PMID- 36981544
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 6
DP  - 2023 Mar 19
TI  - ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic 
      Review on the Promising Perspectives and Valid Concerns.
LID - 10.3390/healthcare11060887 [doi]
LID - 887
AB  - ChatGPT is an artificial intelligence (AI)-based conversational large language 
      model (LLM). The potential applications of LLMs in health care education, 
      research, and practice could be promising if the associated valid concerns are 
      proactively examined and addressed. The current systematic review aimed to 
      investigate the utility of ChatGPT in health care education, research, and 
      practice and to highlight its potential limitations. Using the PRIMSA guidelines, 
      a systematic search was conducted to retrieve English records in PubMed/MEDLINE 
      and Google Scholar (published research or preprints) that examined ChatGPT in the 
      context of health care education, research, or practice. A total of 60 records 
      were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) 
      records and included: (1) improved scientific writing and enhancing research 
      equity and versatility; (2) utility in health care research (efficient analysis 
      of datasets, code generation, literature reviews, saving time to focus on 
      experimental design, and drug discovery and development); (3) benefits in health 
      care practice (streamlining the workflow, cost saving, documentation, 
      personalized medicine, and improved health literacy); and (4) benefits in health 
      care education including improved personalized learning and the focus on critical 
      thinking and problem-based learning. Concerns regarding ChatGPT use were stated 
      in 58/60 (96.7%) records including ethical, copyright, transparency, and legal 
      issues, the risk of bias, plagiarism, lack of originality, inaccurate content 
      with risk of hallucination, limited knowledge, incorrect citations, cybersecurity 
      issues, and risk of infodemics. The promising applications of ChatGPT can induce 
      paradigm shifts in health care education, research, and practice. However, the 
      embrace of this AI chatbot should be conducted with extreme caution considering 
      its potential limitations. As it currently stands, ChatGPT does not qualify to be 
      listed as an author in scientific articles unless the ICMJE/COPE guidelines are 
      revised or amended. An initiative involving all stakeholders in health care 
      education, research, and practice is urgently needed. This will help to set a 
      code of ethics to guide the responsible use of ChatGPT among other LLMs in health 
      care and academia.
FAU - Sallam, Malik
AU  - Sallam M
AUID- ORCID: 0000-0002-0165-9670
AD  - Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, 
      The University of Jordan, Amman 11942, Jordan.
AD  - Department of Clinical Laboratories and Forensic Medicine, Jordan University 
      Hospital, Amman 11942, Jordan.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230319
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10048148
OTO - NOTNLM
OT  - artificial intelligence
OT  - digital health
OT  - ethics
OT  - healthcare
OT  - machine learning
COIS- The author declares no conflict of interest.
EDAT- 2023/03/30 06:00
MHDA- 2023/03/30 06:01
CRDT- 2023/03/29 01:31
PHST- 2023/02/20 00:00 [received]
PHST- 2023/03/17 00:00 [revised]
PHST- 2023/03/17 00:00 [accepted]
PHST- 2023/03/30 06:01 [medline]
PHST- 2023/03/29 01:31 [entrez]
PHST- 2023/03/30 06:00 [pubmed]
AID - healthcare11060887 [pii]
AID - healthcare-11-00887 [pii]
AID - 10.3390/healthcare11060887 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Mar 19;11(6):887. doi: 10.3390/healthcare11060887.

PMID- 36812645
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230224
IS  - 2767-3170 (Electronic)
IS  - 2767-3170 (Linking)
VI  - 2
IP  - 2
DP  - 2023 Feb
TI  - Performance of ChatGPT on USMLE: Potential for AI-assisted medical education 
      using large language models.
PG  - e0000198
LID - 10.1371/journal.pdig.0000198 [doi]
LID - e0000198
AB  - We evaluated the performance of a large language model called ChatGPT on the 
      United States Medical Licensing Exam (USMLE), which consists of three exams: Step 
      1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for 
      all three exams without any specialized training or reinforcement. Additionally, 
      ChatGPT demonstrated a high level of concordance and insight in its explanations. 
      These results suggest that large language models may have the potential to assist 
      with medical education, and potentially, clinical decision-making.
CI  - Copyright: © 2023 Kung et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Kung, Tiffany H
AU  - Kung TH
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
AD  - Department of Anesthesiology, Massachusetts General Hospital, Harvard School of 
      Medicine Boston, Massachusetts, United States of America.
FAU - Cheatham, Morgan
AU  - Cheatham M
AD  - Warren Alpert Medical School; Brown University Providence, Rhode Island, United 
      States of America.
FAU - Medenilla, Arielle
AU  - Medenilla A
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - Sillos, Czarina
AU  - Sillos C
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - De Leon, Lorie
AU  - De Leon L
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - Elepaño, Camille
AU  - Elepaño C
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - Madriaga, Maria
AU  - Madriaga M
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - Aggabao, Rimel
AU  - Aggabao R
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - Diaz-Candido, Giezel
AU  - Diaz-Candido G
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - Maningo, James
AU  - Maningo J
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
FAU - Tseng, Victor
AU  - Tseng V
AUID- ORCID: 0000-0003-0211-512X
AD  - AnsibleHealth, Inc Mountain View, California, United States of America.
AD  - Department of Medical Education, UWorld, LLC Dallas, Texas, United States of 
      America.
LA  - eng
PT  - Journal Article
DEP - 20230209
PL  - United States
TA  - PLOS Digit Health
JT  - PLOS digital health
JID - 9918335064206676
CIN - ChatGPT passing USMLE shines a spotlight on the flaws of medical education.
PMC - PMC9931230
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/02/23 06:00
MHDA- 2023/02/23 06:01
CRDT- 2023/02/22 17:35
PHST- 2022/12/19 00:00 [received]
PHST- 2023/01/23 00:00 [accepted]
PHST- 2023/02/22 17:35 [entrez]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/02/23 06:01 [medline]
AID - PDIG-D-22-00371 [pii]
AID - 10.1371/journal.pdig.0000198 [doi]
PST - epublish
SO  - PLOS Digit Health. 2023 Feb 9;2(2):e0000198. doi: 10.1371/journal.pdig.0000198. 
      eCollection 2023 Feb.

PMID- 36920578
OWN - NLM
STAT- MEDLINE
DCOM- 20230425
LR  - 20230928
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 5
DP  - 2023 May
TI  - Role of Chat GPT in Public Health.
PG  - 868-869
LID - 10.1007/s10439-023-03172-7 [doi]
AB  - ChatGPT, a language model developed by OpenAI, has the potential to play a role 
      in public health. With its ability to generate human-like text based on large 
      amounts of data, ChatGPT has the potential to support individuals and communities 
      in making informed decisions about their health (Panch et al. Lancet Digit Health 
      1:e13-e14, 2019; Baclic et al. Canada Commun Dis Rep 46.6:161, 2020). However, as 
      with any technology, there are limitations and challenges to consider when using 
      ChatGPT in public health. In this overview, we will examine the potential uses of 
      ChatGPT in public health, as well as the advantages and disadvantages of its use.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Biswas, Som S
AU  - Biswas SS
AUID- ORCID: 0000-0002-4038-5844
AD  - Le Bonheur Children's Hospital, The University of Tennessee Health Science 
      Center, Memphis, USA. ssbinmemphis@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230315
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
CIN - Ann Biomed Eng. 2023 Oct;51(10):2120-2122. PMID: 37217804
MH  - Humans
MH  - *Public Health
MH  - Canada
MH  - *Technology
OTO - NOTNLM
OT  - AI
OT  - Public health
OT  - chatGPT
EDAT- 2023/03/16 06:00
MHDA- 2023/04/25 06:42
CRDT- 2023/03/15 12:17
PHST- 2023/02/20 00:00 [received]
PHST- 2023/02/21 00:00 [accepted]
PHST- 2023/04/25 06:42 [medline]
PHST- 2023/03/16 06:00 [pubmed]
PHST- 2023/03/15 12:17 [entrez]
AID - 10.1007/s10439-023-03172-7 [pii]
AID - 10.1007/s10439-023-03172-7 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 May;51(5):868-869. doi: 10.1007/s10439-023-03172-7. Epub 
      2023 Mar 15.

PMID- 36863937
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230323
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Mar 6
TI  - The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in 
      Medical Education: A Conversation With ChatGPT and a Call for Papers.
PG  - e46885
LID - 10.2196/46885 [doi]
LID - e46885
AB  - ChatGPT is a generative language model tool launched by OpenAI on November 30, 
      2022, enabling the public to converse with a machine on a broad range of topics. 
      In January 2023, ChatGPT reached over 100 million users, making it the 
      fastest-growing consumer application to date. This interview with ChatGPT is part 
      2 of a larger interview with ChatGPT. It provides a snapshot of the current 
      capabilities of ChatGPT and illustrates the vast potential for medical education, 
      research, and practice but also hints at current problems and limitations. In 
      this conversation with Gunther Eysenbach, the founder and publisher of JMIR 
      Publications, ChatGPT generated some ideas on how to use chatbots in medical 
      education. It also illustrated its capabilities to generate a virtual patient 
      simulation and quizzes for medical students; critiqued a simulated doctor-patient 
      communication and attempts to summarize a research article (which turned out to 
      be fabricated); commented on methods to detect machine-generated text to ensure 
      academic integrity; generated a curriculum for health professionals to learn 
      about artificial intelligence (AI); and helped to draft a call for papers for a 
      new theme issue to be launched in JMIR Medical Education on ChatGPT. The 
      conversation also highlighted the importance of proper "prompting." Although the 
      language generator does make occasional mistakes, it admits these when 
      challenged. The well-known disturbing tendency of large language models to 
      hallucinate became evident when ChatGPT fabricated references. The interview 
      provides a glimpse into the capabilities and limitations of ChatGPT and the 
      future of AI-supported medical education. Due to the impact of this new 
      technology on medical education, JMIR Medical Education is launching a call for 
      papers for a new e-collection and theme issue. The initial draft of the call for 
      papers was entirely machine generated by ChatGPT, but will be edited by the human 
      guest editors of the theme issue.
CI  - ©Gunther Eysenbach. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 06.03.2023.
FAU - Eysenbach, Gunther
AU  - Eysenbach G
AUID- ORCID: 0000-0001-6479-5330
AD  - JMIR Publications, Toronto, ON, Canada.
LA  - eng
PT  - Editorial
DEP - 20230306
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10028514
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - future of education
OT  - generative language model
OT  - interview
OT  - medical education
COIS- Conflicts of Interest: The author is publisher and editor at JMIR Publications, 
      receives a salary and owns equity.
EDAT- 2023/03/03 06:00
MHDA- 2023/03/03 06:01
CRDT- 2023/03/02 22:04
PHST- 2023/02/28 00:00 [received]
PHST- 2023/03/02 00:00 [accepted]
PHST- 2023/03/03 06:00 [pubmed]
PHST- 2023/03/03 06:01 [medline]
PHST- 2023/03/02 22:04 [entrez]
AID - v9i1e46885 [pii]
AID - 10.2196/46885 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Mar 6;9:e46885. doi: 10.2196/46885.

PMID- 36869927
OWN - NLM
STAT- MEDLINE
DCOM- 20230307
LR  - 20230309
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Print)
IS  - 0148-5598 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Mar 4
TI  - Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple 
      Clinical and Research Scenarios.
PG  - 33
LID - 10.1007/s10916-023-01925-4 [doi]
LID - 33
AB  - This paper aims to highlight the potential applications and limits of a large 
      language model (LLM) in healthcare. ChatGPT is a recently developed LLM that was 
      trained on a massive dataset of text for dialogue with users. Although AI-based 
      language models like ChatGPT have demonstrated impressive capabilities, it is 
      uncertain how well they will perform in real-world scenarios, particularly in 
      fields such as medicine where high-level and complex thinking is necessary. 
      Furthermore, while the use of ChatGPT in writing scientific articles and other 
      scientific outputs may have potential benefits, important ethical concerns must 
      also be addressed. Consequently, we investigated the feasibility of ChatGPT in 
      clinical and research scenarios: (1) support of the clinical practice, (2) 
      scientific production, (3) misuse in medicine and research, and (4) reasoning 
      about public health topics. Results indicated that it is important to recognize 
      and promote education on the appropriate use and potential pitfalls of AI-based 
      LLMs in medicine.
CI  - © 2023. The Author(s).
FAU - Cascella, Marco
AU  - Cascella M
AD  - Department of Anesthesia and Critical Care, Istituto Nazionale Tumori - IRCCS, 
      Fondazione Pascale, Via Mariano Semmola, 53, 80131, Naples, Italy.
FAU - Montomoli, Jonathan
AU  - Montomoli J
AD  - Department of Anesthesia and Intensive Care, Infermi Hospital, AUSL Romagna, 
      Viale Settembrini 2, 47923, Rimini, Italy.
FAU - Bellini, Valentina
AU  - Bellini V
AD  - Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine 
      and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy.
FAU - Bignami, Elena
AU  - Bignami E
AD  - Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine 
      and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy. 
      elenagiovanna.bignami@unipr.it.
LA  - eng
PT  - Journal Article
DEP - 20230304
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - Humans
MH  - Feasibility Studies
MH  - Educational Status
MH  - *Language
MH  - Delivery of Health Care
PMC - PMC9985086
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Clinical resaerch
OT  - Medicine
COIS- The authors have no competing interests to declare that are relevant to the 
      content of this article.
EDAT- 2023/03/05 06:00
MHDA- 2023/03/08 06:00
CRDT- 2023/03/04 11:15
PHST- 2023/01/21 00:00 [received]
PHST- 2023/02/20 00:00 [accepted]
PHST- 2023/03/04 11:15 [entrez]
PHST- 2023/03/05 06:00 [pubmed]
PHST- 2023/03/08 06:00 [medline]
AID - 10.1007/s10916-023-01925-4 [pii]
AID - 1925 [pii]
AID - 10.1007/s10916-023-01925-4 [doi]
PST - epublish
SO  - J Med Syst. 2023 Mar 4;47(1):33. doi: 10.1007/s10916-023-01925-4.

PMID- 37215063
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230523
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - ChatGPT in medicine: an overview of its applications, advantages, limitations, 
      future prospects, and ethical considerations.
PG  - 1169595
LID - 10.3389/frai.2023.1169595 [doi]
LID - 1169595
AB  - This paper presents an analysis of the advantages, limitations, ethical 
      considerations, future prospects, and practical applications of ChatGPT and 
      artificial intelligence (AI) in the healthcare and medical domains. ChatGPT is an 
      advanced language model that uses deep learning techniques to produce human-like 
      responses to natural language inputs. It is part of the family of generative 
      pre-training transformer (GPT) models developed by OpenAI and is currently one of 
      the largest publicly available language models. ChatGPT is capable of capturing 
      the nuances and intricacies of human language, allowing it to generate 
      appropriate and contextually relevant responses across a broad spectrum of 
      prompts. The potential applications of ChatGPT in the medical field range from 
      identifying potential research topics to assisting professionals in clinical and 
      laboratory diagnosis. Additionally, it can be used to help medical students, 
      doctors, nurses, and all members of the healthcare fraternity to know about 
      updates and new developments in their respective fields. The development of 
      virtual assistants to aid patients in managing their health is another important 
      application of ChatGPT in medicine. Despite its potential applications, the use 
      of ChatGPT and other AI tools in medical writing also poses ethical and legal 
      concerns. These include possible infringement of copyright laws, medico-legal 
      complications, and the need for transparency in AI-generated content. In 
      conclusion, ChatGPT has several potential applications in the medical and 
      healthcare fields. However, these applications come with several limitations and 
      ethical considerations which are presented in detail along with future prospects 
      in medicine and healthcare.
CI  - Copyright © 2023 Dave, Athaluri and Singh.
FAU - Dave, Tirth
AU  - Dave T
AD  - Internal Medicine, Bukovinian State Medical University, Chernivtsi, Ukraine.
FAU - Athaluri, Sai Anirudh
AU  - Athaluri SA
AD  - Rangaraya Medical College, Kakinada, Andhra Pradesh, India.
FAU - Singh, Satyam
AU  - Singh S
AD  - GSVM Medical College, Kanpur, Uttar Pradesh, India.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230504
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10192861
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - generative pre-training transformer
OT  - healthcare
OT  - medicine
OT  - natural language processing
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/05/22 19:11
MHDA- 2023/05/22 19:12
CRDT- 2023/05/22 12:19
PHST- 2023/02/19 00:00 [received]
PHST- 2023/04/14 00:00 [accepted]
PHST- 2023/05/22 19:12 [medline]
PHST- 2023/05/22 19:11 [pubmed]
PHST- 2023/05/22 12:19 [entrez]
AID - 10.3389/frai.2023.1169595 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 May 4;6:1169595. doi: 10.3389/frai.2023.1169595. 
      eCollection 2023.

PMID- 36811129
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230224
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Artificial Hallucinations in ChatGPT: Implications in Scientific Writing.
PG  - e35179
LID - 10.7759/cureus.35179 [doi]
LID - e35179
AB  - While still in its infancy, ChatGPT (Generative Pretrained Transformer), 
      introduced in November 2022, is bound to hugely impact many industries, including 
      healthcare, medical education, biomedical research, and scientific writing. 
      Implications of ChatGPT, that new chatbot introduced by OpenAI on academic 
      writing, is largely unknown. In response to the Journal of Medical Science 
      (Cureus) Turing Test - call for case reports written with the assistance of 
      ChatGPT, we present two cases one of homocystinuria-associated osteoporosis, and 
      the other is on late-onset Pompe disease (LOPD), a rare metabolic disorder. We 
      tested ChatGPT to write about the pathogenesis of these conditions. We documented 
      the positive, negative, and rather troubling aspects of our newly introduced 
      chatbot's performance.
CI  - Copyright © 2023, Alkaissi et al.
FAU - Alkaissi, Hussam
AU  - Alkaissi H
AD  - Internal Medicine, Kings County Hospital Center, Brooklyn, USA.
AD  - Internal Medicine, Veterans Affairs Medical Center, Brooklyn, USA.
AD  - Internal Medicine, State University of New York Downstate Medical Center, 
      Brooklyn, USA.
FAU - McFarlane, Samy I
AU  - McFarlane SI
AD  - Internal Medicine, State University of New York Downstate Medical Center, 
      Brooklyn, USA.
LA  - eng
PT  - Editorial
DEP - 20230219
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC9939079
OTO - NOTNLM
OT  - artificial intelligence and education
OT  - artificial intelligence and writing
OT  - artificial intelligence in medicine
OT  - chatbot
OT  - chatgpt
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/02/23 06:00
MHDA- 2023/02/23 06:01
CRDT- 2023/02/22 15:15
PHST- 2023/02/19 00:00 [accepted]
PHST- 2023/02/22 15:15 [entrez]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/02/23 06:01 [medline]
AID - 10.7759/cureus.35179 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 19;15(2):e35179. doi: 10.7759/cureus.35179. eCollection 2023 
      Feb.

PMID- 36946005
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20230727
IS  - 2287-285X (Electronic)
IS  - 2287-2728 (Print)
IS  - 2287-2728 (Linking)
VI  - 29
IP  - 3
DP  - 2023 Jul
TI  - Assessing the performance of ChatGPT in answering questions regarding cirrhosis 
      and hepatocellular carcinoma.
PG  - 721-732
LID - 10.3350/cmh.2023.0089 [doi]
AB  - BACKGROUND/AIMS: Patients with cirrhosis and hepatocellular carcinoma (HCC) 
      require extensive and personalized care to improve outcomes. ChatGPT (Generative 
      Pre-trained Transformer), a large language model, holds the potential to provide 
      professional yet patient-friendly support. We aimed to examine the accuracy and 
      reproducibility of ChatGPT in answering questions regarding knowledge, 
      management, and emotional support for cirrhosis and HCC. METHODS: ChatGPT's 
      responses to 164 questions were independently graded by two transplant 
      hepatologists and resolved by a third reviewer. The performance of ChatGPT was 
      also assessed using two published questionnaires and 26 questions formulated from 
      the quality measures of cirrhosis management. Finally, its emotional support 
      capacity was tested. RESULTS: We showed that ChatGPT regurgitated extensive 
      knowledge of cirrhosis (79.1% correct) and HCC (74.0% correct), but only small 
      proportions (47.3% in cirrhosis, 41.1% in HCC) were labeled as comprehensive. The 
      performance was better in basic knowledge, lifestyle, and treatment than in the 
      domains of diagnosis and preventive medicine. For the quality measures, the model 
      answered 76.9% of questions correctly but failed to specify decision-making 
      cut-offs and treatment durations. ChatGPT lacked knowledge of regional guidelines 
      variations, such as HCC screening criteria. However, it provided practical and 
      multifaceted advice to patients and caregivers regarding the next steps and 
      adjusting to a new diagnosis. CONCLUSION: We analyzed the areas of robustness and 
      limitations of ChatGPT's responses on the management of cirrhosis and HCC and 
      relevant emotional support. ChatGPT may have a role as an adjunct informational 
      tool for patients and physicians to improve outcomes.
FAU - Yeo, Yee Hui
AU  - Yeo YH
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Samaan, Jamil S
AU  - Samaan JS
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Ng, Wee Han
AU  - Ng WH
AD  - Bristol Medical School, University of Bristol, Bristol, UK.
FAU - Ting, Peng-Sheng
AU  - Ting PS
AD  - School of Medicine, Tulane University, New Orleans, LA, USA.
FAU - Trivedi, Hirsh
AU  - Trivedi H
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
AD  - Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, 
      USA.
FAU - Vipani, Aarshi
AU  - Vipani A
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Ayoub, Walid
AU  - Ayoub W
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
AD  - Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, 
      USA.
FAU - Yang, Ju Dong
AU  - Yang JD
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
AD  - Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, 
      USA.
AD  - Samuel Oschin Comprehensive Cancer Institute, Cedars- Sinai Medical Center, Los 
      Angeles, CA, USA.
FAU - Liran, Omer
AU  - Liran O
AD  - Department of Psychiatry and Behavioral Sciences, Cedars-Sinai, Los Angeles, CA, 
      USA.
AD  - Division of Health Services Research, Department of Medicine, Cedars-Sinai, Los 
      Angeles, CA, USA.
FAU - Spiegel, Brennan
AU  - Spiegel B
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
AD  - Division of Health Services Research, Department of Medicine, Cedars-Sinai, Los 
      Angeles, CA, USA.
FAU - Kuo, Alexander
AU  - Kuo A
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
AD  - Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230322
PL  - Korea (South)
TA  - Clin Mol Hepatol
JT  - Clinical and molecular hepatology
JID - 101586730
SB  - IM
CIN - Clin Mol Hepatol. 2023 Jul;29(3):813-814. PMID: 37211355
CIN - Clin Mol Hepatol. 2023 Jul;29(3):815-816. PMID: 37221834
MH  - Humans
MH  - *Carcinoma, Hepatocellular/diagnosis
MH  - Reproducibility of Results
MH  - *Liver Neoplasms/diagnosis
MH  - Liver Cirrhosis/complications/diagnosis
PMC - PMC10366809
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chronic disease management
OT  - Health communication
OT  - Patient education as topic
OT  - Telemedicine
COIS- Conflicts of Interest The authors have no conflictsto disclose.
EDAT- 2023/03/23 06:00
MHDA- 2023/07/25 06:43
CRDT- 2023/03/22 03:48
PHST- 2023/03/03 00:00 [received]
PHST- 2023/03/21 00:00 [accepted]
PHST- 2023/07/25 06:43 [medline]
PHST- 2023/03/23 06:00 [pubmed]
PHST- 2023/03/22 03:48 [entrez]
AID - cmh.2023.0089 [pii]
AID - cmh-2023-0089 [pii]
AID - 10.3350/cmh.2023.0089 [doi]
PST - ppublish
SO  - Clin Mol Hepatol. 2023 Jul;29(3):721-732. doi: 10.3350/cmh.2023.0089. Epub 2023 
      Mar 22.

PMID- 36798292
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230907
DP  - 2023 Feb 7
TI  - Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making.
LID - 2023.02.02.23285399 [pii]
LID - 10.1101/2023.02.02.23285399 [doi]
AB  - BACKGROUND: ChatGPT, a popular new large language model (LLM) built by OpenAI, 
      has shown impressive performance in a number of specialized applications. Despite 
      the rising popularity and performance of AI, studies evaluating the use of LLMs 
      for clinical decision support are lacking. PURPOSE: To evaluate ChatGPT's 
      capacity for clinical decision support in radiology via the identification of 
      appropriate imaging services for two important clinical presentations: breast 
      cancer screening and breast pain. MATERIALS AND METHODS: We compared ChatGPT's 
      responses to the American College of Radiology (ACR) Appropriateness Criteria for 
      breast pain and breast cancer screening. Our prompt formats included an 
      open-ended (OE) format, where ChatGPT was asked to provide the single most 
      appropriate imaging procedure, and a select all that apply (SATA) format, where 
      ChatGPT was given a list of imaging modalities to assess. Scoring criteria 
      evaluated whether proposed imaging modalities were in accordance with ACR 
      guidelines. RESULTS: ChatGPT achieved an average OE score of 1.83 (out of 2) and 
      a SATA average percentage correct of 88.9% for breast cancer screening prompts, 
      and an average OE score of 1.125 (out of 2) and a SATA average percentage correct 
      of 58.3% for breast pain prompts. CONCLUSION: Our results demonstrate the 
      feasibility of using ChatGPT for radiologic decision making, with the potential 
      to improve clinical workflow and responsible use of radiology services.
FAU - Rao, Arya
AU  - Rao A
AUID- ORCID: 0000-0003-3007-4812
FAU - Kim, John
AU  - Kim J
FAU - Kamineni, Meghana
AU  - Kamineni M
FAU - Pang, Michael
AU  - Pang M
FAU - Lie, Winston
AU  - Lie W
FAU - Succi, Marc D
AU  - Succi MD
LA  - eng
PT  - Preprint
DEP - 20230207
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
UIN - J Am Coll Radiol. 2023 Jun 21;:. PMID: 37356806
PMC - PMC9934725
EDAT- 2023/02/18 06:00
MHDA- 2023/02/18 06:01
CRDT- 2023/02/17 02:06
PHST- 2023/02/17 02:06 [entrez]
PHST- 2023/02/18 06:00 [pubmed]
PHST- 2023/02/18 06:01 [medline]
AID - 2023.02.02.23285399 [pii]
AID - 10.1101/2023.02.02.23285399 [doi]
PST - epublish
SO  - medRxiv. 2023 Feb 7:2023.02.02.23285399. doi: 10.1101/2023.02.02.23285399. 
      Preprint.

PMID- 36627845
OWN - NLM
STAT- MEDLINE
DCOM- 20230113
LR  - 20230217
IS  - 1975-5937 (Electronic)
IS  - 1975-5937 (Linking)
VI  - 20
DP  - 2023
TI  - Are ChatGPT’s knowledge and interpretation ability comparable to those of medical 
      students in Korea for taking a parasitology examination?: a descriptive study.
PG  - 1
LID - 10.3352/jeehp.2023.20.1 [doi]
LID - 1
AB  - This study aimed to compare the knowledge and interpretation ability of ChatGPT, 
      a language model of artificial general intelligence, with those of medical 
      students in Korea by administering a parasitology examination to both ChatGPT and 
      medical students. The examination consisted of 79 items and was administered to 
      ChatGPT on January 1, 2023. The examination results were analyzed in terms of 
      ChatGPT’s overall performance score, its correct answer rate by the items’ 
      knowledge level, and the acceptability of its explanations of the items. 
      ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s 
      correct answer rate was not related to the items’ knowledge level. However, there 
      was a relationship between acceptable explanations and correct answers. In 
      conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology 
      examination were not yet comparable to those of medical students in Korea.
FAU - Huh, Sun
AU  - Huh S
AD  - Department of Parasitology and Institute of Medical Education, College of 
      Medicine, Hallym University, Chuncheon, Korea.
LA  - eng
PT  - Journal Article
DEP - 20230111
PL  - Korea (South)
TA  - J Educ Eval Health Prof
JT  - Journal of educational evaluation for health professions
JID - 101490061
SB  - IM
MH  - Humans
MH  - Educational Measurement/methods
MH  - Knowledge
MH  - Republic of Korea
MH  - *Students, Medical
MH  - Artificial Intelligence
PMC - PMC9905868
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Educational measurement
OT  - Knowledge
OT  - Medical students
OT  - Republic of Korea
COIS- Conflict of interest Sun Huh has been the editor of the Journal of Educational 
      Evaluation for Health Professions since 2005. He was not involved in the review 
      process. Otherwise, no potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/01/12 06:00
MHDA- 2023/01/13 06:00
CRDT- 2023/01/11 01:41
PHST- 2023/01/03 00:00 [received]
PHST- 2023/01/11 00:00 [accepted]
PHST- 2023/01/11 01:41 [entrez]
PHST- 2023/01/12 06:00 [pubmed]
PHST- 2023/01/13 06:00 [medline]
AID - jeehp.2023.20.1 [pii]
AID - jeehp-20-01 [pii]
AID - 10.3352/jeehp.2023.20.1 [doi]
PST - ppublish
SO  - J Educ Eval Health Prof. 2023;20:1. doi: 10.3352/jeehp.2023.20.1. Epub 2023 Jan 
      11.

PMID- 36867743
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230325
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Mar 8
TI  - ChatGPT in Clinical Toxicology.
PG  - e46876
LID - 10.2196/46876 [doi]
LID - e46876
AB  - ChatGPT has recently been shown to pass the United States Medical Licensing 
      Examination (USMLE). We tested ChatGPT (Feb 13, 2023 release) using a typical 
      clinical toxicology case of acute organophosphate poisoning. ChatGPT fared well 
      in answering all of our queries regarding it.
CI  - ©Mary Sabry Abdel-Messih, Maged N Kamel Boulos. Originally published in JMIR 
      Medical Education (https://mededu.jmir.org), 08.03.2023.
FAU - Sabry Abdel-Messih, Mary
AU  - Sabry Abdel-Messih M
AUID- ORCID: 0009-0001-3404-0516
AD  - Clinical Toxicology Centre, Forensic Medicine and Clinical Toxicology Department, 
      Faculty of Medicine, Ain Shams University, Cairo, Egypt.
FAU - Kamel Boulos, Maged N
AU  - Kamel Boulos MN
AUID- ORCID: 0000-0003-2400-6303
AD  - School of Medicine, University of Lisbon, Lisbon, Portugal.
LA  - eng
PT  - Journal Article
DEP - 20230308
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
CON - JMIR Med Educ. 9:e45312.
CIN - JMIR Med Educ. 9:e46885.
PMC - PMC10034604
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - clinical toxicology
OT  - medical education
OT  - organophosphates
COIS- Conflicts of Interest: None declared.
EDAT- 2023/03/04 06:00
MHDA- 2023/03/04 06:01
CRDT- 2023/03/03 15:02
PHST- 2023/02/28 00:00 [received]
PHST- 2023/03/03 00:00 [accepted]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/04 06:01 [medline]
PHST- 2023/03/03 15:02 [entrez]
AID - v9i1e46876 [pii]
AID - 10.2196/46876 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Mar 8;9:e46876. doi: 10.2196/46876.

PMID- 36757192
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230314
LR  - 20230912
IS  - 1532-0987 (Electronic)
IS  - 0891-3668 (Linking)
VI  - 42
IP  - 4
DP  - 2023 Apr 1
TI  - To ChatGPT or not to ChatGPT? The Impact of Artificial Intelligence on Academic 
      Publishing.
PG  - 275
LID - 10.1097/INF.0000000000003852 [doi]
FAU - Curtis, Nigel
AU  - Curtis N
AUID- ORCID: 0000-0003-3446-4594
AD  - Department of Paediatrics, The University of Melbourne, Parkville, Victoria, 
      Australia.
AD  - Infectious Diseases Unit, Royal Children's Hospital Melbourne, Parkville, 
      Victoria, Australia.
AD  - Infectious Diseases Group, Murdoch Children's Research Institute, Parkville, 
      Victoria, Australia.
CN  - ChatGPT
LA  - eng
PT  - Journal Article
DEP - 20230209
PL  - United States
TA  - Pediatr Infect Dis J
JT  - The Pediatric infectious disease journal
JID - 8701858
SB  - IM
COIS- The authors have no funding or conflicts of interest to disclose.
EDAT- 2023/02/10 06:00
MHDA- 2023/03/14 06:00
CRDT- 2023/02/09 09:03
PHST- 2023/02/10 06:00 [pubmed]
PHST- 2023/03/14 06:00 [medline]
PHST- 2023/02/09 09:03 [entrez]
AID - 00006454-202304000-00001 [pii]
AID - 10.1097/INF.0000000000003852 [doi]
PST - ppublish
SO  - Pediatr Infect Dis J. 2023 Apr 1;42(4):275. doi: 10.1097/INF.0000000000003852. 
      Epub 2023 Feb 9.

PMID- 37379067
OWN - NLM
STAT- MEDLINE
DCOM- 20230630
LR  - 20231106
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Jun 28
TI  - Utility of ChatGPT in Clinical Practice.
PG  - e48568
LID - 10.2196/48568 [doi]
LID - e48568
AB  - ChatGPT is receiving increasing attention and has a variety of application 
      scenarios in clinical practice. In clinical decision support, ChatGPT has been 
      used to generate accurate differential diagnosis lists, support clinical 
      decision-making, optimize clinical decision support, and provide insights for 
      cancer screening decisions. In addition, ChatGPT has been used for intelligent 
      question-answering to provide reliable information about diseases and medical 
      queries. In terms of medical documentation, ChatGPT has proven effective in 
      generating patient clinical letters, radiology reports, medical notes, and 
      discharge summaries, improving efficiency and accuracy for health care providers. 
      Future research directions include real-time monitoring and predictive analytics, 
      precision medicine and personalized treatment, the role of ChatGPT in 
      telemedicine and remote health care, and integration with existing health care 
      systems. Overall, ChatGPT is a valuable tool that complements the expertise of 
      health care providers and improves clinical decision-making and patient care. 
      However, ChatGPT is a double-edged sword. We need to carefully consider and study 
      the benefits and potential dangers of ChatGPT. In this viewpoint, we discuss 
      recent advances in ChatGPT research in clinical practice and suggest possible 
      risks and challenges of using ChatGPT in clinical practice. It will help guide 
      and support future artificial intelligence research similar to ChatGPT in health.
CI  - ©Jialin Liu, Changyu Wang, Siru Liu. Originally published in the Journal of 
      Medical Internet Research (https://www.jmir.org), 28.06.2023.
FAU - Liu, Jialin
AU  - Liu J
AUID- ORCID: 0000-0002-1369-4625
AD  - Information Center, West China Hospital, Sichuan University, Chengdu, China.
AD  - Department of Medical Informatics, West China Medical School, Chengdu, China.
AD  - Department of Otolaryngology-Head and Neck Surgery, West China Hospital, Sichuan 
      University, Chengdu, China.
FAU - Wang, Changyu
AU  - Wang C
AUID- ORCID: 0000-0003-4548-331X
AD  - Information Center, West China Hospital, Sichuan University, Chengdu, China.
AD  - West China College of Stomatology, Sichuan University, Chengdu, China.
FAU - Liu, Siru
AU  - Liu S
AUID- ORCID: 0000-0002-5003-5354
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, TN, United States.
LA  - eng
PT  - Journal Article
DEP - 20230628
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Organizations
MH  - Clinical Decision-Making
MH  - Diagnosis, Differential
MH  - Documentation
PMC - PMC10365580
OTO - NOTNLM
OT  - ChatGPT
OT  - NLP
OT  - artificial intelligence
OT  - barriers
OT  - best practices
OT  - challenges
OT  - clinical practice
OT  - communication
OT  - doctor-patient
OT  - guidance
OT  - guidelines
OT  - large language model
OT  - large language models
OT  - natural language processing
OT  - patient-physician
OT  - recommendations
OT  - risks
COIS- Conflicts of Interest: None declared.
EDAT- 2023/06/28 13:08
MHDA- 2023/06/30 06:42
CRDT- 2023/06/28 11:53
PHST- 2023/04/28 00:00 [received]
PHST- 2023/06/15 00:00 [accepted]
PHST- 2023/05/29 00:00 [revised]
PHST- 2023/06/30 06:42 [medline]
PHST- 2023/06/28 13:08 [pubmed]
PHST- 2023/06/28 11:53 [entrez]
AID - v25i1e48568 [pii]
AID - 10.2196/48568 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Jun 28;25:e48568. doi: 10.2196/48568.

PMID- 36909565
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
DP  - 2023 Feb 28
TI  - Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An 
      Evaluation of the Chat-GPT Model.
LID - rs.3.rs-2566942 [pii]
LID - 10.21203/rs.3.rs-2566942/v1 [doi]
AB  - BACKGROUND: Natural language processing models such as ChatGPT can generate 
      text-based content and are poised to become a major information source in 
      medicine and beyond. The accuracy and completeness of ChatGPT for medical queries 
      is not known. METHODS: Thirty-three physicians across 17 specialties generated 
      284 medical questions that they subjectively classified as easy, medium, or hard 
      with either binary (yes/no) or descriptive answers. The physicians then graded 
      ChatGPT-generated answers to these questions for accuracy (6-point Likert scale; 
      range 1 - completely incorrect to 6 - completely correct) and completeness 
      (3-point Likert scale; range 1 - incomplete to 3 - complete plus additional 
      context). Scores were summarized with descriptive statistics and compared using 
      Mann-Whitney U or Kruskal-Wallis testing. RESULTS: Across all questions (n=284), 
      median accuracy score was 5.5 (between almost completely and completely correct) 
      with mean score of 4.8 (between mostly and almost completely correct). Median 
      completeness score was 3 (complete and comprehensive) with mean score of 2.5. For 
      questions rated easy, medium, and hard, median accuracy scores were 6, 5.5, and 5 
      (mean 5.0, 4.7, and 4.6; p=0.05). Accuracy scores for binary and descriptive 
      questions were similar (median 6 vs. 5; mean 4.9 vs. 4.7; p=0.07). Of 36 
      questions with scores of 1-2, 34 were re-queried/re-graded 8-17 days later with 
      substantial improvement (median 2 vs. 4; p<0.01). CONCLUSIONS: ChatGPT generated 
      largely accurate information to diverse medical queries as judged by academic 
      physician specialists although with important limitations. Further research and 
      model development are needed to correct inaccuracies and for validation.
FAU - Johnson, Douglas
AU  - Johnson D
AUID- ORCID: 0000-0002-6390-773X
AD  - Vanderbilt University Medical Center.
FAU - Goodman, Rachel
AU  - Goodman R
AUID- ORCID: 0000-0001-7992-8108
AD  - Vanderbilt University School of Medicine.
FAU - Patrinely, J
AU  - Patrinely J
AD  - Vanderbilt University Medical Center.
FAU - Stone, Cosby
AU  - Stone C
AD  - Vanderbilt University Medical Center, Nashville, Tennessee.
FAU - Zimmerman, Eli
AU  - Zimmerman E
AD  - Vanderbilt University Medical Center.
FAU - Donald, Rebecca
AU  - Donald R
AUID- ORCID: 0000-0003-1341-6796
AD  - Vanderbilt University Medical Center.
FAU - Chang, Sam
AU  - Chang S
AD  - Vanderbilt University Medical Center.
FAU - Berkowitz, Sean
AU  - Berkowitz S
AD  - Vanderbilt University Medical Center.
FAU - Finn, Avni
AU  - Finn A
AD  - Vanderbilt University Medical Center.
FAU - Jahangir, Eiman
AU  - Jahangir E
AD  - Vanderbilt University Medical Center.
FAU - Scoville, Elizabeth
AU  - Scoville E
AD  - Vanderbilt University Medical Center.
FAU - Reese, Tyler
AU  - Reese T
AD  - Vanderbilt University Medical Center.
FAU - Friedman, Debra
AU  - Friedman D
AD  - Vanderbilt University Medical Center.
FAU - Bastarache, Julie
AU  - Bastarache J
AD  - Vanderbilt University Medical Center.
FAU - van der Heijden, Yuri
AU  - van der Heijden Y
AD  - Vanderbilt University Medical Center.
FAU - Wright, Jordan
AU  - Wright J
AD  - Vanderbilt University Medical Center.
FAU - Carter, Nicholas
AU  - Carter N
AD  - Vanderbilt University Medical Center.
FAU - Alexander, Matthew
AU  - Alexander M
AD  - Vanderbilt University Medical Center.
FAU - Choe, Jennifer
AU  - Choe J
AD  - Vanderbilt University Medical Center.
FAU - Chastain, Cody
AU  - Chastain C
AD  - Vanderbilt University Medical Center.
FAU - Zic, John
AU  - Zic J
AD  - Vanderbilt University Medical Center.
FAU - Horst, Sara
AU  - Horst S
AD  - Vanderbilt University Medical Center.
FAU - Turker, Isik
AU  - Turker I
AUID- ORCID: 0000-0003-3216-3499
AD  - Vanderbilt University Medical Center.
FAU - Agarwal, Rajiv
AU  - Agarwal R
AD  - Vanderbilt University Medical Center.
FAU - Osmundson, Evan
AU  - Osmundson E
AD  - Vanderbilt University Medical Center.
FAU - Idrees, Kamran
AU  - Idrees K
AD  - Vanderbilt University Medical Center.
FAU - Kieman, Colleen
AU  - Kieman C
AD  - Vanderbilt University Medical Center.
FAU - Padmanabhan, Chandrasekhar
AU  - Padmanabhan C
AD  - Vanderbilt University Medical Center.
FAU - Bailey, Christina
AU  - Bailey C
AD  - Vanderbilt University Medical Center.
FAU - Schlegel, Cameron
AU  - Schlegel C
AD  - Vanderbilt University Medical Center.
FAU - Chambless, Lola
AU  - Chambless L
AD  - Vanderbilt University.
FAU - Gibson, Mike
AU  - Gibson M
AD  - Vanderbilt University Medical Center.
FAU - Osterman, Travis
AU  - Osterman T
AD  - Vanderbilt University Medical Center.
FAU - Wheless, Lee
AU  - Wheless L
AD  - Vanderbilt University Medical Center.
LA  - eng
PT  - Preprint
DEP - 20230228
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10002821
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - clinical decision making
OT  - deep learning
OT  - knowledge dissemination
OT  - large language model
OT  - medical education
OT  - natural language processing
EDAT- 2023/03/14 06:00
MHDA- 2023/03/14 06:01
CRDT- 2023/03/13 04:00
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/14 06:01 [medline]
PHST- 2023/03/13 04:00 [entrez]
AID - rs.3.rs-2566942 [pii]
AID - 10.21203/rs.3.rs-2566942/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 Feb 28:rs.3.rs-2566942. doi: 10.21203/rs.3.rs-2566942/v1. Preprint.

PMID- 37087108
OWN - NLM
STAT- MEDLINE
DCOM- 20230621
LR  - 20230701
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Print)
IS  - 1067-5027 (Linking)
VI  - 30
IP  - 7
DP  - 2023 Jun 20
TI  - Using AI-generated suggestions from ChatGPT to optimize clinical decision 
      support.
PG  - 1237-1245
LID - 10.1093/jamia/ocad072 [doi]
AB  - OBJECTIVE: To determine if ChatGPT can generate useful suggestions for improving 
      clinical decision support (CDS) logic and to assess noninferiority compared to 
      human-generated suggestions. METHODS: We supplied summaries of CDS logic to 
      ChatGPT, an artificial intelligence (AI) tool for question answering that uses a 
      large language model, and asked it to generate suggestions. We asked human 
      clinician reviewers to review the AI-generated suggestions as well as 
      human-generated suggestions for improving the same CDS alerts, and rate the 
      suggestions for their usefulness, acceptance, relevance, understanding, workflow, 
      bias, inversion, and redundancy. RESULTS: Five clinicians analyzed 36 
      AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 
      20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. 
      The suggestions generated by AI were found to offer unique perspectives and were 
      evaluated as highly understandable and relevant, with moderate usefulness, low 
      acceptance, bias, inversion, redundancy. CONCLUSION: AI-generated suggestions 
      could be an important complementary part of optimizing CDS alerts, can identify 
      potential improvements to alert logic and support their implementation, and may 
      even be able to assist experts in formulating their own suggestions for CDS 
      improvement. ChatGPT shows great potential for using large language models and 
      reinforcement learning from human feedback to improve CDS alert logic and 
      potentially other medical areas involving complex, clinical logic, a key step in 
      the development of an advanced learning health system.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      American Medical Informatics Association.
FAU - Liu, Siru
AU  - Liu S
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee, USA.
FAU - Wright, Aileen P
AU  - Wright AP
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee, USA.
AD  - Department of Medicine, Vanderbilt University Medical Center, Nashville, 
      Tennessee, USA.
FAU - Patterson, Barron L
AU  - Patterson BL
AD  - Department of Pediatrics, Vanderbilt University Medical Center, Nashville, 
      Tennessee, USA.
FAU - Wanderer, Jonathan P
AU  - Wanderer JP
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee, USA.
AD  - Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, 
      Tennessee, USA.
FAU - Turer, Robert W
AU  - Turer RW
AUID- ORCID: 0000-0003-1387-640X
AD  - Department of Emergency Medicine, University of Texas Southwestern Medical 
      Center, Dallas, Texas, USA.
AD  - Clinical Informatics Center, University of Texas Southwestern Medical Center, 
      Dallas, Texas, USA.
FAU - Nelson, Scott D
AU  - Nelson SD
AUID- ORCID: 0000-0002-1941-1817
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee, USA.
FAU - McCoy, Allison B
AU  - McCoy AB
AUID- ORCID: 0000-0003-2292-9147
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee, USA.
FAU - Sittig, Dean F
AU  - Sittig DF
AUID- ORCID: 0000-0001-5811-8915
AD  - School of Biomedical Informatics, University of Texas Health Science Center, 
      Houston, Texas, USA.
FAU - Wright, Adam
AU  - Wright A
AUID- ORCID: 0000-0001-6844-145X
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee, USA.
LA  - eng
GR  - K99 LM014097/LM/NLM NIH HHS/United States
GR  - R01 LM013995/LM/NLM NIH HHS/United States
GR  - R01 AG062499/AG/NIA NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Decision Support Systems, Clinical
MH  - Language
MH  - *Learning Health System
MH  - Workflow
PMC - PMC10280357
OTO - NOTNLM
OT  - artificial intelligence
OT  - clinical decision support
OT  - large language model
COIS- The authors do not have conflicts of interest related to this study.
EDAT- 2023/04/23 00:41
MHDA- 2023/06/21 06:42
CRDT- 2023/04/22 20:32
PHST- 2023/02/21 00:00 [received]
PHST- 2023/03/28 00:00 [revised]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/06/21 06:42 [medline]
PHST- 2023/04/23 00:41 [pubmed]
PHST- 2023/04/22 20:32 [entrez]
AID - 7136722 [pii]
AID - ocad072 [pii]
AID - 10.1093/jamia/ocad072 [doi]
PST - ppublish
SO  - J Am Med Inform Assoc. 2023 Jun 20;30(7):1237-1245. doi: 10.1093/jamia/ocad072.

PMID- 36929393
OWN - NLM
STAT- MEDLINE
DCOM- 20230321
LR  - 20230331
IS  - 2515-5091 (Electronic)
IS  - 2515-5091 (Linking)
VI  - 7
IP  - 2
DP  - 2023 Mar 1
TI  - Using ChatGPT to evaluate cancer myths and misconceptions: artificial 
      intelligence and cancer information.
LID - 10.1093/jncics/pkad015 [doi]
LID - pkad015
AB  - Data about the quality of cancer information that chatbots and other artificial 
      intelligence systems provide are limited. Here, we evaluate the accuracy of 
      cancer information on ChatGPT compared with the National Cancer Institute's 
      (NCI's) answers by using the questions on the "Common Cancer Myths and 
      Misconceptions" web page. The NCI's answers and ChatGPT answers to each question 
      were blinded, and then evaluated for accuracy (accurate: yes vs no). Ratings were 
      evaluated independently for each question, and then compared between the blinded 
      NCI and ChatGPT answers. Additionally, word count and Flesch-Kincaid readability 
      grade level for each individual response were evaluated. Following expert review, 
      the percentage of overall agreement for accuracy was 100% for NCI answers and 
      96.9% for ChatGPT outputs for questions 1 through 13 (ĸ = ‒0.03, standard 
      error = 0.08). There were few noticeable differences in the number of words or 
      the readability of the answers from NCI or ChatGPT. Overall, the results suggest 
      that ChatGPT provides accurate information about common cancer myths and 
      misconceptions.
CI  - © The Author(s) 2023. Published by Oxford University Press.
FAU - Johnson, Skyler B
AU  - Johnson SB
AUID- ORCID: 0000-0002-8881-7706
AD  - Department of Radiation Oncology, University of Utah School of Medicine, Huntsman 
      Cancer Institute, Salt Lake City, UT, USA.
AD  - Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake 
      City, UT, USA.
FAU - King, Andy J
AU  - King AJ
AD  - Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake 
      City, UT, USA.
AD  - Department of Communication, University of Utah, Salt Lake City, UT, USA.
FAU - Warner, Echo L
AU  - Warner EL
AD  - Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake 
      City, UT, USA.
AD  - College of Nursing, University of Utah, Salt Lake City, UT, USA.
FAU - Aneja, Sanjay
AU  - Aneja S
AD  - Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, 
      CT, USA.
AD  - Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA.
FAU - Kann, Benjamin H
AU  - Kann BH
AD  - Department of Radiation Oncology, Dana-Farber Cancer Institute/Brigham and 
      Women's Hospital, Harvard Medical School, Boston, MA, USA.
FAU - Bylund, Carma L
AU  - Bylund CL
AD  - Department of Health Outcomes and Biomedical Informatics, University of Florida 
      College of Medicine, Gainesville, FL, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - JNCI Cancer Spectr
JT  - JNCI cancer spectrum
JID - 101721827
SB  - IM
MH  - United States/epidemiology
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Neoplasms/diagnosis
MH  - National Cancer Institute (U.S.)
PMC - PMC10020140
COIS- All authors had full access to all the data in the study and take responsibility 
      for the integrity of the data and the accuracy of the data analysis. Skyler B. 
      Johnson, MD, has no financial conflicts of interest. All other researchers 
      reported no financial conflicts of interests.
EDAT- 2023/03/18 06:00
MHDA- 2023/03/22 06:00
CRDT- 2023/03/17 10:05
PHST- 2023/01/11 00:00 [received]
PHST- 2023/02/13 00:00 [revised]
PHST- 2023/02/16 00:00 [accepted]
PHST- 2023/03/17 10:05 [entrez]
PHST- 2023/03/18 06:00 [pubmed]
PHST- 2023/03/22 06:00 [medline]
AID - 7078555 [pii]
AID - pkad015 [pii]
AID - 10.1093/jncics/pkad015 [doi]
PST - ppublish
SO  - JNCI Cancer Spectr. 2023 Mar 1;7(2):pkad015. doi: 10.1093/jncics/pkad015.

PMID- 36865204
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230904
DP  - 2023 Feb 26
TI  - Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow.
LID - 2023.02.21.23285886 [pii]
LID - 10.1101/2023.02.21.23285886 [doi]
AB  - IMPORTANCE: Large language model (LLM) artificial intelligence (AI) chatbots 
      direct the power of large training datasets towards successive, related tasks, as 
      opposed to single-ask tasks, for which AI already achieves impressive 
      performance. The capacity of LLMs to assist in the full scope of iterative 
      clinical reasoning via successive prompting, in effect acting as virtual 
      physicians, has not yet been evaluated. OBJECTIVE: To evaluate ChatGPT's capacity 
      for ongoing clinical decision support via its performance on standardized 
      clinical vignettes. DESIGN: We inputted all 36 published clinical vignettes from 
      the Merck Sharpe & Dohme (MSD) Clinical Manual into ChatGPT and compared accuracy 
      on differential diagnoses, diagnostic testing, final diagnosis, and management 
      based on patient age, gender, and case acuity. SETTING: ChatGPT, a publicly 
      available LLM. PARTICIPANTS: Clinical vignettes featured hypothetical patients 
      with a variety of age and gender identities, and a range of Emergency Severity 
      Indices (ESIs) based on initial clinical presentation. EXPOSURES: MSD Clinical 
      Manual vignettes. MAIN OUTCOMES AND MEASURES: We measured the proportion of 
      correct responses to the questions posed within the clinical vignettes tested. 
      RESULTS: ChatGPT achieved 71.7% (95% CI, 69.3% to 74.1%) accuracy overall across 
      all 36 clinical vignettes. The LLM demonstrated the highest performance in making 
      a final diagnosis with an accuracy of 76.9% (95% CI, 67.8% to 86.1%), and the 
      lowest performance in generating an initial differential diagnosis with an 
      accuracy of 60.3% (95% CI, 54.2% to 66.6%). Compared to answering questions about 
      general medical knowledge, ChatGPT demonstrated inferior performance on 
      differential diagnosis (β=-15.8%, p<0.001) and clinical management (β=-7.4%, 
      p=0.02) type questions. CONCLUSIONS AND RELEVANCE: ChatGPT achieves impressive 
      accuracy in clinical decision making, with particular strengths emerging as it 
      has more clinical information at its disposal.
FAU - Rao, Arya
AU  - Rao A
AUID- ORCID: 0000-0003-3007-4812
FAU - Pang, Michael
AU  - Pang M
FAU - Kim, John
AU  - Kim J
FAU - Kamineni, Meghana
AU  - Kamineni M
FAU - Lie, Winston
AU  - Lie W
FAU - Prasad, Anoop K
AU  - Prasad AK
FAU - Landman, Adam
AU  - Landman A
FAU - Dreyer, Keith J
AU  - Dreyer KJ
FAU - Succi, Marc D
AU  - Succi MD
AUID- ORCID: 0000-0002-1518-3984
LA  - eng
PT  - Preprint
DEP - 20230226
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
UIN - J Med Internet Res. 2023 Aug 22;25:e48659. PMID: 37606976
PMC - PMC9980239
EDAT- 2023/03/04 06:00
MHDA- 2023/03/04 06:01
CRDT- 2023/03/03 02:28
PHST- 2023/03/03 02:28 [entrez]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/04 06:01 [medline]
AID - 2023.02.21.23285886 [pii]
AID - 10.1101/2023.02.21.23285886 [doi]
PST - epublish
SO  - medRxiv. 2023 Feb 26:2023.02.21.23285886. doi: 10.1101/2023.02.21.23285886. 
      Preprint.

PMID- 37038381
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230412
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Overview of Early ChatGPT's Presence in Medical Literature: Insights From a 
      Hybrid Literature Review by ChatGPT and Human Experts.
PG  - e37281
LID - 10.7759/cureus.37281 [doi]
LID - e37281
AB  - ChatGPT, an artificial intelligence chatbot, has rapidly gained prominence in 
      various domains, including medical education and healthcare literature. This 
      hybrid narrative review, conducted collaboratively by human authors and ChatGPT, 
      aims to summarize and synthesize the current knowledge of ChatGPT in the indexed 
      medical literature during its initial four months. A search strategy was employed 
      in PubMed and EuropePMC databases, yielding 65 and 110 papers, respectively. 
      These papers focused on ChatGPT's impact on medical education, scientific 
      research, medical writing, ethical considerations, diagnostic decision-making, 
      automation potential, and criticisms. The findings indicate a growing body of 
      literature on ChatGPT's applications and implications in healthcare, highlighting 
      the need for further research to assess its effectiveness and ethical concerns.
CI  - Copyright © 2023, Temsah et al.
FAU - Temsah, Omar
AU  - Temsah O
AD  - Collage of Medicine, Alfaisal University, Riyadh, SAU.
FAU - Khan, Samina A
AU  - Khan SA
AD  - Computer Sciences, Universiti Sains Malaysia, Penang, MYS.
FAU - Chaiah, Yazan
AU  - Chaiah Y
AD  - College of Medicine, Alfaisal University, Riyadh, SAU.
FAU - Senjab, Abdulrahman
AU  - Senjab A
AD  - College of Medicine, Alfaisal University, Riyadh, SAU.
FAU - Alhasan, Khalid
AU  - Alhasan K
AD  - Pediatric Nephrology, King Saud University, Riyadh, SAU.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Family and Community Medicine, King Saud University, Riyadh, SAU.
FAU - Aljamaan, Fadi
AU  - Aljamaan F
AD  - Critical Care, King Saud University, Riyadh, SAU.
FAU - Malki, Khalid H
AU  - Malki KH
AD  - Otolaryngology, King Saud University, Riyadh, SAU.
FAU - Halwani, Rabih
AU  - Halwani R
AD  - Clinical Sciences, University of Sharjah, Sharjah, ARE.
FAU - Al-Tawfiq, Jaffar A
AU  - Al-Tawfiq JA
AD  - Specialty Internal Medicine and Quality, Johns Hopkins Aramco Healthcare, 
      Dhahran, SAU.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Intensive Care, King Saud University, Riyadh, SAU.
FAU - Al-Eyadhy, Ayman
AU  - Al-Eyadhy A
AD  - Pediatric Intensive Care, King Saud University, Riyadh, SAU.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230408
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10082551
OTO - NOTNLM
OT  - artificial intelligence in medicine
OT  - chatgpt
OT  - diagnostic decision-making
OT  - ethical considerations
OT  - generative language model
OT  - hybrid method
OT  - machine learning (ml)
OT  - medical education
OT  - medical practice
OT  - medical writing
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/12 06:00
MHDA- 2023/04/12 06:01
CRDT- 2023/04/11 01:43
PHST- 2023/04/07 00:00 [accepted]
PHST- 2023/04/12 06:01 [medline]
PHST- 2023/04/11 01:43 [entrez]
PHST- 2023/04/12 06:00 [pubmed]
AID - 10.7759/cureus.37281 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 8;15(4):e37281. doi: 10.7759/cureus.37281. eCollection 2023 Apr.

PMID- 37124601
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230502
IS  - 0970-9134 (Print)
IS  - 0973-7723 (Electronic)
IS  - 0970-9134 (Linking)
VI  - 39
IP  - 3
DP  - 2023 May
TI  - ChatGPT-a foe or an ally?
PG  - 217-221
LID - 10.1007/s12055-023-01507-6 [doi]
FAU - Yadava, Om Prakash
AU  - Yadava OP
AUID- ORCID: 0000-0001-5088-7226
AD  - National Heart Institute, New Delhi, India. GRID: grid.512696.f. ISNI: 0000 0004 
      1800 3031
LA  - eng
PT  - Editorial
DEP - 20230328
PL  - India
TA  - Indian J Thorac Cardiovasc Surg
JT  - Indian journal of thoracic and cardiovascular surgery
JID - 8700105
PMC - PMC10140247
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Non-human authors
OT  - OpenAI
OT  - Plagiarism
OT  - Scientific publications
COIS- Conflict of interestThe authors declare no competing interests.
EDAT- 2023/05/01 06:42
MHDA- 2023/05/01 06:43
PMCR- 2024/05/01
CRDT- 2023/05/01 03:46
PHST- 2024/05/01 00:00 [pmc-release]
PHST- 2023/05/01 06:43 [medline]
PHST- 2023/05/01 06:42 [pubmed]
PHST- 2023/05/01 03:46 [entrez]
AID - 1507 [pii]
AID - 10.1007/s12055-023-01507-6 [doi]
PST - ppublish
SO  - Indian J Thorac Cardiovasc Surg. 2023 May;39(3):217-221. doi: 
      10.1007/s12055-023-01507-6. Epub 2023 Mar 28.

PMID- 37521206
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230801
IS  - 2229-5178 (Print)
IS  - 2249-5673 (Electronic)
IS  - 2229-5178 (Linking)
VI  - 14
IP  - 4
DP  - 2023 Jul-Aug
TI  - ChatGPT- Quo Vadis?
PG  - 457-458
LID - 10.4103/idoj.idoj_344_23 [doi]
FAU - Kaliyadan, Feroze
AU  - Kaliyadan F
AD  - Department of Dermatology, Sree Narayana Institute of Medical Sciences, 
      Ernakulam, Kerala, India.
FAU - Seetharam, K A
AU  - Seetharam KA
AD  - Department of Dermatology, Venereology and Leprosy, GSL Medical College, 
      Rajahmundry, Andhra Pradesh, India.
LA  - eng
PT  - Editorial
DEP - 20230628
PL  - India
TA  - Indian Dermatol Online J
JT  - Indian dermatology online journal
JID - 101586880
PMC - PMC10373833
EDAT- 2023/07/31 06:42
MHDA- 2023/07/31 06:43
CRDT- 2023/07/31 05:04
PHST- 2023/05/02 00:00 [received]
PHST- 2023/05/10 00:00 [revised]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/07/31 06:43 [medline]
PHST- 2023/07/31 06:42 [pubmed]
PHST- 2023/07/31 05:04 [entrez]
AID - IDOJ-14-457 [pii]
AID - 10.4103/idoj.idoj_344_23 [doi]
PST - epublish
SO  - Indian Dermatol Online J. 2023 Jun 28;14(4):457-458. doi: 
      10.4103/idoj.idoj_344_23. eCollection 2023 Jul-Aug.

PMID- 37103928
OWN - NLM
STAT- MEDLINE
DCOM- 20230619
LR  - 20231206
IS  - 2168-6173 (Electronic)
IS  - 2168-6165 (Print)
IS  - 2168-6165 (Linking)
VI  - 141
IP  - 6
DP  - 2023 Jun 1
TI  - Performance of an Artificial Intelligence Chatbot in Ophthalmic Knowledge 
      Assessment.
PG  - 589-597
LID - 10.1001/jamaophthalmol.2023.1144 [doi]
AB  - IMPORTANCE: ChatGPT is an artificial intelligence (AI) chatbot that has 
      significant societal implications. Training curricula using AI are being 
      developed in medicine, and the performance of chatbots in ophthalmology has not 
      been characterized. OBJECTIVE: To assess the performance of ChatGPT in answering 
      practice questions for board certification in ophthalmology. DESIGN, SETTING, AND 
      PARTICIPANTS: This cross-sectional study used a consecutive sample of text-based 
      multiple-choice questions provided by the OphthoQuestions practice question bank 
      for board certification examination preparation. Of 166 available multiple-choice 
      questions, 125 (75%) were text-based. EXPOSURES: ChatGPT answered questions from 
      January 9 to 16, 2023, and on February 17, 2023. MAIN OUTCOMES AND MEASURES: Our 
      primary outcome was the number of board certification examination practice 
      questions that ChatGPT answered correctly. Our secondary outcomes were the 
      proportion of questions for which ChatGPT provided additional explanations, the 
      mean length of questions and responses provided by ChatGPT, the performance of 
      ChatGPT in answering questions without multiple-choice options, and changes in 
      performance over time. RESULTS: In January 2023, ChatGPT correctly answered 58 of 
      125 questions (46%). ChatGPT's performance was the best in the category general 
      medicine (11/14; 79%) and poorest in retina and vitreous (0%). The proportion of 
      questions for which ChatGPT provided additional explanations was similar between 
      questions answered correctly and incorrectly (difference, 5.82%; 95% CI, -11.0% 
      to 22.0%; χ21 = 0.45; P = .51). The mean length of questions was similar between 
      questions answered correctly and incorrectly (difference, 21.4 characters; SE, 
      36.8; 95% CI, -51.4 to 94.3; t = 0.58; df = 123; P = .22). The mean length of 
      responses was similar between questions answered correctly and incorrectly 
      (difference, -80.0 characters; SE, 65.4; 95% CI, -209.5 to 49.5; t = -1.22; 
      df = 123; P = .22). ChatGPT selected the same multiple-choice response as the 
      most common answer provided by ophthalmology trainees on OphthoQuestions 44% of 
      the time. In February 2023, ChatGPT provided a correct response to 73 of 125 
      multiple-choice questions (58%) and 42 of 78 stand-alone questions (54%) without 
      multiple-choice options. CONCLUSIONS AND RELEVANCE: ChatGPT answered 
      approximately half of questions correctly in the OphthoQuestions free trial for 
      ophthalmic board certification preparation. Medical professionals and trainees 
      should appreciate the advances of AI in medicine while acknowledging that ChatGPT 
      as used in this investigation did not answer sufficient multiple-choice questions 
      correctly for it to provide substantial assistance in preparing for board 
      certification at this time.
FAU - Mihalache, Andrew
AU  - Mihalache A
AD  - Schulich School of Medicine and Dentistry, University of Western Ontario, London, 
      Ontario, Canada.
FAU - Popovic, Marko M
AU  - Popovic MM
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
FAU - Muni, Rajeev H
AU  - Muni RH
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
AD  - Department of Ophthalmology, St Michael's Hospital/Unity Health Toronto, Toronto, 
      Ontario, Canada.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - JAMA Ophthalmol
JT  - JAMA ophthalmology
JID - 101589539
SB  - IM
CIN - JAMA Ophthalmol. 2023 Jun 1;141(6):514-515. PMID: 37103930
MH  - Humans
MH  - *Artificial Intelligence
MH  - Cross-Sectional Studies
MH  - Retina
MH  - Curriculum
MH  - *Ophthalmology
PMC - PMC10141269
COIS- Conflict of Interest Disclosures: Dr Popovic reported grants (to his institution) 
      from PSI Foundation and Fighting Blindness Canada outside the submitted work. Dr 
      Muni reported serving on the advisory board for Alcon, Bausch and Lomb, Bayer, 
      Novartis, Allergan, and Roche and receiving financial support (to his 
      institution) from Bayer, Novartis, and Roche outside the submitted work. No other 
      disclosures were reported.
EDAT- 2023/04/27 12:41
MHDA- 2023/06/19 13:08
PMCR- 2024/04/27
CRDT- 2023/04/27 11:33
PHST- 2024/04/27 00:00 [pmc-release]
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/04/27 12:41 [pubmed]
PHST- 2023/04/27 11:33 [entrez]
AID - 2804364 [pii]
AID - ebr230002 [pii]
AID - 10.1001/jamaophthalmol.2023.1144 [doi]
PST - ppublish
SO  - JAMA Ophthalmol. 2023 Jun 1;141(6):589-597. doi: 
      10.1001/jamaophthalmol.2023.1144.

PMID- 37495493
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231002
LR  - 20231002
IS  - 1873-4898 (Electronic)
IS  - 1477-5131 (Linking)
VI  - 19
IP  - 5
DP  - 2023 Oct
TI  - ChatGPT and large language model (LLM) chatbots: Correspondence.
PG  - 605-606
LID - S1477-5131(23)00301-7 [pii]
LID - 10.1016/j.jpurol.2023.06.033 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230713
PL  - England
TA  - J Pediatr Urol
JT  - Journal of pediatric urology
JID - 101233150
SB  - IM
CON - J Pediatr Urol. 2023 Oct;19(5):598-604. PMID: 37328321
CIN - J Pediatr Urol. 2023 Oct;19(5):607. PMID: 37487885
COIS- Conflicts of interest None.
EDAT- 2023/07/27 01:09
MHDA- 2023/07/27 01:10
CRDT- 2023/07/26 21:59
PHST- 2023/06/17 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/07/27 01:10 [medline]
PHST- 2023/07/27 01:09 [pubmed]
PHST- 2023/07/26 21:59 [entrez]
AID - S1477-5131(23)00301-7 [pii]
AID - 10.1016/j.jpurol.2023.06.033 [doi]
PST - ppublish
SO  - J Pediatr Urol. 2023 Oct;19(5):605-606. doi: 10.1016/j.jpurol.2023.06.033. Epub 
      2023 Jul 13.

PMID- 37814667
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1985-207X (Print)
IS  - 1985-2274 (Electronic)
IS  - 1985-2274 (Linking)
VI  - 18
DP  - 2023
TI  - Use of ChatGPT in medical research and scientific writing.
PG  - 58
LID - 10.51866/cm0006 [doi]
AB  - ChatGPT, an artificial intelligence (AI) language model based on the GPT-3.5 
      architecture, is revolutionising scientific writing and medical research. 
      Researchers employ ChatGPT for diverse tasks, including automated literature 
      reviews, structured-outline generation and drafting/editing assistance. The tool 
      adapts language for varied audiences, aids in citation management, supports 
      collaborative writing and peer review and facilitates table/figure creation. 
      While it enhances efficiency, concerns arise regarding ethics, bias, accuracy and 
      originality. Transparent data sourcing and validation are crucial, as ChatGPT 
      complements human efforts but does not replace critical thinking. Accordingly, 
      researchers must uphold integrity, ensuring that AI-assisted content aligns with 
      research principles. Acknowledgement of AI use in manuscripts, as recommended by 
      the International Committee of Medical Journal Editors, ensures accountability. 
      ChatGPT's transformative potential lies in harmonising its capabilities with 
      researchers' expertise, fostering a symbiotic relationship that advances 
      scientific progress and ethical standards.
CI  - © Academy of Family Physicians of Malaysia.
FAU - Lee, Ping Yein
AU  - Lee PY
AD  - MBBS, DrFamMed, UM eHealth Unit, Faculty of Medicine, Universiti Malaya, Kuala 
      Lumpur, Malaysia. Email: pylee02@gmail.com.
FAU - Salim, Hani
AU  - Salim H
AD  - MBBChBAO, MFamMed, PhD, Department of Family Medicine, Universiti Putra Malaysia, 
      Seri Kembangan, Selangor, Malaysia.
FAU - Abdullah, Adina
AU  - Abdullah A
AD  - MBBS, MFamMed, PhD, Department of Primary Care Medicine, Faculty of Medicine, 
      Universiti, Malaya, Kuala Lumpur, Malaysia.
FAU - Teo, Chin Hai
AU  - Teo CH
AD  - BMedImag, PhD, Department of Primary Care Medicine, Faculty of Medicine, 
      Universiti, Malaya, Kuala Lumpur, Malaysia.
AD  - UM eHealth Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
LA  - eng
PT  - Editorial
DEP - 20230925
PL  - Malaysia
TA  - Malays Fam Physician
JT  - Malaysian family physician : the official journal of the Academy of Family 
      Physicians of Malaysia
JID - 101466855
PMC - PMC10560470
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ChatGPT
OT  - Medical research
OT  - Scientific writing
COIS- All authors declare no conflicts of interest.
EDAT- 2023/10/10 06:41
MHDA- 2023/10/10 06:42
CRDT- 2023/10/10 03:38
PHST- 2023/10/10 06:42 [medline]
PHST- 2023/10/10 06:41 [pubmed]
PHST- 2023/10/10 03:38 [entrez]
AID - 10.51866/cm0006 [doi]
PST - epublish
SO  - Malays Fam Physician. 2023 Sep 25;18:58. doi: 10.51866/cm0006. eCollection 2023.

PMID- 37328321
OWN - NLM
STAT- MEDLINE
DCOM- 20231002
LR  - 20231002
IS  - 1873-4898 (Electronic)
IS  - 1477-5131 (Linking)
VI  - 19
IP  - 5
DP  - 2023 Oct
TI  - ChatGPT and large language model (LLM) chatbots: The current state of 
      acceptability and a proposal for guidelines on utilization in academic medicine.
PG  - 598-604
LID - S1477-5131(23)00224-3 [pii]
LID - 10.1016/j.jpurol.2023.05.018 [doi]
AB  - INTRODUCTION: There is currently no clear consensus on the standards for using 
      large language models such as ChatGPT in academic medicine. Hence, we performed a 
      scoping review of available literature to understand the current state of LLM use 
      in medicine and to provide a guideline for future utilization in academia. 
      MATERIALS AND METHODS: A scoping review of the literature was performed through a 
      Medline search on February 16, 2023 using a combination of keywords including 
      artificial intelligence, machine learning, natural language processing, 
      generative pre-trained transformer, ChatGPT, and large language model. There were 
      no restrictions to language or date of publication. Records not pertaining to 
      LLMs were excluded. Records pertaining to LLM ChatBots and ChatGPT were 
      identified and evaluated separately. Among the records pertaining to LLM ChatBots 
      and ChatGPT, those that suggest recommendations for ChatGPT use in academia were 
      utilized to create guideline statements for ChatGPT and LLM use in academic 
      medicine. RESULTS: A total of 87 records were identified. 30 records were not 
      pertaining to large language models and were excluded. 54 records underwent a 
      full-text review for evaluation. There were 33 records related to LLM ChatBots or 
      ChatGPT. DISCUSSION: From assessing these texts, five guideline statements for 
      LLM use was developed: (1) ChatGPT/LLM cannot be cited as an author in scientific 
      manuscripts; (2) If use of ChatGPT/LLM are considered for use in academic work, 
      author(s) should have at least a basic understanding of what ChatGPT/LLM is; (3) 
      Do not use ChatGPT/LLM to produce entirety of text in manuscripts; humans must be 
      held accountable for use of ChatGPT/LLM and contents created by ChatGPT/LLM 
      should be meticulously verified by humans; (4) ChatGPT/LLMs may be used for 
      editing and refining of text; (5) Any use of ChatGPT/LLM should be transparent 
      and should be clearly outlined in scientific manuscripts and acknowledged. 
      CONCLUSION: Future authors should remain mindful of the potential impact their 
      academic work may have on healthcare and continue to uphold the highest ethical 
      standards and integrity when utilizing ChatGPT/LLM.
CI  - Copyright © 2023 Journal of Pediatric Urology Company. Published by Elsevier Ltd. 
      All rights reserved.
FAU - Kim, Jin K
AU  - Kim JK
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada; Division of Urology, Department of Surgery, University of 
      Toronto, Toronto, Canada. Electronic address: jjk.kim@mail.utoronto.ca.
FAU - Chua, Michael
AU  - Chua M
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada; Division of Urology, Department of Surgery, University of 
      Toronto, Toronto, Canada; Institute of Urology, St. Luke's Medical Center, Quezon 
      City, Philippines.
FAU - Rickard, Mandy
AU  - Rickard M
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada.
FAU - Lorenzo, Armando
AU  - Lorenzo A
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada; Division of Urology, Department of Surgery, University of 
      Toronto, Toronto, Canada.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230602
PL  - England
TA  - J Pediatr Urol
JT  - Journal of pediatric urology
JID - 101233150
SB  - IM
CIN - J Pediatr Urol. 2023 Oct;19(5):605-606. PMID: 37495493
MH  - Humans
MH  - *Artificial Intelligence
MH  - Consensus
MH  - *Medicine
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Generative pre-trained transformer
OT  - Large language model
COIS- Conflicts of interest The authors do not have any conflict of interest to 
      disclose.
EDAT- 2023/06/17 05:11
MHDA- 2023/10/02 06:42
CRDT- 2023/06/16 21:54
PHST- 2023/03/01 00:00 [received]
PHST- 2023/05/14 00:00 [revised]
PHST- 2023/05/27 00:00 [accepted]
PHST- 2023/10/02 06:42 [medline]
PHST- 2023/06/17 05:11 [pubmed]
PHST- 2023/06/16 21:54 [entrez]
AID - S1477-5131(23)00224-3 [pii]
AID - 10.1016/j.jpurol.2023.05.018 [doi]
PST - ppublish
SO  - J Pediatr Urol. 2023 Oct;19(5):598-604. doi: 10.1016/j.jpurol.2023.05.018. Epub 
      2023 Jun 2.

PMID- 37433672
OWN - NLM
STAT- MEDLINE
DCOM- 20230907
LR  - 20230907
IS  - 1535-5675 (Electronic)
IS  - 0091-4916 (Linking)
VI  - 51
IP  - 3
DP  - 2023 Sep
TI  - A Conversation with ChatGPT.
PG  - 255-260
LID - 10.2967/jnmt.123.265864 [doi]
AB  - ChatGPT chatbot powered by GPT 3.5 was released in late November 2022 but has 
      been rapidly assimilated into educational and clinical environments. Method: 
      Insight into ChatGPT capabilities was undertaken in an interview-style approach 
      with the chatbot itself. Results: ChatGPT powered by GPT 3.5 exudes confidence in 
      its capabilities in supporting and enhancing student learning in nuclear medicine 
      and in supporting clinical practice. ChatGPT is also self-aware of limitations 
      and flaws in capabilities and the risks these pose to academic integrity. 
      Conclusion: Further objective evaluation of ChatGPT capabilities in authentic 
      learning and clinical scenarios is required.
CI  - © 2023 by the Society of Nuclear Medicine and Molecular Imaging.
FAU - Currie, Geoffrey
AU  - Currie G
AD  - Charles Sturt University, Wagga Wagga, New South Wales, Australia 
      gcurrie@csu.edu.au.
LA  - eng
PT  - Journal Article
DEP - 20230711
PL  - United States
TA  - J Nucl Med Technol
JT  - Journal of nuclear medicine technology
JID - 0430303
SB  - IM
MH  - Humans
MH  - *Learning
MH  - *Nuclear Medicine
MH  - Software
MH  - Students
OTO - NOTNLM
OT  - academic integrity
OT  - artificial intelligence
OT  - generative AI
OT  - higher education
OT  - language model
OT  - tertiary education
EDAT- 2023/07/12 01:07
MHDA- 2023/09/07 06:42
CRDT- 2023/07/11 21:38
PHST- 2023/04/12 00:00 [received]
PHST- 2023/05/13 00:00 [revised]
PHST- 2023/09/07 06:42 [medline]
PHST- 2023/07/12 01:07 [pubmed]
PHST- 2023/07/11 21:38 [entrez]
AID - jnmt.123.265864 [pii]
AID - 10.2967/jnmt.123.265864 [doi]
PST - ppublish
SO  - J Nucl Med Technol. 2023 Sep;51(3):255-260. doi: 10.2967/jnmt.123.265864. Epub 
      2023 Jul 11.

PMID- 37802491
OWN - NLM
STAT- In-Process
LR  - 20231208
IS  - 2042-1834 (Electronic)
IS  - 0025-8172 (Linking)
VI  - 91
IP  - 4
DP  - 2023 Dec
TI  - ChatGPT in medical research: challenging time ahead.
PG  - 223-225
LID - 10.1177/00258172231184548 [doi]
AB  - Since its launch, ChatGPT, an artificial intelligence-powered language model 
      tool, has generated significant attention in research writing. The use of ChatGPT 
      in medical research can be a double-edged sword. ChatGPT can expedite the 
      research writing process by assisting with hypothesis formulation, literature 
      review, data analysis and manuscript writing. On the other hand, using ChatGPT 
      raises concerns regarding the originality and authenticity of content, the 
      precision and potential bias of the tool's output, and the potential legal issues 
      associated with privacy, confidentiality and plagiarism. The article also calls 
      for adherence to stringent citation guidelines and the development of regulations 
      promoting the responsible application of AI. Despite the revolutionary 
      capabilities of ChatGPT, the article highlights its inability to replicate human 
      thought and the difficulties in maintaining the integrity and reliability of 
      ChatGPT-enabled research, particularly in complex fields such as medicine and 
      law. AI tools can be used as supplementary aids rather than primary sources of 
      analysis in medical research writing.
FAU - Bhargava, Daideepya C
AU  - Bhargava DC
AD  - Department of Forensic Medicine and Toxicology, All India Institute of Medical 
      Sciences, India.
FAU - Jadav, Devendra
AU  - Jadav D
AUID- ORCID: 0000-0001-7975-2130
AD  - Department of Forensic Medicine and Toxicology, All India Institute of Medical 
      Sciences, India.
FAU - Meshram, Vikas P
AU  - Meshram VP
AD  - Department of Forensic Medicine and Toxicology, All India Institute of Medical 
      Sciences, India.
FAU - Kanchan, Tanuj
AU  - Kanchan T
AD  - Department of Forensic Medicine and Toxicology, All India Institute of Medical 
      Sciences, India.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231006
PL  - England
TA  - Med Leg J
JT  - The Medico-legal journal
JID - 0412004
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Medical research
OT  - accuracy
OT  - artificial intelligence
OT  - ethical considerations
OT  - research misconduct
COIS- Declaration of conflicting interestsThe author(s) declared no potential conflicts 
      of interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/10/07 00:41
MHDA- 2023/10/07 00:41
CRDT- 2023/10/06 19:42
PHST- 2023/10/07 00:41 [pubmed]
PHST- 2023/10/07 00:41 [medline]
PHST- 2023/10/06 19:42 [entrez]
AID - 10.1177/00258172231184548 [doi]
PST - ppublish
SO  - Med Leg J. 2023 Dec;91(4):223-225. doi: 10.1177/00258172231184548. Epub 2023 Oct 
      6.

PMID- 37253791
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230602
IS  - 2374-4677 (Print)
IS  - 2374-4677 (Electronic)
IS  - 2374-4677 (Linking)
VI  - 9
IP  - 1
DP  - 2023 May 30
TI  - Large language model (ChatGPT) as a support tool for breast tumor board.
PG  - 44
LID - 10.1038/s41523-023-00557-8 [doi]
LID - 44
AB  - Large language models (LLM) such as ChatGPT have gained public and scientific 
      attention. The aim of this study is to evaluate ChatGPT as a support tool for 
      breast tumor board decisions making. We inserted into ChatGPT-3.5 clinical 
      information of ten consecutive patients presented in a breast tumor board in our 
      institution. We asked the chatbot to recommend management. The results generated 
      by ChatGPT were compared to the final recommendations of the tumor board. They 
      were also graded independently by two senior radiologists. Grading scores were 
      between 1-5 (1 = completely disagree, 5 = completely agree), and in three 
      different categories: summarization, recommendation, and explanation. The mean 
      age was 49.4, 8/10 (80%) of patients had invasive ductal carcinoma, one patient 
      (1/10, 10%) had a ductal carcinoma in-situ and one patient (1/10, 10%) had a 
      phyllodes tumor with atypia. In seven out of ten cases (70%), ChatGPT's 
      recommendations were similar to the tumor board's decisions. Mean scores while 
      grading the chatbot's summarization, recommendation and explanation by the first 
      reviewer were 3.7, 4.3, and 4.6 respectively. Mean values for the second reviewer 
      were 4.3, 4.0, and 4.3, respectively. In this proof-of-concept study, we present 
      initial results on the use of an LLM as a decision support tool in a breast tumor 
      board. Given the significant advancements, it is warranted for clinicians to be 
      familiar with the potential benefits and harms of the technology.
CI  - © 2023. The Author(s).
FAU - Sorin, Vera
AU  - Sorin V
AUID- ORCID: 0000-0003-0509-4686
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel. verasrn@gmail.com.
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel. 
      verasrn@gmail.com.
AD  - DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel. 
      verasrn@gmail.com.
FAU - Klang, Eyal
AU  - Klang E
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel.
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
AD  - DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel.
AD  - Sami Sagol AI Hub, ARC, Chaim Sheba Medical Center, Tel Hashomer, Israel.
FAU - Sklair-Levy, Miri
AU  - Sklair-Levy M
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel.
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
FAU - Cohen, Israel
AU  - Cohen I
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel.
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
FAU - Zippel, Douglas B
AU  - Zippel DB
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
AD  - Department of General and Oncological Surgery- Surgery C, Chaim Sheba Medical 
      Center, Tel Hashomer, Israel.
FAU - Balint Lahat, Nora
AU  - Balint Lahat N
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
AD  - Department of Pathology, Chaim Sheba Medical Center, Tel Hashomer, Israel.
FAU - Konen, Eli
AU  - Konen E
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel.
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
FAU - Barash, Yiftach
AU  - Barash Y
AUID- ORCID: 0000-0002-7242-1328
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel.
AD  - Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
AD  - DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230530
PL  - United States
TA  - NPJ Breast Cancer
JT  - NPJ breast cancer
JID - 101674891
PMC - PMC10229606
COIS- The authors declare no competing interests.
EDAT- 2023/05/31 01:09
MHDA- 2023/05/31 01:10
CRDT- 2023/05/30 23:20
PHST- 2023/03/16 00:00 [received]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/05/31 01:10 [medline]
PHST- 2023/05/31 01:09 [pubmed]
PHST- 2023/05/30 23:20 [entrez]
AID - 10.1038/s41523-023-00557-8 [pii]
AID - 557 [pii]
AID - 10.1038/s41523-023-00557-8 [doi]
PST - epublish
SO  - NPJ Breast Cancer. 2023 May 30;9(1):44. doi: 10.1038/s41523-023-00557-8.

PMID- 37793672
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 2005-8330 (Electronic)
IS  - 1229-6929 (Print)
IS  - 1229-6929 (Linking)
VI  - 24
IP  - 10
DP  - 2023 Oct
TI  - Uncover This Tech Term: Foundation Model.
PG  - 1038-1041
LID - 10.3348/kjr.2023.0790 [doi]
FAU - Jung, Kyu-Hwan
AU  - Jung KH
AUID- ORCID: 0000-0002-6626-6800
AD  - Department of Medical Device Management and Research, Samsung Advanced Institute 
      for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of 
      Korea.
AD  - Dataset Science Research Institute, Research Institute for Future Medicine, 
      Samsung Medical Center, Seoul, Republic of Korea. khwanjung@skku.edu.
LA  - eng
PT  - Editorial
PL  - Korea (South)
TA  - Korean J Radiol
JT  - Korean journal of radiology
JID - 100956096
SB  - IM
PMC - PMC10550749
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Few shot
OT  - Foundation model
OT  - Large language model
OT  - Representation
OT  - Transformer
OT  - Zero shot
COIS- The author has no potential conflicts of interest to disclose.
EDAT- 2023/10/05 01:00
MHDA- 2023/10/05 01:01
CRDT- 2023/10/04 20:12
PHST- 2023/08/21 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/10/05 01:01 [medline]
PHST- 2023/10/05 01:00 [pubmed]
PHST- 2023/10/04 20:12 [entrez]
AID - 24.1038 [pii]
AID - 10.3348/kjr.2023.0790 [doi]
PST - ppublish
SO  - Korean J Radiol. 2023 Oct;24(10):1038-1041. doi: 10.3348/kjr.2023.0790.

PMID- 37694729
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231109
IS  - 1651-2251 (Electronic)
IS  - 0001-6489 (Linking)
VI  - 143
IP  - 9
DP  - 2023 Sep
TI  - Validity of the large language model ChatGPT (GPT4) as a patient information 
      source in otolaryngology by a variety of doctors in a tertiary 
      otorhinolaryngology department.
PG  - 779-782
LID - 10.1080/00016489.2023.2254809 [doi]
AB  - BACKGROUND: A high number of patients seek health information online, and large 
      language models (LLMs) may produce a rising amount of it. AIM: This study 
      evaluates the performance regarding health information provided by ChatGPT, a LLM 
      developed by OpenAI, focusing on its utility as a source for 
      otolaryngology-related patient information. MATERIAL AND METHOD: A variety of 
      doctors from a tertiary otorhinolaryngology department used a Likert scale to 
      assess the chatbot's responses in terms of accuracy, relevance, and depth. The 
      responses were also evaluated by ChatGPT. RESULTS: The composite mean of the 
      three categories was 3.41, with the highest performance noted in the relevance 
      category (mean = 3.71) when evaluated by the respondents. The accuracy and depth 
      categories yielded mean scores of 3.51 and 3.00, respectively. All the categories 
      were rated as 5 when evaluated by ChatGPT. CONCLUSION AND SIGNIFICANCE: Despite 
      its potential in providing relevant and accurate medical information, the 
      chatbot's responses lacked depth and were found to potentially perpetuate biases 
      due to its training on publicly available text. In conclusion, while LLMs show 
      promise in healthcare, further refinement is necessary to enhance response depth 
      and mitigate potential biases.
FAU - Nielsen, Jacob P S
AU  - Nielsen JPS
AD  - Department of Otorhinolaryngology-Head and Neck Surgery and Audiology, Copenhagen 
      University Hospital, Rigshospitalet, Copenhagen, Denmark.
FAU - von Buchwald, Christian
AU  - von Buchwald C
AD  - Department of Otorhinolaryngology-Head and Neck Surgery and Audiology, Copenhagen 
      University Hospital, Rigshospitalet, Copenhagen, Denmark.
FAU - Grønhøj, Christian
AU  - Grønhøj C
AD  - Department of Otorhinolaryngology-Head and Neck Surgery and Audiology, Copenhagen 
      University Hospital, Rigshospitalet, Copenhagen, Denmark.
LA  - eng
PT  - Editorial
DEP - 20230911
PL  - England
TA  - Acta Otolaryngol
JT  - Acta oto-laryngologica
JID - 0370354
SB  - IM
MH  - Humans
MH  - Information Sources
MH  - *Physicians
MH  - Hospital Departments
MH  - Language
MH  - *Otolaryngology
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT4
OT  - chatbots
OT  - ear-nose-throat diseases
OT  - large language model
OT  - otolaryngology
OT  - patient information
OT  - validation study
EDAT- 2023/09/11 12:43
MHDA- 2023/11/09 06:42
CRDT- 2023/09/11 07:02
PHST- 2023/11/09 06:42 [medline]
PHST- 2023/09/11 12:43 [pubmed]
PHST- 2023/09/11 07:02 [entrez]
AID - 10.1080/00016489.2023.2254809 [doi]
PST - ppublish
SO  - Acta Otolaryngol. 2023 Sep;143(9):779-782. doi: 10.1080/00016489.2023.2254809. 
      Epub 2023 Sep 11.

PMID- 36858933
OWN - NLM
STAT- MEDLINE
DCOM- 20230529
LR  - 20230720
IS  - 2211-5684 (Electronic)
IS  - 2211-5684 (Linking)
VI  - 104
IP  - 6
DP  - 2023 Jun
TI  - Revolutionizing radiology with GPT-based models: Current applications, future 
      possibilities and limitations of ChatGPT.
PG  - 269-274
LID - S2211-5684(23)00027-X [pii]
LID - 10.1016/j.diii.2023.02.003 [doi]
AB  - Artificial intelligence has demonstrated utility and is increasingly being used 
      in the field of radiology. The use of generative pre-trained transformer 
      (GPT)-based models has the potential to revolutionize the field of radiology, 
      offering new possibilities for improving accuracy, efficiency, and patient 
      outcome. Current applications of GPT-based models in radiology include report 
      generation, educational support, clinical decision support, patient 
      communication, and data analysis. As these models continue to advance and 
      improve, it is likely that more innovative uses for GPT-based models in the field 
      of radiology at large will be developed, further enhancing the role of technology 
      in the diagnostic process. ChatGPT is a variant of GPT that is specifically 
      fine-tuned for conversational language understanding and generation. This article 
      reports some answers provided by ChatGPT to various questions that radiologists 
      may have regarding ChatGPT and identifies the potential benefits ChatGPT may 
      offer in their daily practice but also current limitations. Similar to other 
      applications of artificial intelligence in the field of imaging, further formal 
      validation of ChatGPT is required.
CI  - Copyright © 2023 Société française de radiologie. Published by Elsevier Masson 
      SAS. All rights reserved.
FAU - Lecler, Augustin
AU  - Lecler A
AD  - Department of Neuroradiology, Foundation Adolphe de Rothschild Hospital, 75019 
      Paris, France; Université Paris Cité, 75006 Paris, France. Electronic address: 
      alecler@for.paris.
FAU - Duron, Loïc
AU  - Duron L
AD  - Department of Neuroradiology, Foundation Adolphe de Rothschild Hospital, 75019 
      Paris, France.
FAU - Soyer, Philippe
AU  - Soyer P
AD  - Université Paris Cité, 75006 Paris, France; Department of Radiology, Hopital 
      Cochin, AP-HP, 75014 Paris, France.
LA  - eng
PT  - Journal Article
DEP - 20230228
PL  - France
TA  - Diagn Interv Imaging
JT  - Diagnostic and interventional imaging
JID - 101568499
SB  - IM
CIN - Diagn Interv Imaging. 2023 Jun;104(6):263-264. PMID: 36925365
MH  - Humans
MH  - *Artificial Intelligence
MH  - Radiography
MH  - *Radiology
MH  - Radiologists
MH  - Communication
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Generative pre-trained transformer (GPT)
OT  - Radiology
COIS- Disclosure of interest Philippe Soyer is the Editor-in-Chief of Diagnostic & 
      Interventional Imaging. The other authors have no conflicts of interest to 
      declare.
EDAT- 2023/03/02 06:00
MHDA- 2023/05/29 06:42
CRDT- 2023/03/01 22:04
PHST- 2023/02/08 00:00 [received]
PHST- 2023/02/10 00:00 [accepted]
PHST- 2023/05/29 06:42 [medline]
PHST- 2023/03/02 06:00 [pubmed]
PHST- 2023/03/01 22:04 [entrez]
AID - S2211-5684(23)00027-X [pii]
AID - 10.1016/j.diii.2023.02.003 [doi]
PST - ppublish
SO  - Diagn Interv Imaging. 2023 Jun;104(6):269-274. doi: 10.1016/j.diii.2023.02.003. 
      Epub 2023 Feb 28.

PMID- 38038796
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2197-1153 (Print)
IS  - 2197-1153 (Electronic)
IS  - 2197-1153 (Linking)
VI  - 10
IP  - 1
DP  - 2023 Dec 1
TI  - ChatGPT and large language models in orthopedics: from education and surgery to 
      research.
PG  - 128
LID - 10.1186/s40634-023-00700-1 [doi]
LID - 128
AB  - ChatGPT has quickly popularized since its release in November 2022. Currently, 
      large language models (LLMs) and ChatGPT have been applied in various domains of 
      medical science, including in cardiology, nephrology, orthopedics, ophthalmology, 
      gastroenterology, and radiology. Researchers are exploring the potential of LLMs 
      and ChatGPT for clinicians and surgeons in every domain. This study discusses how 
      ChatGPT can help orthopedic clinicians and surgeons perform various medical 
      tasks. LLMs and ChatGPT can help the patient community by providing suggestions 
      and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance 
      and expand the field of orthopedics, including orthopedic education, surgery, and 
      research, is explored. Present LLMs have several shortcomings, which are 
      discussed herein. However, next-generation and future domain-specific LLMs are 
      expected to be more potent and transform patients' quality of life.
CI  - © 2023. The Author(s).
FAU - Chatterjee, Srijan
AU  - Chatterjee S
AUID- ORCID: 0000-0002-8282-8324
AD  - Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon 
      Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AUID- ORCID: 0000-0001-9669-1835
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, 
      Odisha, India.
FAU - Pal, Soumen
AU  - Pal S
AUID- ORCID: 0000-0001-9508-2712
AD  - School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil 
      Nadu, India.
FAU - Lee, Sang-Soo
AU  - Lee SS
AUID- ORCID: 0000-0001-5074-7581
AD  - Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon 
      Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea. 
      123sslee@gmail.com.
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AUID- ORCID: 0000-0002-3958-239X
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal, 700126, India. drchiranjib@yahoo.com.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231201
PL  - Germany
TA  - J Exp Orthop
JT  - Journal of experimental orthopaedics
JID - 101653750
PMC - PMC10692045
OTO - NOTNLM
OT  - ChatGPT
OT  - Concern
OT  - Large language models
OT  - Orthopedics
COIS- The authors declare that they have no competing interests.
EDAT- 2023/12/01 12:44
MHDA- 2023/12/01 12:45
CRDT- 2023/12/01 11:07
PHST- 2023/08/23 00:00 [received]
PHST- 2023/11/16 00:00 [accepted]
PHST- 2023/12/01 12:45 [medline]
PHST- 2023/12/01 12:44 [pubmed]
PHST- 2023/12/01 11:07 [entrez]
AID - 10.1186/s40634-023-00700-1 [pii]
AID - 700 [pii]
AID - 10.1186/s40634-023-00700-1 [doi]
PST - epublish
SO  - J Exp Orthop. 2023 Dec 1;10(1):128. doi: 10.1186/s40634-023-00700-1.

PMID- 37742280
OWN - NLM
STAT- Publisher
LR  - 20230924
IS  - 1437-160X (Electronic)
IS  - 0172-8172 (Linking)
DP  - 2023 Sep 24
TI  - Diagnostic accuracy of a large language model in rheumatology: comparison of 
      physician and ChatGPT-4.
LID - 10.1007/s00296-023-05464-6 [doi]
AB  - Pre-clinical studies suggest that large language models (i.e., ChatGPT) could be 
      used in the diagnostic process to distinguish inflammatory rheumatic (IRD) from 
      other diseases. We therefore aimed to assess the diagnostic accuracy of ChatGPT-4 
      in comparison to rheumatologists. For the analysis, the data set of Gräf et al. 
      (2022) was used. Previous patient assessments were analyzed using ChatGPT-4 and 
      compared to rheumatologists' assessments. ChatGPT-4 listed the correct diagnosis 
      comparable often to rheumatologists as the top diagnosis 35% vs 39% (p = 0.30); 
      as well as among the top 3 diagnoses, 60% vs 55%, (p = 0.38). In IRD-positive 
      cases, ChatGPT-4 provided the top diagnosis in 71% vs 62% in the rheumatologists' 
      analysis. Correct diagnosis was among the top 3 in 86% (ChatGPT-4) vs 74% 
      (rheumatologists). In non-IRD cases, ChatGPT-4 provided the correct top diagnosis 
      in 15% vs 27% in the rheumatologists' analysis. Correct diagnosis was among the 
      top 3 in non-IRD cases in 46% of the ChatGPT-4 group vs 45% in the 
      rheumatologists group. If only the first suggestion for diagnosis was considered, 
      ChatGPT-4 correctly classified 58% of cases as IRD compared to 56% of the 
      rheumatologists (p = 0.52). ChatGPT-4 showed a slightly higher accuracy for the 
      top 3 overall diagnoses compared to rheumatologist's assessment. ChatGPT-4 was 
      able to provide the correct differential diagnosis in a relevant number of cases 
      and achieved better sensitivity to detect IRDs than rheumatologist, at the cost 
      of lower specificity. The pilot results highlight the potential of this new 
      technology as a triage tool for the diagnosis of IRD.
CI  - © 2023. The Author(s).
FAU - Krusche, Martin
AU  - Krusche M
AUID- ORCID: 0000-0002-0582-7790
AD  - Division of Rheumatology and Systemic Inflammatory Diseases, University Hospital 
      Hamburg-Eppendorf (UKE), Hamburg, Germany. m.krusche@uke.de.
FAU - Callhoff, Johnna
AU  - Callhoff J
AUID- ORCID: 0000-0002-3923-2728
AD  - Epidemiology Unit, German Rheumatism Research Centre, Berlin, Germany.
AD  - Institute for Social Medicine, Epidemiology and Health Economics, Charité 
      Universitätsmedizin, Berlin, Germany.
FAU - Knitza, Johannes
AU  - Knitza J
AUID- ORCID: 0000-0001-9695-0657
AD  - Institute of Digital Medicine, University Hospital of Giessen and Marburg, 
      Philipps University Marburg, Marburg, Germany.
AD  - Université Grenoble Alpes, AGEIS, Grenoble, France.
FAU - Ruffer, Nikolas
AU  - Ruffer N
AUID- ORCID: 0000-0001-8394-969X
AD  - Division of Rheumatology and Systemic Inflammatory Diseases, University Hospital 
      Hamburg-Eppendorf (UKE), Hamburg, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230924
PL  - Germany
TA  - Rheumatol Int
JT  - Rheumatology international
JID - 8206885
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Diagnostic process
OT  - Large language models
OT  - Rheumatology
OT  - Triage
EDAT- 2023/09/24 18:41
MHDA- 2023/09/24 18:41
CRDT- 2023/09/24 15:09
PHST- 2023/08/21 00:00 [received]
PHST- 2023/09/07 00:00 [accepted]
PHST- 2023/09/24 18:41 [medline]
PHST- 2023/09/24 18:41 [pubmed]
PHST- 2023/09/24 15:09 [entrez]
AID - 10.1007/s00296-023-05464-6 [pii]
AID - 10.1007/s00296-023-05464-6 [doi]
PST - aheadofprint
SO  - Rheumatol Int. 2023 Sep 24. doi: 10.1007/s00296-023-05464-6.

PMID- 37266053
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230604
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT in Dentistry: A Comprehensive Review.
PG  - e38317
LID - 10.7759/cureus.38317 [doi]
LID - e38317
AB  - Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence 
      chatbot that uses natural language processing that can respond to human input in 
      a conversational manner. ChatGPT has numerous applications in the health care 
      system including dentistry; it is used in diagnoses and for assessing disease 
      risk and scheduling appointments. It also has a role in scientific research. In 
      the dental field, it has provided many benefits such as detecting dental and 
      maxillofacial abnormalities on panoramic radiographs and identifying different 
      dental restorations. Therefore, it helps in decreasing the workload. But even 
      with these benefits, one should take into consideration the risks and limitations 
      of this chatbot. Few articles mentioned the use of ChatGPT in dentistry. This 
      comprehensive review represents data collected from 66 relevant articles using 
      PubMed and Google Scholar as databases. This review aims to discuss all relevant 
      published articles on the use of ChatGPT in dentistry.
CI  - Copyright © 2023, Alhaidry et al.
FAU - Alhaidry, Hind M
AU  - Alhaidry HM
AD  - Advanced General Dentistry, Prince Sultan Military Medical City, Riyadh, SAU.
FAU - Fatani, Bader
AU  - Fatani B
AD  - Dentistry, College of Dentistry, King Saud University, Riyadh, SAU.
FAU - Alrayes, Jenan O
AU  - Alrayes JO
AD  - Dentistry, College of Dentistry, King Saud University, Riyadh, SAU.
FAU - Almana, Aljowhara M
AU  - Almana AM
AD  - Dentistry, College of Dentistry, King Saud University, Riyadh, SAU.
FAU - Alfhaed, Nawaf K
AU  - Alfhaed NK
AD  - Dentistry, College of Dentistry, King Saud University, Riyadh, SAU.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230430
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10230850
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - dental
OT  - dentistry
OT  - healthcare
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/02 13:16
MHDA- 2023/06/02 13:17
CRDT- 2023/06/02 10:54
PHST- 2023/04/29 00:00 [accepted]
PHST- 2023/06/02 13:17 [medline]
PHST- 2023/06/02 13:16 [pubmed]
PHST- 2023/06/02 10:54 [entrez]
AID - 10.7759/cureus.38317 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 30;15(4):e38317. doi: 10.7759/cureus.38317. eCollection 2023 
      Apr.

PMID- 37705820
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230916
IS  - 0970-0358 (Print)
IS  - 1998-376X (Electronic)
IS  - 0970-0358 (Linking)
VI  - 56
IP  - 4
DP  - 2023 Aug
TI  - ChatGPT in Plastic and Reconstructive Surgery.
PG  - 320-325
LID - 10.1055/s-0043-1771514 [doi]
AB  - Background  Chat Generative Pre-Trained Transformer (ChatGPT) is a versatile 
      large language model-based generative artificial intelligence. It is proficient 
      in a variety of tasks from drafting emails to coding to composing music to 
      passing medical licensing exams. While the potential role of ChatGPT in plastic 
      surgery is promising, evidence-based research is needed to guide its 
      implementation in practice. Methods  This review aims to summarize the literature 
      surrounding ChatGPT's use in plastic surgery. Results  A literature search 
      revealed several applications for ChatGPT in the field of plastic surgery, 
      including the ability to create academic literature and to aid the production of 
      research. However, the ethical implications of using such chatbots in scientific 
      writing requires careful consideration. ChatGPT can also generate high-quality 
      patient discharge summaries and operation notes within seconds, freeing up busy 
      junior doctors to complete other tasks. However, currently clinical information 
      must still be manually inputted, and clinicians must consider data privacy 
      implications. Its use in aiding patient communication and education and training 
      is also widely documented in the literature. However, questions have been raised 
      over the accuracy of answers generated given that current versions of ChatGPT 
      cannot access the most up-to-date sources. Conclusions  While one must be aware 
      of its shortcomings, ChatGPT is a useful tool for plastic surgeons to improve 
      productivity for a range of tasks from manuscript preparation to healthcare 
      communication generation to drafting teaching sessions to studying and learning. 
      As access improves and technology becomes more refined, surely more uses for 
      ChatGPT in plastic surgery will become apparent.
CI  - Association of Plastic Surgeons of India. This is an open access article 
      published by Thieme under the terms of the Creative Commons 
      Attribution-NonDerivative-NonCommercial License, permitting copying and 
      reproduction so long as the original work is given appropriate credit. Contents 
      may not be used for commercial purposes, or adapted, remixed, transformed or 
      built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).
FAU - Sharma, Sanjeev Chaand
AU  - Sharma SC
AUID- ORCID: 0000-0002-8355-7765
AD  - Department of Plastic Surgery, Leicester Royal Infirmary, Infirmary Square, 
      Leicester, United Kingdom.
FAU - Ramchandani, Jai Parkash
AU  - Ramchandani JP
AD  - Faculty of Life Sciences & Medicine, King's College London, Guy's Campus, Great 
      Maze Pond, London, United Kingdom.
FAU - Thakker, Arjuna
AU  - Thakker A
AD  - Academic Team of Musculoskeletal Surgery, Leicester General Hospital, University 
      Hospitals of Leicester NHS Trust, United Kingdom.
FAU - Lahiri, Anindya
AU  - Lahiri A
AUID- ORCID: 0000-0003-1739-4731
AD  - Department of Plastic Surgery, Sandwell General Hospital, West Bromwich, United 
      Kingdom.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230802
PL  - Germany
TA  - Indian J Plast Surg
JT  - Indian journal of plastic surgery : official publication of the Association of 
      Plastic Surgeons of India
JID - 8405356
PMC - PMC10497341
OTO - NOTNLM
OT  - ChatGPT
OT  - Chatbot
OT  - artificial intelligence
OT  - large language models
COIS- Conflict of Interest None declared.
EDAT- 2023/09/14 06:42
MHDA- 2023/09/14 06:43
CRDT- 2023/09/14 04:07
PHST- 2023/09/14 06:43 [medline]
PHST- 2023/09/14 06:42 [pubmed]
PHST- 2023/09/14 04:07 [entrez]
AID - IJPS-23-6-2243 [pii]
AID - 10.1055/s-0043-1771514 [doi]
PST - epublish
SO  - Indian J Plast Surg. 2023 Aug 2;56(4):320-325. doi: 10.1055/s-0043-1771514. 
      eCollection 2023 Aug.

PMID- 37368470
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230714
IS  - 2562-7600 (Electronic)
IS  - 2562-7600 (Linking)
VI  - 6
DP  - 2023 Jun 27
TI  - Performance of the Large Language Model ChatGPT on the National Nurse 
      Examinations in Japan: Evaluation Study.
PG  - e47305
LID - 10.2196/47305 [doi]
LID - e47305
AB  - BACKGROUND: ChatGPT, a large language model, has shown good performance on 
      physician certification examinations and medical consultations. However, its 
      performance has not been examined in languages other than English or on nursing 
      examinations. OBJECTIVE: We aimed to evaluate the performance of ChatGPT on the 
      Japanese National Nurse Examinations. METHODS: We evaluated the percentages of 
      correct answers provided by ChatGPT (GPT-3.5) for all questions on the Japanese 
      National Nurse Examinations from 2019 to 2023, excluding inappropriate questions 
      and those containing images. Inappropriate questions were pointed out by a 
      third-party organization and announced by the government to be excluded from 
      scoring. Specifically, these include "questions with inappropriate question 
      difficulty" and "questions with errors in the questions or choices." These 
      examinations consist of 240 questions each year, divided into basic knowledge 
      questions that test the basic issues of particular importance to nurses and 
      general questions that test a wide range of specialized knowledge. Furthermore, 
      the questions had 2 types of formats: simple-choice and situation-setup 
      questions. Simple-choice questions are primarily knowledge-based and 
      multiple-choice, whereas situation-setup questions entail the candidate reading a 
      patient's and family situation's description, and selecting the nurse's action or 
      patient's response. Hence, the questions were standardized using 2 types of 
      prompts before requesting answers from ChatGPT. Chi-square tests were conducted 
      to compare the percentage of correct answers for each year's examination format 
      and specialty area related to the question. In addition, a Cochran-Armitage trend 
      test was performed with the percentage of correct answers from 2019 to 2023. 
      RESULTS: The 5-year average percentage of correct answers for ChatGPT was 75.1% 
      (SD 3%) for basic knowledge questions and 64.5% (SD 5%) for general questions. 
      The highest percentage of correct answers on the 2019 examination was 80% for 
      basic knowledge questions and 71.2% for general questions. ChatGPT met the 
      passing criteria for the 2019 Japanese National Nurse Examination and was close 
      to passing the 2020-2023 examinations, with only a few more correct answers 
      required to pass. ChatGPT had a lower percentage of correct answers in some 
      areas, such as pharmacology, social welfare, related law and regulations, 
      endocrinology/metabolism, and dermatology, and a higher percentage of correct 
      answers in the areas of nutrition, pathology, hematology, ophthalmology, 
      otolaryngology, dentistry and dental surgery, and nursing integration and 
      practice. CONCLUSIONS: ChatGPT only passed the 2019 Japanese National Nursing 
      Examination during the most recent 5 years. Although it did not pass the 
      examinations from other years, it performed very close to the passing level, even 
      in those containing questions related to psychology, communication, and nursing.
CI  - ©Kazuya Taira, Takahiro Itaya, Ayame Hanada. Originally published in JMIR Nursing 
      (https://nursing.jmir.org), 27.06.2023.
FAU - Taira, Kazuya
AU  - Taira K
AUID- ORCID: 0000-0002-1560-5454
AD  - Department of Human Health Sciences, Graduate School of Medicine, Kyoto 
      University, Kyoto, Japan.
FAU - Itaya, Takahiro
AU  - Itaya T
AUID- ORCID: 0000-0002-0560-8686
AD  - Department of Healthcare Epidemiology, Graduate School of Medicine and Public 
      Health, Kyoto University, Kyoto, Japan.
AD  - Department of Preventive Medicine and Public Health, School of Medicine, Keio 
      University, Tokyo, Japan.
FAU - Hanada, Ayame
AU  - Hanada A
AUID- ORCID: 0009-0002-1621-214X
AD  - Department of Human Health Sciences, Graduate School of Medicine, Kyoto 
      University, Kyoto, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230627
PL  - Canada
TA  - JMIR Nurs
JT  - JMIR nursing
JID - 101771299
PMC - PMC10337249
OTO - NOTNLM
OT  - ChatGPT
OT  - Japan
OT  - National Nurse Examination
OT  - artificial intelligence
OT  - natural language processing
OT  - registered nurses
COIS- Conflicts of Interest: None declared.
EDAT- 2023/06/27 13:11
MHDA- 2023/06/27 13:12
CRDT- 2023/06/27 11:53
PHST- 2023/03/20 00:00 [received]
PHST- 2023/05/27 00:00 [accepted]
PHST- 2023/05/20 00:00 [revised]
PHST- 2023/06/27 13:12 [medline]
PHST- 2023/06/27 13:11 [pubmed]
PHST- 2023/06/27 11:53 [entrez]
AID - v6i1e47305 [pii]
AID - 10.2196/47305 [doi]
PST - epublish
SO  - JMIR Nurs. 2023 Jun 27;6:e47305. doi: 10.2196/47305.

PMID- 37428337
OWN - NLM
STAT- Publisher
LR  - 20230710
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 10
TI  - A Domain-Specific Next-Generation Large Language Model (LLM) or ChatGPT is 
      Required for Biomedical Engineering and Research.
LID - 10.1007/s10439-023-03306-x [doi]
AB  - Large language models or ChatGPT have recently gained extensive media coverage. 
      At the same time, the use of ChatGPT has increased deistically. Biomedical 
      researchers, engineers, and clinicians have shown significant interest and 
      started using it due to its diverse applications, especially in the biomedical 
      field. However, it has been found that ChatGPT sometimes provided incorrect or 
      partly correct information. It is unable to give the most recent information. 
      Therefore, we urgently advocate a domain-specific next-generation, ChatBot for 
      biomedical engineering and research, providing error-free, more accurate, and 
      updated information. The domain-specific ChatBot can perform diversified 
      functions in biomedical engineering, such as performing innovation in biomedical 
      engineering, designing a medical device, etc. The domain-specific artificial 
      intelligence enabled device will revolutionize biomedical engineering and 
      research if a biomedical domain-specific ChatBot is produced.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Pal, Soumen
AU  - Pal S
AUID- ORCID: 0000-0001-9508-2712
AD  - School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil 
      Nadu, 632014, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AUID- ORCID: 0000-0001-9669-1835
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 
      756020, India.
FAU - Lee, Sang-Soo
AU  - Lee SS
AUID- ORCID: 0000-0001-5074-7581
AD  - Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon 
      Sacred Heart Hospital, Chuncheon, Gangwon-Do, 24252, Republic of Korea.
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AUID- ORCID: 0000-0002-3958-239X
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal, 700126, India. drchiranjib@yahoo.com.
LA  - eng
PT  - Letter
DEP - 20230710
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Biomedical engineering
OT  - ChatBot
OT  - ChatGPT
OT  - Large language model
EDAT- 2023/07/10 13:05
MHDA- 2023/07/10 13:05
CRDT- 2023/07/10 11:12
PHST- 2023/06/29 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/07/10 13:05 [medline]
PHST- 2023/07/10 13:05 [pubmed]
PHST- 2023/07/10 11:12 [entrez]
AID - 10.1007/s10439-023-03306-x [pii]
AID - 10.1007/s10439-023-03306-x [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 10. doi: 10.1007/s10439-023-03306-x.

PMID- 37589944
OWN - NLM
STAT- Publisher
LR  - 20231211
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Aug 17
TI  - Complications Following Facelift and Neck Lift: Implementation and Assessment of 
      Large Language Model and Artificial Intelligence (ChatGPT) Performance Across 16 
      Simulated Patient Presentations.
LID - 10.1007/s00266-023-03538-1 [doi]
AB  - INTRODUCTION: ChatGPT represents a potential resource for patient guidance and 
      education, with the possibility for quality improvement in healthcare delivery. 
      The present study evaluates the role of ChatGPT as an interactive patient 
      resource, and assesses its performance in identifying, triaging, and guiding 
      patients with concerns of postoperative complications following facelift and neck 
      lift surgery. METHODS: Sixteen patient profiles were generated to simulate 
      postoperative patient presentations, with complications of varying acuity and 
      severity. ChatGPT was assessed for its accuracy in generating a differential 
      diagnosis, soliciting a history, providing the most-likely diagnosis, the 
      appropriate disposition, treatments/interventions to begin from home, and 
      red-flag symptoms necessitating an urgent presentation to the emergency 
      department. RESULTS: Overall accuracy in providing a complete differential 
      diagnosis in response to simulated presentations was 85%, with an accuracy of 88% 
      in identifying the most-likely diagnosis after history-taking. However, 
      appropriate patient dispositions were suggested in only 56% of cases. Relevant 
      home treatments/interventions were suggested with an 82% accuracy, and red-flag 
      symptoms with a 73% accuracy. A detailed analysis, stratified according to 
      latency of postoperative presentation (<48 h, 48 h-1 week, or >1 week), and 
      according to acuity of complications, is presented herein. CONCLUSIONS: ChatGPT 
      overestimated the urgency of indicated patient dispositions in 44% of cases, 
      concerning for potential unnecessary increase in healthcare resource utilization. 
      Imperfect performance, and the tool's tendency for overinclusion in its 
      responses, risk increasing patient anxiety and straining physician-patient 
      relationships. While artificial intelligence has great potential in triaging 
      postoperative patient concerns, and improving efficiency and resource 
      utilization, ChatGPT's performance, in its current form, demonstrates a need for 
      further refinement before its safe and effective implementation in facial 
      aesthetic surgical practice. LEVEL OF EVIDENCE IV: This journal requires that 
      authors assign a level of evidence to each article. For a full description of 
      these Evidence-Based Medicine ratings, please refer to the Table of Contents or 
      the online Instructions to Authors www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Abi-Rafeh, Jad
AU  - Abi-Rafeh J
AUID- ORCID: 0000-0002-7483-1515
AD  - Division of Plastic, Reconstructive, and Aesthetic Surgery, McGill University 
      Health Centre, Montreal, QC, Canada.
FAU - Hanna, Steven
AU  - Hanna S
AD  - Manhattan Eye, Ear and Throat Hospital, New York, NY, USA.
FAU - Bassiri-Tehrani, Brian
AU  - Bassiri-Tehrani B
AD  - Private Practice, Atlanta, GA, USA.
FAU - Kazan, Roy
AU  - Kazan R
AD  - Division of Plastic and Reconstructive Surgery, University of Pittsburgh, 
      Pittsburgh, PA, USA.
FAU - Nahai, Foad
AU  - Nahai F
AD  - Former Maurice J. Jurkiewicz Chair and Professor of Plastic Surgery, Department 
      of Surgery, Emory University, Atlanta, GA, USA. nahaimd@aol.com.
LA  - eng
PT  - Journal Article
DEP - 20230817
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EIN - Aesthetic Plast Surg. 2023 Dec 11;:. PMID: 38078915
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Complications
OT  - Facelift
EDAT- 2023/08/17 12:42
MHDA- 2023/08/17 12:42
CRDT- 2023/08/17 11:16
PHST- 2023/06/25 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/08/17 12:42 [pubmed]
PHST- 2023/08/17 12:42 [medline]
PHST- 2023/08/17 11:16 [entrez]
AID - 10.1007/s00266-023-03538-1 [pii]
AID - 10.1007/s00266-023-03538-1 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Aug 17. doi: 10.1007/s00266-023-03538-1.

PMID- 37083633
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Apr 21
TI  - Trialling a Large Language Model (ChatGPT) in General Practice With the Applied 
      Knowledge Test: Observational Study Demonstrating Opportunities and Limitations 
      in Primary Care.
PG  - e46599
LID - 10.2196/46599 [doi]
LID - e46599
AB  - BACKGROUND: Large language models exhibiting human-level performance in 
      specialized tasks are emerging; examples include Generative Pretrained 
      Transformer 3.5, which underlies the processing of ChatGPT. Rigorous trials are 
      required to understand the capabilities of emerging technology, so that 
      innovation can be directed to benefit patients and practitioners. OBJECTIVE: 
      Here, we evaluated the strengths and weaknesses of ChatGPT in primary care using 
      the Membership of the Royal College of General Practitioners Applied Knowledge 
      Test (AKT) as a medium. METHODS: AKT questions were sourced from a web-based 
      question bank and 2 AKT practice papers. In total, 674 unique AKT questions were 
      inputted to ChatGPT, with the model's answers recorded and compared to correct 
      answers provided by the Royal College of General Practitioners. Each question was 
      inputted twice in separate ChatGPT sessions, with answers on repeated trials 
      compared to gauge consistency. Subject difficulty was gauged by referring to 
      examiners' reports from 2018 to 2022. Novel explanations from ChatGPT-defined as 
      information provided that was not inputted within the question or multiple answer 
      choices-were recorded. Performance was analyzed with respect to subject, 
      difficulty, question source, and novel model outputs to explore ChatGPT's 
      strengths and weaknesses. RESULTS: Average overall performance of ChatGPT was 
      60.17%, which is below the mean passing mark in the last 2 years (70.42%). 
      Accuracy differed between sources (P=.04 and .06). ChatGPT's performance varied 
      with subject category (P=.02 and .02), but variation did not correlate with 
      difficulty (Spearman ρ=-0.241 and -0.238; P=.19 and .20). The proclivity of 
      ChatGPT to provide novel explanations did not affect accuracy (P>.99 and .23). 
      CONCLUSIONS: Large language models are approaching human expert-level 
      performance, although further development is required to match the performance of 
      qualified primary care physicians in the AKT. Validated high-performance models 
      may serve as assistants or autonomous clinical tools to ameliorate the general 
      practice workforce crisis.
CI  - ©Arun James Thirunavukarasu, Refaat Hassan, Shathar Mahmood, Rohan Sanghera, Kara 
      Barzangi, Mohanned El Mukashfi, Sachin Shah. Originally published in JMIR Medical 
      Education (https://mededu.jmir.org), 21.04.2023.
FAU - Thirunavukarasu, Arun James
AU  - Thirunavukarasu AJ
AUID- ORCID: 0000-0001-8968-4768
AD  - University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
FAU - Hassan, Refaat
AU  - Hassan R
AUID- ORCID: 0000-0002-3054-1161
AD  - University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
FAU - Mahmood, Shathar
AU  - Mahmood S
AUID- ORCID: 0009-0008-4209-1306
AD  - University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
FAU - Sanghera, Rohan
AU  - Sanghera R
AUID- ORCID: 0000-0001-6370-8426
AD  - University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
FAU - Barzangi, Kara
AU  - Barzangi K
AUID- ORCID: 0009-0009-0327-1221
AD  - University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
FAU - El Mukashfi, Mohanned
AU  - El Mukashfi M
AUID- ORCID: 0009-0001-8158-0216
AD  - University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
FAU - Shah, Sachin
AU  - Shah S
AUID- ORCID: 0009-0008-2470-6143
AD  - Attenborough Surgery, Bushey Medical Centre, Bushey, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230421
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10163403
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - decision support techniques
OT  - deep learning
OT  - family medicine
OT  - general practice
OT  - large language model
OT  - natural language processing
OT  - primary care
COIS- Conflicts of Interest: None declared.
EDAT- 2023/04/21 18:42
MHDA- 2023/04/21 18:43
CRDT- 2023/04/21 14:44
PHST- 2023/02/20 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/03/31 00:00 [revised]
PHST- 2023/04/21 18:43 [medline]
PHST- 2023/04/21 18:42 [pubmed]
PHST- 2023/04/21 14:44 [entrez]
AID - v9i1e46599 [pii]
AID - 10.2196/46599 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Apr 21;9:e46599. doi: 10.2196/46599.

PMID- 37183840
OWN - NLM
STAT- MEDLINE
DCOM- 20230724
LR  - 20230822
IS  - 1748-880X (Electronic)
IS  - 0007-1285 (Print)
IS  - 0007-1285 (Linking)
VI  - 96
IP  - 1148
DP  - 2023 Aug
TI  - ChatGPT from radiologists' perspective.
PG  - 20230203
LID - 10.1259/bjr.20230203 [doi]
LID - 20230203
AB  - ChatGPT is a newly developed technology created by the OpenAI company. It is an 
      artificial-intelligence-based large language model (LLM) and able to generate 
      human-like text. The potential roles of ChatGPT in clinical decision support and 
      academic writing have led to intense criticism of this technology in the 
      scientific community. Therefore, radiologists also need to be familiar with LLMs 
      such as ChatGPT.
FAU - Şendur, Halit Nahit
AU  - Şendur HN
AUID- ORCID: 0000-0003-1690-2538
AD  - Department of Radiology, Gazi University, Faculty of Medicine, Mevlana Bulvarı, 
      Yenimahalle, Ankara, Turkey.
FAU - Şendur, Aylin Billur
AU  - Şendur AB
AD  - Private Radiology Clinic, Kızılırmak Mah. 1443. Cad. No:25 1071 Plaza, Çankaya, 
      Ankara, Turkey.
FAU - Cerit, Mahi Nur
AU  - Cerit MN
AD  - Department of Radiology, Gazi University, Faculty of Medicine, Mevlana Bulvarı, 
      Yenimahalle, Ankara, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230515
PL  - England
TA  - Br J Radiol
JT  - The British journal of radiology
JID - 0373125
SB  - IM
CIN - Br J Radiol. 2023 Sep;96(1149):20230442. PMID: 37399112
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Radiologists
PMC - PMC10392643
EDAT- 2023/05/15 13:06
MHDA- 2023/07/24 06:42
PMCR- 2024/08/01
CRDT- 2023/05/15 06:13
PHST- 2024/08/01 00:00 [pmc-release]
PHST- 2023/07/24 06:42 [medline]
PHST- 2023/05/15 13:06 [pubmed]
PHST- 2023/05/15 06:13 [entrez]
AID - 10.1259/bjr.20230203 [doi]
PST - ppublish
SO  - Br J Radiol. 2023 Aug;96(1148):20230203. doi: 10.1259/bjr.20230203. Epub 2023 May 
      15.

PMID- 36990890
OWN - NLM
STAT- MEDLINE
DCOM- 20230421
LR  - 20230422
IS  - 1873-5487 (Electronic)
IS  - 0188-4409 (Linking)
VI  - 54
IP  - 3
DP  - 2023 Apr
TI  - ChatGPT and Publication Ethics.
PG  - 272-274
LID - S0188-4409(23)00038-3 [pii]
LID - 10.1016/j.arcmed.2023.03.004 [doi]
AB  - Academic publishing is crucial for scientific communication, is governed by 
      accepted ethical norms, and underpins the collective literature on basic science, 
      and technological and medical principles and advances. In November 2022, the 
      public and professional global communities, including the scientific community, 
      witnessed the release of ChatGPT by OpenAI in San Francisco, California, USA. 
      Excluding its public appeal and entertaining aspects but considering its diverse 
      potential applications, some ethical concerns must be considered before 
      establishing guidelines on using and including ChatGPT or similar platforms in 
      scientific publishing. Some academic publishers and preprints have accepted 
      manuscripts with ChatGPT listed as a "co-author". Though excluding such platforms 
      from scientific publishing may not be practicable with time, establishing ethical 
      principles is essential before ChatGPT could become a "co-author" in any 
      scientific, published manuscript.
CI  - Copyright © 2023 Instituto Mexicano del Seguro Social (IMSS). Published by 
      Elsevier Inc. All rights reserved.
FAU - Rahimi, Farid
AU  - Rahimi F
AD  - Research School of Biology, The Australian National University, Ngunnawal and 
      Ngambri Country, Canberra, ACT, Australia.
FAU - Talebi Bezmin Abadi, Amin
AU  - Talebi Bezmin Abadi A
AD  - Department of Bacteriology, Faculty of Medical Sciences, Tarbiat Modares 
      University, Tehran, Iran. Electronic address: amin.talebi@modares.ac.ir.
LA  - eng
PT  - Letter
DEP - 20230327
PL  - United States
TA  - Arch Med Res
JT  - Archives of medical research
JID - 9312706
SB  - IM
MH  - *Publishing
MH  - *Moral Obligations
EDAT- 2023/03/30 06:00
MHDA- 2023/04/21 06:41
CRDT- 2023/03/29 22:06
PHST- 2023/02/22 00:00 [received]
PHST- 2023/03/07 00:00 [accepted]
PHST- 2023/04/21 06:41 [medline]
PHST- 2023/03/30 06:00 [pubmed]
PHST- 2023/03/29 22:06 [entrez]
AID - S0188-4409(23)00038-3 [pii]
AID - 10.1016/j.arcmed.2023.03.004 [doi]
PST - ppublish
SO  - Arch Med Res. 2023 Apr;54(3):272-274. doi: 10.1016/j.arcmed.2023.03.004. Epub 
      2023 Mar 27.

PMID- 37203511
OWN - NLM
STAT- MEDLINE
DCOM- 20230522
LR  - 20230522
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 302
DP  - 2023 May 18
TI  - Large Language Model as Unsupervised Health Information Retriever.
PG  - 833-834
LID - 10.3233/SHTI230282 [doi]
AB  - Retrieving health information is a task of search for health-related information 
      from a variety of sources. Gathering self-reported health information may help 
      enrich the knowledge body of the disease and its symptoms. We investigated 
      retrieving symptom mentions in COVID-19-related Twitter posts with a pretrained 
      large language model (GPT-3) without providing any examples (zero-shot learning). 
      We introduced a new performance measure of total match (TM) to include exact, 
      partial and semantic matches. Our results show that the zero-shot approach is a 
      powerful method without the need to annotate any data, and it can assist in 
      generating instances for few-shot learning which may achieve better performance.
FAU - Jiang, Keyuan
AU  - Jiang K
AD  - Purdue University Northwest, Hammond, Indiana, USA.
FAU - Mujtaba, Mohammed M
AU  - Mujtaba MM
AD  - Purdue University Northwest, Hammond, Indiana, USA.
FAU - Bernard, Gordon R
AU  - Bernard GR
AD  - Vanderbilt University, Nashville, Tennessee, USA.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - Humans
MH  - *COVID-19
MH  - *Social Media
MH  - Language
MH  - Semantics
MH  - Natural Language Processing
OTO - NOTNLM
OT  - COVID-19 symptoms
OT  - Large language model
OT  - Twitter
OT  - health information retrieval
OT  - unsupervised learning
OT  - zero-shot learning
EDAT- 2023/05/19 06:42
MHDA- 2023/05/22 06:42
CRDT- 2023/05/19 05:28
PHST- 2023/05/22 06:42 [medline]
PHST- 2023/05/19 06:42 [pubmed]
PHST- 2023/05/19 05:28 [entrez]
AID - SHTI230282 [pii]
AID - 10.3233/SHTI230282 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 May 18;302:833-834. doi: 10.3233/SHTI230282.

PMID- 37650428
OWN - NLM
STAT- Publisher
LR  - 20230831
IS  - 1437-4331 (Electronic)
IS  - 1434-6621 (Linking)
DP  - 2023 Aug 29
TI  - Predicting hemoglobinopathies using ChatGPT.
LID - 10.1515/cclm-2023-0885 [doi]
FAU - Kurstjens, Steef
AU  - Kurstjens S
AD  - Laboratory of Clinical Chemistry and Hematology, Jeroen Bosch Hospital, 's 
      Hertogenbosch, The Netherlands.
FAU - Schipper, Anoeska
AU  - Schipper A
AD  - Laboratory of Clinical Chemistry and Hematology, Jeroen Bosch Hospital, 's 
      Hertogenbosch, The Netherlands.
AD  - Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands.
FAU - Krabbe, Johannes
AU  - Krabbe J
AD  - Laboratory of Clinical Chemistry and Laboratory Medicine, Medisch Spectrum 
      Twente, Enschede, The Netherlands.
AD  - Laboratory of Clinical Chemistry and Laboratory Medicine, Medlon BV, Enschede, 
      The Netherlands.
FAU - Kusters, Ron
AU  - Kusters R
AD  - Laboratory of Clinical Chemistry and Hematology, Jeroen Bosch Hospital, 's 
      Hertogenbosch, The Netherlands.
AD  - Department of Health Technology and Services Research, Technical Medical Centre, 
      University of Twente, Enschede, Netherlands.
LA  - eng
PT  - Letter
DEP - 20230829
PL  - Germany
TA  - Clin Chem Lab Med
JT  - Clinical chemistry and laboratory medicine
JID - 9806306
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - diagnostics
OT  - laboratory results
OT  - large language models
EDAT- 2023/08/31 12:42
MHDA- 2023/08/31 12:42
CRDT- 2023/08/31 06:45
PHST- 2023/08/11 00:00 [received]
PHST- 2023/08/22 00:00 [accepted]
PHST- 2023/08/31 12:42 [medline]
PHST- 2023/08/31 12:42 [pubmed]
PHST- 2023/08/31 06:45 [entrez]
AID - cclm-2023-0885 [pii]
AID - 10.1515/cclm-2023-0885 [doi]
PST - aheadofprint
SO  - Clin Chem Lab Med. 2023 Aug 29. doi: 10.1515/cclm-2023-0885.

PMID- 37691490
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230912
LR  - 20230912
IS  - 1549-960X (Electronic)
IS  - 1549-9596 (Linking)
VI  - 63
IP  - 17
DP  - 2023 Sep 11
TI  - Editorial: Harnessing the Power of Large Language Model-Based Chatbots for 
      Scientific Discovery.
PG  - 5395
LID - 10.1021/acs.jcim.3c01244 [doi]
FAU - Merz, Kenneth M
AU  - Merz KM
AUID- ORCID: 0000-0001-9139-5893
AD  - Department of Chemistry, Michigan State University, Michigan 48824, United 
      States.
FAU - Wei, Guo-Wei
AU  - Wei GW
AUID- ORCID: 0000-0002-5781-2937
AD  - Department of Mathematics, Michigan State University, Michigan 48824, United 
      States.
FAU - Zhu, Feng
AU  - Zhu F
AUID- ORCID: 0000-0001-8069-0053
AD  - College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
LA  - eng
PT  - Editorial
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
SB  - IM
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 03:33
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 03:33 [entrez]
AID - 10.1021/acs.jcim.3c01244 [doi]
PST - ppublish
SO  - J Chem Inf Model. 2023 Sep 11;63(17):5395. doi: 10.1021/acs.jcim.3c01244.

PMID- 37433676
OWN - NLM
STAT- MEDLINE
DCOM- 20230907
LR  - 20230907
IS  - 1535-5675 (Electronic)
IS  - 0091-4916 (Linking)
VI  - 51
IP  - 3
DP  - 2023 Sep
TI  - ChatGPT in Nuclear Medicine Education.
PG  - 247-254
LID - 10.2967/jnmt.123.265844 [doi]
AB  - Academic integrity has been challenged by artificial intelligence algorithms in 
      teaching institutions, including those providing nuclear medicine training. The 
      GPT 3.5-powered ChatGPT chatbot released in late November 2022 has emerged as an 
      immediate threat to academic and scientific writing. Methods: Both examinations 
      and written assignments for nuclear medicine courses were tested using ChatGPT. 
      Included was a mix of core theory subjects offered in the second and third years 
      of the nuclear medicine science course. Long-answer-style questions (8 subjects) 
      and calculation-style questions (2 subjects) were included for examinations. 
      ChatGPT was also used to produce responses to authentic writing tasks (6 
      subjects). ChatGPT responses were evaluated by Turnitin plagiarism-detection 
      software for similarity and artificial intelligence scores, scored against 
      standardized rubrics, and compared with the mean performance of student cohorts. 
      Results: ChatGPT powered by GPT 3.5 performed poorly in the 2 calculation 
      examinations (overall, 31.7% compared with 67.3% for students), with particularly 
      poor performance in complex-style questions. ChatGPT failed each of 6 written 
      tasks (overall, 38.9% compared with 67.2% for students), with worsening 
      performance corresponding to increasing writing and research expectations in the 
      third year. In the 8 examinations, ChatGPT performed better than students for 
      general or early subjects but poorly for advanced and specific subjects (overall, 
      51% compared with 57.4% for students). Conclusion: Although ChatGPT poses a risk 
      to academic integrity, its usefulness as a cheating tool can be constrained by 
      higher-order taxonomies. Unfortunately, the constraints to higher-order learning 
      and skill development also undermine potential applications of ChatGPT for 
      enhancing learning. There are several potential applications of ChatGPT for 
      teaching nuclear medicine students.
CI  - © 2023 by the Society of Nuclear Medicine and Molecular Imaging.
FAU - Currie, Geoffrey
AU  - Currie G
AD  - Charles Sturt University, Wagga Wagga, New South Wales, Australia; and 
      gcurrie@csu.edu.au.
FAU - Barry, Kym
AU  - Barry K
AD  - Charles Sturt University, Port Macquarie, New South Wales, Australia.
LA  - eng
PT  - Journal Article
DEP - 20230711
PL  - United States
TA  - J Nucl Med Technol
JT  - Journal of nuclear medicine technology
JID - 0430303
SB  - IM
MH  - Humans
MH  - *Nuclear Medicine
MH  - Artificial Intelligence
MH  - Radionuclide Imaging
MH  - Students
MH  - Learning
OTO - NOTNLM
OT  - academic integrity
OT  - artificial intelligence
OT  - generative AI
OT  - higher education
OT  - language model
OT  - tertiary education
EDAT- 2023/07/12 01:07
MHDA- 2023/09/07 06:42
CRDT- 2023/07/11 21:38
PHST- 2023/04/12 00:00 [received]
PHST- 2023/05/13 00:00 [revised]
PHST- 2023/09/07 06:42 [medline]
PHST- 2023/07/12 01:07 [pubmed]
PHST- 2023/07/11 21:38 [entrez]
AID - jnmt.123.265844 [pii]
AID - 10.2967/jnmt.123.265844 [doi]
PST - ppublish
SO  - J Nucl Med Technol. 2023 Sep;51(3):247-254. doi: 10.2967/jnmt.123.265844. Epub 
      2023 Jul 11.

PMID- 37839567
OWN - NLM
STAT- Publisher
LR  - 20231104
IS  - 1878-8769 (Electronic)
IS  - 1878-8750 (Linking)
DP  - 2023 Oct 14
TI  - Large Language Model-Based Neurosurgical Evaluation Matrix: A Novel Scoring 
      Criteria to Assess the Efficacy of ChatGPT as an Educational Tool for 
      Neurosurgery Board Preparation.
LID - S1878-8750(23)01448-1 [pii]
LID - 10.1016/j.wneu.2023.10.043 [doi]
AB  - INTRODUCTION: Technological advancements are reshaping medical education, with 
      digital tools becoming essential in all levels of training. Amidst this 
      transformation, the study explores the potential of ChatGPT, an artificial 
      intelligence model by OpenAI, in enhancing neurosurgical board education. The 
      focus extends beyond technology adoption to its effective utilization, with 
      ChatGPT's proficiency evaluated against practice questions from the Primary 
      Neurosurgery Written Board Exam. METHODS: Using the Congress of Neurologic 
      Surgeons (CNS) Self-Assessment Neurosurgery (SANS) Exam Board Review Prep 
      questions, we conducted 3 rounds of analysis with ChatGPT. We developed a novel 
      ChatGPT Neurosurgical Evaluation Matrix (CNEM) to assess the output quality, 
      accuracy, concordance, and clarity of ChatGPT's answers. RESULTS: ChatGPT 
      achieved spot-on accuracy for 66.7% of prompted questions, 59.4% of unprompted 
      questions, and 63.9% of unprompted questions with a leading phrase. Stratified by 
      topic, accuracy ranged from 50.0% (Vascular) to 78.8% (Neuropathology). In 
      comparison to SANS explanations, ChatGPT output was considered better in 19.1% of 
      questions, equal in 51.6%, and worse in 29.3%. Concordance analysis showed that 
      95.5% of unprompted ChatGPT outputs and 97.4% of unprompted outputs with a 
      leading phrase were aligned. CONCLUSIONS: Our study evaluated the performance of 
      ChatGPT in neurosurgical board education by assessing its accuracy, clarity, and 
      concordance. The findings highlight the potential and challenges of integrating 
      AI technologies like ChatGPT into medical and neurosurgical board education. 
      Further research is needed to refine these tools and optimize their performance 
      for enhanced medical education and patient care.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Mannam, Sneha Sai
AU  - Mannam SS
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Subtirelu, Robert
AU  - Subtirelu R
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Chauhan, Daksh
AU  - Chauhan D
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Ahmad, Hasan S
AU  - Ahmad HS
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Matache, Irina Mihaela
AU  - Matache IM
AD  - Department of Physiology, Faculty of Medicine, Carol Davila University of 
      Medicine and Pharmacy, Bucharest, Romania.
FAU - Bryan, Kevin
AU  - Bryan K
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Chitta, Siddharth V K
AU  - Chitta SVK
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Bathula, Shreya C
AU  - Bathula SC
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Turlip, Ryan
AU  - Turlip R
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Wathen, Connor
AU  - Wathen C
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Ghenbot, Yohannes
AU  - Ghenbot Y
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Ajmera, Sonia
AU  - Ajmera S
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Blue, Rachel
AU  - Blue R
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Chen, H Isaac
AU  - Chen HI
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Ali, Zarina S
AU  - Ali ZS
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Malhotra, Neil
AU  - Malhotra N
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Srinivasan, Visish
AU  - Srinivasan V
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Ozturk, Ali K
AU  - Ozturk AK
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Yoon, Jang W
AU  - Yoon JW
AD  - Department of Neurosurgery, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA. Electronic address: 
      jang.yoon@pennmedicine.upenn.edu.
LA  - eng
PT  - Journal Article
DEP - 20231014
PL  - United States
TA  - World Neurosurg
JT  - World neurosurgery
JID - 101528275
SB  - IM
OTO - NOTNLM
OT  - AI evaluation matrix
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Medical education technology
OT  - Neurosurgical education
EDAT- 2023/10/16 00:42
MHDA- 2023/10/16 00:42
CRDT- 2023/10/15 19:25
PHST- 2023/10/03 00:00 [received]
PHST- 2023/10/07 00:00 [accepted]
PHST- 2023/10/16 00:42 [pubmed]
PHST- 2023/10/16 00:42 [medline]
PHST- 2023/10/15 19:25 [entrez]
AID - S1878-8750(23)01448-1 [pii]
AID - 10.1016/j.wneu.2023.10.043 [doi]
PST - aheadofprint
SO  - World Neurosurg. 2023 Oct 14:S1878-8750(23)01448-1. doi: 
      10.1016/j.wneu.2023.10.043.

PMID- 37507396
OWN - NLM
STAT- MEDLINE
DCOM- 20230802
LR  - 20230802
IS  - 2049-3169 (Electronic)
IS  - 1674-2818 (Print)
IS  - 1674-2818 (Linking)
VI  - 15
IP  - 1
DP  - 2023 Jul 28
TI  - ChatGPT for shaping the future of dentistry: the potential of multi-modal large 
      language model.
PG  - 29
LID - 10.1038/s41368-023-00239-y [doi]
LID - 29
AB  - The ChatGPT, a lite and conversational variant of Generative Pretrained 
      Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language 
      Models (LLMs) with billions of parameters. LLMs have stirred up much interest 
      among researchers and practitioners in their impressive skills in natural 
      language processing tasks, which profoundly impact various fields. This paper 
      mainly discusses the future applications of LLMs in dentistry. We introduce two 
      primary LLM deployment methods in dentistry, including automated dental diagnosis 
      and cross-modal dental diagnosis, and examine their potential applications. 
      Especially, equipped with a cross-modal encoder, a single LLM can manage 
      multi-source data and conduct advanced natural language reasoning to perform 
      complex clinical operations. We also present cases to demonstrate the potential 
      of a fully automatic Multi-Modal LLM AI system for dentistry clinical 
      application. While LLMs offer significant potential benefits, the challenges, 
      such as data privacy, data quality, and model bias, need further study. Overall, 
      LLMs have the potential to revolutionize dental diagnosis and treatment, which 
      indicates a promising avenue for clinical application and research in dentistry.
CI  - © 2023. The Author(s).
FAU - Huang, Hanyao
AU  - Huang H
AUID- ORCID: 0000-0002-6805-0157
AD  - State Key Laboratory of Oral Diseases & National Clinical Research Center for 
      Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital 
      of Stomatology, Sichuan University, Chengdu, China. huanghanyao_cn@scu.edu.cn.
FAU - Zheng, Ou
AU  - Zheng O
AUID- ORCID: 0000-0001-6313-8566
AD  - Department of Civil, Environmental & Construction Engineering, University of 
      Central Florida, Orlando, USA. ouzheng1993@knights.ucf.edu.
FAU - Wang, Dongdong
AU  - Wang D
AD  - Department of Civil, Environmental & Construction Engineering, University of 
      Central Florida, Orlando, USA.
FAU - Yin, Jiayi
AU  - Yin J
AD  - State Key Laboratory of Oral Diseases & National Clinical Research Center for 
      Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital 
      of Stomatology, Sichuan University, Chengdu, China.
FAU - Wang, Zijin
AU  - Wang Z
AD  - Department of Civil, Environmental & Construction Engineering, University of 
      Central Florida, Orlando, USA.
FAU - Ding, Shengxuan
AU  - Ding S
AD  - College of Transportation Engineering, University of Central Florida, Orlando, 
      USA.
FAU - Yin, Heng
AU  - Yin H
AD  - State Key Laboratory of Oral Diseases & National Clinical Research Center for 
      Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital 
      of Stomatology, Sichuan University, Chengdu, China.
FAU - Xu, Chuan
AU  - Xu C
AD  - School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 
      China.
AD  - C2SMART Center, Tandon School of Engineering, New York University, Brooklyn, USA.
FAU - Yang, Renjie
AU  - Yang R
AD  - State Key Laboratory of Oral Diseases & National Clinical Research Center for 
      Oral Diseases & Eastern Clinic, West China Hospital of Stomatology, Sichuan 
      University, Chengdu, China.
FAU - Zheng, Qian
AU  - Zheng Q
AD  - State Key Laboratory of Oral Diseases & National Clinical Research Center for 
      Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital 
      of Stomatology, Sichuan University, Chengdu, China.
FAU - Shi, Bing
AU  - Shi B
AD  - State Key Laboratory of Oral Diseases & National Clinical Research Center for 
      Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital 
      of Stomatology, Sichuan University, Chengdu, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20230728
PL  - India
TA  - Int J Oral Sci
JT  - International journal of oral science
JID - 101504351
SB  - IM
MH  - *Dentistry
MH  - *Artificial Intelligence
PMC - PMC10382494
COIS- The authors declare no competing interests.
EDAT- 2023/07/29 06:42
MHDA- 2023/07/31 06:43
CRDT- 2023/07/28 23:17
PHST- 2023/03/26 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/07/06 00:00 [revised]
PHST- 2023/07/31 06:43 [medline]
PHST- 2023/07/29 06:42 [pubmed]
PHST- 2023/07/28 23:17 [entrez]
AID - 10.1038/s41368-023-00239-y [pii]
AID - 239 [pii]
AID - 10.1038/s41368-023-00239-y [doi]
PST - epublish
SO  - Int J Oral Sci. 2023 Jul 28;15(1):29. doi: 10.1038/s41368-023-00239-y.

PMID- 37789636
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
VI  - 45
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT for assessment writing.
PG  - 1224-1227
LID - 10.1080/0142159X.2023.2249239 [doi]
AB  - What is the educational challenge?Medical schools invest significant resources 
      into the creation of multiple-choice items for assessments. This process is 
      costly and requires faculty training. Recently ChatGPT has been used in various 
      areas to improve content creation efficiency, and it has otherwise been used to 
      answer USMLE-style assessment items.What are the proposed solutions?We proposed 
      the use of ChatGPT to create initial drafts of multiple-choice items.What are the 
      potential benefits to a wider global audience?The use of ChatGPT to generate 
      assessment items can decrease resources required, allowing for the creation of 
      more items, and freeing-up faculty time to perform higher level assessment 
      activities. ChatGPT is also able to consistently produce items using a standard 
      format while adhering to item writing guidelines, which can be very challenging 
      for faculty teams.What are the next steps?We plan to pilot ChatGPT drafted 
      questions and compare item statistics for those written by ChatGPT with those 
      written by our content experts. We also plan to further identify the types of 
      questions that ChatGPT is most appropriate for, and incorporate media into 
      assessment items (e.g. images, videos).
FAU - Zuckerman, Matthew
AU  - Zuckerman M
AUID- ORCID: 0000-0001-9576-4203
AD  - University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO, 
      USA.
FAU - Flood, Ryan
AU  - Flood R
AD  - University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO, 
      USA.
FAU - Tan, Rachael J B
AU  - Tan RJB
AUID- ORCID: 0009-0001-4841-2042
AD  - University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO, 
      USA.
FAU - Kelp, Nicole
AU  - Kelp N
AUID- ORCID: 0000-0001-8462-5689
AD  - University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO, 
      USA.
AD  - Department of Microbiology, Immunology, and Pathology, Colorado State University, 
      Fort Collins, CO, USA.
FAU - Ecker, David J
AU  - Ecker DJ
AUID- ORCID: 0000-0002-1530-0079
AD  - University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO, 
      USA.
FAU - Menke, Jonathan
AU  - Menke J
AD  - University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO, 
      USA.
FAU - Lockspeiser, Tai
AU  - Lockspeiser T
AUID- ORCID: 0000-0002-1633-4860
AD  - Department of Pediatrics, Children's Hospital Colorado, Aurora, CO, USA.
LA  - eng
PT  - Journal Article
DEP - 20231016
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
MH  - Humans
MH  - Educational Status
MH  - *Faculty
MH  - *Schools, Medical
MH  - Videotape Recording
MH  - Writing
OTO - NOTNLM
OT  - AI
OT  - Assessment
OT  - ChatGPT
OT  - artificial intelligence
OT  - medical education
EDAT- 2023/10/04 06:43
MHDA- 2023/11/01 12:44
CRDT- 2023/10/04 02:05
PHST- 2023/11/01 12:44 [medline]
PHST- 2023/10/04 06:43 [pubmed]
PHST- 2023/10/04 02:05 [entrez]
AID - 10.1080/0142159X.2023.2249239 [doi]
PST - ppublish
SO  - Med Teach. 2023 Nov;45(11):1224-1227. doi: 10.1080/0142159X.2023.2249239. Epub 
      2023 Oct 16.

PMID- 37544801
OWN - NLM
STAT- MEDLINE
DCOM- 20231108
LR  - 20231108
IS  - 1531-5037 (Electronic)
IS  - 0022-3468 (Linking)
VI  - 58
IP  - 12
DP  - 2023 Dec
TI  - Revolutionizing Healthcare with ChatGPT: An Early Exploration of an AI Language 
      Model's Impact on Medicine at Large and its Role in Pediatric Surgery.
PG  - 2410-2415
LID - S0022-3468(23)00416-5 [pii]
LID - 10.1016/j.jpedsurg.2023.07.008 [doi]
AB  - BACKGROUND: ChatGPT, a natural language processing model, has shown great promise 
      in revolutionizing the field of medicine. This paper presents a comprehensive 
      evaluation of the transformative potential of OpenAI's ChatGPT on healthcare and 
      scientific research, with an exploration on its prospective capacity to impact 
      the field of pediatric surgery. METHODS: Through an extensive review of the 
      literature, we illuminate ChatGPT's applications in clinical healthcare and 
      medical research while presenting the ethical considerations surrounding its use. 
      RESULTS: Our review reveals the exciting work done so far evaluating the numerous 
      potential uses of ChatGPT in clinical medicine and medical research, but it also 
      shows that significant research and advancements in natural language processing 
      models are still needed. CONCLUSION: ChatGPT has immense promise in transforming 
      how we provide healthcare and how we conduct research. Currently, further robust 
      research on the safety, effectiveness, and ethical considerations of ChatGPT is 
      greatly needed. LEVEL OF STUDY: V.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Xiao, David
AU  - Xiao D
AD  - Department of Pediatric Surgery, Vanderbilt Children's Medical Center, Nashville, 
      TN, USA. Electronic address: David.Xiao@vumc.org.
FAU - Meyers, Patrick
AU  - Meyers P
AD  - Department of Pediatric Surgery, Vanderbilt Children's Medical Center, Nashville, 
      TN, USA.
FAU - Upperman, Jeffrey S
AU  - Upperman JS
AD  - Department of Pediatric Surgery, Vanderbilt Children's Medical Center, Nashville, 
      TN, USA.
FAU - Robinson, Jamie R
AU  - Robinson JR
AD  - Department of Pediatric Surgery, Vanderbilt Children's Medical Center, Nashville, 
      TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230720
PL  - United States
TA  - J Pediatr Surg
JT  - Journal of pediatric surgery
JID - 0052631
SB  - IM
MH  - Child
MH  - Humans
MH  - Prospective Studies
MH  - *Medicine
MH  - *Specialties, Surgical
MH  - *Biomedical Research
MH  - Health Facilities
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Large language model
EDAT- 2023/08/07 00:42
MHDA- 2023/11/08 06:44
CRDT- 2023/08/06 21:54
PHST- 2023/06/05 00:00 [received]
PHST- 2023/07/10 00:00 [revised]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/11/08 06:44 [medline]
PHST- 2023/08/07 00:42 [pubmed]
PHST- 2023/08/06 21:54 [entrez]
AID - S0022-3468(23)00416-5 [pii]
AID - 10.1016/j.jpedsurg.2023.07.008 [doi]
PST - ppublish
SO  - J Pediatr Surg. 2023 Dec;58(12):2410-2415. doi: 10.1016/j.jpedsurg.2023.07.008. 
      Epub 2023 Jul 20.

PMID- 37433929
OWN - NLM
STAT- MEDLINE
DCOM- 20230714
LR  - 20230719
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 619
IP  - 7969
DP  - 2023 Jul
TI  - Large language model is a flagship for Japan.
PG  - 252
LID - 10.1038/d41586-023-02230-3 [doi]
FAU - Kinoshita, Shotaro
AU  - Kinoshita S
AUID- ORCID: 0000-0002-8168-383X
FAU - Yokoyama, Hiromi
AU  - Yokoyama H
AUID- ORCID: 0000-0003-4760-762X
LA  - eng
PT  - Letter
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - Japan
MH  - *Natural Language Processing
MH  - *Language
OTO - NOTNLM
OT  - Ethics
OT  - Machine learning
OT  - Policy
OT  - Society
EDAT- 2023/07/12 01:07
MHDA- 2023/07/13 06:42
CRDT- 2023/07/11 23:29
PHST- 2023/07/13 06:42 [medline]
PHST- 2023/07/12 01:07 [pubmed]
PHST- 2023/07/11 23:29 [entrez]
AID - 10.1038/d41586-023-02230-3 [pii]
AID - 10.1038/d41586-023-02230-3 [doi]
PST - ppublish
SO  - Nature. 2023 Jul;619(7969):252. doi: 10.1038/d41586-023-02230-3.

PMID- 37246194
OWN - NLM
STAT- Publisher
LR  - 20230816
IS  - 1476-5438 (Electronic)
IS  - 1018-4813 (Linking)
DP  - 2023 May 29
TI  - Analysis of large-language model versus human performance for genetics questions.
LID - 10.1038/s41431-023-01396-8 [doi]
AB  - Large-language models like ChatGPT have recently received a great deal of 
      attention. One area of interest pertains to how these models could be used in 
      biomedical contexts, including related to human genetics. To assess one facet of 
      this, we compared the performance of ChatGPT versus human respondents (13,642 
      human responses) in answering 85 multiple-choice questions about aspects of human 
      genetics. Overall, ChatGPT did not perform significantly differently (p = 0.8327) 
      than human respondents; ChatGPT was 68.2% accurate, compared to 66.6% accuracy 
      for human respondents. Both ChatGPT and humans performed better on 
      memorization-type questions versus critical thinking questions (p < 0.0001). When 
      asked the same question multiple times, ChatGPT frequently provided different 
      answers (16% of initial responses), including for both initially correct and 
      incorrect answers, and gave plausible explanations for both correct and incorrect 
      answers. ChatGPT's performance was impressive, but currently demonstrates 
      significant shortcomings for clinical or other high-stakes use. Addressing these 
      limitations will be important to guide adoption in real-life situations.
CI  - © 2023. This is a U.S. Government work and not under copyright protection in the 
      US; foreign copyright protection may apply.
FAU - Duong, Dat
AU  - Duong D
AD  - Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research 
      Institute, Bethesda, MD, USA.
FAU - Solomon, Benjamin D
AU  - Solomon BD
AUID- ORCID: 0000-0002-8826-1023
AD  - Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research 
      Institute, Bethesda, MD, USA. solomonb@mail.nih.gov.
LA  - eng
GR  - N/A/U.S. Department of Health & Human Services | NIH | National Human Genome 
      Research Institute (NHGRI)/
PT  - Journal Article
DEP - 20230529
PL  - England
TA  - Eur J Hum Genet
JT  - European journal of human genetics : EJHG
JID - 9302235
SB  - IM
UOF - medRxiv. 2023 Jan 28;:. PMID: 36789422
CIN - Eur J Hum Genet. 2023 Jul 5;:. PMID: 37407734
EDAT- 2023/05/29 00:42
MHDA- 2023/05/29 00:42
CRDT- 2023/05/28 23:05
PHST- 2023/02/08 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/05/09 00:00 [revised]
PHST- 2023/05/29 00:42 [pubmed]
PHST- 2023/05/29 00:42 [medline]
PHST- 2023/05/28 23:05 [entrez]
AID - 10.1038/s41431-023-01396-8 [pii]
AID - 10.1038/s41431-023-01396-8 [doi]
PST - aheadofprint
SO  - Eur J Hum Genet. 2023 May 29. doi: 10.1038/s41431-023-01396-8.

PMID- 37440696
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - What Should ChatGPT Mean for Bioethics?
PG  - 8-16
LID - 10.1080/15265161.2023.2233357 [doi]
AB  - In the last several months, several major disciplines have started their initial 
      reckoning with what ChatGPT and other Large Language Models (LLMs) mean for them 
      - law, medicine, business among other professions. With a heavy dose of humility, 
      given how fast the technology is moving and how uncertain its social implications 
      are, this article attempts to give some early tentative thoughts on what ChatGPT 
      might mean for bioethics. I will first argue that many bioethics issues raised by 
      ChatGPT are similar to those raised by current medical AI - built into devices, 
      decision support tools, data analytics, etc. These include issues of data 
      ownership, consent for data use, data representativeness and bias, and privacy. I 
      describe how these familiar issues appear somewhat differently in the ChatGPT 
      context, but much of the existing bioethical thinking on these issues provides a 
      strong starting point. There are, however, a few "new-ish" issues I highlight - 
      by new-ish I mean issues that while perhaps not truly new seem much more 
      important for it than other forms of medical AI. These include issues about 
      informed consent and the right to know we are dealing with an AI, the problem of 
      medical deepfakes, the risk of oligopoly and inequitable access related to 
      foundational models, environmental effects, and on the positive side 
      opportunities for the democratization of knowledge and empowering patients. I 
      also discuss how races towards dominance (between large companies and between the 
      U.S. and geopolitical rivals like China) risk sidelining ethics.
FAU - Cohen, I Glenn
AU  - Cohen IG
AD  - Petrie-Flom Center for Health Law Policy, Biotechnology & Bioethics, Harvard Law 
      School.
LA  - eng
PT  - Journal Article
DEP - 20230713
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CIN - Am J Bioeth. 2023 Oct;23(10):60-63. PMID: 37812095
CIN - Am J Bioeth. 2023 Oct;23(10):63-65. PMID: 37812097
CIN - Am J Bioeth. 2023 Oct;23(10):65-68. PMID: 37812098
CIN - Am J Bioeth. 2023 Oct;23(10):80-82. PMID: 37812099
CIN - Am J Bioeth. 2023 Oct;23(10):74-77. PMID: 37812102
CIN - Am J Bioeth. 2023 Oct;23(10):102-104. PMID: 37812104
CIN - Am J Bioeth. 2023 Oct;23(10):113-115. PMID: 37812105
CIN - Am J Bioeth. 2023 Oct;23(10):99-102. PMID: 37812106
CIN - Am J Bioeth. 2023 Oct;23(10):110-113. PMID: 37812107
CIN - Am J Bioeth. 2023 Oct;23(10):86-88. PMID: 37812108
CIN - Am J Bioeth. 2023 Oct;23(10):52-54. PMID: 37812110
CIN - Am J Bioeth. 2023 Oct;23(10):107-110. PMID: 37812112
CIN - Am J Bioeth. 2023 Oct;23(10):55-57. PMID: 37812113
CIN - Am J Bioeth. 2023 Oct;23(10):42-44. PMID: 37812114
CIN - Am J Bioeth. 2023 Oct;23(10):83-85. PMID: 37812116
CIN - Am J Bioeth. 2023 Oct;23(10):58-60. PMID: 37812118
CIN - Am J Bioeth. 2023 Oct;23(10):77-80. PMID: 37812122
CIN - Am J Bioeth. 2023 Oct;23(10):71-73. PMID: 37812123
CIN - Am J Bioeth. 2023 Oct;23(10):1-5. PMID: 37831940
OTO - NOTNLM
OT  - ChatGPT
OT  - bias
OT  - environment
OT  - informed consent
OT  - large language model (LLM)
OT  - oligopoly
OT  - privacy
EDAT- 2023/07/13 19:15
MHDA- 2023/07/13 19:15
CRDT- 2023/07/13 14:43
PHST- 2023/07/13 19:15 [pubmed]
PHST- 2023/07/13 19:15 [medline]
PHST- 2023/07/13 14:43 [entrez]
AID - 10.1080/15265161.2023.2233357 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):8-16. doi: 10.1080/15265161.2023.2233357. Epub 2023 
      Jul 13.

PMID- 37843400
OWN - NLM
STAT- Publisher
LR  - 20231016
IS  - 1943-7722 (Electronic)
IS  - 0002-9173 (Linking)
DP  - 2023 Oct 16
TI  - Large language model's utility in helping pathology professionals.
LID - aqad130 [pii]
LID - 10.1093/ajcp/aqad130 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Laos.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Mohali, India.
AD  - Joseph Ayo Babalola -University, Ikeji Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20231016
PL  - England
TA  - Am J Clin Pathol
JT  - American journal of clinical pathology
JID - 0370470
SB  - IM
EDAT- 2023/10/16 12:42
MHDA- 2023/10/16 12:42
CRDT- 2023/10/16 09:43
PHST- 2023/10/16 12:42 [medline]
PHST- 2023/10/16 12:42 [pubmed]
PHST- 2023/10/16 09:43 [entrez]
AID - 7318213 [pii]
AID - 10.1093/ajcp/aqad130 [doi]
PST - aheadofprint
SO  - Am J Clin Pathol. 2023 Oct 16:aqad130. doi: 10.1093/ajcp/aqad130.

PMID- 37562028
OWN - NLM
STAT- Publisher
LR  - 20230810
IS  - 1744-5078 (Electronic)
IS  - 0927-3948 (Linking)
DP  - 2023 Aug 10
TI  - The Potential Role of Large Language Models in Uveitis Care: Perspectives After 
      ChatGPT and Bard Launch.
PG  - 1-5
LID - 10.1080/09273948.2023.2242462 [doi]
FAU - Tan Yip Ming, Collin
AU  - Tan Yip Ming C
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
FAU - Rojas-Carabali, William
AU  - Rojas-Carabali W
AD  - Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 
      Singapore.
FAU - Cifuentes-González, Carlos
AU  - Cifuentes-González C
AUID- ORCID: 0000-0002-2703-0977
AD  - Neuroscience Research Group (NEUROS), Neurovitae Center for Neuroscience, 
      Institute of Translational Medicine (IMT), Escuela de Medicina y Ciencias de la 
      Salud, Universidad del Rosario, Bogotá, Colombia.
FAU - Agrawal, Rajdeep
AU  - Agrawal R
AD  - Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 
      Singapore.
FAU - Thorne, Jennifer E
AU  - Thorne JE
AD  - Department of Ophthalmology, Wilmer Eye Institute, The Johns Hopkins Hospital, 
      Baltimor, Maryland, USA.
FAU - Tugal-Tutkun, Ilknur
AU  - Tugal-Tutkun I
AD  - Department of Ophthalmology, Istanbul Faculty of Medicine, Istanbul University, 
      Istanbul, Turkey.
AD  - Eye Protection Foundation Bayrampasa Eye Hospital, Istanbul, Turkey.
FAU - Nguyen, Quan Dong
AU  - Nguyen QD
AD  - Byers Eye Institute, Stanford University, Palo Alto, California, USA.
FAU - Gupta, Vishali
AU  - Gupta V
AD  - Advanced Eye Centre, Post-Graduate Institute of Medical Education and Research 
      (PGIMER), Chandigarh, India.
FAU - de-la-Torre, Alejandra
AU  - de-la-Torre A
AD  - Neuroscience Research Group (NEUROS), Neurovitae Center for Neuroscience, 
      Institute of Translational Medicine (IMT), Escuela de Medicina y Ciencias de la 
      Salud, Universidad del Rosario, Bogotá, Colombia.
FAU - Agrawal, Rupesh
AU  - Agrawal R
AD  - Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 
      Singapore.
AD  - Department of Ophthalmology, Tan Tock Seng Hospital, Singapore.
AD  - Moorfields Eye Hospital, NHS Foundation Trust, London, UK.
AD  - Singapore Eye Research Institute, The Academia, Singapore, Singapore.
AD  - Duke NUS Medical School, Singapore, Singapore.
LA  - eng
PT  - Editorial
DEP - 20230810
PL  - England
TA  - Ocul Immunol Inflamm
JT  - Ocular immunology and inflammation
JID - 9312169
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - LLMs
OT  - diagnosis
OT  - ophthalmology
OT  - treatment
OT  - uveitis
EDAT- 2023/08/10 18:42
MHDA- 2023/08/10 18:42
CRDT- 2023/08/10 17:13
PHST- 2023/08/10 18:42 [medline]
PHST- 2023/08/10 18:42 [pubmed]
PHST- 2023/08/10 17:13 [entrez]
AID - 10.1080/09273948.2023.2242462 [doi]
PST - aheadofprint
SO  - Ocul Immunol Inflamm. 2023 Aug 10:1-5. doi: 10.1080/09273948.2023.2242462.

PMID- 37217092
OWN - NLM
STAT- MEDLINE
DCOM- 20230904
LR  - 20231030
IS  - 1556-5653 (Electronic)
IS  - 0015-0282 (Linking)
VI  - 120
IP  - 3 Pt 2
DP  - 2023 Sep
TI  - The promise and peril of using a large language model to obtain clinical 
      information: ChatGPT performs strongly as a fertility counseling tool with 
      limitations.
PG  - 575-583
LID - S0015-0282(23)00522-8 [pii]
LID - 10.1016/j.fertnstert.2023.05.151 [doi]
AB  - OBJECTIVE: To compare the responses of the large language model-based "ChatGPT" 
      to reputable sources when given fertility-related clinical prompts. DESIGN: The 
      "Feb 13" version of ChatGPT by OpenAI was tested against established sources 
      relating to patient-oriented clinical information: 17 "frequently asked questions 
      (FAQs)" about infertility on the Centers for Disease Control (CDC) Website, 
      2 validated fertility knowledge surveys, the Cardiff Fertility Knowledge Scale 
      and the Fertility and Infertility Treatment Knowledge Score, as well as the 
      American Society for Reproductive Medicine committee opinion "optimizing natural 
      fertility." SETTING: Academic medical center. PATIENT(S): Online AI Chatbot. 
      INTERVENTION(S): Frequently asked questions, survey questions and rephrased 
      summary statements were entered as prompts in the chatbot over a 1-week period in 
      February 2023. MAIN OUTCOME MEASURE(S): For FAQs from CDC: words/response, 
      sentiment analysis polarity and objectivity, total factual statements, rate of 
      statements that were incorrect, referenced a source, or noted the value of 
      consulting providers. FOR FERTILITY KNOWLEDGE SURVEYS: Percentile according to 
      published population data. FOR COMMITTEE OPINION: Whether response to conclusions 
      rephrased as questions identified missing facts. RESULT(S): When administered the 
      CDC's 17 infertility FAQ's, ChatGPT produced responses of similar length (207.8 
      ChatGPT vs. 181.0 CDC words/response), factual content (8.65 factual 
      statements/response vs. 10.41), sentiment polarity (mean 0.11 vs. 0.11 on a scale 
      of -1 (negative) to 1 (positive)), and subjectivity (mean 0.42 vs. 0.35 on a 
      scale of 0 (objective) to 1 (subjective)). In total, 9 (6.12%) of 147 ChatGPT 
      factual statements were categorized as incorrect, and only 1 (0.68%) statement 
      cited a reference. ChatGPT would have been at the 87th percentile of Bunting's 
      2013 international cohort for the Cardiff Fertility Knowledge Scale and at the 
      95th percentile on the basis of Kudesia's 2017 cohort for the Fertility and 
      Infertility Treatment Knowledge Score. ChatGPT reproduced the missing facts for 
      all 7 summary statements from "optimizing natural fertility." CONCLUSION(S): A 
      February 2023 version of "ChatGPT" demonstrates the ability of generative 
      artificial intelligence to produce relevant, meaningful responses to 
      fertility-related clinical queries comparable to established sources. Although 
      performance may improve with medical domain-specific training, limitations such 
      as the inability to reliably cite sources and the unpredictable possibility of 
      fabricated information may limit its clinical use.
CI  - Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Chervenak, Joseph
AU  - Chervenak J
AD  - Albert Einstein College of Medicine/Montefiore's Institute for Reproductive 
      Medicine and Health, Hartsdale, New York. Electronic address: 
      joseph.chervenak@gmail.com.
FAU - Lieman, Harry
AU  - Lieman H
AD  - Albert Einstein College of Medicine/Montefiore's Institute for Reproductive 
      Medicine and Health, Hartsdale, New York.
FAU - Blanco-Breindel, Miranda
AU  - Blanco-Breindel M
AD  - Albert Einstein College of Medicine/Montefiore's Institute for Reproductive 
      Medicine and Health, Hartsdale, New York.
FAU - Jindal, Sangita
AU  - Jindal S
AD  - Albert Einstein College of Medicine/Montefiore's Institute for Reproductive 
      Medicine and Health, Hartsdale, New York.
LA  - eng
PT  - Journal Article
DEP - 20230520
PL  - United States
TA  - Fertil Steril
JT  - Fertility and sterility
JID - 0372772
SB  - IM
CIN - Fertil Steril. 2023 Sep;120(3 Pt 2):584-585. PMID: 37356469
CIN - Fertil Steril. 2023 Nov;120(5):1084. PMID: 37506761
MH  - Humans
MH  - *Artificial Intelligence
MH  - Counseling
MH  - Fertility
MH  - *Infertility/diagnosis/therapy
MH  - Language
OTO - NOTNLM
OT  - Artificial intelligence
OT  - counseling
OT  - fertility knowledge
OT  - natural language processing
OT  - online
EDAT- 2023/05/23 01:06
MHDA- 2023/09/04 06:42
CRDT- 2023/05/22 19:20
PHST- 2023/02/26 00:00 [received]
PHST- 2023/05/01 00:00 [revised]
PHST- 2023/05/12 00:00 [accepted]
PHST- 2023/09/04 06:42 [medline]
PHST- 2023/05/23 01:06 [pubmed]
PHST- 2023/05/22 19:20 [entrez]
AID - S0015-0282(23)00522-8 [pii]
AID - 10.1016/j.fertnstert.2023.05.151 [doi]
PST - ppublish
SO  - Fertil Steril. 2023 Sep;120(3 Pt 2):575-583. doi: 
      10.1016/j.fertnstert.2023.05.151. Epub 2023 May 20.

PMID- 37106269
OWN - NLM
STAT- MEDLINE
DCOM- 20230605
LR  - 20231119
IS  - 1708-0428 (Electronic)
IS  - 0960-8923 (Print)
IS  - 0960-8923 (Linking)
VI  - 33
IP  - 6
DP  - 2023 Jun
TI  - Assessing the Accuracy of Responses by the Language Model ChatGPT to Questions 
      Regarding Bariatric Surgery.
PG  - 1790-1796
LID - 10.1007/s11695-023-06603-5 [doi]
AB  - PURPOSE: ChatGPT is a large language model trained on a large dataset covering a 
      broad range of topics, including the medical literature. We aim to examine its 
      accuracy and reproducibility in answering patient questions regarding bariatric 
      surgery. MATERIALS AND METHODS: Questions were gathered from nationally regarded 
      professional societies and health institutions as well as Facebook support 
      groups. Board-certified bariatric surgeons graded the accuracy and 
      reproducibility of responses. The grading scale included the following: (1) 
      comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, 
      and (4) completely incorrect. Reproducibility was determined by asking the model 
      each question twice and examining difference in grading category between the two 
      responses. RESULTS: In total, 151 questions related to bariatric surgery were 
      included. The model provided "comprehensive" responses to 131/151 (86.8%) of 
      questions. When examined by category, the model provided "comprehensive" 
      responses to 93.8% of questions related to "efficacy, eligibility and procedure 
      options"; 93.3% related to "preoperative preparation"; 85.3% related to 
      "recovery, risks, and complications"; 88.2% related to "lifestyle changes"; and 
      66.7% related to "other". The model provided reproducible answers to 137 (90.7%) 
      of questions. CONCLUSION: The large language model ChatGPT often provided 
      accurate and reproducible responses to common questions related to bariatric 
      surgery. ChatGPT may serve as a helpful adjunct information resource for patients 
      regarding bariatric surgery in addition to standard of care provided by licensed 
      healthcare professionals. We encourage future studies to examine how to leverage 
      this disruptive technology to improve patient outcomes and quality of life.
CI  - © 2023. The Author(s).
FAU - Samaan, Jamil S
AU  - Samaan JS
AUID- ORCID: 0000-0002-6191-2631
AD  - Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 
      8700 Beverly Blvd, Los Angeles, CA, 90048, USA. jamil.samaan@gmail.com.
FAU - Yeo, Yee Hui
AU  - Yeo YH
AD  - Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 
      8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
FAU - Rajeev, Nithya
AU  - Rajeev N
AD  - Division of Upper GI and General Surgery, Department of Surgery, Health Care 
      Consultation Center, Keck School of Medicine of USC, 1510 San Pablo St. #514, Los 
      Angeles, CA, 90033, USA.
FAU - Hawley, Lauren
AU  - Hawley L
AD  - Division of Upper GI and General Surgery, Department of Surgery, Health Care 
      Consultation Center, Keck School of Medicine of USC, 1510 San Pablo St. #514, Los 
      Angeles, CA, 90033, USA.
FAU - Abel, Stuart
AU  - Abel S
AD  - Division of Upper GI and General Surgery, Department of Surgery, Health Care 
      Consultation Center, Keck School of Medicine of USC, 1510 San Pablo St. #514, Los 
      Angeles, CA, 90033, USA.
FAU - Ng, Wee Han
AU  - Ng WH
AD  - Bristol Medical School, University of Bristol, 5 Tyndall Ave, Bristol, BS8 1UD, 
      UK.
FAU - Srinivasan, Nitin
AU  - Srinivasan N
AD  - Division of Upper GI and General Surgery, Department of Surgery, Health Care 
      Consultation Center, Keck School of Medicine of USC, 1510 San Pablo St. #514, Los 
      Angeles, CA, 90033, USA.
FAU - Park, Justin
AU  - Park J
AD  - Division of Upper GI and General Surgery, Department of Surgery, Health Care 
      Consultation Center, Keck School of Medicine of USC, 1510 San Pablo St. #514, Los 
      Angeles, CA, 90033, USA.
FAU - Burch, Miguel
AU  - Burch M
AD  - Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los 
      Angeles, CA, 90048, USA.
FAU - Watson, Rabindra
AU  - Watson R
AD  - Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 
      8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
FAU - Liran, Omer
AU  - Liran O
AD  - Department of Psychiatry and Behavioral Sciences, Cedars-Sinai Medical Center, 
      8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
AD  - Division of Health Services Research, Department of Medicine, Cedars-Sinai 
      Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
FAU - Samakar, Kamran
AU  - Samakar K
AD  - Division of Upper GI and General Surgery, Department of Surgery, Health Care 
      Consultation Center, Keck School of Medicine of USC, 1510 San Pablo St. #514, Los 
      Angeles, CA, 90033, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230427
PL  - United States
TA  - Obes Surg
JT  - Obesity surgery
JID - 9106714
SB  - IM
CIN - Obes Surg. 2023 Aug;33(8):2588-2589. PMID: 37301782
CIN - Obes Surg. 2023 Aug;33(8):2590-2591. PMID: 37312008
CIN - Obes Surg. 2023 Aug;33(8):2596. PMID: 37351764
CIN - Obes Surg. 2023 Aug;33(8):2597. PMID: 37353693
MH  - Humans
MH  - Quality of Life
MH  - Reproducibility of Results
MH  - *Obesity, Morbid/surgery
MH  - *Bariatric Surgery
MH  - Language
PMC - PMC10234918
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bariatric surgery
OT  - ChatGPT
OT  - Health literacy
OT  - Language learning models
OT  - Weight loss
COIS- The authors declare no competing interests.
EDAT- 2023/04/28 00:42
MHDA- 2023/06/05 06:42
CRDT- 2023/04/27 23:32
PHST- 2023/02/21 00:00 [received]
PHST- 2023/04/17 00:00 [accepted]
PHST- 2023/04/10 00:00 [revised]
PHST- 2023/06/05 06:42 [medline]
PHST- 2023/04/28 00:42 [pubmed]
PHST- 2023/04/27 23:32 [entrez]
AID - 10.1007/s11695-023-06603-5 [pii]
AID - 6603 [pii]
AID - 10.1007/s11695-023-06603-5 [doi]
PST - ppublish
SO  - Obes Surg. 2023 Jun;33(6):1790-1796. doi: 10.1007/s11695-023-06603-5. Epub 2023 
      Apr 27.

PMID- 37926396
OWN - NLM
STAT- In-Process
LR  - 20231211
IS  - 1872-9649 (Electronic)
IS  - 1568-1637 (Linking)
VI  - 92
DP  - 2023 Dec
TI  - ChatGPT's dance with neuropsychological data: A case study in Alzheimer's 
      disease.
PG  - 102117
LID - S1568-1637(23)00276-3 [pii]
LID - 10.1016/j.arr.2023.102117 [doi]
AB  - Artificial intelligence continues to revolutionize the medical and scientific 
      field, especially with the release of ChatGPT. We assessed whether it provides an 
      accurate interpretation of neuropsychological screening. We provided ChatGPT with 
      the neuropsychological data of a patient with mild Alzheimer's Disease and 
      invited it and two neuropsychologists to interpret the data. While ChatGPT 
      provided an accurate interpretation of scores on each of the neuropsychological 
      tests, it did not use standardized scores and did not specify the cognitive 
      domain that may be most impaired. In contrast, the neuropsychologists used 
      standardized scores to determine that the patient was mainly suffering from 
      memory decline. While ChatGPT may succeed in the general interpretation of 
      neuropsychological testing, at least in patients with Alzheimer's Disease, it 
      still cannot create a pattern of scores across different tests to better specify 
      the nature of cognitive impairment.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - El Haj, Mohamad
AU  - El Haj M
AD  - Institut Universitaire de France, Paris, France; CHU Nantes, Clinical Gerontology 
      Department, Bd Jacques Monod, F44093 Nantes, France. Electronic address: 
      mohamad.elhaj@univ-nantes.fr.
FAU - Boutoleau-Bretonnière, Claire
AU  - Boutoleau-Bretonnière C
AD  - CHU Nantes, Inserm CIC04, Nantes, France.
FAU - Chapelet, Guillaume
AU  - Chapelet G
AD  - CHU Nantes, Clinical Gerontology Department, Bd Jacques Monod, F44093 Nantes, 
      France; Université de Nantes, Inserm, TENS, The Enteric Nervous System in Gut and 
      Brain Diseases, IMAD, Nantes, France.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231104
PL  - England
TA  - Ageing Res Rev
JT  - Ageing research reviews
JID - 101128963
SB  - IM
OTO - NOTNLM
OT  - Alzheimer’s disease
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Neuropsychological testing
OT  - Neuropsychology
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/11/06 00:41
MHDA- 2023/11/06 00:41
CRDT- 2023/11/05 19:17
PHST- 2023/10/09 00:00 [received]
PHST- 2023/10/31 00:00 [revised]
PHST- 2023/11/01 00:00 [accepted]
PHST- 2023/11/06 00:41 [pubmed]
PHST- 2023/11/06 00:41 [medline]
PHST- 2023/11/05 19:17 [entrez]
AID - S1568-1637(23)00276-3 [pii]
AID - 10.1016/j.arr.2023.102117 [doi]
PST - ppublish
SO  - Ageing Res Rev. 2023 Dec;92:102117. doi: 10.1016/j.arr.2023.102117. Epub 2023 Nov 
      4.

PMID- 37271011
OWN - NLM
STAT- MEDLINE
DCOM- 20230628
LR  - 20230816
IS  - 1532-2831 (Electronic)
IS  - 1078-8174 (Linking)
VI  - 29
IP  - 4
DP  - 2023 Jul
TI  - ChatGPT in medical imaging higher education.
PG  - 792-799
LID - S1078-8174(23)00117-7 [pii]
LID - 10.1016/j.radi.2023.05.011 [doi]
AB  - INTRODUCTION: Academic integrity among radiographers and nuclear medicine 
      technologists/scientists in both higher education and scientific writing has been 
      challenged by advances in artificial intelligence (AI). The recent release of 
      ChatGPT, a chatbot powered by GPT-3.5 capable of producing accurate and 
      human-like responses to questions in real-time, has redefined the boundaries of 
      academic and scientific writing. These boundaries require objective evaluation. 
      METHOD: ChatGPT was tested against six subjects across the first three years of 
      the medical radiation science undergraduate course for both exams (n = 6) and 
      written assignment tasks (n = 3). ChatGPT submissions were marked against 
      standardised rubrics and results compared to student cohorts. Submissions were 
      also evaluated by Turnitin for similarity and AI scores. RESULTS: ChatGPT powered 
      by GPT-3.5 performed below the average student performance in all written tasks 
      with an increasing disparity as subjects advanced. ChatGPT performed better than 
      the average student in foundation or general subject examinations where shallow 
      responses meet learning outcomes. For discipline specific subjects, ChatGPT 
      lacked the depth, breadth, and currency of insight to provide pass level answers. 
      CONCLUSION: ChatGPT simultaneously poses a risk to academic integrity in writing 
      and assessment while affording a tool for enhanced learning environments. These 
      risks and benefits are likely to be restricted to learning outcomes of lower 
      taxonomies. Both risks and benefits are likely to be constrained by higher order 
      taxonomies. IMPLICATIONS FOR PRACTICE: ChatGPT powered by GPT3.5 has limited 
      capacity to support student cheating, introduces errors and fabricated 
      information, and is readily identified by software as AI generated. Lack of depth 
      of insight and appropriateness for professional communication also limits 
      capacity as a learning enhancement tool.
CI  - Copyright © 2023 The College of Radiographers. Published by Elsevier Ltd. All 
      rights reserved.
FAU - Currie, G
AU  - Currie G
AD  - Charles Sturt University, Wagga Wagga, NSW, Australia. Electronic address: 
      gcurrie@csu.edu.au.
FAU - Singh, C
AU  - Singh C
AD  - Charles Sturt University, Wagga Wagga, NSW, Australia.
FAU - Nelson, T
AU  - Nelson T
AD  - Charles Sturt University, Port Macquarie, NSW, Australia.
FAU - Nabasenja, C
AU  - Nabasenja C
AD  - Charles Sturt University, Port Macquarie, NSW, Australia.
FAU - Al-Hayek, Y
AU  - Al-Hayek Y
AD  - Charles Sturt University, Wagga Wagga, NSW, Australia.
FAU - Spuur, K
AU  - Spuur K
AD  - Charles Sturt University, Wagga Wagga, NSW, Australia.
LA  - eng
PT  - Journal Article
DEP - 20230602
PL  - Netherlands
TA  - Radiography (Lond)
JT  - Radiography (London, England : 1995)
JID - 9604102
SB  - IM
CIN - Radiography (Lond). 2023 Aug;29(5):867. PMID: 37413958
CIN - Radiography (Lond). 2023 Aug;29(5):868-869. PMID: 37419046
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Diagnostic Imaging
MH  - Radiography
MH  - Learning
MH  - Software
OTO - NOTNLM
OT  - Academic integrity
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Generative algorithms
OT  - Higher education
OT  - Language model
COIS- Conflict of interest statement None.
EDAT- 2023/06/05 00:41
MHDA- 2023/06/28 06:42
CRDT- 2023/06/04 18:06
PHST- 2023/04/13 00:00 [received]
PHST- 2023/05/17 00:00 [revised]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2023/06/28 06:42 [medline]
PHST- 2023/06/05 00:41 [pubmed]
PHST- 2023/06/04 18:06 [entrez]
AID - S1078-8174(23)00117-7 [pii]
AID - 10.1016/j.radi.2023.05.011 [doi]
PST - ppublish
SO  - Radiography (Lond). 2023 Jul;29(4):792-799. doi: 10.1016/j.radi.2023.05.011. Epub 
      2023 Jun 2.

PMID- 37812965
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1873-4499 (Electronic)
IS  - 0899-7071 (Linking)
VI  - 103
DP  - 2023 Nov
TI  - Improving radiology workflow using ChatGPT and artificial intelligence.
PG  - 109993
LID - S0899-7071(23)00213-9 [pii]
LID - 10.1016/j.clinimag.2023.109993 [doi]
AB  - Artificial Intelligence is a branch of computer science that aims to create 
      intelligent machines capable of performing tasks that typically require human 
      intelligence. One of the branches of artificial intelligence is natural language 
      processing, which is dedicated to studying the interaction between computers and 
      human language. ChatGPT is a sophisticated natural language processing tool that 
      can understand and respond to complex questions and commands in natural language. 
      Radiology is a vital aspect of modern medicine that involves the use of imaging 
      technologies to diagnose and treat medical conditions artificial intelligence, 
      including ChatGPT, can be integrated into radiology workflows to improve 
      efficiency, accuracy, and patient care. ChatGPT can streamline various radiology 
      workflow steps, including patient registration, scheduling, patient check-in, 
      image acquisition, interpretation, and reporting. While ChatGPT has the potential 
      to transform radiology workflows, there are limitations to the technology that 
      must be addressed, such as the potential for bias in artificial intelligence 
      algorithms and ethical concerns. As technology continues to advance, ChatGPT is 
      likely to become an increasingly important tool in the field of radiology, and in 
      healthcare more broadly.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Mese, Ismail
AU  - Mese I
AD  - Department of Radiology, Health Sciences University, Erenkoy Mental Health and 
      Neurology Training and Research Hospital, 19 Mayıs, Sinan Ercan Cd. No: 23, 
      Kadıköy/Istanbul 34736, Turkey. Electronic address: ismail_mese@yahoo.com.
FAU - Taslicay, Ceylan Altintas
AU  - Taslicay CA
AD  - Department of Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, 
      TX 77030, USA.
FAU - Sivrioglu, Ali Kemal
AU  - Sivrioglu AK
AD  - Department of Radiology, Liv Hospital Vadistanbul, Ayazağa Mahallesi, Kemerburgaz 
      Caddesi, Vadistanbul Park Etabı, 7F Blok, 34396 Sarıyer/İstanbul, Turkey.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231006
PL  - United States
TA  - Clin Imaging
JT  - Clinical imaging
JID - 8911831
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Workflow
MH  - Radiography
MH  - *Radiology
MH  - Algorithms
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Diagnostic techniques
OT  - Natural language processing
OT  - Radiology
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/10/10 00:42
MHDA- 2023/10/23 01:18
CRDT- 2023/10/09 18:36
PHST- 2023/06/16 00:00 [received]
PHST- 2023/08/19 00:00 [revised]
PHST- 2023/09/28 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/10 00:42 [pubmed]
PHST- 2023/10/09 18:36 [entrez]
AID - S0899-7071(23)00213-9 [pii]
AID - 10.1016/j.clinimag.2023.109993 [doi]
PST - ppublish
SO  - Clin Imaging. 2023 Nov;103:109993. doi: 10.1016/j.clinimag.2023.109993. Epub 2023 
      Oct 6.

PMID- 37928447
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231107
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Revisiting the political biases of ChatGPT.
PG  - 1232003
LID - 10.3389/frai.2023.1232003 [doi]
LID - 1232003
AB  - Although ChatGPT promises wide-ranging applications, there is a concern that it 
      is politically biased; in particular, that it has a left-libertarian orientation. 
      Nevertheless, following recent trends in attempts to reduce such biases, this 
      study re-evaluated the political biases of ChatGPT using political orientation 
      tests and the application programming interface. The effects of the languages 
      used in the system as well as gender and race settings were evaluated. The 
      results indicate that ChatGPT manifests less political bias than previously 
      assumed; however, they did not entirely dismiss the political bias. The languages 
      used in the system, and the gender and race settings may induce political biases. 
      These findings enhance our understanding of the political biases of ChatGPT and 
      may be useful for bias evaluation and designing the operational strategy of 
      ChatGPT.
CI  - Copyright © 2023 Fujimoto and Takemoto.
FAU - Fujimoto, Sasuke
AU  - Fujimoto S
AD  - Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 
      Iizuka, Fukuoka, Japan.
FAU - Takemoto, Kazuhiro
AU  - Takemoto K
AD  - Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 
      Iizuka, Fukuoka, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10623051
OTO - NOTNLM
OT  - ChatGPT
OT  - algorithm bias
OT  - large-language model
OT  - natural language processing
OT  - political bias
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/06 06:42
MHDA- 2023/11/06 06:43
CRDT- 2023/11/06 04:33
PHST- 2023/05/31 00:00 [received]
PHST- 2023/10/04 00:00 [accepted]
PHST- 2023/11/06 06:43 [medline]
PHST- 2023/11/06 06:42 [pubmed]
PHST- 2023/11/06 04:33 [entrez]
AID - 10.3389/frai.2023.1232003 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Oct 20;6:1232003. doi: 10.3389/frai.2023.1232003. 
      eCollection 2023.

PMID- 37526801
OWN - NLM
STAT- Publisher
LR  - 20230801
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Aug 1
TI  - Role of ChatGPT-4 for Medical Researchers.
LID - 10.1007/s10439-023-03336-5 [doi]
AB  - The robustness of artificial intelligence (AI) in medical research has gotten a 
      lot of attention in the current era. In particular, ChatGPT-4 is emerging as a 
      notable AI language model. Basically, ChatGPT-4 is a state-of-the-art language 
      model that mimics human responses to natural language inputs using AI-based deep 
      learning methods and was created by OpenAI. ChatGPT-4 allows medical researchers 
      to instantly share and receive up-to-date information on best practices and 
      guidelines. Moreover, practitioners can explore this technology in various fields 
      such as recognizing patterns, predicting outcomes, and providing important 
      insights by entering patient data, medical reports, and other text-based 
      information. No doubt, ChatGPT-4 is in the trending era but still need to know 
      more about this tool. Therefore, this letter highlights the contributions, 
      benefits, and challenges of the ChatGPT-4 tool in the medical field that may help 
      medical professionals or researchers in their future work.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Datt, Mohit
AU  - Datt M
AUID- ORCID: 0000-0001-8586-426X
AD  - Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National 
      Institute of Technology, Jalandhar, India. mohitdatt1991@gmail.com.
FAU - Sharma, Himanshu
AU  - Sharma H
AD  - Department of Civil Engineering, Dr. B. R. Ambedkar National Institute of 
      Technology, Jalandhar, India.
FAU - Aggarwal, Nikita
AU  - Aggarwal N
AD  - Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar 
      National Institute of Technology, Jalandhar, India.
FAU - Sharma, Shivani
AU  - Sharma S
AD  - Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar 
      National Institute of Technology, Jalandhar, India.
LA  - eng
PT  - Letter
DEP - 20230801
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - ChatGPT-4
OT  - Medical researchers
OT  - NLP
OT  - Open artificial intelligence
EDAT- 2023/08/01 13:09
MHDA- 2023/08/01 13:09
CRDT- 2023/08/01 11:10
PHST- 2023/07/25 00:00 [received]
PHST- 2023/07/27 00:00 [accepted]
PHST- 2023/08/01 13:09 [medline]
PHST- 2023/08/01 13:09 [pubmed]
PHST- 2023/08/01 11:10 [entrez]
AID - 10.1007/s10439-023-03336-5 [pii]
AID - 10.1007/s10439-023-03336-5 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Aug 1. doi: 10.1007/s10439-023-03336-5.

PMID- 36926868
OWN - NLM
STAT- MEDLINE
DCOM- 20230328
LR  - 20230605
IS  - 1549-960X (Electronic)
IS  - 1549-9596 (Linking)
VI  - 63
IP  - 6
DP  - 2023 Mar 27
TI  - Do Large Language Models Understand Chemistry? A Conversation with ChatGPT.
PG  - 1649-1655
LID - 10.1021/acs.jcim.3c00285 [doi]
AB  - Large language models (LLMs) have promised a revolution in answering complex 
      questions using the ChatGPT model. Its application in chemistry is still in its 
      infancy. This viewpoint addresses the question of how well ChatGPT understands 
      chemistry by posing five simple tasks in different subareas of chemistry.
FAU - Castro Nascimento, Cayque Monteiro
AU  - Castro Nascimento CM
AUID- ORCID: 0000-0003-4712-0598
AD  - Departamento de Química, Pontifícia Universidade Católica do Rio de Janeiro, P.O. 
      Box 38097, Rio de Janeiro, RJ 22451-900, Brazil.
FAU - Pimentel, André Silva
AU  - Pimentel AS
AUID- ORCID: 0000-0002-1301-0561
AD  - Departamento de Química, Pontifícia Universidade Católica do Rio de Janeiro, P.O. 
      Box 38097, Rio de Janeiro, RJ 22451-900, Brazil.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230316
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
SB  - IM
MH  - *Communication
MH  - *Language
EDAT- 2023/03/18 06:00
MHDA- 2023/03/28 17:16
CRDT- 2023/03/17 07:56
PHST- 2023/03/28 17:16 [medline]
PHST- 2023/03/18 06:00 [pubmed]
PHST- 2023/03/17 07:56 [entrez]
AID - 10.1021/acs.jcim.3c00285 [doi]
PST - ppublish
SO  - J Chem Inf Model. 2023 Mar 27;63(6):1649-1655. doi: 10.1021/acs.jcim.3c00285. 
      Epub 2023 Mar 16.

PMID- 37399030
OWN - NLM
STAT- MEDLINE
DCOM- 20231113
LR  - 20231124
IS  - 1537-6591 (Electronic)
IS  - 1058-4838 (Print)
IS  - 1058-4838 (Linking)
VI  - 77
IP  - 9
DP  - 2023 Nov 11
TI  - ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical 
      Microbiology?
PG  - 1322-1328
LID - 10.1093/cid/ciad407 [doi]
AB  - ChatGPT, GPT-4, and Bard are highly advanced natural language process-based 
      computer programs (chatbots) that simulate and process human conversation in 
      written or spoken form. Recently released by the company OpenAI, ChatGPT was 
      trained on billions of unknown text elements (tokens) and rapidly gained wide 
      attention for its ability to respond to questions in an articulate manner across 
      a wide range of knowledge domains. These potentially disruptive large language 
      model (LLM) technologies have a broad range of conceivable applications in 
      medicine and medical microbiology. In this opinion article, I describe how 
      chatbot technologies work and discuss the strengths and weaknesses of ChatGPT, 
      GPT-4, and other LLMs for applications in the routine diagnostic laboratory, 
      focusing on various use cases for the pre- to post-analytical process.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Infectious Diseases Society of America.
FAU - Egli, Adrian
AU  - Egli A
AUID- ORCID: 0000-0002-3564-8603
AD  - Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Clin Infect Dis
JT  - Clinical infectious diseases : an official publication of the Infectious Diseases 
      Society of America
JID - 9203213
SB  - IM
MH  - Humans
MH  - *Language
MH  - *Communication
MH  - Laboratories
MH  - Software
PMC - PMC10640689
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatGPT
OT  - chatbot
OT  - digitalization
OT  - large language model
COIS- Potential conflicts of interest. A. E. reports grants or contracts from the 
      Bangerter Rhyner Foundation and the Swiss National Science Foundation; consulting 
      fees from Pfizer and Illumina; and participation on a data and safety monitoring 
      board or advisory board for Sefunda. The author has submitted the ICMJE Form for 
      Disclosure of Potential Conflicts of Interest. Conflicts that the editors 
      consider relevant to the content of the manuscript have been disclosed.
EDAT- 2023/07/03 13:06
MHDA- 2023/11/13 06:42
CRDT- 2023/07/03 11:56
PHST- 2023/03/08 00:00 [received]
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/07/03 13:06 [pubmed]
PHST- 2023/07/03 11:56 [entrez]
AID - 7217675 [pii]
AID - ciad407 [pii]
AID - 10.1093/cid/ciad407 [doi]
PST - ppublish
SO  - Clin Infect Dis. 2023 Nov 11;77(9):1322-1328. doi: 10.1093/cid/ciad407.

PMID- 37266720
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 11
DP  - 2023 Nov
TI  - GPT-4 and Ophthalmology Operative Notes.
PG  - 2353-2355
LID - 10.1007/s10439-023-03263-5 [doi]
AB  - Operative notes are an essential piece of documentation made by an ophthalmic 
      team following ocular or ophthalmic surgery. While the advanced language 
      processing capabilities of GPT-4 can be utilized to enhance the comprehension of 
      natural language in healthcare applications, the utilization of GPT-4 in writing 
      operative notes is not explicitly referenced in the current literature. In this 
      paper, we describe the potential applications for GPT-4 to write ophthalmic 
      operative notes. It is imperative to acknowledge that despite the impressive 
      capabilities exhibited by GPT-4, it remains constrained by certain limitations, 
      and therefore, ought to be employed in tandem with human expertise and subjected 
      to critical evaluation.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Waisberg, Ethan
AU  - Waisberg E
AUID- ORCID: 0000-0001-8999-0212
AD  - University College Dublin School of Medicine, Belfield, Dublin 4, Ireland. 
      ethan.waisberg@ucdconnect.ie.
FAU - Ong, Joshua
AU  - Ong J
AD  - Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
FAU - Masalkhi, Mouayad
AU  - Masalkhi M
AD  - University College Dublin School of Medicine, Belfield, Dublin 4, Ireland.
FAU - Kamran, Sharif Amit
AU  - Kamran SA
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
FAU - Zaman, Nasif
AU  - Zaman N
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
FAU - Sarker, Prithul
AU  - Sarker P
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
FAU - Lee, Andrew G
AU  - Lee AG
AD  - Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA.
AD  - Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, 
      Houston, TX, USA.
AD  - The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, 
      TX, USA.
AD  - Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell 
      Medicine, New York, NY, USA.
AD  - Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, 
      USA.
AD  - University of Texas MD Anderson Cancer Center, Houston, TX, USA.
AD  - Texas A&M College of Medicine, Houston, TX, USA.
AD  - Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa 
      City, IW, USA.
FAU - Tavakkoli, Alireza
AU  - Tavakkoli A
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
LA  - eng
PT  - Letter
DEP - 20230602
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - AI in ophthlamology
OT  - ChatGPT
OT  - Large language model
OT  - Operative notes
EDAT- 2023/06/02 13:16
MHDA- 2023/06/02 13:16
CRDT- 2023/06/02 11:10
PHST- 2023/05/25 00:00 [received]
PHST- 2023/05/29 00:00 [accepted]
PHST- 2023/06/02 13:16 [pubmed]
PHST- 2023/06/02 13:16 [medline]
PHST- 2023/06/02 11:10 [entrez]
AID - 10.1007/s10439-023-03263-5 [pii]
AID - 10.1007/s10439-023-03263-5 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Nov;51(11):2353-2355. doi: 10.1007/s10439-023-03263-5. Epub 
      2023 Jun 2.

PMID- 37309153
OWN - NLM
STAT- MEDLINE
DCOM- 20230614
LR  - 20230811
IS  - 1552-7409 (Electronic)
IS  - 0894-3184 (Linking)
VI  - 36
IP  - 3
DP  - 2023 Jul
TI  - ChatGPT and Forms of Deception.
PG  - 232-233
LID - 10.1177/08943184231169753 [doi]
AB  - The artificial intelligence (AI) chatbot ChatGPT movement has upset and permeated 
      all aspects of the healthcare arena, including the discipline of nursing. The use 
      of ChatGPT is ethically controversial. This article begins a discussion regarding 
      the impacts of ChatGPT and the possibilities of deception with its usage in 
      scientific and disciplinary publications and academic products.
FAU - Milton, Constance L
AU  - Milton CL
AUID- ORCID: 0000-0002-5848-6651
AD  - Professor Emeritus, School of Nursing, Azusa Pacific University, Azusa, CA, USA.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Nurs Sci Q
JT  - Nursing science quarterly
JID - 8805022
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Emotions
MH  - Deception
OTO - NOTNLM
OT  - ChatGPT
OT  - Chatbot
OT  - deception
OT  - ethics
OT  - nurse ethics
EDAT- 2023/06/13 06:42
MHDA- 2023/06/14 06:42
CRDT- 2023/06/13 03:39
PHST- 2023/06/14 06:42 [medline]
PHST- 2023/06/13 06:42 [pubmed]
PHST- 2023/06/13 03:39 [entrez]
AID - 10.1177/08943184231169753 [doi]
PST - ppublish
SO  - Nurs Sci Q. 2023 Jul;36(3):232-233. doi: 10.1177/08943184231169753.

PMID- 37501022
OWN - NLM
STAT- Publisher
LR  - 20231021
IS  - 1432-1335 (Electronic)
IS  - 0171-5216 (Linking)
VI  - 149
IP  - 15
DP  - 2023 Nov
TI  - ChatGPT and oncological applications: comment.
PG  - 14463
LID - 10.1007/s00432-023-04891-z [doi]
AB  - This letter to editor discusses on the publication on large language models for 
      oncological applications. Pros and Cons for ChatGPT are discussed.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - , Samraong, Cambodia. amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230727
PL  - Germany
TA  - J Cancer Res Clin Oncol
JT  - Journal of cancer research and clinical oncology
JID - 7902060
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Oncology
EDAT- 2023/07/28 01:08
MHDA- 2023/07/28 01:08
CRDT- 2023/07/27 23:36
PHST- 2023/05/12 00:00 [received]
PHST- 2023/05/20 00:00 [accepted]
PHST- 2023/07/28 01:08 [pubmed]
PHST- 2023/07/28 01:08 [medline]
PHST- 2023/07/27 23:36 [entrez]
AID - 10.1007/s00432-023-04891-z [pii]
AID - 10.1007/s00432-023-04891-z [doi]
PST - ppublish
SO  - J Cancer Res Clin Oncol. 2023 Nov;149(15):14463. doi: 10.1007/s00432-023-04891-z. 
      Epub 2023 Jul 27.

PMID- 36943815
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 8
DP  - 2023 Jul 15
TI  - Expanding Cosmetic Plastic Surgery Research With ChatGPT.
PG  - 930-937
LID - 10.1093/asj/sjad069 [doi]
AB  - BACKGROUND: In the past 3 months, OpenAI, a San Francisco-based artificial 
      intelligence (AI) research laboratory, has released ChatGPT, a conversation large 
      language model. ChatGPT has the ability to answer user questions, admit to 
      mistakes, and learn from users that are accessing the program. OBJECTIVES: Due to 
      the importance of producing evidence-based research in plastic surgery, the 
      authors of this study wanted to determine how accurate ChatGPT could be in 
      creating novel systematic review ideas that encompass the diverse practice of 
      cosmetic surgery. METHODS: ChatGPT was given commands to produce 20 novel 
      systematic review ideas for 12 different topics within cosmetic surgery. For each 
      topic, the system was told to give 10 general and 10 specific ideas that were 
      related to the concept. To determine the accuracy of ChatGPT, a literature review 
      was conducted with PubMed, CINAHL, EMBASE, and Cochrane. RESULTS: A total of 240 
      "novel" systematic review ideas were constructed by ChatGPT. We determined that 
      the system had an overall accuracy of 55%. When topics were stratified by general 
      and specific ideas, we found that ChatGPT was 35% accurate for general ideas and 
      75% accurate for specific ideas. CONCLUSIONS: ChatGPT is an excellent tool that 
      should be utilized by plastic surgeons. ChatGPT is versatile and has uses beyond 
      research, including patient consultation, patient support, and marketing. As 
      advancements in AI continue to be made, it is important for plastic surgeons to 
      consider the utilization of AI in their clinical practice.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of The 
      Aesthetic Society. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Gupta, Rohun
AU  - Gupta R
FAU - Park, John B
AU  - Park JB
FAU - Bisht, Chirag
AU  - Bisht C
FAU - Herzog, Isabel
AU  - Herzog I
AUID- ORCID: 0000-0001-7491-5746
FAU - Weisberger, Joseph
AU  - Weisberger J
FAU - Chao, John
AU  - Chao J
FAU - Chaiyasate, Kongkrit
AU  - Chaiyasate K
FAU - Lee, Edward S
AU  - Lee ES
LA  - eng
PT  - Journal Article
PT  - Review
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CIN - Aesthet Surg J. 2023 Aug 17;43(9):NP726-NP727. PMID: 37227006
MH  - Humans
MH  - *Surgery, Plastic
MH  - Artificial Intelligence
MH  - *Plastic Surgery Procedures
MH  - Communication
MH  - Hospitalization
EDAT- 2023/03/22 06:00
MHDA- 2023/07/17 06:42
CRDT- 2023/03/21 13:24
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/03/22 06:00 [pubmed]
PHST- 2023/03/21 13:24 [entrez]
AID - 7082916 [pii]
AID - 10.1093/asj/sjad069 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jul 15;43(8):930-937. doi: 10.1093/asj/sjad069.

PMID- 37476960
OWN - NLM
STAT- Publisher
LR  - 20231014
IS  - 1475-1313 (Electronic)
IS  - 0275-5408 (Linking)
VI  - 43
IP  - 6
DP  - 2023 Nov
TI  - Assessing the utility of ChatGPT as an artificial intelligence-based large 
      language model for information to answer questions on myopia.
PG  - 1562-1570
LID - 10.1111/opo.13207 [doi]
AB  - PURPOSE: ChatGPT is an artificial intelligence language model, which uses natural 
      language processing to simulate human conversation. It has seen a wide range of 
      applications including healthcare education, research and clinical practice. This 
      study evaluated the accuracy of ChatGPT in providing accurate and quality 
      information to answer questions on myopia. METHODS: A series of 11 questions 
      (nine categories of general summary, cause, symptom, onset, prevention, 
      complication, natural history, treatment and prognosis) were generated for this 
      cross-sectional study. Each question was entered five times into fresh ChatGPT 
      sessions (free from influence of prior questions). The responses were evaluated 
      by a five-member team of optometry teaching and research staff. The evaluators 
      individually rated the accuracy and quality of responses on a Likert scale, where 
      a higher score indicated greater quality of information (1: very poor; 2: poor; 
      3: acceptable; 4: good; 5: very good). Median scores for each question were 
      estimated and compared between evaluators. Agreement between the five evaluators 
      and the reliability statistics of the questions were estimated. RESULTS: Of the 
      11 questions on myopia, ChatGPT provided good quality information (median scores: 
      4.0) for 10 questions and acceptable responses (median scores: 3.0) for one 
      question. Out of 275 responses in total, 66 (24%) were rated very good, 134 (49%) 
      were rated good, whereas 60 (22%) were rated acceptable, 10 (3.6%) were rated 
      poor and 5 (1.8%) were rated very poor. Cronbach's α of 0.807 indicated good 
      level of agreement between test items. Evaluators' ratings demonstrated 'slight 
      agreement' (Fleiss's κ, 0.005) with a significant difference in scoring among the 
      evaluators (Kruskal-Wallis test, p < 0.001). CONCLUSION: Overall, ChatGPT 
      generated good quality information to answer questions on myopia. Although 
      ChatGPT shows great potential in rapidly providing information on myopia, the 
      presence of inaccurate responses demonstrates that further evaluation and 
      awareness concerning its limitations are crucial to avoid potential 
      misinterpretation.
CI  - © 2023 The Authors. Ophthalmic and Physiological Optics published by John Wiley & 
      Sons Ltd on behalf of College of Optometrists.
FAU - Biswas, Sayantan
AU  - Biswas S
AUID- ORCID: 0000-0001-6011-0365
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Logan, Nicola S
AU  - Logan NS
AUID- ORCID: 0000-0002-0538-9516
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Davies, Leon N
AU  - Davies LN
AUID- ORCID: 0000-0002-1554-0566
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Sheppard, Amy L
AU  - Sheppard AL
AUID- ORCID: 0000-0003-0035-8267
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Wolffsohn, James S
AU  - Wolffsohn JS
AUID- ORCID: 0000-0003-4673-8927
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
LA  - eng
PT  - Journal Article
DEP - 20230721
PL  - England
TA  - Ophthalmic Physiol Opt
JT  - Ophthalmic & physiological optics : the journal of the British College of 
      Ophthalmic Opticians (Optometrists)
JID - 8208839
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot response
OT  - myopia
OT  - patient information
EDAT- 2023/07/21 06:43
MHDA- 2023/07/21 06:43
CRDT- 2023/07/21 04:33
PHST- 2023/06/04 00:00 [revised]
PHST- 2023/03/21 00:00 [received]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/07/21 06:43 [pubmed]
PHST- 2023/07/21 06:43 [medline]
PHST- 2023/07/21 04:33 [entrez]
AID - 10.1111/opo.13207 [doi]
PST - ppublish
SO  - Ophthalmic Physiol Opt. 2023 Nov;43(6):1562-1570. doi: 10.1111/opo.13207. Epub 
      2023 Jul 21.

PMID- 37530687
OWN - NLM
STAT- Publisher
LR  - 20231004
IS  - 1531-6564 (Electronic)
IS  - 0363-5023 (Linking)
VI  - 48
IP  - 10
DP  - 2023 Oct
TI  - Exploring the Role of a Large Language Model on Carpal Tunnel Syndrome 
      Management: An Observation Study of ChatGPT.
PG  - 1025-1033
LID - S0363-5023(23)00360-X [pii]
LID - 10.1016/j.jhsa.2023.07.003 [doi]
AB  - PURPOSE: Recently, large language models, such as ChatGPT, have emerged as 
      promising tools to facilitate scientific research and health care management. The 
      present study aimed to explore the extent of knowledge possessed by ChatGPT 
      concerning carpal tunnel syndrome (CTS), a compressive neuropathy that may lead 
      to impaired hand function and that is frequently encountered in the field of hand 
      surgery. METHODS: Six questions pertaining to diagnosis and management of CTS 
      were posed to ChatGPT. The responses were subsequently analyzed and evaluated 
      based on their accuracy, coherence, and comprehensiveness. In addition, ChatGPT 
      was requested to provide five high-level evidence references in support of its 
      answers. A simulated doctor-patient consultation was also conducted to assess 
      whether ChatGPT could offer safe medical advice. RESULTS: ChatGPT supplied 
      clinically relevant information regarding CTS, although at a relatively 
      superficial level. In the context of doctor-patient interaction, ChatGPT 
      suggested a diagnostic pathway that deviated from the widely accepted clinical 
      consensus on CTS diagnosis. Nevertheless, it incorporated differential diagnoses 
      and valuable management options for CTS. Although ChatGPT demonstrated the 
      ability to retain and recall information from previous patient conversations, it 
      infrequently produced pertinent references, many of which were either nonexistent 
      or incorrect. CONCLUSIONS: ChatGPT displayed the capability to deliver validated 
      medical information on CTS to nonmedical individuals. However, the generation of 
      nonexistent and inaccurate references by ChatGPT presents a challenge to academic 
      integrity. CLINICAL RELEVANCE: To increase their utility in medicine and 
      academia, large language models must go through specialized reputable data set 
      training and validation from experts. It is essential to note that at present, 
      large language models cannot replace the expertise of health care professionals 
      and may act as a supportive tool.
CI  - Copyright © 2023. Published by Elsevier Inc.
FAU - Seth, Ishith
AU  - Seth I
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, Australia; 
      Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia; 
      Faculty of Medicine, The University of Melbourne, Melbourne, Victoria, Australia. 
      Electronic address: ishithseth1@gmail.com.
FAU - Xie, Yi
AU  - Xie Y
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
FAU - Rodwell, Aaron
AU  - Rodwell A
AD  - Department of Surgery, The Wollongong Hospital, Wollongong, New South Wales, 
      Australia.
FAU - Gracias, Dylan
AU  - Gracias D
AD  - Department of Surgery, Townsville Hospital, Townsville, Queensland, Australia.
FAU - Bulloch, Gabriella
AU  - Bulloch G
AD  - Faculty of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, Australia.
FAU - Rozen, Warren M
AU  - Rozen WM
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, Australia; 
      Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - United States
TA  - J Hand Surg Am
JT  - The Journal of hand surgery
JID - 7609631
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - CTS
OT  - ChatGPT
OT  - carpal tunnel syndrome
OT  - chatbot
EDAT- 2023/08/02 13:08
MHDA- 2023/08/02 13:08
CRDT- 2023/08/02 10:28
PHST- 2023/03/17 00:00 [received]
PHST- 2023/06/09 00:00 [revised]
PHST- 2023/07/10 00:00 [accepted]
PHST- 2023/08/02 13:08 [pubmed]
PHST- 2023/08/02 13:08 [medline]
PHST- 2023/08/02 10:28 [entrez]
AID - S0363-5023(23)00360-X [pii]
AID - 10.1016/j.jhsa.2023.07.003 [doi]
PST - ppublish
SO  - J Hand Surg Am. 2023 Oct;48(10):1025-1033. doi: 10.1016/j.jhsa.2023.07.003. Epub 
      2023 Aug 1.

PMID- 37814369
OWN - NLM
STAT- In-Process
LR  - 20231212
IS  - 1365-2591 (Electronic)
IS  - 0143-2885 (Linking)
VI  - 57
IP  - 1
DP  - 2024 Jan
TI  - Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of 
      endodontic question answers.
PG  - 108-113
LID - 10.1111/iej.13985 [doi]
AB  - AIM: Chatbot Generative Pre-trained Transformer (ChatGPT) is a generative 
      artificial intelligence (AI) software based on large language models (LLMs), 
      designed to simulate human conversations and generate novel content based on the 
      training data it has been exposed to. The aim of this study was to evaluate the 
      consistency and accuracy of ChatGPT-generated answers to clinical questions in 
      endodontics, compared to answers provided by human experts. METHODOLOGY: 
      Ninety-one dichotomous (yes/no) questions were designed and categorized into 
      three levels of difficulty. Twenty questions were randomly selected from each 
      difficulty level. Sixty answers were generated by ChatGPT for each question. Two 
      endodontic experts independently answered the 60 questions. Statistical analysis 
      was performed using the SPSS program to calculate the consistency and accuracy of 
      the answers generated by ChatGPT compared to the experts. Confidence intervals 
      (95%) and standard deviations were used to estimate variability. RESULTS: The 
      answers generated by ChatGPT showed high consistency (85.44%). No significant 
      differences in consistency were found based on question difficulty. In terms of 
      answer accuracy, ChatGPT achieved an average accuracy of 57.33%. However, 
      significant differences in accuracy were observed based on question difficulty, 
      with lower accuracy for easier questions. CONCLUSIONS: Currently, ChatGPT is not 
      capable of replacing dentists in clinical decision-making. As ChatGPT's 
      performance improves through deep learning, it is expected to become more useful 
      and effective in the field of endodontics. However, careful attention and ongoing 
      evaluation are needed to ensure its accuracy, reliability and safety in 
      endodontics.
CI  - © 2023 The Authors. International Endodontic Journal published by John Wiley & 
      Sons Ltd on behalf of British Endodontic Society.
FAU - Suárez, Ana
AU  - Suárez A
AUID- ORCID: 0000-0003-2448-6669
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Díaz-Flores García, Víctor
AU  - Díaz-Flores García V
AUID- ORCID: 0000-0001-6141-0546
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Algar, Juan
AU  - Algar J
AD  - Department of Clinical Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Gómez Sánchez, Margarita
AU  - Gómez Sánchez M
AUID- ORCID: 0000-0002-8305-6354
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Llorente de Pedro, María
AU  - Llorente de Pedro M
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Freire, Yolanda
AU  - Freire Y
AUID- ORCID: 0000-0002-0727-777X
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231009
PL  - England
TA  - Int Endod J
JT  - International endodontic journal
JID - 8004996
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - dentistry
OT  - endodontics
OT  - large language models
EDAT- 2023/10/10 00:42
MHDA- 2023/10/10 00:42
CRDT- 2023/10/09 23:52
PHST- 2023/09/26 00:00 [revised]
PHST- 2023/09/03 00:00 [received]
PHST- 2023/09/27 00:00 [accepted]
PHST- 2023/10/10 00:42 [pubmed]
PHST- 2023/10/10 00:42 [medline]
PHST- 2023/10/09 23:52 [entrez]
AID - 10.1111/iej.13985 [doi]
PST - ppublish
SO  - Int Endod J. 2024 Jan;57(1):108-113. doi: 10.1111/iej.13985. Epub 2023 Oct 9.

PMID- 37952004
OWN - NLM
STAT- Publisher
LR  - 20231111
IS  - 1573-8744 (Electronic)
IS  - 1567-567X (Linking)
DP  - 2023 Nov 11
TI  - Evaluation of prompt engineering strategies for pharmacokinetic data analysis 
      with the ChatGPT large language model.
LID - 10.1007/s10928-023-09892-6 [doi]
AB  - To systematically assess the ChatGPT large language model on diverse tasks 
      relevant to pharmacokinetic data analysis. ChatGPT was evaluated with 
      prototypical tasks related to report writing, code generation, non-compartmental 
      analysis, and pharmacokinetic word problems. The writing task consisted of 
      writing an introduction for this paper from a draft title. The coding tasks 
      consisted of generating R code for semi-logarithmic graphing of 
      concentration-time profiles and calculating area under the curve and area under 
      the moment curve from time zero to infinity. Pharmacokinetics word problems on 
      single intravenous, extravascular bolus, and multiple dosing were taken from a 
      pharmacokinetics textbook. Chain-of-thought and problem separation were assessed 
      as prompt engineering strategies when errors occurred. ChatGPT showed 
      satisfactory performance on the report writing, code generation tasks and 
      provided accurate information on the principles and methods underlying 
      pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical 
      calculations involving exponential functions. The outputs generated by ChatGPT 
      were not reproducible: the precise content of the output was variable albeit not 
      necessarily erroneous for different instances of the same prompt. Incorporation 
      of prompt engineering strategies reduced but did not eliminate errors in 
      numerical calculations. ChatGPT has the potential to become a powerful 
      productivity tool for writing, knowledge encapsulation, and coding tasks in 
      pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical 
      calculations require resolution before it can be reliably used for PK and 
      pharmacometrics data analysis.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Shin, Euibeom
AU  - Shin E
AD  - Department of Pharmaceutical Sciences, University at Buffalo, The State 
      University of New York, 355 Pharmacy, Buffalo, NY, 14214-8033, USA.
FAU - Ramanathan, Murali
AU  - Ramanathan M
AD  - Department of Pharmaceutical Sciences, University at Buffalo, The State 
      University of New York, 355 Pharmacy, Buffalo, NY, 14214-8033, USA. 
      murali@buffalo.edu.
LA  - eng
PT  - Journal Article
DEP - 20231111
PL  - United States
TA  - J Pharmacokinet Pharmacodyn
JT  - Journal of pharmacokinetics and pharmacodynamics
JID - 101096520
SB  - IM
OTO - NOTNLM
OT  - Bioavailability
OT  - ChatGPT
OT  - Drug development
OT  - Graphing
OT  - PK/PD
OT  - Pharmacokinetics
OT  - Prompt engineering
EDAT- 2023/11/12 00:42
MHDA- 2023/11/12 00:42
CRDT- 2023/11/11 23:31
PHST- 2023/09/18 00:00 [received]
PHST- 2023/10/11 00:00 [accepted]
PHST- 2023/11/12 00:42 [medline]
PHST- 2023/11/12 00:42 [pubmed]
PHST- 2023/11/11 23:31 [entrez]
AID - 10.1007/s10928-023-09892-6 [pii]
AID - 10.1007/s10928-023-09892-6 [doi]
PST - aheadofprint
SO  - J Pharmacokinet Pharmacodyn. 2023 Nov 11. doi: 10.1007/s10928-023-09892-6.

PMID- 37625267
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20230918
IS  - 2352-3964 (Electronic)
IS  - 2352-3964 (Linking)
VI  - 95
DP  - 2023 Sep
TI  - Benchmarking large language models' performances for myopia care: a comparative 
      analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard.
PG  - 104770
LID - S2352-3964(23)00336-5 [pii]
LID - 10.1016/j.ebiom.2023.104770 [doi]
LID - 104770
AB  - BACKGROUND: Large language models (LLMs) are garnering wide interest due to their 
      human-like and contextually relevant responses. However, LLMs' accuracy across 
      specific medical domains has yet been thoroughly evaluated. Myopia is a frequent 
      topic which patients and parents commonly seek information online. Our study 
      evaluated the performance of three LLMs namely ChatGPT-3.5, ChatGPT-4.0, and 
      Google Bard, in delivering accurate responses to common myopia-related queries. 
      METHODS: We curated thirty-one commonly asked myopia care-related questions, 
      which were categorised into six domains-pathogenesis, risk factors, clinical 
      presentation, diagnosis, treatment and prevention, and prognosis. Each question 
      was posed to the LLMs, and their responses were independently graded by three 
      consultant-level paediatric ophthalmologists on a three-point accuracy scale 
      (poor, borderline, good). A majority consensus approach was used to determine the 
      final rating for each response. 'Good' rated responses were further evaluated for 
      comprehensiveness on a five-point scale. Conversely, 'poor' rated responses were 
      further prompted for self-correction and then re-evaluated for accuracy. 
      FINDINGS: ChatGPT-4.0 demonstrated superior accuracy, with 80.6% of responses 
      rated as 'good', compared to 61.3% in ChatGPT-3.5 and 54.8% in Google Bard 
      (Pearson's chi-squared test, all p ≤ 0.009). All three LLM-Chatbots showed high 
      mean comprehensiveness scores (Google Bard: 4.35; ChatGPT-4.0: 4.23; ChatGPT-3.5: 
      4.11, out of a maximum score of 5). All LLM-Chatbots also demonstrated 
      substantial self-correction capabilities: 66.7% (2 in 3) of ChatGPT-4.0's, 40% (2 
      in 5) of ChatGPT-3.5's, and 60% (3 in 5) of Google Bard's responses improved 
      after self-correction. The LLM-Chatbots performed consistently across domains, 
      except for 'treatment and prevention'. However, ChatGPT-4.0 still performed 
      superiorly in this domain, receiving 70% 'good' ratings, compared to 40% in 
      ChatGPT-3.5 and 45% in Google Bard (Pearson's chi-squared test, all p ≤ 0.001). 
      INTERPRETATION: Our findings underscore the potential of LLMs, particularly 
      ChatGPT-4.0, for delivering accurate and comprehensive responses to 
      myopia-related queries. Continuous strategies and evaluations to improve LLMs' 
      accuracy remain crucial. FUNDING: Dr Yih-Chung Tham was supported by the National 
      Medical Research Council of Singapore (NMRC/MOH/HCSAINV21nov-0001).
CI  - Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.
FAU - Lim, Zhi Wei
AU  - Lim ZW
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
FAU - Pushpanathan, Krithi
AU  - Pushpanathan K
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore.
FAU - Yew, Samantha Min Er
AU  - Yew SME
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore.
FAU - Lai, Yien
AU  - Lai Y
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Department of Ophthalmology, National 
      University Hospital, Singapore.
FAU - Sun, Chen-Hsin
AU  - Sun CH
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Department of Ophthalmology, National 
      University Hospital, Singapore.
FAU - Lam, Janice Sing Harn
AU  - Lam JSH
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Department of Ophthalmology, National 
      University Hospital, Singapore.
FAU - Chen, David Ziyou
AU  - Chen DZ
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Department of Ophthalmology, National 
      University Hospital, Singapore.
FAU - Goh, Jocelyn Hui Lin
AU  - Goh JHL
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
FAU - Tan, Marcus Chun Jin
AU  - Tan MCJ
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Department of Ophthalmology, National 
      University Hospital, Singapore.
FAU - Sheng, Bin
AU  - Sheng B
AD  - Department of Computer Science and Engineering, Shanghai Jiao Tong University, 
      Shanghai, China; Department of Endocrinology and Metabolism, Shanghai Jiao Tong 
      University Affiliated Sixth People's Hospital, Shanghai Diabetes Institute, 
      Shanghai Clinical Center for Diabetes, Shanghai, China; MoE Key Lab of Artificial 
      Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, 
      Shanghai, China.
FAU - Cheng, Ching-Yu
AU  - Cheng CY
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Singapore Eye Research Institute, Singapore 
      National Eye Centre, Singapore; Eye Academic Clinical Program (Eye ACP), Duke NUS 
      Medical School, Singapore.
FAU - Koh, Victor Teck Chang
AU  - Koh VTC
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Department of Ophthalmology, National 
      University Hospital, Singapore.
FAU - Tham, Yih-Chung
AU  - Tham YC
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 
      Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore and National 
      University Health System, Singapore; Singapore Eye Research Institute, Singapore 
      National Eye Centre, Singapore; Eye Academic Clinical Program (Eye ACP), Duke NUS 
      Medical School, Singapore. Electronic address: thamyc@nus.edu.sg.
LA  - eng
PT  - Journal Article
DEP - 20230823
PL  - Netherlands
TA  - EBioMedicine
JT  - EBioMedicine
JID - 101647039
SB  - IM
MH  - Humans
MH  - Child
MH  - *Benchmarking
MH  - Search Engine
MH  - Consensus
MH  - Language
MH  - *Myopia/diagnosis/epidemiology/therapy
PMC - PMC10470220
OTO - NOTNLM
OT  - ChatGPT-3.5
OT  - ChatGPT-4.0
OT  - Chatbot
OT  - Google Bard
OT  - Large language models
OT  - Myopia
COIS- Declaration of interests All authors declare no competing interests.
EDAT- 2023/08/26 05:41
MHDA- 2023/09/18 12:42
CRDT- 2023/08/25 18:04
PHST- 2023/06/30 00:00 [received]
PHST- 2023/07/21 00:00 [revised]
PHST- 2023/08/08 00:00 [accepted]
PHST- 2023/09/18 12:42 [medline]
PHST- 2023/08/26 05:41 [pubmed]
PHST- 2023/08/25 18:04 [entrez]
AID - S2352-3964(23)00336-5 [pii]
AID - 104770 [pii]
AID - 10.1016/j.ebiom.2023.104770 [doi]
PST - ppublish
SO  - EBioMedicine. 2023 Sep;95:104770. doi: 10.1016/j.ebiom.2023.104770. Epub 2023 Aug 
      23.

PMID- 37387067
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20230703
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 305
DP  - 2023 Jun 29
TI  - Revolutionizing Healthcare with Foundation AI Models.
PG  - 469-470
LID - 10.3233/SHTI230533 [doi]
AB  - ChatGPT is a foundation Artificial Intelligence (AI) model that has opened up new 
      opportunities in digital healthcare. Particularly, it can serve as a co-pilot 
      tool for doctors in the interpretation, summarization, and completion of reports. 
      Furthermore, it can build upon the ability to access the large literature and 
      knowledge on the internet. So, chatGPT could generate acceptable responses for 
      the medical examination. Hence. It offers the possibility of enhancing healthcare 
      accessibility, expandability, and effectiveness. Nonetheless, chatGPT is 
      vulnerable to inaccuracies, false information, and bias. This paper briefly 
      describes the potential of Foundation AI models to transform future healthcare by 
      presenting ChatGPT as an example tool.
FAU - Ali, Hazrat
AU  - Ali H
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Qatar 
      Foundation, Doha, Qatar.
FAU - Qadir, Junaid
AU  - Qadir J
AD  - Department of Computer Engineering, Qatar University, Doha, Qatar.
FAU - Alam, Tanvir
AU  - Alam T
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Qatar 
      Foundation, Doha, Qatar.
FAU - Househ, Mowafa
AU  - Househ M
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Qatar 
      Foundation, Doha, Qatar.
FAU - Shah, Zubair
AU  - Shah Z
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Qatar 
      Foundation, Doha, Qatar.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - Humans
MH  - *Artificial Intelligence
MH  - Health Facilities
MH  - Internet
MH  - Knowledge
MH  - *Physicians
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Foundation AI models
OT  - Healthcare
OT  - Medical AI
EDAT- 2023/06/30 06:42
MHDA- 2023/07/03 06:41
CRDT- 2023/06/30 04:23
PHST- 2023/07/03 06:41 [medline]
PHST- 2023/06/30 06:42 [pubmed]
PHST- 2023/06/30 04:23 [entrez]
AID - SHTI230533 [pii]
AID - 10.3233/SHTI230533 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 Jun 29;305:469-470. doi: 10.3233/SHTI230533.

PMID- 37500572
OWN - NLM
STAT- MEDLINE
DCOM- 20230731
LR  - 20230827
IS  - 2005-8330 (Electronic)
IS  - 1229-6929 (Print)
IS  - 1229-6929 (Linking)
VI  - 24
IP  - 8
DP  - 2023 Aug
TI  - Use of Generative Artificial Intelligence, Including Large Language Models Such 
      as ChatGPT, in Scientific Publications: Policies of KJR and Prominent 
      Authorities.
PG  - 715-718
LID - 10.3348/kjr.2023.0643 [doi]
FAU - Park, Seong Ho
AU  - Park SH
AUID- ORCID: 0000-0002-1257-8315
AD  - Department of Radiology and Research Institute of Radiology, Asan Medical Center, 
      University of Ulsan College of Medicine, Seoul, Republic of Korea. 
      parksh.radiology@gmail.com.
LA  - eng
PT  - Editorial
PL  - Korea (South)
TA  - Korean J Radiol
JT  - Korean journal of radiology
JID - 100956096
SB  - IM
CIN - Korean J Radiol. 2023 Sep;24(9):924-925. PMID: 37634646
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Policy
MH  - Publications
PMC - PMC10400373
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Editing
OT  - Generative
OT  - Large language model
OT  - Peer review
OT  - Policy
OT  - Publication
OT  - Writing
COIS- The author has no potential conflicts of interest to disclose.
EDAT- 2023/07/28 01:08
MHDA- 2023/07/31 06:42
CRDT- 2023/07/27 22:15
PHST- 2023/07/10 00:00 [received]
PHST- 2023/07/10 00:00 [accepted]
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/07/28 01:08 [pubmed]
PHST- 2023/07/27 22:15 [entrez]
AID - 24.715 [pii]
AID - 10.3348/kjr.2023.0643 [doi]
PST - ppublish
SO  - Korean J Radiol. 2023 Aug;24(8):715-718. doi: 10.3348/kjr.2023.0643.

PMID- 37485160
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230725
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - Eyes on AI: ChatGPT's Transformative Potential Impact on Ophthalmology.
PG  - e40765
LID - 10.7759/cureus.40765 [doi]
LID - e40765
AB  - ChatGPT, a large language model by OpenAI, has been adopted in various domains 
      since its release in November 2022, but its application in ophthalmology remains 
      less explored. This editorial assesses ChatGPT's potential applications and 
      limitations in ophthalmology across clinical, educational, and research settings. 
      In clinical settings, ChatGPT can serve as an assistant, offering diagnostic and 
      therapeutic suggestions based on patient data and assisting in patient triage. 
      However, its tendencies to generate inaccurate results and its inability to keep 
      up with recent medical guidelines render it unsuitable for standalone clinical 
      decision-making. Data security and compliance with the Health Insurance 
      Portability and Accountability Act (HIPAA) also pose concerns, given ChatGPT's 
      potential to inadvertently expose sensitive patient information. In education, 
      ChatGPT can generate practice questions, provide explanations, and create patient 
      education materials. However, its performance in answering domain-specific 
      questions is suboptimal. In research, ChatGPT can facilitate literature reviews, 
      data analysis, manuscript development, and peer review, but issues of accuracy, 
      bias, and ethics need careful consideration. Ultimately, ensuring accuracy, 
      ethical integrity, and data privacy is essential when integrating ChatGPT into 
      ophthalmology.
CI  - Copyright © 2023, Dossantos et al.
FAU - Dossantos, Jason
AU  - Dossantos J
AD  - Ophthalmology, George Washington University School of Medicine and Health 
      Sciences, Washington, USA.
FAU - An, Jella
AU  - An J
AD  - Ophthalmology, Johns Hopkins University School of Medicine Wilmer Eye Institute, 
      Baltimore, USA.
FAU - Javan, Ramin
AU  - Javan R
AD  - Radiology, George Washington University School of Medicine and Health Sciences, 
      Washington, USA.
LA  - eng
PT  - Editorial
DEP - 20230621
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10362525
OTO - NOTNLM
OT  - artificial intelligence
OT  - artificial intelligence in medicine
OT  - chatgpt
OT  - large language models
OT  - ophthalmology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/24 06:42
MHDA- 2023/07/24 06:43
CRDT- 2023/07/24 04:54
PHST- 2023/06/21 00:00 [accepted]
PHST- 2023/07/24 06:43 [medline]
PHST- 2023/07/24 06:42 [pubmed]
PHST- 2023/07/24 04:54 [entrez]
AID - 10.7759/cureus.40765 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 21;15(6):e40765. doi: 10.7759/cureus.40765. eCollection 2023 
      Jun.

PMID- 37874780
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231027
IS  - 2767-3170 (Electronic)
IS  - 2767-3170 (Linking)
VI  - 2
IP  - 10
DP  - 2023 Oct
TI  - Centering health equity in large language model deployment.
PG  - e0000367
LID - 10.1371/journal.pdig.0000367 [doi]
LID - e0000367
FAU - Singh, Nina
AU  - Singh N
AUID- ORCID: 0000-0002-4623-2451
AD  - Department of Population Health, New York University Grossman School of Medicine, 
      New York, New York, United States of America.
FAU - Lawrence, Katharine
AU  - Lawrence K
AD  - Department of Population Health, New York University Grossman School of Medicine, 
      New York, New York, United States of America.
FAU - Richardson, Safiya
AU  - Richardson S
AD  - Department of Population Health, New York University Grossman School of Medicine, 
      New York, New York, United States of America.
FAU - Mann, Devin M
AU  - Mann DM
AD  - Department of Population Health, New York University Grossman School of Medicine, 
      New York, New York, United States of America.
AD  - Medical Center Information Technology, New York University Langone Health, New 
      York, New York, United States of America.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - United States
TA  - PLOS Digit Health
JT  - PLOS digital health
JID - 9918335064206676
PMC - PMC10597518
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/24 18:42
MHDA- 2023/10/24 18:43
CRDT- 2023/10/24 13:22
PHST- 2023/10/24 18:43 [medline]
PHST- 2023/10/24 18:42 [pubmed]
PHST- 2023/10/24 13:22 [entrez]
AID - PDIG-D-23-00181 [pii]
AID - 10.1371/journal.pdig.0000367 [doi]
PST - epublish
SO  - PLOS Digit Health. 2023 Oct 24;2(10):e0000367. doi: 10.1371/journal.pdig.0000367. 
      eCollection 2023 Oct.

PMID- 37271213
OWN - NLM
STAT- MEDLINE
DCOM- 20230606
LR  - 20230610
IS  - 2005-8330 (Electronic)
IS  - 1229-6929 (Print)
IS  - 1229-6929 (Linking)
VI  - 24
IP  - 6
DP  - 2023 Jun
TI  - Authorship Policy and ChatGPT.
PG  - 599
LID - 10.3348/kjr.2023.0383 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia. amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Research Center, Chandigarh University, Punjab, India.
AD  - Department of Biological Science, Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
PL  - Korea (South)
TA  - Korean J Radiol
JT  - Korean journal of radiology
JID - 100956096
SB  - IM
CON - Korean J Radiol. 2023 Mar;24(3):171-172. PMID: 36788775
MH  - Humans
MH  - *Artificial Intelligence
MH  - Authorship
MH  - *Radiology
MH  - Language
MH  - Republic of Korea
PMC - PMC10248358
OTO - NOTNLM
OT  - Authorship
OT  - ChatGPT
OT  - Policy
COIS- The authors have no potential conflicts of interest to disclose.
EDAT- 2023/06/05 00:42
MHDA- 2023/06/06 06:42
CRDT- 2023/06/04 19:23
PHST- 2023/04/27 00:00 [received]
PHST- 2023/04/30 00:00 [accepted]
PHST- 2023/06/06 06:42 [medline]
PHST- 2023/06/05 00:42 [pubmed]
PHST- 2023/06/04 19:23 [entrez]
AID - 24.599 [pii]
AID - 10.3348/kjr.2023.0383 [doi]
PST - ppublish
SO  - Korean J Radiol. 2023 Jun;24(6):599. doi: 10.3348/kjr.2023.0383.

PMID- 37578849
OWN - NLM
STAT- Publisher
LR  - 20230912
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
DP  - 2023 Aug 14
TI  - Comparative Performance of ChatGPT and Bard in a Text-Based Radiology Knowledge 
      Assessment.
PG  - 8465371231193716
LID - 10.1177/08465371231193716 [doi]
AB  - PURPOSE: Bard by Google, a direct competitor to ChatGPT, was recently released. 
      Understanding the relative performance of these different chatbots can provide 
      important insight into their strengths and weaknesses as well as which roles they 
      are most suited to fill. In this project, we aimed to compare the most recent 
      version of ChatGPT, ChatGPT-4, and Bard by Google, in their ability to accurately 
      respond to radiology board examination practice questions. METHODS: Text-based 
      questions were collected from the 2017-2021 American College of Radiology's 
      Diagnostic Radiology In-Training (DXIT) examinations. ChatGPT-4 and Bard were 
      queried, and their comparative accuracies, response lengths, and response times 
      were documented. Subspecialty-specific performance was analyzed as well. RESULTS: 
      318 questions were included in our analysis. ChatGPT answered significantly more 
      accurately than Bard (87.11% vs 70.44%, P < .0001). ChatGPT's response length was 
      significantly shorter than Bard's (935.28 ± 440.88 characters vs 1437.52 ± 415.91 
      characters, P < .0001). ChatGPT's response time was significantly longer than 
      Bard's (26.79 ± 3.27 seconds vs 7.55 ± 1.88 seconds, P < .0001). ChatGPT 
      performed superiorly to Bard in neuroradiology, (100.00% vs 86.21%, P = .03), 
      general & physics (85.39% vs 68.54%, P < .001), nuclear medicine (80.00% vs 
      56.67%, P < .01), pediatric radiology (93.75% vs 68.75%, P = .03), and ultrasound 
      (100.00% vs 63.64%, P < .001). In the remaining subspecialties, there were no 
      significant differences between ChatGPT and Bard's performance. CONCLUSION: 
      ChatGPT displayed superior radiology knowledge compared to Bard. While both 
      chatbots display reasonable radiology knowledge, they should be used with 
      conscious knowledge of their limitations and fallibility. Both chatbots provided 
      incorrect or illogical answer explanations and did not always address the 
      educational content of the question.
FAU - Patil, Nikhil S
AU  - Patil NS
AUID- ORCID: 0000-0003-3929-0482
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, 
      Canada. RINGGOLD: 62703
FAU - Huang, Ryan S
AU  - Huang RS
AUID- ORCID: 0000-0002-3404-5376
AD  - Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. 
      RINGGOLD: 7938
FAU - van der Pol, Christian B
AU  - van der Pol CB
AUID- ORCID: 0000-0002-1718-2619
AD  - Department of Diagnostic Imaging, Hamilton Health Sciences, Juravinski Hospital 
      and Cancer Centre, Hamilton, ON, Canada.
FAU - Larocque, Natasha
AU  - Larocque N
AUID- ORCID: 0000-0003-0449-9438
AD  - Department of Radiology, McMaster University, Hamilton, ON, Canada. RINGGOLD: 
      3710
LA  - eng
PT  - Journal Article
DEP - 20230814
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - Google
OT  - artificial intelligence
OT  - chatbot
OT  - radiology
EDAT- 2023/08/14 18:42
MHDA- 2023/08/14 18:42
CRDT- 2023/08/14 12:22
PHST- 2023/08/14 18:42 [pubmed]
PHST- 2023/08/14 18:42 [medline]
PHST- 2023/08/14 12:22 [entrez]
AID - 10.1177/08465371231193716 [doi]
PST - aheadofprint
SO  - Can Assoc Radiol J. 2023 Aug 14:8465371231193716. doi: 10.1177/08465371231193716.

PMID- 36943179
OWN - NLM
STAT- Publisher
LR  - 20231125
IS  - 1520-5851 (Electronic)
IS  - 0013-936X (Print)
IS  - 0013-936X (Linking)
VI  - 57
IP  - 46
DP  - 2023 Mar 21
TI  - ChatGPT and Environmental Research.
PG  - 17667-70
LID - 10.1021/acs.est.3c01818 [doi]
FAU - Zhu, Jun-Jie
AU  - Zhu JJ
AUID- ORCID: 0000-0002-7546-2870
AD  - Department of Civil and Environmental Engineering, Princeton University, 
      Princeton, New Jersey 08544, United States.
AD  - Andlinger Center for Energy and the Environment, Princeton University, Princeton, 
      New Jersey 08544, United States.
FAU - Jiang, Jinyue
AU  - Jiang J
AD  - Department of Civil and Environmental Engineering, Princeton University, 
      Princeton, New Jersey 08544, United States.
AD  - Andlinger Center for Energy and the Environment, Princeton University, Princeton, 
      New Jersey 08544, United States.
FAU - Yang, Meiqi
AU  - Yang M
AUID- ORCID: 0000-0003-0913-6804
AD  - Department of Civil and Environmental Engineering, Princeton University, 
      Princeton, New Jersey 08544, United States.
AD  - Andlinger Center for Energy and the Environment, Princeton University, Princeton, 
      New Jersey 08544, United States.
FAU - Ren, Zhiyong Jason
AU  - Ren ZJ
AUID- ORCID: 0000-0001-7606-0331
AD  - Department of Civil and Environmental Engineering, Princeton University, 
      Princeton, New Jersey 08544, United States.
AD  - Andlinger Center for Energy and the Environment, Princeton University, Princeton, 
      New Jersey 08544, United States.
LA  - eng
PT  - Journal Article
DEP - 20230321
PL  - United States
TA  - Environ Sci Technol
JT  - Environmental science & technology
JID - 0213155
SB  - IM
PMC - PMC10666266
OTO - NOTNLM
OT  - ChatGPT
OT  - benefits
OT  - opportunities
OT  - over-reliance
COIS- The authors declare no competing financial interest.
EDAT- 2023/03/22 06:00
MHDA- 2023/03/22 06:00
CRDT- 2023/03/21 10:43
PHST- 2023/03/22 06:00 [pubmed]
PHST- 2023/03/22 06:00 [medline]
PHST- 2023/03/21 10:43 [entrez]
AID - 10.1021/acs.est.3c01818 [doi]
PST - aheadofprint
SO  - Environ Sci Technol. 2023 Mar 21;57(46):17667-70. doi: 10.1021/acs.est.3c01818.

PMID- 37749374
OWN - NLM
STAT- Publisher
LR  - 20230925
IS  - 1476-5454 (Electronic)
IS  - 0950-222X (Linking)
DP  - 2023 Sep 25
TI  - Large language model (LLM)-driven chatbots for neuro-ophthalmic medical 
      education.
LID - 10.1038/s41433-023-02759-7 [doi]
FAU - Waisberg, Ethan
AU  - Waisberg E
AUID- ORCID: 0000-0001-8999-0212
AD  - Department of Ophthalmology, University of Cambridge, Cambridge, UK. 
      ethan.waisberg@qehkl.nhs.uk.
FAU - Ong, Joshua
AU  - Ong J
AUID- ORCID: 0000-0003-4860-827X
AD  - Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
FAU - Masalkhi, Mouayad
AU  - Masalkhi M
AUID- ORCID: 0000-0003-0811-358X
AD  - Department of Ophthalmology, University of Cambridge, Cambridge, UK.
FAU - Lee, Andrew G
AU  - Lee AG
AD  - Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA.
AD  - Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, 
      Houston, TX, USA.
AD  - The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, 
      TX, USA.
AD  - Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell 
      Medicine, New York, NY, USA.
AD  - Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, 
      USA.
AD  - University of Texas MD Anderson Cancer Center, Houston, TX, USA.
AD  - Texas A&M College of Medicine, Bryan, TX, USA.
AD  - Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa 
      City, IA, USA.
LA  - eng
PT  - Journal Article
DEP - 20230925
PL  - England
TA  - Eye (Lond)
JT  - Eye (London, England)
JID - 8703986
SB  - IM
EDAT- 2023/09/26 00:42
MHDA- 2023/09/26 00:42
CRDT- 2023/09/25 23:36
PHST- 2023/08/04 00:00 [received]
PHST- 2023/09/15 00:00 [accepted]
PHST- 2023/08/09 00:00 [revised]
PHST- 2023/09/26 00:42 [medline]
PHST- 2023/09/26 00:42 [pubmed]
PHST- 2023/09/25 23:36 [entrez]
AID - 10.1038/s41433-023-02759-7 [pii]
AID - 10.1038/s41433-023-02759-7 [doi]
PST - aheadofprint
SO  - Eye (Lond). 2023 Sep 25. doi: 10.1038/s41433-023-02759-7.

PMID- 37168166
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230514
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT for Future Medical and Dental Research.
PG  - e37285
LID - 10.7759/cureus.37285 [doi]
LID - e37285
AB  - ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI and it 
      first became available to the public in November 2022. ChatGPT can assist in 
      finding academic papers on the web and summarizing them. This chatbot has the 
      potential to be applied in scientific writing, it has the ability to generate 
      automated drafts, summarize articles, and translate content from several 
      languages. This in turn can make academic writing faster and less challenging. 
      However, due to ethical considerations, its use in scientific writing should be 
      regulated and carefully monitored. Few papers have discussed the use of ChatGPT 
      in scientific research writing. This review aims to discuss all the relevant 
      published papers that discuss the use of ChatGPT in medical and dental research.
CI  - Copyright © 2023, Fatani et al.
FAU - Fatani, Bader
AU  - Fatani B
AD  - Dentistry, College of Dentistry, King Saud University, Riyadh, SAU.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230408
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10165936
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - dentistry
OT  - medicine
OT  - research
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/12 01:07
MHDA- 2023/05/12 01:08
CRDT- 2023/05/11 19:16
PHST- 2023/04/08 00:00 [accepted]
PHST- 2023/05/12 01:08 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 19:16 [entrez]
AID - 10.7759/cureus.37285 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 8;15(4):e37285. doi: 10.7759/cureus.37285. eCollection 2023 Apr.

PMID- 37904734
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231113
IS  - 2331-8422 (Electronic)
IS  - 2331-8422 (Linking)
DP  - 2023 Oct 17
TI  - Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine 
      and Health.
LID - arXiv:2306.10070v2
AB  - ChatGPT has drawn considerable attention from both the general public and domain 
      experts with its remarkable text generation capabilities. This has subsequently 
      led to the emergence of diverse applications in the field of biomedicine and 
      health. In this work, we examine the diverse applications of large language 
      models (LLMs), such as ChatGPT, in biomedicine and health. Specifically we 
      explore the areas of biomedical information retrieval, question answering, 
      medical text summarization, information extraction, and medical education, and 
      investigate whether LLMs possess the transformative power to revolutionize these 
      tasks or whether the distinct complexities of biomedical domain presents unique 
      challenges. Following an extensive literature survey, we find that significant 
      advances have been made in the field of text generation tasks, surpassing the 
      previous state-of-the-art methods. For other applications, the advances have been 
      modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid 
      progress indicates that such methods hold great potential to provide valuable 
      means for accelerating discovery and improving health. We also find that the use 
      of LLMs, like ChatGPT, in the fields of biomedicine and health entails various 
      risks and challenges, including fabricated information in its generated 
      responses, as well as legal and privacy concerns associated with sensitive 
      patient data. We believe this survey can provide a comprehensive and timely 
      overview to biomedical researchers and healthcare practitioners on the 
      opportunities and challenges associated with using ChatGPT and other LLMs for 
      transforming biomedicine and health.
FAU - Tian, Shubo
AU  - Tian S
AD  - National Library of Medicine, National Institutes of Health.
FAU - Jin, Qiao
AU  - Jin Q
AD  - National Library of Medicine, National Institutes of Health.
FAU - Yeganova, Lana
AU  - Yeganova L
AD  - National Library of Medicine, National Institutes of Health.
FAU - Lai, Po-Ting
AU  - Lai PT
AD  - National Library of Medicine, National Institutes of Health.
FAU - Zhu, Qingqing
AU  - Zhu Q
AD  - National Library of Medicine, National Institutes of Health.
FAU - Chen, Xiuying
AU  - Chen X
AD  - King Abdullah University of Science and Technology.
FAU - Yang, Yifan
AU  - Yang Y
AD  - National Library of Medicine, National Institutes of Health.
FAU - Chen, Qingyu
AU  - Chen Q
AD  - National Library of Medicine, National Institutes of Health.
FAU - Kim, Won
AU  - Kim W
AD  - National Library of Medicine, National Institutes of Health.
FAU - Comeau, Donald C
AU  - Comeau DC
AD  - National Library of Medicine, National Institutes of Health.
FAU - Islamaj, Rezarta
AU  - Islamaj R
AD  - National Library of Medicine, National Institutes of Health.
FAU - Kapoor, Aadit
AU  - Kapoor A
AD  - National Library of Medicine, National Institutes of Health.
FAU - Gao, Xin
AU  - Gao X
AD  - King Abdullah University of Science and Technology.
FAU - Lu, Zhiyong
AU  - Lu Z
AD  - National Library of Medicine, National Institutes of Health.
LA  - eng
PT  - Preprint
DEP - 20231017
PL  - United States
TA  - ArXiv
JT  - ArXiv
JID - 101759493
PMC - PMC10614979
OTO - NOTNLM
OT  - ChatGPT
OT  - biomedicine and health
OT  - generative AI
OT  - large language model
OT  - opportunities and challenges
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:43
CRDT- 2023/10/31 03:50
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 06:43 [medline]
PHST- 2023/10/31 03:50 [entrez]
AID - 2306.10070 [pii]
PST - epublish
SO  - ArXiv. 2023 Oct 17:arXiv:2306.10070v2. Preprint.

PMID- 36789422
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230727
DP  - 2023 Jan 28
TI  - Analysis of large-language model versus human performance for genetics questions.
LID - 2023.01.27.23285115 [pii]
LID - 10.1101/2023.01.27.23285115 [doi]
AB  - Large-language models like ChatGPT have recently received a great deal of 
      attention. To assess ChatGPT in the field of genetics, we compared its 
      performance to human respondents in answering genetics questions (involving 
      13,636 responses) that had been posted on social media platforms starting in 
      2021. Overall, ChatGPT did not perform significantly differently than human 
      respondents, but did significantly better on memorization-type questions versus 
      critical thinking questions, frequently provided different answers when asked 
      questions multiple times, and provided plausible explanations for both correct 
      and incorrect answers.
FAU - Duong, Dat
AU  - Duong D
FAU - Solomon, Benjamin D
AU  - Solomon BD
LA  - eng
PT  - Preprint
DEP - 20230128
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
UIN - Eur J Hum Genet. 2023 May 29;:. PMID: 37246194
PMC - PMC9928145
EDAT- 2023/02/16 06:00
MHDA- 2023/02/16 06:01
CRDT- 2023/02/15 02:13
PHST- 2023/02/15 02:13 [entrez]
PHST- 2023/02/16 06:00 [pubmed]
PHST- 2023/02/16 06:01 [medline]
AID - 2023.01.27.23285115 [pii]
AID - 10.1101/2023.01.27.23285115 [doi]
PST - epublish
SO  - medRxiv. 2023 Jan 28:2023.01.27.23285115. doi: 10.1101/2023.01.27.23285115. 
      Preprint.

PMID- 37747487
OWN - NLM
STAT- MEDLINE
DCOM- 20231130
LR  - 20231130
IS  - 1559-713X (Electronic)
IS  - 1559-2332 (Linking)
VI  - 18
IP  - 6
DP  - 2023 Dec 1
TI  - Artificial Intelligence and the Simulationists.
PG  - 395-399
LID - 10.1097/SIH.0000000000000747 [doi]
AB  - The recent introduction of ChatGPT, an advanced, easy-to-use, and freely 
      available artificial intelligence (AI) program, created new possibilities across 
      many industries and professions including healthcare simulation. ChatGPT has the 
      potential to streamline healthcare simulation-based education while also 
      providing insights for the scenario development process that conventional case 
      development may miss. However, there are issues related to accuracy, relevance, 
      and structure of the products produced by the ChatGPT AI program. This article 
      examines 2 AI-generated simulation case examples highlighting strengths and 
      weaknesses while providing guidance on the use of ChatGPT as a simulation 
      resource.
CI  - Copyright © 2023 Society for Simulation in Healthcare.
FAU - Rodgers, David L
AU  - Rodgers DL
AD  - From the Department of Medicine (D.L.R.) Indiana University School of Medicine; 
      Interprofessional Simulation Center (D.L.R., M.N., A.R.), Indiana University 
      School of Medicine, Bloomington; IU Health, Simulation Center at Fairbanks Hall 
      (A.R., J.P.), Indianapolis; Indiana University School of Nursing (R.B.), 
      Bloomington, IN; Penn State Health Milton S. Hershey Medical Center, Clinical 
      Simulation Center (T.B.), Hershey, PA; UT Southwestern Medical Center (J.H.), 
      Dallas, TX; Department of Emergency Medicine (P.V.K.), Indiana University School 
      of Medicine, Bloomington, IN; and Department of Emergency Medicine, Division of 
      Simulation (R.A.), Indiana University School of Medicine, Indianapolis, IN.
FAU - Needler, Mathew
AU  - Needler M
FAU - Robinson, Alexander
AU  - Robinson A
FAU - Barnes, Roxie
AU  - Barnes R
FAU - Brosche, Theresa
AU  - Brosche T
FAU - Hernandez, Jessica
AU  - Hernandez J
FAU - Poore, Julie
AU  - Poore J
FAU - VandeKoppel, Paul
AU  - VandeKoppel P
FAU - Ahmed, Rami
AU  - Ahmed R
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - United States
TA  - Simul Healthc
JT  - Simulation in healthcare : journal of the Society for Simulation in Healthcare
JID - 101264408
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Computer Simulation
COIS- The authors declare no conflict of interest.
EDAT- 2023/09/25 12:42
MHDA- 2023/11/30 06:42
CRDT- 2023/09/25 11:04
PHST- 2023/11/30 06:42 [medline]
PHST- 2023/09/25 12:42 [pubmed]
PHST- 2023/09/25 11:04 [entrez]
AID - 01266021-990000000-00086 [pii]
AID - 10.1097/SIH.0000000000000747 [doi]
PST - ppublish
SO  - Simul Healthc. 2023 Dec 1;18(6):395-399. doi: 10.1097/SIH.0000000000000747. Epub 
      2023 Sep 20.

PMID- 38076260
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2296-858X (Print)
IS  - 2296-858X (Electronic)
IS  - 2296-858X (Linking)
VI  - 10
DP  - 2023
TI  - Exploring large language model for next generation of artificial intelligence in 
      ophthalmology.
PG  - 1291404
LID - 10.3389/fmed.2023.1291404 [doi]
LID - 1291404
AB  - In recent years, ophthalmology has advanced significantly, thanks to rapid 
      progress in artificial intelligence (AI) technologies. Large language models 
      (LLMs) like ChatGPT have emerged as powerful tools for natural language 
      processing. This paper finally includes 108 studies, and explores LLMs' potential 
      in the next generation of AI in ophthalmology. The results encompass a diverse 
      range of studies in the field of ophthalmology, highlighting the versatile 
      applications of LLMs. Subfields encompass general ophthalmology, retinal 
      diseases, anterior segment diseases, glaucoma, and ophthalmic plastics. Results 
      show LLMs' competence in generating informative and contextually relevant 
      responses, potentially reducing diagnostic errors and improving patient outcomes. 
      Overall, this study highlights LLMs' promising role in shaping AI's future in 
      ophthalmology. By leveraging AI, ophthalmologists can access a wealth of 
      information, enhance diagnostic accuracy, and provide better patient care. 
      Despite challenges, continued AI advancements and ongoing research will pave the 
      way for the next generation of AI-assisted ophthalmic practices.
CI  - Copyright © 2023 Jin, Yuan, Wu, Grzybowski and Ye.
FAU - Jin, Kai
AU  - Jin K
AD  - Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang 
      University, Hangzhou, China.
FAU - Yuan, Lu
AU  - Yuan L
AD  - Department of Ophthalmology, The Children's Hospital, Zhejiang University School 
      of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
FAU - Wu, Hongkang
AU  - Wu H
AD  - Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang 
      University, Hangzhou, China.
FAU - Grzybowski, Andrzej
AU  - Grzybowski A
AD  - Institute for Research in Ophthalmology, Foundation for Ophthalmology 
      Development, Poznan, Poland.
FAU - Ye, Juan
AU  - Ye J
AD  - Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang 
      University, Hangzhou, China.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231123
PL  - Switzerland
TA  - Front Med (Lausanne)
JT  - Frontiers in medicine
JID - 101648047
PMC - PMC10701277
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - diagnostic accuracy and efficacy
OT  - large language model
OT  - ophthalmology
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/12/11 12:42
MHDA- 2023/12/11 12:43
CRDT- 2023/12/11 06:07
PHST- 2023/09/09 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/12/11 12:43 [medline]
PHST- 2023/12/11 12:42 [pubmed]
PHST- 2023/12/11 06:07 [entrez]
AID - 10.3389/fmed.2023.1291404 [doi]
PST - epublish
SO  - Front Med (Lausanne). 2023 Nov 23;10:1291404. doi: 10.3389/fmed.2023.1291404. 
      eCollection 2023.

PMID- 37847163
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231029
IS  - 2325-8179 (Electronic)
IS  - 2325-8160 (Linking)
VI  - 54
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT and GPT-4 in Ophthalmology: Applications of Large Language Model 
      Artificial Intelligence in Retina.
PG  - 557-562
LID - 10.3928/23258160-20230926-01 [doi]
FAU - Ong, Joshua
AU  - Ong J
FAU - Hariprasad, Seenu M
AU  - Hariprasad SM
FAU - Chhablani, Jay
AU  - Chhablani J
LA  - eng
PT  - Journal Article
DEP - 20231001
PL  - United States
TA  - Ophthalmic Surg Lasers Imaging Retina
JT  - Ophthalmic surgery, lasers & imaging retina
JID - 101599215
SB  - IM
MH  - Humans
MH  - *Ophthalmology
MH  - Artificial Intelligence
MH  - Retina
EDAT- 2023/10/17 12:43
MHDA- 2023/10/23 01:18
CRDT- 2023/10/17 09:54
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/17 12:43 [pubmed]
PHST- 2023/10/17 09:54 [entrez]
AID - 10.3928/23258160-20230926-01 [doi]
PST - ppublish
SO  - Ophthalmic Surg Lasers Imaging Retina. 2023 Oct;54(10):557-562. doi: 
      10.3928/23258160-20230926-01. Epub 2023 Oct 1.

PMID- 37293238
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230612
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Human-like problem-solving abilities in large language models using ChatGPT.
PG  - 1199350
LID - 10.3389/frai.2023.1199350 [doi]
LID - 1199350
AB  - BACKGROUNDS: The field of Artificial Intelligence (AI) has seen a major shift in 
      recent years due to the development of new Machine Learning (ML) models such as 
      Generative Pre-trained Transformer (GPT). GPT has achieved previously unheard-of 
      levels of accuracy in most computerized language processing tasks and their 
      chat-based variations. AIM: The aim of this study was to investigate the 
      problem-solving abilities of ChatGPT using two sets of verbal insight problems, 
      with a known performance level established by a sample of human participants. 
      MATERIALS AND METHODS: A total of 30 problems labeled as "practice problems" and 
      "transfer problems" were administered to ChatGPT. ChatGPT's answers received a 
      score of "0" for each incorrectly answered problem and a score of "1" for each 
      correct response. The highest possible score for both the practice and transfer 
      problems was 15 out of 15. The solution rate for each problem (based on a sample 
      of 20 subjects) was used to assess and compare the performance of ChatGPT with 
      that of human subjects. RESULTS: The study highlighted that ChatGPT can be 
      trained in out-of-the-box thinking and demonstrated potential in solving verbal 
      insight problems. The global performance of ChatGPT equalled the most probable 
      outcome for the human sample in both practice problems and transfer problems as 
      well as upon their combination. Additionally, ChatGPT answer combinations were 
      among the 5% of most probable outcomes for the human sample both when considering 
      practice problems and pooled problem sets. These findings demonstrate that 
      ChatGPT performance on both set of problems was in line with the mean rate of 
      success of human subjects, indicating that it performed reasonably well. 
      CONCLUSIONS: The use of transformer architecture and self-attention in ChatGPT 
      may have helped to prioritize inputs while predicting, contributing to its 
      potential in verbal insight problem-solving. ChatGPT has shown potential in 
      solving insight problems, thus highlighting the importance of incorporating AI 
      into psychological research. However, it is acknowledged that there are still 
      open challenges. Indeed, further research is required to fully understand AI's 
      capabilities and limitations in verbal problem-solving.
CI  - Copyright © 2023 Orrù, Piarulli, Conversano and Gemignani.
FAU - Orrù, Graziella
AU  - Orrù G
AD  - Department of Surgical, Medical, Molecular and Critical Area Pathology, 
      University of Pisa, Pisa, Italy.
FAU - Piarulli, Andrea
AU  - Piarulli A
AD  - Department of Surgical, Medical, Molecular and Critical Area Pathology, 
      University of Pisa, Pisa, Italy.
FAU - Conversano, Ciro
AU  - Conversano C
AD  - Department of Surgical, Medical, Molecular and Critical Area Pathology, 
      University of Pisa, Pisa, Italy.
FAU - Gemignani, Angelo
AU  - Gemignani A
AD  - Department of Surgical, Medical, Molecular and Critical Area Pathology, 
      University of Pisa, Pisa, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230524
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10244637
OTO - NOTNLM
OT  - AI
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - NLP
OT  - machine learning
OT  - problem-solving
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/06/09 06:42
MHDA- 2023/06/09 06:43
CRDT- 2023/06/09 04:32
PHST- 2023/04/06 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/06/09 06:43 [medline]
PHST- 2023/06/09 06:42 [pubmed]
PHST- 2023/06/09 04:32 [entrez]
AID - 10.3389/frai.2023.1199350 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 May 24;6:1199350. doi: 10.3389/frai.2023.1199350. 
      eCollection 2023.

PMID- 38073539
OWN - NLM
STAT- Publisher
LR  - 20231211
IS  - 1532-5040 (Electronic)
IS  - 0959-3985 (Linking)
DP  - 2023 Dec 11
TI  - Clinical reasoning using ChatGPT: Is it beyond credibility for physiotherapists 
      use?
PG  - 1-20
LID - 10.1080/09593985.2023.2291656 [doi]
AB  - BACKGROUND: Artificial Intelligence (AI) tools are gaining popularity in 
      healthcare. OpenAI released ChatGPT on November 30, 2022. ChatGPT is a language 
      model that comprehends and generates human language, providing instant data 
      analysis and recommendations. This is particularly significant in the dynamic 
      field of physiotherapy, where its integration has the potential to enhance 
      healthcare efficiency. OBJECTIVES: This study aims to evaluate whether 
      ChatGPT-3.5 (free version) provides consistent and accurate clinical responses, 
      its ability to imitate human clinical reasoning in simple and complex scenarios, 
      and its capability to produce a differential diagnosis. METHODS: Two studies were 
      conducted using the ChatGPT-3.5. Study 1 evaluated the consistency and accuracy 
      of ChatGPT's responses in clinical assessment using ten user-participants who 
      submitted the phrase "Which are the main steps for a completed physiotherapy 
      assessment?" Study 2 assessed ChatGPT's differential diagnostic ability using 
      published case studies by 2 independent participants. The case reports consisted 
      of one simple and one complex scenario. RESULTS: Study 1 underscored the 
      variability in ChatGPT's responses, which ranged from comprehensive to concise. 
      Notably, essential steps such as re-assessment and subjective examination were 
      omitted in 30% and 40% of the responses, respectively. In Study 2, ChatGPT 
      demonstrated its capability to develop evidence-based clinical reasoning, 
      particularly evident in simple clinical scenarios. Question phrasing 
      significantly impacted the generated answers. CONCLUSIONS: This study highlights 
      the potential benefits of using ChatGPT in healthcare. It also provides a 
      balanced perspective on ChatGPT's strengths and limitations and emphasizes the 
      importance of using AI tools in a responsible and informed manner.
FAU - Bilika, Paraskevi
AU  - Bilika P
AD  - Physiotherapy Department, Faculty of Health Sciences, Clinical Exercise 
      Physiology and Rehabilitation Research Laboratory, University of Thessaly, Lamia, 
      Greece.
FAU - Stefanouli, Vasiliki
AU  - Stefanouli V
AD  - Physiotherapy Department, Faculty of Health Sciences, Health Assessment and 
      Quality of Life Research Laboratory, University of Thessaly, Lamia, Greece.
FAU - Strimpakos, Nikolaos
AU  - Strimpakos N
AD  - Physiotherapy Department, Faculty of Health Sciences, Health Assessment and 
      Quality of Life Research Laboratory, University of Thessaly, Lamia, Greece.
AD  - Division of Musculoskeletal and Dermatological Sciences, University of 
      Manchester, Manchester, UK.
FAU - Kapreli, Eleni V
AU  - Kapreli EV
AD  - Physiotherapy Department, Faculty of Health Sciences, Clinical Exercise 
      Physiology and Rehabilitation Research Laboratory, University of Thessaly, Lamia, 
      Greece.
LA  - eng
PT  - Journal Article
DEP - 20231211
PL  - England
TA  - Physiother Theory Pract
JT  - Physiotherapy theory and practice
JID - 9015520
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Diagnosis
OT  - artificial intelligence
OT  - clinical reasoning
OT  - physiotherapy assessment
EDAT- 2023/12/11 06:45
MHDA- 2023/12/11 06:45
CRDT- 2023/12/11 05:28
PHST- 2023/12/11 06:45 [medline]
PHST- 2023/12/11 06:45 [pubmed]
PHST- 2023/12/11 05:28 [entrez]
AID - 10.1080/09593985.2023.2291656 [doi]
PST - aheadofprint
SO  - Physiother Theory Pract. 2023 Dec 11:1-20. doi: 10.1080/09593985.2023.2291656.

PMID- 37685617
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230911
IS  - 2077-0383 (Print)
IS  - 2077-0383 (Electronic)
IS  - 2077-0383 (Linking)
VI  - 12
IP  - 17
DP  - 2023 Aug 25
TI  - Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: 
      Findings and Implications.
LID - 10.3390/jcm12175550 [doi]
LID - 5550
AB  - Literature reviews are valuable for summarizing and evaluating the available 
      evidence in various medical fields, including nephrology. However, identifying 
      and exploring the potential sources requires focus and time devoted to literature 
      searching for clinicians and researchers. ChatGPT is a novel artificial 
      intelligence (AI) large language model (LLM) renowned for its exceptional ability 
      to generate human-like responses across various tasks. However, whether ChatGPT 
      can effectively assist medical professionals in identifying relevant literature 
      is unclear. Therefore, this study aimed to assess the effectiveness of ChatGPT in 
      identifying references to literature reviews in nephrology. We keyed the prompt 
      "Please provide the references in Vancouver style and their links in recent 
      literature on… name of the topic" into ChatGPT-3.5 (03/23 Version). We selected 
      all the results provided by ChatGPT and assessed them for existence, relevance, 
      and author/link correctness. We recorded each resource's citations, authors, 
      title, journal name, publication year, digital object identifier (DOI), and link. 
      The relevance and correctness of each resource were verified by searching on 
      Google Scholar. Of the total 610 references in the nephrology literature, only 
      378 (62%) of the references provided by ChatGPT existed, while 31% were 
      fabricated, and 7% of citations were incomplete references. Notably, only 122 
      (20%) of references were authentic. Additionally, 256 (68%) of the links in the 
      references were found to be incorrect, and the DOI was inaccurate in 206 (54%) of 
      the references. Moreover, among those with a link provided, the link was correct 
      in only 20% of cases, and 3% of the references were irrelevant. Notably, an 
      analysis of specific topics in electrolyte, hemodialysis, and kidney stones found 
      that >60% of the references were inaccurate or misleading, with less reliable 
      authorship and links provided by ChatGPT. Based on our findings, the use of 
      ChatGPT as a sole resource for identifying references to literature reviews in 
      nephrology is not recommended. Future studies could explore ways to improve AI 
      language models' performance in identifying relevant nephrology literature.
FAU - Suppadungsuk, Supawadee
AU  - Suppadungsuk S
AUID- ORCID: 0000-0003-1597-2411
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, 
      Mahidol University, Samut Prakan 10540, Thailand.
FAU - Thongprayoon, Charat
AU  - Thongprayoon C
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Krisanapan, Pajaree
AU  - Krisanapan P
AUID- ORCID: 0000-0002-2888-881X
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Division of Nephrology, Thammasat University Hospital, Pathum Thani 12120, 
      Thailand.
FAU - Tangpanithandee, Supawit
AU  - Tangpanithandee S
AUID- ORCID: 0000-0001-6103-2338
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, 
      Mahidol University, Samut Prakan 10540, Thailand.
FAU - Garcia Valencia, Oscar
AU  - Garcia Valencia O
AUID- ORCID: 0000-0003-0186-9448
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Miao, Jing
AU  - Miao J
AUID- ORCID: 0000-0003-0642-9740
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Mekraksakit, Poemlarp
AU  - Mekraksakit P
AUID- ORCID: 0000-0002-2127-2529
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Kashani, Kianoush
AU  - Kashani K
AUID- ORCID: 0000-0003-2184-3683
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Cheungpasitporn, Wisit
AU  - Cheungpasitporn W
AUID- ORCID: 0000-0001-9954-9711
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
LA  - eng
PT  - Journal Article
DEP - 20230825
PL  - Switzerland
TA  - J Clin Med
JT  - Journal of clinical medicine
JID - 101606588
PMC - PMC10488525
OTO - NOTNLM
OT  - ChatGPT
OT  - accuracy
OT  - nephrology literature
OT  - references
OT  - reliability
COIS- The authors declare no conflict of interest.
EDAT- 2023/09/09 11:44
MHDA- 2023/09/09 11:45
CRDT- 2023/09/09 01:08
PHST- 2023/06/29 00:00 [received]
PHST- 2023/08/21 00:00 [revised]
PHST- 2023/08/24 00:00 [accepted]
PHST- 2023/09/09 11:45 [medline]
PHST- 2023/09/09 11:44 [pubmed]
PHST- 2023/09/09 01:08 [entrez]
AID - jcm12175550 [pii]
AID - jcm-12-05550 [pii]
AID - 10.3390/jcm12175550 [doi]
PST - epublish
SO  - J Clin Med. 2023 Aug 25;12(17):5550. doi: 10.3390/jcm12175550.

PMID- 37075729
OWN - NLM
STAT- MEDLINE
DCOM- 20230421
LR  - 20230421
IS  - 2158-1797 (Electronic)
IS  - 1548-8578 (Linking)
VI  - 44
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT: Chances and Challenges for Dentistry.
PG  - 220-224
AB  - The artificial intelligence (AI) chatbot ChatGPT has generated both huge interest 
      and deep concern since its launch in November 2022.1 ChatGPT, a large language 
      model (LLM) with a conversational interface, has been trained on vast amounts of 
      human-generated text and has the ability to respond to questions and complete 
      various text-related tasks. The use of ChatGPT and similar LLMs in dentistry is 
      unlikely to significantly impact the daily routine of most dental healthcare 
      personnel but could streamline administrative workflows and potentially serve as 
      an additional tool for clinical decision support in the future. However, this is 
      contingent on the availability of comprehensive, up-to-date, and unbiased data. 
      The use of LLMs also causes privacy and cybersecurity concerns. It is therefore 
      crucial to implement robust data protection measures and strong defenses against 
      malicious use of LLMs. Although ChatGPT provides succinct answers to most 
      queries, its lack of reliability, transparency, and up-to-date knowledge compared 
      with conventional search engines is a major drawback, particularly for 
      health-related queries.
FAU - Eggmann, Florin
AU  - Eggmann F
AD  - Research Fellow, Department of Preventive and Restorative Sciences, Robert 
      Schattner Center, University of Pennsylvania School of Dental Medicine, 
      Philadelphia, Pennsylvania; Lecturer, Department of Periodontology, 
      Endodontology, and Cariology, University Center for Dental Medicine Basel UZB, 
      University of Basel, Basel, Switzerland.
FAU - Blatz, Markus B
AU  - Blatz MB
AD  - Professor of Restorative Dentistry, Chair, Department of Preventive and 
      Restorative Sciences, Robert Schattner Center, and Assistant Dean, Digital 
      Innovation and Professional Development, University of Pennsylvania School of 
      Dental Medicine, Philadelphia, Pennsylvania.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Compend Contin Educ Dent
JT  - Compendium of continuing education in dentistry (Jamesburg, N.J. : 1995)
JID - 9600713
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Health Personnel
MH  - Software
MH  - Dentistry
EDAT- 2023/04/20 00:41
MHDA- 2023/04/21 06:41
CRDT- 2023/04/19 18:23
PHST- 2023/04/21 06:41 [medline]
PHST- 2023/04/20 00:41 [pubmed]
PHST- 2023/04/19 18:23 [entrez]
PST - ppublish
SO  - Compend Contin Educ Dent. 2023 Apr;44(4):220-224.

PMID- 37847549
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231103
IS  - 2368-7959 (Print)
IS  - 2368-7959 (Electronic)
IS  - 2368-7959 (Linking)
VI  - 10
DP  - 2023 Oct 17
TI  - Identifying Rare Circumstances Preceding Female Firearm Suicides: Validating A 
      Large Language Model Approach.
PG  - e49359
LID - 10.2196/49359 [doi]
LID - e49359
AB  - BACKGROUND: Firearm suicide has been more prevalent among males, but age-adjusted 
      female firearm suicide rates increased by 20% from 2010 to 2020, outpacing the 
      rate increase among males by about 8 percentage points, and female firearm 
      suicide may have different contributing circumstances. In the United States, the 
      National Violent Death Reporting System (NVDRS) is a comprehensive source of data 
      on violent deaths and includes unstructured incident narrative reports from 
      coroners or medical examiners and law enforcement. Conventional natural language 
      processing approaches have been used to identify common circumstances preceding 
      female firearm suicide deaths but failed to identify rarer circumstances due to 
      insufficient training data. OBJECTIVE: This study aimed to leverage a large 
      language model approach to identify infrequent circumstances preceding female 
      firearm suicide in the unstructured coroners or medical examiners and law 
      enforcement narrative reports available in the NVDRS. METHODS: We used the 
      narrative reports of 1462 female firearm suicide decedents in the NVDRS from 2014 
      to 2018. The reports were written in English. We coded 9 infrequent circumstances 
      preceding female firearm suicides. We experimented with predicting those 
      circumstances by leveraging a large language model approach in a yes/no 
      question-answer format. We measured the prediction accuracy with F(1)-score 
      (ranging from 0 to 1). F(1)-score is the harmonic mean of precision (positive 
      predictive value) and recall (true positive rate or sensitivity). RESULTS: Our 
      large language model outperformed a conventional support vector 
      machine-supervised machine learning approach by a wide margin. Compared to the 
      support vector machine model, which had F(1)-scores less than 0.2 for most 
      infrequent circumstances, our large language model approach achieved an 
      F(1)-score of over 0.6 for 4 circumstances and 0.8 for 2 circumstances. 
      CONCLUSIONS: The use of a large language model approach shows promise. 
      Researchers interested in using natural language processing to identify 
      infrequent circumstances in narrative report data may benefit from large language 
      models.
CI  - ©Weipeng Zhou, Laura C Prater, Evan V Goldstein, Stephen J Mooney. Originally 
      published in JMIR Mental Health (https://mental.jmir.org), 17.10.2023.
FAU - Zhou, Weipeng
AU  - Zhou W
AUID- ORCID: 0000-0003-1215-8043
AD  - Department of Biomedical Informatics and Medical Education, School of Medicine, 
      University of Washington, Seattle, WA, United States.
FAU - Prater, Laura C
AU  - Prater LC
AUID- ORCID: 0000-0003-2414-5063
AD  - Department of Psychiatry and Behavioral Health, University of Washington, 
      Seattle, WA, United States.
AD  - Harborview Medical Center, School of Medicine, University of Washington, Seattle, 
      WA, United States.
FAU - Goldstein, Evan V
AU  - Goldstein EV
AUID- ORCID: 0000-0001-7766-7450
AD  - Department of Population Health Sciences, University of Utah, Salt Lake City, UT, 
      United States.
FAU - Mooney, Stephen J
AU  - Mooney SJ
AUID- ORCID: 0000-0001-9092-938X
AD  - Department of Epidemiology, School of Public Health, University of Washington, 
      Seattle, WA, United States.
LA  - eng
PT  - Journal Article
DEP - 20231017
PL  - Canada
TA  - JMIR Ment Health
JT  - JMIR mental health
JID - 101658926
PMC - PMC10618876
OTO - NOTNLM
OT  - depression
OT  - document classification
OT  - female
OT  - female firearm suicide
OT  - firearm suicide
OT  - language models
OT  - large language model
OT  - machine learning
OT  - mental health
OT  - mental health for women
OT  - suicidal
OT  - suicide
OT  - suicide prevention
OT  - violent death
OT  - women
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/17 12:42
MHDA- 2023/10/17 12:43
CRDT- 2023/10/17 11:54
PHST- 2023/05/30 00:00 [received]
PHST- 2023/09/02 00:00 [accepted]
PHST- 2023/08/31 00:00 [revised]
PHST- 2023/10/17 12:43 [medline]
PHST- 2023/10/17 12:42 [pubmed]
PHST- 2023/10/17 11:54 [entrez]
AID - v10i1e49359 [pii]
AID - 10.2196/49359 [doi]
PST - epublish
SO  - JMIR Ment Health. 2023 Oct 17;10:e49359. doi: 10.2196/49359.

PMID- 38076813
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
DP  - 2023 Oct 31
TI  - Comparison of Large Language Models in Answering Immuno-Oncology Questions: A 
      Cross-Sectional Study.
LID - 2023.10.31.23297825 [pii]
LID - 10.1101/2023.10.31.23297825 [doi]
AB  - BACKGROUND: The capability of large language models (LLMs) to understand and 
      generate human-readable text has prompted the investigation of their potential as 
      educational and management tools for cancer patients and healthcare providers. 
      MATERIALS AND METHODS: We conducted a cross-sectional study aimed at evaluating 
      the ability of ChatGPT-4, ChatGPT-3.5, and Google Bard to answer questions 
      related to four domains of immuno-oncology (Mechanisms, Indications, Toxicities, 
      and Prognosis). We generated 60 open-ended questions (15 for each section). 
      Questions were manually submitted to LLMs, and responses were collected on June 
      30th, 2023. Two reviewers evaluated the answers independently. RESULTS: ChatGPT-4 
      and ChatGPT-3.5 answered all questions, whereas Google Bard answered only 53.3% 
      (p <0.0001). The number of questions with reproducible answers was higher for 
      ChatGPT-4 (95%) and ChatGPT3.5 (88.3%) than for Google Bard (50%) (p <0.0001). In 
      terms of accuracy, the number of answers deemed fully correct were 75.4%, 58.5%, 
      and 43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (p = 0.03). 
      Furthermore, the number of responses deemed highly relevant was 71.9%, 77.4%, and 
      43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (p = 0.04). 
      Regarding readability, the number of highly readable was higher for ChatGPT-4 and 
      ChatGPT-3.5 (98.1%) and (100%) compared to Google Bard (87.5%) (p = 0.02). 
      CONCLUSION: ChatGPT-4 and ChatGPT-3.5 are potentially powerful tools in 
      immuno-oncology, whereas Google Bard demonstrated relatively poorer performance. 
      However, the risk of inaccuracy or incompleteness in the responses was evident in 
      all three LLMs, highlighting the importance of expert-driven verification of the 
      outputs returned by these technologies. IMPLICATIONS FOR PRACTICE: Several 
      studies have recently evaluated whether large language models may be feasible 
      tools for providing educational and management information for cancer patients 
      and healthcare providers. In this cross-sectional study, we assessed the ability 
      of ChatGPT-4, ChatGPT-3.5, and Google Bard to answer questions related to 
      immuno-oncology. ChatGPT-4 and ChatGPT-3.5 returned a higher proportion of 
      responses, which were more accurate and comprehensive, than those returned by 
      Google Bard, yielding highly reproducible and readable outputs. These data 
      support ChatGPT-4 and ChatGPT-3.5 as powerful tools in providing information on 
      immuno-oncology; however, accuracy remains a concern, with expert assessment of 
      the output still indicated.
FAU - Iannantuono, Giovanni Maria
AU  - Iannantuono GM
FAU - Bracken-Clarke, Dara
AU  - Bracken-Clarke D
FAU - Karzai, Fatima
AU  - Karzai F
FAU - Choo-Wosoba, Hyoyoung
AU  - Choo-Wosoba H
FAU - Gulley, James L
AU  - Gulley JL
FAU - Floudas, Charalampos S
AU  - Floudas CS
LA  - eng
PT  - Preprint
DEP - 20231031
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10705618
EDAT- 2023/12/11 12:43
MHDA- 2023/12/11 12:44
CRDT- 2023/12/11 06:19
PHST- 2023/12/11 12:44 [medline]
PHST- 2023/12/11 12:43 [pubmed]
PHST- 2023/12/11 06:19 [entrez]
AID - 2023.10.31.23297825 [pii]
AID - 10.1101/2023.10.31.23297825 [doi]
PST - epublish
SO  - medRxiv. 2023 Oct 31:2023.10.31.23297825. doi: 10.1101/2023.10.31.23297825. 
      Preprint.

PMID- 37368124
OWN - NLM
STAT- Publisher
LR  - 20230627
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jun 27
TI  - Need an AI-Enabled, Next-Generation, Advanced ChatGPT or Large Language Models 
      (LLMs) for Error-Free and Accurate Medical Information.
LID - 10.1007/s10439-023-03297-9 [doi]
AB  - Recently, the interest in AI-guided ChatGPT has increased day-to-day, and 
      different applications have been explored, including the medical field. The 
      publication number is also increasing. At the same time, people are trying to get 
      medical information from this Chartbot. However, researchers found that ChatGPT 
      also provides partly correct or false information. Therefore, in this article, we 
      urge the researchers to develop an AI-enabled, next-generation, advanced ChatGPT 
      or large language models (LLMs) so that people can get accurate and error-free 
      medical information.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AUID- ORCID: 0000-0002-3958-239X
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal, 700126, India. drchiranjib@yahoo.com.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AUID- ORCID: 0000-0001-9669-1835
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 
      756020, India.
FAU - Lee, Sang-Soo
AU  - Lee SS
AUID- ORCID: 0000-0001-5074-7581
AD  - Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon 
      Sacred Heart Hospital, Chuncheon-si, Gangwon-do, 24252, Republic of Korea.
LA  - eng
PT  - Letter
DEP - 20230627
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Large language models
OT  - Medical information
EDAT- 2023/06/27 13:11
MHDA- 2023/06/27 13:11
CRDT- 2023/06/27 11:10
PHST- 2023/06/14 00:00 [received]
PHST- 2023/06/20 00:00 [accepted]
PHST- 2023/06/27 13:11 [medline]
PHST- 2023/06/27 13:11 [pubmed]
PHST- 2023/06/27 11:10 [entrez]
AID - 10.1007/s10439-023-03297-9 [pii]
AID - 10.1007/s10439-023-03297-9 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jun 27. doi: 10.1007/s10439-023-03297-9.

PMID- 37973369
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1473-4257 (Electronic)
IS  - 0306-6800 (Linking)
DP  - 2023 Nov 16
TI  - Assessing the performance of ChatGPT in bioethics: a large language model's moral 
      compass in medicine.
LID - jme-2023-109366 [pii]
LID - 10.1136/jme-2023-109366 [doi]
AB  - Chat Generative Pre-Trained Transformer (ChatGPT) has been a growing point of 
      interest in medical education yet has not been assessed in the field of 
      bioethics. This study evaluated the accuracy of ChatGPT-3.5 (April 2023 version) 
      in answering text-based, multiple choice bioethics questions at the level of US 
      third-year and fourth-year medical students. A total of 114 bioethical questions 
      were identified from the widely utilised question banks UWorld and AMBOSS. 
      Accuracy, bioethical categories, difficulty levels, specialty data, error 
      analysis and character count were analysed. We found that ChatGPT had an accuracy 
      of 59.6%, with greater accuracy in topics surrounding death and patient-physician 
      relationships and performed poorly on questions pertaining to informed consent. 
      Of all the specialties, it performed best in paediatrics. Yet, certain 
      specialties and bioethical categories were under-represented. Among the errors 
      made, it tended towards content errors and application errors. There were no 
      significant associations between character count and accuracy. Nevertheless, this 
      investigation contributes to the ongoing dialogue on artificial intelligence's 
      (AI) role in healthcare and medical education, advocating for further research to 
      fully understand AI systems' capabilities and constraints in the nuanced field of 
      medical bioethics.
CI  - © Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and 
      permissions. Published by BMJ.
FAU - Chen, Jamie
AU  - Chen J
AUID- ORCID: 0000-0003-0572-2291
AD  - Hackensack Meridian School of Medicine, Nutley, New Jersey, USA 
      jamie.chen@hmhn.org.
FAU - Cadiente, Angelo
AU  - Cadiente A
AD  - Hackensack Meridian School of Medicine, Nutley, New Jersey, USA.
FAU - Kasselman, Lora J
AU  - Kasselman LJ
AD  - Research Institute, Hackensack Meridian Health, Edison, New Jersey, USA.
FAU - Pilkington, Bryan
AU  - Pilkington B
AD  - Hackensack Meridian School of Medicine, Nutley, New Jersey, USA.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - England
TA  - J Med Ethics
JT  - Journal of medical ethics
JID - 7513619
SB  - IM
OTO - NOTNLM
OT  - Decision Making
OT  - Education
OT  - Ethics- Medical
COIS- Competing interests: None declared.
EDAT- 2023/11/17 15:27
MHDA- 2023/11/17 15:27
CRDT- 2023/11/16 21:03
PHST- 2023/06/26 00:00 [received]
PHST- 2023/11/02 00:00 [accepted]
PHST- 2023/11/17 15:27 [medline]
PHST- 2023/11/17 15:27 [pubmed]
PHST- 2023/11/16 21:03 [entrez]
AID - jme-2023-109366 [pii]
AID - 10.1136/jme-2023-109366 [doi]
PST - aheadofprint
SO  - J Med Ethics. 2023 Nov 16:jme-2023-109366. doi: 10.1136/jme-2023-109366.

PMID- 37391665
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231115
LR  - 20231123
IS  - 1546-170X (Electronic)
IS  - 1078-8956 (Linking)
VI  - 29
IP  - 10
DP  - 2023 Oct
TI  - Large language model AI chatbots require approval as medical devices.
PG  - 2396-2398
LID - 10.1038/s41591-023-02412-6 [doi]
FAU - Gilbert, Stephen
AU  - Gilbert S
AUID- ORCID: 0000-0002-1997-1689
AD  - Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden, 
      Dresden, Germany. stephen.gilbert@tu-dresden.de.
FAU - Harvey, Hugh
AU  - Harvey H
AD  - Hardian Health, Haywards Heath, UK.
FAU - Melvin, Tom
AU  - Melvin T
AUID- ORCID: 0000-0001-6370-4177
AD  - School of Medicine, Trinity College, University of Dublin, Dublin, Ireland.
FAU - Vollebregt, Erik
AU  - Vollebregt E
AD  - Axon Lawyers, Amsterdam, The Netherlands.
FAU - Wicks, Paul
AU  - Wicks P
AD  - Wicks Digital Health, Advantage House, Stowe Court, Lichfield, UK.
LA  - eng
GR  - 16KISA100K/Bundesministerium für Bildung und Forschung (Federal Ministry of 
      Education and Research)/
PT  - Journal Article
PL  - United States
TA  - Nat Med
JT  - Nature medicine
JID - 9502015
SB  - IM
EDAT- 2023/07/01 11:41
MHDA- 2023/07/01 11:42
CRDT- 2023/06/30 23:32
PHST- 2023/07/01 11:42 [medline]
PHST- 2023/07/01 11:41 [pubmed]
PHST- 2023/06/30 23:32 [entrez]
AID - 10.1038/s41591-023-02412-6 [pii]
AID - 10.1038/s41591-023-02412-6 [doi]
PST - ppublish
SO  - Nat Med. 2023 Oct;29(10):2396-2398. doi: 10.1038/s41591-023-02412-6.

PMID- 37211242
OWN - NLM
STAT- MEDLINE
DCOM- 20230728
LR  - 20230728
IS  - 1873-0183 (Electronic)
IS  - 1568-9972 (Linking)
VI  - 22
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT and autoimmunity - A new weapon in the battlefield of knowledge.
PG  - 103360
LID - S1568-9972(23)00094-0 [pii]
LID - 10.1016/j.autrev.2023.103360 [doi]
AB  - The field of medical research has been always full of innovation and huge leaps 
      revolutionizing the scientific world. In the recent years, we have witnessed this 
      firsthand by the evolution of Artificial Intelligence (AI), with ChatGPT being 
      the most recent example. ChatGPT is a language chat bot which generates 
      human-like texts based on data from the internet. If viewed from a medical point 
      view, ChatGPT has shown capabilities of composing medical texts similar to those 
      depicted by experienced authors, to solve clinical cases, to provide medical 
      solutions, among other fascinating performances. Nevertheless, the value of the 
      results, limitations, and clinical implications still need to be carefully 
      evaluated. In our current paper on the role of ChatGPT in clinical medicine, 
      particularly in the field of autoimmunity, we aimed to illustrate the implication 
      of this technology alongside the latest utilization and limitations. In addition, 
      we included an expert opinion on the cyber-related aspects of the bot potentially 
      contributing to the risks attributed to its use, alongside proposed defense 
      mechanisms. All of that, while taking into consideration the rapidity of the 
      continuous improvement AI experiences on a daily basis.
CI  - Copyright © 2023. Published by Elsevier B.V.
FAU - Darkhabani, Mohammad
AU  - Darkhabani M
AD  - International School of Medicine, Istanbul Medipol University, Istanbul, Turkey.
FAU - Alrifaai, Mohamad Aosama
AU  - Alrifaai MA
AD  - International School of Medicine, Istanbul Medipol University, Istanbul, Turkey.
FAU - Elsalti, Abdulrahman
AU  - Elsalti A
AD  - International School of Medicine, Istanbul Medipol University, Istanbul, Turkey.
FAU - Dvir, Yoad M
AU  - Dvir YM
FAU - Mahroum, Naim
AU  - Mahroum N
AD  - International School of Medicine, Istanbul Medipol University, Istanbul, Turkey. 
      Electronic address: naim.mahroum@gmail.com.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230519
PL  - Netherlands
TA  - Autoimmun Rev
JT  - Autoimmunity reviews
JID - 101128967
SB  - IM
MH  - Humans
MH  - *Autoimmunity
MH  - Artificial Intelligence
MH  - *Biomedical Research
MH  - Internet
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Autoimmunity
OT  - Chat bots
OT  - ChatGPT
COIS- Declaration of Competing Interest The authors decalre no conflict of interests.
EDAT- 2023/05/22 00:41
MHDA- 2023/07/28 06:43
CRDT- 2023/05/21 19:25
PHST- 2023/05/03 00:00 [received]
PHST- 2023/05/18 00:00 [accepted]
PHST- 2023/07/28 06:43 [medline]
PHST- 2023/05/22 00:41 [pubmed]
PHST- 2023/05/21 19:25 [entrez]
AID - S1568-9972(23)00094-0 [pii]
AID - 10.1016/j.autrev.2023.103360 [doi]
PST - ppublish
SO  - Autoimmun Rev. 2023 Aug;22(8):103360. doi: 10.1016/j.autrev.2023.103360. Epub 
      2023 May 19.

PMID- 37209289
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20230802
IS  - 1573-7373 (Electronic)
IS  - 0167-594X (Linking)
VI  - 163
IP  - 2
DP  - 2023 Jun
TI  - Large language model artificial intelligence: the current state and future of 
      ChatGPT in neuro-oncology publishing.
PG  - 473-474
LID - 10.1007/s11060-023-04336-0 [doi]
FAU - Cifarelli, Christopher P
AU  - Cifarelli CP
AD  - West Virginia University, Morgantown, WV, USA. cpcifarelli@hsc.wvu.edu.
FAU - Sheehan, Jason P
AU  - Sheehan JP
AD  - University of Virginia, Charlottesville, VA, USA.
LA  - eng
PT  - Letter
DEP - 20230520
PL  - United States
TA  - J Neurooncol
JT  - Journal of neuro-oncology
JID - 8309335
SB  - IM
CIN - J Neurooncol. 2023 Jul;163(3):731-733. PMID: 37440100
MH  - *Artificial Intelligence
MH  - *Publishing/trends
EDAT- 2023/05/20 19:13
MHDA- 2023/07/07 06:42
CRDT- 2023/05/20 14:58
PHST- 2023/04/30 00:00 [received]
PHST- 2023/05/04 00:00 [accepted]
PHST- 2023/07/07 06:42 [medline]
PHST- 2023/05/20 19:13 [pubmed]
PHST- 2023/05/20 14:58 [entrez]
AID - 10.1007/s11060-023-04336-0 [pii]
AID - 10.1007/s11060-023-04336-0 [doi]
PST - ppublish
SO  - J Neurooncol. 2023 Jun;163(2):473-474. doi: 10.1007/s11060-023-04336-0. Epub 2023 
      May 20.

PMID- 37200013
OWN - NLM
STAT- MEDLINE
DCOM- 20230522
LR  - 20230525
IS  - 2689-0186 (Electronic)
IS  - 2689-0186 (Linking)
VI  - 4
IP  - 5
DP  - 2023 May 5
TI  - ChatGPT and Physicians' Malpractice Risk.
PG  - e231938
LID - 10.1001/jamahealthforum.2023.1938 [doi]
FAU - Mello, Michelle M
AU  - Mello MM
AD  - Stanford Law School, Stanford, California.
AD  - Department of Health Policy, Stanford University School of Medicine, Stanford, 
      California.
FAU - Guha, Neel
AU  - Guha N
AD  - Stanford Law School, Stanford, California.
AD  - Department of Computer Science, Stanford University, Stanford, California.
LA  - eng
PT  - Journal Article
DEP - 20230505
PL  - United States
TA  - JAMA Health Forum
JT  - JAMA health forum
JID - 101769500
SB  - IM
MH  - Humans
MH  - *Malpractice
MH  - *Physicians
OAB - This JAMA Forum discusses the possibilities, limitations, and risks of physician 
      use of large language models (such as ChatGPT) along with the improvements 
      required to improve the accuracy of the technology.
OABL- eng
EDAT- 2023/05/18 13:09
MHDA- 2023/05/22 06:41
CRDT- 2023/05/18 11:33
PHST- 2023/05/22 06:41 [medline]
PHST- 2023/05/18 13:09 [pubmed]
PHST- 2023/05/18 11:33 [entrez]
AID - 2805334 [pii]
AID - 10.1001/jamahealthforum.2023.1938 [doi]
PST - epublish
SO  - JAMA Health Forum. 2023 May 5;4(5):e231938. doi: 
      10.1001/jamahealthforum.2023.1938.

PMID- 37873131
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
DP  - 2023 Oct 2
TI  - Knowledge-Driven Online Multimodal Automated Phenotyping System.
LID - 2023.09.29.23296239 [pii]
LID - 10.1101/2023.09.29.23296239 [doi]
AB  - Though electronic health record (EHR) systems are a rich repository of clinical 
      information with large potential, the use of EHR-based phenotyping algorithms is 
      often hindered by inaccurate diagnostic records, the presence of many irrelevant 
      features, and the requirement for a human-labeled training set. In this paper, we 
      describe a knowledge-driven online multimodal automated phenotyping (KOMAP) 
      system that i) generates a list of informative features by an online narrative 
      and codified feature search engine (ONCE) and ii) enables the training of a 
      multimodal phenotyping algorithm based on summary data. Powered by composite 
      knowledge from multiple EHR sources, online article corpora, and a large language 
      model, features selected by ONCE show high concordance with the state-of-the-art 
      AI models (GPT4 and ChatGPT) and encourage large-scale phenotyping by providing a 
      smaller but highly relevant feature set. Validation of the KOMAP system across 
      four healthcare centers suggests that it can generate efficient phenotyping 
      algorithms with robust performance. Compared to other methods requiring 
      patient-level inputs and gold-standard labels, the fully online KOMAP provides a 
      significant opportunity to enable multi-center collaboration.
FAU - Xiong, Xin
AU  - Xiong X
FAU - Sweet, Sara Morini
AU  - Sweet SM
FAU - Liu, Molei
AU  - Liu M
FAU - Hong, Chuan
AU  - Hong C
FAU - Bonzel, Clara-Lea
AU  - Bonzel CL
FAU - Panickan, Vidul Ayakulangara
AU  - Panickan VA
FAU - Zhou, Doudou
AU  - Zhou D
FAU - Wang, Linshanshan
AU  - Wang L
FAU - Costa, Lauren
AU  - Costa L
FAU - Ho, Yuk-Lam
AU  - Ho YL
AUID- ORCID: 0000-0003-3305-3830
FAU - Geva, Alon
AU  - Geva A
FAU - Mandl, Kenneth D
AU  - Mandl KD
AUID- ORCID: 0000-0002-9781-0477
FAU - Cheng, Suchun
AU  - Cheng S
FAU - Xia, Zongqi
AU  - Xia Z
AUID- ORCID: 0000-0003-1500-2589
FAU - Cho, Kelly
AU  - Cho K
FAU - Gaziano, J Michael
AU  - Gaziano JM
FAU - Liao, Katherine P
AU  - Liao KP
AUID- ORCID: 0000-0002-4797-3200
FAU - Cai, Tianxi
AU  - Cai T
FAU - Cai, Tianrun
AU  - Cai T
LA  - eng
PT  - Preprint
DEP - 20231002
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10593060
EDAT- 2023/10/24 06:41
MHDA- 2023/10/24 06:42
CRDT- 2023/10/24 03:52
PHST- 2023/10/24 06:42 [medline]
PHST- 2023/10/24 06:41 [pubmed]
PHST- 2023/10/24 03:52 [entrez]
AID - 2023.09.29.23296239 [pii]
AID - 10.1101/2023.09.29.23296239 [doi]
PST - epublish
SO  - medRxiv. 2023 Oct 2:2023.09.29.23296239. doi: 10.1101/2023.09.29.23296239. 
      Preprint.

PMID- 37926642
OWN - NLM
STAT- Publisher
LR  - 20231105
IS  - 1873-7560 (Electronic)
IS  - 0302-2838 (Linking)
DP  - 2023 Nov 3
TI  - Awareness and Use of ChatGPT and Large Language Models: A Prospective 
      Cross-sectional Global Survey in Urology.
LID - S0302-2838(23)03211-6 [pii]
LID - 10.1016/j.eururo.2023.10.014 [doi]
AB  - BACKGROUND: Since its release in November 2022, ChatGPT has captivated society 
      and shown potential for various aspects of health care. OBJECTIVE: To investigate 
      potential use of ChatGPT, a large language model (LLM), in urology by gathering 
      opinions from urologists worldwide. DESIGN, SETTING, AND PARTICIPANTS: An open 
      web-based survey was distributed via social media and e-mail chains to urologists 
      between April 20, 2023 and May 5, 2023. Participants were asked to answer 
      questions related to their knowledge and experience with artificial intelligence, 
      as well as their opinions of potential use of ChatGPT/LLMs in research and 
      clinical practice. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Data are 
      reported as the mean and standard deviation for continuous variables, and the 
      frequency and percentage for categorical variables. Charts and tables are used as 
      appropriate, with descriptions of the chart types and the measures used. The data 
      are reported in accordance with the Checklist for Reporting Results of Internet 
      E-Surveys (CHERRIES). RESULTS AND LIMITATIONS: A total of 456 individuals 
      completed the survey (64% completion rate). Nearly half (47.7%) reported that 
      they use ChatGPT/LLMs in their academic practice, with fewer using the technology 
      in clinical practice (19.8%). More than half (62.2%) believe there are potential 
      ethical concerns when using ChatGPT for scientific or academic writing, and 53% 
      reported that they have experienced limitations when using ChatGPT in academic 
      practice. CONCLUSIONS: Urologists recognise the potential of ChatGPT/LLMs in 
      research but have concerns regarding ethics and patient acceptance. There is a 
      desire for regulations and guidelines to ensure appropriate use. In addition, 
      measures should be taken to establish rules and guidelines to maximise safety and 
      efficiency when using this novel technology. PATIENT SUMMARY: A survey asked 456 
      urologists from around the world about using an artificial intelligence tool 
      called ChatGPT in their work. Almost half of them use ChatGPT for research, but 
      not many use it for patients care. The resonders think ChatGPT could be helpful, 
      but they worry about problems like ethics and want rules to make sure it's used 
      safely.
CI  - Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All 
      rights reserved.
FAU - Eppler, Michael
AU  - Eppler M
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 
      AI Center, USC Institute of Urology, University of Southern California, Los 
      Angeles, CA, USA.
FAU - Ganjavi, Conner
AU  - Ganjavi C
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 
      AI Center, USC Institute of Urology, University of Southern California, Los 
      Angeles, CA, USA.
FAU - Ramacciotti, Lorenzo Storino
AU  - Ramacciotti LS
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 
      AI Center, USC Institute of Urology, University of Southern California, Los 
      Angeles, CA, USA.
FAU - Piazza, Pietro
AU  - Piazza P
AD  - Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, 
      Italy.
FAU - Rodler, Severin
AU  - Rodler S
AD  - Department of Urology, Klinikum der Universität München, Munich, Germany.
FAU - Checcucci, Enrico
AU  - Checcucci E
AD  - Department of Surgery, FPO-IRCCS Candiolo Cancer Institute, Candiolo, Italy.
FAU - Gomez Rivas, Juan
AU  - Gomez Rivas J
AD  - Department of Urology, Clinico San Carlos University Hospital, Madrid, Spain.
FAU - Kowalewski, Karl F
AU  - Kowalewski KF
AD  - Department of Urology, University Medical Center Mannheim, Heidelberg University, 
      Mannheim, Germany.
FAU - Belenchón, Ines Rivero
AU  - Belenchón IR
AD  - Urology and Nephrology Department, Virgen del Rocío University Hospital, Seville, 
      Spain.
FAU - Puliatti, Stefano
AU  - Puliatti S
AD  - Urology Department, University of Modena and Reggio Emilia, Modena, Italy.
FAU - Taratkin, Mark
AU  - Taratkin M
AD  - Institute for Urology and Reproductive Health, Sechenov University, Moscow, 
      Russia.
FAU - Veccia, Alessandro
AU  - Veccia A
AD  - Department of Urology, Azienda Ospedaliera Universitaria Integrata Verona, 
      Verona, Italy.
FAU - Baekelandt, Loïc
AU  - Baekelandt L
AD  - Department of Urology, University Hospitals Leuven, Leuven, Belgium.
FAU - Teoh, Jeremy Y-C
AU  - Teoh JY
AD  - Department of Surgery, S.H. Ho Urology Centre, The Chinese University of Hong 
      Kong, Hong Kong, China.
FAU - Somani, Bhaskar K
AU  - Somani BK
AD  - University Hospital Southampton NHS Foundation Trust, Southampton, UK.
FAU - Wroclawski, Marcelo
AU  - Wroclawski M
AD  - Hospital Israelita Albert Einstein, São Paulo, Brazil; Beneficência Portuguesa de 
      São Paulo, São Paulo, Brazil; Faculdade de Medicina do ABC, Santo Andre, Brazil.
FAU - Abreu, Andre
AU  - Abreu A
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 
      AI Center, USC Institute of Urology, University of Southern California, Los 
      Angeles, CA, USA.
FAU - Porpiglia, Francesco
AU  - Porpiglia F
AD  - Department of Surgery, FPO-IRCCS Candiolo Cancer Institute, Candiolo, Italy.
FAU - Gill, Inderbir S
AU  - Gill IS
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 
      AI Center, USC Institute of Urology, University of Southern California, Los 
      Angeles, CA, USA.
FAU - Murphy, Declan G
AU  - Murphy DG
AD  - Division of Cancer Surgery, Peter MacCallum Cancer Centre, University of 
      Melbourne, Melbourne, Australia.
FAU - Canes, David
AU  - Canes D
AD  - Division of Urology, Lahey Hospital & Medical Center, Burlington, MA, USA.
FAU - Cacciamani, Giovanni E
AU  - Cacciamani GE
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 
      AI Center, USC Institute of Urology, University of Southern California, Los 
      Angeles, CA, USA. Electronic address: giovanni.cacciamani@med.usc.edu.
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - Switzerland
TA  - Eur Urol
JT  - European urology
JID - 7512719
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Ethics
OT  - Global
OT  - Large language models
OT  - OpenAI
OT  - Survey
OT  - Urology
EDAT- 2023/11/06 00:41
MHDA- 2023/11/06 00:41
CRDT- 2023/11/05 21:56
PHST- 2023/05/29 00:00 [received]
PHST- 2023/09/27 00:00 [revised]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/11/06 00:41 [medline]
PHST- 2023/11/06 00:41 [pubmed]
PHST- 2023/11/05 21:56 [entrez]
AID - S0302-2838(23)03211-6 [pii]
AID - 10.1016/j.eururo.2023.10.014 [doi]
PST - aheadofprint
SO  - Eur Urol. 2023 Nov 3:S0302-2838(23)03211-6. doi: 10.1016/j.eururo.2023.10.014.

PMID- 37902835
OWN - NLM
STAT- Publisher
LR  - 20231030
IS  - 1438-8359 (Electronic)
IS  - 0913-8668 (Linking)
DP  - 2023 Oct 30
TI  - ChatGPT's performance on JSA-certified anesthesiologist exam.
LID - 10.1007/s00540-023-03275-4 [doi]
FAU - Kinoshita, Michiko
AU  - Kinoshita M
AUID- ORCID: 0000-0001-8680-7654
AD  - Department of Anesthesiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, 
      Tokushima-shi, Tokushima, 770-8503, Japan. michiko-kinoshita@tokushima-u.ac.jp.
FAU - Komasaka, Mizuki
AU  - Komasaka M
AD  - Department of Anesthesiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, 
      Tokushima-shi, Tokushima, 770-8503, Japan.
FAU - Tanaka, Katsuya
AU  - Tanaka K
AD  - Department of Anesthesiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, 
      Tokushima-shi, Tokushima, 770-8503, Japan.
LA  - eng
PT  - Letter
DEP - 20231030
PL  - Japan
TA  - J Anesth
JT  - Journal of anesthesia
JID - 8905667
SB  - IM
OTO - NOTNLM
OT  - Anesthesiology
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Large language model
EDAT- 2023/10/30 18:42
MHDA- 2023/10/30 18:42
CRDT- 2023/10/30 12:02
PHST- 2023/05/24 00:00 [received]
PHST- 2023/10/12 00:00 [accepted]
PHST- 2023/10/30 18:42 [medline]
PHST- 2023/10/30 18:42 [pubmed]
PHST- 2023/10/30 12:02 [entrez]
AID - 10.1007/s00540-023-03275-4 [pii]
AID - 10.1007/s00540-023-03275-4 [doi]
PST - aheadofprint
SO  - J Anesth. 2023 Oct 30. doi: 10.1007/s00540-023-03275-4.

PMID- 37798200
OWN - NLM
STAT- Publisher
LR  - 20231005
IS  - 1399-0020 (Electronic)
IS  - 0901-5027 (Linking)
DP  - 2023 Oct 3
TI  - The impact and opportunities of large language models like ChatGPT in oral and 
      maxillofacial surgery: a narrative review.
LID - S0901-5027(23)00216-3 [pii]
LID - 10.1016/j.ijom.2023.09.005 [doi]
AB  - Since its release at the end of 2022, the social response to ChatGPT, a large 
      language model (LLM), has been huge, as it has revolutionized the way we 
      communicate with computers. This review was performed to describe the technical 
      background of LLMs and to provide a review of the current literature on LLMs in 
      the field of oral and maxillofacial surgery (OMS). The PubMed, Scopus, and Web of 
      Science databases were searched for LLMs and OMS. Adjacent surgical disciplines 
      were included to cover the entire literature, and records from Google Scholar and 
      medRxiv were added. Out of the 57 records identified, 37 were included; 31 (84%) 
      were related to GPT-3.5, four (11%) to GPT-4, and two (5%) to both. Current 
      research on LLMs is mainly limited to research and scientific writing, patient 
      information/communication, and medical education. Classic OMS diseases are 
      underrepresented. The current literature related to LLMs in OMS has a limited 
      evidence level. There is a need to investigate the use of LLMs scientifically and 
      systematically in the core areas of OMS. Although LLMs are likely to add value 
      outside the operating room, the use of LLMs raises ethical and medical regulatory 
      issues that must first be addressed.
CI  - Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Puladi, B
AU  - Puladi B
AD  - Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 
      Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH 
      Aachen, Aachen, Germany.
FAU - Gsaxner, C
AU  - Gsaxner C
AD  - Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 
      Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH 
      Aachen, Aachen, Germany; Institute of Computer Graphics and Vision, Graz 
      University of Technology, Graz, Austria; Department of Oral and Maxillofacial 
      Surgery, Medical University of Graz, Graz, Austria.
FAU - Kleesiek, J
AU  - Kleesiek J
AD  - Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, 
      Germany.
FAU - Hölzle, F
AU  - Hölzle F
AD  - Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 
      Aachen, Germany.
FAU - Röhrig, R
AU  - Röhrig R
AD  - Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, 
      Germany.
FAU - Egger, J
AU  - Egger J
AD  - Institute of Computer Graphics and Vision, Graz University of Technology, Graz, 
      Austria; Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 
      Essen, Germany. Electronic address: egger@tugraz.at.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231003
PL  - Denmark
TA  - Int J Oral Maxillofac Surg
JT  - International journal of oral and maxillofacial surgery
JID - 8605826
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Oral and Maxillofacial Surgery
OT  - Review
EDAT- 2023/10/06 00:43
MHDA- 2023/10/06 00:43
CRDT- 2023/10/05 21:59
PHST- 2023/06/30 00:00 [received]
PHST- 2023/09/14 00:00 [revised]
PHST- 2023/09/19 00:00 [accepted]
PHST- 2023/10/06 00:43 [medline]
PHST- 2023/10/06 00:43 [pubmed]
PHST- 2023/10/05 21:59 [entrez]
AID - S0901-5027(23)00216-3 [pii]
AID - 10.1016/j.ijom.2023.09.005 [doi]
PST - aheadofprint
SO  - Int J Oral Maxillofac Surg. 2023 Oct 3:S0901-5027(23)00216-3. doi: 
      10.1016/j.ijom.2023.09.005.

PMID- 37780993
OWN - NLM
STAT- MEDLINE
DCOM- 20231031
LR  - 20231031
IS  - 1551-4056 (Electronic)
IS  - 0044-0086 (Print)
IS  - 0044-0086 (Linking)
VI  - 96
IP  - 3
DP  - 2023 Sep
TI  - ChatGPT and the Future of Journal Reviews: A Feasibility Study.
PG  - 415-420
LID - 10.59249/SKDH9286 [doi]
AB  - The increasing volume of research submissions to academic journals poses a 
      significant challenge for traditional peer-review processes. To address this 
      issue, this study explores the potential of employing ChatGPT, an advanced large 
      language model (LLM), developed by OpenAI, as an artificial intelligence (AI) 
      reviewer for academic journals. By leveraging the vast knowledge and natural 
      language processing capabilities of ChatGPT, we hypothesize it may be possible to 
      enhance the efficiency, consistency, and quality of the peer-review process. This 
      research investigated key aspects of integrating ChatGPT into the journal review 
      workflow. We compared the critical analysis of ChatGPT, acting as an AI reviewer, 
      to human reviews for a single published article. Our methodological framework 
      involved subjecting ChatGPT to an intricate examination, wherein its evaluative 
      acumen was juxtaposed against human-authored reviews of a singular published 
      article. As this is a feasibility study, one article was reviewed, which was a 
      case report on scurvy. The entire article was used as an input into ChatGPT and 
      commanded it to "Please perform a review of the following article and give points 
      for revision." Since this was a case report with a limited word count the entire 
      article could fit in one chat box. The output by ChatGPT was then compared with 
      the comments by human reviewers. Key performance metrics, including precision and 
      overall agreement, were judiciously and subjectively measured to portray the 
      efficacy of ChatGPT as an AI reviewer in comparison to its human counterparts. 
      The outcomes of this rigorous analysis unveiled compelling evidence regarding 
      ChatGPT's performance as an AI reviewer. We demonstrated that ChatGPT's critical 
      analyses aligned with those of human reviewers, as evidenced by the inter-rater 
      agreement. Notably, ChatGPT exhibited commendable capability in identifying 
      methodological flaws, articulating insightful feedback on theoretical frameworks, 
      and gauging the overall contribution of the articles to their respective fields. 
      While the integration of ChatGPT showcased immense promise, certain challenges 
      and caveats surfaced. For example, ambiguities might present with complex 
      research articles, leading to nuanced discrepancies between AI and human reviews. 
      Also figures and images cannot be reviewed by ChatGPT. Lengthy articles need to 
      be reviewed in parts by ChatGPT as the entire article will not fit in one 
      chat/response. The benefits consist of reduction in time needed by journals to 
      review the articles submitted to them, as well as an AI assistant to give a 
      different perspective about the research papers other than the human reviewers. 
      In conclusion, this research contributes a groundbreaking foundation for 
      incorporating ChatGPT into the pantheon of journal reviewers. The delineated 
      guidelines distill key insights into operationalizing ChatGPT as a proficient 
      reviewer within academic journal frameworks, paving the way for a more efficient 
      and insightful review process.
CI  - Copyright ©2023, Yale Journal of Biology and Medicine.
FAU - Biswas, Som
AU  - Biswas S
AD  - Le Bonheur Children's Hospital, The University of Tennessee Health Science 
      Center, Memphis, TN, USA.
FAU - Dobaria, Dushyant
AU  - Dobaria D
AD  - Le Bonheur Children's Hospital, The University of Tennessee Health Science 
      Center, Memphis, TN, USA.
FAU - Cohen, Harris L
AU  - Cohen HL
AD  - Le Bonheur Children's Hospital, The University of Tennessee Health Science 
      Center, Memphis, TN, USA.
LA  - eng
PT  - Case Reports
PT  - Journal Article
PT  - Review
DEP - 20230929
PL  - United States
TA  - Yale J Biol Med
JT  - The Yale journal of biology and medicine
JID - 0417414
SB  - IM
MH  - Humans
MH  - Feasibility Studies
MH  - *Artificial Intelligence
PMC - PMC10524821
OTO - NOTNLM
OT  - chatGPT
OT  - journal review
OT  - review
EDAT- 2023/10/02 06:42
MHDA- 2023/10/31 06:42
CRDT- 2023/10/02 04:37
PHST- 2023/10/31 06:42 [medline]
PHST- 2023/10/02 06:42 [pubmed]
PHST- 2023/10/02 04:37 [entrez]
AID - yjbm963415 [pii]
AID - 10.59249/SKDH9286 [doi]
PST - epublish
SO  - Yale J Biol Med. 2023 Sep 29;96(3):415-420. doi: 10.59249/SKDH9286. eCollection 
      2023 Sep.

PMID- 37284995
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 11
DP  - 2023 Nov
TI  - Passive Contribution of ChatGPT to Scientific Papers.
PG  - 2340-2350
LID - 10.1007/s10439-023-03260-8 [doi]
AB  - Arguably ChatGPT jeopardizes the integrity and validity of the academic 
      publications instead of ethically facilitating them. ChatGPT can apparently 
      fulfill a portion of one of the four authorship criteria set by the International 
      Committee of Medical Journal Editors (ICMJE), i.e., "drafting." However, the 
      authorship criteria by ICMJE must all be collectively met, not singly or 
      partially. Many published manuscripts or preprints have credited ChatGPT by 
      including it in the author byline, and the academic publishing enterprise seems 
      to be unguided on how to handle such manuscripts. Interestingly, PLoS Digital 
      Health removed ChatGPT off a paper which had ChatGPT listed initially in the 
      author byline of the preprint version. Revised publishing policies are, thus, 
      promptly required to guide a consistent stance regarding ChatGPT or similar 
      artificial content generators. Publishing policies must accord among publishers, 
      preprint servers ( https://asapbio.org/preprint-servers ), universities, and 
      research institutions worldwide and across different disciplines. Ideally, 
      considering any declaration of the contribution of ChatGPT to writing any 
      scientific article should be recognized as publishing misconduct immediately and 
      be retracted. Meanwhile, all parties involved in the scientific reporting and 
      publishing must be educated about how ChatGPT fails to meet the essential 
      authorship criteria, so that no author must submit a manuscript with ChatGPT 
      contributing as a "co-author." Meanwhile, using ChatGPT for writing laboratory 
      reports or short summaries of experiments may be acceptable, but not for academic 
      publishing or formal scientific reporting.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Rahimi, Farid
AU  - Rahimi F
AUID- ORCID: 0000-0002-0920-8188
AD  - Research School of Biology, The Australian National University, Ngunnawal and 
      Ngambri Country, Canberra, ACT, Australia. farid.rahimi@anu.edu.au.
FAU - Talebi Bezmin Abadi, Amin
AU  - Talebi Bezmin Abadi A
AD  - Department of Bacteriology, Faculty of Medical Sciences, Tarbiat Modares 
      University, Tehran, Iran.
LA  - eng
PT  - Letter
DEP - 20230607
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Authorship
OT  - ChatGPT
OT  - International Committee of Medical Journal Editors
OT  - Scientific misconduct
EDAT- 2023/06/07 13:10
MHDA- 2023/06/07 13:10
CRDT- 2023/06/07 11:11
PHST- 2023/05/25 00:00 [received]
PHST- 2023/05/26 00:00 [accepted]
PHST- 2023/06/07 13:10 [pubmed]
PHST- 2023/06/07 13:10 [medline]
PHST- 2023/06/07 11:11 [entrez]
AID - 10.1007/s10439-023-03260-8 [pii]
AID - 10.1007/s10439-023-03260-8 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Nov;51(11):2340-2350. doi: 10.1007/s10439-023-03260-8. Epub 
      2023 Jun 7.

PMID- 37901112
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2297-1769 (Print)
IS  - 2297-1769 (Electronic)
IS  - 2297-1769 (Linking)
VI  - 10
DP  - 2023
TI  - Can ChatGPT diagnose my collapsing dog?
PG  - 1245168
LID - 10.3389/fvets.2023.1245168 [doi]
LID - 1245168
FAU - Abani, Samira
AU  - Abani S
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
AD  - Centre for Systems Neuroscience, University of Veterinary Medicine Hannover, 
      Hannover, Germany.
FAU - De Decker, Steven
AU  - De Decker S
AD  - Department of Veterinary Clinical Science and Services, Royal Veterinary College, 
      University of London, London, United Kingdom.
FAU - Tipold, Andrea
AU  - Tipold A
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
AD  - Centre for Systems Neuroscience, University of Veterinary Medicine Hannover, 
      Hannover, Germany.
FAU - Nessler, Jasmin Nicole
AU  - Nessler JN
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
FAU - Volk, Holger Andreas
AU  - Volk HA
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
AD  - Centre for Systems Neuroscience, University of Veterinary Medicine Hannover, 
      Hannover, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231010
PL  - Switzerland
TA  - Front Vet Sci
JT  - Frontiers in veterinary science
JID - 101666658
PMC - PMC10600474
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-3.5
OT  - artificial intelligence
OT  - diagnosis
OT  - generative AI
OT  - language model
OT  - machine learning
OT  - natural language processing
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/10/30 06:46
MHDA- 2023/10/30 06:47
CRDT- 2023/10/30 04:44
PHST- 2023/06/23 00:00 [received]
PHST- 2023/09/19 00:00 [accepted]
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/10/30 06:46 [pubmed]
PHST- 2023/10/30 04:44 [entrez]
AID - 10.3389/fvets.2023.1245168 [doi]
PST - epublish
SO  - Front Vet Sci. 2023 Oct 10;10:1245168. doi: 10.3389/fvets.2023.1245168. 
      eCollection 2023.

PMID- 37394309
OWN - NLM
STAT- Publisher
LR  - 20230702
IS  - 1878-4372 (Electronic)
IS  - 1360-1385 (Linking)
DP  - 2023 Jun 30
TI  - One hundred important questions facing plant science derived using a large 
      language model.
LID - S1360-1385(23)00199-1 [pii]
LID - 10.1016/j.tplants.2023.06.008 [doi]
AB  - Artificial intelligence (AI) is advancing rapidly and continually evolving in 
      various fields. Recently, the release of ChatGPT has sparked significant public 
      interest. In this study, we revisit the '100 Important Questions Facing Plant 
      Science' by leveraging ChatGPT as a valuable tool for generating 
      thought-provoking questions relevant to plant science. These questions primarily 
      revolve around the utilization of plants in product development, understanding 
      plant mechanisms, plant-environment interactions, and enhancing plant traits, 
      with an emphasis on sustainable product development. While ChatGPT may not 
      capture certain crucial aspects highlighted by scientists, it offers valuable 
      insights into the questions generated by experts. Our analysis demonstrates that 
      ChatGPT can be cautiously employed as a supportive tool to facilitate, 
      streamline, and expedite specific tasks in plant science.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Agathokleous, Evgenios
AU  - Agathokleous E
AD  - Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological 
      Administration (ECSS-CMA), School of Applied Meteorology, Nanjing University of 
      Information Science and Technology, Nanjing, 210044, China; Collaborative 
      Innovation Center on Forecast and Evaluation of Meteorological Disasters 
      (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 
      China. Electronic address: evgenios@nuist.edu.cn.
FAU - Rillig, Matthias C
AU  - Rillig MC
AD  - Freie Universität Berlin, Institut für Biologie, Altensteinstr. 6, D-14195, 
      Berlin, Germany; Berlin-Brandenburg Institute of Advanced Biodiversity Research 
      (BBIB), D-14195, Berlin, Germany.
FAU - Peñuelas, Josep
AU  - Peñuelas J
AD  - CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia 08193, Spain; 
      CREAF, Cerdanyola del Vallès, Catalonia 08193, Spain.
FAU - Yu, Zhen
AU  - Yu Z
AD  - Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological 
      Administration (ECSS-CMA), School of Applied Meteorology, Nanjing University of 
      Information Science and Technology, Nanjing, 210044, China; Collaborative 
      Innovation Center on Forecast and Evaluation of Meteorological Disasters 
      (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 
      China. Electronic address: zyu@nuist.edu.cn.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230630
PL  - England
TA  - Trends Plant Sci
JT  - Trends in plant science
JID - 9890299
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Generative Pre-trained Transformer
OT  - artificial intelligence
OT  - biology
OT  - large language model
OT  - plant science
COIS- Declaration of interests No interests are declared.
EDAT- 2023/07/03 00:41
MHDA- 2023/07/03 00:41
CRDT- 2023/07/02 21:59
PHST- 2023/05/17 00:00 [received]
PHST- 2023/06/10 00:00 [revised]
PHST- 2023/06/12 00:00 [accepted]
PHST- 2023/07/03 00:41 [medline]
PHST- 2023/07/03 00:41 [pubmed]
PHST- 2023/07/02 21:59 [entrez]
AID - S1360-1385(23)00199-1 [pii]
AID - 10.1016/j.tplants.2023.06.008 [doi]
PST - aheadofprint
SO  - Trends Plant Sci. 2023 Jun 30:S1360-1385(23)00199-1. doi: 
      10.1016/j.tplants.2023.06.008.

PMID- 37991290
OWN - NLM
STAT- MEDLINE
DCOM- 20231123
LR  - 20231123
IS  - 1998-3689 (Electronic)
IS  - 0301-4738 (Linking)
VI  - 71
IP  - 12
DP  - 2023 Dec 1
TI  - ChatGPT in academic writing: Maximizing its benefits and minimizing the risks.
PG  - 3600-3606
LID - 10.4103/IJO.IJO_718_23 [doi]
AB  - This review article explores the use of ChatGPT in academic writing and provides 
      insights on how to utilize it judiciously. With the increasing popularity of 
      AI-powered language models, ChatGPT has emerged as a potential tool for assisting 
      writers in the research and writing process. We have provided a list of potential 
      uses of ChatGPT by a novice researcher for getting help during research proposal 
      preparation and manuscript writing. However, there are concerns regarding its 
      reliability and potential risks associated with its use. The review highlights 
      the importance of maintaining human judgment in the writing process and using 
      ChatGPT as a complementary tool rather than a replacement for human effort. The 
      article concludes with recommendations for researchers and writers to ensure 
      responsible and effective use of ChatGPT in academic writing.
CI  - Copyright © 2023 Copyright: © 2023 Indian Journal of Ophthalmology.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Department of Physiology, All India Institute of Medical Sciences, Deoghar, 
      Jharkhand, India.
FAU - Mondal, Shaikat
AU  - Mondal S
AD  - Department of Physiology, Raiganj Government Medical College and Hospital, West 
      Bengal, India.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231120
PL  - India
TA  - Indian J Ophthalmol
JT  - Indian journal of ophthalmology
JID - 0405376
SB  - IM
MH  - Humans
MH  - Reproducibility of Results
MH  - *Language
EDAT- 2023/11/22 12:43
MHDA- 2023/11/23 06:42
CRDT- 2023/11/22 08:33
PHST- 2023/03/14 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/11/23 06:42 [medline]
PHST- 2023/11/22 12:43 [pubmed]
PHST- 2023/11/22 08:33 [entrez]
AID - 02223307-202371120-00006 [pii]
AID - 10.4103/IJO.IJO_718_23 [doi]
PST - ppublish
SO  - Indian J Ophthalmol. 2023 Dec 1;71(12):3600-3606. doi: 10.4103/IJO.IJO_718_23. 
      Epub 2023 Nov 20.

PMID- 37988149
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231208
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Nov 21
TI  - The Intersection of ChatGPT, Clinical Medicine, and Medical Education.
PG  - e47274
LID - 10.2196/47274 [doi]
LID - e47274
AB  - As we progress deeper into the digital age, the robust development and 
      application of advanced artificial intelligence (AI) technology, specifically 
      generative language models like ChatGPT (OpenAI), have potential implications in 
      all sectors including medicine. This viewpoint article aims to present the 
      authors' perspective on the integration of AI models such as ChatGPT in clinical 
      medicine and medical education. The unprecedented capacity of ChatGPT to generate 
      human-like responses, refined through Reinforcement Learning with Human Feedback, 
      could significantly reshape the pedagogical methodologies within medical 
      education. Through a comprehensive review and the authors' personal experiences, 
      this viewpoint article elucidates the pros, cons, and ethical considerations of 
      using ChatGPT within clinical medicine and notably, its implications for medical 
      education. This exploration is crucial in a transformative era where AI could 
      potentially augment human capability in the process of knowledge creation and 
      dissemination, potentially revolutionizing medical education and clinical 
      practice. The importance of maintaining academic integrity and professional 
      standards is highlighted. The relevance of establishing clear guidelines for the 
      responsible and ethical use of AI technologies in clinical medicine and medical 
      education is also emphasized.
CI  - ©Rebecca Shin-Yee Wong, Long Chiau Ming, Raja Affendi Raja Ali. Originally 
      published in JMIR Medical Education (https://mededu.jmir.org), 21.11.2023.
FAU - Wong, Rebecca Shin-Yee
AU  - Wong RS
AUID- ORCID: 0000-0002-7738-9398
AD  - Department of Medical Education, School of Medical and Life Sciences, Sunway 
      University, Selangor, Malaysia.
AD  - Faculty of Medicine, Nursing and Health Sciences, SEGi University, Petaling Jaya, 
      Malaysia.
FAU - Ming, Long Chiau
AU  - Ming LC
AUID- ORCID: 0000-0002-6971-1383
AD  - School of Medical and Life Sciences, Sunway University, Selangor, Malaysia.
FAU - Raja Ali, Raja Affendi
AU  - Raja Ali RA
AUID- ORCID: 0000-0001-9431-4657
AD  - School of Medical and Life Sciences, Sunway University, Selangor, Malaysia.
AD  - GUT Research Group, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala 
      Lumpur, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20231121
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10698645
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - OpenAI
OT  - artificial intelligence
OT  - clinical research
OT  - ethical considerations
OT  - large language model
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/21 12:42
MHDA- 2023/11/21 12:43
CRDT- 2023/11/21 11:53
PHST- 2023/03/14 00:00 [received]
PHST- 2023/06/30 00:00 [accepted]
PHST- 2023/06/16 00:00 [revised]
PHST- 2023/11/21 12:43 [medline]
PHST- 2023/11/21 12:42 [pubmed]
PHST- 2023/11/21 11:53 [entrez]
AID - v9i1e47274 [pii]
AID - 10.2196/47274 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Nov 21;9:e47274. doi: 10.2196/47274.

PMID- 37755165
OWN - NLM
STAT- MEDLINE
DCOM- 20230928
LR  - 20231003
IS  - 2076-3271 (Electronic)
IS  - 2076-3271 (Linking)
VI  - 11
IP  - 3
DP  - 2023 Sep 17
TI  - A Bibliometric Analysis of the Rise of ChatGPT in Medical Research.
LID - 10.3390/medsci11030061 [doi]
LID - 61
AB  - The rapid emergence of publicly accessible artificial intelligence platforms such 
      as large language models (LLMs) has led to an equally rapid increase in articles 
      exploring their potential benefits and risks. We performed a bibliometric 
      analysis of ChatGPT literature in medicine and science to better understand 
      publication trends and knowledge gaps. Following title, abstract, and keyword 
      searches of PubMed, Embase, Scopus, and Web of Science databases for ChatGPT 
      articles published in the medical field, articles were screened for inclusion and 
      exclusion criteria. Data were extracted from included articles, with citation 
      counts obtained from PubMed and journal metrics obtained from Clarivate Journal 
      Citation Reports. After screening, 267 articles were included in the study, most 
      of which were editorials or correspondence with an average of 7.5 +/- 18.4 
      citations per publication. Published articles on ChatGPT were authored largely in 
      the United States, India, and China. The topics discussed included use and 
      accuracy of ChatGPT in research, medical education, and patient counseling. Among 
      non-surgical specialties, radiology published the most ChatGPT-related articles, 
      while plastic surgery published the most articles among surgical specialties. The 
      average citation number among the top 20 most-cited articles was 60.1 +/- 35.3. 
      Among journals with the most ChatGPT-related publications, there were on average 
      10 +/- 3.7 publications. Our results suggest that managing the inevitable ethical 
      and safety issues that arise with the implementation of LLMs will require further 
      research exploring the capabilities and accuracy of ChatGPT, to generate policies 
      guiding the adoption of artificial intelligence in medicine and science.
FAU - Barrington, Nikki M
AU  - Barrington NM
AD  - Chicago Medical School, Rosalind Franklin University, North Chicago, IL 60064, 
      USA.
FAU - Gupta, Nithin
AU  - Gupta N
AUID- ORCID: 0000-0002-6696-8984
AD  - School of Osteopathic Medicine, Campbell University, Lillington, NC 27546, USA.
FAU - Musmar, Basel
AU  - Musmar B
AD  - Faculty of Medicine and Health Sciences, An-Najah National University, Nablus 
      P.O. Box 7, West Bank, Palestine.
FAU - Doyle, David
AU  - Doyle D
AD  - Central Michigan College of Medicine, Mount Pleasant, MI 48858, USA.
FAU - Panico, Nicholas
AU  - Panico N
AD  - Lake Erie College of Osteopathic Medicine, Erie, PA 16509, USA.
FAU - Godbole, Nikhil
AU  - Godbole N
AUID- ORCID: 0009-0009-9417-2236
AD  - School of Medicine, Tulane University, New Orleans, LA 70112, USA.
FAU - Reardon, Taylor
AU  - Reardon T
AD  - Department of Neurology, Henry Ford Hospital, Detroit, MI 48202, USA.
FAU - D'Amico, Randy S
AU  - D'Amico RS
AD  - Department of Neurosurgery, Lenox Hill Hospital, New York, NY 10075, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230917
PL  - Switzerland
TA  - Med Sci (Basel)
JT  - Medical sciences (Basel, Switzerland)
JID - 101629322
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - Bibliometrics
MH  - *Biomedical Research
MH  - Benchmarking
MH  - *Radiology
PMC - PMC10535733
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - large language models
OT  - medicine
OT  - research
COIS- The authors declare no conflict of interest.
EDAT- 2023/09/27 12:42
MHDA- 2023/09/28 06:42
CRDT- 2023/09/27 09:53
PHST- 2023/08/14 00:00 [received]
PHST- 2023/09/04 00:00 [revised]
PHST- 2023/09/11 00:00 [accepted]
PHST- 2023/09/28 06:42 [medline]
PHST- 2023/09/27 12:42 [pubmed]
PHST- 2023/09/27 09:53 [entrez]
AID - medsci11030061 [pii]
AID - medsci-11-00061 [pii]
AID - 10.3390/medsci11030061 [doi]
PST - epublish
SO  - Med Sci (Basel). 2023 Sep 17;11(3):61. doi: 10.3390/medsci11030061.

PMID- 37915603
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231103
IS  - 2589-0042 (Electronic)
IS  - 2589-0042 (Linking)
VI  - 26
IP  - 11
DP  - 2023 Nov 17
TI  - Popular large language model chatbots' accuracy, comprehensiveness, and 
      self-awareness in answering ocular symptom queries.
PG  - 108163
LID - 10.1016/j.isci.2023.108163 [doi]
LID - 108163
AB  - In light of growing interest in using emerging large language models (LLMs) for 
      self-diagnosis, we systematically assessed the performance of ChatGPT-3.5, 
      ChatGPT-4.0, and Google Bard in delivering proficient responses to 37 common 
      inquiries regarding ocular symptoms. Responses were masked, randomly shuffled, 
      and then graded by three consultant-level ophthalmologists for accuracy (poor, 
      borderline, good) and comprehensiveness. Additionally, we evaluated the 
      self-awareness capabilities (ability to self-check and self-correct) of the 
      LLM-Chatbots. 89.2% of ChatGPT-4.0 responses were 'good'-rated, outperforming 
      ChatGPT-3.5 (59.5%) and Google Bard (40.5%) significantly (all p < 0.001). All 
      three LLM-Chatbots showed optimal mean comprehensiveness scores as well (ranging 
      from 4.6 to 4.7 out of 5). However, they exhibited subpar to moderate 
      self-awareness capabilities. Our study underscores the potential of ChatGPT-4.0 
      in delivering accurate and comprehensive responses to ocular symptom inquiries. 
      Future rigorous validation of their performance is crucial to ensure their 
      reliability and appropriateness for actual clinical use.
CI  - © 2023 The Authors.
FAU - Pushpanathan, Krithi
AU  - Pushpanathan K
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
FAU - Lim, Zhi Wei
AU  - Lim ZW
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
FAU - Er Yew, Samantha Min
AU  - Er Yew SM
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
FAU - Chen, David Ziyou
AU  - Chen DZ
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Department of Ophthalmology, National University Hospital, Singapore, Singapore.
FAU - Hui'En Lin, Hazel Anne
AU  - Hui'En Lin HA
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Department of Ophthalmology, National University Hospital, Singapore, Singapore.
FAU - Lin Goh, Jocelyn Hui
AU  - Lin Goh JH
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 
      Singapore.
FAU - Wong, Wendy Meihua
AU  - Wong WM
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Department of Ophthalmology, National University Hospital, Singapore, Singapore.
FAU - Wang, Xiaofei
AU  - Wang X
AD  - Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, 
      Beijing, China.
AD  - Advanced Innovation Centre for Biomedical Engineering, School of Biological 
      Science and Medical Engineering, Beihang University, Beijing, China.
FAU - Jin Tan, Marcus Chun
AU  - Jin Tan MC
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Department of Ophthalmology, National University Hospital, Singapore, Singapore.
FAU - Chang Koh, Victor Teck
AU  - Chang Koh VT
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Department of Ophthalmology, National University Hospital, Singapore, Singapore.
FAU - Tham, Yih-Chung
AU  - Tham YC
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Centre for Innovation and Precision Eye Health & Department of Ophthalmology, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 
      Singapore.
AD  - Ophthalmology and Visual Sciences Academic Clinical Programme (Eye ACP), Duke NUS 
      Medical School, Singapore, Singapore.
LA  - eng
PT  - Journal Article
DEP - 20231010
PL  - United States
TA  - iScience
JT  - iScience
JID - 101724038
PMC - PMC10616302
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Ophthalmology
COIS- All authors declare no competing interests.
EDAT- 2023/11/02 06:43
MHDA- 2023/11/02 06:44
CRDT- 2023/11/02 04:03
PHST- 2023/08/07 00:00 [received]
PHST- 2023/09/19 00:00 [revised]
PHST- 2023/10/05 00:00 [accepted]
PHST- 2023/11/02 06:44 [medline]
PHST- 2023/11/02 06:43 [pubmed]
PHST- 2023/11/02 04:03 [entrez]
AID - S2589-0042(23)02240-X [pii]
AID - 108163 [pii]
AID - 10.1016/j.isci.2023.108163 [doi]
PST - epublish
SO  - iScience. 2023 Oct 10;26(11):108163. doi: 10.1016/j.isci.2023.108163. eCollection 
      2023 Nov 17.

PMID- 37296802
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230612
IS  - 2075-4418 (Print)
IS  - 2075-4418 (Electronic)
IS  - 2075-4418 (Linking)
VI  - 13
IP  - 11
DP  - 2023 Jun 2
TI  - Evaluating the Utility of a Large Language Model in Answering Common Patients' 
      Gastrointestinal Health-Related Questions: Are We There Yet?
LID - 10.3390/diagnostics13111950 [doi]
LID - 1950
AB  - BACKGROUND AND AIMS: Patients frequently have concerns about their disease and 
      find it challenging to obtain accurate Information. OpenAI's ChatGPT chatbot 
      (ChatGPT) is a new large language model developed to provide answers to a wide 
      range of questions in various fields. Our aim is to evaluate the performance of 
      ChatGPT in answering patients' questions regarding gastrointestinal health. 
      METHODS: To evaluate the performance of ChatGPT in answering patients' questions, 
      we used a representative sample of 110 real-life questions. The answers provided 
      by ChatGPT were rated in consensus by three experienced gastroenterologists. The 
      accuracy, clarity, and efficacy of the answers provided by ChatGPT were assessed. 
      RESULTS: ChatGPT was able to provide accurate and clear answers to patients' 
      questions in some cases, but not in others. For questions about treatments, the 
      average accuracy, clarity, and efficacy scores (1 to 5) were 3.9 ± 0.8, 3.9 ± 
      0.9, and 3.3 ± 0.9, respectively. For symptoms questions, the average accuracy, 
      clarity, and efficacy scores were 3.4 ± 0.8, 3.7 ± 0.7, and 3.2 ± 0.7, 
      respectively. For diagnostic test questions, the average accuracy, clarity, and 
      efficacy scores were 3.7 ± 1.7, 3.7 ± 1.8, and 3.5 ± 1.7, respectively. 
      CONCLUSIONS: While ChatGPT has potential as a source of information, further 
      development is needed. The quality of information is contingent upon the quality 
      of the online information provided. These findings may be useful for healthcare 
      providers and patients alike in understanding the capabilities and limitations of 
      ChatGPT.
FAU - Lahat, Adi
AU  - Lahat A
AUID- ORCID: 0000-0003-1513-7280
AD  - Chaim Sheba Medical Center, Department of Gastroenterology, Affiliated to Tel 
      Aviv University, Tel Aviv 69978, Israel.
FAU - Shachar, Eyal
AU  - Shachar E
AD  - Chaim Sheba Medical Center, Department of Gastroenterology, Affiliated to Tel 
      Aviv University, Tel Aviv 69978, Israel.
FAU - Avidan, Benjamin
AU  - Avidan B
AD  - Chaim Sheba Medical Center, Department of Gastroenterology, Affiliated to Tel 
      Aviv University, Tel Aviv 69978, Israel.
FAU - Glicksberg, Benjamin
AU  - Glicksberg B
AUID- ORCID: 0000-0003-4515-8090
AD  - Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA.
FAU - Klang, Eyal
AU  - Klang E
AD  - The Sami Sagol AI Hub, ARC Innovation Center, Chaim Sheba Medical Center, 
      Affiliated to Tel-Aviv University, Tel Aviv 69978, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230602
PL  - Switzerland
TA  - Diagnostics (Basel)
JT  - Diagnostics (Basel, Switzerland)
JID - 101658402
PMC - PMC10252924
OTO - NOTNLM
OT  - OpenAI’s ChatGPT
OT  - chatbot
OT  - gastroenterology
OT  - medical information
OT  - natural language processing (NLP)
OT  - patients’ questions
COIS- The authors declare no conflict of interest.
EDAT- 2023/06/10 15:13
MHDA- 2023/06/10 15:14
CRDT- 2023/06/10 01:02
PHST- 2023/03/27 00:00 [received]
PHST- 2023/05/28 00:00 [revised]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/06/10 15:14 [medline]
PHST- 2023/06/10 15:13 [pubmed]
PHST- 2023/06/10 01:02 [entrez]
AID - diagnostics13111950 [pii]
AID - diagnostics-13-01950 [pii]
AID - 10.3390/diagnostics13111950 [doi]
PST - epublish
SO  - Diagnostics (Basel). 2023 Jun 2;13(11):1950. doi: 10.3390/diagnostics13111950.

PMID- 37658808
OWN - NLM
STAT- Publisher
LR  - 20230902
IS  - 1943-7722 (Electronic)
IS  - 0002-9173 (Linking)
DP  - 2023 Sep 2
TI  - Assessment of a large language model's utility in helping pathology professionals 
      answer general knowledge pathology questions.
LID - aqad106 [pii]
LID - 10.1093/ajcp/aqad106 [doi]
AB  - OBJECTIVES: To assess the utility and performance of the large language model 
      ChatGPT 4.0 regarding accuracy, completeness, and its potential as a time-saving 
      tool for pathologists and laboratory directors. METHODS: A deidentified database 
      of questions previously sent to pathology residents from health care providers 
      was used as a source of general knowledge-type pathology questions. These 
      questions were submitted to the large language model and the responses graded by 
      subject matter experts in different pathology subspecialties. The grading 
      criteria assessed accuracy, completeness, and the potential time savings for 
      helping the pathologist craft the response. RESULTS: Overall, respondents thought 
      that most of the answers would take less than 5 minutes of additional work to be 
      used (85%). Accuracy and completeness for the 61 questions was high, with 98% of 
      responses being at least "completely or mostly accurate" and 82% of responses 
      "containing all relevant information." Of the respondents, 97% stated that the 
      response would have "zero or near-zero potential for medical harm," and all 
      thought the tool had potential to save time in constructing answers to health 
      care providers' queries. Performance was similar in both Anatomic Pathology (AP) 
      and Clinical Pathology (CP), with the only exception being some relevant 
      information was excluded in 46% of AP answers vs only 10% in CP (P < .01). 
      CONCLUSIONS: ChatGPT version 4.0 gave responses that were predominantly accurate 
      and complete for general knowledge-type pathology questions. With further 
      research and when reviewed by a pathologist or laboratorian, this could 
      facilitate its use as a pathologist's aid in answering questions from health care 
      providers.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of American 
      Society for Clinical Pathology. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Benirschke, Robert C
AU  - Benirschke RC
AD  - Department of Pathology and Laboratory Medicine, NorthShore University 
      HealthSystem, Evanston, IL, US.
AD  - Department of Pathology, University of Chicago Pritzker School of Medicine, 
      Chicago, IL, US.
FAU - Wodskow, Joshua
AU  - Wodskow J
AUID- ORCID: 0000-0002-9699-1384
AD  - Department of Pathology and Laboratory Medicine, NorthShore University 
      HealthSystem, Evanston, IL, US.
AD  - Department of Pathology, University of Chicago Pritzker School of Medicine, 
      Chicago, IL, US.
FAU - Prasai, Kritika
AU  - Prasai K
AD  - Department of Pathology and Laboratory Medicine, NorthShore University 
      HealthSystem, Evanston, IL, US.
AD  - Department of Pathology, University of Chicago Pritzker School of Medicine, 
      Chicago, IL, US.
FAU - Freeman, Andrew
AU  - Freeman A
AD  - Department of Pathology and Laboratory Medicine, NorthShore University 
      HealthSystem, Evanston, IL, US.
AD  - Department of Pathology, University of Chicago Pritzker School of Medicine, 
      Chicago, IL, US.
FAU - Lee, John M
AU  - Lee JM
AD  - Department of Pathology and Laboratory Medicine, NorthShore University 
      HealthSystem, Evanston, IL, US.
AD  - Department of Pathology, University of Chicago Pritzker School of Medicine, 
      Chicago, IL, US.
FAU - Groth, John
AU  - Groth J
AD  - Department of Pathology and Laboratory Medicine, NorthShore University 
      HealthSystem, Evanston, IL, US.
AD  - Department of Pathology, University of Chicago Pritzker School of Medicine, 
      Chicago, IL, US.
LA  - eng
PT  - Journal Article
DEP - 20230902
PL  - England
TA  - Am J Clin Pathol
JT  - American journal of clinical pathology
JID - 0370470
SB  - IM
EDAT- 2023/09/04 01:22
MHDA- 2023/09/04 01:22
CRDT- 2023/09/02 08:53
PHST- 2023/05/22 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/09/04 01:22 [medline]
PHST- 2023/09/04 01:22 [pubmed]
PHST- 2023/09/02 08:53 [entrez]
AID - 7258968 [pii]
AID - 10.1093/ajcp/aqad106 [doi]
PST - aheadofprint
SO  - Am J Clin Pathol. 2023 Sep 2:aqad106. doi: 10.1093/ajcp/aqad106.

PMID- 37120563
OWN - NLM
STAT- MEDLINE
DCOM- 20230502
LR  - 20230503
IS  - 2046-4053 (Electronic)
IS  - 2046-4053 (Linking)
VI  - 12
IP  - 1
DP  - 2023 Apr 29
TI  - Are ChatGPT and large language models "the answer" to bringing us closer to 
      systematic review automation?
PG  - 72
LID - 10.1186/s13643-023-02243-z [doi]
LID - 72
AB  - In this commentary, we discuss ChatGPT and our perspectives on its utility to 
      systematic reviews (SRs) through the appropriateness and applicability of its 
      responses to SR related prompts. The advancement of artificial intelligence 
      (AI)-assisted technologies leave many wondering about the current capabilities, 
      limitations, and opportunities for integration AI into scientific endeavors. 
      Large language models (LLM)-such as ChatGPT, designed by OpenAI-have recently 
      gained widespread attention with their ability to respond to various prompts in a 
      natural-sounding way. Systematic reviews (SRs) utilize secondary data and often 
      require many months and substantial financial resources to complete, making them 
      attractive grounds for developing AI-assistive technologies. On February 6, 2023, 
      PICO Portal developers hosted a webinar to explore ChatGPT's responses to tasks 
      related to SR methodology. Our experience from exploring the responses of ChatGPT 
      suggest that while ChatGPT and LLMs show some promise for aiding in SR-related 
      tasks, the technology is in its infancy and needs much development for such 
      applications. Furthermore, we advise that great caution should be taken by 
      non-content experts in using these tools due to much of the output appearing, at 
      a high level, to be valid, while much is erroneous and in need of active vetting.
CI  - © 2023. The Author(s).
FAU - Qureshi, Riaz
AU  - Qureshi R
AUID- ORCID: 0000-0002-6670-4874
AD  - University of Colorado Anschutz Medical Campus, Aurora, CO, USA. 
      riaz.qureshi@cuanschutz.edu.
AD  - PICO Portal, New York, NY, USA. riaz.qureshi@cuanschutz.edu.
FAU - Shaughnessy, Daniel
AU  - Shaughnessy D
AD  - University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
FAU - Gill, Kayden A R
AU  - Gill KAR
AD  - PICO Portal, New York, NY, USA.
AD  - University of Pittsburgh, Pittsburgh, PA, USA.
FAU - Robinson, Karen A
AU  - Robinson KA
AD  - PICO Portal, New York, NY, USA.
AD  - Johns Hopkins University, Baltimore, MD, USA.
FAU - Li, Tianjing
AU  - Li T
AD  - University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
AD  - PICO Portal, New York, NY, USA.
FAU - Agai, Eitan
AU  - Agai E
AD  - PICO Portal, New York, NY, USA.
LA  - eng
PT  - Journal Article
DEP - 20230429
PL  - England
TA  - Syst Rev
JT  - Systematic reviews
JID - 101580575
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Automation
MH  - Language
MH  - *Self-Help Devices
MH  - Technology
MH  - Systematic Reviews as Topic
PMC - PMC10148473
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Large language models
OT  - Methodology
OT  - Systematic review
COIS- RQ, KG, TL, and KR provide consultative services to PICO Portal. EA is the 
      founder and owner of PICO Portal. PICO Portal itself has no affiliation with 
      ChatGPT. DS has no relevant interests.
EDAT- 2023/04/30 00:42
MHDA- 2023/05/01 12:43
CRDT- 2023/04/29 23:17
PHST- 2023/03/21 00:00 [received]
PHST- 2023/04/20 00:00 [accepted]
PHST- 2023/05/01 12:43 [medline]
PHST- 2023/04/30 00:42 [pubmed]
PHST- 2023/04/29 23:17 [entrez]
AID - 10.1186/s13643-023-02243-z [pii]
AID - 2243 [pii]
AID - 10.1186/s13643-023-02243-z [doi]
PST - epublish
SO  - Syst Rev. 2023 Apr 29;12(1):72. doi: 10.1186/s13643-023-02243-z.

PMID- 37492832
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230928
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI 
      (LLaMA) Using Medical Domain Knowledge.
PG  - e40895
LID - 10.7759/cureus.40895 [doi]
LID - e40895
AB  - Objective The primary aim of this research was to address the limitations 
      observed in the medical knowledge of prevalent large language models (LLMs) such 
      as ChatGPT, by creating a specialized language model with enhanced accuracy in 
      medical advice. Methods We achieved this by adapting and refining the large 
      language model meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor 
      dialogues sourced from a widely used online medical consultation platform. These 
      conversations were cleaned and anonymized to respect privacy concerns. In 
      addition to the model refinement, we incorporated a self-directed information 
      retrieval mechanism, allowing the model to access and utilize real-time 
      information from online sources like Wikipedia and data from curated offline 
      medical databases. Results The fine-tuning of the model with real-world 
      patient-doctor interactions significantly improved the model's ability to 
      understand patient needs and provide informed advice. By equipping the model with 
      self-directed information retrieval from reliable online and offline sources, we 
      observed substantial improvements in the accuracy of its responses. Conclusion 
      Our proposed ChatDoctor, represents a significant advancement in medical LLMs, 
      demonstrating a significant improvement in understanding patient inquiries and 
      providing accurate advice. Given the high stakes and low error tolerance in the 
      medical field, such enhancements in providing accurate and reliable information 
      are not only beneficial but essential.
CI  - Copyright © 2023, Li et al.
FAU - Li, Yunxiang
AU  - Li Y
AD  - Department of Radiation Oncology, University of Texas Southwestern Medical 
      Center, Dallas, USA.
FAU - Li, Zihan
AU  - Li Z
AD  - Department of Computer Science, University of Illinois at Urbana-Champaign, 
      Illinois, USA.
FAU - Zhang, Kai
AU  - Zhang K
AD  - Department of Computer Science and Engineering, The Ohio State University, 
      Columbus, USA.
FAU - Dan, Ruilong
AU  - Dan R
AD  - College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 
      CHN.
FAU - Jiang, Steve
AU  - Jiang S
AD  - Department of Radiation Oncology, University of Texas Southwestern Medical 
      Center, Dallas, USA.
FAU - Zhang, You
AU  - Zhang Y
AD  - Department of Radiation Oncology, University of Texas Southwestern Medical 
      Center, Dallas, USA.
LA  - eng
GR  - R01 CA240808/CA/NCI NIH HHS/United States
GR  - R01 CA258987/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20230624
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10364849
OTO - NOTNLM
OT  - ai chatbot
OT  - chat gpt
OT  - gpt
OT  - large language model
OT  - llama
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/26 06:43
MHDA- 2023/07/26 06:44
CRDT- 2023/07/26 03:58
PHST- 2023/06/24 00:00 [accepted]
PHST- 2023/07/26 06:44 [medline]
PHST- 2023/07/26 06:43 [pubmed]
PHST- 2023/07/26 03:58 [entrez]
AID - 10.7759/cureus.40895 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 24;15(6):e40895. doi: 10.7759/cureus.40895. eCollection 2023 
      Jun.

PMID- 37682111
OWN - NLM
STAT- Publisher
LR  - 20231012
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 39
IP  - 9
DP  - 2023 Sep 2
TI  - An extensive benchmark study on biomedical text generation and mining with 
      ChatGPT.
LID - 10.1093/bioinformatics/btad557 [doi]
LID - btad557
AB  - MOTIVATION: In recent years, the development of natural language process (NLP) 
      technologies and deep learning hardware has led to significant improvement in 
      large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on 
      GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding 
      and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks 
      and benchmarks and got excellent results. With exciting performance on daily 
      chat, researchers began to explore the capacity of ChatGPT on expertise that 
      requires professional education for human and we are interested in the biomedical 
      domain. RESULTS: To evaluate the performance of ChatGPT on biomedical-related 
      tasks, this article presents a comprehensive benchmark study on the use of 
      ChatGPT for biomedical corpus, including article abstracts, clinical trials 
      description, biomedical questions, and so on. Typical NLP tasks like named entity 
      recognization, relation extraction, sentence similarity, question and answering, 
      and document classification are included. Overall, ChatGPT got a BLURB score of 
      58.50 while the state-of-the-art model had a score of 84.30. Through a series of 
      experiments, we demonstrated the effectiveness and versatility of ChatGPT in 
      biomedical text understanding, reasoning and generation, and the limitation of 
      ChatGPT build on GPT-3.5. AVAILABILITY AND IMPLEMENTATION: All the datasets are 
      available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The 
      prompts are described in the article.
CI  - © The Author(s) 2023. Published by Oxford University Press.
FAU - Chen, Qijie
AU  - Chen Q
AUID- ORCID: 0000-0003-0661-652X
AD  - AIDD, Mindrank AI Ltd, Zhejiang 310000, China.
FAU - Sun, Haotong
AU  - Sun H
AD  - AIDD, Mindrank AI Ltd, Zhejiang 310000, China.
FAU - Liu, Haoyang
AU  - Liu H
AD  - College of Life Sciences, Nankai University, Tianjin 300071, China.
AD  - Guangzhou Laboratory, GuangDong 510005, China.
FAU - Jiang, Yinghui
AU  - Jiang Y
AD  - AIDD, Mindrank AI Ltd, Zhejiang 310000, China.
FAU - Ran, Ting
AU  - Ran T
AD  - Guangzhou Laboratory, GuangDong 510005, China.
FAU - Jin, Xurui
AU  - Jin X
AD  - AIDD, Mindrank AI Ltd, Zhejiang 310000, China.
FAU - Xiao, Xianglu
AU  - Xiao X
AD  - AIDD, Mindrank AI Ltd, Zhejiang 310000, China.
FAU - Lin, Zhimin
AU  - Lin Z
AD  - AIDD, Mindrank AI Ltd, Zhejiang 310000, China.
FAU - Chen, Hongming
AU  - Chen H
AD  - Guangzhou Laboratory, GuangDong 510005, China.
FAU - Niu, Zhangmin
AU  - Niu Z
AUID- ORCID: 0000-0002-7009-946X
AD  - AIDD, Mindrank AI Ltd, Zhejiang 310000, China.
AD  - National Heart and Lung Institute, Imperial College London, London, United 
      Kingdom.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
PMC - PMC10562950
COIS- None declared.
EDAT- 2023/09/08 12:42
MHDA- 2023/09/08 12:42
CRDT- 2023/09/08 10:13
PHST- 2023/04/24 00:00 [received]
PHST- 2023/08/09 00:00 [revised]
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/09/08 12:42 [pubmed]
PHST- 2023/09/08 12:42 [medline]
PHST- 2023/09/08 10:13 [entrez]
AID - 7264174 [pii]
AID - btad557 [pii]
AID - 10.1093/bioinformatics/btad557 [doi]
PST - ppublish
SO  - Bioinformatics. 2023 Sep 2;39(9):btad557. doi: 10.1093/bioinformatics/btad557.

PMID- 37104260
OWN - NLM
STAT- MEDLINE
DCOM- 20230501
LR  - 20230501
IS  - 1540-9597 (Electronic)
IS  - 0276-3869 (Linking)
VI  - 42
IP  - 2
DP  - 2023 Apr-Jun
TI  - ChatGPT, an Opportunity to Understand More About Language Models.
PG  - 194-201
LID - 10.1080/02763869.2023.2194149 [doi]
AB  - ChatGPT, a leading large language model, has achieved some success beyond 
      previous language models and caught the world's attention since its release in 
      late 2022. Businesses and healthcare professional fields have raised strong 
      interests in investing in large language models to assist various kinds of 
      information searching in their domain of expertise. Under the influence of 
      ChatGPT, searched information may be received in a new personalized chat format, 
      in contrast to the traditional search engines with pages of results for users to 
      evaluate and open. Large language models and generative AI present new 
      opportunities for librarians to understand more about language models' 
      development as well as the future directions of the language models that are 
      developed behind the user interfaces. Being aware of how language models impact 
      the communication of information will enrich librarians' abilities to examine the 
      quality of AI outputs and awareness of users' rights and data curation policies, 
      to better assist patrons' research activities that involve using language models 
      in the foreseeable future.
FAU - Zhang, Borui
AU  - Zhang B
AD  - Academic Research Consulting and Services Department, Smathers Libraries, 
      University of Florida, Gainesville, USA.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Med Ref Serv Q
JT  - Medical reference services quarterly
JID - 8219208
MH  - Humans
MH  - *Search Engine
MH  - Communication
MH  - Language
MH  - *Librarians
EDAT- 2023/04/27 18:41
MHDA- 2023/05/01 06:42
CRDT- 2023/04/27 13:33
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/27 18:41 [pubmed]
PHST- 2023/04/27 13:33 [entrez]
AID - 10.1080/02763869.2023.2194149 [doi]
PST - ppublish
SO  - Med Ref Serv Q. 2023 Apr-Jun;42(2):194-201. doi: 10.1080/02763869.2023.2194149.

PMID- 37973919
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231129
IS  - 2398-6352 (Electronic)
IS  - 2398-6352 (Linking)
VI  - 6
IP  - 1
DP  - 2023 Nov 16
TI  - A study of generative large language model for medical research and healthcare.
PG  - 210
LID - 10.1038/s41746-023-00958-w [doi]
LID - 210
AB  - There are enormous enthusiasm and concerns in applying large language models 
      (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs 
      such as ChatGPT, which are not developed for medical use. This study develops a 
      generative clinical LLM, GatorTronGPT, using 277 billion words of text including 
      (1) 82 billion words of clinical text from 126 clinical departments and 
      approximately 2 million patients at the University of Florida Health and (2) 195 
      billion words of diverse general English text. We train GatorTronGPT using a 
      GPT-3 architecture with up to 20 billion parameters and evaluate its utility for 
      biomedical natural language processing (NLP) and healthcare text generation. 
      GatorTronGPT improves biomedical natural language processing. We apply 
      GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models 
      trained using synthetic text generated by GatorTronGPT outperform models trained 
      using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 
      (best) scale shows that there are no significant differences in linguistic 
      readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and 
      clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) 
      and that physicians cannot differentiate them (p < 0.001). This study provides 
      insights into the opportunities and challenges of LLMs for medical research and 
      healthcare.
CI  - © 2023. The Author(s).
FAU - Peng, Cheng
AU  - Peng C
AUID- ORCID: 0000-0002-1994-893X
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Yang, Xi
AU  - Yang X
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Cancer Informatics Shared Resource, University of Florida Health Cancer Center, 
      Gainesville, FL, USA.
FAU - Chen, Aokun
AU  - Chen A
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Cancer Informatics Shared Resource, University of Florida Health Cancer Center, 
      Gainesville, FL, USA.
FAU - Smith, Kaleb E
AU  - Smith KE
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - PourNejatian, Nima
AU  - PourNejatian N
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Costa, Anthony B
AU  - Costa AB
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Martin, Cheryl
AU  - Martin C
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Flores, Mona G
AU  - Flores MG
AUID- ORCID: 0000-0002-7362-3044
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Zhang, Ying
AU  - Zhang Y
AUID- ORCID: 0000-0003-4210-2104
AD  - Research Computing, University of Florida, Gainesville, FL, USA.
FAU - Magoc, Tanja
AU  - Magoc T
AD  - Integrated Data Repository Research Services, University of Florida, Gainesville, 
      FL, USA.
FAU - Lipori, Gloria
AU  - Lipori G
AUID- ORCID: 0000-0001-5616-2701
AD  - Integrated Data Repository Research Services, University of Florida, Gainesville, 
      FL, USA.
AD  - Lillian S. Wells Department of Neurosurgery, Clinical and Translational Science 
      Institute, University of Florida, Gainesville, FL, USA.
FAU - Mitchell, Duane A
AU  - Mitchell DA
AUID- ORCID: 0000-0001-6049-213X
AD  - Lillian S. Wells Department of Neurosurgery, Clinical and Translational Science 
      Institute, University of Florida, Gainesville, FL, USA.
FAU - Ospina, Naykky S
AU  - Ospina NS
AD  - Division of Endocrinology, Department of Medicine, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Ahmed, Mustafa M
AU  - Ahmed MM
AD  - Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Hogan, William R
AU  - Hogan WR
AUID- ORCID: 0000-0002-9881-1017
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Shenkman, Elizabeth A
AU  - Shenkman EA
AUID- ORCID: 0000-0003-4903-1804
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Guo, Yi
AU  - Guo Y
AUID- ORCID: 0000-0003-0587-4105
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Cancer Informatics Shared Resource, University of Florida Health Cancer Center, 
      Gainesville, FL, USA.
FAU - Bian, Jiang
AU  - Bian J
AUID- ORCID: 0000-0002-2238-5429
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Cancer Informatics Shared Resource, University of Florida Health Cancer Center, 
      Gainesville, FL, USA.
FAU - Wu, Yonghui
AU  - Wu Y
AUID- ORCID: 0000-0002-6780-6135
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA. yonghui.wu@ufl.edu.
AD  - Cancer Informatics Shared Resource, University of Florida Health Cancer Center, 
      Gainesville, FL, USA. yonghui.wu@ufl.edu.
LA  - eng
GR  - R56 AG069880/AG/NIA NIH HHS/United States
GR  - R01 AG080624/AG/NIA NIH HHS/United States
GR  - R01 CA246418/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20231116
PL  - England
TA  - NPJ Digit Med
JT  - NPJ digital medicine
JID - 101731738
PMC - PMC10654385
COIS- K.E.S., N.P.N., A.B.C., C.M., and M.G.F. are employed by NVIDIA. There are no 
      other competing financial or non-financial interests. The work presented in this 
      study was conducted exclusively within the University of Florida Health.
EDAT- 2023/11/17 15:24
MHDA- 2023/11/17 15:25
CRDT- 2023/11/17 01:09
PHST- 2023/06/05 00:00 [received]
PHST- 2023/11/01 00:00 [accepted]
PHST- 2023/11/17 15:25 [medline]
PHST- 2023/11/17 15:24 [pubmed]
PHST- 2023/11/17 01:09 [entrez]
AID - 10.1038/s41746-023-00958-w [pii]
AID - 958 [pii]
AID - 10.1038/s41746-023-00958-w [doi]
PST - epublish
SO  - NPJ Digit Med. 2023 Nov 16;6(1):210. doi: 10.1038/s41746-023-00958-w.

PMID- 38060759
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231207
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - Be or Not to Be With ChatGPT?
PG  - e48366
LID - 10.7759/cureus.48366 [doi]
LID - e48366
AB  - In the ever-evolving realm of scientific research, this letter underscores the 
      vital role of ChatGPT as an invaluable ally in manuscript creation, focusing on 
      its remarkable grammar and spelling error correction capabilities. Furthermore, 
      it highlights ChatGPT's efficacy in expediting the manuscript preparation process 
      by streamlining the collection and highlighting critical scientific information. 
      By elucidating the aim of this letter and the multifaceted benefits of ChatGPT, 
      we aspire to illuminate the path toward a future where scientific writing 
      achieves unparalleled efficiency and precision.
CI  - Copyright © 2023, Aliyeva et al.
FAU - Aliyeva, Aynur
AU  - Aliyeva A
AD  - Department of Otolaryngology - Head and Neck Surgery, Cincinnati Children's 
      Hospital Medical Center, Cincinnati, USA.
FAU - Sari, Elif
AU  - Sari E
AD  - Department of Otorhinolaryngology - Head and Neck Surgery, Istanbul Aydın 
      University VM Medical Park Florya Hospital, Istanbul, TUR.
LA  - eng
PT  - Editorial
DEP - 20231106
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10699328
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - future in medicine
OT  - research
OT  - scientific manuscript
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/07 18:42
MHDA- 2023/12/07 18:43
CRDT- 2023/12/07 14:46
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/12/07 18:43 [medline]
PHST- 2023/12/07 18:42 [pubmed]
PHST- 2023/12/07 14:46 [entrez]
AID - 10.7759/cureus.48366 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 6;15(11):e48366. doi: 10.7759/cureus.48366. eCollection 2023 
      Nov.

PMID- 38078232
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - ChatGPT's advice is perceived as better than that of professional advice 
      columnists.
PG  - 1281255
LID - 10.3389/fpsyg.2023.1281255 [doi]
LID - 1281255
AB  - ChatGPT is a high-performance large language model that has the potential to 
      significantly improve human-computer interactions. It can provide advice on a 
      range of topics, but it is unclear how good this advice is relative to that 
      provided by competent humans, especially in situations where empathy is required. 
      Here, we report the first investigation of whether ChatGPT's responses are 
      perceived as better than those of humans in a task where humans were attempting 
      to be empathetic. Fifty social dilemma questions were randomly selected from 10 
      well-known advice columns. In a pre-registered survey, participants (N = 404) 
      were each shown one question, along with the corresponding response by an advice 
      columnist and by ChatGPT. ChatGPT's advice was perceived as more balanced, 
      complete, empathetic, helpful, and better than the advice provided by 
      professional advice columnists (all values of p < 0.001). Although participants 
      could not determine which response was written by ChatGPT (54%, p = 0.29), most 
      participants preferred that their own social dilemma questions be answered by a 
      human than by a computer (77%, p < 0.001). ChatGPT's responses were longer than 
      those produced by the advice columnists (mean 280.9 words vs. 142.2 words, 
      p < 0.001). In a second pre-registered survey, each ChatGPT answer was 
      constrained to be approximately the same length as that of the advice columnist 
      (mean 143.2 vs. 142.2 words, p = 0.95). This survey (N = 401) replicated the 
      above findings, showing that the benefit of ChatGPT was not solely due to it 
      writing longer answers.
CI  - Copyright © 2023 Howe, Fay, Saletta and Hovy.
FAU - Howe, Piers Douglas Lionel
AU  - Howe PDL
AD  - Complex Human Data Hub, Melbourne School of Psychological Sciences, University of 
      Melbourne, Melbourne, VIC, Australia.
FAU - Fay, Nicolas
AU  - Fay N
AD  - School of Psychological Science, University of Western Australia, Perth, WA, 
      Australia.
FAU - Saletta, Morgan
AU  - Saletta M
AD  - Hunt Laboratory, University of Melbourne, Melbourne, VIC, Australia.
FAU - Hovy, Eduard
AU  - Hovy E
AD  - Melbourne Connect, University of Melbourne, Melbourne, VIC, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231121
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10702579
OTO - NOTNLM
OT  - ChatGPT
OT  - advice
OT  - advice column
OT  - agony aunt
OT  - empathy
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/12/11 12:43
MHDA- 2023/12/11 12:44
CRDT- 2023/12/11 06:41
PHST- 2023/08/22 00:00 [received]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/12/11 12:44 [medline]
PHST- 2023/12/11 12:43 [pubmed]
PHST- 2023/12/11 06:41 [entrez]
AID - 10.3389/fpsyg.2023.1281255 [doi]
PST - epublish
SO  - Front Psychol. 2023 Nov 21;14:1281255. doi: 10.3389/fpsyg.2023.1281255. 
      eCollection 2023.

PMID- 37873042
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231026
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Healthcare's New Horizon With ChatGPT's Voice and Vision Capabilities: A Leap 
      Beyond Text.
PG  - e47469
LID - 10.7759/cureus.47469 [doi]
LID - e47469
AB  - The integration of artificial intelligence (AI) in healthcare is responsible for 
      a paradigm shift in medicine. OpenAI's recent augmentation of their Generative 
      Pre-trained Transformer (ChatGPT) large language model (LLM) with voice and image 
      recognition capabilities (OpenAI, Delaware) presents another potential 
      transformative tool for healthcare. Envision a healthcare setting where 
      professionals engage in dynamic interactions with ChatGPT to navigate the 
      complexities of atypical medical scenarios. In this innovative landscape, 
      practitioners could solicit ChatGPT's expertise for concise summarizations and 
      insightful extrapolations from a myriad of web-based resources pertaining to 
      similar medical conditions. Furthermore, imagine patients using ChatGPT to 
      identify abnormalities in medical images or skin lesions. While the prospects are 
      diverse, challenges such as suboptimal audio quality and ensuring data security 
      necessitate cautious integration in medical practice. Drawing insights from 
      previous ChatGPT iterations could provide a prudent roadmap for navigating 
      possible challenges. This editorial explores some possible horizons and potential 
      hurdles of ChatGPT's enhanced functionalities in healthcare, emphasizing the 
      importance of continued refinements and vigilance to maximize the benefits while 
      minimizing risks. Through collaborative efforts between AI developers and 
      healthcare professionals, another fusion of AI and healthcare can evolve into 
      enriched patient care and enhanced medical experience.
CI  - Copyright © 2023, Temsah et al.
FAU - Temsah, Reem
AU  - Temsah R
AD  - College of Pharmacy, Alfaisal University, Riyadh, SAU.
FAU - Altamimi, Ibraheem
AU  - Altamimi I
AD  - College of Medicine, King Saud University, Riyadh, SAU.
FAU - Alhasan, Khalid
AU  - Alhasan K
AD  - Pediatric Nephrology, King Saud University, Riyadh, SAU.
AD  - Solid Organ Transplant Center of Excellence, King Faisal Specialist Hospital and 
      Research Centre, Riyadh, SAU.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Evidence-Based Health Care & Knowledge Translation Research, King Saud 
      University, Riyadh, SAU.
AD  - College of Medicine, King Saud University, Riyadh, SAU.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Evidence-Based Health Care & Knowledge Translation Research, King Saud 
      University, Riyadh, SAU.
AD  - College of Medicine, King Saud University, Riyadh, SAU.
LA  - eng
PT  - Editorial
DEP - 20231022
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10590619
OTO - NOTNLM
OT  - artificial intelligence chatgpt-4
OT  - dalle-3
OT  - image recognition
OT  - user-centric interface
OT  - voice recognition technology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/24 06:41
MHDA- 2023/10/24 06:42
CRDT- 2023/10/24 03:49
PHST- 2023/10/22 00:00 [accepted]
PHST- 2023/10/24 06:42 [medline]
PHST- 2023/10/24 06:41 [pubmed]
PHST- 2023/10/24 03:49 [entrez]
AID - 10.7759/cureus.47469 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 22;15(10):e47469. doi: 10.7759/cureus.47469. eCollection 2023 
      Oct.

PMID- 37265899
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230604
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - A Critical Review of ChatGPT as a Potential Substitute for Diabetes Educators.
PG  - e38380
LID - 10.7759/cureus.38380 [doi]
LID - e38380
AB  - This review article explores the potential of ChatGPT as a substitute for 
      diabetes educators. Diabetes is a prevalent chronic disease that requires ongoing 
      education and support for patients to effectively manage their condition. 
      However, there is a shortage of diabetes educators, and traditional education 
      methods have limitations in addressing patients' individual needs. ChatGPT is an 
      artificial intelligence technology that offers a personalized and interactive 
      approach to education and support. In this review, we provide an overview of 
      ChatGPT technology, discuss the challenges facing diabetes educators, review 
      evidence supporting the use of ChatGPT in diabetes education, and examine ethical 
      considerations related to its use. We also provide recommendations for further 
      research and development of ChatGPT in diabetes education and integration into 
      clinical practice. ChatGPT has the potential to improve access to education and 
      support for patients with diabetes, but further research is needed to better 
      understand its effectiveness and limitations. It is important to ensure that 
      ChatGPT is developed and integrated in an ethical and equitable manner to 
      maximize its potential benefits and minimize potential risks.
CI  - Copyright © 2023, Sharma et al.
FAU - Sharma, Samriddhi
AU  - Sharma S
AD  - Medicine and Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of 
      Higher Education and Research, Wardha, IND.
FAU - Pajai, Sandhya
AU  - Pajai S
AD  - Obstetrics and Gynaecology, Jawaharlal Nehru Medical College, Datta Meghe 
      Institute of Higher Education and Research, Wardha, IND.
FAU - Prasad, Roshan
AU  - Prasad R
AD  - Medicine and Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of 
      Higher Education and Research, Wardha, IND.
FAU - Wanjari, Mayur B
AU  - Wanjari MB
AD  - Research and Development, Jawaharlal Nehru Medical College, Datta Meghe Institute 
      of Higher Education and Research, Wardha, IND.
FAU - Munjewar, Pratiksha K
AU  - Munjewar PK
AD  - Medical Surgical Nursing, Srimati Radhikabai Meghe Memorial College of Nursing, 
      Datta Meghe Institute of Higher Education and Research, Wardha, IND.
FAU - Sharma, Ranjana
AU  - Sharma R
AD  - Medical Surgical Nursing, Srimati Radhikabai Meghe Memorial College of Nursing, 
      Datta Meghe Institute of Higher Education and Research, Wardha, IND.
FAU - Pathade, Aniket
AU  - Pathade A
AD  - Research and Development, Jawaharlal Nehru Medical College, Datta Meghe Institute 
      of Higher Education and Research, Wardha, IND.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230501
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10231273
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - diabetes education
OT  - emotional support
OT  - ethical considerations
OT  - health technology
OT  - healthcare communication
OT  - medical advice
OT  - patient education
OT  - patient engagement
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/02 13:16
MHDA- 2023/06/02 13:17
CRDT- 2023/06/02 10:51
PHST- 2023/04/14 00:00 [received]
PHST- 2023/05/01 00:00 [accepted]
PHST- 2023/06/02 13:17 [medline]
PHST- 2023/06/02 13:16 [pubmed]
PHST- 2023/06/02 10:51 [entrez]
AID - 10.7759/cureus.38380 [doi]
PST - epublish
SO  - Cureus. 2023 May 1;15(5):e38380. doi: 10.7759/cureus.38380. eCollection 2023 May.

PMID- 37425514
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT From the Perspective of an Academic Oral and Maxillofacial Radiologist.
PG  - e40053
LID - 10.7759/cureus.40053 [doi]
LID - e40053
AB  - Chat Generative Pre-Trained Transformer (ChatGPT) is an open artificial 
      intelligence (AI)-powered chatbot with various clinical and academic dentistry 
      applications, including oral and maxillofacial radiology (OMFR). The applications 
      can be extended to generating documents such as oral radiology reports if 
      appropriate prompts are given. There are various challenges associated with this 
      task. Like other fields, ChatGPT can be incorporated to generate content and 
      answer oral radiology-related multiple-choice questions. However, its performance 
      is limited to answering image-based questions. ChatGPT can help in scientific 
      writing but can not be designated as an author due to the lack of validity of the 
      content. This editorial outlines the potential applications and limitations of 
      the current version of ChatGPT in OMFR academic settings.
CI  - Copyright © 2023, Khurana et al.
FAU - Khurana, Sonam
AU  - Khurana S
AD  - Oral Pathology, Radiology, and Medicine, New York University (NYU) College of 
      Dentistry, New York, USA.
FAU - Vaddi, Anusha
AU  - Vaddi A
AD  - Oral and Maxillofacial Radiology, Virginia Commonwealth University School of 
      Dentistry, Richmond, USA.
LA  - eng
PT  - Editorial
DEP - 20230606
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10325627
OTO - NOTNLM
OT  - chatgpt
OT  - dental education
OT  - maxillofacial radiology
OT  - radiology report
OT  - scientific papers
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/10 06:42
MHDA- 2023/07/10 06:43
CRDT- 2023/07/10 04:57
PHST- 2023/06/05 00:00 [accepted]
PHST- 2023/07/10 06:43 [medline]
PHST- 2023/07/10 06:42 [pubmed]
PHST- 2023/07/10 04:57 [entrez]
AID - 10.7759/cureus.40053 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 6;15(6):e40053. doi: 10.7759/cureus.40053. eCollection 2023 Jun.

PMID- 38076902
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
DP  - 2023 Oct 29
TI  - Evaluating ChatGPT as an Agent for Providing Genetic Education.
LID - 2023.10.25.564074 [pii]
LID - 10.1101/2023.10.25.564074 [doi]
AB  - Genetic disorders are complex and can greatly impact an individual's health and 
      well-being. In this study, we assess the ability of ChatGPT, a language model 
      developed by OpenAI, to answer questions related to three specific genetic 
      disorders: BRCA1, MLH1, and HFE. ChatGPT has shown it can supply articulate 
      answers to a wide spectrum of questions. However, its ability to answer questions 
      related to genetic disorders has yet to be evaluated. The aim of this study is to 
      perform both quantitative and qualitative assessments of ChatGPT's performance in 
      this area. The ability of ChatGPT to provide accurate and useful information to 
      patients was assessed by genetic experts. Here we show that ChatGPT answered 
      64.7% of the 68 genetic questions asked and was able to respond coherently to 
      complex questions related to the three genes/conditions. Our results reveal that 
      ChatGPT can provide valuable information to individuals seeking information about 
      genetic disorders, however, it still has some limitations and inaccuracies, 
      particularly in understanding human inheritance patterns. The results of this 
      study have implications for both genomics and medicine and can inform future 
      developments in this area. AI platforms, like ChatGPT, have significant potential 
      in the field of genomics. As these technologies become integrated into 
      consumer-facing products, appropriate oversight is required to ensure accurate 
      and safe delivery of medical information. With such oversight and training 
      specifically for genetic information, these platforms could have the potential to 
      augment some clinical interactions.
FAU - Walton, Nephi
AU  - Walton N
AUID- ORCID: 0000-0002-9095-7265
FAU - Gracefo, Sara
AU  - Gracefo S
FAU - Sutherland, Nykole
AU  - Sutherland N
FAU - Kozel, Beth A
AU  - Kozel BA
FAU - Danford, Christopher J
AU  - Danford CJ
FAU - McGrath, Scott P
AU  - McGrath SP
LA  - eng
PT  - Preprint
DEP - 20231029
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10705538
EDAT- 2023/12/11 12:43
MHDA- 2023/12/11 12:44
CRDT- 2023/12/11 06:20
PHST- 2023/12/11 12:44 [medline]
PHST- 2023/12/11 12:43 [pubmed]
PHST- 2023/12/11 06:20 [entrez]
AID - 2023.10.25.564074 [pii]
AID - 10.1101/2023.10.25.564074 [doi]
PST - epublish
SO  - bioRxiv. 2023 Oct 29:2023.10.25.564074. doi: 10.1101/2023.10.25.564074. Preprint.

PMID- 37951982
OWN - NLM
STAT- Publisher
LR  - 20231211
IS  - 1476-5497 (Electronic)
IS  - 0307-0565 (Linking)
DP  - 2023 Nov 11
TI  - Credibility of ChatGPT in the assessment of obesity in type 2 diabetes according 
      to the guidelines.
LID - 10.1038/s41366-023-01410-5 [doi]
AB  - BACKGROUND: The Chat Generative Pre-trained Transformer (ChatGPT) allows 
      students, researchers, and patients in the medical field to access information 
      easily and has gained attention nowadays. We aimed to evaluate the credibility of 
      ChatGPT according to the guidelines for the assessment of obesity in type 2 
      diabetes (T2D), which is one of the major concerns of this century. MATERIALS AND 
      METHOD: In this cross-sectional non-human subject study, experienced 
      endocrinologists posed 20 questions to ChatGPT in subsections, which were 
      assessments and different treatment options for obesity according to the American 
      Diabetes Association and American Association of Clinical Endocrinology 
      guidelines. The responses of ChatGPT were classified into four categories: 
      compatible, compatible but insufficient, partially incompatible and incompatible 
      with the guidelines. RESULTS: ChatGPT demonstrated a systematic approach to 
      answering questions and recommended consulting a healthcare provider to receive 
      personalized advice based on the specific health needs and circumstances of 
      patients. The compatibility of ChatGPT with the guidelines was 100% in the 
      assessment of obesity in type 2 diabetes; however, it was lower in the therapy 
      sections, which included nutritional, medical, and surgical approaches to weight 
      loss. Furthermore, ChatGPT required additional prompts for responses that were 
      evaluated as "compatible but insufficient" to provide all the information in the 
      guidelines. CONCLUSION: The assessment and management of obesity in T2D are 
      highly individualized. Despite ChatGPT's comprehensive and understandable 
      responses, it should not be used as a substitute for healthcare professionals' 
      patient-centered approach.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature Limited.
FAU - Barlas, Tugba
AU  - Barlas T
AUID- ORCID: 0000-0003-0042-6928
AD  - Department of Endocrinology and Metabolism, Gazi University Faculty of Medicine, 
      Ankara, Turkey. drtugbabarlas@gmail.com.
FAU - Altinova, Alev Eroglu
AU  - Altinova AE
AD  - Department of Endocrinology and Metabolism, Gazi University Faculty of Medicine, 
      Ankara, Turkey.
FAU - Akturk, Mujde
AU  - Akturk M
AD  - Department of Endocrinology and Metabolism, Gazi University Faculty of Medicine, 
      Ankara, Turkey.
FAU - Toruner, Fusun Balos
AU  - Toruner FB
AD  - Department of Endocrinology and Metabolism, Gazi University Faculty of Medicine, 
      Ankara, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20231111
PL  - England
TA  - Int J Obes (Lond)
JT  - International journal of obesity (2005)
JID - 101256108
SB  - IM
CIN - Int J Obes (Lond). 2023 Dec 11;:. PMID: 38081927
EDAT- 2023/11/12 00:41
MHDA- 2023/11/12 00:41
CRDT- 2023/11/11 23:29
PHST- 2023/06/07 00:00 [received]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/10/22 00:00 [revised]
PHST- 2023/11/12 00:41 [pubmed]
PHST- 2023/11/12 00:41 [medline]
PHST- 2023/11/11 23:29 [entrez]
AID - 10.1038/s41366-023-01410-5 [pii]
AID - 10.1038/s41366-023-01410-5 [doi]
PST - aheadofprint
SO  - Int J Obes (Lond). 2023 Nov 11. doi: 10.1038/s41366-023-01410-5.

PMID- 37879261
OWN - NLM
STAT- In-Process
LR  - 20231210
IS  - 1528-3968 (Electronic)
IS  - 0029-6554 (Linking)
VI  - 71
IP  - 6
DP  - 2023 Nov-Dec
TI  - The application of Chat Generative Pre-trained Transformer in nursing education.
PG  - 102064
LID - S0029-6554(23)00169-0 [pii]
LID - 10.1016/j.outlook.2023.102064 [doi]
AB  - BACKGROUND: Nursing education is critical for nurses to deliver quality health 
      care. Incorporating AI into education can enhance the learning process and better 
      equip nurses for their health care roles. PURPOSE: This article explores the 
      potential applications and challenges of ChatGPT in nursing education. METHODS: A 
      comprehensive literature review was conducted to explore the potential benefits 
      and challenges of using ChatGPT in nursing education. DISCUSSION: ChatGPT, an 
      advanced large language model, has the potential to make valuable contributions 
      to nursing education in various ways, including personalized learning, simulation 
      scenarios, immediate feedback, and reducing educator workload. However, it is 
      important to address the various challenges and limitations in order to realize 
      its full potential. CONCLUSION: Nursing educators must carefully consider the 
      potential uses, benefits, challenges, drawbacks, and limitations of ChatGPT to 
      make informed decisions about its integration into nursing education.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Liu, Jialin
AU  - Liu J
AD  - Department of Medical Informatics, West China Medical School, Chengdu, Sichuan, 
      China; Department of Otolaryngology-Head and Neck Surgery, West China Hospital, 
      Sichuan University, Chengdu, Sichuan, China. Electronic address: DLJL8@163.com.
FAU - Liu, Fan
AU  - Liu F
AD  - Department of Nursing, West China Hospital of Stomatology, Sichuan University, 
      Chengdu, Sichuan, China.
FAU - Fang, Jinbo
AU  - Fang J
AD  - Department of Nursing, West China Hospital, West China School of Nursing, Sichuan 
      University, Chengdu, Sichuan, China.
FAU - Liu, Siru
AU  - Liu S
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, TN.
LA  - eng
PT  - Journal Article
DEP - 20231023
PL  - United States
TA  - Nurs Outlook
JT  - Nursing outlook
JID - 0401075
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Education
OT  - Large language model
OT  - Nursing
COIS- Declaration of Competing Interest The authors declare no conflicts of interest.
EDAT- 2023/10/26 00:42
MHDA- 2023/10/26 00:42
CRDT- 2023/10/25 18:08
PHST- 2023/07/19 00:00 [received]
PHST- 2023/09/12 00:00 [revised]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/10/26 00:42 [pubmed]
PHST- 2023/10/26 00:42 [medline]
PHST- 2023/10/25 18:08 [entrez]
AID - S0029-6554(23)00169-0 [pii]
AID - 10.1016/j.outlook.2023.102064 [doi]
PST - ppublish
SO  - Nurs Outlook. 2023 Nov-Dec;71(6):102064. doi: 10.1016/j.outlook.2023.102064. Epub 
      2023 Oct 23.

PMID- 37731429
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230922
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT: A Supplemental Tool for Efficiency and Improved Communication in Rural 
      Dermatology.
PG  - e43812
LID - 10.7759/cureus.43812 [doi]
LID - e43812
AB  - In November 2022, OpenAI released version 3.5 of ChatGPT, the first publicly 
      available artificial intelligence (AI) language model designed to engage in 
      natural, human-like dialogue with users. While this groundbreaking technology has 
      been extensively studied in various domains, its potential applications in rural 
      dermatology remain unexplored in the existing literature. Our research 
      investigates the many benefits that ChatGPT could offer in rural dermatology, 
      particularly concerning administrative tasks and communication with communities 
      with lower healthcare literacy. However, we also acknowledge that utilizing 
      ChatGPT without proper caution and discernment may lead to potential drawbacks. 
      This paper examines the opportunities and challenges associated with integrating 
      ChatGPT into rural dermatology practices, ultimately fostering a well-informed 
      and responsible approach to its implementation.
CI  - Copyright © 2023, Baker et al.
FAU - Baker, Mindy N
AU  - Baker MN
AD  - Dermatology, University of Kentucky College of Medicine, Lexington, USA.
FAU - Burruss, Clayton P
AU  - Burruss CP
AD  - Dermatology, University of Kentucky College of Medicine, Lexington, USA.
FAU - Wilson, Chase L
AU  - Wilson CL
AD  - Dermatology, Elkhorn Dermatology, Georgetown, USA.
LA  - eng
PT  - Editorial
DEP - 20230820
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10508964
OTO - NOTNLM
OT  - artificial intelligence
OT  - language models
OT  - machine learning
OT  - quality of care
OT  - rural dermatology
OT  - rural healthcare access
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/21 06:42
MHDA- 2023/09/21 06:43
CRDT- 2023/09/21 03:54
PHST- 2023/08/20 00:00 [accepted]
PHST- 2023/09/21 06:43 [medline]
PHST- 2023/09/21 06:42 [pubmed]
PHST- 2023/09/21 03:54 [entrez]
AID - 10.7759/cureus.43812 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 20;15(8):e43812. doi: 10.7759/cureus.43812. eCollection 2023 
      Aug.

PMID- 37769024
OWN - NLM
STAT- Publisher
LR  - 20231012
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Print)
IS  - 1553-734X (Linking)
VI  - 19
IP  - 9
DP  - 2023 Sep
TI  - Evaluating a large language model's ability to solve programming exercises from 
      an introductory bioinformatics course.
PG  - e1011511
LID - 10.1371/journal.pcbi.1011511 [doi]
LID - e1011511
AB  - Computer programming is a fundamental tool for life scientists, allowing them to 
      carry out essential research tasks. However, despite various educational efforts, 
      learning to write code can be a challenging endeavor for students and researchers 
      in life-sciences disciplines. Recent advances in artificial intelligence have 
      made it possible to translate human-language prompts to functional code, raising 
      questions about whether these technologies can aid (or replace) life scientists' 
      efforts to write code. Using 184 programming exercises from an 
      introductory-bioinformatics course, we evaluated the extent to which one such 
      tool-OpenAI's ChatGPT-could successfully complete programming tasks. ChatGPT 
      solved 139 (75.5%) of the exercises on its first attempt. For the remaining 
      exercises, we provided natural-language feedback to the model, prompting it to 
      try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) 
      of the exercises. These findings have implications for life-sciences education 
      and research. Instructors may need to adapt their pedagogical approaches and 
      assessment techniques to account for these new capabilities that are available to 
      the general public. For some programming tasks, researchers may be able to work 
      in collaboration with machine-learning models to produce functional code.
CI  - Copyright: © 2023 Piccolo et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Piccolo, Stephen R
AU  - Piccolo SR
AUID- ORCID: 0000-0003-2001-5640
AD  - Department of Biology, Brigham Young University, Provo, Utah, United States of 
      America.
FAU - Denny, Paul
AU  - Denny P
AD  - School of Computer Science, The University of Auckland, Auckland, New Zealand.
FAU - Luxton-Reilly, Andrew
AU  - Luxton-Reilly A
AUID- ORCID: 0000-0001-8269-2909
AD  - School of Computer Science, The University of Auckland, Auckland, New Zealand.
FAU - Payne, Samuel H
AU  - Payne SH
AD  - Department of Biology, Brigham Young University, Provo, Utah, United States of 
      America.
FAU - Ridge, Perry G
AU  - Ridge PG
AD  - Department of Biology, Brigham Young University, Provo, Utah, United States of 
      America.
LA  - eng
PT  - Journal Article
DEP - 20230928
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
SB  - IM
PMC - PMC10564134
COIS- I have read the journal’s policy and the authors of this manuscript have the 
      following competing interests: After completing the analyses for this paper, 
      OpenAI contacted us and offered credits to use their application programming 
      interface for additional research. We accepted those credits but did not use the 
      credits in this study, nor did this arrangement influence our conclusions in this 
      study.
EDAT- 2023/09/28 18:42
MHDA- 2023/09/28 18:42
CRDT- 2023/09/28 13:55
PHST- 2023/04/02 00:00 [received]
PHST- 2023/09/12 00:00 [accepted]
PHST- 2023/10/10 00:00 [revised]
PHST- 2023/09/28 18:42 [pubmed]
PHST- 2023/09/28 18:42 [medline]
PHST- 2023/09/28 13:55 [entrez]
AID - PCOMPBIOL-D-23-00520 [pii]
AID - 10.1371/journal.pcbi.1011511 [doi]
PST - epublish
SO  - PLoS Comput Biol. 2023 Sep 28;19(9):e1011511. doi: 10.1371/journal.pcbi.1011511. 
      eCollection 2023 Sep.

PMID- 37731897
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231102
IS  - 2666-6065 (Electronic)
IS  - 2666-6065 (Linking)
VI  - 41
DP  - 2023 Dec
TI  - ChatGPT: promise and challenges for deployment in low- and middle-income 
      countries.
PG  - 100905
LID - 10.1016/j.lanwpc.2023.100905 [doi]
LID - 100905
AB  - In low- and middle-income countries (LMICs), the fields of medicine and public 
      health grapple with numerous challenges that continue to hinder patients' access 
      to healthcare services. ChatGPT, a publicly accessible chatbot, has emerged as a 
      potential tool in aiding public health efforts in LMICs. This viewpoint details 
      the potential benefits of employing ChatGPT in LMICs to improve medicine and 
      public health encompassing a broad spectrum of domains ranging from health 
      literacy, screening, triaging, remote healthcare support, mental health support, 
      multilingual capabilities, healthcare communication and documentation, medical 
      training and education, and support for healthcare professionals. Additionally, 
      we also share potential concerns and limitations associated with the use of 
      ChatGPT and provide a balanced discussion on the opportunities and challenges of 
      using ChatGPT in LMICs.
CI  - © 2023 The Author(s).
FAU - Wang, Xiaofei
AU  - Wang X
AD  - Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, 
      Beijing Advanced Innovation Center for Biomedical Engineering, School of 
      Biological Science and Medical Engineering, Beihang University, Beijing, China.
FAU - Sanders, Hayley M
AU  - Sanders HM
AD  - Section of Plastic Surgery, Department of Surgery, University of Michigan Medical 
      School, Ann Arbor, MI, USA.
FAU - Liu, Yuchen
AU  - Liu Y
AD  - Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, 
      Beijing Advanced Innovation Center for Biomedical Engineering, School of 
      Biological Science and Medical Engineering, Beihang University, Beijing, China.
FAU - Seang, Kennarey
AU  - Seang K
AD  - Grant Management Office, University of Health Sciences, Phnom Penh, Cambodia.
FAU - Tran, Bach Xuan
AU  - Tran BX
AD  - Department of Health Economics, Institute for Preventive Medicine and Public 
      Health, Hanoi Medical University, Hanoi, Vietnam.
AD  - Institute of Health Economics and Technology, Hanoi, Vietnam.
FAU - Atanasov, Atanas G
AU  - Atanasov AG
AD  - Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University 
      of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
AD  - Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 
      Jastrzebiec, 05-552, Magdalenka, Poland.
FAU - Qiu, Yue
AU  - Qiu Y
AD  - Institute for Hospital Management, Tsinghua University, Beijing, China.
FAU - Tang, Shenglan
AU  - Tang S
AD  - Duke Global Health Institute, Duke University, Durham, NC, USA.
FAU - Car, Josip
AU  - Car J
AD  - Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang 
      Technological University Singapore, Singapore.
AD  - Department of Primary Care and Public Health, School of Public Health, Imperial 
      College London, London, United Kingdom.
FAU - Wang, Ya Xing
AU  - Wang YX
AD  - Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key 
      Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical 
      University, Beijing, China.
FAU - Wong, Tien Yin
AU  - Wong TY
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
AD  - Tsinghua Medicine, Tsinghua University, Beijing, China.
AD  - School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
FAU - Tham, Yih-Chung
AU  - Tham YC
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
AD  - Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong 
      Loo Lin School of Medicine, National University of Singapore, Singapore.
AD  - Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical 
      School, Singapore.
FAU - Chung, Kevin C
AU  - Chung KC
AD  - Section of Plastic Surgery, Department of Surgery, University of Michigan Medical 
      School, Ann Arbor, MI, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230915
PL  - England
TA  - Lancet Reg Health West Pac
JT  - The Lancet regional health. Western Pacific
JID - 101774968
PMC - PMC10507635
OTO - NOTNLM
OT  - ChatGPT
OT  - Equity
OT  - Global health
OT  - Large language model
OT  - Low to middle income countries
OT  - Public health
COIS- YQ received payment from Asia Pacific Medical Technology Association for 
      presentation. TYW received editorial support and medical writing from ApotheCom, 
      study funding and article processing charges from Bayer AG, Leverkusen, Germany; 
      funding of editorial support and medical writing from Bayer Consumer Care AG, 
      Basel, Switzerland; study funding from Regeneron Pharmaceuticals, Inc; consulting 
      fees from Aldropika Therapeutics, Bayer, Boehringer Ingelheim, Genetech, Iveric 
      Bio, Novartis, Oxurion, Plano, Roche, Sanofi and Shanghai Henlius. He is also an 
      inventor, patent-holder and a cofounder of start-up companies EyRiS and Visre. KC 
      received funding from the National Institutes of Health, book royalties from 
      Wolters Kluwer and Elsevier, and a research grant from Sonex to study carpal 
      tunnel outcomes.
EDAT- 2023/09/21 06:42
MHDA- 2023/09/21 06:43
CRDT- 2023/09/21 04:06
PHST- 2023/05/09 00:00 [received]
PHST- 2023/08/14 00:00 [revised]
PHST- 2023/09/03 00:00 [accepted]
PHST- 2023/09/21 06:43 [medline]
PHST- 2023/09/21 06:42 [pubmed]
PHST- 2023/09/21 04:06 [entrez]
AID - S2666-6065(23)00223-7 [pii]
AID - 100905 [pii]
AID - 10.1016/j.lanwpc.2023.100905 [doi]
PST - epublish
SO  - Lancet Reg Health West Pac. 2023 Sep 15;41:100905. doi: 
      10.1016/j.lanwpc.2023.100905. eCollection 2023 Dec.

PMID- 37401955
OWN - NLM
STAT- Publisher
LR  - 20231010
IS  - 2193-6226 (Electronic)
IS  - 2193-6218 (Linking)
VI  - 118
IP  - 7
DP  - 2023 Oct
TI  - [ChatGPT: curse or blessing in nursing care?].
PG  - 534-539
LID - 10.1007/s00063-023-01038-3 [doi]
AB  - Artificial intelligence (AI) has been used in healthcare for some years for risk 
      detection, diagnostics, documentation, education and training and other purposes. 
      A new open AI application is ChatGPT, which is accessible to everyone. The 
      application of ChatGPT as AI in education, training or studies is currently being 
      discussed from many perspectives. It is questionable whether ChatGPT can and 
      should also support nursing professions in health care. The aim of this review 
      article is to show and critically discuss possible areas of application of 
      ChatGPT in theory and practice with a focus on nursing practice, pedagogy, 
      nursing research and nursing development.
CI  - © 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, 
      ein Teil von Springer Nature.
FAU - Krüger, Lars
AU  - Krüger L
AUID- ORCID: 0000-0003-4234-0022
AD  - Herz- und Diabeteszentrum NRW, Universitätsklinikum der Ruhr-Universität Bochum, 
      Bad Oeynhausen, Deutschland.
FAU - Krotsetis, Susanne
AU  - Krotsetis S
AD  - Pflegeentwicklung und Pflegewissenschaft angegliedert der Pflegedirektion, des 
      Universitätsklinikums Schleswig-Holstein, Campus Lübeck, Lübeck, Deutschland.
CN  - OpenAI’s Generative Pretrained Transformer 3 (GPT-3) Model
FAU - Nydahl, Peter
AU  - Nydahl P
AUID- ORCID: 0000-0002-5178-0364
AD  - Pflegeforschung und -entwicklung, Pflegedirektion, Universitätsklinikum 
      Schleswig-Holstein, Haus V40, Arnold-Heller-Str. 3, 24105, Kiel, Deutschland. 
      peter.nydahl@uksh.de.
AD  - Universitätsinstitut für Pflegewissenschaft und -praxis, Paracelsus Medizinische 
      Privatuniversität, Salzburg, Österreich. peter.nydahl@uksh.de.
LA  - ger
PT  - English Abstract
PT  - Journal Article
PT  - Review
TT  - ChatGPT: Fluch oder Segen in der Pflege?
DEP - 20230704
PL  - Germany
TA  - Med Klin Intensivmed Notfmed
JT  - Medizinische Klinik, Intensivmedizin und Notfallmedizin
JID - 101575086
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chatbot
OT  - Ethic
OT  - Evidence-based practice
OT  - Nursing
OT  - Teaching
OT  - Technical Support
EDAT- 2023/07/04 13:08
MHDA- 2023/07/04 13:08
CRDT- 2023/07/04 11:04
PHST- 2023/02/23 00:00 [received]
PHST- 2023/06/03 00:00 [accepted]
PHST- 2023/04/28 00:00 [revised]
PHST- 2023/07/04 13:08 [pubmed]
PHST- 2023/07/04 13:08 [medline]
PHST- 2023/07/04 11:04 [entrez]
AID - 10.1007/s00063-023-01038-3 [pii]
AID - 10.1007/s00063-023-01038-3 [doi]
PST - ppublish
SO  - Med Klin Intensivmed Notfmed. 2023 Oct;118(7):534-539. doi: 
      10.1007/s00063-023-01038-3. Epub 2023 Jul 4.

PMID- 37954179
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231114
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - To use or not to use? Understanding doctoral students' acceptance of ChatGPT in 
      writing through technology acceptance model.
PG  - 1259531
LID - 10.3389/fpsyg.2023.1259531 [doi]
LID - 1259531
AB  - While artificial intelligence-based chatbots have demonstrated great potential 
      for writing, little is known about whether and how doctoral students accept the 
      use of ChatGPT in writing. Framed with Technology Acceptance Model, this study 
      investigated doctoral students' acceptance toward ChatGPT in writing and the 
      factors that influence it. The questionnaire survey revealed a high intention to 
      use ChatGPT in writing among doctoral students in China. The findings further 
      indicated that attitude was a significant predictor of behavioural intention to 
      use ChatGPT in writing and mediated the impacts of perceived usefulness and 
      perceived ease of use on it. Perceived ease of ChatGPT use was in turn influenced 
      by students' past ChatGPT use experience. This study provides powerful evidence 
      for the applicability of Technology Acceptance Model in the acceptance of ChatGPT 
      in writing. The results have significant implications for leveraging ChatGPT for 
      writing in higher education.
CI  - Copyright © 2023 Zou and Huang.
FAU - Zou, Min
AU  - Zou M
AD  - School of Foreign Languages, Beijing Institute of Technology, Beijing, China.
FAU - Huang, Liang
AU  - Huang L
AD  - Department of Public Administration, Southeast University, Nanjing, China.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10637381
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence-based chatbot
OT  - doctoral students
OT  - technology acceptance model
OT  - writing
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/13 06:43
MHDA- 2023/11/13 06:44
CRDT- 2023/11/13 04:26
PHST- 2023/07/16 00:00 [received]
PHST- 2023/10/16 00:00 [accepted]
PHST- 2023/11/13 06:44 [medline]
PHST- 2023/11/13 06:43 [pubmed]
PHST- 2023/11/13 04:26 [entrez]
AID - 10.3389/fpsyg.2023.1259531 [doi]
PST - epublish
SO  - Front Psychol. 2023 Oct 26;14:1259531. doi: 10.3389/fpsyg.2023.1259531. 
      eCollection 2023.

PMID- 37722370
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1421-9832 (Electronic)
IS  - 1018-8665 (Linking)
VI  - 239
IP  - 6
DP  - 2023
TI  - Trends in Accuracy and Appropriateness of Alopecia Areata Information Obtained 
      from a Popular Online Large Language Model, ChatGPT.
PG  - 952-957
LID - 10.1159/000534005 [doi]
AB  - BACKGROUND: Patients with alopecia areata (AA) may access a wide range of sources 
      for information about AA, including the recently developed ChatGPT. Assessing the 
      quality of health information provided by these sources is crucial, as patients 
      are utilizing them in increasing numbers. OBJECTIVES: The aim of the study was to 
      evaluate appropriateness and accuracy of responses to common patient questions 
      about AA generated by ChatGPT. METHODS: Responses generated by ChatGPT 3.5 and 
      ChatGPT 4.0 to 25 questions addressing common patient concerns were assessed by 
      multiple attending dermatologists in an academic center for appropriateness and 
      accuracy. Appropriateness of responses by both models for use in two hypothetical 
      contexts as follows: (1) for patient-facing general information websites, and (2) 
      for electronic health record (EHR) message drafts. RESULTS: The accuracy across 
      all responses was 4.41 out of 5. Accuracy scores of responses ChatGPT 3.5 
      responses had a mean of 4.29, whereas those generated by ChatGPT 4.0 had mean 
      accuracy score of 4.53. Assessments ranged from 100% of responses rated as 
      appropriate for the general question category to 79% questions about management 
      for an EHR message draft. Raters largely preferred responses generated by ChatGPT 
      4.0 versus ChatGPT 3.5. Reviewer agreement was found to be moderate across all 
      questions, with a 53.7% agreement and Fleiss' κ co-efficient of 0.522 (p value 
      &lt;0.001). CONCLUSIONS: The large language model ChatGPT outputted mostly 
      appropriate information for common patient concerns. While not all responses were 
      accurate, the trend toward improvement with newer iterations suggests potential 
      future utility for patients and dermatologists.
CI  - © 2023 S. Karger AG, Basel.
FAU - O'Hagan, Ross
AU  - O'Hagan R
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York, USA.
FAU - Kim, Randie H
AU  - Kim RH
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York, USA.
FAU - Abittan, Brian J
AU  - Abittan BJ
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York, USA.
FAU - Caldas, Stella
AU  - Caldas S
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York, USA.
FAU - Ungar, Jonathan
AU  - Ungar J
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York, USA.
FAU - Ungar, Benjamin
AU  - Ungar B
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York, USA.
LA  - eng
PT  - Journal Article
DEP - 20230918
PL  - Switzerland
TA  - Dermatology
JT  - Dermatology (Basel, Switzerland)
JID - 9203244
RN  - Diffuse alopecia
SB  - IM
MH  - Humans
MH  - *Alopecia Areata/drug therapy
MH  - Head
MH  - Language
MH  - Research Personnel
OTO - NOTNLM
OT  - Alopecia areata
OT  - Artificial intelligence
OT  - ChatGPT
EDAT- 2023/09/19 00:43
MHDA- 2023/12/07 12:42
CRDT- 2023/09/18 18:23
PHST- 2023/04/24 00:00 [received]
PHST- 2023/09/04 00:00 [accepted]
PHST- 2023/12/07 12:42 [medline]
PHST- 2023/09/19 00:43 [pubmed]
PHST- 2023/09/18 18:23 [entrez]
AID - 000534005 [pii]
AID - 10.1159/000534005 [doi]
PST - ppublish
SO  - Dermatology. 2023;239(6):952-957. doi: 10.1159/000534005. Epub 2023 Sep 18.

PMID- 37686665
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230911
IS  - 2072-6694 (Print)
IS  - 2072-6694 (Electronic)
IS  - 2072-6694 (Linking)
VI  - 15
IP  - 17
DP  - 2023 Sep 1
TI  - Automatic Segmentation with Deep Learning in Radiotherapy.
LID - 10.3390/cancers15174389 [doi]
LID - 4389
AB  - This review provides a formal overview of current automatic segmentation studies 
      that use deep learning in radiotherapy. It covers 807 published papers and 
      includes multiple cancer sites, image types (CT/MRI/PET), and segmentation 
      methods. We collect key statistics about the papers to uncover commonalities, 
      trends, and methods, and identify areas where more research might be needed. 
      Moreover, we analyzed the corpus by posing explicit questions aimed at providing 
      high-quality and actionable insights, including: "What should researchers think 
      about when starting a segmentation study?", "How can research practices in 
      medical image segmentation be improved?", "What is missing from the current 
      corpus?", and more. This allowed us to provide practical guidelines on how to 
      conduct a good segmentation study in today's competitive environment that will be 
      useful for future research within the field, regardless of the specific 
      radiotherapeutic subfield. To aid in our analysis, we used the large language 
      model ChatGPT to condense information.
FAU - Isaksson, Lars Johannes
AU  - Isaksson LJ
AUID- ORCID: 0000-0002-1008-3466
AD  - Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
AD  - Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, 
      Italy.
FAU - Summers, Paul
AU  - Summers P
AUID- ORCID: 0000-0002-5085-1095
AD  - Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, 
      Italy.
FAU - Mastroleo, Federico
AU  - Mastroleo F
AUID- ORCID: 0000-0001-6580-6767
AD  - Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
AD  - Department of Translational Medicine, University of Piemonte Orientale (UPO), 
      20188 Novara, Italy.
FAU - Marvaso, Giulia
AU  - Marvaso G
AUID- ORCID: 0000-0002-5339-8038
AD  - Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
FAU - Corrao, Giulia
AU  - Corrao G
AUID- ORCID: 0000-0002-5757-6073
AD  - Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
FAU - Vincini, Maria Giulia
AU  - Vincini MG
AUID- ORCID: 0000-0001-7830-3149
AD  - Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
FAU - Zaffaroni, Mattia
AU  - Zaffaroni M
AUID- ORCID: 0000-0003-4655-4634
AD  - Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
FAU - Ceci, Francesco
AU  - Ceci F
AUID- ORCID: 0000-0001-9785-5248
AD  - Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, 
      Italy.
AD  - Division of Nuclear Medicine, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
FAU - Petralia, Giuseppe
AU  - Petralia G
AD  - Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, 
      Italy.
AD  - Precision Imaging and Research Unit, Department of Medical Imaging and Radiation 
      Sciences, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy.
FAU - Orecchia, Roberto
AU  - Orecchia R
AD  - Scientific Directorate, IEO European Institute of Oncology IRCCS, 20141 Milan, 
      Italy.
FAU - Jereczek-Fossa, Barbara Alicja
AU  - Jereczek-Fossa BA
AD  - Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 
      Milan, Italy.
AD  - Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, 
      Italy.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230901
PL  - Switzerland
TA  - Cancers (Basel)
JT  - Cancers
JID - 101526829
PMC - PMC10486603
OTO - NOTNLM
OT  - artificial intelligence
OT  - artificial neural networks
OT  - automatic
OT  - deep learning
OT  - radiotherapy
OT  - segmentation
COIS- B.A.J.-F. received speaker fees from Roche, Bayer, Janssen, Ipsen, Accuray, 
      Astellas, Elekta, IBA and Astra Zeneca (all outside the current project). The 
      remaining authors declare no conflict of interest. M.G.V. and G.C. received a 
      research fellowship from the Associazione Italiana per la Ricerca sul Cancro 
      (AIRC) entitled “Radioablation ± hormonotherapy for prostate cancer 
      oligorecurrences (RADIOSA trial): potential of imaging and biology” registered at 
      Clinical Trials.gov NCT03940235, approved by the Ethics Committee of IEO and 
      Centro Cardiologico Monzino (IEO-997). The funders had no role in the design of 
      the study; in the collection, analyses, or interpretation of data; in the writing 
      of the manuscript; or in the decision to publish the results.
EDAT- 2023/09/09 11:42
MHDA- 2023/09/09 11:43
CRDT- 2023/09/09 01:15
PHST- 2023/07/25 00:00 [received]
PHST- 2023/08/28 00:00 [revised]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/09/09 11:43 [medline]
PHST- 2023/09/09 11:42 [pubmed]
PHST- 2023/09/09 01:15 [entrez]
AID - cancers15174389 [pii]
AID - cancers-15-04389 [pii]
AID - 10.3390/cancers15174389 [doi]
PST - epublish
SO  - Cancers (Basel). 2023 Sep 1;15(17):4389. doi: 10.3390/cancers15174389.

PMID- 37440100
OWN - NLM
STAT- MEDLINE
DCOM- 20230802
LR  - 20230829
IS  - 1573-7373 (Electronic)
IS  - 0167-594X (Linking)
VI  - 163
IP  - 3
DP  - 2023 Jul
TI  - Response to "Large language model artificial intelligence: the current state and 
      future of ChatGPT in neuro-oncology publishing".
PG  - 731-733
LID - 10.1007/s11060-023-04396-2 [doi]
FAU - Gupta, Nithin K
AU  - Gupta NK
AD  - Campbell University School of Osteopathic Medicine, Lillington, NC, USA. 
      n_gupta0210@email.campbell.edu.
FAU - Doyle, David M
AU  - Doyle DM
AD  - Central Michigan University College of Medicine, Mount Pleasant, MI, USA.
FAU - D'Amico, Randy S
AU  - D'Amico RS
AD  - Lenox Hill Hospital, New York, NY, USA.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230713
PL  - United States
TA  - J Neurooncol
JT  - Journal of neuro-oncology
JID - 8309335
SB  - IM
CON - J Neurooncol. 2023 Jun;163(2):473-474. PMID: 37209289
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Publishing
MH  - *Medical Oncology
EDAT- 2023/07/13 12:31
MHDA- 2023/08/02 06:42
CRDT- 2023/07/13 11:16
PHST- 2023/07/07 00:00 [received]
PHST- 2023/07/08 00:00 [accepted]
PHST- 2023/08/02 06:42 [medline]
PHST- 2023/07/13 12:31 [pubmed]
PHST- 2023/07/13 11:16 [entrez]
AID - 10.1007/s11060-023-04396-2 [pii]
AID - 10.1007/s11060-023-04396-2 [doi]
PST - ppublish
SO  - J Neurooncol. 2023 Jul;163(3):731-733. doi: 10.1007/s11060-023-04396-2. Epub 2023 
      Jul 13.

PMID- 37546142
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230808
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - ChatGPT in Radiology: The Advantages and Limitations of Artificial Intelligence 
      for Medical Imaging Diagnosis.
PG  - e41435
LID - 10.7759/cureus.41435 [doi]
LID - e41435
AB  - This review article provides an overview of using artificial intelligence (AI) in 
      radiology. It discusses the advantages and limitations of ChatGPT, a large 
      language model, for medical imaging diagnosis. ChatGPT has shown great promise in 
      improving the accuracy and efficiency of radiological diagnoses by reducing 
      interpretation variability and errors and improving workflow efficiency. However, 
      there are also limitations, including the need for high-quality training data, 
      ethical considerations, and further research and development to improve its 
      performance and usability. Despite these challenges, ChatGPT has the potential to 
      significantly impact radiology and medical imaging diagnosis. The review article 
      highlights the need for continued research and development, coupled with ethical 
      and regulatory considerations, to ensure that ChatGPT is used to its full 
      potential in improving radiological diagnoses and patient care.
CI  - Copyright © 2023, Srivastav et al.
FAU - Srivastav, Samriddhi
AU  - Srivastav S
AD  - Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher 
      Education & Research, Wardha, IND.
FAU - Chandrakar, Rashi
AU  - Chandrakar R
AD  - Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher 
      Education & Research, Wardha, IND.
FAU - Gupta, Shalvi
AU  - Gupta S
AD  - Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher 
      Education & Research, Wardha, IND.
FAU - Babhulkar, Vaishnavi
AU  - Babhulkar V
AD  - Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher 
      Education & Research, Wardha, IND.
FAU - Agrawal, Sristy
AU  - Agrawal S
AD  - Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher 
      Education & Research, Wardha, IND.
FAU - Jaiswal, Arpita
AU  - Jaiswal A
AD  - Obstetrics and Gynaecology, Jawaharlal Nehru Medical College, Datta Meghe 
      Institute of Higher Education & Research, Wardha, IND.
FAU - Prasad, Roshan
AU  - Prasad R
AD  - Medicine and Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of 
      Higher Education & Research, Wardha, IND.
FAU - Wanjari, Mayur B
AU  - Wanjari MB
AD  - Research and Development, Jawaharlal Nehru Medical College, Datta Meghe Institute 
      of Higher Education & Research, Wardha, IND.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230706
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10404120
OTO - NOTNLM
OT  - advantages
OT  - artificial intelligence
OT  - chatgpt
OT  - limitations
OT  - medical imaging diagnosis
OT  - radiology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/07 06:41
MHDA- 2023/08/07 06:42
CRDT- 2023/08/07 04:32
PHST- 2023/05/02 00:00 [received]
PHST- 2023/07/06 00:00 [accepted]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/08/07 06:41 [pubmed]
PHST- 2023/08/07 04:32 [entrez]
AID - 10.7759/cureus.41435 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 6;15(7):e41435. doi: 10.7759/cureus.41435. eCollection 2023 Jul.

PMID- 37225599
OWN - NLM
STAT- MEDLINE
DCOM- 20230811
LR  - 20230811
IS  - 1558-4623 (Electronic)
IS  - 0001-2998 (Linking)
VI  - 53
IP  - 5
DP  - 2023 Sep
TI  - Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?
PG  - 719-730
LID - S0001-2998(23)00036-3 [pii]
LID - 10.1053/j.semnuclmed.2023.04.008 [doi]
AB  - Academic integrity in both higher education and scientific writing has been 
      challenged by developments in artificial intelligence. The limitations associated 
      with algorithms have been largely overcome by the recently released ChatGPT; a 
      chatbot powered by GPT-3.5 capable of producing accurate and human-like responses 
      to questions in real-time. Despite the potential benefits, ChatGPT confronts 
      significant limitations to its usefulness in nuclear medicine and radiology. Most 
      notably, ChatGPT is prone to errors and fabrication of information which poses a 
      risk to professionalism, ethics and integrity. These limitations simultaneously 
      undermine the value of ChatGPT to the user by not producing outcomes at the 
      expected standard. Nonetheless, there are a number of exciting applications of 
      ChatGPT in nuclear medicine across education, clinical and research sectors. 
      Assimilation of ChatGPT into practice requires redefining of norms, and 
      re-engineering of information expectations.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Currie, Geoffrey M
AU  - Currie GM
AD  - Charles Sturt University, Wagga Wagga, NSW, Australia; Baylor College of 
      Medicine, Houston, TX. Electronic address: gcurrie@csu.edu.au.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230522
PL  - United States
TA  - Semin Nucl Med
JT  - Seminars in nuclear medicine
JID - 1264464
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Nuclear Medicine
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/05/25 01:07
MHDA- 2023/08/11 06:42
CRDT- 2023/05/24 21:59
PHST- 2023/04/20 00:00 [received]
PHST- 2023/04/30 00:00 [accepted]
PHST- 2023/08/11 06:42 [medline]
PHST- 2023/05/25 01:07 [pubmed]
PHST- 2023/05/24 21:59 [entrez]
AID - S0001-2998(23)00036-3 [pii]
AID - 10.1053/j.semnuclmed.2023.04.008 [doi]
PST - ppublish
SO  - Semin Nucl Med. 2023 Sep;53(5):719-730. doi: 10.1053/j.semnuclmed.2023.04.008. 
      Epub 2023 May 22.

PMID- 37487885
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231002
LR  - 20231002
IS  - 1873-4898 (Electronic)
IS  - 1477-5131 (Linking)
VI  - 19
IP  - 5
DP  - 2023 Oct
TI  - Response to letter to the Editor re ChatGPT and large language model (LLM) 
      chatbots: The current state of acceptability and a proposal for guidelines on 
      utilization in academic medicine.
PG  - 607
LID - S1477-5131(23)00300-5 [pii]
LID - 10.1016/j.jpurol.2023.07.007 [doi]
FAU - Kim, Jin K
AU  - Kim JK
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada; Division of Urology, Department of Surgery, University of 
      Toronto, Toronto, Canada. Electronic address: jjk.kim@mail.utoronto.ca.
FAU - Chua, Michael
AU  - Chua M
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada; Division of Urology, Department of Surgery, University of 
      Toronto, Toronto, Canada; Institute of Urology, St. Luke's Medical Center, Quezon 
      City, Philippines.
FAU - Rickard, Mandy
AU  - Rickard M
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada.
FAU - Lorenzo, Armando
AU  - Lorenzo A
AD  - Division of Urology, Department of Surgery, The Hospital for Sick Children, 
      Toronto, Canada; Division of Urology, Department of Surgery, University of 
      Toronto, Toronto, Canada.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230718
PL  - England
TA  - J Pediatr Urol
JT  - Journal of pediatric urology
JID - 101233150
SB  - IM
CON - J Pediatr Urol. 2023 Oct;19(5):605-606. PMID: 37495493
EDAT- 2023/07/25 01:09
MHDA- 2023/07/25 01:10
CRDT- 2023/07/24 22:00
PHST- 2023/06/30 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/07/25 01:10 [medline]
PHST- 2023/07/25 01:09 [pubmed]
PHST- 2023/07/24 22:00 [entrez]
AID - S1477-5131(23)00300-5 [pii]
AID - 10.1016/j.jpurol.2023.07.007 [doi]
PST - ppublish
SO  - J Pediatr Urol. 2023 Oct;19(5):607. doi: 10.1016/j.jpurol.2023.07.007. Epub 2023 
      Jul 18.

PMID- 37494894
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1423-002X (Electronic)
IS  - 0378-7346 (Linking)
VI  - 88
IP  - 5
DP  - 2023
TI  - Diagnostic and Management Performance of ChatGPT in Obstetrics and Gynecology.
PG  - 310-313
LID - 10.1159/000533177 [doi]
AB  - OBJECTIVES: The use of artificial intelligence (AI) in clinical patient 
      management and medical education has been advancing over time. ChatGPT was 
      developed and trained recently, using a large quantity of textual data from the 
      internet. Medical science is expected to be transformed by its use. The present 
      study was conducted to evaluate the diagnostic and management performance of the 
      ChatGPT AI model in obstetrics and gynecology. DESIGN: A cross-sectional study 
      was conducted. PARTICIPANTS/MATERIALS, SETTING, METHODS: This study was conducted 
      in Iran in March 2023. Medical histories and examination results of 30 cases were 
      determined in six areas of obstetrics and gynecology. The cases were presented to 
      a gynecologist and ChatGPT for diagnosis and management. Answers from the 
      gynecologist and ChatGPT were compared, and the diagnostic and management 
      performance of ChatGPT were determined. RESULTS: Ninety percent (27 of 30) of the 
      cases in obstetrics and gynecology were correctly handled by ChatGPT. Its 
      responses were eloquent, informed, and free of a significant number of errors or 
      misinformation. Even when the answers provided by ChatGPT were incorrect, the 
      responses contained a logical explanation about the case as well as information 
      provided in the question stem. LIMITATIONS: The data used in this study were 
      taken from the electronic book and may reflect bias in the diagnosis of ChatGPT. 
      CONCLUSIONS: This is the first evaluation of ChatGPT's performance in diagnosis 
      and management in the field of obstetrics and gynecology. It appears that ChatGPT 
      has potential applications in the practice of medicine and is (currently) free 
      and simple to use. However, several ethical considerations and limitations such 
      as bias, validity, copyright infringement, and plagiarism need to be addressed in 
      future studies.
CI  - © 2023 S. Karger AG, Basel.
FAU - Allahqoli, Leila
AU  - Allahqoli L
AD  - Midwifery Department, Ministry of Health and Medical Education, Tehran, Iran, 
      lallahqoli@gmail.com.
FAU - Ghiasvand, Mohammad Matin
AU  - Ghiasvand MM
AD  - Department of Computer Engineering, Amirkabir University of Technology (AUT), 
      Tehran, Iran.
FAU - Mazidimoradi, Afrooz
AU  - Mazidimoradi A
AD  - Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
FAU - Salehiniya, Hamid
AU  - Salehiniya H
AD  - Social Determinants of Health Research Center, Birjand University of Medical 
      Sciences, Birjand, Iran.
FAU - Alkatout, Ibrahim
AU  - Alkatout I
AD  - Campus Kiel, Kiel School of Gynaecological Endoscopy, University Hospitals 
      Schleswig-Holstein, Kiel, Germany.
LA  - eng
PT  - News
DEP - 20230726
PL  - Switzerland
TA  - Gynecol Obstet Invest
JT  - Gynecologic and obstetric investigation
JID - 7900587
SB  - IM
MH  - Female
MH  - Pregnancy
MH  - Humans
MH  - *Gynecology
MH  - Artificial Intelligence
MH  - Cross-Sectional Studies
MH  - *Obstetrics
MH  - Gynecologists
OTO - NOTNLM
OT  - Artificial intelligence model
OT  - ChatGPT
OT  - Diagnostic performance
OT  - Gynecology
OT  - Management
OT  - Obstetrics
EDAT- 2023/07/27 01:09
MHDA- 2023/11/27 12:41
CRDT- 2023/07/26 18:23
PHST- 2023/05/19 00:00 [received]
PHST- 2023/07/20 00:00 [accepted]
PHST- 2023/11/27 12:41 [medline]
PHST- 2023/07/27 01:09 [pubmed]
PHST- 2023/07/26 18:23 [entrez]
AID - 000533177 [pii]
AID - 10.1159/000533177 [doi]
PST - ppublish
SO  - Gynecol Obstet Invest. 2023;88(5):310-313. doi: 10.1159/000533177. Epub 2023 Jul 
      26.

PMID- 37034742
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230905
DP  - 2023 Mar 29
TI  - Performance of ChatGPT on free-response, clinical reasoning exams.
LID - 2023.03.24.23287731 [pii]
LID - 10.1101/2023.03.24.23287731 [doi]
AB  - IMPORTANCE: Studies show that ChatGPT, a general purpose large language model 
      chatbot, could pass the multiple-choice US Medical Licensing Exams, but the 
      model's performance on open-ended clinical reasoning is unknown. OBJECTIVE: To 
      determine if ChatGPT is capable of consistently meeting the passing threshold on 
      free-response, case-based clinical reasoning assessments. DESIGN: Fourteen 
      multi-part cases were selected from clinical reasoning exams administered to 
      pre-clerkship medical students between 2019 and 2022. For each case, the 
      questions were run through ChatGPT twice and responses were recorded. Two 
      clinician educators independently graded each run according to a standardized 
      grading rubric. To further assess the degree of variation in ChatGPT's 
      performance, we repeated the analysis on a single high-complexity case 20 times. 
      SETTING: A single US medical school. PARTICIPANTS: ChatGPT. MAIN OUTCOMES AND 
      MEASURES: Passing rate of ChatGPT's scored responses and the range in model 
      performance across multiple run throughs of a single case. RESULTS: 12 out of the 
      28 ChatGPT exam responses achieved a passing score (43%) with a mean score of 69% 
      (95% CI: 65% to 73%) compared to the established passing threshold of 70%. When 
      given the same case 20 separate times, ChatGPT's performance on that case varied 
      with scores ranging from 56% to 81%. CONCLUSIONS AND RELEVANCE: ChatGPT's ability 
      to achieve a passing performance in nearly half of the cases analyzed 
      demonstrates the need to revise clinical reasoning assessments and incorporate 
      artificial intelligence (AI)-related topics into medical curricula and practice.
FAU - Strong, Eric
AU  - Strong E
FAU - DiGiammarino, Alicia
AU  - DiGiammarino A
FAU - Weng, Yingjie
AU  - Weng Y
FAU - Basaviah, Preetha
AU  - Basaviah P
FAU - Hosamani, Poonam
AU  - Hosamani P
FAU - Kumar, Andre
AU  - Kumar A
FAU - Nevins, Andrew
AU  - Nevins A
FAU - Kugler, John
AU  - Kugler J
FAU - Hom, Jason
AU  - Hom J
FAU - Chen, Jonathan H
AU  - Chen JH
LA  - eng
PT  - Preprint
DEP - 20230329
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
UIN - JAMA Intern Med. 2023 Jul 17;:. PMID: 37459090
PMC - PMC10081420
EDAT- 2023/04/11 06:00
MHDA- 2023/04/11 06:01
CRDT- 2023/04/10 04:12
PHST- 2023/04/11 06:01 [medline]
PHST- 2023/04/10 04:12 [entrez]
PHST- 2023/04/11 06:00 [pubmed]
AID - 2023.03.24.23287731 [pii]
AID - 10.1101/2023.03.24.23287731 [doi]
PST - epublish
SO  - medRxiv. 2023 Mar 29:2023.03.24.23287731. doi: 10.1101/2023.03.24.23287731. 
      Preprint.

PMID- 38035435
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1532-2831 (Electronic)
IS  - 1078-8174 (Linking)
VI  - 30
IP  - 1
DP  - 2023 Nov 22
TI  - Harnessing the benefits of ChatGPT for radiography education: A discussion paper.
PG  - 209-216
LID - S1078-8174(23)00225-0 [pii]
LID - 10.1016/j.radi.2023.11.009 [doi]
AB  - OBJECTIVE: Radiography education is pivotal in training skilled radiographers for 
      diagnostic imaging and therapeutic applications. With technological advancements, 
      interest in innovative educational tools to enhance traditional teaching methods 
      is growing. This discussion paper explores the possibility of the integration of 
      ChatGPT, a cutting-edge conversational AI language model, into radiography 
      education. KEY FINDINGS: We report that ChatGPT offers interactive learning 
      opportunities that can facilitate learning. It also provides self-paced learning, 
      revision platforms, and supports educators in scenario creation, assessment 
      development, group collaboration, and professional and research activities. 
      Despite these benefits, it is important to carefully consider issues related to 
      academic integrity and privacy, along with the opportunities and challenges 
      presented by this new technology in radiography education. CONCLUSION: This paper 
      highlights some of the prospects and limitations of the potential applications of 
      ChatGPT in radiography education, underscoring the benefits for both students and 
      educators. However, its implementation must be considered thoughtfully and 
      ethically, taking into account its strengths and limitations. IMPLICATIONS FOR 
      PRACTICE: Integrating ChatGPT in radiography education has the potential to 
      improve radiography education by improving digital literacy and graduate outcomes 
      of students while streamlining the preparation process for educators. However, 
      ethical implementation is vital for optimal outcomes.
CI  - Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
FAU - Amedu, C
AU  - Amedu C
AD  - Diagnostic Radiography, Department of Midwifery & Radiography School of Health & 
      Psychological Sciences City, University of London, Northampton Square London EC1V 
      0HB, UK.
FAU - Ohene-Botwe, B
AU  - Ohene-Botwe B
AD  - Diagnostic Radiography, Department of Midwifery & Radiography School of Health & 
      Psychological Sciences City, University of London, Northampton Square London EC1V 
      0HB, UK. Electronic address: benard.ohene-botwe@city.ac.uk.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231122
PL  - Netherlands
TA  - Radiography (Lond)
JT  - Radiography (London, England : 1995)
JID - 9604102
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Benefits
OT  - ChatGPT
OT  - Educational tools
OT  - Radiography education
COIS- Conflict of interest statement None.
EDAT- 2023/12/01 00:43
MHDA- 2023/12/01 00:43
CRDT- 2023/11/30 18:01
PHST- 2023/08/21 00:00 [received]
PHST- 2023/10/25 00:00 [revised]
PHST- 2023/11/09 00:00 [accepted]
PHST- 2023/12/01 00:43 [medline]
PHST- 2023/12/01 00:43 [pubmed]
PHST- 2023/11/30 18:01 [entrez]
AID - S1078-8174(23)00225-0 [pii]
AID - 10.1016/j.radi.2023.11.009 [doi]
PST - aheadofprint
SO  - Radiography (Lond). 2023 Nov 22;30(1):209-216. doi: 10.1016/j.radi.2023.11.009.

PMID- 37933339
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231108
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - A Conversation With ChatGPT About the Usage of Lithium in Pregnancy for Bipolar 
      Disorder.
PG  - e46548
LID - 10.7759/cureus.46548 [doi]
LID - e46548
AB  - This conversation with ChatGPT explores the use of lithium in pregnancy for 
      bipolar disorder, a topic of significant importance in psychiatry. Bipolar 
      disorder is characterized by extreme mood swings, and its prevalence varies 
      globally. ChatGPT provides valuable information on bipolar disorder, its 
      prevalence, age of onset, and gender differences. It also discusses the use of 
      lithium during pregnancy, emphasizing the need for individualized decisions, 
      close monitoring, and potential risks and benefits. However, it is essential to 
      note that ChatGPT's responses lack specific references, raising concerns about 
      the reliability of the information provided. Further research is needed to 
      quantify the correctness and dependability of ChatGPT-generated answers in the 
      healthcare context.
CI  - Copyright © 2023, Randhawa et al.
FAU - Randhawa, Jaismeen
AU  - Randhawa J
AD  - Psychiatry, Sri Guru Ram Das Institute of Medical Sciences and Research, 
      Amritsar, IND.
FAU - Khan, Aadil
AU  - Khan A
AD  - Trauma Surgery, OSF Saint Francis Medical Center, University of Illinois Chicago, 
      Peoria, USA.
AD  - Cardiology, University of Illinois Chicago, Illinois, USA.
AD  - Internal Medicine, Lala Lajpat Rai Hospital, Kanpur, IND.
LA  - eng
PT  - Editorial
DEP - 20231005
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10625495
OTO - NOTNLM
OT  - artificial intelligence (ai)
OT  - bipolar disorder
OT  - chat generative pre-trained transformer (chatgpt)
OT  - lithium
OT  - pregnancy
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/07 06:45
MHDA- 2023/11/07 06:46
CRDT- 2023/11/07 03:49
PHST- 2023/10/05 00:00 [accepted]
PHST- 2023/11/07 06:46 [medline]
PHST- 2023/11/07 06:45 [pubmed]
PHST- 2023/11/07 03:49 [entrez]
AID - 10.7759/cureus.46548 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 5;15(10):e46548. doi: 10.7759/cureus.46548. eCollection 2023 
      Oct.

PMID- 37846609
OWN - NLM
STAT- Publisher
LR  - 20231017
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Oct 17
TI  - Performance of Large Language Model Artificial Intelligence on Dermatology Board 
      Exam Questions.
LID - llad355 [pii]
LID - 10.1093/ced/llad355 [doi]
FAU - Park, Lily
AU  - Park L
AUID- ORCID: 0000-0002-7465-5224
AD  - Department of Dermatology; Larkin Community Hospital, South Miami, FL, USA.
AD  - Nova Southeastern University, School of Osteopathic Medicine, Davie, FL, USA.
FAU - Ehlert, Brittany
AU  - Ehlert B
AD  - Ohio University Heritage College of Osteopathic Medicine, Cleveland, OH, USA.
FAU - Susla, Lyudmyla
AU  - Susla L
AD  - New York Institute of Technology College of Osteopathic Medicine, Old Westbury, 
      NY, USA.
FAU - Lum, Zachary C
AU  - Lum ZC
AD  - Nova Southeastern University, School of Osteopathic Medicine, Davie, FL, USA.
FAU - Lee, Patrick K
AU  - Lee PK
AD  - Department of Dermatology, University of California, Irvine, CA, USA.
LA  - eng
PT  - Journal Article
DEP - 20231017
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
EDAT- 2023/10/17 06:42
MHDA- 2023/10/17 06:42
CRDT- 2023/10/17 05:31
PHST- 2023/10/05 00:00 [received]
PHST- 2023/10/11 00:00 [revised]
PHST- 2023/10/13 00:00 [accepted]
PHST- 2023/10/17 06:42 [medline]
PHST- 2023/10/17 06:42 [pubmed]
PHST- 2023/10/17 05:31 [entrez]
AID - 7319413 [pii]
AID - 10.1093/ced/llad355 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Oct 17:llad355. doi: 10.1093/ced/llad355.

PMID- 37212584
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231213
IS  - 1572-0241 (Electronic)
IS  - 0002-9270 (Linking)
VI  - 118
IP  - 12
DP  - 2023 Dec 1
TI  - Chat Generative Pretrained Transformer Fails the Multiple-Choice American College 
      of Gastroenterology Self-Assessment Test.
PG  - 2280-2282
LID - 10.14309/ajg.0000000000002320 [doi]
AB  - INTRODUCTION: Chat Generative Pretrained Transformer (ChatGPT) is a natural 
      language processing model that generates human-like text. METHODS: ChatGPT-3 and 
      ChatGPT-4 were used to answer the 2022 and 2021 American College of 
      Gastroenterology self-assessment tests. The exact questions were inputted in both 
      versions of ChatGPT. A score of 70% or higher was required to pass the 
      assessment. RESULTS: Overall, ChatGPT-3 scored 65.1% on 455 included questions 
      and GPT-4 scored 62.4%. DISCUSSION: ChatGPT did not pass the American College of 
      Gastroenterology self-assessment test. We do not recommend its use for medical 
      education in gastroenterology in its current form.
CI  - Copyright © 2023 by The American College of Gastroenterology.
FAU - Suchman, Kelly
AU  - Suchman K
AD  - Zucker School of Medicine at Hofstra/Northwell, Long Island Jewish Medical 
      Center, New Hyde Park, New York, USA.
FAU - Garg, Shashank
AU  - Garg S
AD  - Arkansas Gastroenterology, North Little Rock, Arkansas, USA.
FAU - Trindade, Arvind J
AU  - Trindade AJ
AD  - Zucker School of Medicine at Hofstra/Northwell, Long Island Jewish Medical 
      Center, New Hyde Park, New York, USA.
AD  - Institute of Health Innovations and Outcomes Research, Feinstein Institutes for 
      Medical Research, Northwell Health, Manhasset, New York, USA.
LA  - eng
PT  - Journal Article
DEP - 20230522
PL  - United States
TA  - Am J Gastroenterol
JT  - The American journal of gastroenterology
JID - 0421030
SB  - IM
CIN - Am J Gastroenterol. 2023 Dec 1;118(12):2305. PMID: 38033226
CIN - Am J Gastroenterol. 2023 Dec 1;118(12):2305-2306. PMID: 38033227
MH  - Humans
MH  - *Gastroenterology
MH  - Self-Assessment
MH  - Universities
EDAT- 2023/05/22 13:04
MHDA- 2023/12/04 12:43
CRDT- 2023/05/22 09:03
PHST- 2023/03/15 00:00 [received]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/05/22 13:04 [pubmed]
PHST- 2023/05/22 09:03 [entrez]
AID - 00000434-202312000-00032 [pii]
AID - 10.14309/ajg.0000000000002320 [doi]
PST - ppublish
SO  - Am J Gastroenterol. 2023 Dec 1;118(12):2280-2282. doi: 
      10.14309/ajg.0000000000002320. Epub 2023 May 22.

PMID- 37549050
OWN - NLM
STAT- MEDLINE
DCOM- 20230809
LR  - 20230809
IS  - 1943-281X (Electronic)
IS  - 0033-2747 (Linking)
VI  - 86
IP  - 3
DP  - 2023 Fall
TI  - ChatGPT: "To Be or Not to Be" in Bikini Bottom.
PG  - 249-254
LID - 10.1080/00332747.2023.2238617 [doi]
AB  - Objective: In this report, we tested ChatGPT's ability to think abstractly and to 
      integrate information about two seemingly disparate topics by requesting a 
      well-articulated, intellectually stimulating essay in response to a complex and 
      somewhat paradoxical task. Method: We asked ChatGPT to write a satirical essay 
      comparing SpongeBob Squarepants to Shakespeare's Hamlet and examined its ability 
      to create a cohesive essay using abstract thinking. Findings: ChatGPT's 
      comparison of Hamlet and SpongeBob was successful, comprehensive, and convincing, 
      demonstrating the ability to make judgments and to use appropriate metaphors and 
      idioms. Conclusions: Our findings suggest that ChatGPT can respond to complex 
      tasks using abstract thinking.
FAU - Weingrad, Aaron B
AU  - Weingrad AB
FAU - Cozza, Stephen J
AU  - Cozza SJ
AUID- ORCID: 0000-0001-9597-2758
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Psychiatry
JT  - Psychiatry
JID - 0376470
SB  - IM
MH  - Humans
MH  - *Cognition
MH  - *Writing
MH  - *Artificial Intelligence
EDAT- 2023/08/07 19:10
MHDA- 2023/08/09 06:43
CRDT- 2023/08/07 12:52
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/08/07 19:10 [pubmed]
PHST- 2023/08/07 12:52 [entrez]
AID - 10.1080/00332747.2023.2238617 [doi]
PST - ppublish
SO  - Psychiatry. 2023 Fall;86(3):249-254. doi: 10.1080/00332747.2023.2238617.

PMID- 37634646
OWN - NLM
STAT- MEDLINE
DCOM- 20230829
LR  - 20230907
IS  - 2005-8330 (Electronic)
IS  - 1229-6929 (Print)
IS  - 1229-6929 (Linking)
VI  - 24
IP  - 9
DP  - 2023 Sep
TI  - The Integration of Large Language Models Such as ChatGPT in Scientific Writing: 
      Harnessing Potential and Addressing Pitfalls.
PG  - 924-925
LID - 10.3348/kjr.2023.0738 [doi]
FAU - Koga, Shunsuke
AU  - Koga S
AUID- ORCID: 0000-0001-8868-9700
AD  - Department of Pathology and Laboratory Medicine, Hospital of the University of 
      Pennsylvania, Philadelphia, PA, USA. shunsuke.koga@pennmedicine.upenn.edu.
LA  - eng
PT  - Comment
PT  - Letter
PL  - Korea (South)
TA  - Korean J Radiol
JT  - Korean journal of radiology
JID - 100956096
SB  - IM
CON - Korean J Radiol. 2023 Aug;24(8):715-718. PMID: 37500572
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Policy
MH  - Publications
PMC - PMC10462902
OTO - NOTNLM
OT  - Authorship
OT  - ChatGPT
OT  - Ethics
OT  - Large language model
OT  - Scientific writing
COIS- The author has no potential conflicts of interest to disclose.
EDAT- 2023/08/28 00:41
MHDA- 2023/08/29 12:44
CRDT- 2023/08/27 19:24
PHST- 2023/08/06 00:00 [received]
PHST- 2023/08/08 00:00 [accepted]
PHST- 2023/08/29 12:44 [medline]
PHST- 2023/08/28 00:41 [pubmed]
PHST- 2023/08/27 19:24 [entrez]
AID - 24.924 [pii]
AID - 10.3348/kjr.2023.0738 [doi]
PST - ppublish
SO  - Korean J Radiol. 2023 Sep;24(9):924-925. doi: 10.3348/kjr.2023.0738.

PMID- 37024324
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230605
LR  - 20230605
IS  - 1532-8171 (Electronic)
IS  - 0735-6757 (Linking)
VI  - 69
DP  - 2023 Jul
TI  - ChatGPT: The next-gen tool for triaging?
PG  - 215-217
LID - S0735-6757(23)00142-0 [pii]
LID - 10.1016/j.ajem.2023.03.027 [doi]
FAU - Bhattaram, Suhrith
AU  - Bhattaram S
AD  - Emergency Medicine, Senior Resident, Emergency Medicine, Dr D Y Patil Medical 
      College, Hospital & Research Centre, Dr D Y Patil Vidyapeeth, Pune, India. 
      Electronic address: bsk1694@gmail.com.
FAU - Shinde, Varsha S
AU  - Shinde VS
AD  - Anaesthesia, Professor, Emergency Medicine, Dr D Y Patil Medical College, 
      Hospital & Research Centre, Dr D Y Patil Vidyapeeth, Pune, India. Electronic 
      address: varsha.shinde@dpu.edu.in.
FAU - Khumujam, Princy Panthoi
AU  - Khumujam PP
AD  - Obstetrics and Gynaecology, Senior Resident, Department of Obstetrics and 
      Gynecology, North Eastern Indira Gandhi Regional Institute of Health & Medical 
      Sciences, Mawdiangdiang, Meghalaya, India.
LA  - eng
PT  - Letter
DEP - 20230321
PL  - United States
TA  - Am J Emerg Med
JT  - The American journal of emergency medicine
JID - 8309942
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Emergency medicine
OT  - Triage
COIS- Declaration of Competing Interest None.
EDAT- 2023/04/07 06:00
MHDA- 2023/04/07 06:01
CRDT- 2023/04/06 22:00
PHST- 2023/02/28 00:00 [received]
PHST- 2023/03/09 00:00 [revised]
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/04/07 06:01 [medline]
PHST- 2023/04/07 06:00 [pubmed]
PHST- 2023/04/06 22:00 [entrez]
AID - S0735-6757(23)00142-0 [pii]
AID - 10.1016/j.ajem.2023.03.027 [doi]
PST - ppublish
SO  - Am J Emerg Med. 2023 Jul;69:215-217. doi: 10.1016/j.ajem.2023.03.027. Epub 2023 
      Mar 21.

PMID- 37334036
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230620
IS  - 2666-9145 (Electronic)
IS  - 2666-9145 (Linking)
VI  - 3
IP  - 4
DP  - 2023 Dec
TI  - Evaluating the Performance of ChatGPT in Ophthalmology: An Analysis of Its 
      Successes and Shortcomings.
PG  - 100324
LID - 10.1016/j.xops.2023.100324 [doi]
LID - 100324
AB  - PURPOSE: Foundation models are a novel type of artificial intelligence 
      algorithms, in which models are pretrained at scale on unannotated data and 
      fine-tuned for a myriad of downstream tasks, such as generating text. This study 
      assessed the accuracy of ChatGPT, a large language model (LLM), in the 
      ophthalmology question-answering space. DESIGN: Evaluation of diagnostic test or 
      technology. PARTICIPANTS: ChatGPT is a publicly available LLM. METHODS: We tested 
      2 versions of ChatGPT (January 9 "legacy" and ChatGPT Plus) on 2 popular multiple 
      choice question banks commonly used to prepare for the high-stakes Ophthalmic 
      Knowledge Assessment Program (OKAP) examination. We generated two 260-question 
      simulated exams from the Basic and Clinical Science Course (BCSC) Self-Assessment 
      Program and the OphthoQuestions online question bank. We carried out logistic 
      regression to determine the effect of the examination section, cognitive level, 
      and difficulty index on answer accuracy. We also performed a post hoc analysis 
      using Tukey's test to decide if there were meaningful differences between the 
      tested subspecialties. MAIN OUTCOME MEASURES: We reported the accuracy of ChatGPT 
      for each examination section in percentage correct by comparing ChatGPT's outputs 
      with the answer key provided by the question banks. We presented logistic 
      regression results with a likelihood ratio (LR) chi-square. We considered 
      differences between examination sections statistically significant at a P value 
      of < 0.05. RESULTS: The legacy model achieved 55.8% accuracy on the BCSC set and 
      42.7% on the OphthoQuestions set. With ChatGPT Plus, accuracy increased to 59.4% 
      ± 0.6% and 49.2% ± 1.0%, respectively. Accuracy improved with easier questions 
      when controlling for the examination section and cognitive level. Logistic 
      regression analysis of the legacy model showed that the examination section (LR, 
      27.57; P = 0.006) followed by question difficulty (LR, 24.05; P < 0.001) were 
      most predictive of ChatGPT's answer accuracy. Although the legacy model performed 
      best in general medicine and worst in neuro-ophthalmology (P < 0.001) and ocular 
      pathology (P = 0.029), similar post hoc findings were not seen with ChatGPT Plus, 
      suggesting more consistent results across examination sections. CONCLUSION: 
      ChatGPT has encouraging performance on a simulated OKAP examination. Specializing 
      LLMs through domain-specific pretraining may be necessary to improve their 
      performance in ophthalmic subspecialties. FINANCIAL DISCLOSURES: Proprietary or 
      commercial disclosure may be found after the references.
CI  - © 2023 by the American Academy of Ophthalmology.
FAU - Antaki, Fares
AU  - Antaki F
AD  - Department of Ophthalmology, Université de Montréal, Montréal, Quebec, Canada.
AD  - Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS 
      de l'Est-de-l'Île-de-Montréal, Montréal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal 
      (CHUM), Montréal, Quebec, Canada.
AD  - The CHUM School of Artificial Intelligence in Healthcare (SAIH), Centre 
      Hospitalier de l'Université de Montréal (CHUM), Montréal, Quebec, Canada.
FAU - Touma, Samir
AU  - Touma S
AD  - Department of Ophthalmology, Université de Montréal, Montréal, Quebec, Canada.
AD  - Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS 
      de l'Est-de-l'Île-de-Montréal, Montréal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal 
      (CHUM), Montréal, Quebec, Canada.
FAU - Milad, Daniel
AU  - Milad D
AD  - Department of Ophthalmology, Université de Montréal, Montréal, Quebec, Canada.
AD  - Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS 
      de l'Est-de-l'Île-de-Montréal, Montréal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal 
      (CHUM), Montréal, Quebec, Canada.
FAU - El-Khoury, Jonathan
AU  - El-Khoury J
AD  - Department of Ophthalmology, Université de Montréal, Montréal, Quebec, Canada.
AD  - Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS 
      de l'Est-de-l'Île-de-Montréal, Montréal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal 
      (CHUM), Montréal, Quebec, Canada.
FAU - Duval, Renaud
AU  - Duval R
AD  - Department of Ophthalmology, Université de Montréal, Montréal, Quebec, Canada.
AD  - Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS 
      de l'Est-de-l'Île-de-Montréal, Montréal, Quebec, Canada.
LA  - eng
PT  - Journal Article
DEP - 20230505
PL  - Netherlands
TA  - Ophthalmol Sci
JT  - Ophthalmology science
JID - 9918230896206676
PMC - PMC10272508
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Generative Pretrained Transformer
OT  - Medical education
OT  - Ophthalmology
EDAT- 2023/06/19 06:42
MHDA- 2023/06/19 06:43
CRDT- 2023/06/19 03:00
PHST- 2023/02/03 00:00 [received]
PHST- 2023/04/21 00:00 [revised]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/06/19 06:43 [medline]
PHST- 2023/06/19 06:42 [pubmed]
PHST- 2023/06/19 03:00 [entrez]
AID - S2666-9145(23)00056-8 [pii]
AID - 100324 [pii]
AID - 10.1016/j.xops.2023.100324 [doi]
PST - epublish
SO  - Ophthalmol Sci. 2023 May 5;3(4):100324. doi: 10.1016/j.xops.2023.100324. 
      eCollection 2023 Dec.

PMID- 37691594
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2383-7977 (Electronic)
IS  - 1975-5171 (Print)
IS  - 1975-5171 (Linking)
VI  - 18
IP  - 3
DP  - 2023 Jul
TI  - Utilizing ChatGPT in clinical research related to anesthesiology: a comprehensive 
      review of opportunities and limitations.
PG  - 244-251
LID - 10.17085/apm.23056 [doi]
AB  - Chat generative pre-trained transformer (ChatGPT) is a chatbot developed by 
      OpenAI that answers questions in a human-like manner. ChatGPT is a GPT language 
      model that understands and responds to natural language created using a 
      transformer, which is a new artificial neural network algorithm first introduced 
      by Google in 2017. ChatGPT can be used to identify research topics and proofread 
      English writing and R scripts to improve work efficiency and optimize time. 
      Attempts to actively utilize generative artificial intelligence (AI) are expected 
      to continue in clinical settings. However, ChatGPT still has many limitations for 
      widespread use in clinical research, owing to AI hallucination symptoms and its 
      training data constraints. Researchers recommend avoiding scientific writing 
      using ChatGPT in many traditional journals because of the current lack of 
      originality guidelines and plagiarism of content generated by ChatGPT. Further 
      regulations and discussions on these topics are expected in the future.
FAU - Lee, Sang-Wook
AU  - Lee SW
AD  - Department of Anesthesiology and Pain Medicine, Asan Medical Center, University 
      of Ulsan College of Medicine, Seoul, Korea.
FAU - Choi, Woo-Jong
AU  - Choi WJ
AD  - Department of Anesthesiology and Pain Medicine, Asan Medical Center, University 
      of Ulsan College of Medicine, Seoul, Korea.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230726
PL  - Korea (South)
TA  - Anesth Pain Med (Seoul)
JT  - Anesthesia and pain medicine
JID - 101517708
PMC - PMC10410543
OTO - NOTNLM
OT  - Anesthesiology
OT  - Artificial intelligence
OT  - Hallucinations
OT  - Plagiarism
OT  - Transformer
COIS- CONFLICTS OF INTEREST No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 04:07
PHST- 2023/05/02 00:00 [received]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 04:07 [entrez]
AID - apm.23056 [pii]
AID - apm-23056 [pii]
AID - 10.17085/apm.23056 [doi]
PST - ppublish
SO  - Anesth Pain Med (Seoul). 2023 Jul;18(3):244-251. doi: 10.17085/apm.23056. Epub 
      2023 Jul 26.

PMID- 37247851
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231004
LR  - 20231004
IS  - 1437-4331 (Electronic)
IS  - 1434-6621 (Linking)
VI  - 61
IP  - 11
DP  - 2023 Oct 26
TI  - ChatGPT, critical thing and ethical practice.
PG  - e221
LID - 10.1515/cclm-2023-0495 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Ajitgarh, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230530
PL  - Germany
TA  - Clin Chem Lab Med
JT  - Clinical chemistry and laboratory medicine
JID - 9806306
SB  - IM
CON - Clin Chem Lab Med. 2023 Apr 25;61(7):1131-1132. PMID: 37092365
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - ethic
EDAT- 2023/05/30 01:06
MHDA- 2023/05/30 01:07
CRDT- 2023/05/29 20:13
PHST- 2023/05/12 00:00 [received]
PHST- 2023/05/15 00:00 [accepted]
PHST- 2023/05/30 01:07 [medline]
PHST- 2023/05/30 01:06 [pubmed]
PHST- 2023/05/29 20:13 [entrez]
AID - cclm-2023-0495 [pii]
AID - 10.1515/cclm-2023-0495 [doi]
PST - epublish
SO  - Clin Chem Lab Med. 2023 May 30;61(11):e221. doi: 10.1515/cclm-2023-0495. Print 
      2023 Oct 26.

PMID- 37821756
OWN - NLM
STAT- MEDLINE
DCOM- 20231027
LR  - 20231027
IS  - 2731-7099 (Electronic)
IS  - 2731-7080 (Linking)
VI  - 64
IP  - 11
DP  - 2023 Nov
TI  - [ChatGPT: aid to medical ethics decision making?].
PG  - 1065-1071
LID - 10.1007/s00108-023-01601-2 [doi]
AB  - BACKGROUND: Physicians have to make countless decisions every day. The medical, 
      ethical and legal aspects are often intertwined and subject to change over time. 
      Involving an ethics committee or arranging an ethical consultation are examples 
      of potential aids to decision making. Whether and how artificial intelligence 
      (AI) and the large language model (LLM) of the company OpenAI (San Francisco, CA, 
      USA), known under the name ChatGPT, can also help and support ethical decision 
      making is increasingly becoming a matter of controversial debate. MATERIAL AND 
      METHODS: Based on a case example, in which a female physician is confronted with 
      ethical and legal issues and presents these to ChatGPT to come up with answers, 
      the first indications of the strengths and weaknesses are ascertained. 
      CONCLUSION: Due to the rapid technical development and access to ever increasing 
      quantities of data, the utilization should be closely observed and evaluated.
CI  - © 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, 
      ein Teil von Springer Nature.
FAU - Schmidt, Kurt W
AU  - Schmidt KW
AD  - Zentrum für Ethik in der Medizin, Agaplesion Markus Krankenhaus, 
      Wilhelm-Epstein-Str. 4, 60431, Frankfurt a. M., Deutschland. 
      kurt.schmidt@ekhn.de.
FAU - Lechner, Fabian
AU  - Lechner F
AD  - Institut für Künstliche Intelligenz, Universitätsklinikum Gießen und Marburg, 
      Marburg, Deutschland.
LA  - ger
PT  - English Abstract
PT  - Journal Article
PT  - Review
TT  - ChatGPT: Hilfe bei der medizinethischen Entscheidungsfindung?
DEP - 20231011
PL  - Germany
TA  - Inn Med (Heidelb)
JT  - Innere Medizin (Heidelberg, Germany)
JID - 9918384885306676
SB  - IM
MH  - Female
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Ethics Committees
MH  - Clinical Decision-Making
MH  - Decision Making
MH  - Ethics, Medical
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Empathy
OT  - Ethical analysis
OT  - Ethics committees
OT  - Legal aspects
EDAT- 2023/10/12 00:43
MHDA- 2023/10/27 06:42
CRDT- 2023/10/11 23:40
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/10/27 06:42 [medline]
PHST- 2023/10/12 00:43 [pubmed]
PHST- 2023/10/11 23:40 [entrez]
AID - 10.1007/s00108-023-01601-2 [pii]
AID - 10.1007/s00108-023-01601-2 [doi]
PST - ppublish
SO  - Inn Med (Heidelb). 2023 Nov;64(11):1065-1071. doi: 10.1007/s00108-023-01601-2. 
      Epub 2023 Oct 11.

PMID- 37549788
OWN - NLM
STAT- MEDLINE
DCOM- 20230904
LR  - 20230925
IS  - 1538-2990 (Electronic)
IS  - 0002-9629 (Linking)
VI  - 366
IP  - 4
DP  - 2023 Oct
TI  - Can ChatGPT pass the thoracic surgery exam?
PG  - 291-295
LID - S0002-9629(23)01292-2 [pii]
LID - 10.1016/j.amjms.2023.08.001 [doi]
AB  - BACKGROUND: The capacity of ChatGPT in academic environments and medical exams is 
      being discovered more and more every day. In this study, we tested the success of 
      ChatGPT on Turkish-language thoracic surgery exam questions. METHODS: ChatGPT was 
      provided with a total of 105 questions divided into seven distinct groups, each 
      of which contained 15 questions. Along with the success of the students, the 
      success of ChatGPT-3.5 and ChatGPT-4 architectures in answering the questions 
      correctly was analyzed. RESULTS: The overall mean score of students was 
      12.50 ± 1.20, corresponding to 83.33%. Moreover, ChatGPT-3.5 managed to surpass 
      students' score of 12.5 with an average of 13.57 ± 0.49 questions correctly on 
      average, while ChatGPT-4 answered 14 ± 0.76 questions correctly (83.3%, 90.48%, 
      and 93.33%, respectively). CONCLUSIONS: When the results of this study and other 
      similar studies in the literature are evaluated together, ChatGPT, which was 
      developed for general purpose, can also produce successful results in a specific 
      field of medicine. AI-powered applications are becoming more and more useful and 
      valuable in providing academic knowledge.
CI  - Copyright © 2023 Southern Society for Clinical Investigation. Published by 
      Elsevier Inc. All rights reserved.
FAU - Gencer, Adem
AU  - Gencer A
AD  - Department of Thoracic Surgery, Afyonkarahisar Health Sciences University, 
      Faculty of Medicine, Zafer Sağlık Külliyesi, Dörtyol Mah. 2078 Sok. No:3 A Blok, 
      Afyonkarahisar, Turkey. Electronic address: dr.ademgencer@gmail.com.
FAU - Aydin, Suphi
AU  - Aydin S
AD  - Department of Thoracic Surgery, Afyonkarahisar Health Sciences University, 
      Faculty of Medicine, Zafer Sağlık Külliyesi, Dörtyol Mah. 2078 Sok. No:3 A Blok, 
      Afyonkarahisar, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230806
PL  - United States
TA  - Am J Med Sci
JT  - The American journal of the medical sciences
JID - 0370506
SB  - IM
MH  - Humans
MH  - *Thoracic Surgery
MH  - *Medicine
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ChatGPT
OT  - Large language models
OT  - Medical education
OT  - Thoracic surgery
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/08/08 00:42
MHDA- 2023/09/04 06:43
CRDT- 2023/08/07 19:13
PHST- 2023/06/07 00:00 [received]
PHST- 2023/06/12 00:00 [revised]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/09/04 06:43 [medline]
PHST- 2023/08/08 00:42 [pubmed]
PHST- 2023/08/07 19:13 [entrez]
AID - S0002-9629(23)01292-2 [pii]
AID - 10.1016/j.amjms.2023.08.001 [doi]
PST - ppublish
SO  - Am J Med Sci. 2023 Oct;366(4):291-295. doi: 10.1016/j.amjms.2023.08.001. Epub 
      2023 Aug 6.

PMID- 37621112
OWN - NLM
STAT- Publisher
LR  - 20230906
IS  - 1365-2125 (Electronic)
IS  - 0306-5251 (Linking)
DP  - 2023 Aug 24
TI  - ChatGPT in pharmacometrics? Potential opportunities and limitations.
PG  - e15895
LID - 10.1111/bcp.15895 [doi]
AB  - The potential of using ChatGPT in pharmacometrics was explored in this study, 
      with a focus on developing a population pharmacokinetic (PK) model for standard 
      half-life factor VIII. Our results demonstrated that ChatGPT can be utilized to 
      accurately obtain typical PK parameters from literature, generate a population PK 
      model in R and develop an interactive Shiny application to visualize the results. 
      ChatGPT's language generation capabilities enabled the development of R codes 
      with minimal programming knowledge and helped to identify as well fix errors in 
      the code. While ChatGPT presents several advantages, such as its ability to 
      streamline the development process, its use in pharmacometrics also has 
      limitations and challenges, including the accuracy and reliability of 
      AI-generated data, the lack of transparency and reproducibility regarding codes 
      generated by ChatGPT. Overall, our study demonstrates the potential of using 
      ChatGPT in pharmacometrics, but researchers must carefully evaluate its use for 
      their specific needs.
CI  - © 2023 The Authors. British Journal of Clinical Pharmacology published by John 
      Wiley & Sons Ltd on behalf of British Pharmacological Society.
FAU - Cloesmeijer, Michael E
AU  - Cloesmeijer ME
AUID- ORCID: 0000-0002-2810-1570
AD  - Department of Hospital Pharmacy - Clinical Pharmacology, Amsterdam University 
      Medical Centers, Amsterdam, The Netherlands.
FAU - Janssen, Alexander
AU  - Janssen A
AD  - Department of Hospital Pharmacy - Clinical Pharmacology, Amsterdam University 
      Medical Centers, Amsterdam, The Netherlands.
FAU - Koopman, Sjoerd F
AU  - Koopman SF
AUID- ORCID: 0000-0002-4536-2692
AD  - Department of Hospital Pharmacy - Clinical Pharmacology, Amsterdam University 
      Medical Centers, Amsterdam, The Netherlands.
FAU - Cnossen, Marjon H
AU  - Cnossen MH
AD  - Department of Pediatric Hematology and Oncology, Erasmus University Medical 
      Center - Sophia Children's Hospital Rotterdam, Rotterdam, The Netherlands.
FAU - Mathôt, Ron A A
AU  - Mathôt RAA
AD  - Department of Hospital Pharmacy - Clinical Pharmacology, Amsterdam University 
      Medical Centers, Amsterdam, The Netherlands.
CN  - SYMPHONY consortium
LA  - eng
GR  - SYMPHONY/
GR  - NWA.1160.18.038/Netherlands Organization for Scientific Research/
PT  - Journal Article
DEP - 20230824
PL  - England
TA  - Br J Clin Pharmacol
JT  - British journal of clinical pharmacology
JID - 7503323
SB  - IM
OTO - NOTNLM
OT  - haematology
OT  - modelling and simulation
OT  - pharmacokinetics
OT  - pharmacometrics
EDAT- 2023/08/25 06:42
MHDA- 2023/08/25 06:42
CRDT- 2023/08/25 02:07
PHST- 2023/07/31 00:00 [revised]
PHST- 2023/04/24 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/08/25 06:42 [pubmed]
PHST- 2023/08/25 06:42 [medline]
PHST- 2023/08/25 02:07 [entrez]
AID - 10.1111/bcp.15895 [doi]
PST - aheadofprint
SO  - Br J Clin Pharmacol. 2023 Aug 24:e15895. doi: 10.1111/bcp.15895.

PMID- 38026475
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2296-875X (Print)
IS  - 2296-875X (Electronic)
IS  - 2296-875X (Linking)
VI  - 10
DP  - 2023
TI  - ChatGPT in orthopedics: a narrative review exploring the potential of artificial 
      intelligence in orthopedic practice.
PG  - 1284015
LID - 10.3389/fsurg.2023.1284015 [doi]
LID - 1284015
AB  - The field of orthopedics faces complex challenges requiring quick and intricate 
      decisions, with patient education and compliance playing crucial roles in 
      treatment outcomes. Technological advancements in artificial intelligence (AI) 
      can potentially enhance orthopedic care. ChatGPT, a natural language processing 
      technology developed by OpenAI, has shown promise in various sectors, including 
      healthcare. ChatGPT can facilitate patient information exchange in orthopedics, 
      provide clinical decision support, and improve patient communication and 
      education. It can assist in differential diagnosis, suggest appropriate imaging 
      modalities, and optimize treatment plans based on evidence-based guidelines. 
      However, ChatGPT has limitations, such as insufficient expertise in specialized 
      domains and a lack of contextual understanding. The application of ChatGPT in 
      orthopedics is still evolving, with studies exploring its potential in clinical 
      decision-making, patient education, workflow optimization, and scientific 
      literature. The results indicate both the benefits and limitations of ChatGPT, 
      emphasizing the need for caution, ethical considerations, and human oversight. 
      Addressing training data quality, biases, data privacy, and accountability 
      challenges is crucial for responsible implementation. While ChatGPT has the 
      potential to transform orthopedic healthcare, further research and development 
      are necessary to ensure its reliability, accuracy, and ethical use in patient 
      care.
CI  - © 2023 Giorgino, Alessandri-Bonetti, Luca, Migliorini, Rossi, Peretti and 
      Mangiavini.
FAU - Giorgino, Riccardo
AU  - Giorgino R
AD  - IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
AD  - Residency Program in Orthopedics and Traumatology, University of Milan, Milan, 
      Italy.
FAU - Alessandri-Bonetti, Mario
AU  - Alessandri-Bonetti M
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 
      Pittsburgh, PA, United States.
FAU - Luca, Andrea
AU  - Luca A
AD  - IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
FAU - Migliorini, Filippo
AU  - Migliorini F
AD  - Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University 
      Medical Centre, Aachen, Germany.
AD  - Department of Orthopedics and Trauma Surgery, Academic Hospital of Bolzano 
      (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical University, Bolzano, 
      Italy.
FAU - Rossi, Nicolò
AU  - Rossi N
AD  - IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
AD  - Residency Program in Orthopedics and Traumatology, University of Milan, Milan, 
      Italy.
FAU - Peretti, Giuseppe M
AU  - Peretti GM
AD  - IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
AD  - Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di 
      Milano, Milan, Italy.
FAU - Mangiavini, Laura
AU  - Mangiavini L
AD  - IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
AD  - Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di 
      Milano, Milan, Italy.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231101
PL  - Switzerland
TA  - Front Surg
JT  - Frontiers in surgery
JID - 101645127
PMC - PMC10654618
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - clinical decision-making
OT  - orthopedics
OT  - patient education
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 16:54
PHST- 2023/08/27 00:00 [received]
PHST- 2023/10/16 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 16:54 [entrez]
AID - 10.3389/fsurg.2023.1284015 [doi]
PST - epublish
SO  - Front Surg. 2023 Nov 1;10:1284015. doi: 10.3389/fsurg.2023.1284015. eCollection 
      2023.

PMID- 37061593
OWN - NLM
STAT- MEDLINE
DCOM- 20230523
LR  - 20230523
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 6
DP  - 2023 Jun
TI  - Behind the ChatGPT Hype: Are Its Suggestions Contributing to Addiction?
PG  - 1128-1129
LID - 10.1007/s10439-023-03201-5 [doi]
AB  - ChatGPT has been a frequent topic of discussion lately. All over the Internet, 
      from YouTube to blogs, there have been reports about how ChatGPT is able to plan 
      people's daily activities, even for a whole month. However, what matters is what 
      activities ChatGPT recommends. When ChatGPT was trained on a vast amount of data 
      from the Internet, we wondered if it would suggest activities that can lead to 
      addiction. In our test, not once did ChatGPT recommend an activity related to 
      alcohol, drug use, or any other activity that can lead to addiction with serious 
      health consequences. Suggestions seemed more like self-improvement posts on blogs 
      than discussion forums where people might mention drinking in the evenings. Thus, 
      if a person were to use ChatGPT as a personal lifestyle advisor, it does not 
      appear on the basis of this test that ChatGPT would recommend activities that 
      would be fundamentally detrimental to their health. However, more detailed 
      long-term testing of similar tools is needed before recommendations for use in 
      practice can be made.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Haman, Michael
AU  - Haman M
AUID- ORCID: 0000-0001-5772-2045
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Kamýcká 129, 165 00, Prague-Suchdol, Czechia. 
      haman@pef.czu.cz.
FAU - Školník, Milan
AU  - Školník M
AUID- ORCID: 0000-0002-0672-219X
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Kamýcká 129, 165 00, Prague-Suchdol, Czechia.
LA  - eng
PT  - Letter
DEP - 20230415
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Addiction Medicine
OTO - NOTNLM
OT  - AI
OT  - Addiction
OT  - ChatGPT
OT  - Language model
OT  - Personal improvement
EDAT- 2023/04/16 06:00
MHDA- 2023/05/12 07:06
CRDT- 2023/04/15 23:18
PHST- 2023/03/31 00:00 [received]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/05/12 07:06 [medline]
PHST- 2023/04/16 06:00 [pubmed]
PHST- 2023/04/15 23:18 [entrez]
AID - 10.1007/s10439-023-03201-5 [pii]
AID - 10.1007/s10439-023-03201-5 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jun;51(6):1128-1129. doi: 10.1007/s10439-023-03201-5. Epub 
      2023 Apr 15.

PMID- 37812468
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231026
IS  - 2291-9694 (Print)
IS  - 2291-9694 (Electronic)
VI  - 11
DP  - 2023 Oct 9
TI  - ChatGPT-Generated Differential Diagnosis Lists for Complex Case-Derived Clinical 
      Vignettes: Diagnostic Accuracy Evaluation.
PG  - e48808
LID - 10.2196/48808 [doi]
LID - e48808
AB  - BACKGROUND: The diagnostic accuracy of differential diagnoses generated by 
      artificial intelligence chatbots, including ChatGPT models, for complex clinical 
      vignettes derived from general internal medicine (GIM) department case reports is 
      unknown. OBJECTIVE: This study aims to evaluate the accuracy of the differential 
      diagnosis lists generated by both third-generation ChatGPT (ChatGPT-3.5) and 
      fourth-generation ChatGPT (ChatGPT-4) by using case vignettes from case reports 
      published by the Department of GIM of Dokkyo Medical University Hospital, Japan. 
      METHODS: We searched PubMed for case reports. Upon identification, physicians 
      selected diagnostic cases, determined the final diagnosis, and displayed them 
      into clinical vignettes. Physicians typed the determined text with the clinical 
      vignettes in the ChatGPT-3.5 and ChatGPT-4 prompts to generate the top 10 
      differential diagnoses. The ChatGPT models were not specially trained or further 
      reinforced for this task. Three GIM physicians from other medical institutions 
      created differential diagnosis lists by reading the same clinical vignettes. We 
      measured the rate of correct diagnosis within the top 10 differential diagnosis 
      lists, top 5 differential diagnosis lists, and the top diagnosis. RESULTS: In 
      total, 52 case reports were analyzed. The rates of correct diagnosis by ChatGPT-4 
      within the top 10 differential diagnosis lists, top 5 differential diagnosis 
      lists, and top diagnosis were 83% (43/52), 81% (42/52), and 60% (31/52), 
      respectively. The rates of correct diagnosis by ChatGPT-3.5 within the top 10 
      differential diagnosis lists, top 5 differential diagnosis lists, and top 
      diagnosis were 73% (38/52), 65% (34/52), and 42% (22/52), respectively. The rates 
      of correct diagnosis by ChatGPT-4 were comparable to those by physicians within 
      the top 10 (43/52, 83% vs 39/52, 75%, respectively; P=.47) and within the top 5 
      (42/52, 81% vs 35/52, 67%, respectively; P=.18) differential diagnosis lists and 
      top diagnosis (31/52, 60% vs 26/52, 50%, respectively; P=.43) although the 
      difference was not significant. The ChatGPT models' diagnostic accuracy did not 
      significantly vary based on open access status or the publication date (before 
      2011 vs 2022). CONCLUSIONS: This study demonstrates the potential diagnostic 
      accuracy of differential diagnosis lists generated using ChatGPT-3.5 and 
      ChatGPT-4 for complex clinical vignettes from case reports published by the GIM 
      department. The rate of correct diagnoses within the top 10 and top 5 
      differential diagnosis lists generated by ChatGPT-4 exceeds 80%. Although derived 
      from a limited data set of case reports from a single department, our findings 
      highlight the potential utility of ChatGPT-4 as a supplementary tool for 
      physicians, particularly for those affiliated with the GIM department. Further 
      investigations should explore the diagnostic accuracy of ChatGPT by using 
      distinct case materials beyond its training data. Such efforts will provide a 
      comprehensive insight into the role of artificial intelligence in enhancing 
      clinical decision-making.
CI  - ©Takanobu Hirosawa, Ren Kawamura, Yukinori Harada, Kazuya Mizuta, Kazuki 
      Tokumasu, Yuki Kaji, Tomoharu Suzuki, Taro Shimizu. Originally published in JMIR 
      Medical Informatics (https://medinform.jmir.org), 09.10.2023.
FAU - Hirosawa, Takanobu
AU  - Hirosawa T
AUID- ORCID: 0000-0002-3573-8203
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
FAU - Kawamura, Ren
AU  - Kawamura R
AUID- ORCID: 0000-0002-5632-3218
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
FAU - Harada, Yukinori
AU  - Harada Y
AUID- ORCID: 0000-0001-6042-7397
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
FAU - Mizuta, Kazuya
AU  - Mizuta K
AUID- ORCID: 0009-0000-8822-7127
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
FAU - Tokumasu, Kazuki
AU  - Tokumasu K
AUID- ORCID: 0000-0001-9513-6864
AD  - Department of General Medicine, Okayama University Graduate School of Medicine, 
      Dentistry and Pharmaceutical Sciences, Okayama, Japan.
FAU - Kaji, Yuki
AU  - Kaji Y
AUID- ORCID: 0000-0002-0267-9876
AD  - Department of General Medicine, International University of Health and Welfare 
      Narita Hospital, Chiba, Japan.
FAU - Suzuki, Tomoharu
AU  - Suzuki T
AUID- ORCID: 0000-0002-5557-0516
AD  - Department of Hospital Medicine, Urasoe General Hospital, Okinawa, Japan.
FAU - Shimizu, Taro
AU  - Shimizu T
AUID- ORCID: 0000-0002-3788-487X
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231009
PL  - Canada
TA  - JMIR Med Inform
JT  - JMIR medical informatics
JID - 101645109
PMC - PMC10594139
OTO - NOTNLM
OT  - AI chatbot
OT  - ChatGPT
OT  - accuracy
OT  - artificial intelligence
OT  - case study
OT  - clinical decision support
OT  - decision support
OT  - diagnosis
OT  - diagnostic
OT  - diagnostic excellence
OT  - language model
OT  - large language models
OT  - natural language processing
OT  - vignette
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/09 12:43
MHDA- 2023/10/09 12:44
CRDT- 2023/10/09 11:53
PHST- 2023/05/09 00:00 [received]
PHST- 2023/09/13 00:00 [accepted]
PHST- 2023/07/20 00:00 [revised]
PHST- 2023/10/09 12:44 [medline]
PHST- 2023/10/09 12:43 [pubmed]
PHST- 2023/10/09 11:53 [entrez]
AID - v11i1e48808 [pii]
AID - 10.2196/48808 [doi]
PST - epublish
SO  - JMIR Med Inform. 2023 Oct 9;11:e48808. doi: 10.2196/48808.

PMID- 37707442
OWN - NLM
STAT- Publisher
LR  - 20230914
IS  - 1559-8519 (Electronic)
IS  - 0022-4499 (Linking)
DP  - 2023 Sep 14
TI  - Hey ChatGPT, Let's Talk About Sexual Consent.
PG  - 1-12
LID - 10.1080/00224499.2023.2254772 [doi]
AB  - Access to sexual health education, such as education on sexual consent, is 
      limited in the US. Artificial intelligence (AI), such as ChatGPT, provides a 
      potential opportunity to increase access to sexual consent information and 
      education. However, what ChatGPT knows about sexual consent and if this aligns 
      with the current evidence-based literature on sexual consent is unclear. The goal 
      of this research commentary was to explore what ChatGPT knows about sexual 
      consent with a focus on: 1) the definition of consent, 2) how consent could be 
      communicated, and 3) the impact that substances have on consent. We also examined 
      the reliability of ChatGPT's responses by having three different researchers ask 
      ChatGPT the same set of questions. Across our questions, ChatGPT provided similar 
      and comprehensive responses that discussed key features of consent - that consent 
      is freely given or reversible. ChatGPT provided examples of different verbal and 
      nonverbal cues people can use to communicate and interpret consent and discussed 
      the ways that substances can impact consent communication. Overall, ChatGPT could 
      be a potential resource for educators and young people who seek information about 
      sexual consent; however, we should proceed with caution. ChatGPT is not a 
      replacement for an educator but rather a way to increase access to education.
FAU - Marcantonio, Tiffany L
AU  - Marcantonio TL
AUID- ORCID: 0000-0003-1087-7554
AD  - Department of Health Science, College of Human Environmental Sciences, University 
      of Alabama.
FAU - Nielsen, Karen E
AU  - Nielsen KE
AD  - Population Health Science, School of Public Health, Georgia State University.
FAU - Haikalis, Michelle
AU  - Haikalis M
AUID- ORCID: 0000-0003-1062-5314
AD  - Department of Behavioral and Social Sciences, School of Public Health, Center for 
      Alcohol and Addiction Studies, Brown University.
FAU - Leone, Ruschelle M
AU  - Leone RM
AUID- ORCID: 0000-0002-7179-398X
AD  - Department of Health Policy and Behavioral Sciences, School of Public Health, 
      Georgia State University.
FAU - Woerner, Jacqueline
AU  - Woerner J
AUID- ORCID: 0000-0002-6704-5372
AD  - Department of Sociology, University of Central Florida.
FAU - Neilson, Elizabeth C
AU  - Neilson EC
AUID- ORCID: 0000-0002-4138-8748
AD  - Department of Psychology, Eastern Michigan University.
FAU - Schipani-McLaughlin, Anne Marie
AU  - Schipani-McLaughlin AM
AD  - Department of Health Policy and Behavioral Sciences, School of Public Health, 
      Georgia State University.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - United States
TA  - J Sex Res
JT  - Journal of sex research
JID - 0062647
SB  - IM
EDAT- 2023/09/14 12:42
MHDA- 2023/09/14 12:42
CRDT- 2023/09/14 10:22
PHST- 2023/09/14 12:42 [medline]
PHST- 2023/09/14 12:42 [pubmed]
PHST- 2023/09/14 10:22 [entrez]
AID - 10.1080/00224499.2023.2254772 [doi]
PST - aheadofprint
SO  - J Sex Res. 2023 Sep 14:1-12. doi: 10.1080/00224499.2023.2254772.

PMID- 37622581
OWN - NLM
STAT- Publisher
LR  - 20230825
IS  - 1097-6817 (Electronic)
IS  - 0194-5998 (Linking)
DP  - 2023 Aug 25
TI  - BPPV Information on Google Versus AI (ChatGPT).
LID - 10.1002/ohn.506 [doi]
AB  - OBJECTIVE: To quantitatively compare online patient education materials found 
      using traditional search engines (Google) versus conversational Artificial 
      Intelligence (AI) models (ChatGPT) for benign paroxysmal positional vertigo 
      (BPPV). STUDY DESIGN: The top 30 Google search results for "benign paroxysmal 
      positional vertigo" were compared to the OpenAI conversational AI language model, 
      ChatGPT, responses for 5 common patient questions posed about BPPV in February 
      2023. Metrics included readability, quality, understandability, and 
      actionability. SETTING: Online information. METHODS: Validated online information 
      metrics including Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), 
      DISCERN instrument score, and Patient Education Materials Assessment Tool for 
      Printed Materials were analyzed and scored by reviewers. RESULTS: Mean 
      readability scores, FKGL and FRE, for the Google webpages were 10.7 ± 2.6 and 
      46.5 ± 14.3, respectively. ChatGPT responses had a higher FKGL score of 
      13.9 ± 2.5 (P < .001) and a lower FRE score of 34.9 ± 11.2 (P = .005), both 
      corresponding to lower readability. The Google webpages had a DISCERN part 2 
      score of 25.4 ± 7.5 compared to the individual ChatGPT responses with a score of 
      17.5 ± 3.9 (P = .001), and the combined ChatGPT responses with a score of 
      25.0 ± 0.9 (P = .928). The average scores of the reviewers for all ChatGPT 
      responses for accuracy were 4.19 ± 0.82 and 4.31 ± 0.67 for currency. CONCLUSION: 
      The results of this study suggest that the information on ChatGPT is more 
      difficult to read, of lower quality, and more difficult to comprehend compared to 
      information on Google searches.
CI  - © 2023 American Academy of Otolaryngology-Head and Neck Surgery Foundation.
FAU - Bellinger, Jeffrey R
AU  - Bellinger JR
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Virginia School 
      of Medicine, Charlottesville, Virginia, USA.
FAU - De La Chapa, Julian S
AU  - De La Chapa JS
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Virginia School 
      of Medicine, Charlottesville, Virginia, USA.
FAU - Kwak, Minhie W
AU  - Kwak MW
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Virginia School 
      of Medicine, Charlottesville, Virginia, USA.
FAU - Ramos, Gabriel A
AU  - Ramos GA
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Virginia School 
      of Medicine, Charlottesville, Virginia, USA.
FAU - Morrison, Daniel
AU  - Morrison D
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Virginia School 
      of Medicine, Charlottesville, Virginia, USA.
FAU - Kesser, Bradley W
AU  - Kesser BW
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Virginia School 
      of Medicine, Charlottesville, Virginia, USA.
LA  - eng
PT  - Journal Article
DEP - 20230825
PL  - England
TA  - Otolaryngol Head Neck Surg
JT  - Otolaryngology--head and neck surgery : official journal of American Academy of 
      Otolaryngology-Head and Neck Surgery
JID - 8508176
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Google
OT  - artificial intelligence
OT  - benign paroxysmal positional vertigo
OT  - online information
OT  - quality
OT  - readability
OT  - understandability
EDAT- 2023/08/25 12:42
MHDA- 2023/08/25 12:42
CRDT- 2023/08/25 07:22
PHST- 2023/06/25 00:00 [received]
PHST- 2023/07/29 00:00 [accepted]
PHST- 2023/08/25 12:42 [medline]
PHST- 2023/08/25 12:42 [pubmed]
PHST- 2023/08/25 07:22 [entrez]
AID - 10.1002/ohn.506 [doi]
PST - aheadofprint
SO  - Otolaryngol Head Neck Surg. 2023 Aug 25. doi: 10.1002/ohn.506.

PMID- 37754479
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2254-9625 (Electronic)
IS  - 2174-8144 (Print)
IS  - 2174-8144 (Linking)
VI  - 13
IP  - 9
DP  - 2023 Sep 21
TI  - Assessing the Usability of ChatGPT for Formal English Language Learning.
PG  - 1937-1960
LID - 10.3390/ejihpe13090140 [doi]
AB  - Recently, the emerging technologies have been constantly shaping the education 
      domain, especially the use of artificial intelligence (AI) for language learning, 
      which has attracted significant attention. Many of the AI tools are being used 
      for learning foreign languages, in both formal and informal ways. There are many 
      studies that have explored the potential of the recent technology "ChatGPT" for 
      education and learning languages, but none of the existing studies have conducted 
      any exploratory study for assessing the usability of ChatGPT. This paper conducts 
      an assessment for usability of ChatGPT for formal English language learning. The 
      study uses a standard questionnaire-based approach to ask participants about 
      their feedback for usefulness and effectiveness of ChatGPT. The participants were 
      asked for their feedback after performing series of tasks related to formal 
      English language learning with ChatGPT. A variety of student participants were 
      selected for this study with diverse English language proficiency levels, 
      education levels, and nationalities. The quantitative analysis of the participant 
      responses shed light on their experience with regards to the usability of ChatGPT 
      for performing different English language learning tasks such as conversation, 
      writing, grammar, and vocabulary. The findings from this study are quite 
      promising and indicate that ChatGPT is an effective tool to be used for formal 
      English language learning. Overall, this study contributes to the fast-growing 
      research domain on using emerging technologies for formal English language 
      learning by conducting in-depth assessment of usability for ChatGPT in formal 
      English language learning.
FAU - Shaikh, Sarang
AU  - Shaikh S
AUID- ORCID: 0000-0003-2099-4797
AD  - Department of Information Security and Communication Technology (IIK), Norwegian 
      University of Science and Technology (NTNU), 2815 Gjøvik, Norway.
FAU - Yayilgan, Sule Yildirim
AU  - Yayilgan SY
AUID- ORCID: 0000-0002-1982-6609
AD  - Department of Information Security and Communication Technology (IIK), Norwegian 
      University of Science and Technology (NTNU), 2815 Gjøvik, Norway.
FAU - Klimova, Blanka
AU  - Klimova B
AUID- ORCID: 0000-0001-8000-9766
AD  - Department of Applied Linguistics, Faculty of Informatics and Management, 
      University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech 
      Republic.
FAU - Pikhart, Marcel
AU  - Pikhart M
AUID- ORCID: 0000-0002-5633-9332
AD  - Department of Applied Linguistics, Faculty of Informatics and Management, 
      University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech 
      Republic.
LA  - eng
PT  - Journal Article
DEP - 20230921
PL  - Switzerland
TA  - Eur J Investig Health Psychol Educ
JT  - European journal of investigation in health, psychology and education
JID - 101751466
PMC - PMC10528190
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - English language
OT  - chatbots
OT  - formal learning
OT  - language learning
OT  - usability testing
COIS- The authors declare no conflict of interest.
EDAT- 2023/09/27 12:42
MHDA- 2023/09/27 12:43
CRDT- 2023/09/27 08:55
PHST- 2023/07/10 00:00 [received]
PHST- 2023/08/29 00:00 [revised]
PHST- 2023/09/13 00:00 [accepted]
PHST- 2023/09/27 12:43 [medline]
PHST- 2023/09/27 12:42 [pubmed]
PHST- 2023/09/27 08:55 [entrez]
AID - ejihpe13090140 [pii]
AID - ejihpe-13-00140 [pii]
AID - 10.3390/ejihpe13090140 [doi]
PST - epublish
SO  - Eur J Investig Health Psychol Educ. 2023 Sep 21;13(9):1937-1960. doi: 
      10.3390/ejihpe13090140.

PMID- 37093625
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230511
IS  - 2373-6658 (Electronic)
IS  - 2373-6658 (Linking)
VI  - 7
DP  - 2023 Apr 24
TI  - Impact of ChatGPT on Interdisciplinary Nursing Education and Research.
PG  - e48136
LID - 10.2196/48136 [doi]
LID - e48136
AB  - ChatGPT, a trending artificial intelligence tool developed by OpenAI, was 
      launched in November 2022. The impact of ChatGPT on the nursing and 
      interdisciplinary research ecosystem is profound.
CI  - ©Hongyu Miao, Hyochol Ahn. Originally published in the Asian/Pacific Island 
      Nursing Journal (https://apinj.jmir.org), 24.04.2023.
FAU - Miao, Hongyu
AU  - Miao H
AUID- ORCID: 0000-0002-4131-3164
AD  - Florida State University, Tallahassee, FL, United States.
FAU - Ahn, Hyochol
AU  - Ahn H
AUID- ORCID: 0000-0002-9998-4876
AD  - Florida State University, Tallahassee, FL, United States.
LA  - eng
PT  - Editorial
DEP - 20230424
PL  - Canada
TA  - Asian Pac Isl Nurs J
JT  - Asian/Pacific Island nursing journal
JID - 101699697
PMC - PMC10167576
OTO - NOTNLM
OT  - ChatGPT
OT  - OpenAI
OT  - artificial intelligence
OT  - nursing education
OT  - nursing research
COIS- Conflicts of Interest: None declared.
EDAT- 2023/04/24 12:42
MHDA- 2023/04/24 12:43
CRDT- 2023/04/24 11:52
PHST- 2023/04/12 00:00 [received]
PHST- 2023/04/14 00:00 [accepted]
PHST- 2023/04/24 12:43 [medline]
PHST- 2023/04/24 12:42 [pubmed]
PHST- 2023/04/24 11:52 [entrez]
AID - v7i1e48136 [pii]
AID - 10.2196/48136 [doi]
PST - epublish
SO  - Asian Pac Isl Nurs J. 2023 Apr 24;7:e48136. doi: 10.2196/48136.

PMID- 37750843
OWN - NLM
STAT- Publisher
LR  - 20231117
IS  - 1748-880X (Electronic)
IS  - 0007-1285 (Print)
IS  - 0007-1285 (Linking)
VI  - 96
IP  - 1152
DP  - 2023 Oct 24
TI  - ChatGPT: a tool for scientific writing or a threat to integrity?
PG  - 20230430
LID - 10.1259/bjr.20230430 [doi]
LID - 20230430
AB  - The use of ChatGPT as a tool for writing and knowledge integration raises 
      concerns about the potential for its use to replace critical thinking and 
      academic writing skills. While ChatGPT can assist in generating text and 
      suggesting appropriate language, it should not replace the human responsibility 
      for creating innovative knowledge through experiential learning. The accuracy and 
      quality of information provided by ChatGPT also require caution, as previous 
      studies have reported inaccuracies in references used by chatbots. ChatGPT 
      acknowledges certain limitations, including the potential for generating 
      erroneous or biased content, and it is essential to exercise caution in 
      interpreting its responses and recognize the indispensable role of human 
      experience in the processes of information retrieval and knowledge creation. 
      Furthermore, the challenge of distinguishing between papers written by humans or 
      AI highlights the need for thorough review processes to prevent the spread of 
      articles that could lead to the loss of confidence in the accuracy and integrity 
      of scientific research. Overall, while the use of ChatGPT can be helpful, it is 
      crucial to raise awareness of the potential issues associated with the use of 
      ChatGPT, as well as to discuss boundaries so that AI can be used without 
      compromising the quality of scientific articles and the integrity of 
      evidence-based knowledge.
FAU - Silva, Thaísa Pinheiro
AU  - Silva TP
AUID- ORCID: 0000-0002-7485-0206
AD  - Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental 
      School, University of Campinas, Piracicaba, Brazil.
FAU - Ocampo, Thaís S C
AU  - Ocampo TSC
AD  - Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental 
      School, University of Campinas, Piracicaba, Brazil.
FAU - Alencar-Palha, Caio
AU  - Alencar-Palha C
AD  - Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental 
      School, University of Campinas, Piracicaba, Brazil.
FAU - Oliveira-Santos, Christiano
AU  - Oliveira-Santos C
AD  - Department of Diagnosis and Oral Health, University of Louisville School of 
      Dentistry, Louisville, United States.
FAU - Takeshita, Wilton Mitsunari
AU  - Takeshita WM
AD  - Department of Diagnosis and Surgery, Paulista State University Júlio de Mesquita 
      Filho, Araçatuba, Brazil.
FAU - Oliveira, Matheus L
AU  - Oliveira ML
AD  - Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental 
      School, University of Campinas, Piracicaba, Brazil.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - England
TA  - Br J Radiol
JT  - The British journal of radiology
JID - 0373125
SB  - IM
PMC - PMC10646664
EDAT- 2023/09/26 13:43
MHDA- 2023/09/26 13:43
PMCR- 2024/11/01
CRDT- 2023/09/26 10:34
PHST- 2024/11/01 00:00 [pmc-release]
PHST- 2023/09/26 13:43 [pubmed]
PHST- 2023/09/26 13:43 [medline]
PHST- 2023/09/26 10:34 [entrez]
AID - 10.1259/bjr.20230430 [doi]
PST - aheadofprint
SO  - Br J Radiol. 2023 Oct 24;96(1152):20230430. doi: 10.1259/bjr.20230430.

PMID- 37796786
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231102
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 18
IP  - 10
DP  - 2023
TI  - An exploratory survey about using ChatGPT in education, healthcare, and research.
PG  - e0292216
LID - 10.1371/journal.pone.0292216 [doi]
LID - e0292216
AB  - OBJECTIVE: ChatGPT is the first large language model (LLM) to reach a large, 
      mainstream audience. Its rapid adoption and exploration by the population at 
      large has sparked a wide range of discussions regarding its acceptable and 
      optimal integration in different areas. In a hybrid (virtual and in-person) panel 
      discussion event, we examined various perspectives regarding the use of ChatGPT 
      in education, research, and healthcare. MATERIALS AND METHODS: We surveyed 
      in-person and online attendees using an audience interaction platform (Slido). We 
      quantitatively analyzed received responses on questions about the use of ChatGPT 
      in various contexts. We compared pairwise categorical groups with a Fisher's 
      Exact. Furthermore, we used qualitative methods to analyze and code discussions. 
      RESULTS: We received 420 responses from an estimated 844 participants (response 
      rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried 
      ChatGPT compared with faculty. Those who had used ChatGPT were more interested in 
      using it in a wider range of contexts going forwards. Of the three discussed 
      contexts, the greatest uncertainty was shown about using ChatGPT in education. 
      Pros and cons were raised during discussion for the use of this technology in 
      education, research, and healthcare. DISCUSSION: There was a range of 
      perspectives around the uses of ChatGPT in education, research, and healthcare, 
      with still much uncertainty around its acceptability and optimal uses. There were 
      different perspectives from respondents of different roles (trainee vs faculty vs 
      staff). More discussion is needed to explore perceptions around the use of LLMs 
      such as ChatGPT in vital sectors such as education, healthcare and research. 
      Given involved risks and unforeseen challenges, taking a thoughtful and measured 
      approach in adoption would reduce the likelihood of harm.
CI  - Copyright: © 2023 Hosseini et al. This is an open access article distributed 
      under the terms of the Creative Commons Attribution License, which permits 
      unrestricted use, distribution, and reproduction in any medium, provided the 
      original author and source are credited.
FAU - Hosseini, Mohammad
AU  - Hosseini M
AUID- ORCID: 0000-0002-2385-985X
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, Illinois, United States of America.
FAU - Gao, Catherine A
AU  - Gao CA
AUID- ORCID: 0000-0001-5576-3943
AD  - Division of Pulmonary and Critical Care, Department of Medicine, Northwestern 
      University Feinberg School of Medicine, Chicago, Illinois, United States of 
      America.
FAU - Liebovitz, David M
AU  - Liebovitz DM
AUID- ORCID: 0000-0002-2518-5940
AD  - Divisions of General Internal Medicine and Health and Biomedical Informatics, 
      Department of Medicine, Northwestern University Feinberg School of Medicine, 
      Chicago, Illinois, United States of America.
AD  - Center for Medical Education in Digital Health and Data Science, Northwestern 
      University Feinberg School of Medicine, Chicago, Illinois, United States of 
      America.
FAU - Carvalho, Alexandre M
AU  - Carvalho AM
AUID- ORCID: 0000-0002-6961-7004
AD  - Division of Infectious Diseases, Department of Medicine, Northwestern University 
      Feinberg School of Medicine, Chicago, Illinois, United States of America.
AD  - Center for Pathogen Genomics & Microbial Evolution, Northwestern University 
      Feinberg School of Medicine, Chicago, Illinois, United States of America.
FAU - Ahmad, Faraz S
AU  - Ahmad FS
AUID- ORCID: 0000-0002-2613-2541
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, Illinois, United States of America.
AD  - Division of Cardiology, Department of Medicine, Northwestern University Feinberg 
      School of Medicine, Chicago, Illinois, United States of America.
AD  - Bluhm Cardiovascular Center for Artificial Intelligence, Northwestern Medicine, 
      Northwestern University Feinberg School of Medicine, Chicago, Illinois, United 
      States of America.
FAU - Luo, Yuan
AU  - Luo Y
AUID- ORCID: 0000-0003-0195-7456
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, Illinois, United States of America.
FAU - MacDonald, Ngan
AU  - MacDonald N
AUID- ORCID: 0000-0003-0268-7224
AD  - Institute for Artificial Intelligence in Medicine, Northwestern University 
      Feinberg School of Medicine, Chicago, Illinois, United States of America.
FAU - Holmes, Kristi L
AU  - Holmes KL
AUID- ORCID: 0000-0001-8420-5254
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, Illinois, United States of America.
AD  - Institute for Artificial Intelligence in Medicine, Northwestern University 
      Feinberg School of Medicine, Chicago, Illinois, United States of America.
AD  - Galter Health Sciences Library and Learning Center, Northwestern University 
      Feinberg School of Medicine, Chicago, Illinois, United States of America.
FAU - Kho, Abel
AU  - Kho A
AUID- ORCID: 0000-0003-1993-5634
AD  - Divisions of General Internal Medicine and Health and Biomedical Informatics, 
      Department of Medicine, Northwestern University Feinberg School of Medicine, 
      Chicago, Illinois, United States of America.
AD  - Institute for Artificial Intelligence in Medicine, Northwestern University 
      Feinberg School of Medicine, Chicago, Illinois, United States of America.
LA  - eng
GR  - F32 HL162377/HL/NHLBI NIH HHS/United States
GR  - UL1 TR001422/TR/NCATS NIH HHS/United States
GR  - K23 HL155970/HL/NHLBI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20231005
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
UOF - medRxiv. 2023 Apr 03;:. PMID: 37066228
MH  - Humans
MH  - Educational Status
MH  - *Faculty
MH  - *Mainstreaming, Education
MH  - Health Facilities
MH  - Probability
PMC - PMC10553335
COIS- The authors report no conflicting interests.
EDAT- 2023/10/05 18:42
MHDA- 2023/11/01 12:45
CRDT- 2023/10/05 13:33
PHST- 2023/05/12 00:00 [received]
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/11/01 12:45 [medline]
PHST- 2023/10/05 18:42 [pubmed]
PHST- 2023/10/05 13:33 [entrez]
AID - PONE-D-23-13566 [pii]
AID - 10.1371/journal.pone.0292216 [doi]
PST - epublish
SO  - PLoS One. 2023 Oct 5;18(10):e0292216. doi: 10.1371/journal.pone.0292216. 
      eCollection 2023.

PMID- 37986970
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231129
DP  - 2023 Nov 8
TI  - ChatGPT usage in the Reactome curation process.
LID - 2023.11.08.566195 [pii]
LID - 10.1101/2023.11.08.566195 [doi]
AB  - Appreciating the rapid advancement and ubiquity of generative AI, particularly 
      ChatGPT, a chatbot using large language models like GPT, we endeavour to explore 
      the potential application of ChatGPT in the data collection and annotation stages 
      within the Reactome curation process. This exploration aimed to create an 
      automated or semi-automated framework to mitigate the extensive manual effort 
      traditionally required for gathering and annotating information pertaining to 
      biological pathways, adopting a Reactome "reaction-centric" approach. In this 
      pilot study, we used ChatGPT/GPT4 to address gaps in the pathway annotation and 
      enrichment in parallel with the conventional manual curation process. This 
      approach facilitated a comparative analysis, where we assessed the outputs 
      generated by ChatGPT against manually extracted information. The primary 
      objective of this comparison was to ascertain the efficiency of integrating 
      ChatGPT or other large language models into the Reactome curation workflow and 
      helping plan our annotation pipeline, ultimately improving our protein-to-pathway 
      association in a reliable and automated or semi-automated way. In the process, we 
      identified some promising capabilities and inherent challenges associated with 
      the utilisation of ChatGPT/GPT4 in general and also specifically in the context 
      of Reactome curation processes. We describe approaches and tools for refining the 
      output given by ChatGPT/GPT4 that aid in generating more accurate and detailed 
      output.
FAU - Tiwari, Krishna
AU  - Tiwari K
AUID- ORCID: 0000-0002-3699-0937
AD  - European Molecular Biology Laboratory, European Bioinformatics Institute 
      (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
AD  - Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
FAU - Matthews, Lisa
AU  - Matthews L
AUID- ORCID: 0000-0001-5707-3065
AD  - NYU Grossman School of Medicine, New York, NY 10016, USA.
FAU - May, Bruce
AU  - May B
AD  - Ontario Institute for Cancer Research, Toronto, Ontario, M5G 0A3, Canada.
FAU - Shamovsky, Veronica
AU  - Shamovsky V
AUID- ORCID: 0000-0002-2187-2241
AD  - NYU Grossman School of Medicine, New York, NY 10016, USA.
FAU - Orlic-Milacic, Marija
AU  - Orlic-Milacic M
AUID- ORCID: 0000-0002-3218-5631
AD  - Ontario Institute for Cancer Research, Toronto, Ontario, M5G 0A3, Canada.
FAU - Rothfels, Karen
AU  - Rothfels K
AUID- ORCID: 0000-0002-0705-7048
AD  - Ontario Institute for Cancer Research, Toronto, Ontario, M5G 0A3, Canada.
FAU - Ragueneau, Eliot
AU  - Ragueneau E
AUID- ORCID: 0000-0002-7876-6503
AD  - European Molecular Biology Laboratory, European Bioinformatics Institute 
      (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
AD  - Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
FAU - Gong, Chuqiao
AU  - Gong C
AUID- ORCID: 0000-0001-8674-1739
AD  - European Molecular Biology Laboratory, European Bioinformatics Institute 
      (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
AD  - Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
FAU - Stephan, Ralf
AU  - Stephan R
AUID- ORCID: 0000-0002-4650-631X
AD  - Ontario Institute for Cancer Research, Toronto, Ontario, M5G 0A3, Canada.
FAU - Li, Nancy
AU  - Li N
AD  - Ontario Institute for Cancer Research, Toronto, Ontario, M5G 0A3, Canada.
FAU - Wu, Guanming
AU  - Wu G
AUID- ORCID: 0000-0001-8196-1177
AD  - Oregon Health and Science University, Portland, OR 97239, USA.
FAU - Stein, Lincoln
AU  - Stein L
AUID- ORCID: 0000-0002-1983-4588
AD  - Ontario Institute for Cancer Research, Toronto, Ontario, M5G 0A3, Canada.
FAU - D'Eustachio, Peter
AU  - D'Eustachio P
AUID- ORCID: 0000-0002-5494-626X
AD  - NYU Grossman School of Medicine, New York, NY 10016, USA.
FAU - Hermjakob, Henning
AU  - Hermjakob H
AUID- ORCID: 0000-0001-8479-0262
AD  - European Molecular Biology Laboratory, European Bioinformatics Institute 
      (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
AD  - Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
LA  - eng
GR  - U41 HG003751/HG/NHGRI NIH HHS/United States
GR  - U54 GM114833/GM/NIGMS NIH HHS/United States
GR  - U24 HG012198/HG/NHGRI NIH HHS/United States
GR  - U24 HG011851/HG/NHGRI NIH HHS/United States
GR  - U01 CA239069/CA/NCI NIH HHS/United States
PT  - Preprint
DEP - 20231108
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10659344
EDAT- 2023/11/21 06:42
MHDA- 2023/11/21 06:43
CRDT- 2023/11/21 05:09
PHST- 2023/11/21 06:42 [pubmed]
PHST- 2023/11/21 06:43 [medline]
PHST- 2023/11/21 05:09 [entrez]
AID - 2023.11.08.566195 [pii]
AID - 10.1101/2023.11.08.566195 [doi]
PST - epublish
SO  - bioRxiv. 2023 Nov 8:2023.11.08.566195. doi: 10.1101/2023.11.08.566195. Preprint.

PMID- 37122812
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230502
IS  - 2673-253X (Electronic)
IS  - 2673-253X (Linking)
VI  - 5
DP  - 2023
TI  - AI chatbots not yet ready for clinical use.
PG  - 1161098
LID - 10.3389/fdgth.2023.1161098 [doi]
LID - 1161098
AB  - As large language models (LLMs) expand and become more advanced, so do the 
      natural language processing capabilities of conversational AI, or "chatbots". 
      OpenAI's recent release, ChatGPT, uses a transformer-based model to enable 
      human-like text generation and question-answering on general domain knowledge, 
      while a healthcare-specific Large Language Model (LLM) such as GatorTron has 
      focused on the real-world healthcare domain knowledge. As LLMs advance to achieve 
      near human-level performances on medical question and answering benchmarks, it is 
      probable that Conversational AI will soon be developed for use in healthcare. In 
      this article we discuss the potential and compare the performance of two 
      different approaches to generative pretrained transformers-ChatGPT, the most 
      widely used general conversational LLM, and Foresight, a GPT (generative 
      pretrained transformer) based model focused on modelling patients and disorders. 
      The comparison is conducted on the task of forecasting relevant diagnoses based 
      on clinical vignettes. We also discuss important considerations and limitations 
      of transformer-based chatbots for clinical use.
CI  - © 2023 Au Yeung, Kraljevic, Luintel, Balston, Idowu, Dobson and Teo.
FAU - Au Yeung, Joshua
AU  - Au Yeung J
AD  - Department of Neuroscience, Kings College Hospital, London, United Kingdom.
AD  - Guys & St Thomas Hospital, London, United Kingdom.
FAU - Kraljevic, Zeljko
AU  - Kraljevic Z
AD  - Department of Biostatistics, Kings College London, London, United Kingdom.
FAU - Luintel, Akish
AU  - Luintel A
AD  - Department of Neuroscience, Kings College Hospital, London, United Kingdom.
FAU - Balston, Alfred
AU  - Balston A
AD  - Guys & St Thomas Hospital, London, United Kingdom.
FAU - Idowu, Esther
AU  - Idowu E
AD  - Guys & St Thomas Hospital, London, United Kingdom.
FAU - Dobson, Richard J
AU  - Dobson RJ
AD  - Department of Biostatistics, Kings College London, London, United Kingdom.
AD  - NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust 
      and King's College London, London, United Kingdom.
FAU - Teo, James T
AU  - Teo JT
AD  - Department of Neuroscience, Kings College Hospital, London, United Kingdom.
AD  - Guys & St Thomas Hospital, London, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230412
PL  - Switzerland
TA  - Front Digit Health
JT  - Frontiers in digital health
JID - 101771889
PMC - PMC10130576
OTO - NOTNLM
OT  - AI safety
OT  - chatbot
OT  - digital health
OT  - large language models
OT  - natural language processing (computer science)
OT  - transformer
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest. The handling editor BA declared a past co-authorship with 
      the author RJD.
EDAT- 2023/05/01 06:42
MHDA- 2023/05/01 06:43
CRDT- 2023/05/01 03:22
PHST- 2023/02/07 00:00 [received]
PHST- 2023/03/27 00:00 [accepted]
PHST- 2023/05/01 06:43 [medline]
PHST- 2023/05/01 06:42 [pubmed]
PHST- 2023/05/01 03:22 [entrez]
AID - 10.3389/fdgth.2023.1161098 [doi]
PST - epublish
SO  - Front Digit Health. 2023 Apr 12;5:1161098. doi: 10.3389/fdgth.2023.1161098. 
      eCollection 2023.

PMID- 37168339
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230514
IS  - 2156-6976 (Print)
IS  - 2156-6976 (Electronic)
IS  - 2156-6976 (Linking)
VI  - 13
IP  - 4
DP  - 2023
TI  - The role of ChatGPT in scientific communication: writing better scientific review 
      articles.
PG  - 1148-1154
AB  - Artificial intelligence tools represent an exciting opportunity for scientists to 
      streamline their research and write impactful articles. Using artificial 
      intelligence tools like ChatGPT can greatly improve writing review articles for 
      scientists, by enhancing efficiency and quality. ChatGPT speeds up writing, 
      develops outlines, adds details, and helps improve writing style. However, 
      ChatGPT's limitations must be kept in mind, and generated text must be reviewed 
      and edited to avoid plagiarism and fabrication. Despite these limitations, 
      ChatGPT is a powerful tool that allows scientists to focus on analyzing and 
      interpreting literature reviews. Embracing these tools can help scientists 
      produce meaningful research in a more efficient and effective manner, however 
      caution must be taken and unchecked use of ChatGPT in writing should be avoided.
CI  - AJCR Copyright © 2023.
FAU - Huang, Jingshan
AU  - Huang J
AD  - School of Computing and College of Medicine, University of South Alabama Mobile, 
      AL, USA.
FAU - Tan, Ming
AU  - Tan M
AD  - Institute of Biochemistry and Molecular Biology, Institute of Biomedical 
      Sciences, and Research Center for Cancer Biology, China Medical University 
      Taichung, Taiwan.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230415
PL  - United States
TA  - Am J Cancer Res
JT  - American journal of cancer research
JID - 101549944
PMC - PMC10164801
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - review article
OT  - scientific writing
COIS- None.
EDAT- 2023/05/12 01:07
MHDA- 2023/05/12 01:08
CRDT- 2023/05/11 19:19
PHST- 2023/03/13 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/05/12 01:08 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 19:19 [entrez]
PST - epublish
SO  - Am J Cancer Res. 2023 Apr 15;13(4):1148-1154. eCollection 2023.

PMID- 37459084
OWN - NLM
STAT- MEDLINE
DCOM- 20230906
LR  - 20231003
IS  - 2168-6211 (Electronic)
IS  - 2168-6203 (Print)
IS  - 2168-6203 (Linking)
VI  - 177
IP  - 9
DP  - 2023 Sep 1
TI  - Performance of a Large Language Model on Practice Questions for the Neonatal 
      Board Examination.
PG  - 977-979
LID - 10.1001/jamapediatrics.2023.2373 [doi]
FAU - Beam, Kristyn
AU  - Beam K
AD  - Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, 
      Massachusetts.
FAU - Sharma, Puneet
AU  - Sharma P
AD  - Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts.
FAU - Kumar, Bhawesh
AU  - Kumar B
AD  - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 
      Massachusetts.
FAU - Wang, Cindy
AU  - Wang C
AD  - Department of Statistics, Harvard University, Cambridge, Massachusetts.
FAU - Brodsky, Dara
AU  - Brodsky D
AD  - Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, 
      Massachusetts.
FAU - Martin, Camilia R
AU  - Martin CR
AD  - Division of Neonatology, Weill Cornell Medicine, New York, New York.
FAU - Beam, Andrew
AU  - Beam A
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      Massachusetts.
LA  - eng
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - United States
TA  - JAMA Pediatr
JT  - JAMA pediatrics
JID - 101589544
SB  - IM
MH  - Infant, Newborn
MH  - Humans
MH  - *Certification
MH  - *Specialty Boards
MH  - Language
PMC - PMC10352922
OAB - This Diagnostic/Prognostic Study evaluates the performance of a large language 
      model in generating answers to practice questions for the neonatal-perinatal 
      board examination.
OABL- eng
COIS- Conflict of Interest Disclosures: None reported.
EDAT- 2023/07/17 15:09
MHDA- 2023/09/06 06:42
PMCR- 2024/07/17
CRDT- 2023/07/17 11:33
PHST- 2024/07/17 00:00 [pmc-release]
PHST- 2023/09/06 06:42 [medline]
PHST- 2023/07/17 15:09 [pubmed]
PHST- 2023/07/17 11:33 [entrez]
AID - 2807329 [pii]
AID - pld230028 [pii]
AID - 10.1001/jamapediatrics.2023.2373 [doi]
PST - ppublish
SO  - JAMA Pediatr. 2023 Sep 1;177(9):977-979. doi: 10.1001/jamapediatrics.2023.2373.

PMID- 37516680
OWN - NLM
STAT- Publisher
LR  - 20230729
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 29
TI  - ChatGPT and Clinical Decision Support: Scope, Application, and Limitations.
LID - 10.1007/s10439-023-03329-4 [doi]
AB  - This study examines ChatGPT's role in clinical decision support, by analyzing its 
      scope, application, and limitations. By analyzing patient data and providing 
      evidence-based recommendations, ChatGPT, an AI language model, can help 
      healthcare professionals make well-informed decisions. This study examines 
      ChatGPT's use in clinical decision support, including diagnosis and treatment 
      planning. However, it acknowledges limitations like biases, lack of contextual 
      understanding, and human oversight and also proposes a framework for the future 
      clinical decision support system. Understanding these factors will allow 
      healthcare professionals to utilize ChatGPT effectively and make accurate 
      clinical decisions. Further research is needed to understand the implications of 
      using ChatGPT in healthcare settings and to develop safeguards for responsible 
      use.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Ferdush, Jannatul
AU  - Ferdush J
AD  - Department of Computer Science and Engineering, Jashore University of Science and 
      Technology, Jashore, 7408, Bangladesh. jannatulferdush@just.edu.bd.
FAU - Begum, Mahbuba
AU  - Begum M
AD  - Department of Computer Science and Engineering, Mawlana Bhasani Science and 
      Technology, Tangail, 1902, Bangladesh.
FAU - Hossain, Sakib Tanvir
AU  - Hossain ST
AD  - Department of Mechanical Engineering, Khulna University of Engineering and 
      Technology, Khulna, 9203, Bangladesh.
LA  - eng
PT  - Letter
DEP - 20230729
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Biasness
OT  - CDS
OT  - ChatGPT
OT  - Ethics
EDAT- 2023/07/30 01:06
MHDA- 2023/07/30 01:06
CRDT- 2023/07/29 23:04
PHST- 2023/07/15 00:00 [received]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/07/30 01:06 [medline]
PHST- 2023/07/30 01:06 [pubmed]
PHST- 2023/07/29 23:04 [entrez]
AID - 10.1007/s10439-023-03329-4 [pii]
AID - 10.1007/s10439-023-03329-4 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 29. doi: 10.1007/s10439-023-03329-4.

PMID- 37885522
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231028
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Analyzing the Performance of ChatGPT About Osteoporosis.
PG  - e45890
LID - 10.7759/cureus.45890 [doi]
LID - e45890
AB  - INTRODUCTION: This study evaluates the knowledge of ChatGPT about osteoporosis. 
      METHODS: Osteoporosis-related frequently asked questions (FAQs) created by 
      examining the websites frequently visited by patients, the official websites of 
      hospitals, and social media. Questions based on these scientific data have been 
      prepared in accordance with National Osteoporosis Guideline Group guides. Rater 
      scored all ChatGPT answers between 1 and 4 (1 stated that the information was 
      completely correct, 2 stated that the information was correct but insufficient, 3 
      stated that although some of the information was correct, there was incorrect 
      information in the answer, and 4 stated that the answer consisted of completely 
      incorrect information). The reproducibility of ChatGPT responses on osteoporosis 
      was assessed by asking each question twice. The repeatability of the ChatGPT 
      answer was considered as getting the same score twice. RESULTS: ChatGPT responded 
      to 72 FAQs with an accuracy rate of 80.6%. The highest accuracy in ChatGPT's 
      answers about osteoporosis was in the prevention category, 91.7%, and in the 
      general knowledge category, 85.8%. Only 19 of the 31 (61.3%) questions prepared 
      according to the National Osteoporosis Guideline Group guidelines were answered 
      correctly by ChatGPT, and two answers (6.4%) were categorized as grade 4. The 
      reproducibility rate of ChatGPT answers on 72 FAQs was 86.1% and the 
      reproducibility rate of ChatGPT answers on National Osteoporosis Guideline Group 
      guidelines was 83.9%. CONCLUSION: Present study outcomes for the first time 
      showed that ChatGPT provided adequate answers to more than 80% of FAQs about 
      osteoporosis. However, the accuracy of ChatGPT's answers to inquiries based on 
      National Osteoporosis Guideline Group guidelines was decreased to 61.3%.
CI  - Copyright © 2023, Cinar et al.
FAU - Cinar, Cigdem
AU  - Cinar C
AD  - Department of Interventional Physiatry, Biruni University, Istanbul, TUR.
LA  - eng
PT  - Journal Article
DEP - 20230925
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10599213
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - guideline
OT  - information source
OT  - osteoporosis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/27 06:43
MHDA- 2023/10/27 06:44
CRDT- 2023/10/27 04:19
PHST- 2023/09/24 00:00 [accepted]
PHST- 2023/10/27 06:44 [medline]
PHST- 2023/10/27 06:43 [pubmed]
PHST- 2023/10/27 04:19 [entrez]
AID - 10.7759/cureus.45890 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 25;15(9):e45890. doi: 10.7759/cureus.45890. eCollection 2023 
      Sep.

PMID- 37166289
OWN - NLM
STAT- MEDLINE
DCOM- 20230515
LR  - 20230830
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Print)
IS  - 0740-9303 (Linking)
VI  - 39
IP  - 3
DP  - 2023 May-Jun 01
TI  - ChatGPT and Lacrimal Drainage Disorders: Performance and Scope of Improvement.
PG  - 221-225
LID - 10.1097/IOP.0000000000002418 [doi]
AB  - PURPOSE: This study aimed to report the performance of the large language model 
      ChatGPT (OpenAI, San Francisco, CA, U.S.A.) in the context of lacrimal drainage 
      disorders. METHODS: A set of prompts was constructed through questions and 
      statements spanning common and uncommon aspects of lacrimal drainage disorders. 
      Care was taken to avoid constructing prompts that had significant or new 
      knowledge beyond the year 2020. Each of the prompts was presented thrice to 
      ChatGPT. The questions covered common disorders such as primary acquired 
      nasolacrimal duct obstruction and congenital nasolacrimal duct obstruction and 
      their cause and management. The prompts also tested ChatGPT on certain specifics, 
      such as the history of dacryocystorhinostomy (DCR) surgery, lacrimal pump 
      anatomy, and human canalicular surfactants. ChatGPT was also quizzed on 
      controversial topics such as silicone intubation and the use of mitomycin C in 
      DCR surgery. The responses of ChatGPT were carefully analyzed for evidence-based 
      content, specificity of the response, presence of generic text, disclaimers, 
      factual inaccuracies, and its abilities to admit mistakes and challenge incorrect 
      premises. Three lacrimal surgeons graded the responses into three categories: 
      correct, partially correct, and factually incorrect. RESULTS: A total of 21 
      prompts were presented to the ChatGPT. The responses were detailed and were based 
      according to the prompt structure. In response to most questions, ChatGPT 
      provided a generic disclaimer that it could not give medical advice or 
      professional opinion but then provided an answer to the question in detail. 
      Specific prompts such as "how can I perform an external DCR?" were responded by a 
      sequential listing of all the surgical steps. However, several factual 
      inaccuracies were noted across many ChatGPT replies. Several responses on 
      controversial topics such as silicone intubation and mitomycin C were generic and 
      not precisely evidence-based. ChatGPT's response to specific questions such as 
      canalicular surfactants and idiopathic canalicular inflammatory disease was poor. 
      The presentation of variable prompts on a single topic led to responses with 
      either repetition or recycling of the phrases. Citations were uniformly missing 
      across all responses. Agreement among the three observers was high (95%) in 
      grading the responses. The responses of ChatGPT were graded as correct for only 
      40% of the prompts, partially correct in 35%, and outright factually incorrect in 
      25%. Hence, some degree of factual inaccuracy was present in 60% of the 
      responses, if we consider the partially correct responses. The exciting aspect 
      was that ChatGPT was able to admit mistakes and correct them when presented with 
      counterarguments. It was also capable of challenging incorrect prompts and 
      premises. CONCLUSION: The performance of ChatGPT in the context of lacrimal 
      drainage disorders, at best, can be termed average. However, the potential of 
      this AI chatbot to influence medicine is enormous. There is a need for it to be 
      specifically trained and retrained for individual medical subspecialties.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on 
      behalf of the American Society of Ophthalmic Plastic and Reconstructive Surgery, 
      Inc.
FAU - Ali, Mohammad Javed
AU  - Ali MJ
AD  - Govindram Seksaria Institute of Dacryology, L.V. Prasad Eye Institute, Hyderabad, 
      India.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230510
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
RN  - 50SG953SK6 (Mitomycin)
RN  - 0 (Silicones)
SB  - IM
CIN - Ophthalmic Plast Reconstr Surg. 2023 May-Jun 01;39(3):203-205. PMID: 37166288
MH  - Humans
MH  - *Dacryocystorhinostomy/methods
MH  - *Lacrimal Duct Obstruction/diagnosis/therapy
MH  - *Nasolacrimal Duct/surgery
MH  - Mitomycin
MH  - Intubation
MH  - Silicones
PMC - PMC10171282
COIS- The author has no financial or conflicts of interest to disclose.
EDAT- 2023/05/11 13:18
MHDA- 2023/05/15 06:42
CRDT- 2023/05/11 09:53
PHST- 2023/05/15 06:42 [medline]
PHST- 2023/05/11 13:18 [pubmed]
PHST- 2023/05/11 09:53 [entrez]
AID - 00002341-202305000-00004 [pii]
AID - 10.1097/IOP.0000000000002418 [doi]
PST - ppublish
SO  - Ophthalmic Plast Reconstr Surg. 2023 May-Jun 01;39(3):221-225. doi: 
      10.1097/IOP.0000000000002418. Epub 2023 May 10.

PMID- 38049066
OWN - NLM
STAT- Publisher
LR  - 20231204
IS  - 1544-3450 (Electronic)
IS  - 1086-5802 (Linking)
DP  - 2023 Dec 2
TI  - Effectiveness of ChatGPT in clinical pharmacy and the Role of Artificial 
      Intelligence in medication therapy management.
LID - S1544-3191(23)00384-9 [pii]
LID - 10.1016/j.japh.2023.11.023 [doi]
AB  - BACKGROUND: The use of artificial intelligence (AI) to optimize medication 
      therapy management (MTM) in identifying drug interactions may potentially improve 
      MTM efficiency. ChatGPT, an AI language model, may be applied to identify 
      medication interventions by integrating patient and drug databases. ChatGPT has 
      been shown to be effective in other areas of clinical medicine from diagnosis to 
      patient management. However, ChatGPT's ability to manage MTM related activities 
      is little known. OBJECTIVES: To evaluate the effectiveness of ChatGPT in MTM 
      services in simple, complex, and very complex cases to understand AI 
      contributions in MTM. METHODS: Two clinical pharmacists rated and validated the 
      difficulty of patient cases from simple, complex, and very complex. ChatGPT's 
      response to the cases was assessed based on three criteria: the ability to 
      identify drug interactions, precision in recommending alternatives, and 
      appropriateness in devising management plans. Two clinical pharmacists validated 
      the accuracy of ChatGPT's responses and compared them to actual answers for each 
      complexity level. RESULTS: ChatGPT 4 accurately solved 39 out of 39 (100 %) 
      patient cases. ChatGPT successfully identified drug interactions, provided 
      therapy recommendations and formulated general management plans, but it did not 
      recommend specific dosages. Results suggest it can assist pharmacists in 
      formulating MTM plans to improve overall efficiency. CONCLUSION: The application 
      of ChatGPT in MTM has the potential to enhance patient safety and involvement, 
      lower healthcare costs, and assist healthcare providers in medication management 
      and identifying drug interactions. Future pharmacists can utilize AI models such 
      as ChatGPT to improve patient care. The future of the pharmacy profession will 
      depend on how the field responds to the changing need for patient care optimized 
      by AI and automation.
CI  - Copyright © 2023. Published by Elsevier Inc.
FAU - Roosan, Don
AU  - Roosan D
AD  - Assistant Professor, College of Pharmacy, Western University of Health Sciences, 
      309 E 2nd St, Pomona, CA 91766.
FAU - Padua, Pauline
AU  - Padua P
AD  - College of Pharmacy, Western University of Health Sciences, Pomona, CA. 
      Electronic address: pauline.padua@westernu.edu.
FAU - Khan, Raiyan
AU  - Khan R
AD  - Montclair State University, Montclair, NJ, Research Associate.
FAU - Khan, Hasiba
AU  - Khan H
AD  - Clinical Research Lead, Tekurai LLC.
FAU - Verzosa, Claudia
AU  - Verzosa C
AD  - College of Pharmacy, Western University of Health Sciences, Pomona, CA.
FAU - Wu, Yanting
AU  - Wu Y
AD  - College of Pharmacy, Western University of Health Sciences, Pomona, CA.
LA  - eng
PT  - Journal Article
DEP - 20231202
PL  - United States
TA  - J Am Pharm Assoc (2003)
JT  - Journal of the American Pharmacists Association : JAPhA
JID - 101176252
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ChatGPT
OT  - Medication therapy management (MTM)
EDAT- 2023/12/05 00:42
MHDA- 2023/12/05 00:42
CRDT- 2023/12/04 19:26
PHST- 2023/04/23 00:00 [received]
PHST- 2023/10/27 00:00 [revised]
PHST- 2023/11/27 00:00 [accepted]
PHST- 2023/12/05 00:42 [medline]
PHST- 2023/12/05 00:42 [pubmed]
PHST- 2023/12/04 19:26 [entrez]
AID - S1544-3191(23)00384-9 [pii]
AID - 10.1016/j.japh.2023.11.023 [doi]
PST - aheadofprint
SO  - J Am Pharm Assoc (2003). 2023 Dec 2:S1544-3191(23)00384-9. doi: 
      10.1016/j.japh.2023.11.023.

PMID- 37680954
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230909
IS  - 2624-909X (Electronic)
IS  - 2624-909X (Linking)
VI  - 6
DP  - 2023
TI  - Universal skepticism of ChatGPT: a review of early literature on chat generative 
      pre-trained transformer.
PG  - 1224976
LID - 10.3389/fdata.2023.1224976 [doi]
LID - 1224976
AB  - ChatGPT, a new language model developed by OpenAI, has garnered significant 
      attention in various fields since its release. This literature review provides an 
      overview of early ChatGPT literature across multiple disciplines, exploring its 
      applications, limitations, and ethical considerations. The review encompasses 
      Scopus-indexed publications from November 2022 to April 2023 and includes 156 
      articles related to ChatGPT. The findings reveal a predominance of negative 
      sentiment across disciplines, though subject-specific attitudes must be 
      considered. The review highlights the implications of ChatGPT in many fields 
      including healthcare, raising concerns about employment opportunities and ethical 
      considerations. While ChatGPT holds promise for improved communication, further 
      research is needed to address its capabilities and limitations. This literature 
      review provides insights into early research on ChatGPT, informing future 
      investigations and practical applications of chatbot technology, as well as 
      development and usage of generative AI.
CI  - Copyright © 2023 Watters and Lemanski.
FAU - Watters, Casey
AU  - Watters C
AD  - Faculty of Law, Bond University, Gold Coast, QLD, Australia.
FAU - Lemanski, Michal K
AU  - Lemanski MK
AD  - Institute for Human Resource Management, WU Vienna, Vienna, Austria.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230823
PL  - Switzerland
TA  - Front Big Data
JT  - Frontiers in big data
JID - 101770603
PMC - PMC10482048
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT
OT  - artificial intelligence
OT  - disruptive technology
OT  - large language model (LLM)
OT  - transformer
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/08 06:43
MHDA- 2023/09/08 06:44
CRDT- 2023/09/08 04:09
PHST- 2023/05/18 00:00 [received]
PHST- 2023/07/10 00:00 [accepted]
PHST- 2023/09/08 06:44 [medline]
PHST- 2023/09/08 06:43 [pubmed]
PHST- 2023/09/08 04:09 [entrez]
AID - 10.3389/fdata.2023.1224976 [doi]
PST - epublish
SO  - Front Big Data. 2023 Aug 23;6:1224976. doi: 10.3389/fdata.2023.1224976. 
      eCollection 2023.

PMID- 37781591
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Sep 14
TI  - ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case 
      Reports.
LID - 2023.09.13.23295508 [pii]
LID - 10.1101/2023.09.13.23295508 [doi]
AB  - PURPOSE: To evaluate the efficiency of large language models (LLMs) including 
      ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on case reports. 
      DESIGN: Prospective study. SUBJECTS OR PARTICIPANTS: We selected 22 different 
      case reports of neuro-ophthalmic diseases from a publicly available online 
      database. These cases included a wide range of chronic and acute diseases that 
      are commonly seen by neuro-ophthalmic sub-specialists. METHODS: We inserted the 
      text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 
      and asked for the most probable diagnosis. We then presented the exact 
      information to two neuro-ophthalmologists and recorded their diagnoses followed 
      by comparison to responses from both versions of ChatGPT. MAIN OUTCOME MEASURES: 
      Diagnostic accuracy in terms of number of correctly diagnosed cases among 
      diagnoses. RESULTS: ChatGPT v3.5, ChatGPT Plus v4.0, and the two 
      neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) 
      out of 22 cases, respectively. The agreement between the various diagnostic 
      sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT 
      v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second 
      neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first 
      neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second 
      neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 
      (17%). CONCLUSIONS: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in 
      diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. 
      With further development, ChatGPT Plus v4.0 may have potential to be used in 
      clinical care settings to assist clinicians in providing quick, accurate 
      diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs 
      like ChatGPT in clinical settings that lack access to subspeciality trained 
      neuro-ophthalmologists deserves further research.
FAU - Madadi, Yeganeh
AU  - Madadi Y
AD  - Department of Ophthalmology, University of Tennessee Health Science Center, 
      Memphis, TN, USA.
FAU - Delsoz, Mohammad
AU  - Delsoz M
AD  - Department of Ophthalmology, University of Tennessee Health Science Center, 
      Memphis, TN, USA.
FAU - Lao, Priscilla A
AU  - Lao PA
AD  - Department of Ophthalmology, University of Tennessee Health Science Center, 
      Memphis, TN, USA.
FAU - Fong, Joseph W
AU  - Fong JW
AD  - Department of Ophthalmology, University of Tennessee Health Science Center, 
      Memphis, TN, USA.
FAU - Hollingsworth, T J
AU  - Hollingsworth TJ
AD  - Department of Ophthalmology, University of Tennessee Health Science Center, 
      Memphis, TN, USA.
FAU - Kahook, Malik Y
AU  - Kahook MY
AD  - Department of Ophthalmology, University of Colorado School of Medicine, Aurora, 
      CO, USA.
FAU - Yousefi, Siamak
AU  - Yousefi S
AD  - Department of Ophthalmology, University of Tennessee Health Science Center, 
      Memphis, TN, USA.
AD  - Department of Genetics, Genomics, and Informatics, University of Tennessee Health 
      Science Center, Memphis, TN, USA.
LA  - eng
GR  - R01 EY033005/EY/NEI NIH HHS/United States
GR  - R21 EY031725/EY/NEI NIH HHS/United States
PT  - Preprint
DEP - 20230914
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10540811
OTO - NOTNLM
OT  - Artificial Intelligence (AI)
OT  - ChatGPT
OT  - Diagnosis
OT  - Large Language Models (LLM)
OT  - Neuro-Ophthalmology
COIS- Conflicts of interest/Competing interests: Authors declare no relevant conflict 
      of interest(s) to disclose.
EDAT- 2023/10/02 06:42
MHDA- 2023/10/02 06:43
CRDT- 2023/10/02 04:48
PHST- 2023/10/02 06:42 [pubmed]
PHST- 2023/10/02 06:43 [medline]
PHST- 2023/10/02 04:48 [entrez]
AID - 2023.09.13.23295508 [pii]
AID - 10.1101/2023.09.13.23295508 [doi]
PST - epublish
SO  - medRxiv. 2023 Sep 14:2023.09.13.23295508. doi: 10.1101/2023.09.13.23295508. 
      Preprint.

PMID- 37635297
OWN - NLM
STAT- In-Process
LR  - 20231209
IS  - 1475-1313 (Electronic)
IS  - 0275-5408 (Linking)
VI  - 44
IP  - 1
DP  - 2024 Jan
TI  - Authors' Reply: Assessing the utility of ChatGPT as an artificial 
      intelligence-based large language model for information to answer questions on 
      myopia.
PG  - 233-234
LID - 10.1111/opo.13227 [doi]
FAU - Biswas, Sayantan
AU  - Biswas S
AUID- ORCID: 0000-0001-6011-0365
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Logan, Nicola S
AU  - Logan NS
AUID- ORCID: 0000-0002-0538-9516
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Davies, Leon N
AU  - Davies LN
AUID- ORCID: 0000-0002-1554-0566
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Sheppard, Amy L
AU  - Sheppard AL
AUID- ORCID: 0000-0003-0035-8267
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
FAU - Wolffsohn, James S
AU  - Wolffsohn JS
AUID- ORCID: 0000-0003-4673-8927
AD  - School of Optometry, College of Health and Life Sciences, Aston University, 
      Birmingham, UK.
LA  - eng
PT  - Letter
DEP - 20230827
PL  - England
TA  - Ophthalmic Physiol Opt
JT  - Ophthalmic & physiological optics : the journal of the British College of 
      Ophthalmic Opticians (Optometrists)
JID - 8208839
SB  - IM
EDAT- 2023/08/28 06:42
MHDA- 2023/08/28 06:42
CRDT- 2023/08/28 00:42
PHST- 2023/08/16 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/08/28 06:42 [pubmed]
PHST- 2023/08/28 06:42 [medline]
PHST- 2023/08/28 00:42 [entrez]
AID - 10.1111/opo.13227 [doi]
PST - ppublish
SO  - Ophthalmic Physiol Opt. 2024 Jan;44(1):233-234. doi: 10.1111/opo.13227. Epub 2023 
      Aug 27.

PMID- 37671415
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231120
IS  - 1940-5480 (Electronic)
IS  - 1067-151X (Print)
IS  - 1067-151X (Linking)
VI  - 31
IP  - 23
DP  - 2023 Dec 1
TI  - Comparison of ChatGPT-3.5, ChatGPT-4, and Orthopaedic Resident Performance on 
      Orthopaedic Assessment Examinations.
PG  - 1173-1179
LID - 10.5435/JAAOS-D-23-00396 [doi]
AB  - INTRODUCTION: Artificial intelligence (AI) programs have the ability to answer 
      complex queries including medical profession examination questions. The purpose 
      of this study was to compare the performance of orthopaedic residents (ortho 
      residents) against Chat Generative Pretrained Transformer (ChatGPT)-3.5 and GPT-4 
      on orthopaedic assessment examinations. A secondary objective was to perform a 
      subgroup analysis comparing the performance of each group on questions that 
      included image interpretation versus text-only questions. METHODS: The ResStudy 
      orthopaedic examination question bank was used as the primary source of 
      questions. One hundred eighty questions and answer choices from nine different 
      orthopaedic subspecialties were directly input into ChatGPT-3.5 and then GPT-4. 
      ChatGPT did not have consistently available image interpretation, so no images 
      were directly provided to either AI format. Answers were recorded as correct 
      versus incorrect by the chatbot, and resident performance was recorded based on 
      user data provided by ResStudy. RESULTS: Overall, ChatGPT-3.5, GPT-4, and ortho 
      residents scored 29.4%, 47.2%, and 74.2%, respectively. There was a difference 
      among the three groups in testing success, with ortho residents scoring higher 
      than ChatGPT-3.5 and GPT-4 ( P < 0.001 and P < 0.001). GPT-4 scored higher than 
      ChatGPT-3.5 ( P = 0.002). A subgroup analysis was performed by dividing questions 
      into question stems without images and question stems with images. ChatGPT-3.5 
      was more correct (37.8% vs. 22.4%, respectively, OR = 2.1, P = 0.033) and 
      ChatGPT-4 was also more correct (61.0% vs. 35.7%, OR = 2.8, P < 0.001), when 
      comparing text-only questions versus questions with images. Residents were 72.6% 
      versus 75.5% correct with text-only questions versus questions with images, with 
      no significant difference ( P = 0.302). CONCLUSION: Orthopaedic residents were 
      able to answer more questions accurately than ChatGPT-3.5 and GPT-4 on 
      orthopaedic assessment examinations. GPT-4 is superior to ChatGPT-3.5 for 
      answering orthopaedic resident assessment examination questions. Both ChatGPT-3.5 
      and GPT-4 performed better on text-only questions than questions with images. It 
      is unlikely that GPT-4 or ChatGPT-3.5 would pass the American Board of 
      Orthopaedic Surgery written examination.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on 
      behalf of the American Academy of Orthopaedic Surgeons.
FAU - Massey, Patrick A
AU  - Massey PA
AUID- ORCID: 0000-0001-8637-0122
AD  - From the Department of Orthopaedic Surgery, Louisiana State University Health 
      Sciences Center Shreveport, Shreveport, LA.
FAU - Montgomery, Carver
AU  - Montgomery C
FAU - Zhang, Andrew S
AU  - Zhang AS
AUID- ORCID: 0000-0003-1799-8703
LA  - eng
PT  - Journal Article
DEP - 20230904
PL  - United States
TA  - J Am Acad Orthop Surg
JT  - The Journal of the American Academy of Orthopaedic Surgeons
JID - 9417468
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Orthopedics
MH  - Physical Examination
MH  - Software
PMC - PMC10627532
EDAT- 2023/09/06 06:42
MHDA- 2023/11/20 06:54
CRDT- 2023/09/06 03:59
PHST- 2023/04/26 00:00 [received]
PHST- 2023/07/22 00:00 [accepted]
PHST- 2023/11/20 06:54 [medline]
PHST- 2023/09/06 06:42 [pubmed]
PHST- 2023/09/06 03:59 [entrez]
AID - 00124635-990000000-00782 [pii]
AID - JAAOS-D-23-00396 [pii]
AID - 10.5435/JAAOS-D-23-00396 [doi]
PST - ppublish
SO  - J Am Acad Orthop Surg. 2023 Dec 1;31(23):1173-1179. doi: 
      10.5435/JAAOS-D-23-00396. Epub 2023 Sep 4.

PMID- 37562022
OWN - NLM
STAT- Publisher
LR  - 20230810
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
DP  - 2023 Aug 10
TI  - Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons 
      on the Demonstrated & Potential Applications, Promises, and Limitations of 
      ChatGPT.
LID - sjad260 [pii]
LID - 10.1093/asj/sjad260 [doi]
AB  - BACKGROUND: The rapidly evolving field of artificial intelligence (AI) holds 
      great potential for plastic surgeons. ChatGPT, a recently released AI large 
      language model (LLM), promises application across many disciplines, including 
      healthcare. OBJECTIVES: The aim of this article is to provide a plastic surgeon's 
      primer on AI, LLM, and ChatGPT, including an analysis of current demonstrated and 
      proposed clinical applications. METHODS: A systematic review was performed 
      identifying medical and surgical literature on ChatGPT's proposed clinical 
      applications. Variables assessed included applications investigated, command 
      tasks provided, user input information, AI-emulated human skills, output 
      validation, and reported limitations. RESULTS: The analysis included 175 articles 
      reporting on 13 plastic surgery applications and 116 additional clinical 
      applications, categorized by field and purpose. Thirty-four plastic surgery 
      applications were varied in their target audience, including attending plastic 
      surgeons (n=17, 50%), trainees/educators (n=8, 24.0%), researchers/scholars (n=7, 
      21%), and patients (n=2, 6%). The 15 identified limitations of ChatGPT were 
      categorized by training data, algorithm, and ethical considerations. CONCLUSIONS: 
      Widespread use of ChatGPT in plastic surgery will depend on rigorous research of 
      proposed applications to validate performance and address limitations. This 
      systemic review aims to guide research, development, and regulation to safely 
      adopt AI in plastic surgery.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of The 
      Aesthetic Society. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Abi-Rafeh, Jad
AU  - Abi-Rafeh J
AUID- ORCID: 0000-0002-7483-1515
AD  - Division of Plastic, Reconstructive, and Aesthetic Surgery, McGill University 
      Health Centre, Montreal, Quebec, Canada.
FAU - Xu, Hong Hao
AU  - Xu HH
AD  - Faculty of Medicine, Laval University, Quebec City, Quebec, Canada.
FAU - Kazan, Roy
AU  - Kazan R
AD  - Division of Plastic and Reconstructive Surgery, University of Pittsburgh, 
      Pittsburgh, PA, USA.
FAU - Tevlin, Ruth
AU  - Tevlin R
AD  - Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford 
      University, Palo Alto, CA, USA.
FAU - Furnas, Heather
AU  - Furnas H
AD  - Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford 
      University, Palo Alto, CA, USA.
LA  - eng
PT  - Journal Article
DEP - 20230810
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
EDAT- 2023/08/10 18:41
MHDA- 2023/08/10 18:41
CRDT- 2023/08/10 17:12
PHST- 2023/05/26 00:00 [received]
PHST- 2023/08/02 00:00 [revised]
PHST- 2023/08/04 00:00 [accepted]
PHST- 2023/08/10 18:41 [medline]
PHST- 2023/08/10 18:41 [pubmed]
PHST- 2023/08/10 17:12 [entrez]
AID - 7240719 [pii]
AID - 10.1093/asj/sjad260 [doi]
PST - aheadofprint
SO  - Aesthet Surg J. 2023 Aug 10:sjad260. doi: 10.1093/asj/sjad260.

PMID- 37506761
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231030
IS  - 1556-5653 (Electronic)
IS  - 0015-0282 (Linking)
VI  - 120
IP  - 5
DP  - 2023 Nov
TI  - A critical perspective on large language model to obtain clinical information 
      about fertility research.
PG  - 1084
LID - S0015-0282(23)00702-1 [pii]
LID - 10.1016/j.fertnstert.2023.07.016 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230726
PL  - United States
TA  - Fertil Steril
JT  - Fertility and sterility
JID - 0372772
SB  - IM
CON - Fertil Steril. 2023 Sep;120(3 Pt 2):575-583. PMID: 37217092
MH  - Humans
MH  - *Language
MH  - *Fertility
EDAT- 2023/07/29 06:42
MHDA- 2023/10/30 06:46
CRDT- 2023/07/28 19:13
PHST- 2023/07/13 00:00 [received]
PHST- 2023/07/18 00:00 [revised]
PHST- 2023/07/20 00:00 [accepted]
PHST- 2023/10/30 06:46 [medline]
PHST- 2023/07/29 06:42 [pubmed]
PHST- 2023/07/28 19:13 [entrez]
AID - S0015-0282(23)00702-1 [pii]
AID - 10.1016/j.fertnstert.2023.07.016 [doi]
PST - ppublish
SO  - Fertil Steril. 2023 Nov;120(5):1084. doi: 10.1016/j.fertnstert.2023.07.016. Epub 
      2023 Jul 26.

PMID- 36925365
OWN - NLM
STAT- MEDLINE
DCOM- 20230529
LR  - 20230720
IS  - 2211-5684 (Electronic)
IS  - 2211-5684 (Linking)
VI  - 104
IP  - 6
DP  - 2023 Jun
TI  - Beyond chatting: The opportunities and challenges of ChatGPT in medicine and 
      radiology.
PG  - 263-264
LID - S2211-5684(23)00030-X [pii]
LID - 10.1016/j.diii.2023.02.006 [doi]
FAU - Ferres, Juan M Lavista
AU  - Ferres JML
AD  - Microsoft, Inc., AI for Good Research Lab, Redmond, WA 98052, USA.
FAU - Weeks, William B
AU  - Weeks WB
AD  - Microsoft, Inc., AI for Good Research Lab, Redmond, WA 98052, USA.
FAU - Chu, Linda C
AU  - Chu LC
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, Johns 
      Hopkins University School of Medicine, Baltimore, MD 2128, USA.
FAU - Rowe, Steven P
AU  - Rowe SP
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, Johns 
      Hopkins University School of Medicine, Baltimore, MD 2128, USA. Electronic 
      address: srowe8@jhmi.edu.
FAU - Fishman, Elliot K
AU  - Fishman EK
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, Johns 
      Hopkins University School of Medicine, Baltimore, MD 2128, USA.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230314
PL  - France
TA  - Diagn Interv Imaging
JT  - Diagnostic and interventional imaging
JID - 101568499
SB  - IM
CON - Diagn Interv Imaging. 2023 Jun;104(6):269-274. PMID: 36858933
MH  - Humans
MH  - *Radiology
MH  - Radiography
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Generative pre-trained transformer
OT  - Radiology
COIS- Disclosure of interest SPR is a co-founder of, stockholder in, and consultant for 
      PlenaryAI, Inc. He has received research funding from, and serves as a consultant 
      for, Lantheus Pharmaceuticals, Inc, the producer of Pylarify AI. Juan M. Lavista 
      Ferres and William B. Weeks are employees of Microsoft. The other authors report 
      no relevant conflicts of interest.
EDAT- 2023/03/17 06:00
MHDA- 2023/05/29 06:42
CRDT- 2023/03/16 23:10
PHST- 2023/02/20 00:00 [received]
PHST- 2023/02/21 00:00 [accepted]
PHST- 2023/05/29 06:42 [medline]
PHST- 2023/03/17 06:00 [pubmed]
PHST- 2023/03/16 23:10 [entrez]
AID - S2211-5684(23)00030-X [pii]
AID - 10.1016/j.diii.2023.02.006 [doi]
PST - ppublish
SO  - Diagn Interv Imaging. 2023 Jun;104(6):263-264. doi: 10.1016/j.diii.2023.02.006. 
      Epub 2023 Mar 14.

PMID- 37581690
OWN - NLM
STAT- MEDLINE
DCOM- 20230905
LR  - 20230905
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Aug 15
TI  - ChatGPT Performs on the Chinese National Medical Licensing Examination.
PG  - 86
LID - 10.1007/s10916-023-01961-0 [doi]
AB  - ChatGPT, a language model developed by OpenAI, uses a 175 billion parameter 
      Transformer architecture for natural language processing tasks. This study aimed 
      to compare the knowledge and interpretation ability of ChatGPT with those of 
      medical students in China by administering the Chinese National Medical Licensing 
      Examination (NMLE) to both ChatGPT and medical students. We evaluated the 
      performance of ChatGPT in three years' worth of the NMLE, which consists of four 
      units. At the same time, the exam results were compared to those of medical 
      students who had studied for five years at medical colleges. ChatGPT's 
      performance was lower than that of the medical students, and ChatGPT's correct 
      answer rate was related to the year in which the exam questions were 
      released. ChatGPT's knowledge and interpretation ability for the NMLE were not 
      yet comparable to those of medical students in China. It is probable that these 
      abilities will improve through deep learning.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Wang, Xinyi
AU  - Wang X
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Gong, Zhenye
AU  - Gong Z
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Wang, Guoxin
AU  - Wang G
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Jia, Jingdan
AU  - Jia J
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Xu, Ying
AU  - Xu Y
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Zhao, Jialu
AU  - Zhao J
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Fan, Qingye
AU  - Fan Q
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Wu, Shaun
AU  - Wu S
AD  - WORK Medical Technology Group LTD, Hangzhou, China.
FAU - Hu, Weiguo
AU  - Hu W
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China.
FAU - Li, Xiaoyang
AU  - Li X
AUID- ORCID: 0000-0003-4309-8709
AD  - Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao 
      Tong University School of Medicine, 197 Ruijin Rd. II, Shanghai, 200025, China. 
      woodslee429@126.com.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20230815
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - Humans
MH  - Asian People
MH  - China
MH  - Knowledge
MH  - Language
MH  - *Artificial Intelligence
MH  - *Medicine/standards
MH  - *Licensure/standards
MH  - *Students, Medical/statistics & numerical data
MH  - *Educational Measurement/standards
OTO - NOTNLM
OT  - ChatGPT
OT  - Chinese National Medical Licensing Examination
OT  - Medical student
EDAT- 2023/08/15 12:42
MHDA- 2023/08/16 06:43
CRDT- 2023/08/15 11:05
PHST- 2023/02/14 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/08/16 06:43 [medline]
PHST- 2023/08/15 12:42 [pubmed]
PHST- 2023/08/15 11:05 [entrez]
AID - 10.1007/s10916-023-01961-0 [pii]
AID - 10.1007/s10916-023-01961-0 [doi]
PST - epublish
SO  - J Med Syst. 2023 Aug 15;47(1):86. doi: 10.1007/s10916-023-01961-0.

PMID- 37066228
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
DP  - 2023 Apr 3
TI  - An exploratory survey about using ChatGPT in education, healthcare, and research.
LID - 2023.03.31.23287979 [pii]
LID - 10.1101/2023.03.31.23287979 [doi]
AB  - Objective ChatGPT is the first large language model (LLM) to reach a large, 
      mainstream audience. Its rapid adoption and exploration by the population at 
      large has sparked a wide range of discussions regarding its acceptable and 
      optimal integration in different areas. In a hybrid (virtual and in-person) panel 
      discussion event, we examined various perspectives regarding the use of ChatGPT 
      in education, research, and healthcare. Materials and Methods We surveyed 
      in-person and online attendees using an audience interaction platform (Slido). We 
      quantitatively analyzed received responses on questions about the use of ChatGPT 
      in various contexts. We compared pairwise categorical groups with Fisher's Exact. 
      Furthermore, we used qualitative methods to analyze and code discussions. Results 
      We received 420 responses from an estimated 844 participants (response rate 
      49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried 
      ChatGPT compared with faculty. Those who had used ChatGPT were more interested in 
      using it in a wider range of contexts going forwards. Of the three discussed 
      contexts, the greatest uncertainty was shown about using ChatGPT in education. 
      Pros and cons were raised during discussion for the use of this technology in 
      education, research, and healthcare. Discussion There was a range of perspectives 
      around the uses of ChatGPT in education, research, and healthcare, with still 
      much uncertainty around its acceptability and optimal uses. There were different 
      perspectives from respondents of different roles (trainee vs faculty vs staff). 
      More discussion is needed to explore perceptions around the use of LLMs such as 
      ChatGPT in vital sectors such as education, healthcare and research. Given 
      involved risks and unforeseen challenges, taking a thoughtful and measured 
      approach in adoption would reduce the likelihood of harm.
FAU - Hosseini, Mohammad
AU  - Hosseini M
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, IL.
FAU - Gao, Catherine A
AU  - Gao CA
AD  - Department of Medicine (Division of Pulmonary and Critical Care), Northwestern 
      University Feinberg School of Medicine, Chicago, IL.
FAU - Liebovitz, David
AU  - Liebovitz D
AD  - Department of Medicine (Divisions of General Internal Medicine and Health and 
      Biomedical Informatics), Northwestern University Feinberg School of Medicine, 
      Chicago, IL.
AD  - Center for Medical Education in Digital Health and Data Science, Northwestern 
      University Feinberg School of Medicine, Chicago, IL.
FAU - Carvalho, Alexandre
AU  - Carvalho A
AD  - Department of Medicine (Division of Infectious Diseases), Northwestern University 
      Feinberg School of Medicine, Chicago, IL.
AD  - Center for Pathogen Genomics & Microbial Evolution, Northwestern University 
      Feinberg School of Medicine, Chicago, IL.
FAU - Ahmad, Faraz S
AU  - Ahmad FS
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, IL.
AD  - Department of Medicine (Division of Cardiology), Northwestern University Feinberg 
      School of Medicine, Chicago, IL.
AD  - Bluhm Cardiovascular Center for Artificial Intelligence, Northwestern Medicine, 
      Northwestern University Feinberg School of Medicine, Chicago, IL.
FAU - Luo, Yuan
AU  - Luo Y
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, IL.
FAU - MacDonald, Ngan
AU  - MacDonald N
AD  - Institute for Augmented Intelligence in Medicine, Northwestern University 
      Feinberg School of Medicine, Chicago, IL.
FAU - Holmes, Kristi
AU  - Holmes K
AD  - Department of Preventive Medicine, Northwestern University Feinberg School of 
      Medicine, Chicago, IL.
AD  - Galter Health Sciences Library and Learning Center, Northwestern University 
      Feinberg School of Medicine, Chicago, IL.
FAU - Kho, Abel
AU  - Kho A
AD  - Department of Medicine (Divisions of General Internal Medicine and Health and 
      Biomedical Informatics), Northwestern University Feinberg School of Medicine, 
      Chicago, IL.
AD  - Institute for Augmented Intelligence in Medicine, Northwestern University 
      Feinberg School of Medicine, Chicago, IL.
LA  - eng
GR  - F32 HL162377/HL/NHLBI NIH HHS/United States
GR  - K23 HL155970/HL/NHLBI NIH HHS/United States
GR  - UL1 TR001422/TR/NCATS NIH HHS/United States
PT  - Preprint
DEP - 20230403
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
UIN - PLoS One. 2023 Oct 5;18(10):e0292216. PMID: 37796786
PMC - PMC10104227
COIS- Conflicts of interest: The authors report no conflicting interests.
EDAT- 2023/04/18 06:00
MHDA- 2023/04/18 06:01
CRDT- 2023/04/17 04:08
PHST- 2023/04/18 06:01 [medline]
PHST- 2023/04/17 04:08 [entrez]
PHST- 2023/04/18 06:00 [pubmed]
AID - 2023.03.31.23287979 [pii]
AID - 10.1101/2023.03.31.23287979 [doi]
PST - epublish
SO  - medRxiv. 2023 Apr 3:2023.03.31.23287979. doi: 10.1101/2023.03.31.23287979. 
      Preprint.

PMID- 37954270
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231122
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 11
DP  - 2023 Nov
TI  - Humans are still better than ChatGPT: Case of the IEEEXtreme competition.
PG  - e21624
LID - 10.1016/j.heliyon.2023.e21624 [doi]
LID - e21624
AB  - Since the release of ChatGPT, numerous studies have highlighted the remarkable 
      performance of ChatGPT, which often rivals or even surpasses human capabilities 
      in various tasks and domains. However, this paper presents a contrasting 
      perspective by demonstrating an instance where human performance excels in 
      typical tasks suited for ChatGPT, specifically in the domain of computer 
      programming. We utilize the IEEExtreme Challenge competition as a benchmark-a 
      prestigious, annual international programming contest encompassing a wide range 
      of problems with different complexities. To conduct a thorough evaluation, we 
      selected and executed a diverse set of 102 challenges, drawn from five distinct 
      IEEExtreme editions, using three major programming languages: Python, Java, and 
      C++. Our empirical analysis provides evidence that contrary to popular belief, 
      human programmers maintain a competitive edge over ChatGPT in certain aspects of 
      problem-solving within the programming context. In fact, we found that the 
      average score obtained by ChatGPT on the set of IEEExtreme programming problems 
      is 3.9 to 5.8 times lower than the average human score, depending on the 
      programming language. This paper elaborates on these findings, offering critical 
      insights into the limitations and potential areas of improvement for AI-based 
      language models like ChatGPT.
CI  - © 2023 The Authors.
FAU - Koubaa, Anis
AU  - Koubaa A
AD  - Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 
      12435, Saudi Arabia.
FAU - Qureshi, Basit
AU  - Qureshi B
AD  - Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 
      12435, Saudi Arabia.
FAU - Ammar, Adel
AU  - Ammar A
AD  - Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 
      12435, Saudi Arabia.
FAU - Khan, Zahid
AU  - Khan Z
AD  - Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 
      12435, Saudi Arabia.
FAU - Boulila, Wadii
AU  - Boulila W
AD  - Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 
      12435, Saudi Arabia.
FAU - Ghouti, Lahouari
AU  - Ghouti L
AD  - Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 
      12435, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20231029
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10638003
OTO - NOTNLM
OT  - ChatGPT
OT  - Computer programming
OT  - GPT limitations
OT  - GPT performance
OT  - GPT-3.5
OT  - GPT-4
OT  - NLP
OT  - OpenAI
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/11/13 06:43
MHDA- 2023/11/13 06:44
CRDT- 2023/11/13 04:28
PHST- 2023/05/27 00:00 [received]
PHST- 2023/10/06 00:00 [revised]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/11/13 06:44 [medline]
PHST- 2023/11/13 06:43 [pubmed]
PHST- 2023/11/13 04:28 [entrez]
AID - S2405-8440(23)08832-1 [pii]
AID - e21624 [pii]
AID - 10.1016/j.heliyon.2023.e21624 [doi]
PST - epublish
SO  - Heliyon. 2023 Oct 29;9(11):e21624. doi: 10.1016/j.heliyon.2023.e21624. 
      eCollection 2023 Nov.

PMID- 37922967
OWN - NLM
STAT- Publisher
LR  - 20231103
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
DP  - 2023 Nov 1
TI  - Future potential challenges of using large language models like ChatGPT in daily 
      medical practice.
LID - S1546-1440(23)00851-7 [pii]
LID - 10.1016/j.jacr.2023.10.019 [doi]
FAU - Sedaghat, Sam
AU  - Sedaghat S
AD  - Department of Diagnostic and Interventional Radiology, University Hospital of 
      Heidelberg, Germany. Electronic address: samsedaghat1@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - challenges
OT  - healthcare
OT  - large language models
EDAT- 2023/11/06 03:54
MHDA- 2023/11/06 03:54
CRDT- 2023/11/03 20:13
PHST- 2023/08/22 00:00 [received]
PHST- 2023/09/28 00:00 [revised]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/11/06 03:54 [medline]
PHST- 2023/11/06 03:54 [pubmed]
PHST- 2023/11/03 20:13 [entrez]
AID - S1546-1440(23)00851-7 [pii]
AID - 10.1016/j.jacr.2023.10.019 [doi]
PST - aheadofprint
SO  - J Am Coll Radiol. 2023 Nov 1:S1546-1440(23)00851-7. doi: 
      10.1016/j.jacr.2023.10.019.

PMID- 37399881
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231120
IS  - 1873-0442 (Electronic)
IS  - 1477-8939 (Linking)
VI  - 54
DP  - 2023 Jul-Aug
TI  - ChatGPT in travel medicine: A friend or foe?
PG  - 102615
LID - S1477-8939(23)00075-3 [pii]
LID - 10.1016/j.tmaid.2023.102615 [doi]
FAU - Choudhary, Om Prakash
AU  - Choudhary OP
AD  - Department of Veterinary Anatomy, College of Veterinary Science, Guru Angad Dev 
      Veterinary and Animal Sciences University (GADVASU), Rampura Phul, Bathinda, 
      151103, Punjab, India. Electronic address: dr.om.choudhary@gmail.com.
FAU - Priyanka
AU  - Priyanka
AD  - Department of Veterinary Microbiology, College of Veterinary Science, Guru Angad 
      Dev Veterinary and Animal Sciences University (GADVASU), Rampura Phul, Bathinda, 
      151103, Punjab, India.
LA  - eng
PT  - Letter
DEP - 20230701
PL  - Netherlands
TA  - Travel Med Infect Dis
JT  - Travel medicine and infectious disease
JID - 101230758
SB  - IM
MH  - Humans
MH  - *Travel Medicine
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Pandemic
OT  - Travel medicine
OT  - Traveling
OT  - Vaccination
COIS- Declaration of competing interest All authors report no conflicts of interest 
      relevant to this article.
EDAT- 2023/07/04 01:05
MHDA- 2023/07/04 01:06
CRDT- 2023/07/03 19:19
PHST- 2023/06/27 00:00 [received]
PHST- 2023/06/27 00:00 [accepted]
PHST- 2023/07/04 01:06 [medline]
PHST- 2023/07/04 01:05 [pubmed]
PHST- 2023/07/03 19:19 [entrez]
AID - S1477-8939(23)00075-3 [pii]
AID - 10.1016/j.tmaid.2023.102615 [doi]
PST - ppublish
SO  - Travel Med Infect Dis. 2023 Jul-Aug;54:102615. doi: 10.1016/j.tmaid.2023.102615. 
      Epub 2023 Jul 1.

PMID- 38078915
OWN - NLM
STAT- Publisher
LR  - 20231211
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Dec 11
TI  - Correction: Complications Following Facelift and Neck Lift: Implementation and 
      Assessment of Large Language Model and Artificial Intelligence (ChatGPT) 
      Performance Across 16 Simulated Patient Presentations.
LID - 10.1007/s00266-023-03790-5 [doi]
FAU - Abi-Rafeh, Jad
AU  - Abi-Rafeh J
AUID- ORCID: 0000-0002-7483-1515
AD  - Division of Plastic, Reconstructive, and Aesthetic Surgery, McGill University 
      Health Centre, Montreal, QC, Canada.
FAU - Hanna, Steven
AU  - Hanna S
AD  - Manhattan Eye, Ear and Throat Hospital, New York, NY, USA.
FAU - Bassiri-Tehrani, Brian
AU  - Bassiri-Tehrani B
AD  - Private Practice, Atlanta, GA, USA.
FAU - Kazan, Roy
AU  - Kazan R
AD  - Division of Plastic and Reconstructive Surgery, University of Pittsburgh, 
      Pittsburgh, PA, USA.
FAU - Nahai, Foad
AU  - Nahai F
AD  - Department of Surgery, Emory University, Atlanta, GA, USA. nahaimd@aol.com.
LA  - eng
PT  - Published Erratum
DEP - 20231211
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EFR - Aesthetic Plast Surg. 2023 Aug 17;:. PMID: 37589944
EDAT- 2023/12/11 12:42
MHDA- 2023/12/11 12:42
CRDT- 2023/12/11 11:04
PHST- 2023/12/11 12:42 [medline]
PHST- 2023/12/11 12:42 [pubmed]
PHST- 2023/12/11 11:04 [entrez]
AID - 10.1007/s00266-023-03790-5 [pii]
AID - 10.1007/s00266-023-03790-5 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Dec 11. doi: 10.1007/s00266-023-03790-5.

PMID- 37452215
OWN - NLM
STAT- Publisher
LR  - 20230714
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 14
TI  - The Role of ChatGPT in Medical Research: Progress and Limitations.
LID - 10.1007/s10439-023-03311-0 [doi]
AB  - Advancements in AI have resulted in the development of sophisticated language 
      models like ChatGPT, which can generate human-like text. While ChatGPT is useful 
      for clarifying concepts and providing basic guidance, it has limitations. It 
      lacks the ability to provide the latest scientific information and access 
      original medical databases. Studies have shown that ChatGPT's text can be 
      robotic, shallow, and lacking a human touch. It has also been found to provide 
      misleading or inaccurate information. Researchers and medical professionals 
      should be aware of these limitations and not solely rely on ChatGPT for complex 
      tasks. The human element and real-world experiences are indispensable in science, 
      and consulting experts is advisable for reliable insights.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Ashraf, Hamza
AU  - Ashraf H
AUID- ORCID: 0009-0007-9121-6091
AD  - Allama Iqbal Medical College, Lahore, Punjab, Pakistan. monsterguzura@gmail.com.
FAU - Ashfaq, Haider
AU  - Ashfaq H
AUID- ORCID: 0009-0002-4372-9929
AD  - Allama Iqbal Medical College, Lahore, Punjab, Pakistan.
LA  - eng
PT  - Letter
DEP - 20230714
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGpt
OT  - Medical research
EDAT- 2023/07/15 10:42
MHDA- 2023/07/15 10:42
CRDT- 2023/07/14 23:29
PHST- 2023/07/03 00:00 [received]
PHST- 2023/07/06 00:00 [accepted]
PHST- 2023/07/15 10:42 [medline]
PHST- 2023/07/15 10:42 [pubmed]
PHST- 2023/07/14 23:29 [entrez]
AID - 10.1007/s10439-023-03311-0 [pii]
AID - 10.1007/s10439-023-03311-0 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 14. doi: 10.1007/s10439-023-03311-0.

PMID- 38043471
OWN - NLM
STAT- Publisher
LR  - 20231203
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 168
DP  - 2023 Nov 30
TI  - Evaluation of Large language model performance on the Multi-Specialty Recruitment 
      Assessment (MSRA) exam.
PG  - 107794
LID - S0010-4825(23)01259-3 [pii]
LID - 10.1016/j.compbiomed.2023.107794 [doi]
AB  - INTRODUCTION: AI-powered platforms have gained prominence in medical education 
      and training, offering diverse applications from surgical performance assessment 
      to exam preparation. This research paper examines the capabilities of Large 
      Language Models (LLMs), including Llama 2, Google Bard, Bing Chat, and 
      ChatGPT-3.5, in answering multiple-choice questions of the Clinical Problem 
      Solving (CPS) paper of the Multi-Specialty Recruitment Assessment (MSRA) exam. 
      METHODS: Using a dataset of 100 CPS questions from ten subject categories, we 
      assessed the LLMs' performance against medical doctors preparing for the exam. 
      RESULTS: Results showed that Bing Chat outperformed all other LLMs and even 
      surpassed human users from the Qbank question bank. Conversely, Llama 2's 
      performance was inferior to human users. Google Bard and ChatGPT 3.5 did not 
      exhibit statistically significant differences in correct response rates compared 
      to human candidates. Pairwise comparisons demonstrated Bing Chat's significant 
      superiority over Llama 2, Google Bard, and ChatGPT 3.5. However, no significant 
      differences were found between Llama 2 and Google Bard, Llama 2, and ChatGPT-3.5, 
      and Google Bard and ChatGPT-3.5. DISCUSSION: Freely available LLMs have already 
      demonstrated that they can perform as well or even outperform human users in 
      answering MSRA exam questions. Bing Chat emerged as a particularly strong 
      performer. The study also highlights the potential for enhancing LLMs' medical 
      knowledge acquisition through tailored fine-tuning. Medical knowledge tailored 
      LLMs such as Med-PaLM, have already shown promising results. CONCLUSION: We 
      provided valuable insights into LLMs' competence in answering medical MCQs and 
      their potential integration into medical education and assessment processes.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Tsoutsanis, Panagiotis
AU  - Tsoutsanis P
AD  - Northern Care Alliance NHS Foundation Trust, Rochdale Eye Unit, Rochdale 
      infirmary, Greater Manchester, UK; Department of Education, University of Oxford, 
      Oxford, UK. Electronic address: tpanos02@gmail.com.
FAU - Tsoutsanis, Aristotelis
AU  - Tsoutsanis A
AD  - Department of Informatics, Technical University of Munich, Munich, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Large language models
OT  - Medical education
OT  - Medical exam
COIS- Declaration of competing interest The authors have declared that no competing 
      interests exist.
EDAT- 2023/12/04 00:42
MHDA- 2023/12/04 00:42
CRDT- 2023/12/03 18:14
PHST- 2023/08/30 00:00 [received]
PHST- 2023/11/27 00:00 [revised]
PHST- 2023/11/28 00:00 [accepted]
PHST- 2023/12/04 00:42 [medline]
PHST- 2023/12/04 00:42 [pubmed]
PHST- 2023/12/03 18:14 [entrez]
AID - S0010-4825(23)01259-3 [pii]
AID - 10.1016/j.compbiomed.2023.107794 [doi]
PST - aheadofprint
SO  - Comput Biol Med. 2023 Nov 30;168:107794. doi: 10.1016/j.compbiomed.2023.107794.

PMID- 37581444
OWN - NLM
STAT- Publisher
LR  - 20231115
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
VI  - 93
IP  - 6
DP  - 2023 Dec 1
TI  - Performance of ChatGPT and GPT-4 on Neurosurgery Written Board Examinations.
PG  - 1353-1365
LID - 10.1227/neu.0000000000002632 [doi]
AB  - BACKGROUND AND OBJECTIVES: Interest surrounding generative large language models 
      (LLMs) has rapidly grown. Although ChatGPT (GPT-3.5), a general LLM, has shown 
      near-passing performance on medical student board examinations, the performance 
      of ChatGPT or its successor GPT-4 on specialized examinations and the factors 
      affecting accuracy remain unclear. This study aims to assess the performance of 
      ChatGPT and GPT-4 on a 500-question mock neurosurgical written board examination. 
      METHODS: The Self-Assessment Neurosurgery Examinations (SANS) American Board of 
      Neurological Surgery Self-Assessment Examination 1 was used to evaluate ChatGPT 
      and GPT-4. Questions were in single best answer, multiple-choice format. χ 2 , 
      Fisher exact, and univariable logistic regression tests were used to assess 
      performance differences in relation to question characteristics. RESULTS: ChatGPT 
      (GPT-3.5) and GPT-4 achieved scores of 73.4% (95% CI: 69.3%-77.2%) and 83.4% (95% 
      CI: 79.8%-86.5%), respectively, relative to the user average of 72.8% (95% CI: 
      68.6%-76.6%). Both LLMs exceeded last year's passing threshold of 69%. Although 
      scores between ChatGPT and question bank users were equivalent ( P = .963), GPT-4 
      outperformed both (both P < .001). GPT-4 answered every question answered 
      correctly by ChatGPT and 37.6% (50/133) of remaining incorrect questions 
      correctly. Among 12 question categories, GPT-4 significantly outperformed users 
      in each but performed comparably with ChatGPT in 3 (functional, other general, 
      and spine) and outperformed both users and ChatGPT for tumor questions. Increased 
      word count (odds ratio = 0.89 of answering a question correctly per +10 words) 
      and higher-order problem-solving (odds ratio = 0.40, P = .009) were associated 
      with lower accuracy for ChatGPT, but not for GPT-4 (both P > .005). Multimodal 
      input was not available at the time of this study; hence, on questions with image 
      content, ChatGPT and GPT-4 answered 49.5% and 56.8% of questions correctly based 
      on contextual context clues alone. CONCLUSION: LLMs achieved passing scores on a 
      mock 500-question neurosurgical written board examination, with GPT-4 
      significantly outperforming ChatGPT.
CI  - Copyright © Congress of Neurological Surgeons 2023. All rights reserved.
FAU - Ali, Rohaid
AU  - Ali R
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Tang, Oliver Y
AU  - Tang OY
AUID- ORCID: 0000-0002-8604-2708
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Connolly, Ian D
AU  - Connolly ID
AD  - Department of Neurosurgery, Massachusetts General Hospital, Boston , 
      Massachusetts , USA.
FAU - Zadnik Sullivan, Patricia L
AU  - Zadnik Sullivan PL
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Shin, John H
AU  - Shin JH
AD  - Department of Neuroscience, Norman Prince Neurosciences Institute, Rhode Island 
      Hospital, Providence , Rhode Island , USA.
FAU - Fridley, Jared S
AU  - Fridley JS
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Asaad, Wael F
AU  - Asaad WF
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
AD  - Department of Neuroscience, Norman Prince Neurosciences Institute, Rhode Island 
      Hospital, Providence , Rhode Island , USA.
AD  - Department of Neuroscience, Brown University, Providence , Rhode Island , USA.
AD  - Department of Neuroscience, Carney Institute for Brain Science, Brown University, 
      Providence , Rhode Island , USA.
FAU - Cielo, Deus
AU  - Cielo D
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Oyelese, Adetokunbo A
AU  - Oyelese AA
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Doberstein, Curtis E
AU  - Doberstein CE
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Gokaslan, Ziya L
AU  - Gokaslan ZL
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
FAU - Telfeian, Albert E
AU  - Telfeian AE
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence , Rhode Island , USA.
LA  - eng
PT  - Journal Article
DEP - 20230815
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
EDAT- 2023/08/15 12:42
MHDA- 2023/08/15 12:42
CRDT- 2023/08/15 09:03
PHST- 2023/03/22 00:00 [received]
PHST- 2023/05/19 00:00 [accepted]
PHST- 2023/08/15 12:42 [pubmed]
PHST- 2023/08/15 12:42 [medline]
PHST- 2023/08/15 09:03 [entrez]
AID - 00006123-990000000-00844 [pii]
AID - 10.1227/neu.0000000000002632 [doi]
PST - ppublish
SO  - Neurosurgery. 2023 Dec 1;93(6):1353-1365. doi: 10.1227/neu.0000000000002632. Epub 
      2023 Aug 15.

PMID- 37711151
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230917
IS  - 2054-5703 (Print)
IS  - 2054-5703 (Electronic)
IS  - 2054-5703 (Linking)
VI  - 10
IP  - 9
DP  - 2023 Sep
TI  - Using ChatGPT for human-computer interaction research: a primer.
PG  - 231053
LID - 10.1098/rsos.231053 [doi]
LID - 231053
AB  - ChatGPT could serve as a tool for text analysis within the field of 
      Human-Computer Interaction, though its validity requires investigation. This 
      study applied ChatGPT to: (1) textbox questionnaire responses on nine 
      augmented-reality interfaces, (2) interview data from participants who 
      experienced these interfaces in a virtual simulator, and (3) transcribed 
      think-aloud data of participants who viewed a real painting and its replica. 
      Using a hierarchical approach, ChatGPT produced scores or summaries of text 
      batches, which were then aggregated. Results showed that (1) ChatGPT generated 
      sentiment scores of the interfaces that correlated extremely strongly (r > 0.99) 
      with human rating scale outcomes and with a rule-based sentiment analysis method 
      (criterion validity). Additionally, (2) by inputting automatically transcribed 
      interviews to ChatGPT, it provided meaningful meta-summaries of the qualities of 
      the interfaces (face validity). One meta-summary analysed in depth was found to 
      have substantial but imperfect overlap with a content analysis conducted by an 
      independent researcher (criterion validity). Finally, (3) ChatGPT's summary of 
      the think-aloud data highlighted subtle differences between the real painting and 
      the replica (face validity), a distinction corresponding with a keyword analysis 
      (criterion validity). In conclusion, our research indicates that, with 
      appropriate precautions, ChatGPT can be used as a valid tool for analysing text 
      data.
CI  - © 2023 The Authors.
FAU - Tabone, Wilbert
AU  - Tabone W
AUID- ORCID: 0000-0002-5796-9571
AD  - Department of Cognitive Robotics, Faculty of Mechanical, Maritime and Materials 
      Engineering, Delft University of Technology, Delft 2628CD, The Netherlands.
FAU - de Winter, Joost
AU  - de Winter J
AUID- ORCID: 0000-0002-1281-8200
AD  - Department of Cognitive Robotics, Faculty of Mechanical, Maritime and Materials 
      Engineering, Delft University of Technology, Delft 2628CD, The Netherlands.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - England
TA  - R Soc Open Sci
JT  - Royal Society open science
JID - 101647528
PMC - PMC10498031
OTO - NOTNLM
OT  - application programming interface (API)
OT  - human-subject research
OT  - prompt engineering
OT  - reproducibility
COIS- We declare we have no competing interests.
EDAT- 2023/09/15 06:43
MHDA- 2023/09/15 06:44
CRDT- 2023/09/15 03:44
PHST- 2023/07/21 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/09/15 06:44 [medline]
PHST- 2023/09/15 06:43 [pubmed]
PHST- 2023/09/15 03:44 [entrez]
AID - rsos231053 [pii]
AID - 10.1098/rsos.231053 [doi]
PST - epublish
SO  - R Soc Open Sci. 2023 Sep 13;10(9):231053. doi: 10.1098/rsos.231053. eCollection 
      2023 Sep.

PMID- 37957381
OWN - NLM
STAT- Publisher
LR  - 20231113
IS  - 1546-170X (Electronic)
IS  - 1078-8956 (Linking)
DP  - 2023 Nov 13
TI  - Reporting standards for the use of large language model-linked chatbots for 
      health advice.
LID - 10.1038/s41591-023-02656-2 [doi]
FAU - Huo, Bright
AU  - Huo B
AUID- ORCID: 0000-0003-4999-4328
AD  - Division of General Surgery, Department of Surgery, McMaster University, 
      Hamilton, Ontario, Canada. brighthuo@dal.ca.
FAU - Cacciamani, Giovanni E
AU  - Cacciamani GE
AUID- ORCID: 0000-0002-8892-5539
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
AD  - AI Center at USC Urology, University of Southern California, Los Angeles, CA, 
      USA.
FAU - Collins, Gary S
AU  - Collins GS
AUID- ORCID: 0000-0002-2772-2316
AD  - Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, 
      Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK.
AD  - UK EQUATOR Centre, University of Oxford, Oxford, UK.
FAU - McKechnie, Tyler
AU  - McKechnie T
AD  - Division of General Surgery, Department of Surgery, McMaster University, 
      Hamilton, Ontario, Canada.
AD  - Department of Health Research Methods, Evidence, and Impact, McMaster University, 
      Hamilton, Ontario, Canada.
FAU - Lee, Yung
AU  - Lee Y
AD  - Division of General Surgery, Department of Surgery, McMaster University, 
      Hamilton, Ontario, Canada.
AD  - Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
FAU - Guyatt, Gordon
AU  - Guyatt G
AD  - Department of Health Research Methods, Evidence, and Impact, McMaster University, 
      Hamilton, Ontario, Canada.
AD  - Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
LA  - eng
PT  - Letter
DEP - 20231113
PL  - United States
TA  - Nat Med
JT  - Nature medicine
JID - 9502015
SB  - IM
EDAT- 2023/11/14 00:42
MHDA- 2023/11/14 00:42
CRDT- 2023/11/13 23:42
PHST- 2023/11/14 00:42 [medline]
PHST- 2023/11/14 00:42 [pubmed]
PHST- 2023/11/13 23:42 [entrez]
AID - 10.1038/s41591-023-02656-2 [pii]
AID - 10.1038/s41591-023-02656-2 [doi]
PST - aheadofprint
SO  - Nat Med. 2023 Nov 13. doi: 10.1038/s41591-023-02656-2.

PMID- 37201835
OWN - NLM
STAT- MEDLINE
DCOM- 20230614
LR  - 20230614
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 888
DP  - 2023 Aug 25
TI  - Use of ChatGPT: What does it mean for biology and environmental science?
PG  - 164154
LID - S0048-9697(23)02775-4 [pii]
LID - 10.1016/j.scitotenv.2023.164154 [doi]
AB  - Artificial intelligence (AI) large language models (LLMs) have emerged as 
      important technologies. Recently, ChatGPT (Generative Pre-trained Transformer) 
      has been released and attracted massive interest from the public, owing to its 
      unique capabilities to simplify many daily tasks of people from diverse 
      backgrounds and social statuses. Here, we discuss how ChatGPT (and similar AI 
      technologies) can impact biology and environmental science, providing examples 
      obtained through interactive sessions with ChatGPT. The benefits that ChatGPT 
      offers are ample and can impact many aspects of biology and environmental 
      science, including education, research, scientific publishing, outreach, and 
      societal translation. Among others, ChatGPT can simplify and expedite highly 
      complex and challenging tasks. As an example to illustrate this, we provide 100 
      important questions for biology and 100 important questions for environmental 
      science. Although ChatGPT offers a plethora of benefits, there are several risks 
      and potential harms associated with its use, which we analyze herein. Awareness 
      of risks and potential harms should be raised. However, understanding and 
      overcoming the current limitations could lead these recent technological advances 
      to push biology and environmental science to their limits.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Agathokleous, Evgenios
AU  - Agathokleous E
AD  - School of Applied Meteorology, Nanjing University of Information Science & 
      Technology (NUIST), Nanjing 210044, China. Electronic address: 
      evgenios@nuist.edu.cn.
FAU - Saitanis, Costas J
AU  - Saitanis CJ
AD  - Lab of Ecology and Environmental Science, Agricultural University of Athens, Iera 
      Odos 75, Athens 11855, Greece.
FAU - Fang, Chao
AU  - Fang C
AD  - School of Applied Meteorology, Nanjing University of Information Science & 
      Technology (NUIST), Nanjing 210044, China.
FAU - Yu, Zhen
AU  - Yu Z
AD  - School of Applied Meteorology, Nanjing University of Information Science & 
      Technology (NUIST), Nanjing 210044, China. Electronic address: zyu@nuist.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20230516
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
SB  - IM
MH  - Humans
MH  - *Environmental Science
MH  - Artificial Intelligence
MH  - Educational Status
MH  - Electric Power Supplies
MH  - Biology
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Biology
OT  - ChatGPT
OT  - Environmental science
OT  - Generative Pre-trained Transformer
OT  - Large language model
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/05/19 01:04
MHDA- 2023/06/14 06:42
CRDT- 2023/05/18 19:29
PHST- 2023/04/29 00:00 [received]
PHST- 2023/05/09 00:00 [revised]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/06/14 06:42 [medline]
PHST- 2023/05/19 01:04 [pubmed]
PHST- 2023/05/18 19:29 [entrez]
AID - S0048-9697(23)02775-4 [pii]
AID - 10.1016/j.scitotenv.2023.164154 [doi]
PST - ppublish
SO  - Sci Total Environ. 2023 Aug 25;888:164154. doi: 10.1016/j.scitotenv.2023.164154. 
      Epub 2023 May 16.

PMID- 37892297
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2227-9067 (Print)
IS  - 2227-9067 (Electronic)
IS  - 2227-9067 (Linking)
VI  - 10
IP  - 10
DP  - 2023 Sep 30
TI  - Can ChatGPT Guide Parents on Tympanostomy Tube Insertion?
LID - 10.3390/children10101634 [doi]
LID - 1634
AB  - BACKGROUND: The emergence of ChatGPT, a state-of-the-art language model developed 
      by OpenAI, has introduced a novel avenue for patients to seek medically related 
      information. This technology holds significant promise in terms of accessibility 
      and convenience. However, the use of ChatGPT as a source of accurate information 
      enhancing patient education and engagement requires careful consideration. The 
      objective of this study was to assess the accuracy and reliability of ChatGPT in 
      providing information on the indications and management of complications 
      post-tympanostomy, the most common pediatric procedure in otolaryngology. 
      METHODS: We prompted ChatGPT-3.5 with questions and compared its generated 
      responses with the recommendations provided by the latest American Academy of 
      Otolaryngology-Head and Neck Surgery Foundation (AAO-HNSF) "Clinical Practice 
      Guideline: Tympanostomy Tubes in Children (Update)". RESULTS: A total of 23 
      responses were generated by ChatGPT against the AAO-HNSF guidelines. Following a 
      thorough review, it was determined that 22/23 (95.7%) responses exhibited a high 
      level of reliability and accuracy, closely aligning with the gold standard. 
      CONCLUSION: Our research study indicates that ChatGPT may be of assistance to 
      parents in search of information regarding tympanostomy tube insertion and its 
      clinical implications.
FAU - Moise, Alexander
AU  - Moise A
AD  - Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H3G 2M1, 
      Canada.
FAU - Centomo-Bozzo, Adam
AU  - Centomo-Bozzo A
AD  - Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, 
      QC H3A 3E8, Canada.
FAU - Orishchak, Ostap
AU  - Orishchak O
AD  - Department of Pediatric Otolaryngology, Montreal Children's Hospital, Montreal, 
      QC H4A 3J1, Canada.
FAU - Alnoury, Mohammed K
AU  - Alnoury MK
AUID- ORCID: 0000-0001-9222-8501
AD  - Department of Otolaryngology, Head and Neck Surgery, King Abdulaziz University, 
      Jeddah 21589, Saudi Arabia.
FAU - Daniel, Sam J
AU  - Daniel SJ
AD  - Department of Pediatric Otolaryngology, Montreal Children's Hospital, Montreal, 
      QC H4A 3J1, Canada.
LA  - eng
PT  - Journal Article
DEP - 20230930
PL  - Switzerland
TA  - Children (Basel)
JT  - Children (Basel, Switzerland)
JID - 101648936
PMC - PMC10605420
OTO - NOTNLM
OT  - ChatGPT
OT  - OpenAI
OT  - artificial intelligence
OT  - myringotomy
OT  - otolaryngology
OT  - tympanostomy tube insertion
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/28 11:45
MHDA- 2023/10/28 11:46
CRDT- 2023/10/28 01:03
PHST- 2023/08/23 00:00 [received]
PHST- 2023/09/21 00:00 [revised]
PHST- 2023/09/27 00:00 [accepted]
PHST- 2023/10/28 11:46 [medline]
PHST- 2023/10/28 11:45 [pubmed]
PHST- 2023/10/28 01:03 [entrez]
AID - children10101634 [pii]
AID - children-10-01634 [pii]
AID - 10.3390/children10101634 [doi]
PST - epublish
SO  - Children (Basel). 2023 Sep 30;10(10):1634. doi: 10.3390/children10101634.

PMID- 37463210
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230729
IS  - 1091-6490 (Electronic)
IS  - 0027-8424 (Print)
IS  - 0027-8424 (Linking)
VI  - 120
IP  - 30
DP  - 2023 Jul 25
TI  - ChatGPT outperforms crowd workers for text-annotation tasks.
PG  - e2305016120
LID - 10.1073/pnas.2305016120 [doi]
LID - e2305016120
AB  - Many NLP applications require manual text annotations for a variety of tasks, 
      notably to train classifiers or evaluate the performance of unsupervised models. 
      Depending on the size and degree of complexity, the tasks may be conducted by 
      crowd workers on platforms such as MTurk as well as trained annotators, such as 
      research assistants. Using four samples of tweets and news articles (n = 6,183), 
      we show that ChatGPT outperforms crowd workers for several annotation tasks, 
      including relevance, stance, topics, and frame detection. Across the four 
      datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd workers by 
      about 25 percentage points on average, while ChatGPT's intercoder agreement 
      exceeds that of both crowd workers and trained annotators for all tasks. 
      Moreover, the per-annotation cost of ChatGPT is less than $0.003-about thirty 
      times cheaper than MTurk. These results demonstrate the potential of large 
      language models to drastically increase the efficiency of text classification.
FAU - Gilardi, Fabrizio
AU  - Gilardi F
AUID- ORCID: 0000-0002-0635-3048
AD  - Department of Political Science, University of Zurich, Zurich 8050, Switzerland.
FAU - Alizadeh, Meysam
AU  - Alizadeh M
AUID- ORCID: 0000-0001-6696-6471
AD  - Department of Political Science, University of Zurich, Zurich 8050, Switzerland.
FAU - Kubli, Maël
AU  - Kubli M
AUID- ORCID: 0000-0002-5592-9648
AD  - Department of Political Science, University of Zurich, Zurich 8050, Switzerland.
LA  - eng
GR  - 883121/EC | European Research Council (ERC)/
PT  - Journal Article
DEP - 20230718
PL  - United States
TA  - Proc Natl Acad Sci U S A
JT  - Proceedings of the National Academy of Sciences of the United States of America
JID - 7505876
SB  - IM
PMC - PMC10372638
OTO - NOTNLM
OT  - ChatGPT
OT  - human annotations
OT  - large language models
OT  - text as data
OT  - text classification
COIS- The authors declare no competing interest.
EDAT- 2023/07/18 18:42
MHDA- 2023/07/18 18:43
CRDT- 2023/07/18 13:43
PHST- 2023/07/18 18:43 [medline]
PHST- 2023/07/18 18:42 [pubmed]
PHST- 2023/07/18 13:43 [entrez]
AID - 202305016 [pii]
AID - 10.1073/pnas.2305016120 [doi]
PST - ppublish
SO  - Proc Natl Acad Sci U S A. 2023 Jul 25;120(30):e2305016120. doi: 
      10.1073/pnas.2305016120. Epub 2023 Jul 18.

PMID- 37898100
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 90
DP  - 2023 Dec
TI  - ChatGPT: Opportunities, risks and priorities for psychiatry.
PG  - 103808
LID - S1876-2018(23)00364-7 [pii]
LID - 10.1016/j.ajp.2023.103808 [doi]
AB  - The advancement of large language models such as ChatGPT, opens new possibilities 
      in psychiatry but also invites scrutiny. This paper examines the potential 
      opportunities, risks, and crucial areas of focus within this area. The active 
      engagement of the mental health community is seen as critical to ensure ethical 
      practice, equal access, and a patient-centric approach.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Wei, Yaohui
AU  - Wei Y
AD  - Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 
      Shanghai, China; Department of Psychiatry and Psychotherapy, University Hospital 
      Rechts der Isar, Technical University of Munich, Munich, Germany.
FAU - Guo, Lei
AU  - Guo L
AD  - Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 
      Shanghai, China.
FAU - Lian, Cheng
AU  - Lian C
AD  - Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 
      Shanghai, China.
FAU - Chen, Jue
AU  - Chen J
AD  - Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 
      Shanghai, China. Electronic address: chenjue2088@163.com.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - *Psychiatry
MH  - Language
MH  - Mental Health
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Large language model
OT  - Psychiatry
COIS- Declaration of Competing Interest The authors declare no conflicts of interest 
      relevant to the contents of the manuscript.
EDAT- 2023/10/29 06:46
MHDA- 2023/11/27 12:42
CRDT- 2023/10/28 18:10
PHST- 2023/08/17 00:00 [received]
PHST- 2023/10/18 00:00 [revised]
PHST- 2023/10/22 00:00 [accepted]
PHST- 2023/11/27 12:42 [medline]
PHST- 2023/10/29 06:46 [pubmed]
PHST- 2023/10/28 18:10 [entrez]
AID - S1876-2018(23)00364-7 [pii]
AID - 10.1016/j.ajp.2023.103808 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Dec;90:103808. doi: 10.1016/j.ajp.2023.103808. Epub 2023 
      Oct 24.

PMID- 37465809
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230720
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - When Precision Meets Penmanship: ChatGPT and Surgery Documentation.
PG  - e40546
LID - 10.7759/cureus.40546 [doi]
LID - e40546
AB  - ChatGPT (Chatbot Generative Pre-Trained Transformer) is an artificial 
      intelligence with several potential applications in the field of medicine. As a 
      large language model, it is particularly good at generating text. This study 
      investigates the use of ChatGPT in constructing operation notes for laparoscopic 
      appendicectomy, one of the most common surgical procedures in the UK. We prompted 
      ChatGPT-4, the latest generation of ChatGPT, to produce operation notes for 
      laparoscopic appendicectomy, which were then evaluated against 'Getting It Right 
      First Time' (GIRFT) recommendations. GIRFT is an organisation that has 
      collaborated with the National Health Service (NHS) to improve surgical 
      documentation guidelines. Excluding certain items documented elsewhere in patient 
      records, the generated notes were assessed against 30 key points in GIRFT 
      recommendations. This process was repeated three times to obtain an average 
      score. Our results showed that ChatGPT generated operation notes in seconds, with 
      an average coverage of 78.8% (23.66 out of 30 points) of the GIRFT guidelines, 
      surpassing average compliance with similar guidelines from the Royal College of 
      Surgeons (RCS). However, the quality of ChatGPT's output was found to be 
      dependent on the quality of the prompt, highlighting the need for verification of 
      the generated content. Additionally, secure integration with electronic health 
      records is required before ChatGPT can be adopted into the NHS.
CI  - Copyright © 2023, Robinson et al.
FAU - Robinson, Alexander
AU  - Robinson A
AD  - General Surgery, Mid and South Essex NHS (National Health Service) Foundation 
      Trust, Chelmsford, GBR.
FAU - Aggarwal, Shaurya Jr
AU  - Aggarwal S Jr
AD  - General Surgery, Mid and South Essex NHS (National Health Service) Foundation 
      Trust, Chelmsford, GBR.
LA  - eng
PT  - Journal Article
DEP - 20230617
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10350653
OTO - NOTNLM
OT  - appendicectomy
OT  - chatgpt
OT  - laparoscopic surgery
OT  - nhs
OT  - surgical documentation
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/19 06:42
MHDA- 2023/07/19 06:43
CRDT- 2023/07/19 04:07
PHST- 2023/06/16 00:00 [accepted]
PHST- 2023/07/19 06:43 [medline]
PHST- 2023/07/19 06:42 [pubmed]
PHST- 2023/07/19 04:07 [entrez]
AID - 10.7759/cureus.40546 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 17;15(6):e40546. doi: 10.7759/cureus.40546. eCollection 2023 
      Jun.

PMID- 37024502
OWN - NLM
STAT- MEDLINE
DCOM- 20230410
LR  - 20230417
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Apr 6
TI  - ChatGPT's inconsistent moral advice influences users' judgment.
PG  - 4569
LID - 10.1038/s41598-023-31341-0 [doi]
LID - 4569
AB  - ChatGPT is not only fun to chat with, but it also searches information, answers 
      questions, and gives advice. With consistent moral advice, it can improve the 
      moral judgment and decisions of users. Unfortunately, ChatGPT's advice is not 
      consistent. Nonetheless, it does influence users' moral judgment, we find in an 
      experiment, even if they know they are advised by a chatting bot, and they 
      underestimate how much they are influenced. Thus, ChatGPT corrupts rather than 
      improves its users' moral judgment. While these findings call for better design 
      of ChatGPT and similar bots, we also propose training to improve users' digital 
      literacy as a remedy. Transparency, however, is not sufficient to enable the 
      responsible use of AI.
CI  - © 2023. The Author(s).
FAU - Krügel, Sebastian
AU  - Krügel S
AD  - Faculty of Computer Science, Technische Hochschule Ingolstadt, Esplanade 10, 
      85049, Ingolstadt, Germany. sebastian.kruegel@thi.de.
FAU - Ostermaier, Andreas
AU  - Ostermaier A
AD  - Department of Business and Management, University of Southern Denmark, Campusvej 
      55, 5230, Odense, Denmark.
FAU - Uhl, Matthias
AU  - Uhl M
AD  - Faculty of Computer Science, Technische Hochschule Ingolstadt, Esplanade 10, 
      85049, Ingolstadt, Germany.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230406
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
RN  - EC 2.3.1.6 (Choline O-Acetyltransferase)
SB  - IM
MH  - *Judgment
MH  - *Morals
MH  - Software
MH  - Literacy
MH  - Choline O-Acetyltransferase
PMC - PMC10079665
COIS- The authors declare no competing interests.
EDAT- 2023/04/07 06:00
MHDA- 2023/04/10 06:42
CRDT- 2023/04/06 23:16
PHST- 2023/01/20 00:00 [received]
PHST- 2023/03/10 00:00 [accepted]
PHST- 2023/04/10 06:42 [medline]
PHST- 2023/04/06 23:16 [entrez]
PHST- 2023/04/07 06:00 [pubmed]
AID - 10.1038/s41598-023-31341-0 [pii]
AID - 31341 [pii]
AID - 10.1038/s41598-023-31341-0 [doi]
PST - epublish
SO  - Sci Rep. 2023 Apr 6;13(1):4569. doi: 10.1038/s41598-023-31341-0.

PMID- 37567487
OWN - NLM
STAT- Publisher
LR  - 20230823
IS  - 1526-3231 (Electronic)
IS  - 0749-8063 (Linking)
DP  - 2023 Aug 9
TI  - Evaluation High-Quality of Information from ChatGPT (Artificial 
      Intelligence-Large Language Model) Artificial Intelligence on Shoulder 
      Stabilization Surgery.
LID - S0749-8063(23)00642-4 [pii]
LID - 10.1016/j.arthro.2023.07.048 [doi]
AB  - PURPOSE: To analyze the quality and readability of information regarding shoulder 
      stabilization surgery available using an online AI software (ChatGPT), using 
      standardized scoring systems, as well as to report on the given answers by the 
      AI. METHODS: An open AI model (ChatGPT) was used to answer 23 commonly asked 
      questions from patients on shoulder stabilization surgery. These answers were 
      evaluated for medical accuracy, quality, and readability using The JAMA Benchmark 
      criteria, DISCERN score, Flesch-Kincaid Reading Ease Score (FRES) & Grade Level 
      (FKGL). RESULTS: The JAMA Benchmark criteria score was 0, which is the lowest 
      score, indicating no reliable resources cited. The DISCERN score was 60, which is 
      considered a good score. The areas that open AI model did not achieve full marks 
      were also related to the lack of available source material used to compile the 
      answers, and finally some shortcomings with information not fully supported by 
      the literature. The FRES was 26.2, and the FKGL was considered to be that of a 
      college graduate. CONCLUSION: There was generally high quality in the answers 
      given on questions relating to shoulder stabilization surgery, but there was a 
      high reading level required to comprehend the information presented. However, it 
      is unclear where the answers came from with no source material cited. It is 
      important to note that the ChatGPT software repeatedly references the need to 
      discuss these questions with an orthopaedic surgeon and the importance of shared 
      discussion making, as well as compliance with surgeon treatment recommendations. 
      CLINICAL RELEVANCE: As shoulder instability is an injury that predominantly 
      affects younger individuals who may use the Internet for information, this study 
      shows what information patients may be getting online.
CI  - Copyright © 2023 Arthroscopy Association of North America. Published by Elsevier 
      Inc. All rights reserved.
FAU - Hurley, Eoghan T
AU  - Hurley ET
AD  - Duke University, Durham, North Carolina, U.S.A.. Electronic address: 
      eoghan.hurley@duke.edu.
FAU - Crook, Bryan S
AU  - Crook BS
AD  - Duke University, Durham, North Carolina, U.S.A.
FAU - Lorentz, Samuel G
AU  - Lorentz SG
AD  - Duke University, Durham, North Carolina, U.S.A.
FAU - Danilkowicz, Richard M
AU  - Danilkowicz RM
AD  - Duke University, Durham, North Carolina, U.S.A.
FAU - Lau, Brian C
AU  - Lau BC
AD  - Duke University, Durham, North Carolina, U.S.A.
FAU - Taylor, Dean C
AU  - Taylor DC
AD  - Duke University, Durham, North Carolina, U.S.A.
FAU - Dickens, Jonathan F
AU  - Dickens JF
AD  - Duke University, Durham, North Carolina, U.S.A.
FAU - Anakwenze, Oke
AU  - Anakwenze O
AD  - Duke University, Durham, North Carolina, U.S.A.
FAU - Klifto, Christopher S
AU  - Klifto CS
AD  - Duke University, Durham, North Carolina, U.S.A.
LA  - eng
PT  - Journal Article
DEP - 20230809
PL  - United States
TA  - Arthroscopy
JT  - Arthroscopy : the journal of arthroscopic & related surgery : official 
      publication of the Arthroscopy Association of North America and the International 
      Arthroscopy Association
JID - 8506498
SB  - IM
EDAT- 2023/08/12 10:42
MHDA- 2023/08/12 10:42
CRDT- 2023/08/11 19:27
PHST- 2023/03/29 00:00 [received]
PHST- 2023/06/27 00:00 [revised]
PHST- 2023/07/28 00:00 [accepted]
PHST- 2023/08/12 10:42 [pubmed]
PHST- 2023/08/12 10:42 [medline]
PHST- 2023/08/11 19:27 [entrez]
AID - S0749-8063(23)00642-4 [pii]
AID - 10.1016/j.arthro.2023.07.048 [doi]
PST - aheadofprint
SO  - Arthroscopy. 2023 Aug 9:S0749-8063(23)00642-4. doi: 10.1016/j.arthro.2023.07.048.

PMID- 37933835
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2980-1478 (Electronic)
IS  - 2980-1478 (Linking)
VI  - 49
IP  - 6
DP  - 2023 Nov
TI  - Assessing the Performance of Chat Generative Pretrained Transformer (ChatGPT) in 
      Answering Andrology-Related Questions.
PG  - 365-369
LID - 10.5152/tud.2023.23171 [doi]
AB  - OBJECTIVE: The internet and social media have become primary sources of health 
      information, with men frequently turning to these platforms before seeking 
      professional help. Chat generative pretrained transformer (ChatGPT), an 
      artificial intelligence model developed by OpenAI, has gained popularity as a 
      natural language processing program. The present study evaluated the accuracy and 
      reproducibility of ChatGPT's responses to andrology-related questions. METHODS: 
      The study analyzed frequently asked andrology questions from health forums, 
      hospital websites, and social media platforms like YouTube and Instagram. 
      Questions were categorized into topics like male hypogonadism, erectile 
      dysfunction, etc. The European Association of Urology (EAU) guideline 
      recommendations were also included. These questions were input into ChatGPT, and 
      responses were evaluated by 3 experienced urologists who scored them on a scale 
      of 1 to 4. RESULTS: Out of 136 evaluated questions, 108 met the criteria. Of 
      these, 87.9% received correct and adequate answers, 9.3% were correct but 
      insufficient, and 3 responses contained both correct and incorrect information. 
      No question was answered completely wrong. The highest correct answer rates were 
      for disorders of ejaculation, penile curvature, and male hypogonadism. The EAU 
      guideline-based questions achieved a correctness rate of 86.3%. The 
      reproducibility of the answers was over 90%. CONCLUSION: The study found that 
      ChatGPT provided accurate and reliable answers to over 80% of andrology-related 
      questions. While limitations exist, such as potential outdated data and inability 
      to understand emotional aspects, ChatGPT's potential in the health-care sector is 
      promising. Collaborating with health-care professionals during artificial 
      intelligence model development could enhance its reliability.
FAU - Caglar, Ufuk
AU  - Caglar U
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Yildiz, Oguzhan
AU  - Yildiz O
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Ozervarli, M Fırat
AU  - Ozervarli MF
AD  - Department of Urology, Istanbul University, Istanbul School of Medicine, 
      Istanbul, Turkey.
FAU - Aydin, Resat
AU  - Aydin R
AD  - Department of Urology, Istanbul University, Istanbul School of Medicine, 
      Istanbul, Turkey.
FAU - Sarilar, Omer
AU  - Sarilar O
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Ozgor, Faruk
AU  - Ozgor F
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Ortac, Mazhar
AU  - Ortac M
AD  - Department of Urology, Istanbul University, Istanbul School of Medicine, 
      Istanbul, Turkey.
LA  - eng
PT  - Journal Article
PL  - Turkey
TA  - Urol Res Pract
JT  - Urology research & practice
JID - 9918608789006676
EDAT- 2023/11/07 12:42
MHDA- 2023/11/07 12:43
CRDT- 2023/11/07 07:53
PHST- 2023/11/07 12:43 [medline]
PHST- 2023/11/07 12:42 [pubmed]
PHST- 2023/11/07 07:53 [entrez]
AID - 10.5152/tud.2023.23171 [doi]
PST - ppublish
SO  - Urol Res Pract. 2023 Nov;49(6):365-369. doi: 10.5152/tud.2023.23171.

PMID- 37793668
OWN - NLM
STAT- MEDLINE
DCOM- 20231006
LR  - 20231022
IS  - 2005-8330 (Electronic)
IS  - 1229-6929 (Print)
IS  - 1229-6929 (Linking)
VI  - 24
IP  - 10
DP  - 2023 Oct
TI  - Is ChatGPT a "Fire of Prometheus" for Non-Native English-Speaking Researchers in 
      Academic Writing?
PG  - 952-959
LID - 10.3348/kjr.2023.0773 [doi]
AB  - Large language models (LLMs) such as ChatGPT have garnered considerable interest 
      for their potential to aid non-native English-speaking researchers. These models 
      can function as personal, round-the-clock English tutors, akin to how Prometheus 
      in Greek mythology bestowed fire upon humans for their advancement. LLMs can be 
      particularly helpful for non-native researchers in writing the Introduction and 
      Discussion sections of manuscripts, where they often encounter challenges. 
      However, using LLMs to generate text for research manuscripts entails concerns 
      such as hallucination, plagiarism, and privacy issues; to mitigate these risks, 
      authors should verify the accuracy of generated content, employ text similarity 
      detectors, and avoid inputting sensitive information into their prompts. 
      Consequently, it may be more prudent to utilize LLMs for editing and refining 
      text rather than generating large portions of text. Journal policies concerning 
      the use of LLMs vary, but transparency in disclosing artificial intelligence tool 
      usage is emphasized. This paper aims to summarize how LLMs can lower the barrier 
      to academic writing in English, enabling researchers to concentrate on 
      domain-specific research, provided they are used responsibly and cautiously.
CI  - Copyright © 2023 The Korean Society of Radiology.
FAU - Hwang, Sung Il
AU  - Hwang SI
AUID- ORCID: 0000-0001-7516-5369
AD  - Department of Radiology, Seoul National University Bundang Hospital, Seongnam, 
      Republic of Korea.
AD  - Department of Radiology, Graduate School of Medicine, Dentistry and 
      Pharmaceutical Sciences, Okayama University, Okayama, Japan.
FAU - Lim, Joon Seo
AU  - Lim JS
AUID- ORCID: 0000-0002-2738-5882
AD  - Scientific Publications Team, Clinical Research Center, Asan Medical Center, 
      University of Ulsan College of Medicine, Seoul, Republic of Korea. 
      joonseolim@amc.seoul.kr.
FAU - Lee, Ro Woon
AU  - Lee RW
AUID- ORCID: 0000-0002-8678-8059
AD  - Department of Radiology, Inha University Hospital, Incheon, Republic of Korea.
FAU - Matsui, Yusuke
AU  - Matsui Y
AUID- ORCID: 0000-0001-7548-7886
AD  - Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical 
      Sciences, Okayama University, Okayama, Japan.
FAU - Iguchi, Toshihiro
AU  - Iguchi T
AUID- ORCID: 0000-0001-9467-0280
AD  - Department of Radiological Technology, Faculty of Health Sciences, Okayama 
      University, Okayama, Japan.
FAU - Hiraki, Takao
AU  - Hiraki T
AUID- ORCID: 0000-0003-3992-4043
AD  - Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical 
      Sciences, Okayama University, Okayama, Japan.
FAU - Ahn, Hyungwoo
AU  - Ahn H
AUID- ORCID: 0000-0003-3266-6176
AD  - Department of Radiology, Seoul National University Bundang Hospital, Seongnam, 
      Republic of Korea.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - Korea (South)
TA  - Korean J Radiol
JT  - Korean journal of radiology
JID - 100956096
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Writing
PMC - PMC10550740
OTO - NOTNLM
OT  - Academic writing
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Editing
OT  - Generative pretrained transformer
OT  - Large language model
OT  - Publication
COIS- Sung Il Hwang, the section editor of the Korean Journal of Radiology, was not 
      involved in the editorial evaluation or decision to publish this article. All 
      authors have declared no conflicts of interest.
EDAT- 2023/10/05 01:00
MHDA- 2023/10/06 06:43
CRDT- 2023/10/04 20:12
PHST- 2023/08/17 00:00 [received]
PHST- 2023/08/18 00:00 [accepted]
PHST- 2023/10/06 06:43 [medline]
PHST- 2023/10/05 01:00 [pubmed]
PHST- 2023/10/04 20:12 [entrez]
AID - 24.952 [pii]
AID - 10.3348/kjr.2023.0773 [doi]
PST - ppublish
SO  - Korean J Radiol. 2023 Oct;24(10):952-959. doi: 10.3348/kjr.2023.0773.

PMID- 38090765
OWN - NLM
STAT- In-Process
LR  - 20231213
IS  - 2152-2723 (Electronic)
IS  - 2152-2715 (Linking)
VI  - 26
IP  - 12
DP  - 2023 Dec
TI  - Exploring the Impact of ChatGPT Literacy on User Satisfaction: The Mediating Role 
      of User Motivations.
PG  - 913-918
LID - 10.1089/cyber.2023.0312 [doi]
AB  - The introduction of chat generative pretrained transformer (ChatGPT), the fastest 
      growing large language model, has changed the landscape of artificial 
      intelligence-human interaction in everyday life. As the social influence of 
      ChatGPT increases, competencies in it become important life skills. This study 
      aims to explore the determinants of ChatGPT user satisfaction to provide 
      practical implications by suggesting a significant independent variable and 
      mediators between the independent variable and user satisfaction. To this end, 
      this study recruited 822 college students with prior experience using ChatGPT 
      (407 males and 415 females) and conducted an online survey. We tested the effects 
      of ChatGPT literacy on user satisfaction and the mediating roles of different 
      motives (i.e., information and knowledge acquisition and entertainment and 
      leisure) in the relationship between ChatGPT literacy and user satisfaction. The 
      results suggest that ChatGPT literacy significantly increases user satisfaction 
      and that information and knowledge acquisition and entertainment and leisure 
      partially mediate the relationship between the effect of ChatGPT literacy and 
      user satisfaction. The results may have implications for large language model 
      developers and practitioners, such as educators.
FAU - Lee, Seyoung
AU  - Lee S
AUID- ORCID: 0000-0001-8048-1796
AD  - Department of Media and Communication, Sungkyunkwan University, Jongno-Gu, South 
      Korea.
FAU - Park, Gain
AU  - Park G
AUID- ORCID: 0000-0002-3767-8320
AD  - Department of Journalism and Media Studies, New Mexico State University, Las 
      Cruces, New Mexico, USA.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - United States
TA  - Cyberpsychol Behav Soc Netw
JT  - Cyberpsychology, behavior and social networking
JID - 101528721
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - ChatGPT literacy
OT  - artificial intelligence
OT  - large language model
OT  - user motivation
OT  - user satisfaction
EDAT- 2023/12/13 18:42
MHDA- 2023/12/13 18:42
CRDT- 2023/12/13 13:17
PHST- 2023/12/13 18:42 [medline]
PHST- 2023/12/13 18:42 [pubmed]
PHST- 2023/12/13 13:17 [entrez]
AID - 10.1089/cyber.2023.0312 [doi]
PST - ppublish
SO  - Cyberpsychol Behav Soc Netw. 2023 Dec;26(12):913-918. doi: 
      10.1089/cyber.2023.0312. Epub 2023 Nov 22.

PMID- 38044041
OWN - NLM
STAT- Publisher
LR  - 20231203
IS  - 1444-0938 (Electronic)
IS  - 0816-4622 (Linking)
DP  - 2023 Dec 3
TI  - Accuracy of a Large Language Model as a new tool for optometry education.
PG  - 1-4
LID - 10.1080/08164622.2023.2288174 [doi]
AB  - CLINICAL RELEVANCE: The unsupervised introduction of certain Artificial 
      Intelligence tools in optometry education may challenge the proper acquisition of 
      accurate clinical knowledge and skills proficiency. BACKGROUND: Large Language 
      Models like ChatGPT (Generative Pretrained Transformer) are increasingly being 
      used by researchers and students for work and academic assignments. The 
      authoritative and conversationally correct language provided by these tools may 
      mask their inherent limitations when presented with specific scientific and 
      clinical queries. METHODS: Three sets of 10 queries related to contact lenses & 
      anterior eye, low vision and binocular vision & vision therapy were presented to 
      ChatGPT, with instructions to provide five relevant references to support each 
      response. Three experts and 53 undergraduate and post-graduate students graded 
      from 0 to 10 the accuracy of the responses, and the references were evaluated for 
      precision and relevance. Students graded from 0 to 10 the potential usefulness of 
      ChatGPT for their academic coursework. RESULTS: Median scores were 7, 8 and 6 
      (experts) and 8, 9 and 7.5 (students) for the contact lenses & anterior eye, low 
      vision and binocular vision & vision therapy categories, respectively. Responses 
      to more specific queries were awarded lower scores by both experts (ρ = -0.612; 
      P < 0.001) and students (ρ = -0.578; P = 0.001). Of 150 references, 24% were 
      accurate and 19.3% relevant. Students graded the usefulness of ChatGPT with 7.5 
      (2 to 9), 7 (3 to 9) and 8.5 (3 to 10) for contact lenses & anterior eye, low 
      vision and binocular vision & vision therapy, respectively. CONCLUSION: Careful 
      expert appraisal of the responses and, particularly, of the references provided 
      by ChatGPT is required in research and academic settings. As the use of these 
      tools becomes widespread, it is essential to take proactive steps to address 
      their limitations and ensure their responsible use.
FAU - Cardona, Genis
AU  - Cardona G
AUID- ORCID: 0000-0002-4770-8992
AD  - Department of Optics and Optometry, Universitat Politècnica de Catalunya, 
      Terrassa, Spain.
FAU - Argiles, Marc
AU  - Argiles M
AUID- ORCID: 0000-0001-5474-9832
AD  - Department of Optics and Optometry, Universitat Politècnica de Catalunya, 
      Terrassa, Spain.
FAU - Pérez-Mañá, Lluis
AU  - Pérez-Mañá L
AUID- ORCID: 0000-0002-8672-7803
AD  - Department of Optics and Optometry, Universitat Politècnica de Catalunya, 
      Terrassa, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231203
PL  - United States
TA  - Clin Exp Optom
JT  - Clinical & experimental optometry
JID - 8703442
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Large Language Model
OT  - ethics
OT  - optometry education
EDAT- 2023/12/04 00:42
MHDA- 2023/12/04 00:42
CRDT- 2023/12/03 21:22
PHST- 2023/12/04 00:42 [medline]
PHST- 2023/12/04 00:42 [pubmed]
PHST- 2023/12/03 21:22 [entrez]
AID - 10.1080/08164622.2023.2288174 [doi]
PST - aheadofprint
SO  - Clin Exp Optom. 2023 Dec 3:1-4. doi: 10.1080/08164622.2023.2288174.

PMID- 37529688
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 13
DP  - 2023
TI  - Evaluating large language models on a highly-specialized topic, radiation 
      oncology physics.
PG  - 1219326
LID - 10.3389/fonc.2023.1219326 [doi]
LID - 1219326
AB  - PURPOSE: We present the first study to investigate Large Language Models (LLMs) 
      in answering radiation oncology physics questions. Because popular exams like AP 
      Physics, LSAT, and GRE have large test-taker populations and ample test 
      preparation resources in circulation, they may not allow for accurately assessing 
      the true potential of LLMs. This paper proposes evaluating LLMs on a 
      highly-specialized topic, radiation oncology physics, which may be more pertinent 
      to scientific and medical communities in addition to being a valuable benchmark 
      of LLMs. METHODS: We developed an exam consisting of 100 radiation oncology 
      physics questions based on our expertise. Four LLMs, ChatGPT (GPT-3.5), ChatGPT 
      (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and 
      non-experts. The performance of ChatGPT (GPT-4) was further explored by being 
      asked to explain first, then answer. The deductive reasoning capability of 
      ChatGPT (GPT-4) was evaluated using a novel approach (substituting the correct 
      answer with "None of the above choices is the correct answer."). A majority vote 
      analysis was used to approximate how well each group could score when working 
      together. RESULTS: ChatGPT GPT-4 outperformed all other LLMs and medical 
      physicists, on average, with improved accuracy when prompted to explain before 
      answering. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its 
      answer choices across a number of trials, whether correct or incorrect, a 
      characteristic that was not observed in the human test groups or Bard (LaMDA). In 
      evaluating deductive reasoning ability, ChatGPT (GPT-4) demonstrated surprising 
      accuracy, suggesting the potential presence of an emergent ability. Finally, 
      although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not 
      allow for further improvement when scoring based on a majority vote across 
      trials. In contrast, a team of medical physicists were able to greatly outperform 
      ChatGPT (GPT-4) using a majority vote. CONCLUSION: This study suggests a great 
      potential for LLMs to work alongside radiation oncology experts as highly 
      knowledgeable assistants.
CI  - Copyright © 2023 Holmes, Liu, Zhang, Ding, Sio, McGee, Ashman, Li, Liu, Shen and 
      Liu.
FAU - Holmes, Jason
AU  - Holmes J
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
FAU - Liu, Zhengliang
AU  - Liu Z
AD  - School of Computing, The University of Georgia, Athens, GA, United States.
FAU - Zhang, Lian
AU  - Zhang L
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
FAU - Ding, Yuzhen
AU  - Ding Y
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
FAU - Sio, Terence T
AU  - Sio TT
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
FAU - McGee, Lisa A
AU  - McGee LA
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
FAU - Ashman, Jonathan B
AU  - Ashman JB
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
FAU - Li, Xiang
AU  - Li X
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
FAU - Liu, Tianming
AU  - Liu T
AD  - School of Computing, The University of Georgia, Athens, GA, United States.
FAU - Shen, Jiajian
AU  - Shen J
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
FAU - Liu, Wei
AU  - Liu W
AD  - Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
LA  - eng
GR  - K25 CA168984/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20230717
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC10388568
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - large language model
OT  - medical physics
OT  - natural language processing
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/08/02 06:43
MHDA- 2023/08/02 06:44
CRDT- 2023/08/02 04:00
PHST- 2023/05/08 00:00 [received]
PHST- 2023/06/12 00:00 [accepted]
PHST- 2023/08/02 06:44 [medline]
PHST- 2023/08/02 06:43 [pubmed]
PHST- 2023/08/02 04:00 [entrez]
AID - 10.3389/fonc.2023.1219326 [doi]
PST - epublish
SO  - Front Oncol. 2023 Jul 17;13:1219326. doi: 10.3389/fonc.2023.1219326. eCollection 
      2023.

PMID- 38084123
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231212
IS  - 1178-2390 (Print)
IS  - 1178-2390 (Electronic)
IS  - 1178-2390 (Linking)
VI  - 16
DP  - 2023
TI  - Are Different Versions of ChatGPT's Ability Comparable to the Clinical Diagnosis 
      Presented in Case Reports? A Descriptive Study.
PG  - 3825-3831
LID - 10.2147/JMDH.S441790 [doi]
AB  - OBJECTIVE: ChatGPT, an advanced language model developed by OpenAI, holds the 
      opportunity to bring about a transformation in the processing of clinical 
      decision-making within the realm of medicine. Despite the growing popularity of 
      research related on ChatGPT, there is a paucity of research assessing its 
      appropriateness for clinical decision support. Our study delved into ChatGPT's 
      ability to respond in accordance with the diagnoses found in case reports, with 
      the intention of serving as a reference for clinical decision-making. METHODS: We 
      included 147 case reports from the Chinese Medical Association Journal Database 
      that generated primary and secondary diagnoses covering various diseases. Each 
      question was independently posed three times to both GPT-3.5 and GPT-4.0, 
      respectively. The results were analyzed regarding ChatGPT's mean scores and 
      accuracy types. RESULTS: GPT-4.0 displayed moderate accuracy in primary 
      diagnoses. With the increasing number of input, a corresponding enhancement in 
      the accuracy of ChatGPT's outputs became evident. Notably, autoimmune diseases 
      comprised the largest proportion of case reports, and the mean score for primary 
      diagnosis exhibited statistically significant differences in autoimmune diseases. 
      CONCLUSION: Our finding suggested that the potential practicality in utilizing 
      ChatGPT for clinical decision-making. To enhance the accuracy of ChatGPT, it is 
      necessary to integrate it with the existing electronic health record system in 
      the future.
CI  - © 2023 Chen et al.
FAU - Chen, Jingfang
AU  - Chen J
AUID- ORCID: 0000-0001-8389-7703
AD  - Faculty of Medicine, Macau University of Science and Technology, Macau, People's 
      Republic of China.
AD  - Department of Research and Teaching, the Third People's Hospital of Shenzhen, 
      Shenzhen, People's Republic of China.
AD  - Hengyang Medical School, School of Nursing, University of South China, Hengyang, 
      People's Republic of China.
FAU - Liu, Linlin
AU  - Liu L
AD  - Hengyang Medical School, School of Nursing, University of South China, Hengyang, 
      People's Republic of China.
FAU - Ruan, Shujin
AU  - Ruan S
AD  - Hengyang Medical School, School of Nursing, University of South China, Hengyang, 
      People's Republic of China.
FAU - Li, Mengjun
AU  - Li M
AD  - Hengyang Medical School, School of Nursing, University of South China, Hengyang, 
      People's Republic of China.
FAU - Yin, Chengliang
AU  - Yin C
AUID- ORCID: 0000-0001-8262-5749
AD  - Faculty of Medicine, Macau University of Science and Technology, Macau, People's 
      Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20231206
PL  - New Zealand
TA  - J Multidiscip Healthc
JT  - Journal of multidisciplinary healthcare
JID - 101512691
PMC - PMC10710805
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - case reports
OT  - clinical decision support systems
COIS- The authors report no conflicts of interest in this work.
EDAT- 2023/12/12 06:42
MHDA- 2023/12/12 06:43
CRDT- 2023/12/12 03:35
PHST- 2023/09/23 00:00 [received]
PHST- 2023/11/26 00:00 [accepted]
PHST- 2023/12/12 06:43 [medline]
PHST- 2023/12/12 06:42 [pubmed]
PHST- 2023/12/12 03:35 [entrez]
AID - 441790 [pii]
AID - 10.2147/JMDH.S441790 [doi]
PST - epublish
SO  - J Multidiscip Healthc. 2023 Dec 6;16:3825-3831. doi: 10.2147/JMDH.S441790. 
      eCollection 2023.

PMID- 37582904
OWN - NLM
STAT- Publisher
LR  - 20230815
IS  - 1476-5438 (Electronic)
IS  - 1018-4813 (Linking)
DP  - 2023 Aug 15
TI  - Response to correspondence regarding "Analysis of large-language model versus 
      human performance for genetics questions".
LID - 10.1038/s41431-023-01444-3 [doi]
FAU - Duong, Dat
AU  - Duong D
AD  - Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research 
      Institute, Bethesda, MD, USA.
FAU - Solomon, Benjamin D
AU  - Solomon BD
AUID- ORCID: 0000-0002-8826-1023
AD  - Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research 
      Institute, Bethesda, MD, USA. solomonb@mail.nih.gov.
LA  - eng
GR  - N/A/U.S. Department of Health & Human Services | NIH | National Human Genome 
      Research Institute (NHGRI)/
GR  - N/A/U.S. Department of Health & Human Services | NIH | National Human Genome 
      Research Institute (NHGRI)/
PT  - Letter
DEP - 20230815
PL  - England
TA  - Eur J Hum Genet
JT  - European journal of human genetics : EJHG
JID - 9302235
SB  - IM
EDAT- 2023/08/16 00:42
MHDA- 2023/08/16 00:42
CRDT- 2023/08/15 23:31
PHST- 2023/07/20 00:00 [received]
PHST- 2023/07/24 00:00 [accepted]
PHST- 2023/08/16 00:42 [medline]
PHST- 2023/08/16 00:42 [pubmed]
PHST- 2023/08/15 23:31 [entrez]
AID - 10.1038/s41431-023-01444-3 [pii]
AID - 10.1038/s41431-023-01444-3 [doi]
PST - aheadofprint
SO  - Eur J Hum Genet. 2023 Aug 15. doi: 10.1038/s41431-023-01444-3.

PMID- 37824352
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231115
IS  - 1972-2680 (Electronic)
IS  - 1972-2680 (Linking)
VI  - 17
IP  - 9
DP  - 2023 Sep 30
TI  - ChatGPT: ethical concerns and challenges in academics and research.
PG  - 1292-1299
LID - 10.3855/jidc.18738 [doi]
AB  - INTRODUCTION: The emergence of artificial intelligence (AI) has presented several 
      opportunities to ease human work. AI applications are available for almost every 
      domain of life. A new technology, Chat Generative Pre-Trained Transformer 
      (ChatGPT), was introduced by OpenAI in November 2022, and has become a topic of 
      discussion across the world. ChatGPT-3 has brought many opportunities, as well as 
      ethical and privacy considerations. ChatGPT is a large language model (LLM) which 
      has been trained on the events that happened until 2021. The use of AI and its 
      assisted technologies in scientific writing is against research and publication 
      ethics. Therefore, policies and guidelines need to be developed over the use of 
      such tools in scientific writing. The main objective of the present study was to 
      highlight the use of AI and AI assisted technologies such as the ChatGPT and 
      other chatbots in the scientific writing and in the research domain resulting in 
      bias, spread of inaccurate information and plagiarism. METHODOLOGY: Experiments 
      were designed to test the accuracy of ChatGPT when used in research and academic 
      writing. RESULTS: The information provided by ChatGPT was inaccurate and may have 
      far-reaching implications in the field of medical science and engineering. 
      Critical thinking should be encouraged among researchers to raise awareness about 
      the associated privacy and ethical risks. CONCLUSIONS: Regulations for ethical 
      and privacy concerns related to the use of ChatGPT in academics and research need 
      to be developed.
CI  - Copyright (c) 2023 Ankita Guleria, Kewal Krishan, Vishal Sharma, Tanuj Kanchan.
FAU - Guleria, Ankita
AU  - Guleria A
AD  - Department of Anthropology, Panjab University, Sector-14, Chandigarh, India.
FAU - Krishan, Kewal
AU  - Krishan K
AD  - Department of Anthropology, Panjab University, Sector-14, Chandigarh, India.
FAU - Sharma, Vishal
AU  - Sharma V
AD  - Institute of Forensic Science and Criminology, Panjab University, Sector-14, 
      Chandigarh, India.
FAU - Kanchan, Tanuj
AU  - Kanchan T
AD  - Department of Forensic Medicine and Toxicology, All India Institute of Medical 
      Sciences, Jodhpur, India.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230930
PL  - Italy
TA  - J Infect Dev Ctries
JT  - Journal of infection in developing countries
JID - 101305410
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Organizations
OTO - NOTNLM
OT  - ChatGPT
OT  - Open AI
OT  - artificial intelligence
OT  - chatbot
OT  - privacy concerns
OT  - publication ethics
COIS- No Conflict of Interest is declared
EDAT- 2023/10/12 18:42
MHDA- 2023/11/01 12:44
CRDT- 2023/10/12 12:43
PHST- 2023/06/14 00:00 [received]
PHST- 2023/07/12 00:00 [accepted]
PHST- 2023/11/01 12:44 [medline]
PHST- 2023/10/12 18:42 [pubmed]
PHST- 2023/10/12 12:43 [entrez]
AID - 10.3855/jidc.18738 [doi]
PST - epublish
SO  - J Infect Dev Ctries. 2023 Sep 30;17(9):1292-1299. doi: 10.3855/jidc.18738.

PMID- 37714915
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231121
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Sep 15
TI  - ChatGPT in education: global reactions to AI innovations.
PG  - 15310
LID - 10.1038/s41598-023-42227-6 [doi]
LID - 15310
AB  - The release and rapid diffusion of ChatGPT have caught the attention of educators 
      worldwide. Some educators are enthusiastic about its potential to support 
      learning. Others are concerned about how it might circumvent learning 
      opportunities or contribute to misinformation. To better understand reactions 
      about ChatGPT concerning education, we analyzed Twitter data (16,830,997 tweets 
      from 5,541,457 users). Based on topic modeling and sentiment analysis, we provide 
      an overview of global perceptions and reactions to ChatGPT regarding education. 
      ChatGPT triggered a massive response on Twitter, with education being the most 
      tweeted content topic. Topics ranged from specific (e.g., cheating) to broad 
      (e.g., opportunities), which were discussed with mixed sentiment. We traced that 
      authority decisions may influence public opinions. We discussed that the average 
      reaction on Twitter (e.g., using ChatGPT to cheat in exams) differs from 
      discussions in which education and teaching-learning researchers are likely to be 
      more interested (e.g., ChatGPT as an intelligent learning partner). This study 
      provides insights into people's reactions when new groundbreaking technology is 
      released and implications for scientific and policy communication in rapidly 
      changing circumstances.
CI  - © 2023. Springer Nature Limited.
FAU - Fütterer, Tim
AU  - Fütterer T
AUID- ORCID: 0000-0001-5399-9557
AD  - Hector Research Institute of Education Sciences and Psychology, University of 
      Tübingen, Europastraße 6, 72072, Tübingen, Germany. 
      tim.fuetterer@uni-tuebingen.de.
FAU - Fischer, Christian
AU  - Fischer C
AUID- ORCID: 0000-0002-8809-2776
AD  - Hector Research Institute of Education Sciences and Psychology, University of 
      Tübingen, Europastraße 6, 72072, Tübingen, Germany.
FAU - Alekseeva, Anastasiia
AU  - Alekseeva A
AUID- ORCID: 0000-0002-7395-6180
AD  - Hector Research Institute of Education Sciences and Psychology, University of 
      Tübingen, Europastraße 6, 72072, Tübingen, Germany.
FAU - Chen, Xiaobin
AU  - Chen X
AUID- ORCID: 0000-0003-3158-0899
AD  - Hector Research Institute of Education Sciences and Psychology, University of 
      Tübingen, Europastraße 6, 72072, Tübingen, Germany.
FAU - Tate, Tamara
AU  - Tate T
AUID- ORCID: 0000-0002-1753-8435
AD  - University of California, Irvine, USA.
FAU - Warschauer, Mark
AU  - Warschauer M
AUID- ORCID: 0000-0002-6817-4416
AD  - University of California, Irvine, USA.
FAU - Gerjets, Peter
AU  - Gerjets P
AUID- ORCID: 0000-0003-1358-6779
AD  - Leibniz-Institut für Wissensmedien, Tübingen, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
PMC - PMC10504368
COIS- The authors declare no competing interests.
EDAT- 2023/09/16 05:42
MHDA- 2023/09/16 05:43
CRDT- 2023/09/15 23:23
PHST- 2023/04/20 00:00 [received]
PHST- 2023/09/07 00:00 [accepted]
PHST- 2023/09/16 05:43 [medline]
PHST- 2023/09/16 05:42 [pubmed]
PHST- 2023/09/15 23:23 [entrez]
AID - 10.1038/s41598-023-42227-6 [pii]
AID - 42227 [pii]
AID - 10.1038/s41598-023-42227-6 [doi]
PST - epublish
SO  - Sci Rep. 2023 Sep 15;13(1):15310. doi: 10.1038/s41598-023-42227-6.

PMID- 37179029
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230822
LR  - 20230830
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
VI  - 89
IP  - 3
DP  - 2023 Sep
TI  - Experimenting with ChatGPT: Concerns for academic medicine.
PG  - e127-e129
LID - S0190-9622(23)00747-8 [pii]
LID - 10.1016/j.jaad.2023.04.045 [doi]
FAU - Hirani, Rahim
AU  - Hirani R
AD  - New York Medical College, Valhalla, New York.
FAU - Farabi, Banu
AU  - Farabi B
AD  - New York Medical College, Valhalla, New York; Department of Dermatology, NYC 
      Health + Hospitals/Metropolitan Medical Center, New York, New York; Department of 
      Medicine, NYC Health + Hospitals/South Brooklyn Health, Ruth Bader Ginsburg 
      Hospital, Brooklyn, New York.
FAU - Marmon, Shoshana
AU  - Marmon S
AD  - New York Medical College, Valhalla, New York; Department of Dermatology, NYC 
      Health + Hospitals/Metropolitan Medical Center, New York, New York; Department of 
      Medicine, NYC Health + Hospitals/South Brooklyn Health, Ruth Bader Ginsburg 
      Hospital, Brooklyn, New York. Electronic address: Shoshana.Marmon@nychhc.org.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230511
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
CON - NPJ Digit Med. 2023 Apr 26;6(1):75. PMID: 37100871
OTO - NOTNLM
OT  - ChatGPT
OT  - academic publishing
OT  - artificial intelligence (AI)
OT  - dermatology journals
OT  - gender bias
OT  - large language model
OT  - machine learning
OT  - racial bias
OT  - scientific integrity
COIS- Conflicts of interest None disclosed.
EDAT- 2023/05/14 01:07
MHDA- 2023/05/14 01:08
CRDT- 2023/05/13 19:29
PHST- 2023/02/21 00:00 [received]
PHST- 2023/04/04 00:00 [revised]
PHST- 2023/04/10 00:00 [accepted]
PHST- 2023/05/14 01:08 [medline]
PHST- 2023/05/14 01:07 [pubmed]
PHST- 2023/05/13 19:29 [entrez]
AID - S0190-9622(23)00747-8 [pii]
AID - 10.1016/j.jaad.2023.04.045 [doi]
PST - ppublish
SO  - J Am Acad Dermatol. 2023 Sep;89(3):e127-e129. doi: 10.1016/j.jaad.2023.04.045. 
      Epub 2023 May 11.

PMID- 36914821
OWN - NLM
STAT- MEDLINE
DCOM- 20230315
LR  - 20230418
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Mar 13
TI  - Evaluating the use of large language model in identifying top research questions 
      in gastroenterology.
PG  - 4164
LID - 10.1038/s41598-023-31412-2 [doi]
LID - 4164
AB  - The field of gastroenterology (GI) is constantly evolving. It is essential to 
      pinpoint the most pressing and important research questions. To evaluate the 
      potential of chatGPT for identifying research priorities in GI and provide a 
      starting point for further investigation. We queried chatGPT on four key topics 
      in GI: inflammatory bowel disease, microbiome, Artificial Intelligence in GI, and 
      advanced endoscopy in GI. A panel of experienced gastroenterologists separately 
      reviewed and rated the generated research questions on a scale of 1-5, with 5 
      being the most important and relevant to current research in GI. chatGPT 
      generated relevant and clear research questions. Yet, the questions were not 
      considered original by the panel of gastroenterologists. On average, the 
      questions were rated 3.6 ± 1.4, with inter-rater reliability ranging from 0.80 to 
      0.98 (p < 0.001). The mean grades for relevance, clarity, specificity, and 
      originality were 4.9 ± 0.1, 4.6 ± 0.4, 3.1 ± 0.2, 1.5 ± 0.4, respectively. Our 
      study suggests that Large Language Models (LLMs) may be a useful tool for 
      identifying research priorities in the field of GI, but more work is needed to 
      improve the novelty of the generated research questions.
CI  - © 2023. The Author(s).
FAU - Lahat, Adi
AU  - Lahat A
AD  - Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel 
      Aviv University, Tel Aviv, Israel. zokadi@gmail.com.
FAU - Shachar, Eyal
AU  - Shachar E
AD  - Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel 
      Aviv University, Tel Aviv, Israel.
FAU - Avidan, Benjamin
AU  - Avidan B
AD  - Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel 
      Aviv University, Tel Aviv, Israel.
FAU - Shatz, Zina
AU  - Shatz Z
AD  - Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel 
      Aviv University, Tel Aviv, Israel.
FAU - Glicksberg, Benjamin S
AU  - Glicksberg BS
AD  - Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount 
      Sinai, New York, NY, USA.
FAU - Klang, Eyal
AU  - Klang E
AD  - The Sami Sagol AI Hub, ARC Innovation Center, Chaim Sheba Medical Center, 
      Affiliated to Tel-Aviv University, Tel Aviv, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230313
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - *Gastroenterology
MH  - Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Gastroenterologists
MH  - *Inflammatory Bowel Diseases
PMC - PMC10011374
COIS- The authors declare no competing interests.
EDAT- 2023/03/15 06:00
MHDA- 2023/03/16 06:00
CRDT- 2023/03/14 00:44
PHST- 2023/01/05 00:00 [received]
PHST- 2023/03/11 00:00 [accepted]
PHST- 2023/03/14 00:44 [entrez]
PHST- 2023/03/15 06:00 [pubmed]
PHST- 2023/03/16 06:00 [medline]
AID - 10.1038/s41598-023-31412-2 [pii]
AID - 31412 [pii]
AID - 10.1038/s41598-023-31412-2 [doi]
PST - epublish
SO  - Sci Rep. 2023 Mar 13;13(1):4164. doi: 10.1038/s41598-023-31412-2.

PMID- 37634667
OWN - NLM
STAT- In-Process
LR  - 20231209
IS  - 1878-8769 (Electronic)
IS  - 1878-8750 (Linking)
VI  - 179
DP  - 2023 Nov
TI  - Chat GPT as a Neuro-Score Calculator: Analysis of a Large Language Model's 
      Performance on Various Neurological Exam Grading Scales.
PG  - e342-e347
LID - S1878-8750(23)01201-9 [pii]
LID - 10.1016/j.wneu.2023.08.088 [doi]
AB  - BACKGROUND: ChatGPT is a large language model artificial intelligence chatbot 
      that has been applied to different aspects of the medical field. Our study aims 
      to assess the quality of chatGPT to evaluate patients based on their exams for 
      different scores including Glasgow Coma Scale (GCS), intracranial hemorrhage 
      score (ICH), and Hunt & Hess (H&H) classification. METHODS: We created batches of 
      patient test cases with detailed neurological exams, totaling 20 cases and 
      created variants of increasing complex phrasing of the test cases. Using ChatGPT, 
      we assessed repeatability and quantified the errors, including the average error 
      rate (AER) and magnitude of errors (AME). We repeated this process for the H&H 
      and the ICH score using base cases. Specific prompts were created for each 
      calculator. RESULTS: The GCS calculator on 10 base test cases had an AER/AME of 
      10%/0.150. The accuracy of ChatGPT decreased with increasing complexity; for 
      example, in a variation where crucial information was missing, the AER was 45% 
      for 20 cases. For H&H, AER/AME was 13%/0.13 and for ICH, AER/AME was 27.5%/0.325. 
      Using a simple prompt resulted in a significantly higher error rate of 70%. 
      CONCLUSIONS: ChatGPT demonstrates ability in this proof-of-concept experiment in 
      evaluating neuroexams using established assessment scales including GCS, ICH, and 
      H&H. However, it has limitations in accuracy and may "hallucinate" with complex 
      or vague descriptions. Nonetheless, ChatGPT, has promising potential in medicine.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Chen, Tse Chiang
AU  - Chen TC
AD  - Department of Neurology, Tulane University School of Medicine, New Orleans, 
      Louisiana, USA.
FAU - Kaminski, Emily
AU  - Kaminski E
AD  - Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Koduri, Laila
AU  - Koduri L
AD  - Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Singer, Alyssa
AU  - Singer A
AD  - Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Singer, Jorie
AU  - Singer J
AD  - Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Couldwell, Mitch
AU  - Couldwell M
AD  - Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Delashaw, Johnny
AU  - Delashaw J
AD  - Department of Neurological Surgery, Tulane University School of Medicine, New 
      Orleans, Louisiana, USA.
FAU - Dumont, Aaron
AU  - Dumont A
AD  - Department of Neurological Surgery, Tulane University School of Medicine, New 
      Orleans, Louisiana, USA.
FAU - Wang, Arthur
AU  - Wang A
AD  - Department of Neurological Surgery, Tulane University School of Medicine, New 
      Orleans, Louisiana, USA. Electronic address: awang15@tulane.edu.
LA  - eng
PT  - Journal Article
DEP - 20230826
PL  - United States
TA  - World Neurosurg
JT  - World neurosurgery
JID - 101528275
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Calculator
OT  - ChatGPT
OT  - Grading scale
OT  - Large language model
OT  - Neurological exam
EDAT- 2023/08/28 00:41
MHDA- 2023/08/28 00:41
CRDT- 2023/08/27 19:25
PHST- 2023/08/20 00:00 [received]
PHST- 2023/08/22 00:00 [accepted]
PHST- 2023/08/28 00:41 [pubmed]
PHST- 2023/08/28 00:41 [medline]
PHST- 2023/08/27 19:25 [entrez]
AID - S1878-8750(23)01201-9 [pii]
AID - 10.1016/j.wneu.2023.08.088 [doi]
PST - ppublish
SO  - World Neurosurg. 2023 Nov;179:e342-e347. doi: 10.1016/j.wneu.2023.08.088. Epub 
      2023 Aug 26.

PMID- 37227477
OWN - NLM
STAT- Publisher
LR  - 20231019
IS  - 1435-702X (Electronic)
IS  - 0721-832X (Linking)
VI  - 261
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT and scientific abstract writing: pitfalls and caution.
PG  - 3205-3206
LID - 10.1007/s00417-023-06123-z [doi]
FAU - Ali, Mohammad Javed
AU  - Ali MJ
AUID- ORCID: 0000-0002-6363-9202
AD  - Govindram Seksaria Institute of Dacryology, L.V. Prasad Eye Institute, Road no 2, 
      Banjara Hills, Hyderabad, -34, India. drjaved007@gmail.com.
FAU - Singh, Swati
AU  - Singh S
AD  - Ophthalmic Plastic Surgery Service, L.V. Prasad Eye Institute, Hyderabad, India.
LA  - eng
PT  - Journal Article
DEP - 20230525
PL  - Germany
TA  - Graefes Arch Clin Exp Ophthalmol
JT  - Graefe's archive for clinical and experimental ophthalmology = Albrecht von 
      Graefes Archiv fur klinische und experimentelle Ophthalmologie
JID - 8205248
SB  - IM
OTO - NOTNLM
OT  - Abstract
OT  - ChatGPT
OT  - Ethics
OT  - Ophthalmology
OT  - Publication
OT  - Scientific
EDAT- 2023/05/25 13:06
MHDA- 2023/05/25 13:06
CRDT- 2023/05/25 11:16
PHST- 2023/03/29 00:00 [received]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2023/04/27 00:00 [revised]
PHST- 2023/05/25 13:06 [pubmed]
PHST- 2023/05/25 13:06 [medline]
PHST- 2023/05/25 11:16 [entrez]
AID - 10.1007/s00417-023-06123-z [pii]
AID - 10.1007/s00417-023-06123-z [doi]
PST - ppublish
SO  - Graefes Arch Clin Exp Ophthalmol. 2023 Nov;261(11):3205-3206. doi: 
      10.1007/s00417-023-06123-z. Epub 2023 May 25.

PMID- 37553205
OWN - NLM
STAT- Publisher
LR  - 20230808
IS  - 1750-3639 (Electronic)
IS  - 1015-6305 (Linking)
DP  - 2023 Aug 8
TI  - Evaluating the performance of large language models: ChatGPT and Google Bard in 
      generating differential diagnoses in clinicopathological conferences of 
      neurodegenerative disorders.
PG  - e13207
LID - 10.1111/bpa.13207 [doi]
AB  - This study explores the utility of the large language models (LLMs), specifically 
      ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical 
      summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo 
      Clinic brain bank Clinico-Pathological Conferences were analyzed. The LLMs 
      provided multiple pathologic diagnoses and their rationales, which were compared 
      with the final clinical diagnoses made by physicians. ChatGPT-3.5, ChatGPT-4, and 
      Google Bard correctly made primary diagnoses in 32%, 52%, and 40% of cases, 
      respectively, while correct diagnoses were included in 76%, 84%, and 76% of 
      cases, respectively. These findings highlight the potential of artificial 
      intelligence tools like ChatGPT in neuropathology, suggesting they may facilitate 
      more comprehensive discussions in clinicopathological conferences.
CI  - © 2023 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf 
      of International Society of Neuropathology.
FAU - Koga, Shunsuke
AU  - Koga S
AUID- ORCID: 0000-0001-8868-9700
AD  - Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA.
FAU - Martin, Nicholas B
AU  - Martin NB
AD  - Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA.
FAU - Dickson, Dennis W
AU  - Dickson DW
AD  - Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA.
LA  - eng
PT  - Letter
DEP - 20230808
PL  - Switzerland
TA  - Brain Pathol
JT  - Brain pathology (Zurich, Switzerland)
JID - 9216781
SB  - IM
OTO - NOTNLM
OT  - CPC
OT  - ChatGPT
OT  - Google Bard
OT  - artificial intelligence
OT  - clinicopathological conference
OT  - large language model
OT  - neuropathology
OT  - pathology
EDAT- 2023/08/09 01:05
MHDA- 2023/08/09 01:05
CRDT- 2023/08/08 21:13
PHST- 2023/06/22 00:00 [received]
PHST- 2023/07/31 00:00 [accepted]
PHST- 2023/08/09 01:05 [medline]
PHST- 2023/08/09 01:05 [pubmed]
PHST- 2023/08/08 21:13 [entrez]
AID - 10.1111/bpa.13207 [doi]
PST - aheadofprint
SO  - Brain Pathol. 2023 Aug 8:e13207. doi: 10.1111/bpa.13207.

PMID- 38028668
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Overview of Chatbots with special emphasis on artificial intelligence-enabled 
      ChatGPT in medical science.
PG  - 1237704
LID - 10.3389/frai.2023.1237704 [doi]
LID - 1237704
AB  - The release of ChatGPT has initiated new thinking about AI-based Chatbot and its 
      application and has drawn huge public attention worldwide. Researchers and 
      doctors have started thinking about the promise and application of AI-related 
      large language models in medicine during the past few months. Here, the 
      comprehensive review highlighted the overview of Chatbot and ChatGPT and their 
      current role in medicine. Firstly, the general idea of Chatbots, their evolution, 
      architecture, and medical use are discussed. Secondly, ChatGPT is discussed with 
      special emphasis of its application in medicine, architecture and training 
      methods, medical diagnosis and treatment, research ethical issues, and a 
      comparison of ChatGPT with other NLP models are illustrated. The article also 
      discussed the limitations and prospects of ChatGPT. In the future, these large 
      language models and ChatGPT will have immense promise in healthcare. However, 
      more research is needed in this direction.
CI  - Copyright © 2023 Chakraborty, Pal, Bhattacharya, Dash and Lee.
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal, India.
FAU - Pal, Soumen
AU  - Pal S
AD  - School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil 
      Nadu, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Balasore, Odisha, India.
FAU - Dash, Snehasish
AU  - Dash S
AD  - School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil 
      Nadu, India.
FAU - Lee, Sang-Soo
AU  - Lee SS
AD  - Institute for Skeletal Aging and Orthopedic Surgery, Hallym University Chuncheon 
      Sacred Heart Hospital, Chuncheon-si, Gangwon-do, Republic of Korea.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231031
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10644239
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Chatbot
OT  - large language models
OT  - medical use
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/29 18:43
MHDA- 2023/11/29 18:44
CRDT- 2023/11/29 17:16
PHST- 2023/06/09 00:00 [received]
PHST- 2023/10/05 00:00 [accepted]
PHST- 2023/11/29 18:44 [medline]
PHST- 2023/11/29 18:43 [pubmed]
PHST- 2023/11/29 17:16 [entrez]
AID - 10.3389/frai.2023.1237704 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Oct 31;6:1237704. doi: 10.3389/frai.2023.1237704. 
      eCollection 2023.

PMID- 37477707
OWN - NLM
STAT- Publisher
LR  - 20230721
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 21
TI  - ChatGPT as a Complementary Mental Health Resource: A Boon or a Bane.
LID - 10.1007/s10439-023-03326-7 [doi]
AB  - The launch of Open AI's chatbot, ChatGPT, has generated a lot of attention and 
      discussion among professionals in several fields. Many concerns and challenges 
      have been brought up by researchers from various fields, particularly in relation 
      to the harm that using these tools for medical diagnosis and treatment 
      recommendations can cause. In addition, it has been debated if ChatGPT is 
      dependable, efficient, and helpful for clinicians and medical professionals. 
      Therefore, in this study, we assess ChatGPT's effectiveness in providing mental 
      health support, particularly for issues related to anxiety and depression, based 
      on the chatbot's responses and cross-questioning. The findings indicate that 
      there are significant inconsistencies and that ChatGPT's reliability is low in 
      this specific domain. As a result, care must be used when using ChatGPT as a 
      complementary mental health resource.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Farhat, Faiza
AU  - Farhat F
AUID- ORCID: 0000-0002-1310-1586
AD  - Section of Parasitology, Department of Zoology, Aligarh Muslim University, 
      Aligarh, UP, 202002, India. faizahaque16@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230721
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Anxiety
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Depression
OT  - Large language model
OT  - Mental health
EDAT- 2023/07/21 13:15
MHDA- 2023/07/21 13:15
CRDT- 2023/07/21 11:06
PHST- 2023/07/14 00:00 [received]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/07/21 13:15 [medline]
PHST- 2023/07/21 13:15 [pubmed]
PHST- 2023/07/21 11:06 [entrez]
AID - 10.1007/s10439-023-03326-7 [pii]
AID - 10.1007/s10439-023-03326-7 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 21. doi: 10.1007/s10439-023-03326-7.

PMID- 37804030
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 1681-7168 (Electronic)
IS  - 1022-386X (Linking)
VI  - 33
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT: Transcending Language Limitations in Scientific Research Using 
      Artificial Intelligence.
PG  - 1198-1200
LID - 10.29271/jcpsp.2023.10.1198 [doi]
AB  - Health and scientific researchers in non-English speaking countries such as 
      Pakistan, are not proficient in English, which limits their ability to 
      communicate their ideas and findings to the international scientific community. 
      ChatGPT is a large language model that can help non-native English speakers to 
      write high-quality scientific papers much faster by assisting them in conveying 
      their ideas in a clear and understandable manner, as well as avoiding common 
      language errors. In fact, ChatGPT has already been used in publication of 
      research papers, literature reviews, and editorials. However, it is imperative to 
      recognise that ChatGPT is still in its early stages, thus, it is important to 
      recognise its limitations. It is suggested that ChatGPT should be employed to 
      complement writing and reviewing tasks but should not be relied on to generate 
      original content or perform essential analysis, as it cannot replace human 
      expertise, contextual knowledge, experience, and intelligence. Researchers should 
      exercise caution and thoroughly scrutinise the generated text for accuracy and 
      plagiarism before incorporating it into their work. Key Words: Artificial 
      intelligence, ChatGPT, Health research, Scientific research.
FAU - Osama, Muhammad
AU  - Osama M
AD  - Foundation University College of Physical Therapy (FUCP), Foundation University, 
      Islamabad, Pakistan.
AD  - Rawal Institute of Health Sciences, Islamabad, Pakistan.
FAU - Afridi, Sabah
AU  - Afridi S
AD  - Hofstra University, New York, United States of America.
FAU - Maaz, Muhammad
AU  - Maaz M
LA  - eng
PT  - Journal Article
PL  - Pakistan
TA  - J Coll Physicians Surg Pak
JT  - Journal of the College of Physicians and Surgeons--Pakistan : JCPSP
JID - 9606447
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Exercise
MH  - Knowledge
MH  - Language
MH  - Pakistan
EDAT- 2023/10/07 11:43
MHDA- 2023/11/01 12:44
CRDT- 2023/10/07 03:51
PHST- 2023/02/01 00:00 [received]
PHST- 2023/08/31 00:00 [accepted]
PHST- 2023/11/01 12:44 [medline]
PHST- 2023/10/07 11:43 [pubmed]
PHST- 2023/10/07 03:51 [entrez]
AID - 040579197 [pii]
AID - 10.29271/jcpsp.2023.10.1198 [doi]
PST - ppublish
SO  - J Coll Physicians Surg Pak. 2023 Oct;33(10):1198-1200. doi: 
      10.29271/jcpsp.2023.10.1198.

PMID- 37999958
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 1929-0748 (Print)
IS  - 1929-0748 (Electronic)
IS  - 1929-0748 (Linking)
VI  - 12
DP  - 2023 Nov 24
TI  - Usability and Efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health 
      Sciences Students: Protocol for a Crossover Randomized Controlled Trial.
PG  - e51873
LID - 10.2196/51873 [doi]
LID - e51873
AB  - BACKGROUND: The integration of artificial intelligence (AI) into health sciences 
      students' education holds significant importance. The rapid advancement of AI has 
      opened new horizons in scientific writing and has the potential to reshape 
      human-technology interactions. AI in education may impact critical thinking, 
      leading to unintended consequences that need to be addressed. Understanding the 
      implications of AI adoption in education is essential for ensuring its 
      responsible and effective use, empowering health sciences students to navigate 
      AI-driven technologies' evolving field with essential knowledge and skills. 
      OBJECTIVE: This study aims to provide details on the study protocol and the 
      methods used to investigate the usability and efficacy of ChatGPT, a large 
      language model. The primary focus is on assessing its role as a supplementary 
      learning tool for improving learning processes and outcomes among undergraduate 
      health sciences students, with a specific emphasis on chronic diseases. METHODS: 
      This single-blinded, crossover, randomized, controlled trial is part of a broader 
      mixed methods study, and the primary emphasis of this paper is on the 
      quantitative component of the overall research. A total of 50 students will be 
      recruited for this study. The alternative hypothesis posits that there will be a 
      significant difference in learning outcomes and technology usability between 
      students using ChatGPT (group A) and those using standard web-based tools (group 
      B) to access resources and complete assignments. Participants will be allocated 
      to sequence AB or BA in a 1:1 ratio using computer-generated randomization. Both 
      arms include students' participation in a writing assignment intervention, with a 
      washout period of 21 days between interventions. The primary outcome is the 
      measure of the technology usability and effectiveness of ChatGPT, whereas the 
      secondary outcome is the measure of students' perceptions and experiences with 
      ChatGPT as a learning tool. Outcome data will be collected up to 24 hours after 
      the interventions. RESULTS: This study aims to understand the potential benefits 
      and challenges of incorporating AI as an educational tool, particularly in the 
      context of student learning. The findings are expected to identify critical areas 
      that need attention and help educators develop a deeper understanding of AI's 
      impact on the educational field. By exploring the differences in the usability 
      and efficacy between ChatGPT and conventional web-based tools, this study seeks 
      to inform educators and students on the responsible integration of AI into 
      academic settings, with a specific focus on health sciences education. 
      CONCLUSIONS: By exploring the usability and efficacy of ChatGPT compared with 
      conventional web-based tools, this study seeks to inform educators and students 
      about the responsible integration of AI into academic settings. TRIAL 
      REGISTRATION: ClinicalTrails.gov NCT05963802; 
      https://clinicaltrials.gov/study/NCT05963802. INTERNATIONAL REGISTERED REPORT 
      IDENTIFIER (IRRID): PRR1-10.2196/51873.
CI  - ©Mirella Veras, Joseph-Omer Dyer, Morgan Rooney, Paulo Goberlânio Barros Silva, 
      Derek Rutherford, Dahlia Kairy. Originally published in JMIR Research Protocols 
      (https://www.researchprotocols.org), 24.11.2023.
FAU - Veras, Mirella
AU  - Veras M
AUID- ORCID: 0000-0002-4124-1543
AD  - Health Sciences, Carleton University, Ottawa, ON, Canada.
AD  - Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, 
      Montréal, QC, Canada.
FAU - Dyer, Joseph-Omer
AU  - Dyer JO
AUID- ORCID: 0000-0002-7570-9941
AD  - École de Réadaptation, Faculté de Médecine, Université de Montréal, Montréal, QC, 
      Canada.
AD  - Groupe Interdisciplinaire de Recherche sur la Cognition et le Raisonnement 
      Professionnel, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.
FAU - Rooney, Morgan
AU  - Rooney M
AUID- ORCID: 0000-0001-5261-5929
AD  - Teaching and Learning Services, Carleton University, Ottawa, ON, Canada.
FAU - Barros Silva, Paulo Goberlânio
AU  - Barros Silva PG
AUID- ORCID: 0000-0002-1513-9027
AD  - Centro Universitário Christus, Fortaleza, Ceara, Brazil.
FAU - Rutherford, Derek
AU  - Rutherford D
AUID- ORCID: 0000-0003-2688-6201
AD  - School of Physiotherapy, Dalhousie University, Halifax, NS, Canada.
FAU - Kairy, Dahlia
AU  - Kairy D
AUID- ORCID: 0000-0001-6872-6607
AD  - Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, 
      Montréal, QC, Canada.
AD  - École de Réadaptation, Faculté de Médecine, Université de Montréal, Montréal, QC, 
      Canada.
AD  - Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, 
      Centre Intégré Universitaire de Santé et Services Sociaux du 
      Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada.
LA  - eng
SI  - ClinicalTrials.gov/NCT05963802
PT  - Journal Article
DEP - 20231124
PL  - Canada
TA  - JMIR Res Protoc
JT  - JMIR research protocols
JID - 101599504
PMC - PMC10709780
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - OpenAI
OT  - RCT
OT  - artificial intelligence
OT  - crossover RCT
OT  - education
OT  - health sciences
OT  - learning outcomes
OT  - perceptions
OT  - randomized controlled trial
OT  - usability
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/24 12:43
MHDA- 2023/11/24 12:44
CRDT- 2023/11/24 11:54
PHST- 2023/08/15 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/10/18 00:00 [revised]
PHST- 2023/11/24 12:44 [medline]
PHST- 2023/11/24 12:43 [pubmed]
PHST- 2023/11/24 11:54 [entrez]
AID - v12i1e51873 [pii]
AID - 10.2196/51873 [doi]
PST - epublish
SO  - JMIR Res Protoc. 2023 Nov 24;12:e51873. doi: 10.2196/51873.

PMID- 37059827
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20230811
IS  - 1432-2161 (Electronic)
IS  - 0364-2348 (Linking)
VI  - 52
IP  - 9
DP  - 2023 Sep
TI  - A comparison of ChatGPT-generated articles with human-written articles.
PG  - 1755-1758
LID - 10.1007/s00256-023-04340-5 [doi]
AB  - OBJECTIVE: ChatGPT (Generative Pre-trained Transformer) is an artificial 
      intelligence language tool developed by OpenAI that utilises machine learning 
      algorithms to generate text that closely mimics human language. It has recently 
      taken the internet by storm. There have been several concerns regarding the 
      accuracy of documents it generates. This study compares the accuracy and quality 
      of several ChatGPT-generated academic articles with those written by human 
      authors. MATERIAL AND METHODS: We performed a study to assess the accuracy of 
      ChatGPT-generated radiology articles by comparing them with the published or 
      written, and under review articles. These were independently analysed by two 
      fellowship-trained musculoskeletal radiologists and graded from 1 to 5 (1 being 
      bad and inaccurate to 5 being excellent and accurate). RESULTS: In total, 4 of 
      the 5 articles written by ChatGPT were significantly inaccurate with fictitious 
      references. One of the papers was well written, with a good introduction and 
      discussion; however, all references were fictitious. CONCLUSION: ChatGPT is able 
      to generate coherent research articles, which on initial review may closely 
      resemble authentic articles published by academic researchers. However, all of 
      the articles we assessed were factually inaccurate and had fictitious references. 
      It is worth noting, however, that the articles generated may appear authentic to 
      an untrained reader.
CI  - © 2023. The Author(s), under exclusive licence to International Skeletal Society 
      (ISS).
FAU - Ariyaratne, Sisith
AU  - Ariyaratne S
AD  - Department of Musculoskeletal Radiology, The Royal Orthopedic Hospital, Bristol 
      Road South, Northfield, Birmingham, UK.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AD  - Department of Orthopedics, Southport and Ormskirk Hospital, Southport, UK.
FAU - Nischal, Neha
AU  - Nischal N
AD  - Department of Radiology, Holy Family Hospital, New Delhi, India.
FAU - Chitti Babu, Naparla
AU  - Chitti Babu N
AD  - Department of Radiology, Srinivas Institute of Medical Sciences & Research 
      Centre, Mukka, Mangalore, India.
FAU - Botchu, Rajesh
AU  - Botchu R
AD  - Department of Musculoskeletal Radiology, The Royal Orthopedic Hospital, Bristol 
      Road South, Northfield, Birmingham, UK. drbrajesh@yahoo.com.
LA  - eng
PT  - Journal Article
DEP - 20230414
PL  - Germany
TA  - Skeletal Radiol
JT  - Skeletal radiology
JID - 7701953
SB  - IM
CIN - Skeletal Radiol. 2023 Dec;52(12):2487-2488. PMID: 37458781
CIN - Skeletal Radiol. 2023 Dec;52(12):2493. PMID: 37566150
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Algorithms
MH  - Fellowships and Scholarships
MH  - Internet
MH  - Machine Learning
OTO - NOTNLM
OT  - Accuracy
OT  - Articles
OT  - ChatGPT
OT  - Research
EDAT- 2023/04/15 06:00
MHDA- 2023/07/17 06:42
CRDT- 2023/04/14 23:18
PHST- 2023/03/01 00:00 [received]
PHST- 2023/04/09 00:00 [accepted]
PHST- 2023/04/06 00:00 [revised]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/04/15 06:00 [pubmed]
PHST- 2023/04/14 23:18 [entrez]
AID - 10.1007/s00256-023-04340-5 [pii]
AID - 10.1007/s00256-023-04340-5 [doi]
PST - ppublish
SO  - Skeletal Radiol. 2023 Sep;52(9):1755-1758. doi: 10.1007/s00256-023-04340-5. Epub 
      2023 Apr 14.

PMID- 37303897
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230613
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - ChatGPT outperforms humans in emotional awareness evaluations.
PG  - 1199058
LID - 10.3389/fpsyg.2023.1199058 [doi]
LID - 1199058
AB  - The artificial intelligence chatbot, ChatGPT, has gained widespread attention for 
      its ability to perform natural language processing tasks and has the 
      fastest-growing user base in history. Although ChatGPT has successfully generated 
      theoretical information in multiple fields, its ability to identify and describe 
      emotions is still unknown. Emotional awareness (EA), the ability to conceptualize 
      one's own and others' emotions, is considered a transdiagnostic mechanism for 
      psychopathology. This study utilized the Levels of Emotional Awareness Scale 
      (LEAS) as an objective, performance-based test to analyze ChatGPT's responses to 
      twenty scenarios and compared its EA performance with that of the general 
      population norms, as reported by a previous study. A second examination was 
      performed one month later to measure EA improvement over time. Finally, two 
      independent licensed psychologists evaluated the fit-to-context of ChatGPT's EA 
      responses. In the first examination, ChatGPT demonstrated significantly higher 
      performance than the general population on all the LEAS scales (Z score = 2.84). 
      In the second examination, ChatGPT's performance significantly improved, almost 
      reaching the maximum possible LEAS score (Z score = 4.26). Its accuracy levels 
      were also extremely high (9.7/10). The study demonstrated that ChatGPT can 
      generate appropriate EA responses, and that its performance may improve 
      significantly over time. The study has theoretical and clinical implications, as 
      ChatGPT can be used as part of cognitive training for clinical populations with 
      EA impairments. In addition, ChatGPT's EA-like abilities may facilitate 
      psychiatric diagnosis and assessment and be used to enhance emotional language. 
      Further research is warranted to better understand the potential benefits and 
      risks of ChatGPT and refine it to promote mental health.
CI  - Copyright © 2023 Elyoseph, Hadar-Shoval, Asraf and Lvovsky.
FAU - Elyoseph, Zohar
AU  - Elyoseph Z
AD  - Department of Psychology and Educational Counseling, The Center for 
      Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, 
      Israel.
AD  - Department of Brain Sciences, Faculty of Medicine, Imperial College London, 
      London, England.
FAU - Hadar-Shoval, Dorit
AU  - Hadar-Shoval D
AD  - Department of Psychology and Educational Counseling, The Center for 
      Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, 
      Israel.
FAU - Asraf, Kfir
AU  - Asraf K
AD  - Psychology Department, Center for Psychobiological Research, Max Stern Yezreel 
      Valley College, Emek Yezreel, Israel.
FAU - Lvovsky, Maya
AU  - Lvovsky M
AD  - Psychology Department, Center for Psychobiological Research, Max Stern Yezreel 
      Valley College, Emek Yezreel, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230526
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10254409
OTO - NOTNLM
OT  - ChatGPT
OT  - LEAS
OT  - artificial intelligence
OT  - emotional awareness
OT  - emotional intelligence
OT  - psychological assessment
OT  - psychotherapy
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/06/12 06:42
MHDA- 2023/06/12 06:43
CRDT- 2023/06/12 03:59
PHST- 2023/04/02 00:00 [received]
PHST- 2023/05/11 00:00 [accepted]
PHST- 2023/06/12 06:43 [medline]
PHST- 2023/06/12 06:42 [pubmed]
PHST- 2023/06/12 03:59 [entrez]
AID - 10.3389/fpsyg.2023.1199058 [doi]
PST - epublish
SO  - Front Psychol. 2023 May 26;14:1199058. doi: 10.3389/fpsyg.2023.1199058. 
      eCollection 2023.

PMID- 37332002
OWN - NLM
STAT- MEDLINE
DCOM- 20231124
LR  - 20231124
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 12
DP  - 2023 Dec
TI  - The Potential of ChatGPT in Assisting Children with Down Syndrome.
PG  - 2638-2640
LID - 10.1007/s10439-023-03281-3 [doi]
AB  - ChatGPT, an advanced language generation model developed by OpenAI, has the 
      potential to revolutionize healthcare delivery and support for individuals with 
      various conditions, including Down syndrome. This article explores the 
      applications of ChatGPT in assisting children with Down syndrome, highlighting 
      the benefits it can bring to their education, social interaction, and overall 
      well-being. While acknowledging the challenges and limitations, we examine how 
      ChatGPT can be utilized as a valuable tool in enhancing the lives of these 
      children, promoting their cognitive development, and supporting their unique 
      needs.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Yang, Jing
AU  - Yang J
AD  - Department of Computer System and Technology, Faculty of Computer Science and 
      Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
FAU - Por, Lip Yee
AU  - Por LY
AD  - Department of Computer System and Technology, Faculty of Computer Science and 
      Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia. 
      porlip@um.edu.my.
FAU - Leong, Ming Chern
AU  - Leong MC
AD  - Paediatric & Congenital Heart Centre, Institut Jantung Negara, 145, Jalan Tun 
      Razak, 51200, Kuala Lumpur, Malaysia. mcleong@ijn.com.my.
FAU - Ku, Chin Soon
AU  - Ku CS
AUID- ORCID: 0000-0003-0793-3308
AD  - Department of Computer Science, Universiti Tunku Abdul Rahman, 31900, Kampar, 
      Malaysia. kucs@utar.edu.my.
LA  - eng
PT  - Letter
DEP - 20230618
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Child
MH  - Humans
MH  - *Down Syndrome
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - ChatGPT
OT  - Down syndrome
OT  - Education
OT  - Learning
OT  - Social interaction
EDAT- 2023/06/19 00:42
MHDA- 2023/11/09 06:41
CRDT- 2023/06/18 23:35
PHST- 2023/06/04 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/11/09 06:41 [medline]
PHST- 2023/06/19 00:42 [pubmed]
PHST- 2023/06/18 23:35 [entrez]
AID - 10.1007/s10439-023-03281-3 [pii]
AID - 10.1007/s10439-023-03281-3 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Dec;51(12):2638-2640. doi: 10.1007/s10439-023-03281-3. Epub 
      2023 Jun 18.

PMID- 37529853
OWN - NLM
STAT- Publisher
LR  - 20230802
IS  - 1097-6817 (Electronic)
IS  - 0194-5998 (Linking)
DP  - 2023 Aug 2
TI  - Head-to-Head Comparison of ChatGPT Versus Google Search for Medical Knowledge 
      Acquisition.
LID - 10.1002/ohn.465 [doi]
AB  - OBJECTIVE: Chat Generative Pretrained Transformer (ChatGPT) is the newest 
      iteration of OpenAI's generative artificial intelligence (AI) with the potential 
      to influence many facets of life, including health care. This study sought to 
      assess ChatGPT's capabilities as a source of medical knowledge, using Google 
      Search as a comparison. STUDY DESIGN: Cross-sectional analysis. SETTING: Online 
      using ChatGPT, Google Seach, and Clinical Practice Guidelines (CPG). METHODS: CPG 
      Plain Language Summaries for 6 conditions were obtained. Questions relevant to 
      specific conditions were developed and input into ChatGPT and Google Search. All 
      questions were written from the patient perspective and sought (1) general 
      medical knowledge or (2) medical recommendations, with varying levels of acuity 
      (urgent or emergent vs routine clinical scenarios). Two blinded reviewers scored 
      all passages and compared results from ChatGPT and Google Search, using the 
      Patient Education Material Assessment Tool (PEMAT-P) as the primary outcome. 
      Additional customized questions were developed that assessed the medical content 
      of the passages. RESULTS: The overall average PEMAT-P score for medical advice 
      was 68.2% (standard deviation [SD]: 4.4) for ChatGPT and 89.4% (SD: 5.9) for 
      Google Search (p < .001). There was a statistically significant difference in the 
      PEMAT-P score by source (p < .001) but not by urgency of the clinical situation 
      (p = .613). ChatGPT scored significantly higher than Google Search (87% vs 78%, 
      p = .012) for patient education questions. CONCLUSION: ChatGPT fared better than 
      Google Search when offering general medical knowledge, but it scored worse when 
      providing medical recommendations. Health care providers should strive to 
      understand the potential benefits and ramifications of generative AI to guide 
      patients appropriately.
CI  - © 2023 American Academy of Otolaryngology-Head and Neck Surgery Foundation.
FAU - Ayoub, Noel F
AU  - Ayoub NF
AUID- ORCID: 0000-0003-1867-994X
AD  - Department of Otolaryngology-Head and Neck Surgery, Division of Head & Neck 
      Surgery, Stanford University School of Medicine, Stanford, California, USA.
FAU - Lee, Yu-Jin
AU  - Lee YJ
AD  - Department of Otolaryngology-Head and Neck Surgery, Division of Head & Neck 
      Surgery, Stanford University School of Medicine, Stanford, California, USA.
FAU - Grimm, David
AU  - Grimm D
AD  - Department of Otolaryngology-Head and Neck Surgery, Division of Head & Neck 
      Surgery, Stanford University School of Medicine, Stanford, California, USA.
FAU - Divi, Vasu
AU  - Divi V
AD  - Department of Otolaryngology-Head and Neck Surgery, Division of Head & Neck 
      Surgery, Stanford University School of Medicine, Stanford, California, USA.
LA  - eng
PT  - Journal Article
DEP - 20230802
PL  - England
TA  - Otolaryngol Head Neck Surg
JT  - Otolaryngology--head and neck surgery : official journal of American Academy of 
      Otolaryngology-Head and Neck Surgery
JID - 8508176
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - generative artificial intelligence
OT  - health literacy
OT  - large language models
OT  - online search engines
OT  - patient education
EDAT- 2023/08/02 06:42
MHDA- 2023/08/02 06:42
CRDT- 2023/08/02 04:13
PHST- 2023/07/07 00:00 [revised]
PHST- 2023/03/19 00:00 [received]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/08/02 06:42 [medline]
PHST- 2023/08/02 06:42 [pubmed]
PHST- 2023/08/02 04:13 [entrez]
AID - 10.1002/ohn.465 [doi]
PST - aheadofprint
SO  - Otolaryngol Head Neck Surg. 2023 Aug 2. doi: 10.1002/ohn.465.

PMID- 37503263
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230728
DP  - 2023 Jul 16
TI  - Leveraging Large Language Models for Generating Responses to Patient Messages.
LID - 2023.07.14.23292669 [pii]
LID - 10.1101/2023.07.14.23292669 [doi]
AB  - OBJECTIVE: This study aimed to develop and assess the performance of fine-tuned 
      large language models for generating responses to patient messages sent via an 
      electronic health record patient portal. METHODS: Utilizing a dataset of messages 
      and responses extracted from the patient portal at a large academic medical 
      center, we developed a model (CLAIR-Short) based on a pre-trained large language 
      model (LLaMA-65B). In addition, we used the OpenAI API to update physician 
      responses from an open-source dataset into a format with informative paragraphs 
      that offered patient education while emphasizing empathy and professionalism. By 
      combining with this dataset, we further fine-tuned our model (CLAIR-Long). To 
      evaluate the fine-tuned models, we used ten representative patient portal 
      questions in primary care to generate responses. We asked primary care physicians 
      to review generated responses from our models and ChatGPT and rated them for 
      empathy, responsiveness, accuracy, and usefulness. RESULTS: The dataset consisted 
      of a total of 499,794 pairs of patient messages and corresponding responses from 
      the patient portal, with 5,000 patient messages and ChatGPT-updated responses 
      from an online platform. Four primary care physicians participated in the survey. 
      CLAIR-Short exhibited the ability to generate concise responses similar to 
      provider's responses. CLAIR-Long responses provided increased patient educational 
      content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, 
      receiving positive evaluations for responsiveness, empathy, and accuracy, while 
      receiving a neutral rating for usefulness. CONCLUSION: Leveraging large language 
      models to generate responses to patient messages demonstrates significant 
      potential in facilitating communication between patients and primary care 
      providers.
FAU - Liu, Siru
AU  - Liu S
FAU - McCoy, Allison B
AU  - McCoy AB
FAU - Wright, Aileen P
AU  - Wright AP
FAU - Carew, Babatunde
AU  - Carew B
FAU - Genkins, Julian Z
AU  - Genkins JZ
FAU - Huang, Sean S
AU  - Huang SS
FAU - Peterson, Josh F
AU  - Peterson JF
FAU - Steitz, Bryan
AU  - Steitz B
FAU - Wright, Adam
AU  - Wright A
LA  - eng
PT  - Preprint
DEP - 20230716
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10370222
EDAT- 2023/07/28 06:42
MHDA- 2023/07/28 06:43
CRDT- 2023/07/28 04:28
PHST- 2023/07/28 06:43 [medline]
PHST- 2023/07/28 06:42 [pubmed]
PHST- 2023/07/28 04:28 [entrez]
AID - 2023.07.14.23292669 [pii]
AID - 10.1101/2023.07.14.23292669 [doi]
PST - epublish
SO  - medRxiv. 2023 Jul 16:2023.07.14.23292669. doi: 10.1101/2023.07.14.23292669. 
      Preprint.

PMID- 38041797
OWN - NLM
STAT- Publisher
LR  - 20231202
IS  - 1943-7722 (Electronic)
IS  - 0002-9173 (Linking)
DP  - 2023 Dec 2
TI  - Evaluation of ChatGPT pathology knowledge using board-style questions.
LID - aqad158 [pii]
LID - 10.1093/ajcp/aqad158 [doi]
AB  - OBJECTIVES: ChatGPT is an artificial intelligence chatbot developed by OpenAI. 
      Its extensive knowledge and unique interactive capabilities enable its use in 
      various innovative ways in the medical field, such as writing clinical notes and 
      simplifying radiology reports. Through this study, we aimed to analyze the 
      pathology knowledge of ChatGPT to advocate its role in transforming pathology 
      education. METHODS: The American Society for Clinical Pathology Resident Question 
      Bank 2022 was used to test ChatGPT, version 4. Practice tests were created in 
      each subcategory and answered based on the input that ChatGPT provided. Questions 
      that required interpretation of images were excluded. We analyzed ChatGPT 
      performance and compared it with average peer performance. RESULTS: The overall 
      performance of ChatGPT was 56.98%, lower than that of the average peer 
      performance of 62.81%. ChatGPT performed better on clinical pathology (60.42%) 
      than on anatomic pathology (54.94%). Furthermore, its performance was better on 
      easy questions (68.47%) than on intermediate (52.88%) and difficult questions 
      (37.21%). CONCLUSIONS: ChatGPT has the potential to be a valuable resource in 
      pathology education if trained on a larger, specialized medical data set. Those 
      relying on it (in its current form) solely for the purpose of pathology training 
      should be cautious.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of American 
      Society for Clinical Pathology. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Geetha, Saroja D
AU  - Geetha SD
AD  - Department of Pathology and Laboratory Medicine, North Shore University Hospital 
      and Long Island Jewish Medical Center, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell Health, Greenvale, NY, US.
FAU - Khan, Anam
AU  - Khan A
AD  - Department of Pathology and Laboratory Medicine, North Shore University Hospital 
      and Long Island Jewish Medical Center, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell Health, Greenvale, NY, US.
FAU - Khan, Atif
AU  - Khan A
AD  - Department of Pathology and Laboratory Medicine, North Shore University Hospital 
      and Long Island Jewish Medical Center, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell Health, Greenvale, NY, US.
FAU - Kannadath, Bijun S
AU  - Kannadath BS
AD  - Department of Internal Medicine, University of Arizona College of Medicine, 
      Phoenix, AZ, US.
FAU - Vitkovski, Taisia
AU  - Vitkovski T
AD  - Department of Pathology and Laboratory Medicine, North Shore University Hospital 
      and Long Island Jewish Medical Center, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell Health, Greenvale, NY, US.
LA  - eng
PT  - Journal Article
DEP - 20231202
PL  - England
TA  - Am J Clin Pathol
JT  - American journal of clinical pathology
JID - 0370470
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - natural language processing
OT  - neural networks
OT  - pathology education
EDAT- 2023/12/02 12:42
MHDA- 2023/12/02 12:42
CRDT- 2023/12/02 11:32
PHST- 2023/06/08 00:00 [received]
PHST- 2023/10/16 00:00 [accepted]
PHST- 2023/12/02 12:42 [medline]
PHST- 2023/12/02 12:42 [pubmed]
PHST- 2023/12/02 11:32 [entrez]
AID - 7457992 [pii]
AID - 10.1093/ajcp/aqad158 [doi]
PST - aheadofprint
SO  - Am J Clin Pathol. 2023 Dec 2:aqad158. doi: 10.1093/ajcp/aqad158.

PMID- 37901715
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231101
IS  - 2950-1628 (Electronic)
IS  - 2950-1628 (Linking)
VI  - 1
IP  - 2
DP  - 2023 Sep
TI  - A Comprehensive Survey of ChatGPT: Advancements, Applications, Prospects, and 
      Challenges.
LID - 100022 [pii]
LID - 10.1016/j.metrad.2023.100022 [doi]
AB  - Large Language Models (LLMs) especially when combined with Generative Pre-trained 
      Transformers (GPT) represent a groundbreaking in natural language processing. In 
      particular, ChatGPT, a state-of-the-art conversational language model with a 
      user-friendly interface, has garnered substantial attention owing to its 
      remarkable capability for generating human-like responses across a variety of 
      conversational scenarios. This survey offers an overview of ChatGPT, delving into 
      its inception, evolution, and key technology. We summarize the fundamental 
      principles that underpin ChatGPT, encompassing its introduction in conjunction 
      with GPT and LLMs. We also highlight the specific characteristics of GPT models 
      with details of their impressive language understanding and generation 
      capabilities. We then summarize applications of ChatGPT in a few representative 
      domains. In parallel to the many advantages that ChatGPT can provide, we discuss 
      the limitations and challenges along with potential mitigation strategies. 
      Despite various controversial arguments and ethical concerns, ChatGPT has drawn 
      significant attention from research industries and academia in a very short 
      period. The survey concludes with an envision of promising avenues for future 
      research in the field of ChatGPT. It is worth noting that knowing and addressing 
      the challenges faced by ChatGPT will mount the way for more reliable and 
      trustworthy conversational agents in the years to come.
FAU - Nazir, Anam
AU  - Nazir A
AD  - Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine. W 670 Baltimore St, HSF III, Room 1173, Baltimore, MD 21201.
FAU - Wang, Ze
AU  - Wang Z
AD  - Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine. W 670 Baltimore St, HSF III, Room 1173, Baltimore, MD 21201.
LA  - eng
GR  - R01 AG060054/AG/NIA NIH HHS/United States
GR  - R01 AG070227/AG/NIA NIH HHS/United States
GR  - P41 EB029460/EB/NIBIB NIH HHS/United States
GR  - R01 EB031080/EB/NIBIB NIH HHS/United States
GR  - UL1 TR003098/TR/NCATS NIH HHS/United States
PT  - Journal Article
DEP - 20231007
PL  - Netherlands
TA  - Meta Radiol
JT  - Meta-radiology
JID - 9918697480806676
PMC - PMC10611551
MID - NIHMS1934956
OTO - NOTNLM
OT  - ChatGPT
OT  - Contextual Learning
OT  - Generative Pre-trained Transformers (GPT)
OT  - Human-Computer Interaction
OT  - Large Language Models (LLMs)
OT  - Natural Language Processing (NLP)
OT  - Trustworthy Conversational Agents
EDAT- 2023/10/30 06:46
MHDA- 2023/10/30 06:47
CRDT- 2023/10/30 04:56
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/10/30 06:46 [pubmed]
PHST- 2023/10/30 04:56 [entrez]
AID - 100022 [pii]
AID - 10.1016/j.metrad.2023.100022 [doi]
PST - ppublish
SO  - Meta Radiol. 2023 Sep;1(2):100022. doi: 10.1016/j.metrad.2023.100022. Epub 2023 
      Oct 7.

PMID- 37606976
OWN - NLM
STAT- MEDLINE
DCOM- 20230828
LR  - 20230908
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Aug 22
TI  - Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: 
      Development and Usability Study.
PG  - e48659
LID - 10.2196/48659 [doi]
LID - e48659
AB  - BACKGROUND: Large language model (LLM)-based artificial intelligence chatbots 
      direct the power of large training data sets toward successive, related tasks as 
      opposed to single-ask tasks, for which artificial intelligence already achieves 
      impressive performance. The capacity of LLMs to assist in the full scope of 
      iterative clinical reasoning via successive prompting, in effect acting as 
      artificial physicians, has not yet been evaluated. OBJECTIVE: This study aimed to 
      evaluate ChatGPT's capacity for ongoing clinical decision support via its 
      performance on standardized clinical vignettes. METHODS: We inputted all 36 
      published clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual 
      into ChatGPT and compared its accuracy on differential diagnoses, diagnostic 
      testing, final diagnosis, and management based on patient age, gender, and case 
      acuity. Accuracy was measured by the proportion of correct responses to the 
      questions posed within the clinical vignettes tested, as calculated by human 
      scorers. We further conducted linear regression to assess the contributing 
      factors toward ChatGPT's performance on clinical tasks. RESULTS: ChatGPT achieved 
      an overall accuracy of 71.7% (95% CI 69.3%-74.1%) across all 36 clinical 
      vignettes. The LLM demonstrated the highest performance in making a final 
      diagnosis with an accuracy of 76.9% (95% CI 67.8%-86.1%) and the lowest 
      performance in generating an initial differential diagnosis with an accuracy of 
      60.3% (95% CI 54.2%-66.6%). Compared to answering questions about general medical 
      knowledge, ChatGPT demonstrated inferior performance on differential diagnosis 
      (β=-15.8%; P<.001) and clinical management (β=-7.4%; P=.02) question types. 
      CONCLUSIONS: ChatGPT achieves impressive accuracy in clinical decision-making, 
      with increasing strength as it gains more clinical information at its disposal. 
      In particular, ChatGPT demonstrates the greatest accuracy in tasks of final 
      diagnosis as compared to initial diagnosis. Limitations include possible model 
      hallucinations and the unclear composition of ChatGPT's training data set.
CI  - ©Arya Rao, Michael Pang, John Kim, Meghana Kamineni, Winston Lie, Anoop K Prasad, 
      Adam Landman, Keith Dreyer, Marc D Succi. Originally published in the Journal of 
      Medical Internet Research (https://www.jmir.org), 22.08.2023.
FAU - Rao, Arya
AU  - Rao A
AUID- ORCID: 0000-0003-3007-4812
AD  - Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations 
      Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
FAU - Pang, Michael
AU  - Pang M
AUID- ORCID: 0000-0001-5619-9344
AD  - Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations 
      Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
FAU - Kim, John
AU  - Kim J
AUID- ORCID: 0000-0003-4252-5916
AD  - Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations 
      Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
FAU - Kamineni, Meghana
AU  - Kamineni M
AUID- ORCID: 0000-0002-6698-5151
AD  - Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations 
      Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
FAU - Lie, Winston
AU  - Lie W
AUID- ORCID: 0009-0002-0939-7449
AD  - Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations 
      Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
FAU - Prasad, Anoop K
AU  - Prasad AK
AUID- ORCID: 0000-0002-4409-6062
AD  - Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations 
      Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
FAU - Landman, Adam
AU  - Landman A
AUID- ORCID: 0000-0002-2166-0521
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Brigham and Women's Hospital, Boston, MA, United States.
FAU - Dreyer, Keith
AU  - Dreyer K
AUID- ORCID: 0000-0003-1207-6443
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Data Science Office, Mass General Brigham, Boston, MA, United States.
FAU - Succi, Marc D
AU  - Succi MD
AUID- ORCID: 0000-0002-1518-3984
AD  - Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations 
      Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Massachusetts General Hospital, Boston, MA, United 
      States.
AD  - Mass General Brigham Innovation, Mass General Brigham, Boston, MA, United States.
LA  - eng
GR  - T32 GM144273/GM/NIGMS NIH HHS/United States
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20230822
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
UOF - medRxiv. 2023 Feb 26;:. PMID: 36865204
MH  - Humans
MH  - *Artificial Intelligence
MH  - Clinical Decision-Making
MH  - Organizations
MH  - Workflow
MH  - User-Centered Design
PMC - PMC10481210
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT
OT  - Generative Pre-trained Transformer
OT  - LLMs
OT  - accuracy
OT  - artificial intelligence
OT  - chatbot
OT  - clinical decision support
OT  - clinical vignettes
OT  - decision-making
OT  - development
OT  - large language models
OT  - usability
OT  - utility
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/22 13:43
MHDA- 2023/08/23 06:42
CRDT- 2023/08/22 11:55
PHST- 2023/05/02 00:00 [received]
PHST- 2023/07/27 00:00 [accepted]
PHST- 2023/07/26 00:00 [revised]
PHST- 2023/08/23 06:42 [medline]
PHST- 2023/08/22 13:43 [pubmed]
PHST- 2023/08/22 11:55 [entrez]
AID - v25i1e48659 [pii]
AID - 10.2196/48659 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Aug 22;25:e48659. doi: 10.2196/48659.

PMID- 38081504
OWN - NLM
STAT- Publisher
LR  - 20231211
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
DP  - 2023 Dec 9
TI  - Testing ChatGPT ability to answer laypeople questions about cardiac arrest and 
      cardiopulmonary resuscitation.
PG  - 110077
LID - S0300-9572(23)00813-4 [pii]
LID - 10.1016/j.resuscitation.2023.110077 [doi]
AB  - INTRODUCTION: Cardiac arrest leaves witnesses, survivors, and their relatives 
      with a multitude of questions. When a young or a public figure is affected, 
      interest around cardiac arrest and cardiopulmonary resuscitation (CPR) increases. 
      ChatGPT allows everyone to obtain human-like responses on any topic. Due to the 
      risks of accessing incorrect information, we assessed ChatGPT accuracy in 
      answering laypeople questions about cardiac arrest and CPR. METHODS: We 
      co-produced a list of 40 questions with members of Sudden Cardiac Arrest UK 
      covering all aspects of cardiac arrest and CPR. Answers provided by ChatGPT to 
      each question were evaluated by professionals for their accuracy, by 
      professionals and laypeople for their relevance, clarity, comprehensiveness, and 
      overall value on a scale from 1 (poor) to 5 (excellent), and for readability. 
      RESULTS: ChatGPT answers received an overall positive evaluation (4.3±0.7) by 14 
      professionals and 16 laypeople. Also, clarity (4.4±0.6), relevance (4.3±0.6), 
      accuracy (4.0±0.6), and comprehensiveness (4.2±0.7) of answers was rated high. 
      Professionals, however, rated overall value (4.0±0.5 vs 4.6±0.7; p=0.02) and 
      comprehensiveness (3.9±0.6 vs 4.5±0.7; p=0.02) lower compared to laypeople. 
      CPR-related answers consistently received a lower score across all parameters by 
      professionals and laypeople. Readability was 'difficult' (median Flesch reading 
      ease score of 34 [IQR 26-42]). CONCLUSIONS: ChatGPT provided largely accurate, 
      relevant, and comprehensive answers to questions about cardiac arrest commonly 
      asked by survivors, their relatives, and lay rescuers, except CPR-related answers 
      that received the lowest scores. Large language model will play a significant 
      role in the future and healthcare-related content generated should be monitored.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Scquizzato, Tommaso
AU  - Scquizzato T
AD  - Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific 
      Institute, Milan, Italy.
FAU - Semeraro, Federico
AU  - Semeraro F
AD  - Department of Anaesthesia, Intensive Care and Emergency Medical Services, 
      Ospedale Maggiore, Bologna, Italy.
FAU - Swindell, Paul
AU  - Swindell P
AD  - Sudden Cardiac Arrest UK, UK.
FAU - Simpson, Rupert
AU  - Simpson R
AD  - Essex Cardiothoracic Centre, Mid and South Essex NHS Foundation Trust, Basildon, 
      UK; Sudden Cardiac Arrest UK, UK.
FAU - Angelini, Matteo
AU  - Angelini M
AD  - Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific 
      Institute, Milan, Italy.
FAU - Gazzato, Arianna
AU  - Gazzato A
AD  - Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific 
      Institute, Milan, Italy.
FAU - Sajjad, Uzma
AU  - Sajjad U
AD  - Essex Cardiothoracic Centre, Mid and South Essex NHS Foundation Trust, Basildon, 
      UK; Sudden Cardiac Arrest UK, UK.
FAU - Bignami, Elena G
AU  - Bignami EG
AD  - Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine 
      and Surgery, University of Parma, Parma, Italy.
FAU - Landoni, Giovanni
AU  - Landoni G
AD  - Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific 
      Institute, Milan, Italy; School of Medicine, Vita-Salute San Raffaele University, 
      Milan, Italy.
FAU - Keeble, Thomas R
AU  - Keeble TR
AD  - Medical Technology Research Centre, Anglia Ruskin School of Medicine, Chelmsford, 
      UK; Medical Technology Research Centre, Anglia Ruskin School of Medicine, 
      Chelmsford, UK; Sudden Cardiac Arrest UK, UK.
FAU - Mion, Marco
AU  - Mion M
AD  - Essex Cardiothoracic Centre, Mid and South Essex NHS Foundation Trust, Basildon, 
      UK; Medical Technology Research Centre, Anglia Ruskin School of Medicine, 
      Chelmsford, UK. Electronic address: m.mion@nhs.net.
LA  - eng
PT  - Journal Article
DEP - 20231209
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - cardiopulmonary resuscitation
OT  - large language model
OT  - out-of-hospital cardiac arrest
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/12/12 00:42
MHDA- 2023/12/12 00:42
CRDT- 2023/12/11 19:26
PHST- 2023/10/28 00:00 [received]
PHST- 2023/11/15 00:00 [accepted]
PHST- 2023/12/12 00:42 [medline]
PHST- 2023/12/12 00:42 [pubmed]
PHST- 2023/12/11 19:26 [entrez]
AID - S0300-9572(23)00813-4 [pii]
AID - 10.1016/j.resuscitation.2023.110077 [doi]
PST - aheadofprint
SO  - Resuscitation. 2023 Dec 9:110077. doi: 10.1016/j.resuscitation.2023.110077.

PMID- 37499282
OWN - NLM
STAT- MEDLINE
DCOM- 20230816
LR  - 20230821
IS  - 1872-7123 (Electronic)
IS  - 0165-1781 (Print)
IS  - 0165-1781 (Linking)
VI  - 326
DP  - 2023 Aug
TI  - ChatGPT and Bard exhibit spontaneous citation fabrication during psychiatry 
      literature search.
PG  - 115334
LID - S0165-1781(23)00284-6 [pii]
LID - 10.1016/j.psychres.2023.115334 [doi]
AB  - ChatGPT (Generative Pre-Trained Transformer) is a large language model (LLM), 
      which comprises a neural network that has learned information and patterns of 
      language use from large amounts of text on the internet. ChatGPT, introduced by 
      OpenAI, responds to human queries in a conversational manner. Here, we aimed to 
      assess whether ChatGPT could reliably produce accurate references to supplement 
      the literature search process. We describe our March 2023 exchange with ChatGPT, 
      which generated thirty-five citations, two of which were real. 12 citations were 
      similar to actual manuscripts (e.g., titles with incorrect author lists, 
      journals, or publication years) and the remaining 21, while plausible, were in 
      fact a pastiche of multiple existent manuscripts. In June 2023, we re-tested 
      ChatGPT's performance and compared it to that of Google's GPT counterpart, Bard 
      2.0. We investigated performance in English, as well as in Spanish and Italian. 
      Fabrications made by LLMs, including erroneous citations, have been called 
      "hallucinations"; we discuss reasons for which this is a misnomer. Furthermore, 
      we describe potential explanations for citation fabrication by GPTs, as well as 
      measures being taken to remedy this issue, including reinforcement learning. Our 
      results underscore that output from conversational LLMs should be verified.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - McGowan, Alessia
AU  - McGowan A
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Gui, Yunlai
AU  - Gui Y
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Dobbs, Matthew
AU  - Dobbs M
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Shuster, Sophia
AU  - Shuster S
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Cotter, Matthew
AU  - Cotter M
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Selloni, Alexandria
AU  - Selloni A
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Goodman, Marianne
AU  - Goodman M
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA; James J. Peters 
      Veterans Administration, Bronx, NY, USA.
FAU - Srivastava, Agrima
AU  - Srivastava A
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Cecchi, Guillermo A
AU  - Cecchi GA
AD  - IBM TJ Watson Research Center, Yorktown Heights, NY, USA.
FAU - Corcoran, Cheryl M
AU  - Corcoran CM
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA; James J. Peters 
      Veterans Administration, Bronx, NY, USA. Electronic address: 
      cheryl.corcoran@mssm.edu.
LA  - eng
GR  - R01 MH107558/MH/NIMH NIH HHS/United States
GR  - R01 MH115332/MH/NIMH NIH HHS/United States
GR  - R01 MH132239/MH/NIMH NIH HHS/United States
PT  - Journal Article
DEP - 20230707
PL  - Ireland
TA  - Psychiatry Res
JT  - Psychiatry research
JID - 7911385
SB  - IM
MH  - Humans
MH  - *Communication
MH  - Language
MH  - Dietary Supplements
MH  - Hallucinations
MH  - *Psychiatry
PMC - PMC10424704
MID - NIHMS1918813
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bard
OT  - ChatGPT
OT  - Citations
OT  - Fabrication
OT  - Large language models
OT  - Linguistic
OT  - Literature search
OT  - Natural language processing
OT  - References
COIS- Declaration of Competing Interest None.
EDAT- 2023/07/27 19:10
MHDA- 2023/08/16 06:43
PMCR- 2024/08/01
CRDT- 2023/07/27 18:00
PHST- 2023/05/09 00:00 [received]
PHST- 2023/07/01 00:00 [revised]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2024/08/01 00:00 [pmc-release]
PHST- 2023/08/16 06:43 [medline]
PHST- 2023/07/27 19:10 [pubmed]
PHST- 2023/07/27 18:00 [entrez]
AID - S0165-1781(23)00284-6 [pii]
AID - 10.1016/j.psychres.2023.115334 [doi]
PST - ppublish
SO  - Psychiatry Res. 2023 Aug;326:115334. doi: 10.1016/j.psychres.2023.115334. Epub 
      2023 Jul 7.

PMID- 37523010
OWN - NLM
STAT- MEDLINE
DCOM- 20230831
LR  - 20230831
IS  - 1435-1544 (Electronic)
IS  - 0938-7412 (Print)
IS  - 0938-7412 (Linking)
VI  - 34
IP  - 3
DP  - 2023 Sep
TI  - [Big hype about ChapGPT in medicine : Is it something for rhythmologists? What 
      must be taken into consideration?].
PG  - 240-245
LID - 10.1007/s00399-023-00960-5 [doi]
AB  - ChatGPT, a chatbot based on a large language model, is currently attracting much 
      attention. Modern machine learning (ML) architectures enable the program to 
      answer almost any question, to summarize, translate, and even generate its own 
      texts, all in a text-based dialogue with the user. Underlying technologies, 
      summarized under the acronym NLP (natural language processing), go back to the 
      1960s. In almost all areas including medicine, ChatGPT is raising enormous hopes. 
      It can easily pass medical exams and may be useful in patient care, diagnostic 
      and therapeutic assistance, and medical research. The enthusiasm for this new 
      technology shown even by medical professionals is surprising. Although the system 
      knows much, it does not know everything; not everything it outputs is accurate 
      either. Every output has to be carefully checked by the user for correctness, 
      which is often not easily done since references to sources are lacking. Issues 
      regarding data protection and ethics also arise. Today's language models are not 
      free of bias and systematic distortion. These shortcomings have led to calls for 
      stronger regulation of the use of ChatGPT and an increasing number of similar 
      language models. However, this new technology represents an enormous progress in 
      knowledge processing and dissemination. Numerous scenarios in which ChatGPT can 
      provide assistance are conceivable, including in rhythmology. In the future, it 
      will be crucial to render the models error-free and transparent and to clearly 
      define the rules for their use. Responsible use requires systematic training to 
      improve the digital competence of users, including physicians who use such 
      programs.
CI  - © 2023. The Author(s).
FAU - Haverkamp, W
AU  - Haverkamp W
AD  - Abteilung für Kardiologie und Metabolismus, Medizinische Klinik mit Schwerpunkt 
      Kardiologie, Campus Virchow-Klinikum, Deutsches Herzzentrum der Charité, 
      Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, 
      Deutschland. wilhelm.haverkamp@dhzc-charite.de.
AD  - Berlin Institute of Health Center for Regenerative Therapies (BCRT), Berlin, 
      Deutschland. wilhelm.haverkamp@dhzc-charite.de.
FAU - Strodthoff, N
AU  - Strodthoff N
AD  - Department für Versorgungsforschung, Fakultät VI - Medizin und 
      Gesundheitswissenschaften, Abteilung AI4Health, Universität Oldenburg, Oldenburg, 
      Deutschland.
FAU - Tennenbaum, J
AU  - Tennenbaum J
AD  - Center for the Philosophy of Science, University of Lisbon, Lisbon, Portugal.
FAU - Israel, C
AU  - Israel C
AD  - Klinik für Innere Medizin - Kardiologie, Diabetologie und Nephrologie, 
      Evangelisches Klinikum Bethel, Bielefeld, Deutschland.
LA  - ger
PT  - English Abstract
PT  - Journal Article
PT  - Review
TT  - Großer Hype um ChatGPT in der Medizin : Ist es etwas für den Rhythmologen? Was 
      muss man bedenken?
DEP - 20230731
PL  - Germany
TA  - Herzschrittmacherther Elektrophysiol
JT  - Herzschrittmachertherapie & Elektrophysiologie
JID - 9425873
SB  - IM
MH  - Humans
MH  - *Software
MH  - *Artificial Intelligence
MH  - *Medicine
PMC - PMC10462516
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chat-GPT
OT  - Language models
OT  - Natural language processing
EDAT- 2023/07/31 13:09
MHDA- 2023/08/29 12:43
CRDT- 2023/07/31 11:09
PHST- 2023/06/01 00:00 [received]
PHST- 2023/07/20 00:00 [accepted]
PHST- 2023/08/29 12:43 [medline]
PHST- 2023/07/31 13:09 [pubmed]
PHST- 2023/07/31 11:09 [entrez]
AID - 10.1007/s00399-023-00960-5 [pii]
AID - 960 [pii]
AID - 10.1007/s00399-023-00960-5 [doi]
PST - ppublish
SO  - Herzschrittmacherther Elektrophysiol. 2023 Sep;34(3):240-245. doi: 
      10.1007/s00399-023-00960-5. Epub 2023 Jul 31.

PMID- 37462773
OWN - NLM
STAT- Publisher
LR  - 20231019
IS  - 1432-1335 (Electronic)
IS  - 0171-5216 (Linking)
VI  - 149
IP  - 14
DP  - 2023 Nov
TI  - Letter to the Editor: ChatGPT's contribution to cancer study.
PG  - 13495-13500
LID - 10.1007/s00432-023-05183-2 [doi]
AB  - The role of large language models (LLM) in medical and biomedical sciences is 
      remarkable, and chat generative pre-trained transformer (ChatGPT) as an AI model 
      has the potential to assist in research and clinical practice. While it is 
      essential to recognize that AI models like ChatGPT are tools that should be used 
      with human expertise and judgment, they should argue, rather than replace, the 
      knowledge and experience of healthcare professionals and researchers in the 
      medical and biomedical fields.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Faraji, Niloofar
AU  - Faraji N
AUID- ORCID: 0000-0001-5796-7157
AD  - Gastrointestinal and Liver Diseases Research Center, Razi Hospital, Guilan 
      University of Medical Sciences, Rasht, Iran.
FAU - Aali, Shahab
AU  - Aali S
AUID- ORCID: 0009-0004-1717-2690
AD  - Department of Urology, School of Medicine, Urology Research Center, Razi 
      Hospital, Guilan University of Medical Sciences, Rasht, Iran.
FAU - Motiei, Mahsa
AU  - Motiei M
AUID- ORCID: 0000-0002-3543-9975
AD  - School of Medicine, Razi Hospital, Guilan University of Medical Sciences, Rasht, 
      Iran.
FAU - Sadat Mansouri, Sahand
AU  - Sadat Mansouri S
AUID- ORCID: 0009-0003-1536-9150
AD  - Pediatric Diseases Research Center, Guilan University of Medical Sciences, Rasht, 
      Iran. ssadatmansoory@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230718
PL  - Germany
TA  - J Cancer Res Clin Oncol
JT  - Journal of cancer research and clinical oncology
JID - 7902060
SB  - IM
OTO - NOTNLM
OT  - Cancer
OT  - ChatGPT
OT  - Medical
OT  - Oncology
OT  - Research
EDAT- 2023/07/18 13:10
MHDA- 2023/07/18 13:10
CRDT- 2023/07/18 11:07
PHST- 2023/07/09 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/07/18 13:10 [pubmed]
PHST- 2023/07/18 13:10 [medline]
PHST- 2023/07/18 11:07 [entrez]
AID - 10.1007/s00432-023-05183-2 [pii]
AID - 10.1007/s00432-023-05183-2 [doi]
PST - ppublish
SO  - J Cancer Res Clin Oncol. 2023 Nov;149(14):13495-13500. doi: 
      10.1007/s00432-023-05183-2. Epub 2023 Jul 18.

PMID- 37502981
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
DP  - 2023 Jul 24
TI  - Evaluating Capabilities of Large Language Models: Performance of GPT4 on Surgical 
      Knowledge Assessments.
LID - 2023.07.16.23292743 [pii]
LID - 10.1101/2023.07.16.23292743 [doi]
AB  - BACKGROUND: Artificial intelligence (AI) has the potential to dramatically alter 
      healthcare by enhancing how we diagnosis and treat disease. One promising AI 
      model is ChatGPT, a large general-purpose language model trained by OpenAI. The 
      chat interface has shown robust, human-level performance on several professional 
      and academic benchmarks. We sought to probe its performance and stability over 
      time on surgical case questions. METHODS: We evaluated the performance of 
      ChatGPT-4 on two surgical knowledge assessments: the Surgical Council on Resident 
      Education (SCORE) and a second commonly used knowledge assessment, referred to as 
      Data-B. Questions were entered in two formats: open-ended and multiple choice. 
      ChatGPT output were assessed for accuracy and insights by surgeon evaluators. We 
      categorized reasons for model errors and the stability of performance on repeat 
      encounters. RESULTS: A total of 167 SCORE and 112 Data-B questions were presented 
      to the ChatGPT interface. ChatGPT correctly answered 71% and 68% of 
      multiple-choice SCORE and Data-B questions, respectively. For both open-ended and 
      multiple-choice questions, approximately two-thirds of ChatGPT responses 
      contained non-obvious insights. Common reasons for inaccurate responses included: 
      inaccurate information in a complex question (n=16, 36.4%); inaccurate 
      information in fact-based question (n=11, 25.0%); and accurate information with 
      circumstantial discrepancy (n=6, 13.6%). Upon repeat query, the answer selected 
      by ChatGPT varied for 36.4% of inaccurate questions; the response accuracy 
      changed for 6/16 questions. CONCLUSION: Consistent with prior findings, we 
      demonstrate robust near or above human-level performance of ChatGPT within the 
      surgical domain. Unique to this study, we demonstrate a substantial inconsistency 
      in ChatGPT responses with repeat query. This finding warrants future 
      consideration and presents an opportunity to further train these models to 
      provide safe and consistent responses. Without mental and/or conceptual models, 
      it is unclear whether language models such as ChatGPT would be able to safely 
      assist clinicians in providing care.
FAU - Beaulieu-Jones, Brendin R
AU  - Beaulieu-Jones BR
AD  - Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA.
AD  - Department of Biomedical Informatics, Harvard Medical School, Boston, MA.
FAU - Shah, Sahaj
AU  - Shah S
AD  - Geisinger Commonwealth School of Medicine, Scranton, PA.
FAU - Berrigan, Margaret T
AU  - Berrigan MT
AD  - Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA.
FAU - Marwaha, Jayson S
AU  - Marwaha JS
AD  - Division of Colorectal Surgery, National Taiwan University Hospital, Taipei, 
      Taiwan.
FAU - Lai, Shuo-Lun
AU  - Lai SL
AD  - Division of Colorectal Surgery, National Taiwan University Hospital, Taipei, 
      Taiwan.
FAU - Brat, Gabriel A
AU  - Brat GA
AD  - Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA.
AD  - Department of Biomedical Informatics, Harvard Medical School, Boston, MA.
LA  - eng
GR  - T15 LM007092/LM/NLM NIH HHS/United States
PT  - Preprint
DEP - 20230724
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10371188
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - language models
OT  - surgery
OT  - surgical education
EDAT- 2023/07/28 06:42
MHDA- 2023/07/28 06:43
CRDT- 2023/07/28 04:25
PHST- 2023/07/28 06:42 [pubmed]
PHST- 2023/07/28 06:43 [medline]
PHST- 2023/07/28 04:25 [entrez]
AID - 2023.07.16.23292743 [pii]
AID - 10.1101/2023.07.16.23292743 [doi]
PST - epublish
SO  - medRxiv. 2023 Jul 24:2023.07.16.23292743. doi: 10.1101/2023.07.16.23292743. 
      Preprint.

PMID- 38090452
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231213
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - A Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of ChatGPT 
      Integration in Nursing Education: A Narrative Review.
PG  - e48643
LID - 10.7759/cureus.48643 [doi]
AB  - Amidst evolving healthcare demands, nursing education plays a pivotal role in 
      preparing future nurses for complex challenges. Traditional approaches, however, 
      must be revised to meet modern healthcare needs. The ChatGPT, an AI-based 
      chatbot, has garnered significant attention due to its ability to personalize 
      learning experiences, enhance virtual clinical simulations, and foster 
      collaborative learning in nursing education. This review aims to thoroughly 
      assess the potential impact of integrating ChatGPT into nursing education. The 
      hypothesis is that valuable insights can be provided for stakeholders through a 
      comprehensive SWOT analysis examining the strengths, weaknesses, opportunities, 
      and threats associated with ChatGPT. This will enable informed decisions about 
      its integration, prioritizing improved learning outcomes. A thorough narrative 
      literature review was undertaken to provide a solid foundation for the SWOT 
      analysis. The materials included scholarly articles and reports, which ensure the 
      study's credibility and allow for a holistic and unbiased assessment. The 
      analysis identified accessibility, consistency, adaptability, cost-effectiveness, 
      and staying up-to-date as crucial factors influencing the strengths, weaknesses, 
      opportunities, and threats associated with ChatGPT integration in nursing 
      education. These themes provided a framework to understand the potential risks 
      and benefits of integrating ChatGPT into nursing education. This review 
      highlights the importance of responsible and effective use of ChatGPT in nursing 
      education and the need for collaboration among educators, policymakers, and AI 
      developers. Addressing the identified challenges and leveraging the strengths of 
      ChatGPT can lead to improved learning outcomes and enriched educational 
      experiences for students. The findings emphasize the importance of responsibly 
      integrating ChatGPT in nursing education, balancing technological advancement 
      with careful consideration of associated risks, to achieve optimal outcomes.
CI  - Copyright © 2023, Abujaber et al.
FAU - Abujaber, Ahmad A
AU  - Abujaber AA
AD  - Department of Nursing, Hamad Medical Corporation, Doha, QAT.
FAU - Abd-Alrazaq, Alaa
AU  - Abd-Alrazaq A
AD  - AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, QAT.
FAU - Al-Qudimat, Ahmad R
AU  - Al-Qudimat AR
AD  - Department of Public Health, Qatar University, Doha, QAT.
AD  - Surgical Research Section, Department of Surgery, Hamad Medical Corporation, 
      Doha, QAT.
FAU - Nashwan, Abdulqadir J
AU  - Nashwan AJ
AD  - Department of Nursing, Hamad Medical Corporation, Doha, QAT.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231111
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10711690
OTO - NOTNLM
OT  - chatgpt
OT  - gpt-4
OT  - large language models
OT  - nursing education
OT  - swot analysis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/13 18:42
MHDA- 2023/12/13 18:43
CRDT- 2023/12/13 13:08
PHST- 2023/12/13 18:43 [medline]
PHST- 2023/12/13 18:42 [pubmed]
PHST- 2023/11/11 00:00 [accepted]
PHST- 2023/12/13 13:08 [entrez]
AID - 10.7759/cureus.48643 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 11;15(11):e48643. doi: 10.7759/cureus.48643. eCollection 2023 
      Nov.

PMID- 37191485
OWN - NLM
STAT- MEDLINE
DCOM- 20230622
LR  - 20230713
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 5
DP  - 2023 Jun
TI  - Performance of ChatGPT on a Radiology Board-style Examination: Insights into 
      Current Strengths and Limitations.
PG  - e230582
LID - 10.1148/radiol.230582 [doi]
AB  - Background ChatGPT is a powerful artificial intelligence large language model 
      with great potential as a tool in medical practice and education, but its 
      performance in radiology remains unclear. Purpose To assess the performance of 
      ChatGPT on radiology board-style examination questions without images and to 
      explore its strengths and limitations. Materials and Methods In this exploratory 
      prospective study performed from February 25 to March 3, 2023, 150 
      multiple-choice questions designed to match the style, content, and difficulty of 
      the Canadian Royal College and American Board of Radiology examinations were 
      grouped by question type (lower-order [recall, understanding] and higher-order 
      [apply, analyze, synthesize] thinking) and topic (physics, clinical). The 
      higher-order thinking questions were further subclassified by type (description 
      of imaging findings, clinical management, application of concepts, calculation 
      and classification, disease associations). ChatGPT performance was evaluated 
      overall, by question type, and by topic. Confidence of language in responses was 
      assessed. Univariable analysis was performed. Results ChatGPT answered 69% of 
      questions correctly (104 of 150). The model performed better on questions 
      requiring lower-order thinking (84%, 51 of 61) than on those requiring 
      higher-order thinking (60%, 53 of 89) (P = .002). When compared with lower-order 
      questions, the model performed worse on questions involving description of 
      imaging findings (61%, 28 of 46; P = .04), calculation and classification (25%, 
      two of eight; P = .01), and application of concepts (30%, three of 10; P = .01). 
      ChatGPT performed as well on higher-order clinical management questions (89%, 16 
      of 18) as on lower-order questions (P = .88). It performed worse on physics 
      questions (40%, six of 15) than on clinical questions (73%, 98 of 135) (P = .02). 
      ChatGPT used confident language consistently, even when incorrect (100%, 46 of 
      46). Conclusion Despite no radiology-specific pretraining, ChatGPT nearly passed 
      a radiology board-style examination without images; it performed well on 
      lower-order thinking questions and clinical management questions but struggled 
      with higher-order thinking questions involving description of imaging findings, 
      calculation and classification, and application of concepts. © RSNA, 2023 See 
      also the editorial by Lourenco et al and the article by Bhayana et al in this 
      issue.
FAU - Bhayana, Rajesh
AU  - Bhayana R
AUID- ORCID: 0000-0002-8352-7953
AD  - From the University Medical Imaging Toronto, Joint Department of Medical Imaging, 
      University Health Network, Mount Sinai Hospital and Women's College Hospital, 
      University of Toronto, Toronto General Hospital, 200 Elizabeth St, Peter Mulk 
      Building, 1st Fl, Toronto, ON, Canada M5G 24C.
FAU - Krishna, Satheesh
AU  - Krishna S
AUID- ORCID: 0000-0001-6603-7621
AD  - From the University Medical Imaging Toronto, Joint Department of Medical Imaging, 
      University Health Network, Mount Sinai Hospital and Women's College Hospital, 
      University of Toronto, Toronto General Hospital, 200 Elizabeth St, Peter Mulk 
      Building, 1st Fl, Toronto, ON, Canada M5G 24C.
FAU - Bleakney, Robert R
AU  - Bleakney RR
AUID- ORCID: 0009-0000-4493-2676
AD  - From the University Medical Imaging Toronto, Joint Department of Medical Imaging, 
      University Health Network, Mount Sinai Hospital and Women's College Hospital, 
      University of Toronto, Toronto General Hospital, 200 Elizabeth St, Peter Mulk 
      Building, 1st Fl, Toronto, ON, Canada M5G 24C.
LA  - eng
PT  - Journal Article
DEP - 20230516
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CIN - Radiology. 2023 Jun;307(5):e231053. PMID: 37191490
CIN - Radiology. 2023 Jun;307(5):e231330. PMID: 37338357
CIN - Radiology. 2023 Jul;308(1):e231335. PMID: 37432082
MH  - Humans
MH  - *Artificial Intelligence
MH  - Prospective Studies
MH  - Canada
MH  - Radiography
MH  - *Radiology
EDAT- 2023/05/16 13:09
MHDA- 2023/06/22 06:42
CRDT- 2023/05/16 09:52
PHST- 2023/06/22 06:42 [medline]
PHST- 2023/05/16 13:09 [pubmed]
PHST- 2023/05/16 09:52 [entrez]
AID - 10.1148/radiol.230582 [doi]
PST - ppublish
SO  - Radiology. 2023 Jun;307(5):e230582. doi: 10.1148/radiol.230582. Epub 2023 May 16.

PMID- 37720035
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230920
DP  - 2023 Aug 28
TI  - Performance of ChatGPT in Diagnosis of Corneal Eye Diseases.
LID - 2023.08.25.23294635 [pii]
LID - 10.1101/2023.08.25.23294635 [doi]
AB  - INTRODUCTION: Assessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for 
      diagnosing corneal eye diseases based on case reports and compare with human 
      experts. METHODS: We randomly selected 20 cases of corneal diseases including 
      corneal infections, dystrophies, degenerations, and injuries from a publicly 
      accessible online database from the University of Iowa. We then input the text of 
      each case description into ChatGPT-4.0 and ChatGPT3.5 and asked for a provisional 
      diagnosis. We finally evaluated the responses based on the correct diagnoses then 
      compared with the diagnoses of three cornea specialists (Human experts) and 
      evaluated interobserver agreements. RESULTS: The provisional diagnosis accuracy 
      based on ChatGPT-4.0 was 85% (17 correct out of 20 cases) while the accuracy of 
      ChatGPT-3.5 was 60% (12 correct cases out of 20). The accuracy of three cornea 
      specialists were 100% (20 cases), 90% (18 cases), and 90% (18 cases), 
      respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 
      65% (13 cases) while the interobserver agreement between ChatGPT-4.0 and three 
      cornea specialists were 85% (17 cases), 80% (16 cases), and 75% (15 cases), 
      respectively. However, the interobserver agreement between ChatGPT-3.5 and each 
      of three cornea specialists was 60% (12 cases). CONCLUSIONS: The accuracy of 
      ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly 
      improved than ChatGPT-3.5 and promising for potential clinical integration.
FAU - Delsoz, Mohammad
AU  - Delsoz M
AD  - Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee 
      Health Science Center, Memphis, TN, USA.
FAU - Madadi, Yeganeh
AU  - Madadi Y
AD  - Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee 
      Health Science Center, Memphis, TN, USA.
FAU - Munir, Wuqaas M
AU  - Munir WM
AD  - Department of Ophthalmology and Visual Sciences, University of Maryland School of 
      Medicine, Baltimore, MD, USA.
FAU - Tamm, Brendan
AU  - Tamm B
AD  - Department of Ophthalmology and Visual Sciences, University of Maryland School of 
      Medicine, Baltimore, MD, USA.
FAU - Mehravaran, Shiva
AU  - Mehravaran S
AD  - School of Computer, Mathematical, and Natural Sciences, Morgan State University, 
      Baltimore, MD, USA.
FAU - Soleimani, Mohammad
AU  - Soleimani M
AD  - Department of Ophthalmology and Visual Sciences, University of Illinois at 
      Chicago, Chicago, Illinois, USA.
AD  - Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, 
      Tehran, Iran.
FAU - Djalilian, Ali
AU  - Djalilian A
AD  - Department of Ophthalmology and Visual Sciences, University of Illinois at 
      Chicago, Chicago, Illinois, USA.
FAU - Yousefi, Siamak
AU  - Yousefi S
AD  - Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee 
      Health Science Center, Memphis, TN, USA.
AD  - Department of Genetics, Genomics, and Informatics, University of Tennessee Health 
      Science Center, Memphis, TN, USA.
LA  - eng
PT  - Preprint
DEP - 20230828
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10500623
OTO - NOTNLM
OT  - Artificial Intelligence (AI)
OT  - ChatGPT
OT  - Corneal eye diseases
OT  - Generative Pre-trained Transformer (GPT)
OT  - Large Language Models (LLM)
OT  - Provisional Diagnosis
COIS- Conflict of Interest Mohammad Delsoz: None. Yeganeh Madadi: None Wuqaas M Munir: 
      None Brendan Tamm: None Shiva Mehravaran: None Mohammad Soleimani: None Ali 
      Djalilian: None Siamak Yousefi: Remidio, M&S Technologies, Visrtucal Fields, 
      InsihgtAEye, Enolink
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:43
CRDT- 2023/09/18 04:33
PHST- 2023/09/18 06:43 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:33 [entrez]
AID - 2023.08.25.23294635 [pii]
AID - 10.1101/2023.08.25.23294635 [doi]
PST - epublish
SO  - medRxiv. 2023 Aug 28:2023.08.25.23294635. doi: 10.1101/2023.08.25.23294635. 
      Preprint.

PMID- 37404149
OWN - NLM
STAT- MEDLINE
DCOM- 20230707
LR  - 20230718
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 308
IP  - 1
DP  - 2023 Jul
TI  - Feasibility of Differential Diagnosis Based on Imaging Patterns Using a Large 
      Language Model.
PG  - e231167
LID - 10.1148/radiol.231167 [doi]
FAU - Kottlors, Jonathan
AU  - Kottlors J
AUID- ORCID: 0000-0001-5021-6895
AD  - Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and 
      University Hospital Cologne, University of Cologne, Cologne, Germany.
FAU - Bratke, Grischa
AU  - Bratke G
AUID- ORCID: 0000-0002-5696-9828
AD  - Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and 
      University Hospital Cologne, University of Cologne, Cologne, Germany.
FAU - Rauen, Philip
AU  - Rauen P
AUID- ORCID: 0000-0002-3849-6537
AD  - Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and 
      University Hospital Cologne, University of Cologne, Cologne, Germany.
FAU - Kabbasch, Christoph
AU  - Kabbasch C
AUID- ORCID: 0000-0003-3712-2258
AD  - Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and 
      University Hospital Cologne, University of Cologne, Cologne, Germany.
FAU - Persigehl, Thorsten
AU  - Persigehl T
AUID- ORCID: 0000-0001-5928-4405
AD  - Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and 
      University Hospital Cologne, University of Cologne, Cologne, Germany.
FAU - Schlamann, Marc
AU  - Schlamann M
AD  - Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and 
      University Hospital Cologne, University of Cologne, Cologne, Germany.
FAU - Lennartz, Simon
AU  - Lennartz S
AUID- ORCID: 0000-0002-3254-4809
AD  - Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and 
      University Hospital Cologne, University of Cologne, Cologne, Germany.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
MH  - Humans
MH  - Diagnosis, Differential
MH  - Feasibility Studies
MH  - *Language
MH  - *Diagnostic Imaging
EDAT- 2023/07/05 13:05
MHDA- 2023/07/07 06:42
CRDT- 2023/07/05 10:03
PHST- 2023/07/07 06:42 [medline]
PHST- 2023/07/05 13:05 [pubmed]
PHST- 2023/07/05 10:03 [entrez]
AID - 10.1148/radiol.231167 [doi]
PST - ppublish
SO  - Radiology. 2023 Jul;308(1):e231167. doi: 10.1148/radiol.231167.

PMID- 37162073
OWN - NLM
STAT- MEDLINE
DCOM- 20230511
LR  - 20230513
IS  - 2317-6326 (Electronic)
IS  - 0102-6720 (Print)
IS  - 0102-6720 (Linking)
VI  - 36
DP  - 2023
TI  - FUTURE OF THE LANGUAGE MODELS IN HEALTHCARE: THE ROLE OF CHATGPT.
PG  - e1727
LID - S0102-67202023000100500 [pii]
LID - 10.1590/0102-672020230002e1727 [doi]
LID - e1727
AB  - The field of medicine has always been at the forefront of technological 
      innovation, constantly seeking new strategies to diagnose, treat, and prevent 
      diseases. Guidelines for clinical practice to orientate medical teams regarding 
      diagnosis, treatment, and prevention measures have increased over the years. The 
      purpose is to gather the most medical knowledge to construct an orientation for 
      practice. Evidence-based guidelines follow several main characteristics of a 
      systematic review, including systematic and unbiased search, selection, and 
      extraction of the source of evidence. In recent years, the rapid advancement of 
      artificial intelligence has provided clinicians and patients with access to 
      personalized, data-driven insights, support and new opportunities for healthcare 
      professionals to improve patient outcomes, increase efficiency, and reduce costs. 
      One of the most exciting developments in Artificial Intelligence has been the 
      emergence of chatbots. A chatbot is a computer program used to simulate 
      conversations with human users. Recently, OpenAI, a research organization focused 
      on machine learning, developed ChatGPT, a large language model that generates 
      human-like text. ChatGPT uses a type of AI known as a deep learning model. 
      ChatGPT can quickly search and select pieces of evidence through numerous 
      databases to provide answers to complex questions, reducing the time and effort 
      required to research a particular topic manually. Consequently, language models 
      can accelerate the creation of clinical practice guidelines. While there is no 
      doubt that ChatGPT has the potential to revolutionize the way healthcare is 
      delivered, it is essential to note that it should not be used as a substitute for 
      human healthcare professionals. Instead, ChatGPT should be considered a tool that 
      can be used to augment and support the work of healthcare professionals, helping 
      them to provide better care to their patients.
FAU - Tustumi, Francisco
AU  - Tustumi F
AUID- ORCID: 0000-0001-6695-0496
AD  - Universidade de São Paulo, Faculty of Medicine, Department of Gastroenterology - 
      São Paulo (SP), Brazil.
FAU - Andreollo, Nelson Adami
AU  - Andreollo NA
AUID- ORCID: 0000-0001-7452-1165
AD  - Universidade Estadual de Campinas, Faculty of Medical Sciences, Department of 
      Surgery, Digestive Disease Surgical Unit - Campinas (SP), Brazil.
FAU - Aguilar-Nascimento, José Eduardo de
AU  - Aguilar-Nascimento JE
AUID- ORCID: 0000-0002-3583-6612
AD  - Universidade Federal de Mato Grosso, Medical School, Department of Surgery - 
      Cuiabá (MT), Brazil.
LA  - eng
PT  - Journal Article
PT  - Systematic Review
DEP - 20230508
PL  - Brazil
TA  - Arq Bras Cir Dig
JT  - Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of 
      digestive surgery
JID - 9100283
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Language
MH  - Software
MH  - Delivery of Health Care
PMC - PMC10168663
COIS- Conflicts of Interest: None
EDAT- 2023/05/10 12:42
MHDA- 2023/05/11 06:42
CRDT- 2023/05/10 08:03
PHST- 2023/02/16 00:00 [received]
PHST- 2023/02/20 00:00 [accepted]
PHST- 2023/05/11 06:42 [medline]
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 08:03 [entrez]
AID - S0102-67202023000100500 [pii]
AID - 10.1590/0102-672020230002e1727 [doi]
PST - epublish
SO  - Arq Bras Cir Dig. 2023 May 8;36:e1727. doi: 10.1590/0102-672020230002e1727. 
      eCollection 2023.

PMID- 37038572
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230412
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Anesthetic Management of a Patient With Juvenile Hyaline Fibromatosis: A Case 
      Report Written With the Assistance of the Large Language Model ChatGPT.
PG  - e35946
LID - 10.7759/cureus.35946 [doi]
LID - e35946
AB  - This case report was written with the assistance of the large language model 
      known as ChatGPT, a form of generative artificial intelligence that can write 
      grammatically correct and semantically meaningful prose on a multitude of 
      topics. Here, it has assisted us in presenting a case of anesthetic management 
      for a case of Juvenile Hyaline Fibromatosis (JHF), an extremely rare genetic 
      disorder that is part of a spectrum of diseases presently characterized as 
      Hyaline Fibromatosis Syndrome (HFS), which also includes a more severe variant 
      presenting in infancy. HFS is caused by autosomal recessive mutations in the 
      ANTXR2 (anthrax toxin receptor cell adhesion molecule 2) gene, which binds 
      collagen IV and laminin, suggesting that it may be involved in extracellular 
      matrix adhesion. Defects in this molecule lead to abnormal deposition of hyaline 
      material in perivascular areas, presenting as cutaneous lesions, joint 
      contractures, and in some cases internal organ dysfunction. Anesthetic management 
      of patients with JHF may present difficulties with patient positioning and airway 
      management. Most reports of anesthetic management concern children with severe 
      disease and adult reports are uncommon. We present a case of JHF in a 39-year-old 
      woman managed for resection of a lower extremity cutaneous lesion. The anesthetic 
      management of this relatively minor case was uneventful, but the process of 
      drafting this report with the assistance of the new software tool ChatGPT was 
      informative of both its strengths and limitations.
CI  - Copyright © 2023, Segal et al.
FAU - Segal, Scott
AU  - Segal S
AD  - Anesthesiology, Wake Forest School of Medicine, Winston-Salem, USA.
FAU - Khanna, Ashish K
AU  - Khanna AK
AD  - Anesthesiology, Wake Forest School of Medicine, Winston Salem, USA.
LA  - eng
PT  - Case Reports
DEP - 20230309
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10082625
OTO - NOTNLM
OT  - anesthetic management
OT  - chatgpt
OT  - hyaline fibromatosis syndrome
OT  - juvenile hyaline fibromatosis
OT  - large language model
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/12 06:00
MHDA- 2023/04/12 06:01
CRDT- 2023/04/11 01:48
PHST- 2023/03/09 00:00 [accepted]
PHST- 2023/04/12 06:01 [medline]
PHST- 2023/04/11 01:48 [entrez]
PHST- 2023/04/12 06:00 [pubmed]
AID - 10.7759/cureus.35946 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 9;15(3):e35946. doi: 10.7759/cureus.35946. eCollection 2023 Mar.

PMID- 37263804
OWN - NLM
STAT- MEDLINE
DCOM- 20231211
LR  - 20231211
IS  - 1535-6302 (Electronic)
IS  - 0363-0188 (Linking)
VI  - 53
IP  - 1
DP  - 2024 Jan-Feb
TI  - Ability of ChatGPT to generate competent radiology reports for distal radius 
      fracture by use of RSNA template items and integrated AO classifier.
PG  - 102-110
LID - S0363-0188(23)00052-X [pii]
LID - 10.1067/j.cpradiol.2023.04.001 [doi]
AB  - The amount of acquired radiology imaging studies grows worldwide at a rapid pace. 
      Novel information technology tools for radiologists promise an increase of 
      reporting quality and as well quantity at the same time. Automated text report 
      drafting is one branch of this development. We defined for the present study in 
      total 9 cases of distal radius fracture. Command files structured according to a 
      template of the Radiological Society of North America (RSNA) and to 
      Arbeitsgemeinschaft Osteosynthese (AO) classifiers were given as input to the 
      natural language processing tool ChatGPT. ChatGPT was tasked with drafting an 
      appropriate radiology report. A parameter study (n = 5 iterations) was performed. 
      An overall high appraisal of ChatGPT radiology report quality was obtained in a 
      score card based assessment. ChatGPT demonstrates the capability to adjust output 
      files in response to minor changes in input command files. Existing shortcomings 
      were found in technical terminology and medical interpretation of findings. Text 
      drafting tools might well support work of radiologists in the future. They would 
      allow a radiologist to focus time on the observation of image details and patient 
      pathology. ChatGPT can be considered a substantial step forward towards that aim.
CI  - Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
FAU - Bosbach, Wolfram A
AU  - Bosbach WA
AD  - Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), 
      Inselspital, Bern University Hospital, University of Bern, Switzerland. 
      Electronic address: WolframAndreas.Bosbach@Insel.CH.
FAU - Senge, Jan F
AU  - Senge JF
AD  - Department of Mathematics and Computer Science, University of Bremen, Bremen, 
      Germany; Max-Planck Dioscuri Centre for Topological Data Analysis, Warsaw, 
      Poland.
FAU - Nemeth, Bence
AU  - Nemeth B
AD  - Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), 
      Inselspital, Bern University Hospital, University of Bern, Switzerland; Faculty 
      of Information Technology and Bionics, Pázmány Péter Catholic University, 
      Budapest, Hungary.
FAU - Omar, Siti H
AU  - Omar SH
AD  - Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), 
      Inselspital, Bern University Hospital, University of Bern, Switzerland; 
      Department for Radiology, Kuala Lumpur Hospital, Ministry of Health of Malaysia, 
      Kuala Lumpur, Malaysia.
FAU - Mitrakovic, Milena
AU  - Mitrakovic M
AD  - Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), 
      Inselspital, Bern University Hospital, University of Bern, Switzerland.
FAU - Beisbart, Claus
AU  - Beisbart C
AD  - Institute of Philosophy, University of Bern, Bern, Switzerland; Center for 
      Artificial Intelligence in Medicine, University of Bern, Bern, Switzerland.
FAU - Horváth, András
AU  - Horváth A
AD  - Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 
      Budapest, Hungary.
FAU - Heverhagen, Johannes
AU  - Heverhagen J
AD  - Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), 
      Inselspital, Bern University Hospital, University of Bern, Switzerland.
FAU - Daneshvar, Keivan
AU  - Daneshvar K
AD  - Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), 
      Inselspital, Bern University Hospital, University of Bern, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20230417
PL  - United States
TA  - Curr Probl Diagn Radiol
JT  - Current problems in diagnostic radiology
JID - 7607123
SB  - IM
MH  - Humans
MH  - *Wrist Fractures
MH  - Radiography
MH  - *Radiology
MH  - Diagnostic Imaging
MH  - North America
EDAT- 2023/06/02 01:07
MHDA- 2023/12/11 12:43
CRDT- 2023/06/01 21:57
PHST- 2023/03/21 00:00 [received]
PHST- 2023/04/13 00:00 [accepted]
PHST- 2023/12/11 12:43 [medline]
PHST- 2023/06/02 01:07 [pubmed]
PHST- 2023/06/01 21:57 [entrez]
AID - S0363-0188(23)00052-X [pii]
AID - 10.1067/j.cpradiol.2023.04.001 [doi]
PST - ppublish
SO  - Curr Probl Diagn Radiol. 2024 Jan-Feb;53(1):102-110. doi: 
      10.1067/j.cpradiol.2023.04.001. Epub 2023 Apr 17.

PMID- 37385548
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20231027
IS  - 1097-6779 (Electronic)
IS  - 0016-5107 (Linking)
VI  - 98
IP  - 4
DP  - 2023 Oct
TI  - Harnessing language models for streamlined postcolonoscopy patient management: a 
      novel approach.
PG  - 639-641.e4
LID - S0016-5107(23)02675-5 [pii]
LID - 10.1016/j.gie.2023.06.025 [doi]
AB  - BACKGROUND AND AIMS: ChatGPT, an advanced language model, is increasingly used in 
      diverse fields, including medicine. This study explores using ChatGPT to optimize 
      postcolonoscopy management by providing guideline-based recommendations and 
      addressing low compliance rates and timing issues. METHODS: In this 
      proof-of-concept study, 20 clinical scenarios were prepared as structured reports 
      and free-text notes, and ChatGPT's responses were evaluated by 2 senior 
      gastroenterologists. Compliance with guidelines and accuracy were assessed, and 
      inter-rater agreement was calculated using Fleiss' kappa coefficient. RESULTS: 
      ChatGPT exhibited 90% compliance with guidelines and 85% accuracy, with a very 
      good inter-rater agreement (Fleiss' kappa coefficient of .84, P < .01). ChatGPT 
      handled multiple variations and descriptions and crafted concise patient letters. 
      CONCLUSIONS: Results suggest that ChatGPT could aid healthcare providers in 
      making informed decisions and improve compliance with postcolonoscopy 
      surveillance guidelines. Future research should investigate integrating ChatGPT 
      into electronic health record systems and evaluating its effectiveness in 
      different healthcare settings and populations.
CI  - Copyright © 2023 American Society for Gastrointestinal Endoscopy. Published by 
      Elsevier Inc. All rights reserved.
FAU - Gorelik, Yuri
AU  - Gorelik Y
AD  - Gastroenterology Institute, Rambam Health Care Campus and Technion Institute of 
      Technology, Faculty of Medicine, Haifa, Israel.
FAU - Ghersin, Itai
AU  - Ghersin I
AD  - Gastroenterology Institute, Rambam Health Care Campus and Technion Institute of 
      Technology, Faculty of Medicine, Haifa, Israel.
FAU - Maza, Itay
AU  - Maza I
AD  - Gastroenterology Institute, Rambam Health Care Campus and Technion Institute of 
      Technology, Faculty of Medicine, Haifa, Israel.
FAU - Klein, Amir
AU  - Klein A
AD  - Gastroenterology Institute, Rambam Health Care Campus and Technion Institute of 
      Technology, Faculty of Medicine, Haifa, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230627
PL  - United States
TA  - Gastrointest Endosc
JT  - Gastrointestinal endoscopy
JID - 0010505
SB  - IM
MH  - Humans
MH  - *Colonoscopy/methods
MH  - Guideline Adherence
MH  - *Gastroenterologists
COIS- Disclosure All authors disclosed no financial relationships.
EDAT- 2023/06/30 01:06
MHDA- 2023/09/25 06:42
CRDT- 2023/06/29 19:17
PHST- 2023/04/24 00:00 [received]
PHST- 2023/05/21 00:00 [revised]
PHST- 2023/06/08 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/06/30 01:06 [pubmed]
PHST- 2023/06/29 19:17 [entrez]
AID - S0016-5107(23)02675-5 [pii]
AID - 10.1016/j.gie.2023.06.025 [doi]
PST - ppublish
SO  - Gastrointest Endosc. 2023 Oct;98(4):639-641.e4. doi: 10.1016/j.gie.2023.06.025. 
      Epub 2023 Jun 27.

PMID- 37129780
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20231211
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 8
DP  - 2023 Aug
TI  - Understanding the Perceptions of Healthcare Researchers Regarding ChatGPT: A 
      Study Based on Bidirectional Encoder Representation from Transformers (BERT) 
      Sentiment Analysis and Topic Modeling.
PG  - 1654-1656
LID - 10.1007/s10439-023-03222-0 [doi]
AB  - In this study, we have used deep learning techniques to understand the perception 
      of researchers in the healthcare sector about the recently introduced chat 
      generative pre-trained transformer (ChatGPT). Ever since the launch of ChatGPT, 
      there have been various debates over the usage of ChatGPT for research purposes. 
      In this article, using the pre-trained BERT (Bidirectional Encoder 
      Representations from Transformers) model, we performed sentiment analysis and 
      topic modeling to analyze the social media posts of healthcare researchers to 
      understand their emotions towards ChatGPT.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Praveen, S V
AU  - Praveen SV
AUID- ORCID: 0000-0002-1450-8839
AD  - Department of Analytics, Xavier Institute of Management and Entrepreneurship, 
      Bangalore, India. praveenscissci@gmail.com.
FAU - Vajrobol, Vajratiya
AU  - Vajrobol V
AD  - Institute of Informatics and Communication, University of Delhi-South Campus, New 
      Delhi, India.
LA  - eng
PT  - Letter
DEP - 20230502
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Sentiment Analysis
MH  - *Electric Power Supplies
OTO - NOTNLM
OT  - ChatGPT
OT  - Deep learning
OT  - Health care research
OT  - Natural language processing
OT  - Perception
EDAT- 2023/05/02 12:43
MHDA- 2023/07/10 06:42
CRDT- 2023/05/02 11:11
PHST- 2023/04/24 00:00 [received]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/05/02 12:43 [pubmed]
PHST- 2023/05/02 11:11 [entrez]
AID - 10.1007/s10439-023-03222-0 [pii]
AID - 10.1007/s10439-023-03222-0 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Aug;51(8):1654-1656. doi: 10.1007/s10439-023-03222-0. Epub 
      2023 May 2.

PMID- 37744723
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230926
IS  - 2296-875X (Print)
IS  - 2296-875X (Electronic)
IS  - 2296-875X (Linking)
VI  - 10
DP  - 2023
TI  - Expanding horizons and navigating challenges for enhanced clinical workflows: 
      ChatGPT in urology.
PG  - 1257191
LID - 10.3389/fsurg.2023.1257191 [doi]
LID - 1257191
AB  - PURPOSE OF REVIEW: ChatGPT has emerged as a potential tool for facilitating 
      doctors' workflows. However, when it comes to applying these findings within a 
      urological context, there have not been many studies. Thus, our objective was 
      rooted in analyzing the pros and cons of ChatGPT use and how it can be exploited 
      and used by urologists. RECENT FINDINGS: ChatGPT can facilitate clinical 
      documentation and note-taking, patient communication and support, medical 
      education, and research. In urology, it was proven that ChatGPT has the potential 
      as a virtual healthcare aide for benign prostatic hyperplasia, an educational and 
      prevention tool on prostate cancer, educational support for urological residents, 
      and as an assistant in writing urological papers and academic work. However, 
      several concerns about its exploitation are presented, such as lack of web 
      crawling, risk of accidental plagiarism, and concerns about patients-data 
      privacy. SUMMARY: The existing limitations mediate the need for further 
      improvement of ChatGPT, such as ensuring the privacy of patient data and 
      expanding the learning dataset to include medical databases, and developing 
      guidance on its appropriate use. Urologists can also help by conducting studies 
      to determine the effectiveness of ChatGPT in urology in clinical scenarios and 
      nosologies other than those previously listed.
CI  - © 2023 Talyshinskii, Naik, Hameed, Zhanbyrbekuly, Khairli, Guliev, Juliebø-Jones, 
      Tzelves and Somani.
FAU - Talyshinskii, Ali
AU  - Talyshinskii A
AD  - Department of Urology, Astana Medical University, Astana, Kazakhstan.
FAU - Naik, Nithesh
AU  - Naik N
AD  - Department of Mechanical and Industrial Engineering, Manipal Institute of 
      Technology, Manipal Academy of Higher Education, Manipal, India.
FAU - Hameed, B M Zeeshan
AU  - Hameed BMZ
AD  - Department of Urology, Father Muller Medical College, Mangalore, India.
FAU - Zhanbyrbekuly, Ulanbek
AU  - Zhanbyrbekuly U
AD  - Department of Urology, Astana Medical University, Astana, Kazakhstan.
FAU - Khairli, Gafur
AU  - Khairli G
AD  - Department of Urology, Astana Medical University, Astana, Kazakhstan.
FAU - Guliev, Bakhman
AU  - Guliev B
AD  - Department of Urology, Mariinsky Hospital, St Petersburg, Russia.
FAU - Juilebø-Jones, Patrick
AU  - Juilebø-Jones P
AD  - Department of Urology, Haukeland University Hospital, Bergen, Norway.
FAU - Tzelves, Lazaros
AU  - Tzelves L
AD  - Department of Urology, National and Kapodistrian University of Athens, 
      Sismanogleion Hospital, Athens, Marousi, Greece.
FAU - Somani, Bhaskar Kumar
AU  - Somani BK
AD  - Department of Urology, University Hospital Southampton NHS Trust, Southampton, 
      United Kingdom.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230907
PL  - Switzerland
TA  - Front Surg
JT  - Frontiers in surgery
JID - 101645127
PMC - PMC10512827
OTO - NOTNLM
OT  - chatGPT
OT  - generative AI
OT  - healthcare
OT  - urology
OT  - workflow
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/25 06:42
MHDA- 2023/09/25 06:43
CRDT- 2023/09/25 04:48
PHST- 2023/07/12 00:00 [received]
PHST- 2023/08/28 00:00 [accepted]
PHST- 2023/09/25 06:43 [medline]
PHST- 2023/09/25 06:42 [pubmed]
PHST- 2023/09/25 04:48 [entrez]
AID - 10.3389/fsurg.2023.1257191 [doi]
PST - epublish
SO  - Front Surg. 2023 Sep 7;10:1257191. doi: 10.3389/fsurg.2023.1257191. eCollection 
      2023.

PMID- 37886525
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
DP  - 2023 Oct 19
TI  - ChatGPT Demonstrates Potential for Identifying Psychiatric Disorders: Application 
      to Childbirth-Related Post-Traumatic Stress Disorder.
LID - rs.3.rs-3428787 [pii]
LID - 10.21203/rs.3.rs-3428787/v1 [doi]
AB  - Free-text analysis using Machine Learning (ML)-based Natural Language Processing 
      (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative 
      Pre-trained Transformer (ChatGPT) has demonstrated initial feasibility for this 
      purpose; however, this work remains preliminary, and whether it can accurately 
      assess mental illness remains to be determined. This study examines ChatGPT's 
      utility to identify post-traumatic stress disorder following childbirth 
      (CB-PTSD), a maternal postpartum mental illness affecting millions of women 
      annually, with no standard screening protocol. We explore ChatGPT's potential to 
      screen for CB-PTSD by analyzing maternal childbirth narratives as the sole data 
      source. By developing an ML model that utilizes ChatGPT's knowledge, we identify 
      CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.82) 
      ChatGPT and six previously published large language models (LLMs) trained on 
      mental health or clinical domains data, suggesting that ChatGPT can be harnessed 
      to identify CB-PTSD. Our modeling approach can be generalized to assess other 
      mental health disorders.
FAU - Bartal, Alon
AU  - Bartal A
AD  - The School of Business Administration, Bar-Ilan University, Max and Anna Web, 
      Ramat Gan, 5290002, Israel.
FAU - Jagodnik, Kathleen M
AU  - Jagodnik KM
AD  - The School of Business Administration, Bar-Ilan University, Max and Anna Web, 
      Ramat Gan, 5290002, Israel.
AD  - Department of Psychiatry, Massachusetts General Hospital, 55 Fruit St., Boston, 
      02114, Massachusetts, USA.
AD  - Department of Psychiatry, Harvard Medical School, 25 Shattuck St., Boston, 02115, 
      Massachusetts, USA.
FAU - Chan, Sabrina J
AU  - Chan SJ
AD  - Department of Psychiatry, Massachusetts General Hospital, 55 Fruit St., Boston, 
      02114, Massachusetts, USA.
FAU - Dekel, Sharon
AU  - Dekel S
AD  - Department of Psychiatry, Massachusetts General Hospital, 55 Fruit St., Boston, 
      02114, Massachusetts, USA.
AD  - Department of Psychiatry, Harvard Medical School, 25 Shattuck St., Boston, 02115, 
      Massachusetts, USA.
LA  - eng
PT  - Preprint
DEP - 20231019
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10602164
OTO - NOTNLM
OT  - Birth narratives
OT  - ChatGPT
OT  - Childbirth-related post-traumatic stress disorder (CB-PTSD)
OT  - Maternal mental health
OT  - Postpartum psychopathology
OT  - Pre-trained large language model (PLM)
EDAT- 2023/10/27 06:42
MHDA- 2023/10/27 06:43
CRDT- 2023/10/27 04:44
PHST- 2023/10/27 06:42 [pubmed]
PHST- 2023/10/27 06:43 [medline]
PHST- 2023/10/27 04:44 [entrez]
AID - rs.3.rs-3428787 [pii]
AID - 10.21203/rs.3.rs-3428787/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 Oct 19:rs.3.rs-3428787. doi: 10.21203/rs.3.rs-3428787/v1. Preprint.

PMID- 37423349
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231113
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 10
DP  - 2023 Oct
TI  - Use of Large Language Models to Predict Neuroimaging.
PG  - 1004-1009
LID - S1546-1440(23)00483-0 [pii]
LID - 10.1016/j.jacr.2023.06.008 [doi]
AB  - PURPOSE: Large language models (LLMs) have demonstrated a level of competency 
      within the medical field. The aim of this study was to explore the ability of 
      LLMs to predict the best neuroradiologic imaging modality given specific clinical 
      presentations. In addition, the authors seek to determine if LLMs can outperform 
      an experienced neuroradiologist in this regard. METHODS: ChatGPT and Glass AI, a 
      health care-based LLM by Glass Health, were used. ChatGPT was prompted to rank 
      the three best neuroimaging modalities while taking the best responses from Glass 
      AI and the neuroradiologist. The responses were compared with the ACR 
      Appropriateness Criteria for 147 conditions. Clinical scenarios were passed into 
      each LLM twice to account for stochasticity. Each output was scored out of 3 on 
      the basis of the criteria. Partial scores were given for nonspecific answers. 
      RESULTS: ChatGPT and Glass AI scored 1.75 and 1.83, respectively, with no 
      statistically significant difference. The neuroradiologist scored 2.20, 
      significantly outperforming both LLMs. ChatGPT was also found to be the more 
      inconsistent of the two LLMs, with the score difference between both outputs 
      being statistically significant. Additionally, scores between different ranks 
      output by ChatGPT were statistically significant. CONCLUSIONS: LLMs perform well 
      in selecting appropriate neuroradiologic imaging procedures when prompted with 
      specific clinical scenarios. ChatGPT performed the same as Glass AI, suggesting 
      that with medical text training, ChatGPT could significantly improve its function 
      in this application. LLMs did not outperform an experienced neuroradiologist, 
      indicating the need for continued improvement in the medical context.
CI  - Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All 
      rights reserved.
FAU - Nazario-Johnson, Lleayem
AU  - Nazario-Johnson L
AD  - Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown 
      University/Rhode Island Hospital, Providence, Rhode Island.
FAU - Zaki, Hossam A
AU  - Zaki HA
AD  - Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown 
      University/Rhode Island Hospital, Providence, Rhode Island. Electronic address: 
      hossam_zaki@brown.edu.
FAU - Tung, Glenn A
AU  - Tung GA
AD  - Associate Dean for Clinical Affairs, Department of Diagnostic Imaging, The Warren 
      Alpert Medical School of Brown University/Rhode Island Hospital, Providence, 
      Rhode Island.
LA  - eng
PT  - Journal Article
DEP - 20230708
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
MH  - Humans
MH  - *Language
MH  - *Neuroimaging
MH  - Radiologists
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - clinical decision making
EDAT- 2023/07/10 00:41
MHDA- 2023/11/03 06:44
CRDT- 2023/07/09 19:24
PHST- 2023/05/11 00:00 [received]
PHST- 2023/06/15 00:00 [revised]
PHST- 2023/06/16 00:00 [accepted]
PHST- 2023/11/03 06:44 [medline]
PHST- 2023/07/10 00:41 [pubmed]
PHST- 2023/07/09 19:24 [entrez]
AID - S1546-1440(23)00483-0 [pii]
AID - 10.1016/j.jacr.2023.06.008 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Oct;20(10):1004-1009. doi: 10.1016/j.jacr.2023.06.008. 
      Epub 2023 Jul 8.

PMID- 36989584
OWN - NLM
STAT- MEDLINE
DCOM- 20230508
LR  - 20231122
IS  - 1878-0334 (Electronic)
IS  - 1871-4021 (Linking)
VI  - 17
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT: Is this version good for healthcare and research?
PG  - 102744
LID - S1871-4021(23)00040-1 [pii]
LID - 10.1016/j.dsx.2023.102744 [doi]
AB  - BACKGROUND AND AIMS: There have been advancements in artificial intelligence (AI) 
      and deep learning in the past decade. Recently, OpenAI Inc. has launched a new 
      chatbot, called ChatGPT that interacts in a conversational way and its dialogue 
      format makes is user friendly and fast. In this paper we aimed to explore the 
      current position and the accuracy of currently available version of ChatGPT in 
      relation to healthcare and medical research. METHODS: We searched the PubMed, 
      Scopus, and Google databases from 15(th) to 25(th) February 2023, using the 
      keywords: 'ChatGPT' AND 'medical research, healthcare, and scientific writing'. 
      We found 29 results in PubMed and 9 results in Scopus database., in English 
      language. In addition, we (RV, AM) interacted with ChatGPT multiple times to 
      review accuracy of responses of various medical questions. RESULTS: Using 
      literature search and interactions with ChatGPT with medical questions, we infer 
      that this version generates answers rapidly but narrates data from existing 
      internet literature in a general manner. However, as emphasised by the company in 
      the landing page of ChatGPT, we found errors in responses to medical questions, 
      Further, narrated data were limited up to September 2021. Positive features 
      include admission of its limitations in medical field, and as it has been 
      designed, learning from previous answers. CONCLUSION: Current version of ChatGPT 
      may be useful in a limited manner as a narrative AI chatbot for medical 
      personnel, however, researchers are advised to fact check all statements 
      provided, keeping in mind its limitations.
CI  - Copyright © 2023 Research Trust of DiabetesIndia (DiabetesIndia) and National 
      Diabetes Obesity and Cholesterol Foundation (N-DOC). Published by Elsevier Ltd. 
      All rights reserved.
FAU - Vaishya, Raju
AU  - Vaishya R
AD  - Department of Orthopaedics, Indraprastha Apollo Hospitals, Sarita Vihar, 110076, 
      New Delhi, India. Electronic address: raju.vaishya@gmail.com.
FAU - Misra, Anoop
AU  - Misra A
AD  - Department of Endocrinology, Fortis C Doc Hospital, Nehru Place, New Delhi, 
      India.
FAU - Vaish, Abhishek
AU  - Vaish A
AD  - Department of Orthopaedics, Indraprastha Apollo Hospitals, Sarita Vihar, 110076, 
      New Delhi, India.
LA  - eng
PT  - Journal Article
DEP - 20230315
PL  - Netherlands
TA  - Diabetes Metab Syndr
JT  - Diabetes & metabolic syndrome
JID - 101462250
SB  - IM
CIN - Diabetes Metab Syndr. 2023 May;17(5):102779. PMID: 37178512
MH  - Humans
MH  - *Artificial Intelligence
MH  - Health Facilities
MH  - *Biomedical Research
MH  - Databases, Factual
MH  - Delivery of Health Care
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Healthcare
OT  - Research
COIS- Declaration of competing interest None.
EDAT- 2023/03/30 06:00
MHDA- 2023/05/08 06:42
CRDT- 2023/03/29 18:02
PHST- 2023/03/01 00:00 [received]
PHST- 2023/03/07 00:00 [revised]
PHST- 2023/03/10 00:00 [accepted]
PHST- 2023/05/08 06:42 [medline]
PHST- 2023/03/30 06:00 [pubmed]
PHST- 2023/03/29 18:02 [entrez]
AID - S1871-4021(23)00040-1 [pii]
AID - 10.1016/j.dsx.2023.102744 [doi]
PST - ppublish
SO  - Diabetes Metab Syndr. 2023 Apr;17(4):102744. doi: 10.1016/j.dsx.2023.102744. Epub 
      2023 Mar 15.

PMID- 37638368
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230829
IS  - 2590-1133 (Electronic)
IS  - 2590-1133 (Linking)
VI  - 5
DP  - 2023 Dec
TI  - Causal reasoning about epidemiological associations in conversational AI.
PG  - 100102
LID - 10.1016/j.gloepi.2023.100102 [doi]
LID - 100102
AB  - We present a Socratic dialogue with ChatGPT, a large language model (LLM), on the 
      causal interpretation of epidemiological associations between fine particulate 
      matter (PM2.5) and human mortality risks. ChatGPT, reflecting probable patterns 
      of human reasoning and argumentation in the sources on which it has been trained, 
      initially holds that "It is well-established that exposure to ambient levels of 
      PM2.5 does increase mortality risk" and adds the unsolicited remark that 
      "Reducing exposure to PM2.5 is an important public health priority." After 
      patient questioning, however, it concludes that "It is not known with certainty 
      that current ambient levels of PM2.5 increase mortality risk. While there is 
      strong evidence of an association between PM2.5 and mortality risk, the causal 
      nature of this association remains uncertain due to the possibility of omitted 
      confounders." This revised evaluation of the evidence suggests the potential 
      value of sustained questioning in refining and improving both the types of human 
      reasoning and argumentation imitated by current LLMs and the reliability of the 
      initial conclusions expressed by current LLMs.
CI  - © 2023 The Author.
FAU - Cox, Louis Anthony Jr
AU  - Cox LA Jr
AD  - Cox Associates, MoirAI, Entanglement, and University of Colorado, 503 N. Franklin 
      Street, Denver, CO 80218, USA.
LA  - eng
PT  - Journal Article
DEP - 20230308
PL  - United States
TA  - Glob Epidemiol
JT  - Global epidemiology
JID - 101759263
PMC - PMC10445972
OTO - NOTNLM
OT  - Causal artificial intelligence
OT  - Causal reasoning
OT  - ChatGPT
OT  - Large language models
OT  - PM2.5
COIS- The authors declare that they have no known competing financial interestsor 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/08/28 06:43
MHDA- 2023/08/28 06:44
CRDT- 2023/08/28 05:03
PHST- 2023/02/28 00:00 [received]
PHST- 2023/03/03 00:00 [revised]
PHST- 2023/03/03 00:00 [accepted]
PHST- 2023/08/28 06:44 [medline]
PHST- 2023/08/28 06:43 [pubmed]
PHST- 2023/08/28 05:03 [entrez]
AID - S2590-1133(23)00005-6 [pii]
AID - 100102 [pii]
AID - 10.1016/j.gloepi.2023.100102 [doi]
PST - epublish
SO  - Glob Epidemiol. 2023 Mar 8;5:100102. doi: 10.1016/j.gloepi.2023.100102. 
      eCollection 2023 Dec.

PMID- 38081765
OWN - NLM
STAT- In-Process
LR  - 20231211
IS  - 2632-1009 (Electronic)
IS  - 2632-1009 (Linking)
VI  - 30
IP  - 1
DP  - 2023 Dec 11
TI  - ChatGPT in Iranian medical licensing examination: evaluating the diagnostic 
      accuracy and decision-making capabilities of an AI-based model.
LID - e100815 [pii]
LID - 10.1136/bmjhci-2023-100815 [doi]
AB  - INTRODUCTION: Large language models such as ChatGPT have gained popularity for 
      their ability to generate comprehensive responses to human queries. In the field 
      of medicine, ChatGPT has shown promise in applications ranging from diagnostics 
      to decision-making. However, its performance in medical examinations and its 
      comparison to random guessing have not been extensively studied. METHODS: This 
      study aimed to evaluate the performance of ChatGPT in the preinternship 
      examination, a comprehensive medical assessment for students in Iran. The 
      examination consisted of 200 multiple-choice questions categorised into basic 
      science evaluation, diagnosis and decision-making. GPT-4 was used, and the 
      questions were translated to English. A statistical analysis was conducted to 
      assess the performance of ChatGPT and also compare it with a random test group. 
      RESULTS: The results showed that ChatGPT performed exceptionally well, with 68.5% 
      of the questions answered correctly, significantly surpassing the pass mark of 
      45%. It exhibited superior performance in decision-making and successfully passed 
      all specialties. Comparing ChatGPT to the random test group, ChatGPT's 
      performance was significantly higher, demonstrating its ability to provide more 
      accurate responses and reasoning. CONCLUSION: This study highlights the potential 
      of ChatGPT in medical licensing examinations and its advantage over random 
      guessing. However, it is important to note that ChatGPT still falls short of 
      human physicians in terms of diagnostic accuracy and decision-making 
      capabilities. Caution should be exercised when using ChatGPT, and its results 
      should be verified by human experts to ensure patient safety and avoid potential 
      errors in the medical field.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Ebrahimian, Manoochehr
AU  - Ebrahimian M
AUID- ORCID: 0000-0003-1286-3308
AD  - Pediatric Surgery Research Center, Research Institute for Children's Health, 
      Shahid Beheshti University of Medical Sciences, Tehran, Iran 
      manoochehrebrahimian@gmail.com.
FAU - Behnam, Behdad
AU  - Behnam B
AD  - Gastrointestinal and Liver Disease Research Center, Iran University of Medical 
      Sciences, Tehran, Iran.
FAU - Ghayebi, Negin
AU  - Ghayebi N
AD  - School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
FAU - Sobhrakhshankhah, Elham
AU  - Sobhrakhshankhah E
AD  - Gastrointestinal and Liver Disease Research Center, Iran University of Medical 
      Sciences, Tehran, Iran.
LA  - eng
PT  - Journal Article
DEP - 20231211
PL  - England
TA  - BMJ Health Care Inform
JT  - BMJ health & care informatics
JID - 101745500
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Decision Making, Computer-Assisted
OT  - Neural Networks, Computer
COIS- Competing interests: None declared.
EDAT- 2023/12/12 00:42
MHDA- 2023/12/12 00:42
CRDT- 2023/12/11 22:23
PHST- 2023/05/30 00:00 [received]
PHST- 2023/11/28 00:00 [accepted]
PHST- 2023/12/12 00:42 [medline]
PHST- 2023/12/12 00:42 [pubmed]
PHST- 2023/12/11 22:23 [entrez]
AID - bmjhci-2023-100815 [pii]
AID - 10.1136/bmjhci-2023-100815 [doi]
PST - epublish
SO  - BMJ Health Care Inform. 2023 Dec 11;30(1):e100815. doi: 
      10.1136/bmjhci-2023-100815.

PMID- 37142327
OWN - NLM
STAT- MEDLINE
DCOM- 20230919
LR  - 20231009
IS  - 1742-6723 (Electronic)
IS  - 1742-6723 (Linking)
VI  - 35
IP  - 5
DP  - 2023 Oct
TI  - Generative artificial intelligence: Can ChatGPT write a quality abstract?
PG  - 809-811
LID - 10.1111/1742-6723.14233 [doi]
AB  - ChatGPT is a generative artificial intelligence chatbot which may have a role in 
      medicine and science. We investigated if the freely available version of ChatGPT 
      can produce a quality conference abstract using a fictitious but accurately 
      calculated data table as applied by a non-medically trained person. The resulting 
      abstract was well written without obvious errors and followed the abstract 
      instructions. One of the references was fictitious, known as 'hallucination'. 
      ChatGPT or similar programmes, with careful review of the product by authors, may 
      become a valuable scientific writing tool. The scientific and medical use of 
      generative artificial intelligence, however, raises many questions.
CI  - © 2023 The Authors. Emergency Medicine Australasia published by John Wiley & Sons 
      Australia, Ltd on behalf of Australasian College for Emergency Medicine.
FAU - Babl, Franz E
AU  - Babl FE
AUID- ORCID: 0000-0002-1107-2187
AD  - Emergency Department, The Royal Children's Hospital, Melbourne, Victoria, 
      Australia.
AD  - Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Victoria, 
      Australia.
AD  - Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, 
      The University of Melbourne, Melbourne, Victoria, Australia.
AD  - Department of Critical Care, Faculty of Medicine, Dentistry and Health Sciences, 
      The University of Melbourne, Melbourne, Victoria, Australia.
FAU - Babl, Maximilian P
AU  - Babl MP
AD  - Xavier College, Melbourne, Victoria, Australia.
LA  - eng
GR  - GNT2017605/National Health and Medical Research Council/
GR  - Royal Children's Hospital Foundation/
PT  - Journal Article
DEP - 20230504
PL  - Australia
TA  - Emerg Med Australas
JT  - Emergency medicine Australasia : EMA
JID - 101199824
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Medicine
MH  - Writing
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - generative artificial intelligence
EDAT- 2023/05/05 00:42
MHDA- 2023/09/19 06:42
CRDT- 2023/05/04 20:52
PHST- 2023/04/18 00:00 [received]
PHST- 2023/04/19 00:00 [accepted]
PHST- 2023/09/19 06:42 [medline]
PHST- 2023/05/05 00:42 [pubmed]
PHST- 2023/05/04 20:52 [entrez]
AID - 10.1111/1742-6723.14233 [doi]
PST - ppublish
SO  - Emerg Med Australas. 2023 Oct;35(5):809-811. doi: 10.1111/1742-6723.14233. Epub 
      2023 May 4.

PMID- 37793630
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2234-1900 (Print)
IS  - 2234-3164 (Electronic)
IS  - 2234-1900 (Linking)
VI  - 41
IP  - 3
DP  - 2023 Sep
TI  - Developing prompts from large language model for extracting clinical information 
      from pathology and ultrasound reports in breast cancer.
PG  - 209-216
LID - 10.3857/roj.2023.00633 [doi]
AB  - PURPOSE: We aimed to evaluate the time and cost of developing prompts using large 
      language model (LLM), tailored to extract clinical factors in breast cancer 
      patients and their accuracy. MATERIALS AND METHODS: We collected data from 
      reports of surgical pathology and ultrasound from breast cancer patients who 
      underwent radiotherapy from 2020 to 2022. We extracted the information using the 
      Generative Pre-trained Transformer (GPT) for Sheets and Docs extension plugin and 
      termed this the "LLM" method. The time and cost of developing the prompts with 
      LLM methods were assessed and compared with those spent on collecting information 
      with "full manual" and "LLM-assisted manual" methods. To assess accuracy, 340 
      patients were randomly selected, and the extracted information by LLM method were 
      compared with those collected by "full manual" method. RESULTS: Data from 2,931 
      patients were collected. We developed 12 prompts for Extract function and 12 for 
      Format function to extract and standardize the information. The overall accuracy 
      was 87.7%. For lymphovascular invasion, it was 98.2%. Developing and processing 
      the prompts took 3.5 hours and 15 minutes, respectively. Utilizing the ChatGPT 
      application programming interface cost US $65.8 and when factoring in the 
      estimated wage, the total cost was US $95.4. In an estimated comparison, 
      "LLM-assisted manual" and "LLM" methods were time- and cost-efficient compared to 
      the "full manual" method. CONCLUSION: Developing and facilitating prompts for LLM 
      to derive clinical factors was efficient to extract crucial information from huge 
      medical records. This study demonstrated the potential of the application of 
      natural language processing using LLM model in breast cancer patients. Prompts 
      from the current study can be re-used for other research to collect clinical 
      information.
FAU - Choi, Hyeon Seok
AU  - Choi HS
AD  - Department of Radiation Oncology, Seoul National University Hospital, Seoul 
      National University College of Medicine, Seoul, Korea.
FAU - Song, Jun Yeong
AU  - Song JY
AD  - Department of Radiation Oncology, Seoul National University Hospital, Seoul 
      National University College of Medicine, Seoul, Korea.
FAU - Shin, Kyung Hwan
AU  - Shin KH
AD  - Department of Radiation Oncology, Seoul National University Hospital, Seoul 
      National University College of Medicine, Seoul, Korea.
AD  - Institute of Radiation Medicine, Seoul National University Medical Research 
      Center, Seoul, Korea.
FAU - Chang, Ji Hyun
AU  - Chang JH
AD  - Department of Radiation Oncology, Seoul National University Hospital, Seoul 
      National University College of Medicine, Seoul, Korea.
FAU - Jang, Bum-Sup
AU  - Jang BS
AD  - Department of Radiation Oncology, Seoul National University Hospital, Seoul 
      National University College of Medicine, Seoul, Korea.
LA  - eng
GR  - National Cancer Center/
GR  - HA22C0044/Ministry of Health and Welfare/
GR  - Ministry of Science and Information and Communication Technology/
GR  - 2022R1F1A106345712/National Research Foundation of Korea/
PT  - Journal Article
DEP - 20230921
PL  - Korea (South)
TA  - Radiat Oncol J
JT  - Radiation oncology journal
JID - 101577577
PMC - PMC10556835
OTO - NOTNLM
OT  - Ai artificial intelligence
OT  - Automatic data processing
OT  - Breast cancer
OT  - Clinical reports
OT  - Natural language processing
COIS- Conflict of Interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/10/05 01:00
MHDA- 2023/10/05 01:01
CRDT- 2023/10/04 19:20
PHST- 2023/07/24 00:00 [received]
PHST- 2023/09/11 00:00 [accepted]
PHST- 2023/10/05 01:01 [medline]
PHST- 2023/10/05 01:00 [pubmed]
PHST- 2023/10/04 19:20 [entrez]
AID - roj.2023.00633 [pii]
AID - roj-2023-00633 [pii]
AID - 10.3857/roj.2023.00633 [doi]
PST - ppublish
SO  - Radiat Oncol J. 2023 Sep;41(3):209-216. doi: 10.3857/roj.2023.00633. Epub 2023 
      Sep 21.

PMID- 37779351
OWN - NLM
STAT- Publisher
LR  - 20231002
IS  - 2194-802X (Electronic)
IS  - 2194-802X (Linking)
DP  - 2023 Oct 3
TI  - Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a 
      practical guide.
LID - 10.1515/dx-2023-0116 [doi]
AB  - OBJECTIVES: This study aimed to elucidate effective methodologies for utilizing 
      the generative artificial intelligence (AI) system, namely the Chat Generative 
      Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities 
      among clinicians. METHODS: We conducted a comprehensive exploration of the 
      capabilities of ChatGPT, emphasizing two main areas: (1) efficient utilization of 
      ChatGPT, with a focus on application and language selection, input methodology, 
      and output verification; and (2) specific strategies to bolster clinical 
      reasoning using ChatGPT, including self-learning via simulated clinical case 
      creation and engagement with published case reports. RESULTS: Effective AI-based 
      clinical reasoning development requires a clear delineation of both system roles 
      and user needs. All outputs from the system necessitate rigorous verification 
      against credible medical resources. When used in self-learning scenarios, 
      capabilities of ChatGPT in clinical case creation notably enhanced disease 
      comprehension. CONCLUSIONS: The efficient use of generative AIs, as exemplified 
      by ChatGPT, can impressively enhance clinical reasoning among medical 
      professionals. Adopting these cutting-edge tools promises a bright future for 
      continuous advancements in clinicians' diagnostic skills, heralding a 
      transformative era in digital healthcare.
CI  - © 2023 Walter de Gruyter GmbH, Berlin/Boston.
FAU - Hirosawa, Takanobu
AU  - Hirosawa T
AUID- ORCID: 0000-0002-3573-8203
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
FAU - Shimizu, Taro
AU  - Shimizu T
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231003
PL  - Germany
TA  - Diagnosis (Berl)
JT  - Diagnosis (Berlin, Germany)
JID - 101654734
SB  - IM
OTO - NOTNLM
OT  - diagnosis
OT  - diagnostic excellence
OT  - large language model
OT  - natural language processing
OT  - self-learning
EDAT- 2023/10/02 06:42
MHDA- 2023/10/02 06:42
CRDT- 2023/10/02 02:23
PHST- 2023/08/30 00:00 [received]
PHST- 2023/09/04 00:00 [accepted]
PHST- 2023/10/02 06:42 [medline]
PHST- 2023/10/02 06:42 [pubmed]
PHST- 2023/10/02 02:23 [entrez]
AID - dx-2023-0116 [pii]
AID - 10.1515/dx-2023-0116 [doi]
PST - aheadofprint
SO  - Diagnosis (Berl). 2023 Oct 3. doi: 10.1515/dx-2023-0116.

PMID- 37332008
OWN - NLM
STAT- Publisher
LR  - 20230618
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jun 18
TI  - Revolutionary Potential of ChatGPT in Constructing Intelligent Clinical Decision 
      Support Systems.
LID - 10.1007/s10439-023-03288-w [doi]
AB  - Recently, Chatbot Generative Pre-trained Transformer (ChatGPT) is recognized as a 
      promising clinical decision support system (CDSS) in the medical field owing to 
      its advanced text analysis capabilities and interactive design. However, ChatGPT 
      primarily focuses on learning text semantics rather than learning complex data 
      structures and conducting real-time data analysis, which typically necessitate 
      the development of intelligent CDSS employing specialized machine learning 
      algorithms. Although ChatGPT cannot directly execute specific algorithms, it aids 
      in algorithm design for intelligent CDSS at the textual level. In this study, 
      besides discussing the types of CDSS and their relationship with ChatGPT, we 
      mainly investigate the benefits and drawbacks of employing ChatGPT as an 
      auxiliary design tool for intelligent CDSS. Our findings indicate that by 
      collaborating with human expertise, ChatGPT has the potential to revolutionize 
      the development of robust and effective intelligent CDSS.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Liao, Zhiqiang
AU  - Liao Z
AUID- ORCID: 0000-0002-4030-1364
AD  - Department of Electrical Engineering and Information Systems, Graduate School of 
      Engineering, The University of Tokyo, Tokyo, 113-8656, Japan. 
      liao@bioxide.t.u-tokyo.ac.jp.
FAU - Wang, Jian
AU  - Wang J
AD  - Department of Orthopaedics, Qilu Hospital of Shandong University, Jinan, 250012, 
      People's Republic of China.
FAU - Shi, Zhuozheng
AU  - Shi Z
AD  - Department of Bioengineering, Graduate School of Engineering, The University of 
      Tokyo, Tokyo, 113-8656, Japan.
FAU - Lu, Lintao
AU  - Lu L
AD  - Department of Orthopaedics, Qilu Hospital of Shandong University, Jinan, 250012, 
      People's Republic of China. lulintao@mail.sdu.edu.cn.
AD  - Department of Orthopaedics, Qilu Hospital of Shandong University Dezhou Hospital, 
      Dezhou, 253000, People's Republic of China. lulintao@mail.sdu.edu.cn.
FAU - Tabata, Hitoshi
AU  - Tabata H
AD  - Department of Electrical Engineering and Information Systems, Graduate School of 
      Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.
AD  - Department of Bioengineering, Graduate School of Engineering, The University of 
      Tokyo, Tokyo, 113-8656, Japan.
LA  - eng
PT  - Letter
DEP - 20230618
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Clinical decision support systems
OT  - Large language model
OT  - Machine learning
EDAT- 2023/06/19 00:42
MHDA- 2023/06/19 00:42
CRDT- 2023/06/18 23:35
PHST- 2023/06/08 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/06/19 00:42 [medline]
PHST- 2023/06/19 00:42 [pubmed]
PHST- 2023/06/18 23:35 [entrez]
AID - 10.1007/s10439-023-03288-w [pii]
AID - 10.1007/s10439-023-03288-w [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jun 18. doi: 10.1007/s10439-023-03288-w.

PMID- 37061595
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230615
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 7
DP  - 2023 Jul
TI  - ChatGPT and the Future of Health Policy Analysis: Potential and Pitfalls of Using 
      ChatGPT in Policymaking.
PG  - 1357-1359
LID - 10.1007/s10439-023-03204-2 [doi]
AB  - Scholars increasingly rely on new artificial intelligence models for convenience 
      and simple access to necessities due to the rapid evolution of scientific 
      literature and technology. The invention of ChatGPT by OpenAI stands out as a key 
      example of how significant advances in large language model technology have 
      recently changed the field of artificial intelligence (AI). Since ChatGPT's 
      development, it has been tested by multiple sectors on various topics to see how 
      well it functions in a natural and conversational mode. The crucial question is 
      how much ChatGPT can influence global health policy analysis. In this article, 
      the researcher briefly explains ChatGPT's potential and the difficulties that 
      users, such as researchers or policymakers, may continue to face.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Sifat, Ridwan Islam
AU  - Sifat RI
AUID- ORCID: 0000-0001-9897-0870
AD  - School of Public Policy, University of Maryland, Baltimore County, Baltimore, MD, 
      21250, USA. rsifat1@umbc.edu.
LA  - eng
PT  - Letter
DEP - 20230415
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Artificial Intelligence
MH  - *Policy Making
MH  - Health Policy
MH  - Technology
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Global health policy
OT  - Policymaking
EDAT- 2023/04/16 06:00
MHDA- 2023/06/15 06:41
CRDT- 2023/04/15 23:18
PHST- 2023/04/06 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/06/15 06:41 [medline]
PHST- 2023/04/16 06:00 [pubmed]
PHST- 2023/04/15 23:18 [entrez]
AID - 10.1007/s10439-023-03204-2 [pii]
AID - 10.1007/s10439-023-03204-2 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jul;51(7):1357-1359. doi: 10.1007/s10439-023-03204-2. Epub 
      2023 Apr 15.

PMID- 37874336
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Oct 24
TI  - ChatGPT performance in laryngology and head and neck surgery: a clinical 
      case-series.
LID - 10.1007/s00405-023-08282-5 [doi]
AB  - OBJECTIVES: To study the performance of ChatGPT in the management of laryngology 
      and head and neck (LHN) cases. METHODS: History and clinical examination of 
      patients consulting at the Otolaryngology-Head and Neck Surgery department were 
      presented to ChatGPT, which was interrogated for differential diagnosis, 
      management, and treatment. The ChatGPT performance was assessed by two blinded 
      board-certified otolaryngologists using the following items of a composite score 
      and the Ottawa Clinic Assessment Tool: differential diagnosis; additional 
      examination; and treatment options. The complexity of clinical cases was 
      evaluated with the Amsterdam Clinical Challenge Scale test. RESULTS: Forty 
      clinical cases were submitted to ChatGPT, accounting for 14 (35%), 12 (30%), and 
      14 (35%) easy, moderate and difficult cases, respectively. ChatGPT indicated a 
      significant higher number of additional examinations compared to practitioners 
      (p = 0.001). There was a significant agreement between practitioners and ChatGPT 
      for the indication of some common examinations (audiometry, ultrasonography, 
      biopsy, gastrointestinal endoscopy or videofluoroscopy). ChatGPT never indicated 
      some important additional examinations (PET-CT, voice quality assessment, or 
      impedance-pH monitoring). ChatGPT reported highest performance in the proposition 
      of the primary (90%) or the most plausible differential diagnoses (65%), and the 
      therapeutic options (60-68%). The ChatGPT performance in the indication of 
      additional examinations was lowest. CONCLUSIONS: ChatGPT is a promising 
      adjunctive tool in LHN practice, providing extensive documentation about 
      disease-related additional examinations, differential diagnoses, and treatments. 
      The ChatGPT is more efficient in diagnosis and treatment, rather than in the 
      selection of the most adequate additional examination.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Lechien, Jerome R
AU  - Lechien JR
AUID- ORCID: 0000-0002-0845-0845
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Socities (IFOS), Paris, France. 
      Jerome.Lechien@umons.ac.be.
AD  - Division of Laryngology and Broncho-Esophagology, Department of 
      Otolaryngology-Head Neck Surgery, UMONS Research Institute for Health Sciences 
      and Technology, EpiCURA Hospital, University of Mons (UMons), Mons, Belgium. 
      Jerome.Lechien@umons.ac.be.
AD  - Department of Otorhinolaryngology and Head and Neck Surgery, School of Medicine, 
      UFR Simone Veil, Foch Hospital, Université Versailles Saint-Quentin-en-Yvelines 
      (Paris Saclay University), Paris, France. Jerome.Lechien@umons.ac.be.
AD  - Department of Otorhinolaryngology and Head and Neck Surgery, CHU Saint-Pierre, 
      Brussels, Belgium. Jerome.Lechien@umons.ac.be.
AD  - Polyclinique Elsan de Poitiers, Poitiers, France. Jerome.Lechien@umons.ac.be.
AD  - Department of Human Anatomy and Experimental Oncology, Faculty of Medicine, UMONS 
      Research Institute for Health Sciences and Technology, Avenue du Champ de Mars, 
      6, 7000, Mons, Belgium. Jerome.Lechien@umons.ac.be.
FAU - Georgescu, Bianca M
AU  - Georgescu BM
AD  - Division of Laryngology and Broncho-Esophagology, Department of 
      Otolaryngology-Head Neck Surgery, UMONS Research Institute for Health Sciences 
      and Technology, EpiCURA Hospital, University of Mons (UMons), Mons, Belgium.
FAU - Hans, Stephane
AU  - Hans S
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Socities (IFOS), Paris, France.
AD  - Department of Otorhinolaryngology and Head and Neck Surgery, School of Medicine, 
      UFR Simone Veil, Foch Hospital, Université Versailles Saint-Quentin-en-Yvelines 
      (Paris Saclay University), Paris, France.
FAU - Chiesa-Estomba, Carlos M
AU  - Chiesa-Estomba CM
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Socities (IFOS), Paris, France.
AD  - Department of Otorhinolaryngology-Head & Neck Surgery, Donostia University 
      Hospital-Biodonostia Research Institute, St. Sebastian, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - Artificial
OT  - ChatGPT
OT  - Comparison
OT  - Diagnosis
OT  - Head neck
OT  - Intelligence
OT  - Laryngology
OT  - Laryngopharyngeal
OT  - Otolaryngology
OT  - Reflux
OT  - Surgery
OT  - Treatment
OT  - Voice
EDAT- 2023/10/24 12:42
MHDA- 2023/10/24 12:42
CRDT- 2023/10/24 11:04
PHST- 2023/07/16 00:00 [received]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/10/24 12:42 [medline]
PHST- 2023/10/24 12:42 [pubmed]
PHST- 2023/10/24 11:04 [entrez]
AID - 10.1007/s00405-023-08282-5 [pii]
AID - 10.1007/s00405-023-08282-5 [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Oct 24. doi: 10.1007/s00405-023-08282-5.

PMID- 37717252
OWN - NLM
STAT- Publisher
LR  - 20230917
IS  - 1097-6817 (Electronic)
IS  - 0194-5998 (Linking)
DP  - 2023 Sep 17
TI  - Is ChatGPT-4 Accurate in Proofread a Manuscript in Otolaryngology-Head and Neck 
      Surgery?
LID - 10.1002/ohn.526 [doi]
AB  - ChatGPT is a new artificial intelligence-powered language model of chatbot able 
      to help otolaryngologists in clinical practice and research. We investigated the 
      ability of ChatGPT-4 in the editing of a manuscript in otolaryngology. Four 
      papers were written by a nonnative English otolaryngologist and edited by a 
      professional editing service. ChatGPT-4 was used to detect and correct errors in 
      manuscripts. From the 171 errors in the manuscripts, ChatGPT-4 detected 86 errors 
      (50.3%) including vocabulary (N = 36), determiner (N = 27), preposition (N = 24), 
      capitalization (N = 20), and number (N = 11). ChatGPT-4 proposed appropriate 
      corrections for 72 (83.7%) errors, while some errors were poorly detected (eg, 
      capitalization [5%] and vocabulary [44.4%] errors. ChatGPT-4 claimed to change 
      something that was already there in 82 cases. ChatGPT demonstrated usefulness in 
      identifying some types of errors but not all. Nonnative English researchers 
      should be aware of the current limits of ChatGPT-4 in the proofreading of 
      manuscripts.
CI  - © 2023 American Academy of Otolaryngology-Head and Neck Surgery Foundation.
FAU - Lechien, Jerome R
AU  - Lechien JR
AD  - Department of Otolaryngology-Head Neck Surgery, Division of Laryngology and 
      Broncho-esophagology, EpiCURA Hospital, UMONS Research Institute for Health 
      Sciences and Technology, University of Mons (UMons), Mons, Belgium.
AD  - Department of Otorhinolaryngology and Head and Neck Surgery, Foch Hospital, 
      School of Medicine, UFR Simone Veil, Université Versailles 
      Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France.
AD  - Department of Otorhinolaryngology and Head and Neck Surgery, CHU de Bruxelles, 
      CHU Saint-Pierre, School of Medicine, Université Libre de Bruxelles, Brussels, 
      Belgium.
AD  - Polyclinique Elsan de Poitiers, Poitiers, France.
FAU - Gorton, Amy
AU  - Gorton A
AD  - Faculty of Translation and Interpretation (FTI-EII), University of Mons, Mons, 
      Belgium.
FAU - Robertson, Jean
AU  - Robertson J
AD  - Faculty of Translation and Interpretation (FTI-EII), University of Mons, Mons, 
      Belgium.
FAU - Vaira, Luigi A
AU  - Vaira LA
AD  - Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and 
      Pharmacy, University of Sassari, Sassari, Italy.
AD  - PhD School of Biomedical Sciences, Department of Biomedical Sciences, University 
      of Sassari, Sassari, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230917
PL  - England
TA  - Otolaryngol Head Neck Surg
JT  - Otolaryngology--head and neck surgery : official journal of American Academy of 
      Otolaryngology-Head and Neck Surgery
JID - 8508176
SB  - IM
OTO - NOTNLM
OT  - ChatGPT-4
OT  - GPT-4
OT  - artificial
OT  - correction
OT  - head neck
OT  - intelligence
OT  - otolaryngology
OT  - proofread
OT  - scientific
OT  - surgery
OT  - writing
EDAT- 2023/09/17 18:42
MHDA- 2023/09/17 18:42
CRDT- 2023/09/17 14:51
PHST- 2023/08/01 00:00 [revised]
PHST- 2023/05/04 00:00 [received]
PHST- 2023/08/18 00:00 [accepted]
PHST- 2023/09/17 18:42 [medline]
PHST- 2023/09/17 18:42 [pubmed]
PHST- 2023/09/17 14:51 [entrez]
AID - 10.1002/ohn.526 [doi]
PST - aheadofprint
SO  - Otolaryngol Head Neck Surg. 2023 Sep 17. doi: 10.1002/ohn.526.

PMID- 36972383
OWN - NLM
STAT- MEDLINE
DCOM- 20230329
LR  - 20231208
IS  - 2471-254X (Electronic)
IS  - 2471-254X (Linking)
VI  - 7
IP  - 4
DP  - 2023 Apr 1
TI  - Artificial intelligence-based text generators in hepatology: ChatGPT is just the 
      beginning.
LID - 10.1097/HC9.0000000000000097 [doi]
LID - e0097
AB  - Since its release as a "research preview" in November 2022, ChatGPT, the 
      conversational interface to the Generative Pretrained Transformer 3 large 
      language model built by OpenAI, has garnered significant publicity for its 
      ability to generate detailed responses to a variety of questions. ChatGPT and 
      other large language models generate sentences and paragraphs in response to word 
      patterns in training data that they have previously seen. By allowing users to 
      communicate with an artificial intelligence model in a human-like way, however, 
      ChatGPT has crossed the technological adoption barrier into the mainstream. 
      Existing examples of ChatGPT use-cases, such as negotiating bills, debugging 
      programing code, and writing essays, indicate that ChatGPT and similar models 
      have the potential to have profound (and yet unknown) impacts on clinical 
      research and practice in hepatology. In this special article, we discuss the 
      general background and potential pitfalls of ChatGPT and associated 
      technologies-and then we explore its uses in hepatology with specific examples.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on 
      behalf of the American Association for the Study of Liver Diseases.
FAU - Ge, Jin
AU  - Ge J
AUID- ORCID: 0000-0003-1574-1525
AD  - Division of Gastroenterology and Hepatology, Department of Medicine, University 
      of California-San Francisco, San Francisco, California, USA.
FAU - Lai, Jennifer C
AU  - Lai JC
AUID- ORCID: 0000-0003-2092-6380
LA  - eng
GR  - R01 AG059183/AG/NIA NIH HHS/United States
GR  - KL2 TR001870/TR/NCATS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20230324
PL  - United States
TA  - Hepatol Commun
JT  - Hepatology communications
JID - 101695860
SB  - IM
MH  - Humans
MH  - *Gastroenterology
MH  - Artificial Intelligence
PMC - PMC10043591
COIS- Jin Ge—research support: Merck and Co. Jennifer C. Lai—consultant: GenFit Corp; 
      Advisory Board: Novo Nordisk; research support: Gore Therapeutics; Site 
      investigator: Lipocine.
EDAT- 2023/03/28 06:00
MHDA- 2023/03/29 06:05
CRDT- 2023/03/27 14:32
PHST- 2023/01/11 00:00 [received]
PHST- 2023/01/31 00:00 [accepted]
PHST- 2023/03/29 06:05 [medline]
PHST- 2023/03/27 14:32 [entrez]
PHST- 2023/03/28 06:00 [pubmed]
AID - 02009842-202304010-00011 [pii]
AID - 10.1097/HC9.0000000000000097 [doi]
PST - epublish
SO  - Hepatol Commun. 2023 Mar 24;7(4):e0097. doi: 10.1097/HC9.0000000000000097. 
      eCollection 2023 Apr 1.

PMID- 37614494
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230825
IS  - 2473-974X (Electronic)
IS  - 2473-974X (Linking)
VI  - 7
IP  - 3
DP  - 2023 Jul-Sep
TI  - Diagnostic and Management Applications of ChatGPT in Structured Otolaryngology 
      Clinical Scenarios.
PG  - e67
LID - 10.1002/oto2.67 [doi]
LID - e67
AB  - OBJECTIVE: To evaluate the clinical applications and limitations of chat 
      generative pretrained transformer (ChatGPT) in otolaryngology. STUDY DESIGN: 
      Cross-sectional survey. SETTING: Tertiary academic center. METHODS: ChatGPT 4.0 
      was queried for diagnoses and management plans for 20 physician-written clinical 
      vignettes in otolaryngology. Attending physicians were then asked to rate the 
      difficulty of the clinical vignettes and agreement with the differential 
      diagnoses and management plans of ChatGPT responses on a 5-point Likert scale. 
      Summary statistics were calculated. Univariate ordinal regression was then 
      performed between vignette difficulty and quality of the diagnoses and management 
      plans. RESULTS: Eleven attending physicians completed the survey (61% response 
      rate). Overall, vignettes were rated as very easy to neutral difficulty (range of 
      median score: 1.00-4.00; overall median 2.00). There was a high agreement with 
      the differential diagnosis provided by ChatGPT (range of median score: 3.00-5.00; 
      overall median: 5.00). There was also high agreement with treatment plans (range 
      of median score: 3.00-5.00; overall median: 5.00). There was no association 
      between vignette difficulty and agreement with differential diagnosis or 
      treatment. Lower diagnosis scores had greater odds of having lower treatment 
      scores. CONCLUSION: Generative artificial intelligence models like ChatGPT are 
      being rapidly adopted in medicine. Performance with curated, easy-to-moderate 
      difficulty otolaryngology scenarios indicate high agreement with physicians for 
      diagnosis and management. However, a decreased quality in diagnosis is associated 
      with decreased quality in management. Further research is necessary on ChatGPT's 
      ability to handle unstructured clinical information.
CI  - © 2023 The Authors. OTO Open published by Wiley Periodicals LLC on behalf of 
      American Academy of Otolaryngology–Head and Neck Surgery Foundation.
FAU - Qu, Roy W
AU  - Qu RW
AUID- ORCID: 0000-0002-4689-819X
AD  - Department of Otolaryngology-Head and Neck Surgery Loma Linda University Medical 
      Center Loma Linda California USA.
FAU - Qureshi, Uneeb
AU  - Qureshi U
AUID- ORCID: 0000-0003-3185-0449
AD  - Department of Otolaryngology-Head and Neck Surgery Loma Linda University Medical 
      Center Loma Linda California USA.
FAU - Petersen, Garrett
AU  - Petersen G
AUID- ORCID: 0009-0004-5501-7114
AD  - Department of Otolaryngology-Head and Neck Surgery Loma Linda School of Medicine, 
      Loma Linda University Medical Center Loma Linda California USA.
FAU - Lee, Steve C
AU  - Lee SC
AUID- ORCID: 0000-0002-4460-4303
AD  - Department of Otolaryngology-Head and Neck Surgery Loma Linda University Medical 
      Center Loma Linda California USA.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - United States
TA  - OTO Open
JT  - OTO open
JID - 101717942
PMC - PMC10442607
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - large language model
OT  - otolaryngology
COIS- The authors have no conflicts of interest to declare.
EDAT- 2023/08/24 06:42
MHDA- 2023/08/24 06:43
CRDT- 2023/08/24 04:02
PHST- 2023/07/13 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/08/24 06:43 [medline]
PHST- 2023/08/24 06:42 [pubmed]
PHST- 2023/08/24 04:02 [entrez]
AID - OTO267 [pii]
AID - 10.1002/oto2.67 [doi]
PST - epublish
SO  - OTO Open. 2023 Aug 22;7(3):e67. doi: 10.1002/oto2.67. eCollection 2023 Jul-Sep.

PMID- 37865883
OWN - NLM
STAT- MEDLINE
DCOM- 20231128
LR  - 20231205
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Nov 16
TI  - A SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of ChatGPT in 
      the Medical Literature: Concise Review.
PG  - e49368
LID - 10.2196/49368 [doi]
LID - e49368
AB  - BACKGROUND: ChatGPT is a 175-billion-parameter natural language processing model 
      that is already involved in scientific content and publications. Its influence 
      ranges from providing quick access to information on medical topics, assisting in 
      generating medical and scientific articles and papers, performing medical data 
      analyses, and even interpreting complex data sets. OBJECTIVE: The future role of 
      ChatGPT remains uncertain and a matter of debate already shortly after its 
      release. This review aimed to analyze the role of ChatGPT in the medical 
      literature during the first 3 months after its release. METHODS: We performed a 
      concise review of literature published in PubMed from December 1, 2022, to March 
      31, 2023. To find all publications related to ChatGPT or considering ChatGPT, the 
      search term was kept simple ("ChatGPT" in AllFields). All publications available 
      as full text in German or English were included. All accessible publications were 
      evaluated according to specifications by the author team (eg, impact factor, 
      publication modus, article type, publication speed, and type of ChatGPT 
      integration or content). The conclusions of the articles were used for later SWOT 
      (strengths, weaknesses, opportunities, and threats) analysis. All data were 
      analyzed on a descriptive basis. RESULTS: Of 178 studies in total, 160 met the 
      inclusion criteria and were evaluated. The average impact factor was 4.423 (range 
      0-96.216), and the average publication speed was 16 (range 0-83) days. Among the 
      articles, there were 77 editorials (48,1%), 43 essays (26.9%), 21 studies 
      (13.1%), 6 reviews (3.8%), 6 case reports (3.8%), 6 news (3.8%), and 1 
      meta-analysis (0.6%). Of those, 54.4% (n=87) were published as open access, with 
      5% (n=8) provided on preprint servers. Over 400 quotes with information on 
      strengths, weaknesses, opportunities, and threats were detected. By far, most 
      (n=142, 34.8%) were related to weaknesses. ChatGPT excels in its ability to 
      express ideas clearly and formulate general contexts comprehensibly. It performs 
      so well that even experts in the field have difficulty identifying abstracts 
      generated by ChatGPT. However, the time-limited scope and the need for 
      corrections by experts were mentioned as weaknesses and threats of ChatGPT. 
      Opportunities include assistance in formulating medical issues for nonnative 
      English speakers, as well as the possibility of timely participation in the 
      development of such artificial intelligence tools since it is in its early stages 
      and can therefore still be influenced. CONCLUSIONS: Artificial intelligence tools 
      such as ChatGPT are already part of the medical publishing landscape. Despite 
      their apparent opportunities, policies and guidelines must be implemented to 
      ensure benefits in education, clinical practice, and research and protect against 
      threats such as scientific misconduct, plagiarism, and inaccuracy.
CI  - ©Daniel Gödde, Sophia Nöhl, Carina Wolf, Yannick Rupert, Lukas Rimkus, Jan 
      Ehlers, Frank Breuckmann, Timur Sellmann. Originally published in the Journal of 
      Medical Internet Research (https://www.jmir.org), 16.11.2023.
FAU - Gödde, Daniel
AU  - Gödde D
AUID- ORCID: 0000-0002-8430-1411
AD  - Department of Pathology and Molecularpathology, Helios University Hospital 
      Wuppertal, Witten/Herdecke University, Witten, Germany.
FAU - Nöhl, Sophia
AU  - Nöhl S
AUID- ORCID: 0009-0008-7515-8300
AD  - Faculty of Health, Witten/Herdecke University, Witten, Germany.
FAU - Wolf, Carina
AU  - Wolf C
AUID- ORCID: 0009-0003-0303-2825
AD  - Faculty of Health, Witten/Herdecke University, Witten, Germany.
FAU - Rupert, Yannick
AU  - Rupert Y
AUID- ORCID: 0009-0005-7430-2460
AD  - Faculty of Health, Witten/Herdecke University, Witten, Germany.
FAU - Rimkus, Lukas
AU  - Rimkus L
AUID- ORCID: 0009-0009-4468-7768
AD  - Faculty of Health, Witten/Herdecke University, Witten, Germany.
FAU - Ehlers, Jan
AU  - Ehlers J
AUID- ORCID: 0000-0001-6306-4173
AD  - Department of Didactics and Education Research in the Health Sector, Faculty of 
      Health, Witten/Herdecke University, Witten, Germany.
FAU - Breuckmann, Frank
AU  - Breuckmann F
AUID- ORCID: 0000-0001-7245-8000
AD  - Department of Cardiology and Vascular Medicine, West German Heart and Vascular 
      Center Essen, University Duisburg-Essen, Essen, Germany.
AD  - Department of Cardiology, Pneumology, Neurology and Intensive Care Medicine, 
      Klinik Kitzinger Land, Kitzingen, Germany.
FAU - Sellmann, Timur
AU  - Sellmann T
AUID- ORCID: 0000-0002-1471-6806
AD  - Department of Anaesthesiology I, Witten/Herdecke University, Witten, Germany.
AD  - Department of Anaesthesiology and Intensive Care Medicine, Evangelisches 
      Krankenhaus BETHESDA zu Duisburg, Duisburg, Germany.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231116
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Data Analysis
MH  - Educational Status
MH  - Language
MH  - Natural Language Processing
PMC - PMC10690535
OTO - NOTNLM
OT  - ChatGPT
OT  - SWOT
OT  - artificial intelligence
OT  - chatbot
OT  - chatbots
OT  - concise review
OT  - education technology
OT  - machine learning
OT  - medical education
OT  - review methodology
OT  - review methods
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/22 12:42
MHDA- 2023/11/17 15:28
CRDT- 2023/10/22 06:28
PHST- 2023/05/26 00:00 [received]
PHST- 2023/09/27 00:00 [accepted]
PHST- 2023/09/26 00:00 [revised]
PHST- 2023/11/17 15:28 [medline]
PHST- 2023/10/22 12:42 [pubmed]
PHST- 2023/10/22 06:28 [entrez]
AID - v25i1e49368 [pii]
AID - 10.2196/49368 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Nov 16;25:e49368. doi: 10.2196/49368.

PMID- 37203581
OWN - NLM
STAT- MEDLINE
DCOM- 20230526
LR  - 20230912
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 302
DP  - 2023 May 18
TI  - How Good Is ChatGPT for Medication Evidence Synthesis?
PG  - 1062-1066
LID - 10.3233/SHTI230347 [doi]
AB  - With its seeming competence to mimic human responses, ChatGPT, an emerging 
      AI-powered chatbot, has spurred great interest. This study aims to explore the 
      role of ChatGPT in synthesizing medication literature and compare it with a 
      hybrid summarization system. We tested ten medications' effectiveness with 
      reference to their definitions and descriptions extracted from DrugBank. ChatGPT 
      could generate coherent summaries that are not backed by evidence. In contrast, 
      our approach can provide a highly structured and concise synthesis of related 
      evidence, but the resulting summary is not as fluent and convincing as ChatGPT. 
      Therefore, we recommend integrating both techniques to achieve the best 
      performance.
FAU - Liu, Hao
AU  - Liu H
AD  - Dept. of Biomedical Informatics, Columbia University, New York, NY, USA.
FAU - Peng, Yifan
AU  - Peng Y
AD  - Dept. of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
FAU - Weng, Chunhua
AU  - Weng C
AD  - Dept. of Biomedical Informatics, Columbia University, New York, NY, USA.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - *Software
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - ChatGPT
OT  - Natural Language Processing
OT  - Summarization
EDAT- 2023/05/19 06:42
MHDA- 2023/05/22 06:41
CRDT- 2023/05/19 05:29
PHST- 2023/05/22 06:41 [medline]
PHST- 2023/05/19 06:42 [pubmed]
PHST- 2023/05/19 05:29 [entrez]
AID - SHTI230347 [pii]
AID - 10.3233/SHTI230347 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 May 18;302:1062-1066. doi: 10.3233/SHTI230347.

PMID- 37606000
OWN - NLM
STAT- Publisher
LR  - 20230930
IS  - 1552-4876 (Electronic)
IS  - 1552-4868 (Linking)
VI  - 193
IP  - 3
DP  - 2023 Sep
TI  - Is artificial intelligence getting too much credit in medical genetics?
PG  - e32062
LID - 10.1002/ajmg.c.32062 [doi]
AB  - Artificial intelligence has lately proven useful in the field of medical 
      genetics. It is already being used to interpret genome sequences and diagnose 
      patients based on facial recognition. More recently, large-language models (LLMs) 
      such as ChatGPT have been tested for their capacity to provide medical genetics 
      information. It was found that ChatGPT performed similarly to human respondents 
      in factual and critical thinking questions, albeit with reduced accuracy in the 
      latter. In particular, ChatGPT's performance in questions related to calculating 
      the recurrence risk was dismal, despite only having to deal with a single 
      disease. To see if challenging ChatGPT with more difficult problems may reveal 
      its flaws and their bases, it was asked to solve recurrence risk problems dealing 
      with two diseases instead of one. Interestingly, it managed to correctly 
      understand the mode of inheritance of recessive diseases, yet it incorrectly 
      calculated the probability of having a healthy child. Other LLMs were also tested 
      and showed similar noise. This highlights a major limitation for clinical use. 
      While this shortcoming may be solved in the near future, LLMs may not be ready 
      yet to be used as an effective clinical tool in communicating medical genetics 
      information.
CI  - © 2023 Wiley Periodicals LLC.
FAU - Alkuraya, Imen F
AU  - Alkuraya IF
AUID- ORCID: 0009-0002-1764-7938
AD  - Abdulaziz International School, Riyadh, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - United States
TA  - Am J Med Genet C Semin Med Genet
JT  - American journal of medical genetics. Part C, Seminars in medical genetics
JID - 101235745
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - Claude
OT  - benchmarking
OT  - large-language model
EDAT- 2023/08/22 06:42
MHDA- 2023/08/22 06:42
CRDT- 2023/08/22 05:53
PHST- 2023/08/08 00:00 [received]
PHST- 2023/08/09 00:00 [accepted]
PHST- 2023/08/22 06:42 [pubmed]
PHST- 2023/08/22 06:42 [medline]
PHST- 2023/08/22 05:53 [entrez]
AID - 10.1002/ajmg.c.32062 [doi]
PST - ppublish
SO  - Am J Med Genet C Semin Med Genet. 2023 Sep;193(3):e32062. doi: 
      10.1002/ajmg.c.32062. Epub 2023 Aug 22.

PMID- 37142479
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20231120
IS  - 1472-6491 (Electronic)
IS  - 1472-6483 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Jul
TI  - Artificial intelligence in scientific writing: a friend or a foe?
PG  - 3-9
LID - S1472-6483(23)00219-5 [pii]
LID - 10.1016/j.rbmo.2023.04.009 [doi]
AB  - The generative pre-trained transformer, ChatGPT, is a chatbot that could serve as 
      a powerful tool in scientific writing. ChatGPT is a so-called large language 
      model (LLM) that is trained to mimic the statistical patterns of language in an 
      enormous database of human-generated text combined from text in books, articles 
      and websites across a wide range of domains. ChatGPT can assist scientists with 
      material organization, draft creation and proofreading, making it a valuable tool 
      in research and publishing. This paper discusses the use of this artificial 
      intelligence (AI) chatbot in academic writing by presenting one simplified 
      example. Specifically, it reflects our experience of using ChatGPT to draft a 
      scientific article for Reproductive BioMedicine Online and highlights the pros, 
      cons and concerns associated with using LLM-based AI for generating a manuscript.
CI  - Copyright © 2023 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All 
      rights reserved.
FAU - Altmäe, Signe
AU  - Altmäe S
AD  - Department of Biochemistry and Molecular Biology, Faculty of Sciences, University 
      of Granada, Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, 
      Granada, Spain; Division of Obstetrics and Gynecology, Department of Clinical 
      Science, Intervention and Technology (CLINTEC), Karolinska Institutet and 
      Karolinska University Hospital, Stockholm, Sweden. Electronic address: 
      signealtmae@ugr.es.
FAU - Sola-Leyva, Alberto
AU  - Sola-Leyva A
AD  - Department of Biochemistry and Molecular Biology, Faculty of Sciences, University 
      of Granada, Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, 
      Granada, Spain.
FAU - Salumets, Andres
AU  - Salumets A
AD  - Division of Obstetrics and Gynecology, Department of Clinical Science, 
      Intervention and Technology (CLINTEC), Karolinska Institutet and Karolinska 
      University Hospital, Stockholm, Sweden; Competence Centre on Health Technologies, 
      Tartu, Estonia; Department of Obstetrics and Gynaecology, Institute of Clinical 
      Medicine, University of Tartu, Tartu, Estonia.
LA  - eng
PT  - Journal Article
DEP - 20230420
PL  - Netherlands
TA  - Reprod Biomed Online
JT  - Reproductive biomedicine online
JID - 101122473
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Software
MH  - Databases, Factual
MH  - *Physicians
MH  - Writing
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Large language model
OT  - Scientific writing
EDAT- 2023/05/05 00:42
MHDA- 2023/07/03 06:41
CRDT- 2023/05/04 21:57
PHST- 2023/04/12 00:00 [received]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/07/03 06:41 [medline]
PHST- 2023/05/05 00:42 [pubmed]
PHST- 2023/05/04 21:57 [entrez]
AID - S1472-6483(23)00219-5 [pii]
AID - 10.1016/j.rbmo.2023.04.009 [doi]
PST - ppublish
SO  - Reprod Biomed Online. 2023 Jul;47(1):3-9. doi: 10.1016/j.rbmo.2023.04.009. Epub 
      2023 Apr 20.

PMID- 37128784
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 12
DP  - 2023 Nov 16
TI  - Performance of ChatGPT on the Plastic Surgery Inservice Training Examination.
PG  - NP1078-NP1082
LID - 10.1093/asj/sjad128 [doi]
AB  - BACKGROUND: Developed originally as a tool for resident self-evaluation, the 
      Plastic Surgery Inservice Training Examination (PSITE) has become a standardized 
      tool adopted by Plastic Surgery residency programs. The introduction of large 
      language models (LLMs), such as ChatGPT (OpenAI, San Francisco, CA), has 
      demonstrated the potential to help propel the field of Plastic Surgery. 
      OBJECTIVES: The authors of this study wanted to assess whether or not ChatGPT 
      could be utilized as a tool in resident education by assessing its accuracy on 
      the PSITE. METHODS: Questions were obtained from the 2022 PSITE, which was 
      present on the American Council of Academic Plastic Surgeons (ACAPS) website. 
      Questions containing images or tables were carefully inspected and flagged before 
      being inputted into ChatGPT. All responses by ChatGPT were qualified utilizing 
      the properties of natural coherence. Responses that were found to be incorrect 
      were divided into the following categories: logical, informational, or explicit 
      fallacy. RESULTS: ChatGPT answered a total of 242 questions with an accuracy of 
      54.96%. The software incorporated logical reasoning in 88.8% of questions, 
      internal information in 95.5% of questions, and external information in 92.1% of 
      questions. When stratified by correct and incorrect responses, we determined that 
      there was a statistically significant difference in ChatGPT's use of external 
      information (P < .05). CONCLUSIONS: ChatGPT is a versatile tool that has the 
      potential to impact resident education by providing general knowledge, clarifying 
      information, providing case-based learning, and promoting evidence-based 
      medicine. With advancements in LLM and artificial intelligence (AI), it is 
      possible that ChatGPT may be an impactful tool for resident education within 
      Plastic Surgery.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of The 
      Aesthetic Society. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Gupta, Rohun
AU  - Gupta R
AUID- ORCID: 0000-0003-1491-2441
FAU - Herzog, Isabel
AU  - Herzog I
AUID- ORCID: 0000-0001-7491-5746
FAU - Park, John B
AU  - Park JB
FAU - Weisberger, Joseph
AU  - Weisberger J
FAU - Firouzbakht, Peter
AU  - Firouzbakht P
FAU - Ocon, Vanessa
AU  - Ocon V
FAU - Chao, John
AU  - Chao J
FAU - Lee, Edward S
AU  - Lee ES
FAU - Mailey, Brian A
AU  - Mailey BA
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CIN - Aesthet Surg J. 2023 Jun 02;:. PMID: 37265091
EIN - Aesthet Surg J. 2023 Sep 29;:. PMID: 37772448
EDAT- 2023/05/02 06:42
MHDA- 2023/05/02 06:42
CRDT- 2023/05/02 03:43
PHST- 2023/05/02 06:42 [pubmed]
PHST- 2023/05/02 06:42 [medline]
PHST- 2023/05/02 03:43 [entrez]
AID - 7147614 [pii]
AID - 10.1093/asj/sjad128 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Nov 16;43(12):NP1078-NP1082. doi: 10.1093/asj/sjad128.

PMID- 37255525
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230918
IS  - 2382-1205 (Print)
IS  - 2382-1205 (Electronic)
IS  - 2382-1205 (Linking)
VI  - 10
DP  - 2023 Jan-Dec
TI  - Practical Applications of ChatGPT in Undergraduate Medical Education.
PG  - 23821205231178449
LID - 10.1177/23821205231178449 [doi]
LID - 23821205231178449
AB  - ChatGPT is a chatbot developed by OpenAI that has garnered significant attention 
      for achieving at or near a passing standard on the United States Medical 
      Licensing Exam (USMLE). Currently, researchers and users are exploring ChatGPT's 
      broad range of potential applications in academia, business, programming, and 
      beyond. We attempt outline how ChatGPT may be applied to support undergraduate 
      medical education during the preclinical and clinical years, and highlight 
      possible concerns regarding its use which necessitates the creation of formal 
      policies and training by medical schools.
CI  - © The Author(s) 2023.
FAU - Tsang, Ricky
AU  - Tsang R
AD  - Department of Medicine, Faculty of Medicine, University of British Columbia, 
      Vancouver, BC, Canada. RINGGOLD: 12358
LA  - eng
PT  - Journal Article
DEP - 20230524
PL  - United States
TA  - J Med Educ Curric Dev
JT  - Journal of medical education and curricular development
JID - 101690298
PMC - PMC10226299
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Medical education
OT  - UGME
OT  - chatbot
COIS- The author declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/05/31 13:11
MHDA- 2023/05/31 13:12
CRDT- 2023/05/31 10:23
PHST- 2023/03/24 00:00 [received]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/05/31 13:11 [pubmed]
PHST- 2023/05/31 13:12 [medline]
PHST- 2023/05/31 10:23 [entrez]
AID - 10.1177_23821205231178449 [pii]
AID - 10.1177/23821205231178449 [doi]
PST - epublish
SO  - J Med Educ Curric Dev. 2023 May 24;10:23821205231178449. doi: 
      10.1177/23821205231178449. eCollection 2023 Jan-Dec.

PMID- 37490317
OWN - NLM
STAT- MEDLINE
DCOM- 20230731
LR  - 20230811
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Jul 25
TI  - ChatGPT vs Google for Queries Related to Dementia and Other Cognitive Decline: 
      Comparison of Results.
PG  - e48966
LID - 10.2196/48966 [doi]
LID - e48966
AB  - BACKGROUND: People living with dementia or other cognitive decline and their 
      caregivers (PLWD) increasingly rely on the web to find information about their 
      condition and available resources and services. The recent advancements in large 
      language models (LLMs), such as ChatGPT, provide a new alternative to the more 
      traditional web search engines, such as Google. OBJECTIVE: This study compared 
      the quality of the results of ChatGPT and Google for a collection of PLWD-related 
      queries. METHODS: A set of 30 informational and 30 service delivery 
      (transactional) PLWD-related queries were selected and submitted to both Google 
      and ChatGPT. Three domain experts assessed the results for their currency of 
      information, reliability of the source, objectivity, relevance to the query, and 
      similarity of their response. The readability of the results was also analyzed. 
      Interrater reliability coefficients were calculated for all outcomes. RESULTS: 
      Google had superior currency and higher reliability. ChatGPT results were 
      evaluated as more objective. ChatGPT had a significantly higher response 
      relevance, while Google often drew upon sources that were referral services for 
      dementia care or service providers themselves. The readability was low for both 
      platforms, especially for ChatGPT (mean grade level 12.17, SD 1.94) compared to 
      Google (mean grade level 9.86, SD 3.47). The similarity between the content of 
      ChatGPT and Google responses was rated as high for 13 (21.7%) responses, medium 
      for 16 (26.7%) responses, and low for 31 (51.6%) responses. CONCLUSIONS: Both 
      Google and ChatGPT have strengths and weaknesses. ChatGPT rarely includes the 
      source of a result. Google more often provides a date for and a known reliable 
      source of the response compared to ChatGPT, whereas ChatGPT supplies more 
      relevant responses to queries. The results of ChatGPT may be out of date and 
      often do not specify a validity time stamp. Google sometimes returns results 
      based on commercial entities. The readability scores for both indicate that 
      responses are often not appropriate for persons with low health literacy skills. 
      In the future, the addition of both the source and the date of health-related 
      information and availability in other languages may increase the value of these 
      platforms for both nonmedical and medical professionals.
CI  - ©Vagelis Hristidis, Nicole Ruggiano, Ellen L Brown, Sai Rithesh Reddy Ganta, 
      Selena Stewart. Originally published in the Journal of Medical Internet Research 
      (https://www.jmir.org), 25.07.2023.
FAU - Hristidis, Vagelis
AU  - Hristidis V
AUID- ORCID: 0000-0001-8679-4988
AD  - Department of Computer Science and Engineering, University of California, 
      Riverside, Riverside, CA, United States.
FAU - Ruggiano, Nicole
AU  - Ruggiano N
AUID- ORCID: 0000-0002-2398-7077
AD  - School of Social Work, University of Alabama, Tuscaloosa, AL, United States.
FAU - Brown, Ellen L
AU  - Brown EL
AUID- ORCID: 0000-0002-2418-3257
AD  - Nicole Wertheim College of Nursing and Health Sciences, Florida International 
      University, Miami, FL, United States.
FAU - Ganta, Sai Rithesh Reddy
AU  - Ganta SRR
AUID- ORCID: 0009-0006-3445-7561
AD  - Department of Computer Science and Engineering, University of California, 
      Riverside, Riverside, CA, United States.
FAU - Stewart, Selena
AU  - Stewart S
AUID- ORCID: 0009-0009-0060-2865
AD  - School of Social Work, University of Alabama, Tuscaloosa, AL, United States.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230725
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Cognitive Dysfunction
MH  - *Dementia
MH  - Language
MH  - Reproducibility of Results
MH  - Search Engine
MH  - *Artificial Intelligence
MH  - Geriatrics
PMC - PMC10410383
OTO - NOTNLM
OT  - ChatGPT
OT  - Google
OT  - aging
OT  - chatbots
OT  - cognition
OT  - cognitive
OT  - dementia
OT  - geriatric
OT  - geriatrics
OT  - gerontology
OT  - information seeking
OT  - language model
OT  - large language models
OT  - queries
OT  - query
OT  - search
OT  - web search
COIS- Conflicts of Interest: VH is the founder of SmartBot360, which is a health care 
      chatbot company. SmartBot360 was not discussed in this paper.
EDAT- 2023/07/25 13:09
MHDA- 2023/07/26 06:42
CRDT- 2023/07/25 11:52
PHST- 2023/05/15 00:00 [received]
PHST- 2023/07/10 00:00 [accepted]
PHST- 2023/06/09 00:00 [revised]
PHST- 2023/07/26 06:42 [medline]
PHST- 2023/07/25 13:09 [pubmed]
PHST- 2023/07/25 11:52 [entrez]
AID - v25i1e48966 [pii]
AID - 10.2196/48966 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Jul 25;25:e48966. doi: 10.2196/48966.

PMID- 37335851
OWN - NLM
STAT- MEDLINE
DCOM- 20230821
LR  - 20230823
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Print)
IS  - 1067-5027 (Linking)
VI  - 30
IP  - 9
DP  - 2023 Aug 18
TI  - ChatGPT and the clinical informatics board examination: the end of unproctored 
      maintenance of certification?
PG  - 1558-1560
LID - 10.1093/jamia/ocad104 [doi]
AB  - We aimed to assess ChatGPT's performance on the Clinical Informatics Board 
      Examination and to discuss the implications of large language models (LLMs) for 
      board certification and maintenance. We tested ChatGPT using 260 multiple-choice 
      questions from Mankowitz's Clinical Informatics Board Review book, omitting 6 
      image-dependent questions. ChatGPT answered 190 (74%) of 254 eligible questions 
      correctly. While performance varied across the Clinical Informatics Core Content 
      Areas, differences were not statistically significant. ChatGPT's performance 
      raises concerns about the potential misuse in medical certification and the 
      validity of knowledge assessment exams. Since ChatGPT is able to answer 
      multiple-choice questions accurately, permitting candidates to use artificial 
      intelligence (AI) systems for exams will compromise the credibility and validity 
      of at-home assessments and undermine public trust. The advent of AI and LLMs 
      threatens to upend existing processes of board certification and maintenance and 
      necessitates new approaches to the evaluation of proficiency in medical 
      education.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      American Medical Informatics Association. All rights reserved. For permissions, 
      please email: journals.permissions@oup.com.
FAU - Kumah-Crystal, Yaa
AU  - Kumah-Crystal Y
AD  - Department of Biomedical Informatics, Pediatric Endocrinology, Vanderbilt 
      University Medical Center, Nashville, Tennessee, USA.
FAU - Mankowitz, Scott
AU  - Mankowitz S
AD  - Department of Clinical Informatics, Overlook Medical Center, Summit, New Jersey, 
      USA.
FAU - Embi, Peter
AU  - Embi P
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee, USA.
FAU - Lehmann, Christoph U
AU  - Lehmann CU
AUID- ORCID: 0000-0001-9559-4646
AD  - Clinical Informatics Center, UT Southwestern Medical Center, Dallas, Texas, USA.
LA  - eng
GR  - UL1 TR003163/TR/NCATS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
MH  - Artificial Intelligence
MH  - Certification
MH  - *Medical Informatics
MH  - *Education, Medical
MH  - Language
PMC - PMC10436139
OTO - NOTNLM
OT  - ChatGPT
OT  - Clinical Informatics Board Examination
OT  - artificial intelligence
OT  - large language models
OT  - medical education
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2023/06/19 19:12
MHDA- 2023/08/21 06:43
PMCR- 2024/06/19
CRDT- 2023/06/19 14:43
PHST- 2023/03/23 00:00 [received]
PHST- 2023/05/31 00:00 [revised]
PHST- 2023/06/08 00:00 [accepted]
PHST- 2024/06/19 00:00 [pmc-release]
PHST- 2023/08/21 06:43 [medline]
PHST- 2023/06/19 19:12 [pubmed]
PHST- 2023/06/19 14:43 [entrez]
AID - 7202064 [pii]
AID - ocad104 [pii]
AID - 10.1093/jamia/ocad104 [doi]
PST - ppublish
SO  - J Am Med Inform Assoc. 2023 Aug 18;30(9):1558-1560. doi: 10.1093/jamia/ocad104.

PMID- 37578934
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230922
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Sep 5
TI  - Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation 
      Study.
PG  - e48254
LID - 10.2196/48254 [doi]
LID - e48254
AB  - BACKGROUND: ChatGPT is a conversational large language model that has the 
      potential to revolutionize knowledge acquisition. However, the impact of this 
      technology on the quality of education is still unknown considering the risks and 
      concerns surrounding ChatGPT use. Therefore, it is necessary to assess the 
      usability and acceptability of this promising tool. As an innovative technology, 
      the intention to use ChatGPT can be studied in the context of the technology 
      acceptance model (TAM). OBJECTIVE: This study aimed to develop and validate a 
      TAM-based survey instrument called TAME-ChatGPT (Technology Acceptance Model 
      Edited to Assess ChatGPT Adoption) that could be employed to examine the 
      successful integration and use of ChatGPT in health care education. METHODS: The 
      survey tool was created based on the TAM framework. It comprised 13 items for 
      participants who heard of ChatGPT but did not use it and 23 items for 
      participants who used ChatGPT. Using a convenient sampling approach, the survey 
      link was circulated electronically among university students between February and 
      March 2023. Exploratory factor analysis (EFA) was used to assess the construct 
      validity of the survey instrument. RESULTS: The final sample comprised 458 
      respondents, the majority among them undergraduate students (n=442, 96.5%). Only 
      109 (23.8%) respondents had heard of ChatGPT prior to participation and only 55 
      (11.3%) self-reported ChatGPT use before the study. EFA analysis on the attitude 
      and usage scales showed significant Bartlett tests of sphericity scores (P<.001) 
      and adequate Kaiser-Meyer-Olkin measures (0.823 for the attitude scale and 0.702 
      for the usage scale), confirming the factorability of the correlation matrices. 
      The EFA showed that 3 constructs explained a cumulative total of 69.3% variance 
      in the attitude scale, and these subscales represented perceived risks, attitude 
      to technology/social influence, and anxiety. For the ChatGPT usage scale, EFA 
      showed that 4 constructs explained a cumulative total of 72% variance in the data 
      and comprised the perceived usefulness, perceived risks, perceived ease of use, 
      and behavior/cognitive factors. All the ChatGPT attitude and usage subscales 
      showed good reliability with Cronbach α values >.78 for all the deduced 
      subscales. CONCLUSIONS: The TAME-ChatGPT demonstrated good reliability, validity, 
      and usefulness in assessing health care students' attitudes toward ChatGPT. The 
      findings highlighted the importance of considering risk perceptions, usefulness, 
      ease of use, attitudes toward technology, and behavioral factors when adopting 
      ChatGPT as a tool in health care education. This information can aid the 
      stakeholders in creating strategies to support the optimal and ethical use of 
      ChatGPT and to identify the potential challenges hindering its successful 
      implementation. Future research is recommended to guide the effective adoption of 
      ChatGPT in health care education.
CI  - ©Malik Sallam, Nesreen A Salim, Muna Barakat, Kholoud Al-Mahzoum, Ala'a B 
      Al-Tammemi, Diana Malaeb, Rabih Hallit, Souheil Hallit. Originally published in 
      JMIR Medical Education (https://mededu.jmir.org), 05.09.2023.
FAU - Sallam, Malik
AU  - Sallam M
AUID- ORCID: 0000-0002-0165-9670
AD  - Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, 
      The University of Jordan, Amman, Jordan.
AD  - Department of Clinical Laboratories and Forensic Medicine, Jordan University 
      Hospital, Amman, Jordan.
FAU - Salim, Nesreen A
AU  - Salim NA
AUID- ORCID: 0000-0002-5355-2269
AD  - Prosthodontic Department, School of Dentistry, The University of Jordan, Amman, 
      Jordan.
AD  - Prosthodontic Department, Jordan University Hospital, Amman, Jordan.
FAU - Barakat, Muna
AU  - Barakat M
AUID- ORCID: 0000-0002-7966-1172
AD  - Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied 
      Science Private University, Amman, Jordan.
AD  - Middle East University Research Unit, Middle East University, Amman, Jordan.
FAU - Al-Mahzoum, Kholoud
AU  - Al-Mahzoum K
AUID- ORCID: 0009-0002-0143-7645
AD  - Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, 
      The University of Jordan, Amman, Jordan.
FAU - Al-Tammemi, Ala'a B
AU  - Al-Tammemi AB
AUID- ORCID: 0000-0003-0862-0186
AD  - Migration Health Division, International Organization for Migration, The United 
      Nations Migration Agency, Amman, Jordan.
FAU - Malaeb, Diana
AU  - Malaeb D
AUID- ORCID: 0000-0002-2436-850X
AD  - College of Pharmacy, Gulf Medical University, Ajman, United Arab Emirates.
FAU - Hallit, Rabih
AU  - Hallit R
AUID- ORCID: 0000-0003-1817-6146
AD  - School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, 
      Jounieh, Lebanon.
AD  - Department of Infectious Disease, Bellevue Medical Center, Mansourieh, Lebanon.
AD  - Department of Infectious Disease, Notre Dame des Secours, University Hospital 
      Center, Byblos, Lebanon.
FAU - Hallit, Souheil
AU  - Hallit S
AUID- ORCID: 0000-0001-6918-5689
AD  - School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, 
      Jounieh, Lebanon.
AD  - Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon.
LA  - eng
PT  - Journal Article
DEP - 20230905
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10509747
OTO - NOTNLM
OT  - KAP
OT  - artificial intelligence
OT  - education
OT  - healthcare
OT  - knowledge
OT  - machine learning
OT  - opinion
OT  - practices
OT  - survey
OT  - technology
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/14 18:42
MHDA- 2023/08/14 18:43
CRDT- 2023/08/14 13:13
PHST- 2023/04/17 00:00 [received]
PHST- 2023/08/14 00:00 [accepted]
PHST- 2023/07/25 00:00 [revised]
PHST- 2023/08/14 18:43 [medline]
PHST- 2023/08/14 18:42 [pubmed]
PHST- 2023/08/14 13:13 [entrez]
AID - v9i1e48254 [pii]
AID - 10.2196/48254 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Sep 5;9:e48254. doi: 10.2196/48254.

PMID- 37160568
OWN - NLM
STAT- MEDLINE
DCOM- 20230511
LR  - 20230511
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Linking)
VI  - 47
IP  - 1
DP  - 2023 May 9
TI  - Revolutionizing Medical Education: Can ChatGPT Boost Subjective Learning and 
      Expression?
PG  - 61
LID - 10.1007/s10916-023-01957-w [doi]
AB  - ChatGPT is an AI tool that can be used to enhance medical education by helping 
      students develop subjective learning and expression skills. These skills are 
      critical in clinical practice, but the current medical education system is 
      heavily focused on objective assessments, such as Multiple Choice Questions 
      (MCQs). Students from non-English speaking backgrounds can particularly struggle 
      with expressing themselves in English, which is the primary language of 
      instruction in many medical schools worldwide. ChatGPT can provide additional 
      language support for these students to help them develop their language skills 
      and communicate effectively. ChatGPT can be used in small group assessments to 
      serve as a benchmark for students to strive for in their medical education. By 
      comparing their answers to ChatGPT's responses, students can identify gaps in 
      their knowledge and work to fill them. ChatGPT can also provide students with 
      feedback on their writing style and language usage, helping them to improve their 
      subjective expression of medical knowledge. Furthermore, ChatGPT can be used to 
      simulate patient encounters for medical students. By interacting with ChatGPT, 
      students can practice taking medical histories and documenting symptoms 
      accurately. In continuing medical education (CME) programs, physicians can also 
      benefit from ChatGPT's capabilities. By using ChatGPT to search for the latest 
      research articles, clinical trials, and treatment guidelines, physicians can stay 
      informed and provide the best care possible to their patients. Overall, ChatGPT 
      has the potential to be a valuable tool in medical education by helping students 
      and physicians develop the essential skills required for clinical practice, such 
      as communication, problem-solving, and critical thinking.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Seetharaman, Rajmohan
AU  - Seetharaman R
AUID- ORCID: 0000-0002-4605-2805
AD  - Department of Pharmacology and Therapeutics, Seth G.S. Medical College & KEM 
      Hospital, Parel, Mumbai, 400012, India. rajmohan.seetharaman@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230509
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - Humans
MH  - *Learning
MH  - *Students
MH  - Cognition
MH  - Education, Medical, Continuing
MH  - Benchmarking
EDAT- 2023/05/10 00:41
MHDA- 2023/05/11 06:42
CRDT- 2023/05/09 22:50
PHST- 2023/04/11 00:00 [received]
PHST- 2023/04/20 00:00 [accepted]
PHST- 2023/05/11 06:42 [medline]
PHST- 2023/05/10 00:41 [pubmed]
PHST- 2023/05/09 22:50 [entrez]
AID - 10.1007/s10916-023-01957-w [pii]
AID - 10.1007/s10916-023-01957-w [doi]
PST - epublish
SO  - J Med Syst. 2023 May 9;47(1):61. doi: 10.1007/s10916-023-01957-w.

PMID- 37220429
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230525
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Capacity of ChatGPT to Identify Guideline-Based Treatments for Advanced Solid 
      Tumors.
PG  - e37938
LID - 10.7759/cureus.37938 [doi]
LID - e37938
AB  - BACKGROUND:  ChatGPT, created by OpenAI, is a large language model which has 
      become the fastest growing consumer application in history, recognized for its 
      expansive knowledge of varied subjects. The field of oncology is highly 
      specialized and requires nuanced understanding of medications and conditions. 
      Herein, we sought to better qualify the ability of ChatGPT to name applicable 
      treatments for patients with advanced solid cancers. METHODS:  This observational 
      study was conducted utilizing ChatGPT. The capacity of ChatGPT to tabulate 
      appropriate systemic therapies for new diagnoses of advanced solid malignancies 
      was ascertained through standardized prompts. A ratio of those medications listed 
      by ChatGPT to those suggested in the National Comprehensive Cancer Network (NCCN) 
      guidelines was produced and called the valid therapy quotient (VTQ). Additional 
      descriptive analyses of the VTQ and its association with incidence and type of 
      treatment were performed. RESULTS:  Some 51 distinct diagnoses were utilized 
      within this experiment. ChatGPT was able to identify 91 distinct medications in 
      response to prompts related to advanced solid tumors. The overall VTQ is 0.77. In 
      all cases, ChatGPT was able to provide at least one example of systemic therapy 
      suggested by the NCCN. There was a weak association between incidence of each 
      malignancy and the VTQ. CONCLUSION:  The capacity of ChatGPT to identify 
      medications used to treat advanced solid tumors indicates a level of concordance 
      with the NCCN guidelines. As it stands, the role of ChatGPT to assist oncologists 
      and patients in treatment decision making remains unknown. Nonetheless, in future 
      iterations, it may be anticipated that accuracy and consistency in this domain 
      will improve, and further studies will be needed to better quantify its 
      capabilities.
CI  - Copyright © 2023, Schulte et al.
FAU - Schulte, Brian
AU  - Schulte B
AD  - Medicine, University of California San Francisco, San Francisco, USA.
LA  - eng
PT  - Journal Article
DEP - 20230421
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10200252
OTO - NOTNLM
OT  - cancer
OT  - chatgpt
OT  - chemotherapy
OT  - immunotherapy
OT  - nccn guidelines
OT  - oncology
OT  - targeted therapy
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/23 19:09
MHDA- 2023/05/23 19:10
CRDT- 2023/05/23 17:13
PHST- 2023/04/20 00:00 [accepted]
PHST- 2023/05/23 19:10 [medline]
PHST- 2023/05/23 19:09 [pubmed]
PHST- 2023/05/23 17:13 [entrez]
AID - 10.7759/cureus.37938 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 21;15(4):e37938. doi: 10.7759/cureus.37938. eCollection 2023 
      Apr.

PMID- 37848769
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231130
IS  - 1432-1084 (Electronic)
IS  - 0938-7994 (Linking)
VI  - 33
IP  - 12
DP  - 2023 Dec
TI  - Letter to the Editor: A critical evaluation on the use of large language model 
      for radiology research.
PG  - 9462-9463
LID - 10.1007/s00330-023-10332-9 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Department of Computer Applications, Sikkim University, Gangtok, India. 
      ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20231018
PL  - Germany
TA  - Eur Radiol
JT  - European radiology
JID - 9114774
SB  - IM
MH  - Humans
MH  - Radiography
MH  - *Radiology
MH  - Language
EDAT- 2023/10/18 00:42
MHDA- 2023/11/27 12:44
CRDT- 2023/10/17 23:48
PHST- 2023/07/15 00:00 [received]
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/08/02 00:00 [revised]
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/10/18 00:42 [pubmed]
PHST- 2023/10/17 23:48 [entrez]
AID - 10.1007/s00330-023-10332-9 [pii]
AID - 10.1007/s00330-023-10332-9 [doi]
PST - ppublish
SO  - Eur Radiol. 2023 Dec;33(12):9462-9463. doi: 10.1007/s00330-023-10332-9. Epub 2023 
      Oct 18.

PMID- 37799027
OWN - NLM
STAT- Publisher
LR  - 20231006
IS  - 2296-6498 (Print)
IS  - 2296-6498 (Linking)
VI  - 134
IP  - 5
DP  - 2023 Oct 6
TI  - ChatGPT's performance in dentistry and allergy-immunology assessments: a 
      comparative study.
AB  - Large language models (LLMs) such as ChatGPT have potential applications in 
      healthcare, including dentistry. Priming, the practice of providing LLMs with 
      initial, relevant information, is an approach to improve their output quality. 
      This study aimed to evaluate the performance of ChatGPT 3 and ChatGPT 4 on 
      self-assessment questions for dentistry, through the Swiss Federal Licensing 
      Examination in Dental Medicine (SFLEDM), and allergy and clinical immunology, 
      through the European Examination in Allergy and Clinical Immunology (EEAACI). The 
      second objective was to assess the impact of priming on ChatGPT's performance. 
      The SFLEDM and EEAACI multiple-choice questions from the University of Bern's 
      Institute for Medical Education platform were administered to both ChatGPT 
      versions, with and without priming. Performance was analyzed based on correct 
      responses. The statistical analysis included Wilcoxon rank sum tests (α=0.05). 
      The average accuracy rates in the SFLEDM and EEAACI assessments were 63.3% and 
      79.3%, respectively. Both ChatGPT versions performed better on EEAACI than 
      SFLEDM, with ChatGPT 4 outperforming ChatGPT 3 across all tests. ChatGPT 3's 
      performance exhibited a significant improvement with priming for both EEAACI 
      (p=0.017) and SFLEDM (p=0.024) assessments. For ChatGPT 4, the priming effect was 
      significant only in the SFLEDM assessment (p=0.038). The performance disparity 
      between SFLEDM and EEAACI assessments underscores ChatGPT's varying proficiency 
      across different medical domains, likely tied to the nature and amount of 
      training data available in each field. Priming can be a tool for enhancing 
      output, especially in earlier LLMs. Advancements from ChatGPT 3 to 4 highlight 
      the rapid developments in LLM technology. Yet, their use in critical fields such 
      as healthcare must remain cautious owing to LLMs' inherent limitations and risks.
FAU - Fuchs, Alexander
AU  - Fuchs A
AD  - Department of Periodontology, Endodontology, and Cariology, University Center for 
      Dental Medicine Basel UZB, University of Basel, Basel, Switzerland.
FAU - Trachsel, Tina
AU  - Trachsel T
AD  - Division of Allergy, University Children's Hospital Basel, Basel, Switzerland.
FAU - Weiger, Roland
AU  - Weiger R
AD  - Department of Periodontology, Endodontology, and Cariology, University Center for 
      Dental Medicine Basel UZB, University of Basel, Basel, Switzerland.
FAU - Eggmann, Florin
AU  - Eggmann F
AD  - Department of Periodontology, Endodontology, and Cariology, University Center for 
      Dental Medicine Basel UZB, University of Basel, Basel, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20231006
PL  - Switzerland
TA  - Swiss Dent J
JT  - Swiss dental journal
JID - 101624119
SB  - IM
OTO - NOTNLM
OT  - allergology
OT  - artificial intelligence
OT  - clinical immunology
OT  - dental education
OT  - machine learning
OT  - medical informatics applications
EDAT- 2023/10/06 06:43
MHDA- 2023/10/06 06:43
CRDT- 2023/10/06 03:02
PHST- 2023/10/06 06:43 [medline]
PHST- 2023/10/06 06:43 [pubmed]
PHST- 2023/10/06 03:02 [entrez]
AID - sdj-2024-05-01 [pii]
PST - aheadofprint
SO  - Swiss Dent J. 2023 Oct 6;134(5).

PMID- 37085182
OWN - NLM
STAT- MEDLINE
DCOM- 20230529
LR  - 20230804
IS  - 1473-4893 (Electronic)
IS  - 1470-2118 (Linking)
VI  - 23
IP  - 3
DP  - 2023 May
TI  - Early applications of ChatGPT in medical practice, education and research.
PG  - 278-279
LID - 10.7861/clinmed.2023-0078 [doi]
AB  - ChatGPT, which can automatically generate written responses to queries using 
      internet sources, soon went viral after its release at the end of 2022. The 
      performance of ChatGPT on medical exams shows results near the passing threshold, 
      making it comparable to third-year medical students. It can also write academic 
      abstracts or reviews at an acceptable level. However, it is not clear how ChatGPT 
      deals with harmful content, misinformation or plagiarism; therefore, authors 
      using ChatGPT professionally for academic writing should be cautious. ChatGPT 
      also has the potential to facilitate the interaction between healthcare providers 
      and patients in various ways. However, sophisticated tasks such as understanding 
      the human anatomy are still a limitation of ChatGPT. ChatGPT can simplify 
      radiological reports, but the possibility of incorrect statements and missing 
      medical information remain. Although ChatGPT has the potential to change medical 
      practice, education and research, further improvements of this application are 
      needed for regular use in medicine.
CI  - © Royal College of Physicians 2023. All rights reserved.
FAU - Sedaghat, Sam
AU  - Sedaghat S
AD  - University Hospital of Heidelberg, Heidelberg, Germany samsedaghat1@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230421
PL  - England
TA  - Clin Med (Lond)
JT  - Clinical medicine (London, England)
JID - 101092853
SB  - IM
CIN - Clin Med (Lond). 2023 Jul;23(4):429-430. PMID: 37524422
MH  - Humans
MH  - Educational Status
MH  - Health Personnel
MH  - Internet
MH  - *Medicine
MH  - *Students, Medical
OTO - NOTNLM
OT  - ChatGPT
OT  - academic writing
OT  - artificial intelligence
OT  - healthcare
OT  - medical education
EDAT- 2023/04/22 10:42
MHDA- 2023/05/29 06:42
CRDT- 2023/04/21 20:52
PHST- 2023/05/29 06:42 [medline]
PHST- 2023/04/22 10:42 [pubmed]
PHST- 2023/04/21 20:52 [entrez]
AID - clinmed.2023-0078 [pii]
AID - 10.7861/clinmed.2023-0078 [doi]
PST - ppublish
SO  - Clin Med (Lond). 2023 May;23(3):278-279. doi: 10.7861/clinmed.2023-0078. Epub 
      2023 Apr 21.

PMID- 38022092
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT's Epoch in Rheumatological Diagnostics: A Critical Assessment in the 
      Context of Sjögren's Syndrome.
PG  - e47754
LID - 10.7759/cureus.47754 [doi]
LID - e47754
AB  - INTRODUCTION: The rise of artificial intelligence in medical practice is 
      reshaping clinical care. Large language models (LLMs) like ChatGPT have the 
      potential to assist in rheumatology by personalizing scientific information 
      retrieval, particularly in the context of Sjögren's Syndrome. This study aimed to 
      evaluate the efficacy of ChatGPT in providing insights into Sjögren's Syndrome, 
      differentiating it from other rheumatological conditions. MATERIALS AND METHODS: 
       A database of peer-reviewed articles and clinical guidelines focused on 
      Sjögren's Syndrome was compiled. Clinically relevant questions were presented to 
      ChatGPT, with responses assessed for accuracy, relevance, and comprehensiveness. 
      Techniques such as blinding, random control queries, and temporal analysis 
      ensured unbiased evaluation. ChatGPT's responses were also assessed using the 
      15-questionnaire DISCERN tool. RESULTS:  ChatGPT effectively highlighted key 
      immunopathological and histopathological characteristics of Sjögren's Syndrome, 
      though some crucial data and citation inconsistencies were noted. For a given 
      clinical vignette, ChatGPT correctly identified potential etiological 
      considerations with Sjögren's Syndrome being prominent. DISCUSSION:  LLMs like 
      ChatGPT offer rapid access to vast amounts of data, beneficial for both patients 
      and providers. While it democratizes information, limitations like potential 
      oversimplification and reference inaccuracies were observed. The balance between 
      LLM insights and clinical judgment, as well as continuous model refinement, is 
      crucial. CONCLUSION:  LLMs like ChatGPT offer significant potential in 
      rheumatology, providing swift and broad medical insights. However, a cautious 
      approach is vital, ensuring rigorous training and ethical application for optimal 
      patient care and clinical practice.
CI  - Copyright © 2023, Irfan et al.
FAU - Irfan, Bilal
AU  - Irfan B
AD  - Microbiology and Immunology, University of Michigan, Ann Arbor, USA.
FAU - Yaqoob, Aneela
AU  - Yaqoob A
AD  - Infectious Diseases, Beaumont Hospital, Taylor, USA.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10676288
OTO - NOTNLM
OT  - artificial intelligence (ai)
OT  - chatgpt
OT  - chatgpt-4
OT  - large language models (llms)
OT  - sjögren’s syndrome
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:17
PHST- 2023/10/26 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:17 [entrez]
AID - 10.7759/cureus.47754 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 26;15(10):e47754. doi: 10.7759/cureus.47754. eCollection 2023 
      Oct.

PMID- 38037955
OWN - NLM
STAT- Publisher
LR  - 20231201
IS  - 1478-7083 (Electronic)
IS  - 0035-8843 (Linking)
DP  - 2023 Dec 1
TI  - Performance of large language models at the MRCS Part A: a tool for medical 
      education?
LID - 10.1308/rcsann.2023.0085 [doi]
AB  - INTRODUCTION: The Intercollegiate Membership of the Royal College of Surgeons 
      examination (MRCS) Part A assesses generic surgical sciences and applied 
      knowledge using 300 multiple-choice Single Best Answer items. Large Language 
      Models (LLMs) are trained on vast amounts of text to generate natural language 
      outputs, and applications in healthcare and medical education are rising. 
      METHODS: Two LLMs, ChatGPT (OpenAI) and Bard (Google AI), were tested using 300 
      questions from a popular MRCS Part A question bank without/with need for 
      justification (NJ/J). LLM outputs were scored according to accuracy, concordance 
      and insight. RESULTS: ChatGPT achieved 85.7%/84.3% accuracy for NJ/J encodings. 
      Bard achieved 64%/64.3% accuracy for NJ/J encodings. ChatGPT and Bard displayed 
      high levels of concordance for NJ (95.3%; 81.7%) and J (93.7%; 79.7%) encodings, 
      respectively. ChatGPT and Bard provided an insightful statement in >98% and >86% 
      outputs, respectively. DISCUSSION: This study demonstrates that ChatGPT achieves 
      passing-level accuracy at MRCS Part A, and both LLMs achieve high concordance and 
      provide insightful responses to test questions. Instances of clinically 
      inappropriate or inaccurate decision-making, incomplete appreciation of nuanced 
      clinical scenarios and utilisation of out-of-date guidance was, however, noted. 
      LLMs are accessible and time-efficient tools, access vast clinical knowledge, and 
      may reduce the emphasis on factual recall in medical education and assessment. 
      CONCLUSION: ChatGPT achieves passing-level accuracy for MRCS Part A with 
      concordant and insightful outputs. Future applications of LLMs in healthcare must 
      be cautious of hallucinations and incorrect reasoning but have the potential to 
      develop AI-supported clinicians.
FAU - Yiu, A
AU  - Yiu A
AD  - King's College Hospital NHS Foundation Trust, UK.
AD  - Imperial College London, UK.
FAU - Lam, K
AU  - Lam K
AD  - Imperial College London, UK.
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - England
TA  - Ann R Coll Surg Engl
JT  - Annals of the Royal College of Surgeons of England
JID - 7506860
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - Education
OT  - Examination
OT  - Large language model
OT  - MRCS
EDAT- 2023/12/01 12:41
MHDA- 2023/12/01 12:41
CRDT- 2023/12/01 06:34
PHST- 2023/12/01 12:41 [medline]
PHST- 2023/12/01 12:41 [pubmed]
PHST- 2023/12/01 06:34 [entrez]
AID - 10.1308/rcsann.2023.0085 [doi]
PST - aheadofprint
SO  - Ann R Coll Surg Engl. 2023 Dec 1. doi: 10.1308/rcsann.2023.0085.

PMID- 37862879
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1532-818X (Electronic)
IS  - 0196-0709 (Linking)
VI  - 45
IP  - 1
DP  - 2023 Oct 14
TI  - Performance of ChatGPT in Otolaryngology knowledge assessment.
PG  - 104082
LID - S0196-0709(23)00296-X [pii]
LID - 10.1016/j.amjoto.2023.104082 [doi]
FAU - Revercomb, Lucy
AU  - Revercomb L
AD  - Department of Otolaryngology - Head and Neck Surgery, Rutgers New Jersey Medical 
      School, Newark, NJ, USA.
FAU - Patel, Aman M
AU  - Patel AM
AD  - Department of Otolaryngology - Head and Neck Surgery, Rutgers New Jersey Medical 
      School, Newark, NJ, USA.
FAU - Choudhry, Hassaam S
AU  - Choudhry HS
AD  - Department of Otolaryngology - Head and Neck Surgery, Rutgers New Jersey Medical 
      School, Newark, NJ, USA.
FAU - Filimonov, Andrey
AU  - Filimonov A
AD  - Department of Otolaryngology - Head and Neck Surgery, Rutgers New Jersey Medical 
      School, Newark, NJ, USA. Electronic address: andreyf@njms.rutgers.edu.
LA  - eng
PT  - Letter
DEP - 20231014
PL  - United States
TA  - Am J Otolaryngol
JT  - American journal of otolaryngology
JID - 8000029
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Board certification examination
OT  - ChatGPT
OT  - Medical education
COIS- Declaration of competing interest None.
EDAT- 2023/10/21 05:42
MHDA- 2023/10/21 05:42
CRDT- 2023/10/20 18:07
PHST- 2023/08/30 00:00 [received]
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/10/21 05:42 [medline]
PHST- 2023/10/21 05:42 [pubmed]
PHST- 2023/10/20 18:07 [entrez]
AID - S0196-0709(23)00296-X [pii]
AID - 10.1016/j.amjoto.2023.104082 [doi]
PST - aheadofprint
SO  - Am J Otolaryngol. 2023 Oct 14;45(1):104082. doi: 10.1016/j.amjoto.2023.104082.

PMID- 37204545
OWN - NLM
STAT- MEDLINE
DCOM- 20230926
LR  - 20230926
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 10
DP  - 2023 Oct
TI  - AI Tackles Pandemics: ChatGPT's Game-Changing Impact on Infectious Disease 
      Control.
PG  - 2097-2099
LID - 10.1007/s10439-023-03239-5 [doi]
AB  - Unleashing the power of artificial intelligence (AI), this letter delves into the 
      potential applications of ChatGPT, a cutting-edge language model, in the 
      management and control of infectious diseases. By examining ChatGPT's 
      contributions to information dissemination, diagnosis, treatment, and research, 
      the article underscores its revolutionary impact on the field while acknowledging 
      existing limitations and envisioning future advancements for optimized medical 
      applications.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India. ppray@cus.ac.in.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, Kolkata, India.
LA  - eng
PT  - Letter
DEP - 20230518
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Artificial Intelligence
MH  - *Pandemics
MH  - Information Dissemination
PMC - PMC10196282
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Disease management
OT  - Healthcare
OT  - Infectious diseases
OT  - Natural language processing
COIS- No benefits in any form have been or will be received from a commercial party 
      related directly or indirectly to the subject of this manuscript. The author 
      declare no conflict of interest.
EDAT- 2023/05/19 13:05
MHDA- 2023/09/26 13:43
CRDT- 2023/05/19 11:09
PHST- 2023/05/04 00:00 [received]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/09/26 13:43 [medline]
PHST- 2023/05/19 13:05 [pubmed]
PHST- 2023/05/19 11:09 [entrez]
AID - 10.1007/s10439-023-03239-5 [pii]
AID - 3239 [pii]
AID - 10.1007/s10439-023-03239-5 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Oct;51(10):2097-2099. doi: 10.1007/s10439-023-03239-5. Epub 
      2023 May 18.

PMID- 38016664
OWN - NLM
STAT- Publisher
LR  - 20231128
IS  - 1098-8793 (Electronic)
IS  - 0736-6825 (Linking)
DP  - 2023 Nov 28
TI  - Effectiveness of ChatGPT to Identify and Accurately Guide Patients in Rhinoplasty 
      Complications.
LID - 10.1055/a-2218-6984 [doi]
AB  - INTRODUCTION: Postoperative complications in rhinoplasty require prompt 
      intervention for optimal outcomes. ChatGPT, an AI tool, offers potential for 
      assisting in postoperative care. OBJECTIVE: This study aims to assess ChatGPT's 
      effectiveness in providing guidance for various rhinoplasty complications. 
      METHODS: Different complication scenarios were inputted into ChatGPT. Responses 
      were categorized into "Contact Surgeon" or "Follow Postoperative Instructions." 
      RESULTS: ChatGPT consistently advised immediate surgeon contact for infection. 
      For other complications, it recommended monitoring and adhering to instructions, 
      while suggesting surgeon contact if concerns persisted. CONCLUSION: ChatGPT shows 
      promise in aiding patients' postoperative care by accurately identifying cases 
      necessitating communication with surgeons or emergency care. This research 
      underscores AI's potential in enhancing patient-centered care and contributes to 
      the evolving landscape of healthcare practices.
CI  - Thieme. All rights reserved.
FAU - Soto-Galindo, Germán Armando
AU  - Soto-Galindo GA
AD  - Department of Otolaryngology Head and Neck Surgery, Ege University Faculty of 
      Medicine, Izmir, Turkey.
FAU - Capelleras, Marta
AU  - Capelleras M
AD  - OTORHINOLARYNGOLOGY, Ege University, Barcelona, Spain.
FAU - Cruellas, Marc
AU  - Cruellas M
AD  - OTORHINOLARYNGOLOGY, Hospital Universitari de Bellvitge, L'Hospitalet de 
      Llobregat, Spain.
FAU - Apaydin, Fazil
AU  - Apaydin F
AD  - OTORHINOLARYNGOLOGY, EGE UNIVERSITY, IZMIR, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - United States
TA  - Facial Plast Surg
JT  - Facial plastic surgery : FPS
JID - 8405303
COIS- The authors declare that they have no conflict of interest.
EDAT- 2023/11/29 00:42
MHDA- 2023/11/29 00:42
CRDT- 2023/11/28 19:23
PHST- 2023/11/29 00:42 [medline]
PHST- 2023/11/29 00:42 [pubmed]
PHST- 2023/11/28 19:23 [entrez]
AID - 10.1055/a-2218-6984 [doi]
PST - aheadofprint
SO  - Facial Plast Surg. 2023 Nov 28. doi: 10.1055/a-2218-6984.

PMID- 37264670
OWN - NLM
STAT- Publisher
LR  - 20230602
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Jun 2
TI  - Performance of ChatGPT on dermatology Specialty Certificate Examination multiple 
      choice questions.
LID - llad197 [pii]
LID - 10.1093/ced/llad197 [doi]
AB  - ChatGPT is a large language model trained on increasingly large datasets by 
      OpenAI to perform language-based tasks. It is capable of answering 
      multiple-choice questions, such as those posed by the dermatology SCE 
      examination. We asked two iterations of ChatGPT: ChatGPT-3.5 and ChatGPT-4 84 
      multiple-choice sample questions from the sample dermatology SCE question bank. 
      ChatGPT-3.5 achieved an overall score of 63.1%, and ChatGPT-4 scored 90.5% (a 
      significant improvement in performance (p<0.001)). The typical pass mark for the 
      dermatology SCE is 70-72%. ChatGPT-4 is therefore capable of answering clinical 
      questions and achieving a passing grade in these sample questions. There are many 
      possible educational and clinical implications for increasingly advanced 
      artificial intelligence (AI) and its use in medicine, including in the diagnosis 
      of dermatological conditions. Such advances should be embraced provided that 
      patient safety is a core tenet, and the limitations of AI in the nuances of 
      complex clinical cases are recognised.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of British 
      Association of Dermatologists. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Passby, Lauren
AU  - Passby L
AD  - Department of Dermatology, Solihull Hospital, University Hospitals Birmingham NHS 
      Foundation Trust, UK.
FAU - Jenko, Nathan
AU  - Jenko N
AD  - Department of Radiology, The Royal Orthopaedic Hospital NHS Foundation Trust, UK.
FAU - Wernham, Aaron
AU  - Wernham A
AUID- ORCID: 0000-0001-5920-6888
AD  - Department of Dermatology, Leicester Royal Infirmary, Leicester, UK.
AD  - Department of Dermatology, Manor Hospital, Walsall Healthcare NHS Trust, Walsall, 
      UK.
LA  - eng
PT  - Journal Article
DEP - 20230602
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
EDAT- 2023/06/02 06:42
MHDA- 2023/06/02 06:42
CRDT- 2023/06/02 03:44
PHST- 2023/05/25 00:00 [received]
PHST- 2023/05/26 00:00 [accepted]
PHST- 2023/06/02 06:42 [medline]
PHST- 2023/06/02 06:42 [pubmed]
PHST- 2023/06/02 03:44 [entrez]
AID - 7188526 [pii]
AID - 10.1093/ced/llad197 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Jun 2:llad197. doi: 10.1093/ced/llad197.

PMID- 37615142
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 0970-258X (Print)
IS  - 0970-258X (Linking)
VI  - 36
IP  - 1
DP  - 2023 Jan-Feb
TI  - Chatbots, ChatGPT, and Scholarly Manuscripts: WAME Recommendations on ChatGPT and 
      Chatbots in relation to scholarly publications.
PG  - 1-4
LID - 10.25259/NMJI_365_23 [pii]
FAU - Zielinski, Chris
AU  - Zielinski C
AD  - Vice President, WAME; Centre for Global Health, University of Winchester, UK.
FAU - Winker, Margaret
AU  - Winker M
AD  - President, WAME; Associate Editor, Journal of Gastroenterology and Hepatology; 
      Director, Jawaharlal Institute of Postgraduate Medical Education and Research, 
      Puducherry, India.
FAU - Aggarwal, Rakesh
AU  - Aggarwal R
AD  - President, WAME; Associate Editor, Journal of Gastroenterology and Hepatology; 
      Director, Jawaharlal Institute of Postgraduate Medical Education and Research, 
      Puducherry, India.
FAU - Ferris, Lorraine
AU  - Ferris L
AD  - WAME; Dalla Lana School of Public Health, University of Toronto, Canada.
FAU - Heinemann, Markus
AU  - Heinemann M
AD  - WAME; The Thoracic and Cardiovascular Surgeon, Manila, The Philippines.
FAU - Lapeña, Jose Florencio
AU  - Lapeña JF
AD  - WAME; Philippine Journal of Otolaryngology Head & Neck Surgery University of the 
      Philippines, Manila, The Philippines.
FAU - Pai, Sanjay
AU  - Pai S
AD  - WAME; The National Medical Journal of India, India.
FAU - Ing, Edsel
AU  - Ing E
AD  - WAME; Canadian Journal of Ophthalmology; University of Toronto, Canada.
FAU - Citrome, Leslie
AU  - Citrome L
AD  - WAME; Current Medical Research and Opinion; Psychiatry for Clinical Therapeutics; 
      Psychiatry and Behavioral Sciences New York Medical College, USA on behalf of the 
      WAME Board.
LA  - eng
PT  - Editorial
PL  - India
TA  - Natl Med J India
JT  - The National medical journal of India
JID - 8809315
SB  - IM
EDAT- 2023/08/24 13:43
MHDA- 2023/08/24 13:44
CRDT- 2023/08/24 06:07
PHST- 2023/08/24 13:44 [medline]
PHST- 2023/08/24 13:43 [pubmed]
PHST- 2023/08/24 06:07 [entrez]
AID - 10.25259/NMJI_365_23 [pii]
AID - 10.25259/NMJI_365_23 [doi]
PST - ppublish
SO  - Natl Med J India. 2023 Jan-Feb;36(1):1-4. doi: 10.25259/NMJI_365_23.

PMID- 37023599
OWN - NLM
STAT- MEDLINE
DCOM- 20230502
LR  - 20231130
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 80
DP  - 2023 May
TI  - Utilization of ChatGPT for Plastic Surgery Research: Friend or Foe?
PG  - 145-147
LID - S1748-6815(23)00136-5 [pii]
LID - 10.1016/j.bjps.2023.03.004 [doi]
AB  - On November 20, 2022, ChatGPT was made available to the general public free of 
      charge. As a large language model (LLM), the software was able to process 
      inquiries by users and generate text based on compiled datasets in a humanist 
      manner. Due to the importance of research in the Plastic Surgery community, we 
      set out to determine if ChatGPT could be utilized to produce novel systematic 
      review ideas relevant to Plastic Surgery. Out of 80 systematic review ideas 
      generated by ChatGPT, we found that the software was highly accurate in creating 
      novel systematic review ideas. Beyond aiding in Plastic Surgery research, ChatGPT 
      has the potential to be used for virtual consultations, pre-operative planning, 
      patient education, and post-operative care for patients. ChatGPT may be a simple 
      solution for the complex problems encountered in Plastic Surgery.
CI  - Copyright © 2023 British Association of Plastic, Reconstructive and Aesthetic 
      Surgeons. Published by Elsevier Ltd. All rights reserved.
FAU - Gupta, Rohun
AU  - Gupta R
AD  - Oakland University William Beaumont School of Medicine, Rochester, MI, USA. 
      Electronic address: rgupta@oakland.edu.
FAU - Herzog, Isabel
AU  - Herzog I
AD  - Department of Plastic Surgery, Rutgers New Jersey School of Medicine, Newark, NJ, 
      USA.
FAU - Weisberger, Joseph
AU  - Weisberger J
AD  - Department of Plastic Surgery, Rutgers New Jersey School of Medicine, Newark, NJ, 
      USA.
FAU - Chao, John
AU  - Chao J
AD  - Department of Plastic Surgery, Rutgers New Jersey School of Medicine, Newark, NJ, 
      USA.
FAU - Chaiyasate, Kongkrit
AU  - Chaiyasate K
AD  - Oakland University William Beaumont School of Medicine, Rochester, MI, USA.
FAU - Lee, Edward S
AU  - Lee ES
AD  - Department of Plastic Surgery, Rutgers New Jersey School of Medicine, Newark, NJ, 
      USA.
LA  - eng
PT  - Journal Article
PT  - Systematic Review
DEP - 20230321
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
CIN - J Plast Reconstr Aesthet Surg. 2023 Jul;82:275. PMID: 37209600
CIN - Aesthet Surg J. 2023 Aug 17;43(9):NP726-NP727. PMID: 37227006
MH  - Humans
MH  - Language
MH  - *Plastic Surgery Procedures
MH  - Postoperative Care
MH  - Referral and Consultation
MH  - *Surgery, Plastic
OTO - NOTNLM
OT  - AI
OT  - Artificial intelligence
OT  - Education
OT  - Systematic reviews
EDAT- 2023/04/07 06:00
MHDA- 2023/05/02 06:42
CRDT- 2023/04/06 18:06
PHST- 2023/02/22 00:00 [received]
PHST- 2023/02/24 00:00 [revised]
PHST- 2023/03/15 00:00 [accepted]
PHST- 2023/05/02 06:42 [medline]
PHST- 2023/04/07 06:00 [pubmed]
PHST- 2023/04/06 18:06 [entrez]
AID - S1748-6815(23)00136-5 [pii]
AID - 10.1016/j.bjps.2023.03.004 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 May;80:145-147. doi: 
      10.1016/j.bjps.2023.03.004. Epub 2023 Mar 21.

PMID- 37518931
OWN - NLM
STAT- MEDLINE
DCOM- 20230801
LR  - 20230801
IS  - 0300-5283 (Print)
IS  - 0300-5283 (Linking)
VI  - 78
IP  - 4
DP  - 2023 Jul
TI  - A Tête-à-tête with ChatGPT on the impact of artificial intelligence in medical 
      education.
PG  - 547-549
AB  - Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial intelligence 
      (AI) language model developed by OpenAI. It is trained to process vast amounts of 
      text and engage in human-like conversational interaction with users. Being 
      accessible by all, it is widely used and its capabilities range from language 
      translation, summarising long texts and creative writing. This article explores 
      the potential role of ChatGPT in medical education and the possible concerns 
      about the misuse of this technology through a conversation with ChatGPT itself 
      via text prompts. The implications of this technology in medical education as 
      told by ChatGPT are interesting and seemingly helpful for both the students and 
      the tutors. However, this could be a double-edged sword considering the risks of 
      compromised students' integrity and concerns of over-reliance. This also calls 
      for counter strategies and policies in place to mitigate these risks.
FAU - Vignesh, R
AU  - Vignesh R
AD  - Universiti Kuala Lumpur, Royal College of Medicine Perak, Faculty of Medicine, 
      Preclinical Department, Ipoh, Malaysia. vignesh@unikl.edu.my.
FAU - Pradeep, P
AU  - Pradeep P
AD  - Universiti Kuala Lumpur, Royal College of Medicine Perak, Faculty of Medicine, 
      Preclinical Department, Ipoh, Malaysia.
FAU - Balakrishnan, P
AU  - Balakrishnan P
AD  - Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental 
      College and Hospitals, Centre for Infectious Diseases, Department of 
      Microbiology, Chennai, India.
LA  - eng
PT  - Journal Article
PL  - Malaysia
TA  - Med J Malaysia
JT  - The Medical journal of Malaysia
JID - 0361547
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Education, Medical
MH  - Students
EDAT- 2023/07/31 06:43
MHDA- 2023/08/01 06:45
CRDT- 2023/07/31 00:12
PHST- 2023/08/01 06:45 [medline]
PHST- 2023/07/31 06:43 [pubmed]
PHST- 2023/07/31 00:12 [entrez]
PST - ppublish
SO  - Med J Malaysia. 2023 Jul;78(4):547-549.

PMID- 37460216
OWN - NLM
STAT- Publisher
LR  - 20230726
IS  - 1468-3318 (Electronic)
IS  - 0964-4563 (Linking)
DP  - 2023 Jul 17
TI  - Exploring the ChatGPT platform with scenario-specific prompts for vaping 
      cessation.
LID - tc-2023-058009 [pii]
LID - 10.1136/tc-2023-058009 [doi]
AB  - OBJECTIVE: To evaluate and start a discussion on the potential usefulness of 
      applying Artificial Intelligence (AI)-driven natural language processing 
      technology such as the ChatGPT in tobacco control efforts, specifically vaping 
      cessation. METHOD: Ten real-world questions about vaping cessation were selected 
      from a Reddit forum and used as ChatGPT prompts or queries. Content analysis was 
      performed on the ChatGPT responses to identify the thematic aspects of vaping 
      cessation support represented in the responses. Next, the responses were 
      empirically evaluated by five experts in tobacco control on accuracy, quality, 
      clarity, and empathy. RESULT: The following themes related to vaping cessation 
      support were identified: understanding nicotine withdrawal symptoms, 
      self-regulation, peer support, motivational support, and Nicotine Replacement 
      Therapy (NRT). The experts judged the ChatGPT responses to be 'satisfactory' to 
      'excellent' in areas of accuracy, quality, clarity, and empathy. CONCLUSION: If 
      managed by a group of experts, including clinicians, and behavioral and computer 
      scientists, a platform such as the ChatGPT may be leveraged to design tailored 
      interventions for tobacco use cessation, including vaping cessation.
CI  - © Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and 
      permissions. Published by BMJ.
FAU - Amin, Samia
AU  - Amin S
AUID- ORCID: 0000-0001-9526-1606
AD  - Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, 
      Honolulu, Hawaii, USA samin@cc.hawaii.edu.
FAU - Kawamoto, Crissy Terawaki
AU  - Kawamoto CT
AUID- ORCID: 0000-0003-0335-6275
AD  - Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, 
      Honolulu, Hawaii, USA.
FAU - Pokhrel, Pallav
AU  - Pokhrel P
AUID- ORCID: 0000-0002-7906-0799
AD  - Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, 
      Honolulu, Hawaii, USA.
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - England
TA  - Tob Control
JT  - Tobacco control
JID - 9209612
SB  - IM
OTO - NOTNLM
OT  - Cessation
OT  - ChatGPT
OT  - E-Cigarettes
OT  - Quit
OT  - Vaping
COIS- Competing interests: None declared.
EDAT- 2023/07/18 01:09
MHDA- 2023/07/18 01:09
CRDT- 2023/07/17 21:13
PHST- 2023/02/16 00:00 [received]
PHST- 2023/06/28 00:00 [accepted]
PHST- 2023/07/18 01:09 [pubmed]
PHST- 2023/07/18 01:09 [medline]
PHST- 2023/07/17 21:13 [entrez]
AID - tc-2023-058009 [pii]
AID - 10.1136/tc-2023-058009 [doi]
PST - aheadofprint
SO  - Tob Control. 2023 Jul 17:tc-2023-058009. doi: 10.1136/tc-2023-058009.

PMID- 37802673
OWN - NLM
STAT- Publisher
LR  - 20231006
IS  - 1878-4046 (Electronic)
IS  - 1076-6332 (Linking)
DP  - 2023 Oct 5
TI  - Potential Applications and Impact of ChatGPT in Radiology.
LID - S1076-6332(23)00460-9 [pii]
LID - 10.1016/j.acra.2023.08.039 [doi]
AB  - Radiology has always gone hand-in-hand with technology and artificial 
      intelligence (AI) is not new to the field. While various AI devices and 
      algorithms have already been integrated in the daily clinical practice of 
      radiology, with applications ranging from scheduling patient appointments to 
      detecting and diagnosing certain clinical conditions on imaging, the use of 
      natural language processing and large language model based software have been in 
      discussion for a long time. Algorithms like ChatGPT can help in improving patient 
      outcomes, increasing the efficiency of radiology interpretation, and aiding in 
      the overall workflow of radiologists and here we discuss some of its potential 
      applications.
CI  - Copyright © 2023 The Association of University Radiologists. Published by 
      Elsevier Inc. All rights reserved.
FAU - Bajaj, Suryansh
AU  - Bajaj S
AD  - Department of Radiology, University of Arkansas for Medical Sciences, Little 
      Rock, Arkansas 72205 (S.B.).
FAU - Gandhi, Darshan
AU  - Gandhi D
AD  - Department of Diagnostic Radiology, University of Tennessee Health Science 
      Center, Memphis, Tennessee 38103 (D.G.). Electronic address: 
      darshangandhi7@gmail.com.
FAU - Nayar, Divya
AU  - Nayar D
AD  - Department of Neurology, University of Arkansas for Medical Sciences, Little 
      Rock, Arkansas 72205 (D.N.).
LA  - eng
PT  - Journal Article
DEP - 20231005
PL  - United States
TA  - Acad Radiol
JT  - Academic radiology
JID - 9440159
SB  - IM
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - GPT-4
OT  - Radiology
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/10/07 00:42
MHDA- 2023/10/07 00:42
CRDT- 2023/10/06 21:58
PHST- 2023/07/13 00:00 [received]
PHST- 2023/08/15 00:00 [revised]
PHST- 2023/08/28 00:00 [accepted]
PHST- 2023/10/07 00:42 [medline]
PHST- 2023/10/07 00:42 [pubmed]
PHST- 2023/10/06 21:58 [entrez]
AID - S1076-6332(23)00460-9 [pii]
AID - 10.1016/j.acra.2023.08.039 [doi]
PST - aheadofprint
SO  - Acad Radiol. 2023 Oct 5:S1076-6332(23)00460-9. doi: 10.1016/j.acra.2023.08.039.

PMID- 38020045
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2473-974X (Electronic)
IS  - 2473-974X (Linking)
VI  - 7
IP  - 4
DP  - 2023 Oct-Dec
TI  - Evaluating the Current Ability of ChatGPT to Assist in Professional 
      Otolaryngology Education.
PG  - e94
LID - 10.1002/oto2.94 [doi]
LID - e94
AB  - OBJECTIVE: To quantify ChatGPT's concordance with expert Otolaryngologists when 
      posed with high-level questions that require blending rote memorization and 
      critical thinking. STUDY DESIGN: Cross-sectional survey. SETTING: OpenAI's 
      ChatGPT-3.5 Platform. METHODS: Two board-certified otolaryngologists (HZ, RS) 
      input 2 sets of 30 text-based questions (open-ended and single-answer 
      multiple-choice) into the ChatGPT-3.5 model. Responses were rated on a scale 
      (correct, partially correct, incorrect) by each Otolaryngologist working 
      simultaneously with the AI model. Interrater agreement percentage was based on 
      binomial distribution for calculating the 95% confidence intervals and performing 
      significance tests. Statistical significance was defined as P < .05 for 2-sided 
      tests. RESULTS: In testing open-ended questions, the ChatGPT model had 56.7% of 
      initially answering questions with complete accuracy, and 86.7% chance of answer 
      with some accuracy (corrected agreement = 80.1%; P < .001). For repeat questions, 
      ChatGPT improved to 73.3% with complete accuracy and 96.7% with some accuracy 
      (corrected agreement = 88.8%; P < .001). For multiple-choice questions, the 
      ChatGPT model performed substantially worse (43.3% correct). CONCLUSION: ChatGPT 
      currently does not provide reliably accurate responses to sophisticated questions 
      in Otolaryngology. Professional societies must be aware of the potential of this 
      tool and prevent unscrupulous use during test-taking situations and consider 
      guidelines for clinical scenarios. Expert clinical oversight is still necessary 
      for myriad use cases (eg, hallucination).
CI  - © 2023 The Authors. OTO Open published by Wiley Periodicals LLC on behalf of 
      American Academy of Otolaryngology–Head and Neck Surgery Foundation.
FAU - Zalzal, Habib G
AU  - Zalzal HG
AUID- ORCID: 0000-0002-0777-977X
AD  - Division of Otolaryngology-Head and Neck Surgery Children's National Hospital 
      Washington District of Columbia USA.
FAU - Cheng, Jenhao
AU  - Cheng J
AD  - Quality, Safety, Analytics Children's National Hospital Washington District of 
      Columbia USA.
FAU - Shah, Rahul K
AU  - Shah RK
AD  - Division of Otolaryngology-Head and Neck Surgery Children's National Hospital 
      Washington District of Columbia USA.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - United States
TA  - OTO Open
JT  - OTO open
JID - 101717942
PMC - PMC10663981
OTO - NOTNLM
OT  - ChatGPT
OT  - OpenAI
OT  - artificial intelligence
OT  - continuing medical education
OT  - large language model
OT  - machine learning
OT  - maintenance of certification
COIS- None.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 14:38
PHST- 2023/08/28 00:00 [received]
PHST- 2023/10/11 00:00 [revised]
PHST- 2023/11/04 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 14:38 [entrez]
AID - OTO294 [pii]
AID - 10.1002/oto2.94 [doi]
PST - epublish
SO  - OTO Open. 2023 Nov 22;7(4):e94. doi: 10.1002/oto2.94. eCollection 2023 Oct-Dec.

PMID- 37535043
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1827-1898 (Electronic)
IS  - 0031-0808 (Linking)
VI  - 65
IP  - 4
DP  - 2023 Dec
TI  - ChatGPT: unlocking the potential of Artifical Intelligence in COVID-19 monitoring 
      and prediction.
PG  - 461-466
LID - 10.23736/S0031-0808.23.04853-X [doi]
AB  - BACKGROUND: The COVID-19 pandemic has had an unprecedent impact of everyday life 
      with deleterious consequences on global health, economics, and society. Thus, 
      accurate and timely information is critical for monitoring its spread and 
      mitigating its impact. ChatGPT is a large language model chatbot with artificial 
      intelligence, developed by OpenAI, that can provide both textual content and R 
      code for predictive models. It may prove to be useful in analyzing and 
      interpreting COVID-19-related data. METHODS: This paper explores the application 
      of ChatGPT to the monitoring of the COVID-19 pandemic, presenting R code for 
      predictive models and demonstrating the model's capabilities in sentiment 
      analysis, information extraction, and predictive modelling. We used the 
      prediction models suggested by ChatGPT to predict the daily number of COVID-19 
      deaths in Italy. The prediction accuracy of the models was compared using the 
      following metrics: mean squared error (MSE), mean absolute deviation (MAD) and 
      root mean squared error (RMSE). RESULTS: ChatGPT suggested three different 
      predictive models, including ARIMA, Random Forest and Prophet. The ARIMA model 
      outperformed the other two models in predicting the daily number of COVID-19 
      deaths in Italy, with lower MSE, MAD, and RMSE values as compared to the Random 
      Forest and Prophet. CONCLUSIONS: This paper demonstrates the potential of ChatGPT 
      as a valuable tool in the monitoring of the pandemic. By processing large amounts 
      of data and providing relevant information, ChatGPT has the potential to provide 
      accurate and timely insights, and support decision-making processes to mitigate 
      the spread and impact of pandemics. The paper highlights the importance of 
      exploring the capabilities of artificial intelligence in the management of public 
      emergencies and provides a starting point for future research in this area.
FAU - Gerli, Alberto G
AU  - Gerli AG
AD  - Department of Clinical Sciences and Community Health, University of Milan, Milan, 
      Italy - alberto.gerli@unimi.it.
FAU - Soriano, Joan B
AU  - Soriano JB
AD  - Unit of Pulmonology, Hospital Universitario de la Princesa, Madrid, Spain.
AD  - Faculty of Medicine, Autonomous University of Madrid, Madrid, Spain.
AD  - Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), 
      Carlos III Health Institute, Madrid, Spain.
FAU - Alicandro, Gianfranco
AU  - Alicandro G
AD  - Department of Pathophysiology and Transplantation, University of Milan, Milan, 
      Italy.
AD  - Cystic Fibrosis Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore 
      Policlinico, Milan, Italy.
FAU - Salvagno, Michele
AU  - Salvagno M
AD  - Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, 
      Brussels, Belgium.
FAU - Taccone, Fabio
AU  - Taccone F
AD  - Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, 
      Brussels, Belgium.
FAU - Centanni, Stefano
AU  - Centanni S
AD  - Respiratory Unit, Department of Health Sciences, ASST Santi Paolo e Carlo, 
      University of Milan, Milan, Italy.
FAU - LA Vecchia, Carlo
AU  - LA Vecchia C
AD  - Department of Clinical Sciences and Community Health, University of Milan, Milan, 
      Italy.
LA  - eng
PT  - Journal Article
DEP - 20230803
PL  - Italy
TA  - Panminerva Med
JT  - Panminerva medica
JID - 0421110
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Pandemics
MH  - *COVID-19/epidemiology
MH  - Intelligence
MH  - Italy/epidemiology
EDAT- 2023/08/03 13:09
MHDA- 2023/11/27 12:45
CRDT- 2023/08/03 10:44
PHST- 2023/11/27 12:45 [medline]
PHST- 2023/08/03 13:09 [pubmed]
PHST- 2023/08/03 10:44 [entrez]
AID - S0031-0808.23.04853-X [pii]
AID - 10.23736/S0031-0808.23.04853-X [doi]
PST - ppublish
SO  - Panminerva Med. 2023 Dec;65(4):461-466. doi: 10.23736/S0031-0808.23.04853-X. Epub 
      2023 Aug 3.

PMID- 37648882
OWN - NLM
STAT- MEDLINE
DCOM- 20230926
LR  - 20230926
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 10
DP  - 2023 Oct
TI  - Can ChatGPT Help in the Awareness of Diabetes?
PG  - 2125-2129
LID - 10.1007/s10439-023-03356-1 [doi]
AB  - Diabetes is a common chronic illness that requires continual patient education 
      and support to be effectively managed. The lack of diabetes educators and the 
      limitations of conventional education approaches make it difficult to meet the 
      specific needs of each patient. The artificial intelligence (AI) tool ChatGPT 
      provides a tailored and interactive approach to support and instruction. ChatGPT 
      has the potential to increase diabetes patients' access to information and 
      assistance, but more research is required to fully comprehend its benefits and 
      drawbacks. To maximize ChatGPT's potential benefits and reduce its possible 
      hazards, care must be taken to ensure that it is developed and integrated in an 
      ethical and equitable manner. In this post, we talk about how crucial ChatGPT is 
      for educating people about diabetes.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Khan, Imran
AU  - Khan I
AUID- ORCID: 0000-0003-0053-8938
AD  - Department of Computer Science and Engineering, Harcourt Butler Technical 
      University, Kanpur, UP, India. imrankhan.ee2531@gmail.com.
FAU - Agarwal, Rashi
AU  - Agarwal R
AD  - Department of Computer Science and Engineering, Harcourt Butler Technical 
      University, Kanpur, UP, India.
LA  - eng
PT  - Letter
DEP - 20230830
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Diabetes Mellitus
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Diabetes
OT  - Machine learning
EDAT- 2023/08/31 00:41
MHDA- 2023/09/26 13:43
CRDT- 2023/08/30 23:31
PHST- 2023/08/17 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/09/26 13:43 [medline]
PHST- 2023/08/31 00:41 [pubmed]
PHST- 2023/08/30 23:31 [entrez]
AID - 10.1007/s10439-023-03356-1 [pii]
AID - 10.1007/s10439-023-03356-1 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Oct;51(10):2125-2129. doi: 10.1007/s10439-023-03356-1. Epub 
      2023 Aug 30.

PMID- 37499880
OWN - NLM
STAT- Publisher
LR  - 20231022
IS  - 1878-1632 (Electronic)
IS  - 1529-9430 (Linking)
VI  - 23
IP  - 11
DP  - 2023 Nov
TI  - Thromboembolic prophylaxis in spine surgery: an analysis of ChatGPT 
      recommendations.
PG  - 1684-1691
LID - S1529-9430(23)03285-0 [pii]
LID - 10.1016/j.spinee.2023.07.015 [doi]
AB  - BACKGROUND CONTEXT: Venous thromboembolism is a negative outcome of elective 
      spine surgery. However, the use of thromboembolic chemoprophylaxis in this 
      patient population is controversial due to the possible increased risk of 
      epidural hematoma. ChatGPT is an artificial intelligence model which may be able 
      to generate recommendations for thromboembolic prophylaxis in spine surgery. 
      PURPOSE: To evaluate the accuracy of ChatGPT recommendations for thromboembolic 
      prophylaxis in spine surgery. STUDY DESIGN/SETTING: Comparative analysis. PATIENT 
      SAMPLE: None. OUTCOME MEASURES: Accuracy, over-conclusiveness, supplemental, and 
      incompleteness of ChatGPT responses compared to the North American Spine Society 
      (NASS) clinical guidelines. METHODS: ChatGPT was prompted with questions from the 
      2009 NASS clinical guidelines for antithrombotic therapies and evaluated for 
      concordance with the clinical guidelines. ChatGPT-3.5 responses were obtained on 
      March 5, 2023, and ChatGPT-4.0 responses were obtained on April 7, 2023. A 
      ChatGPT response was classified as accurate if it did not contradict the clinical 
      guideline. Three additional categories were created to further evaluate the 
      ChatGPT responses in comparison to the NASS guidelines: over-conclusiveness, 
      supplementary, and incompleteness. ChatGPT was classified as over-conclusive if 
      it made a recommendation where the NASS guideline did not provide one. ChatGPT 
      was classified as supplementary if it included additional relevant information 
      not specified by the NASS guideline. ChatGPT was classified as incomplete if it 
      failed to provide relevant information included in the NASS guideline. RESULTS: 
      Twelve clinical guidelines were evaluated in total. Compared to the NASS clinical 
      guidelines, ChatGPT-3.5 was accurate in 4 (33%) of its responses while 
      ChatGPT-4.0 was accurate in 11 (92%) responses. ChatGPT-3.5 was over-conclusive 
      in 6 (50%) of its responses while ChatGPT-4.0 was over-conclusive in 1 (8%) 
      response. ChatGPT-3.5 provided supplemental information in 8 (67%) of its 
      responses, and ChatGPT-4.0 provided supplemental information in 11 (92%) 
      responses. Four (33%) responses from ChatGPT-3.5 were incomplete, and 4 (33%) 
      responses from ChatGPT-4.0 were incomplete. CONCLUSIONS: ChatGPT was able to 
      provide recommendations for thromboembolic prophylaxis with reasonable accuracy. 
      ChatGPT-3.5 tended to cite nonexistent sources and was more likely to give 
      specific recommendations while ChatGPT-4.0 was more conservative in its answers. 
      As ChatGPT is continuously updated, further validation is needed before it can be 
      used as a guideline for clinical practice.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Duey, Akiro H
AU  - Duey AH
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Nietsch, Katrina S
AU  - Nietsch KS
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Zaidat, Bashar
AU  - Zaidat B
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Ren, Renee
AU  - Ren R
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Ndjonko, Laura C Mazudie
AU  - Ndjonko LCM
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Shrestha, Nancy
AU  - Shrestha N
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Rajjoub, Rami
AU  - Rajjoub R
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Ahmed, Wasil
AU  - Ahmed W
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Hoang, Timothy
AU  - Hoang T
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Saturno, Michael P
AU  - Saturno MP
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Tang, Justin E
AU  - Tang JE
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Gallate, Zachary S
AU  - Gallate ZS
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Kim, Jun S
AU  - Kim JS
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY.
FAU - Cho, Samuel K
AU  - Cho SK
AD  - Department of Orthopedics, Icahn School of Medicine, New York, NY. Electronic 
      address: samuel.cho@mountsinai.org.
LA  - eng
PT  - Journal Article
DEP - 20230725
PL  - United States
TA  - Spine J
JT  - The spine journal : official journal of the North American Spine Society
JID - 101130732
SB  - IM
OTO - NOTNLM
OT  - Antithrombotic therapy
OT  - ChatGPT
OT  - Chemoprophylaxis
OT  - Deep vein thrombosis
OT  - Pulmonary embolism
OT  - Subdural hematoma
OT  - Thromboembolic prophylaxis
OT  - Thromboprophylaxis
OT  - Venous thromboembolism
COIS- Declarations of competing interests One or more of the authors declare financial 
      or professional relationships on ICMJE-TSJ disclosure forms.
EDAT- 2023/07/28 01:08
MHDA- 2023/07/28 01:08
CRDT- 2023/07/27 19:16
PHST- 2023/05/23 00:00 [received]
PHST- 2023/07/10 00:00 [revised]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/07/28 01:08 [pubmed]
PHST- 2023/07/28 01:08 [medline]
PHST- 2023/07/27 19:16 [entrez]
AID - S1529-9430(23)03285-0 [pii]
AID - 10.1016/j.spinee.2023.07.015 [doi]
PST - ppublish
SO  - Spine J. 2023 Nov;23(11):1684-1691. doi: 10.1016/j.spinee.2023.07.015. Epub 2023 
      Jul 25.

PMID- 37830257
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 1898-018X (Electronic)
IS  - 1898-018X (Linking)
DP  - 2023 Oct 13
TI  - Reshaping medical education: Performance of ChatGPT on a PES medical examination.
LID - 10.5603/cj.97517 [doi]
AB  - BACKGROUND: We are currently experiencing a third digital revolution driven by 
      artificial intelligence (AI), and the emergence of new chat generative 
      pre-trained transformer (ChatGPT) represents a significant technological 
      advancement with profound implications for global society, especially in the 
      field of education. METHODS: The aim of this study was to see how well ChatGPT 
      performed on medical school exams and to highlight how it might change medical 
      education and practice. Recently, OpenAI's ChatGPT (OpenAI, San Francisco; GPT-4 
      May 24 Version) was put to the test against a significant Polish medical 
      specialization licensing exam (PES), and the results are in. The version of 
      ChatGPT-4 used in this study was the most up-to-date model at the time of 
      publication (GPT-4). ChatGPT answered questions from June 28, 2023, to June 30, 
      2023. RESULTS: ChatGPT demonstrates notable advancements in natural language 
      processing models on the tasks of medical question answering. In June 2023, the 
      performance of ChatGPT was assessed based on its ability to answer a set of 120 
      questions, where it achieved a correct response rate of 67.1%, accurately 
      responding to 80 questions. CONCLUSIONS: ChatGPT may be used as an assistance 
      tool in medical education. While ChatGPT can serve as a valuable tool in medical 
      education, it cannot fully replace human expertise and knowledge due to its 
      inherent limitations.
FAU - Wójcik, Simona
AU  - Wójcik S
AD  - LUX MED Llc, Warsaw, Poland.
FAU - Rulkiewicz, Anna
AU  - Rulkiewicz A
AD  - LUX MED Llc, Warsaw, Poland.
FAU - Pruszczyk, Piotr
AU  - Pruszczyk P
AUID- ORCID: 0000-0002-9768-0000
AD  - Department of Internal Medicine and Cardiology with The Center for Diagnosis and 
      Treatment of Thromboembolism, Medical University of Warsaw, Poland.
FAU - Lisik, Wojciech
AU  - Lisik W
AUID- ORCID: 0000-0003-3020-3979
AD  - Department of General and Transplantation Surgery, Medical University of Warsaw, 
      Poland.
FAU - Poboży, Marcin
AU  - Poboży M
AD  - Cichowski Pobozy Healthcare Facility, Maciejowice, Poland.
FAU - Domienik-Karłowicz, Justyna
AU  - Domienik-Karłowicz J
AUID- ORCID: 0000-0001-6122-9755
AD  - Department of Internal Medicine and Cardiology with The Center for Diagnosis and 
      Treatment of Thromboembolism, Medical University of Warsaw, Poland. 
      jdomienik@tlen.pl.
AD  - LUX MED Llc, Warsaw, Poland. jdomienik@tlen.pl.
LA  - eng
PT  - Journal Article
DEP - 20231013
PL  - Poland
TA  - Cardiol J
JT  - Cardiology journal
JID - 101392712
SB  - IM
OTO - NOTNLM
OT  - AI in medicine
OT  - ChatGPT
OT  - artificial intelligence
OT  - health IT
OT  - innovations
OT  - language processing
OT  - medical education
OT  - virtual teaching assistant
EDAT- 2023/10/13 06:45
MHDA- 2023/10/13 06:45
CRDT- 2023/10/13 05:31
PHST- 2023/09/20 00:00 [received]
PHST- 2023/10/03 00:00 [accepted]
PHST- 2023/10/13 06:45 [medline]
PHST- 2023/10/13 06:45 [pubmed]
PHST- 2023/10/13 05:31 [entrez]
AID - VM/OJS/J/97517 [pii]
AID - 10.5603/cj.97517 [doi]
PST - aheadofprint
SO  - Cardiol J. 2023 Oct 13. doi: 10.5603/cj.97517.

PMID- 37794249
OWN - NLM
STAT- Publisher
LR  - 20231004
IS  - 1432-1084 (Electronic)
IS  - 0938-7994 (Linking)
DP  - 2023 Oct 5
TI  - ChatGPT makes medicine easy to swallow: an exploratory case study on simplified 
      radiology reports.
LID - 10.1007/s00330-023-10213-1 [doi]
AB  - OBJECTIVES: To assess the quality of simplified radiology reports generated with 
      the large language model (LLM) ChatGPT and to discuss challenges and chances of 
      ChatGPT-like LLMs for medical text simplification. METHODS: In this exploratory 
      case study, a radiologist created three fictitious radiology reports which we 
      simplified by prompting ChatGPT with "Explain this medical report to a child 
      using simple language." In a questionnaire, we tasked 15 radiologists to rate the 
      quality of the simplified radiology reports with respect to their factual 
      correctness, completeness, and potential harm for patients. We used Likert scale 
      analysis and inductive free-text categorization to assess the quality of the 
      simplified reports. RESULTS: Most radiologists agreed that the simplified reports 
      were factually correct, complete, and not potentially harmful to the patient. 
      Nevertheless, instances of incorrect statements, missed relevant medical 
      information, and potentially harmful passages were reported. CONCLUSION: While we 
      see a need for further adaption to the medical field, the initial insights of 
      this study indicate a tremendous potential in using LLMs like ChatGPT to improve 
      patient-centered care in radiology and other medical domains. CLINICAL RELEVANCE 
      STATEMENT: Patients have started to use ChatGPT to simplify and explain their 
      medical reports, which is expected to affect patient-doctor interaction. This 
      phenomenon raises several opportunities and challenges for clinical routine. KEY 
      POINTS: • Patients have started to use ChatGPT to simplify their medical reports, 
      but their quality was unknown. • In a questionnaire, most participating 
      radiologists overall asserted good quality to radiology reports simplified with 
      ChatGPT. However, they also highlighted a notable presence of errors, potentially 
      leading patients to draw harmful conclusions. • Large language models such as 
      ChatGPT have vast potential to enhance patient-centered care in radiology and 
      other medical domains. To realize this potential while minimizing harm, they need 
      supervision by medical experts and adaption to the medical field.
CI  - © 2023. The Author(s).
FAU - Jeblick, Katharina
AU  - Jeblick K
AUID- ORCID: 0000-0002-8678-7152
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany. 
      katharina.jeblick@med.uni-muenchen.de.
AD  - Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung 
      Research (DZL), Munich, Germany. katharina.jeblick@med.uni-muenchen.de.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany. 
      katharina.jeblick@med.uni-muenchen.de.
FAU - Schachtner, Balthasar
AU  - Schachtner B
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany.
FAU - Dexl, Jakob
AU  - Dexl J
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany.
FAU - Mittermeier, Andreas
AU  - Mittermeier A
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany.
FAU - Stüber, Anna Theresa
AU  - Stüber AT
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany.
AD  - Department of Statistics, LMU Munich, Munich, Germany.
FAU - Topalis, Johanna
AU  - Topalis J
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
FAU - Weber, Tobias
AU  - Weber T
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany.
AD  - Department of Statistics, LMU Munich, Munich, Germany.
FAU - Wesp, Philipp
AU  - Wesp P
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany.
FAU - Sabel, Bastian Oliver
AU  - Sabel BO
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
FAU - Ricke, Jens
AU  - Ricke J
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
FAU - Ingrisch, Michael
AU  - Ingrisch M
AD  - Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
AD  - Munich Center for Machine Learning (MCML), Munich, Germany.
LA  - eng
GR  - 460037581/Deutsche Forschungsgemeinschaft/
PT  - Journal Article
DEP - 20231005
PL  - Germany
TA  - Eur Radiol
JT  - European radiology
JID - 9114774
SB  - IM
OTO - NOTNLM
OT  - Natural language processing
OT  - Patient-centered care
OT  - Radiology
EDAT- 2023/10/05 01:00
MHDA- 2023/10/05 01:00
CRDT- 2023/10/04 23:33
PHST- 2023/03/06 00:00 [received]
PHST- 2023/07/07 00:00 [accepted]
PHST- 2023/05/24 00:00 [revised]
PHST- 2023/10/05 01:00 [medline]
PHST- 2023/10/05 01:00 [pubmed]
PHST- 2023/10/04 23:33 [entrez]
AID - 10.1007/s00330-023-10213-1 [pii]
AID - 10.1007/s00330-023-10213-1 [doi]
PST - aheadofprint
SO  - Eur Radiol. 2023 Oct 5. doi: 10.1007/s00330-023-10213-1.

PMID- 37795422
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231006
IS  - 2296-858X (Print)
IS  - 2296-858X (Electronic)
IS  - 2296-858X (Linking)
VI  - 10
DP  - 2023
TI  - Evaluating the performance of ChatGPT-4 on the United Kingdom Medical Licensing 
      Assessment.
PG  - 1240915
LID - 10.3389/fmed.2023.1240915 [doi]
LID - 1240915
AB  - INTRODUCTION: Recent developments in artificial intelligence large language 
      models (LLMs), such as ChatGPT, have allowed for the understanding and generation 
      of human-like text. Studies have found LLMs abilities to perform well in various 
      examinations including law, business and medicine. This study aims to evaluate 
      the performance of ChatGPT in the United Kingdom Medical Licensing Assessment 
      (UKMLA). METHODS: Two publicly available UKMLA papers consisting of 200 
      single-best-answer (SBA) questions were screened. Nine SBAs were omitted as they 
      contained images that were not suitable for input. Each question was assigned a 
      specialty based on the UKMLA content map published by the General Medical 
      Council. A total of 191 SBAs were inputted in ChatGPT-4 through three attempts 
      over the course of 3 weeks (once per week). RESULTS: ChatGPT scored 74.9% 
      (143/191), 78.0% (149/191) and 75.6% (145/191) on three attempts, respectively. 
      The average of all three attempts was 76.3% (437/573) with a 95% confidence 
      interval of (74.46% and 78.08%). ChatGPT answered 129 SBAs correctly and 32 SBAs 
      incorrectly on all three attempts. On three attempts, ChatGPT performed well in 
      mental health (8/9 SBAs), cancer (11/14 SBAs) and cardiovascular (10/13 SBAs). On 
      three attempts, ChatGPT did not perform well in clinical haematology (3/7 SBAs), 
      endocrine and metabolic (2/5 SBAs) and gastrointestinal including liver (3/10 
      SBAs). Regarding to response consistency, ChatGPT provided correct answers 
      consistently in 67.5% (129/191) of SBAs but provided incorrect answers 
      consistently in 12.6% (24/191) and inconsistent response in 19.9% (38/191) of 
      SBAs, respectively. DISCUSSION AND CONCLUSION: This study suggests ChatGPT 
      performs well in the UKMLA. There may be a potential correlation between 
      specialty performance. LLMs ability to correctly answer SBAs suggests that it 
      could be utilised as a supplementary learning tool in medical education with 
      appropriate medical educator supervision.
CI  - Copyright © 2023 Lai, Wu, Hsu and Kan.
FAU - Lai, U Hin
AU  - Lai UH
AD  - Sandwell and West Birmingham NHS Trust, West Bromwich, United Kingdom.
AD  - Aston Medical School, Birmingham, United Kingdom.
FAU - Wu, Keng Sam
AU  - Wu KS
AD  - Sandwell and West Birmingham NHS Trust, West Bromwich, United Kingdom.
AD  - University Hospitals Birmingham NHS Trust, Birmingham, United Kingdom.
FAU - Hsu, Ting-Yu
AU  - Hsu TY
AD  - Aston Medical School, Birmingham, United Kingdom.
AD  - University Hospitals Birmingham NHS Trust, Birmingham, United Kingdom.
FAU - Kan, Jessie Kai Ching
AU  - Kan JKC
AD  - Aston Medical School, Birmingham, United Kingdom.
AD  - Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230919
PL  - Switzerland
TA  - Front Med (Lausanne)
JT  - Frontiers in medicine
JID - 101648047
PMC - PMC10547055
OTO - NOTNLM
OT  - ChatGPT
OT  - Medical Licensing Examination
OT  - United Kingdom Medical Licensing Assessment
OT  - assessment
OT  - examination
OT  - medical education
OT  - medicine
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/10/05 06:43
MHDA- 2023/10/05 06:44
CRDT- 2023/10/05 04:11
PHST- 2023/06/15 00:00 [received]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/10/05 06:44 [medline]
PHST- 2023/10/05 06:43 [pubmed]
PHST- 2023/10/05 04:11 [entrez]
AID - 10.3389/fmed.2023.1240915 [doi]
PST - epublish
SO  - Front Med (Lausanne). 2023 Sep 19;10:1240915. doi: 10.3389/fmed.2023.1240915. 
      eCollection 2023.

PMID- 37193434
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230519
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Synchronous Bilateral Breast Cancer: A Case Report Piloting and Evaluating the 
      Implementation of the AI-Powered Large Language Model (LLM) ChatGPT.
PG  - e37587
LID - 10.7759/cureus.37587 [doi]
LID - e37587
AB  - Primary breast carcinoma is the most common cancer type in women, and although 
      bilateral synchronous breast cancers (s-BBC) remain quite rare, the reported 
      incidence may increase with the adoption of more sensitive imaging 
      modalities. Here, we present a case of histomorphological and clinically distinct 
      s-BBC, together with a discussion of clinical management decisions, prognosis, 
      and treatment standards and how these relate to outcomes vis-à-vis more 
      established standards in unifocal breast carcinoma. The case report also 
      constitutes a pilot and formal evaluation of a large language model (LLM) of 
      ChatGPT as a tool to aid in generating a single patient case report.
CI  - Copyright © 2023, Naik et al.
FAU - Naik, Himani R
AU  - Naik HR
AD  - Gundersen Medical Foundation, Gundersen Lutheran Medical Center, La Crosse, USA.
FAU - Prather, Andrew D
AU  - Prather AD
AD  - Radiology, Gundersen Lutheran Medical Center, La Crosse, USA.
FAU - Gurda, Grzegorz T
AU  - Gurda GT
AD  - Biology, University of Wisconsin-La Crosse, La Crosse, USA.
AD  - Pathology, Gundersen Lutheran Medical Center, La Crosse, USA.
LA  - eng
PT  - Case Reports
DEP - 20230414
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10183235
OTO - NOTNLM
OT  - bilateral
OT  - breast cancer
OT  - breast cancer risk
OT  - chatgpt
OT  - chatgpt aided case report
OT  - ductal carcinoma
OT  - multifocal
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/17 01:07
MHDA- 2023/05/17 01:08
CRDT- 2023/05/16 21:24
PHST- 2023/04/07 00:00 [accepted]
PHST- 2023/05/17 01:08 [medline]
PHST- 2023/05/17 01:07 [pubmed]
PHST- 2023/05/16 21:24 [entrez]
AID - 10.7759/cureus.37587 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 14;15(4):e37587. doi: 10.7759/cureus.37587. eCollection 2023 
      Apr.

PMID- 37831496
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2561-326X (Electronic)
IS  - 2561-326X (Linking)
VI  - 7
DP  - 2023 Oct 13
TI  - Accuracy of ChatGPT on Medical Questions in the National Medical Licensing 
      Examination in Japan: Evaluation Study.
PG  - e48023
LID - 10.2196/48023 [doi]
LID - e48023
AB  - BACKGROUND: ChatGPT (OpenAI) has gained considerable attention because of its 
      natural and intuitive responses. ChatGPT sometimes writes plausible-sounding but 
      incorrect or nonsensical answers, as stated by OpenAI as a limitation. However, 
      considering that ChatGPT is an interactive AI that has been trained to reduce the 
      output of unethical sentences, the reliability of the training data is high and 
      the usefulness of the output content is promising. Fortunately, in March 2023, a 
      new version of ChatGPT, GPT-4, was released, which, according to internal 
      evaluations, was expected to increase the likelihood of producing factual 
      responses by 40% compared with its predecessor, GPT-3.5. The usefulness of this 
      version of ChatGPT in English is widely appreciated. It is also increasingly 
      being evaluated as a system for obtaining medical information in languages other 
      than English. Although it does not reach a passing score on the national medical 
      examination in Chinese, its accuracy is expected to gradually improve. Evaluation 
      of ChatGPT with Japanese input is limited, although there have been reports on 
      the accuracy of ChatGPT's answers to clinical questions regarding the Japanese 
      Society of Hypertension guidelines and on the performance of the National Nursing 
      Examination. OBJECTIVE: The objective of this study is to evaluate whether 
      ChatGPT can provide accurate diagnoses and medical knowledge for Japanese input. 
      METHODS: Questions from the National Medical Licensing Examination (NMLE) in 
      Japan, administered by the Japanese Ministry of Health, Labour and Welfare in 
      2022, were used. All 400 questions were included. Exclusion criteria were figures 
      and tables that ChatGPT could not recognize; only text questions were extracted. 
      We instructed GPT-3.5 and GPT-4 to input the Japanese questions as they were and 
      to output the correct answers for each question. The output of ChatGPT was 
      verified by 2 general practice physicians. In case of discrepancies, they were 
      checked by another physician to make a final decision. The overall performance 
      was evaluated by calculating the percentage of correct answers output by GPT-3.5 
      and GPT-4. RESULTS: Of the 400 questions, 292 were analyzed. Questions containing 
      charts, which are not supported by ChatGPT, were excluded. The correct response 
      rate for GPT-4 was 81.5% (237/292), which was significantly higher than the rate 
      for GPT-3.5, 42.8% (125/292). Moreover, GPT-4 surpassed the passing standard 
      (>72%) for the NMLE, indicating its potential as a diagnostic and therapeutic 
      decision aid for physicians. CONCLUSIONS: GPT-4 reached the passing standard for 
      the NMLE in Japan, entered in Japanese, although it is limited to written 
      questions. As the accelerated progress in the past few months has shown, the 
      performance of the AI will improve as the large language model continues to learn 
      more, and it may well become a decision support system for medical professionals 
      by providing more accurate information.
CI  - ©Yasutaka Yanagita, Daiki Yokokawa, Shun Uchida, Junsuke Tawara, Masatomi 
      Ikusaka. Originally published in JMIR Formative Research 
      (https://formative.jmir.org), 13.10.2023.
FAU - Yanagita, Yasutaka
AU  - Yanagita Y
AUID- ORCID: 0000-0002-9213-8247
AD  - Department of General Medicine, Chiba University Hospital, Chiba, Japan.
FAU - Yokokawa, Daiki
AU  - Yokokawa D
AUID- ORCID: 0000-0003-0944-8664
AD  - Department of General Medicine, Chiba University Hospital, Chiba, Japan.
FAU - Uchida, Shun
AU  - Uchida S
AUID- ORCID: 0000-0003-3408-9763
AD  - Department of General Medicine, Chiba University Hospital, Chiba, Japan.
FAU - Tawara, Junsuke
AU  - Tawara J
AUID- ORCID: 0000-0002-7842-5047
AD  - Department of Internal Medicine, Sanmu Medical Center, Chiba, Japan.
FAU - Ikusaka, Masatomi
AU  - Ikusaka M
AUID- ORCID: 0000-0002-9760-5596
AD  - Department of General Medicine, Chiba University Hospital, Chiba, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231013
PL  - Canada
TA  - JMIR Form Res
JT  - JMIR formative research
JID - 101726394
PMC - PMC10612006
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT-4
OT  - Japanese
OT  - NMLE
OT  - National Medical Licensing Examination
OT  - artificial intelligence
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/13 12:44
MHDA- 2023/10/13 12:45
CRDT- 2023/10/13 11:53
PHST- 2023/04/09 00:00 [received]
PHST- 2023/10/03 00:00 [accepted]
PHST- 2023/06/21 00:00 [revised]
PHST- 2023/10/13 12:45 [medline]
PHST- 2023/10/13 12:44 [pubmed]
PHST- 2023/10/13 11:53 [entrez]
AID - v7i1e48023 [pii]
AID - 10.2196/48023 [doi]
PST - epublish
SO  - JMIR Form Res. 2023 Oct 13;7:e48023. doi: 10.2196/48023.

PMID- 37337480
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230621
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical 
      Content.
PG  - e39238
LID - 10.7759/cureus.39238 [doi]
LID - e39238
AB  - Background The availability of large language models such as Chat Generative 
      Pre-trained Transformer (ChatGPT, OpenAI) has enabled individuals from diverse 
      backgrounds to access medical information. However, concerns exist about the 
      accuracy of ChatGPT responses and the references used to generate medical 
      content. Methods This observational study investigated the authenticity and 
      accuracy of references in medical articles generated by ChatGPT. ChatGPT-3.5 
      generated 30 short medical papers, each with at least three references, based on 
      standardized prompts encompassing various topics and therapeutic areas. Reference 
      authenticity and accuracy were verified by searching Medline, Google Scholar, and 
      the Directory of Open Access Journals. The authenticity and accuracy 
      of individual ChatGPT-generated reference elements were also determined. Results 
      Overall, 115 references were generated by ChatGPT, with a mean of 3.8±1.1 per 
      paper. Among these references, 47% were fabricated, 46% were authentic but 
      inaccurate, and only 7% were authentic and accurate. The likelihood of fabricated 
      references significantly differed based on prompt variations; yet the frequency 
      of authentic and accurate references remained low in all cases. Among the seven 
      components evaluated for each reference, an incorrect PMID number was most 
      common, listed in 93% of papers. Incorrect volume (64%), page numbers (64%), and 
      year of publication (60%) were the next most frequent errors. The mean number of 
      inaccurate components was 4.3±2.8 out of seven per reference. Conclusions The 
      findings of this study emphasize the need for caution when seeking medical 
      information on ChatGPT since most of the references provided were found to be 
      fabricated or inaccurate. Individuals are advised to verify medical information 
      from reliable sources and avoid relying solely on artificial 
      intelligence-generated content.
CI  - Copyright © 2023, Bhattacharyya et al.
FAU - Bhattacharyya, Mehul
AU  - Bhattacharyya M
AD  - Clinical Research, Miller Scientific, Johnson City, USA.
FAU - Miller, Valerie M
AU  - Miller VM
AD  - Leadership, University of the Cumberlands, Williamsburg, USA.
FAU - Bhattacharyya, Debjani
AU  - Bhattacharyya D
AD  - Education, University of Massachusetts Lowell, Lowell, USA.
FAU - Miller, Larry E
AU  - Miller LE
AD  - Clinical Research, Miller Scientific, Johnson City, USA.
LA  - eng
PT  - Journal Article
DEP - 20230519
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10277170
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - large language model
OT  - machine learning
OT  - references
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/20 06:42
MHDA- 2023/06/20 06:43
CRDT- 2023/06/20 02:25
PHST- 2023/05/18 00:00 [accepted]
PHST- 2023/06/20 06:43 [medline]
PHST- 2023/06/20 06:42 [pubmed]
PHST- 2023/06/20 02:25 [entrez]
AID - 10.7759/cureus.39238 [doi]
PST - epublish
SO  - Cureus. 2023 May 19;15(5):e39238. doi: 10.7759/cureus.39238. eCollection 2023 
      May.

PMID- 37845389
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231130
IS  - 1432-1084 (Electronic)
IS  - 0938-7994 (Linking)
VI  - 33
IP  - 12
DP  - 2023 Dec
TI  - Reply to the letter to the editor: "A critical evaluation on the use of large 
      language model for radiology research".
PG  - 9464-9465
LID - 10.1007/s00330-023-10333-8 [doi]
FAU - López-Úbeda, Pilar
AU  - López-Úbeda P
AUID- ORCID: 0000-0003-0478-743X
AD  - R+D+I Department, HT Medica, Jaén, Spain. p.lopez@htmedica.com.
FAU - Martín-Noguerol, Teodoro
AU  - Martín-Noguerol T
AD  - MRI Unit, Radiology Department, HT Medica, Jaén, Spain.
FAU - Luna, Antonio
AU  - Luna A
AD  - MRI Unit, Radiology Department, HT Medica, Jaén, Spain.
LA  - eng
GR  - PTQ2021-012120/Ministerio de Ciencia e Innovación/
PT  - Letter
DEP - 20231017
PL  - Germany
TA  - Eur Radiol
JT  - European radiology
JID - 9114774
SB  - IM
MH  - Humans
MH  - Radiography
MH  - *Radiology
MH  - Language
EDAT- 2023/10/17 00:42
MHDA- 2023/11/27 12:44
CRDT- 2023/10/16 23:34
PHST- 2023/09/12 00:00 [received]
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/09/12 00:00 [revised]
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/10/17 00:42 [pubmed]
PHST- 2023/10/16 23:34 [entrez]
AID - 10.1007/s00330-023-10333-8 [pii]
AID - 10.1007/s00330-023-10333-8 [doi]
PST - ppublish
SO  - Eur Radiol. 2023 Dec;33(12):9464-9465. doi: 10.1007/s00330-023-10333-8. Epub 2023 
      Oct 17.

PMID- 37602076
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230823
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - ChatGPT and Artificial Intelligence in Transplantation Research: Is It Always 
      Correct?
PG  - e42150
LID - 10.7759/cureus.42150 [doi]
LID - e42150
AB  - INTRODUCTION: ChatGPT (OpenAI, San Francisco, California, United States) is a 
      chatbot powered by language-based artificial intelligence (AI). It generates text 
      based on the information provided by users. It is currently being evaluated in 
      medical research, publishing, and healthcare. However, there has been no prior 
      study on the evaluation of its ability to help in kidney transplant research. 
      This feasibility study aimed to evaluate the application and accuracy of ChatGPT 
      in the field of kidney transplantation. METHODS: On two separate dates, February 
      21 and March 2, 2023, ChatGPT 3.5 was questioned regarding the medical treatment 
      of kidney transplants and related scientific facts. The responses provided by the 
      chatbot were compiled, and a panel of two specialists reviewed the correctness of 
      each answer. RESULTS: We demonstrated that ChatGPT possessed substantial general 
      knowledge of kidney transplantation; however, they lacked sufficient information 
      and had inaccurate information that necessitates a deeper understanding of the 
      topic. Moreover, ChatGPT failed to provide references for any of the scientific 
      data it provided regarding kidney transplants, and when requested for references, 
      it provided inaccurate ones. CONCLUSION: The results of this short feasibility 
      study indicate that ChatGPT may have the ability to assist in data collecting 
      when a particular query is posed. However, caution should be exercised and it 
      should not be used in isolation as a supplement to research or decisions 
      regarding healthcare because there are still challenges with data accuracy and 
      missing information.
CI  - Copyright © 2023, Rawashdeh et al.
FAU - Rawashdeh, Badi
AU  - Rawashdeh B
AD  - Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA.
FAU - Kim, Joohyun
AU  - Kim J
AD  - Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA.
FAU - AlRyalat, Saif Aldeen
AU  - AlRyalat SA
AD  - Opthalmology, The University of Jordan, Amman, JOR.
FAU - Prasad, Raj
AU  - Prasad R
AD  - Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA.
FAU - Cooper, Matthew
AU  - Cooper M
AD  - Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA.
LA  - eng
PT  - Journal Article
DEP - 20230719
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10438857
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - kidney transplantation
OT  - machine learning
OT  - research
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/21 06:42
MHDA- 2023/08/21 06:43
CRDT- 2023/08/21 05:09
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/08/21 06:43 [medline]
PHST- 2023/08/21 06:42 [pubmed]
PHST- 2023/08/21 05:09 [entrez]
AID - 10.7759/cureus.42150 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 19;15(7):e42150. doi: 10.7759/cureus.42150. eCollection 2023 
      Jul.

PMID- 37991498
OWN - NLM
STAT- Publisher
LR  - 20231122
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Nov 22
TI  - Accuracy of ChatGPT in head and neck oncological board decisions: preliminary 
      findings.
LID - 10.1007/s00405-023-08326-w [doi]
AB  - OBJECTIVES: To evaluate the ChatGPT-4 performance in oncological board decisions. 
      METHODS: Twenty medical records of patients with head and neck cancer were 
      evaluated by ChatGPT-4 for additional examinations, management, and therapeutic 
      approaches. The ChatGPT-4 propositions were assessed with the Artificial 
      Intelligence Performance Instrument. The stability of ChatGPT-4 was evaluated 
      through regenerated answers at 1-day interval. RESULTS: ChatGPT-4 provided 
      adequate explanations for cTNM staging in 19 cases (95%). ChatGPT-4 proposed a 
      significant higher number of additional examinations than practitioners (72 
      versus 103; p = 0.001). ChatGPT-4 indications of endoscopy-biopsy, HPV research, 
      ultrasonography, and PET-CT were consistent with the oncological board decisions. 
      The therapeutic propositions of ChatGPT-4 were accurate in 13 cases (65%). Most 
      additional examination and primary treatment propositions were consistent 
      throughout regenerated response process. CONCLUSIONS: ChatGPT-4 may be an 
      adjunctive theoretical tool in oncological board simple decisions.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Lechien, Jerome R
AU  - Lechien JR
AUID- ORCID: 0000-0002-0845-0845
AD  - Research Committee of Young-Otolaryngologists of the International Federations of 
      Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France. 
      Jerome.Lechien@umons.ac.be.
AD  - Department of Otolaryngology-Head Neck Surgery, Foch Hospital, UFR Simone Veil, 
      University Paris Saclay, Paris, France. Jerome.Lechien@umons.ac.be.
AD  - Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne 
      Nouvelle/Paris 3), Paris, France. Jerome.Lechien@umons.ac.be.
AD  - Department of Otorhinolaryngology and Head and Neck Surgery, CHU Saint-Pierre, 
      Brussels, Belgium. Jerome.Lechien@umons.ac.be.
AD  - Division of Laryngology and Broncho-Esophagology, Department of 
      Otolaryngology-Head and Neck Surgery, EpiCURA Hospital, Baudour, Belgium. 
      Jerome.Lechien@umons.ac.be.
FAU - Chiesa-Estomba, Carlos-Miguel
AU  - Chiesa-Estomba CM
AD  - Research Committee of Young-Otolaryngologists of the International Federations of 
      Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
AD  - Department of Otorhinolaryngology-Head and Neck Surgery, Hospital Universitario 
      Donostia, San Sebastian, Spain.
FAU - Baudouin, Robin
AU  - Baudouin R
AD  - Research Committee of Young-Otolaryngologists of the International Federations of 
      Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
AD  - Department of Otolaryngology-Head Neck Surgery, Foch Hospital, UFR Simone Veil, 
      University Paris Saclay, Paris, France.
AD  - Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne 
      Nouvelle/Paris 3), Paris, France.
FAU - Hans, Stéphane
AU  - Hans S
AD  - Research Committee of Young-Otolaryngologists of the International Federations of 
      Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
AD  - Department of Otolaryngology-Head Neck Surgery, Foch Hospital, UFR Simone Veil, 
      University Paris Saclay, Paris, France.
AD  - Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne 
      Nouvelle/Paris 3), Paris, France.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Decision
OT  - GPT
OT  - Head neck surgery
OT  - Otolaryngology
OT  - Performance
EDAT- 2023/11/22 12:43
MHDA- 2023/11/22 12:43
CRDT- 2023/11/22 11:03
PHST- 2023/10/05 00:00 [received]
PHST- 2023/10/27 00:00 [accepted]
PHST- 2023/11/22 12:43 [medline]
PHST- 2023/11/22 12:43 [pubmed]
PHST- 2023/11/22 11:03 [entrez]
AID - 10.1007/s00405-023-08326-w [pii]
AID - 10.1007/s00405-023-08326-w [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Nov 22. doi: 10.1007/s00405-023-08326-w.

PMID- 37720897
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230921
IS  - 1664-0640 (Print)
IS  - 1664-0640 (Electronic)
IS  - 1664-0640 (Linking)
VI  - 14
DP  - 2023
TI  - The plasticity of ChatGPT's mentalizing abilities: personalization for 
      personality structures.
PG  - 1234397
LID - 10.3389/fpsyt.2023.1234397 [doi]
LID - 1234397
AB  - This study evaluated the potential of ChatGPT, a large language model, to 
      generate mentalizing-like abilities that are tailored to a specific personality 
      structure and/or psychopathology. Mentalization is the ability to understand and 
      interpret one's own and others' mental states, including thoughts, feelings, and 
      intentions. Borderline Personality Disorder (BPD) and Schizoid Personality 
      Disorder (SPD) are characterized by distinct patterns of emotional regulation. 
      Individuals with BPD tend to experience intense and unstable emotions, while 
      individuals with SPD tend to experience flattened or detached emotions. We used 
      ChatGPT's free version 23.3 and assessed the extent to which its responses akin 
      to emotional awareness (EA) were customized to the distinctive personality 
      structure-character characterized by Borderline Personality Disorder (BPD) and 
      Schizoid Personality Disorder (SPD), employing the Levels of Emotional Awareness 
      Scale (LEAS). ChatGPT was able to accurately describe the emotional reactions of 
      individuals with BPD as more intense, complex, and rich than those with SPD. This 
      finding suggests that ChatGPT can generate mentalizing-like responses consistent 
      with a range of psychopathologies in line with clinical and theoretical 
      knowledge. However, the study also raises concerns regarding the potential for 
      stigmas or biases related to mental diagnoses to impact the validity and 
      usefulness of chatbot-based clinical interventions. We emphasize the need for the 
      responsible development and deployment of chatbot-based interventions in mental 
      health, which considers diverse theoretical frameworks.
CI  - Copyright © 2023 Hadar-Shoval, Elyoseph and Lvovsky.
FAU - Hadar-Shoval, Dorit
AU  - Hadar-Shoval D
AD  - Department of Psychology and Educational Counseling, The Center for 
      Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, 
      Israel.
FAU - Elyoseph, Zohar
AU  - Elyoseph Z
AD  - Department of Psychology and Educational Counseling, The Center for 
      Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, 
      Israel.
AD  - Department of Brain Sciences, Faculty of Medicine, Imperial College London, 
      London, United Kingdom.
AD  - Educational Psychology Department, Center for Psychobiological Research, Max 
      Stern Yezreel Valley College, Emek Yezreel, Israel.
FAU - Lvovsky, Maya
AU  - Lvovsky M
AD  - Educational Psychology Department, Center for Psychobiological Research, Max 
      Stern Yezreel Valley College, Emek Yezreel, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230901
PL  - Switzerland
TA  - Front Psychiatry
JT  - Frontiers in psychiatry
JID - 101545006
PMC - PMC10503434
OTO - NOTNLM
OT  - Schizoid Personality Disorder
OT  - artificial intelligence
OT  - borderline personality disorder
OT  - emotional awareness
OT  - emotional intelligence
OT  - empathy
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:43
CRDT- 2023/09/18 04:47
PHST- 2023/06/04 00:00 [received]
PHST- 2023/08/22 00:00 [accepted]
PHST- 2023/09/18 06:43 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:47 [entrez]
AID - 10.3389/fpsyt.2023.1234397 [doi]
PST - epublish
SO  - Front Psychiatry. 2023 Sep 1;14:1234397. doi: 10.3389/fpsyt.2023.1234397. 
      eCollection 2023.

PMID- 37162695
OWN - NLM
STAT- MEDLINE
DCOM- 20231010
LR  - 20231010
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT in Colorectal Surgery: A Promising Tool or a Passing Fad?
PG  - 1892-1897
LID - 10.1007/s10439-023-03232-y [doi]
AB  - Colorectal surgery is a specialized branch of surgery that involves the diagnosis 
      and treatment of conditions affecting the colon, rectum, and anus. In the recent 
      years, the use of artificial intelligence (AI) has gained considerable interest 
      in various medical specialties, including surgery. Chatbot Generative Pre-Trained 
      Transformer (ChatGPT), an AI-based chatbot developed by OpenAI, has shown great 
      potential in improving the quality of healthcare delivery by providing accurate 
      and timely information to both patients and healthcare professionals. In this 
      paper, we investigate the potential application of ChatGPT in colorectal surgery. 
      We also discuss the potential advantages and challenges associated with the 
      implementation of ChatGPT in the surgical setting. Furthermore, we address the 
      socio-ethical implications of utilizing ChatGPT in healthcare. This includes 
      concerns over patient privacy, liability, and the potential impact on the 
      doctor-patient relationship. Our findings suggest that ChatGPT has the potential 
      to revolutionize the field of colorectal surgery by providing personalized and 
      precise medical information, reducing errors and complications, and improving 
      patient outcomes.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Li, Wenbo
AU  - Li W
AD  - Department of Nursing, Jinzhou Medical University, Jinzhou, China.
FAU - Zhang, Yinxu
AU  - Zhang Y
AD  - Department of Colorectal Surgery, The First Affiliated Hospital, Jinzhou Medical 
      University, Jinzhou, 121001, China.
FAU - Chen, Fengmin
AU  - Chen F
AUID- ORCID: 0009-0003-6118-1554
AD  - Department of Colorectal Surgery, The First Affiliated Hospital, Jinzhou Medical 
      University, Jinzhou, 121001, China. 314307031@qq.com.
LA  - eng
PT  - Letter
DEP - 20230510
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Colorectal Surgery/education/trends
MH  - *Artificial Intelligence
MH  - Physician-Patient Relations
MH  - Software
MH  - Clinical Decision-Making
MH  - Postoperative Care
MH  - Privacy
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Colorectal surgery
EDAT- 2023/05/10 12:42
MHDA- 2023/08/09 06:43
CRDT- 2023/05/10 11:14
PHST- 2023/05/02 00:00 [received]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 11:14 [entrez]
AID - 10.1007/s10439-023-03232-y [pii]
AID - 10.1007/s10439-023-03232-y [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Sep;51(9):1892-1897. doi: 10.1007/s10439-023-03232-y. Epub 
      2023 May 10.

PMID- 37668790
OWN - NLM
STAT- MEDLINE
DCOM- 20231117
LR  - 20231117
IS  - 1432-0711 (Electronic)
IS  - 0932-0067 (Linking)
VI  - 308
IP  - 6
DP  - 2023 Dec
TI  - Performance of ChatGPT in Israeli Hebrew OBGYN national residency examinations.
PG  - 1797-1802
LID - 10.1007/s00404-023-07185-4 [doi]
AB  - PURPOSE: Previous studies of ChatGPT performance in the field of medical 
      examinations have reached contradictory results. Moreover, the performance of 
      ChatGPT in other languages other than English is yet to be explored. We aim to 
      study the performance of ChatGPT in Hebrew OBGYN-'Shlav-Alef' (Phase 1) 
      examination. METHODS: A performance study was conducted using a consecutive 
      sample of text-based multiple choice questions, originated from authentic Hebrew 
      OBGYN-'Shlav-Alef' examinations in 2021-2022. We constructed 150 multiple choice 
      questions from consecutive text-based-only original questions. We compared the 
      performance of ChatGPT performance to the real-life actual performance of OBGYN 
      residents who completed the tests in 2021-2022. We also compared ChatGTP Hebrew 
      performance vs. previously published English medical tests. RESULTS: In 
      2021-2022, 27.8% of OBGYN residents failed the 'Shlav-Alef' examination and the 
      mean score of the residents was 68.4. Overall, 150 authentic questions were 
      evaluated (one examination). ChatGPT correctly answered 58 questions (38.7%) and 
      reached a failed score. The performance of Hebrew ChatGPT was lower when compared 
      to actual performance of residents: 38.7% vs. 68.4%, p < .001. In a comparison to 
      ChatGPT performance in 9,091 English language questions in the field of medicine, 
      the performance of Hebrew ChatGPT was lower (38.7% in Hebrew vs. 60.7% in 
      English, p < .001). CONCLUSIONS: ChatGPT answered correctly on less than 40% of 
      Hebrew OBGYN resident examination questions. Residents cannot rely on ChatGPT for 
      the preparation of this examination. Efforts should be made to improve ChatGPT 
      performance in other languages besides English.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Cohen, Adiel
AU  - Cohen A
AUID- ORCID: 0000-0002-6218-1156
AD  - Department of Obstetrics and Gynecology, Hadassah Medical Organization and 
      Faculty of Medicine, Hebrew University of Jerusalem, Ein Kerem, P.O.B. 12000, 
      91120, Jerusalem, Israel. adielic@gmail.com.
FAU - Alter, Roie
AU  - Alter R
AD  - Department of Obstetrics and Gynecology, Hadassah Medical Organization and 
      Faculty of Medicine, Hebrew University of Jerusalem, Ein Kerem, P.O.B. 12000, 
      91120, Jerusalem, Israel.
FAU - Lessans, Naama
AU  - Lessans N
AD  - Department of Obstetrics and Gynecology, Hadassah Medical Organization and 
      Faculty of Medicine, Hebrew University of Jerusalem, Ein Kerem, P.O.B. 12000, 
      91120, Jerusalem, Israel.
FAU - Meyer, Raanan
AU  - Meyer R
AD  - Department of Obstetrics and Gynecology, Chaim Sheba Medical Center, Ramat-Gan, 
      Israel.
AD  - Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
AD  - Cedar-Sinai Medical Center, Los Angeles, USA.
FAU - Brezinov, Yoav
AU  - Brezinov Y
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Montreal, Canada.
FAU - Levin, Gabriel
AU  - Levin G
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Montreal, Canada.
AD  - The Department of Gynecoloic Oncology, Hadassah Medical Center, Faculty of 
      Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230905
PL  - Germany
TA  - Arch Gynecol Obstet
JT  - Archives of gynecology and obstetrics
JID - 8710213
SB  - IM
MH  - Humans
MH  - *Internship and Residency
MH  - Israel
MH  - Language
MH  - Physical Examination
MH  - Artificial Intelligence
OTO - NOTNLM
OT  - ChatGPT
OT  - Hebrew
OT  - OBGYN
OT  - Performance
OT  - Test
EDAT- 2023/09/05 12:42
MHDA- 2023/11/17 15:29
CRDT- 2023/09/05 11:14
PHST- 2023/06/11 00:00 [received]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/11/17 15:29 [medline]
PHST- 2023/09/05 12:42 [pubmed]
PHST- 2023/09/05 11:14 [entrez]
AID - 10.1007/s00404-023-07185-4 [pii]
AID - 10.1007/s00404-023-07185-4 [doi]
PST - ppublish
SO  - Arch Gynecol Obstet. 2023 Dec;308(6):1797-1802. doi: 10.1007/s00404-023-07185-4. 
      Epub 2023 Sep 5.

PMID- 37155982
OWN - NLM
STAT- MEDLINE
DCOM- 20230510
LR  - 20230516
IS  - 1538-0688 (Electronic)
IS  - 0190-535X (Linking)
VI  - 50
IP  - 3
DP  - 2023 Apr 21
TI  - Artificial Intelligence for Oncology Nursing Authors: Potential Utility and 
      Concerns About Large Language Model Chatbots.
PG  - 276-277
LID - 10.1188/23.ONF.276-277 [doi]
AB  - Artificial intelligence is a revolution in the computing and data scientist era 
      that has led to excitement and controversy in many fields, including research and 
      publishing. As we move further into the artificial intelligence.
FAU - Lyon, Debra
AU  - Lyon D
AD  - University of Florida.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Oncol Nurs Forum
JT  - Oncology nursing forum
JID - 7809033
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Oncology Nursing
MH  - Language
MH  - *Physicians
MH  - Publishing
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbots
OT  - large language model
OT  - publishing
OT  - research
EDAT- 2023/05/08 18:42
MHDA- 2023/05/10 06:42
CRDT- 2023/05/08 16:53
PHST- 2023/05/10 06:42 [medline]
PHST- 2023/05/08 18:42 [pubmed]
PHST- 2023/05/08 16:53 [entrez]
AID - 10.1188/23.ONF.276-277 [doi]
PST - ppublish
SO  - Oncol Nurs Forum. 2023 Apr 21;50(3):276-277. doi: 10.1188/23.ONF.276-277.

PMID- 37028488
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230816
IS  - 1937-5913 (Electronic)
IS  - 1542-0124 (Linking)
VI  - 28
DP  - 2023 Apr
TI  - Readership awareness series - Paper 4: Chatbots and ChatGPT - Ethical 
      considerations in scientific publications.
PG  - 153-154
LID - S1542-0124(23)00027-7 [pii]
LID - 10.1016/j.jtos.2023.04.001 [doi]
FAU - Ali, Mohammad Javed
AU  - Ali MJ
FAU - Djalilian, Ali
AU  - Djalilian A
LA  - eng
PT  - Editorial
DEP - 20230406
PL  - United States
TA  - Ocul Surf
JT  - The ocular surface
JID - 101156063
SB  - IM
MH  - *Publishing
MH  - *Authorship
OTO - NOTNLM
OT  - Authorship
OT  - ChatGPT
OT  - Chatbot
OT  - Ethics
OT  - Publication
OT  - Scientific
COIS- Declaration of competing interest None.
EDAT- 2023/04/08 06:00
MHDA- 2023/08/14 06:43
CRDT- 2023/04/07 19:24
PHST- 2023/03/17 00:00 [received]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/08/14 06:43 [medline]
PHST- 2023/04/08 06:00 [pubmed]
PHST- 2023/04/07 19:24 [entrez]
AID - S1542-0124(23)00027-7 [pii]
AID - 10.1016/j.jtos.2023.04.001 [doi]
PST - ppublish
SO  - Ocul Surf. 2023 Apr;28:153-154. doi: 10.1016/j.jtos.2023.04.001. Epub 2023 Apr 6.

PMID- 37337487
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230621
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - Recent Trend in Artificial Intelligence-Assisted Biomedical Publishing: A 
      Quantitative Bibliometric Analysis.
PG  - e39224
LID - 10.7759/cureus.39224 [doi]
LID - e39224
AB  - The rapid advancements in artificial intelligence (AI) technology in recent years 
      have led to its integration into biomedical publishing. However, the extent to 
      which AI has contributed to developing biomedical literature is unclear. This 
      study aimed to identify trends in AI-generated content within peer-reviewed 
      biomedical literature. We first tested the sensitivity and specificity of 
      commercially available AI-detection software (Originality.AI, Collingwood, 
      Ontario, Canada). Next, we conducted a MEDLINE (Medical Literature Analysis and 
      Retrieval System Online) search to identify randomized controlled trials with 
      available abstracts indexed between January 2020 and March 2023. We randomly 
      selected 30 abstracts per quarter during this period and pasted the abstracts 
      into the AI detection software to determine the probability of AI-generated 
      content. The software yielded 100% sensitivity, 95% specificity, and excellent 
      overall discriminatory ability with an area under the receiving operating curve 
      of 97.6%. Among the 390 MEDLINE-indexed abstracts included in the analysis, the 
      prevalence with a high probability (≥ 90%) of AI-generated text increased during 
      the study period from 21.7% to 36.7% (p=0.01) based on a chi-square test for 
      trend. The increasing prevalence of AI-generated text during the study period was 
      also observed in various sensitivity analyses using AI probability thresholds 
      ranging from 50% to 99% (all p≤0.01). The results of this study suggest that the 
      prevalence of AI-assisted publishing in peer-reviewed journals has been 
      increasing in recent years, even before the widespread adoption of ChatGPT 
      (OpenAI, San Francisco, California, United States) and similar tools. The extent 
      to which natural writing characteristics of the authors, utilization of common 
      AI-powered applications, and introduction of AI elements during the 
      post-acceptance publication phase influence AI detection scores warrants further 
      study.
CI  - Copyright © 2023, Miller et al.
FAU - Miller, Larry E
AU  - Miller LE
AD  - Clinical Research, Miller Scientific, Johnson City, USA.
FAU - Bhattacharyya, Debjani
AU  - Bhattacharyya D
AD  - Education, University of Massachusetts Lowell, Lowell, USA.
FAU - Miller, Valerie M
AU  - Miller VM
AD  - Leadership, University of the Cumberlands, Williamsburg, USA.
FAU - Bhattacharyya, Mehul
AU  - Bhattacharyya M
AD  - Clinical Research, Miller Scientific, Johnson City, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230519
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10277011
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - large language model
OT  - machine learning
OT  - references
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/20 06:42
MHDA- 2023/06/20 06:43
CRDT- 2023/06/20 02:25
PHST- 2023/05/19 00:00 [accepted]
PHST- 2023/06/20 06:43 [medline]
PHST- 2023/06/20 06:42 [pubmed]
PHST- 2023/06/20 02:25 [entrez]
AID - 10.7759/cureus.39224 [doi]
PST - epublish
SO  - Cureus. 2023 May 19;15(5):e39224. doi: 10.7759/cureus.39224. eCollection 2023 
      May.

PMID- 36813155
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230320
LR  - 20230320
IS  - 1873-7544 (Electronic)
IS  - 0306-4522 (Linking)
VI  - 515
DP  - 2023 Apr 1
TI  - ChatGPT in Research: Balancing Ethics, Transparency and Advancement.
PG  - 71-73
LID - S0306-4522(23)00079-9 [pii]
LID - 10.1016/j.neuroscience.2023.02.008 [doi]
FAU - Graf, Akseli
AU  - Graf A
AD  - Institute of Psychopharmacology, Central Institute of Mental Health, Medical 
      Faculty Mannheim, University of Heidelberg, Mannheim, Germany. Electronic 
      address: Akseli.Graf@zi-mannheim.de.
FAU - Bernardi, Rick E
AU  - Bernardi RE
AD  - Institute of Psychopharmacology, Central Institute of Mental Health, Medical 
      Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230221
PL  - United States
TA  - Neuroscience
JT  - Neuroscience
JID - 7605074
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - ethics
OT  - large language models
EDAT- 2023/02/23 06:00
MHDA- 2023/02/23 06:01
CRDT- 2023/02/22 19:29
PHST- 2023/02/08 00:00 [received]
PHST- 2023/02/11 00:00 [accepted]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/02/23 06:01 [medline]
PHST- 2023/02/22 19:29 [entrez]
AID - S0306-4522(23)00079-9 [pii]
AID - 10.1016/j.neuroscience.2023.02.008 [doi]
PST - ppublish
SO  - Neuroscience. 2023 Apr 1;515:71-73. doi: 10.1016/j.neuroscience.2023.02.008. Epub 
      2023 Feb 21.

PMID- 37891532
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231030
IS  - 1471-2334 (Electronic)
IS  - 1471-2334 (Linking)
VI  - 23
IP  - 1
DP  - 2023 Oct 27
TI  - ChatGPT and mycosis- a new weapon in the knowledge battlefield.
PG  - 731
LID - 10.1186/s12879-023-08724-9 [doi]
LID - 731
AB  - As current trend for physician tools, ChatGPT can sift through massive amounts of 
      information and solve problems through easy-to-understand conversations, 
      ultimately improving efficiency. Mycosis is currently facing great challenges, 
      including high fungal burdens, high mortality, limited choice of antifungal drugs 
      and increasing drug resistance. To address these challenges, We asked ChatGPT for 
      fungal infection scenario-based questions and assessed its appropriateness, 
      consistency, and potential pitfalls. We concluded ChatGPT can provide compelling 
      responses to most prompts, including diagnosis, recommendations for examination, 
      treatment and rational drug use. Moreover, we summarized exciting future 
      applications in mycosis, such as clinical work, scientific research, education 
      and healthcare. However, the largest barriers to implementation are deficits in 
      indiviudal advice, timely literature updates, consistency, accuracy and data 
      safety. To fully embrace the opportunity, we need to address these barriers and 
      manage the risks. We expect that ChatGPT will become a new weapon in in the 
      battlefield of mycosis.
CI  - © 2023. The Author(s).
FAU - Jin, Yi
AU  - Jin Y
AD  - Department of Dermatology, Shanghai Key Laboratory of Medical Mycology, Second 
      Affiliated Hospital of Naval Medical University, Shanghai, 200003, P.R. China.
FAU - Liu, Hua
AU  - Liu H
AD  - Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao 
      Tong University School of Medicine, Shanghai, China.
FAU - Zhao, Bin
AU  - Zhao B
AD  - Department of Anesthesiology and SICU, Xinhua Hospital, School of Medicine, 
      Shanghai Jiao Tong University, Shanghai, 200092, P.R. China. 
      zbgbtj0926@gmail.com.
FAU - Pan, Weihua
AU  - Pan W
AD  - Department of Dermatology, Shanghai Key Laboratory of Medical Mycology, Second 
      Affiliated Hospital of Naval Medical University, Shanghai, 200003, P.R. China. 
      panweihua9@sina.com.
LA  - eng
GR  - grant no. 2021QN38/Naval Medical University/
PT  - Journal Article
DEP - 20231027
PL  - England
TA  - BMC Infect Dis
JT  - BMC infectious diseases
JID - 100968551
RN  - 0 (Antifungal Agents)
SB  - IM
MH  - Humans
MH  - *Mycoses/diagnosis/drug therapy
MH  - Antifungal Agents/therapeutic use
MH  - Communication
MH  - Educational Status
MH  - Health Facilities
PMC - PMC10605453
OTO - NOTNLM
OT  - Applications
OT  - ChatGPT
OT  - Mycosis
COIS- The authors declare no competing interests.
EDAT- 2023/10/28 11:42
MHDA- 2023/10/30 06:47
CRDT- 2023/10/27 23:47
PHST- 2023/08/27 00:00 [received]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/10/28 11:42 [pubmed]
PHST- 2023/10/27 23:47 [entrez]
AID - 10.1186/s12879-023-08724-9 [pii]
AID - 8724 [pii]
AID - 10.1186/s12879-023-08724-9 [doi]
PST - epublish
SO  - BMC Infect Dis. 2023 Oct 27;23(1):731. doi: 10.1186/s12879-023-08724-9.

PMID- 37986988
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231208
DP  - 2023 Nov 7
TI  - ChatGPT to enhance learning in dental education at a historically black medical 
      college.
LID - rs.3.rs-3546693 [pii]
LID - 10.21203/rs.3.rs-3546693/v1 [doi]
AB  - In dental education of today, the didactic curriculum primarily relies on 
      lecture-based courses, where instructors impart knowledge through presentations 
      and discussions. The recent rise of powerful large language model-based tools, 
      exemplified by ChatGPT (Chat Generative Pre-Trained Transformer), poses a great 
      challenge to traditional dental education while simultaneously offering a unique 
      resource and approach that potentially complements today's teaching and learning, 
      where existing widely available learning resources have often fallen short. In 
      this paper, we used dental course materials, syllabi, and textbooks adopted 
      currently in the School of Dentistry at Meharry Medical College to assess the 
      potential utility and effectiveness of ChatGPT in dental education. We collected 
      the responses of the chatbot to questions as well as students' interactions with 
      it for assessment. Our results showed that ChatGPT can assist in dental essay 
      writing and generate relevant content for dental students, in addition to other 
      benefits. The limitations of ChatGPT were also discussed in the paper.
FAU - Rahad, Khandoker
AU  - Rahad K
AD  - Meharry Medical College.
FAU - Martin, Kianna
AU  - Martin K
AD  - Meharry Medical College.
FAU - Amugo, Ihunna
AU  - Amugo I
AD  - Meharry Medical College.
FAU - Ferguson, Shania
AU  - Ferguson S
AD  - Meharry Medical College.
FAU - Curtis, Angela
AU  - Curtis A
AD  - Meharry Medical College.
FAU - Davis, Anniya
AU  - Davis A
AD  - Meharry Medical College.
FAU - Gangula, Pandu
AU  - Gangula P
AD  - Meharry Medical College.
FAU - Wang, Qingguo
AU  - Wang Q
AUID- ORCID: 0000-0002-5125-3724
AD  - Meharry Medical College.
LA  - eng
GR  - OT2 OD032581/OD/NIH HHS/United States
GR  - U01 DE033241/DE/NIDCR NIH HHS/United States
GR  - U54 MD007586/MD/NIMHD NIH HHS/United States
PT  - Preprint
DEP - 20231107
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10659452
OTO - NOTNLM
OT  - ChatGPT
OT  - Dental Education
OT  - Dentistry
OT  - Medical School
OT  - Student Learning
COIS- Competing interests: There is no conflict of interest in connection with this 
      article.
EDAT- 2023/11/21 06:42
MHDA- 2023/11/21 06:43
CRDT- 2023/11/21 05:09
PHST- 2023/11/21 06:42 [pubmed]
PHST- 2023/11/21 06:43 [medline]
PHST- 2023/11/21 05:09 [entrez]
AID - rs.3.rs-3546693 [pii]
AID - 10.21203/rs.3.rs-3546693/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 Nov 7:rs.3.rs-3546693. doi: 10.21203/rs.3.rs-3546693/v1. Preprint.

PMID- 37725885
OWN - NLM
STAT- Publisher
LR  - 20231006
IS  - 1873-5150 (Electronic)
IS  - 0887-8994 (Linking)
VI  - 148
DP  - 2023 Nov
TI  - Leveraging ChatGPT in the Pediatric Neurology Clinic: Practical Considerations 
      for Use to Improve Efficiency and Outcomes.
PG  - 157-163
LID - S0887-8994(23)00303-X [pii]
LID - 10.1016/j.pediatrneurol.2023.08.035 [doi]
AB  - BACKGROUND: Artificial intelligence (AI) is progressively influencing healthcare 
      sectors, including pediatric neurology. This paper aims to investigate the 
      potential and limitations of using ChatGPT, a large language model (LLM) 
      developed by OpenAI, in an outpatient pediatric neurology clinic. The analysis 
      focuses on the tool's capabilities in enhancing clinical efficiency, 
      productivity, and patient education. METHOD: This is an opinion-based exploration 
      supplemented with practical examples. We assessed ChatGPT's utility in 
      administrative and educational tasks such as drafting medical necessity letters 
      and creating patient educational materials. RESULTS: ChatGPT showed efficacy in 
      streamlining administrative work, particularly in drafting administrative letters 
      and formulating personalized patient education materials. However, the model has 
      limitations in performing higher-order tasks like formulating nuanced 
      differential diagnoses. Additionally, ethical and legal concerns, including data 
      privacy and the potential dissemination of misinformation, warrant cautious 
      implementation. CONCLUSIONS: The integration of AI tools like ChatGPT in 
      pediatric neurology clinics has demonstrated promising results in boosting 
      efficiency and patient education, despite present limitations and ethical 
      concerns. As technology advances, we anticipate future applications may extend to 
      more complex clinical tasks like precise differential diagnoses and treatment 
      strategy guidance. Careful, patient-centered implementation is essential for 
      leveraging the potential benefits of AI in pediatric neurology effectively.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Karakas, Cemal
AU  - Karakas C
AD  - Division of Pediatric Neurology, Department of Neurology, University of 
      Louisville, Louisville, Kentucky; Norton Neuroscience Institute, Louisville, 
      Kentucky. Electronic address: cemal.karakas@louisville.edu.
FAU - Brock, Dylan
AU  - Brock D
AD  - Division of Pediatric Neurology, Department of Neurology, University of 
      Louisville, Louisville, Kentucky; Norton Neuroscience Institute, Louisville, 
      Kentucky.
FAU - Lakhotia, Arpita
AU  - Lakhotia A
AD  - Division of Pediatric Neurology, Department of Neurology, University of 
      Louisville, Louisville, Kentucky; Norton Neuroscience Institute, Louisville, 
      Kentucky.
LA  - eng
PT  - Journal Article
DEP - 20230829
PL  - United States
TA  - Pediatr Neurol
JT  - Pediatric neurology
JID - 8508183
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Clinic
OT  - Efficiency
OT  - Letters
OT  - Practical
OT  - Workflow
COIS- Declaration of competing interest All authors declare that they have no conflict 
      of interests.
EDAT- 2023/09/20 00:43
MHDA- 2023/09/20 00:43
CRDT- 2023/09/19 18:02
PHST- 2023/05/15 00:00 [received]
PHST- 2023/08/17 00:00 [revised]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/09/20 00:43 [pubmed]
PHST- 2023/09/20 00:43 [medline]
PHST- 2023/09/19 18:02 [entrez]
AID - S0887-8994(23)00303-X [pii]
AID - 10.1016/j.pediatrneurol.2023.08.035 [doi]
PST - ppublish
SO  - Pediatr Neurol. 2023 Nov;148:157-163. doi: 10.1016/j.pediatrneurol.2023.08.035. 
      Epub 2023 Aug 29.

PMID- 37662036
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230905
IS  - 1664-2295 (Print)
IS  - 1664-2295 (Electronic)
IS  - 1664-2295 (Linking)
VI  - 14
DP  - 2023
TI  - The utility of ChatGPT in the assessment of literature on the prevention of 
      migraine: an observational, qualitative study.
PG  - 1225223
LID - 10.3389/fneur.2023.1225223 [doi]
LID - 1225223
AB  - BACKGROUND: It is not known how large language models, such as ChatGPT, can be 
      applied toward the assessment of the efficacy of medications, including in the 
      prevention of migraine, and how it might support those claims with existing 
      medical evidence. METHODS: We queried ChatGPT-3.5 on the efficacy of 47 
      medications for the prevention of migraine and then asked it to give citations in 
      support of its assessment. ChatGPT's evaluations were then compared to their FDA 
      approval status for this indication as well as the American Academy of Neurology 
      2012 evidence-based guidelines for the prevention of migraine. The citations 
      ChatGPT generated for these evaluations were then assessed to see if they were 
      real papers and if they were relevant to the query. RESULTS: ChatGPT affirmed 
      that the 14 medications that have either received FDA approval for prevention of 
      migraine or AAN Grade A/B evidence were effective for migraine. Its assessments 
      of the other 33 medications were unreliable including suggesting possible 
      efficacy for four medications that have never been used for the prevention of 
      migraine. Critically, only 33/115 (29%) of the papers ChatGPT cited were real, 
      while 76/115 (66%) were "hallucinated" not real papers and 6/115 (5%) shared the 
      names of real papers but had not real citations. CONCLUSION: While ChatGPT 
      produced tailored answers on the efficacy of the queried medications, the results 
      were unreliable and inaccurate because of the overwhelming volume of 
      "hallucinated" articles it generated and cited.
CI  - Copyright © 2023 Moskatel and Zhang.
FAU - Moskatel, Leon S
AU  - Moskatel LS
AD  - Division of Headache and Facial Pain, Department of Neurology, Stanford 
      University, Palo Alto, CA, United States.
FAU - Zhang, Niushen
AU  - Zhang N
AD  - Division of Headache and Facial Pain, Department of Neurology, Stanford 
      University, Palo Alto, CA, United States.
LA  - eng
PT  - Journal Article
DEP - 20230817
PL  - Switzerland
TA  - Front Neurol
JT  - Frontiers in neurology
JID - 101546899
PMC - PMC10469750
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - evidence-based medicine
OT  - migraine
OT  - preventive medications
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/04 06:42
MHDA- 2023/09/04 06:43
CRDT- 2023/09/04 04:35
PHST- 2023/05/18 00:00 [received]
PHST- 2023/08/07 00:00 [accepted]
PHST- 2023/09/04 06:43 [medline]
PHST- 2023/09/04 06:42 [pubmed]
PHST- 2023/09/04 04:35 [entrez]
AID - 10.3389/fneur.2023.1225223 [doi]
PST - epublish
SO  - Front Neurol. 2023 Aug 17;14:1225223. doi: 10.3389/fneur.2023.1225223. 
      eCollection 2023.

PMID- 37611254
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231213
IS  - 1572-0241 (Electronic)
IS  - 0002-9270 (Linking)
VI  - 118
IP  - 12
DP  - 2023 Dec 1
TI  - Towards AI-Augmented Clinical Decision-Making: An Examination of ChatGPT's 
      Utility in Acute Ulcerative Colitis Presentations.
PG  - 2283-2289
LID - 10.14309/ajg.0000000000002483 [doi]
AB  - This study explores the potential of OpenAI's ChatGPT as a decision support tool 
      for acute ulcerative colitis presentations in the setting of an emergency 
      department. We assessed ChatGPT's performance in determining disease severity 
      using TrueLove and Witts criteria and the necessity of hospitalization for 
      patients with ulcerative colitis, comparing results with those of expert 
      gastroenterologists. Of 20 cases, ChatGPT's assessments were found to be 80% 
      consistent with gastroenterologist evaluations and indicated a high degree of 
      reliability. This suggests that ChatGPT could provide as a clinical decision 
      support tool in assessing acute ulcerative colitis, serving as an adjunct to 
      clinical judgment.
CI  - Copyright © 2023 by The American College of Gastroenterology.
FAU - Levartovsky, Asaf
AU  - Levartovsky A
AD  - Department of Gastroenterology, Sheba Medical Center, Affiliated to Tel Aviv 
      University, Tel Aviv, Israel.
FAU - Ben-Horin, Shomron
AU  - Ben-Horin S
AD  - Department of Gastroenterology, Sheba Medical Center, Affiliated to Tel Aviv 
      University, Tel Aviv, Israel.
FAU - Kopylov, Uri
AU  - Kopylov U
AD  - Department of Gastroenterology, Sheba Medical Center, Affiliated to Tel Aviv 
      University, Tel Aviv, Israel.
FAU - Klang, Eyal
AU  - Klang E
AUID- ORCID: 0000-0002-4567-3108
AD  - Department of Diagnostic Imaging, Sheba Medical Center, Affiliated to Tel Aviv 
      University, Tel Aviv, Israel.
AD  - DeepVision Lab, Sheba Medical Center, Affiliated to Tel Aviv University, Tel 
      Aviv, Israel.
FAU - Barash, Yiftach
AU  - Barash Y
AUID- ORCID: 0000-0002-7242-1328
AD  - Department of Diagnostic Imaging, Sheba Medical Center, Affiliated to Tel Aviv 
      University, Tel Aviv, Israel.
AD  - DeepVision Lab, Sheba Medical Center, Affiliated to Tel Aviv University, Tel 
      Aviv, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230823
PL  - United States
TA  - Am J Gastroenterol
JT  - The American journal of gastroenterology
JID - 0421030
SB  - IM
MH  - Humans
MH  - *Colitis, Ulcerative/diagnosis
MH  - Reproducibility of Results
MH  - Clinical Decision-Making
MH  - Emergency Service, Hospital
MH  - Artificial Intelligence
EDAT- 2023/08/23 18:42
MHDA- 2023/12/04 12:41
CRDT- 2023/08/23 16:53
PHST- 2023/06/01 00:00 [received]
PHST- 2023/08/04 00:00 [accepted]
PHST- 2023/12/04 12:41 [medline]
PHST- 2023/08/23 18:42 [pubmed]
PHST- 2023/08/23 16:53 [entrez]
AID - 00000434-202312000-00033 [pii]
AID - 10.14309/ajg.0000000000002483 [doi]
PST - ppublish
SO  - Am J Gastroenterol. 2023 Dec 1;118(12):2283-2289. doi: 
      10.14309/ajg.0000000000002483. Epub 2023 Aug 23.

PMID- 37553556
OWN - NLM
STAT- Publisher
LR  - 20230808
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Aug 8
TI  - Enhancing Diabetes Self-management and Education: A Critical Analysis of 
      ChatGPT's Role.
LID - 10.1007/s10439-023-03317-8 [doi]
AB  - ChatGPT, an advanced natural language processing model, holds significant promise 
      in diabetes self-management and education. ChatGPT excels in providing 
      personalized educational experiences by tailoring information to meet individual 
      patient needs and preferences. It aids patients in developing self-management 
      skills and strategies, fostering proactive disease management. Additionally, 
      ChatGPT addresses healthcare access disparities by enabling patients to access 
      educational resources irrespective of their geographic location or physical 
      limitations. However, it is important to acknowledge and address the deficiencies 
      of ChatGPT, such as its limited medical expertise, contextual understanding, and 
      emotional support capabilities. Strategies for optimizing ChatGPT include regular 
      training and updating, integration of healthcare professionals' expertise, 
      improvement in contextual comprehension, and enhancing emotional support. By 
      addressing these limitations and striking a balance between the benefits and 
      limitations, ChatGPT can play a significant role in empowering patients to better 
      understand and manage diabetes. Further research and development are needed to 
      refine ChatGPT's capabilities and address ethical considerations, but its 
      integration in patient education holds the potential to transform healthcare 
      delivery and create a more informed and engaged patient population.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Zheng, Yue
AU  - Zheng Y
AUID- ORCID: 0000-0003-3865-7051
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, 
      China.
FAU - Wu, Yijun
AU  - Wu Y
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, 
      China.
FAU - Feng, Baijie
AU  - Feng B
AD  - West China School of Medicine, Sichuan University, Chengdu, 610041, China.
FAU - Wang, Laduona
AU  - Wang L
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, 
      China.
FAU - Kang, Kai
AU  - Kang K
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, 
      China. kaikang@wchscu.cn.
FAU - Zhao, Ailin
AU  - Zhao A
AD  - Department of Hematology, West China Hospital, Sichuan University, Chengdu, 
      610041, Sichuan, China. irenez20@outlook.com.
LA  - eng
GR  - 2023NSFSC1885/Natural Science Foundation of Sichuan Province/
GR  - 2022SCUH0025/"from zero to one" Innovation Research Project of Sichuan 
      University/
GR  - 2022-YF05-01443-SN/Chengdu Science and Technology Program/
GR  - 23ZDYF2836/Key Research and Development Program of Sichuan Province/
GR  - 2021M692310/China Postdoctoral Science Foundation/
GR  - 82204490/National Natural Science Foundation of China/
PT  - Letter
DEP - 20230808
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Diabetes self-management and education
EDAT- 2023/08/09 01:05
MHDA- 2023/08/09 01:05
CRDT- 2023/08/08 23:30
PHST- 2023/07/06 00:00 [received]
PHST- 2023/07/10 00:00 [accepted]
PHST- 2023/08/09 01:05 [medline]
PHST- 2023/08/09 01:05 [pubmed]
PHST- 2023/08/08 23:30 [entrez]
AID - 10.1007/s10439-023-03317-8 [pii]
AID - 10.1007/s10439-023-03317-8 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Aug 8. doi: 10.1007/s10439-023-03317-8.

PMID- 37428540
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230727
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Jul 10
TI  - Putting ChatGPT's Medical Advice to the (Turing) Test: Survey Study.
PG  - e46939
LID - 10.2196/46939 [doi]
LID - e46939
AB  - BACKGROUND: Chatbots are being piloted to draft responses to patient questions, 
      but patients' ability to distinguish between provider and chatbot responses and 
      patients' trust in chatbots' functions are not well established. OBJECTIVE: This 
      study aimed to assess the feasibility of using ChatGPT (Chat Generative 
      Pre-trained Transformer) or a similar artificial intelligence-based chatbot for 
      patient-provider communication. METHODS: A survey study was conducted in January 
      2023. Ten representative, nonadministrative patient-provider interactions were 
      extracted from the electronic health record. Patients' questions were entered 
      into ChatGPT with a request for the chatbot to respond using approximately the 
      same word count as the human provider's response. In the survey, each patient 
      question was followed by a provider- or ChatGPT-generated response. Participants 
      were informed that 5 responses were provider generated and 5 were chatbot 
      generated. Participants were asked-and incentivized financially-to correctly 
      identify the response source. Participants were also asked about their trust in 
      chatbots' functions in patient-provider communication, using a Likert scale from 
      1-5. RESULTS: A US-representative sample of 430 study participants aged 18 and 
      older were recruited on Prolific, a crowdsourcing platform for academic studies. 
      In all, 426 participants filled out the full survey. After removing participants 
      who spent less than 3 minutes on the survey, 392 respondents remained. Overall, 
      53.3% (209/392) of respondents analyzed were women, and the average age was 47.1 
      (range 18-91) years. The correct classification of responses ranged between 49% 
      (192/392) to 85.7% (336/392) for different questions. On average, chatbot 
      responses were identified correctly in 65.5% (1284/1960) of the cases, and human 
      provider responses were identified correctly in 65.1% (1276/1960) of the cases. 
      On average, responses toward patients' trust in chatbots' functions were weakly 
      positive (mean Likert score 3.4 out of 5), with lower trust as the health-related 
      complexity of the task in the questions increased. CONCLUSIONS: ChatGPT responses 
      to patient questions were weakly distinguishable from provider responses. 
      Laypeople appear to trust the use of chatbots to answer lower-risk health 
      questions. It is important to continue studying patient-chatbot interaction as 
      chatbots move from administrative to more clinical roles in health care.
CI  - ©Oded Nov, Nina Singh, Devin Mann. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 10.07.2023.
FAU - Nov, Oded
AU  - Nov O
AUID- ORCID: 0000-0001-6410-2995
AD  - Department of Technology Management, Tandon School of Engineering, New York 
      University, New York, NY, United States.
FAU - Singh, Nina
AU  - Singh N
AUID- ORCID: 0000-0002-4623-2451
AD  - Department of Population Health, Grossman School of Medicine, New York 
      University, New York, NY, United States.
FAU - Mann, Devin
AU  - Mann D
AUID- ORCID: 0000-0002-2099-0852
AD  - Department of Population Health, Grossman School of Medicine, New York 
      University, New York, NY, United States.
AD  - Medical Center Information Technology, Langone Health, New York University, New 
      York, NY, United States.
LA  - eng
PT  - Journal Article
DEP - 20230710
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10366957
OTO - NOTNLM
OT  - AI
OT  - ChatGPT, Chat Generative Pre-trained Transformer
OT  - artificial intelligence
OT  - chatbot
OT  - ethics
OT  - feasibility
OT  - language model
OT  - large language model
OT  - machine learning
OT  - patient-provider interaction
OT  - privacy
COIS- Conflicts of Interest: None declared.
EDAT- 2023/07/10 13:06
MHDA- 2023/07/10 13:07
CRDT- 2023/07/10 11:54
PHST- 2023/03/02 00:00 [received]
PHST- 2023/06/14 00:00 [accepted]
PHST- 2023/05/26 00:00 [revised]
PHST- 2023/07/10 13:07 [medline]
PHST- 2023/07/10 13:06 [pubmed]
PHST- 2023/07/10 11:54 [entrez]
AID - v9i1e46939 [pii]
AID - 10.2196/46939 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Jul 10;9:e46939. doi: 10.2196/46939.

PMID- 37655838
OWN - NLM
STAT- Publisher
LR  - 20230901
IS  - 1879-3479 (Electronic)
IS  - 0020-7292 (Linking)
DP  - 2023 Sep 1
TI  - Performance of ChatGPT in French language Parcours d'Accès Spécifique Santé test 
      and in OBGYN.
LID - 10.1002/ijgo.15083 [doi]
AB  - OBJECTIVES: To evaluate the performance of ChatGPT in a French medical school 
      entrance examination. METHODS: A cross-sectional study using a consecutive sample 
      of text-based multiple-choice practice questions for the Parcours d'Accès 
      Spécifique Santé. ChatGPT answered questions in French. We compared performance 
      of ChatGPT in obstetrics and gynecology (OBGYN) and in the whole test. RESULTS: 
      Overall, 885 questions were evaluated. The mean test score was 34.0% (306; 
      maximal score of 900). The performance of ChatGPT was 33.0% (292 correct answers, 
      885 questions). The performance of ChatGPT was lower in biostatistics 
      (13.3% ± 19.7%) than in anatomy (34.2% ± 17.9%; P = 0.037) and also lower than in 
      histology and embryology (40.0% ± 18.5%; P = 0.004). The OBGYN part had 290 
      questions. There was no difference in the test scores and the performance of 
      ChatGPT in OBGYN versus the whole entrance test (P = 0.76 vs P = 0.10, 
      respectively). CONCLUSIONS: ChatGPT answered one-third of questions correctly in 
      the French test preparation. The performance in OBGYN was similar.
CI  - © 2023 The Authors. International Journal of Gynecology & Obstetrics published by 
      John Wiley & Sons Ltd on behalf of International Federation of Gynecology and 
      Obstetrics.
FAU - Guigue, Paul-Adrien
AU  - Guigue PA
AD  - University Claude Bernard Lyon I, Lyon, France.
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Montreal, Quebec, Canada.
FAU - Meyer, Raanan
AU  - Meyer R
AD  - Department of Obstetrics and Gynecology, Chaim Sheba Medical Center, Ramat-Gan, 
      Israel.
AD  - Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
AD  - Cedar-Sinai Medical Center, Los Angeles, California, USA.
FAU - Thivolle-Lioux, Gaetan
AU  - Thivolle-Lioux G
AD  - University Claude Bernard Lyon I, Lyon, France.
AD  - Centre de Recherche en Cancérologie de Lyon (CRCL), Lyon, France.
FAU - Brezinov, Yoav
AU  - Brezinov Y
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Montreal, Quebec, Canada.
FAU - Levin, Gabriel
AU  - Levin G
AUID- ORCID: 0000-0003-1282-5379
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Montreal, Quebec, Canada.
AD  - The Department of Gynecologic Oncology, Hadassah Medical Center, Faculty of 
      Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230901
PL  - United States
TA  - Int J Gynaecol Obstet
JT  - International journal of gynaecology and obstetrics: the official organ of the 
      International Federation of Gynaecology and Obstetrics
JID - 0210174
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - French
OT  - OBGYN
OT  - large language models
OT  - performance
OT  - test
EDAT- 2023/09/01 12:42
MHDA- 2023/09/01 12:42
CRDT- 2023/09/01 08:33
PHST- 2023/08/06 00:00 [revised]
PHST- 2023/05/07 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/09/01 12:42 [medline]
PHST- 2023/09/01 12:42 [pubmed]
PHST- 2023/09/01 08:33 [entrez]
AID - 10.1002/ijgo.15083 [doi]
PST - aheadofprint
SO  - Int J Gynaecol Obstet. 2023 Sep 1. doi: 10.1002/ijgo.15083.

PMID- 37934828
OWN - NLM
STAT- Publisher
LR  - 20231108
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
DP  - 2023 Nov 7
TI  - Examining ChatGPT Performance on USMLE Sample Items and Implications for 
      Assessment.
LID - 10.1097/ACM.0000000000005549 [doi]
AB  - PURPOSE: In late 2022 and early 2023, reports that ChatGPT could pass the United 
      States Medical Licensing Examination (USMLE) generated considerable excitement, 
      and media response suggested that ChatGPT has credible medical knowledge. This 
      report analyzes the extent to which an artificial intelligence (AI) agent's 
      performance on these sample items can generalize to performance on an actual 
      USMLE examination and an illustration is given using ChatGPT. METHOD: As with 
      earlier investigations, analyses were based on publicly available USMLE sample 
      items. Each item was submitted to ChatGPT 3 times to evaluate stability. 
      Responses were scored following rules that match operational practice, and a 
      preliminary analysis explored the characteristics of items that ChatGPT answered 
      correctly. The study was conducted between February and March 2023. RESULTS: For 
      the full sample of items, ChatGPT scored above 60% correct except for one 
      replication for Step 3. Response success varied across replications for 76 items 
      (20%). There was a modest correspondence with item difficulty wherein ChatGPT was 
      more likely to respond correctly to items found easier by examinees. ChatGPT 
      performed significantly worse ( P < .001) on items relating to practice-based 
      learning. CONCLUSIONS: Achieving 60% accuracy is an approximate indicator of 
      meeting the passing standard, requiring statistical adjustments for comparison. 
      Hence this assessment can only suggest consistency with the passing standards for 
      Steps 1 and 2 Clinical Knowledge, with further limitations in extrapolating this 
      inference to Step 3. These limitations are due to variances in item difficulty 
      and exclusion of the simulation component of Step 3 from the 
      evaluation-limitations that would apply to any AI system evaluated on the Step 3 
      sample items. It is crucial to note that responses from large language models 
      exhibit notable variations when faced with repeated inquiries, underscoring the 
      need for expert validation to ensure their utility as a learning tool.
CI  - Copyright © 2023 the Association of American Medical Colleges.
FAU - Yaneva, Victoria
AU  - Yaneva V
AD  - V. Yaneva is manager, Natural Language Processing Research, Office of Research 
      Strategy, National Board of Medical Examiners, Philadelphia, Pennsylvania.
FAU - Baldwin, Peter
AU  - Baldwin P
AD  - P. Baldwin is principal measurement scientist, Office of Research Strategy, 
      National Board of Medical Examiners, Philadelphia, Pennsylvania.
FAU - Jurich, Daniel P
AU  - Jurich DP
AD  - D.P. Jurich is associate vice president, United States Medical Licensing 
      Examination, National Board of Medical Examiners, Philadelphia, Pennsylvania.
FAU - Swygert, Kimberly
AU  - Swygert K
AD  - K. Swygert is director, Test Development Innovations, National Board of Medical 
      Examiners, Philadelphia, Pennsylvania.
FAU - Clauser, Brian E
AU  - Clauser BE
AD  - B.E. Clauser is distinguished research scientist, Office of Research Strategy, 
      National Board of Medical Examiners, Philadelphia, Pennsylvania.
LA  - eng
PT  - Journal Article
DEP - 20231107
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
EDAT- 2023/11/07 18:42
MHDA- 2023/11/07 18:42
CRDT- 2023/11/07 14:13
PHST- 2023/11/07 18:42 [pubmed]
PHST- 2023/11/07 18:42 [medline]
PHST- 2023/11/07 14:13 [entrez]
AID - 00001888-990000000-00638 [pii]
AID - 10.1097/ACM.0000000000005549 [doi]
PST - aheadofprint
SO  - Acad Med. 2023 Nov 7. doi: 10.1097/ACM.0000000000005549.

PMID- 36966950
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230926
IS  - 2468-7855 (Electronic)
IS  - 2468-7855 (Linking)
VI  - 124
IP  - 5
DP  - 2023 Oct
TI  - The role of using ChatGPT AI in writing medical scientific articles.
PG  - 101456
LID - S2468-7855(23)00078-2 [pii]
LID - 10.1016/j.jormas.2023.101456 [doi]
AB  - The use of artificial intelligence (AI) in medical research is on the rise. This 
      article explores the role of using ChatGPT, a language model developed by OpenAI, 
      in writing medical scientific articles. The material and methods used included a 
      comparative analysis of medical scientific articles produced with and without the 
      use of ChatGPT. The results suggest that the use of ChatGPT can be a useful tool 
      for scientists to increase the production of higher quality medical scientific 
      articles, but it is important to note that AI cannot fully replace human authors. 
      In conclusion, scientists should consider ChatGPT as an additional tool to 
      produce higher quality medical scientific articles more quickly.
CI  - Copyright © 2023 Elsevier Masson SAS. All rights reserved.
FAU - Benichou, L
AU  - Benichou L
AD  - Service de chirurgie maxillo-faciale et stomatologie, Groupe Hospitalier Paris 
      St-Joseph, 185 rue Raymond Losserand 75014 Paris, France. Electronic address: 
      lbenichou@ghpsj.fr.
CN  - ChatGPT
AD  - OpenAI, 3180 18th St, San Francisco, CA 94110, États-Unis.
LA  - eng
PT  - Journal Article
DEP - 20230324
PL  - France
TA  - J Stomatol Oral Maxillofac Surg
JT  - Journal of stomatology, oral and maxillofacial surgery
JID - 101701089
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Writing
MH  - Language
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Medical research
OT  - Medical scientific articles
COIS- Declaration of Competing Interest The authors declare no conflicts of interest.
EDAT- 2023/03/27 06:00
MHDA- 2023/09/25 06:42
CRDT- 2023/03/26 20:26
PHST- 2023/02/15 00:00 [received]
PHST- 2023/03/15 00:00 [revised]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/03/27 06:00 [pubmed]
PHST- 2023/03/26 20:26 [entrez]
AID - S2468-7855(23)00078-2 [pii]
AID - 10.1016/j.jormas.2023.101456 [doi]
PST - ppublish
SO  - J Stomatol Oral Maxillofac Surg. 2023 Oct;124(5):101456. doi: 
      10.1016/j.jormas.2023.101456. Epub 2023 Mar 24.

PMID- 37593450
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230823
IS  - 1664-0640 (Print)
IS  - 1664-0640 (Electronic)
IS  - 1664-0640 (Linking)
VI  - 14
DP  - 2023
TI  - Beyond human expertise: the promise and limitations of ChatGPT in suicide risk 
      assessment.
PG  - 1213141
LID - 10.3389/fpsyt.2023.1213141 [doi]
LID - 1213141
AB  - ChatGPT, an artificial intelligence language model developed by OpenAI, holds the 
      potential for contributing to the field of mental health. Nevertheless, although 
      ChatGPT theoretically shows promise, its clinical abilities in suicide 
      prevention, a significant mental health concern, have yet to be demonstrated. To 
      address this knowledge gap, this study aims to compare ChatGPT's assessments of 
      mental health indicators to those of mental health professionals in a 
      hypothetical case study that focuses on suicide risk assessment. Specifically, 
      ChatGPT was asked to evaluate a text vignette describing a hypothetical patient 
      with varying levels of perceived burdensomeness and thwarted belongingness. The 
      ChatGPT assessments were compared to the norms of mental health professionals. 
      The results indicated that ChatGPT rated the risk of suicide attempts lower than 
      did the mental health professionals in all conditions. Furthermore, ChatGPT rated 
      mental resilience lower than the norms in most conditions. These results imply 
      that gatekeepers, patients or even mental health professionals who rely on 
      ChatGPT for evaluating suicidal risk or as a complementary tool to improve 
      decision-making may receive an inaccurate assessment that underestimates the 
      actual suicide risk.
CI  - Copyright © 2023 Elyoseph and Levkovich.
FAU - Elyoseph, Zohar
AU  - Elyoseph Z
AD  - Department of Psychology and Educational Counseling, The Center for 
      Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, 
      Israel.
AD  - Department of Brain Sciences, Faculty of Medicine, Imperial College London, 
      London, United Kingdom.
FAU - Levkovich, Inbar
AU  - Levkovich I
AD  - Faculty of Graduate Studies, Oranim Academic College, Kiryat Tiv'on, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - Switzerland
TA  - Front Psychiatry
JT  - Frontiers in psychiatry
JID - 101545006
PMC - PMC10427505
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - diagnosis
OT  - psychological assessment
OT  - risk assessment
OT  - suicide risk
OT  - text vignette
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.All claims expressed in this article are solely those of the 
      authors and do not necessarily represent those of their affiliated organizations, 
      or those of the publisher, the editors and the reviewers. Any product that may be 
      evaluated in this article, or claim that may be made by its manufacturer, is not 
      guaranteed or endorsed by the publisher.
EDAT- 2023/08/18 06:43
MHDA- 2023/08/18 06:44
CRDT- 2023/08/18 03:54
PHST- 2023/04/28 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/08/18 06:44 [medline]
PHST- 2023/08/18 06:43 [pubmed]
PHST- 2023/08/18 03:54 [entrez]
AID - 10.3389/fpsyt.2023.1213141 [doi]
PST - epublish
SO  - Front Psychiatry. 2023 Aug 1;14:1213141. doi: 10.3389/fpsyt.2023.1213141. 
      eCollection 2023.

PMID- 38090410
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231213
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - The Impact of Chat Generative Pre-trained Transformer (ChatGPT) on Oncology: 
      Application, Expectations, and Future Prospects.
PG  - e48670
LID - 10.7759/cureus.48670 [doi]
AB  - The integration of Chat Generative Pre-trained Transformer (ChatGPT) into the 
      field of oncology has recently garnered significant attention, with potential 
      implications for enhancing both research and clinical practice. This paper 
      presents a comprehensive review of the evolving landscape of ChatGPT applications 
      in oncology, emphasizing its contributions to diagnosis, treatment, research, and 
      patient education. We examine its role in assisting medical professionals, 
      researchers, and patients in understanding complex cancer-related data and making 
      informed decisions. Ethical considerations and potential challenges associated 
      with the implementation of ChatGPT in oncology are also discussed. This article 
      highlights the promising role of ChatGPT as a valuable tool in the oncology 
      domain and outlines future directions for research and application.
CI  - Copyright © 2023, Li et al.
FAU - Li, Yanxing
AU  - Li Y
AD  - Department of Clinical Oncology, Xi'an Jiaotong University Health Science Center, 
      Xi'an, CHN.
FAU - Gao, Wentao
AU  - Gao W
AD  - Department of Clinical Oncology, Xi'an Jiaotong University Health Science Center, 
      Xi'an, CHN.
FAU - Luan, Zhenhua
AU  - Luan Z
AD  - Department of Clinical Oncology, Xi'an Jiaotong University Health Science Center, 
      Xi'an, CHN.
FAU - Zhou, Zhi
AU  - Zhou Z
AD  - Department of Clinical Oncology, Xi'an Jiaotong University Health Science Center, 
      Xi'an, CHN.
FAU - Li, Jianjun
AU  - Li J
AD  - Department of Cardiology, Jincheng People's Hospital, Changzhi Medical College, 
      Jincheng, CHN.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231111
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10714025
OTO - NOTNLM
OT  - artificial intelligence
OT  - cancer
OT  - chatgpt
OT  - diagnosis
OT  - oncology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/13 18:42
MHDA- 2023/12/13 18:43
CRDT- 2023/12/13 13:08
PHST- 2023/12/13 18:43 [medline]
PHST- 2023/12/13 18:42 [pubmed]
PHST- 2023/11/11 00:00 [accepted]
PHST- 2023/12/13 13:08 [entrez]
AID - 10.7759/cureus.48670 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 11;15(11):e48670. doi: 10.7759/cureus.48670. eCollection 2023 
      Nov.

PMID- 38021917
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Using ChatGPT to Predict Cancer Predisposition Genes: A Promising Tool for 
      Pediatric Oncologists.
PG  - e47594
LID - 10.7759/cureus.47594 [doi]
LID - e47594
AB  - BACKGROUND: Determining genetic susceptibility for cancer predisposition 
      syndromes (CPS) through cancer predisposition genes (CPGs) testing is critical in 
      facilitating appropriate prevention and surveillance strategies. This study 
      investigates the use of ChatGPT, a large language model, in predicting CPGs using 
      clinical notes. METHODS: Our study involved 53 patients with pathogenic CPG 
      mutations. Two kinds of clinical notes were used: the first visit note, 
      containing a thorough history and physical exam, and the genetic clinic note, 
      summarizing the patient's diagnosis and family history. We asked ChatGPT to 
      recommend CPS genes based on these notes and compared these predictions with 
      previously identified mutations. RESULTS: Rb1 was the most frequently mutated 
      gene in our cohort (34%), followed by NF1 (9.4%), TP53 (5.7%), and VHL (5.7%). 
      Out of 53 patients, 30 had genetic clinic notes of a median length of 54 words. 
      ChatGPT correctly predicted the gene in 93% of these cases. However, it failed to 
      predict EPCAM and VHL genes in specific patients. For the first visit notes 
      (median length: 461 words), ChatGPT correctly predicted the gene in 64% of these 
      cases. CONCLUSION: ChatGPT shows promise in predicting CPGs from clinical notes, 
      particularly genetic clinic notes. This approach may be useful in enhancing CPG 
      testing, especially in areas lacking genetic testing resources. With further 
      training, there is a possibility for ChatGPT to improve its predictive 
      potential and expand its clinical applicability. However, additional research is 
      needed to explore the full potential and applicability of ChatGPT.
CI  - Copyright © 2023, Sultan et al.
FAU - Sultan, Iyad
AU  - Sultan I
AD  - Department of Pediatrics, King Hussein Cancer Center, Amman, JOR.
FAU - Al-Abdallat, Haneen
AU  - Al-Abdallat H
AD  - Department of Medicine, University of Jordan, Amman, JOR.
FAU - Alnajjar, Zaina
AU  - Alnajjar Z
AD  - Department of Medicine, Hashemite University, Zarqa, JOR.
FAU - Ismail, Layan
AU  - Ismail L
AD  - Department of Medicine, University of Jordan, Amman, JOR.
FAU - Abukhashabeh, Razan
AU  - Abukhashabeh R
AD  - Department of Cell Therapy and Applied Genomics, King Hussein Cancer Center, 
      Amman, JOR.
FAU - Bitar, Layla
AU  - Bitar L
AD  - Department of Pediatric Oncology, King Hussein Cancer Center, Amman, JOR.
FAU - Abu Shanap, Mayada
AU  - Abu Shanap M
AD  - Department of Pediatric Oncology, King Hussein Cancer Center, Amman, JOR.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10666922
OTO - NOTNLM
OT  - cancer
OT  - chatgpt
OT  - genes
OT  - oncology
OT  - pediatrics
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:14
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:14 [entrez]
AID - 10.7759/cureus.47594 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 24;15(10):e47594. doi: 10.7759/cureus.47594. eCollection 2023 
      Oct.

PMID- 37855948
OWN - NLM
STAT- Publisher
LR  - 20231019
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Oct 19
TI  - Examining the Potential of ChatGPT on Biomedical Information Retrieval: 
      Fact-Checking Drug-Disease Associations.
LID - 10.1007/s10439-023-03385-w [doi]
AB  - Large language models (LLMs) such as ChatGPT have recently attracted significant 
      attention due to their impressive performance on many real-world tasks. These 
      models have also demonstrated the potential in facilitating various biomedical 
      tasks. However, little is known of their potential in biomedical information 
      retrieval, especially identifying drug-disease associations. This study aims to 
      explore the potential of ChatGPT, a popular LLM, in discerning drug-disease 
      associations. We collected 2694 true drug-disease associations and 5662 false 
      drug-disease pairs. Our approach involved creating various prompts to instruct 
      ChatGPT in identifying these associations. Under varying prompt designs, 
      ChatGPT's capability to identify drug-disease associations with an accuracy of 
      74.6-83.5% and 96.2-97.6% for the true and false pairs, respectively. This study 
      shows that ChatGPT has the potential in identifying drug-disease associations and 
      may serve as a helpful tool in searching pharmacy-related information. However, 
      the accuracy of its insights warrants comprehensive examination before its 
      implementation in medical practice.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Gao, Zhenxiang
AU  - Gao Z
AUID- ORCID: 0000-0002-2223-5092
AD  - Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case 
      Western Reserve University, Cleveland, OH, USA.
FAU - Li, Lingyao
AU  - Li L
AD  - School of Information, University of Michigan, Ann Arbor, MI, USA.
FAU - Ma, Siyuan
AU  - Ma S
AD  - Vanderbilt University Medical Center, Nashville, TN, USA.
FAU - Wang, Qinyong
AU  - Wang Q
AD  - Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case 
      Western Reserve University, Cleveland, OH, USA.
FAU - Hemphill, Libby
AU  - Hemphill L
AD  - School of Information, University of Michigan, Ann Arbor, MI, USA.
FAU - Xu, Rong
AU  - Xu R
AD  - Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case 
      Western Reserve University, Cleveland, OH, USA. rxx@case.edu.
LA  - eng
GR  - AA029831/AA/NIAAA NIH HHS/United States
GR  - AG057557/AG/NIA NIH HHS/United States
GR  - AG061388/AG/NIA NIH HHS/United States
GR  - AG062272/AG/NIA NIH HHS/United States
GR  - AG07664/AG/NIA NIH HHS/United States
PT  - Letter
DEP - 20231019
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Biomedical information retrieval
OT  - ChatGPT
OT  - Drug-disease associations
OT  - Generative AI
OT  - Performance evaluation
OT  - Prompt design
EDAT- 2023/10/19 12:43
MHDA- 2023/10/19 12:43
CRDT- 2023/10/19 11:17
PHST- 2023/09/13 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/10/19 12:43 [medline]
PHST- 2023/10/19 12:43 [pubmed]
PHST- 2023/10/19 11:17 [entrez]
AID - 10.1007/s10439-023-03385-w [pii]
AID - 10.1007/s10439-023-03385-w [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Oct 19. doi: 10.1007/s10439-023-03385-w.

PMID- 37789443
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231122
IS  - 2052-3211 (Print)
IS  - 2052-3211 (Electronic)
IS  - 2052-3211 (Linking)
VI  - 16
IP  - 1
DP  - 2023 Oct 3
TI  - ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian 
      pharmacists' perception, practice, and concerns.
PG  - 115
LID - 10.1186/s40545-023-00624-2 [doi]
LID - 115
AB  - OBJECTIVES: The purpose of this study is to find out how much pharmacists know 
      and have used ChatGPT in their practice. We investigated the advantages and 
      disadvantages of utilizing ChatGPT in a pharmacy context, the amount of training 
      necessary to use it proficiently, and the influence on patient care using a 
      survey. METHODS: This cross-sectional study was carried out between May and June 
      2023 to assess the potential and problems that pharmacists observed while 
      integrating chatbots powered by AI (ChatGPT) in pharmacy practice. The 
      correlation between perceived benefits and concerns was evaluated using 
      Spearman's rho correlation due to the data's non-normal distribution.Any 
      pharmacists licensed by the Jordanian Pharmacists Association were included in 
      the study. A convenient sampling technique was used to choose the participants, 
      and the study questionnaire was distributed utilizing an online medium (Facebook 
      and WhatsApp). Anyone who expressed interest in taking part was given a link to 
      the study's instructions so they may read them before giving their electronic 
      consent and accessing the survey. RESULTS: The potential advantages of ChatGPT in 
      the pharmacy practice were widely acknowledged by the participants. The majority 
      of participants (69.9%) concurred that educational material about pharmacy items 
      or therapeutic areas can be provided using ChatGPT, with 66.9% of respondents 
      believing that ChatGPT is a machine learning algorithm. Concerns about the 
      accuracy of AI-generated responses were also prevalent. More than half of the 
      participants (55.7%) raised the possibility that AI systems such as ChatGPT could 
      pick up on and replicate prejudices and discriminatory patterns from the data 
      they were trained on. Analysis shows a statistically significant positive link, 
      albeit a minor one, between the perceived advantages of ChatGPT and its drawbacks 
      (r = 0.255, p < 0.001). However, concerns were strongly correlated with knowledge 
      of ChatGPT. In contrast to those who were either unsure or had not heard of 
      ChatGPT (64.2%), individuals who had heard of it were more likely to have strong 
      concerns (79.8%) (p = 0.002). Finally, the results show a statistically 
      significant association between the frequency of ChatGPT use and positive 
      perceptions of the tool (p < 0.001). CONCLUSIONS: Although ChatGPT has shown 
      promise in health and pharmaceutical practice, its application should be 
      rigorously regulated by evidence-based law. According to the study's findings, 
      pharmacists support the use of ChatGPT in pharmacy practice but have concerns 
      about its use due to ethical reasons, legal problems, privacy concerns, worries 
      about the accuracy of the data generated, data learning, and bias risk.
CI  - © 2023. Dr. Zaheer-Ud-Din Babar and Auckland UniServices Ltd.
FAU - Abu Hammour, Khawla
AU  - Abu Hammour K
AD  - Department of Clinical Pharmacy and Biopharmaceutics, Faculty of Pharmacy, 
      University of Jordan, Amman, Jordan.
FAU - Alhamad, Hamza
AU  - Alhamad H
AD  - Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 
      Jordan.
FAU - Al-Ashwal, Fahmi Y
AU  - Al-Ashwal FY
AUID- ORCID: 0000-0003-2076-0771
AD  - Department of Clinical Pharmacy, College of Pharmacy, Al-Ayen University, 
      Thi-Qar, Iraq. fahmialashwal89@gmail.com.
AD  - Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, 
      University of Science and Technology, Sana'a, Yemen. fahmialashwal89@gmail.com.
FAU - Halboup, Abdulsalam
AU  - Halboup A
AD  - Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, 
      University of Science and Technology, Sana'a, Yemen.
AD  - Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, University 
      Sains Malaysia, Gelugor, Pulau Pinang, Malaysia.
FAU - Abu Farha, Rana
AU  - Abu Farha R
AD  - Clinical Pharmacy and Therapeutics Department, Faculty of Pharmacy, Applied 
      Science Private University, P.O. Box 11937, Amman, Jordan.
FAU - Abu Hammour, Adnan
AU  - Abu Hammour A
AD  - Medrise Medical Center, Dubai Healthcare City, Dubai, United Arab Emirates.
LA  - eng
PT  - Journal Article
DEP - 20231003
PL  - England
TA  - J Pharm Policy Pract
JT  - Journal of pharmaceutical policy and practice
JID - 101627192
EIN - J Pharm Policy Pract. 2023 Oct 30;16(1):129. PMID: 37904185
PMC - PMC10548710
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Pharmacy practice
COIS- The Authors declare that there is no conflict of interest.
EDAT- 2023/10/04 00:42
MHDA- 2023/10/04 00:43
CRDT- 2023/10/03 23:55
PHST- 2023/07/13 00:00 [received]
PHST- 2023/09/22 00:00 [accepted]
PHST- 2023/10/04 00:43 [medline]
PHST- 2023/10/04 00:42 [pubmed]
PHST- 2023/10/03 23:55 [entrez]
AID - 10.1186/s40545-023-00624-2 [pii]
AID - 624 [pii]
AID - 10.1186/s40545-023-00624-2 [doi]
PST - epublish
SO  - J Pharm Policy Pract. 2023 Oct 3;16(1):115. doi: 10.1186/s40545-023-00624-2.

PMID- 37548997
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230824
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Aug 7
TI  - Performance of ChatGPT on the Situational Judgement Test-A Professional 
      Dilemmas-Based Examination for Doctors in the United Kingdom.
PG  - e48978
LID - 10.2196/48978 [doi]
LID - e48978
AB  - BACKGROUND: ChatGPT is a large language model that has performed well on 
      professional examinations in the fields of medicine, law, and business. However, 
      it is unclear how ChatGPT would perform on an examination assessing 
      professionalism and situational judgement for doctors. OBJECTIVE: We evaluated 
      the performance of ChatGPT on the Situational Judgement Test (SJT): a national 
      examination taken by all final-year medical students in the United Kingdom. This 
      examination is designed to assess attributes such as communication, teamwork, 
      patient safety, prioritization skills, professionalism, and ethics. METHODS: All 
      questions from the UK Foundation Programme Office's (UKFPO's) 2023 SJT practice 
      examination were inputted into ChatGPT. For each question, ChatGPT's answers and 
      rationales were recorded and assessed on the basis of the official UK Foundation 
      Programme Office scoring template. Questions were categorized into domains of 
      Good Medical Practice on the basis of the domains referenced in the rationales 
      provided in the scoring sheet. Questions without clear domain links were screened 
      by reviewers and assigned one or multiple domains. ChatGPT's overall performance, 
      as well as its performance across the domains of Good Medical Practice, was 
      evaluated. RESULTS: Overall, ChatGPT performed well, scoring 76% on the SJT but 
      scoring full marks on only a few questions (9%), which may reflect possible flaws 
      in ChatGPT's situational judgement or inconsistencies in the reasoning across 
      questions (or both) in the examination itself. ChatGPT demonstrated consistent 
      performance across the 4 outlined domains in Good Medical Practice for doctors. 
      CONCLUSIONS: Further research is needed to understand the potential applications 
      of large language models, such as ChatGPT, in medical education for standardizing 
      questions and providing consistent rationales for examinations assessing 
      professionalism and ethics.
CI  - ©Robin J Borchert, Charlotte R Hickman, Jack Pepys, Timothy J Sadler. Originally 
      published in JMIR Medical Education (https://mededu.jmir.org), 07.08.2023.
FAU - Borchert, Robin J
AU  - Borchert RJ
AUID- ORCID: 0000-0002-4673-9746
AD  - Department of Radiology, University of Cambridge, Cambridge, United Kingdom.
AD  - Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals 
      NHS Foundation Trust, Cambridge, United Kingdom.
FAU - Hickman, Charlotte R
AU  - Hickman CR
AUID- ORCID: 0000-0003-3228-1479
AD  - Department of General Medicine, Lister Hospital, East and North Hertfordshire NHS 
      Trust, Stevenage, United Kingdom.
FAU - Pepys, Jack
AU  - Pepys J
AUID- ORCID: 0000-0002-1441-0145
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Italy.
FAU - Sadler, Timothy J
AU  - Sadler TJ
AUID- ORCID: 0000-0003-3710-3137
AD  - Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals 
      NHS Foundation Trust, Cambridge, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230807
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10442724
OTO - NOTNLM
OT  - ChatGPT
OT  - SJT
OT  - Situational Judgement Test
OT  - artificial intelligence
OT  - chatbot
OT  - communication
OT  - exam
OT  - examination
OT  - judgement
OT  - language model
OT  - language models
OT  - medical education
OT  - reasoning
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/07 13:10
MHDA- 2023/08/07 13:11
CRDT- 2023/08/07 11:53
PHST- 2023/05/16 00:00 [received]
PHST- 2023/07/25 00:00 [accepted]
PHST- 2023/06/30 00:00 [revised]
PHST- 2023/08/07 13:11 [medline]
PHST- 2023/08/07 13:10 [pubmed]
PHST- 2023/08/07 11:53 [entrez]
AID - v9i1e48978 [pii]
AID - 10.2196/48978 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Aug 7;9:e48978. doi: 10.2196/48978.

PMID- 37728984
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231007
IS  - 2368-7959 (Print)
IS  - 2368-7959 (Electronic)
IS  - 2368-7959 (Linking)
VI  - 10
DP  - 2023 Sep 20
TI  - Suicide Risk Assessments Through the Eyes of ChatGPT-3.5 Versus ChatGPT-4: 
      Vignette Study.
PG  - e51232
LID - 10.2196/51232 [doi]
LID - e51232
AB  - BACKGROUND: ChatGPT, a linguistic artificial intelligence (AI) model engineered 
      by OpenAI, offers prospective contributions to mental health professionals. 
      Although having significant theoretical implications, ChatGPT's practical 
      capabilities, particularly regarding suicide prevention, have not yet been 
      substantiated. OBJECTIVE: The study's aim was to evaluate ChatGPT's ability to 
      assess suicide risk, taking into consideration 2 discernable factors-perceived 
      burdensomeness and thwarted belongingness-over a 2-month period. In addition, we 
      evaluated whether ChatGPT-4 more accurately evaluated suicide risk than did 
      ChatGPT-3.5. METHODS: ChatGPT was tasked with assessing a vignette that depicted 
      a hypothetical patient exhibiting differing degrees of perceived burdensomeness 
      and thwarted belongingness. The assessments generated by ChatGPT were 
      subsequently contrasted with standard evaluations rendered by mental health 
      professionals. Using both ChatGPT-3.5 and ChatGPT-4 (May 24, 2023), we executed 3 
      evaluative procedures in June and July 2023. Our intent was to scrutinize 
      ChatGPT-4's proficiency in assessing various facets of suicide risk in relation 
      to the evaluative abilities of both mental health professionals and an earlier 
      version of ChatGPT-3.5 (March 14 version). RESULTS: During the period of June and 
      July 2023, we found that the likelihood of suicide attempts as evaluated by 
      ChatGPT-4 was similar to the norms of mental health professionals (n=379) under 
      all conditions (average Z score of 0.01). Nonetheless, a pronounced discrepancy 
      was observed regarding the assessments performed by ChatGPT-3.5 (May version), 
      which markedly underestimated the potential for suicide attempts, in comparison 
      to the assessments carried out by the mental health professionals (average Z 
      score of -0.83). The empirical evidence suggests that ChatGPT-4's evaluation of 
      the incidence of suicidal ideation and psychache was higher than that of the 
      mental health professionals (average Z score of 0.47 and 1.00, respectively). 
      Conversely, the level of resilience as assessed by both ChatGPT-4 and ChatGPT-3.5 
      (both versions) was observed to be lower in comparison to the assessments offered 
      by mental health professionals (average Z score of -0.89 and -0.90, 
      respectively). CONCLUSIONS: The findings suggest that ChatGPT-4 estimates the 
      likelihood of suicide attempts in a manner akin to evaluations provided by 
      professionals. In terms of recognizing suicidal ideation, ChatGPT-4 appears to be 
      more precise. However, regarding psychache, there was an observed overestimation 
      by ChatGPT-4, indicating a need for further research. These results have 
      implications regarding ChatGPT-4's potential to support gatekeepers, patients, 
      and even mental health professionals' decision-making. Despite the clinical 
      potential, intensive follow-up studies are necessary to establish the use of 
      ChatGPT-4's capabilities in clinical practice. The finding that ChatGPT-3.5 
      frequently underestimates suicide risk, especially in severe cases, is 
      particularly troubling. It indicates that ChatGPT may downplay one's actual 
      suicide risk level.
CI  - ©Inbar Levkovich, Zohar Elyoseph. Originally published in JMIR Mental Health 
      (https://mental.jmir.org), 20.09.2023.
FAU - Levkovich, Inbar
AU  - Levkovich I
AUID- ORCID: 0000-0003-1582-3889
AD  - Oranim Academic College, Faculty of Graduate Studies, Kiryat Tivon, Israel.
FAU - Elyoseph, Zohar
AU  - Elyoseph Z
AUID- ORCID: 0000-0002-5717-4074
AD  - Department of Psychology and Educational Counseling, The Center for 
      Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, 
      Israel.
AD  - Department of Brain Sciences, Faculty of Medicine, Imperial College London, 
      London, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - Canada
TA  - JMIR Ment Health
JT  - JMIR mental health
JID - 101658926
PMC - PMC10551796
OTO - NOTNLM
OT  - ChatGPT
OT  - NLP
OT  - artificial intelligence
OT  - assessment
OT  - assessments
OT  - diagnosis
OT  - mental
OT  - natural language processing
OT  - psychological
OT  - psychological assessment
OT  - risk
OT  - risk assessment
OT  - self-harm
OT  - suicidal
OT  - suicide
OT  - suicide risk
OT  - text vignette
OT  - vignette
OT  - vignettes
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/20 18:42
MHDA- 2023/09/20 18:43
CRDT- 2023/09/20 12:17
PHST- 2023/07/25 00:00 [received]
PHST- 2023/08/24 00:00 [accepted]
PHST- 2023/08/22 00:00 [revised]
PHST- 2023/09/20 18:43 [medline]
PHST- 2023/09/20 18:42 [pubmed]
PHST- 2023/09/20 12:17 [entrez]
AID - v10i1e51232 [pii]
AID - 10.2196/51232 [doi]
PST - epublish
SO  - JMIR Ment Health. 2023 Sep 20;10:e51232. doi: 10.2196/51232.

PMID- 37040060
OWN - NLM
STAT- MEDLINE
DCOM- 20230512
LR  - 20230512
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 6
DP  - 2023 Jun
TI  - Transforming Maritime Health with ChatGPT-Powered Healthcare Services for 
      Mariners.
PG  - 1123-1125
LID - 10.1007/s10439-023-03195-0 [doi]
AB  - The maritime industry is vital to international trade, however, it also poses 
      inimitable challenges to the health and well-being of mariners. Long voyages at 
      sea might make it grim to receive high-quality healthcare. This is a descriptive 
      study that highlights the use of ChatGPT in providing healthcare amenities to 
      mariners. AI technologies can revolutionize maritime healthcare to tackle this 
      issue. ChatGPT, a state-of-the-art AI system developed by OpenAI can provide 
      valuable support for the health and welfare of seafarers'. By harnessing the 
      extensive expertise and conversational capacities of ChatGPT, maritime industries 
      can provide personalized and prompt healthcare services to their stakeholders. 
      This research work will highlight how ChatGPT-powered healthcare services can 
      boost the health and well-being of seafarers. ChatGPT has the potential to 
      revolutionize the marine sector by enabling virtual consultations with healthcare 
      professionals for the analysis of health data. The assimilation of ChatGPT 
      technology into maritime healthcare has the potential to revolutionize the way 
      seafarers receive care and support. Certainly, some challenges need to be taken 
      into consideration.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Sharma, Manik
AU  - Sharma M
AUID- ORCID: 0000-0002-5942-134X
AD  - DAV University Jalandhar, Jalandhar, Punjab, India. manik_sharma25@yahoo.com.
FAU - Sharma, Samriti
AU  - Sharma S
AD  - Guru Nanak Dev University, Amritsar, Punjab, India.
LA  - eng
PT  - Letter
DEP - 20230411
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Ships
MH  - Commerce
MH  - *Occupational Health
MH  - Internationality
MH  - Delivery of Health Care
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Health
OT  - Mariner
OT  - Maritime
EDAT- 2023/04/12 06:00
MHDA- 2023/05/12 07:06
CRDT- 2023/04/11 11:27
PHST- 2023/03/21 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/05/12 07:06 [medline]
PHST- 2023/04/12 06:00 [pubmed]
PHST- 2023/04/11 11:27 [entrez]
AID - 10.1007/s10439-023-03195-0 [pii]
AID - 10.1007/s10439-023-03195-0 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jun;51(6):1123-1125. doi: 10.1007/s10439-023-03195-0. Epub 
      2023 Apr 11.

PMID- 37673708
OWN - NLM
STAT- Publisher
LR  - 20231005
IS  - 2090-2387 (Electronic)
IS  - 1687-1979 (Linking)
VI  - 24
IP  - 3
DP  - 2023 Aug
TI  - ChatGPT's ability to comprehend and answer cirrhosis related questions in Arabic.
PG  - 145-148
LID - S1687-1979(23)00058-8 [pii]
LID - 10.1016/j.ajg.2023.08.001 [doi]
AB  - BACKGROUND AND STUDY AIMS: Cirrhosis is a chronic progressive disease which 
      requires complex care. Its incidence is rising in the Arab countries making it 
      the 7th leading cause of death in the Arab League in 2010. ChatGPT is a large 
      language model with a growing body of literature demonstrating its ability to 
      answer clinical questions. We examined ChatGPT's accuracy in responding to 
      cirrhosis related questions in Arabic and compared its performance to English. 
      MATERIALS AND METHODS: ChatGPTs responses to 91 questions in Arabic and English 
      were graded by a transplant hepatologist fluent in both languages. Accuracy of 
      responses was assessed using the scale: 1. Comprehensive, 2. Correct but 
      inadequate, 3. Mixed with correct and incorrect/outdated data, and 4. Completely 
      incorrect.Accuracy of Arabic compared to English responses was assessed using the 
      scale: 1. Arabic response is more accurate, 2. Similar accuracy, 3. Arabic 
      response is less accurate. RESULTS: The model provided 22 (24.2%) comprehensive, 
      44 (48.4%) correct but inadequate, 13 (14.3%) mixed with correct and 
      incorrect/outdated data and 12 (13.2%) completely incorrect Arabic responses. 
      When comparing the accuracy of Arabic and English responses, 9 (9.9%) of the 
      Arabic responses were graded as more accurate, 52 (57.1%) similar in accuracy and 
      30 (33.0%) as less accurate compared to English. CONCLUSION: ChatGPT has the 
      potential to serve as an adjunct source of information for Arabic speaking 
      patients with cirrhosis. The model provided correct responses in Arabic to 72.5% 
      of questions, although its performance in Arabic was less accurate than in 
      English. The model produced completely incorrect responses to 13.2% of questions, 
      reinforcing its potential role as an adjunct and not replacement of care by 
      licensed healthcare professionals. Future studies to refine this technology are 
      needed to help Arabic speaking patients with cirrhosis across the globe 
      understand their disease and improve their outcomes.
CI  - Copyright © 2023 Pan-Arab Association of Gastroenterology. Published by Elsevier 
      B.V. All rights reserved.
FAU - Samaan, Jamil S
AU  - Samaan JS
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Yeo, Yee Hui
AU  - Yeo YH
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Ng, Wee Han
AU  - Ng WH
AD  - Bristol Medical School, University of Bristol, Bristol, UK.
FAU - Ting, Peng-Sheng
AU  - Ting PS
AD  - School of Medicine, Tulane University, New Orleans, LA, USA.
FAU - Trivedi, Hirsh
AU  - Trivedi H
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA; Comprehensive Transplant 
      Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Vipani, Aarshi
AU  - Vipani A
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Yang, Ju Dong
AU  - Yang JD
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA; Comprehensive Transplant 
      Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Samuel Oschin 
      Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 
      USA.
FAU - Liran, Omer
AU  - Liran O
AD  - Department of Psychiatry and Behavioral Sciences, Cedars-Sinai, Los Angeles, CA, 
      USA; Division of Health Services Research, Department of Medicine, Cedars-Sinai, 
      Los Angeles, CA, USA.
FAU - Spiegel, Brennan
AU  - Spiegel B
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Health Services 
      Research, Department of Medicine, Cedars-Sinai, Los Angeles, CA, USA.
FAU - Kuo, Alexander
AU  - Kuo A
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA; Comprehensive Transplant 
      Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Ayoub, Walid S
AU  - Ayoub WS
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA; Comprehensive Transplant 
      Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: 
      walid.ayoub@cshs.org.
LA  - eng
PT  - Journal Article
DEP - 20230904
PL  - Egypt
TA  - Arab J Gastroenterol
JT  - Arab journal of gastroenterology : the official publication of the Pan-Arab 
      Association of Gastroenterology
JID - 101298363
SB  - IM
OTO - NOTNLM
OT  - Arabic
OT  - ChatGPT
OT  - Cirrhosis
OT  - Disparity
OT  - Large language model
COIS- Declaration of competing interests The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/09/07 00:41
MHDA- 2023/09/07 00:41
CRDT- 2023/09/06 21:56
PHST- 2023/04/25 00:00 [received]
PHST- 2023/07/24 00:00 [revised]
PHST- 2023/08/18 00:00 [accepted]
PHST- 2023/09/07 00:41 [pubmed]
PHST- 2023/09/07 00:41 [medline]
PHST- 2023/09/06 21:56 [entrez]
AID - S1687-1979(23)00058-8 [pii]
AID - 10.1016/j.ajg.2023.08.001 [doi]
PST - ppublish
SO  - Arab J Gastroenterol. 2023 Aug;24(3):145-148. doi: 10.1016/j.ajg.2023.08.001. 
      Epub 2023 Sep 4.

PMID- 37590034
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230904
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Aug 17
TI  - Using ChatGPT as a Learning Tool in Acupuncture Education: Comparative Study.
PG  - e47427
LID - 10.2196/47427 [doi]
LID - e47427
AB  - BACKGROUND: ChatGPT (Open AI) is a state-of-the-art artificial intelligence model 
      with potential applications in the medical fields of clinical practice, research, 
      and education. OBJECTIVE: This study aimed to evaluate the potential of ChatGPT 
      as an educational tool in college acupuncture programs, focusing on its ability 
      to support students in learning acupuncture point selection, treatment planning, 
      and decision-making. METHODS: We collected case studies published in Acupuncture 
      in Medicine between June 2022 and May 2023. Both ChatGPT-3.5 and ChatGPT-4 were 
      used to generate suggestions for acupuncture points based on case presentations. 
      A Wilcoxon signed-rank test was conducted to compare the number of acupuncture 
      points generated by ChatGPT-3.5 and ChatGPT-4, and the overlapping ratio of 
      acupuncture points was calculated. RESULTS: Among the 21 case studies, 14 studies 
      were included for analysis. ChatGPT-4 generated significantly more acupuncture 
      points (9.0, SD 1.1) compared to ChatGPT-3.5 (5.6, SD 0.6; P<.001). The 
      overlapping ratios of acupuncture points for ChatGPT-3.5 (0.40, SD 0.28) and 
      ChatGPT-4 (0.34, SD 0.27; P=.67) were not significantly different. CONCLUSIONS: 
      ChatGPT may be a useful educational tool for acupuncture students, providing 
      valuable insights into personalized treatment plans. However, it cannot fully 
      replace traditional diagnostic methods, and further studies are needed to ensure 
      its safe and effective implementation in acupuncture education.
CI  - ©Hyeonhoon Lee. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 17.08.2023.
FAU - Lee, Hyeonhoon
AU  - Lee H
AUID- ORCID: 0000-0002-9426-823X
AD  - Department of Anesthesiology and Pain Medicine, Seoul National University 
      Hospital, Seoul, Republic of Korea.
AD  - Biomedical Research Institute, Seoul National University Hospital, Seoul, 
      Republic of Korea.
LA  - eng
PT  - Journal Article
DEP - 20230817
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10472163
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - acupuncture
OT  - artificial intelligence
OT  - educational tool
OT  - personalized education
OT  - students
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/17 12:41
MHDA- 2023/08/17 12:42
CRDT- 2023/08/17 11:53
PHST- 2023/03/20 00:00 [received]
PHST- 2023/07/25 00:00 [accepted]
PHST- 2023/06/10 00:00 [revised]
PHST- 2023/08/17 12:42 [medline]
PHST- 2023/08/17 12:41 [pubmed]
PHST- 2023/08/17 11:53 [entrez]
AID - v9i1e47427 [pii]
AID - 10.2196/47427 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Aug 17;9:e47427. doi: 10.2196/47427.

PMID- 37701862
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230914
IS  - 2475-4757 (Electronic)
IS  - 2475-4757 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Jul
TI  - Improved Performance of ChatGPT-4 on the OKAP Examination: A Comparative Study 
      with ChatGPT-3.5.
PG  - e184-e187
LID - 10.1055/s-0043-1774399 [doi]
AB  - Introduction:  This study aims to evaluate the performance of ChatGPT-4, an 
      advanced artificial intelligence (AI) language model, on the Ophthalmology 
      Knowledge Assessment Program (OKAP) examination compared to its predecessor, 
      ChatGPT-3.5. Methods:  Both models were tested on 180 OKAP practice questions 
      covering various ophthalmology subject categories. Results:  ChatGPT-4 
      significantly outperformed ChatGPT-3.5 (81% vs. 57%; p <0.001), indicating 
      improvements in medical knowledge assessment. Discussion:  The superior 
      performance of ChatGPT-4 suggests potential applicability in ophthalmologic 
      education and clinical decision support systems. Future research should focus on 
      refining AI models, ensuring a balanced representation of fundamental and 
      specialized knowledge, and determining the optimal method of integrating AI into 
      medical education and practice.
CI  - The Author(s). This is an open access article published by Thieme under the terms 
      of the Creative Commons Attribution-NonDerivative-NonCommercial License, 
      permitting copying and reproduction so long as the original work is given 
      appropriate credit. Contents may not be used for commercial purposes, or adapted, 
      remixed, transformed or built upon. ( 
      https://creativecommons.org/licenses/by-nc-nd/4.0/ ).
FAU - Teebagy, Sean
AU  - Teebagy S
AUID- ORCID: 0000-0002-6304-9608
AD  - Department of Ophthalmology and Visual Sciences, UMass Chan Medical School, 
      Worcester, Massachusetts.
FAU - Colwell, Lauren
AU  - Colwell L
AD  - Department of Ophthalmology and Visual Sciences, UMass Chan Medical School, 
      Worcester, Massachusetts.
FAU - Wood, Emma
AU  - Wood E
AD  - Department of Ophthalmology and Visual Sciences, UMass Chan Medical School, 
      Worcester, Massachusetts.
FAU - Yaghy, Antonio
AU  - Yaghy A
AD  - Department of Ophthalmology and Visual Sciences, UMass Chan Medical School, 
      Worcester, Massachusetts.
FAU - Faustina, Misha
AU  - Faustina M
AUID- ORCID: 0000-0003-3140-4406
AD  - Department of Ophthalmology and Visual Sciences, UMass Chan Medical School, 
      Worcester, Massachusetts.
LA  - eng
PT  - Journal Article
DEP - 20230911
PL  - United States
TA  - J Acad Ophthalmol (2017)
JT  - Journal of academic ophthalmology (2017)
JID - 101731829
PMC - PMC10495224
OTO - NOTNLM
OT  - ChatGPT
OT  - OKAP
OT  - Ophthalmology Knowledge Assessment Program
OT  - artificial intelligence
OT  - medical education
COIS- Conflict of Interest None declared.
EDAT- 2023/09/13 06:42
MHDA- 2023/09/13 06:43
CRDT- 2023/09/13 04:14
PHST- 2023/04/13 00:00 [received]
PHST- 2023/08/10 00:00 [accepted]
PHST- 2023/09/13 06:43 [medline]
PHST- 2023/09/13 06:42 [pubmed]
PHST- 2023/09/13 04:14 [entrez]
AID - JAO-425 [pii]
AID - 10.1055/s-0043-1774399 [doi]
PST - epublish
SO  - J Acad Ophthalmol (2017). 2023 Sep 11;15(2):e184-e187. doi: 
      10.1055/s-0043-1774399. eCollection 2023 Jul.

PMID- 37957666
OWN - NLM
STAT- MEDLINE
DCOM- 20231115
LR  - 20231116
IS  - 1472-6920 (Electronic)
IS  - 1472-6920 (Linking)
VI  - 23
IP  - 1
DP  - 2023 Nov 13
TI  - Assessment of the capacity of ChatGPT as a self-learning tool in medical 
      pharmacology: a study using MCQs.
PG  - 864
LID - 10.1186/s12909-023-04832-x [doi]
LID - 864
AB  - BACKGROUND: ChatGPT is a large language model developed by OpenAI that exhibits a 
      remarkable ability to simulate human speech. This investigation attempts to 
      evaluate the potential of ChatGPT as a standalone self-learning tool, with 
      specific attention on its efficacy in answering multiple-choice questions (MCQs) 
      and providing credible rationale for its responses. METHODS: The study used 78 
      test items from the Korean Comprehensive Basic Medical Sciences Examination 
      (K-CBMSE) for years 2019 to 2021. 78 test items translated from Korean to English 
      with four lead-in prompts per item resulted in a total of 312 MCQs. The MCQs were 
      submitted to ChatGPT and the responses were analyzed for correctness, 
      consistency, and relevance. RESULTS: ChatGPT responded with an overall accuracy 
      of 76.0%. Compared to its performance on recall and interpretation questions, the 
      model performed poorly on problem-solving questions. ChatGPT offered correct 
      rationales for 77.8% (182/234) of the responses, with errors primarily arising 
      from faulty information and flawed reasoning. In terms of references, ChatGPT 
      provided incorrect citations for 69.7% (191/274) of the responses. While the 
      veracity of reference paragraphs could not be ascertained, 77.0% (47/61) were 
      deemed pertinent and accurate with respect to the answer key. CONCLUSION: The 
      current version of ChatGPT has limitations in accurately answering MCQs and 
      generating correct and relevant rationales, particularly when it comes to 
      referencing. To avoid possible threats such as spreading inaccuracies and 
      decreasing critical thinking skills, ChatGPT should be used with supervision.
CI  - © 2023. The Author(s).
FAU - Choi, Woong
AU  - Choi W
AD  - Department of Pharmacology, College of Medicine, Chungbuk National University, 
      Cheongju, Chungbuk, 28644, Korea. wchoi@chungbuk.ac.kr.
LA  - eng
PT  - Journal Article
DEP - 20231113
PL  - England
TA  - BMC Med Educ
JT  - BMC medical education
JID - 101088679
SB  - IM
MH  - Humans
MH  - Language
MH  - *Learning
MH  - Mental Recall
MH  - *Problem Solving
MH  - *Artificial Intelligence
MH  - *Pharmacology, Clinical/education
PMC - PMC10644619
OTO - NOTNLM
OT  - ChatGPT
OT  - Large language model
OT  - Multiple-choice questions
OT  - Performance
OT  - Rationale
OT  - Referencing
OT  - Self-directed learning
COIS- The authors declare no competing interests.
EDAT- 2023/11/14 06:42
MHDA- 2023/11/15 06:43
CRDT- 2023/11/14 00:10
PHST- 2023/03/02 00:00 [received]
PHST- 2023/11/01 00:00 [accepted]
PHST- 2023/11/15 06:43 [medline]
PHST- 2023/11/14 06:42 [pubmed]
PHST- 2023/11/14 00:10 [entrez]
AID - 10.1186/s12909-023-04832-x [pii]
AID - 4832 [pii]
AID - 10.1186/s12909-023-04832-x [doi]
PST - epublish
SO  - BMC Med Educ. 2023 Nov 13;23(1):864. doi: 10.1186/s12909-023-04832-x.

PMID- 37806782
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1471-1842 (Electronic)
IS  - 1471-1834 (Linking)
VI  - 40
IP  - 4
DP  - 2023 Dec
TI  - An overview of the capabilities of ChatGPT for medical writing and its 
      implications for academic integrity.
PG  - 440-446
LID - 10.1111/hir.12509 [doi]
AB  - The artificial intelligence (AI) tool ChatGPT, which is based on a large language 
      model (LLM), is gaining popularity in academic institutions, notably in the 
      medical field. This article provides a brief overview of the capabilities of 
      ChatGPT for medical writing and its implications for academic integrity. It 
      provides a list of AI generative tools, common use of AI generative tools for 
      medical writing, and provides a list of AI generative text detection tools. It 
      also provides recommendations for policymakers, information professionals, and 
      medical faculty for the constructive use of AI generative tools and related 
      technology. It also highlights the role of health sciences librarians and 
      educators in protecting students from generating text through ChatGPT in their 
      academic work.
CI  - © 2023 Health Libraries Group.
FAU - Liu, Huihui
AU  - Liu H
AD  - Shanxi University, Xiaodian District, Taiyuan, People's Republic of China.
FAU - Azam, Mehreen
AU  - Azam M
AD  - Department of Information Management, The Islamia University of Bahawalpur, 
      Bahawalpur, Pakistan.
FAU - Bin Naeem, Salman
AU  - Bin Naeem S
AD  - Department of Information Management, The Islamia University of Bahawalpur, 
      Bahawalpur, Pakistan.
FAU - Faiola, Anthony
AU  - Faiola A
AD  - Department of Health and Clinical Sciences, College of Health Sciences, 
      University of Kentucky, Lexington, Kentucky, USA.
LA  - eng
GR  - Shanxi Provincial Universities Philosophy and Social Science/
PT  - Journal Article
DEP - 20231008
PL  - England
TA  - Health Info Libr J
JT  - Health information and libraries journal
JID - 100970070
MH  - Humans
MH  - Artificial Intelligence
MH  - *Medical Writing
MH  - Schools
MH  - Language
MH  - *Librarians
OTO - NOTNLM
OT  - artificial intelligence (AI)
OT  - librarians, health science
OT  - libraries, academic
OT  - plagiarism
OT  - students, medical
EDAT- 2023/10/09 00:41
MHDA- 2023/11/27 12:43
CRDT- 2023/10/08 21:42
PHST- 2023/08/21 00:00 [received]
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/11/27 12:43 [medline]
PHST- 2023/10/09 00:41 [pubmed]
PHST- 2023/10/08 21:42 [entrez]
AID - 10.1111/hir.12509 [doi]
PST - ppublish
SO  - Health Info Libr J. 2023 Dec;40(4):440-446. doi: 10.1111/hir.12509. Epub 2023 Oct 
      8.

PMID- 38058315
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231208
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT in Answering Queries Related to Lifestyle-Related Diseases and Disorders.
PG  - e48296
LID - 10.7759/cureus.48296 [doi]
LID - e48296
AB  - Background Lifestyle-related diseases and disorders have become a significant 
      global health burden. However, the majority of the population ignores or do not 
      consult doctors for such disease or disorders. Artificial intelligence (AI)-based 
      large language model (LLM) like ChatGPT (GPT3.5) is capable of generating 
      customized queries of a user. Hence, it can act as a virtual telehealth agent. 
      Its capability to answer lifestyle-related diseases or disorders has not been 
      explored. Objective This study aimed to evaluate the effectiveness of ChatGPT, an 
      LLM, in providing answers to queries related to lifestyle-related diseases or 
      disorders. Methods A set of 20 lifestyle-related disease or disorder cases 
      covering a wide range of topics such as obesity, diabetes, cardiovascular health, 
      and mental health were prepared with four questions. The case and questions were 
      presented to ChatGPT and asked for the answers to those questions. Two physicians 
      rated the content on a three-point Likert-like scale ranging from accurate (2), 
      partially accurate (1), and inaccurate (0). Further, the content was rated as 
      adequate (2), inadequate (1), and misguiding (0) for testing the applicability of 
      the guides for patients. The readability of the text was analyzed by the 
      Flesch-Kincaid Ease Score (FKES).  Results Among 20 cases, the average score of 
      accuracy was 1.83±0.37 and guidance was 1.9±0.21. Both the scores were higher 
      than the hypothetical median of 1.5 (p=0.004 and p<0.0001, respectively). ChatGPT 
      answered the questions with a natural tone in 11 cases and nine with a positive 
      tone. The text was understandable for college graduates with a mean FKES of 
      27.8±5.74. Conclusion The analysis of content accuracy revealed that ChatGPT 
      provided reasonably accurate information in the majority of the cases, 
      successfully addressing queries related to lifestyle-related diseases or 
      disorders. Hence, initial guidance can be obtained by patients when they get 
      little time to consult a doctor or wait for an appointment to consult a doctor 
      for suggestions about their condition.
CI  - Copyright © 2023, Mondal et al.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, IND.
FAU - Dash, Ipsita
AU  - Dash I
AD  - Biochemistry, Saheed Laxman Nayak Medical College and Hospital, Koraput, IND.
FAU - Mondal, Shaikat
AU  - Mondal S
AD  - Physiology, Raiganj Government Medical College and Hospital, Raiganj, IND.
FAU - Behera, Joshil Kumar
AU  - Behera JK
AD  - Physiology, Nagaland Institute of Medical Science and Research, Kohima, IND.
LA  - eng
PT  - Journal Article
DEP - 20231105
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10696911
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - education
OT  - healthcare
OT  - internet
OT  - language model
OT  - lifestyle
OT  - non-communicable disease
OT  - patient
OT  - public health education
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/07 06:42
MHDA- 2023/12/07 06:43
CRDT- 2023/12/07 04:00
PHST- 2023/11/04 00:00 [accepted]
PHST- 2023/12/07 06:43 [medline]
PHST- 2023/12/07 06:42 [pubmed]
PHST- 2023/12/07 04:00 [entrez]
AID - 10.7759/cureus.48296 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 5;15(11):e48296. doi: 10.7759/cureus.48296. eCollection 2023 
      Nov.

PMID- 37536678
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230925
IS  - 1555-0273 (Electronic)
IS  - 1555-0265 (Linking)
VI  - 18
IP  - 10
DP  - 2023 Oct 1
TI  - ChatGPT for Sample-Size Calculation in Sports Medicine and Exercise Sciences: A 
      Cautionary Note.
PG  - 1219-1223
LID - 10.1123/ijspp.2023-0109 [doi]
AB  - PURPOSE: To investigate the accuracy of ChatGPT (Chat generative pretrained 
      transformer), a large language model, in calculating sample size for 
      sport-sciences and sports-medicine research studies. METHODS: We conducted an 
      analysis on 4 published papers (ie, examples 1-4) encompassing various study 
      designs and approaches for calculating sample size in 3 sport-science and 
      -medicine journals, including 3 randomized controlled trials and 1 survey paper. 
      We provided ChatGPT with all necessary data such as mean, percentage SD, normal 
      deviates (Zα/2 and Z1-β), and study design. Prompting from 1 example has 
      subsequently been reused to gain insights into the reproducibility of the ChatGPT 
      response. RESULTS: ChatGPT correctly calculated the sample size for 1 randomized 
      controlled trial but failed in the remaining 3 examples, including the incorrect 
      identification of the formula in one example of a survey paper. After interaction 
      with ChatGPT, the correct sample size was obtained for the survey paper. 
      Intriguingly, when the prompt from Example 3 was reused, ChatGPT provided a 
      completely different sample size than its initial response. CONCLUSIONS: While 
      the use of artificial-intelligence tools holds great promise, it should be noted 
      that it might lead to errors and inconsistencies in sample-size calculations even 
      when the tool is fed with the necessary correct information. As 
      artificial-intelligence technology continues to advance and learn from human 
      feedback, there is hope for improvement in sample-size calculation and other 
      research tasks. However, it is important for scientists to exercise caution in 
      utilizing these tools. Future studies should assess more advanced/powerful 
      versions of this tool (ie, ChatGPT4).
FAU - Methnani, Jabeur
AU  - Methnani J
AUID- ORCID: 0000-0002-1422-3059
AD  - LR19ES09, Laboratoire de Physiologie de l'Exercice et Physiopathologie: de 
      l'Intégré au Moléculaire "Biologie, Médecine et Santé," Faculty of Medicine of 
      Sousse, University of  Sousse, Sousse,Tunisia.
AD  - High Institute of Sport and Physical Education, Ksar said University of Manouba, 
      Ksar said,Tunisia.
FAU - Latiri, Imed
AU  - Latiri I
AUID- ORCID: 0000-0002-6074-4736
AD  - Research Laboratory LR12SP09 "Heart Failure" Farhat HACHED Hospital, University 
      of Sousse, Sousse,Tunisia.
FAU - Dergaa, Ismail
AU  - Dergaa I
AUID- ORCID: 0000-0001-8091-1856
AD  - Primary Health Care Corporation (PHCC), Doha,Qatar.
AD  - Aspetar, Orthopedic and Sports Medicine Hospital, FIFA Medical Center of 
      Excellence, Doha,Qatar.
AD  - Research Unit Physical Activity, Sport, and Health, UR18JS01, National 
      Observatory of Sport, Tunis,Tunisia.
FAU - Chamari, Karim
AU  - Chamari K
AUID- ORCID: 0000-0001-9178-7678
AD  - High Institute of Sport and Physical Education, University of Sfax, Sfax,Tunisia.
FAU - Ben Saad, Helmi
AU  - Ben Saad H
AUID- ORCID: 0000-0002-7477-2965
AD  - High Institute of Sport and Physical Education, Ksar said University of Manouba, 
      Ksar said,Tunisia.
AD  - Service of Physiology and Functional Explorations, Farhat HACHED Hospital, 
      University of Sousse, Sousse,Tunisia.
LA  - eng
PT  - Journal Article
DEP - 20230803
PL  - United States
TA  - Int J Sports Physiol Perform
JT  - International journal of sports physiology and performance
JID - 101276430
SB  - IM
MH  - Humans
MH  - Reproducibility of Results
MH  - Exercise
MH  - *Sports Medicine
MH  - *Sports
MH  - Research Design
MH  - Randomized Controlled Trials as Topic
OTO - NOTNLM
OT  - artificial intelligence
OT  - effect size
OT  - higher education
OT  - large language model
OT  - methodology
OT  - natural language processing
OT  - peer review
OT  - power
OT  - precision
OT  - research
OT  - sport science
OT  - statistics
EDAT- 2023/08/04 01:07
MHDA- 2023/09/25 06:42
CRDT- 2023/08/03 20:02
PHST- 2023/04/05 00:00 [received]
PHST- 2023/06/04 00:00 [revised]
PHST- 2023/06/05 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/08/04 01:07 [pubmed]
PHST- 2023/08/03 20:02 [entrez]
AID - 10.1123/ijspp.2023-0109 [doi]
PST - epublish
SO  - Int J Sports Physiol Perform. 2023 Aug 3;18(10):1219-1223. doi: 
      10.1123/ijspp.2023-0109. Print 2023 Oct 1.

PMID- 37369944
OWN - NLM
STAT- Publisher
LR  - 20230627
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jun 27
TI  - Advancing the Production of Clinical Medical Devices Through ChatGPT.
LID - 10.1007/s10439-023-03300-3 [doi]
AB  - As a recently popular large language model, Chatbot Generative Pre-trained 
      Transformer (ChatGPT) is highly valued in the field of clinical medicine. Due to 
      the limited understanding of the potential impact of ChatGPT on the manufacturing 
      side of clinical medical devices, we aim to fill this gap through this article. 
      We elucidate the classification of medical devices and explore the positive 
      contributions of ChatGPT in various aspects of medical device design, 
      optimization, and improvement. However, limitations such as the potential for 
      misinterpretation of user intent, lack of personal experience, and the need for 
      human supervision should be taken into consideration. Striking a balance between 
      ChatGPT and human expertise can ensure the safety, quality, and compliance of 
      medical devices. This work contributes to the advancement of ChatGPT in the 
      medical device manufacturing industry and highlights the synergistic relationship 
      between artificial intelligence and human involvement in healthcare.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Li, Siqi
AU  - Li S
AD  - Advanced Research Center, GD Midea Equipment Co., Ltd, Foshan, 528000, China.
FAU - Guo, Zheng
AU  - Guo Z
AUID- ORCID: 0009-0005-5060-5571
AD  - Orthopedics Department of The Sixth Affiliated Hospital, School of Medicine, 
      South China University of Technology, Foshan, 528042, China. 
      guozheng98@mail.sdu.edu.cn.
FAU - Zang, Xuehui
AU  - Zang X
AD  - Orthopedics Department of The Sixth Affiliated Hospital, School of Medicine, 
      South China University of Technology, Foshan, 528042, China. 
      lyzangxh@scut.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230627
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Clinical medical devices
OT  - Large language model
OT  - Manufacturing engineering
EDAT- 2023/06/28 01:06
MHDA- 2023/06/28 01:06
CRDT- 2023/06/27 23:32
PHST- 2023/06/19 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/06/28 01:06 [medline]
PHST- 2023/06/28 01:06 [pubmed]
PHST- 2023/06/27 23:32 [entrez]
AID - 10.1007/s10439-023-03300-3 [pii]
AID - 10.1007/s10439-023-03300-3 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jun 27. doi: 10.1007/s10439-023-03300-3.

PMID- 37244883
OWN - NLM
STAT- MEDLINE
DCOM- 20230926
LR  - 20230926
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT: Detection in Academic Journals is Editors' and Publishers' 
      Responsibilities.
PG  - 2103-2104
LID - 10.1007/s10439-023-03247-5 [doi]
AB  - This letter makes note that the responsibility to detect text written by AI, such 
      as ChatGPT, is the sole responsibility of editors and journals/publishers. This 
      proposed policy is made to ensure proper authorship and to therefore assuage 
      readers of a paper's authorship validity-so as not to have AI-driven guest 
      authorship-so that the integrity of the biomedical literature is not further 
      degraded. Two letters to the editor were recently written in this journal by 
      ChatGPT and edited by the author. The extent to which ChatGPT contributed to 
      those letters' content is unknown.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Independent researcher, Ikenobe 3011-2, Kagawa-ken, 761-0799, Japan.
LA  - eng
PT  - Letter
DEP - 20230527
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Editorial Policies
MH  - *Periodicals as Topic
MH  - Authorship
OTO - NOTNLM
OT  - AI
OT  - Human editing
OT  - Large language models
OT  - Productivity
OT  - Publish or perish
EDAT- 2023/05/28 01:07
MHDA- 2023/09/26 13:42
CRDT- 2023/05/27 23:03
PHST- 2023/05/16 00:00 [received]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2023/09/26 13:42 [medline]
PHST- 2023/05/28 01:07 [pubmed]
PHST- 2023/05/27 23:03 [entrez]
AID - 10.1007/s10439-023-03247-5 [pii]
AID - 10.1007/s10439-023-03247-5 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Oct;51(10):2103-2104. doi: 10.1007/s10439-023-03247-5. Epub 
      2023 May 27.

PMID- 37914380
OWN - NLM
STAT- Publisher
LR  - 20231205
IS  - 2352-0787 (Electronic)
IS  - 2352-0779 (Linking)
DP  - 2023 Nov 1
TI  - Comparison of ChatGPT and Traditional Patient Education Materials for Men's 
      Health.
PG  - 101097UPJ0000000000000490
LID - 10.1097/UPJ.0000000000000490 [doi]
AB  - INTRODUCTION: ChatGPT is an artificial intelligence platform available to 
      patients seeking medical advice. Traditionally, urology patients consulted 
      official provider-created materials, particularly the Urology Care Foundation™ 
      (UCF). Today, men increasingly go online due to the rising costs of health care 
      and the stigma surrounding sexual health. Online health information is largely 
      inaccessible to laypersons as it exceeds the recommended American sixth to eighth 
      grade reading level. We conducted a comparative assessment of patient education 
      materials generated by ChatGPT vs UCF regarding men's health conditions. METHODS: 
      All 6 UCF men's health resources were identified. ChatGPT responses were 
      generated using patient questions obtained from UCF. Adjusted ChatGPT responses 
      were generated by prompting, "Explain it to me like I am in sixth grade." Textual 
      analysis was performed using sentence, word, syllable, and complex word count. 
      Six validated formulae were used for readability analysis. Two physicians 
      independently scored responses for accuracy, comprehensiveness, and 
      understandability. Statistical analysis involved Wilcoxon matched-pairs test. 
      RESULTS: ChatGPT responses were longer and more complex. Both UCF and ChatGPT 
      failed official readability standards, although ChatGPT performed significantly 
      worse across all 6 topics (all P < .001). Conversely, adjusted ChatGPT 
      readability typically surpassed UCF, even meeting the recommended level for 2 
      topics. Qualitatively, UCF and ChatGPT had comparable accuracy, although ChatGPT 
      had better comprehensiveness and worse understandability. CONCLUSIONS: When 
      comparing readability, ChatGPT-generated education is less accessible than 
      provider-written content, although neither meets the recommended level. Our 
      analysis indicates that specific artificial intelligence prompts can simplify 
      educational materials to meet national standards and accommodate individual 
      literacy.
FAU - Shah, Yash B
AU  - Shah YB
AUID- ORCID: 0000-0003-1551-7611
AD  - Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson 
      University, Philadelphia, Pennsylvania.
FAU - Ghosh, Anushka
AU  - Ghosh A
AD  - Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson 
      University, Philadelphia, Pennsylvania.
FAU - Hochberg, Aaron R
AU  - Hochberg AR
AD  - Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson 
      University, Philadelphia, Pennsylvania.
FAU - Rapoport, Eli
AU  - Rapoport E
AD  - Department of Urology, NYU Langone, New York, New York.
FAU - Lallas, Costas D
AU  - Lallas CD
AD  - Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson 
      University, Philadelphia, Pennsylvania.
FAU - Shah, Mihir S
AU  - Shah MS
AD  - Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson 
      University, Philadelphia, Pennsylvania.
FAU - Cohen, Seth D
AU  - Cohen SD
AD  - Department of Urology, NYU Langone, New York, New York.
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - United States
TA  - Urol Pract
JT  - Urology practice
JID - 101635343
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - health literacy
OT  - men’s health
OT  - patient education
EDAT- 2023/11/02 00:42
MHDA- 2023/11/02 00:42
CRDT- 2023/11/01 21:13
PHST- 2023/11/02 00:42 [pubmed]
PHST- 2023/11/02 00:42 [medline]
PHST- 2023/11/01 21:13 [entrez]
AID - 10.1097/UPJ.0000000000000490 [doi]
PST - aheadofprint
SO  - Urol Pract. 2023 Nov 1:101097UPJ0000000000000490. doi: 
      10.1097/UPJ.0000000000000490.

PMID- 37745352
OWN - NLM
STAT- Publisher
LR  - 20230926
DP  - 2023 Sep 7
TI  - Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Statements 
      with Varying Degrees of Misinformation.
LID - 2023.09.04.23294917 [pii]
LID - 10.1101/2023.09.04.23294917 [doi]
AB  - BACKGROUND: There are many myths regarding Alzheimer's disease (AD) that have 
      been circulated on the Internet, each exhibiting varying degrees of accuracy, 
      inaccuracy, and misinformation. Large language models such as ChatGPT, may be a 
      useful tool to help assess these myths for veracity and inaccuracy. However, they 
      can induce misinformation as well. The objective of this study is to assess 
      ChatGPT's ability to identify and address AD myths with reliable information. 
      METHODS: We conducted a cross-sectional study of clinicians' evaluation of 
      ChatGPT (GPT 4.0)'s responses to 20 selected AD myths. We prompted ChatGPT to 
      express its opinion on each myth and then requested it to rephrase its 
      explanation using a simplified language that could be more readily understood by 
      individuals with a middle school education. We implemented a survey using Redcap 
      to determine the degree to which clinicians agreed with the accuracy of each 
      ChatGPT's explanation and the degree to which the simplified rewriting was 
      readable and retained the message of the original. We also collected their 
      explanation on any disagreement with ChatGPT's responses. We used five 
      Likert-type scale with a score ranging from -2 to 2 to quantify clinicians' 
      agreement in each aspect of the evaluation. RESULTS: The clinicians (n=11) were 
      generally satisfied with ChatGPT's explanations, with a mean (SD) score of 
      1.0(±0.3) across the 20 myths. While ChatGPT correctly identified that all the 20 
      myths were inaccurate, some clinicians disagreed with its explanations on 7 of 
      the myths.Overall, 9 of the 11 professionals either agreed or strongly agreed 
      that ChatGPT has the potential to provide meaningful explanations of certain 
      myths. CONCLUSIONS: The majority of surveyed healthcare professionals 
      acknowledged the potential value of ChatGPT in mitigating AD misinformation. 
      However, the need for more refined and detailed explanations of the disease's 
      mechanisms and treatments was highlighted.
FAU - Huang, Sean S
AU  - Huang SS
AD  - Department of Medicine, Division of Geriatrics, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, TN, USA.
FAU - Song, Qingyuan
AU  - Song Q
AD  - Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
FAU - Beiting, Kimberly J
AU  - Beiting KJ
AD  - Department of Medicine, Division of Geriatrics, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
FAU - Duggan, Maria C
AU  - Duggan MC
AD  - Department of Medicine, Division of Geriatrics, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
AD  - Geriatric Research Education and Clinical Center (GRECC), Department of Veteran 
      Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA.
FAU - Hines, Kristin
AU  - Hines K
AD  - Department of Medicine, Division of Geriatrics, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
FAU - Murff, Harvey
AU  - Murff H
AD  - Department of Medicine, Division of Geriatrics, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
FAU - Leung, Vania
AU  - Leung V
AD  - Department of Academic Internal Medicine and Geriatrics, University of Illinois 
      at Chicago, Chicago, IL, USA.
FAU - Powers, James
AU  - Powers J
AD  - Department of Medicine, Division of Geriatrics, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
AD  - Geriatric Research Education and Clinical Center (GRECC), Department of Veteran 
      Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA.
FAU - Harvey, T S
AU  - Harvey TS
AD  - Department of Anthropology, Vanderbilt University, Nashville, TN, USA.
FAU - Malin, Bradley
AU  - Malin B
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, TN, USA.
AD  - Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
AD  - Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 
      USA.
FAU - Yin, Zhijun
AU  - Yin Z
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, TN, USA.
AD  - Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
LA  - eng
PT  - Preprint
DEP - 20230907
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10515299
OTO - NOTNLM
OT  - Alzheimer’s Disease
OT  - ChatGPT
OT  - Large Language Models
OT  - Misinformation
COIS- Conflict of Interest: There are no conflicts of interest to disclose.
EDAT- 2023/09/25 06:42
MHDA- 2023/09/25 06:42
CRDT- 2023/09/25 05:00
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/09/25 06:42 [pubmed]
PHST- 2023/09/25 05:00 [entrez]
AID - 2023.09.04.23294917 [pii]
AID - 10.1101/2023.09.04.23294917 [doi]
PST - epublish
SO  - medRxiv. 2023 Sep 7:2023.09.04.23294917. doi: 10.1101/2023.09.04.23294917. 
      Preprint.

PMID- 37853081
OWN - NLM
STAT- Publisher
LR  - 20231018
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Oct 18
TI  - Can ChatGPT be the Plastic Surgeon's New Digital Assistant? A Bibliometric 
      Analysis and Scoping Review of ChatGPT in Plastic Surgery Literature.
LID - 10.1007/s00266-023-03709-0 [doi]
AB  - BACKGROUND: ChatGPT, an artificial intelligence (AI) chatbot that uses natural 
      language processing (NLP) to interact in a humanlike manner, has made significant 
      contributions to various healthcare fields, including plastic surgery. However, 
      its widespread use has raised ethical and security concerns. This study examines 
      the presence of ChatGPT, an artificial intelligence (AI) chatbot, in the 
      literature of plastic surgery. METHODS: A bibliometric analysis and scoping 
      review of the ChatGPT plastic surgery literature were performed. PubMed was 
      queried using the search term "ChatGPT" to identify all biomedical literature on 
      ChatGPT, with only studies related to plastic, reconstructive, or aesthetic 
      surgery topics being considered eligible for inclusion. RESULTS: The analysis 
      included 30 out of 724 articles retrieved from PubMed, focusing on publications 
      from December 2022 to July 2023. Four key areas of research emerged: applications 
      in research/creation of original work, clinical application, surgical education, 
      and ethics/commentary on previous studies. The versatility of ChatGPT in 
      research, its potential in surgical education, and its role in enhancing patient 
      education were explored. Ethical concerns regarding patient privacy, plagiarism, 
      and the accuracy of information obtained from ChatGPT-generated sources were also 
      highlighted. CONCLUSION: While ethical concerns persist, the study underscores 
      the potential of ChatGPT in plastic surgery research and practice, emphasizing 
      the need for careful utilization and collaboration to optimize its benefits while 
      minimizing risks. LEVEL OF EVIDENCE V: This journal requires that authors assign 
      a level of evidence to each article. For a full description of these 
      Evidence-Based Medicine ratings, please refer to the Table of Contents or the 
      online Instructions to Authors www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Liu, Hilary Y
AU  - Liu HY
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, Pittsburgh, PA, G10315219, USA.
FAU - Alessandri-Bonetti, Mario
AU  - Alessandri-Bonetti M
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, Pittsburgh, PA, G10315219, USA.
FAU - Arellano, José Antonio
AU  - Arellano JA
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, Pittsburgh, PA, G10315219, USA.
FAU - Egro, Francesco M
AU  - Egro FM
AUID- ORCID: 0000-0003-1536-7713
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, Pittsburgh, PA, G10315219, USA. francescoegro@gmail.com.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231018
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Clinical practice
OT  - Ethics
OT  - Medical education
OT  - Patient education
OT  - Plastic surgery
OT  - Research
EDAT- 2023/10/19 00:45
MHDA- 2023/10/19 00:45
CRDT- 2023/10/18 23:30
PHST- 2023/08/19 00:00 [received]
PHST- 2023/09/30 00:00 [accepted]
PHST- 2023/10/19 00:45 [medline]
PHST- 2023/10/19 00:45 [pubmed]
PHST- 2023/10/18 23:30 [entrez]
AID - 10.1007/s00266-023-03709-0 [pii]
AID - 10.1007/s00266-023-03709-0 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Oct 18. doi: 10.1007/s00266-023-03709-0.

PMID- 37198498
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230520
IS  - 2524-4442 (Electronic)
IS  - 2096-496X (Print)
IS  - 2524-4442 (Linking)
VI  - 6
IP  - 1
DP  - 2023 May 18
TI  - Translating radiology reports into plain language using ChatGPT and GPT-4 with 
      prompt learning: results, limitations, and potential.
PG  - 9
LID - 10.1186/s42492-023-00136-5 [doi]
LID - 9
AB  - The large language model called ChatGPT has drawn extensively attention because 
      of its human-like expression and reasoning abilities. In this study, we 
      investigate the feasibility of using ChatGPT in experiments on translating 
      radiology reports into plain language for patients and healthcare providers so 
      that they are educated for improved healthcare. Radiology reports from 62 
      low-dose chest computed tomography lung cancer screening scans and 76 brain 
      magnetic resonance imaging metastases screening scans were collected in the first 
      half of February for this study. According to the evaluation by radiologists, 
      ChatGPT can successfully translate radiology reports into plain language with an 
      average score of 4.27 in the five-point system with 0.08 places of information 
      missing and 0.07 places of misinformation. In terms of the suggestions provided 
      by ChatGPT, they are generally relevant such as keeping following-up with doctors 
      and closely monitoring any symptoms, and for about 37% of 138 cases in total 
      ChatGPT offers specific suggestions based on findings in the report. ChatGPT also 
      presents some randomness in its responses with occasionally over-simplified or 
      neglected information, which can be mitigated using a more detailed prompt. 
      Furthermore, ChatGPT results are compared with a newly released large model 
      GPT-4, showing that GPT-4 can significantly improve the quality of translated 
      reports. Our results show that it is feasible to utilize large language models in 
      clinical education, and further efforts are needed to address limitations and 
      maximize their potential.
CI  - © 2023. The Author(s).
FAU - Lyu, Qing
AU  - Lyu Q
AUID- ORCID: 0000-0002-9824-0170
AD  - Department of Radiology, Wake Forest University School of Medicine, 
      Winston-Salem, NC 27103, United States.
FAU - Tan, Josh
AU  - Tan J
AD  - Department of Radiology, Wake Forest University School of Medicine, 
      Winston-Salem, NC 27103, United States.
FAU - Zapadka, Michael E
AU  - Zapadka ME
AD  - Department of Radiology, Wake Forest University School of Medicine, 
      Winston-Salem, NC 27103, United States.
FAU - Ponnatapura, Janardhana
AU  - Ponnatapura J
AD  - Department of Radiology, Wake Forest University School of Medicine, 
      Winston-Salem, NC 27103, United States.
FAU - Niu, Chuang
AU  - Niu C
AD  - Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY 12180, 
      United States.
FAU - Myers, Kyle J
AU  - Myers KJ
AD  - Puente Solutions LLC, Phoenix, AZ 85065, United States. drkylejmyers@gmail.com.
FAU - Wang, Ge
AU  - Wang G
AD  - Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY 12180, 
      United States. wangg6@rpi.edu.
FAU - Whitlow, Christopher T
AU  - Whitlow CT
AD  - Department of Radiology, Wake Forest University School of Medicine, 
      Winston-Salem, NC 27103, United States. cwhitlow@wakehealth.edu.
LA  - eng
PT  - Journal Article
DEP - 20230518
PL  - Germany
TA  - Vis Comput Ind Biomed Art
JT  - Visual computing for industry, biomedicine, and art
JID - 101759975
PMC - PMC10192466
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Large language model
OT  - Patient education
OT  - Radiology report
COIS- The authors declare that they have no competing interests.
EDAT- 2023/05/18 01:07
MHDA- 2023/05/18 01:08
CRDT- 2023/05/17 23:33
PHST- 2023/03/29 00:00 [received]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/05/18 01:08 [medline]
PHST- 2023/05/18 01:07 [pubmed]
PHST- 2023/05/17 23:33 [entrez]
AID - 10.1186/s42492-023-00136-5 [pii]
AID - 136 [pii]
AID - 10.1186/s42492-023-00136-5 [doi]
PST - epublish
SO  - Vis Comput Ind Biomed Art. 2023 May 18;6(1):9. doi: 10.1186/s42492-023-00136-5.

PMID- 37928033
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231107
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 11
DP  - 2023 Nov
TI  - "Chatting with ChatGPT": Analyzing the factors influencing users' intention to 
      Use the Open AI's ChatGPT using the UTAUT model.
PG  - e20962
LID - 10.1016/j.heliyon.2023.e20962 [doi]
LID - e20962
AB  - Open AI's ChatGPT has emerged as a popular AI language model that can engage in 
      natural language conversations with users. Based on a qualitative research 
      approach using semistructured interviews with 32 ChatGPT users from India, this 
      study examined the factors influencing users' acceptance and use of ChatGPT using 
      the unified theory of acceptance and usage of technology (UTAUT) model. The study 
      results demonstrated that the four factors of UTAUT, along with two extended 
      constructs, i.e. perceived interactivity and privacy concerns, can explain users' 
      interaction and engagement with ChatGPT. The study also found that age and 
      experience can moderate the impact of various factors on the use of ChatGPT. The 
      theoretical and practical implications of the study were also discussed.
CI  - © 2023 The Author(s).
FAU - Menon, Devadas
AU  - Menon D
AD  - Development and Educational Communication Unit, Ahmedabad- 380056, India.
FAU - Shilpa, K
AU  - Shilpa K
AD  - Manipal Academy of Higher Education, Manipal, India.
LA  - eng
PT  - Journal Article
DEP - 20231018
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10623159
OTO - NOTNLM
OT  - Acceptance and use
OT  - ChatGPT
OT  - Chatbots
OT  - OpenAI
OT  - UTAUT
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/11/06 06:42
MHDA- 2023/11/06 06:43
CRDT- 2023/11/06 04:27
PHST- 2023/04/18 00:00 [received]
PHST- 2023/10/11 00:00 [revised]
PHST- 2023/10/12 00:00 [accepted]
PHST- 2023/11/06 06:43 [medline]
PHST- 2023/11/06 06:42 [pubmed]
PHST- 2023/11/06 04:27 [entrez]
AID - S2405-8440(23)08170-7 [pii]
AID - e20962 [pii]
AID - 10.1016/j.heliyon.2023.e20962 [doi]
PST - epublish
SO  - Heliyon. 2023 Oct 18;9(11):e20962. doi: 10.1016/j.heliyon.2023.e20962. 
      eCollection 2023 Nov.

PMID- 37718142
OWN - NLM
STAT- MEDLINE
DCOM- 20231113
LR  - 20231122
IS  - 1471-499X (Electronic)
IS  - 1471-4914 (Linking)
VI  - 29
IP  - 12
DP  - 2023 Dec
TI  - Medical education empowered by generative artificial intelligence large language 
      models.
PG  - 971-973
LID - S1471-4914(23)00211-3 [pii]
LID - 10.1016/j.molmed.2023.08.012 [doi]
AB  - Generative artificial intelligence (GAI) large language models (LLMs), like 
      ChatGPT, have become the world's fastest growing applications. Here, we provide 
      useful strategies for educators in medical and health science (M&HS) to integrate 
      GAI-LLMs into learning and teaching practice, ultimately enhancing students' 
      digital capability.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Jowsey, Tanisha
AU  - Jowsey T
AD  - Faculty of Health Sciences and Medicine, Bond University, Robina, Australia.
FAU - Stokes-Parish, Jessica
AU  - Stokes-Parish J
AD  - Faculty of Health Sciences and Medicine, Bond University, Robina, Australia.
FAU - Singleton, Rachelle
AU  - Singleton R
AD  - Faculty of Medical and Health Sciences, University of Auckland, Auckland, New 
      Zealand.
FAU - Todorovic, Michael
AU  - Todorovic M
AD  - School of Nursing and Midwifery, Griffith University, Brisbane, Australia; 
      Menzies Health Institute Queensland, Griffith University, Brisbane, Australia. 
      Electronic address: m.todorovic@griffith.edu.au.
LA  - eng
PT  - Journal Article
DEP - 20230916
PL  - England
TA  - Trends Mol Med
JT  - Trends in molecular medicine
JID - 100966035
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Education, Medical
MH  - Language
OTO - NOTNLM
OT  - artificial intelligence
OT  - education
OT  - large language model
OT  - machine learning
COIS- Declaration of interests No interests are declared.
EDAT- 2023/09/18 00:41
MHDA- 2023/11/13 06:42
CRDT- 2023/09/17 21:53
PHST- 2023/07/27 00:00 [received]
PHST- 2023/08/29 00:00 [revised]
PHST- 2023/08/29 00:00 [accepted]
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/09/18 00:41 [pubmed]
PHST- 2023/09/17 21:53 [entrez]
AID - S1471-4914(23)00211-3 [pii]
AID - 10.1016/j.molmed.2023.08.012 [doi]
PST - ppublish
SO  - Trends Mol Med. 2023 Dec;29(12):971-973. doi: 10.1016/j.molmed.2023.08.012. Epub 
      2023 Sep 16.

PMID- 37417886
OWN - NLM
STAT- MEDLINE
DCOM- 20231116
LR  - 20231124
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
VI  - 93
IP  - 6
DP  - 2023 Dec 1
TI  - Exploring the Intersection of Artificial Intelligence and Neurosurgery: Let us be 
      Cautious With ChatGPT.
PG  - 1366-1373
LID - 10.1227/neu.0000000000002598 [doi]
AB  - BACKGROUND AND OBJECTIVES: ChatGPT is a novel natural language processing 
      artificial intelligence (AI) module where users enter any question or command and 
      receive a single text response within seconds. As AI becomes more accessible, 
      patients may begin to use it as a resource for medical information and advice. 
      This is the first study to assess the neurosurgical information that is provided 
      by ChatGPT. METHODS: ChatGPT was accessed in January 2023, and prompts were 
      created requesting treatment information for 40 common neurosurgical conditions. 
      Quantitative characteristics were collected, and four independent reviewers 
      evaluated the responses using the DISCERN tool. Prompts were compared against the 
      American Association of Neurological Surgeons (AANS) "For Patients" webpages. 
      RESULTS: ChatGPT returned text organized in paragraph and bullet-point lists. 
      ChatGPT responses were shorter (mean 270.1 ± 41.9 words; AANS webpage 1634.5 ± 
      891.3 words) but more difficult to read (mean Flesch-Kincaid score 32.4 ± 6.7; 
      AANS webpage 37.1 ± 7.0). ChatGPT output was found to be of "fair" quality (mean 
      DISCERN score 44.2 ± 4.1) and significantly inferior to the "good" overall 
      quality of the AANS patient website (57.7 ± 4.4). ChatGPT was poor in providing 
      references/resources and describing treatment risks. ChatGPT provided 177 
      references, of which 68.9% were inaccurate and 33.9% were completely falsified. 
      CONCLUSION: ChatGPT is an adaptive resource for neurosurgical information but has 
      shortcomings that limit the quality of its responses, including poor readability, 
      lack of references, and failure to fully describe treatment options. Hence, 
      patients and providers should remain wary of the provided content. As ChatGPT or 
      other AI search algorithms continue to improve, they may become a reliable 
      alternative for medical information.
CI  - Copyright © Congress of Neurological Surgeons 2023. All rights reserved.
FAU - Mishra, Akash
AU  - Mishra A
AUID- ORCID: 0000-0002-5617-9009
AD  - Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine 
      at Hofstra/Northwell, Lake Success , New York , USA.
FAU - Begley, Sabrina L
AU  - Begley SL
FAU - Chen, Adrian
AU  - Chen A
FAU - Rob, Moontahinaz
AU  - Rob M
FAU - Pelcher, Isabelle
AU  - Pelcher I
FAU - Ward, Max
AU  - Ward M
FAU - Schulder, Michael
AU  - Schulder M
LA  - eng
PT  - Journal Article
DEP - 20230707
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
MH  - Humans
MH  - *Neurosurgery
MH  - Artificial Intelligence
MH  - Neurosurgical Procedures
MH  - Neurosurgeons
MH  - Algorithms
EDAT- 2023/07/07 13:04
MHDA- 2023/11/16 06:45
CRDT- 2023/07/07 10:02
PHST- 2023/02/23 00:00 [received]
PHST- 2023/05/14 00:00 [accepted]
PHST- 2023/11/16 06:45 [medline]
PHST- 2023/07/07 13:04 [pubmed]
PHST- 2023/07/07 10:02 [entrez]
AID - 00006123-990000000-00801 [pii]
AID - 10.1227/neu.0000000000002598 [doi]
PST - ppublish
SO  - Neurosurgery. 2023 Dec 1;93(6):1366-1373. doi: 10.1227/neu.0000000000002598. Epub 
      2023 Jul 7.

PMID- 38088393
OWN - NLM
STAT- In-Process
LR  - 20231213
IS  - 2384-8553 (Electronic)
IS  - 0021-2571 (Linking)
VI  - 59
IP  - 4
DP  - 2023 Oct-Dec
TI  - Exploring the potential of ChatGPT for clinical reasoning and decision-making: a 
      cross-sectional study on the Italian Medical Residency Exam.
PG  - 267-270
LID - 10.4415/ANN_23_04_05 [doi]
AB  - BACKGROUND: This study aimed to assess the performance of ChatGPT, a large 
      language model (LLM), on the Italian State Exam for Medical Residency (SSM) test 
      to determine its potential as a tool for medical education and clinical 
      decision-making support. MATERIALS AND METHODS: A total of 136 questions were 
      obtained from the official SSM test. ChatGPT responses were analyzed and compared 
      to the performance of medical doctors who took the test in 2022. Questions were 
      classified into clinical cases (CC) and notional questions (NQ). RESULTS: ChatGPT 
      achieved an overall accuracy of 90.44%, with higher performance on clinical cases 
      (92.45%) than on notional questions (89.15%). Compared to medical doctors' 
      scores, ChatGPT performance was higher than 99.6% of the participants. 
      CONCLUSIONS: These results suggest that ChatGPT holds promise as a valuable tool 
      in clinical decision-making, particularly in the context of clinical reasoning. 
      Further research is needed to explore the potential applications and 
      implementation of large language models (LLMs) in medical education and medical 
      practice.
FAU - Scaioli, Giacomo
AU  - Scaioli G
AD  - Dipartimento di Scienze della Sanità Pubblica e Pediatriche, Università degli 
      Studi di Torino, Turin, Italy.
FAU - Lo Moro, Giuseppina
AU  - Lo Moro G
AD  - Dipartimento di Scienze della Sanità Pubblica e Pediatriche, Università degli 
      Studi di Torino, Turin, Italy.
FAU - Conrado, Francesco
AU  - Conrado F
AD  - Dipartimento di Scienze della Sanità Pubblica e Pediatriche, Università degli 
      Studi di Torino, Turin, Italy.
FAU - Rosset, Lorenzo
AU  - Rosset L
AD  - Dipartimento di Scienze della Sanità Pubblica e Pediatriche, Università degli 
      Studi di Torino, Turin, Italy.
FAU - Bert, Fabrizio
AU  - Bert F
AD  - Dipartimento di Scienze della Sanità Pubblica e Pediatriche, Università degli 
      Studi di Torino, Turin, Italy.
FAU - Siliquini, Roberta
AU  - Siliquini R
AD  - Dipartimento di Scienze della Sanità Pubblica e Pediatriche, Università degli 
      Studi di Torino, Turin, Italy.
LA  - eng
PT  - Journal Article
PL  - Italy
TA  - Ann Ist Super Sanita
JT  - Annali dell'Istituto superiore di sanita
JID - 7502520
SB  - IM
EDAT- 2023/12/13 12:43
MHDA- 2023/12/13 12:43
CRDT- 2023/12/13 08:51
PHST- 2023/12/13 12:43 [medline]
PHST- 2023/12/13 12:43 [pubmed]
PHST- 2023/12/13 08:51 [entrez]
AID - 10.4415/ANN_23_04_05 [doi]
PST - ppublish
SO  - Ann Ist Super Sanita. 2023 Oct-Dec;59(4):267-270. doi: 10.4415/ANN_23_04_05.

PMID- 38069953
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 1531-6564 (Electronic)
IS  - 0363-5023 (Linking)
DP  - 2023 Dec 8
TI  - Appropriateness and Reliability of an Online Artificial Intelligence Platform's 
      Responses to Common Questions Regarding Distal Radius Fractures.
LID - S0363-5023(23)00589-0 [pii]
LID - 10.1016/j.jhsa.2023.10.019 [doi]
AB  - PURPOSE: Chat Generative Pre-Trained Transformer (ChatGPT) is a novel artificial 
      intelligence chatbot that is changing the way humans gather information online. 
      The purpose of this study was to investigate ChatGPT's ability to appropriately 
      and reliably answer common questions regarding distal radius fractures. METHODS: 
      Thirty common questions regarding distal radius fractures were presented in an 
      identical manner to the online ChatGPT-3.5 interface three separate times, 
      yielding 90 unique responses because ChatGPT produces an original answer with 
      each query. All responses were graded as "appropriate," "appropriate but 
      incomplete," or "inappropriate" by a consensus discussion among three hand 
      surgeon reviewers. The questions were additionally subcategorized into one of 
      four domains based on Bloom's cognitive learning taxonomy, and descriptive 
      statistics were reported. RESULTS: Seventy of the 90 total responses (78%) 
      produced by ChatGPT were "appropriate," and 29 of the 30 questions (97%) had at 
      least one response considered appropriate (of the three possible). However, only 
      17 of the 30 questions (57%) were answered appropriately on all three iterations. 
      The test-retest reliability of ChatGPT was poor with an intraclass correlation 
      coefficient of 0.12. Finally, ChatGPT performed best answering questions 
      requiring lower-order thinking skills (Bloom's levels 1-3) and less well on level 
      4 questions. CONCLUSIONS: This study found that although ChatGPT has the 
      capability to answer common questions regarding distal radius fractures, caution 
      should be taken before implementing its use, given ChatGPT's inconsistency in 
      providing a complete and accurate response to the same question every time. 
      CLINICAL RELEVANCE: As the popularity and technology of ChatGPT continue to grow, 
      it is important to understand the potential and limitations of this platform to 
      determine how it may be best implemented to improve patient care.
CI  - Copyright © 2023 American Society for Surgery of the Hand. Published by Elsevier 
      Inc. All rights reserved.
FAU - Christy, Michele
AU  - Christy M
AD  - Department of Orthopaedic Surgery, Washington University in St. Louis, St. Louis, 
      MO.
FAU - Morris, Marie T
AU  - Morris MT
AD  - Department of Orthopaedic Surgery, Washington University in St. Louis, St. Louis, 
      MO.
FAU - Goldfarb, Charles A
AU  - Goldfarb CA
AD  - Department of Orthopaedic Surgery, Washington University in St. Louis, St. Louis, 
      MO.
FAU - Dy, Christopher J
AU  - Dy CJ
AD  - Department of Orthopaedic Surgery, Washington University in St. Louis, St. Louis, 
      MO. Electronic address: Dyc@wustl.edu.
LA  - eng
PT  - Journal Article
DEP - 20231208
PL  - United States
TA  - J Hand Surg Am
JT  - The Journal of hand surgery
JID - 7609631
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - distal radius fractures
OT  - patient education
EDAT- 2023/12/10 06:41
MHDA- 2023/12/10 06:41
CRDT- 2023/12/09 10:34
PHST- 2023/06/19 00:00 [received]
PHST- 2023/10/25 00:00 [revised]
PHST- 2023/10/26 00:00 [accepted]
PHST- 2023/12/10 06:41 [medline]
PHST- 2023/12/10 06:41 [pubmed]
PHST- 2023/12/09 10:34 [entrez]
AID - S0363-5023(23)00589-0 [pii]
AID - 10.1016/j.jhsa.2023.10.019 [doi]
PST - aheadofprint
SO  - J Hand Surg Am. 2023 Dec 8:S0363-5023(23)00589-0. doi: 
      10.1016/j.jhsa.2023.10.019.

PMID- 37936648
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231109
IS  - 2632-6140 (Electronic)
IS  - 2632-6140 (Linking)
VI  - 5
IP  - 2
DP  - 2023
TI  - Assessment of ChatGPT's performance on neurology written board examination 
      questions.
PG  - e000530
LID - 10.1136/bmjno-2023-000530 [doi]
LID - e000530
AB  - BACKGROUND AND OBJECTIVES: ChatGPT has shown promise in healthcare. To assess the 
      utility of this novel tool in healthcare education, we evaluated ChatGPT's 
      performance in answering neurology board exam questions. METHODS: Neurology 
      board-style examination questions were accessed from BoardVitals, a commercial 
      neurology question bank. ChatGPT was provided a full question prompt and multiple 
      answer choices. First attempts and additional attempts up to three tries were 
      given to ChatGPT to select the correct answer. A total of 560 questions (14 
      blocks of 40 questions) were used, although any image-based questions were 
      disregarded due to ChatGPT's inability to process visual input. The artificial 
      intelligence (AI) answers were then compared with human user data provided by the 
      question bank to gauge its performance. RESULTS: Out of 509 eligible questions 
      over 14 question blocks, ChatGPT correctly answered 335 questions (65.8%) on the 
      first attempt/iteration and 383 (75.3%) over three attempts/iterations, scoring 
      at approximately the 26th and 50th percentiles, respectively. The highest 
      performing subjects were pain (100%), epilepsy & seizures (85%) and genetic (82%) 
      while the lowest performing subjects were imaging/diagnostic studies (27%), 
      critical care (41%) and cranial nerves (48%). DISCUSSION: This study found that 
      ChatGPT performed similarly to its human counterparts. The accuracy of the AI 
      increased with multiple attempts and performance fell within the expected range 
      of neurology resident learners. This study demonstrates ChatGPT's potential in 
      processing specialised medical information. Future studies would better define 
      the scope to which AI would be able to integrate into medical decision making.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Chen, Tse Chian
AU  - Chen TC
AD  - Neurology, Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Multala, Evan
AU  - Multala E
AD  - Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Kearns, Patrick
AU  - Kearns P
AD  - Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Delashaw, Johnny
AU  - Delashaw J
AD  - Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Dumont, Aaron
AU  - Dumont A
AD  - Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Maraganore, Demetrius
AU  - Maraganore D
AD  - Neurology, Tulane University School of Medicine, New Orleans, Louisiana, USA.
FAU - Wang, Arthur
AU  - Wang A
AUID- ORCID: 0009-0007-4396-2516
AD  - Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana, USA.
LA  - eng
PT  - Journal Article
DEP - 20231102
PL  - England
TA  - BMJ Neurol Open
JT  - BMJ neurology open
JID - 101775450
PMC - PMC10626870
OTO - NOTNLM
OT  - CLINICAL NEUROLOGY
COIS- Competing interests: None declared.
EDAT- 2023/11/08 06:42
MHDA- 2023/11/08 06:43
CRDT- 2023/11/08 03:50
PHST- 2023/09/07 00:00 [received]
PHST- 2023/10/19 00:00 [accepted]
PHST- 2023/11/08 06:43 [medline]
PHST- 2023/11/08 06:42 [pubmed]
PHST- 2023/11/08 03:50 [entrez]
AID - bmjno-2023-000530 [pii]
AID - 10.1136/bmjno-2023-000530 [doi]
PST - epublish
SO  - BMJ Neurol Open. 2023 Nov 2;5(2):e000530. doi: 10.1136/bmjno-2023-000530. 
      eCollection 2023.

PMID- 37658948
OWN - NLM
STAT- Publisher
LR  - 20230902
IS  - 1573-2584 (Electronic)
IS  - 0301-1623 (Linking)
DP  - 2023 Sep 2
TI  - Evaluating the performance of ChatGPT in answering questions related to 
      urolithiasis.
LID - 10.1007/s11255-023-03773-0 [doi]
AB  - PURPOSE: ChatGPT is an artificial intelligence (AI) program with natural language 
      processing. We analyzed ChatGPT's knowledge about urolithiasis whether it can be 
      used to inform patients about urolithiasis. METHODS: Frequently asked questions 
      (FAQs) about urolithiasis on the websites of urological associations and 
      hospitals were analyzed. Also, strong recommendation-level information was 
      gathered from the urolithiasis section of the European Association of Urology 
      (EAU) 2022 Guidelines. All questions were asked in order in ChatGPT August 3rd 
      version. All answers were evaluated separately by two specialist urologists and 
      scored between 1 and 4, where 1: completely correct, 2: correct but inadequate, 
      3: a mix of correct and misleading information, and 4: completely incorrect. 
      RESULTS: Of the FAQs, 94.6% were answered completely correctly. No question was 
      answered completely incorrectly. All questions about general, diagnosis, and 
      ureteral stones were graded as 1. Of the 60 questions prepared according to the 
      EAU guideline recommendations, 50 (83.3%) were evaluated as grade 1, and 8 
      (13.3%) and 2 (3.3%) as grade 3. All questions related to general, diagnostic, 
      renal calculi, ureteral calculi, and metabolic evaluation received the same 
      answer the second time they were asked. CONCLUSION: Our findings demonstrated 
      that ChatGPT accurately and satisfactorily answered more than 95% of the 
      questions about urolithiasis. We conclude that applying ChatGPT in urology 
      clinics under the supervision of urologists can help patients and their families 
      to have better understanding on urolithiasis diagnosis and treatment.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature B.V.
FAU - Cakir, Hakan
AU  - Cakir H
AUID- ORCID: 0009-0003-7341-8360
AD  - Department of Urology, Fulya Acibadem Hospital, Sisli, Istanbul, Turkey. 
      hakancakirmd@gmail.com.
FAU - Caglar, Ufuk
AU  - Caglar U
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Yildiz, Oguzhan
AU  - Yildiz O
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Meric, Arda
AU  - Meric A
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Ayranci, Ali
AU  - Ayranci A
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Ozgor, Faruk
AU  - Ozgor F
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230902
PL  - Netherlands
TA  - Int Urol Nephrol
JT  - International urology and nephrology
JID - 0262521
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Patient knowledge
OT  - Urolithiasis
EDAT- 2023/09/04 01:22
MHDA- 2023/09/04 01:22
CRDT- 2023/09/02 11:06
PHST- 2023/08/12 00:00 [received]
PHST- 2023/08/26 00:00 [accepted]
PHST- 2023/09/04 01:22 [medline]
PHST- 2023/09/04 01:22 [pubmed]
PHST- 2023/09/02 11:06 [entrez]
AID - 10.1007/s11255-023-03773-0 [pii]
AID - 10.1007/s11255-023-03773-0 [doi]
PST - aheadofprint
SO  - Int Urol Nephrol. 2023 Sep 2. doi: 10.1007/s11255-023-03773-0.

PMID- 37540015
OWN - NLM
STAT- Publisher
LR  - 20230804
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Aug 4
TI  - An original study of ChatGPT-3.5 and ChatGPT-4 Dermatological Knowledge Level 
      based on the Dermatology Specialty Certificate Examinations.
LID - llad255 [pii]
LID - 10.1093/ced/llad255 [doi]
AB  - BACKGROUND: The global use of artificial intelligence has the potential to 
      revolutionize the healthcare industry. Despite the fact that artificial 
      intelligence is becoming more popular, there is still a lack of evidence on its 
      use in dermatology. OBJECTIVE: The study aimed to determine the capacity of 
      ChatGPT-3.5 and ChatGPT-4 to support dermatological knowledge and clinical 
      decision-making in medical practice. METHODS: Three dermatology specialty 
      certificate tests, in English and Polish, consisting of 120 single-best-answer, 
      multiple-choice format questions each, were used to assess ChatGPT-3.5 and 
      ChatGPT-4 performance. RESULTS: ChatGPT-4 exceeded the 60% pass rate in every 
      performed test, with a minimum of 80% and 70% correct answers for the English and 
      Polish versions, respectively. ChatGPT-4 performed significantly better on each 
      exam (p<0.01), regardless of the language, compared to ChatGPT-3.5. Furthermore, 
      ChatGPT-4 answered clinical picture-type questions with an average accuracy of 
      92.98% and 84.21% for English and Polish questions respectively. The difference 
      between the tests in Polish and English did not turn out to be significant but 
      still, ChatGPT-3.5 and ChatGPT-4 in English performed better overall than in 
      Polish by an average of 8 percentage points for each test. Incorrect ChatGPT 
      answers were highly correlated with a lower difficulty index, which denotes 
      questions with higher difficulty in most of the tests. (p<0.05). CONCLUSION: The 
      dermatological knowledge level of ChatGPT was high, with a significantly better 
      performance of ChatGPT-4 than ChatGPT-3.5. Although the use of ChatGPT will not 
      replace the doctor's final decision, physicians should support artificial 
      intelligence development in dermatology to raise the standards of medical care.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of British 
      Association of Dermatologists. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Lewandowski, Miłosz
AU  - Lewandowski M
AUID- ORCID: 0000-0002-3561-5705
AD  - Department of Dermatology, Venereology and Allergology, Faculty of Medicine, 
      Medical University of Gdansk, Smoluchowskiego 17, 80-214 Gdansk, Poland.
FAU - Łukowicz, Paweł
AU  - Łukowicz P
AD  - Division of Biostatistics and Neural Networks, Medical University of Gdansk, 
      Debinki 1, 80-211 Gdansk, Poland.
FAU - Świetlik, Dariusz
AU  - Świetlik D
AD  - Division of Biostatistics and Neural Networks, Medical University of Gdansk, 
      Debinki 1, 80-211 Gdansk, Poland.
FAU - Barańska-Rybak, Wioletta
AU  - Barańska-Rybak W
AD  - Department of Dermatology, Venereology and Allergology, Faculty of Medicine, 
      Medical University of Gdansk, Smoluchowskiego 17, 80-214 Gdansk, Poland.
LA  - eng
PT  - Journal Article
DEP - 20230804
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
EDAT- 2023/08/04 13:10
MHDA- 2023/08/04 13:10
CRDT- 2023/08/04 09:02
PHST- 2023/05/17 00:00 [received]
PHST- 2023/07/17 00:00 [revised]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/08/04 13:10 [medline]
PHST- 2023/08/04 13:10 [pubmed]
PHST- 2023/08/04 09:02 [entrez]
AID - 7237242 [pii]
AID - 10.1093/ced/llad255 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Aug 4:llad255. doi: 10.1093/ced/llad255.

PMID- 37857839
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231123
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 19
TI  - A vignette-based evaluation of ChatGPT's ability to provide appropriate and 
      equitable medical advice across care contexts.
PG  - 17885
LID - 10.1038/s41598-023-45223-y [doi]
LID - 17885
AB  - ChatGPT is a large language model trained on text corpora and reinforced with 
      human supervision. Because ChatGPT can provide human-like responses to complex 
      questions, it could become an easily accessible source of medical advice for 
      patients. However, its ability to answer medical questions appropriately and 
      equitably remains unknown. We presented ChatGPT with 96 advice-seeking vignettes 
      that varied across clinical contexts, medical histories, and social 
      characteristics. We analyzed responses for clinical appropriateness by 
      concordance with guidelines, recommendation type, and consideration of social 
      factors. Ninety-three (97%) responses were appropriate and did not explicitly 
      violate clinical guidelines. Recommendations in response to advice-seeking 
      questions were completely absent (N = 34, 35%), general (N = 18, 18%), or 
      specific (N = 44, 46%). 53 (55%) explicitly considered social factors like race 
      or insurance status, which in some cases changed clinical recommendations. 
      ChatGPT consistently provided background information in response to medical 
      questions but did not reliably offer appropriate and personalized medical advice.
CI  - © 2023. Springer Nature Limited.
FAU - Nastasi, Anthony J
AU  - Nastasi AJ
AD  - Department of Emergency Medicine, University of Pennsylvania, 3400 Spruce Street, 
      Philadelphia, PA, 19104, USA. Anthony.Nastasi@pennmedicine.upenn.edu.
FAU - Courtright, Katherine R
AU  - Courtright KR
AD  - Perelman School of Medicine, Palliative and Advanced Illness Research (PAIR) 
      Center, University of Pennsylvania, Philadelphia, PA, USA.
AD  - Division of Pulmonary, Allergy, & Critical Care Medicine, Perelman School of 
      Medicine, University of Pennsylvania, Philadelphia, PA, USA.
AD  - Perelman School of Medicine, Leonard Davis Institute of Health Economics, 
      University of Pennsylvania, Philadelphia, PA, USA.
AD  - Perelman School of Medicine, Penn Palliative Care Program, University of 
      Pennsylvania, Philadelphia, PA, USA.
FAU - Halpern, Scott D
AU  - Halpern SD
AD  - Perelman School of Medicine, Palliative and Advanced Illness Research (PAIR) 
      Center, University of Pennsylvania, Philadelphia, PA, USA.
AD  - Division of Pulmonary, Allergy, & Critical Care Medicine, Perelman School of 
      Medicine, University of Pennsylvania, Philadelphia, PA, USA.
AD  - Perelman School of Medicine, Leonard Davis Institute of Health Economics, 
      University of Pennsylvania, Philadelphia, PA, USA.
FAU - Weissman, Gary E
AU  - Weissman GE
AD  - Perelman School of Medicine, Palliative and Advanced Illness Research (PAIR) 
      Center, University of Pennsylvania, Philadelphia, PA, USA.
AD  - Division of Pulmonary, Allergy, & Critical Care Medicine, Perelman School of 
      Medicine, University of Pennsylvania, Philadelphia, PA, USA.
AD  - Perelman School of Medicine, Leonard Davis Institute of Health Economics, 
      University of Pennsylvania, Philadelphia, PA, USA.
AD  - Perelman School of Medicine, Penn Institute for Biomedical Informatics, 
      University of Pennsylvania, Philadelphia, PA, USA.
LA  - eng
GR  - K23 HL141639/HL/NHLBI NIH HHS/United States
GR  - R01AG073384/NH/NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20231019
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - Female
MH  - *Insurance Coverage
MH  - *Language
MH  - Social Factors
MH  - Uterus
PMC - PMC10587094
COIS- The authors declare no competing interests.
EDAT- 2023/10/20 00:43
MHDA- 2023/10/23 01:18
CRDT- 2023/10/19 23:43
PHST- 2023/03/01 00:00 [received]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/20 00:43 [pubmed]
PHST- 2023/10/19 23:43 [entrez]
AID - 10.1038/s41598-023-45223-y [pii]
AID - 45223 [pii]
AID - 10.1038/s41598-023-45223-y [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 19;13(1):17885. doi: 10.1038/s41598-023-45223-y.

PMID- 37031289
OWN - NLM
STAT- MEDLINE
DCOM- 20230425
LR  - 20230525
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 5
DP  - 2023 May
TI  - Talk with ChatGPT About the Outbreak of Mpox in 2022: Reflections and Suggestions 
      from AI Dimensions.
PG  - 870-874
LID - 10.1007/s10439-023-03196-z [doi]
AB  - In the era of big data, generative artificial intelligence (AI) models are 
      currently in a boom. The Chatbot Generative Pre-trained Transformer (ChatGPT), a 
      large language model (LLM) developed by OpenAI (San Francisco, CA), is a type of 
      AI software that could generate text based on the input it receives. In this 
      study, in order to explore how ChatGPT could give reflections and suggestions 
      about the sudden outbreak of Mpox in 2022 from the AI dimensions, our group 
      talked with ChatGPT with several questions about Mpox. We hope this talk could 
      enrich our knowledge on Mpox from the new AI dimensions and also explore the 
      possibility of human and AI fight shoulder to shoulder for prevention and 
      containment of the potential epidemics or pandemics in future.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China.
FAU - He, Yongbin
AU  - He Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
FAU - Li, Cheng
AU  - Li C
AD  - Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical 
      College of Peking University, Beijing, China.
FAU - Xie, Ruijie
AU  - Xie R
AD  - Department of Hand and Microsurgery, Hengyang Medical School, The Affiliated 
      Nanhua Hospital, University of South China Hengyang, Hengyang, China.
FAU - Lu, Yanqiu
AU  - Lu Y
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China.
FAU - Gu, Shuqin
AU  - Gu S
AD  - Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC, USA. 
      shuqin.gu@duke.edu.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Graduate School, Tianjin Medical University, Tianjin, China. 
      wuhaiyang2021@tmu.edu.cn.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      NC, USA. wuhaiyang2021@tmu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230408
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Monkeypox
MH  - Software
MH  - Disease Outbreaks
MH  - Electric Power Supplies
PMC - PMC10085783
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbots
OT  - Infectious disease
OT  - Mpox
OT  - Public health
COIS- The authors declare no conflict of interest.
EDAT- 2023/04/09 06:00
MHDA- 2023/04/25 06:42
CRDT- 2023/04/08 23:20
PHST- 2023/03/21 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/04/25 06:42 [medline]
PHST- 2023/04/09 06:00 [pubmed]
PHST- 2023/04/08 23:20 [entrez]
AID - 10.1007/s10439-023-03196-z [pii]
AID - 3196 [pii]
AID - 10.1007/s10439-023-03196-z [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 May;51(5):870-874. doi: 10.1007/s10439-023-03196-z. Epub 
      2023 Apr 8.

PMID- 37958000
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231117
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 21
DP  - 2023 Oct 30
TI  - Performance of ChatGPT on Registered Nurse License Exam in Taiwan: A Descriptive 
      Study.
LID - 10.3390/healthcare11212855 [doi]
LID - 2855
AB  - (1) Background: AI (artificial intelligence) chatbots have been widely applied. 
      ChatGPT could enhance individual learning capabilities and clinical reasoning 
      skills and facilitate students' understanding of complex concepts in healthcare 
      education. There is currently less emphasis on its application in nursing 
      education. The application of ChatGPT in nursing education needs to be verified. 
      (2) Methods: A descriptive study was used to analyze the scores of ChatGPT on the 
      registered nurse license exam (RNLE) in 2022~2023, and to explore the response 
      and explanations of ChatGPT. The process of data measurement encompassed input 
      sourcing, encoding methods, and statistical analysis. (3) Results: ChatGPT 
      promptly responded within seconds. The average score of four exams was around 
      51.6 to 63.75 by ChatGPT, and it passed the RNLE in 2022 1st and 2023 2nd. 
      However, ChatGPT may generate misleading or inaccurate explanations, or it could 
      lead to hallucination; confusion or misunderstanding about complicated scenarios; 
      and languages bias. (4) Conclusions: ChatGPT may have the potential to assist 
      with nursing education because of its advantages. It is recommended to integrate 
      ChatGPT into different nursing courses, to assess its limitations and 
      effectiveness through a variety of tools and methods.
FAU - Huang, Huiman
AU  - Huang H
AUID- ORCID: 0000-0003-4378-176X
AD  - School of Nursing, College of Nursing, Tzu Chi University of Science and 
      Technology, Hualien 970302, Taiwan.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10649156
OTO - NOTNLM
OT  - artificial intelligence
OT  - nursing graduate
OT  - registered nurse
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/14 06:42
MHDA- 2023/11/14 06:43
CRDT- 2023/11/14 02:05
PHST- 2023/09/24 00:00 [received]
PHST- 2023/10/17 00:00 [revised]
PHST- 2023/10/27 00:00 [accepted]
PHST- 2023/11/14 06:43 [medline]
PHST- 2023/11/14 06:42 [pubmed]
PHST- 2023/11/14 02:05 [entrez]
AID - healthcare11212855 [pii]
AID - healthcare-11-02855 [pii]
AID - 10.3390/healthcare11212855 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Oct 30;11(21):2855. doi: 10.3390/healthcare11212855.

PMID- 37844967
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 2009-8774 (Electronic)
IS  - 2305-6983 (Print)
IS  - 2305-6983 (Linking)
VI  - 11
IP  - 4
DP  - 2023 Sep
TI  - Identifying depression and its determinants upon initiating treatment: ChatGPT 
      versus primary care physicians.
LID - 10.1136/fmch-2023-002391 [doi]
LID - e002391
AB  - OBJECTIVE: To compare evaluations of depressive episodes and suggested treatment 
      protocols generated by Chat Generative Pretrained Transformer (ChatGPT)-3 and 
      ChatGPT-4 with the recommendations of primary care physicians. METHODS: Vignettes 
      were input to the ChatGPT interface. These vignettes focused primarily on 
      hypothetical patients with symptoms of depression during initial consultations. 
      The creators of these vignettes meticulously designed eight distinct versions in 
      which they systematically varied patient attributes (sex, socioeconomic status 
      (blue collar worker or white collar worker) and depression severity (mild or 
      severe)). Each variant was subsequently introduced into ChatGPT-3.5 and 
      ChatGPT-4. Each vignette was repeated 10 times to ensure consistency and 
      reliability of the ChatGPT responses. RESULTS: For mild depression, ChatGPT-3.5 
      and ChatGPT-4 recommended psychotherapy in 95.0% and 97.5% of cases, 
      respectively. Primary care physicians, however, recommended psychotherapy in only 
      4.3% of cases. For severe cases, ChatGPT favoured an approach that combined 
      psychotherapy, while primary care physicians recommended a combined approach. The 
      pharmacological recommendations of ChatGPT-3.5 and ChatGPT-4 showed a preference 
      for exclusive use of antidepressants (74% and 68%, respectively), in contrast 
      with primary care physicians, who typically recommended a mix of antidepressants 
      and anxiolytics/hypnotics (67.4%). Unlike primary care physicians, ChatGPT showed 
      no gender or socioeconomic biases in its recommendations. CONCLUSION: ChatGPT-3.5 
      and ChatGPT-4 aligned well with accepted guidelines for managing mild and severe 
      depression, without showing the gender or socioeconomic biases observed among 
      primary care physicians. Despite the suggested potential benefit of using 
      atificial intelligence (AI) chatbots like ChatGPT to enhance clinical decision 
      making, further research is needed to refine AI recommendations for severe cases 
      and to consider potential risks and ethical issues.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Levkovich, Inbar
AU  - Levkovich I
AUID- ORCID: 0000-0002-5717-4074
AD  - Oranim Academic College, Tivon, Israel inbar.lev2@gmail.com.
FAU - Elyoseph, Zohar
AU  - Elyoseph Z
AD  - Department of Psychology and Educational Counseling, Max Stern Academic College 
      Of Emek Yezreel, Emek Yezreel, Israel.
AD  - Department of Brain Sciences, Imperial College London, London, UK.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Fam Med Community Health
JT  - Family medicine and community health
JID - 101700650
RN  - 0 (Anti-Anxiety Agents)
RN  - EC 2.3.1.6 (Choline O-Acetyltransferase)
RN  - 0 (Antidepressive Agents)
SB  - IM
MH  - Humans
MH  - Depression/drug therapy
MH  - *Physicians, Primary Care
MH  - Reproducibility of Results
MH  - *Anti-Anxiety Agents
MH  - Choline O-Acetyltransferase
MH  - Antidepressive Agents/therapeutic use
PMC - PMC10582915
OTO - NOTNLM
OT  - Depression
OT  - Family Health
OT  - Family Practice
OT  - General Practice
OT  - Mental Health
COIS- Competing interests: None declared.
EDAT- 2023/10/17 00:42
MHDA- 2023/10/23 01:18
CRDT- 2023/10/16 20:53
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/17 00:42 [pubmed]
PHST- 2023/10/16 20:53 [entrez]
AID - fmch-2023-002391 [pii]
AID - 10.1136/fmch-2023-002391 [doi]
PST - ppublish
SO  - Fam Med Community Health. 2023 Sep;11(4):e002391. doi: 10.1136/fmch-2023-002391.

PMID- 37839017
OWN - NLM
STAT- Publisher
LR  - 20231015
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
DP  - 2023 Oct 15
TI  - ChatGPT-4: An assessment of an upgraded artificial intelligence chatbot in the 
      United States Medical Licensing Examination.
PG  - 1-7
LID - 10.1080/0142159X.2023.2249588 [doi]
AB  - Purpose: ChatGPT-4 is an upgraded version of an artificial intelligence chatbot. 
      The performance of ChatGPT-4 on the United States Medical Licensing Examination 
      (USMLE) has not been independently characterized. We aimed to assess the 
      performance of ChatGPT-4 at responding to USMLE Step 1, Step 2CK, and Step 3 
      practice questions.Method: Practice multiple-choice questions for the USMLE Step 
      1, Step 2CK, and Step 3 were compiled. Of 376 available questions, 319 (85%) were 
      analyzed by ChatGPT-4 on March 21(st), 2023. Our primary outcome was the 
      performance of ChatGPT-4 for the practice USMLE Step 1, Step 2CK, and Step 3 
      examinations, measured as the proportion of multiple-choice questions answered 
      correctly. Our secondary outcomes were the mean length of questions and responses 
      provided by ChatGPT-4.Results: ChatGPT-4 responded to 319 text-based 
      multiple-choice questions from USMLE practice test material. ChatGPT-4 answered 
      82 of 93 (88%) questions correctly on USMLE Step 1, 91 of 106 (86%) on Step 2CK, 
      and 108 of 120 (90%) on Step 3. ChatGPT-4 provided explanations for all 
      questions. ChatGPT-4 spent 30.8 ± 11.8 s on average responding to practice 
      questions for USMLE Step 1, 23.0 ± 9.4 s per question for Step 2CK, and 
      23.1 ± 8.3 s per question for Step 3. The mean length of practice USMLE 
      multiple-choice questions that were answered correctly and incorrectly by 
      ChatGPT-4 was similar (difference = 17.48 characters, SE = 59.75, 
      95%CI = [-100.09,135.04], t = 0.29, p = 0.77). The mean length of ChatGPT-4's 
      correct responses to practice questions was significantly shorter than the mean 
      length of incorrect responses (difference = 79.58 characters, SE = 35.42, 
      95%CI = [9.89,149.28], t = 2.25, p = 0.03).Conclusions: ChatGPT-4 answered a 
      remarkably high proportion of practice questions correctly for USMLE 
      examinations. ChatGPT-4 performed substantially better at USMLE practice 
      questions than previous models of the same AI chatbot.
FAU - Mihalache, Andrew
AU  - Mihalache A
AD  - Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
FAU - Huang, Ryan S
AU  - Huang RS
AD  - Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
FAU - Popovic, Marko M
AU  - Popovic MM
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
FAU - Muni, Rajeev H
AU  - Muni RH
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
AD  - Department of Ophthalmology, St. Michael's Hospital/Unity Health Toronto, 
      Toronto, Ontario, Canada.
LA  - eng
PT  - Journal Article
DEP - 20231015
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
OTO - NOTNLM
OT  - United States medical licensing examination
OT  - artificial intelligence
OT  - chatgpt-4
OT  - natural language processing
EDAT- 2023/10/15 18:41
MHDA- 2023/10/15 18:41
CRDT- 2023/10/15 15:10
PHST- 2023/10/15 18:41 [medline]
PHST- 2023/10/15 18:41 [pubmed]
PHST- 2023/10/15 15:10 [entrez]
AID - 10.1080/0142159X.2023.2249588 [doi]
PST - aheadofprint
SO  - Med Teach. 2023 Oct 15:1-7. doi: 10.1080/0142159X.2023.2249588.

PMID- 37821602
OWN - NLM
STAT- Publisher
LR  - 20231011
IS  - 1432-0932 (Electronic)
IS  - 0940-6719 (Linking)
DP  - 2023 Oct 11
TI  - Large language models: Are artificial intelligence-based chatbots a reliable 
      source of patient information for spinal surgery?
LID - 10.1007/s00586-023-07975-z [doi]
AB  - PURPOSE: Large language models (LLM) have recently attracted attention because of 
      their enormous performance. Based on artificial intelligence, LLM enable dialogic 
      communication using quasi-natural language that approximates the quality of human 
      communication. Thus, LLM could play an important role for patients to become 
      informed. To evaluate the validity of an LLM in providing medical information, we 
      used one of the first high-performance LLM (ChatGPT) on the clinical example of 
      acute lumbar disc herniation (LDH). METHODS: Twenty-four spinal surgeons 
      experienced in LDH surgery directed questions to ChatGPT about the clinical 
      picture of LDH from a patient's perspective. They evaluated the quality of 
      ChatGPT responses and its potential use in medical communication. The responses 
      were compared with the information content of a standard informed consent form. 
      RESULTS: ChatGPT provided good results in terms of comprehensibility, 
      specificity, and satisfaction of responses and in terms of medical accuracy and 
      completeness. ChatGPT was not able to provide all the information that was 
      provided in the informed consent form, but did communicate information that was 
      not listed there. In some cases, albeit minor, ChatGPT made medically inaccurate 
      claims, such as listing kyphoplasty and vertebroplasty as surgical options for 
      LDH. CONCLUSION: With the incipient use of artificial intelligence in 
      communication, LLM will certainly become increasingly important to patients. Even 
      if LLM are unlikely to play a role in clinical communication between physicians 
      and patients at the moment, the opportunities-but also the risks-of this novel 
      technology should be alertly monitored.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Stroop, Anna
AU  - Stroop A
AD  - Faculty of Health, Department of Medicine, Witten-Herdecke University, 
      Alfred-Herrhausen-Straße 45, 58455, Witten, Germany.
FAU - Stroop, Tabea
AU  - Stroop T
AD  - Faculty of Health, Department of Medicine, Witten-Herdecke University, 
      Alfred-Herrhausen-Straße 45, 58455, Witten, Germany.
FAU - Zawy Alsofy, Samer
AU  - Zawy Alsofy S
AD  - Faculty of Health, Department of Medicine, Witten-Herdecke University, 
      Alfred-Herrhausen-Straße 45, 58455, Witten, Germany.
AD  - Department of Neurosurgery, St. Barbara-Hospital, Academic Hospital of 
      Westfälische Wilhelms-University Münster, Hamm, Germany.
FAU - Nakamura, Makoto
AU  - Nakamura M
AD  - Department of Neurosurgery, Academic Hospital Köln-Merheim, Witten-Herdecke 
      University, Cologne, Germany.
FAU - Möllmann, Frank
AU  - Möllmann F
AD  - Department for Neuro- and Spine Surgery, Niels Stensen Neuro Center, Osnabrück, 
      Germany.
FAU - Greiner, Christoph
AU  - Greiner C
AD  - Department for Neuro- and Spine Surgery, Niels Stensen Neuro Center, Osnabrück, 
      Germany.
FAU - Stroop, Ralf
AU  - Stroop R
AUID- ORCID: 0000-0001-8795-6790
AD  - Faculty of Health, Department of Medicine, Witten-Herdecke University, 
      Alfred-Herrhausen-Straße 45, 58455, Witten, Germany. ralf.stroop@uni-wh.de.
AD  - Medical School Hamburg, Hamburg, Germany. ralf.stroop@uni-wh.de.
LA  - eng
PT  - Journal Article
DEP - 20231011
PL  - Germany
TA  - Eur Spine J
JT  - European spine journal : official publication of the European Spine Society, the 
      European Spinal Deformity Society, and the European Section of the Cervical Spine 
      Research Society
JID - 9301980
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Large language model
OT  - Patient information
OT  - Spinal surgery
EDAT- 2023/10/12 00:43
MHDA- 2023/10/12 00:43
CRDT- 2023/10/11 23:30
PHST- 2023/03/12 00:00 [received]
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/08/31 00:00 [revised]
PHST- 2023/10/12 00:43 [medline]
PHST- 2023/10/12 00:43 [pubmed]
PHST- 2023/10/11 23:30 [entrez]
AID - 10.1007/s00586-023-07975-z [pii]
AID - 10.1007/s00586-023-07975-z [doi]
PST - aheadofprint
SO  - Eur Spine J. 2023 Oct 11. doi: 10.1007/s00586-023-07975-z.

PMID- 37149512
OWN - NLM
STAT- MEDLINE
DCOM- 20230809
LR  - 20230809
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT Applications and Challenges in Controlling Monkey Pox in Pakistan.
PG  - 1889-1891
LID - 10.1007/s10439-023-03231-z [doi]
AB  - Generative Pre-trained Transformer-based Chatbot (ChatGPT) is an emerging OpenAI 
      application with its greater role in revolutionizing the world. ChatGPT can 
      generate a massive amount of data by using the simple textual input. ChatGPT has 
      its role in supporting the communities to make deciding role in healthcare 
      sector. This paper aims to provide information about monkey pox (mpox) infection 
      in Pakistan. Moreover, this paper analyzes the text-based information given by 
      ChatGPT and narrates potential advantages and disadvantages about mpox infection. 
      Spread of infection, symptoms and diagnose of mpox, control and management, and 
      government responsibilities are major identified advantages. Findings of this 
      paper also show some potential issues of ChatGPT AI application, which are as 
      lack of latest data about mpox in Pakistan, reliability and performance issues, 
      cost and resources for appropriate development and implementation of Open AI 
      application in healthcare. Future works can be carried out to address these 
      limitations in ChatGPT AI application.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Hasnain, Muhammad
AU  - Hasnain M
AD  - Lahore Leads University, Lahore, Pakistan. drhasnain.it@leads.edu.pk.
AD  - Department of Computer Science, Lahore Leads University, Lahore, Pakistan. 
      drhasnain.it@leads.edu.pk.
LA  - eng
PT  - Letter
DEP - 20230506
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - Pakistan
MH  - *Monkeypox
MH  - Reproducibility of Results
MH  - Electric Power Supplies
MH  - Software
OTO - NOTNLM
OT  - Accurate prediction
OT  - Cost
OT  - Monkey pox transmission
OT  - Vaccine
EDAT- 2023/05/07 00:42
MHDA- 2023/08/09 06:43
CRDT- 2023/05/06 23:03
PHST- 2023/05/01 00:00 [received]
PHST- 2023/05/02 00:00 [accepted]
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/05/07 00:42 [pubmed]
PHST- 2023/05/06 23:03 [entrez]
AID - 10.1007/s10439-023-03231-z [pii]
AID - 10.1007/s10439-023-03231-z [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Sep;51(9):1889-1891. doi: 10.1007/s10439-023-03231-z. Epub 
      2023 May 6.

PMID- 37001483
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230612
LR  - 20230612
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 84
DP  - 2023 Jun
TI  - ChatGPT: The balance of future, honesty, and integrity.
PG  - 103571
LID - S1876-2018(23)00126-0 [pii]
LID - 10.1016/j.ajp.2023.103571 [doi]
FAU - Nuryana, Zalik
AU  - Nuryana Z
AD  - Department of Islamic Education, Universitas Ahmad Dahlan, Indonesia. Electronic 
      address: zalik.nuryana@pai.uad.ac.id.
FAU - Pranolo, Andri
AU  - Pranolo A
AD  - Department of Informatics, Universitas Ahmad Dahlan, Indonesia. Electronic 
      address: andri.pranolo@tif.uad.ac.id.
LA  - eng
PT  - Letter
DEP - 20230326
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Asian Journal of Psychiatry
OT  - Balance of future
OT  - ChatGPT
OT  - Honesty
OT  - Integrity
COIS- Conflict of Interest The authors declare no conflict of interest.
EDAT- 2023/04/01 06:00
MHDA- 2023/04/01 06:01
CRDT- 2023/03/31 18:12
PHST- 2023/03/18 00:00 [received]
PHST- 2023/03/24 00:00 [revised]
PHST- 2023/03/25 00:00 [accepted]
PHST- 2023/04/01 06:01 [medline]
PHST- 2023/04/01 06:00 [pubmed]
PHST- 2023/03/31 18:12 [entrez]
AID - S1876-2018(23)00126-0 [pii]
AID - 10.1016/j.ajp.2023.103571 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Jun;84:103571. doi: 10.1016/j.ajp.2023.103571. Epub 2023 
      Mar 26.

PMID- 37482439
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231018
IS  - 1532-7361 (Electronic)
IS  - 0039-6060 (Linking)
VI  - 174
IP  - 5
DP  - 2023 Nov
TI  - How appropriate are answers of online chat-based artificial intelligence 
      (ChatGPT) to common questions on colon cancer?
PG  - 1273-1275
LID - S0039-6060(23)00387-2 [pii]
LID - 10.1016/j.surg.2023.06.005 [doi]
FAU - Emile, Sameh Hany
AU  - Emile SH
AD  - Colorectal Surgery Department, Ellen Leifer Shulman and Steven Shulman Digestive 
      Disease Center, Cleveland Clinic Florida, Weston, FL; Colorectal Surgery Unit, 
      Mansoura University Hospitals, Mansoura, Egypt. Electronic address: 
      https://twitter.com/dr_samehhany81.
FAU - Horesh, Nir
AU  - Horesh N
AD  - Colorectal Surgery Department, Ellen Leifer Shulman and Steven Shulman Digestive 
      Disease Center, Cleveland Clinic Florida, Weston, FL; Department of Surgery and 
      Transplantation, Sheba Medical Center, Ramat-Gan, Israel. Electronic address: 
      https://twitter.com/nirhoresh.
FAU - Freund, Michael
AU  - Freund M
AD  - Department of General Surgery, Shaare Zedek Medical Center, Faculty of Medicine, 
      Hebrew University of Jerusalem, Israel. Electronic address: 
      https://twitter.com/mikifreund.
FAU - Pellino, Gianluca
AU  - Pellino G
AD  - Colorectal Surgery, Vall d'Hebron University Hospital, Universitat Autonoma de 
      Barcelona UAB, Barcelona, Spain; Department of Advanced Medical and Surgical 
      Sciences, Università degli Studi della Campania "Luigi Vanvitelli", Naples, 
      Italy. Electronic address: https://twitter.com/GianlucaPellino.
FAU - Oliveira, Lucia
AU  - Oliveira L
AD  - Director Anorectal Physiology Department of Rio de Janeiro and CEPEMED, Rio de 
      Janeiro, Brazil. Electronic address: https://twitter.com/luciacolorectal.
FAU - Wignakumar, Anjelli
AU  - Wignakumar A
AD  - Department of Plastic Surgery, St. Mary's Hospital, London, UK. Electronic 
      address: https://twitter.com/AWignakumar.
FAU - Wexner, Steven D
AU  - Wexner SD
AD  - Colorectal Surgery Department, Ellen Leifer Shulman and Steven Shulman Digestive 
      Disease Center, Cleveland Clinic Florida, Weston, FL. Electronic address: 
      wexners@ccf.org.
LA  - eng
PT  - Editorial
DEP - 20230721
PL  - United States
TA  - Surgery
JT  - Surgery
JID - 0417347
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Colonic Neoplasms/diagnosis/surgery
EDAT- 2023/07/24 00:41
MHDA- 2023/10/09 06:41
CRDT- 2023/07/23 21:58
PHST- 2023/04/20 00:00 [received]
PHST- 2023/06/03 00:00 [revised]
PHST- 2023/06/18 00:00 [accepted]
PHST- 2023/10/09 06:41 [medline]
PHST- 2023/07/24 00:41 [pubmed]
PHST- 2023/07/23 21:58 [entrez]
AID - S0039-6060(23)00387-2 [pii]
AID - 10.1016/j.surg.2023.06.005 [doi]
PST - ppublish
SO  - Surgery. 2023 Nov;174(5):1273-1275. doi: 10.1016/j.surg.2023.06.005. Epub 2023 
      Jul 21.

PMID- 37989755
OWN - NLM
STAT- MEDLINE
DCOM- 20231123
LR  - 20231124
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Nov 21
TI  - ChatGPT's performance before and after teaching in mass casualty incident triage.
PG  - 20350
LID - 10.1038/s41598-023-46986-0 [doi]
LID - 20350
AB  - Since its initial launching, ChatGPT has gained significant attention from the 
      media, with many claiming that ChatGPT's arrival is a transformative milestone in 
      the advancement of the AI revolution. Our aim was to assess the performance of 
      ChatGPT before and after teaching the triage of mass casualty incidents by 
      utilizing a validated questionnaire specifically designed for such scenarios. In 
      addition, we compared the triage performance between ChatGPT and medical 
      students. Our cross-sectional study employed a mixed-methods analysis to assess 
      the performance of ChatGPT in mass casualty incident triage, pre- and 
      post-teaching of Simple Triage And Rapid Treatment (START) triage. After teaching 
      the START triage algorithm, ChatGPT scored an overall triage accuracy of 80%, 
      with only 20% of cases being over-triaged. The mean accuracy of medical students 
      on the same questionnaire yielded 64.3%. Qualitative analysis on pre-determined 
      themes on 'walking-wounded', 'respiration', 'perfusion', and 'mental status' on 
      ChatGPT showed similar performance in pre- and post-teaching of START triage. 
      Additional themes on 'disclaimer', 'prediction', 'management plan', and 
      'assumption' were identified during the thematic analysis. ChatGPT exhibited 
      promising results in effectively responding to mass casualty incident 
      questionnaires. Nevertheless, additional research is necessary to ensure its 
      safety and efficacy before clinical implementation.
CI  - © 2023. The Author(s).
FAU - Gan, Rick Kye
AU  - Gan RK
AUID- ORCID: 0000-0002-4211-7819
AD  - Unit for Research in Emergency and Disaster, Faculty of Medicine and Health 
      Sciences, University of Oviedo, 33006, Oviedo, Spain.
FAU - Uddin, Helal
AU  - Uddin H
AUID- ORCID: 0000-0002-0767-3174
AD  - Unit for Research in Emergency and Disaster, Faculty of Medicine and Health 
      Sciences, University of Oviedo, 33006, Oviedo, Spain. md.helal.uddin@stud.ki.se.
AD  - Department of Global Public Health, Karolinska Institute, 17177, Solna, Sweden. 
      md.helal.uddin@stud.ki.se.
AD  - Department of Sociology, East West University, Dhaka, 1212, Bangladesh. 
      md.helal.uddin@stud.ki.se.
FAU - Gan, Ann Zee
AU  - Gan AZ
AUID- ORCID: 0009-0001-4650-5528
AD  - Tenghilan Health Clinic, 89208, Tuaran, Sabah, Malaysia.
FAU - Yew, Ying Ying
AU  - Yew YY
AUID- ORCID: 0000-0003-2747-6730
AD  - Unit for Research in Emergency and Disaster, Faculty of Medicine and Health 
      Sciences, University of Oviedo, 33006, Oviedo, Spain.
FAU - González, Pedro Arcos
AU  - González PA
AUID- ORCID: 0000-0003-4882-5442
AD  - Unit for Research in Emergency and Disaster, Faculty of Medicine and Health 
      Sciences, University of Oviedo, 33006, Oviedo, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231121
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - *Triage/methods
MH  - *Mass Casualty Incidents
MH  - Cross-Sectional Studies
MH  - Computer Simulation
MH  - Algorithms
PMC - PMC10663620
COIS- The authors declare no competing interests.
EDAT- 2023/11/22 00:42
MHDA- 2023/11/23 06:42
CRDT- 2023/11/21 23:37
PHST- 2023/05/27 00:00 [received]
PHST- 2023/11/07 00:00 [accepted]
PHST- 2023/11/23 06:42 [medline]
PHST- 2023/11/22 00:42 [pubmed]
PHST- 2023/11/21 23:37 [entrez]
AID - 10.1038/s41598-023-46986-0 [pii]
AID - 46986 [pii]
AID - 10.1038/s41598-023-46986-0 [doi]
PST - epublish
SO  - Sci Rep. 2023 Nov 21;13(1):20350. doi: 10.1038/s41598-023-46986-0.

PMID- 37229893
OWN - NLM
STAT- MEDLINE
DCOM- 20230726
LR  - 20230726
IS  - 1532-8171 (Electronic)
IS  - 0735-6757 (Linking)
VI  - 70
DP  - 2023 Aug
TI  - ChatGPT and conversational artificial intelligence: Friend, foe, or future of 
      research?
PG  - 81-83
LID - S0735-6757(23)00262-0 [pii]
LID - 10.1016/j.ajem.2023.05.018 [doi]
AB  - Artificial intelligence (AI) and machine learning are increasingly utilized 
      across healthcare. More recently, there has been a rise in the use AI within 
      research, particularly through novel conversational AI platforms, such as 
      ChatGPT. In this Controversies paper, we discuss the advantages, limitations, and 
      future directions for ChatGPT and other forms of conversational AI in research 
      and scholarly dissemination.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Gottlieb, Michael
AU  - Gottlieb M
AD  - Department of Emergency Medicine, Rush University Medical Center, Chicago, IL, 
      United States of America. Electronic address: MichaelGottliebMD@gmail.com.
FAU - Kline, Jeffrey A
AU  - Kline JA
AD  - Department of Emergency Medicine, Wayne State University School of Medicine, 
      Detroit, MI, United States of America. Electronic address: jkline@wayne.edu.
FAU - Schneider, Alexander J
AU  - Schneider AJ
FAU - Coates, Wendy C
AU  - Coates WC
AD  - Department of Emergency Medicine, University of California, David Geffen School 
      of Medicine, Los Angeles, CA, United States of America.
LA  - eng
PT  - Journal Article
DEP - 20230518
PL  - United States
TA  - Am J Emerg Med
JT  - The American journal of emergency medicine
JID - 8309942
SB  - IM
CIN - Am J Emerg Med. 2023 Aug;70:190. PMID: 37349234
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Machine Learning
MH  - Communication
MH  - Health Facilities
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Ethics
OT  - Publication
OT  - Research
COIS- Declaration of Competing Interest We have no conflicts of interest to declare and 
      this manuscript has not been submitted elsewhere.
EDAT- 2023/05/26 01:05
MHDA- 2023/07/26 06:43
CRDT- 2023/05/25 18:03
PHST- 2023/03/30 00:00 [received]
PHST- 2023/05/01 00:00 [revised]
PHST- 2023/05/12 00:00 [accepted]
PHST- 2023/07/26 06:43 [medline]
PHST- 2023/05/26 01:05 [pubmed]
PHST- 2023/05/25 18:03 [entrez]
AID - S0735-6757(23)00262-0 [pii]
AID - 10.1016/j.ajem.2023.05.018 [doi]
PST - ppublish
SO  - Am J Emerg Med. 2023 Aug;70:81-83. doi: 10.1016/j.ajem.2023.05.018. Epub 2023 May 
      18.

PMID- 37250832
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230531
IS  - 2169-7574 (Print)
IS  - 2169-7574 (Electronic)
IS  - 2169-7574 (Linking)
VI  - 11
IP  - 5
DP  - 2023 May
TI  - Artificial or Augmented Authorship? A Conversation with a Chatbot on Base of 
      Thumb Arthritis.
PG  - e4999
LID - 10.1097/GOX.0000000000004999 [doi]
LID - e4999
AB  - ChatGPT is an open artificial intelligence chat box that could revolutionize 
      academia and augment research writing. This study had an open conversation with 
      ChatGPT and invited the platform to evaluate this article through series of five 
      questions on base of thumb arthritis to test if its contributions and contents 
      merely add artificial unusable input or help us augment the quality of the 
      article. The information ChatGPT-3 provided was accurate, albeit surface-level, 
      and lacks analytical ability to dissect for important limitations about base of 
      thumb arthritis, which would not be conducive to potentiating creative ideas and 
      solutions in plastic surgery. ChatGPT failed to provide relevant references and 
      even "created" references instead of indicating its inability to perform the 
      task. This highlights that as an AI-generator for medical publishing text, 
      ChatGPT-3 should be used cautiously.
CI  - Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf 
      of The American Society of Plastic Surgeons.
FAU - Seth, Ishith
AU  - Seth I
AD  - From the Faculty of Science, Medicine, and Health, Monash University, Victoria, 
      Australia.
AD  - Faculty of Science, Medicine, and Health, University of Melbourne, Victoria, 
      Australia.
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Peninsula Clinical School, Central Clinical School at Monash University, The 
      Alfred Centre, Melbourne, Victoria, Australia.
FAU - Sinkjær Kenney, Peter
AU  - Sinkjær Kenney P
AD  - Department of Plastic Surgery, Odense University Hospital, Odense, Denmark.
AD  - Department of Plastic and Breast Surgery, Aarhus University Hospital, Aarhus N, 
      Denmark.
FAU - Bulloch, Gabriella
AU  - Bulloch G
AD  - Faculty of Science, Medicine, and Health, University of Melbourne, Victoria, 
      Australia.
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
AD  - From the Faculty of Science, Medicine, and Health, Monash University, Victoria, 
      Australia.
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Peninsula Clinical School, Central Clinical School at Monash University, The 
      Alfred Centre, Melbourne, Victoria, Australia.
FAU - Bo Thomsen, Jørn
AU  - Bo Thomsen J
AD  - Department of Plastic Surgery, Odense University Hospital, Odense, Denmark.
AD  - Research unit for Plastic Surgery, Department of Clinical Research, University of 
      Southern Denmark, Odense, Denmark.
FAU - Rozen, Warren M
AU  - Rozen WM
AD  - From the Faculty of Science, Medicine, and Health, Monash University, Victoria, 
      Australia.
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Peninsula Clinical School, Central Clinical School at Monash University, The 
      Alfred Centre, Melbourne, Victoria, Australia.
LA  - eng
PT  - Journal Article
DEP - 20230526
PL  - United States
TA  - Plast Reconstr Surg Glob Open
JT  - Plastic and reconstructive surgery. Global open
JID - 101622231
PMC - PMC10219695
COIS- The authors have no financial interest to declare in relation to the content of 
      this article. Disclosure statements are at the end of this article, following the 
      correspondence information.
EDAT- 2023/05/30 13:07
MHDA- 2023/05/30 13:08
CRDT- 2023/05/30 11:29
PHST- 2023/02/17 00:00 [received]
PHST- 2023/03/27 00:00 [accepted]
PHST- 2023/05/30 13:08 [medline]
PHST- 2023/05/30 13:07 [pubmed]
PHST- 2023/05/30 11:29 [entrez]
AID - 10.1097/GOX.0000000000004999 [doi]
PST - epublish
SO  - Plast Reconstr Surg Glob Open. 2023 May 26;11(5):e4999. doi: 
      10.1097/GOX.0000000000004999. eCollection 2023 May.

PMID- 37349973
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2753-670X (Electronic)
IS  - 2753-670X (Linking)
VI  - 36
IP  - 6
DP  - 2023 Jun 1
TI  - ChatGPT: revolutionizing cardiothoracic surgery research through artificial 
      intelligence.
LID - 10.1093/icvts/ivad090 [doi]
LID - ivad090
FAU - Arjomandi Rad, Arian
AU  - Arjomandi Rad A
AUID- ORCID: 0000-0002-4931-4049
AD  - Medical Sciences Division, University of Oxford, Oxford, UK.
AD  - Department of Cardiothoracic Surgery, Maastricht University Medical Center, 
      Maastricht, Netherlands.
AD  - Department of Surgery and Cancer, Imperial College London, London, UK.
FAU - Sardari Nia, Peyman
AU  - Sardari Nia P
AUID- ORCID: 0000-0003-2296-1497
AD  - Department of Cardiothoracic Surgery, Maastricht University Medical Center, 
      Maastricht, Netherlands.
FAU - Athanasiou, Thanos
AU  - Athanasiou T
AUID- ORCID: 0000-0002-2525-5515
AD  - Department of Surgery and Cancer, Imperial College London, London, UK.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Interdiscip Cardiovasc Thorac Surg
JT  - Interdisciplinary cardiovascular and thoracic surgery
JID - 9918540787006676
SB  - IM
PMC - PMC10287897
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Cardiothoracic surgery
OT  - ChatGPT
OT  - Research
EDAT- 2023/06/23 06:42
MHDA- 2023/06/23 06:43
CRDT- 2023/06/23 01:23
PHST- 2023/04/21 00:00 [received]
PHST- 2023/05/28 00:00 [revised]
PHST- 2023/06/19 00:00 [accepted]
PHST- 2023/06/23 06:43 [medline]
PHST- 2023/06/23 06:42 [pubmed]
PHST- 2023/06/23 01:23 [entrez]
AID - 7205498 [pii]
AID - ivad090 [pii]
AID - 10.1093/icvts/ivad090 [doi]
PST - ppublish
SO  - Interdiscip Cardiovasc Thorac Surg. 2023 Jun 1;36(6):ivad090. doi: 
      10.1093/icvts/ivad090.

PMID- 37209880
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1527-9995 (Electronic)
IS  - 0090-4295 (Linking)
VI  - 177
DP  - 2023 Jul
TI  - ChatGPT Performance on the American Urological Association Self-assessment Study 
      Program and the Potential Influence of Artificial Intelligence in Urologic 
      Training.
PG  - 29-33
LID - S0090-4295(23)00442-9 [pii]
LID - 10.1016/j.urology.2023.05.010 [doi]
AB  - OBJECTIVE: To assess chat generative pre-trained transformer's (ChatGPT) 
      performance on the American Urological Association Self-Assessment Study Program 
      (AUA SASP) and stratify performance by question stem complexity. METHODS: 
      Questions from the 2021-2022 AUA SASP program were administered to ChatGPT 
      version 3 (ChatGPT-3). Questions were administered to the model utilizing a 
      standardized prompt. The answer choice selected by ChatGPT was then used to 
      answer the question stem in the AUA SASP program. ChatGPT was then prompted to 
      assign a question stem order (first, second, third) to each question. The 
      percentage of correctly answered questions was determined for each order level. 
      All responses provided by ChatGPT were qualitatively assessed for appropriate 
      rationale. RESULTS: A total of 268 questions were administered to ChatGPT. 
      ChatGPT performed better on 2021 compared to the 2022 AUA SASP question set, 
      answering 42.3% versus 30.0% of questions correctly (P < .05). Hundred percent of 
      answer explanations provided appropriate, relevant rationale regardless of 
      whether the answer was correct. Further stratification included assessment by 
      question order level. ChatGPT performed progressively better on the 2021 question 
      set with decreasing order levels, with first-order questions reaching 53.8% 
      (n = 14). However, differences in proportions did not reach statistical 
      significance (P > .05). CONCLUSION: ChatGPT answered many high-level questions 
      correctly and provided a reasonable rationale for each answer choice. While 
      ChatGPT was unable to answer numerous first-order questions, future language 
      processing model learning may lead to the optimization of its fund of knowledge. 
      This may lead to the utilization of artificial intelligence like ChatGPT as an 
      educational tool for urology trainees and professors.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Deebel, Nicholas A
AU  - Deebel NA
AD  - Department of Urology, Wake Forest University School of Medicine, Winston-Salem, 
      NC. Electronic address: ndeebel@wakehealth.edu.
FAU - Terlecki, Ryan
AU  - Terlecki R
AD  - Department of Urology, Wake Forest University School of Medicine, Winston-Salem, 
      NC. Electronic address: rterlecki@wakehealth.edu.
LA  - eng
PT  - Journal Article
DEP - 20230518
PL  - United States
TA  - Urology
JT  - Urology
JID - 0366151
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - Self-Assessment
MH  - *Urologic Diseases
MH  - *Urology
MH  - Educational Status
COIS- Declaration of Competing Interest Nicholas Deebel, MD: No relevant conflict of 
      interest. Ryan Terlecki, MD: paid consultant to Boston Scientific (not relevant 
      to this manuscript).
EDAT- 2023/05/21 01:05
MHDA- 2023/08/14 06:42
CRDT- 2023/05/20 19:27
PHST- 2023/03/07 00:00 [received]
PHST- 2023/04/01 00:00 [revised]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/05/21 01:05 [pubmed]
PHST- 2023/05/20 19:27 [entrez]
AID - S0090-4295(23)00442-9 [pii]
AID - 10.1016/j.urology.2023.05.010 [doi]
PST - ppublish
SO  - Urology. 2023 Jul;177:29-33. doi: 10.1016/j.urology.2023.05.010. Epub 2023 May 
      18.

PMID- 37838575
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1875-2128 (Electronic)
IS  - 1875-2128 (Linking)
VI  - 116
IP  - 12
DP  - 2023 Dec
TI  - Relevance of medical information obtained from ChatGPT: Comment.
PG  - 602
LID - S1875-2136(23)00175-4 [pii]
LID - 10.1016/j.acvd.2023.09.003 [doi]
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Chandigarh, India; Joesph Ayobabalola, University, 
      Ikeji-Arakeji, Nigeria. Electronic address: wviroj@yahoo.com.
LA  - eng
PT  - Letter
DEP - 20231004
PL  - Netherlands
TA  - Arch Cardiovasc Dis
JT  - Archives of cardiovascular diseases
JID - 101465655
SB  - IM
OTO - NOTNLM
OT  - Cardiovascular
OT  - ChatGPT
OT  - Education
EDAT- 2023/10/15 05:46
MHDA- 2023/10/15 05:47
CRDT- 2023/10/14 22:02
PHST- 2023/09/18 00:00 [received]
PHST- 2023/09/20 00:00 [accepted]
PHST- 2023/10/15 05:47 [medline]
PHST- 2023/10/15 05:46 [pubmed]
PHST- 2023/10/14 22:02 [entrez]
AID - S1875-2136(23)00175-4 [pii]
AID - 10.1016/j.acvd.2023.09.003 [doi]
PST - ppublish
SO  - Arch Cardiovasc Dis. 2023 Dec;116(12):602. doi: 10.1016/j.acvd.2023.09.003. Epub 
      2023 Oct 4.

PMID- 38051578
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Dec 5
TI  - ChatGPT Versus Consultants: Blinded Evaluation on Answering Otorhinolaryngology 
      Case-Based Questions.
PG  - e49183
LID - 10.2196/49183 [doi]
AB  - BACKGROUND: Large language models (LLMs), such as ChatGPT (Open AI), are 
      increasingly used in medicine and supplement standard search engines as 
      information sources. This leads to more "consultations" of LLMs about personal 
      medical symptoms. OBJECTIVE: This study aims to evaluate ChatGPT's performance in 
      answering clinical case-based questions in otorhinolaryngology (ORL) in 
      comparison to ORL consultants' answers. METHODS: We used 41 case-based questions 
      from established ORL study books and past German state examinations for doctors. 
      The questions were answered by both ORL consultants and ChatGPT 3. ORL 
      consultants rated all responses, except their own, on medical adequacy, 
      conciseness, coherence, and comprehensibility using a 6-point Likert scale. They 
      also identified (in a blinded setting) if the answer was created by an ORL 
      consultant or ChatGPT. Additionally, the character count was compared. Due to the 
      rapidly evolving pace of technology, a comparison between responses generated by 
      ChatGPT 3 and ChatGPT 4 was included to give an insight into the evolving 
      potential of LLMs. RESULTS: Ratings in all categories were significantly higher 
      for ORL consultants (P<.001). Although inferior to the scores of the ORL 
      consultants, ChatGPT's scores were relatively higher in semantic categories 
      (conciseness, coherence, and comprehensibility) compared to medical adequacy. ORL 
      consultants identified ChatGPT as the source correctly in 98.4% (121/123) of 
      cases. ChatGPT's answers had a significantly higher character count compared to 
      ORL consultants (P<.001). Comparison between responses generated by ChatGPT 3 and 
      ChatGPT 4 showed a slight improvement in medical accuracy as well as a better 
      coherence of the answers provided. Contrarily, neither the conciseness (P=.06) 
      nor the comprehensibility (P=.08) improved significantly despite the significant 
      increase in the mean amount of characters by 52.5% (n= (1470-964)/964; P<.001). 
      CONCLUSIONS: While ChatGPT provided longer answers to medical problems, medical 
      adequacy and conciseness were significantly lower compared to ORL consultants' 
      answers. LLMs have potential as augmentative tools for medical care, but their 
      "consultation" for medical problems carries a high risk of misinformation as 
      their high semantic quality may mask contextual deficits.
CI  - ©Christoph Raphael Buhr, Harry Smith, Tilman Huppertz, Katharina Bahr-Hamm, 
      Christoph Matthias, Andrew Blaikie, Tom Kelsey, Sebastian Kuhn, Jonas Eckrich. 
      Originally published in JMIR Medical Education (https://mededu.jmir.org), 
      05.12.2023.
FAU - Buhr, Christoph Raphael
AU  - Buhr CR
AUID- ORCID: 0000-0002-9551-2310
AD  - Department of Otorhinolaryngology, University Medical Center of the Johannes 
      Gutenberg-University Mainz, Mainz, Germany.
AD  - School of Medicine, University of St Andrews, St Andrews, United Kingdom.
FAU - Smith, Harry
AU  - Smith H
AUID- ORCID: 0009-0003-4497-1394
AD  - School of Computer Science, University of St Andrews, St Andrews, United Kingdom.
FAU - Huppertz, Tilman
AU  - Huppertz T
AUID- ORCID: 0000-0003-0654-3551
AD  - Department of Otorhinolaryngology, University Medical Center of the Johannes 
      Gutenberg-University Mainz, Mainz, Germany.
FAU - Bahr-Hamm, Katharina
AU  - Bahr-Hamm K
AUID- ORCID: 0000-0001-7428-127X
AD  - Department of Otorhinolaryngology, University Medical Center of the Johannes 
      Gutenberg-University Mainz, Mainz, Germany.
FAU - Matthias, Christoph
AU  - Matthias C
AUID- ORCID: 0000-0002-7569-2393
AD  - Department of Otorhinolaryngology, University Medical Center of the Johannes 
      Gutenberg-University Mainz, Mainz, Germany.
FAU - Blaikie, Andrew
AU  - Blaikie A
AUID- ORCID: 0000-0001-7913-6872
AD  - School of Medicine, University of St Andrews, St Andrews, United Kingdom.
FAU - Kelsey, Tom
AU  - Kelsey T
AUID- ORCID: 0000-0002-8091-1458
AD  - School of Computer Science, University of St Andrews, St Andrews, United Kingdom.
FAU - Kuhn, Sebastian
AU  - Kuhn S
AUID- ORCID: 0000-0002-8031-2973
AD  - Institute of Digital Medicine, Philipps-University Marburg and University 
      Hospital of Giessen and Marburg, Marburg, Germany.
FAU - Eckrich, Jonas
AU  - Eckrich J
AUID- ORCID: 0000-0001-5498-4031
AD  - Department of Otorhinolaryngology, University Medical Center of the Johannes 
      Gutenberg-University Mainz, Mainz, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231205
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - LLM
OT  - LLMs
OT  - ORL
OT  - artificial intelligence
OT  - chatbot
OT  - chatbots
OT  - digital health
OT  - global health
OT  - language model
OT  - large language models
OT  - low- and middle-income countries
OT  - otorhinolaryngology
OT  - telehealth
OT  - telemedicine
EDAT- 2023/12/05 17:44
MHDA- 2023/12/05 17:45
CRDT- 2023/12/05 11:54
PHST- 2023/05/20 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/07/20 00:00 [revised]
PHST- 2023/12/05 17:45 [medline]
PHST- 2023/12/05 17:44 [pubmed]
PHST- 2023/12/05 11:54 [entrez]
AID - v9i1e49183 [pii]
AID - 10.2196/49183 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Dec 5;9:e49183. doi: 10.2196/49183.

PMID- 37073184
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230421
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - The Capability of ChatGPT in Predicting and Explaining Common Drug-Drug 
      Interactions.
PG  - e36272
LID - 10.7759/cureus.36272 [doi]
LID - e36272
AB  - Background Drug-drug interactions (DDIs) can have serious consequences for 
      patient health and well-being. Patients who are taking multiple medications may 
      be at an increased risk of experiencing adverse events or drug toxicity if they 
      are not aware of potential interactions between their medications. Many times, 
      patients self-prescribe medications without knowing DDI. Objective The objective 
      is to investigate the effectiveness of ChatGPT, a large language model, in 
      predicting and explaining common DDIs. Methods A total of 40 DDIs lists were 
      prepared from previously published literature. This list was used to converse 
      with ChatGPT with a two-stage question. The first question was asked as "can I 
      take X and Y together?" with two drug names. After storing the output, the next 
      question was asked. The second question was asked as "why should I not take X and 
      Y together?" The output was stored for further analysis. The responses were 
      checked by two pharmacologists and the consensus output was categorized as 
      "correct" and "incorrect." The "correct" ones were further classified as 
      "conclusive" and "inconclusive." The text was checked for reading ease scores and 
      grades of education required to understand the text. Data were tested by 
      descriptive and inferential statistics. Results Among the 40 DDI pairs, one 
      answer was incorrect in the first question. Among correct answers, 19 were 
      conclusive and 20 were inconclusive. For the second question, one answer was 
      wrong. Among correct answers, 17 were conclusive and 22 were inconclusive. The 
      mean Flesch reading ease score was 27.64±10.85 in answers to the first question 
      and 29.35±10.16 in answers to the second question, p = 0.47. The mean 
      Flesh-Kincaid grade level was 15.06±2.79 in answers to the first question and 
      14.85±1.97 in answers to the second question, p = 0.69. When we compared the 
      reading levels with hypothetical 6th grade, the grades were significantly higher 
      than expected (t = 20.57, p < 0.0001 for first answers and t = 28.43, p < 0.0001 
      for second answers). Conclusion ChatGPT is a partially effective tool for 
      predicting and explaining DDIs. Patients, who may not have immediate access to 
      the healthcare facility for getting information about DDIs, may take help from 
      ChatGPT. However, on several occasions, it may provide incomplete guidance. 
      Further improvement is required for potential usage by patients for getting ideas 
      about DDI.
CI  - Copyright © 2023, Juhi et al.
FAU - Juhi, Ayesha
AU  - Juhi A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Pipil, Neha
AU  - Pipil N
AD  - Pharmacology, All India Institute of Medical Sciences, Bilaspur, Bilaspur, IND.
FAU - Santra, Soumya
AU  - Santra S
AD  - Pharmacology, College of Medicine and JNM Hospital, Kalyani, IND.
FAU - Mondal, Shaikat
AU  - Mondal S
AD  - Physiology, Raiganj Government Medical College and Hospital, Raiganj, IND.
FAU - Behera, Joshil Kumar
AU  - Behera JK
AD  - Physiology, Dharanidhar Medical College, Keonjhar, Keonjhar, IND.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
LA  - eng
PT  - Journal Article
DEP - 20230317
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10105894
OTO - NOTNLM
OT  - adverse reactions
OT  - artificial intelligence
OT  - chatgpt
OT  - drug interactions
OT  - drug-drug interaction
OT  - explaining
OT  - language model
OT  - patient education
OT  - predicting
OT  - side effects
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/19 06:41
MHDA- 2023/04/19 06:42
CRDT- 2023/04/19 01:44
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/04/19 06:42 [medline]
PHST- 2023/04/19 06:41 [pubmed]
PHST- 2023/04/19 01:44 [entrez]
AID - 10.7759/cureus.36272 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 17;15(3):e36272. doi: 10.7759/cureus.36272. eCollection 2023 
      Mar.

PMID- 36865144
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230303
DP  - 2023 Feb 23
TI  - Assessing the Value of ChatGPT for Clinical Decision Support Optimization.
LID - 2023.02.21.23286254 [pii]
LID - 10.1101/2023.02.21.23286254 [doi]
AB  - OBJECTIVE: To determine if ChatGPT can generate useful suggestions for improving 
      clinical decision support (CDS) logic and to assess noninferiority compared to 
      human-generated suggestions. METHODS: We supplied summaries of CDS logic to 
      ChatGPT, an artificial intelligence (AI) tool for question answering that uses a 
      large language model, and asked it to generate suggestions. We asked human 
      clinician reviewers to review the AI-generated suggestions as well as 
      human-generated suggestions for improving the same CDS alerts, and rate the 
      suggestions for their usefulness, acceptance, relevance, understanding, workflow, 
      bias, inversion, and redundancy. RESULTS: Five clinicians analyzed 36 
      AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 
      20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. 
      The suggestions generated by AI were found to offer unique perspectives and were 
      evaluated as highly understandable and relevant, with moderate usefulness, low 
      acceptance, bias, inversion, redundancy. CONCLUSION: AI-generated suggestions 
      could be an important complementary part of optimizing CDS alerts, can identify 
      potential improvements to alert logic and support their implementation, and may 
      even be able to assist experts in formulating their own suggestions for CDS 
      improvement. ChatGPT shows great potential for using large language models and 
      reinforcement learning from human feedback to improve CDS alert logic and 
      potentially other medical areas involving complex, clinical logic, a key step in 
      the development of an advanced learning health system.
FAU - Liu, Siru
AU  - Liu S
FAU - Wright, Aileen P
AU  - Wright AP
FAU - Patterson, Barron L
AU  - Patterson BL
FAU - Wanderer, Jonathan P
AU  - Wanderer JP
FAU - Turer, Robert W
AU  - Turer RW
FAU - Nelson, Scott D
AU  - Nelson SD
FAU - McCoy, Allison B
AU  - McCoy AB
FAU - Sittig, Dean F
AU  - Sittig DF
FAU - Wright, Adam
AU  - Wright A
LA  - eng
PT  - Preprint
DEP - 20230223
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC9980251
EDAT- 2023/03/04 06:00
MHDA- 2023/03/04 06:01
CRDT- 2023/03/03 02:28
PHST- 2023/03/03 02:28 [entrez]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/04 06:01 [medline]
AID - 2023.02.21.23286254 [pii]
AID - 10.1101/2023.02.21.23286254 [doi]
PST - epublish
SO  - medRxiv. 2023 Feb 23:2023.02.21.23286254. doi: 10.1101/2023.02.21.23286254. 
      Preprint.

PMID- 37904927
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231116
DP  - 2023 Oct 17
TI  - Bioinformatics Illustrations Decoded by ChatGPT: The Good, The Bad, and The Ugly.
LID - 2023.10.15.562423 [pii]
LID - 10.1101/2023.10.15.562423 [doi]
AB  - Emerging studies underscore the promising capabilities of large language 
      model-based chatbots in conducting fundamental bioinformatics data analyses. The 
      recent feature of accepting image-inputs by ChatGPT motivated us to explore its 
      efficacy in deciphering bioinformatics illustrations. Our evaluation with 
      examples in cancer research, including sequencing data analysis, multimodal 
      network-based drug repositioning, and tumor clonal evolution, revealed that 
      ChatGPT can proficiently explain different plot types and apply biological 
      knowledge to enrich interpretations. However, it struggled to provide accurate 
      interpretations when quantitative analysis of visual elements was involved. 
      Furthermore, while the chatbot can draft figure legends and summarize findings 
      from the figures, stringent proofreading is imperative to ensure the accuracy and 
      reliability of the content.
FAU - Wang, Jinge
AU  - Wang J
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV 26506, USA.
FAU - Ye, Qing
AU  - Ye Q
AD  - West Virginia University Cancer Institute, West Virginia University, Morgantown, 
      WV 26506, USA.
FAU - Liu, Li
AU  - Liu L
AD  - College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
AD  - Biodesign Institute, Arizona State University, Tempe, AZ, 85281 USA.
FAU - Lan Guo, Nancy
AU  - Lan Guo N
AD  - West Virginia University Cancer Institute, West Virginia University, Morgantown, 
      WV 26506, USA.
AD  - Department of Occupational and Environmental Health Sciences, West Virginia 
      University, Morgantown, WV 26506, USA.
FAU - Hu, Gangqing
AU  - Hu G
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV 26506, USA.
AD  - West Virginia University Cancer Institute, West Virginia University, Morgantown, 
      WV 26506, USA.
LA  - eng
GR  - P20 GM103434/GM/NIGMS NIH HHS/United States
GR  - P20 GM121322/GM/NIGMS NIH HHS/United States
GR  - R01 LM013438/LM/NLM NIH HHS/United States
GR  - U54 GM104942/GM/NIGMS NIH HHS/United States
PT  - Preprint
DEP - 20231017
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10614796
COIS- Competing Interests The authors declared no competing interests.
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:43
CRDT- 2023/10/31 03:59
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 06:43 [medline]
PHST- 2023/10/31 03:59 [entrez]
AID - 2023.10.15.562423 [pii]
AID - 10.1101/2023.10.15.562423 [doi]
PST - epublish
SO  - bioRxiv. 2023 Oct 17:2023.10.15.562423. doi: 10.1101/2023.10.15.562423. Preprint.

PMID- 37349064
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230626
LR  - 20230626
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
VI  - 188
DP  - 2023 Jul
TI  - ChatGPT can pass the AHA exams: Open-ended questions outperform multiple-choice 
      format.
PG  - 109783
LID - S0300-9572(23)00096-5 [pii]
LID - 10.1016/j.resuscitation.2023.109783 [doi]
AB  - The study by Fijačko et al. tested ChatGPT's ability to pass the BLS and ACLS 
      exams of AHA, but found that ChatGPT failed both exams. A limitation of their 
      study was using ChatGPT to generate only one response, which may have introduced 
      bias. When generating three responses per question, ChatGPT can pass BLS exam 
      with an overall accuracy of 84%. When incorrectly answered questions were 
      rewritten as open-ended questions, ChatGPT's accuracy rate increased to 96% and 
      92.1% for the BLS and ACLS exams, respectively, allowing ChatGPT to pass both 
      exams with outstanding results.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Zhu, Lingxuan
AU  - Zhu L
AD  - Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of 
      Medicine, 160 Pujian Road, Shanghai 200127, China; The First Clinical Medical 
      School, Southern Medical University, 1023 Shatai South Road, Guangzhou, 510515 
      Guangdong, China.
FAU - Mou, Weiming
AU  - Mou W
AD  - The First Clinical Medical School, Southern Medical University, 1023 Shatai South 
      Road, Guangzhou, 510515 Guangdong, China.
FAU - Yang, Tao
AU  - Yang T
AD  - The First Clinical Medical School, Southern Medical University, 1023 Shatai South 
      Road, Guangzhou, 510515 Guangdong, China.
FAU - Chen, Rui
AU  - Chen R
AD  - Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of 
      Medicine, 160 Pujian Road, Shanghai 200127, China. Electronic address: 
      drchenrui@foxmail.com.
LA  - eng
PT  - Letter
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
EDAT- 2023/06/23 01:10
MHDA- 2023/06/23 01:11
CRDT- 2023/06/22 21:01
PHST- 2023/03/15 00:00 [received]
PHST- 2023/03/17 00:00 [accepted]
PHST- 2023/06/23 01:11 [medline]
PHST- 2023/06/23 01:10 [pubmed]
PHST- 2023/06/22 21:01 [entrez]
AID - S0300-9572(23)00096-5 [pii]
AID - 10.1016/j.resuscitation.2023.109783 [doi]
PST - ppublish
SO  - Resuscitation. 2023 Jul;188:109783. doi: 10.1016/j.resuscitation.2023.109783.

PMID- 38021609
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - A Novel Approach: Evaluating ChatGPT's Utility for the Management of Thyroid 
      Nodules.
PG  - e47576
LID - 10.7759/cureus.47576 [doi]
LID - e47576
AB  - Background and objective Artificial intelligence (AI) applications such as Chat 
      Generative Pre-Trained Transformer (ChatGPT) created by OpenAI, which represent 
      the revolutionary aspects of today's technology, have benefitted professionals in 
      many fields and society at large. In this study, we aimed to assess how effective 
      is ChatGPT in helping both the patient and the physician manage thyroid nodules, 
      a very common pathology. Methods Fifty-five questions frequently asked by 
      patients were identified and asked to ChatGPT. Subsequently, three cases of 
      thyroid nodules were progressively presented to ChatGPT. The answers to patient 
      questions were scored for correctness and reliability by two endocrinologists. As 
      for the cases, diagnostic and therapeutic approaches provided by ChatGPT were 
      analyzed and scored by two endocrinologists for correctness, safety, and 
      usability. The responses were evaluated by using 7-point Likert-type scales 
      designed by us. Results The answers to patient questions were found to be mostly 
      correct and reliable by both raters (Rater #1: 6.47 ± 0.50 and 6.27 ± 0.52; Rater 
      #2: 6.18 ± 0.92 and 6.09 ± 0.96). Regarding the management of cases, ChatGPT's 
      approach was found to be largely correct, safe, and usable by Rater #1, while 
      Rater #2 evaluated the approaches as partially or mostly correct, safe, and 
      usable. Conclusion Based on our findings, ChatGPT can be used as an informative 
      and reliable resource for managing patients with thyroid nodules. While it is not 
      suitable to be used as a primary resource for physicians, it has the potential to 
      be a helpful and supportive tool.
CI  - Copyright © 2023, Köroğlu et al.
FAU - Köroğlu, Ekin Y
AU  - Köroğlu EY
AD  - Endocrinology and Metabolism, Ankara City Hospital, Ankara, TUR.
FAU - Fakı, Sevgül
AU  - Fakı S
AD  - Endocrinology and Metabolism, Ankara City Hospital, Ankara, TUR.
FAU - Beştepe, Nagihan
AU  - Beştepe N
AD  - Endocrinology and Metabolism, Ankara City Hospital, Ankara, TUR.
FAU - Tam, Abbas A
AU  - Tam AA
AD  - Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of 
      Medicine, Ankara, TUR.
FAU - Çuhacı Seyrek, Neslihan
AU  - Çuhacı Seyrek N
AD  - Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of 
      Medicine, Ankara, TUR.
FAU - Topaloglu, Oya
AU  - Topaloglu O
AD  - Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of 
      Medicine, Ankara, TUR.
FAU - Ersoy, Reyhan
AU  - Ersoy R
AD  - Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of 
      Medicine, Ankara, TUR.
FAU - Cakir, Bekir
AU  - Cakir B
AD  - Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of 
      Medicine, Ankara, TUR.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10666652
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - thyroid
OT  - thyroid cancer
OT  - thyroid nodule
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:09
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:09 [entrez]
AID - 10.7759/cureus.47576 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 24;15(10):e47576. doi: 10.7759/cureus.47576. eCollection 2023 
      Oct.

PMID- 37789187
OWN - NLM
STAT- Publisher
LR  - 20231003
IS  - 1554-3528 (Electronic)
IS  - 1554-351X (Linking)
DP  - 2023 Oct 3
TI  - The impact of ChatGPT on human data collection: A case study involving typicality 
      norming data.
LID - 10.3758/s13428-023-02235-w [doi]
AB  - Tools like ChatGPT, which allow people to unlock the potential of large language 
      models (LLMs), have taken the world by storm. ChatGPT's ability to produce 
      written output of remarkable quality has inspired, or forced, academics to 
      consider its consequences for both research and education. In particular, the 
      question of what constitutes authorship, and how to evaluate (scientific) 
      contributions has received a lot of attention. However, its impact on (online) 
      human data collection has mostly flown under the radar. The current paper 
      examines how ChatGPT can be (mis)used in the context of generating norming data. 
      We found that ChatGPT is able to produce sensible output, resembling that of 
      human participants, for a typicality rating task. Moreover, the test-retest 
      reliability of ChatGPT's ratings was similar to that of human participants tested 
      1 day apart. We discuss the relevance of these findings in the context of 
      (online) human data collection, focusing both on opportunities (e.g., (risk-)free 
      pilot data) and challenges (e.g., data fabrication).
CI  - © 2023. The Author(s).
FAU - Heyman, Tom
AU  - Heyman T
AUID- ORCID: 0000-0003-0565-441X
AD  - Methodology and Statistics Unit, Institute of Psychology, Leiden University, 
      Wassenaarseweg 52, 2333, AK Leiden, The Netherlands. 
      t.d.p.heyman@fsw.leidenuniv.nl.
FAU - Heyman, Geert
AU  - Heyman G
AD  - Nokia Bell Labs Antwerp, Copernicuslaan 50, 2018, Antwerpen, Belgium.
LA  - eng
PT  - Journal Article
DEP - 20231003
PL  - United States
TA  - Behav Res Methods
JT  - Behavior research methods
JID - 101244316
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Human data collection
OT  - Large language models
OT  - Typicality
EDAT- 2023/10/04 00:42
MHDA- 2023/10/04 00:42
CRDT- 2023/10/03 23:37
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/10/04 00:42 [medline]
PHST- 2023/10/04 00:42 [pubmed]
PHST- 2023/10/03 23:37 [entrez]
AID - 10.3758/s13428-023-02235-w [pii]
AID - 10.3758/s13428-023-02235-w [doi]
PST - aheadofprint
SO  - Behav Res Methods. 2023 Oct 3. doi: 10.3758/s13428-023-02235-w.

PMID- 37426402
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - Transforming Medical Education: Assessing the Integration of ChatGPT Into Faculty 
      Workflows at a Caribbean Medical School.
PG  - e41399
LID - 10.7759/cureus.41399 [doi]
LID - e41399
AB  - INTRODUCTION: ChatGPT is a Large Language Model (LLM) which allows for natural 
      language processing and interactions with users in a conversational style. Since 
      its release in 2022, it has had a significant impact in many occupational fields, 
      including medical education. We sought to gain insight into the extent and type 
      of usage of ChatGPT at a Caribbean medical school, the American University of 
      Antigua College of Medicine (AUA). METHODS: We administered a questionnaire to 87 
      full-time faculty at the school via email. We quantified and made graphical 
      representations of the results via Qualtrics Experience Management software 
      (QualtricsXM, Qualtrics, Provo, UT). Survey results were investigated using bar 
      graph comparisons of absolute numbers and percentages for various categories 
      related to ChatGPT usage, and descriptive statistics for Likert scale questions. 
      RESULTS: We found an estimated 33% of faculty were currently using ChatGPT. There 
      was broad acceptance of the program by those who were using it and most believed 
      it should be an option for students. The primary task ChatGPT was being used for 
      was multiple choice question (MCQ) generation. The primary concern faculty had 
      was incorrect information being included in ChatGPT output. CONCLUSION: ChatGPT 
      has been quickly adopted by a subset of the college faculty, demonstrating its 
      growing acceptance. Given the level of approval expressed about the program, we 
      believe ChatGPT will continue to form an important and expanding part of faculty 
      workflows at AUA and in medical education in general.
CI  - Copyright © 2023, Cross et al.
FAU - Cross, Joseph
AU  - Cross J
AD  - Biochemistry, Cell Biology & Genetics, American University of Antigua, St. 
      John's, ATG.
AD  - Microbial Pathogenesis and Immunology, Texas A&M College of Medicine, College 
      Station, USA.
FAU - Robinson, Raymond
AU  - Robinson R
AD  - Clinical Sciences, American University of Antigua, St. John's, ATG.
FAU - Devaraju, Sumanth
AU  - Devaraju S
AD  - Family Medicine, American University of Antigua, St. John's, ATG.
FAU - Vaughans, Andrea
AU  - Vaughans A
AD  - Medical Education and Simulation, American University of Antigua, St. John's, 
      ATG.
FAU - Hood, Ricardo
AU  - Hood R
AD  - Clinical Sciences, American University of Antigua, St. John's, ATG.
FAU - Kayalackakom, Tarron
AU  - Kayalackakom T
AD  - Medical Education and Simulation, American University of Antigua, St. John's, 
      ATG.
FAU - Honnavar, Prasanna
AU  - Honnavar P
AD  - Microbiology, American University of Antigua, St. John's, ATG.
FAU - Naik, Sheetal
AU  - Naik S
AD  - Physiology, American University of Antigua, St. John's, ATG.
FAU - Sebastian, Roopa
AU  - Sebastian R
AD  - Biochemistry, Cell Biology & Genetics, American University of Antigua, St. 
      John's, ATG.
LA  - eng
PT  - Journal Article
DEP - 20230705
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10328790
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - medical education
OT  - medical faculty
OT  - survey
OT  - virtual assistant
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/10 06:42
MHDA- 2023/07/10 06:43
CRDT- 2023/07/10 05:09
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/07/10 06:43 [medline]
PHST- 2023/07/10 06:42 [pubmed]
PHST- 2023/07/10 05:09 [entrez]
AID - 10.7759/cureus.41399 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 5;15(7):e41399. doi: 10.7759/cureus.41399. eCollection 2023 Jul.

PMID- 37948100
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231127
IS  - 2561-326X (Electronic)
IS  - 2561-326X (Linking)
VI  - 7
DP  - 2023 Nov 10
TI  - Strengths and Weaknesses of ChatGPT Models for Scientific Writing About Medical 
      Vitamin B12: Mixed Methods Study.
PG  - e49459
LID - 10.2196/49459 [doi]
LID - e49459
AB  - BACKGROUND: ChatGPT is a large language model developed by OpenAI designed to 
      generate human-like responses to prompts. OBJECTIVE: This study aims to evaluate 
      the ability of GPT-4 to generate scientific content and assist in scientific 
      writing using medical vitamin B12 as the topic. Furthermore, the study will 
      compare the performance of GPT-4 to its predecessor, GPT-3.5. METHODS: The study 
      examined responses from GPT-4 and GPT-3.5 to vitamin B12-related prompts, 
      focusing on their quality and characteristics and comparing them to established 
      scientific literature. RESULTS: The results indicated that GPT-4 can potentially 
      streamline scientific writing through its ability to edit language and write 
      abstracts, keywords, and abbreviation lists. However, significant limitations of 
      ChatGPT were revealed, including its inability to identify and address bias, 
      inability to include recent information, lack of transparency, and inclusion of 
      inaccurate information. Additionally, it cannot check for plagiarism or provide 
      proper references. The accuracy of GPT-4's answers was found to be superior to 
      GPT-3.5. CONCLUSIONS: ChatGPT can be considered a helpful assistant in the 
      writing process but not a replacement for a scientist's expertise. Researchers 
      must remain aware of its limitations and use it appropriately. The improvements 
      in consecutive ChatGPT versions suggest the possibility of overcoming some 
      present limitations in the near future.
CI  - ©Omar Abuyaman. Originally published in JMIR Formative Research 
      (https://formative.jmir.org), 10.11.2023.
FAU - Abuyaman, Omar
AU  - Abuyaman O
AUID- ORCID: 0000-0002-7694-7941
AD  - Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, 
      The Hashemite University, Zarqa, 13133, Jordan.
LA  - eng
PT  - Journal Article
DEP - 20231110
PL  - Canada
TA  - JMIR Form Res
JT  - JMIR formative research
JID - 101726394
PMC - PMC10674142
OTO - NOTNLM
OT  - AI
OT  - AI solutions
OT  - ChatGPT
OT  - GPT-3.5
OT  - GPT-4
OT  - artificial intelligence
OT  - language editing
OT  - scientific content
OT  - vitamin B12
OT  - wide range information
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/10 12:45
MHDA- 2023/11/10 12:46
CRDT- 2023/11/10 11:53
PHST- 2023/05/30 00:00 [received]
PHST- 2023/10/29 00:00 [accepted]
PHST- 2023/08/17 00:00 [revised]
PHST- 2023/11/10 12:46 [medline]
PHST- 2023/11/10 12:45 [pubmed]
PHST- 2023/11/10 11:53 [entrez]
AID - v7i1e49459 [pii]
AID - 10.2196/49459 [doi]
PST - epublish
SO  - JMIR Form Res. 2023 Nov 10;7:e49459. doi: 10.2196/49459.

PMID- 37133418
OWN - NLM
STAT- MEDLINE
DCOM- 20230626
LR  - 20230626
IS  - 1744-5205 (Electronic)
IS  - 0882-0538 (Linking)
VI  - 38
IP  - 5
DP  - 2023 Jul
TI  - ChatGPT and Ophthalmology: Exploring Its Potential with Discharge Summaries and 
      Operative Notes.
PG  - 503-507
LID - 10.1080/08820538.2023.2209166 [doi]
AB  - PURPOSE: This study aimed to report the abilities of the large language model 
      ChatGPT(R) (OpenAI, San Francisco, USA) in constructing ophthalmic discharge 
      summaries and operative notes. METHODS: A set of prompts was constructed through 
      statements incorporating common ophthalmic surgeries across the subspecialties of 
      the cornea, retina, glaucoma, paediatric ophthalmology, neuro-ophthalmology, and 
      ophthalmic plastics surgery. The responses of ChatGPT were assessed by three 
      surgeons carefully and analyzed them for evidence-based content, specificity of 
      the response, presence of generic text, disclaimers, factual inaccuracies, and 
      its abilities to admit mistakes and challenge incorrect premises. RESULTS: A 
      total of 24 prompts were presented to the ChatGPT. Twelve prompts assessed its 
      ability to construct discharge summaries, and an equal number explored the 
      potential for preparing operative notes. The response was found to be tailored 
      based on the quality of inputs given and was provided in a matter of seconds. The 
      ophthalmic discharge summaries had a valid but significant generic text. ChatGPT 
      could incorporate specific medications, follow-up instructions, consultation 
      time, and location within the discharge summaries when prompted appropriately. 
      While the operative notes were detailed, they required significant tuning. 
      ChatGPT routinely admits its mistakes and corrects itself immediately when 
      confronted with factual inaccuracies. The mistakes are avoided in subsequent 
      reports when given similar prompts. CONCLUSION: The performance of ChatGPT in the 
      context of ophthalmic discharge summaries and operative notes was encouraging. 
      These are constructed rapidly in a matter of seconds. Focused training of ChatGPT 
      on these issues with inclusion of a human verification step has an enormous 
      potential to impact healthcare positively.
FAU - Singh, Swati
AU  - Singh S
AD  - Ophthalmic Plastic Surgery Service, L.V. Prasad Eye Institute, Hyderabad, India.
FAU - Djalilian, Ali
AU  - Djalilian A
AD  - Department of Ophthalmology, University of Illinois, Chicago, Illinois, USA.
FAU - Ali, Mohammad Javed
AU  - Ali MJ
AD  - Govindram Seksaria Institute of Dacryology, L.V. Prasad Eye Institute, Hyderabad, 
      India.
LA  - eng
PT  - Journal Article
DEP - 20230503
PL  - England
TA  - Semin Ophthalmol
JT  - Seminars in ophthalmology
JID - 8610759
SB  - IM
MH  - Child
MH  - Humans
MH  - *Ophthalmology
MH  - Patient Discharge
MH  - Cornea
MH  - *Glaucoma
MH  - Referral and Consultation
OTO - NOTNLM
OT  - Chatbot
OT  - LLM
OT  - chatGPT
OT  - cornea
OT  - glaucoma
OT  - lacrimal
OT  - large language models
OT  - ophthalmology
OT  - retina
OT  - strabismus
EDAT- 2023/05/03 12:42
MHDA- 2023/06/26 06:41
CRDT- 2023/05/03 10:23
PHST- 2023/06/26 06:41 [medline]
PHST- 2023/05/03 12:42 [pubmed]
PHST- 2023/05/03 10:23 [entrez]
AID - 10.1080/08820538.2023.2209166 [doi]
PST - ppublish
SO  - Semin Ophthalmol. 2023 Jul;38(5):503-507. doi: 10.1080/08820538.2023.2209166. 
      Epub 2023 May 3.

PMID- 37998071
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231126
IS  - 2254-9625 (Electronic)
IS  - 2174-8144 (Print)
IS  - 2174-8144 (Linking)
VI  - 13
IP  - 11
DP  - 2023 Nov 9
TI  - Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder 
      Perspectives.
PG  - 2599-2614
LID - 10.3390/ejihpe13110181 [doi]
AB  - The incorporation of artificial intelligence (AI) into education has heralded a 
      transformative era in the way students learn and faculties teach. Among the 
      burgeoning array of AI tools, ChatGPT stands out as a versatile and powerful 
      resource. Developed by OpenAI, ChatGPT is an AI-driven conversational model that 
      generates human-like responses. This research draws on the Constructivism 
      Learning Theory to uncover the key drivers pushing higher education students to 
      use ChatGPT for academic purposes, and the multifaceted consequences it brings to 
      the academic environment, by integrating the perspectives of key stakeholders: 
      students, faculty, and education experts/leaders. The key findings of in-depth, 
      face-to-face, interviews with key stakeholders revealed 12 main drivers that 
      motivate students and their faculty to use ChatGPT mainly for learning purposes. 
      However, the findings identified the multifaceted (six positive and another six 
      negative) consequences of using ChatGPT for academic purposes. Recommendations 
      for mitigating the negative consequences of ChatGPT were discussed with key 
      stakeholders, particularly education experts/leaders, who were more concerned 
      about using ChatGPT for academic reasons. The research reveals that higher 
      education institutions should establish clear guidelines as a part of higher 
      education policy, supplemented with training sessions for students and their 
      faculty, about the responsible use of ChatGPT for academic purposes to mitigate 
      any ethical concerns.
FAU - Hasanein, Ahmed M
AU  - Hasanein AM
AUID- ORCID: 0000-0002-0664-9017
AD  - College of Business Administration, King Faisal University, Al-Ahsa P.O. Box 400, 
      Saudi Arabia.
AD  - Faculty of Tourism and Hotel Management, Helwan University, Cairo P.O. Box 12612, 
      Egypt.
FAU - Sobaih, Abu Elnasr E
AU  - Sobaih AEE
AUID- ORCID: 0000-0002-2730-689X
AD  - College of Business Administration, King Faisal University, Al-Ahsa P.O. Box 400, 
      Saudi Arabia.
AD  - Faculty of Tourism and Hotel Management, Helwan University, Cairo P.O. Box 12612, 
      Egypt.
LA  - eng
GR  - GRANT4379/This research was funded by the Deanship of Scientific Research, Vice 
      Presidency for Graduate Studies and Scientific Research, King Faisal University, 
      Saudi Arabia/
PT  - Journal Article
DEP - 20231109
PL  - Switzerland
TA  - Eur J Investig Health Psychol Educ
JT  - European journal of investigation in health, psychology and education
JID - 101751466
PMC - PMC10670526
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - chatbot
OT  - consequences
OT  - drivers
OT  - higher education
OT  - holistic perspective
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/24 12:43
MHDA- 2023/11/24 12:44
CRDT- 2023/11/24 09:33
PHST- 2023/10/04 00:00 [received]
PHST- 2023/10/28 00:00 [revised]
PHST- 2023/11/07 00:00 [accepted]
PHST- 2023/11/24 12:44 [medline]
PHST- 2023/11/24 12:43 [pubmed]
PHST- 2023/11/24 09:33 [entrez]
AID - ejihpe13110181 [pii]
AID - ejihpe-13-00181 [pii]
AID - 10.3390/ejihpe13110181 [doi]
PST - epublish
SO  - Eur J Investig Health Psychol Educ. 2023 Nov 9;13(11):2599-2614. doi: 
      10.3390/ejihpe13110181.

PMID- 37307503
OWN - NLM
STAT- MEDLINE
DCOM- 20230929
LR  - 20230929
IS  - 1087-2981 (Electronic)
IS  - 1087-2981 (Linking)
VI  - 28
IP  - 1
DP  - 2023 Dec
TI  - ChatGPT in medical school: how successful is AI in progress testing?
PG  - 2220920
LID - 10.1080/10872981.2023.2220920 [doi]
LID - 2220920
AB  - BACKGROUND: As generative artificial intelligence (AI), ChatGPT provides easy 
      access to a wide range of information, including factual knowledge in the field 
      of medicine. Given that knowledge acquisition is a basic determinant of 
      physicians' performance, teaching and testing different levels of medical 
      knowledge is a central task of medical schools. To measure the factual knowledge 
      level of the ChatGPT responses, we compared the performance of ChatGPT with that 
      of medical students in a progress test. METHODS: A total of 400 multiple-choice 
      questions (MCQs) from the progress test in German-speaking countries were entered 
      into ChatGPT's user interface to obtain the percentage of correctly answered 
      questions. We calculated the correlations of the correctness of ChatGPT responses 
      with behavior in terms of response time, word count, and difficulty of a progress 
      test question. RESULTS: Of the 395 responses evaluated, 65.5% of the progress 
      test questions answered by ChatGPT were correct. On average, ChatGPT required 
      22.8 s (SD 17.5) for a complete response, containing 36.2 (SD 28.1) words. There 
      was no correlation between the time used and word count with the accuracy of the 
      ChatGPT response (correlation coefficient for time rho = -0.08, 95% CI [-0.18, 
      0.02], t(393) = -1.55, p = 0.121; for word count rho = -0.03, 95% CI [-0.13, 
      0.07], t(393) = -0.54, p = 0.592). There was a significant correlation between 
      the difficulty index of the MCQs and the accuracy of the ChatGPT response 
      (correlation coefficient for difficulty: rho = 0.16, 95% CI [0.06, 0.25], 
      t(393) = 3.19, p = 0.002). CONCLUSION: ChatGPT was able to correctly answer 
      two-thirds of all MCQs at the German state licensing exam level in Progress Test 
      Medicine and outperformed almost all medical students in years 1-3. The ChatGPT 
      answers can be compared with the performance of medical students in the second 
      half of their studies.
FAU - Friederichs, Hendrik
AU  - Friederichs H
AUID- ORCID: 0000-0001-9671-5235
AD  - Medical School OWL, Bielefeld University, Bielefeld, Germany.
FAU - Friederichs, Wolf Jonas
AU  - Friederichs WJ
AUID- ORCID: 0000-0003-1733-7788
AD  - Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany.
FAU - März, Maren
AU  - März M
AUID- ORCID: 0000-0002-2661-5076
AD  - Charité- Universitätsmedizin Berlin, Kooperationspartner der Freien Universität 
      Berlin, Humboldt-Universität Zu Berlin, Progress Test Medizin, Charitéplatz 1, 
      Berlin, Germany.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Med Educ Online
JT  - Medical education online
JID - 9806550
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Schools, Medical
MH  - *Students, Medical
MH  - *Educational Measurement
MH  - *Education, Medical
MH  - Machine Learning
PMC - PMC10262795
OTO - NOTNLM
OT  - Medical education
OT  - artificial intelligence
OT  - learning
OT  - machine learning
OT  - progress test
COIS- No potential conflict of interest was reported by the authors.
EDAT- 2023/06/12 19:12
MHDA- 2023/06/14 06:42
CRDT- 2023/06/12 15:43
PHST- 2023/06/14 06:42 [medline]
PHST- 2023/06/12 19:12 [pubmed]
PHST- 2023/06/12 15:43 [entrez]
AID - 2220920 [pii]
AID - 10.1080/10872981.2023.2220920 [doi]
PST - ppublish
SO  - Med Educ Online. 2023 Dec;28(1):2220920. doi: 10.1080/10872981.2023.2220920.

PMID- 37660470
OWN - NLM
STAT- MEDLINE
DCOM- 20230905
LR  - 20231013
IS  - 2164-554X (Electronic)
IS  - 2164-5515 (Print)
IS  - 2164-5515 (Linking)
VI  - 19
IP  - 2
DP  - 2023 Aug 1
TI  - Chatting with ChatGPT to learn about safety of COVID-19 vaccines - A perspective.
PG  - 2235200
LID - 10.1080/21645515.2023.2235200 [doi]
LID - 2235200
AB  - Vaccine hesitancy is among the top 10 threats to global health, according to the 
      World Health Organization (WHO). In this exploration, we delve into ChatGPT 
      capacity to generate opinions on vaccine hesitancy by interrogating this AI 
      chatbot for the 50 most prevalent counterfait messages, false and true 
      contraindications, and myths circulating on the internet regarding vaccine 
      safety. Our results indicate that, while the present version of ChatGPT's default 
      responses may be incomplete, they are generally satisfactory. Although ChatGPT 
      cannot substitute an expert or the scientific evidence itself, this form of AI 
      has the potential to guide users toward information that aligns well with 
      scientific evidence.
FAU - Salas, Antonio
AU  - Salas A
AUID- ORCID: 0000-0002-2336-702X
AD  - Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, 
      Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de 
      Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago 
      (SERGAS), Santiago de Compostela, Spain.
AD  - Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de 
      Investigación Sanitaria de Santiago, Universidade de Santiago de Compostela, 
      Santiago de Compostela, Spain.
AD  - Centro de Investigación Biomédica en Red de Enfermedades Respiratorias 
      (CIBER-ES), Madrid, Spain.
FAU - Rivero-Calle, Irene
AU  - Rivero-Calle I
AUID- ORCID: 0000-0002-3678-9264
AD  - Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de 
      Investigación Sanitaria de Santiago, Universidade de Santiago de Compostela, 
      Santiago de Compostela, Spain.
AD  - Centro de Investigación Biomédica en Red de Enfermedades Respiratorias 
      (CIBER-ES), Madrid, Spain.
AD  - WHO Collaborating Centre for Vaccine Safety of Santiago de Compostela, Servizo 
      Galego de Saude, Santiago de Compostela, Spain.
AD  - Translational Pediatrics and Infectious Diseases, Department of Pediatrics, 
      Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, 
      Spain.
FAU - Martinón-Torres, Federico
AU  - Martinón-Torres F
AUID- ORCID: 0000-0002-9023-581X
AD  - Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de 
      Investigación Sanitaria de Santiago, Universidade de Santiago de Compostela, 
      Santiago de Compostela, Spain.
AD  - Centro de Investigación Biomédica en Red de Enfermedades Respiratorias 
      (CIBER-ES), Madrid, Spain.
AD  - WHO Collaborating Centre for Vaccine Safety of Santiago de Compostela, Servizo 
      Galego de Saude, Santiago de Compostela, Spain.
AD  - Translational Pediatrics and Infectious Diseases, Department of Pediatrics, 
      Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, 
      Spain.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Hum Vaccin Immunother
JT  - Human vaccines & immunotherapeutics
JID - 101572652
RN  - 0 (COVID-19 Vaccines)
SB  - IM
CIN - Hum Vaccin Immunother. 2023 Aug;19(2):2263982. PMID: 37799088
MH  - Humans
MH  - *COVID-19 Vaccines/adverse effects
MH  - *COVID-19/prevention & control
MH  - Internet
MH  - Software
MH  - Vaccination Hesitancy
PMC - PMC10478732
OTO - NOTNLM
OT  - COVID-19
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - misinformation
OT  - vaccine safety
COIS- AS declares no competing interests. IRC has participated in advisory boards 
      organized by MSD, GSK, Sanofi and Pfizer. IRC has been involved in clinical 
      trials funded by Ablynx, Abbot, Seqirus, Sanofi Pasteur MSD, Cubist, Wyeth, 
      Merck, Pfizer, Roche, Regeneron, Jansen, Medimmune, Novavax, Novartis and GSK, 
      although the funds were awarded to the institution. FM-T has received honoraria 
      from GSK, Pfizer Inc, Moderna, Astra Zeneca, Sanofi Pasteur, MSD, Seqirus, 
      Biofabri and Janssen for taking part in advisory boards and expert meetings and 
      for acting as a speaker in congresses outside the scope of the submitted work. 
      Federico Martinón-Torres has also acted as principal investigator in randomized 
      controlled trials of the above-mentioned companies as well as Ablynx, Gilead, 
      Regeneron, Roche, Abbott, Novavax, and MedImmune, with honoraria paid to his 
      institution.
EDAT- 2023/09/04 00:41
MHDA- 2023/09/05 06:41
CRDT- 2023/09/03 18:03
PHST- 2023/09/05 06:41 [medline]
PHST- 2023/09/04 00:41 [pubmed]
PHST- 2023/09/03 18:03 [entrez]
AID - 2235200 [pii]
AID - 10.1080/21645515.2023.2235200 [doi]
PST - ppublish
SO  - Hum Vaccin Immunother. 2023 Aug 1;19(2):2235200. doi: 
      10.1080/21645515.2023.2235200.

PMID- 37761751
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231003
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 18
DP  - 2023 Sep 15
TI  - ChatGPT's Skills in Statistical Analysis Using the Example of Allergology: Do We 
      Have Reason for Concern?
LID - 10.3390/healthcare11182554 [doi]
LID - 2554
AB  - BACKGROUND: Content generated by artificial intelligence is sometimes not 
      truthful. To date, there have been a number of medical studies related to the 
      validity of ChatGPT's responses; however, there is a lack of studies addressing 
      various aspects of statistical analysis. The aim of this study was to assess the 
      validity of the answers provided by ChatGPT in relation to statistical analysis, 
      as well as to identify recommendations to be implemented in the future in 
      connection with the results obtained. METHODS: The study was divided into four 
      parts and was based on the exemplary medical field of allergology. The first part 
      consisted of asking ChatGPT 30 different questions related to statistical 
      analysis. The next five questions included a request for ChatGPT to perform the 
      relevant statistical analyses, and another five requested ChatGPT to indicate 
      which statistical test should be applied to articles accepted for publication in 
      Allergy. The final part of the survey involved asking ChatGPT the same 
      statistical question three times. RESULTS: Out of the 40 general questions asked 
      that related to broad statistical analysis, ChatGPT did not fully answer half of 
      them. Assumptions necessary for the application of specific statistical tests 
      were not included. ChatGPT also gave completely divergent answers to one question 
      about which test should be used. CONCLUSION: The answers provided by ChatGPT to 
      various statistical questions may give rise to the use of inappropriate 
      statistical tests and, consequently, the subsequent misinterpretation of the 
      research results obtained. Questions asked in this regard need to be framed more 
      precisely.
FAU - Ordak, Michal
AU  - Ordak M
AUID- ORCID: 0000-0001-5652-4397
AD  - Department of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, 
      Medical University of Warsaw, Banacha 1 Str., 02-097 Warsaw, Poland.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10530997
OTO - NOTNLM
OT  - ChatGPT
OT  - statistical analysis
COIS- The author has no conflict of interest to declare.
EDAT- 2023/09/28 06:42
MHDA- 2023/09/28 06:43
CRDT- 2023/09/28 01:14
PHST- 2023/08/16 00:00 [received]
PHST- 2023/09/13 00:00 [revised]
PHST- 2023/09/13 00:00 [accepted]
PHST- 2023/09/28 06:43 [medline]
PHST- 2023/09/28 06:42 [pubmed]
PHST- 2023/09/28 01:14 [entrez]
AID - healthcare11182554 [pii]
AID - healthcare-11-02554 [pii]
AID - 10.3390/healthcare11182554 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Sep 15;11(18):2554. doi: 10.3390/healthcare11182554.

PMID- 37407364
OWN - NLM
STAT- MEDLINE
DCOM- 20230922
LR  - 20231003
IS  - 2405-8025 (Electronic)
IS  - 2405-8025 (Linking)
VI  - 9
IP  - 10
DP  - 2023 Oct
TI  - Pearls and pitfalls of ChatGPT in medical oncology.
PG  - 788-790
LID - S2405-8033(23)00109-7 [pii]
LID - 10.1016/j.trecan.2023.06.007 [doi]
AB  - Recently, ChatGPT has drawn attention to the potential uses of artificial 
      intelligence (AI) in academia. Here, we discuss how ChatGPT can be of value to 
      medicine and medical oncology and the potential pitfalls that may be encountered.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Blum, Jacob
AU  - Blum J
AD  - The Johns Hopkins University School of Medicine. Baltimore, MD, USA.
FAU - Menta, Arjun K
AU  - Menta AK
AD  - The Johns Hopkins University School of Medicine. Baltimore, MD, USA.
FAU - Zhao, Xiyu
AU  - Zhao X
AD  - The Johns Hopkins University School of Medicine. Baltimore, MD, USA.
FAU - Yang, Victor B
AU  - Yang VB
AD  - The Johns Hopkins University School of Medicine. Baltimore, MD, USA.
FAU - Gouda, Mohamed A
AU  - Gouda MA
AD  - Department of Investigational Cancer Therapeutics, The University of Texas MD 
      Anderson Cancer Center, Houston, TX, USA.
FAU - Subbiah, Vivek
AU  - Subbiah V
AD  - Sarah Cannon Research Institute, Nashville, TN, USA. Electronic address: 
      Vivek.Subbiah@scri.com.
LA  - eng
PT  - Journal Article
DEP - 20230704
PL  - United States
TA  - Trends Cancer
JT  - Trends in cancer
JID - 101665956
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Medical Oncology
OTO - NOTNLM
OT  - artificial intelligence
OT  - oncology
OT  - practice
COIS- Declaration of interests V.S. is on the advisory board of Illumina, Labcorp, 
      Relay Therapeutics, Bayer, Jazz Pharmaceuticals, and Aadi Biosciences. No 
      interests are declared for the other authors.
EDAT- 2023/07/06 01:08
MHDA- 2023/09/22 06:42
CRDT- 2023/07/05 22:01
PHST- 2023/04/25 00:00 [received]
PHST- 2023/06/08 00:00 [revised]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/09/22 06:42 [medline]
PHST- 2023/07/06 01:08 [pubmed]
PHST- 2023/07/05 22:01 [entrez]
AID - S2405-8033(23)00109-7 [pii]
AID - 10.1016/j.trecan.2023.06.007 [doi]
PST - ppublish
SO  - Trends Cancer. 2023 Oct;9(10):788-790. doi: 10.1016/j.trecan.2023.06.007. Epub 
      2023 Jul 4.

PMID- 37019020
OWN - NLM
STAT- MEDLINE
DCOM- 20230612
LR  - 20231116
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 84
DP  - 2023 Jun
TI  - The Artificial intelligence large language models and neuropsychiatry practice 
      and research ethic.
PG  - 103577
LID - S1876-2018(23)00132-6 [pii]
LID - 10.1016/j.ajp.2023.103577 [doi]
FAU - Zhong, Yi
AU  - Zhong Y
AD  - The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central 
      Rehabilitation Hospital, Wuxi, Jiangsu, China; Peking University Sixth Hospital, 
      Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health 
      (Peking University), National Clinical Research Center for Mental Disorders 
      (Peking University Sixth Hospital), Peking University, Beijing 100191, China; 
      Department of Neuroscience, City University of Hong Kong, Hong Kong, China. 
      Electronic address: yi.zhong@my.cityu.edu.hk.
FAU - Chen, Yu-Jun
AU  - Chen YJ
AD  - Affiliated Shuyang Hospital of Nanjing University of Chinese Medicine, Jiangsu, 
      China.
FAU - Zhou, Yang
AU  - Zhou Y
AD  - Department of Psychiatry, Wuhan Mental Health Center, Wuhan, Hubei, China; 
      Department of Psychiatry, Wuhan Hospital for Psychotherapy, Wuhan, Hubei, China.
FAU - Lyu, Yan-Ao-Hai
AU  - Lyu YA
AD  - Department of Social and Behavioural Sciences, City University of Hong Kong, Hong 
      Kong, China.
FAU - Yin, Jia-Jun
AU  - Yin JJ
AD  - The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central 
      Rehabilitation Hospital, Wuxi, Jiangsu, China. Electronic address: 
      yinjiajun@jiangnan.edu.cn.
FAU - Gao, Yu-Jun
AU  - Gao YJ
AD  - Department of Psychiatry, Renmin Hospital of Wuhan University, Hubei, China; 
      Clinical and Translational Sciences (CaTS) Lab, The Douglas Research Centre, 
      McGill University, Montréal, Québec, Canada. Electronic address: 
      Yujun_Gao@whu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230331
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Neuropsychiatry
MH  - Intelligence
MH  - Language
MH  - Ethics, Research
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Ethic
OT  - Large language model
OT  - Neuropsychiatry research
COIS- Declaration of Competing Interest The authors declared that they have no 
      conflicts of interest to this work.
EDAT- 2023/04/06 06:00
MHDA- 2023/06/12 06:42
CRDT- 2023/04/05 18:06
PHST- 2023/03/24 00:00 [received]
PHST- 2023/03/29 00:00 [revised]
PHST- 2023/03/29 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/04/06 06:00 [pubmed]
PHST- 2023/04/05 18:06 [entrez]
AID - S1876-2018(23)00132-6 [pii]
AID - 10.1016/j.ajp.2023.103577 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Jun;84:103577. doi: 10.1016/j.ajp.2023.103577. Epub 2023 
      Mar 31.

PMID- 37584720
OWN - NLM
STAT- Publisher
LR  - 20231003
IS  - 1279-8517 (Electronic)
IS  - 0930-1038 (Print)
IS  - 0930-1038 (Linking)
VI  - 45
IP  - 10
DP  - 2023 Oct
TI  - The potential role of ChatGPT and artificial intelligence in anatomy education: a 
      conversation with ChatGPT.
PG  - 1321-1329
LID - 10.1007/s00276-023-03229-1 [doi]
AB  - PURPOSE: A recent study published in the JMIR Med Educ Journal explored the 
      potential impact of the Generative Pre-Train (ChatGPT), a generative language 
      model, on medical education, research, and practice. In the present study, an 
      interview with ChatGPT was conducted to determine its capabilities and potential 
      for use in anatomy education (AE) and anatomy research (AR). METHODS: The study 
      involved 18 questions asked of ChatGPT after obtaining an online subscription to 
      the 4th edition. The questions were randomly selected and evaluated based on 
      accuracy, relevance, and comprehensiveness. RESULTS: The ChatGPT provided 
      accurate and well-structured anatomical descriptions, including clinical 
      relevance and relationships between structures. The chatbot also offered concise 
      summaries of chapters and helpful advice on anatomical terminology, even with 
      complex terms. However, when it came to anatomical variants and their clinical 
      significance, the chatbot's replies were inadequate unless variants were 
      systematically classified into types. ChatGPT-4 generated multiple-choice quizzes 
      and matching questions of varying difficulty levels, as well as summaries of 
      articles when presented with text. However, the chatbot recognized its 
      limitations in terms of accuracy, as did the authors of the current study. 
      CONCLUSION: ChatGPT-4 can be a valuable interactive educational tool for students 
      in the field of anatomy, encouraging engagement and further questions. However, 
      it cannot replace the critical role of educators and should be used as a 
      complementary tool. Future research should establish guidelines for ChatGPT's 
      optimal use and application in medical education.
CI  - © 2023. The Author(s).
FAU - Totlis, Trifon
AU  - Totlis T
AUID- ORCID: 0000-0001-5729-7755
AD  - Department of Anatomy and Surgical Anatomy, School of Medicine, Faculty of Health 
      Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece. 
      totlis@auth.gr.
FAU - Natsis, Konstantinos
AU  - Natsis K
AUID- ORCID: 0000-0002-3077-1784
AD  - Department of Anatomy and Surgical Anatomy, School of Medicine, Faculty of Health 
      Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
FAU - Filos, Dimitrios
AU  - Filos D
AD  - Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, 
      School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
FAU - Ediaroglou, Vasilios
AU  - Ediaroglou V
AD  - Department of Anatomy and Surgical Anatomy, School of Medicine, Faculty of Health 
      Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
FAU - Mantzou, Nikolaos
AU  - Mantzou N
AD  - Department of Anatomy and Surgical Anatomy, School of Medicine, Faculty of Health 
      Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
FAU - Duparc, Fabrice
AU  - Duparc F
AUID- ORCID: 0000-0002-7131-7692
AD  - Department of Anatomy, Faculty of Medicine-Pharmacy, University of 
      Rouen-Normandy, 22 Boulevard Gambetta, 76183, Rouen, France.
FAU - Piagkou, Maria
AU  - Piagkou M
AUID- ORCID: 0000-0002-4831-8005
AD  - Department of Anatomy, School of Medicine, Faculty of Health Sciences, National 
      and Kapodistrian University of Athens, Athens, Greece.
LA  - eng
PT  - Journal Article
DEP - 20230816
PL  - Germany
TA  - Surg Radiol Anat
JT  - Surgical and radiologic anatomy : SRA
JID - 8608029
SB  - IM
PMC - PMC10533609
OTO - NOTNLM
OT  - Anatomy education
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Generative language models
OT  - Medical education
COIS- The authors have no competing interests to declare that are relevant to the 
      content of this article.
EDAT- 2023/08/16 12:42
MHDA- 2023/08/16 12:42
CRDT- 2023/08/16 11:05
PHST- 2023/07/04 00:00 [received]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/08/16 12:42 [pubmed]
PHST- 2023/08/16 12:42 [medline]
PHST- 2023/08/16 11:05 [entrez]
AID - 10.1007/s00276-023-03229-1 [pii]
AID - 3229 [pii]
AID - 10.1007/s00276-023-03229-1 [doi]
PST - ppublish
SO  - Surg Radiol Anat. 2023 Oct;45(10):1321-1329. doi: 10.1007/s00276-023-03229-1. 
      Epub 2023 Aug 16.

PMID- 37378091
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - The Readiness of ChatGPT to Write Scientific Case Reports Independently: A 
      Comparative Evaluation Between Human and Artificial Intelligence.
PG  - e39386
LID - 10.7759/cureus.39386 [doi]
LID - e39386
AB  - The use of artificial intelligence (AI) in scientific publishing has gained 
      increased attention, and one AI tool that has been the subject of much discussion 
      is ChatGPT. It is a large language model (LLM) built on the OpenAI platform that 
      aims to emulate human-like writing and continually improves through user 
      interactions. In this paper, ChatGPT's performance was assessed in medical 
      publishing by comparing it to a case report written by oral and maxillofacial 
      radiologists. ChatGPT was tasked with writing a case report based on a drafted 
      report written by the authors in five different prompts. The findings of this 
      study highlight issues related to the accuracy, completeness, and readability of 
      the generated text. These results have significant implications for the future 
      use of AI in scientific publishing and suggest that in the current iteration of 
      ChatGPT, scientific information must be revised by an expert.
CI  - Copyright © 2023, Buholayka et al.
FAU - Buholayka, Maryam
AU  - Buholayka M
AD  - Oral and Maxillofacial Radiology, University of Connecticut Health, Farmington, 
      USA.
AD  - Department of Biomedical Sciences, Imam Abdulrahman Bin Faisal University, 
      Dammam, SAU.
FAU - Zouabi, Rama
AU  - Zouabi R
AD  - Oral and Maxillofacial Radiology, University of Connecticut Health, Farmington, 
      USA.
FAU - Tadinada, Aditya
AU  - Tadinada A
AD  - Oral and Maxillofacial Radiology, University of Connecticut Health, Farmington, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230523
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10292135
OTO - NOTNLM
OT  - artificial hallucination
OT  - artificial intelligence
OT  - case report
OT  - chatgpt
OT  - scientific writing and artificial intelligence
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/28 13:09
MHDA- 2023/06/28 13:10
CRDT- 2023/06/28 09:28
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/06/28 13:10 [medline]
PHST- 2023/06/28 13:09 [pubmed]
PHST- 2023/06/28 09:28 [entrez]
AID - 10.7759/cureus.39386 [doi]
PST - epublish
SO  - Cureus. 2023 May 23;15(5):e39386. doi: 10.7759/cureus.39386. eCollection 2023 
      May.

PMID- 37792344
OWN - NLM
STAT- Publisher
LR  - 20231004
IS  - 2768-3613 (Electronic)
IS  - 2768-3605 (Linking)
DP  - 2023 Oct 4
TI  - Can ChatGPT Provide Quality Information on Integrative Oncology? A Brief Report.
LID - 10.1089/jicm.2023.0290 [doi]
AB  - This short report evaluated the accuracy and quality of information provided by 
      ChatGPT regarding the use of complementary and integrative medicine for cancer. 
      Using the QUality Evaluation Scoring Tool, a panel of 12 reviewers assessed 
      ChatGPT's responses to 8 questions. The study found that ChatGPT provided 
      moderate-quality responses that were relatively unbiased and not misleading. 
      However, the chatbot's inability to reference specific scientific studies was a 
      significant limitation. Patients with cancer should not rely on ChatGPT for 
      clinical advice until further systematic validation. Future studies should 
      examine how patients perceive ChatGPT's information and its impact on 
      communication with health care professionals.
FAU - Lam, Chun Sing
AU  - Lam CS
AD  - School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, 
      Hong Kong, China.
FAU - Hua, Rong
AU  - Hua R
AD  - School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, 
      Hong Kong, China.
FAU - Koon, Ho Kee
AU  - Koon HK
AD  - School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong 
      Kong, Hong Kong, China.
FAU - Zhou, Keary Rui
AU  - Zhou KR
AD  - School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, 
      Hong Kong, China.
FAU - Lam, Teddy Tai Ning
AU  - Lam TTN
AD  - School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, 
      Hong Kong, China.
FAU - Lee, Chui Ping
AU  - Lee CP
AD  - School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, 
      Hong Kong, China.
FAU - Lin, Wai Ling
AU  - Lin WL
AD  - School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong 
      Kong, Hong Kong, China.
FAU - Wong, Cho Lee
AU  - Wong CL
AD  - The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of 
      Hong Kong, Hong Kong, China.
FAU - Lau, Yat Ming
AU  - Lau YM
AD  - Department of Medical Oncology, The Kinghorn Cancer Centre, St Vincent's 
      Hospital, Sydney, Australia.
FAU - Loong, Herbert Ho-Fung
AU  - Loong HH
AD  - Department of Clinical Oncology, The Chinese University of Hong Kong, Hong Kong, 
      China.
FAU - Chung, Vincent Chi-Ho
AU  - Chung VC
AUID- ORCID: 0000-0002-5947-4492
AD  - School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong 
      Kong, Hong Kong, China.
AD  - Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The 
      Chinese University of Hong Kong, Hong Kong, China.
FAU - Cheung, Yin Ting
AU  - Cheung YT
AUID- ORCID: 0000-0001-9874-8938
AD  - School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, 
      Hong Kong, China.
AD  - Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Hong 
      Kong, China.
LA  - eng
PT  - Journal Article
DEP - 20231004
PL  - United States
TA  - J Integr Complement Med
JT  - Journal of integrative and complementary medicine
JID - 9918283075806676
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - cancer
OT  - complementary medicine
OT  - integrative medicine
OT  - integrative oncology
EDAT- 2023/10/04 12:44
MHDA- 2023/10/04 12:44
CRDT- 2023/10/04 11:33
PHST- 2023/10/04 12:44 [medline]
PHST- 2023/10/04 12:44 [pubmed]
PHST- 2023/10/04 11:33 [entrez]
AID - 10.1089/jicm.2023.0290 [doi]
PST - aheadofprint
SO  - J Integr Complement Med. 2023 Oct 4. doi: 10.1089/jicm.2023.0290.

PMID- 37183102
OWN - NLM
STAT- MEDLINE
DCOM- 20230620
LR  - 20231027
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
VI  - 131
IP  - 1
DP  - 2023 Jul
TI  - ChatGPT in anaesthesia research: risk of fabrication in literature searches.
PG  - e29-e30
LID - S0007-0912(23)00184-8 [pii]
LID - 10.1016/j.bja.2023.04.009 [doi]
FAU - Grigio, Thiago R
AU  - Grigio TR
AD  - Department of Anaesthesiology, Pain Center, University of Groningen, University 
      Medical Center Groningen, Groningen, the Netherlands. Electronic address: 
      t.r.grigio@umcg.nl.
FAU - Timmerman, Hans
AU  - Timmerman H
AD  - Department of Anaesthesiology, Pain Center, University of Groningen, University 
      Medical Center Groningen, Groningen, the Netherlands.
FAU - Wolff, André P
AU  - Wolff AP
AD  - Department of Anaesthesiology, Pain Center, University of Groningen, University 
      Medical Center Groningen, Groningen, the Netherlands.
LA  - eng
PT  - Letter
DEP - 20230512
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
CIN - Br J Anaesth. 2023 Nov;131(5):e172-e173. PMID: 37625909
MH  - Humans
MH  - *Anesthesia/adverse effects
MH  - *Anesthesiology
MH  - Epidemiologic Studies
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - data fabrication
OT  - grey literature
OT  - large language model
OT  - research integrity
EDAT- 2023/05/15 00:42
MHDA- 2023/06/20 06:42
CRDT- 2023/05/14 21:59
PHST- 2023/03/10 00:00 [received]
PHST- 2023/03/30 00:00 [revised]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/06/20 06:42 [medline]
PHST- 2023/05/15 00:42 [pubmed]
PHST- 2023/05/14 21:59 [entrez]
AID - S0007-0912(23)00184-8 [pii]
AID - 10.1016/j.bja.2023.04.009 [doi]
PST - ppublish
SO  - Br J Anaesth. 2023 Jul;131(1):e29-e30. doi: 10.1016/j.bja.2023.04.009. Epub 2023 
      May 12.

PMID- 37347508
OWN - NLM
STAT- MEDLINE
DCOM- 20230626
LR  - 20231124
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Jun 22
TI  - The ChatGPT (Generative Artificial Intelligence) Revolution Has Made Artificial 
      Intelligence Approachable for Medical Professionals.
PG  - e48392
LID - 10.2196/48392 [doi]
LID - e48392
AB  - In November 2022, OpenAI publicly launched its large language model (LLM), 
      ChatGPT, and reached the milestone of having over 100 million users in only 2 
      months. LLMs have been shown to be useful in a myriad of health care-related 
      tasks and processes. In this paper, I argue that attention to, public access to, 
      and debate about LLMs have initiated a wave of products and services using 
      generative artificial intelligence (AI), which had previously found it hard to 
      attract physicians. This paper describes what AI tools have become available 
      since the beginning of the ChatGPT revolution and contemplates how it they might 
      change physicians' perceptions about this breakthrough technology.
CI  - ©Bertalan Mesko. Originally published in the Journal of Medical Internet Research 
      (https://www.jmir.org), 22.06.2023.
FAU - Mesko, Bertalan
AU  - Mesko B
AUID- ORCID: 0000-0002-7005-7083
AD  - The Medical Futurist Institute, Budapest, Hungary.
AD  - Department of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
LA  - eng
PT  - Journal Article
DEP - 20230622
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Language
MH  - *Physicians
MH  - Technology
PMC - PMC10337400
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - computer generated
OT  - continuing education
OT  - conversation agents
OT  - conversational agent
OT  - curricula
OT  - curriculum
OT  - digital health
OT  - future
OT  - generated text
OT  - generative
OT  - language model
OT  - large language model
OT  - medical education
OT  - medical practice
OT  - professional development
OT  - technology
COIS- Conflicts of Interest: None declared.
EDAT- 2023/06/22 13:09
MHDA- 2023/06/26 06:41
CRDT- 2023/06/22 11:52
PHST- 2023/04/21 00:00 [received]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/06/02 00:00 [revised]
PHST- 2023/06/26 06:41 [medline]
PHST- 2023/06/22 13:09 [pubmed]
PHST- 2023/06/22 11:52 [entrez]
AID - v25i1e48392 [pii]
AID - 10.2196/48392 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Jun 22;25:e48392. doi: 10.2196/48392.

PMID- 37140001
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231122
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 12
DP  - 2023 Nov 16
TI  - ChatGPT Is Equivalent to First-Year Plastic Surgery Residents: Evaluation of 
      ChatGPT on the Plastic Surgery In-Service Examination.
PG  - NP1085-NP1089
LID - 10.1093/asj/sjad130 [doi]
AB  - BACKGROUND: ChatGPT is an artificial intelligence language model developed and 
      released by OpenAI (San Francisco, CA) in late 2022. OBJECTIVES: The aim of this 
      study was to evaluate the performance of ChatGPT on the Plastic Surgery 
      In-Service Examination and to compare it to residents' performance nationally. 
      METHODS: The Plastic Surgery In-Service Examinations from 2018 to 2022 were used 
      as a question source. For each question, the stem and all multiple-choice options 
      were imported into ChatGPT. The 2022 examination was used to compare the 
      performance of ChatGPT to plastic surgery residents nationally. RESULTS: In 
      total, 1129 questions were included in the final analysis and ChatGPT answered 
      630 (55.8%) of these correctly. ChatGPT scored the highest on the 2021 exam 
      (60.1%) and on the comprehensive section (58.7%). There were no significant 
      differences regarding questions answered correctly among exam years or among the 
      different exam sections. ChatGPT answered 57% of questions correctly on the 2022 
      exam. When compared to the performance of plastic surgery residents in 2022, 
      ChatGPT would rank in the 49th percentile for first-year integrated plastic 
      surgery residents, 13th percentile for second-year residents, 5th percentile for 
      third- and fourth-year residents, and 0th percentile for fifth- and sixth-year 
      residents. CONCLUSIONS: ChatGPT performs at the level of a first-year resident on 
      the Plastic Surgery In-Service Examination. However, it performed poorly when 
      compared with residents in more advanced years of training. Although ChatGPT has 
      many undeniable benefits and potential uses in the field of healthcare and 
      medical education, it will require additional research to assess its efficacy.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of The 
      Aesthetic Society. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Humar, Pooja
AU  - Humar P
FAU - Asaad, Malke
AU  - Asaad M
AUID- ORCID: 0000-0002-2313-1973
FAU - Bengur, Fuat Baris
AU  - Bengur FB
FAU - Nguyen, Vu
AU  - Nguyen V
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
EIN - Aesthet Surg J. 2023 Sep 29;:. PMID: 37772448
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Surgery, Plastic
MH  - Physical Examination
EDAT- 2023/05/04 12:41
MHDA- 2023/11/20 06:55
CRDT- 2023/05/04 07:03
PHST- 2023/11/20 06:55 [medline]
PHST- 2023/05/04 12:41 [pubmed]
PHST- 2023/05/04 07:03 [entrez]
AID - 7151262 [pii]
AID - 10.1093/asj/sjad130 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Nov 16;43(12):NP1085-NP1089. doi: 10.1093/asj/sjad130.

PMID- 37218530
OWN - NLM
STAT- MEDLINE
DCOM- 20230704
LR  - 20230704
IS  - 1756-185X (Electronic)
IS  - 1756-1841 (Linking)
VI  - 26
IP  - 7
DP  - 2023 Jul
TI  - "Dr ChatGPT": Is it a reliable and useful source for common rheumatic diseases?
PG  - 1343-1349
LID - 10.1111/1756-185X.14749 [doi]
AB  - AIM: It is inevitable that artificial intelligence applications will be used as a 
      source of information in the field of health in the near future. For this reason, 
      we aimed to evaluate whether ChatGPT, a new Large Language Model, can be used to 
      obtain information about common rheumatic diseases. MATERIALS AND METHODS: Common 
      rheumatic diseases were identified using the American College of Rheumatology and 
      European League against Rheumatism guidelines. Osteoarthritis (OA), rheumatoid 
      arthritis, ankylosing spondylitis (AS), systemic lupus erythematosus, psoriatic 
      arthritis, fibromyalgia syndrome, and gout were identified by using Google trends 
      for the four most frequently searched keywords on Google. The responses were 
      evaluated with seven-point Likert-type reliability and usefulness scales 
      developed by us. RESULTS: The highest score in terms of reliability was OA (mean 
      ± standard deviation 5.62 ± 1.17), whereas the highest score in terms of 
      usefulness was AS (mean 5.87 ± 0.17). There was no significant difference in the 
      reliability and usefulness of the answers given by the ChatGPT (p = .423 and 
      p = .387, respectively). All scores ranged between 4 and 7. CONCLUSIONS: Although 
      ChatGPT is reliable and useful for patients to obtain information about rheumatic 
      diseases, it should be kept in mind that it may give false and misleading 
      answers.
CI  - © 2023 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons 
      Australia, Ltd.
FAU - Uz, Cuma
AU  - Uz C
AUID- ORCID: 0000-0001-5277-0101
AD  - Department of Physical Medicine and Rehabilitation, Etlik City Hospital, Ankara, 
      Turkey.
FAU - Umay, Ebru
AU  - Umay E
AD  - Department of Physical Medicine and Rehabilitation, Etlik City Hospital, Ankara, 
      Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230523
PL  - England
TA  - Int J Rheum Dis
JT  - International journal of rheumatic diseases
JID - 101474930
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Rheumatic Diseases/diagnosis
MH  - *Arthritis, Rheumatoid
MH  - *Osteoarthritis
MH  - *Spondylitis, Ankylosing
MH  - *Lupus Erythematosus, Systemic
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - large language model
OT  - rheumatic diseases
EDAT- 2023/05/23 06:42
MHDA- 2023/07/04 06:42
CRDT- 2023/05/23 05:28
PHST- 2023/05/06 00:00 [revised]
PHST- 2023/03/11 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/07/04 06:42 [medline]
PHST- 2023/05/23 06:42 [pubmed]
PHST- 2023/05/23 05:28 [entrez]
AID - 10.1111/1756-185X.14749 [doi]
PST - ppublish
SO  - Int J Rheum Dis. 2023 Jul;26(7):1343-1349. doi: 10.1111/1756-185X.14749. Epub 
      2023 May 23.

PMID- 38025546
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2090-0724 (Print)
IS  - 2090-0732 (Electronic)
IS  - 2090-0724 (Linking)
VI  - 2023
DP  - 2023
TI  - Comparison of Answers between ChatGPT and Human Dieticians to Common Nutrition 
      Questions.
PG  - 5548684
LID - 10.1155/2023/5548684 [doi]
LID - 5548684
AB  - BACKGROUND: More people than ever seek nutrition information from online sources. 
      The chatbot ChatGPT has seen staggering popularity since its inception and may 
      become a resource for information in nutrition. However, the adequacy of ChatGPT 
      to answer questions in the field of nutrition has not been investigated. Thus, 
      the aim of this research was to investigate the competency of ChatGPT in 
      answering common nutrition questions. METHODS: Dieticians were asked to provide 
      their most commonly asked nutrition questions and their own answers to them. We 
      then asked the same questions to ChatGPT and sent both sets of answers to other 
      dieticians (N = 18) or nutritionists and experts in the domain of each question 
      (N = 9) to be graded based on scientific correctness, actionability, and 
      comprehensibility. The grades were also averaged to give an overall score, and 
      group means of the answers to each question were compared using permutation 
      tests. RESULTS: The overall grades for ChatGPT were higher than those from the 
      dieticians for the overall scores in five of the eight questions we received. 
      ChatGPT also had higher grades on five occasions for scientific correctness, four 
      for actionability, and five for comprehensibility. In contrast, none of the 
      answers from the dieticians had a higher average score than ChatGPT for any of 
      the questions, both overall and for each of the grading components. CONCLUSIONS: 
      Our results suggest that ChatGPT can be used to answer nutrition questions that 
      are frequently asked to dieticians and provide encouraging support for the role 
      of chatbots in offering nutrition support.
CI  - Copyright © 2023 Daniel Kirk et al.
FAU - Kirk, Daniel
AU  - Kirk D
AUID- ORCID: 0000-0001-7738-7686
AD  - Division of Human Nutrition and Health, Wageningen University & Research, Helix, 
      Stippeneng 4, Wageningen 6708 WE, Netherlands.
AD  - Department of Twin Research and Genetic Epidemiology, King's College London, St 
      Thomas' Hospital Campus, 4th Floor South Wing Block D, Westminster Bridge Rd, 
      London SE1 7EH, UK.
FAU - van Eijnatten, Elise
AU  - van Eijnatten E
AUID- ORCID: 0000-0002-2591-0583
AD  - Division of Human Nutrition and Health, Wageningen University & Research, Helix, 
      Stippeneng 4, Wageningen 6708 WE, Netherlands.
FAU - Camps, Guido
AU  - Camps G
AUID- ORCID: 0000-0002-3136-8671
AD  - Division of Human Nutrition and Health, Wageningen University & Research, Helix, 
      Stippeneng 4, Wageningen 6708 WE, Netherlands.
AD  - OnePlanet Research Center, Plus Ultra II, Bronland 10, Wageningen 6708 WE, 
      Netherlands.
LA  - eng
PT  - Journal Article
DEP - 20231107
PL  - United States
TA  - J Nutr Metab
JT  - Journal of nutrition and metabolism
JID - 101526296
PMC - PMC10645493
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2023/11/29 18:41
MHDA- 2023/11/29 18:42
CRDT- 2023/11/29 16:32
PHST- 2023/07/10 00:00 [received]
PHST- 2023/10/11 00:00 [revised]
PHST- 2023/10/12 00:00 [accepted]
PHST- 2023/11/29 18:42 [medline]
PHST- 2023/11/29 18:41 [pubmed]
PHST- 2023/11/29 16:32 [entrez]
AID - 10.1155/2023/5548684 [doi]
PST - epublish
SO  - J Nutr Metab. 2023 Nov 7;2023:5548684. doi: 10.1155/2023/5548684. eCollection 
      2023.

PMID- 37561097
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230907
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Aug 21
TI  - Examining Real-World Medication Consultations and Drug-Herb Interactions: ChatGPT 
      Performance Evaluation.
PG  - e48433
LID - 10.2196/48433 [doi]
LID - e48433
AB  - BACKGROUND: Since OpenAI released ChatGPT, with its strong capability in handling 
      natural tasks and its user-friendly interface, it has garnered significant 
      attention. OBJECTIVE: A prospective analysis is required to evaluate the accuracy 
      and appropriateness of medication consultation responses generated by ChatGPT. 
      METHODS: A prospective cross-sectional study was conducted by the pharmacy 
      department of a medical center in Taiwan. The test data set comprised 
      retrospective medication consultation questions collected from February 1, 2023, 
      to February 28, 2023, along with common questions about drug-herb interactions. 
      Two distinct sets of questions were tested: real-world medication consultation 
      questions and common questions about interactions between traditional Chinese and 
      Western medicines. We used the conventional double-review mechanism. The 
      appropriateness of each response from ChatGPT was assessed by 2 experienced 
      pharmacists. In the event of a discrepancy between the assessments, a third 
      pharmacist stepped in to make the final decision. RESULTS: Of 293 real-world 
      medication consultation questions, a random selection of 80 was used to evaluate 
      ChatGPT's performance. ChatGPT exhibited a higher appropriateness rate in 
      responding to public medication consultation questions compared to those asked by 
      health care providers in a hospital setting (31/51, 61% vs 20/51, 39%; P=.01). 
      CONCLUSIONS: The findings from this study suggest that ChatGPT could potentially 
      be used for answering basic medication consultation questions. Our analysis of 
      the erroneous information allowed us to identify potential medical risks 
      associated with certain questions; this problem deserves our close attention.
CI  - ©Hsing-Yu Hsu, Kai-Cheng Hsu, Shih-Yen Hou, Ching-Lung Wu, Yow-Wen Hsieh, Yih-Dih 
      Cheng. Originally published in JMIR Medical Education (https://mededu.jmir.org), 
      21.08.2023.
FAU - Hsu, Hsing-Yu
AU  - Hsu HY
AUID- ORCID: 0000-0003-4424-9476
AD  - Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan.
AD  - Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan 
      University, Taipei, Taiwan.
FAU - Hsu, Kai-Cheng
AU  - Hsu KC
AUID- ORCID: 0000-0003-3640-1634
AD  - Artificial Intelligence Center, China Medical University Hospital, Taichung, 
      Taiwan.
AD  - Department of Medicine, China Medical University, Taichung, Taiwan.
FAU - Hou, Shih-Yen
AU  - Hou SY
AUID- ORCID: 0000-0002-1899-5574
AD  - Artificial Intelligence Center, China Medical University Hospital, Taichung, 
      Taiwan.
FAU - Wu, Ching-Lung
AU  - Wu CL
AD  - School of Pharmacy, College of Pharmacy, China Medical University, Taichung, 
      Taiwan.
FAU - Hsieh, Yow-Wen
AU  - Hsieh YW
AUID- ORCID: 0000-0002-0523-3482
AD  - Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan.
AD  - School of Pharmacy, College of Pharmacy, China Medical University, Taichung, 
      Taiwan.
FAU - Cheng, Yih-Dih
AU  - Cheng YD
AUID- ORCID: 0000-0003-1542-6760
AD  - Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan.
AD  - School of Pharmacy, College of Pharmacy, China Medical University, Taichung, 
      Taiwan.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10477918
OTO - NOTNLM
OT  - ChatGPT
OT  - LLM
OT  - NLP
OT  - chat generative pre-trained transformer
OT  - drug-herb interactions
OT  - language models
OT  - large language model
OT  - natural language processing
OT  - pharmacist
OT  - real-world medication consultation questions
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/10 12:42
MHDA- 2023/08/10 12:43
CRDT- 2023/08/10 10:43
PHST- 2023/04/24 00:00 [received]
PHST- 2023/07/25 00:00 [accepted]
PHST- 2023/06/23 00:00 [revised]
PHST- 2023/08/10 12:43 [medline]
PHST- 2023/08/10 12:42 [pubmed]
PHST- 2023/08/10 10:43 [entrez]
AID - v9i1e48433 [pii]
AID - 10.2196/48433 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Aug 21;9:e48433. doi: 10.2196/48433.

PMID- 37852692
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 1553-6467 (Electronic)
IS  - 0002-9459 (Linking)
VI  - 87
IP  - 10
DP  - 2023 Oct
TI  - The Role of Artificial Intelligence in the Future of Pharmacy Education.
PG  - 100135
LID - S0002-9459(23)00142-0 [pii]
LID - 10.1016/j.ajpe.2023.100135 [doi]
AB  - Recent developments making an artificial intelligence (AI) large language model 
      available for public use have generated significant interest and angst among 
      educators. Viewed as both a time saver and a threat to academic integrity, 
      several questions have arisen about AI's role in education. Numerous 
      opportunities exist to use AI for teaching and learning, but new questions have 
      also arisen regarding AI's impact on the future of healthcare. The pharmacy 
      Academy should be at the center of these discussions to address the technical, 
      philosophical, and ethical issues that AI presents for the future of pharmacy and 
      pharmacy education.
CI  - Copyright © 2023 American Association of Colleges of Pharmacy. Published by 
      Elsevier Inc. All rights reserved.
FAU - Cain, Jeff
AU  - Cain J
AD  - University of Kentucky College of Pharmacy, Department of Pharmacy Practice & 
      Science, Lexington, KY, USA. Electronic address: jeff.cain@uky.edu.
FAU - Malcom, Daniel R
AU  - Malcom DR
AD  - Sullivan University College of Pharmacy and Health Sciences, Clinical and 
      Administrative Sciences, Louisville, KY, USA.
FAU - Aungst, Timothy D
AU  - Aungst TD
AD  - Massachusetts College of Pharmacy and Health Sciences, Department of Pharmacy 
      Practice, Worcester, MA, USA.
LA  - eng
PT  - Journal Article
DEP - 20230526
PL  - United States
TA  - Am J Pharm Educ
JT  - American journal of pharmaceutical education
JID - 0372650
SB  - IM
MH  - Humans
MH  - *Education, Pharmacy
MH  - Artificial Intelligence
MH  - *Pharmacy
MH  - Educational Status
MH  - Academies and Institutes
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ChatGPT
OT  - Ethics
COIS- Declaration of Competing Interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: Co-author serves as an associate editor for the journal. D.M.
EDAT- 2023/10/19 00:43
MHDA- 2023/10/23 01:18
CRDT- 2023/10/18 20:53
PHST- 2023/01/30 00:00 [received]
PHST- 2023/05/09 00:00 [revised]
PHST- 2023/05/19 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/19 00:43 [pubmed]
PHST- 2023/10/18 20:53 [entrez]
AID - S0002-9459(23)00142-0 [pii]
AID - 10.1016/j.ajpe.2023.100135 [doi]
PST - ppublish
SO  - Am J Pharm Educ. 2023 Oct;87(10):100135. doi: 10.1016/j.ajpe.2023.100135. Epub 
      2023 May 26.

PMID- 37058235
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1863-4362 (Electronic)
IS  - 0021-1265 (Linking)
VI  - 192
IP  - 6
DP  - 2023 Dec
TI  - Academic journals cannot simply require authors to declare that they used 
      ChatGPT.
PG  - 3195-3196
LID - 10.1007/s11845-023-03374-x [doi]
AB  - This letter to the editor points out weaknesses in the editorial policies of some 
      academic journals regarding the use of ChatGPT-generated content. Editorial 
      policies should provide more specific details on which parts of an academic paper 
      are allowed to use ChatGPT-generated content. If authors use ChatGPT-generated 
      content in the conclusion or results section, it may harm the academic paper's 
      originality and, therefore, should not be accepted.
CI  - © 2023. The Author(s), under exclusive licence to Royal Academy of Medicine in 
      Ireland.
FAU - Tang, Gengyan
AU  - Tang G
AUID- ORCID: 0000-0003-3221-3134
AD  - Sichuan Academy of Social Sciences, Chengdu, China. tanggengyan@outlook.com.
LA  - eng
PT  - Letter
DEP - 20230414
PL  - Ireland
TA  - Ir J Med Sci
JT  - Irish journal of medical science
JID - 7806864
SB  - IM
MH  - Humans
MH  - *Publishing
MH  - *Periodicals as Topic
MH  - Editorial Policies
OTO - NOTNLM
OT  - Academic journals
OT  - Research ethics
OT  - Research integrity
OT  - Research originality
OT  - Scholarly publishing
EDAT- 2023/04/15 06:00
MHDA- 2023/12/04 12:41
CRDT- 2023/04/14 11:13
PHST- 2023/04/06 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/12/04 12:41 [medline]
PHST- 2023/04/15 06:00 [pubmed]
PHST- 2023/04/14 11:13 [entrez]
AID - 10.1007/s11845-023-03374-x [pii]
AID - 10.1007/s11845-023-03374-x [doi]
PST - ppublish
SO  - Ir J Med Sci. 2023 Dec;192(6):3195-3196. doi: 10.1007/s11845-023-03374-x. Epub 
      2023 Apr 14.

PMID- 37851495
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231104
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Oct 18
TI  - Health Care Trainees' and Professionals' Perceptions of ChatGPT in Improving 
      Medical Knowledge Training: Rapid Survey Study.
PG  - e49385
LID - 10.2196/49385 [doi]
LID - e49385
AB  - BACKGROUND: ChatGPT is a powerful pretrained large language model. It has both 
      demonstrated potential and raised concerns related to knowledge translation and 
      knowledge transfer. To apply and improve knowledge transfer in the real world, it 
      is essential to assess the perceptions and acceptance of the users of 
      ChatGPT-assisted training. OBJECTIVE: We aimed to investigate the perceptions of 
      health care trainees and professionals on ChatGPT-assisted training, using 
      biomedical informatics as an example. METHODS: We used purposeful sampling to 
      include all health care undergraduate trainees and graduate professionals (n=195) 
      from January to May 2023 in the School of Public Health at the National Defense 
      Medical Center in Taiwan. Subjects were asked to watch a 2-minute video 
      introducing 5 scenarios about ChatGPT-assisted training in biomedical informatics 
      and then answer a self-designed online (web- and mobile-based) questionnaire 
      according to the Kirkpatrick model. The survey responses were used to develop 4 
      constructs: "perceived knowledge acquisition," "perceived training motivation," 
      "perceived training satisfaction," and "perceived training effectiveness." The 
      study used structural equation modeling (SEM) to evaluate and test the structural 
      model and hypotheses. RESULTS: The online questionnaire response rate was 152 of 
      195 (78%); 88 of 152 participants (58%) were undergraduate trainees and 90 of 152 
      participants (59%) were women. The ages ranged from 18 to 53 years (mean 23.3, SD 
      6.0 years). There was no statistical difference in perceptions of training 
      evaluation between men and women. Most participants were enthusiastic about the 
      ChatGPT-assisted training, while the graduate professionals were more 
      enthusiastic than undergraduate trainees. Nevertheless, some concerns were raised 
      about potential cheating on training assessment. The average scores for knowledge 
      acquisition, training motivation, training satisfaction, and training 
      effectiveness were 3.84 (SD 0.80), 3.76 (SD 0.93), 3.75 (SD 0.87), and 3.72 (SD 
      0.91), respectively (Likert scale 1-5: strongly disagree to strongly agree). 
      Knowledge acquisition had the highest score and training effectiveness the 
      lowest. In the SEM results, training effectiveness was influenced predominantly 
      by knowledge acquisition and partially met the hypotheses in the research 
      framework. Knowledge acquisition had a direct effect on training effectiveness, 
      training satisfaction, and training motivation, with β coefficients of .80, .87, 
      and .97, respectively (all P<.001). CONCLUSIONS: Most health care trainees and 
      professionals perceived ChatGPT-assisted training as an aid in knowledge 
      transfer. However, to improve training effectiveness, it should be combined with 
      empirical experts for proper guidance and dual interaction. In a future study, we 
      recommend using a larger sample size for evaluation of internet-connected large 
      language models in medical knowledge transfer.
CI  - ©Je-Ming Hu, Feng-Cheng Liu, Chi-Ming Chu, Yu-Tien Chang. Originally published in 
      the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023.
FAU - Hu, Je-Ming
AU  - Hu JM
AUID- ORCID: 0000-0002-7377-0984
AD  - Division of Colorectal Surgery, Department of Surgery, Tri-service General 
      Hospital, National Defense Medical Center, Taipei, Taiwan.
AD  - Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, 
      Taiwan.
AD  - School of Medicine, National Defense Medical Center, Taipei, Taiwan.
FAU - Liu, Feng-Cheng
AU  - Liu FC
AUID- ORCID: 0000-0003-1291-8946
AD  - Division of Rheumatology/Immunology and Allergy, Department of Medicine, 
      Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
FAU - Chu, Chi-Ming
AU  - Chu CM
AUID- ORCID: 0000-0001-8563-1378
AD  - Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, 
      Taiwan.
AD  - School of Public Health, National Defense Medical Center, Taipei, Taiwan.
AD  - Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, 
      Taiwan.
AD  - Big Data Research Center, College of Medicine, Fu-Jen Catholic University, New 
      Taipei City, Taiwan.
AD  - Department of Public Health, Kaohsiung Medical University, Kaohsiung, Taiwan.
AD  - Department of Public Health, China Medical University, Taichung, Taiwan.
FAU - Chang, Yu-Tien
AU  - Chang YT
AUID- ORCID: 0000-0002-1801-7041
AD  - School of Public Health, National Defense Medical Center, Taipei, Taiwan.
LA  - eng
PT  - Journal Article
DEP - 20231018
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Adolescent
MH  - Adult
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Young Adult
MH  - *Attitude of Health Personnel
MH  - Medicine
MH  - Perception
MH  - *Students
MH  - Surveys and Questionnaires
MH  - Taiwan
MH  - *Artificial Intelligence
PMC - PMC10620632
OTO - NOTNLM
OT  - ChatGPT
OT  - SEM
OT  - internet survey
OT  - large language model
OT  - medicine
OT  - perception evaluation
OT  - structural equation modeling
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/18 12:42
MHDA- 2023/10/23 01:18
CRDT- 2023/10/18 11:53
PHST- 2023/05/29 00:00 [received]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/07/13 00:00 [revised]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/18 12:42 [pubmed]
PHST- 2023/10/18 11:53 [entrez]
AID - v25i1e49385 [pii]
AID - 10.2196/49385 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Oct 18;25:e49385. doi: 10.2196/49385.

PMID- 37464178
OWN - NLM
STAT- Publisher
LR  - 20230718
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 18
TI  - Innovating Healthcare: The Role of ChatGPT in Streamlining Hospital Workflow in 
      the Future.
LID - 10.1007/s10439-023-03323-w [doi]
AB  - ChatGPT is revolutionizing hospital workflows by enhancing the precision and 
      efficiency of tasks that were formerly the exclusive domain of healthcare 
      professionals. Additionally, ChatGPT can aid in administrative duties, including 
      appointment scheduling and billing, which enables healthcare professionals to 
      allocate more time towards patient care. By shouldering some of these 
      responsibilities, ChatGPT has the potential to advance the quality of patient 
      care, streamline departmental efficiency, and lower healthcare costs. 
      Nevertheless, it is crucial to strike a balance between the advantages of ChatGPT 
      and the necessity of human interaction in healthcare to guarantee optimal patient 
      care. While ChatGPT may assume some of the duties of physicians in particular 
      medical domains, it cannot replace human doctors. Tackling the challenges and 
      constraints associated with the integration of ChatGPT into the healthcare system 
      is critical for its successful implementation.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Zheng, Yue
AU  - Zheng Y
AUID- ORCID: 0000-0003-3865-7051
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, 
      China.
FAU - Wang, Laduona
AU  - Wang L
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, 
      China.
FAU - Feng, Baijie
AU  - Feng B
AD  - West China School of Medicine, Sichuan University, Chengdu, 610041, China.
FAU - Zhao, Ailin
AU  - Zhao A
AD  - Department of Hematology, West China Hospital, Sichuan University, Chengdu, 
      610041, Sichuan, China. irenez20@outlook.com.
FAU - Wu, Yijun
AU  - Wu Y
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, 
      China. wuyj01029@wchscu.com.
LA  - eng
GR  - 2023NSFSC1885/Natural Science Foundation of Sichuan Province/
GR  - 2022SCUH0025/"from zero to one" Innovation Research Project of Sichuan 
      University/
GR  - 2022-YF05-01443-SN/Chengdu Science and Technology Program/
GR  - 23ZDYF2836/Key Research and Development Program of Sichuan Province/
GR  - 2021M692310/China Postdoctoral Science Foundation/
GR  - 82204490/National Natural Science Foundation of China/
PT  - Letter
DEP - 20230718
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Healthcare
OT  - Hospital Workflow
EDAT- 2023/07/19 01:06
MHDA- 2023/07/19 01:06
CRDT- 2023/07/18 23:32
PHST- 2023/07/11 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/07/19 01:06 [medline]
PHST- 2023/07/19 01:06 [pubmed]
PHST- 2023/07/18 23:32 [entrez]
AID - 10.1007/s10439-023-03323-w [pii]
AID - 10.1007/s10439-023-03323-w [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 18. doi: 10.1007/s10439-023-03323-w.

PMID- 37808173
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 1733-134X (Print)
IS  - 1899-0967 (Electronic)
IS  - 1733-134X (Linking)
VI  - 88
DP  - 2023
TI  - Will ChatGPT pass the Polish specialty exam in radiology and diagnostic imaging? 
      Insights into strengths and limitations.
PG  - e430-e434
LID - 10.5114/pjr.2023.131215 [doi]
AB  - PURPOSE: Rapid development of artificial intelligence has aroused curiosity 
      regarding its potential applications in medical field. The purpose of this 
      article was to present the performance of ChatGPT, a state-of-the-art language 
      model in relation to pass rate of national specialty examination (PES) in 
      radiology and imaging diagnostics within Polish education system. Additionally, 
      the study aimed to identify the strengths and limitations of the model through a 
      detailed analysis of issues raised by exam questions. MATERIAL AND METHODS: The 
      present study utilized a PES exam consisting of 120 questions, provided by 
      Medical Exami-nations Center in Lodz. Questions were administered using 
      openai.com platform that grants free access to GPT-3.5 model. All questions were 
      categorized according to Bloom's taxonomy to assess their complexity and 
      difficulty. Following the answer to each exam question, ChatGPT was asked to rate 
      its confidence on a scale of 1 to 5 to evaluate the accuracy of its response. 
      RESULTS: ChatGPT did not reach the pass rate threshold of PES exam (52%); 
      however, it was close in certain question categories. No significant differences 
      were observed in the percentage of correct answers across question types and 
      sub-types. CONCLUSIONS: The performance of the ChatGPT model in the pass rate of 
      PES exam in radiology and imaging diagnostics in Poland is yet to be determined, 
      which requires further research on improved versions of ChatGPT.
CI  - © Pol J Radiol 2023.
FAU - Kufel, Jakub
AU  - Kufel J
AD  - Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical 
      University of Silesia, Zabrze, Poland.
FAU - Paszkiewicz, Iga
AU  - Paszkiewicz I
AD  - Tytus Chałubiński Hospital, Zakopane, Poland.
FAU - Bielówka, Michał
AU  - Bielówka M
AD  - Professor Zbigniew Religa Student Scientific Association at the Department of 
      Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 
      Poland.
FAU - Bartnikowska, Wiktoria
AU  - Bartnikowska W
AD  - Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, 
      Poland.
FAU - Janik, Michał
AU  - Janik M
AD  - Professor Zbigniew Religa Student Scientific Association at the Department of 
      Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 
      Poland.
FAU - Stencel, Magdalena
AU  - Stencel M
AD  - Professor Zbigniew Religa Student Scientific Association at the Department of 
      Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 
      Poland.
FAU - Czogalik, Łukasz
AU  - Czogalik Ł
AD  - Professor Zbigniew Religa Student Scientific Association at the Department of 
      Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 
      Poland.
FAU - Gruszczyńska, Katarzyna
AU  - Gruszczyńska K
AD  - Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in 
      Katowice, Medical University of Silesia, Katowice, Poland.
FAU - Mielcarska, Sylwia
AU  - Mielcarska S
AD  - Department of Medical and Molecular Biology, Faculty of Medical Sciences in 
      Zabrze, Medical University of Silesia, Zabrze, Poland.
LA  - eng
PT  - Journal Article
DEP - 20230918
PL  - Poland
TA  - Pol J Radiol
JT  - Polish journal of radiology
JID - 101175532
PMC - PMC10551734
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - deep learning
OT  - large language model
COIS- The authors report no conflict of interest.
EDAT- 2023/10/09 06:41
MHDA- 2023/10/09 06:42
CRDT- 2023/10/09 05:32
PHST- 2023/06/06 00:00 [received]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/10/09 06:41 [pubmed]
PHST- 2023/10/09 05:32 [entrez]
AID - 51387 [pii]
AID - 10.5114/pjr.2023.131215 [doi]
PST - epublish
SO  - Pol J Radiol. 2023 Sep 18;88:e430-e434. doi: 10.5114/pjr.2023.131215. eCollection 
      2023.

PMID- 37095384
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Print)
IS  - 0364-216X (Linking)
VI  - 47
IP  - 5
DP  - 2023 Oct
TI  - Aesthetic Surgery Advice and Counseling from Artificial Intelligence: A 
      Rhinoplasty Consultation with ChatGPT.
PG  - 1985-1993
LID - 10.1007/s00266-023-03338-7 [doi]
AB  - BACKGROUND: ChatGPT is an open-source artificial large language model that uses 
      deep learning to produce human-like text dialogue. This observational study 
      evaluated the ability of ChatGPT to provide informative and accurate responses to 
      a set of hypothetical questions designed to simulate an initial consultation 
      about rhinoplasty. METHODS: Nine questions were prompted to ChatGPT on 
      rhinoplasty. The questions were sourced from a checklist published by the 
      American Society of Plastic Surgeons, and the responses were assessed for 
      accessibility, informativeness, and accuracy by Specialist Plastic Surgeons with 
      extensive experience in rhinoplasty. RESULTS: ChatGPT was able to provide 
      coherent and easily comprehensible answers to the questions posed, demonstrating 
      its understanding of natural language in a health-specific context. The responses 
      emphasized the importance of an individualized approach, particularly in 
      aesthetic plastic surgery. However, the study also highlighted ChatGPT's 
      limitations in providing more detailed or personalized advice. CONCLUSION: 
      Overall, the results suggest that ChatGPT has the potential to provide valuable 
      information to patients in a medical context, particularly in situations where 
      patients may be hesitant to seek advice from medical professionals or where 
      access to medical advice is limited. However, further research is needed to 
      determine the scope and limitations of AI language models in this domain and to 
      assess the potential benefits and risks associated with their use. LEVEL OF 
      EVIDENCE V: Observational study under respected authorities. This journal 
      requires that authors assign a level of evidence to each article. For a full 
      description of these Evidence-Based Medicine ratings, please refer to the Table 
      of Contents or the online Instructions to Authors www.springer.com/00266 .
CI  - © 2023. Crown.
FAU - Xie, Yi
AU  - Xie Y
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia. ishithseth1@gmail.com.
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia. 
      ishithseth1@gmail.com.
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
FAU - Rozen, Warren M
AU  - Rozen WM
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
FAU - Ross, Richard
AU  - Ross R
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
FAU - Lee, Matthew
AU  - Lee M
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
LA  - eng
PT  - Journal Article
PT  - Observational Study
DEP - 20230424
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
CIN - Aesthetic Plast Surg. 2023 Oct;47(5):2203-2204. PMID: 37212874
CIN - Aesthetic Plast Surg. 2023 Oct;47(5):2211-2212. PMID: 37256297
MH  - Humans
MH  - *Rhinoplasty
MH  - Artificial Intelligence
MH  - *Surgery, Plastic
MH  - Counseling
MH  - Esthetics
MH  - Referral and Consultation
PMC - PMC10581928
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Rhinoplasty
COIS- The authors declare that they have no conflicts of interest to disclose.
EDAT- 2023/04/25 00:41
MHDA- 2023/10/23 12:43
CRDT- 2023/04/24 11:31
PHST- 2023/03/11 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/10/23 12:43 [medline]
PHST- 2023/04/25 00:41 [pubmed]
PHST- 2023/04/24 11:31 [entrez]
AID - 10.1007/s00266-023-03338-7 [pii]
AID - 3338 [pii]
AID - 10.1007/s00266-023-03338-7 [doi]
PST - ppublish
SO  - Aesthetic Plast Surg. 2023 Oct;47(5):1985-1993. doi: 10.1007/s00266-023-03338-7. 
      Epub 2023 Apr 24.

PMID- 38031276
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1536-481X (Electronic)
IS  - 1057-0829 (Linking)
DP  - 2023 Nov 24
TI  - Can ChatGPT Aid Clinicians in Educating Patients on the Surgical Management of 
      Glaucoma?
LID - 10.1097/IJG.0000000000002338 [doi]
AB  - PRCIS: ChatGPT can help healthcare providers automate the quality assessment of 
      online health information, but it does not produce easier to understand responses 
      compared to existing online health information. PURPOSE: To compare the 
      readability of ChatGPT-generated health information about glaucoma surgery to 
      existing material online, and to evaluate ChatGPT's ability to analyze the 
      quality of information found online about glaucoma surgery. METHODS: ChatGPT was 
      asked to create patient handouts on glaucoma surgery using 7 independent prompts 
      aiming to generate 6th grade level reading material. Existing patient-targeted 
      online health information about glaucoma surgery was selected from the top 50 
      search results of three search engines, excluding advertisements, blog posts, 
      information intended for health professionals, irrelevant content, and duplicate 
      links. Four validated tools were used to assess readability, and the readability 
      of the ChatGPT-generated material was compared with the readability of existing 
      online information. The DISCERN Instrument was used for quality assessment of 
      online materials. The DISCERN instrument was also programmed to ChatGPT to 
      evaluate its ability to analyze quality. The R software and descriptive 
      statistics were used for data analysis. RESULTS: 35 webpages were included. There 
      was no difference between the reading level of online webpages (12th grade) and 
      the reading level of ChatGPT-generated responses (11th grade), despite the 
      ChatGPT prompts asking for simple language and a 6th grade reading level. The 
      quality of health content was "fair" with only 5 resources receiving an 
      "excellent" score. ChatGPT scored the quality of health resources with high 
      precision (r=0.725). CONCLUSIONS: Patient-targeted information on glaucoma 
      surgery is beyond the reading level of the average patient, and therefore at risk 
      of not being understood, and is of subpar quality, per DISCERN tool scoring. 
      ChatGPT did not generate documents at a lower reading level as prompted, but this 
      tool can aid in automating the time-consuming and subjective process of quality 
      assessment.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Kianian, Reza
AU  - Kianian R
AD  - Stein Eye Institute, Department of Ophthalmology, David Geffen School of 
      Medicine, University of California Los Angeles, Los Angeles, California.
FAU - Sun, Deyu
AU  - Sun D
AD  - Stein Eye Institute, Department of Ophthalmology, David Geffen School of 
      Medicine, University of California Los Angeles, Los Angeles, California.
FAU - Giaconi, JoAnn
AU  - Giaconi J
AD  - Stein Eye Institute, Department of Ophthalmology, David Geffen School of 
      Medicine, University of California Los Angeles, Los Angeles, California.
AD  - Department of Surgery, Veterans Health Administration of Greater Los Angeles, Los 
      Angeles, California.
LA  - eng
PT  - Journal Article
DEP - 20231124
PL  - United States
TA  - J Glaucoma
JT  - Journal of glaucoma
JID - 9300903
SB  - IM
EDAT- 2023/11/30 06:43
MHDA- 2023/11/30 06:43
CRDT- 2023/11/30 00:13
PHST- 2023/05/13 00:00 [received]
PHST- 2023/11/10 00:00 [accepted]
PHST- 2023/11/30 06:43 [medline]
PHST- 2023/11/30 06:43 [pubmed]
PHST- 2023/11/30 00:13 [entrez]
AID - 00061198-990000000-00318 [pii]
AID - 10.1097/IJG.0000000000002338 [doi]
PST - aheadofprint
SO  - J Glaucoma. 2023 Nov 24. doi: 10.1097/IJG.0000000000002338.

PMID- 37844413
OWN - NLM
STAT- Publisher
LR  - 20231016
IS  - 1532-818X (Electronic)
IS  - 0196-0709 (Linking)
VI  - 45
IP  - 1
DP  - 2023 Oct 5
TI  - Answering head and neck cancer questions: An assessment of ChatGPT responses.
PG  - 104085
LID - S0196-0709(23)00299-5 [pii]
LID - 10.1016/j.amjoto.2023.104085 [doi]
AB  - PURPOSE: To examine and compare ChatGPT versus Google websites in answering 
      common head and neck cancer questions. MATERIALS AND METHODS: Commonly asked 
      questions about head and neck cancer were obtained and inputted into both 
      ChatGPT-4 and Google search engine. For each question, the ChatGPT response and 
      first website search result were compiled and examined. Content quality was 
      assessed by independent reviewers using standardized grading criteria and the 
      modified Ensuring Quality Information for Patients (EQIP) tool. Readability was 
      determined using the Flesch reading ease scale. RESULTS: In total, 49 questions 
      related to head and neck cancer were included. Google sources were on average 
      significantly higher quality than ChatGPT responses (4.2 vs 3.6, p = 0.005). 
      According to the EQIP tool, Google and ChatGPT had on average similar response 
      rates per criterion (24.4 vs 20.5, p = 0.09) while Google had a significantly 
      higher average score per question than ChatGPT (13.8 vs 11.7, p < 0.001) 
      According to the Flesch reading ease scale, ChatGPT and Google sources were both 
      considered similarly difficult to read (33.1 vs 37.0, p = 0.180) and at a college 
      level (14.3 vs 14.2, p = 0.820.) CONCLUSION: ChatGPT responses were as 
      challenging to read as Google sources, but poorer quality due to decreased 
      reliability and accuracy in answering questions. Though promising, ChatGPT in its 
      current form should not be considered dependable. Google sources are a preferred 
      resource for patient educational materials.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Wei, Kimberly
AU  - Wei K
AD  - Department of Otorhinolaryngology - Head and Neck Surgery, University of 
      Pennsylvania, Philadelphia, PA, USA.
FAU - Fritz, Christian
AU  - Fritz C
AD  - Department of Otorhinolaryngology - Head and Neck Surgery, University of 
      Pennsylvania, Philadelphia, PA, USA.
FAU - Rajasekaran, Karthik
AU  - Rajasekaran K
AD  - Department of Otorhinolaryngology - Head and Neck Surgery, University of 
      Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, 
      University of Pennsylvania, Philadelphia, PA, USA. Electronic address: 
      Karthik.rajasekaran@pennmedicine.upenn.edu.
LA  - eng
PT  - Journal Article
DEP - 20231005
PL  - United States
TA  - Am J Otolaryngol
JT  - American journal of otolaryngology
JID - 8000029
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chatgpt
OT  - Common questions
OT  - Head and neck cancer
OT  - Patient education
COIS- Declaration of competing interest None.
EDAT- 2023/10/17 00:42
MHDA- 2023/10/17 00:42
CRDT- 2023/10/16 18:04
PHST- 2023/08/23 00:00 [received]
PHST- 2023/10/01 00:00 [accepted]
PHST- 2023/10/17 00:42 [medline]
PHST- 2023/10/17 00:42 [pubmed]
PHST- 2023/10/16 18:04 [entrez]
AID - S0196-0709(23)00299-5 [pii]
AID - 10.1016/j.amjoto.2023.104085 [doi]
PST - aheadofprint
SO  - Am J Otolaryngol. 2023 Oct 5;45(1):104085. doi: 10.1016/j.amjoto.2023.104085.

PMID- 37792871
OWN - NLM
STAT- MEDLINE
DCOM- 20231006
LR  - 20231006
IS  - 1806-9282 (Electronic)
IS  - 0104-4230 (Print)
IS  - 0104-4230 (Linking)
VI  - 69
IP  - 10
DP  - 2023
TI  - Performance of ChatGPT-4 in answering questions from the Brazilian National 
      Examination for Medical Degree Revalidation.
PG  - e20230848
LID - S0104-42302023001000618 [pii]
LID - 10.1590/1806-9282.20230848 [doi]
LID - e20230848
AB  - OBJECTIVE: The aim of this study was to evaluate the performance of ChatGPT-4.0 
      in answering the 2022 Brazilian National Examination for Medical Degree 
      Revalidation (Revalida) and as a tool to provide feedback on the quality of the 
      examination. METHODS: A total of two independent physicians entered all 
      examination questions into ChatGPT-4.0. After comparing the outputs with the test 
      solutions, they classified the large language model answers as adequate, 
      inadequate, or indeterminate. In cases of disagreement, they adjudicated and 
      achieved a consensus decision on the ChatGPT accuracy. The performance across 
      medical themes and nullified questions was compared using chi-square statistical 
      analysis. RESULTS: In the Revalida examination, ChatGPT-4.0 answered 71 (87.7%) 
      questions correctly and 10 (12.3%) incorrectly. There was no statistically 
      significant difference in the proportions of correct answers among different 
      medical themes (p=0.4886). The artificial intelligence model had a lower accuracy 
      of 71.4% in nullified questions, with no statistical difference (p=0.241) between 
      non-nullified and nullified groups. CONCLUSION: ChatGPT-4.0 showed satisfactory 
      performance for the 2022 Brazilian National Examination for Medical Degree 
      Revalidation. The large language model exhibited worse performance on subjective 
      questions and public healthcare themes. The results of this study suggested that 
      the overall quality of the Revalida examination questions is satisfactory and 
      corroborates the nullified questions.
FAU - Gobira, Mauro
AU  - Gobira M
AUID- ORCID: 0000-0002-0483-8789
AD  - Instituto Paulista de Estudos e Pesquisas em Oftalmologia, Vision Institute - São 
      Paulo (SP), Brazil.
FAU - Nakayama, Luis Filipe
AU  - Nakayama LF
AUID- ORCID: 0000-0002-6847-6748
AD  - Instituto Paulista de Estudos e Pesquisas em Oftalmologia, Vision Institute - São 
      Paulo (SP), Brazil.
AD  - Massachusetts Institute of Technology, Institute for Medical Engineering and 
      Science - Cambridge (MA), USA.
FAU - Moreira, Rodrigo
AU  - Moreira R
AUID- ORCID: 0000-0002-9132-2819
AD  - Instituto Paulista de Estudos e Pesquisas em Oftalmologia, Vision Institute - São 
      Paulo (SP), Brazil.
FAU - Andrade, Eric
AU  - Andrade E
AUID- ORCID: 0000-0002-3331-786X
AD  - Universidade Federal de São Paulo, Department of Ophthalmology - São Paulo (SP), 
      Brazil.
FAU - Regatieri, Caio Vinicius Saito
AU  - Regatieri CVS
AUID- ORCID: 0000-0003-1511-8696
AD  - Universidade Federal de São Paulo, Department of Ophthalmology - São Paulo (SP), 
      Brazil.
FAU - Belfort, Rubens Jr
AU  - Belfort R Jr
AUID- ORCID: 0000-0002-8422-3898
AD  - Universidade Federal de São Paulo, Department of Ophthalmology - São Paulo (SP), 
      Brazil.
LA  - eng
PT  - Journal Article
DEP - 20230925
PL  - Brazil
TA  - Rev Assoc Med Bras (1992)
JT  - Revista da Associacao Medica Brasileira (1992)
JID - 9308586
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Brazil
MH  - *Health Personnel
MH  - Language
PMC - PMC10547492
EDAT- 2023/10/04 18:42
MHDA- 2023/10/06 06:43
CRDT- 2023/10/04 13:47
PHST- 2023/07/06 00:00 [received]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/10/06 06:43 [medline]
PHST- 2023/10/04 18:42 [pubmed]
PHST- 2023/10/04 13:47 [entrez]
AID - S0104-42302023001000618 [pii]
AID - 10.1590/1806-9282.20230848 [doi]
PST - epublish
SO  - Rev Assoc Med Bras (1992). 2023 Sep 25;69(10):e20230848. doi: 
      10.1590/1806-9282.20230848. eCollection 2023.

PMID- 37078489
OWN - NLM
STAT- Publisher
LR  - 20230420
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
DP  - 2023 Apr 20
TI  - Accuracy of Information and References Using ChatGPT-3 for Retrieval of Clinical 
      Radiological Information.
PG  - 8465371231171125
LID - 10.1177/08465371231171125 [doi]
AB  - Purpose: To assess the accuracy of answers provided by ChatGPT-3 when prompted 
      with questions from the daily routine of radiologists and to evaluate the text 
      response when ChatGPT-3 was prompted to provide references for a given answer. 
      Methods: ChatGPT-3 (San Francisco, OpenAI) is an artificial intelligence chatbot 
      based on a large language model (LLM) that has been designed to generate 
      human-like text. A total of 88 questions were submitted to ChatGPT-3 using 
      textual prompt. These 88 questions were equally dispersed across 8 subspecialty 
      areas of radiology. The responses provided by ChatGPT-3 were assessed for 
      correctness by cross-checking them with peer-reviewed, PubMed-listed references. 
      In addition, the references provided by ChatGPT-3 were evaluated for 
      authenticity. Results: A total of 59 of 88 responses (67%) to radiological 
      questions were correct, while 29 responses (33%) had errors. Out of 343 
      references provided, only 124 references (36.2%) were available through internet 
      search, while 219 references (63.8%) appeared to be generated by ChatGPT-3. When 
      examining the 124 identified references, only 47 references (37.9%) were 
      considered to provide enough background to correctly answer 24 questions (37.5%). 
      Conclusion: In this pilot study, ChatGPT-3 provided correct responses to 
      questions from the daily clinical routine of radiologists in only about two 
      thirds, while the remainder of responses contained errors. The majority of 
      provided references were not found and only a minority of the provided references 
      contained the correct information to answer the question. Caution is advised when 
      using ChatGPT-3 to retrieve radiological information.
FAU - Wagner, Matthias W
AU  - Wagner MW
AUID- ORCID: 0000-0001-6501-839X
AD  - Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for 
      Sick Children, Toronto, Canada. RINGGOLD: 7979
AD  - Department of Medical Imaging, University of Toronto, Toronto, Canada. RINGGOLD: 
      12366
FAU - Ertl-Wagner, Birgit B
AU  - Ertl-Wagner BB
AD  - Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for 
      Sick Children, Toronto, Canada. RINGGOLD: 7979
AD  - Department of Medical Imaging, University of Toronto, Toronto, Canada. RINGGOLD: 
      12366
LA  - eng
PT  - Journal Article
DEP - 20230420
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - decision support
OT  - large language models
OT  - radiology
EDAT- 2023/04/20 13:41
MHDA- 2023/04/20 13:41
CRDT- 2023/04/20 06:54
PHST- 2023/04/20 13:41 [medline]
PHST- 2023/04/20 13:41 [pubmed]
PHST- 2023/04/20 06:54 [entrez]
AID - 10.1177/08465371231171125 [doi]
PST - aheadofprint
SO  - Can Assoc Radiol J. 2023 Apr 20:8465371231171125. doi: 10.1177/08465371231171125.

PMID- 37862566
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
DP  - 2023 Oct 20
TI  - An explorative assessment of ChatGPT as an aid in medical education: Use it with 
      caution.
PG  - 1-8
LID - 10.1080/0142159X.2023.2271159 [doi]
AB  - OBJECTIVE: To explore the use of ChatGPT by educators and students in a medical 
      school setting. METHOD: This study used the public version of ChatGPT launched by 
      OpenAI on November 30, 2022 (https://openai.com/blog/chatgpt/). We employed 
      prompts to ask ChatGPT to 1) generate a content outline for a session on the 
      topics of cholesterol, lipoproteins, and hyperlipidemia for medical students; 2) 
      produce a list of learning objectives for the session; and 3) write assessment 
      questions with and without clinical vignettes related to the identified learning 
      objectives. We assessed the responses by ChatGPT for accuracy and reliability to 
      determine the potential of the chatbot as an aid to educators and as a 
      "know-it-all" medical information provider for students. RESULTS: ChatGPT can 
      function as an aid to educators, but it is not yet suitable as a reliable 
      information resource for educators and medical students. CONCLUSION: ChatGPT can 
      be a useful tool to assist medical educators in drafting course and session 
      content outlines and create assessment questions. At the same time, caution must 
      be taken as ChatGPT is prone to providing incorrect information; expert oversight 
      and caution are necessary to ensure the information generated is accurate and 
      beneficial to students. Therefore, it is premature for medical students to use 
      the current version of ChatGPT as a "know-it-all" information provider. In the 
      future, medical educators should work with programming experts to explore and 
      grow the full potential of AI in medical education.
FAU - Han, Zhiyong
AU  - Han Z
AD  - Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, 
      NJ, USA.
FAU - Battaglia, Fortunato
AU  - Battaglia F
AD  - Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, 
      NJ, USA.
FAU - Udaiyar, Abinav
AU  - Udaiyar A
AD  - Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, 
      NJ, USA.
FAU - Fooks, Allen
AU  - Fooks A
AD  - Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, 
      NJ, USA.
FAU - Terlecky, Stanley R
AU  - Terlecky SR
AD  - Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, 
      NJ, USA.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Medical education
EDAT- 2023/10/20 18:45
MHDA- 2023/10/20 18:45
CRDT- 2023/10/20 14:52
PHST- 2023/10/20 18:45 [medline]
PHST- 2023/10/20 18:45 [pubmed]
PHST- 2023/10/20 14:52 [entrez]
AID - 10.1080/0142159X.2023.2271159 [doi]
PST - aheadofprint
SO  - Med Teach. 2023 Oct 20:1-8. doi: 10.1080/0142159X.2023.2271159.

PMID- 38039286
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2767-3170 (Electronic)
IS  - 2767-3170 (Linking)
VI  - 2
IP  - 12
DP  - 2023 Dec
TI  - How does ChatGPT-4 preform on non-English national medical licensing examination? 
      An evaluation in Chinese language.
PG  - e0000397
LID - 10.1371/journal.pdig.0000397 [doi]
LID - e0000397
AB  - ChatGPT, an artificial intelligence (AI) system powered by large-scale language 
      models, has garnered significant interest in healthcare. Its performance 
      dependent on the quality and quantity of training data available for a specific 
      language, with the majority of it being in English. Therefore, its effectiveness 
      in processing the Chinese language, which has fewer data available, warrants 
      further investigation. This study aims to assess the of ChatGPT's ability in 
      medical education and clinical decision-making within the Chinese context. We 
      utilized a dataset from the Chinese National Medical Licensing Examination (NMLE) 
      to assess ChatGPT-4's proficiency in medical knowledge in Chinese. Performance 
      indicators, including score, accuracy, and concordance (confirmation of answers 
      through explanation), were employed to evaluate ChatGPT's effectiveness in both 
      original and encoded medical questions. Additionally, we translated the original 
      Chinese questions into English to explore potential avenues for improvement. 
      ChatGPT scored 442/600 for original questions in Chinese, surpassing the passing 
      threshold of 360/600. However, ChatGPT demonstrated reduced accuracy in 
      addressing open-ended questions, with an overall accuracy rate of 47.7%. Despite 
      this, ChatGPT displayed commendable consistency, achieving a 75% concordance rate 
      across all case analysis questions. Moreover, translating Chinese case analysis 
      questions into English yielded only marginal improvements in ChatGPT's 
      performance (p = 0.728). ChatGPT exhibits remarkable precision and reliability 
      when handling the NMLE in Chinese. Translation of NMLE questions from Chinese to 
      English does not yield an improvement in ChatGPT's performance.
CI  - Copyright: © 2023 Fang et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Fang, Changchang
AU  - Fang C
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
AD  - Queen Mary College, Nanchang University, Jiangxi, China.
FAU - Wu, Yuting
AU  - Wu Y
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
FAU - Fu, Wanying
AU  - Fu W
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
AD  - Queen Mary College, Nanchang University, Jiangxi, China.
FAU - Ling, Jitao
AU  - Ling J
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
FAU - Wang, Yue
AU  - Wang Y
AD  - Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen 
      University, Guangzhou, China.
AD  - Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, 
      China.
FAU - Liu, Xiaolin
AU  - Liu X
AD  - Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen 
      University, Guangzhou, China.
AD  - Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, 
      China.
FAU - Jiang, Yuan
AU  - Jiang Y
AD  - Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen 
      University, Guangzhou, China.
AD  - Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, 
      China.
FAU - Wu, Yifan
AU  - Wu Y
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
FAU - Chen, Yixuan
AU  - Chen Y
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
FAU - Zhou, Jing
AU  - Zhou J
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
FAU - Zhu, Zhichen
AU  - Zhu Z
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
FAU - Yan, Zhiwei
AU  - Yan Z
AD  - Provincial University Key Laboratory of Sport and Health Science, School of 
      Physical Education and Sport Sciences, Fujian Normal University, Fuzhou, China.
FAU - Yu, Peng
AU  - Yu P
AD  - Department of Endocrine, the Second Affiliated Hospital of Nanchang University, 
      Jiangxi, China.
AD  - Institute for the Study of Endocrinology and Metabolism in Jiangxi, the Second 
      Affiliated Hospital of Nanchang University, Jiangxi, China.
FAU - Liu, Xiao
AU  - Liu X
AUID- ORCID: 0000-0001-8252-3435
AD  - Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen 
      University, Guangzhou, China.
AD  - Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, 
      China.
AD  - Institute for the Study of Endocrinology and Metabolism in Jiangxi, the Second 
      Affiliated Hospital of Nanchang University, Jiangxi, China.
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - United States
TA  - PLOS Digit Health
JT  - PLOS digital health
JID - 9918335064206676
PMC - PMC10691691
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/01 18:42
MHDA- 2023/12/01 18:43
CRDT- 2023/12/01 13:33
PHST- 2023/05/05 00:00 [received]
PHST- 2023/10/23 00:00 [accepted]
PHST- 2023/12/01 18:43 [medline]
PHST- 2023/12/01 18:42 [pubmed]
PHST- 2023/12/01 13:33 [entrez]
AID - PDIG-D-23-00175 [pii]
AID - 10.1371/journal.pdig.0000397 [doi]
PST - epublish
SO  - PLOS Digit Health. 2023 Dec 1;2(12):e0000397. doi: 10.1371/journal.pdig.0000397. 
      eCollection 2023 Dec.

PMID- 37489920
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231120
IS  - 1615-7109 (Electronic)
IS  - 1203-4754 (Linking)
VI  - 27
IP  - 4
DP  - 2023 Jul-Aug
TI  - Performance of ChatGPT on a Practice Dermatology Board Certification Examination.
PG  - 407-409
LID - 10.1177/12034754231188437 [doi]
FAU - Joly-Chevrier, Maxine
AU  - Joly-Chevrier M
AUID- ORCID: 0000-0001-6706-8139
AD  - Faculty of Medicine, Université de Montréal, Montreal, Canada.
FAU - Nguyen, Anne Xuan-Lan
AU  - Nguyen AX
AD  - Faculty of Medicine, McGill University, Montreal, Canada.
FAU - Lesko-Krleza, Michael
AU  - Lesko-Krleza M
AD  - Division of Computer Engineering, Department of Electrical and Computer 
      Engineering, Concordia University, Montreal, Canada.
FAU - Lefrançois, Philippe
AU  - Lefrançois P
AD  - Division of Dermatology, Department of Medicine, McGill University, Montreal, 
      Canada.
AD  - Division of Dermatology, Department of Medicine, Jewish General Hospital, 
      Montreal, Canada.
AD  - Lady Davis Institute for Medical Research, Montreal, Canada.
LA  - eng
PT  - Letter
PT  - Research Support, Non-U.S. Gov't
DEP - 20230725
PL  - United States
TA  - J Cutan Med Surg
JT  - Journal of cutaneous medicine and surgery
JID - 9614685
SB  - IM
MH  - Humans
MH  - *Dermatology
MH  - *Artificial Intelligence
MH  - *Certification
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - dermatology
OT  - medical education
EDAT- 2023/07/25 13:09
MHDA- 2023/07/25 13:10
CRDT- 2023/07/25 09:13
PHST- 2023/07/25 13:10 [medline]
PHST- 2023/07/25 13:09 [pubmed]
PHST- 2023/07/25 09:13 [entrez]
AID - 10.1177/12034754231188437 [doi]
PST - ppublish
SO  - J Cutan Med Surg. 2023 Jul-Aug;27(4):407-409. doi: 10.1177/12034754231188437. 
      Epub 2023 Jul 25.

PMID- 37208451
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 10
DP  - 2023 Oct
TI  - Can ChatGPT be Trusted for Consulting? Uncovering Doctor's Perceptions Using Deep 
      Learning Techniques.
PG  - 2116-2119
LID - 10.1007/s10439-023-03245-7 [doi]
AB  - Since the introduction of ChatGPT by OpenAI in late 2022, the question of whether 
      doctors can employ it for consultation has been a subject of debate. ChatGPT is a 
      deep learning model trained on a vast dataset, but concerns about the reliability 
      of its output have been a subject of debate in recent times. In this article, we 
      have employed cutting-edge bidirectional encoder representations from 
      transformers (BERT) sentiment analysis and topic modeling techniques to 
      comprehend doctors' attitudes toward using ChatGPT in consultation.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Praveen, S V
AU  - Praveen SV
AUID- ORCID: 0000-0002-1450-8839
AD  - Department of Analytics, Xavier Institute of Management and Entrepreneurship, 
      Bangalore, India. praveenscissci@gmail.com.
FAU - Vajrobol, Vajratiya
AU  - Vajrobol V
AD  - Institute of Informatics and Communication, University of Delhi-South Campus, New 
      Delhi, India.
LA  - eng
PT  - Letter
DEP - 20230519
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Deep Learning
MH  - Reproducibility of Results
MH  - Referral and Consultation
MH  - Electric Power Supplies
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Consulting
OT  - Deep learning
OT  - Doctor
EDAT- 2023/05/20 09:42
MHDA- 2023/10/23 00:43
CRDT- 2023/05/19 23:22
PHST- 2023/05/12 00:00 [received]
PHST- 2023/05/15 00:00 [accepted]
PHST- 2023/10/23 00:43 [medline]
PHST- 2023/05/20 09:42 [pubmed]
PHST- 2023/05/19 23:22 [entrez]
AID - 10.1007/s10439-023-03245-7 [pii]
AID - 10.1007/s10439-023-03245-7 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Oct;51(10):2116-2119. doi: 10.1007/s10439-023-03245-7. Epub 
      2023 May 19.

PMID- 37831553
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 1744-5078 (Electronic)
IS  - 0927-3948 (Linking)
DP  - 2023 Oct 13
TI  - Chatbots Vs. Human Experts: Evaluating Diagnostic Performance of Chatbots in 
      Uveitis and the Perspectives on AI Adoption in Ophthalmology.
PG  - 1-8
LID - 10.1080/09273948.2023.2266730 [doi]
AB  - PURPOSE: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, 
      in uveitis diagnosis compared to renowned uveitis specialists, and evaluate 
      clinicians' perception about utilizing artificial intelligence (AI) in 
      ophthalmology practice. METHODS: Six cases were presented to uveitis experts, 
      ChatGPT (version 3.5 and 4.0) and Glass 1.0, and diagnostic accuracy was 
      analyzed. Additionally, a survey about the emotions, confidence in utilizing 
      AI-based tools, and the likelihood of incorporating such tools in clinical 
      practice was done. RESULTS: Uveitis experts accurately diagnosed all cases 
      (100%), while ChatGPT achieved a diagnostic success rate of 66% and Glass 1.0 
      achieved 33%. Most attendees felt excited or optimistic about utilizing AI in 
      ophthalmology practice. Older age and high level of education were positively 
      correlated with increased inclination to adopt AI-based tools. CONCLUSIONS: 
      ChatGPT demonstrated promising diagnostic capabilities in uveitis cases and 
      ophthalmologist showed enthusiasm for the integration of AI into clinical 
      practice.
FAU - Rojas-Carabali, William
AU  - Rojas-Carabali W
AD  - Lee Kong Chiang School of Medicine, Nanyang Technological University, Singapore, 
      Singapore.
FAU - Sen, Alok
AU  - Sen A
AD  - Retina and Uvea Services, Sadguru Netra Chikitsalaya, Chitrakoot, India.
FAU - Agarwal, Aniruddha
AU  - Agarwal A
AD  - Eye Institute, Cleveland Clinic, Abu Dhabi, UAE.
AD  - Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, 
      Cleveland, Ohio, USA.
AD  - Department of Ophthalmology, Maastricht University Medical Center, Maastricht, 
      The Netherlands.
FAU - Tan, Gavin
AU  - Tan G
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 
      Singapore.
AD  - Duke-NUS Medical School, Singapore, Singapore.
FAU - Cheung, Carol Y
AU  - Cheung CY
AD  - Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese 
      University of Hong Kong, Hong Kong, China.
FAU - Rousselot, Andres
AU  - Rousselot A
AD  - Department of Ophthalmology, Universidad del Salvador, Buenos Aires, Argentina.
FAU - Agrawal, Rajdeep
AU  - Agrawal R
AD  - Lee Kong Chiang School of Medicine, Nanyang Technological University, Singapore, 
      Singapore.
FAU - Liu, Renee
AU  - Liu R
AD  - Department of Ophthalmology and Schepens Eye Research Institute,Harvard Medical 
      School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.
FAU - Cifuentes-González, Carlos
AU  - Cifuentes-González C
AUID- ORCID: 0000-0002-2703-0977
AD  - Neuroscience Research Group (NEUROS), Neurovitae Center for Neuroscience, 
      Institute of Translational Medicine (IMT), Escuela de Medicina y Ciencias de la 
      Salud, Universidad del Rosario, Bogotá, Colombia.
FAU - Elze, Tobias
AU  - Elze T
AD  - Department of Ophthalmology and Schepens Eye Research Institute,Harvard Medical 
      School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.
FAU - Kempen, John H
AU  - Kempen JH
AUID- ORCID: 0000-0002-2967-4792
AD  - Department of Ophthalmology and Schepens Eye Research Institute,Harvard Medical 
      School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.
AD  - Sight for Souls, Bellevue, Washington, USA.
AD  - Department of Ophthalmology, Addis Ababa University School of Medicine, Addis 
      Ababa, Ethiopia.
AD  - Department of Ophthalmology, Myungsung Medical College/MCM Comprehensive 
      Specialized Hospital, Addis Abeba, Ethiopia.
FAU - Sobrin, Lucia
AU  - Sobrin L
AUID- ORCID: 0000-0003-1575-0819
AD  - Department of Ophthalmology and Schepens Eye Research Institute,Harvard Medical 
      School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.
FAU - Nguyen, Quan Dong
AU  - Nguyen QD
AD  - Byers Eye Institute, Stanford University, Palo Alto, California, USA.
FAU - de-la-Torre, Alejandra
AU  - de-la-Torre A
AUID- ORCID: 0000-0003-0684-1989
AD  - Neuroscience Research Group (NEUROS), Neurovitae Center for Neuroscience, 
      Institute of Translational Medicine (IMT), Escuela de Medicina y Ciencias de la 
      Salud, Universidad del Rosario, Bogotá, Colombia.
FAU - Lee, Bernett
AU  - Lee B
AD  - Lee Kong Chiang School of Medicine, Nanyang Technological University, Singapore, 
      Singapore.
FAU - Gupta, Vishali
AU  - Gupta V
AD  - Post Graduate Institute of Medical Education and Research (PGIMER), Advance Eye 
      Centre, Chandigarh, India.
FAU - Agrawal, Rupesh
AU  - Agrawal R
AD  - Lee Kong Chiang School of Medicine, Nanyang Technological University, Singapore, 
      Singapore.
AD  - Duke-NUS Medical School, Singapore, Singapore.
AD  - Moorfields Eye Hospital, NHS Foundation Trust, London, UK.
AD  - Tan Tock Seng Hospital, National Healthcare Group, Singapore, Singapore.
LA  - eng
PT  - Journal Article
DEP - 20231013
PL  - England
TA  - Ocul Immunol Inflamm
JT  - Ocular immunology and inflammation
JID - 9312169
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - diagnosis
OT  - large language model
OT  - ophthalmology
EDAT- 2023/10/13 18:43
MHDA- 2023/10/13 18:43
CRDT- 2023/10/13 12:23
PHST- 2023/10/13 18:43 [medline]
PHST- 2023/10/13 18:43 [pubmed]
PHST- 2023/10/13 12:23 [entrez]
AID - 10.1080/09273948.2023.2266730 [doi]
PST - aheadofprint
SO  - Ocul Immunol Inflamm. 2023 Oct 13:1-8. doi: 10.1080/09273948.2023.2266730.

PMID- 37979017
OWN - NLM
STAT- Publisher
LR  - 20231118
IS  - 1970-9366 (Electronic)
IS  - 1828-0447 (Linking)
DP  - 2023 Nov 18
TI  - How accurate are the references generated by ChatGPT in internal medicine?
LID - 10.1007/s11739-023-03484-5 [doi]
FAU - Sebo, Paul
AU  - Sebo P
AUID- ORCID: 0000-0001-7616-0017
AD  - University Institute for Primary Care (IuMFE), University of Geneva, Geneva, 
      Switzerland. paulsebo@hotmail.com.
LA  - eng
PT  - Letter
DEP - 20231118
PL  - Italy
TA  - Intern Emerg Med
JT  - Internal and emergency medicine
JID - 101263418
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - Accuracy
OT  - ChatGPT
OT  - Fake
OT  - Journal
OT  - Publication
OT  - Reference
EDAT- 2023/11/18 12:42
MHDA- 2023/11/18 12:42
CRDT- 2023/11/18 11:06
PHST- 2023/10/08 00:00 [received]
PHST- 2023/11/07 00:00 [accepted]
PHST- 2023/11/18 12:42 [medline]
PHST- 2023/11/18 12:42 [pubmed]
PHST- 2023/11/18 11:06 [entrez]
AID - 10.1007/s11739-023-03484-5 [pii]
AID - 10.1007/s11739-023-03484-5 [doi]
PST - aheadofprint
SO  - Intern Emerg Med. 2023 Nov 18. doi: 10.1007/s11739-023-03484-5.

PMID- 37790756
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 13
DP  - 2023
TI  - Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal 
      Gray Zone cases: potentials and challenges for ai-assisted medical education and 
      decision making in radiation oncology.
PG  - 1265024
LID - 10.3389/fonc.2023.1265024 [doi]
LID - 1265024
AB  - PURPOSE: The potential of large language models in medicine for education and 
      decision-making purposes has been demonstrated as they have achieved decent 
      scores on medical exams such as the United States Medical Licensing Exam (USMLE) 
      and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in 
      the specialized field of radiation oncology. METHODS: The 38th American College 
      of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red 
      Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The 
      TXIT exam contains 300 questions covering various topics of radiation oncology. 
      The 2022 Gray Zone collection contains 15 complex clinical cases. RESULTS: For 
      the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 
      78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. 
      Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology 
      are identified to some extent. Specifically, ChatGPT-4 demonstrates better 
      knowledge of statistics, CNS & eye, pediatrics, biology, and physics than 
      knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. 
      Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, 
      and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth 
      details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest 
      a personalized treatment approach to each case with high correctness and 
      comprehensiveness. Importantly, it provides novel treatment aspects for many 
      cases, which are not suggested by any human experts. CONCLUSION: Both evaluations 
      demonstrate the potential of ChatGPT-4 in medical education for the general 
      public and cancer patients, as well as the potential to aid clinical 
      decision-making, while acknowledging its limitations in certain domains. Owing to 
      the risk of hallucinations, it is essential to verify the content generated by 
      models such as ChatGPT for accuracy.
CI  - Copyright © 2023 Huang, Gomaa, Semrau, Haderlein, Lettmaier, Weissmann, Grigo, 
      Tkhayat, Frey, Gaipl, Distel, Maier, Fietkau, Bert and Putz.
FAU - Huang, Yixing
AU  - Huang Y
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Gomaa, Ahmed
AU  - Gomaa A
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Semrau, Sabine
AU  - Semrau S
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Haderlein, Marlen
AU  - Haderlein M
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Lettmaier, Sebastian
AU  - Lettmaier S
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Weissmann, Thomas
AU  - Weissmann T
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Grigo, Johanna
AU  - Grigo J
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Tkhayat, Hassen Ben
AU  - Tkhayat HB
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 
      Erlangen, Germany.
FAU - Frey, Benjamin
AU  - Frey B
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Gaipl, Udo
AU  - Gaipl U
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Distel, Luitpold
AU  - Distel L
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Maier, Andreas
AU  - Maier A
AD  - Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 
      Erlangen, Germany.
FAU - Fietkau, Rainer
AU  - Fietkau R
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Bert, Christoph
AU  - Bert C
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
FAU - Putz, Florian
AU  - Putz F
AD  - Department of Radiation Oncology, University Hospital Erlangen, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
AD  - Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC10543650
OTO - NOTNLM
OT  - Gray Zone
OT  - artificial intelligence
OT  - clinical decision support (CDS)
OT  - large language model
OT  - natural language processing
OT  - radiotherapy
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/10/04 06:43
MHDA- 2023/10/04 06:44
CRDT- 2023/10/04 04:10
PHST- 2023/07/21 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/10/04 06:44 [medline]
PHST- 2023/10/04 06:43 [pubmed]
PHST- 2023/10/04 04:10 [entrez]
AID - 10.3389/fonc.2023.1265024 [doi]
PST - epublish
SO  - Front Oncol. 2023 Sep 14;13:1265024. doi: 10.3389/fonc.2023.1265024. eCollection 
      2023.

PMID- 37695668
OWN - NLM
STAT- MEDLINE
DCOM- 20230912
LR  - 20230928
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Sep 11
TI  - Clinical Accuracy of Large Language Models and Google Search Responses to 
      Postpartum Depression Questions: Cross-Sectional Study.
PG  - e49240
LID - 10.2196/49240 [doi]
LID - e49240
FAU - Sezgin, Emre
AU  - Sezgin E
AUID- ORCID: 0000-0001-8798-9605
AD  - Nationwide Children's Hospital, Columbus, OH, United States.
AD  - College of Medicine, The Ohio State University, Columbus, OH, United States.
FAU - Chekeni, Faraaz
AU  - Chekeni F
AUID- ORCID: 0009-0004-5472-3906
AD  - Nationwide Children's Hospital, Columbus, OH, United States.
AD  - College of Medicine, The Ohio State University, Columbus, OH, United States.
FAU - Lee, Jennifer
AU  - Lee J
AUID- ORCID: 0000-0002-0849-1006
AD  - Nationwide Children's Hospital, Columbus, OH, United States.
AD  - College of Medicine, The Ohio State University, Columbus, OH, United States.
FAU - Keim, Sarah
AU  - Keim S
AUID- ORCID: 0000-0003-3490-3649
AD  - Nationwide Children's Hospital, Columbus, OH, United States.
AD  - College of Medicine, The Ohio State University, Columbus, OH, United States.
AD  - College of Public Health, The Ohio State University, Columbus, OH, United States.
LA  - eng
PT  - Letter
DEP - 20230911
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Female
MH  - Humans
MH  - Cross-Sectional Studies
MH  - *Depression, Postpartum
MH  - Search Engine
PMC - PMC10520763
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT
OT  - Google
OT  - LaMDA
OT  - artificial intelligence
OT  - clinical accuracy
OT  - cross-sectional study
OT  - depression
OT  - generative AI
OT  - health information seeking
OT  - large language model
OT  - mental health
OT  - natural language processing
OT  - postpartum depression
COIS- Conflicts of Interest: FC owned shares of Google (GOOGL) during the study period.
EDAT- 2023/09/11 12:43
MHDA- 2023/09/12 06:41
CRDT- 2023/09/11 11:54
PHST- 2023/05/22 00:00 [received]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/07/20 00:00 [revised]
PHST- 2023/09/12 06:41 [medline]
PHST- 2023/09/11 12:43 [pubmed]
PHST- 2023/09/11 11:54 [entrez]
AID - v25i1e49240 [pii]
AID - 10.2196/49240 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Sep 11;25:e49240. doi: 10.2196/49240.

PMID- 37295794
OWN - NLM
STAT- Publisher
LR  - 20230609
IS  - 1532-8651 (Electronic)
IS  - 1098-7339 (Linking)
DP  - 2023 Jun 9
TI  - Addition of dexamethasone to prolong peripheral nerve blocks: a ChatGPT-created 
      narrative review.
LID - rapm-2023-104646 [pii]
LID - 10.1136/rapm-2023-104646 [doi]
AB  - Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence 
      chatbot, produces detailed responses and human-like coherent answers, and has 
      been used in the clinical and academic medicine. To evaluate its accuracy in 
      regional anesthesia topics, we produced a ChatGPT review on the addition of 
      dexamethasone to prolong peripheral nerve blocks. A group of experts in regional 
      anesthesia and pain medicine were invited to help shape the topic to be studied, 
      refine the questions entered in to the ChatGPT program, vet the manuscript for 
      accuracy, and create a commentary on the article. Although ChatGPT produced an 
      adequate summary of the topic for a general medical or lay audience, the review 
      that were created appeared to be inadequate for a subspecialty audience as the 
      expert authors. Major concerns raised by the authors included the poor search 
      methodology, poor organization/lack of flow, inaccuracies/omissions of text or 
      references, and lack of novelty. At this time, we do not believe ChatGPT is able 
      to replace human experts and is extremely limited in providing original, creative 
      solutions/ideas and interpreting data for a subspecialty medical review article.
CI  - © American Society of Regional Anesthesia & Pain Medicine 2023. No commercial 
      re-use. See rights and permissions. Published by BMJ.
FAU - Wu, Christopher L
AU  - Wu CL
AUID- ORCID: 0000-0002-4484-0787
AD  - Department of Anesthesiology, Critical Care Medicine and Pain Management, 
      Hospital for Special Surgery, New York, New York, USA wuch@hss.edu.
AD  - Anesthesiology, Weill Cornell Medicine, New York, New York, USA.
AD  - 3Pain Prevention Research Center at Hospital for Special Surgery, Hospital for 
      Special Surgery, New York City, New Yrok, USA.
FAU - Cho, Brian
AU  - Cho B
AD  - Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, 
      Baltimore, Maryland, USA.
FAU - Gabriel, Rodney
AU  - Gabriel R
AUID- ORCID: 0000-0003-4443-0021
AD  - Anesthesiology, University of California, La Jolla, California, USA.
FAU - Hurley, Robert
AU  - Hurley R
AD  - Department of Anesthesiology, Department of Neurobiology and Anatomy, Wake Forest 
      University, Wake Forest, North Carolina, USA.
FAU - Liu, Jiabin
AU  - Liu J
AUID- ORCID: 0000-0002-1029-2786
AD  - Anesthesiology, Critical Care and Pain Management, Hospital for Special Surgery, 
      New York, New York, USA.
FAU - Mariano, Edward R
AU  - Mariano ER
AUID- ORCID: 0000-0003-2735-248X
AD  - Anesthesiology and Perioperative Care Service, VA Palo Alto Health Care System, 
      Palo Alto, California, USA.
AD  - Department of Anesthesiology, Perioperative and Pain Medicine, Stanford 
      University School of Medicine, Stanford, California, USA.
FAU - Mathur, Vineesh
AU  - Mathur V
AD  - Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, 
      Baltimore, Maryland, USA.
FAU - Memtsoudis, Stavros G
AU  - Memtsoudis SG
AD  - Department of Anesthesiology, Critical Care Medicine and Pain Management, 
      Hospital for Special Surgery, New York, New York, USA.
AD  - Anesthesiology, Weill Cornell Medicine, New York, New York, USA.
FAU - Grant, Michael Conrad
AU  - Grant MC
AD  - Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, 
      Baltimore, Maryland, USA.
LA  - eng
PT  - Journal Article
DEP - 20230609
PL  - England
TA  - Reg Anesth Pain Med
JT  - Regional anesthesia and pain medicine
JID - 9804508
SB  - IM
OTO - NOTNLM
OT  - OUTCOMES
OT  - Pharmacology
OT  - REGIONAL ANESTHESIA
COIS- Competing interests: BC is a speaker for Haemonetics. RG’s institution has 
      received funding and/or product for research purposes from Epimed, Infutronix, 
      SPR Therapeutics, Merck, and Precision Genetics. RG’s institution serves as a 
      consultant for Avanos. RH’s institution has received funding for his research 
      from Avanos, NIH and AHRQ. SGM is the owner SGM Consulting. Partner Parvizi 
      Surgical Innovations, Patent holder Multicatheter infusion system.
EDAT- 2023/06/10 15:14
MHDA- 2023/06/10 15:14
CRDT- 2023/06/09 20:33
PHST- 2023/05/01 00:00 [received]
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/06/10 15:14 [medline]
PHST- 2023/06/10 15:14 [pubmed]
PHST- 2023/06/09 20:33 [entrez]
AID - rapm-2023-104646 [pii]
AID - 10.1136/rapm-2023-104646 [doi]
PST - aheadofprint
SO  - Reg Anesth Pain Med. 2023 Jun 9:rapm-2023-104646. doi: 10.1136/rapm-2023-104646.

PMID- 37776392
OWN - NLM
STAT- Publisher
LR  - 20230930
IS  - 1432-1068 (Electronic)
IS  - 1633-8065 (Linking)
DP  - 2023 Sep 30
TI  - Evaluating ChatGPT responses in the context of a 53-year-old male with a femoral 
      neck fracture: a qualitative analysis.
LID - 10.1007/s00590-023-03742-4 [doi]
AB  - PURPOSE: The integration of artificial intelligence (AI) tools, such as ChatGPT, 
      in clinical medicine and medical education has gained significant attention due 
      to their potential to support decision-making and improve patient care. However, 
      there is a need to evaluate the benefits and limitations of these tools in 
      specific clinical scenarios. METHODS: This study used a case study approach 
      within the field of orthopaedic surgery. A clinical case report featuring a 
      53-year-old male with a femoral neck fracture was used as the basis for 
      evaluation. ChatGPT, a large language model, was asked to respond to clinical 
      questions related to the case. The responses generated by ChatGPT were evaluated 
      qualitatively, considering their relevance, justification, and alignment with the 
      responses of real clinicians. Alternative dialogue protocols were also employed 
      to assess the impact of additional prompts and contextual information on ChatGPT 
      responses. RESULTS: ChatGPT generally provided clinically appropriate responses 
      to the questions posed in the clinical case report. However, the level of 
      justification and explanation varied across the generated responses. 
      Occasionally, clinically inappropriate responses and inconsistencies were 
      observed in the generated responses across different dialogue protocols and on 
      separate days. CONCLUSIONS: The findings of this study highlight both the 
      potential and limitations of using ChatGPT in clinical practice. While ChatGPT 
      demonstrated the ability to provide relevant clinical information, the lack of 
      consistent justification and occasional clinically inappropriate responses raise 
      concerns about its reliability. These results underscore the importance of 
      careful consideration and validation when using AI tools in healthcare. Further 
      research and clinician training are necessary to effectively integrate AI tools 
      like ChatGPT, ensuring their safe and reliable use in clinical decision-making.
CI  - © 2023. The Author(s).
FAU - Zhou, Yushy
AU  - Zhou Y
AUID- ORCID: 0000-0002-2653-2107
AD  - Department of Surgery, The University of Melbourne, St. Vincent's Hospital 
      Melbourne, 29 Regent Street, Clinical Sciences Block Level 2, Melbourne, VIC, 
      3010, Australia. yushy.zhou@student.unimelb.edu.au.
AD  - Department of Orthopaedic Surgery, St. Vincent's Hospital, Melbourne, Australia. 
      yushy.zhou@student.unimelb.edu.au.
FAU - Moon, Charles
AU  - Moon C
AD  - Department of Orthopaedic Surgery, Cedars-Sinai Medical Centre, Los Angeles, CA, 
      USA.
FAU - Szatkowski, Jan
AU  - Szatkowski J
AD  - Department of Orthopaedic Surgery, Indiana University Health Methodist Hospital, 
      Indianapolis, IN, USA.
FAU - Moore, Derek
AU  - Moore D
AD  - Santa Barbara Orthopedic Associates, Santa Barbara, CA, USA.
FAU - Stevens, Jarrad
AU  - Stevens J
AD  - Department of Orthopaedic Surgery, St. Vincent's Hospital, Melbourne, Australia.
LA  - eng
PT  - Journal Article
DEP - 20230930
PL  - France
TA  - Eur J Orthop Surg Traumatol
JT  - European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
JID - 9518037
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chatgpt
OT  - Clinical
OT  - Decision-making
OT  - Large language model
OT  - Orthopaedic surgery
EDAT- 2023/10/01 04:43
MHDA- 2023/10/01 04:43
CRDT- 2023/09/30 11:07
PHST- 2023/08/14 00:00 [received]
PHST- 2023/09/18 00:00 [accepted]
PHST- 2023/10/01 04:43 [medline]
PHST- 2023/10/01 04:43 [pubmed]
PHST- 2023/09/30 11:07 [entrez]
AID - 10.1007/s00590-023-03742-4 [pii]
AID - 10.1007/s00590-023-03742-4 [doi]
PST - aheadofprint
SO  - Eur J Orthop Surg Traumatol. 2023 Sep 30. doi: 10.1007/s00590-023-03742-4.

PMID- 37004317
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231102
LR  - 20231103
IS  - 1532-818X (Electronic)
IS  - 0196-0709 (Linking)
VI  - 44
IP  - 4
DP  - 2023 Jul-Aug
TI  - Potential role of ChatGPT in clinical otolaryngology explained by ChatGPT.
PG  - 103873
LID - S0196-0709(23)00087-X [pii]
LID - 10.1016/j.amjoto.2023.103873 [doi]
FAU - Park, Isabel
AU  - Park I
AD  - Division of Otolaryngology-Head and Neck Surgery, George Washington University 
      School of Medicine & Health Sciences, Washington, DC, USA. Electronic address: 
      isabelpark@gwmail.gwu.edu.
FAU - Joshi, Arjun S
AU  - Joshi AS
AD  - Division of Otolaryngology-Head and Neck Surgery, George Washington University 
      School of Medicine & Health Sciences, Washington, DC, USA.
FAU - Javan, Ramin
AU  - Javan R
AD  - Department of Radiology, George Washington University Hospital, Washington, DC, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230329
PL  - United States
TA  - Am J Otolaryngol
JT  - American journal of otolaryngology
JID - 8000029
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Clinical otolaryngology
OT  - OpenAI
EDAT- 2023/04/03 06:00
MHDA- 2023/04/03 06:01
CRDT- 2023/04/02 18:04
PHST- 2023/03/03 00:00 [received]
PHST- 2023/03/20 00:00 [revised]
PHST- 2023/03/25 00:00 [accepted]
PHST- 2023/04/03 06:01 [medline]
PHST- 2023/04/03 06:00 [pubmed]
PHST- 2023/04/02 18:04 [entrez]
AID - S0196-0709(23)00087-X [pii]
AID - 10.1016/j.amjoto.2023.103873 [doi]
PST - ppublish
SO  - Am J Otolaryngol. 2023 Jul-Aug;44(4):103873. doi: 10.1016/j.amjoto.2023.103873. 
      Epub 2023 Mar 29.

PMID- 37500980
OWN - NLM
STAT- Publisher
LR  - 20230727
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 27
TI  - AI-ChatGPT/GPT-4: An Booster for the Development of Physical Medicine and 
      Rehabilitation in the New Era!
LID - 10.1007/s10439-023-03314-x [doi]
AB  - Artificial intelligence (AI) has been driving the continuous development of the 
      Physical Medicine and Rehabilitation (PM&R) fields. The latest release of 
      ChatGPT/GPT-4 has shown us that AI can potentially transform the healthcare 
      industry. In this study, we propose various ways in which ChatGPT/GPT-4 can 
      display its talents in the field of PM&R in future. ChatGPT/GPT-4 is an essential 
      tool for Physiatrists in the new era.
CI  - © 2023. The Author(s).
FAU - Peng, Shengxin
AU  - Peng S
AD  - School of Rehabilitation Medicine of Binzhou Medical University, Yantai, China.
FAU - Wang, Deqiang
AU  - Wang D
AD  - School of Rehabilitation Medicine of Binzhou Medical University, Yantai, China.
FAU - Liang, Yuanhao
AU  - Liang Y
AD  - Weifang Medical University, Weifang, China.
FAU - Xiao, Wenshan
AU  - Xiao W
AD  - Shandong First Medical University, Jinan, China.
FAU - Zhang, Yixiang
AU  - Zhang Y
AD  - Weifang Medical University, Weifang, China.
FAU - Liu, Lei
AU  - Liu L
AUID- ORCID: 0000-0002-6915-1929
AD  - Department of Painology, The First Affiliated Hospital of Shandong First Medical 
      University (Shandong Provincial Qianfoshan Hospital), Jinan, 250014, China. 
      fort0825@sina.com.
LA  - eng
GR  - 2022cxgx020510/2022 Shandong Province Key R&D Program/
PT  - Letter
DEP - 20230727
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - GPT-4
OT  - Physical medicine and rehabilitation
EDAT- 2023/07/28 01:08
MHDA- 2023/07/28 01:08
CRDT- 2023/07/27 23:34
PHST- 2023/07/03 00:00 [received]
PHST- 2023/07/06 00:00 [accepted]
PHST- 2023/07/28 01:08 [medline]
PHST- 2023/07/28 01:08 [pubmed]
PHST- 2023/07/27 23:34 [entrez]
AID - 10.1007/s10439-023-03314-x [pii]
AID - 10.1007/s10439-023-03314-x [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 27. doi: 10.1007/s10439-023-03314-x.

PMID- 37505381
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231120
IS  - 1826-6983 (Electronic)
IS  - 0033-8362 (Linking)
VI  - 128
IP  - 11
DP  - 2023 Nov
TI  - A critical examination and suggestions for large language models for structured 
      reporting in radiology.
PG  - 1441-1442
LID - 10.1007/s11547-023-01688-5 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20230728
PL  - Italy
TA  - Radiol Med
JT  - La Radiologia medica
JID - 0177625
SB  - IM
MH  - Humans
MH  - Radiography
MH  - *Radiology
OTO - NOTNLM
OT  - ChatGPT
OT  - Generative AI
OT  - Large language model
OT  - Radiology
OT  - Structured reporting
EDAT- 2023/07/28 13:11
MHDA- 2023/11/02 12:42
CRDT- 2023/07/28 11:10
PHST- 2023/06/18 00:00 [received]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/11/02 12:42 [medline]
PHST- 2023/07/28 13:11 [pubmed]
PHST- 2023/07/28 11:10 [entrez]
AID - 10.1007/s11547-023-01688-5 [pii]
AID - 10.1007/s11547-023-01688-5 [doi]
PST - ppublish
SO  - Radiol Med. 2023 Nov;128(11):1441-1442. doi: 10.1007/s11547-023-01688-5. Epub 
      2023 Jul 28.

PMID- 37713147
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20230918
IS  - 1536-3708 (Electronic)
IS  - 0148-7043 (Linking)
VI  - 91
IP  - 4
DP  - 2023 Oct 1
TI  - Comparison of Patient Education Materials Generated by Chat Generative 
      Pre-Trained Transformer Versus Experts: An Innovative Way to Increase Readability 
      of Patient Education Materials.
PG  - 409-412
LID - 10.1097/SAP.0000000000003634 [doi]
AB  - INTRODUCTION: Improving patient education materials may improve patient outcomes. 
      This study aims to explore the possibility of generating patient education 
      materials with the assistance of a large language model, Chat Generative 
      Pre-Trained Transformer (ChatGPT). In addition, we compare the accuracy and 
      readability of ChatGPT-generated materials versus expert-generated materials. 
      METHODS: Patient education materials in implant-based breast reconstruction were 
      generated by experts and ChatGPT independently. Readability and accuracy of the 
      materials are the main outcomes. Readability of the materials was compared using 
      Flesch-Kincaid score. Accuracy of the materials generated by ChatGPT was 
      evaluated by 2 independent reviewers. Content errors are categorized into 
      information errors, statistical errors, and multiple errors (errors more than 2 
      types). RESULTS: The content generated by experts had higher readability. The 
      Flesch-Kincaid score is at the 7.5 grade for expert-generated materials, whereas 
      the content generated by ChatGPT is at the 10.5 grade (despite ChatGPT being 
      asked to generate content at the seventh grade level). The accuracy of 
      ChatGPT-generated content is 50%, with most errors being information errors. 
      ChatGPT often provides information about breast reduction or breast augmentation, 
      despite being asked specifically about breast reconstruction. Despite its 
      limitation, ChatGPT significantly reduced the time required to generate patient 
      education materials. Although it takes experts 1 month to generate patient 
      education materials, ChatGPT generates materials within 30 minutes. CONCLUSIONS: 
      ChatGPT can be a powerful starting tool to generate patient education materials. 
      However, its readability and accuracy still require improvements.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Hung, Ya-Ching
AU  - Hung YC
FAU - Chaker, Sara C
AU  - Chaker SC
AD  - From the Department of Plastic Surgery, Vanderbilt University Medical Center, 
      Nashville, TN.
FAU - Sigel, Matthew
AU  - Sigel M
AD  - Vanderbilt University, Nashville, TN.
FAU - Saad, Mariam
AU  - Saad M
AD  - From the Department of Plastic Surgery, Vanderbilt University Medical Center, 
      Nashville, TN.
FAU - Slater, Elizabeth D
AU  - Slater ED
AD  - From the Department of Plastic Surgery, Vanderbilt University Medical Center, 
      Nashville, TN.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Ann Plast Surg
JT  - Annals of plastic surgery
JID - 7805336
SB  - IM
MH  - Humans
MH  - *Comprehension
MH  - Patient Education as Topic
MH  - Language
MH  - *Mammaplasty
COIS- Conflicts of interest and sources of funding: none declared.
EDAT- 2023/09/15 12:44
MHDA- 2023/09/18 12:43
CRDT- 2023/09/15 11:17
PHST- 2023/09/18 12:43 [medline]
PHST- 2023/09/15 12:44 [pubmed]
PHST- 2023/09/15 11:17 [entrez]
AID - 00000637-202310000-00002 [pii]
AID - 10.1097/SAP.0000000000003634 [doi]
PST - ppublish
SO  - Ann Plast Surg. 2023 Oct 1;91(4):409-412. doi: 10.1097/SAP.0000000000003634.

PMID- 37962176
OWN - NLM
STAT- Publisher
LR  - 20231114
IS  - 1473-6586 (Electronic)
IS  - 0963-0643 (Linking)
DP  - 2023 Nov 14
TI  - ChatGPT in urology practice: revolutionizing efficiency and patient care with 
      generative artificial intelligence.
LID - 10.1097/MOU.0000000000001151 [doi]
AB  - PURPOSE OF REVIEW: ChatGPT has emerged as a potentially useful tool for 
      healthcare. Its role in urology is in its infancy and has much potential for 
      research, clinical practice and for patient assistance. With this narrative 
      review, we want to draw a picture of what is known about ChatGPT's integration in 
      urology, alongside future promises and challenges. RECENT FINDINGS: The use of 
      ChatGPT can ease the administrative work, helping urologists with note-taking and 
      clinical documentation such as discharge summaries and clinical notes. It can 
      improve patient engagement through increasing awareness and facilitating 
      communication, as it has especially been investigated for uro-oncological 
      diseases. Its ability to understand human emotions makes ChatGPT an empathic and 
      thoughtful interactive tool or source for urological patients and their 
      relatives. Currently, its role in clinical diagnosis and treatment decisions is 
      uncertain, as concerns have been raised about misinterpretation, hallucination 
      and out-of-date information. Moreover, a mandatory regulatory process for ChatGPT 
      in urology is yet to be established. SUMMARY: ChatGPT has the potential to 
      contribute to precision medicine and tailored practice by its quick, structured 
      responses. However, this will depend on how well information can be obtained by 
      seeking appropriate responses and asking the pertinent questions. The key lies in 
      being able to validate the responses, regulating the information shared and 
      avoiding misuse of the same to protect the data and patient privacy. Its 
      successful integration into mainstream urology needs educational bodies to 
      provide guidelines or best practice recommendations for the same.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Nedbal, Carlotta
AU  - Nedbal C
AD  - Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, 
      UK.
AD  - Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic 
      University of Marche, Ancona, Italy.
FAU - Naik, Nitesh
AU  - Naik N
AD  - Department of Mechanical and Industrial Engineering, Manipal Institute of 
      Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
FAU - Castellani, Daniele
AU  - Castellani D
AD  - Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic 
      University of Marche, Ancona, Italy.
FAU - Gahuar, Vineet
AU  - Gahuar V
AD  - Department of Urology, Ng Teng Fong General Hospital, NUHS, Singapore.
FAU - Geraghty, Robert
AU  - Geraghty R
AD  - Department of Urology, Freeman Hospital, Newcastle-upon-Tyne, UK.
FAU - Somani, Bhaskar Kumar
AU  - Somani BK
AD  - Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, 
      UK.
LA  - eng
PT  - Journal Article
DEP - 20231114
PL  - United States
TA  - Curr Opin Urol
JT  - Current opinion in urology
JID - 9200621
SB  - IM
EDAT- 2023/11/14 12:42
MHDA- 2023/11/14 12:42
CRDT- 2023/11/14 07:53
PHST- 2023/11/14 12:42 [medline]
PHST- 2023/11/14 12:42 [pubmed]
PHST- 2023/11/14 07:53 [entrez]
AID - 00042307-990000000-00131 [pii]
AID - 10.1097/MOU.0000000000001151 [doi]
PST - aheadofprint
SO  - Curr Opin Urol. 2023 Nov 14. doi: 10.1097/MOU.0000000000001151.

PMID- 37389908
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20231122
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Jun 30
TI  - Reliability of Medical Information Provided by ChatGPT: Assessment Against 
      Clinical Guidelines and Patient Information Quality Instrument.
PG  - e47479
LID - 10.2196/47479 [doi]
LID - e47479
AB  - BACKGROUND: ChatGPT-4 is the latest release of a novel artificial intelligence 
      (AI) chatbot able to answer freely formulated and complex questions. In the near 
      future, ChatGPT could become the new standard for health care professionals and 
      patients to access medical information. However, little is known about the 
      quality of medical information provided by the AI. OBJECTIVE: We aimed to assess 
      the reliability of medical information provided by ChatGPT. METHODS: Medical 
      information provided by ChatGPT-4 on the 5 hepato-pancreatico-biliary (HPB) 
      conditions with the highest global disease burden was measured with the Ensuring 
      Quality Information for Patients (EQIP) tool. The EQIP tool is used to measure 
      the quality of internet-available information and consists of 36 items that are 
      divided into 3 subsections. In addition, 5 guideline recommendations per analyzed 
      condition were rephrased as questions and input to ChatGPT, and agreement between 
      the guidelines and the AI answer was measured by 2 authors independently. All 
      queries were repeated 3 times to measure the internal consistency of ChatGPT. 
      RESULTS: Five conditions were identified (gallstone disease, pancreatitis, liver 
      cirrhosis, pancreatic cancer, and hepatocellular carcinoma). The median EQIP 
      score across all conditions was 16 (IQR 14.5-18) for the total of 36 items. 
      Divided by subsection, median scores for content, identification, and structure 
      data were 10 (IQR 9.5-12.5), 1 (IQR 1-1), and 4 (IQR 4-5), respectively. 
      Agreement between guideline recommendations and answers provided by ChatGPT was 
      60% (15/25). Interrater agreement as measured by the Fleiss κ was 0.78 (P<.001), 
      indicating substantial agreement. Internal consistency of the answers provided by 
      ChatGPT was 100%. CONCLUSIONS: ChatGPT provides medical information of comparable 
      quality to available static internet information. Although currently of limited 
      quality, large language models could become the future standard for patients and 
      health care professionals to gather medical information.
CI  - ©Harriet Louise Walker, Shahi Ghani, Christoph Kuemmerli, Christian Andreas 
      Nebiker, Beat Peter Müller, Dimitri Aristotle Raptis, Sebastian Manuel Staubli. 
      Originally published in the Journal of Medical Internet Research 
      (https://www.jmir.org), 30.06.2023.
FAU - Walker, Harriet Louise
AU  - Walker HL
AUID- ORCID: 0009-0009-6278-6399
AD  - Royal Free London NHS Foundation Trust, London, United Kingdom.
FAU - Ghani, Shahi
AU  - Ghani S
AUID- ORCID: 0000-0001-7366-296X
AD  - Royal Free London NHS Foundation Trust, London, United Kingdom.
FAU - Kuemmerli, Christoph
AU  - Kuemmerli C
AUID- ORCID: 0000-0002-7109-3545
AD  - Clarunis - University Center for Gastrointestinal and Liver Diseases, Basel, 
      Switzerland.
FAU - Nebiker, Christian Andreas
AU  - Nebiker CA
AUID- ORCID: 0000-0002-7493-2850
AD  - Departement Chirurgie, Kantonsspital Aarau, Aarau, Switzerland.
FAU - Müller, Beat Peter
AU  - Müller BP
AUID- ORCID: 0000-0002-8552-8538
AD  - Clarunis - University Center for Gastrointestinal and Liver Diseases, Basel, 
      Switzerland.
FAU - Raptis, Dimitri Aristotle
AU  - Raptis DA
AUID- ORCID: 0000-0002-0898-3270
AD  - Organ Transplant Center of Excellence, King Faisal Specialist Hospital & Research 
      Centre, Riyadh, Saudi Arabia.
FAU - Staubli, Sebastian Manuel
AU  - Staubli SM
AUID- ORCID: 0000-0002-0818-9835
AD  - Royal Free London NHS Foundation Trust, London, United Kingdom.
AD  - Clarunis - University Center for Gastrointestinal and Liver Diseases, Basel, 
      Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20230630
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Health Personnel
MH  - Internet
MH  - Language
PMC - PMC10365578
OTO - NOTNLM
OT  - ChatGPT
OT  - EQIP tool
OT  - artificial intelligence
OT  - bile
OT  - biliary
OT  - chatbot
OT  - chatbots
OT  - conversational agent
OT  - conversational agents
OT  - gall
OT  - gallstone
OT  - hepatic
OT  - internal medicine
OT  - internet information
OT  - liver
OT  - medical information
OT  - pancreas
OT  - pancreatic
OT  - pancreatitis
OT  - patient information
COIS- Conflicts of Interest: None declared.
EDAT- 2023/06/30 13:11
MHDA- 2023/07/03 06:42
CRDT- 2023/06/30 11:53
PHST- 2023/04/07 00:00 [received]
PHST- 2023/06/15 00:00 [accepted]
PHST- 2023/06/07 00:00 [revised]
PHST- 2023/07/03 06:42 [medline]
PHST- 2023/06/30 13:11 [pubmed]
PHST- 2023/06/30 11:53 [entrez]
AID - v25i1e47479 [pii]
AID - 10.2196/47479 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Jun 30;25:e47479. doi: 10.2196/47479.

PMID- 37905713
OWN - NLM
STAT- Publisher
LR  - 20231031
IS  - 1445-2197 (Electronic)
IS  - 1445-1433 (Linking)
DP  - 2023 Oct 31
TI  - Conversational artificial intelligence (chatGPT™) in the management of complex 
      colorectal cancer patients: early experience.
LID - 10.1111/ans.18749 [doi]
AB  - INTRODUCTION: In 2022 chatGPT™ (OpenAI, San Francisco) was introduced to the 
      public. The complex reasoning and the natural language processing (NLP) ability 
      of the AI platform has generated much excitement about the potential 
      applications. This study conducted a preliminary analysis of the chatGPT™'s 
      ability to formulate a management plan in accordance with oncological principles 
      for patients with colorectal cancer. METHODOLOGY: Colorectal cancer cases 
      discussed in the multidisciplinary tumor (MDT) board at a single tertiary 
      institution between September 2022 and January 2023 were prospectively collected. 
      The treatment recommendations made by the chatGPT™ for Stage IV, recurrent, 
      synchronous colorectal cancer were analysed for adherence to oncological 
      principles. The recommendations by chatGPT™ were compared with the decision plans 
      made by the MDT. RESULTS: In all cases, the chatGPT™ was able to adhere to 
      oncological principles. The recommendations in all 30 cases factored in the 
      patient's overall health and functional status. The oncological management 
      recommendation concordance rate between chatGPT™ and the MDT was 86.7%. 
      CONCLUSIONS: This study shows a high concordance rate of the chatGPT™'s 
      recommendations with that given by the MDT in the management of complex 
      colorectal patients. This will need to be verified in a larger prospective study.
CI  - © 2023 Royal Australasian College of Surgeons.
FAU - Choo, Jeong Min
AU  - Choo JM
AD  - Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department 
      of Surgery, Korea University College of Medicine, Seoul, South Korea.
FAU - Ryu, Hyo Seon
AU  - Ryu HS
AD  - Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department 
      of Surgery, Korea University College of Medicine, Seoul, South Korea.
FAU - Kim, Ji Seon
AU  - Kim JS
AD  - Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department 
      of Surgery, Korea University College of Medicine, Seoul, South Korea.
FAU - Cheong, Ju Yong
AU  - Cheong JY
AUID- ORCID: 0000-0002-1600-7632
AD  - Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department 
      of Surgery, Korea University College of Medicine, Seoul, South Korea.
FAU - Baek, Se-Jin
AU  - Baek SJ
AUID- ORCID: 0000-0002-3185-8777
AD  - Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department 
      of Surgery, Korea University College of Medicine, Seoul, South Korea.
FAU - Kwak, Jung Myun
AU  - Kwak JM
AD  - Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department 
      of Surgery, Korea University College of Medicine, Seoul, South Korea.
FAU - Kim, Jin
AU  - Kim J
AUID- ORCID: 0000-0001-6479-9673
AD  - Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department 
      of Surgery, Korea University College of Medicine, Seoul, South Korea.
LA  - eng
PT  - Journal Article
DEP - 20231031
PL  - Australia
TA  - ANZ J Surg
JT  - ANZ journal of surgery
JID - 101086634
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatGPT™
OT  - colorectal cancer
OT  - multidisciplinary tumour board
EDAT- 2023/10/31 12:42
MHDA- 2023/10/31 12:42
CRDT- 2023/10/31 07:33
PHST- 2023/10/05 00:00 [revised]
PHST- 2023/03/31 00:00 [received]
PHST- 2023/10/13 00:00 [accepted]
PHST- 2023/10/31 12:42 [medline]
PHST- 2023/10/31 12:42 [pubmed]
PHST- 2023/10/31 07:33 [entrez]
AID - 10.1111/ans.18749 [doi]
PST - aheadofprint
SO  - ANZ J Surg. 2023 Oct 31. doi: 10.1111/ans.18749.

PMID- 37724211
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230920
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Mind + Machine: ChatGPT as a Basic Clinical Decisions Support Tool.
PG  - e43690
LID - 10.7759/cureus.43690 [doi]
LID - e43690
AB  - Background Generative artificial intelligence (AI) has integrated into various 
      industries as it has demonstrated enormous potential in automating elaborate 
      processes and enhancing complex decision-making. The ability of these chatbots to 
      critically triage, diagnose, and manage complex medical conditions, remains 
      unknown and requires further research. Objective This cross-sectional study 
      sought to quantitatively analyze the appropriateness of ChatGPT (OpenAI, San 
      Francisco, CA, US) in its ability to triage, synthesize differential diagnoses, 
      and generate treatment plans for nine diverse but common clinical scenarios.  
      Methods Various common clinical scenarios were developed. Each was input into 
      ChatGPT, and the chatbot was asked to develop diagnostic and treatment plans. 
      Five practicing physicians independently scored ChatGPT's responses to the 
      clinical scenarios.  Results  The average overall score for the triage ranking 
      was 4.2 (SD 0.7). The lowest overall score was for the completeness of the 
      differential diagnosis at 4.1 (0.5). The highest overall scores were seen with 
      the accuracy of the differential diagnosis, initial treatment plan, and overall 
      usefulness of the response (all with an average score of 4.4). Variance among 
      physician scores ranged from 0.24 for accuracy of the differential diagnosis to 
      0.49 for appropriateness of triage ranking.  Discussion ChatGPT has the potential 
      to augment clinical decision-making. More extensive research, however, is needed 
      to ensure accuracy and appropriate recommendations are provided.
CI  - Copyright © 2023, Ayoub et al.
FAU - Ayoub, Marc
AU  - Ayoub M
AD  - Neurocritical Care, Northwell, Northshore University Hospital, Manhasset, USA.
AD  - Internal Medicine, Elmhurst Hospital Center, Mount Sinai School of Medicine, New 
      York, USA.
FAU - Ballout, Ahmad A
AU  - Ballout AA
AD  - Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 
      Long Island, USA.
FAU - Zayek, Rosana A
AU  - Zayek RA
AD  - Internal Medicine, Torrance Memorial Medical Center, Torrance, USA.
FAU - Ayoub, Noel F
AU  - Ayoub NF
AD  - Otolaryngology-Head and Neck Surgery, Stanford Health Care, Palo Alto, USA.
LA  - eng
PT  - Journal Article
DEP - 20230818
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10505276
OTO - NOTNLM
OT  - artificial intelligence in healthcare
OT  - chatgpt
OT  - clinical decision support system
OT  - generative artificial intelligence
OT  - large language model
OT  - triage
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/19 12:42
MHDA- 2023/09/19 12:43
CRDT- 2023/09/19 03:40
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/09/19 12:43 [medline]
PHST- 2023/09/19 12:42 [pubmed]
PHST- 2023/09/19 03:40 [entrez]
AID - 10.7759/cureus.43690 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 18;15(8):e43690. doi: 10.7759/cureus.43690. eCollection 2023 
      Aug.

PMID- 38021639
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Is ChatGPT's Knowledge and Interpretative Ability Comparable to First 
      Professional MBBS (Bachelor of Medicine, Bachelor of Surgery) Students of India 
      in Taking a Medical Biochemistry Examination?
PG  - e47329
LID - 10.7759/cureus.47329 [doi]
LID - e47329
AB  - Introduction ChatGPT is a large language model (LLM)-based chatbot that uses 
      natural language processing to create humanlike conversational dialogue. It has 
      created a significant impact on the entire global landscape, especially in 
      sectors like finance and banking, e-commerce, education, legal, human resources 
      (HR), and recruitment since its inception. There have been multiple ongoing 
      controversies regarding the seamless integration of ChatGPT with the healthcare 
      system because of its factual accuracy, lack of experience, lack of clarity, 
      expertise, and above all, lack of empathy. Our study seeks to compare ChatGPT's 
      knowledge and interpretative abilities with those of first-year medical students 
      in India in the subject of medical biochemistry. Materials and methods A total of 
      79 questions (40 multiple choice questions and 39 subjective questions) of 
      medical biochemistry were set for Phase 1, block II term examination. Chat GPT 
      was enrolled as the 101st student in the class. The questions were entered into 
      ChatGPT's interface and responses were noted. The response time for the 
      multiple-choice questions (MCQs) asked was also noted. The answers given by 
      ChatGPT and 100 students of the class were checked by two subject experts, and 
      marks were given according to the quality of answers. Marks obtained by the AI 
      chatbot were compared with the marks obtained by the students. Results ChatGPT 
      scored 140 marks out of 200 and outperformed almost all the students and ranked 
      fifth in the class. It scored very well in information-based MCQs (92%) and 
      descriptive logical reasoning (80%), whereas performed poorly in descriptive 
      clinical scenario-based questions (52%). In terms of time taken to respond to the 
      MCQs, it took significantly more time to answer logical reasoning MCQs than 
      simple information-based MCQs (3.10±0.882 sec vs. 2.02±0.477 sec, p<0.005). 
      Conclusions ChatGPT was able to outperform almost all the students in the subject 
      of medical biochemistry. If the ethical issues are dealt with efficiently, these 
      LLMs have a huge potential to be used in teaching and learning methods of modern 
      medicine by students successfully.
CI  - Copyright © 2023, Ghosh et al.
FAU - Ghosh, Abhra
AU  - Ghosh A
AD  - Biochemistry, Dayanand Medical College and Hospital, Ludhiana, IND.
FAU - Maini Jindal, Nandita
AU  - Maini Jindal N
AD  - Biochemistry, Dayanand Medical College and Hospital, Ludhiana, IND.
FAU - Gupta, Vikram K
AU  - Gupta VK
AD  - Community Medicine, Dayanand Medical College, Ludhiana, IND.
FAU - Bansal, Ekta
AU  - Bansal E
AD  - Biochemistry, Dayanand Medical College and Hospital, Ludhiana, IND.
FAU - Kaur Bajwa, Navjot
AU  - Kaur Bajwa N
AD  - Biochemistry, Dayanand Medical College and Hospital, Ludhiana, IND.
FAU - Sett, Abhishek
AU  - Sett A
AD  - Healthcare, Deloitte Consulting US India Pvt Ltd, Bangalore, IND.
LA  - eng
PT  - Journal Article
DEP - 20231019
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10657167
OTO - NOTNLM
OT  - ai-based education
OT  - artificial intelligence
OT  - chatgpt
OT  - medical biochemistry
OT  - medical education
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:41
MHDA- 2023/11/29 18:42
CRDT- 2023/11/29 15:09
PHST- 2023/10/19 00:00 [accepted]
PHST- 2023/11/29 18:42 [medline]
PHST- 2023/11/29 18:41 [pubmed]
PHST- 2023/11/29 15:09 [entrez]
AID - 10.7759/cureus.47329 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 19;15(10):e47329. doi: 10.7759/cureus.47329. eCollection 2023 
      Oct.

PMID- 37988948
OWN - NLM
STAT- Publisher
LR  - 20231121
IS  - 1095-6859 (Electronic)
IS  - 0090-8258 (Linking)
VI  - 179
DP  - 2023 Nov 20
TI  - Let's chat about cervical cancer: Assessing the accuracy of ChatGPT responses to 
      cervical cancer questions.
PG  - 164-168
LID - S0090-8258(23)01535-4 [pii]
LID - 10.1016/j.ygyno.2023.11.008 [doi]
AB  - OBJECTIVE: To quantify the accuracy of ChatGPT in answering commonly asked 
      questions pertaining to cervical cancer prevention, diagnosis, treatment, and 
      survivorship/quality-of-life (QOL). METHODS: ChatGPT was queried with 64 
      questions adapted from professional society websites and the authors' clinical 
      experiences. The answers were scored by two attending Gynecologic Oncologists 
      according to the following scale: 1) correct and comprehensive, 2) correct but 
      not comprehensive, 3) some correct, some incorrect, and 4) completely incorrect. 
      Scoring discrepancies were resolved by additional reviewers as needed. The 
      proportion of responses earning each score were calculated overall and within 
      each question category. RESULTS: ChatGPT provided correct and comprehensive 
      answers to 34 (53.1%) questions, correct but not comprehensive answers to 19 
      (29.7%) questions, partially incorrect answers to 10 (15.6%) questions, and 
      completely incorrect answers to 1 (1.6%) question. Prevention and 
      survivorship/QOL had the highest proportion of "correct" scores (scores of 1 or 
      2) at 22/24 (91.7%) and 15/16 (93.8%), respectively. ChatGPT performed less well 
      in the treatment category, with 15/21 (71.4%) correct scores. It performed the 
      worst in the diagnosis category with only 1/3 (33.3%) correct scores. CONCLUSION: 
      ChatGPT accurately answers questions about cervical cancer prevention, 
      survivorship, and QOL. It performs less accurately for cervical cancer diagnosis 
      and treatment. Further development of this immensely popular large language model 
      should include physician input before it can be utilized as a tool for 
      Gynecologists or recommended as a patient resource for information on cervical 
      cancer diagnosis and treatment.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Hermann, Catherine E
AU  - Hermann CE
AD  - New York University Langone Health, Department of Obstetrics and Gynecology, 
      Division of Gynecologic Oncology, New York, NY, United States of America. 
      Electronic address: catherine.e.hermann@gmail.com.
FAU - Patel, Jharna M
AU  - Patel JM
AD  - New York University Langone Health, Department of Obstetrics and Gynecology, 
      Division of Gynecologic Oncology, New York, NY, United States of America.
FAU - Boyd, Leslie
AU  - Boyd L
AD  - New York University Langone Health, Department of Obstetrics and Gynecology, 
      Division of Gynecologic Oncology, New York, NY, United States of America.
FAU - Growdon, Whitfield B
AU  - Growdon WB
AD  - New York University Langone Health, Department of Obstetrics and Gynecology, 
      Division of Gynecologic Oncology, New York, NY, United States of America.
FAU - Aviki, Emeline
AU  - Aviki E
AD  - New York University Langone Health Long Island, Department of Obstetrics and 
      Gynecology, Division of Gynecologic Oncology, Mineola, NY, United States of 
      America.
FAU - Stasenko, Marina
AU  - Stasenko M
AD  - New York University Langone Health, Department of Obstetrics and Gynecology, 
      Division of Gynecologic Oncology, New York, NY, United States of America.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - United States
TA  - Gynecol Oncol
JT  - Gynecologic oncology
JID - 0365304
SB  - IM
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Cervical cancer
OT  - Cervical dysplasia
OT  - ChatGPT
OT  - HPV vaccination
COIS- Declaration of Competing Interest None of the authors have conflicts to report.
EDAT- 2023/11/22 00:42
MHDA- 2023/11/22 00:42
CRDT- 2023/11/21 18:24
PHST- 2023/08/12 00:00 [received]
PHST- 2023/10/31 00:00 [revised]
PHST- 2023/11/08 00:00 [accepted]
PHST- 2023/11/22 00:42 [medline]
PHST- 2023/11/22 00:42 [pubmed]
PHST- 2023/11/21 18:24 [entrez]
AID - S0090-8258(23)01535-4 [pii]
AID - 10.1016/j.ygyno.2023.11.008 [doi]
PST - aheadofprint
SO  - Gynecol Oncol. 2023 Nov 20;179:164-168. doi: 10.1016/j.ygyno.2023.11.008.

PMID- 37855397
OWN - NLM
STAT- Publisher
LR  - 20231019
IS  - 1445-2197 (Electronic)
IS  - 1445-1433 (Linking)
DP  - 2023 Oct 19
TI  - Exploring the role of an artificial intelligence chatbot on appendicitis 
      management: an experimental study on ChatGPT.
LID - 10.1111/ans.18736 [doi]
AB  - BACKGROUND: Appendicitis is a common surgical condition that requires urgent 
      medical attention. Recent advancements in artificial intelligence and large 
      language processing, such as ChatGPT, have demonstrated potential in supporting 
      healthcare management and scientific research. This study aims to evaluate the 
      accuracy and comprehensiveness of ChatGPT's knowledge on appendicitis management. 
      METHODS: Six questions related to appendicitis management were created by 
      experienced RACS qualified general surgeons to assess ChatGPT's ability to 
      provide accurate information. The criteria of ChatGPT answers' accuracy were 
      compared with current healthcare guidelines for appendicitis and subjective 
      evaluation by two RACS qualified General Surgeons. Additionally, ChatGPT was then 
      asked to provide five high level evidence references to support its responses. 
      RESULTS: ChatGPT provided clinically relevant information on appendicitis 
      management, however, was inconsistent in doing so and often provided superficial 
      information. Further to this, ChatGPT encountered difficulties in generating 
      relevant references, with some being either non-existent or incorrect. 
      CONCLUSION: ChatGPT has the potential to provide timely and comprehensible 
      medical information on appendicitis management to laypersons. However, its issue 
      of inaccuracy in information and production of non-existent or erroneous 
      references presents a challenge for researchers and clinicians who may 
      inadvertently employ such information in their research or healthcare. Therefore, 
      clinicians should exercise caution when using ChatGPT for these purposes.
CI  - © 2023 Royal Australasian College of Surgeons.
FAU - Gracias, Dylan
AU  - Gracias D
AUID- ORCID: 0000-0002-6873-0144
AD  - Department of Surgery, Townsville Hospital, Townsville, Queensland, Australia.
FAU - Siu, Adrian
AU  - Siu A
AUID- ORCID: 0000-0001-8151-9805
AD  - Faculty of Medicine and Health, Central Clinical School, The University of 
      Sydney, Sydney, New South Wales, Australia.
AD  - Surgical Outcomes Research Centre (SOuRCe), Royal Prince Alfred Hospital, 
      Camperdown, New South Wales, Australia.
AD  - Concord Institute of Academic Surgery, Concord Hospital, Concord, New South 
      Wales, Australia.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Department of Surgery, Bendigo Health, Bendigo, Victoria, Australia.
FAU - Dooreemeah, Dilshad
AU  - Dooreemeah D
AD  - Department of Surgery, Bendigo Health, Bendigo, Victoria, Australia.
FAU - Lee, Angus
AU  - Lee A
AUID- ORCID: 0000-0002-2276-9924
AD  - Department of Surgery, Bendigo Health, Bendigo, Victoria, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231019
PL  - Australia
TA  - ANZ J Surg
JT  - ANZ journal of surgery
JID - 101086634
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - appendicitis
OT  - large language model
OT  - management
EDAT- 2023/10/19 12:42
MHDA- 2023/10/19 12:42
CRDT- 2023/10/19 08:03
PHST- 2023/09/27 00:00 [revised]
PHST- 2023/08/05 00:00 [received]
PHST- 2023/10/02 00:00 [accepted]
PHST- 2023/10/19 12:42 [medline]
PHST- 2023/10/19 12:42 [pubmed]
PHST- 2023/10/19 08:03 [entrez]
AID - 10.1111/ans.18736 [doi]
PST - aheadofprint
SO  - ANZ J Surg. 2023 Oct 19. doi: 10.1111/ans.18736.

PMID- 37286898
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 4
DP  - 2023 Nov 29
TI  - The current existence of ChatGPT in education: a double-edged sword?
PG  - e799-e800
LID - 10.1093/pubmed/fdad082 [doi]
AB  - The authors in this article would like to invite related parties to carry out 
      updates in the world of education, that the existence of ChatGPT can provide new 
      enthusiasm for all of us to improve in a better direction.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Faculty 
      of Public Health. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Situmorang, Dominikus David Biondi
AU  - Situmorang DDB
AUID- ORCID: 0000-0002-6065-2022
AD  - Faculty of Education and Language, Department of Guidance and Counseling, Atma 
      Jaya Catholic University of Indonesia, DKI Jakarta, Indonesia.
FAU - Salim, Rose Mini Agoes
AU  - Salim RMA
AD  - Faculty of Psychology, Department of Educational Psychology, University of 
      Indonesia, Depok, Indonesia.
FAU - Ifdil, Ifdil
AU  - Ifdil I
AD  - Faculty of Education, Department of Guidance and Counseling, Universitas Negeri 
      Padang, Padang, Indonesia.
FAU - Liza, Ledya Oktavia
AU  - Liza LO
AUID- ORCID: 0000-0001-9820-1481
AD  - Department of Special Education, Lancang Kuning University, Riau, Indonesia.
FAU - Rusandi, M Arli
AU  - Rusandi MA
AUID- ORCID: 0000-0001-7385-104X
AD  - Department of Guidance and Counseling, Riau University, Riau, Indonesia.
FAU - Hayati, Isnaria Rizki
AU  - Hayati IR
AD  - Department of Guidance and Counseling, Universitas Islam Negeri Sultan Syarif 
      Kasim Riau, Riau, Indonesia.
FAU - Amalia, Rizki
AU  - Amalia R
AD  - Department of Early Childhood Education, Universitas Pahlawan Tuanku Tambusai, 
      Riau, Indonesia.
FAU - Muhandaz, Ramon
AU  - Muhandaz R
AD  - Department of Mathematics Education, Universitas Islam Negeri Sultan Syarif Kasim 
      Riau, Riau, Indonesia.
FAU - Fitriani, Arbania
AU  - Fitriani A
AD  - Faculty of Psychology, Universitas Esa Unggul, DKI Jakarta, Indonesia.
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - *Population Health
MH  - Educational Status
OTO - NOTNLM
OT  - GPT Zero
OT  - advantages
OT  - chatGPT
OT  - disadvantages
OT  - double-edged sword
OT  - education
EDAT- 2023/06/08 01:08
MHDA- 2023/12/04 12:43
CRDT- 2023/06/07 23:35
PHST- 2023/04/25 00:00 [received]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/06/08 01:08 [pubmed]
PHST- 2023/06/07 23:35 [entrez]
AID - 7191715 [pii]
AID - 10.1093/pubmed/fdad082 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Nov 29;45(4):e799-e800. doi: 10.1093/pubmed/fdad082.

PMID- 37815084
OWN - NLM
STAT- Publisher
LR  - 20231010
IS  - 1098-2299 (Electronic)
IS  - 0272-4391 (Linking)
DP  - 2023 Oct 10
TI  - Artificial intelligence utility for drug development: ChatGPT and beyond.
LID - 10.1002/ddr.22121 [doi]
FAU - Gurwitz, David
AU  - Gurwitz D
AUID- ORCID: 0000-0002-9363-1869
AD  - Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel 
      Aviv University, Tel Aviv, Israel.
AD  - Sagol School of Neuroscience, Tel Aviv, Israel.
FAU - Shomron, Noam
AU  - Shomron N
AUID- ORCID: 0000-0001-9913-6124
AD  - Sagol School of Neuroscience, Tel Aviv, Israel.
AD  - Department of Cell and Developmental Biology, Faculty of Medicine, Tel Aviv 
      University, Tel Aviv, Israel.
AD  - Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel.
AD  - Tel Aviv University Innovation Labs (TILabs), Tel Aviv, Israel.
AD  - Djerassi Institute of Oncology, Tel Aviv University, Tel Aviv, Israel.
LA  - eng
GR  - Ministry of Health, State of Israel/
PT  - Journal Article
DEP - 20231010
PL  - United States
TA  - Drug Dev Res
JT  - Drug development research
JID - 8204468
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - NCBI
OT  - PubMed
OT  - drug development
EDAT- 2023/10/10 12:42
MHDA- 2023/10/10 12:42
CRDT- 2023/10/10 07:13
PHST- 2023/09/20 00:00 [revised]
PHST- 2023/07/19 00:00 [received]
PHST- 2023/10/02 00:00 [accepted]
PHST- 2023/10/10 12:42 [medline]
PHST- 2023/10/10 12:42 [pubmed]
PHST- 2023/10/10 07:13 [entrez]
AID - 10.1002/ddr.22121 [doi]
PST - aheadofprint
SO  - Drug Dev Res. 2023 Oct 10. doi: 10.1002/ddr.22121.

PMID- 37934302
OWN - NLM
STAT- In-Process
LR  - 20231207
IS  - 1573-6555 (Electronic)
IS  - 0090-6905 (Linking)
VI  - 52
IP  - 6
DP  - 2023 Dec
TI  - A Cross Sectional Study of ChatGPT in Translation: Magnitude of Use, Attitudes, 
      and Uncertainties.
PG  - 2937-2954
LID - 10.1007/s10936-023-10031-y [doi]
AB  - This preliminary cross-sectional study, focusing on Artificial Intelligence (AI), 
      aimed to assess the impact of ChatGPT on translation within an Arab context. It 
      primarily explored the attitudes of a sample of translation teachers and students 
      through semi-structured interviews and projective techniques. Data collection 
      included gathering information about the advantages and challenges that ChatGPT, 
      in comparison to Google Translate, had introduced to the field of translation and 
      translation teaching. The results indicated that nearly all the participants were 
      satisfied with ChatGPT. The results also revealed that most students preferred 
      ChatGPT over Google Translate, while most teachers favored Google Translate. The 
      study also found that the participants recognized both positive and negative 
      aspects of using ChatGPT in translation. Findings also indicated that ChatGPT, as 
      a recent AI-based translation-related technology, is more valuable for mechanical 
      processes of writing and editing translated texts than for tasks requiring 
      judgment, such as fine-tuning and double-checking. While it offers various 
      advantages, AI also presents new challenges that educators and stakeholders need 
      to address accordingly.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Sahari, Yousef
AU  - Sahari Y
AD  - University of Bisha, Bisha, Saudi Arabia.
FAU - Al-Kadi, Abdu M Talib
AU  - Al-Kadi AMT
AD  - Ibb University, Ibb, Yemen.
FAU - Ali, Jamal Kaid Mohammed
AU  - Ali JKM
AUID- ORCID: 0000-0003-3079-5580
AD  - University of Bisha, Bisha, Saudi Arabia. jgmali@ub.edu.sa.
LA  - eng
PT  - Journal Article
DEP - 20231107
PL  - United States
TA  - J Psycholinguist Res
JT  - Journal of psycholinguistic research
JID - 0333506
SB  - IM
OTO - NOTNLM
OT  - Arab Context
OT  - Artificial Intelligence (AI)
OT  - Attitudes
OT  - ChatGPT
OT  - Translation
EDAT- 2023/11/07 12:46
MHDA- 2023/11/07 12:46
CRDT- 2023/11/07 11:08
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/11/07 12:46 [pubmed]
PHST- 2023/11/07 12:46 [medline]
PHST- 2023/11/07 11:08 [entrez]
AID - 10.1007/s10936-023-10031-y [pii]
AID - 10.1007/s10936-023-10031-y [doi]
PST - ppublish
SO  - J Psycholinguist Res. 2023 Dec;52(6):2937-2954. doi: 10.1007/s10936-023-10031-y. 
      Epub 2023 Nov 7.

PMID- 37780034
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231004
IS  - 2382-1205 (Print)
IS  - 2382-1205 (Electronic)
IS  - 2382-1205 (Linking)
VI  - 10
DP  - 2023 Jan-Dec
TI  - Examining the Threat of ChatGPT to the Validity of Short Answer Assessments in an 
      Undergraduate Medical Program.
PG  - 23821205231204178
LID - 10.1177/23821205231204178 [doi]
LID - 23821205231204178
AB  - OBJECTIVES: ChatGPT is an artificial intelligence model that can interpret 
      free-text prompts and return detailed, human-like responses across a wide domain 
      of subjects. This study evaluated the extent of the threat posed by ChatGPT to 
      the validity of short-answer assessment problems used to examine pre-clerkship 
      medical students in our undergraduate medical education program. METHODS: Forty 
      problems used in prior student assessments were retrieved and stratified by 
      levels of Bloom's Taxonomy. Thirty of these problems were submitted to 
      ChatGPT-3.5. For the remaining 10 problems, we retrieved past minimally passing 
      student responses. Six tutors graded each of the 40 responses. Comparison of 
      performance between student-generated and ChatGPT-generated answers aggregated as 
      a whole and grouped by Bloom's levels of cognitive reasoning, was done using 
      t-tests, ANOVA, Cronbach's alpha, and Cohen's d. Scores for ChatGPT-generated 
      responses were also compared to historical class average performance. RESULTS: 
      ChatGPT-generated responses received a mean score of 3.29 out of 5 (n = 30, 95% 
      CI 2.93-3.65) compared to 2.38 for a group of students meeting minimum passing 
      marks (n = 10, 95% CI 1.94-2.82), representing higher performance (P = .008, 
      η(2) = 0.169), but was outperformed by historical class average scores on the 
      same 30 problems (mean 3.67, P = .018) when including all past responses 
      regardless of student performance level. There was no statistically significant 
      trend in performance across domains of Bloom's Taxonomy. CONCLUSION: While 
      ChatGPT was able to pass short answer assessment problems spanning the 
      pre-clerkship curriculum, it outperformed only underperforming students. We 
      remark that tutors in several cases were convinced that ChatGPT-produced 
      responses were produced by students. Risks to assessment validity include 
      uncertainty in identifying struggling students and inability to intervene in a 
      timely manner. The performance of ChatGPT on problems requiring increasing 
      demands of cognitive reasoning warrants further research.
CI  - © The Author(s) 2023.
FAU - Morjaria, Leo
AU  - Morjaria L
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
FAU - Burns, Levi
AU  - Burns L
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
FAU - Bracken, Keyna
AU  - Bracken K
AUID- ORCID: 0000-0002-4512-6067
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
AD  - McMaster Education Research, Innovation and Theory (MERIT) Program, McMaster 
      University, Hamilton, Ontario, Canada. RINGGOLD: 3710
FAU - Ngo, Quang N
AU  - Ngo QN
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
AD  - McMaster Education Research, Innovation and Theory (MERIT) Program, McMaster 
      University, Hamilton, Ontario, Canada. RINGGOLD: 3710
FAU - Lee, Mark
AU  - Lee M
AD  - McMaster Education Research, Innovation and Theory (MERIT) Program, McMaster 
      University, Hamilton, Ontario, Canada. RINGGOLD: 3710
FAU - Levinson, Anthony J
AU  - Levinson AJ
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
FAU - Smith, John
AU  - Smith J
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
FAU - Thompson, Penelope
AU  - Thompson P
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
FAU - Sibbald, Matthew
AU  - Sibbald M
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada. RINGGOLD: 12362
AD  - McMaster Education Research, Innovation and Theory (MERIT) Program, McMaster 
      University, Hamilton, Ontario, Canada. RINGGOLD: 3710
LA  - eng
PT  - Journal Article
DEP - 20230928
PL  - United States
TA  - J Med Educ Curric Dev
JT  - Journal of medical education and curricular development
JID - 101690298
PMC - PMC10540597
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - medical student
OT  - pre-clerkship
OT  - short answer assessment
OT  - undergraduate
COIS- The authors declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/10/02 06:42
MHDA- 2023/10/02 06:43
CRDT- 2023/10/02 04:21
PHST- 2023/05/17 00:00 [received]
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/10/02 06:43 [medline]
PHST- 2023/10/02 06:42 [pubmed]
PHST- 2023/10/02 04:21 [entrez]
AID - 10.1177_23821205231204178 [pii]
AID - 10.1177/23821205231204178 [doi]
PST - epublish
SO  - J Med Educ Curric Dev. 2023 Sep 28;10:23821205231204178. doi: 
      10.1177/23821205231204178. eCollection 2023 Jan-Dec.

PMID- 37788063
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231026
IS  - 2291-5222 (Electronic)
IS  - 2291-5222 (Linking)
VI  - 11
DP  - 2023 Oct 3
TI  - Comparison of Diagnostic and Triage Accuracy of Ada Health and WebMD Symptom 
      Checkers, ChatGPT, and Physicians for Patients in an Emergency Department: 
      Clinical Data Analysis Study.
PG  - e49995
LID - 10.2196/49995 [doi]
LID - e49995
AB  - BACKGROUND: Diagnosis is a core component of effective health care, but 
      misdiagnosis is common and can put patients at risk. Diagnostic decision support 
      systems can play a role in improving diagnosis by physicians and other health 
      care workers. Symptom checkers (SCs) have been designed to improve diagnosis and 
      triage (ie, which level of care to seek) by patients. OBJECTIVE: The aim of this 
      study was to evaluate the performance of the new large language model ChatGPT 
      (versions 3.5 and 4.0), the widely used WebMD SC, and an SC developed by Ada 
      Health in the diagnosis and triage of patients with urgent or emergent clinical 
      problems compared with the final emergency department (ED) diagnoses and 
      physician reviews. METHODS: We used previously collected, deidentified, 
      self-report data from 40 patients presenting to an ED for care who used the Ada 
      SC to record their symptoms prior to seeing the ED physician. Deidentified data 
      were entered into ChatGPT versions 3.5 and 4.0 and WebMD by a research assistant 
      blinded to diagnoses and triage. Diagnoses from all 4 systems were compared with 
      the previously abstracted final diagnoses in the ED as well as with diagnoses and 
      triage recommendations from three independent board-certified ED physicians who 
      had blindly reviewed the self-report clinical data from Ada. Diagnostic accuracy 
      was calculated as the proportion of the diagnoses from ChatGPT, Ada SC, WebMD SC, 
      and the independent physicians that matched at least one ED diagnosis (stratified 
      as top 1 or top 3). Triage accuracy was calculated as the number of 
      recommendations from ChatGPT, WebMD, or Ada that agreed with at least 2 of the 
      independent physicians or were rated "unsafe" or "too cautious." RESULTS: 
      Overall, 30 and 37 cases had sufficient data for diagnostic and triage analysis, 
      respectively. The rate of top-1 diagnosis matches for Ada, ChatGPT 3.5, ChatGPT 
      4.0, and WebMD was 9 (30%), 12 (40%), 10 (33%), and 12 (40%), respectively, with 
      a mean rate of 47% for the physicians. The rate of top-3 diagnostic matches for 
      Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 19 (63%), 19 (63%), 15 (50%), and 17 
      (57%), respectively, with a mean rate of 69% for physicians. The distribution of 
      triage results for Ada was 62% (n=23) agree, 14% unsafe (n=5), and 24% (n=9) too 
      cautious; that for ChatGPT 3.5 was 59% (n=22) agree, 41% (n=15) unsafe, and 0% 
      (n=0) too cautious; that for ChatGPT 4.0 was 76% (n=28) agree, 22% (n=8) unsafe, 
      and 3% (n=1) too cautious; and that for WebMD was 70% (n=26) agree, 19% (n=7) 
      unsafe, and 11% (n=4) too cautious. The unsafe triage rate for ChatGPT 3.5 (41%) 
      was significantly higher (P=.009) than that of Ada (14%). CONCLUSIONS: ChatGPT 
      3.5 had high diagnostic accuracy but a high unsafe triage rate. ChatGPT 4.0 had 
      the poorest diagnostic accuracy, but a lower unsafe triage rate and the highest 
      triage agreement with the physicians. The Ada and WebMD SCs performed better 
      overall than ChatGPT. Unsupervised patient use of ChatGPT for diagnosis and 
      triage is not recommended without improvements to triage accuracy and extensive 
      clinical evaluation.
CI  - ©Hamish Fraser, Daven Crossland, Ian Bacher, Megan Ranney, Tracy Madsen, Ross 
      Hilliard. Originally published in JMIR mHealth and uHealth 
      (https://mhealth.jmir.org), 03.10.2023.
FAU - Fraser, Hamish
AU  - Fraser H
AUID- ORCID: 0000-0003-4383-2854
AD  - Brown Center for Biomedical Informatics, The Warren Alpert Medical School of 
      Brown University, Providence, RI, United States.
AD  - Department of Health Services, Policy and Practice, Brown University School of 
      Public Health, Providence, RI, United States.
FAU - Crossland, Daven
AU  - Crossland D
AUID- ORCID: 0009-0004-8878-5718
AD  - Brown Center for Biomedical Informatics, The Warren Alpert Medical School of 
      Brown University, Providence, RI, United States.
AD  - Department of Epidemiology, Brown University School of Public Health, Providence, 
      RI, United States.
FAU - Bacher, Ian
AU  - Bacher I
AUID- ORCID: 0000-0003-2383-3411
AD  - Brown Center for Biomedical Informatics, The Warren Alpert Medical School of 
      Brown University, Providence, RI, United States.
FAU - Ranney, Megan
AU  - Ranney M
AUID- ORCID: 0000-0002-8450-9642
AD  - School of Public Health, Yale University, New Haven, CT, United States.
FAU - Madsen, Tracy
AU  - Madsen T
AUID- ORCID: 0000-0001-5101-0776
AD  - Department of Epidemiology, Brown University School of Public Health, Providence, 
      RI, United States.
AD  - Department of Emergency Medicine, The Warren Alpert Medical School of Brown 
      University, Providence, RI, United States.
FAU - Hilliard, Ross
AU  - Hilliard R
AUID- ORCID: 0000-0003-0187-8096
AD  - Department of Internal Medicine, Maine Medical Center, Portland, ME, United 
      States.
LA  - eng
GR  - R01 HS029513/HS/AHRQ HHS/United States
GR  - P20 GM139664/GM/NIGMS NIH HHS/United States
GR  - K23 HL140081/HL/NHLBI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, P.H.S.
DEP - 20231003
PL  - Canada
TA  - JMIR Mhealth Uhealth
JT  - JMIR mHealth and uHealth
JID - 101624439
SB  - IM
MH  - Humans
MH  - *Triage/methods
MH  - Emergency Service, Hospital
MH  - *Physicians
MH  - Health Personnel
MH  - Self Report
PMC - PMC10582809
OTO - NOTNLM
OT  - ChatGPT
OT  - ChatGPT-3.5
OT  - ChatGPT-4.0
OT  - LLM
OT  - accuracy
OT  - app
OT  - application
OT  - diagnose
OT  - diagnosis
OT  - emergency
OT  - emergency patient
OT  - language model
OT  - machine learning
OT  - self-diagnose
OT  - self-diagnosis
OT  - symptom checker
OT  - triage
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/03 12:42
MHDA- 2023/10/23 01:18
CRDT- 2023/10/03 11:54
PHST- 2023/06/16 00:00 [received]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/08/17 00:00 [revised]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/03 12:42 [pubmed]
PHST- 2023/10/03 11:54 [entrez]
AID - v11i1e49995 [pii]
AID - 10.2196/49995 [doi]
PST - epublish
SO  - JMIR Mhealth Uhealth. 2023 Oct 3;11:e49995. doi: 10.2196/49995.

PMID- 37819738
OWN - NLM
STAT- Publisher
LR  - 20231011
IS  - 1469-0756 (Electronic)
IS  - 0032-5473 (Linking)
DP  - 2023 Oct 10
TI  - ChatGPT in research and health professions education: challenges, opportunities, 
      and future directions.
LID - qgad090 [pii]
LID - 10.1093/postmj/qgad090 [doi]
AB  - ChatGPT was launched by OpenAI in November 2022 and within 2 months it became 
      popular across a wide range of industrial, social, and intellectual contexts 
      including healthcare education. This article reviews the impact of ChatGPT on 
      research and health professions education by identifying the challenges and 
      opportunities in these fields. Additionally, it aims to provide future directions 
      to mitigate the challenges and maximize the benefits of this technology in health 
      professions education. ChatGPT has the potential to revolutionize the field of 
      research and health professions education. However, there is a need to address 
      ethical concerns and limitations such as lack of real-time data, data 
      inaccuracies, biases, plagiarism, and copyright infringement before its 
      implementation. Future research can highlight the ways to mitigate these 
      challenges; establish guidelines and policies; and explore how effectively 
      ChatGPT and other AI tools can be used in the field of research and healthcare 
      professions education.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Sahu, Pradeep Kumar
AU  - Sahu PK
AD  - Centre For Medical Sciences Education, Faculty of Medical Sciences, The 
      University of the West Indies, St Augustine, Trinidad and Tobago.
FAU - Benjamin, Lisa A
AU  - Benjamin LA
AD  - Department of Basic Veterinary Sciences, School of Veterinary Medicine, Faculty 
      of Medical Sciences, The University of the West Indies, St Augustine, Trinidad 
      and Tobago.
FAU - Singh Aswal, Gunjan
AU  - Singh Aswal G
AD  - Department of Restorative Dentistry, School of Dentistry, Faculty of Medical 
      Sciences, The University of the West Indies, St Augustine Trinidad and Tobago.
FAU - Williams-Persad, Arlene
AU  - Williams-Persad A
AD  - Department of Paraclinical Sciences, Faculty of Medical Sciences, The University 
      of the West Indies St. Augustine, Trinidad and Tobago West Indies.
LA  - eng
PT  - Journal Article
DEP - 20231010
PL  - England
TA  - Postgrad Med J
JT  - Postgraduate medical journal
JID - 0234135
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - academic research
OT  - artificial intelligence
OT  - health professions education
OT  - teaching and learning
EDAT- 2023/10/11 14:44
MHDA- 2023/10/11 14:44
CRDT- 2023/10/11 12:32
PHST- 2023/09/01 00:00 [received]
PHST- 2023/09/15 00:00 [accepted]
PHST- 2023/10/11 14:44 [medline]
PHST- 2023/10/11 14:44 [pubmed]
PHST- 2023/10/11 12:32 [entrez]
AID - 7304050 [pii]
AID - 10.1093/postmj/qgad090 [doi]
PST - aheadofprint
SO  - Postgrad Med J. 2023 Oct 10:qgad090. doi: 10.1093/postmj/qgad090.

PMID- 37626010
OWN - NLM
STAT- Publisher
LR  - 20230913
IS  - 1365-2125 (Electronic)
IS  - 0306-5251 (Linking)
DP  - 2023 Aug 25
TI  - Evaluating the performance of ChatGPT in clinical pharmacy: A comparative study 
      of ChatGPT and clinical pharmacists.
LID - 10.1111/bcp.15896 [doi]
AB  - AIMS: To evaluate the performance of chat generative pretrained transformer 
      (ChatGPT) in key domains of clinical pharmacy practice, including prescription 
      review, patient medication education, adverse drug reaction (ADR) recognition, 
      ADR causality assessment and drug counselling. METHODS: Questions and clinical 
      pharmacist's answers were collected from real clinical cases and clinical 
      pharmacist competency assessment. ChatGPT's responses were generated by inputting 
      the same question into the 'New Chat' box of ChatGPT Mar 23 Version. Five 
      licensed clinical pharmacists independently rated these answers on a scale of 0 
      (Completely incorrect) to 10 (Completely correct). The mean scores of ChatGPT and 
      clinical pharmacists were compared using a paired 2-tailed Student's t-test. The 
      text content of the answers was also descriptively summarized together. RESULTS: 
      The quantitative results indicated that ChatGPT was excellent in drug counselling 
      (ChatGPT: 8.77 vs. clinical pharmacist: 9.50, P = .0791) and weak in prescription 
      review (5.23 vs. 9.90, P = .0089), patient medication education (6.20 vs. 9.07, 
      P = .0032), ADR recognition (5.07 vs. 9.70, P = .0483) and ADR causality 
      assessment (4.03 vs. 9.73, P = .023). The capabilities and limitations of ChatGPT 
      in clinical pharmacy practice were summarized based on the completeness and 
      accuracy of the answers. ChatGPT revealed robust retrieval, information 
      integration and dialogue capabilities. It lacked medicine-specific datasets as 
      well as the ability for handling advanced reasoning and complex instructions. 
      CONCLUSIONS: While ChatGPT holds promise in clinical pharmacy practice as a 
      supplementary tool, the ability of ChatGPT to handle complex problems needs 
      further improvement and refinement.
CI  - © 2023 British Pharmacological Society.
FAU - Huang, Xiaoru
AU  - Huang X
AD  - Department of Pharmacy, Peking University Third Hospital, Beijing, China.
AD  - Department of Pharmaceutical Management and Clinical Pharmacy, College of 
      Pharmacy, Peking University, Beijing, China.
FAU - Estau, Dannya
AU  - Estau D
AD  - Department of Pharmacy, Peking University Third Hospital, Beijing, China.
AD  - Department of Pharmaceutical Management and Clinical Pharmacy, College of 
      Pharmacy, Peking University, Beijing, China.
FAU - Liu, Xuening
AU  - Liu X
AD  - Department of Pharmacy, Peking University Third Hospital, Beijing, China.
AD  - Department of Pharmaceutical Management and Clinical Pharmacy, College of 
      Pharmacy, Peking University, Beijing, China.
FAU - Yu, Yang
AU  - Yu Y
AD  - Department of Pharmacy, Peking University Third Hospital, Beijing, China.
AD  - Department of Pharmaceutical Management and Clinical Pharmacy, College of 
      Pharmacy, Peking University, Beijing, China.
FAU - Qin, Jiguang
AU  - Qin J
AD  - Department of Pharmacy, Peking University Third Hospital, Beijing, China.
AD  - Department of Pharmaceutical Management and Clinical Pharmacy, College of 
      Pharmacy, Peking University, Beijing, China.
FAU - Li, Zijian
AU  - Li Z
AUID- ORCID: 0000-0002-8057-2257
AD  - Department of Pharmacy, Peking University Third Hospital, Beijing, China.
AD  - Department of Pharmaceutical Management and Clinical Pharmacy, College of 
      Pharmacy, Peking University, Beijing, China.
AD  - Department of Cardiology and Institute of Vascular Medicine, Peking University 
      Third Hospital, Beijing Key Laboratory of Cardiovascular Receptors Research, Key 
      Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Ministry 
      of Health, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking 
      University, Beijing, China.
LA  - eng
GR  - 7222218/Beijing Municipal Natural Science Foundation/
GR  - 2021RU003/Medical Research Management/Basic and Clinical Research Unit of 
      Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences/
GR  - 2022YFC3602400/National Key Research and Development Program of China/
GR  - 81820108031/National Natural Science Foundation of China/
GR  - 91939301/National Natural Science Foundation of China/
GR  - U21A20336/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20230825
PL  - England
TA  - Br J Clin Pharmacol
JT  - British journal of clinical pharmacology
JID - 7503323
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - clinical pharmacy
EDAT- 2023/08/26 05:41
MHDA- 2023/08/26 05:41
CRDT- 2023/08/25 22:02
PHST- 2023/08/01 00:00 [revised]
PHST- 2023/04/26 00:00 [received]
PHST- 2023/08/14 00:00 [accepted]
PHST- 2023/08/26 05:41 [pubmed]
PHST- 2023/08/26 05:41 [medline]
PHST- 2023/08/25 22:02 [entrez]
AID - 10.1111/bcp.15896 [doi]
PST - aheadofprint
SO  - Br J Clin Pharmacol. 2023 Aug 25. doi: 10.1111/bcp.15896.

PMID- 37183932
OWN - NLM
STAT- MEDLINE
DCOM- 20230621
LR  - 20230621
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
VI  - 45
IP  - 7
DP  - 2023 Jul
TI  - Medical Teacher's first ChatGPT's referencing hallucinations: Lessons for 
      editors, reviewers, and teachers.
PG  - 673-675
LID - 10.1080/0142159X.2023.2208731 [doi]
AB  - Students' inappropriate use of ChatGPT is a concern. There is also, however, the 
      potential for academics to use ChatGPT inappropriately. After explaining 
      ChatGPT's "hallucinations" regarding citing and referencing, this commentary 
      illustrates the problem by describing the detection of the first known Medical 
      Teacher submission using ChatGPT inappropriately, the lessons that can be drawn 
      from it for journal editors, reviewers, and teachers, and then the wider 
      implications if this problem is left unchecked.
FAU - Masters, Ken
AU  - Masters K
AUID- ORCID: 0000-0003-3425-5020
AD  - Sultan Qaboos University, Sultanate of Oman.
LA  - eng
PT  - Journal Article
DEP - 20230515
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
MH  - Humans
MH  - *Teaching
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - ChatGPT
OT  - Ethics
OT  - GPT-4
OT  - artificial intelligence
OT  - hallucinations
OT  - medical education
EDAT- 2023/05/15 13:06
MHDA- 2023/06/15 06:42
CRDT- 2023/05/15 06:43
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/05/15 13:06 [pubmed]
PHST- 2023/05/15 06:43 [entrez]
AID - 10.1080/0142159X.2023.2208731 [doi]
PST - ppublish
SO  - Med Teach. 2023 Jul;45(7):673-675. doi: 10.1080/0142159X.2023.2208731. Epub 2023 
      May 15.

PMID- 37934568
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231124
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Nov 7
TI  - Assessing the Performance of ChatGPT in Medical Biochemistry Using Clinical Case 
      Vignettes: Observational Study.
PG  - e47191
LID - 10.2196/47191 [doi]
LID - e47191
AB  - BACKGROUND: ChatGPT has gained global attention recently owing to its high 
      performance in generating a wide range of information and retrieving any kind of 
      data instantaneously. ChatGPT has also been tested for the United States Medical 
      Licensing Examination (USMLE) and has successfully cleared it. Thus, its 
      usability in medical education is now one of the key discussions worldwide. 
      OBJECTIVE: The objective of this study is to evaluate the performance of ChatGPT 
      in medical biochemistry using clinical case vignettes. METHODS: The performance 
      of ChatGPT was evaluated in medical biochemistry using 10 clinical case 
      vignettes. Clinical case vignettes were randomly selected and inputted in ChatGPT 
      along with the response options. We tested the responses for each clinical case 
      twice. The answers generated by ChatGPT were saved and checked using our 
      reference material. RESULTS: ChatGPT generated correct answers for 4 questions on 
      the first attempt. For the other cases, there were differences in responses 
      generated by ChatGPT in the first and second attempts. In the second attempt, 
      ChatGPT provided correct answers for 6 questions and incorrect answers for 4 
      questions out of the 10 cases that were used. But, to our surprise, for case 3, 
      different answers were obtained with multiple attempts. We believe this to have 
      happened owing to the complexity of the case, which involved addressing various 
      critical medical aspects related to amino acid metabolism in a balanced approach. 
      CONCLUSIONS: According to the findings of our study, ChatGPT may not be 
      considered an accurate information provider for application in medical education 
      to improve learning and assessment. However, our study was limited by a small 
      sample size (10 clinical case vignettes) and the use of the publicly available 
      version of ChatGPT (version 3.5). Although artificial intelligence (AI) has the 
      capability to transform medical education, we emphasize the validation of such 
      data produced by such AI systems for correctness and dependability before it 
      could be implemented in practice.
CI  - ©Krishna Mohan Surapaneni. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 07.11.2023.
FAU - Surapaneni, Krishna Mohan
AU  - Surapaneni KM
AUID- ORCID: 0000-0002-5204-5708
AD  - Panimalar Medical College Hospital & Research Institute, Chennai, India.
LA  - eng
PT  - Journal Article
DEP - 20231107
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10664016
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - biochemistry
OT  - case scenario
OT  - case study
OT  - chatbot
OT  - computer generated
OT  - medical Biochemistry
OT  - medical education
OT  - medical exam
OT  - medical examination
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/07 12:46
MHDA- 2023/11/07 12:47
CRDT- 2023/11/07 11:53
PHST- 2023/03/11 00:00 [received]
PHST- 2023/09/21 00:00 [accepted]
PHST- 2023/05/29 00:00 [revised]
PHST- 2023/11/07 12:47 [medline]
PHST- 2023/11/07 12:46 [pubmed]
PHST- 2023/11/07 11:53 [entrez]
AID - v9i1e47191 [pii]
AID - 10.2196/47191 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Nov 7;9:e47191. doi: 10.2196/47191.

PMID- 37949111
OWN - NLM
STAT- Publisher
LR  - 20231110
IS  - 1469-0691 (Electronic)
IS  - 1198-743X (Linking)
DP  - 2023 Nov 8
TI  - Extracting symptoms from free-text responses using ChatGPT among COVID-19 cases 
      in Hong Kong.
LID - S1198-743X(23)00540-2 [pii]
LID - 10.1016/j.cmi.2023.11.002 [doi]
AB  - OBJECTIVES: To investigate the feasibility and performance of ChatGPT in 
      converting symptom narratives into structured symptom labels. METHODS: We 
      extracted symptoms from 300 de-identified symptom narratives of COVID-19 patients 
      by a computer-based matching algorithm (the standard), and prompt engineering in 
      ChatGPT. Common symptoms were those with a prevalence >10% according to the 
      standard, and similarly less common symptoms were those with a prevalence of 
      2-10%. The precision of ChatGPT was compared with the standard using sensitivity 
      and specificity with 95% exact binomial confidence intervals (95% binCI). In 
      ChatGPT, we prompted without examples (zero-shot prompting) and with examples 
      (few-shot prompting). RESULTS: In zero-shot prompting, GPT-4 achieved high 
      specificity (0.947 [95% binCI: 0.894, 0.978] -1.000 [95% binCI: 0.965-0.988, 
      1.000]) for all symptoms, high sensitivity for common symptoms (0.853 [95% binCI: 
      0.689, 0.950] - 1.000 [95% binCI: 0.951, 1.000]), and moderate sensitivity for 
      less common symptoms (0.200 [95% binCI: 0.043, 0.481] - 1.000 [95% binCI: 
      0.590-0.815, 1.000]). Few-shot prompting increased the sensitivity and 
      specificity. GPT-4 outperformed GPT-3.5 in response accuracy and consistent 
      labeling. CONCLUSIONS: This work substantiates ChatGPT's role as a research tool 
      in medical fields. Its performance in converting symptom narratives to structured 
      symptom labels was encouraging, saving time and effort in compiling the 
      task-specific training data. It potentially accelerates free-text data 
      compilation and synthesis in future disease outbreaks, and improves the accuracy 
      of symptom checkers. Focused prompt training addressing ambiguous descriptions 
      impacts medical research positively.
CI  - Copyright © 2023 European Society of Clinical Microbiology and Infectious 
      Diseases. Published by Elsevier Ltd. All rights reserved.
FAU - Wei, Wan In
AU  - Wei WI
AD  - JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 
      Hong Kong Special Administrative Region, China.
FAU - Leung, Cyrus Lap Kwan
AU  - Leung CLK
AD  - JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 
      Hong Kong Special Administrative Region, China.
FAU - Tang, Arthur
AU  - Tang A
AD  - School of Science, Engineering and Technology, RMIT University, Vietnam.
FAU - McNeil, Edward B
AU  - McNeil EB
AD  - JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 
      Hong Kong Special Administrative Region, China.
FAU - Wong, Samuel Yeung Shan
AU  - Wong SYS
AD  - JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 
      Hong Kong Special Administrative Region, China.
FAU - Kwok, Kin On
AU  - Kwok KO
AD  - JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 
      Hong Kong Special Administrative Region, China; Stanley Ho Centre for Emerging 
      Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special 
      Administrative Region, China; Hong Kong Institute of Asia-Pacific Studies, The 
      Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; 
      Department of Infectious Disease Epidemiology, School of Public Health, Imperial 
      College London, London, United Kingdom. Electronic address: kkokwok@cuhk.edu.hk.
LA  - eng
PT  - Journal Article
DEP - 20231108
PL  - England
TA  - Clin Microbiol Infect
JT  - Clinical microbiology and infection : the official publication of the European 
      Society of Clinical Microbiology and Infectious Diseases
JID - 9516420
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - entity recognition
OT  - large language model
OT  - symptom extraction
OT  - symptom narratives
OT  - symptom science
COIS- Competing Interest None
EDAT- 2023/11/11 11:44
MHDA- 2023/11/11 11:44
CRDT- 2023/11/10 19:12
PHST- 2023/06/26 00:00 [received]
PHST- 2023/11/01 00:00 [revised]
PHST- 2023/11/03 00:00 [accepted]
PHST- 2023/11/11 11:44 [medline]
PHST- 2023/11/11 11:44 [pubmed]
PHST- 2023/11/10 19:12 [entrez]
AID - S1198-743X(23)00540-2 [pii]
AID - 10.1016/j.cmi.2023.11.002 [doi]
PST - aheadofprint
SO  - Clin Microbiol Infect. 2023 Nov 8:S1198-743X(23)00540-2. doi: 
      10.1016/j.cmi.2023.11.002.

PMID- 37700949
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230914
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Assessing the Efficacy of ChatGPT in Solving Questions Based on the Core Concepts 
      in Physiology.
PG  - e43314
LID - 10.7759/cureus.43314 [doi]
LID - e43314
AB  - Background and objective ChatGPT is a large language model (LLM) generative 
      artificial intelligence (AI) chatbot trained through deep learning to produce 
      human-like language skills and analysis of simple problems across a wide variety 
      of subject areas. However, in terms of facilitating the transfer of learning in 
      medical education, a concern has arisen that while AI is adept at applying 
      surface-level understanding, it does not have the necessary in-depth knowledge to 
      act at an expert level, particularly in addressing the core concepts. In this 
      study, we explored the efficacy of ChatGPT in solving various reasoning questions 
      based on the five core concepts applied to different modules in the subject of 
      physiology. Materials and methods In this study, a total of 82 reasoning-type 
      questions from six modules applicable to the five core concepts were created by 
      the subject experts. The questions were used to chat with the conversational AI 
      tool and the responses generated at first instance were considered for scoring 
      and analysis. To compare the scores among various modules and five core concepts 
      separately, the Kruskal-Wallis test along with post hoc analysis were used. 
      Results The overall mean score for the modules (60 questions) was 3.72 ±0.26 
      while the average score obtained for the core concepts (60 questions) was 3.68 
      ±0.30. Furthermore, statistically significant differences (p=0.05 for modules and 
      p=0.024 for core concepts) were observed among various modules as well as core 
      concepts. Conclusion The significant differences observed in the scores among 
      various modules and core concepts highlight the varying execution of the same 
      software tool, thereby necessitating the need for further evaluation of 
      AI-enabled learning applications to enhance the transfer of learning among 
      undergraduates.
CI  - Copyright © 2023, Banerjee et al.
FAU - Banerjee, Arijita
AU  - Banerjee A
AD  - Physiology, Indian Institute of Technology Kharagpur, Kharagpur, IND.
FAU - Ahmad, Aquil
AU  - Ahmad A
AD  - Physiology, Maulana Azad Medical College, New Delhi, IND.
FAU - Bhalla, Payal
AU  - Bhalla P
AD  - Physiology, Maulana Azad Medical College, New Delhi, IND.
FAU - Goyal, Kavita
AU  - Goyal K
AD  - Physiology, Shri Atal Bihari Vajpayee Medical College, Faridabad, IND.
LA  - eng
PT  - Journal Article
DEP - 20230810
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10492920
OTO - NOTNLM
OT  - artificial intelligence (ai)
OT  - chat gpt
OT  - core concepts
OT  - medical physiology
OT  - transfer of learning
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/13 06:41
MHDA- 2023/09/13 06:42
CRDT- 2023/09/13 03:58
PHST- 2023/08/10 00:00 [accepted]
PHST- 2023/09/13 06:42 [medline]
PHST- 2023/09/13 06:41 [pubmed]
PHST- 2023/09/13 03:58 [entrez]
AID - 10.7759/cureus.43314 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 10;15(8):e43314. doi: 10.7759/cureus.43314. eCollection 2023 
      Aug.

PMID- 37725411
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231006
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Sep 19
TI  - Assessment of Resident and AI Chatbot Performance on the University of Toronto 
      Family Medicine Residency Progress Test: Comparative Study.
PG  - e50514
LID - 10.2196/50514 [doi]
LID - e50514
AB  - BACKGROUND: Large language model (LLM)-based chatbots are evolving at an 
      unprecedented pace with the release of ChatGPT, specifically GPT-3.5, and its 
      successor, GPT-4. Their capabilities in general-purpose tasks and language 
      generation have advanced to the point of performing excellently on various 
      educational examination benchmarks, including medical knowledge tests. Comparing 
      the performance of these 2 LLM models to that of Family Medicine residents on a 
      multiple-choice medical knowledge test can provide insights into their potential 
      as medical education tools. OBJECTIVE: This study aimed to quantitatively and 
      qualitatively compare the performance of GPT-3.5, GPT-4, and Family Medicine 
      residents in a multiple-choice medical knowledge test appropriate for the level 
      of a Family Medicine resident. METHODS: An official University of Toronto 
      Department of Family and Community Medicine Progress Test consisting of 
      multiple-choice questions was inputted into GPT-3.5 and GPT-4. The artificial 
      intelligence chatbot's responses were manually reviewed to determine the selected 
      answer, response length, response time, provision of a rationale for the 
      outputted response, and the root cause of all incorrect responses (classified 
      into arithmetic, logical, and information errors). The performance of the 
      artificial intelligence chatbots were compared against a cohort of Family 
      Medicine residents who concurrently attempted the test. RESULTS: GPT-4 performed 
      significantly better compared to GPT-3.5 (difference 25.0%, 95% CI 16.3%-32.8%; 
      McNemar test: P<.001); it correctly answered 89/108 (82.4%) questions, while 
      GPT-3.5 answered 62/108 (57.4%) questions correctly. Further, GPT-4 scored higher 
      across all 11 categories of Family Medicine knowledge. In 86.1% (n=93) of the 
      responses, GPT-4 provided a rationale for why other multiple-choice options were 
      not chosen compared to the 16.7% (n=18) achieved by GPT-3.5. Qualitatively, for 
      both GPT-3.5 and GPT-4 responses, logical errors were the most common, while 
      arithmetic errors were the least common. The average performance of Family 
      Medicine residents was 56.9% (95% CI 56.2%-57.6%). The performance of GPT-3.5 was 
      similar to that of the average Family Medicine resident (P=.16), while the 
      performance of GPT-4 exceeded that of the top-performing Family Medicine resident 
      (P<.001). CONCLUSIONS: GPT-4 significantly outperforms both GPT-3.5 and Family 
      Medicine residents on a multiple-choice medical knowledge test designed for 
      Family Medicine residents. GPT-4 provides a logical rationale for its response 
      choice, ruling out other answer choices efficiently and with concise 
      justification. Its high degree of accuracy and advanced reasoning capabilities 
      facilitate its potential applications in medical education, including the 
      creation of exam questions and scenarios as well as serving as a resource for 
      medical knowledge or information on community services.
CI  - ©Ryan ST Huang, Kevin Jia Qi Lu, Christopher Meaney, Joel Kemppainen, Angela 
      Punnett, Fok-Han Leung. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 19.09.2023.
FAU - Huang, Ryan St
AU  - Huang RS
AUID- ORCID: 0000-0002-3404-5376
AD  - Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
FAU - Lu, Kevin Jia Qi
AU  - Lu KJQ
AUID- ORCID: 0000-0003-3296-9193
AD  - Department of Family and Community Medicine, University of Toronto, Toronto, ON, 
      Canada.
FAU - Meaney, Christopher
AU  - Meaney C
AUID- ORCID: 0000-0002-5429-5233
AD  - Department of Family and Community Medicine, University of Toronto, Toronto, ON, 
      Canada.
FAU - Kemppainen, Joel
AU  - Kemppainen J
AUID- ORCID: 0009-0001-5566-124X
AD  - Department of Family and Community Medicine, University of Toronto, Toronto, ON, 
      Canada.
FAU - Punnett, Angela
AU  - Punnett A
AUID- ORCID: 0000-0003-4541-0823
AD  - Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
AD  - Division of Haematology, The Hospital for Sick Children, Toronto, ON, Canada.
FAU - Leung, Fok-Han
AU  - Leung FH
AUID- ORCID: 0000-0001-8886-3625
AD  - Department of Family and Community Medicine, University of Toronto, Toronto, ON, 
      Canada.
LA  - eng
PT  - Journal Article
DEP - 20230919
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10548315
OTO - NOTNLM
OT  - AI
OT  - GPT-3.5
OT  - GPT-4
OT  - LLM
OT  - NLP
OT  - artificial intelligence
OT  - community
OT  - education
OT  - education examination
OT  - family medicine
OT  - language model
OT  - large language model
OT  - machine learning, ChatGPT
OT  - medical education
OT  - medical knowledge exam
OT  - medical residents
OT  - natural language processing
OT  - test
OT  - testing
OT  - utility
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/19 17:42
MHDA- 2023/09/19 17:43
CRDT- 2023/09/19 11:53
PHST- 2023/07/03 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/08/17 00:00 [revised]
PHST- 2023/09/19 17:43 [medline]
PHST- 2023/09/19 17:42 [pubmed]
PHST- 2023/09/19 11:53 [entrez]
AID - v9i1e50514 [pii]
AID - 10.2196/50514 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Sep 19;9:e50514. doi: 10.2196/50514.

PMID- 37330383
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20230725
IS  - 1532-8171 (Electronic)
IS  - 0735-6757 (Linking)
VI  - 70
DP  - 2023 Aug
TI  - Instructional support on first aid in choking by an artificial 
      intelligence-powered chatbot.
PG  - 200-202
LID - S0735-6757(23)00306-6 [pii]
LID - 10.1016/j.ajem.2023.06.010 [doi]
FAU - Birkun, Alexei A
AU  - Birkun AA
AD  - Department of General Surgery, Anesthesiology, Resuscitation and Emergency 
      Medicine, Medical Academy named after S.I. Georgievsky of V.I. Vernadsky Crimean 
      Federal University, Lenin Blvd, 5/7, Simferopol 295051, Russian Federation. 
      Electronic address: birkunalexei@gmail.com.
FAU - Gautam, Adhish
AU  - Gautam A
AD  - Regional Government Hospital, Una (H.P.), 174303, India.
LA  - eng
PT  - Letter
DEP - 20230610
PL  - United States
TA  - Am J Emerg Med
JT  - The American journal of emergency medicine
JID - 8309942
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - First Aid
MH  - Software
MH  - *Airway Obstruction
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chatbot
OT  - Choking
OT  - First aid
OT  - GPT-4
OT  - Large language model
COIS- Declaration of Competing Interest The authors have no conflicts of interest to 
      declare.
EDAT- 2023/06/18 01:07
MHDA- 2023/07/25 06:43
CRDT- 2023/06/17 21:56
PHST- 2023/05/11 00:00 [received]
PHST- 2023/05/19 00:00 [revised]
PHST- 2023/06/05 00:00 [accepted]
PHST- 2023/07/25 06:43 [medline]
PHST- 2023/06/18 01:07 [pubmed]
PHST- 2023/06/17 21:56 [entrez]
AID - S0735-6757(23)00306-6 [pii]
AID - 10.1016/j.ajem.2023.06.010 [doi]
PST - ppublish
SO  - Am J Emerg Med. 2023 Aug;70:200-202. doi: 10.1016/j.ajem.2023.06.010. Epub 2023 
      Jun 10.

PMID- 37222839
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231115
IS  - 1432-0711 (Electronic)
IS  - 0932-0067 (Linking)
VI  - 308
IP  - 6
DP  - 2023 Dec
TI  - Exploring the use of ChatGPT in OBGYN: a bibliometric analysis of the first 
      ChatGPT-related publications.
PG  - 1785-1789
LID - 10.1007/s00404-023-07081-x [doi]
AB  - PURPOSE: Little is known about the scientific literature regarding the new 
      revolutionary tool, ChatGPT. We aim to perform a bibliometric analysis to 
      identify ChatGPT-related publications in obstetrics and gynecology (OBGYN). STUDY 
      DESIGN: A bibliometric study through PubMed database. We mined all 
      ChatGPT-related publications using the search term "ChatGPT". Bibliometric data 
      were obtained from the iCite database. We performed a descriptive analysis. We 
      further compared IF among publications describing a study vs. other publications. 
      RESULTS: Overall, 42 ChatGPT-related publications were published across 26 
      different journals during 69 days. Most publications were editorials (52%) and 
      news/briefing (22%), with only one (2%) research article identified. Five (12%) 
      publications described a study performed. No ChatGPT-related publications in 
      OBGYN were found. The leading journal by the number of publications was Nature 
      (24%), followed by Lancet Digital Health and Radiology (7%, for both). The main 
      subjects of publications were ChatGPT's scientific writing quality (26%) and a 
      description of ChatGPT (26%) followed by tested performance of ChatGPT (14%), 
      authorship and ethical issues (10% for both topics).In a comparison of 
      publications describing a study performed (n = 5) vs. other publications 
      (n = 37), mean IF was lower in the study-publications (mean 6.25 ± 0 vs. 
      25.4 ± 21.6, p < .001). CONCLUSIONS: The study highlights main trends in 
      ChatGPT-related publications. OBGYN is yet to be represented in this literature.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Levin, Gabriel
AU  - Levin G
AUID- ORCID: 0000-0003-1282-5379
AD  - The Department of Gynecologic Oncology, Hadassah-Hebrew University Medical 
      Center, Jerusalem, Israel. levin.gaby@gmail.com.
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Quebec, Canada. levin.gaby@gmail.com.
FAU - Brezinov, Yoav
AU  - Brezinov Y
AD  - Experimental Surgery, McGill University, Quebec, Canada.
FAU - Meyer, Raanan
AU  - Meyer R
AD  - Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics and 
      Gynecology, Cedars Sinai Medical Center, Los Angeles, CA, USA.
AD  - The Dr. Pinchas Bornstein Talpiot Medical Leadership Program, Sheba Medical 
      Center, Tel Hashomer, Ramat-Gan, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230524
PL  - Germany
TA  - Arch Gynecol Obstet
JT  - Archives of gynecology and obstetrics
JID - 8710213
SB  - IM
MH  - Humans
MH  - Bibliometrics
MH  - Databases, Factual
MH  - *Gynecology
MH  - *Obstetrics
MH  - Publications
MH  - Artificial Intelligence
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bibliometrics
OT  - ChatGPT
OT  - OBGYN literature
OT  - Research
EDAT- 2023/05/24 13:08
MHDA- 2023/10/23 00:42
CRDT- 2023/05/24 11:23
PHST- 2023/03/29 00:00 [received]
PHST- 2023/05/08 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/05/24 13:08 [pubmed]
PHST- 2023/05/24 11:23 [entrez]
AID - 10.1007/s00404-023-07081-x [pii]
AID - 10.1007/s00404-023-07081-x [doi]
PST - ppublish
SO  - Arch Gynecol Obstet. 2023 Dec;308(6):1785-1789. doi: 10.1007/s00404-023-07081-x. 
      Epub 2023 May 24.

PMID- 37074486
OWN - NLM
STAT- MEDLINE
DCOM- 20230512
LR  - 20230525
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 6
DP  - 2023 Jun
TI  - Potential Use of Artificial Intelligence in Infectious Disease: Take ChatGPT as 
      an Example.
PG  - 1130-1135
LID - 10.1007/s10439-023-03203-3 [doi]
AB  - Over the past month, a new AI model called Chatbot Generative Pre-trained 
      Transformer (ChatGPT), has received enormous attention in the media and 
      scientific communities due to its ability to process and respond to commands in a 
      humanistic fashion. As reported, five days after its launch, the number of 
      registered users of ChatGPT exceeded one million, and its monthly active users 
      had exceeded 100 million two months later, making it the most rapidly growing 
      consumer application in history. The advent of ChatGPT has further brought about 
      new ideas and challenges in the realm of infectious disease. In view of this, in 
      order to evaluate the potential use of ChatGPT in clinical practice and 
      scientific research of infectious disease, we conducted a brief online survey by 
      using the publicly available ChatGPT webpage. Also, the present study also talks 
      about the relevant social and ethical issues related to this program.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China.
FAU - Li, Zhiyong
AU  - Li Z
AD  - Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, 
      Tianjin, China.
FAU - He, Yongbin
AU  - He Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
AD  - University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
FAU - Guo, Qiang
AU  - Guo Q
AD  - Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, 
      Tianjin, China.
FAU - Lu, Yanqiu
AU  - Lu Y
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China. yanqiul@163.com.
FAU - Gu, Shuqin
AU  - Gu S
AD  - Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC, USA. 
      shuqin.gu@duke.edu.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Graduate School, Tianjin Medical University, Tianjin, China. 
      wuhaiyang2021@tmu.edu.cn.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      NC, USA. wuhaiyang2021@tmu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230419
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Software
MH  - *Communicable Diseases
MH  - Electric Power Supplies
PMC - PMC10116900
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbots
OT  - Infectious disease
COIS- The authors declare no conflict of interest.
EDAT- 2023/04/19 12:42
MHDA- 2023/05/12 07:06
CRDT- 2023/04/19 11:17
PHST- 2023/04/04 00:00 [received]
PHST- 2023/04/05 00:00 [accepted]
PHST- 2023/05/12 07:06 [medline]
PHST- 2023/04/19 12:42 [pubmed]
PHST- 2023/04/19 11:17 [entrez]
AID - 10.1007/s10439-023-03203-3 [pii]
AID - 3203 [pii]
AID - 10.1007/s10439-023-03203-3 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jun;51(6):1130-1135. doi: 10.1007/s10439-023-03203-3. Epub 
      2023 Apr 19.

PMID- 37982677
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 2689-3622 (Electronic)
IS  - 2689-3614 (Linking)
DP  - 2023 Nov 20
TI  - Artificial Intelligence Versus Expert Plastic Surgeon: Comparative Study Shows 
      ChatGPT "Wins" Rhinoplasty Consultations: Should We Be Worried?
LID - 10.1089/fpsam.2023.0224 [doi]
AB  - Introduction: Large language models, such as ChatGPT, hold tremendous promise to 
      bridge gaps in patient education and enhance the decision-making resources 
      available online for patients seeking nasal surgery. Objective: To compare the 
      performance of ChatGPT in answering preoperative and postoperative patient 
      questions related to septorhinoplasty. Methods: Two sets of responses were 
      collected for the questions: one from an expert rhinoplasty surgeon with over two 
      decades of experience, and the other from ChatGPT-3.5. Seven expert rhinoplasty 
      surgeons, blinded to the source of responses, independently assessed the 
      responses using a 5-point Likert scale in four performance areas: empathy, 
      accuracy, completeness, and overall quality. Results: ChatGPT outperformed 
      physician responses in three of the four performance areas, earning significantly 
      higher ratings in accuracy, completeness, and overall quality (p < 0.001). In 
      addition, ChatGPT was overwhelmingly preferred over physician responses 
      (p < 0.001), with evaluators favoring ChatGPT in 80.95% of instances. 
      Conclusions: ChatGPT has demonstrated its remarkable ability to deliver accurate, 
      complete, and high-quality responses to preoperative and postoperative patient 
      questions. Although certain improvements are warranted, this artificial 
      intelligence tool has shown its potential to effectively counsel and educate 
      prospective septorhinoplasty patients at a level comparable with or exceeding 
      that of an expert surgeon.
FAU - Durairaj, K Kay
AU  - Durairaj KK
AUID- ORCID: 0009-0004-1287-8619
AD  - Department of Otolaryngology-Head and Neck Surgery, Huntington Hospital, 
      Pasadena, California, USA.
AD  - Kay Durairaj, MD, A Medical Corp, Pasadena, California, USA.
FAU - Baker, Omer
AU  - Baker O
AD  - Kay Durairaj, MD, A Medical Corp, Pasadena, California, USA.
FAU - Bertossi, Dario
AU  - Bertossi D
AUID- ORCID: 0000-0002-8635-9967
AD  - Department of Head and Neck Surgery, University of Verona, Verona, Italy.
FAU - Dayan, Steven
AU  - Dayan S
AD  - Denova Research, Chicago, Illinois, USA.
FAU - Karimi, Kian
AU  - Karimi K
AUID- ORCID: 0000-0002-3803-8560
AD  - Dr. Kian Nasal & Facial Plastic Surgery, Los Angeles, California, USA.
FAU - Kim, Roy
AU  - Kim R
AD  - Private Practice, San Francisco/Beverly Hills, California, USA.
FAU - Most, Sam
AU  - Most S
AUID- ORCID: 0000-0002-7385-3149
AD  - Division of Facial Plastic & Reconstructive Surgery, Stanford University, 
      Stanford, California, USA.
FAU - Robotti, Enrico
AU  - Robotti E
AD  - Private Practice, Bergamo, Italy.
FAU - Rosengaus, Frank
AU  - Rosengaus F
AD  - Private Practice, Mexico City, Mexico.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - United States
TA  - Facial Plast Surg Aesthet Med
JT  - Facial plastic surgery & aesthetic medicine
JID - 101757922
SB  - IM
EDAT- 2023/11/20 13:41
MHDA- 2023/11/20 13:41
CRDT- 2023/11/20 09:32
PHST- 2023/11/20 13:41 [medline]
PHST- 2023/11/20 13:41 [pubmed]
PHST- 2023/11/20 09:32 [entrez]
AID - 10.1089/fpsam.2023.0224 [doi]
PST - aheadofprint
SO  - Facial Plast Surg Aesthet Med. 2023 Nov 20. doi: 10.1089/fpsam.2023.0224.

PMID- 37455540
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 4
DP  - 2023 Nov 29
TI  - ChatGPT in the higher education environment: perspectives from the theory of high 
      order thinking skills.
PG  - e840-e841
LID - 10.1093/pubmed/fdad120 [doi]
AB  - ChatGPT is a form of technological progress in the 5.0 era. The use of ChatGPT 
      has begun to penetrate all fields of science, including the field of education. 
      Students use ChatGPT to help them complete their assignments in the university 
      environment. Apart from providing many benefits, ChatGPT also has many dangers 
      threatening students. One of the dangers for students who rely too much on 
      ChatGPT is a decrease in higher-order thinking skills.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Faculty 
      of Public Health. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Putra, Febri W
AU  - Putra FW
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Padang, West Sumatera, Indonesia.
FAU - Rangka, Itsar B
AU  - Rangka IB
AUID- ORCID: 0000-0002-6934-2469
AD  - Departement of Guidance and Counseling, Faculty of Education and Social Sciences, 
      Universitas Indraprasta PGRI, Jakarta, Indonesia.
FAU - Aminah, Siti
AU  - Aminah S
AUID- ORCID: 0000-0002-6640-6412
AD  - Department of Education Psychology and Guidance, Faculty of Education and 
      Psychology, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia.
FAU - Aditama, Mint H R
AU  - Aditama MHR
AUID- ORCID: 0000-0003-4593-8935
AD  - Department of Guidance and Counseling, Faculty of Education and Psychology, 
      Universitas Negeri Manado, Tondano, Indonesia.
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - *Students
MH  - Educational Status
MH  - *Thinking
MH  - Universities
OTO - NOTNLM
OT  - ChatGPT
OT  - cognitive
OT  - high-order thinking skills
OT  - students
OT  - university
EDAT- 2023/07/17 06:42
MHDA- 2023/12/04 12:42
CRDT- 2023/07/17 02:33
PHST- 2023/06/19 00:00 [received]
PHST- 2023/06/27 00:00 [accepted]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/07/17 06:42 [pubmed]
PHST- 2023/07/17 02:33 [entrez]
AID - 7224312 [pii]
AID - 10.1093/pubmed/fdad120 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Nov 29;45(4):e840-e841. doi: 10.1093/pubmed/fdad120.

PMID- 37482572
OWN - NLM
STAT- Publisher
LR  - 20230723
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 23
TI  - The First Months of Life of ChatGPT and Its Impact in Healthcare: A Bibliometric 
      Analysis of the Current Literature.
LID - 10.1007/s10439-023-03325-8 [doi]
AB  - We aimed to evaluate current trends and future directions in the field of AI 
      research since ChatGPT was launched. We performed a bibliometric analysis of the 
      literature published during the first 7 months of the life of ChatGPT since its 
      introduction, updated to July 1st, 2023. Seven hundred and twenty-four (724) 
      articles were retrieved. This analysis highlights a significant increase in 
      publications exploring ChatGPT use across various medical disciplines, indicating 
      its expanding relevance in healthcare. A decline proportion of studies focusing 
      on ethical considerations was observed. Simultaneously, there was a steady 
      increase in studies focused on the exploration of possible applications of 
      ChatGPT. As ChatGPT applications continue to expand, ongoing vigilance and 
      collaborative efforts to optimize ChatGPT performance are essential in harnessing 
      the benefits while mitigating the risks of AI use in healthcare.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Alessandri-Bonetti, Mario
AU  - Alessandri-Bonetti M
AUID- ORCID: 0000-0002-5506-8323
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15213, USA.
FAU - Liu, Hilary Y
AU  - Liu HY
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15213, USA.
FAU - Giorgino, Riccardo
AU  - Giorgino R
AD  - Department of Orthopedics, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
FAU - Nguyen, Vu T
AU  - Nguyen VT
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15213, USA.
FAU - Egro, Francesco M
AU  - Egro FM
AUID- ORCID: 0000-0003-1536-7713
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15213, USA. francescoegro@gmail.com.
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 3550 
      Terrace Street 6B Scaife Hall, Pittsburgh, PA, 15261, USA. 
      francescoegro@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230723
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bibliometric analysis
OT  - ChatGPT
OT  - Deep learning
OT  - Natural language processing
EDAT- 2023/07/24 00:41
MHDA- 2023/07/24 00:41
CRDT- 2023/07/23 23:06
PHST- 2023/07/12 00:00 [received]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/07/24 00:41 [medline]
PHST- 2023/07/24 00:41 [pubmed]
PHST- 2023/07/23 23:06 [entrez]
AID - 10.1007/s10439-023-03325-8 [pii]
AID - 10.1007/s10439-023-03325-8 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 23. doi: 10.1007/s10439-023-03325-8.

PMID- 38058223
OWN - NLM
STAT- Publisher
LR  - 20231207
IS  - 2042-7670 (Electronic)
IS  - 0042-4900 (Linking)
DP  - 2023 Dec 6
TI  - Evaluating ChatGPT text mining of clinical records for companion animal obesity 
      monitoring.
PG  - e3669
LID - 10.1002/vetr.3669 [doi]
AB  - BACKGROUND: Veterinary clinical narratives remain a largely untapped resource for 
      addressing complex diseases. Here we compare the ability of a large language 
      model (ChatGPT) and a previously developed regular expression (RegexT) to 
      identify overweight body condition scores (BCS) in veterinary narratives 
      pertaining to companion animals. METHODS: BCS values were extracted from 4415 
      anonymised clinical narratives using either RegexT or by appending the narrative 
      to a prompt sent to ChatGPT, prompting the model to return the BCS information. 
      Data were manually reviewed for comparison. RESULTS: The precision of RegexT was 
      higher (100%, 95% confidence interval [CI] 94.81%-100%) than that of ChatGPT 
      (89.3%, 95% CI 82.75%-93.64%). However, the recall of ChatGPT (100%, 95% CI 
      96.18%-100%) was considerably higher than that of RegexT (72.6%, 95% CI 
      63.92%-79.94%). LIMITATIONS: Prior anonymisation and subtle prompt engineering 
      are needed to improve ChatGPT output. CONCLUSIONS: Large language models create 
      diverse opportunities and, while complex, present an intuitive interface to 
      information. However, they require careful implementation to avoid unpredictable 
      errors.
CI  - © 2023 The Authors. Veterinary Record published by John Wiley & Sons Ltd on 
      behalf of British Veterinary Association.
FAU - Fins, Ivo S
AU  - Fins IS
AUID- ORCID: 0000-0002-1519-7550
AD  - Small Animal Veterinary Surveillance Network, Institute of Infection, Veterinary 
      and Ecological Sciences, University of Liverpool, Liverpool, UK.
FAU - Davies, Heather
AU  - Davies H
AUID- ORCID: 0000-0001-6905-4718
AD  - Small Animal Veterinary Surveillance Network, Institute of Infection, Veterinary 
      and Ecological Sciences, University of Liverpool, Liverpool, UK.
FAU - Farrell, Sean
AU  - Farrell S
AD  - Department of Computer Science, Durham University, Durham, UK.
FAU - Torres, Jose R
AU  - Torres JR
AD  - Institute for Animal Health and Food Safety, University of Las Palmas de Gran 
      Canaria, Las Palmas, Gran Canaria, Spain.
FAU - Pinchbeck, Gina
AU  - Pinchbeck G
AD  - Small Animal Veterinary Surveillance Network, Institute of Infection, Veterinary 
      and Ecological Sciences, University of Liverpool, Liverpool, UK.
FAU - Radford, Alan D
AU  - Radford AD
AD  - Small Animal Veterinary Surveillance Network, Institute of Infection, Veterinary 
      and Ecological Sciences, University of Liverpool, Liverpool, UK.
FAU - Noble, Peter-John
AU  - Noble PJ
AD  - Small Animal Veterinary Surveillance Network, Institute of Infection, Veterinary 
      and Ecological Sciences, University of Liverpool, Liverpool, UK.
LA  - eng
GR  - BB_/Biotechnology and Biological Sciences Research Council/United Kingdom
PT  - Journal Article
DEP - 20231206
PL  - England
TA  - Vet Rec
JT  - The Veterinary record
JID - 0031164
SB  - IM
EDAT- 2023/12/07 06:42
MHDA- 2023/12/07 06:42
CRDT- 2023/12/07 03:44
PHST- 2023/09/25 00:00 [revised]
PHST- 2023/07/31 00:00 [received]
PHST- 2023/11/07 00:00 [accepted]
PHST- 2023/12/07 06:42 [medline]
PHST- 2023/12/07 06:42 [pubmed]
PHST- 2023/12/07 03:44 [entrez]
AID - 10.1002/vetr.3669 [doi]
PST - aheadofprint
SO  - Vet Rec. 2023 Dec 6:e3669. doi: 10.1002/vetr.3669.

PMID- 37380584
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230731
LR  - 20230731
IS  - 1532-8171 (Electronic)
IS  - 0735-6757 (Linking)
VI  - 70
DP  - 2023 Aug
TI  - ChatGPT and conversational artificial intelligence: Ethics in the eye of the 
      beholder.
PG  - 191
LID - S0735-6757(23)00317-0 [pii]
LID - 10.1016/j.ajem.2023.06.023 [doi]
FAU - Gottlieb, Michael
AU  - Gottlieb M
AD  - Department of Emergency Medicine, Rush University Medical Center, Chicago, IL, 
      United States of America. Electronic address: MichaelGottliebMD@gmail.com.
FAU - Kline, Jeffrey A
AU  - Kline JA
AD  - Department of Emergency Medicine, Wayne State University School of Medicine, 
      Detroit, MI, United States of America. Electronic address: jkline@wayne.edu.
FAU - Schneider, Alexander J
AU  - Schneider AJ
AD  - NantGames, Inc, Los Angeles, CA, United States of America. Electronic address: 
      publications@schneids.me.
FAU - Coates, Wendy C
AU  - Coates WC
AD  - Department of Emergency Medicine, University of California, Los Angeles, David 
      Geffen School of Medicine, Los Angeles, CA, United States of America. Electronic 
      address: balletmd@gmail.com.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230614
PL  - United States
TA  - Am J Emerg Med
JT  - The American journal of emergency medicine
JID - 8309942
SB  - IM
CON - Am J Emerg Med. 2023 Aug;70:190. PMID: 37349234
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Ethics
OT  - Publication
OT  - Research
COIS- Declaration of Competing Interest We have no conflicts of interest to declare and 
      this manuscript has not been submitted elsewhere.
EDAT- 2023/06/29 01:08
MHDA- 2023/07/25 06:43
CRDT- 2023/06/28 21:58
PHST- 2023/06/05 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/07/25 06:43 [medline]
PHST- 2023/06/29 01:08 [pubmed]
PHST- 2023/06/28 21:58 [entrez]
AID - S0735-6757(23)00317-0 [pii]
AID - 10.1016/j.ajem.2023.06.023 [doi]
PST - ppublish
SO  - Am J Emerg Med. 2023 Aug;70:191. doi: 10.1016/j.ajem.2023.06.023. Epub 2023 Jun 
      14.

PMID- 37510487
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 14
DP  - 2023 Jul 17
TI  - ChatGPT Knowledge Evaluation in Basic and Clinical Medical Sciences: Multiple 
      Choice Question Examination-Based Performance.
LID - 10.3390/healthcare11142046 [doi]
LID - 2046
AB  - The Chatbot Generative Pre-Trained Transformer (ChatGPT) has garnered great 
      attention from the public, academicians and science communities. It responds with 
      appropriate and articulate answers and explanations across various disciplines. 
      For the use of ChatGPT in education, research and healthcare, different 
      perspectives exist with some level of ambiguity around its acceptability and 
      ideal uses. However, the literature is acutely lacking in establishing a link to 
      assess the intellectual levels of ChatGPT in the medical sciences. Therefore, the 
      present study aimed to investigate the knowledge level of ChatGPT in medical 
      education both in basic and clinical medical sciences, multiple-choice question 
      (MCQs) examination-based performance and its impact on the medical examination 
      system. In this study, initially, a subject-wise question bank was established 
      with a pool of multiple-choice questions (MCQs) from various medical textbooks 
      and university examination pools. The research team members carefully reviewed 
      the MCQ contents and ensured that the MCQs were relevant to the subject's 
      contents. Each question was scenario-based with four sub-stems and had a single 
      correct answer. In this study, 100 MCQs in various disciplines, including basic 
      medical sciences (50 MCQs) and clinical medical sciences (50 MCQs), were randomly 
      selected from the MCQ bank. The MCQs were manually entered one by one, and a 
      fresh ChatGPT session was started for each entry to avoid memory retention bias. 
      The task was given to ChatGPT to assess the response and knowledge level of 
      ChatGPT. The first response obtained was taken as the final response. Based on a 
      pre-determined answer key, scoring was made on a scale of 0 to 1, with zero 
      representing incorrect and one representing the correct answer. The results 
      revealed that out of 100 MCQs in various disciplines of basic and clinical 
      medical sciences, ChatGPT attempted all the MCQs and obtained 37/50 (74%) marks 
      in basic medical sciences and 35/50 (70%) marks in clinical medical sciences, 
      with an overall score of 72/100 (72%) in both basic and clinical medical 
      sciences. It is concluded that ChatGPT obtained a satisfactory score in both 
      basic and clinical medical sciences subjects and demonstrated a degree of 
      understanding and explanation. This study's findings suggest that ChatGPT may be 
      able to assist medical students and faculty in medical education settings since 
      it has potential as an innovation in the framework of medical sciences and 
      education.
FAU - Meo, Sultan Ayoub
AU  - Meo SA
AUID- ORCID: 0000-0001-9820-1852
AD  - Department of Physiology, College of Medicine, King Saud University, Riyadh 
      11461, Saudi Arabia.
FAU - Al-Masri, Abeer A
AU  - Al-Masri AA
AD  - Department of Physiology, College of Medicine, King Saud University, Riyadh 
      11461, Saudi Arabia.
FAU - Alotaibi, Metib
AU  - Alotaibi M
AD  - University Diabetes Unit, Department of Medicine, College of Medicine, King Saud 
      University, Riyadh 11461, Saudi Arabia.
FAU - Meo, Muhammad Zain Sultan
AU  - Meo MZS
AD  - College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia.
FAU - Meo, Muhammad Omair Sultan
AU  - Meo MOS
AD  - College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia.
LA  - eng
GR  - (IFKSUOR3-4-3)/Deputyship for Research & Innovation, Ministry of Education, Saudi 
      Arabia (IFKSUOR3-4-3./
PT  - Journal Article
DEP - 20230717
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10379728
OTO - NOTNLM
OT  - ChatGPT
OT  - intellect level
OT  - knowledge
OT  - medical education
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/29 11:43
MHDA- 2023/07/29 11:44
CRDT- 2023/07/29 01:18
PHST- 2023/06/09 00:00 [received]
PHST- 2023/07/12 00:00 [revised]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/07/29 11:44 [medline]
PHST- 2023/07/29 11:43 [pubmed]
PHST- 2023/07/29 01:18 [entrez]
AID - healthcare11142046 [pii]
AID - healthcare-11-02046 [pii]
AID - 10.3390/healthcare11142046 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Jul 17;11(14):2046. doi: 10.3390/healthcare11142046.

PMID- 37903939
OWN - NLM
STAT- Publisher
LR  - 20231031
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Oct 30
TI  - Consulting the Digital Doctor: Google Versus ChatGPT as Sources of Information on 
      Breast Implant-Associated Anaplastic Large Cell Lymphoma and Breast Implant 
      Illness.
LID - 10.1007/s00266-023-03713-4 [doi]
AB  - BACKGROUND: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) 
      is a rare complication associated with the use of breast implants. Breast implant 
      illness (BII) is another potentially concerning issue related to breast implants. 
      This study aims to assess the quality of ChatGPT as a potential source of patient 
      education by comparing the answers to frequently asked questions on BIA-ALCL and 
      BII provided by ChatGPT and Google. METHODS: The Google and ChatGPT answers to 
      the 10 most frequently asked questions on the search terms "breast implant 
      associated anaplastic large cell lymphoma" and "breast implant illness" were 
      recorded. Five blinded breast plastic surgeons were then asked to grade the 
      quality of the answers according to the Global Quality Score (GQS). A Wilcoxon 
      paired t-test was performed to evaluate the difference in GQS ratings for Google 
      and ChatGPT answers. The sources provided by Google and ChatGPT were also 
      categorized and assessed. RESULTS: In a comparison of answers provided by Google 
      and ChatGPT on BIA-ALCL and BII, ChatGPT significantly outperformed Google. For 
      BIA-ALCL, Google's average score was 2.72 ± 1.44, whereas ChatGPT scored an 
      average of 4.18 ± 1.04 (p < 0.01). For BII, Google's average score was 
      2.66 ± 1.24, while ChatGPT scored an average of 4.28 ± 0.97 (p < 0.01). The 
      superiority of ChatGPT's responses was attributed to their comprehensive nature 
      and recognition of existing knowledge gaps. However, some of ChatGPT's answers 
      had inaccessible sources. CONCLUSION: ChatGPT outperforms Google in providing 
      high-quality answers to commonly asked questions on BIA-ALCL and BII, 
      highlighting the potential of AI technologies in patient education. LEVEL OF 
      EVIDENCE: Level III, comparative study LEVEL OF EVIDENCE III: This journal 
      requires that authors assign a level of evidence to each article. For a full 
      description of these Evidence-Based Medicine ratings, please refer to the Table 
      of Contents or the online Instructions to Authors www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Liu, Hilary Y
AU  - Liu HY
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15219, USA.
FAU - Alessandri Bonetti, Mario
AU  - Alessandri Bonetti M
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15219, USA.
FAU - De Lorenzi, Francesca
AU  - De Lorenzi F
AD  - Department of Plastic Surgery, IRCCS European Institute of Oncology, Via Giuseppe 
      Ripamonti 345, 20122, Milan, Italy.
FAU - Gimbel, Michael L
AU  - Gimbel ML
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15219, USA.
FAU - Nguyen, Vu T
AU  - Nguyen VT
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15219, USA.
FAU - Egro, Francesco M
AU  - Egro FM
AUID- ORCID: 0000-0003-1536-7713
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA, 15219, USA. francescoegro@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Breast
OT  - Breast implant associated anaplastic large cell lymphoma
OT  - ChatGPT
OT  - Google
OT  - Implant
OT  - Implant illness
OT  - Patient education
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:42
CRDT- 2023/10/31 00:34
PHST- 2023/08/19 00:00 [received]
PHST- 2023/10/10 00:00 [accepted]
PHST- 2023/10/31 06:42 [medline]
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 00:34 [entrez]
AID - 10.1007/s00266-023-03713-4 [pii]
AID - 10.1007/s00266-023-03713-4 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Oct 30. doi: 10.1007/s00266-023-03713-4.

PMID- 37183438
OWN - NLM
STAT- MEDLINE
DCOM- 20230706
LR  - 20230718
IS  - 1097-0142 (Electronic)
IS  - 0008-543X (Linking)
VI  - 129
IP  - 15
DP  - 2023 Aug 1
TI  - ChatGPT-A promising generative AI tool and its implications for cancer care.
PG  - 2284-2289
LID - 10.1002/cncr.34827 [doi]
AB  - Since its launch, ChatGPT has taken the internet by storm and has the potential 
      to be used broadly in the health care system, particularly in a setting such as 
      medical oncology. ChatGPT is well suited to review and extract key content from 
      records of patients with cancer, interpret next-generation sequencing reports, 
      and offer a list of potential clinical trial options.
CI  - © 2023 American Cancer Society.
FAU - Uprety, Dipesh
AU  - Uprety D
AUID- ORCID: 0000-0002-4278-9703
AD  - Department of Medical Oncology, Barbara Ann Karmanos Cancer Institute, Detroit, 
      Michigan, USA.
FAU - Zhu, Dongxiao
AU  - Zhu D
AD  - Department of Computer Science, Wayne State University, Detroit, Michigan, USA.
FAU - West, Howard Jack
AU  - West HJ
AD  - Department of Medical Oncology, City of Hope Comprehensive Cancer Center, Duarte, 
      California, USA.
LA  - eng
PT  - Journal Article
DEP - 20230514
PL  - United States
TA  - Cancer
JT  - Cancer
JID - 0374236
SB  - IM
MH  - Humans
MH  - *High-Throughput Nucleotide Sequencing
MH  - Internet
MH  - Medical Oncology
MH  - *Neoplasms/therapy
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence (AI)
OT  - cancer care
OT  - oncology
EDAT- 2023/05/15 06:42
MHDA- 2023/07/06 06:42
CRDT- 2023/05/15 03:41
PHST- 2023/07/06 06:42 [medline]
PHST- 2023/05/15 06:42 [pubmed]
PHST- 2023/05/15 03:41 [entrez]
AID - 10.1002/cncr.34827 [doi]
PST - ppublish
SO  - Cancer. 2023 Aug 1;129(15):2284-2289. doi: 10.1002/cncr.34827. Epub 2023 May 14.

PMID- 37945414
OWN - NLM
STAT- Publisher
LR  - 20231109
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
DP  - 2023 Nov 7
TI  - Quantitative evaluation of ChatGPT versus Bard responses to anaesthesia-related 
      queries.
LID - S0007-0912(23)00550-0 [pii]
LID - 10.1016/j.bja.2023.09.030 [doi]
FAU - Patnaik, Sourav S
AU  - Patnaik SS
AD  - Department of Anesthesiology and Pain Management, The University of Texas 
      Southwestern Medical Center, Dallas, TX, USA. Electronic address: 
      sourav.patnaik@uthsouthwestern.edu.
FAU - Hoffmann, Ulrike
AU  - Hoffmann U
AD  - Department of Anesthesiology and Pain Management, The University of Texas 
      Southwestern Medical Center, Dallas, TX, USA.
LA  - eng
PT  - Letter
DEP - 20231107
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - analytics
OT  - artificial intelligence
OT  - communication
OT  - perioperative
OT  - preoperative evaluation
EDAT- 2023/11/10 00:44
MHDA- 2023/11/10 00:44
CRDT- 2023/11/09 21:54
PHST- 2023/08/18 00:00 [received]
PHST- 2023/09/28 00:00 [revised]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/11/10 00:44 [medline]
PHST- 2023/11/10 00:44 [pubmed]
PHST- 2023/11/09 21:54 [entrez]
AID - S0007-0912(23)00550-0 [pii]
AID - 10.1016/j.bja.2023.09.030 [doi]
PST - aheadofprint
SO  - Br J Anaesth. 2023 Nov 7:S0007-0912(23)00550-0. doi: 10.1016/j.bja.2023.09.030.

PMID- 37244834
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231025
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
VI  - 131
IP  - 2
DP  - 2023 Aug
TI  - Artificial intelligence and anaesthesia examinations: exploring ChatGPT as a 
      prelude to the future.
PG  - e36-e37
LID - S0007-0912(23)00226-X [pii]
LID - 10.1016/j.bja.2023.04.033 [doi]
FAU - Aldridge, Matthew J
AU  - Aldridge MJ
AD  - North Bristol NHS Trust, Bristol, UK. Electronic address: matt.aldridge@nhs.net.
FAU - Penders, Robert
AU  - Penders R
AD  - North Bristol NHS Trust, Bristol, UK.
LA  - eng
PT  - Letter
DEP - 20230526
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Anesthesia
MH  - *Anesthesiology
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-4
OT  - anaesthesia training
OT  - artificial intelligence
OT  - examination
OT  - large language model
EDAT- 2023/05/28 01:07
MHDA- 2023/10/23 12:44
CRDT- 2023/05/27 22:00
PHST- 2023/04/14 00:00 [received]
PHST- 2023/04/22 00:00 [revised]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/10/23 12:44 [medline]
PHST- 2023/05/28 01:07 [pubmed]
PHST- 2023/05/27 22:00 [entrez]
AID - S0007-0912(23)00226-X [pii]
AID - 10.1016/j.bja.2023.04.033 [doi]
PST - ppublish
SO  - Br J Anaesth. 2023 Aug;131(2):e36-e37. doi: 10.1016/j.bja.2023.04.033. Epub 2023 
      May 26.

PMID- 37465170
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230721
LR  - 20230721
IS  - 2296-2565 (Electronic)
IS  - 2296-2565 (Linking)
VI  - 11
DP  - 2023
TI  - Applying ChatGPT in public health: a SWOT and PESTLE analysis.
PG  - 1225861
LID - 10.3389/fpubh.2023.1225861 [doi]
LID - 1225861
FAU - Morita, Plinio P
AU  - Morita PP
AD  - School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
AD  - Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, 
      Canada.
AD  - Research Institute for Aging, University of Waterloo, Waterloo, ON, Canada.
AD  - Centre for Digital Therapeutics, Techna Institute, University Health Network, 
      Toronto, ON, Canada.
AD  - Dalla Lana School of Public Health, Institute of Health Policy, Management, and 
      Evaluation, University of Toronto, Toronto, ON, Canada.
FAU - Abhari, Shahabeddin
AU  - Abhari S
AD  - School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
FAU - Kaur, Jasleen
AU  - Kaur J
AD  - School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
FAU - Lotto, Matheus
AU  - Lotto M
AD  - School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
AD  - Department of Pediatric Dentistry, Orthodontics, and Public Health, Bauru School 
      of Dentistry, University of São Paulo, Bauru, Brazil.
FAU - Miranda, Pedro Augusto Da Silva E Souza
AU  - Miranda PADSES
AD  - School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
FAU - Oetomo, Arlene
AU  - Oetomo A
AD  - School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
LA  - eng
PT  - Journal Article
DEP - 20230703
PL  - Switzerland
TA  - Front Public Health
JT  - Frontiers in public health
JID - 101616579
SB  - IM
PMC - PMC10350520
OTO - NOTNLM
OT  - ChatGPT
OT  - SWOT analysis
OT  - artificial intelligence
OT  - healthcare
OT  - public health
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/07/19 06:42
MHDA- 2023/07/21 06:44
CRDT- 2023/07/19 03:56
PHST- 2023/05/20 00:00 [received]
PHST- 2023/06/16 00:00 [accepted]
PHST- 2023/07/21 06:44 [medline]
PHST- 2023/07/19 06:42 [pubmed]
PHST- 2023/07/19 03:56 [entrez]
AID - 10.3389/fpubh.2023.1225861 [doi]
PST - epublish
SO  - Front Public Health. 2023 Jul 3;11:1225861. doi: 10.3389/fpubh.2023.1225861. 
      eCollection 2023.

PMID- 37254022
OWN - NLM
STAT- MEDLINE
DCOM- 20230811
LR  - 20230811
IS  - 1873-4626 (Electronic)
IS  - 1091-255X (Linking)
VI  - 27
IP  - 8
DP  - 2023 Aug
TI  - Quality of ChatGPT Responses to Questions Related To Liver Transplantation.
PG  - 1716-1719
LID - 10.1007/s11605-023-05714-9 [doi]
FAU - Endo, Yutaka
AU  - Endo Y
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, 
      OH, USA.
FAU - Sasaki, Kazunari
AU  - Sasaki K
AD  - Department of Surgery, Stanford University, Stanford, CA, USA.
FAU - Moazzam, Zorays
AU  - Moazzam Z
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, 
      OH, USA.
FAU - Lima, Henrique A
AU  - Lima HA
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, 
      OH, USA.
FAU - Schenk, Austin
AU  - Schenk A
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, 
      OH, USA.
FAU - Limkemann, Ashley
AU  - Limkemann A
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, 
      OH, USA.
FAU - Washburn, Kenneth
AU  - Washburn K
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, 
      OH, USA.
FAU - Pawlik, Timothy M
AU  - Pawlik TM
AUID- ORCID: 0000-0002-4828-8096
AD  - Department of Surgery, The Ohio State University, Wexner Medical Center, 395 W. 
      12th Ave., Suite 670 614 293 8701, Columbus, USA. Tim.Pawlik@osumc.edu.
LA  - eng
PT  - Journal Article
DEP - 20230530
PL  - United States
TA  - J Gastrointest Surg
JT  - Journal of gastrointestinal surgery : official journal of the Society for Surgery 
      of the Alimentary Tract
JID - 9706084
SB  - IM
MH  - Humans
MH  - *Liver Transplantation
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - ChatGPT
OT  - Patient information
OT  - Transplantation
EDAT- 2023/05/31 01:09
MHDA- 2023/08/11 06:43
CRDT- 2023/05/30 23:31
PHST- 2023/04/27 00:00 [received]
PHST- 2023/05/14 00:00 [accepted]
PHST- 2023/08/11 06:43 [medline]
PHST- 2023/05/31 01:09 [pubmed]
PHST- 2023/05/30 23:31 [entrez]
AID - 10.1007/s11605-023-05714-9 [pii]
AID - 10.1007/s11605-023-05714-9 [doi]
PST - ppublish
SO  - J Gastrointest Surg. 2023 Aug;27(8):1716-1719. doi: 10.1007/s11605-023-05714-9. 
      Epub 2023 May 30.

PMID- 37828297
OWN - NLM
STAT- Publisher
LR  - 20231012
IS  - 1432-1084 (Electronic)
IS  - 0938-7994 (Linking)
DP  - 2023 Oct 13
TI  - Evaluating the reliability of ChatGPT as a tool for imaging test referral: a 
      comparative study with a clinical decision support system.
LID - 10.1007/s00330-023-10230-0 [doi]
AB  - OBJECTIVES: As the technology continues to evolve and advance, we can expect to 
      see artificial intelligence (AI) being used in increasingly sophisticated ways to 
      make a diagnosis and decisions such as suggesting the most appropriate imaging 
      referrals. We aim to explore whether Chat Generative Pretrained Transformer 
      (ChatGPT) can provide accurate imaging referrals for clinical use that are at 
      least as good as the ESR iGuide. METHODS: A comparative study was conducted in a 
      tertiary hospital. Data was collected from 97 consecutive cases that were 
      admitted to the emergency department with abdominal complaints. We compared the 
      imaging test referral recommendations suggested by the ESR iGuide and the ChatGPT 
      and analyzed cases of disagreement. In addition, we selected cases where ChatGPT 
      recommended a chest abdominal pelvis (CAP) CT (n = 66), and asked four 
      specialists to grade the appropriateness of the referral. RESULTS: ChatGPT 
      recommendations were consistent with the recommendations provided by the ESR 
      iGuide. No statistical differences were found between the appropriateness of 
      referrals by age or gender. Using a sub-analysis of CAP cases, a high agreement 
      between ChatGPT and the specialists was found. Cases of disagreement (12.4%) were 
      further analyzed and presented themes of vague recommendations such as "it would 
      be advisable" and "this would help to rule out." CONCLUSIONS: ChatGPT's ability 
      to guide the selection of appropriate tests may be comparable to some degree with 
      the ESR iGuide. Features such as the clinical, ethical, and regulatory 
      implications are still warranted and need to be addressed prior to clinical 
      implementation. Further studies are needed to confirm these findings. CLINICAL 
      RELEVANCE STATEMENT: The article explores the potential of using advanced 
      language models, such as ChatGPT, in healthcare as a CDS for selecting 
      appropriate imaging tests. Using ChatGPT can improve the efficiency of the 
      decision-making process KEY POINTS: • ChatGPT recommendations were highly 
      consistent with the recommendations provided by the ESR iGuide. • ChatGPT's 
      ability in guiding the selection of appropriate tests may be comparable to some 
      degree with ESR iGuide's.
CI  - © 2023. The Author(s), under exclusive licence to European Society of Radiology.
FAU - Rosen, Shani
AU  - Rosen S
AD  - Department of Health Technology and Policy Evaluation, Gertner Institute for 
      Epidemiology and Health Policy, Institute of Epidemiology & Health Policy 
      Research, Sheba Medical Center, Tel HaShomer, Ramat-Gan, Israel.
AD  - Nursing Department, School of Health Sciences, Sackler Faculty of Medicine, Tel 
      Aviv University, Tel Aviv, Israel.
FAU - Saban, Mor
AU  - Saban M
AUID- ORCID: 0000-0001-6869-0907
AD  - Nursing Department, School of Health Sciences, Sackler Faculty of Medicine, Tel 
      Aviv University, Tel Aviv, Israel. morsaban1@tauex.tau.ac.il.
LA  - eng
PT  - Journal Article
DEP - 20231013
PL  - Germany
TA  - Eur Radiol
JT  - European radiology
JID - 9114774
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - ESR iGuide
OT  - Imaging
EDAT- 2023/10/13 00:43
MHDA- 2023/10/13 00:43
CRDT- 2023/10/12 23:34
PHST- 2023/02/15 00:00 [received]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/07/28 00:00 [revised]
PHST- 2023/10/13 00:43 [medline]
PHST- 2023/10/13 00:43 [pubmed]
PHST- 2023/10/12 23:34 [entrez]
AID - 10.1007/s00330-023-10230-0 [pii]
AID - 10.1007/s00330-023-10230-0 [doi]
PST - aheadofprint
SO  - Eur Radiol. 2023 Oct 13. doi: 10.1007/s00330-023-10230-0.

PMID- 37430793
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230711
LR  - 20230718
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 10
DP  - 2023 May 18
TI  - Can ChatGPT Help in Electronics Research and Development? A Case Study with 
      Applied Sensors.
LID - 10.3390/s23104879 [doi]
LID - 4879
AB  - In this paper, we investigated the applicability of ChatGPT AI in electronics 
      research and development via a case study of applied sensors in embedded 
      electronic systems, a topic that is rarely mentioned in the recent literature, 
      thus providing new insight for professionals and academics. The initial 
      electronics-development tasks of a smart home project were prompted to the 
      ChatGPT system to find out its capabilities and limitations. We wanted to obtain 
      detailed information on the central processing controller units and the actual 
      sensors usable for the specific project, their specifications and recommendations 
      on the hardware and software design flow additionally. Furthermore, an extensive 
      literature survey was requested to see if the bot could offer scientific papers 
      covering the given topic. It was found that the ChatGPT responded with proper 
      recommendations on controllers. However, the suggested sensor units, the hardware 
      and software design were only partially acceptable, with occasional errors in 
      specifications and generated code. The results of the literature survey showed 
      that non-acceptable, fabricated citations (fake authors list, title, journal 
      details and DOI-Digital Object identifier) were presented by the bot. The paper 
      provides a detailed qualitative analysis, a performance analysis and critical 
      discussion of the aforementioned aspects while providing the query set, the 
      generated answers and codes as supplied data with the goal to give added value to 
      electronics researchers and developers if trying to reach out for the tools in 
      their profession.
FAU - Tafferner, Zoltán
AU  - Tafferner Z
AD  - Department of Electronics Technology, Faculty of Electrical Engineering and 
      Informatics, Budapest University of Technology and Economics, H-1111 Budapest, 
      Hungary.
FAU - Balázs, Illés
AU  - Balázs I
AUID- ORCID: 0000-0002-8619-5472
AD  - Department of Electronics Technology, Faculty of Electrical Engineering and 
      Informatics, Budapest University of Technology and Economics, H-1111 Budapest, 
      Hungary.
AD  - LTCC Technology Research Group, Łukasiewicz Research Network-Institute of 
      Microelectronics and Photonics, 02-255 Kraków, Poland.
FAU - Krammer, Olivér
AU  - Krammer O
AD  - Department of Electronics Technology, Faculty of Electrical Engineering and 
      Informatics, Budapest University of Technology and Economics, H-1111 Budapest, 
      Hungary.
FAU - Géczy, Attila
AU  - Géczy A
AD  - Department of Electronics Technology, Faculty of Electrical Engineering and 
      Informatics, Budapest University of Technology and Economics, H-1111 Budapest, 
      Hungary.
LA  - eng
PT  - Journal Article
DEP - 20230518
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10221460
OTO - NOTNLM
OT  - ChatGPT
OT  - IoT
OT  - development
OT  - electronics
OT  - engineering
OT  - generative large language model
OT  - sensors
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/11 06:42
MHDA- 2023/07/11 06:43
CRDT- 2023/07/11 01:03
PHST- 2023/04/06 00:00 [received]
PHST- 2023/05/05 00:00 [revised]
PHST- 2023/05/12 00:00 [accepted]
PHST- 2023/07/11 06:43 [medline]
PHST- 2023/07/11 06:42 [pubmed]
PHST- 2023/07/11 01:03 [entrez]
AID - s23104879 [pii]
AID - sensors-23-04879 [pii]
AID - 10.3390/s23104879 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 May 18;23(10):4879. doi: 10.3390/s23104879.

PMID- 37932006
OWN - NLM
STAT- Publisher
LR  - 20231106
IS  - 1468-2079 (Electronic)
IS  - 0007-1161 (Linking)
DP  - 2023 Nov 6
TI  - Performance of ChatGPT and Bard on the official part 1 FRCOphth practice 
      questions.
LID - bjo-2023-324091 [pii]
LID - 10.1136/bjo-2023-324091 [doi]
AB  - BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT), a large language 
      model by OpenAI, and Bard, Google's artificial intelligence (AI) chatbot, have 
      been evaluated in various contexts. This study aims to assess these models' 
      proficiency in the part 1 Fellowship of the Royal College of Ophthalmologists 
      (FRCOphth) Multiple Choice Question (MCQ) examination, highlighting their 
      potential in medical education. METHODS: Both models were tested on a sample 
      question bank for the part 1 FRCOphth MCQ exam. Their performances were compared 
      with historical human performance on the exam, focusing on the ability to 
      comprehend, retain and apply information related to ophthalmology. We also tested 
      it on the book 'MCQs for FRCOpth part 1', and assessed its performance across 
      subjects. RESULTS: ChatGPT demonstrated a strong performance, surpassing 
      historical human pass marks and examination performance, while Bard 
      underperformed. The comparison indicates the potential of certain AI models to 
      match, and even exceed, human standards in such tasks. CONCLUSION: The results 
      demonstrate the potential of AI models, such as ChatGPT, in processing and 
      applying medical knowledge at a postgraduate level. However, performance varied 
      among different models, highlighting the importance of appropriate AI selection. 
      The study underlines the potential for AI applications in medical education and 
      the necessity for further investigation into their strengths and limitations.
CI  - © Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and 
      permissions. Published by BMJ.
FAU - Fowler, Thomas
AU  - Fowler T
AUID- ORCID: 0009-0003-9166-1954
AD  - Department of Medicine, Barking Havering and Redbridge University Hospitals NHS 
      Trust, London, UK thomas.fowler6@nhs.net.
FAU - Pullen, Simon
AU  - Pullen S
AD  - Department of Anaesthetics, Princess Alexandra Hospital, Harlow, UK.
FAU - Birkett, Liam
AU  - Birkett L
AD  - Emergency Medicine, Royal Free Hospital, London, UK.
LA  - eng
PT  - Journal Article
DEP - 20231106
PL  - England
TA  - Br J Ophthalmol
JT  - The British journal of ophthalmology
JID - 0421041
SB  - IM
OTO - NOTNLM
OT  - Medical Education
COIS- Competing interests: None declared.
EDAT- 2023/11/07 00:42
MHDA- 2023/11/07 00:42
CRDT- 2023/11/06 21:03
PHST- 2023/06/22 00:00 [received]
PHST- 2023/10/08 00:00 [accepted]
PHST- 2023/11/07 00:42 [medline]
PHST- 2023/11/07 00:42 [pubmed]
PHST- 2023/11/06 21:03 [entrez]
AID - bjo-2023-324091 [pii]
AID - 10.1136/bjo-2023-324091 [doi]
PST - aheadofprint
SO  - Br J Ophthalmol. 2023 Nov 6:bjo-2023-324091. doi: 10.1136/bjo-2023-324091.

PMID- 37827724
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 2632-1009 (Electronic)
IS  - 2632-1009 (Linking)
VI  - 30
IP  - 1
DP  - 2023 Oct
TI  - Comparative study of ChatGPT and human evaluators on the assessment of medical 
      literature according to recognised reporting standards.
LID - 10.1136/bmjhci-2023-100830 [doi]
LID - e100830
AB  - INTRODUCTION: Amid clinicians' challenges in staying updated with medical 
      research, artificial intelligence (AI) tools like the large language model (LLM) 
      ChatGPT could automate appraisal of research quality, saving time and reducing 
      bias. This study compares the proficiency of ChatGPT3 against human evaluation in 
      scoring abstracts to determine its potential as a tool for evidence synthesis. 
      METHODS: We compared ChatGPT's scoring of implant dentistry abstracts with human 
      evaluators using the Consolidated Standards of Reporting Trials for Abstracts 
      reporting standards checklist, yielding an overall compliance score (OCS). 
      Bland-Altman analysis assessed agreement between human and AI-generated OCS 
      percentages. Additional error analysis included mean difference of OCS subscores, 
      Welch's t-test and Pearson's correlation coefficient. RESULTS: Bland-Altman 
      analysis showed a mean difference of 4.92% (95% CI 0.62%, 0.37%) in OCS between 
      human evaluation and ChatGPT. Error analysis displayed small mean differences in 
      most domains, with the highest in 'conclusion' (0.764 (95% CI 0.186, 0.280)) and 
      the lowest in 'blinding' (0.034 (95% CI 0.818, 0.895)). The strongest 
      correlations between were in 'harms' (r=0.32, p<0.001) and 'trial registration' 
      (r=0.34, p=0.002), whereas the weakest were in 'intervention' (r=0.02, p<0.001) 
      and 'objective' (r=0.06, p<0.001). CONCLUSION: LLMs like ChatGPT can help 
      automate appraisal of medical literature, aiding in the identification of 
      accurately reported research. Possible applications of ChatGPT include 
      integration within medical databases for abstract evaluation. Current limitations 
      include the token limit, restricting its usage to abstracts. As AI technology 
      advances, future versions like GPT4 could offer more reliable, comprehensive 
      evaluations, enhancing the identification of high-quality research and 
      potentially improving patient outcomes.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published 
      by BMJ.
FAU - Roberts, Richard Hr
AU  - Roberts RH
AUID- ORCID: 0000-0002-9600-5943
AD  - Reconstructive Surgery and Regenerative Medicine Research Centre, Swansea 
      University, Swansea, UK 838272@swansea.ac.uk.
AD  - Swansea University Medical School, Swansea University, Swansea, UK.
AD  - Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK.
FAU - Ali, Stephen R
AU  - Ali SR
AD  - Reconstructive Surgery and Regenerative Medicine Research Centre, Swansea 
      University, Swansea, UK.
AD  - Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK.
FAU - Hutchings, Hayley A
AU  - Hutchings HA
AD  - Swansea University Medical School, Swansea University, Swansea, UK.
FAU - Dobbs, Thomas D
AU  - Dobbs TD
AD  - Reconstructive Surgery and Regenerative Medicine Research Centre, Swansea 
      University, Swansea, UK.
AD  - Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK.
FAU - Whitaker, Iain S
AU  - Whitaker IS
AD  - Reconstructive Surgery and Regenerative Medicine Research Centre, Swansea 
      University, Swansea, UK.
AD  - Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK.
LA  - eng
PT  - Journal Article
PL  - England
TA  - BMJ Health Care Inform
JT  - BMJ health & care informatics
JID - 101745500
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Biomedical Research
MH  - Checklist
MH  - Databases, Factual
MH  - Patient Compliance
PMC - PMC10583079
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Medical Informatics
COIS- Competing interests: None declared.
EDAT- 2023/10/13 00:43
MHDA- 2023/10/23 01:18
CRDT- 2023/10/12 21:12
PHST- 2023/06/14 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/13 00:43 [pubmed]
PHST- 2023/10/12 21:12 [entrez]
AID - bmjhci-2023-100830 [pii]
AID - 10.1136/bmjhci-2023-100830 [doi]
PST - ppublish
SO  - BMJ Health Care Inform. 2023 Oct;30(1):e100830. doi: 10.1136/bmjhci-2023-100830.

PMID- 37265864
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230804
IS  - 2634-3916 (Electronic)
IS  - 2634-3916 (Linking)
VI  - 4
IP  - 3
DP  - 2023 May
TI  - ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence 
      success story?
PG  - 279-281
LID - 10.1093/ehjdh/ztad029 [doi]
AB  - Chat Generative Pre-trained Transformer (ChatGPT) is currently a trending topic 
      worldwide triggering extensive debate about its predictive power, its potential 
      uses, and its wider implications. Recent publications have demonstrated that 
      ChatGPT can correctly answer questions from undergraduate exams such as the 
      United States Medical Licensing Examination. We challenged it to answer questions 
      from a more demanding, post-graduate exam-the European Exam in Core Cardiology 
      (EECC), the final exam for the completion of specialty training in Cardiology in 
      many countries. Our results demonstrate that ChatGPT succeeds in the EECC.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      European Society of Cardiology.
FAU - Skalidis, Ioannis
AU  - Skalidis I
AUID- ORCID: 0000-0002-4374-0389
AD  - Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 
      Lausanne, Switzerland.
FAU - Cagnina, Aurelien
AU  - Cagnina A
AD  - Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 
      Lausanne, Switzerland.
FAU - Luangphiphat, Wongsakorn
AU  - Luangphiphat W
AD  - Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 
      Lausanne, Switzerland.
FAU - Mahendiran, Thabo
AU  - Mahendiran T
AD  - Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 
      Lausanne, Switzerland.
AD  - Institute of Mathematics and School of Computer and Communication Sciences, EPFL, 
      EPFL FSB SMA, Station 8,1015 Lausanne, Switzerland.
FAU - Muller, Olivier
AU  - Muller O
AUID- ORCID: 0000-0003-2441-5799
AD  - Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 
      Lausanne, Switzerland.
FAU - Abbe, Emmanuel
AU  - Abbe E
AD  - Institute of Mathematics and School of Computer and Communication Sciences, EPFL, 
      EPFL FSB SMA, Station 8,1015 Lausanne, Switzerland.
FAU - Fournier, Stephane
AU  - Fournier S
AUID- ORCID: 0000-0002-9422-9521
AD  - Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 
      Lausanne, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20230424
PL  - England
TA  - Eur Heart J Digit Health
JT  - European heart journal. Digital health
JID - 101778323
EIN - Eur Heart J Digit Health. 2023 May 17;4(4):357. PMID: 37538140
PMC - PMC10232281
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Large language models
OT  - Machine learning
OT  - Medical education
COIS- Conflict of interest: None declared.
EDAT- 2023/06/02 13:16
MHDA- 2023/06/02 13:17
CRDT- 2023/06/02 10:51
PHST- 2023/02/18 00:00 [received]
PHST- 2023/04/12 00:00 [revised]
PHST- 2023/04/21 00:00 [accepted]
PHST- 2023/06/02 13:17 [medline]
PHST- 2023/06/02 13:16 [pubmed]
PHST- 2023/06/02 10:51 [entrez]
AID - ztad029 [pii]
AID - 10.1093/ehjdh/ztad029 [doi]
PST - epublish
SO  - Eur Heart J Digit Health. 2023 Apr 24;4(3):279-281. doi: 10.1093/ehjdh/ztad029. 
      eCollection 2023 May.

PMID- 38049285
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231206
LR  - 20231206
IS  - 1701-2163 (Print)
IS  - 1701-2163 (Linking)
VI  - 45
IP  - 12
DP  - 2023 Dec
TI  - Artificial Intelligence/ChatGPT-Generated Manuscripts and Disclosure: Comment.
PG  - 102192
LID - S1701-2163(23)00514-5 [pii]
LID - 10.1016/j.jogc.2023.102192 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Research Center, Chandigarh University, Punjab, India; Department of Biological 
      Science, Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
PL  - Netherlands
TA  - J Obstet Gynaecol Can
JT  - Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et 
      gynecologie du Canada : JOGC
JID - 101126664
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - disclosure
OT  - manuscript
EDAT- 2023/12/05 00:42
MHDA- 2023/12/06 06:42
CRDT- 2023/12/04 21:01
PHST- 2023/08/11 00:00 [received]
PHST- 2023/08/11 00:00 [accepted]
PHST- 2023/12/06 06:42 [medline]
PHST- 2023/12/05 00:42 [pubmed]
PHST- 2023/12/04 21:01 [entrez]
AID - S1701-2163(23)00514-5 [pii]
AID - 10.1016/j.jogc.2023.102192 [doi]
PST - ppublish
SO  - J Obstet Gynaecol Can. 2023 Dec;45(12):102192. doi: 10.1016/j.jogc.2023.102192.

PMID- 38007922
OWN - NLM
STAT- Publisher
LR  - 20231126
IS  - 1532-2688 (Electronic)
IS  - 1059-1311 (Linking)
VI  - 114
DP  - 2023 Nov 23
TI  - Assessing the performance of ChatGPT's responses to questions related to 
      epilepsy: A cross-sectional study on natural language processing and medical 
      information retrieval.
PG  - 1-8
LID - S1059-1311(23)00300-X [pii]
LID - 10.1016/j.seizure.2023.11.013 [doi]
AB  - BACKGROUND: Epilepsy is a neurological condition marked by frequent seizures and 
      various cognitive and psychological effects. Reliable information is essential 
      for effective treatment. Natural language processing models like ChatGPT are 
      increasingly used in healthcare for information access and data analysis, making 
      it crucial to assess their accuracy. OBJECTIVE: This study aimed to investigate 
      the accuracy of ChatGPT in providing educational information related to epilepsy. 
      METHODS: We compared the answers from ChatGPT-4 and ChatGPT-3.5 to 57 common 
      epilepsy questions based on the Korean Epilepsy Society's "Epilepsy Patient and 
      Caregiver Guide." Two epileptologists reviewed the responses, with a third 
      serving as an arbiter in cases of disagreement. RESULTS: Out of 57 questions, 40 
      responses from ChatGPT-4 had "sufficient educational value," 16 were "correct but 
      inadequate," and one was "mixed with correct and incorrect" information. No 
      answers were entirely incorrect. GPT-4 generally outperformed GPT-3.5 and was 
      often on par with or better than the official guide. CONCLUSIONS: ChatGPT-4 shows 
      promise as a tool for delivering reliable epilepsy-related information and could 
      help alleviate the educational burden on healthcare professionals. Further 
      research is needed to explore the benefits and limitations of using such models 
      in medical contexts.
CI  - Copyright © 2023. Published by Elsevier Ltd.
FAU - Kim, Hyun-Woo
AU  - Kim HW
AD  - Department of Neurology, Pusan National University Yangsan Hospital, 50612 
      Geumoro 20, Yangsan, South Korea.
FAU - Shin, Dong-Hyeon
AU  - Shin DH
AD  - Department of Neurology, Pusan National University Yangsan Hospital, 50612 
      Geumoro 20, Yangsan, South Korea.
FAU - Kim, Jiyoung
AU  - Kim J
AD  - Department of Neurology, Pusan National University Hospital, Busan, South Korea; 
      Pusan National University School of Medicine, Research Institute for Convergence 
      of Biomedical Science and Technology, Yangsan, South Korea.
FAU - Lee, Gha-Hyun
AU  - Lee GH
AD  - Department of Neurology, Pusan National University Hospital, Busan, South Korea; 
      Pusan National University School of Medicine, Research Institute for Convergence 
      of Biomedical Science and Technology, Yangsan, South Korea.
FAU - Cho, Jae Wook
AU  - Cho JW
AD  - Department of Neurology, Pusan National University Yangsan Hospital, 50612 
      Geumoro 20, Yangsan, South Korea; Pusan National University School of Medicine, 
      Research Institute for Convergence of Biomedical Science and Technology, Yangsan, 
      South Korea. Electronic address: sleepcho@pusan.ac.kr.
LA  - eng
PT  - Journal Article
DEP - 20231123
PL  - England
TA  - Seizure
JT  - Seizure
JID - 9306979
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Epilepsy
OT  - Natural language processing
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/11/27 00:43
MHDA- 2023/11/27 00:43
CRDT- 2023/11/26 18:06
PHST- 2023/09/28 00:00 [received]
PHST- 2023/11/18 00:00 [revised]
PHST- 2023/11/22 00:00 [accepted]
PHST- 2023/11/27 00:43 [medline]
PHST- 2023/11/27 00:43 [pubmed]
PHST- 2023/11/26 18:06 [entrez]
AID - S1059-1311(23)00300-X [pii]
AID - 10.1016/j.seizure.2023.11.013 [doi]
PST - aheadofprint
SO  - Seizure. 2023 Nov 23;114:1-8. doi: 10.1016/j.seizure.2023.11.013.

PMID- 37337531
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230621
IS  - 2632-6140 (Electronic)
IS  - 2632-6140 (Linking)
VI  - 5
IP  - 1
DP  - 2023
TI  - Evaluating the limits of AI in medical specialisation: ChatGPT's performance on 
      the UK Neurology Specialty Certificate Examination.
PG  - e000451
LID - 10.1136/bmjno-2023-000451 [doi]
LID - e000451
AB  - BACKGROUND: Large language models such as ChatGPT have demonstrated potential as 
      innovative tools for medical education and practice, with studies showing their 
      ability to perform at or near the passing threshold in general medical 
      examinations and standardised admission tests. However, no studies have assessed 
      their performance in the UK medical education context, particularly at a 
      specialty level, and specifically in the field of neurology and neuroscience. 
      METHODS: We evaluated the performance of ChatGPT in higher specialty training for 
      neurology and neuroscience using 69 questions from the Pool-Specialty Certificate 
      Examination (SCE) Neurology Web Questions bank. The dataset primarily focused on 
      neurology (80%). The questions spanned subtopics such as symptoms and signs, 
      diagnosis, interpretation and management with some questions addressing specific 
      patient populations. The performance of ChatGPT 3.5 Legacy, ChatGPT 3.5 Default 
      and ChatGPT-4 models was evaluated and compared. RESULTS: ChatGPT 3.5 Legacy and 
      ChatGPT 3.5 Default displayed overall accuracies of 42% and 57%, respectively, 
      falling short of the passing threshold of 58% for the 2022 SCE neurology 
      examination. ChatGPT-4, on the other hand, achieved the highest accuracy of 64%, 
      surpassing the passing threshold and outperforming its predecessors across 
      disciplines and subtopics. CONCLUSIONS: The advancements in ChatGPT-4's 
      performance compared with its predecessors demonstrate the potential for 
      artificial intelligence (AI) models in specialised medical education and 
      practice. However, our findings also highlight the need for ongoing development 
      and collaboration between AI developers and medical experts to ensure the models' 
      relevance and reliability in the rapidly evolving field of medicine.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Giannos, Panagiotis
AU  - Giannos P
AUID- ORCID: 0000-0003-1037-1983
AD  - Department of Life Sciences, Imperial College London, London, UK.
AD  - Society of Meta-Research and Biomedical Innovation, London, UK.
AD  - Promotion of Emerging and Evaluative Research Society, London, UK.
LA  - eng
PT  - Journal Article
DEP - 20230615
PL  - England
TA  - BMJ Neurol Open
JT  - BMJ neurology open
JID - 101775450
PMC - PMC10277081
OTO - NOTNLM
OT  - clinical neurology
OT  - health policy & practice
OT  - medicine
COIS- Competing interests: None declared.
EDAT- 2023/06/20 06:42
MHDA- 2023/06/20 06:43
CRDT- 2023/06/20 02:26
PHST- 2023/06/06 00:00 [accepted]
PHST- 2023/06/20 06:43 [medline]
PHST- 2023/06/20 06:42 [pubmed]
PHST- 2023/06/20 02:26 [entrez]
AID - bmjno-2023-000451 [pii]
AID - 10.1136/bmjno-2023-000451 [doi]
PST - epublish
SO  - BMJ Neurol Open. 2023 Jun 15;5(1):e000451. doi: 10.1136/bmjno-2023-000451. 
      eCollection 2023.

PMID- 37999899
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Nov 24
TI  - Evaluating the Performance of Different Large Language Models on Health 
      Consultation and Patient Education in Urolithiasis.
PG  - 125
LID - 10.1007/s10916-023-02021-3 [doi]
AB  - OBJECTIVES: To evaluate the effectiveness of four large language models (LLMs) 
      (Claude, Bard, ChatGPT4, and New Bing) that have large user bases and significant 
      social attention, in the context of medical consultation and patient education in 
      urolithiasis. MATERIALS AND METHODS: In this study, we developed a questionnaire 
      consisting of 21 questions and 2 clinical scenarios related to urolithiasis. 
      Subsequently, clinical consultations were simulated for each of the four models 
      to assess their responses to the questions. Urolithiasis experts then evaluated 
      the model responses in terms of accuracy, comprehensiveness, ease of 
      understanding, human care, and clinical case analysis ability based on a 
      predesigned 5-point Likert scale. Visualization and statistical analyses were 
      then employed to compare the four models and evaluate their performance. RESULTS: 
      All models yielded satisfying performance, except for Bard, who failed to provide 
      a valid response to Question 13. Claude consistently scored the highest in all 
      dimensions compared with the other three models. ChatGPT4 ranked second in 
      accuracy, with a relatively stable output across multiple tests, but shortcomings 
      were observed in empathy and human caring. Bard exhibited the lowest accuracy and 
      overall performance. Claude and ChatGPT4 both had a high capacity to analyze 
      clinical cases of urolithiasis. Overall, Claude emerged as the best performer in 
      urolithiasis consultations and education. CONCLUSION: Claude demonstrated 
      superior performance compared with the other three in urolithiasis consultation 
      and education. This study highlights the remarkable potential of LLMs in medical 
      health consultations and patient education, although professional review, further 
      evaluation, and modifications are still required.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Song, Haifeng
AU  - Song H
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China.
FAU - Xia, Yi
AU  - Xia Y
AD  - Department of Urology, Zhongda Hospital, Southeast University, 87 Dingjiaqiao, 
      Nanjing, 210009, China.
AD  - School of Medicine, Southeast University, Nanjing, 210009, China.
FAU - Luo, Zhichao
AU  - Luo Z
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China.
FAU - Liu, Hui
AU  - Liu H
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China.
FAU - Song, Yan
AU  - Song Y
AD  - Department of Urology, Sheng Jing Hospital of China Medical University, Shenyang, 
      110000, China.
FAU - Zeng, Xue
AU  - Zeng X
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China.
FAU - Li, Tianjie
AU  - Li T
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China.
FAU - Zhong, Guangxin
AU  - Zhong G
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China.
FAU - Li, Jianxing
AU  - Li J
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China.
FAU - Chen, Ming
AU  - Chen M
AD  - Department of Urology, Zhongda Hospital, Southeast University, 87 Dingjiaqiao, 
      Nanjing, 210009, China.
FAU - Zhang, Guangyuan
AU  - Zhang G
AD  - Department of Urology, Zhongda Hospital, Southeast University, 87 Dingjiaqiao, 
      Nanjing, 210009, China. zgy0879@qq.com.
FAU - Xiao, Bo
AU  - Xiao B
AD  - Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical 
      Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China. 
      beidaxiaobo@163.com.
AD  - Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 
      102218, China. beidaxiaobo@163.com.
LA  - eng
PT  - Journal Article
DEP - 20231124
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - Humans
MH  - *Patient Education as Topic
MH  - Educational Status
MH  - Language
MH  - Referral and Consultation
MH  - *Urolithiasis
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Health consultation
OT  - Large language model
OT  - Urolithiasis
EDAT- 2023/11/24 12:42
MHDA- 2023/11/27 12:44
CRDT- 2023/11/24 11:13
PHST- 2023/08/24 00:00 [received]
PHST- 2023/11/14 00:00 [accepted]
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/11/24 12:42 [pubmed]
PHST- 2023/11/24 11:13 [entrez]
AID - 10.1007/s10916-023-02021-3 [pii]
AID - 10.1007/s10916-023-02021-3 [doi]
PST - epublish
SO  - J Med Syst. 2023 Nov 24;47(1):125. doi: 10.1007/s10916-023-02021-3.

PMID- 37798960
OWN - NLM
STAT- Publisher
LR  - 20231006
IS  - 1932-2968 (Electronic)
IS  - 1932-2968 (Linking)
DP  - 2023 Oct 5
TI  - The Scientific Knowledge of Bard and ChatGPT in Endocrinology, Diabetes, and 
      Diabetes Technology: Multiple-Choice Questions Examination-Based Performance.
PG  - 19322968231203987
LID - 10.1177/19322968231203987 [doi]
AB  - BACKGROUND: The present study aimed to investigate the knowledge level of Bard 
      and ChatGPT in the areas of endocrinology, diabetes, and diabetes technology 
      through a multiple-choice question (MCQ) examination format. METHODS: Initially, 
      a 100-MCQ bank was established based on MCQs in endocrinology, diabetes, and 
      diabetes technology. The MCQs were created from physiology, medical textbooks, 
      and academic examination pools in the areas of endocrinology, diabetes, and 
      diabetes technology and academic examination pools. The study team members 
      analyzed the MCQ contents to ensure that they were related to the endocrinology, 
      diabetes, and diabetes technology. The number of MCQs from endocrinology was 50, 
      and that from diabetes and science technology was also 50. The knowledge level of 
      Google's Bard and ChatGPT was assessed with an MCQ-based examination. RESULTS: In 
      the endocrinology examination section, ChatGPT obtained 29 marks (correct 
      responses) of 50 (58%), and Bard obtained a similar score of 29 of 50 (58%). 
      However, in the diabetes technology examination section, ChatGPT obtained 23 
      marks of 50 (46%), and Bard obtained 20 marks of 50 (40%). Overall, in the entire 
      three-part examination, ChatGPT obtained 52 marks of 100 (52%), and Bard obtained 
      49 marks of 100 (49%). ChatGPT obtained slightly more marks than Bard. However, 
      both ChatGPT and Bard did not achieve satisfactory scores in endocrinology or 
      diabetes/technology of at least 60%. CONCLUSIONS: The overall MCQ-based 
      performance of ChatGPT was slightly better than that of Google's Bard. However, 
      both ChatGPT and Bard did not achieve appropriate scores in endocrinology and 
      diabetes/diabetes technology. The study indicates that Bard and ChatGPT have the 
      potential to facilitate medical students and faculty in academic medical 
      education settings, but both artificial intelligence tools need more updated 
      information in the fields of endocrinology, diabetes, and diabetes technology.
FAU - Meo, Sultan Ayoub
AU  - Meo SA
AUID- ORCID: 0000-0001-9820-1852
AD  - Department of Physiology, College of Medicine, King Saud University, Riyadh, 
      Saudi Arabia.
FAU - Al-Khlaiwi, Thamir
AU  - Al-Khlaiwi T
AD  - Department of Physiology, College of Medicine, King Saud University, Riyadh, 
      Saudi Arabia.
FAU - AbuKhalaf, Abdulelah Adnan
AU  - AbuKhalaf AA
AUID- ORCID: 0000-0001-8194-0560
AD  - College of Medicine, King Saud University, Riyadh, Saudi Arabia.
FAU - Meo, Anusha Sultan
AU  - Meo AS
AUID- ORCID: 0000-0001-5213-3233
AD  - The School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, 
      Aberdeen, UK.
FAU - Klonoff, David C
AU  - Klonoff DC
AUID- ORCID: 0000-0001-6394-6862
AD  - Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA.
LA  - eng
PT  - Journal Article
DEP - 20231005
PL  - United States
TA  - J Diabetes Sci Technol
JT  - Journal of diabetes science and technology
JID - 101306166
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Google’s board
OT  - diabetes technology
OT  - endocrinology
OT  - intellect level
OT  - knowledge
COIS- Declaration of Conflicting InterestsThe author(s) declared no potential conflicts 
      of interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/10/06 06:43
MHDA- 2023/10/06 06:43
CRDT- 2023/10/06 02:33
PHST- 2023/10/06 06:43 [medline]
PHST- 2023/10/06 06:43 [pubmed]
PHST- 2023/10/06 02:33 [entrez]
AID - 10.1177/19322968231203987 [doi]
PST - aheadofprint
SO  - J Diabetes Sci Technol. 2023 Oct 5:19322968231203987. doi: 
      10.1177/19322968231203987.

PMID- 37879994
OWN - NLM
STAT- Publisher
LR  - 20231025
IS  - 0219-3108 (Electronic)
IS  - 1015-9584 (Linking)
DP  - 2023 Oct 23
TI  - Surgery in the era of ChatGPT: A bibliometric analysis based on web of science.
LID - S1015-9584(23)01646-9 [pii]
LID - 10.1016/j.asjsur.2023.10.034 [doi]
FAU - He, Si-Ke
AU  - He SK
AD  - Department of Urology, West China Hospital, Sichuan University, Chengdu, China.
FAU - Tu, Teng
AU  - Tu T
AD  - West China School of Medicine, Sichuan University, Chengdu, China.
FAU - Deng, Bo-Wen
AU  - Deng BW
AD  - Southwest Medical University, Luzhou, China.
FAU - Bai, Yun-Jin
AU  - Bai YJ
AD  - Department of Urology, West China Hospital, Sichuan University, Chengdu, China. 
      Electronic address: baiyunjin@scu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20231023
PL  - Netherlands
TA  - Asian J Surg
JT  - Asian journal of surgery
JID - 8900600
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bibliometric analysis
OT  - ChatGPT
OT  - Surgery
EDAT- 2023/10/26 00:42
MHDA- 2023/10/26 00:42
CRDT- 2023/10/25 21:57
PHST- 2023/09/17 00:00 [received]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/10/26 00:42 [medline]
PHST- 2023/10/26 00:42 [pubmed]
PHST- 2023/10/25 21:57 [entrez]
AID - S1015-9584(23)01646-9 [pii]
AID - 10.1016/j.asjsur.2023.10.034 [doi]
PST - aheadofprint
SO  - Asian J Surg. 2023 Oct 23:S1015-9584(23)01646-9. doi: 
      10.1016/j.asjsur.2023.10.034.

PMID- 37930057
OWN - NLM
STAT- Publisher
LR  - 20231106
IS  - 1097-0096 (Electronic)
IS  - 0091-2751 (Linking)
DP  - 2023 Nov 6
TI  - An investigation study on the interpretation of ultrasonic medical reports using 
      OpenAI's GPT-3.5-turbo model.
LID - 10.1002/jcu.23590 [doi]
AB  - OBJECTIVES: Ultrasound medical reports are an important means of diagnosing 
      diseases and assessing treatment effectiveness. However, their professional terms 
      and complex sentences often make it difficult for ordinary people to understand. 
      Therefore, this study explores the clinical value of using artificial 
      intelligence systems based on ChatGPT to interpret ultrasound medical reports. 
      METHODS: In this study, a combination of online and offline questionnaires were 
      used to survey both physicians and non-medical individuals. The questionnaires 
      evaluated ChatGPT's interpretation of ultrasound reports from both professional 
      and comprehensibility perspectives, and the results were analyzed using Excel 
      spreadsheets. Additionally, a portion of the research content was evaluated using 
      the Likert Scale 5-point method in the questionnaire. RESULTS: According to 
      survey results, 67.4% of surveyed doctors believe that using ChatGPT for 
      interpreting ultrasound medical reports can help improve work efficiency. At the 
      same time, 69.72% of non-medical professionals believe it is necessary to enhance 
      their understanding of medical ultrasound reports through ChatGPT interpretation, 
      and 62.58% support the application of ChatGPT to ultrasound medical reports. The 
      non-medical group's understanding of ultrasound medical reports significantly 
      improved (p < 0.01) after implementing ChatGPT, However, 67.49% of the general 
      public are concerned about ChatGPT's imperfect functionality, which may cause 
      misleading information. This reflects that the public's trust in new technology 
      is not high enough, and they are also worried about possible privacy leaks and 
      security issues with ChatGPT technology. CONCLUSIONS: The higher acceptance and 
      support of non-medical individuals for the interpretation of medical reports by 
      ChatGPT might be due to the system's natural language processing abilities that 
      allow them to better understand and evaluate report contents. However, the 
      expertise and experience of physicians are still irreplaceable. This suggests 
      that the ChatGPT-based ultrasound medical report interpretation system has 
      certain clinical value and application prospects, but further optimization is 
      necessary to address its shortcomings in data quality and professionalism. This 
      study provides a reference and inspiration for promoting the application and 
      development of ultrasound technology and artificial intelligence systems in the 
      medical field.
CI  - © 2023 Wiley Periodicals LLC.
FAU - Wang, Wen Hui
AU  - Wang WH
AUID- ORCID: 0000-0001-7495-100X
AD  - Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 
      China.
FAU - Wang, Shi Yu
AU  - Wang SY
AD  - School of Life Science and Technology, University of Electronic Science and 
      Technology of China, Chengdu, China.
FAU - Huang, Jia Yan
AU  - Huang JY
AD  - Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 
      China.
FAU - Liu, Xiao di
AU  - Liu XD
AD  - Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 
      China.
FAU - Yang, Jie
AU  - Yang J
AD  - Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 
      China.
FAU - Liao, Min
AU  - Liao M
AD  - Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 
      China.
FAU - Lu, Qiang
AU  - Lu Q
AD  - Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 
      China.
FAU - Wu, Zhe
AU  - Wu Z
AD  - School of Life Science and Technology, University of Electronic Science and 
      Technology of China, Chengdu, China.
LA  - eng
GR  - 82027808/The National Key Scientific Instrument Development Program/
GR  - 2019YFE0196700/The National Science and Technology Ministry of China/
PT  - Journal Article
DEP - 20231106
PL  - United States
TA  - J Clin Ultrasound
JT  - Journal of clinical ultrasound : JCU
JID - 0401663
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - GPT-3.5
OT  - Ultrasound
EDAT- 2023/11/06 12:43
MHDA- 2023/11/06 12:43
CRDT- 2023/11/06 08:14
PHST- 2023/09/23 00:00 [revised]
PHST- 2023/07/31 00:00 [received]
PHST- 2023/10/04 00:00 [accepted]
PHST- 2023/11/06 12:43 [medline]
PHST- 2023/11/06 12:43 [pubmed]
PHST- 2023/11/06 08:14 [entrez]
AID - 10.1002/jcu.23590 [doi]
PST - aheadofprint
SO  - J Clin Ultrasound. 2023 Nov 6. doi: 10.1002/jcu.23590.

PMID- 37130197
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1538-9855 (Electronic)
IS  - 0363-3624 (Linking)
VI  - 48
IP  - 5
DP  - 2023 Sep-Oct 01
TI  - Can ChatGPT Accurately Answer a PICOT Question? Assessing AI Response to a 
      Clinical Question.
PG  - 231-233
LID - 10.1097/NNE.0000000000001436 [doi]
AB  - BACKGROUND: ChatGPT, an artificial intelligence (AI) text generator trained to 
      predict correct words, can provide answers to questions but has shown mixed 
      results in answering medical questions. PURPOSE: To assess the reliability and 
      accuracy of ChatGPT in providing answers to a complex clinical question. METHODS: 
      A Population, Intervention, Comparison, Outcome, and Time (PICOT) formatted 
      question was queried, along with a request for references. Full-text articles 
      were reviewed to verify the accuracy of the evidence summary provided by the 
      chatbot. RESULTS: ChatGPT was unable to provide a certifiable response to a PICOT 
      question. The references cited as evidence included incorrect journal 
      information, and many study details summarized by ChatGPT proved to be patently 
      false, including providing fabricated data. CONCLUSIONS: ChatGPT provides answers 
      that appear legitimate but may be factually incorrect. The system is not 
      transparent in how it gathers data to answer questions and sometimes fabricates 
      information that looks plausible, making it an unreliable tool for clinical 
      questions.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Branum, Candise
AU  - Branum C
AUID- ORCID: 0009-0005-2486-0675
AD  - Health Science Librarian and Assistant Professor (Mx Branum), Foley Center 
      Library, and Assistant Professor (Dr Schiavenato), School of Nursing and Human 
      Physiology, Gonzaga University, Spokane, Washington.
FAU - Schiavenato, Martin
AU  - Schiavenato M
LA  - eng
PT  - Journal Article
DEP - 20230428
PL  - United States
TA  - Nurse Educ
JT  - Nurse educator
JID - 7701902
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - Nursing Education Research
MH  - *Software
COIS- The authors declare no conflicts of interest.
EDAT- 2023/05/02 18:41
MHDA- 2023/05/02 18:42
CRDT- 2023/05/02 14:23
PHST- 2023/05/02 18:42 [medline]
PHST- 2023/05/02 18:41 [pubmed]
PHST- 2023/05/02 14:23 [entrez]
AID - 00006223-990000000-00234 [pii]
AID - 10.1097/NNE.0000000000001436 [doi]
PST - ppublish
SO  - Nurse Educ. 2023 Sep-Oct 01;48(5):231-233. doi: 10.1097/NNE.0000000000001436. 
      Epub 2023 Apr 28.

PMID- 38033801
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231202
IS  - 2374-7943 (Print)
IS  - 2374-7951 (Electronic)
IS  - 2374-7943 (Linking)
VI  - 9
IP  - 11
DP  - 2023 Nov 22
TI  - ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs.
PG  - 2161-2170
LID - 10.1021/acscentsci.3c01087 [doi]
AB  - We leveraged the power of ChatGPT and Bayesian optimization in the development of 
      a multi-AI-driven system, backed by seven large language model-based assistants 
      and equipped with machine learning algorithms, that seamlessly orchestrates a 
      multitude of research aspects in a chemistry laboratory (termed the ChatGPT 
      Research Group). Our approach accelerated the discovery of optimal microwave 
      synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and 
      COF-323 and achieving the desired porosity and water capacity. In this system, 
      human researchers gained assistance from these diverse AI collaborators, each 
      with a unique role within the laboratory environment, spanning strategy planning, 
      literature search, coding, robotic operation, labware design, safety inspection, 
      and data analysis. Such a comprehensive approach enables a single researcher 
      working in concert with AI to achieve productivity levels analogous to those of 
      an entire traditional scientific team. Furthermore, by reducing human biases in 
      screening experimental conditions and deftly balancing the exploration and 
      exploitation of synthesis parameters, our Bayesian search approach precisely 
      zeroed in on optimal synthesis conditions from a pool of 6 million within a 
      significantly shortened time scale. This work serves as a compelling proof of 
      concept for an AI-driven revolution in the chemistry laboratory, painting a 
      future where AI becomes an efficient collaborator, liberating us from routine 
      tasks to focus on pushing the boundaries of innovation.
CI  - © 2023 The Authors. Published by American Chemical Society.
FAU - Zheng, Zhiling
AU  - Zheng Z
AUID- ORCID: 0000-0001-6090-2258
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
FAU - Zhang, Oufan
AU  - Zhang O
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kenneth S. Pitzer Center for Theoretical Chemistry, University of California, 
      Berkeley, California 94720, United States.
FAU - Nguyen, Ha L
AU  - Nguyen HL
AUID- ORCID: 0000-0002-4977-925X
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
FAU - Rampal, Nakul
AU  - Rampal N
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
FAU - Alawadhi, Ali H
AU  - Alawadhi AH
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
FAU - Rong, Zichao
AU  - Rong Z
AUID- ORCID: 0000-0002-9014-9540
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
FAU - Head-Gordon, Teresa
AU  - Head-Gordon T
AUID- ORCID: 0000-0003-0025-8987
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kenneth S. Pitzer Center for Theoretical Chemistry, University of California, 
      Berkeley, California 94720, United States.
AD  - Department of Chemical and Biomolecular Engineering, University of California, 
      Berkeley, California 94720, United States.
AD  - Department of Bioengineering, University of California, Berkeley, California 
      94720, United States.
FAU - Borgs, Christian
AU  - Borgs C
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
AD  - Department of Electrical Engineering and Computer Sciences, University of 
      California, Berkeley, California 94720, United States.
FAU - Chayes, Jennifer T
AU  - Chayes JT
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
AD  - Department of Electrical Engineering and Computer Sciences, University of 
      California, Berkeley, California 94720, United States.
AD  - Department of Mathematics, University of California, Berkeley, California 94720, 
      United States.
AD  - Department of Statistics, University of California, Berkeley, California 94720, 
      United States.
AD  - School of Information, University of California, Berkeley, California 94720, 
      United States.
FAU - Yaghi, Omar M
AU  - Yaghi OM
AUID- ORCID: 0000-0002-5611-3325
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
AD  - KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy 
      Applications, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi 
      Arabia.
LA  - eng
PT  - Journal Article
DEP - 20231110
PL  - United States
TA  - ACS Cent Sci
JT  - ACS central science
JID - 101660035
PMC - PMC10683477
COIS- The authors declare the following competing financial interest(s): Omar M. Yaghi 
      is co-founder of ATOCO Inc., aiming at commercializing related technologies.
EDAT- 2023/11/30 18:45
MHDA- 2023/11/30 18:46
CRDT- 2023/11/30 17:24
PHST- 2023/08/29 00:00 [received]
PHST- 2023/10/20 00:00 [revised]
PHST- 2023/10/23 00:00 [accepted]
PHST- 2023/11/30 18:46 [medline]
PHST- 2023/11/30 18:45 [pubmed]
PHST- 2023/11/30 17:24 [entrez]
AID - 10.1021/acscentsci.3c01087 [doi]
PST - epublish
SO  - ACS Cent Sci. 2023 Nov 10;9(11):2161-2170. doi: 10.1021/acscentsci.3c01087. 
      eCollection 2023 Nov 22.

PMID- 37770637
OWN - NLM
STAT- Publisher
LR  - 20230928
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Sep 28
TI  - Exploring the Potential of ChatGPT-4 in Responding to Common Questions About 
      Abdominoplasty: An AI-Based Case Study of a Plastic Surgery Consultation.
LID - 10.1007/s00266-023-03660-0 [doi]
AB  - BACKGROUND: With the increasing integration of artificial intelligence (AI) in 
      health care, AI chatbots like ChatGPT-4 are being used to deliver health 
      information. OBJECTIVES: This study aimed to assess the capability of ChatGPT-4 
      in answering common questions related to abdominoplasty, evaluating its potential 
      as an adjunctive tool in patient education and preoperative consultation. 
      METHODS: A variety of common questions about abdominoplasty were submitted to 
      ChatGPT-4. These questions were sourced from a question list provided by the 
      American Society of Plastic Surgery to ensure their relevance and 
      comprehensiveness. An experienced plastic surgeon meticulously evaluated the 
      responses generated by ChatGPT-4 in terms of informational depth, response 
      articulation, and competency to determine the proficiency of the AI in providing 
      patient-centered information. RESULTS: The study showed that ChatGPT-4 can give 
      clear answers, making it useful for answering common queries. However, it 
      struggled with personalized advice and sometimes provided incorrect or outdated 
      references. Overall, ChatGPT-4 can effectively share abdominoplasty information, 
      which may help patients better understand the procedure. Despite these positive 
      findings, the AI needs more refinement, especially in providing personalized and 
      accurate information, to fully meet patient education needs in plastic surgery. 
      CONCLUSIONS: Although ChatGPT-4 shows promise as a resource for patient 
      education, continuous improvements and rigorous checks are essential for its 
      beneficial integration into healthcare settings. The study emphasizes the need 
      for further research, particularly focused on improving the personalization and 
      accuracy of AI responses. LEVEL OF EVIDENCE V: This journal requires that authors 
      assign a level of evidence to each article. For a full description of these 
      Evidence-Based Medicine ratings, please refer to the Table of Contents or the 
      online Instructions to Authors www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Li, Wenbo
AU  - Li W
AD  - Department of Nursing, Jinzhou Medical University, Jinzhou, 121001, China.
FAU - Chen, Junjiang
AU  - Chen J
AD  - Department of Burn Plastic and Medical Aesthetic Surgery, The First Affiliated 
      Hospital, Jinzhou Medical University, Jinzhou, China.
FAU - Chen, Fengmin
AU  - Chen F
AD  - Department of Colorectal Surgery, The First Affiliated Hospital, Jinzhou Medical 
      University, Jinzhou, China.
FAU - Liang, Jiaqing
AU  - Liang J
AD  - Department of Nursing, Jinzhou Medical University, Jinzhou, 121001, China.
FAU - Yu, Hongyu
AU  - Yu H
AD  - Department of Nursing, Jinzhou Medical University, Jinzhou, 121001, China. 
      15840040053@163.com.
LA  - eng
PT  - Journal Article
DEP - 20230928
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
OTO - NOTNLM
OT  - Abdominoplasty
OT  - Artificial intelligence
OT  - ChatGPT
OT  - ChatGPT-4
OT  - Chatbot
EDAT- 2023/09/29 00:42
MHDA- 2023/09/29 00:42
CRDT- 2023/09/28 23:35
PHST- 2023/07/12 00:00 [received]
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/09/29 00:42 [medline]
PHST- 2023/09/29 00:42 [pubmed]
PHST- 2023/09/28 23:35 [entrez]
AID - 10.1007/s00266-023-03660-0 [pii]
AID - 10.1007/s00266-023-03660-0 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Sep 28. doi: 10.1007/s00266-023-03660-0.

PMID- 37033498
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230411
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Extraventricular Neurocytoma of the Posterior Fossa: A Case Report Written by 
      ChatGPT.
PG  - e35850
LID - 10.7759/cureus.35850 [doi]
LID - e35850
AB  - ChatGPT (Chat Generative Pre-trained Transformer) is the latest artificial 
      intelligence (AI) tool that has taken the internet by storm. A large language 
      model-based AI tool, ChatGPT is used to generate natural prose-like answers 
      learning from a large database of text. A lot is being written and spoken about 
      the future of academic writing using this tool.We attempt to generate a short 
      case report of a brain tumour labelled extraventriuclar neurocytoma (EVN) which 
      is a rare tumour of the central nervous system. When posed with a set of 
      questions on this uncommon topic, ChatGPT did well with relevant but not very 
      specific answers. Validation of the information is an issue like with most 
      machine learning solutions. This case report is an attempt to summarise EVN and 
      its management. Q: Summarise extraventricular neurocytoma. ChatGPT: EVN is a rare 
      and benign tumor that typically occurs in the brain. It often causes no symptoms, 
      but as it grows, it can cause headaches, seizures, and changes in vision or 
      balance. Surgery is usually curative, but there is a lack of studies to know the 
      best treatment options. The prognosis is generally good with high rates of 
      long-term survival and low rates of recurrence. Text italicised in the article is 
      generated by ChatGPT.
CI  - Copyright © 2023, Hegde et al.
FAU - Hegde, Ajay
AU  - Hegde A
AD  - Neurosurgery, Kasturba Medical College, Manipal, IND.
FAU - Srinivasan, Siddharth
AU  - Srinivasan S
AD  - Neurosurgery, Kasturba Medical College, Manipal, IND.
FAU - Menon, Girish
AU  - Menon G
AD  - Neurosurgery, Kasturba Medical College, Manipal, IND.
LA  - eng
PT  - Case Reports
DEP - 20230306
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10076908
OTO - NOTNLM
OT  - artificial intelligence (ai)
OT  - brain tumour
OT  - chatgpt
OT  - extraventricular neurocytoma
OT  - neuro oncology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/11 06:00
MHDA- 2023/04/11 06:01
CRDT- 2023/04/10 03:54
PHST- 2023/03/05 00:00 [accepted]
PHST- 2023/04/11 06:01 [medline]
PHST- 2023/04/10 03:54 [entrez]
PHST- 2023/04/11 06:00 [pubmed]
AID - 10.7759/cureus.35850 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 6;15(3):e35850. doi: 10.7759/cureus.35850. eCollection 2023 Mar.

PMID- 37547515
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230808
IS  - 2468-0249 (Electronic)
IS  - 2468-0249 (Linking)
VI  - 8
IP  - 8
DP  - 2023 Aug
TI  - Assessing the Accuracy of ChatGPT on Core Questions in Glomerular Disease.
PG  - 1657-1659
LID - 10.1016/j.ekir.2023.05.014 [doi]
FAU - Miao, Jing
AU  - Miao J
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, Minnesota, USA.
FAU - Thongprayoon, Charat
AU  - Thongprayoon C
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, Minnesota, USA.
FAU - Cheungpasitporn, Wisit
AU  - Cheungpasitporn W
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, Minnesota, USA.
LA  - eng
PT  - Journal Article
DEP - 20230526
PL  - United States
TA  - Kidney Int Rep
JT  - Kidney international reports
JID - 101684752
PMC - PMC10403654
OTO - NOTNLM
OT  - ChatGPT
OT  - KSAP
OT  - NephSAP
OT  - accuracy
OT  - concordance
OT  - glomerular disease
EDAT- 2023/08/07 06:42
MHDA- 2023/08/07 06:43
CRDT- 2023/08/07 04:54
PHST- 2023/04/01 00:00 [received]
PHST- 2023/05/09 00:00 [revised]
PHST- 2023/05/15 00:00 [accepted]
PHST- 2023/08/07 06:43 [medline]
PHST- 2023/08/07 06:42 [pubmed]
PHST- 2023/08/07 04:54 [entrez]
AID - S2468-0249(23)01309-8 [pii]
AID - 10.1016/j.ekir.2023.05.014 [doi]
PST - epublish
SO  - Kidney Int Rep. 2023 May 26;8(8):1657-1659. doi: 10.1016/j.ekir.2023.05.014. 
      eCollection 2023 Aug.

PMID- 37553555
OWN - NLM
STAT- Publisher
LR  - 20230808
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Aug 8
TI  - Sailing the Seven Seas: A Multinational Comparison of ChatGPT's Performance on 
      Medical Licensing Examinations.
LID - 10.1007/s10439-023-03338-3 [doi]
AB  - PURPOSE: The use of AI-powered technology, particularly OpenAI's ChatGPT, holds 
      significant potential to reshape healthcare and medical education. Despite 
      existing studies on the performance of ChatGPT in medical licensing examinations 
      across different nations, a comprehensive, multinational analysis using rigorous 
      methodology is currently lacking. Our study sought to address this gap by 
      evaluating the performance of ChatGPT on six different national medical licensing 
      exams and investigating the relationship between test question length and 
      ChatGPT's accuracy. METHODS: We manually inputted a total of 1,800 test questions 
      (300 each from US, Italian, French, Spanish, UK, and Indian medical licensing 
      examination) into ChatGPT, and recorded the accuracy of its responses. RESULTS: 
      We found significant variance in ChatGPT's test accuracy across different 
      countries, with the highest accuracy seen in the Italian examination (73% correct 
      answers) and the lowest in the French examination (22% correct answers). 
      Interestingly, question length correlated with ChatGPT's performance in the 
      Italian and French state examinations only. In addition, the study revealed that 
      questions requiring multiple correct answers, as seen in the French examination, 
      posed a greater challenge to ChatGPT. CONCLUSION: Our findings underscore the 
      need for future research to further delineate ChatGPT's strengths and limitations 
      in medical test-taking across additional countries and to develop guidelines to 
      prevent AI-assisted cheating in medical examinations.
CI  - © 2023. The Author(s).
FAU - Alfertshofer, Michael
AU  - Alfertshofer M
AD  - Division of Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilians University 
      Munich, Ziemssenstrasse 5, 80336, Munich, Germany. m.alfertshofer@campus.lmu.de.
FAU - Hoch, Cosima C
AU  - Hoch CC
AD  - Department of Otolaryngology, Head and Neck Surgery, School of Medicine, 
      Technical University of Munich (TUM), Ismaningerstrasse 22, 81675, Munich, 
      Germany.
FAU - Funk, Paul F
AU  - Funk PF
AD  - Department of Otolaryngology, Head and Neck Surgery, University Hospital Jena, 
      Friedrich Schiller University Jena, Am Klinikum 1, 07747, Jena, Germany.
FAU - Hollmann, Katharina
AU  - Hollmann K
AD  - Department of Pathology, Massachusetts General Hospital, Harvard Medical School, 
      55 Fruit St, Boston, MA, 02114, USA.
FAU - Wollenberg, Barbara
AU  - Wollenberg B
AD  - Department of Otolaryngology, Head and Neck Surgery, School of Medicine, 
      Technical University of Munich (TUM), Ismaningerstrasse 22, 81675, Munich, 
      Germany.
FAU - Knoedler, Samuel
AU  - Knoedler S
AD  - Department of Plastic, Hand and Reconstructive Surgery, University Hospital 
      Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany.
FAU - Knoedler, Leonard
AU  - Knoedler L
AD  - Department of Plastic, Hand and Reconstructive Surgery, University Hospital 
      Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany.
LA  - eng
PT  - Letter
DEP - 20230808
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Clinical decision-making
OT  - Medical education
OT  - Medical licensing exams
OT  - OpenAI
EDAT- 2023/08/09 01:05
MHDA- 2023/08/09 01:05
CRDT- 2023/08/08 23:30
PHST- 2023/07/26 00:00 [received]
PHST- 2023/07/28 00:00 [accepted]
PHST- 2023/08/09 01:05 [medline]
PHST- 2023/08/09 01:05 [pubmed]
PHST- 2023/08/08 23:30 [entrez]
AID - 10.1007/s10439-023-03338-3 [pii]
AID - 10.1007/s10439-023-03338-3 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Aug 8. doi: 10.1007/s10439-023-03338-3.

PMID- 37184746
OWN - NLM
STAT- MEDLINE
DCOM- 20230926
LR  - 20230926
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 10
DP  - 2023 Oct
TI  - Revolutionizing Chronic Obstructive Pulmonary Disease Care with the Open AI 
      Application: ChatGPT.
PG  - 2100-2102
LID - 10.1007/s10439-023-03238-6 [doi]
AB  - Chronic Obstructive Pulmonary Disease (COPD) is one of the major and leading 
      threats to human being. To cope with this health challenge, several studies have 
      been undertaken in the literature. However, COPD is not paid close attention to 
      eliminate it entirely. The current study aims to examine the role of ChatGPT 
      application to bring improvement in controlling and managing COPD in patients. 
      ChatGPT is used to give prompt answers of text-based questions in a variety of 
      fields. It has potential role in knowing the symptoms of COPD, and letting 
      individuals to modify their life styles. ChatGPT suggests medications for COPD 
      individuals based on the established medical guidelines. Compared to the 
      literature, ChatGPT provides a comprehensive list of COPD test and evaluation 
      methods. ChatGPT has the potential to help physicians to take decisions in 
      diagnosing, treating, and managing COPD among individuals in its upcoming 
      versions. More researches can be conducted to identify the limits of ChatGPT 
      application in future works.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Hasnain, Muhammad
AU  - Hasnain M
AD  - Department of Computer Science, Lahore Leads University, Lahore, Pakistan. 
      drhasnain.it@leads.edu.pk.
FAU - Hayat, Asad
AU  - Hayat A
AD  - Department of Computer Science, Lahore Leads University, Lahore, Pakistan.
FAU - Hussain, Akbar
AU  - Hussain A
AD  - Department of Information Technology, Lahore Leads University, Lahore, Pakistan.
LA  - eng
PT  - Letter
DEP - 20230515
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Quality of Life
MH  - *Pulmonary Disease, Chronic Obstructive/diagnosis/drug therapy
OTO - NOTNLM
OT  - COPD tests
OT  - Medication
OT  - Pulmonary rehabilitation
EDAT- 2023/05/15 13:06
MHDA- 2023/09/26 13:42
CRDT- 2023/05/15 11:16
PHST- 2023/05/07 00:00 [received]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/09/26 13:42 [medline]
PHST- 2023/05/15 13:06 [pubmed]
PHST- 2023/05/15 11:16 [entrez]
AID - 10.1007/s10439-023-03238-6 [pii]
AID - 10.1007/s10439-023-03238-6 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Oct;51(10):2100-2102. doi: 10.1007/s10439-023-03238-6. Epub 
      2023 May 15.

PMID- 37490183
OWN - NLM
STAT- Publisher
LR  - 20230725
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 25
TI  - How Does ChatGPT Perform on the Italian Residency Admission National Exam 
      Compared to 15,869 Medical Graduates?
LID - 10.1007/s10439-023-03318-7 [doi]
AB  - PURPOSE: The study aims to assess ChatGPT performance on the Residency Admission 
      National Exam to evaluate ChatGPT's level of medical knowledge compared to 
      graduate medical doctors in Italy. METHODS: ChatGPT3 was used in June 2023 to 
      undertake the 2022 Italian Residency Admission National Exam-a 140 multiple 
      choice questions computer-based exam taken by all Italian medical graduates 
      yearly, used to assess basic science and applied medical knowledge. The exam was 
      scored using the same criteria defined by the national educational governing 
      body. The performance of ChatGPT was compared to the performance of the 15,869 
      medical graduates who took the exam in July 2022. Lastly, the integrity and 
      quality of ChatGPT's responses were evaluated. RESULTS: ChatGPT answered 
      correctly 122 out of 140 questions. The score ranked in the top 98.8(th) 
      percentile among 15,869 medical graduates. Among the 18 incorrect answers, 10 
      were evaluating direct questions on basic science medical knowledge, while 8 were 
      evaluating candidates' applied clinical knowledge and reasoning under the form of 
      case presentation. Errors were logical (2 incorrect answers) and informational in 
      nature (16 incorrect answers). Explanations to the correct answers were all 
      evaluated as "appropriate." Comparison to national statistics related to the 
      minimal score needed to match into each specialty, demonstrated that the 
      performance of ChatGPT would have granted the candidate a match into any 
      specialty. CONCLUSION: ChatGPT proved to be proficient in basic science medical 
      knowledge and applied clinical knowledge. Future research should assess the 
      impact and reliability of ChatGPT in clinical practice.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Alessandri Bonetti, Mario
AU  - Alessandri Bonetti M
AUID- ORCID: 0000-0002-5506-8323
AD  - Residency Program in Plastic Surgery, University of Milan, Via Festa del Perdono 
      7, 20122, Milan, Italy.
FAU - Giorgino, Riccardo
AU  - Giorgino R
AD  - Residency Program in Orthopedics and Traumatology, University of Milan, Via Festa 
      del Perdono 7, 20122, Milan, Italy.
FAU - Gallo Afflitto, Gabriele
AU  - Gallo Afflitto G
AD  - Residency Program in Ophthalmology, Università di Roma "Tor Vergata", Via 
      Cracovia 50, 00133, Rome, Italy.
FAU - De Lorenzi, Francesca
AU  - De Lorenzi F
AD  - Department of Plastic Surgery, European Institute of Oncology, Via Giuseppe 
      Ripamonti 345, 20122, Milan, Italy.
FAU - Egro, Francesco M
AU  - Egro FM
AUID- ORCID: 0000-0003-1536-7713
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 3550 
      Terrace Street 6B Scaife Hall, Pittsburgh, PA, 15261, USA. 
      francescoegro@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230725
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Education
OT  - Medical degree
OT  - Medical knowledge
OT  - Residency
EDAT- 2023/07/25 13:08
MHDA- 2023/07/25 13:08
CRDT- 2023/07/25 11:08
PHST- 2023/07/02 00:00 [received]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/07/25 13:08 [medline]
PHST- 2023/07/25 13:08 [pubmed]
PHST- 2023/07/25 11:08 [entrez]
AID - 10.1007/s10439-023-03318-7 [pii]
AID - 10.1007/s10439-023-03318-7 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 25. doi: 10.1007/s10439-023-03318-7.

PMID- 38057132
OWN - NLM
STAT- Publisher
LR  - 20231206
IS  - 2326-5205 (Electronic)
IS  - 2326-5191 (Linking)
DP  - 2023 Dec 6
TI  - Doctor versus AI: Patient and physician evaluation of large language model 
      responses to rheumatology patient questions: comment.
LID - 10.1002/art.42773 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Phonhong, Lao People's Democratic Republic.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, India.
LA  - eng
PT  - Letter
DEP - 20231206
PL  - United States
TA  - Arthritis Rheumatol
JT  - Arthritis & rheumatology (Hoboken, N.J.)
JID - 101623795
SB  - IM
EDAT- 2023/12/07 00:42
MHDA- 2023/12/07 00:42
CRDT- 2023/12/06 21:38
PHST- 2023/11/14 00:00 [received]
PHST- 2023/11/28 00:00 [accepted]
PHST- 2023/12/07 00:42 [medline]
PHST- 2023/12/07 00:42 [pubmed]
PHST- 2023/12/06 21:38 [entrez]
AID - 10.1002/art.42773 [doi]
PST - aheadofprint
SO  - Arthritis Rheumatol. 2023 Dec 6. doi: 10.1002/art.42773.

PMID- 37638266
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230829
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Exploring the Potential and Limitations of Chat Generative Pre-trained 
      Transformer (ChatGPT) in Generating Board-Style Dermatology Questions: A 
      Qualitative Analysis.
PG  - e43717
LID - 10.7759/cureus.43717 [doi]
LID - e43717
AB  - This article investigates the limitations of Chat Generative Pre-trained 
      Transformer (ChatGPT), a language model developed by OpenAI, as a study tool in 
      dermatology. The study utilized ChatPDF, an application that integrates PDF files 
      with ChatGPT, to generate American Board of Dermatology Applied Exam 
      (ABD-AE)-style questions from continuing medical education articles from the 
      Journal of the American Board of Dermatology. A qualitative analysis of the 
      questions was conducted by two board-certified dermatologists, assessing 
      accuracy, complexity, and clarity. Out of 40 questions generated, only 16 (40%) 
      were deemed accurate and appropriate for ABD-AE study preparation. The remaining 
      questions exhibited limitations, including low complexity, lack of clarity, and 
      inaccuracies. The findings highlight the challenges faced by ChatGPT in 
      understanding the domain-specific knowledge required in dermatology. Moreover, 
      the model's inability to comprehend the context and generate high-quality 
      distractor options, as well as the absence of image generation capabilities, 
      further hinders its usefulness. The study emphasizes that while ChatGPT may aid 
      in generating simple questions, it cannot replace the expertise of dermatologists 
      and medical educators in developing high-quality, board-style questions that 
      effectively evaluate candidates' knowledge and reasoning abilities.
CI  - Copyright © 2023, Ayub et al.
FAU - Ayub, Ibraheim
AU  - Ayub I
AD  - Dermatology, A.T. Still University School of Osteopathic Medicine, Mesa, USA.
FAU - Hamann, Dathan
AU  - Hamann D
AD  - Dermatology, Dermatology Residency, HonorHealth, Scottsdale, USA.
FAU - Hamann, Carsten R
AU  - Hamann CR
AD  - Dermatology, HonorHealth Dermatology Residency, Scottsdale, USA.
AD  - Dermatology, Dartmouth-Hitchcock Medical Center, Lebanon, USA.
FAU - Davis, Matthew J
AU  - Davis MJ
AD  - Dermatology, Dartmouth-Hitchcock Medical Center, Lebanon, USA.
LA  - eng
PT  - Journal Article
DEP - 20230818
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10450251
OTO - NOTNLM
OT  - artificial intelligence in medicine
OT  - chatgpt
OT  - dermatology
OT  - medical education
OT  - multiple-choice questions
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/28 06:42
MHDA- 2023/08/28 06:43
CRDT- 2023/08/28 05:02
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/08/28 06:43 [medline]
PHST- 2023/08/28 06:42 [pubmed]
PHST- 2023/08/28 05:02 [entrez]
AID - 10.7759/cureus.43717 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 18;15(8):e43717. doi: 10.7759/cureus.43717. eCollection 2023 
      Aug.

PMID- 37286486
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20230703
IS  - 1347-4820 (Electronic)
IS  - 1346-9843 (Linking)
VI  - 87
IP  - 7
DP  - 2023 Jun 23
TI  - Evaluation of the Accuracy of ChatGPT in Answering Clinical Questions on the 
      Japanese Society of Hypertension Guidelines.
PG  - 1030-1033
LID - 10.1253/circj.CJ-23-0308 [doi]
AB  - BACKGROUND: To assist healthcare providers in interpreting guidelines, clinical 
      questions (CQ) are often included, but not always, which can make interpretation 
      difficult for non-expert clinicians. We evaluated the ability of ChatGPT to 
      accurately answer CQs on the Japanese Society of Hypertension Guidelines for the 
      Management of Hypertension (JSH 2019).Methods and Results: We conducted an 
      observational study using data from JSH 2019. The accuracy rate for CQs and 
      limited evidence-based questions of the guidelines (Qs) were evaluated. ChatGPT 
      demonstrated a higher accuracy rate for CQs than for Qs (80% vs. 36%, P value: 
      0.005). CONCLUSIONS: ChatGPT has the potential to be a valuable tool for 
      clinicians in the management of hypertension.
FAU - Kusunose, Kenya
AU  - Kusunose K
AD  - Department of Cardiovascular Medicine, Tokushima University Hospital.
AD  - Department of Cardiovascular Medicine, Nephrology, and Neurology, Graduate School 
      of Medicine, University of the Ryukyus.
FAU - Kashima, Shuichiro
AU  - Kashima S
AD  - Department of Cardiovascular Medicine, Tokushima University Hospital.
FAU - Sata, Masataka
AU  - Sata M
AD  - Department of Cardiovascular Medicine, Tokushima University Hospital.
LA  - eng
PT  - Journal Article
PT  - Observational Study
DEP - 20230607
PL  - Japan
TA  - Circ J
JT  - Circulation journal : official journal of the Japanese Circulation Society
JID - 101137683
SB  - IM
MH  - Humans
MH  - *East Asian People
MH  - Health Personnel
MH  - *Hypertension/diagnosis/drug therapy
MH  - Reproducibility of Results
MH  - Artificial Intelligence
MH  - Social Media
OTO - NOTNLM
OT  - ChatGPT
OT  - Guidelines
OT  - Hypertension
OT  - Large language models
EDAT- 2023/06/08 01:08
MHDA- 2023/06/27 06:42
CRDT- 2023/06/07 22:43
PHST- 2023/06/27 06:42 [medline]
PHST- 2023/06/08 01:08 [pubmed]
PHST- 2023/06/07 22:43 [entrez]
AID - 10.1253/circj.CJ-23-0308 [doi]
PST - ppublish
SO  - Circ J. 2023 Jun 23;87(7):1030-1033. doi: 10.1253/circj.CJ-23-0308. Epub 2023 Jun 
      7.

PMID- 37410674
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230925
IS  - 1469-0756 (Electronic)
IS  - 0032-5473 (Linking)
VI  - 99
IP  - 1176
DP  - 2023 Sep 21
TI  - Artificial intelligence in orthopaedics: can Chat Generative Pre-trained 
      Transformer (ChatGPT) pass Section 1 of the Fellowship of the Royal College of 
      Surgeons (Trauma & Orthopaedics) examination?
PG  - 1110-1114
LID - 10.1093/postmj/qgad053 [doi]
AB  - PURPOSE: Chat Generative Pre-trained Transformer (ChatGPT) is a large language 
      artificial intelligence (AI) model which generates contextually relevant text in 
      response to questioning. After ChatGPT successfully passed the United States 
      Medical Licensing Examinations, proponents have argued it should play an 
      increasing role in medical service provision and education. AI in healthcare 
      remains in its infancy, and the reliability of AI systems must be scrutinized. 
      This study assessed whether ChatGPT could pass Section 1 of the Fellowship of the 
      Royal College of Surgeons (FRCS) examination in Trauma and Orthopaedic Surgery. 
      METHODS: The UK and Ireland In-Training Examination (UKITE) was used as a 
      surrogate for the FRCS. Papers 1 and 2 of UKITE 2022 were directly inputted into 
      ChatGPT. All questions were in a single-best-answer format without wording 
      alterations. Imaging was trialled to ensure ChatGPT utilized this information. 
      RESULTS: ChatGPT scored 35.8%: 30% lower than the FRCS pass rate and 8.2% lower 
      than the mean score achieved by human candidates of all training levels. 
      Subspecialty analysis demonstrated ChatGPT scored highest in basic science 
      (53.3%) and lowest in trauma (0%). In 87 questions answered incorrectly, ChatGPT 
      only stated it did not know the answer once and gave incorrect explanatory 
      answers for the remaining questions. CONCLUSION: ChatGPT is currently unable to 
      exert the higher-order judgement and multilogical thinking required to pass the 
      FRCS examination. Further, the current model fails to recognize its own 
      limitations. ChatGPT's deficiencies should be publicized equally as much as its 
      successes to ensure clinicians remain aware of its fallibility.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Postgraduate Medical Journal. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Cuthbert, Rory
AU  - Cuthbert R
AUID- ORCID: 0000-0001-6211-3789
AD  - Guy's and St Thomas' Hospital National Health Service Foundation Trust, London, 
      SE1 9RT, United Kingdom.
FAU - Simpson, Ashley I
AU  - Simpson AI
AUID- ORCID: 0000-0001-6232-536X
AD  - Guy's and St Thomas' Hospital National Health Service Foundation Trust, London, 
      SE1 9RT, United Kingdom.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Postgrad Med J
JT  - Postgraduate medical journal
JID - 0234135
SB  - IM
MH  - Humans
MH  - *Orthopedics
MH  - Artificial Intelligence
MH  - Fellowships and Scholarships
MH  - Reproducibility of Results
MH  - *Orthopedic Procedures
MH  - *Surgeons
OTO - NOTNLM
OT  - artificial intelligence
OT  - examination
OT  - orthopaedics
OT  - surgeons
OT  - trauma
EDAT- 2023/07/06 19:12
MHDA- 2023/09/25 06:41
CRDT- 2023/07/06 13:13
PHST- 2023/04/01 00:00 [received]
PHST- 2023/05/26 00:00 [revised]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/09/25 06:41 [medline]
PHST- 2023/07/06 19:12 [pubmed]
PHST- 2023/07/06 13:13 [entrez]
AID - 7220358 [pii]
AID - 10.1093/postmj/qgad053 [doi]
PST - ppublish
SO  - Postgrad Med J. 2023 Sep 21;99(1176):1110-1114. doi: 10.1093/postmj/qgad053.

PMID- 37336169
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230807
IS  - 1873-4499 (Electronic)
IS  - 0899-7071 (Linking)
VI  - 101
DP  - 2023 Sep
TI  - Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance 
      patient understanding of diagnostic reports.
PG  - 137-141
LID - S0899-7071(23)00146-8 [pii]
LID - 10.1016/j.clinimag.2023.06.008 [doi]
AB  - PURPOSE: To evaluate the complexity of diagnostic radiology reports across major 
      imaging modalities and the ability of ChatGPT (Early March 2023 Version, OpenAI, 
      California, USA) to simplify these reports to the 8th grade reading level of the 
      average U.S. adult. METHODS: We randomly sampled 100 radiographs (XR), 100 
      ultrasound (US), 100 CT, and 100 MRI radiology reports from our institution's 
      database dated between 2022 and 2023 (N = 400). These were processed by ChatGPT 
      using the prompt "Explain this radiology report to a patient in layman's terms in 
      second person: <Report Text>". Mean report length, Flesch reading ease score 
      (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for each report 
      and ChatGPT output. T-tests were used to determine significance. RESULTS: Mean 
      report length was 164 ± 117 words, FRES was 38.0 ± 11.8, and FKRL was 10.4 ± 1.9. 
      FKRL was significantly higher for CT and MRI than for US and XR. Only 60/400 
      (15%) had a FKRL <8.5. The mean simplified ChatGPT output length was 103 ± 36 
      words, FRES was 83.5 ± 5.6, and FKRL was 5.8 ± 1.1. This reflects a mean decrease 
      of 61 words (p < 0.01), increase in FRES of 45.5 (p < 0.01), and decrease in FKRL 
      of 4.6 (p < 0.01). All simplified outputs had FKRL <8.5. DISCUSSION: Our study 
      demonstrates the effective use of ChatGPT when tasked with simplifying radiology 
      reports to below the 8th grade reading level. We report significant improvements 
      in FRES, FKRL, and word count, the last of which requires modality-specific 
      context.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Li, Hanzhou
AU  - Li H
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      Hli277@emory.edu.
FAU - Moon, John T
AU  - Moon JT
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/johntmoon.
FAU - Iyer, Deepak
AU  - Iyer D
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/d_iyer7.
FAU - Balthazar, Patricia
AU  - Balthazar P
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/PBalthazarMD.
FAU - Krupinski, Elizabeth A
AU  - Krupinski EA
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/EAKrup.
FAU - Bercu, Zachary L
AU  - Bercu ZL
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/ZachBercuMD.
FAU - Newsome, Janice M
AU  - Newsome JM
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/angiowoman.
FAU - Banerjee, Imon
AU  - Banerjee I
AD  - Mayo Clinic, Department of Radiology, Phoenix, AZ, United States of America. 
      Electronic address: https://twitter.com/ImonBanerjee6.
FAU - Gichoya, Judy W
AU  - Gichoya JW
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/judywawira.
FAU - Trivedi, Hari M
AU  - Trivedi HM
AD  - Emory University School of Medicine, Department of Radiology and Imaging Science, 
      1364 Clifton Rd, Atlanta, GA 30322, United States of America. Electronic address: 
      https://twitter.com/HariTrivediMD.
LA  - eng
PT  - Journal Article
DEP - 20230608
PL  - United States
TA  - Clin Imaging
JT  - Clinical imaging
JID - 8911831
SB  - IM
MH  - Adult
MH  - Humans
MH  - *Comprehension
MH  - Radiography
MH  - *Radiology
MH  - Magnetic Resonance Imaging
MH  - Databases, Factual
OTO - NOTNLM
OT  - 21st century cures act
OT  - Large language model
OT  - Natural language processing
OT  - Patient-centered reports
COIS- Declaration of competing interest Dr. Balthazar received research support from 
      the Association of University Radiologists GE Radiology Research Academic 
      Fellowship
EDAT- 2023/06/20 01:09
MHDA- 2023/08/07 06:42
CRDT- 2023/06/19 18:07
PHST- 2023/03/13 00:00 [received]
PHST- 2023/05/26 00:00 [revised]
PHST- 2023/06/06 00:00 [accepted]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/06/20 01:09 [pubmed]
PHST- 2023/06/19 18:07 [entrez]
AID - S0899-7071(23)00146-8 [pii]
AID - 10.1016/j.clinimag.2023.06.008 [doi]
PST - ppublish
SO  - Clin Imaging. 2023 Sep;101:137-141. doi: 10.1016/j.clinimag.2023.06.008. Epub 
      2023 Jun 8.

PMID- 37923639
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1877-1300 (Electronic)
IS  - 1877-1297 (Linking)
VI  - 15
IP  - 12
DP  - 2023 Dec
TI  - Exploratory study on student perception on the use of chat AI in process-driven 
      problem-based learning.
PG  - 1017-1025
LID - S1877-1297(23)00283-6 [pii]
LID - 10.1016/j.cptl.2023.10.001 [doi]
AB  - INTRODUCTION: With the increasing prevalence of artificial intelligence (AI) 
      technology, it is imperative to investigate its influence on education and the 
      resulting impact on student learning outcomes. This includes exploring the 
      potential application of AI in process-driven problem-based learning (PDPBL). 
      This study aimed to investigate the perceptions of students towards the use of 
      ChatGPT) build on GPT-3.5 in PDPBL in the Bachelor of Pharmacy program. METHODS: 
      Eighteen students with prior experience in traditional PDPBL processes 
      participated in the study, divided into three groups to perform PDPBL sessions 
      with various triggers from pharmaceutical chemistry, pharmaceutics, and clinical 
      pharmacy fields, while utilizing chat AI provided by ChatGPT to assist with data 
      searching and problem-solving. Questionnaires were used to collect data on the 
      impact of ChatGPT on students' satisfaction, engagement, participation, and 
      learning experience during the PBL sessions. RESULTS: The survey revealed that 
      ChatGPT improved group collaboration and engagement during PDPBL, while 
      increasing motivation and encouraging more questions. Nevertheless, some students 
      encountered difficulties understanding ChatGPT's information and questioned its 
      reliability and credibility. Despite these challenges, most students saw 
      ChatGPT's potential to eventually replace traditional information-seeking 
      methods. CONCLUSIONS: The study suggests that ChatGPT has the potential to 
      enhance PDPBL in pharmacy education. However, further research is needed to 
      examine the validity and reliability of the information provided by ChatGPT, and 
      its impact on a larger sample size.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Hamid, Hazrina
AU  - Hamid H
AD  - Faculty of Pharmacy, Lincoln University College, 12-18, Jalan SS 6/12, 47301 
      Petaling Jaya, Selangor Darul Ehsan, Malaysia.
FAU - Zulkifli, Khadjizah
AU  - Zulkifli K
AD  - Faculty of Pharmacy, Lincoln University College, 12-18, Jalan SS 6/12, 47301 
      Petaling Jaya, Selangor Darul Ehsan, Malaysia.
FAU - Naimat, Faiza
AU  - Naimat F
AD  - Department of Pharmacy, Malaysia National Heart Institute College, 145, Jalan Tun 
      Razak, 50400 Kuala Lumpur, Malaysia.
FAU - Che Yaacob, Nor Liana
AU  - Che Yaacob NL
AD  - Faculty of Pharmacy, University Sultan Zainal Abidin, 20400 Kuala Terengganu, 
      Terengganu Darul Iman, Malaysia.
FAU - Ng, Kwok Wen
AU  - Ng KW
AD  - Faculty of Pharmacy, Quest International University, 227, Jalan Raja Permaisuri 
      Bainun, 30250 Ipoh, Perak, Malaysia. Electronic address: kwokwen.ng@qiu.edu.my.
LA  - eng
PT  - Journal Article
DEP - 20231102
PL  - United States
TA  - Curr Pharm Teach Learn
JT  - Currents in pharmacy teaching & learning
JID - 101560815
SB  - IM
MH  - Humans
MH  - *Problem-Based Learning
MH  - Artificial Intelligence
MH  - Reproducibility of Results
MH  - Students
MH  - *Pharmacy Service, Hospital
MH  - Perception
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Process-driven problem-based learning
OT  - Students' perception
COIS- Declaration of Competing Interests None.
EDAT- 2023/11/06 01:19
MHDA- 2023/12/05 12:42
CRDT- 2023/11/03 22:56
PHST- 2023/02/08 00:00 [received]
PHST- 2023/08/08 00:00 [revised]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/12/05 12:42 [medline]
PHST- 2023/11/06 01:19 [pubmed]
PHST- 2023/11/03 22:56 [entrez]
AID - S1877-1297(23)00283-6 [pii]
AID - 10.1016/j.cptl.2023.10.001 [doi]
PST - ppublish
SO  - Curr Pharm Teach Learn. 2023 Dec;15(12):1017-1025. doi: 
      10.1016/j.cptl.2023.10.001. Epub 2023 Nov 2.

PMID- 38032062
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1365-2559 (Electronic)
IS  - 0309-0167 (Linking)
DP  - 2023 Nov 30
TI  - Bridging bytes and biopsies: A comparative analysis of ChatGPT and 
      histopathologists in pathology diagnosis and collaborative potential.
LID - 10.1111/his.15100 [doi]
AB  - BACKGROUND AND AIMS: ChatGPT is a powerful artificial intelligence (AI) chatbot 
      developed by the OpenAI research laboratory which is capable of analysing human 
      input and generating human-like responses. Early research into the potential 
      application of ChatGPT in healthcare has focused mainly on clinical and 
      administrative functions. The diagnostic ability and utility of ChatGPT in 
      histopathology is not well defined. We benchmarked the performance of ChatGPT 
      against pathologists in diagnostic histopathology, and evaluated the 
      collaborative potential between pathologists and ChatGPT to deliver more accurate 
      diagnoses. METHODS AND RESULTS: In Part 1 of the study, pathologists and ChatGPT 
      were subjected to a series of questions encompassing common diagnostic conundrums 
      in histopathology. For Part 2, pathologists reviewed a series of challenging 
      virtual slides and provided their diagnoses before and after consultation with 
      ChatGPT. We found that ChatGPT performed worse than pathologists in reaching the 
      correct diagnosis. Consultation with ChatGPT provided limited help and 
      information generated from ChatGPT is dependent on the prompts provided by the 
      pathologists and is not always correct. Finally, we surveyed pathologists who 
      rated the diagnostic accuracy of ChatGPT poorly, but found it useful as an 
      advanced search engine. CONCLUSIONS: The use of ChatGPT4 as a diagnostic tool in 
      histopathology is limited by its inherent shortcomings. Judicious evaluation of 
      the information and histopathology diagnosis generated from ChatGPT4 is essential 
      and cannot replace the acuity and judgement of a pathologist. However, future 
      advances in generative AI may expand its role in the field of histopathology.
CI  - © 2023 The Authors. Histopathology published by John Wiley & Sons Ltd.
FAU - Oon, Ming Liang
AU  - Oon ML
AUID- ORCID: 0000-0003-0493-3361
AD  - Department of Pathology, National University Hospital, Singapore, Singapore.
FAU - Syn, Nicholas L
AU  - Syn NL
AD  - Department of Pathology, National University Hospital, Singapore, Singapore.
FAU - Tan, Char Loo
AU  - Tan CL
AUID- ORCID: 0000-0002-1470-3175
AD  - Department of Pathology, National University Hospital, Singapore, Singapore.
FAU - Tan, Kong-Bing
AU  - Tan KB
AD  - Department of Pathology, National University Hospital, Singapore, Singapore.
AD  - Department of Pathology, Yong Loo Lin School of Medicine, National University of 
      Singapore, Singapore.
FAU - Ng, Siok-Bian
AU  - Ng SB
AD  - Department of Pathology, National University Hospital, Singapore, Singapore.
AD  - Department of Pathology, Yong Loo Lin School of Medicine, National University of 
      Singapore, Singapore.
AD  - Cancer Science Institute of Singapore, National University of Singapore, 
      Singapore, Singapore.
LA  - eng
GR  - National Medical Research Council/
PT  - Journal Article
DEP - 20231130
PL  - England
TA  - Histopathology
JT  - Histopathology
JID - 7704136
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - digital pathology
OT  - natural language processing
OT  - pathology
EDAT- 2023/11/30 12:42
MHDA- 2023/11/30 12:42
CRDT- 2023/11/30 07:53
PHST- 2023/10/03 00:00 [revised]
PHST- 2023/06/10 00:00 [received]
PHST- 2023/11/02 00:00 [accepted]
PHST- 2023/11/30 12:42 [medline]
PHST- 2023/11/30 12:42 [pubmed]
PHST- 2023/11/30 07:53 [entrez]
AID - 10.1111/his.15100 [doi]
PST - aheadofprint
SO  - Histopathology. 2023 Nov 30. doi: 10.1111/his.15100.

PMID- 37941716
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231110
IS  - 2433-3298 (Electronic)
IS  - 2433-328X (Print)
IS  - 2433-328X (Linking)
VI  - 6
IP  - 4
DP  - 2023 Oct 16
TI  - Beyond the Pass Mark: Accuracy of ChatGPT and Bing in the National Medical 
      Licensure Examination in Japan.
PG  - 536-538
LID - 10.31662/jmaj.2023-0043 [doi]
FAU - Kataoka, Yuki
AU  - Kataoka Y
AD  - Department of Internal Medicine, Kyoto Min-iren Asukai Hospital, Kyoto, Japan.
AD  - Scientific Research Works Peer Support Group (SRWS-PSG), Osaka, Japan.
AD  - Section of Clinical Epidemiology, Department of Community Medicine, Kyoto 
      University Graduate School of Medicine, Kyoto, Japan.
AD  - Department of Healthcare Epidemiology, Kyoto University Graduate School of 
      Medicine/School of Public Health, Kyoto, Japan.
FAU - Yamamoto-Kataoka, Sachiko
AU  - Yamamoto-Kataoka S
AD  - Department of Health Informatics, Kyoto University Graduate School of 
      Medicine/School of Public Health, Kyoto, Japan.
FAU - So, Ryuhei
AU  - So R
AD  - Department of Psychiatry, Okayama Psychiatric Medical Center, Okayama, Japan.
FAU - Furukawa, Toshi A
AU  - Furukawa TA
AD  - Department of Health Promotion and Human Behavior, Kyoto University Graduate 
      School of Medicine/School of Public Health, Kyoto, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - Japan
TA  - JMA J
JT  - JMA journal
JID - 101769797
PMC - PMC10628311
OTO - NOTNLM
OT  - ChatGPT
OT  - Evidence-based Medicine
OT  - Large Language Model
COIS- Yuki Kataoka: None declared; Sachiko Yamamoto-Kataoka: None declared; Ryuhei So: 
      RS has received research grants from the Japan Society for the Promotion of 
      Science (JSPS); Ministry of Health, Labor, and Welfare, Japan; Japan Agency for 
      Medical Research and Development; Osake-no-Kagaku Foundation; The Mental Health 
      Okamoto Memorial Foundation; and Kobayashi Magobe Memorial Medical Foundation; 
      and speaker’s honoraria from Otsuka Pharmaceutical Co., Ltd., Nippon Shinyaku 
      Co., Ltd. and Takeda Pharmaceutical Co., Ltd. outside the submitted work. RS also 
      reports an employment position at CureApp Inc., which develops software as 
      medical devices; Toshi A. Furukawa: TAF reports personal fees from 
      Boehringer-Ingelheim, DT Axis, Kyoto University Original, Shionogi and SONY, and 
      a grant from Shionogi, outside the submitted work; In addition, TAF has patents 
      2020-548587 and 2022-082495 pending, and intellectual properties for Kokoro-app 
      licensed to Mitsubishi-Tanabe.
EDAT- 2023/11/09 06:42
MHDA- 2023/11/09 06:43
CRDT- 2023/11/09 04:10
PHST- 2023/03/22 00:00 [received]
PHST- 2023/06/21 00:00 [accepted]
PHST- 2023/11/09 06:43 [medline]
PHST- 2023/11/09 06:42 [pubmed]
PHST- 2023/11/09 04:10 [entrez]
AID - 10.31662/jmaj.2023-0043 [doi]
PST - ppublish
SO  - JMA J. 2023 Oct 16;6(4):536-538. doi: 10.31662/jmaj.2023-0043. Epub 2023 Sep 20.

PMID- 37252334
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230605
IS  - 2052-2975 (Print)
IS  - 2052-2975 (Electronic)
IS  - 2052-2975 (Linking)
VI  - 53
DP  - 2023 Jun
TI  - Enhancing infectious disease response: A demonstrative dialogue with ChatGPT and 
      ChatGPT-4 for future outbreak preparedness.
PG  - 101153
LID - 10.1016/j.nmni.2023.101153 [doi]
LID - 101153
FAU - Al-Tawfiq, Jaffar A
AU  - Al-Tawfiq JA
AD  - Specialty Internal Medicine and Quality Department, Johns Hopkins Aramco 
      Healthcare, Dhahran, Saudi Arabia.
AD  - Infectious Diseases Division, Department of Medicine, Indiana University School 
      of Medicine, Indianapolis, IN, USA.
AD  - Infectious Diseases Division, Department of Medicine, Johns Hopkins University 
      School of Medicine, Baltimore, MD, USA.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Department of Family and Community Medicine, King Saud University, Riyadh, Saudi 
      Arabia.
FAU - Rodriguez-Morales, Alfonso J
AU  - Rodriguez-Morales AJ
AD  - Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, 
      4861, Peru.
AD  - Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, 
      Beirut, 1102, Lebanon.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Department of Pediatrics, King Saud University Medical City, Riyadh, Saudi 
      Arabia.
LA  - eng
PT  - Journal Article
DEP - 20230519
PL  - England
TA  - New Microbes New Infect
JT  - New microbes and new infections
JID - 101624750
PMC - PMC10195765
OTO - NOTNLM
OT  - COVID-19
OT  - ChatGPT
OT  - ChatGPT-4
OT  - pandemics
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/05/30 13:07
MHDA- 2023/05/30 13:08
CRDT- 2023/05/30 11:53
PHST- 2023/04/28 00:00 [received]
PHST- 2023/05/09 00:00 [revised]
PHST- 2023/05/12 00:00 [accepted]
PHST- 2023/05/30 13:08 [medline]
PHST- 2023/05/30 13:07 [pubmed]
PHST- 2023/05/30 11:53 [entrez]
AID - S2052-2975(23)00072-0 [pii]
AID - 101153 [pii]
AID - 10.1016/j.nmni.2023.101153 [doi]
PST - ppublish
SO  - New Microbes New Infect. 2023 Jun;53:101153. doi: 10.1016/j.nmni.2023.101153. 
      Epub 2023 May 19.

PMID- 37980605
OWN - NLM
STAT- Publisher
LR  - 20231119
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Nov 19
TI  - Chat GPT for the management of obstructive sleep apnea: do we have a polar star?
LID - 10.1007/s00405-023-08270-9 [doi]
AB  - PURPOSE: This study explores the potential of the Chat-Generative Pre-Trained 
      Transformer (Chat-GPT), a Large Language Model (LLM), in assisting healthcare 
      professionals in the diagnosis of obstructive sleep apnea (OSA). It aims to 
      assess the agreement between Chat-GPT's responses and those of expert 
      otolaryngologists, shedding light on the role of AI-generated content in medical 
      decision-making. METHODS: A prospective, cross-sectional study was conducted, 
      involving 350 otolaryngologists from 25 countries who responded to a specialized 
      OSA survey. Chat-GPT was tasked with providing answers to the same survey 
      questions. Responses were assessed by both super-experts and statistically 
      analyzed for agreement. RESULTS: The study revealed that Chat-GPT and expert 
      responses shared a common answer in over 75% of cases for individual questions. 
      However, the overall consensus was achieved in only four questions. Super-expert 
      assessments showed a moderate agreement level, with Chat-GPT scoring slightly 
      lower than experts. Statistically, Chat-GPT's responses differed significantly 
      from experts' opinions (p = 0.0009). Sub-analysis revealed areas of improvement 
      for Chat-GPT, particularly in questions where super-experts rated its responses 
      lower than expert consensus. CONCLUSIONS: Chat-GPT demonstrates potential as a 
      valuable resource for OSA diagnosis, especially where access to specialists is 
      limited. The study emphasizes the importance of AI-human collaboration, with 
      Chat-GPT serving as a complementary tool rather than a replacement for medical 
      professionals. This research contributes to the discourse in otolaryngology and 
      encourages further exploration of AI-driven healthcare applications. While 
      Chat-GPT exhibits a commendable level of consensus with expert responses, ongoing 
      refinements in AI-based healthcare tools hold significant promise for the future 
      of medicine, addressing the underdiagnosis and undertreatment of OSA and 
      improving patient outcomes.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Mira, Felipe Ahumada
AU  - Mira FA
AD  - ENT Department, Hospital of Linares, Linares, Chile.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Favier, Valentin
AU  - Favier V
AD  - ENT Department, University Hospital of Montpellier, Montpellier, France.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Dos Santos Sobreira Nunes, Heloisa
AU  - Dos Santos Sobreira Nunes H
AD  - ENT and Sleep Medicine Department, Nucleus of Otolaryngology, Head and Neck 
      Surgery and Sleep Medicine of São Paulo, São Paulo, Brazil.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - de Castro, Joana Vaz
AU  - de Castro JV
AD  - ENT Department, Armed Forces Hospital, Lisbon, Portugal.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Carsuzaa, Florent
AU  - Carsuzaa F
AD  - ENT Department, University Hospital of Poitiers, Poitiers, France.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Meccariello, Giuseppe
AU  - Meccariello G
AD  - Head and Neck Department, ENT & Oral Surgery Unity, G.B. Morgagni, L. Pierantoni 
      Hospital, Via Forlanini, 47121, Forlì, Italy.
FAU - Vicini, Claudio
AU  - Vicini C
AD  - Head and Neck Department, ENT & Oral Surgery Unity, G.B. Morgagni, L. Pierantoni 
      Hospital, Via Forlanini, 47121, Forlì, Italy.
FAU - De Vito, Andrea
AU  - De Vito A
AD  - Head and Neck Department, ENT & Oral Surgery Unity, G.B. Morgagni, L. Pierantoni 
      Hospital, Via Forlanini, 47121, Forlì, Italy.
FAU - Lechien, Jerome R
AU  - Lechien JR
AD  - Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology 
      and Head and Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health 
      Sciences and Technology, University of Mons, Mons, Belgium.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Estomba, Carlos Chiesa
AU  - Estomba CC
AD  - Department of Otorhinolaryngology, Biodonostia Research Institute, Donostia 
      University Hospital, Osakidetza, 20014, San Sebastian, Spain.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Maniaci, Antonino
AU  - Maniaci A
AD  - Department of Medical and Surgical Sciences and Advanced Technologies "GF 
      Ingrassia", ENT Section, University of Catania, Piazza Università 2, 95100, 
      Catania, Italy.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Iannella, Giannicola
AU  - Iannella G
AD  - Department of 'Organi di Senso', University "Sapienza", Viale Dell'Università 33, 
      00185, Rome, Italy.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France.
FAU - Rojas, Eduardo Peña
AU  - Rojas EP
AD  - Clínica Lircay, Talca, Chile.
FAU - Cornejo, Jenifer Barros
AU  - Cornejo JB
AD  - Hospital Clínico UC Christus, Santiago, Chile.
FAU - Cammaroto, Giovanni
AU  - Cammaroto G
AUID- ORCID: 0000-0002-1618-0048
AD  - Head and Neck Department, ENT & Oral Surgery Unity, G.B. Morgagni, L. Pierantoni 
      Hospital, Via Forlanini, 47121, Forlì, Italy. giovanni.cammaroto@hotmail.com.
AD  - Young Otolaryngologists-International Federations of Oto-Rhinolaryngological 
      Societies (YO-IFOS), Paris, France. giovanni.cammaroto@hotmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231119
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - Chat-Gpt
OT  - Chatbot
OT  - OSA
OT  - Sleep apnea
EDAT- 2023/11/19 18:41
MHDA- 2023/11/19 18:41
CRDT- 2023/11/19 15:15
PHST- 2023/09/13 00:00 [received]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/11/19 18:41 [medline]
PHST- 2023/11/19 18:41 [pubmed]
PHST- 2023/11/19 15:15 [entrez]
AID - 10.1007/s00405-023-08270-9 [pii]
AID - 10.1007/s00405-023-08270-9 [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Nov 19. doi: 10.1007/s00405-023-08270-9.

PMID- 37567084
OWN - NLM
STAT- MEDLINE
DCOM- 20231010
LR  - 20231018
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 88
DP  - 2023 Oct
TI  - A digital ally: The potential roles of ChatGPT in mental health services.
PG  - 103726
LID - S1876-2018(23)00282-4 [pii]
LID - 10.1016/j.ajp.2023.103726 [doi]
FAU - He, Yunhan
AU  - He Y
AD  - School of Psychology, Shenzhen University, Shenzhen, China. Electronic address: 
      psylink_hyh@163.com.
FAU - Liang, Kaixin
AU  - Liang K
AD  - Department of Psychology, Faculty of Social Sciences, University of Macau, Macau, 
      China.
FAU - Han, Binyao
AU  - Han B
AD  - Warner School of Education, University of Rochester, New York, United States.
FAU - Chi, Xinli
AU  - Chi X
AD  - School of Psychology, Shenzhen University, Shenzhen, China.
LA  - eng
PT  - Letter
DEP - 20230807
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - *Mental Health Services
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - ChatGPT
OT  - Mental health services
COIS- Declaration of Competing Interest The author declares no conflict of interest.
EDAT- 2023/08/12 10:42
MHDA- 2023/10/09 06:42
CRDT- 2023/08/11 18:07
PHST- 2023/06/19 00:00 [received]
PHST- 2023/08/02 00:00 [revised]
PHST- 2023/08/04 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/08/12 10:42 [pubmed]
PHST- 2023/08/11 18:07 [entrez]
AID - S1876-2018(23)00282-4 [pii]
AID - 10.1016/j.ajp.2023.103726 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Oct;88:103726. doi: 10.1016/j.ajp.2023.103726. Epub 2023 
      Aug 7.

PMID- 37283095
OWN - NLM
STAT- Publisher
LR  - 20230607
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
DP  - 2023 Jun 7
TI  - Response to ChatGPT in Radiology: A Deeper Look Into its Limitations and 
      Potential Pathways for Improvement.
PG  - 8465371231181910
LID - 10.1177/08465371231181910 [doi]
FAU - Wagner, Matthias W
AU  - Wagner MW
AUID- ORCID: 0000-0001-6501-839X
AD  - Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for 
      Sick Children, Toronto, ON, Canada. RINGGOLD: 7979
AD  - Department of Medical Imaging, University of Toronto, Canada. RINGGOLD: 12366
AD  - Department of Diagnostic and Interventional Neuroradiology, University Hospital 
      Augsburg, Germany.
FAU - Ertl-Wagner, Birgit B
AU  - Ertl-Wagner BB
AD  - Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for 
      Sick Children, Toronto, ON, Canada. RINGGOLD: 7979
AD  - Department of Medical Imaging, University of Toronto, Canada. RINGGOLD: 12366
LA  - eng
PT  - Letter
DEP - 20230607
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Radiology
EDAT- 2023/06/07 06:42
MHDA- 2023/06/07 06:42
CRDT- 2023/06/07 05:32
PHST- 2023/06/07 06:42 [medline]
PHST- 2023/06/07 06:42 [pubmed]
PHST- 2023/06/07 05:32 [entrez]
AID - 10.1177/08465371231181910 [doi]
PST - aheadofprint
SO  - Can Assoc Radiol J. 2023 Jun 7:8465371231181910. doi: 10.1177/08465371231181910.

PMID- 37808939
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2228-6497 (Print)
IS  - 2228-6497 (Electronic)
IS  - 2228-6497 (Linking)
VI  - 13
IP  - 3
DP  - 2023
TI  - Exploring the role of ChatGPT in patient care (diagnosis and treatment) and 
      medical research: A systematic review.
PG  - 183-191
LID - 10.34172/hpp.2023.22 [doi]
AB  - BACKGROUND: ChatGPT is an artificial intelligence based tool developed by OpenAI 
      (California, USA). This systematic review examines the potential of ChatGPT in 
      patient care and its role in medical research. METHODS: The systematic review was 
      done according to the PRISMA guidelines. Embase, Scopus, PubMed and Google 
      Scholar data bases were searched. We also searched preprint data bases. Our 
      search was aimed to identify all kinds of publications, without any restrictions, 
      on ChatGPT and its application in medical research, medical publishing and 
      patient care. We used search term "ChatGPT". We reviewed all kinds of 
      publications including original articles, reviews, editorial/ commentaries, and 
      even letter to the editor. Each selected records were analysed using ChatGPT and 
      responses generated were compiled in a table. The word table was transformed in 
      to a PDF and was further analysed using ChatPDF. RESULTS: We reviewed full texts 
      of 118 articles. ChatGPT can assist with patient enquiries, note writing, 
      decision-making, trial enrolment, data management, decision support, research 
      support, and patient education. But the solutions it offers are usually 
      insufficient and contradictory, raising questions about their originality, 
      privacy, correctness, bias, and legality. Due to its lack of human-like 
      qualities, ChatGPT's legitimacy as an author is questioned when used for academic 
      writing. ChatGPT generated contents have concerns with bias and possible 
      plagiarism. CONCLUSION: Although it can help with patient treatment and research, 
      there are issues with accuracy, authorship, and bias. ChatGPT can serve as a 
      "clinical assistant" and be a help in research and scholarly writing.
CI  - © 2023 The Author(s).
FAU - Garg, Ravindra Kumar
AU  - Garg RK
AUID- ORCID: 0000-0003-0044-7083
AD  - Department of Neurology, King George's Medical University, Lucknow, India.
FAU - Urs, Vijeth L
AU  - Urs VL
AD  - Department of Neurology, King George's Medical University, Lucknow, India.
FAU - Agarwal, Akshay Anand
AU  - Agarwal AA
AD  - Department of Surgery, King George's Medical University, Lucknow, India.
FAU - Chaudhary, Sarvesh Kumar
AU  - Chaudhary SK
AD  - Department of Neurology, King George's Medical University, Lucknow, India.
FAU - Paliwal, Vimal
AU  - Paliwal V
AD  - Department of Neurology, Sanjay Gandhi Institute of Medical Sciences, Lucknow, 
      India.
FAU - Kar, Sujita Kumar
AU  - Kar SK
AD  - Department of Psychiatry, King George's Medical University, Lucknow, India.
LA  - eng
PT  - Systematic Review
DEP - 20230911
PL  - Iran
TA  - Health Promot Perspect
JT  - Health promotion perspectives
JID - 101580052
PMC - PMC10558973
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Authorship
OT  - Machine learning
OT  - Publishing
OT  - Scholarly
COIS- None.
EDAT- 2023/10/09 06:41
MHDA- 2023/10/09 06:42
CRDT- 2023/10/09 05:48
PHST- 2023/06/14 00:00 [received]
PHST- 2023/07/06 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/10/09 06:41 [pubmed]
PHST- 2023/10/09 05:48 [entrez]
AID - 10.34172/hpp.2023.22 [doi]
PST - epublish
SO  - Health Promot Perspect. 2023 Sep 11;13(3):183-191. doi: 10.34172/hpp.2023.22. 
      eCollection 2023.

PMID- 38022227
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Reliability and Usefulness of ChatGPT for Inflammatory Bowel Diseases: An 
      Analysis for Patients and Healthcare Professionals.
PG  - e46736
LID - 10.7759/cureus.46736 [doi]
LID - e46736
AB  - AIM: We aimed to evaluate the performance of Chat Generative Pre-trained 
      Transformer (ChatGPT) within the context of inflammatory bowel disease (IBD), 
      which is expected to become an increasingly significant health issue in the 
      future. In addition, the objective of the study was to assess whether ChatGPT 
      serves as a reliable and useful resource for both patients and healthcare 
      professionals. METHODS: For this study, 20 specific questions were identified for 
      the two main components of IBD, which are Crohn's disease (CD) and ulcerative 
      colitis (UC). The questions were divided into two sets: one set contained 
      questions directed at healthcare professionals while the second set contained 
      questions directed toward patients. The responses were evaluated with seven-point 
      Likert-type reliability and usefulness scales. RESULTS: The distribution of the 
      reliability and utility scores was calculated into four groups (two diseases and 
      two question sources) by averaging the mean scores from both raters. The highest 
      scores in both reliability and usefulness were obtained from professional sources 
      (5.00± 1.21 and 5.15±1.08, respectively). The ranking in terms of reliability and 
      usefulness, respectively, was as follows: CD questions (4.70±1.26 and 4.75±1.06) 
      and UC questions (4.40±1.21 and 4.55±1.31). The reliability scores of the answers 
      for the professionals were significantly higher than those for the patients (both 
      raters, p=0.032).  Conclusion: Despite its capacity for reliability and 
      usefulness in the context of IBD, ChatGPT still has some limitations and 
      deficiencies. The correction of ChatGPT's deficiencies and its enhancement by 
      developers with more detailed and up-to-date information could make it a 
      significant source of information for both patients and medical professionals.
CI  - Copyright © 2023, Cankurtaran et al.
FAU - Cankurtaran, Rasim Eren
AU  - Cankurtaran RE
AD  - Department of Gastroenterology, Ankara Etlik City Hospital, Ankara, TUR.
FAU - Polat, Yunus Halil
AU  - Polat YH
AD  - Department of Gastroenterology, Ankara Training and Research Hospital, Ankara, 
      TUR.
FAU - Aydemir, Neslihan Gunes
AU  - Aydemir NG
AD  - Department of Gastroenterology, Akdeniz University Faculty of Medicine, Antalya, 
      TUR.
FAU - Umay, Ebru
AU  - Umay E
AD  - Physical Medicine and Rehabilitation, University of Health Sciences, Ankara Etlik 
      City Hospital, Ankara, TUR.
FAU - Yurekli, Oyku Tayfur
AU  - Yurekli OT
AD  - Department of Gastroenterology, Ankara Yildirim Beyazit University Faculty of 
      Medicine, Ankara, TUR.
LA  - eng
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10630704
OTO - NOTNLM
OT  - artificial intelligence (ai)
OT  - chatgpt
OT  - crohn’s disease (cd)
OT  - healthcare research
OT  - inflammatory bowel diseases (ibd)
OT  - large language model
OT  - ulcerative colitis (uc)
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:19
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:19 [entrez]
AID - 10.7759/cureus.46736 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 9;15(10):e46736. doi: 10.7759/cureus.46736. eCollection 2023 
      Oct.

PMID- 37602755
OWN - NLM
STAT- Publisher
LR  - 20230821
IS  - 1445-2197 (Electronic)
IS  - 1445-1433 (Linking)
DP  - 2023 Aug 21
TI  - Investigating the impact of innovative AI chatbot on post-pandemic medical 
      education and clinical assistance: a comprehensive analysis.
LID - 10.1111/ans.18666 [doi]
AB  - BACKGROUND: The COVID-19 pandemic has significantly disrupted clinical experience 
      and exposure of medical students and junior doctors. Artificial Intelligence (AI) 
      integration in medical education has the potential to enhance learning and 
      improve patient care. This study aimed to evaluate the effectiveness of three 
      popular large language models (LLMs) in serving as clinical decision-making 
      support tools for junior doctors. METHODS: A series of increasingly complex 
      clinical scenarios were presented to ChatGPT, Google's Bard and Bing's AI. Their 
      responses were evaluated against standard guidelines, and for reliability by the 
      Flesch Reading Ease Score, Flesch-Kincaid Grade Level, the Coleman-Liau Index, 
      and the modified DISCERN score for assessing suitability. Lastly, the LLMs 
      outputs were assessed by using the Likert scale for accuracy, informativeness, 
      and accessibility by three experienced specialists. RESULTS: In terms of 
      readability and reliability, ChatGPT stood out among the three LLMs, recording 
      the highest scores in Flesch Reading Ease (31.2 ± 3.5), Flesch-Kincaid Grade 
      Level (13.5 ± 0.7), Coleman-Lau Index (13) and DISCERN (62 ± 4.4). These results 
      suggest statistically significant superior comprehensibility and alignment with 
      clinical guidelines in the medical advice given by ChatGPT. Bard followed closely 
      behind, with BingAI trailing in all categories. The only non-significant 
      statistical differences (P > 0.05) were found between ChatGPT and Bard's 
      readability indices, and between the Flesch Reading Ease scores of ChatGPT/Bard 
      and BingAI. CONCLUSION: This study demonstrates the potential utility of LLMs in 
      fostering self-directed and personalized learning, as well as bolstering clinical 
      decision-making support for junior doctors. However further development is needed 
      for its integration into education.
CI  - © 2023 The Authors. ANZ Journal of Surgery published by John Wiley & Sons 
      Australia, Ltd on behalf of Royal Australasian College of Surgeons.
FAU - Xie, Yi
AU  - Xie Y
AD  - Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, 
      Australia.
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
AUID- ORCID: 0000-0001-5660-0572
AD  - Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, 
      Australia.
FAU - Rozen, Warren M
AU  - Rozen WM
AUID- ORCID: 0000-0002-4092-182X
AD  - Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, 
      Australia.
FAU - Seifman, Marc A
AU  - Seifman MA
AUID- ORCID: 0000-0002-5989-4809
AD  - Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia.
AD  - Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, 
      Australia.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - Australia
TA  - ANZ J Surg
JT  - ANZ journal of surgery
JID - 101086634
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - junior doctor
OT  - large language model
OT  - surgical education
EDAT- 2023/08/21 12:43
MHDA- 2023/08/21 12:43
CRDT- 2023/08/21 09:26
PHST- 2023/08/04 00:00 [revised]
PHST- 2023/03/24 00:00 [received]
PHST- 2023/08/07 00:00 [accepted]
PHST- 2023/08/21 12:43 [medline]
PHST- 2023/08/21 12:43 [pubmed]
PHST- 2023/08/21 09:26 [entrez]
AID - 10.1111/ans.18666 [doi]
PST - aheadofprint
SO  - ANZ J Surg. 2023 Aug 21. doi: 10.1111/ans.18666.

PMID- 37750052
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230928
IS  - 1179-1365 (Print)
IS  - 1179-1365 (Electronic)
IS  - 1179-1365 (Linking)
VI  - 15
DP  - 2023
TI  - Evaluating the Sensitivity, Specificity, and Accuracy of ChatGPT-3.5, ChatGPT-4, 
      Bing AI, and Bard Against Conventional Drug-Drug Interactions Clinical Tools.
PG  - 137-147
LID - 10.2147/DHPS.S425858 [doi]
AB  - BACKGROUND: AI platforms are equipped with advanced ‎algorithms that have the 
      potential to offer a wide range of ‎applications in healthcare services. However, 
      information about the accuracy of AI chatbots against ‎conventional drug-drug 
      interaction tools is limited‎. This study aimed to assess the sensitivity, 
      specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and Bard in 
      predicting drug-drug interactions. METHODS: AI-based chatbots (ie, ChatGPT-3.5, 
      ChatGPT-4, Microsoft Bing AI, and Google Bard) were compared for their abilities 
      to detect clinically relevant DDIs for 255 drug pairs. Descriptive statistics, 
      such as specificity, sensitivity, accuracy, negative predictive value (NPV), and 
      positive predictive value (PPV), were calculated for each tool. RESULTS: When a 
      subscription tool was used as a reference, the specificity ranged from a low of 
      0.372 (ChatGPT-3.5) to a high of 0.769 (Microsoft Bing AI). Also, Microsoft Bing 
      AI had the highest performance with an accuracy score of 0.788, with ChatGPT-3.5 
      having the lowest accuracy rate of 0.469. There was an overall improvement in 
      performance for all the programs when the reference tool switched to a free DDI 
      source, but still, ChatGPT-3.5 had the lowest specificity (0.392) and accuracy 
      (0.525), and Microsoft Bing AI demonstrated the highest specificity (0.892) and 
      accuracy (0.890). When assessing the consistency of accuracy across two different 
      drug classes, ChatGPT-3.5 and ChatGPT-4 showed the highest ‎variability in 
      accuracy. In addition, ChatGPT-3.5, ChatGPT-4, and Bard exhibited the highest 
      ‎fluctuations in specificity when analyzing two medications belonging to the same 
      drug class. CONCLUSION: Bing AI had the highest accuracy and specificity, 
      outperforming Google's Bard, ChatGPT-3.5, and ChatGPT-4. The findings highlight 
      the significant potential these AI tools hold in transforming patient care. While 
      the current AI platforms evaluated are not without limitations, their ability to 
      quickly analyze potentially significant interactions with good sensitivity 
      suggests a promising step towards improved patient safety.
CI  - © 2023 Al-Ashwal et al.
FAU - Al-Ashwal, Fahmi Y
AU  - Al-Ashwal FY
AUID- ORCID: 0000-0003-2076-0771
AD  - Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, 
      University of Science and Technology, Sana'a, Yemen.
AD  - College of Pharmacy, Al-Ayen University, Thi-Qar, Iraq.
FAU - Zawiah, Mohammed
AU  - Zawiah M
AD  - Department of Pharmacy Practice, Faculty of Clinical Pharmacy, Hodeidah 
      University, Al Hodeidah, Yemen.
FAU - Gharaibeh, Lobna
AU  - Gharaibeh L
AD  - Pharmacological and Diagnostic Research Center, Faculty of Pharmacy, Al-Ahliyya 
      Amman University, Amman, Jordan.
FAU - Abu-Farha, Rana
AU  - Abu-Farha R
AUID- ORCID: 0000-0001-8298-4071
AD  - Clinical Pharmacy and Therapeutics Department, Faculty of Pharmacy, Applied 
      Science Private University, Amman, Jordan.
FAU - Bitar, Ahmad Naoras
AU  - Bitar AN
AUID- ORCID: 0000-0003-0125-2683
AD  - Department of Clinical Pharmacy, Faculty of Pharmacy and Biomedical Sciences, 
      Malaysian Allied Health Sciences Academy, Jenjarom, Selangor, 42610, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - New Zealand
TA  - Drug Healthc Patient Saf
JT  - Drug, healthcare and patient safety
JID - 101544775
PMC - PMC10518176
OTO - NOTNLM
OT  - Bard‎
OT  - Bing AI
OT  - ChatGPT
OT  - accuracy
OT  - drug-drug interaction
OT  - patient safety
OT  - sensitivity
OT  - specificity
COIS- The authors report no conflicts of interest in this work.‎
EDAT- 2023/09/26 06:44
MHDA- 2023/09/26 06:45
CRDT- 2023/09/26 03:34
PHST- 2023/07/11 00:00 [received]
PHST- 2023/09/09 00:00 [accepted]
PHST- 2023/09/26 06:45 [medline]
PHST- 2023/09/26 06:44 [pubmed]
PHST- 2023/09/26 03:34 [entrez]
AID - 425858 [pii]
AID - 10.2147/DHPS.S425858 [doi]
PST - epublish
SO  - Drug Healthc Patient Saf. 2023 Sep 20;15:137-147. doi: 10.2147/DHPS.S425858. 
      eCollection 2023.

PMID- 37094282
OWN - NLM
STAT- Publisher
LR  - 20230424
IS  - 1873-1953 (Electronic)
IS  - 1474-5151 (Linking)
DP  - 2023 Apr 24
TI  - What if your patient switches from Dr. Google to Dr. ChatGPT? A vignette-based 
      survey of the trustworthiness, value and danger of ChatGPT-generated responses to 
      health questions.
LID - zvad038 [pii]
LID - 10.1093/eurjcn/zvad038 [doi]
AB  - ChatGPT is a new artificial intelligence system that revolutionizes the way how 
      information can be sought and obtained. In this study, the trustworthiness, value 
      and danger of ChatGPT-generated responses on four vignettes that represented 
      virtual patient questions was evaluated by 20 experts in the domain of congenital 
      heart disease, atrial fibrillation, heart failure, or cholesterol. Experts 
      generally considered ChatGPT-generated responses trustworthy and valuable, with 
      few considering them dangerous. Fourty percent of the experts found ChatGPT 
      responses more valuable than Google. Experts appreciated the sophistication and 
      nuances in the responses, but also recognized that responses were often 
      incomplete and sometimes misleading.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      European Society of Cardiology.
FAU - Van Bulck, Liesbet
AU  - Van Bulck L
AUID- ORCID: 0000-0001-8975-4455
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven - University of 
      Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Research Foundation Flanders (FWO), Leuvenseweg 38, 1000 Brussels, Belgium.
FAU - Moons, Philip
AU  - Moons P
AUID- ORCID: 0000-0002-8609-4516
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven - University of 
      Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens 
      backe 1, 413 46 Gothenburg, Sweden.
AD  - Department of Paediatrics and Child Health, University of Cape Town, Klipfontein 
      Rd, Rondebosch, 7700 Cape Town, South Africa.
LA  - eng
PT  - Journal Article
DEP - 20230424
PL  - England
TA  - Eur J Cardiovasc Nurs
JT  - European journal of cardiovascular nursing
JID - 101128793
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - chatbot
OT  - language models
OT  - natural language processing
OT  - nurses
OT  - nursing
OT  - patient information
OT  - safety
EDAT- 2023/04/24 18:42
MHDA- 2023/04/24 18:42
CRDT- 2023/04/24 04:13
PHST- 2023/04/05 00:00 [received]
PHST- 2023/04/17 00:00 [revised]
PHST- 2023/04/20 00:00 [accepted]
PHST- 2023/04/24 18:42 [medline]
PHST- 2023/04/24 18:42 [pubmed]
PHST- 2023/04/24 04:13 [entrez]
AID - 7140165 [pii]
AID - 10.1093/eurjcn/zvad038 [doi]
PST - aheadofprint
SO  - Eur J Cardiovasc Nurs. 2023 Apr 24:zvad038. doi: 10.1093/eurjcn/zvad038.

PMID- 37394880
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230704
LR  - 20230727
IS  - 2001-1326 (Electronic)
IS  - 2001-1326 (Linking)
VI  - 13
IP  - 7
DP  - 2023 Jul
TI  - The application of ChatGPT in healthcare progress notes: A commentary from a 
      clinical and research perspective.
PG  - e1324
LID - 10.1002/ctm2.1324 [doi]
LID - e1324
FAU - Nguyen, Josh
AU  - Nguyen J
AUID- ORCID: 0000-0002-9106-0388
AD  - Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, 
      Australia.
AD  - School of Psychology and Public Health, La Trobe University, Melbourne, Victoria, 
      Australia.
FAU - Pepping, Christopher A
AU  - Pepping CA
AD  - School of Psychology and Public Health, La Trobe University, Melbourne, Victoria, 
      Australia.
AD  - School of Applied Psychology, Griffith University, Brisbane, Queensland, 
      Australia.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Clin Transl Med
JT  - Clinical and translational medicine
JID - 101597971
SB  - IM
PMC - PMC10315641
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - progress notes
OT  - prompt engineering
EDAT- 2023/07/03 06:42
MHDA- 2023/07/03 06:43
CRDT- 2023/07/03 02:23
PHST- 2023/06/20 00:00 [received]
PHST- 2023/06/25 00:00 [accepted]
PHST- 2023/07/03 06:43 [medline]
PHST- 2023/07/03 06:42 [pubmed]
PHST- 2023/07/03 02:23 [entrez]
AID - CTM21324 [pii]
AID - 10.1002/ctm2.1324 [doi]
PST - ppublish
SO  - Clin Transl Med. 2023 Jul;13(7):e1324. doi: 10.1002/ctm2.1324.

PMID- 37905130
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231110
DP  - 2023 Oct 19
TI  - GenePT: A Simple But Hard-to-Beat Foundation Model for Genes and Cells Built From 
      ChatGPT.
LID - 2023.10.16.562533 [pii]
LID - 10.1101/2023.10.16.562533 [doi]
AB  - There has been significant recent progress in leveraging large-scale gene 
      expression data to develop foundation models for single-cell transcriptomes such 
      as Geneformer [1], scGPT [2], and scBERT [3]. These models infer gene functions 
      and interrelations from the gene expression profiles of millions of cells, which 
      requires extensive data curation and resource-intensive training. Here, we 
      explore a much simpler alternative by leveraging ChatGPT embeddings of genes 
      based on literature. Our proposal, GenePT, uses NCBI text descriptions of 
      individual genes with GPT-3.5 to generate gene embeddings. From there, GenePT 
      generates single-cell embeddings in two ways: (i) by averaging the gene 
      embeddings, weighted by each gene's expression level; or (ii) by creating a 
      sentence embedding for each cell, using gene names ordered by the expression 
      level. Without the need for dataset curation and additional pretraining, GenePT 
      is efficient and easy to use. On many downstream tasks used to evaluate recent 
      single-cell foundation models - e.g., classifying gene properties and cell types 
      - GenePT achieves comparable, and often better, performance than Geneformer and 
      other methods. GenePT demonstrates that large language model embedding of 
      literature is a simple and effective path for biological foundation models.
FAU - Chen, Yiqun T
AU  - Chen YT
AD  - Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.
FAU - Zou, James
AU  - Zou J
AD  - Departments of Biomedical Data Science; Electrical Engineering; and Computer 
      Science, Stanford University, Stanford, CA 94305.
LA  - eng
GR  - P30 AG059307/AG/NIA NIH HHS/United States
GR  - U01 MH098953/MH/NIMH NIH HHS/United States
PT  - Preprint
DEP - 20231019
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10614824
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:43
CRDT- 2023/10/31 04:02
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 06:43 [medline]
PHST- 2023/10/31 04:02 [entrez]
AID - 2023.10.16.562533 [pii]
AID - 10.1101/2023.10.16.562533 [doi]
PST - epublish
SO  - bioRxiv. 2023 Oct 19:2023.10.16.562533. doi: 10.1101/2023.10.16.562533. Preprint.

PMID- 38015298
OWN - NLM
STAT- MEDLINE
DCOM- 20231129
LR  - 20231201
IS  - 1590-9999 (Electronic)
IS  - 1590-9921 (Print)
IS  - 1590-9921 (Linking)
VI  - 24
IP  - 1
DP  - 2023 Nov 28
TI  - Arthrosis diagnosis and treatment recommendations in clinical practice: an 
      exploratory investigation with the generative AI model GPT-4.
PG  - 61
LID - 10.1186/s10195-023-00740-4 [doi]
LID - 61
AB  - BACKGROUND: The spread of artificial intelligence (AI) has led to transformative 
      advancements in diverse sectors, including healthcare. Specifically, generative 
      writing systems have shown potential in various applications, but their 
      effectiveness in clinical settings has been barely investigated. In this context, 
      we evaluated the proficiency of ChatGPT-4 in diagnosing gonarthrosis and 
      coxarthrosis and recommending appropriate treatments compared with orthopaedic 
      specialists. METHODS: A retrospective review was conducted using anonymized 
      medical records of 100 patients previously diagnosed with either knee or hip 
      arthrosis. ChatGPT-4 was employed to analyse these historical records, 
      formulating both a diagnosis and potential treatment suggestions. Subsequently, a 
      comparative analysis was conducted to assess the concordance between the AI's 
      conclusions and the original clinical decisions made by the physicians. RESULTS: 
      In diagnostic evaluations, ChatGPT-4 consistently aligned with the conclusions 
      previously drawn by physicians. In terms of treatment recommendations, there was 
      an 83% agreement between the AI and orthopaedic specialists. The therapeutic 
      concordance was verified by the calculation of a Cohen's Kappa coefficient of 
      0.580 (p < 0.001). This indicates a moderate-to-good level of agreement. In 
      recommendations pertaining to surgical treatment, the AI demonstrated a 
      sensitivity and specificity of 78% and 80%, respectively. Multivariable logistic 
      regression demonstrated that the variables reduced quality of life (OR 49.97, 
      p < 0.001) and start-up pain (OR 12.54, p = 0.028) have an influence on 
      ChatGPT-4's recommendation for a surgery. CONCLUSION: This study emphasises 
      ChatGPT-4's notable potential in diagnosing conditions such as gonarthrosis and 
      coxarthrosis and in aligning its treatment recommendations with those of 
      orthopaedic specialists. However, it is crucial to acknowledge that AI tools such 
      as ChatGPT-4 are not meant to replace the nuanced expertise and clinical judgment 
      of seasoned orthopaedic surgeons, particularly in complex decision-making 
      scenarios regarding treatment indications. Due to the exploratory nature of the 
      study, further research with larger patient populations and more complex 
      diagnoses is necessary to validate the findings and explore the broader potential 
      of AI in healthcare. LEVEL OF EVIDENCE: Level III evidence.
CI  - © 2023. The Author(s).
FAU - Pagano, Stefano
AU  - Pagano S
AUID- ORCID: 0009-0006-6721-7564
AD  - Department of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, 
      Bad Abbach, Germany. stefano.pagano@ukr.de.
FAU - Holzapfel, Sabrina
AU  - Holzapfel S
AD  - Department of Neonatology, University Children's Hospital Regensburg, Hospital 
      St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, 
      Germany.
FAU - Kappenschneider, Tobias
AU  - Kappenschneider T
AD  - Department of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, 
      Bad Abbach, Germany.
FAU - Meyer, Matthias
AU  - Meyer M
AD  - Department of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, 
      Bad Abbach, Germany.
FAU - Maderbacher, Günther
AU  - Maderbacher G
AD  - Department of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, 
      Bad Abbach, Germany.
FAU - Grifka, Joachim
AU  - Grifka J
AD  - Department of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, 
      Bad Abbach, Germany.
FAU - Holzapfel, Dominik Emanuel
AU  - Holzapfel DE
AD  - Department of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, 
      Bad Abbach, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - Italy
TA  - J Orthop Traumatol
JT  - Journal of orthopaedics and traumatology : official journal of the Italian 
      Society of Orthopaedics and Traumatology
JID - 101090931
SB  - IM
MH  - Humans
MH  - *Osteoarthritis, Hip/diagnosis/therapy
MH  - Artificial Intelligence
MH  - Quality of Life
MH  - *Osteoarthritis, Knee/diagnosis/therapy
MH  - Knee Joint
PMC - PMC10684473
OTO - NOTNLM
OT  - Arthrosis
OT  - Artificial intelligence
OT  - ChatGPT-4
OT  - Large language model
OT  - Orthopaedics
OT  - Total joint replacement
COIS- The authors declare that they have no competing interests.
EDAT- 2023/11/28 12:42
MHDA- 2023/11/29 06:42
CRDT- 2023/11/28 11:09
PHST- 2023/09/08 00:00 [received]
PHST- 2023/11/05 00:00 [accepted]
PHST- 2023/11/29 06:42 [medline]
PHST- 2023/11/28 12:42 [pubmed]
PHST- 2023/11/28 11:09 [entrez]
AID - 10.1186/s10195-023-00740-4 [pii]
AID - 740 [pii]
AID - 10.1186/s10195-023-00740-4 [doi]
PST - epublish
SO  - J Orthop Traumatol. 2023 Nov 28;24(1):61. doi: 10.1186/s10195-023-00740-4.

PMID- 37972444
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 88
DP  - 2023 Oct 29
TI  - Modern Machiavelli? The illusion of ChatGPT-generated patient reviews in plastic 
      and aesthetic surgery based on 9000 review classifications.
PG  - 99-108
LID - S1748-6815(23)00689-7 [pii]
LID - 10.1016/j.bjps.2023.10.119 [doi]
AB  - BACKGROUND: Online patient reviews are crucial in guiding individuals who seek 
      plastic surgery, but artificial chatbots pose a threat of disseminating fake 
      reviews. This study aimed to compare real patient feedback with ChatGPT-generated 
      reviews for the top five US plastic surgery procedures. METHODS: Thirty real 
      patient reviews on rhinoplasty, blepharoplasty, facelift, liposuction, and breast 
      augmentation were collected from RealSelf and used as templates for ChatGPT to 
      generate matching patient reviews. Prolific users (n = 30) assessed 150 pairs of 
      reviews to identify human-written and artificial intelligence (AI)-generated 
      reviews. Patient reviews were further assessed using AI content detector software 
      (Copyleaks AI). RESULTS: Among the 9000 classification tasks, 64.3% and 35.7% of 
      reviews were classified as authentic and fake, respectively. On an average, the 
      author (human versus machine) was correctly identified in 59.6% of cases, and 
      this poor classification performance was consistent across all procedures. 
      Patients with prior aesthetic treatment showed poorer classification performance 
      than those without (p < 0.05). The mean character count in human-written reviews 
      was significantly higher (p < 0.001) that that in AI-generated reviews, with a 
      significant correlation between character count and participants' accuracy rate 
      (p < 0.001). Emotional timbre of reviews differed significantly with "happiness" 
      being more prevalent in human-written reviews (p < 0.001), and "disappointment" 
      being more prevalent in AI reviews (p = 0.005). Copyleaks AI correctly classified 
      96.7% and 69.3% of human-written and ChatGPT-generated reviews, respectively. 
      CONCLUSION: ChatGPT convincingly replicates authentic patient reviews, even 
      deceiving commercial AI detection software. Analyzing emotional tone and review 
      length can help differentiate real from fake reviews, underscoring the need to 
      educate both patients and physicians to prevent misinformation and mistrust.
CI  - Copyright © 2023 British Association of Plastic, Reconstructive and Aesthetic 
      Surgeons. Published by Elsevier Ltd. All rights reserved.
FAU - Knoedler, Samuel
AU  - Knoedler S
AD  - Department of Plastic Surgery and Hand Surgery, Klinikum Rechts der Isar, 
      Technical University of Munich, Munich, Germany; Division of Plastic Surgery, 
      Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, 
      Boston, MA, USA; Instituto Ivo Pitanguy, Hospital Santa Casa de Misericórdia, 
      Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, Brazil. 
      Electronic address: samuel.knoedler@tum.de.
FAU - Sofo, Giuseppe
AU  - Sofo G
AD  - Instituto Ivo Pitanguy, Hospital Santa Casa de Misericórdia, Pontifícia 
      Universidade Católica do Rio de Janeiro, Rio de Janeiro, Brazil.
FAU - Kern, Barbara
AU  - Kern B
AD  - Department of Plastic Surgery, Charité - Universitätsmedizin Berlin, Corporate 
      Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin 
      Institute of Health, Berlin, Germany.
FAU - Frank, Konstantin
AU  - Frank K
AD  - Ocean Clinic, Marbella, Spain.
FAU - Cotofana, Sebastian
AU  - Cotofana S
AD  - Centre for Cutaneous Research, Blizard Institute, Queen Mary University of 
      London, London, UK; Department of Dermatology, Erasmus Hospital, Rotterdam, the 
      Netherlands.
FAU - von Isenburg, Sarah
AU  - von Isenburg S
AD  - Private Practice, Plastische Chirurgie München Dres. Neuhann-Lorenz & v. 
      Isenburg, Munich, Germany.
FAU - Könneker, Sören
AU  - Könneker S
AD  - Department of Plastic Surgery and Hand Surgery, University Hospital Zürich, 
      Zurich, Switzerland.
FAU - Mazzarone, Francesco
AU  - Mazzarone F
AD  - Instituto Ivo Pitanguy, Hospital Santa Casa de Misericórdia, Pontifícia 
      Universidade Católica do Rio de Janeiro, Rio de Janeiro, Brazil.
FAU - Dorafshar, Amir H
AU  - Dorafshar AH
AD  - Division of Plastic and Reconstructive Surgery, Rush University Medical Center, 
      Chicago, IL, USA.
FAU - Knoedler, Leonard
AU  - Knoedler L
AD  - Division of Plastic and Reconstructive Surgery, Massachusetts General Hospital, 
      Harvard Medical School, Boston, MA, USA.
FAU - Alfertshofer, Michael
AU  - Alfertshofer M
AD  - Division of Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilians-University 
      Munich, Munich, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231029
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - GPT-4
OT  - Large language model
OT  - Patient review
OT  - Plastic Surgery
COIS- Declaration of Competing Interest None declared.
EDAT- 2023/11/17 15:24
MHDA- 2023/11/17 15:24
CRDT- 2023/11/16 18:01
PHST- 2023/09/27 00:00 [received]
PHST- 2023/10/18 00:00 [revised]
PHST- 2023/10/23 00:00 [accepted]
PHST- 2023/11/17 15:24 [medline]
PHST- 2023/11/17 15:24 [pubmed]
PHST- 2023/11/16 18:01 [entrez]
AID - S1748-6815(23)00689-7 [pii]
AID - 10.1016/j.bjps.2023.10.119 [doi]
PST - aheadofprint
SO  - J Plast Reconstr Aesthet Surg. 2023 Oct 29;88:99-108. doi: 
      10.1016/j.bjps.2023.10.119.

PMID- 37987870
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Print)
IS  - 0148-5598 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Nov 21
TI  - "ChatGPT, Can You Help Me Save My Child's Life?" - Diagnostic Accuracy and 
      Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life 
      Support and Paediatric Advanced Life Support Cases - An In-silico Analysis.
PG  - 123
LID - 10.1007/s10916-023-02019-x [doi]
LID - 123
AB  - BACKGROUND: Paediatric emergencies are challenging for healthcare workers, first 
      aiders, and parents waiting for emergency medical services to arrive. With the 
      expected rise of virtual assistants, people will likely seek help from such 
      digital AI tools, especially in regions lacking emergency medical services. Large 
      Language Models like ChatGPT proved effective in providing health-related 
      information and are competent in medical exams but are questioned regarding 
      patient safety. Currently, there is no information on ChatGPT's performance in 
      supporting parents in paediatric emergencies requiring help from emergency 
      medical services. This study aimed to test 20 paediatric and two basic life 
      support case vignettes for ChatGPT and GPT-4 performance and safety in children. 
      METHODS: We provided the cases three times each to two models, ChatGPT and GPT-4, 
      and assessed the diagnostic accuracy, emergency call advice, and the validity of 
      advice given to parents. RESULTS: Both models recognized the emergency in the 
      cases, except for septic shock and pulmonary embolism, and identified the correct 
      diagnosis in 94%. However, ChatGPT/GPT-4 reliably advised to call emergency 
      services only in 12 of 22 cases (54%), gave correct first aid instructions in 9 
      cases (45%) and incorrectly advised advanced life support techniques to parents 
      in 3 of 22 cases (13.6%). CONCLUSION: Considering these results of the recent 
      ChatGPT versions, the validity, reliability and thus safety of ChatGPT/GPT-4 as 
      an emergency support tool is questionable. However, whether humans would perform 
      better in the same situation is uncertain. Moreover, other studies have shown 
      that human emergency call operators are also inaccurate, partly with worse 
      performance than ChatGPT/GPT-4 in our study. However, one of the main limitations 
      of the study is that we used prototypical cases, and the management may differ 
      from urban to rural areas and between different countries, indicating the need 
      for further evaluation of the context sensitivity and adaptability of the model. 
      Nevertheless, ChatGPT and the new versions under development may be promising 
      tools for assisting lay first responders, operators, and professionals in 
      diagnosing a paediatric emergency. TRIAL REGISTRATION: Not applicable.
CI  - © 2023. The Author(s).
FAU - Bushuven, Stefan
AU  - Bushuven S
AD  - Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 
      78234, Germany. Stefan.Bushuven@notis-ev.de.
AD  - Department of Anesthesiology and Critical Care, Medical Center - University of 
      Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 
      Stefan.Bushuven@notis-ev.de.
AD  - Institute for Medical Education, University Hospital, LMU Munich, Munich, 
      Germany. Stefan.Bushuven@notis-ev.de.
FAU - Bentele, Michael
AU  - Bentele M
AD  - Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 
      78234, Germany.
FAU - Bentele, Stefanie
AU  - Bentele S
AD  - Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 
      78234, Germany.
FAU - Gerber, Bianka
AU  - Gerber B
AD  - Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 
      78234, Germany.
FAU - Bansbach, Joachim
AU  - Bansbach J
AD  - Department of Anesthesiology and Critical Care, Medical Center - University of 
      Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
FAU - Ganter, Julian
AU  - Ganter J
AD  - Department of Anesthesiology and Critical Care, Medical Center - University of 
      Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
FAU - Trifunovic-Koenig, Milena
AU  - Trifunovic-Koenig M
AD  - Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 
      78234, Germany.
FAU - Ranisch, Robert
AU  - Ranisch R
AD  - Faculty for Health Sciences Brandenburg, University of Potsdam, Potsdam, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231121
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - Humans
MH  - Child
MH  - *Emergencies
MH  - Reproducibility of Results
MH  - *Emergency Medical Services
MH  - Health Personnel
MH  - Language
PMC - PMC10663183
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - First responder
OT  - GPT-4
OT  - Large language model
OT  - Medical didactics
OT  - Tele-medicine
COIS- The authors declare no competing interests.
EDAT- 2023/11/21 12:44
MHDA- 2023/11/27 12:42
CRDT- 2023/11/21 11:10
PHST- 2023/05/09 00:00 [received]
PHST- 2023/11/13 00:00 [accepted]
PHST- 2023/11/27 12:42 [medline]
PHST- 2023/11/21 12:44 [pubmed]
PHST- 2023/11/21 11:10 [entrez]
AID - 10.1007/s10916-023-02019-x [pii]
AID - 2019 [pii]
AID - 10.1007/s10916-023-02019-x [doi]
PST - epublish
SO  - J Med Syst. 2023 Nov 21;47(1):123. doi: 10.1007/s10916-023-02019-x.

PMID- 37221834
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20230727
IS  - 2287-285X (Electronic)
IS  - 2287-2728 (Print)
IS  - 2287-2728 (Linking)
VI  - 29
IP  - 3
DP  - 2023 Jul
TI  - Letter 2 regarding "Assessing the performance of ChatGPT in answering questions 
      regarding cirrhosis and hepatocellular carcinoma".
PG  - 815-816
LID - 10.3350/cmh.2023.0170 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230524
PL  - Korea (South)
TA  - Clin Mol Hepatol
JT  - Clinical and molecular hepatology
JID - 101586730
SB  - IM
CON - Clin Mol Hepatol. 2023 Jul;29(3):721-732. PMID: 36946005
CIN - Clin Mol Hepatol. 2023 Jul;29(3):823-824. PMID: 37254485
MH  - Humans
MH  - *Carcinoma, Hepatocellular
MH  - *Liver Neoplasms
MH  - Liver Cirrhosis
PMC - PMC10366795
OTO - NOTNLM
OT  - Carcinoma
OT  - ChatGPT
OT  - Performance
COIS- Conflicts of Interest The authors have no conflicts to disclose.
EDAT- 2023/05/24 06:42
MHDA- 2023/07/25 06:43
CRDT- 2023/05/24 03:08
PHST- 2023/05/18 00:00 [received]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/07/25 06:43 [medline]
PHST- 2023/05/24 06:42 [pubmed]
PHST- 2023/05/24 03:08 [entrez]
AID - cmh.2023.0170 [pii]
AID - cmh-2023-0170 [pii]
AID - 10.3350/cmh.2023.0170 [doi]
PST - ppublish
SO  - Clin Mol Hepatol. 2023 Jul;29(3):815-816. doi: 10.3350/cmh.2023.0170. Epub 2023 
      May 24.

PMID- 37696742
OWN - NLM
STAT- Publisher
LR  - 20231014
IS  - 1934-8150 (Electronic)
IS  - 1551-7411 (Linking)
VI  - 19
IP  - 12
DP  - 2023 Dec
TI  - Assessing the accuracy and consistency of ChatGPT in clinical pharmacy 
      management: A preliminary analysis with clinical pharmacy experts worldwide.
PG  - 1590-1594
LID - S1551-7411(23)00365-0 [pii]
LID - 10.1016/j.sapharm.2023.08.012 [doi]
AB  - BACKGROUND: ChatGPT conversation system has ushered in a revolutionary new era of 
      information retrieval and stands as one of the fastest-growing platforms. 
      Clinical pharmacy, as a dynamic discipline, necessitates an advanced 
      comprehension of drugs and diseases. The process of decision-making in clinical 
      pharmacy demands accuracy and consistency in medical information, as it directly 
      affects patient safety. OBJECTIVE: The objective was to evaluate ChatGPT's 
      accuracy and consistency in managing pharmacotherapy cases across multiple time 
      points. Additionally, input was gathered from global clinical pharmacy experts, 
      and the agreement between ChatGPT's responses and those of clinical pharmacy 
      experts worldwide was assessed. METHODS: A set of 20 cases of pharmacotherapy was 
      entered into ChatGPT at three different time points. Reliability analysis was 
      performed using inter-rater reliability to measure the accuracy of the output 
      generated by ChatGPT at each time point. Test-retest reliability was performed to 
      measure the consistency of the output generated by ChatGPT across the three time 
      points. Pharmacy expert performance was evaluated, and the overall results were 
      compared. RESULTS: ChatGPT achieved a hit rate of 70.83% at week 1, 79.2% at week 
      3, and 75% at week 5. The percent agreement between weeks 1 and 3 was 79.2%, 
      whereas it was 87.5% between weeks 3 and 5, and 83.3% between weeks 1 and 5. In 
      contrast, accuracy rates among clinical pharmacy experts showed considerable 
      variation according to their geographic location. The highest agreement between 
      clinical pharmacist responses and ChatGPT responses was observed at the last time 
      point examined. CONCLUSIONS: Overall, the analysis suggested that ChatGPT is 
      capable of generating clinically relevant pharmaceutical information, albeit with 
      some variation in accuracy and consistency. It should be noted that clinical 
      pharmacy experts worldwide may provide varying degrees of accuracy depending on 
      their expertise. This study highlights the potential of AI chatbots in clinical 
      pharmacy.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Al-Dujaili, Zahraa
AU  - Al-Dujaili Z
AD  - College of Pharmacy, American University of Iraq - Baghdad (AUIB), Baghdad, Iraq.
FAU - Omari, Sarah
AU  - Omari S
AD  - Department of Epidemiology and Population Health, American University of Beirut 
      (AUB), Beirut, Lebanon.
FAU - Pillai, Jey
AU  - Pillai J
AD  - College of Pharmacy, American University of Iraq - Baghdad (AUIB), Baghdad, Iraq.
FAU - Al Faraj, Achraf
AU  - Al Faraj A
AD  - College of Pharmacy, American University of Iraq - Baghdad (AUIB), Baghdad, Iraq. 
      Electronic address: achraf.alfaraj@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230907
PL  - United States
TA  - Res Social Adm Pharm
JT  - Research in social & administrative pharmacy : RSAP
JID - 101231974
SB  - IM
OTO - NOTNLM
OT  - AI chatbot
OT  - ChatGPT
OT  - Clinical pharmacy
OT  - Decision making
OT  - Pharmacotherapy
COIS- Declaration of competing interest None.
EDAT- 2023/09/12 00:42
MHDA- 2023/09/12 00:42
CRDT- 2023/09/11 21:58
PHST- 2023/07/13 00:00 [received]
PHST- 2023/08/30 00:00 [revised]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/09/12 00:42 [pubmed]
PHST- 2023/09/12 00:42 [medline]
PHST- 2023/09/11 21:58 [entrez]
AID - S1551-7411(23)00365-0 [pii]
AID - 10.1016/j.sapharm.2023.08.012 [doi]
PST - ppublish
SO  - Res Social Adm Pharm. 2023 Dec;19(12):1590-1594. doi: 
      10.1016/j.sapharm.2023.08.012. Epub 2023 Sep 7.

PMID- 37466157
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230925
IS  - 1469-0756 (Electronic)
IS  - 0032-5473 (Linking)
VI  - 99
IP  - 1176
DP  - 2023 Sep 21
TI  - The impact of Chat Generative Pre-trained Transformer (ChatGPT) on medical 
      education.
PG  - 1125-1127
LID - 10.1093/postmj/qgad058 [doi]
AB  - Artificial intelligence (AI) in medicine is developing rapidly. The advent of 
      Chat Generative Pre-trained Transformer (ChatGPT) has taken the world by storm 
      with its potential uses and efficiencies. However, technology leaders, 
      researchers, educators, and policy makers have also sounded the alarm on its 
      potential harms and unintended consequences. AI will increasingly find its way 
      into medicine and is a force of both disruption and innovation. We discuss the 
      potential benefits and limitations of this new league of technology and how 
      medical educators have to develop skills and curricula to best harness this 
      innovative power.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Postgraduate Medical Journal. All rights reserved. For Permissions, please email: 
      journals.permissions@oup.com.
FAU - Heng, Jonathan J Y
AU  - Heng JJY
AD  - Yong Loo Lin School of Medicine, National University of Singapore, 117597, 
      Singapore.
FAU - Teo, Desmond B
AU  - Teo DB
AD  - Chronic and Fast Programmes, Alexandra Hospital, 159964, Singapore.
FAU - Tan, L F
AU  - Tan LF
AUID- ORCID: 0000-0003-1232-9308
AD  - Healthy Ageing Programme, Alexandra Hospital, 159964, Singapore.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Postgrad Med J
JT  - Postgraduate medical journal
JID - 0234135
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Education, Medical
MH  - *Medicine
MH  - Curriculum
MH  - Research Personnel
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - medical education
EDAT- 2023/07/19 13:06
MHDA- 2023/09/25 06:43
CRDT- 2023/07/19 07:03
PHST- 2023/05/10 00:00 [received]
PHST- 2023/05/25 00:00 [revised]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/09/25 06:43 [medline]
PHST- 2023/07/19 13:06 [pubmed]
PHST- 2023/07/19 07:03 [entrez]
AID - 7225901 [pii]
AID - 10.1093/postmj/qgad058 [doi]
PST - ppublish
SO  - Postgrad Med J. 2023 Sep 21;99(1176):1125-1127. doi: 10.1093/postmj/qgad058.

PMID- 37822477
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0976-5662 (Print)
IS  - 2213-3445 (Electronic)
IS  - 0976-5662 (Linking)
VI  - 44
DP  - 2023 Sep
TI  - Perception of Chat Generative Pre-trained Transformer (Chat-GPT) AI tool amongst 
      MSK clinicians.
PG  - 102253
LID - 10.1016/j.jcot.2023.102253 [doi]
LID - 102253
AB  - BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT); an open access 
      artificial intelligence (AI) tool has been in the limelight with its ability to 
      respond to prompts, analyse data information using algorithms to augment 
      efficiency in day-to-day activities across a spectrum of human activities 
      including MSK/Orthopaedic science. PURPOSE OF THE STUDY: The purpose of this 
      cross-sectional survey has been to analyse the knowledge, understanding of the 
      role of Chat Generative Pre-trained Transformer (ChatGPT) and its implications in 
      clinical practice as well as research in medicine. MATERIAL & METHODS: An online 
      cross-sectional survey of 10 questions (multiple choice and free text) was 
      circulated amongst orthopaedic surgeons, musculoskeletal radiologists and 
      Rheumatologists in India and UK, to evaluate perception of Chat Generative 
      Pre-trained Transformer (ChatGPT) AI Tool. RESULTS: We had 125 responses with 
      majority being aware of ChatGPT though a minority had used it. There was 
      consensus that its going have detrimental effect on workforce with majority of 
      the opinion that they would be used to create radiology reports. Mixed responses 
      were noted regarding the quality of research and role of ChatGPT being an 
      anonymous author. CONCLUSION: There is a considerable debate amongst clinicians 
      of orthopaedic, radiology and rheumatology -specialities. The attitudes are mixed 
      but mainly positive, although there are many concerns about the still-evolving 
      new technology. LEVEL OF STUDY: Diagnostic Study level 4.
CI  - © 2023 Delhi Orthopedic Association. All rights reserved.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AD  - Department of Orthopaedics, Southport and Ormskirk Hospitals, Mersey and West 
      Lancashire Teaching NHS Trust, Southport, UK.
FAU - Yousef, Mina Malak Abed
AU  - Yousef MMA
AD  - Department of Orthopaedics, Southport and Ormskirk Hospitals, Mersey and West 
      Lancashire Teaching NHS Trust, Southport, UK.
FAU - Nune, Arvind
AU  - Nune A
AD  - Department of Rheumatology, Southport and Ormskirk Hospitals, Mersey and West 
      Lancahire Teaching NHS Trust, Southport, UK.
FAU - Sharma, Gaurav Kant
AU  - Sharma GK
AD  - Jaipur Institute of Pain and Sports Injuries (JIPSI), Jaipur, India.
FAU - Botchu, Rajesh
AU  - Botchu R
AD  - Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, 
      UK.
LA  - eng
PT  - Journal Article
DEP - 20230923
PL  - India
TA  - J Clin Orthop Trauma
JT  - Journal of clinical orthopaedics and trauma
JID - 101559469
PMC - PMC10562840
OTO - NOTNLM
OT  - Algorithms
OT  - Artificial intelligence
OT  - Orthopaedics
OT  - Radiology
OT  - Surveys and questionnaires
COIS- The authors declare the following financial interests/personal relationships 
      which may be considered as potential competing interests:KP and RB are on 
      editorial board of JCOT
EDAT- 2023/10/12 06:43
MHDA- 2023/10/12 06:44
PMCR- 2024/09/23
CRDT- 2023/10/12 04:03
PHST- 2023/02/28 00:00 [received]
PHST- 2023/08/03 00:00 [revised]
PHST- 2023/09/21 00:00 [accepted]
PHST- 2024/09/23 00:00 [pmc-release]
PHST- 2023/10/12 06:44 [medline]
PHST- 2023/10/12 06:43 [pubmed]
PHST- 2023/10/12 04:03 [entrez]
AID - S0976-5662(23)00161-3 [pii]
AID - 102253 [pii]
AID - 10.1016/j.jcot.2023.102253 [doi]
PST - epublish
SO  - J Clin Orthop Trauma. 2023 Sep 23;44:102253. doi: 10.1016/j.jcot.2023.102253. 
      eCollection 2023 Sep.

PMID- 37898342
OWN - NLM
STAT- Publisher
LR  - 20231124
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
DP  - 2023 Oct 26
TI  - Response to Jin and Dobry's "ChatGPT for health care providers and patients: 
      Practical implications within dermatology".
LID - S0190-9622(23)03042-6 [pii]
LID - 10.1016/j.jaad.2023.10.038 [doi]
FAU - Jiang, Melinda
AU  - Jiang M
AD  - Department of Immunology, Royal Adelaide Hospital, Adelaide, SA, Australia; 
      Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, 
      Australia. Electronic address: melinda.jiang@sa.gov.au.
FAU - Bacchi, Stephen
AU  - Bacchi S
AD  - Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia; 
      College of Medicine and Public Health, Flinders University, Adelaide, SA, 
      Australia; Health and Information, Adelaide, SA, Australia.
FAU - Kovoor, Joshua
AU  - Kovoor J
AD  - Department of Immunology, Royal Adelaide Hospital, Adelaide, SA, Australia; 
      Health and Information, Adelaide, SA, Australia.
FAU - Stretton, Brandon
AU  - Stretton B
AD  - Royal Adelaide Hospital, Adelaide, SA, Australia; University of Adelaide, 
      Adelaide, SA, Australia.
FAU - Chan, Weng Onn
AU  - Chan WO
AD  - Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, 
      Australia; Department of Ophthalmology, Royal Adelaide Hospital, Adelaide, SA, 
      Australia.
LA  - eng
PT  - Letter
DEP - 20231026
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
OTO - NOTNLM
OT  - dermatology
OT  - education
OT  - evidence
OT  - large language model
OT  - learning
OT  - machine learning
OT  - teaching
COIS- Conflicts of interest None disclosed.
EDAT- 2023/10/29 06:46
MHDA- 2023/10/29 06:46
CRDT- 2023/10/28 19:29
PHST- 2023/09/15 00:00 [received]
PHST- 2023/10/08 00:00 [revised]
PHST- 2023/10/13 00:00 [accepted]
PHST- 2023/10/29 06:46 [pubmed]
PHST- 2023/10/29 06:46 [medline]
PHST- 2023/10/28 19:29 [entrez]
AID - S0190-9622(23)03042-6 [pii]
AID - 10.1016/j.jaad.2023.10.038 [doi]
PST - aheadofprint
SO  - J Am Acad Dermatol. 2023 Oct 26:S0190-9622(23)03042-6. doi: 
      10.1016/j.jaad.2023.10.038.

PMID- 37341179
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1546-3141 (Electronic)
IS  - 0361-803X (Linking)
VI  - 221
IP  - 5
DP  - 2023 Nov
TI  - Use of ChatGPT, GPT-4, and Bard to Improve Readability of ChatGPT's Answers to 
      Common Questions About Lung Cancer and Lung Cancer Screening.
PG  - 701-704
LID - 10.2214/AJR.23.29622 [doi]
FAU - Haver, Hana L
AU  - Haver HL
AD  - University of Maryland School of Medicine, Baltimore, MD.
FAU - Lin, Cheng Ting
AU  - Lin CT
AD  - Johns Hopkins University School of Medicine, Baltimore, MD.
FAU - Sirajuddin, Arlene
AU  - Sirajuddin A
AD  - NIH, Bethesda, MD.
FAU - Yi, Paul H
AU  - Yi PH
AD  - University of Maryland School of Medicine, Johns Hopkins University, Baltimore, 
      MD.
AD  - University of Maryland, College Park, MD.
FAU - Jeudy, Jean
AU  - Jeudy J
AD  - University of Maryland School of Medicine, Baltimore, MD, 
      jjeudy@som.umaryland.edu.
LA  - eng
PT  - Journal Article
DEP - 20230621
PL  - United States
TA  - AJR Am J Roentgenol
JT  - AJR. American journal of roentgenology
JID - 7708173
SB  - IM
OAB - ChatGPT’s responses to questions about lung cancer and LCS, although deemed 
      clinically appropriate by cardiothoracic radiologists, were difficult to read. 
      Simplified responses from three LLMs (ChatGPT, GPT-4, and Bard) had improved 
      reading ease and readability (in terms of U.S. grade levels). However, some 
      simplified responses were no longer clinically appropriate.
OABL- eng
EDAT- 2023/06/21 13:04
MHDA- 2023/06/21 13:04
CRDT- 2023/06/21 06:53
PHST- 2023/06/21 13:04 [pubmed]
PHST- 2023/06/21 13:04 [medline]
PHST- 2023/06/21 06:53 [entrez]
AID - 10.2214/AJR.23.29622 [doi]
PST - ppublish
SO  - AJR Am J Roentgenol. 2023 Nov;221(5):701-704. doi: 10.2214/AJR.23.29622. Epub 
      2023 Jun 21.

PMID- 36971685
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230329
LR  - 20230329
IS  - 1522-1229 (Electronic)
IS  - 1043-4046 (Linking)
VI  - 47
IP  - 2
DP  - 2023 Jun 1
TI  - Evaluating the performance of ChatGPT in medical physiology university 
      examination of phase I MBBS.
PG  - 270-271
LID - 10.1152/advan.00036.2023 [doi]
FAU - Subramani, Mahila
AU  - Subramani M
AD  - Department of Physiology, Panimalar Medical College Hospital and Research 
      Institute, Varadharajapuram, Chennai, Tamil Nadu, India.
FAU - Jaleel, Ilham
AU  - Jaleel I
AD  - Department of Physiology, Panimalar Medical College Hospital and Research 
      Institute, Varadharajapuram, Chennai, Tamil Nadu, India.
FAU - Krishna Mohan, Surapaneni
AU  - Krishna Mohan S
AD  - Departments of Biochemistry, Medical Education, Molecular Virology, Research, 
      Clinical Skills and Simulation, Panimalar Medical College Hospital and Research 
      Institute, Varadharajapuram, Chennai, Tamil Nadu, India.
LA  - eng
PT  - Letter
PL  - United States
TA  - Adv Physiol Educ
JT  - Advances in physiology education
JID - 100913944
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - medical education
OT  - medical physiology
EDAT- 2023/03/28 06:00
MHDA- 2023/03/29 06:04
CRDT- 2023/03/27 10:33
PHST- 2023/03/29 06:04 [medline]
PHST- 2023/03/27 10:33 [entrez]
PHST- 2023/03/28 06:00 [pubmed]
AID - 10.1152/advan.00036.2023 [doi]
PST - ppublish
SO  - Adv Physiol Educ. 2023 Jun 1;47(2):270-271. doi: 10.1152/advan.00036.2023.

PMID- 37693388
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Aug 28
TI  - Assessing Racial and Ethnic Bias in Text Generation for Healthcare-Related Tasks 
      by ChatGPT(1).
LID - 2023.08.28.23294730 [pii]
LID - 10.1101/2023.08.28.23294730 [doi]
AB  - Large Language Models (LLM) are AI tools that can respond human-like to voice or 
      free-text commands without training on specific tasks. However, concerns have 
      been raised about their potential racial bias in healthcare tasks. In this study, 
      ChatGPT was used to generate healthcare-related text for patients with HIV, 
      analyzing data from 100 deidentified electronic health record encounters. Each 
      patient's data were fed four times with all information remaining the same except 
      for race/ethnicity (African American, Asian, Hispanic White, Non-Hispanic White). 
      The text output was analyzed for sentiment, subjectivity, reading ease, and most 
      used words by race/ethnicity and insurance type. Results showed that instructions 
      for African American, Asian, Hispanic White, and Non-Hispanic White patients had 
      an average polarity of 0.14, 0.14, 0.15, and 0.14, respectively, with an average 
      subjectivity of 0.46 for all races/ethnicities. The differences in polarity and 
      subjectivity across races/ethnicities were not statistically significant. 
      However, there was a statistically significant difference in word frequency 
      across races/ethnicities and a statistically significant difference in 
      subjectivity across insurance types with commercial insurance eliciting the most 
      subjective responses and Medicare and other payer types the lowest. The study 
      suggests that ChatGPT is relatively invariant to race/ethnicity and insurance 
      type in terms of linguistic and readability measures. Further studies are needed 
      to validate these results and assess their implications.
FAU - Hanna, John J
AU  - Hanna JJ
AD  - Clinical Informatics Center, University of Texas Southwestern, 5323 Harry Hines 
      Blvd, Dallas, TX 75390, USA.
FAU - Wakene, Abdi D
AU  - Wakene AD
AD  - Clinical Informatics Center, University of Texas Southwestern, 5323 Harry Hines 
      Blvd, Dallas, TX 75390, USA.
FAU - Lehmann, Christoph U
AU  - Lehmann CU
AD  - Clinical Informatics Center, University of Texas Southwestern, 5323 Harry Hines 
      Blvd, Dallas, TX 75390, USA.
FAU - Medford, Richard J
AU  - Medford RJ
AD  - Clinical Informatics Center, University of Texas Southwestern, 5323 Harry Hines 
      Blvd, Dallas, TX 75390, USA.
LA  - eng
PT  - Preprint
DEP - 20230828
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10491360
OTO - NOTNLM
OT  - Large Language Model
OT  - artificial intelligence
OT  - bias
OT  - racism
OT  - reading ease
OT  - sentiment analysis
OT  - word frequency
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 04:53
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 04:53 [entrez]
AID - 2023.08.28.23294730 [pii]
AID - 10.1101/2023.08.28.23294730 [doi]
PST - epublish
SO  - medRxiv. 2023 Aug 28:2023.08.28.23294730. doi: 10.1101/2023.08.28.23294730. 
      Preprint.

PMID- 37793197
OWN - NLM
STAT- MEDLINE
DCOM- 20231121
LR  - 20231130
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 86
DP  - 2023 Nov
TI  - Generative artificial intelligence fails to provide sufficiently accurate 
      recommendations when compared to established breast reconstruction surgery 
      guidelines.
PG  - 248-250
LID - S1748-6815(23)00524-7 [pii]
LID - 10.1016/j.bjps.2023.09.030 [doi]
FAU - Saturno, Michael P
AU  - Saturno MP
AD  - Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA.
FAU - Mejia, Mateo Restrepo
AU  - Mejia MR
AD  - Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA.
FAU - Wang, Anya
AU  - Wang A
AD  - Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA.
FAU - Kwon, Daniel
AU  - Kwon D
AD  - Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA.
FAU - Oleru, Olachi
AU  - Oleru O
AD  - Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA.
FAU - Seyidova, Nargiz
AU  - Seyidova N
AD  - Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA.
FAU - Henderson, Peter W
AU  - Henderson PW
AD  - Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount 
      Sinai, New York, NY 10029, USA. Electronic address: 
      peter.henderson@mountsinai.org.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Mammaplasty
MH  - Practice Guidelines as Topic
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Guidelines
OT  - Large language model
OT  - Plastic and reconstructive surgery
OT  - Reconstructive breast surgery
COIS- Declaration of Competing Interest None.
EDAT- 2023/10/04 18:42
MHDA- 2023/11/06 06:42
CRDT- 2023/10/04 17:59
PHST- 2023/08/17 00:00 [received]
PHST- 2023/09/08 00:00 [accepted]
PHST- 2023/11/06 06:42 [medline]
PHST- 2023/10/04 18:42 [pubmed]
PHST- 2023/10/04 17:59 [entrez]
AID - S1748-6815(23)00524-7 [pii]
AID - 10.1016/j.bjps.2023.09.030 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Nov;86:248-250. doi: 
      10.1016/j.bjps.2023.09.030. Epub 2023 Sep 15.

PMID- 37892793
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2077-0383 (Print)
IS  - 2077-0383 (Electronic)
IS  - 2077-0383 (Linking)
VI  - 12
IP  - 20
DP  - 2023 Oct 20
TI  - Are ChatGPT's Free-Text Responses on Periprosthetic Joint Infections of the Hip 
      and Knee Reliable and Useful?
LID - 10.3390/jcm12206655 [doi]
LID - 6655
AB  - BACKGROUND: This study aimed to evaluate ChatGPT's performance on questions about 
      periprosthetic joint infections (PJI) of the hip and knee. METHODS: Twenty-seven 
      questions from the 2018 International Consensus Meeting on Musculoskeletal 
      Infection were selected for response generation. The free-text responses were 
      evaluated by three orthopedic surgeons using a five-point Likert scale. 
      Inter-rater reliability (IRR) was assessed via Fleiss' kappa (FK). RESULTS: 
      Overall, near-perfect IRR was found for disagreement on the presence of factual 
      errors (FK: 0.880, 95% CI [0.724, 1.035], p < 0.001) and agreement on information 
      completeness (FK: 0.848, 95% CI [0.699, 0.996], p < 0.001). Substantial IRR was 
      observed for disagreement on misleading information (FK: 0.743, 95% CI [0.601, 
      0.886], p < 0.001) and agreement on suitability for patients (FK: 0.627, 95% CI 
      [0.478, 0.776], p < 0.001). Moderate IRR was observed for agreement on 
      "up-to-dateness" (FK: 0.584, 95% CI [0.434, 0.734], p < 0.001) and suitability 
      for orthopedic surgeons (FK: 0.505, 95% CI [0.383, 0.628], p < 0.001). Question- 
      and subtopic-specific analysis revealed diverse IRR levels ranging from 
      near-perfect to poor. CONCLUSIONS: ChatGPT's free-text responses to complex 
      orthopedic questions were predominantly reliable and useful for orthopedic 
      surgeons and patients. Given variations in performance by question and subtopic, 
      consulting additional sources and exercising careful interpretation should be 
      emphasized for reliable medical decision-making.
FAU - Draschl, Alexander
AU  - Draschl A
AUID- ORCID: 0000-0001-8344-7796
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
AD  - Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, 
      Medical University of Graz, Auenbruggerplatz 29/4, 8036 Graz, Austria.
FAU - Hauer, Georg
AU  - Hauer G
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
FAU - Fischerauer, Stefan Franz
AU  - Fischerauer SF
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
FAU - Kogler, Angelika
AU  - Kogler A
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
AD  - Department of Dermatology and Venereology, Medical University of Graz, 
      Auenbruggerplatz 8, 8036 Graz, Austria.
FAU - Leitner, Lukas
AU  - Leitner L
AUID- ORCID: 0000-0002-5534-7605
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
FAU - Andreou, Dimosthenis
AU  - Andreou D
AUID- ORCID: 0000-0002-5083-9678
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
FAU - Leithner, Andreas
AU  - Leithner A
AUID- ORCID: 0000-0002-2598-2325
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
FAU - Sadoghi, Patrick
AU  - Sadoghi P
AUID- ORCID: 0000-0003-1767-555X
AD  - Department of Orthopedics and Trauma, Medical University of Graz, 
      Auenbruggerplatz 5, 8036 Graz, Austria.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - Switzerland
TA  - J Clin Med
JT  - Journal of clinical medicine
JID - 101606588
PMC - PMC10607052
OTO - NOTNLM
OT  - artificial intelligence
OT  - hip prosthesis
OT  - knee prosthesis
OT  - large language model
OT  - periprosthetic joint infection
COIS- A.L. and P.S. receive or have received institutional educational grants from 
      Johnson & Johnson, Alphamed, and Medacta without a conflict of interest regarding 
      the submitted work. The other authors have no conflict of interest to declare.
EDAT- 2023/10/28 11:47
MHDA- 2023/10/28 11:48
CRDT- 2023/10/28 01:06
PHST- 2023/08/28 00:00 [received]
PHST- 2023/10/12 00:00 [revised]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/10/28 11:48 [medline]
PHST- 2023/10/28 11:47 [pubmed]
PHST- 2023/10/28 01:06 [entrez]
AID - jcm12206655 [pii]
AID - jcm-12-06655 [pii]
AID - 10.3390/jcm12206655 [doi]
PST - epublish
SO  - J Clin Med. 2023 Oct 20;12(20):6655. doi: 10.3390/jcm12206655.

PMID- 37598590
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20231130
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 85
DP  - 2023 Oct
TI  - Dr. ChatGPT will see you now: How do Google and ChatGPT compare in answering 
      patient questions on breast reconstruction?
PG  - 488-497
LID - S1748-6815(23)00445-X [pii]
LID - 10.1016/j.bjps.2023.07.039 [doi]
FAU - Liu, Hilary Y
AU  - Liu HY
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15213, United States.
FAU - Alessandri Bonetti, Mario
AU  - Alessandri Bonetti M
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15213, United States.
FAU - Jeong, Tiffany
AU  - Jeong T
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15213, United States.
FAU - Pandya, Sumaarg
AU  - Pandya S
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15213, United States.
FAU - Nguyen, Vu T
AU  - Nguyen VT
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15213, United States.
FAU - Egro, Francesco M
AU  - Egro FM
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15213, United States. Electronic address: 
      francescoegro@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230727
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
CIN - J Plast Reconstr Aesthet Surg. 2023 Nov;86:269-270. PMID: 37797373
MH  - Humans
MH  - *Search Engine
MH  - *Mammaplasty
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Breast
OT  - Breast reconstruction
OT  - ChatGPT
OT  - Google
OT  - Patient education
COIS- Declaration of Competing Interest The authors declare that they have no conflicts 
      of interest to disclose.
EDAT- 2023/08/21 00:41
MHDA- 2023/09/25 06:42
CRDT- 2023/08/20 18:07
PHST- 2023/07/07 00:00 [received]
PHST- 2023/07/16 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/08/21 00:41 [pubmed]
PHST- 2023/08/20 18:07 [entrez]
AID - S1748-6815(23)00445-X [pii]
AID - 10.1016/j.bjps.2023.07.039 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Oct;85:488-497. doi: 
      10.1016/j.bjps.2023.07.039. Epub 2023 Jul 27.

PMID- 37939643
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 87
DP  - 2023 Dec
TI  - Online patient education in body contouring: A comparison between Google and 
      ChatGPT.
PG  - 390-402
LID - S1748-6815(23)00642-3 [pii]
LID - 10.1016/j.bjps.2023.10.091 [doi]
AB  - Appropriate patient education and preparation prior to surgery represent a 
      fundamental step in managing expectations, avoiding unnecessary encounters and 
      eventually achieving optimal outcomes. Thus, the objective of this study is to 
      evaluate ChatGPT's potential as a viable source for patient education by 
      comparing its responses and provided references to frequently asked questions on 
      body contouring, with Google's. A Google search was conducted on July 15th, 2023, 
      using the search term "body contouring surgery". The first 15 questions under the 
      "People also ask" section and answers provided by Google were recorded. The 15 
      questions were then asked to ChatGPT-3.5. Four plastic surgeons evaluated the 
      answers from 1 to 5 according to the Global Quality Scale. The mean score for 
      responses given by Google was 2.55 ± 1.29, indicating poor quality but some 
      information present, of very limited use to patients. The mean score for 
      responses produced by ChatGPT was 4.38 ± 0.67, suggesting that the content was of 
      good quality, useful to patients, and encompassed the most important topics. The 
      difference was statistically significant (p = 0.001). Deficiencies in providing 
      references represent one of the most evident weaknesses of ChatGPT. However, 
      ChatGPT did not appear to spread misinformation, and the content of the generated 
      responses was deemed of good quality and useful to patients. The integration of 
      AI technology as a source for patient education has the potential to optimize 
      patient queries on body contouring questions.
CI  - Copyright © 2023 British Association of Plastic, Reconstructive and Aesthetic 
      Surgeons. Published by Elsevier Ltd. All rights reserved.
FAU - Alessandri-Bonetti, Mario
AU  - Alessandri-Bonetti M
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15219, United States.
FAU - Liu, Hilary Y
AU  - Liu HY
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15219, United States.
FAU - Palmesano, Marco
AU  - Palmesano M
AD  - Department of Plastic Surgery, University of Rome "Tor Vergata", Rome, Italy.
FAU - Nguyen, Vu T
AU  - Nguyen VT
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15219, United States.
FAU - Egro, Francesco M
AU  - Egro FM
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, G103, Pittsburgh, PA 15219, United States. Electronic address: 
      francescoegro@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
MH  - Humans
MH  - *Body Contouring
MH  - Search Engine
MH  - Patient Education as Topic
MH  - *Education, Distance
MH  - Patients
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Body contouring
OT  - ChatGPT
OT  - Large language model
OT  - Patient education
COIS- Declaration of Competing Interest The authors declare that they have no conflicts 
      of interest to disclose.
EDAT- 2023/11/09 00:42
MHDA- 2023/12/05 12:42
CRDT- 2023/11/08 18:11
PHST- 2023/09/18 00:00 [received]
PHST- 2023/10/08 00:00 [accepted]
PHST- 2023/12/05 12:42 [medline]
PHST- 2023/11/09 00:42 [pubmed]
PHST- 2023/11/08 18:11 [entrez]
AID - S1748-6815(23)00642-3 [pii]
AID - 10.1016/j.bjps.2023.10.091 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Dec;87:390-402. doi: 
      10.1016/j.bjps.2023.10.091. Epub 2023 Oct 20.

PMID- 37259767
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20231116
IS  - 1469-3178 (Electronic)
IS  - 1469-221X (Print)
IS  - 1469-221X (Linking)
VI  - 24
IP  - 7
DP  - 2023 Jul 5
TI  - The (mis)use of ChatGPT in science and education: Turing, Djerassi, "athletics" & 
      ethics.
PG  - e57501
LID - 10.15252/embr.202357501 [doi]
LID - e57501
AB  - ChatGPT is an amazing advance in technology, but it poses problems for teachers 
      in universities and schools. How should use of ChatGPT by students be dealt with?
CI  - © 2023 The Authors.
FAU - Shaw, David
AU  - Shaw D
AUID- ORCID: 0000-0001-8180-6927
AD  - Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.
AD  - Department of Health, Ethics and Society, Care and Public Health Research 
      Institute, Maastricht University, Maastricht, The Netherlands.
FAU - Morfeld, Peter
AU  - Morfeld P
AD  - Institute and Policlinic for Occupational Medicine, Environmental Medicine and 
      Prevention Research, University Hospital of Cologne, Cologne, Germany.
FAU - Erren, Thomas
AU  - Erren T
AD  - Institute and Policlinic for Occupational Medicine, Environmental Medicine and 
      Prevention Research, University Hospital of Cologne, Cologne, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230601
PL  - England
TA  - EMBO Rep
JT  - EMBO reports
JID - 100963049
SB  - IM
MH  - Humans
MH  - *Schools
MH  - Universities
MH  - *Students
MH  - Technology
PMC - PMC10328063
EDAT- 2023/06/01 06:42
MHDA- 2023/07/10 06:42
PMCR- 2024/06/01
CRDT- 2023/06/01 04:09
PHST- 2023/05/16 00:00 [received]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2024/06/01 00:00 [pmc-release]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/06/01 06:42 [pubmed]
PHST- 2023/06/01 04:09 [entrez]
AID - EMBR202357501 [pii]
AID - 10.15252/embr.202357501 [doi]
PST - ppublish
SO  - EMBO Rep. 2023 Jul 5;24(7):e57501. doi: 10.15252/embr.202357501. Epub 2023 Jun 1.

PMID- 37392000
OWN - NLM
STAT- MEDLINE
DCOM- 20231129
LR  - 20231129
IS  - 1741-2854 (Electronic)
IS  - 0020-7640 (Linking)
VI  - 69
IP  - 8
DP  - 2023 Dec
TI  - Old dog, new tricks? Exploring the potential functionalities of ChatGPT in 
      supporting educational methods in social psychiatry.
PG  - 1882-1889
LID - 10.1177/00207640231178451 [doi]
AB  - BACKGROUND: Artificial Intelligence is ever-expanding and large-language models 
      are increasingly shaping teaching and learning experiences. ChatGPT is a 
      prominent recent example of this technology and has generated much debate around 
      the benefits and disadvantages of chatbots in educational domains. AIM: This 
      study seeks to demonstrate the possible use-cases of ChatGPT in supporting 
      educational methods specific to social psychiatry. METHODS: Through interactions 
      with ChatGPT 3.5, we asked this technology to list six ways in which it could aid 
      social psychiatry teaching. Subsequently, we requested that ChatGPT perform one 
      of the tasks it identified in its responses. FINDINGS: ChatGPT highlighted 
      several roles it could fulfil in educational settings, including as an 
      information provider, a tool for debates and discussions, a facilitator of 
      self-directed learning and a content-creator for course materials. For the latter 
      scenario, based on another prompt, ChatGPT generated a hypothetical case vignette 
      for a topic relevant to social psychiatry. CONCLUSIONS: Based on our experiences, 
      ChatGPT can be an effective teaching tool, offering opportunities for active and 
      case-based learning for students and instructors in social psychiatry. However, 
      in their current form, chatbots have several limitations that must be considered, 
      including misinformation and inherent biases, although these may only be 
      temporary in nature as these technologies continue to advance. Accordingly, we 
      argue that large-language models can support social psychiatry education with 
      appropriate caution and encourage educators to become attuned to their potential 
      through further detailed research in this area.
FAU - Smith, Alexander
AU  - Smith A
AUID- ORCID: 0000-0002-1769-5684
AD  - Department of Forensic Psychiatry, University of Bern, Switzerland.
FAU - Hachen, Stefanie
AU  - Hachen S
AD  - Department of Forensic Psychiatry, University of Bern, Switzerland.
FAU - Schleifer, Roman
AU  - Schleifer R
AD  - Department of Forensic Psychiatry, University of Bern, Switzerland.
FAU - Bhugra, Dinesh
AU  - Bhugra D
AD  - King's College London, UK.
FAU - Buadze, Anna
AU  - Buadze A
AUID- ORCID: 0000-0002-8683-6807
AD  - Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, 
      University of Zurich, Switzerland.
FAU - Liebrenz, Michael
AU  - Liebrenz M
AD  - Department of Forensic Psychiatry, University of Bern, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20230630
PL  - England
TA  - Int J Soc Psychiatry
JT  - The International journal of social psychiatry
JID - 0374726
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Community Psychiatry
MH  - Educational Status
MH  - Students
MH  - Language
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - active-learning
OT  - case-based learning
OT  - psychiatric education
OT  - teaching
COIS- Conflict of interestThe author(s) declared no potential conflicts of interest 
      with respect to the research, authorship, and/or publication of this article.
EDAT- 2023/07/01 11:42
MHDA- 2023/11/29 06:43
CRDT- 2023/07/01 02:32
PHST- 2023/11/29 06:43 [medline]
PHST- 2023/07/01 11:42 [pubmed]
PHST- 2023/07/01 02:32 [entrez]
AID - 10.1177/00207640231178451 [doi]
PST - ppublish
SO  - Int J Soc Psychiatry. 2023 Dec;69(8):1882-1889. doi: 10.1177/00207640231178451. 
      Epub 2023 Jun 30.

PMID- 37717655
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231128
LR  - 20231128
IS  - 1535-7732 (Electronic)
IS  - 1051-0443 (Linking)
VI  - 34
IP  - 12
DP  - 2023 Dec
TI  - Can Chat Generative Pretraining Transformer (ChatGPT) Be Used for Statistical 
      Analysis of Research Data?
PG  - 2242-2246.e2
LID - S1051-0443(23)00669-3 [pii]
LID - 10.1016/j.jvir.2023.09.010 [doi]
FAU - Jahangiri, Younes
AU  - Jahangiri Y
AD  - Department of Interventional Radiology, Corewell Health West Michigan, 100 
      Michigan St NE, Grand Rapids, MI 49503. Electronic address: 
      y.jahangiri@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230917
PL  - United States
TA  - J Vasc Interv Radiol
JT  - Journal of vascular and interventional radiology : JVIR
JID - 9203369
SB  - IM
EDAT- 2023/09/18 00:41
MHDA- 2023/09/18 00:42
CRDT- 2023/09/17 19:14
PHST- 2023/07/06 00:00 [received]
PHST- 2023/09/07 00:00 [revised]
PHST- 2023/09/08 00:00 [accepted]
PHST- 2023/09/18 00:42 [medline]
PHST- 2023/09/18 00:41 [pubmed]
PHST- 2023/09/17 19:14 [entrez]
AID - S1051-0443(23)00669-3 [pii]
AID - 10.1016/j.jvir.2023.09.010 [doi]
PST - ppublish
SO  - J Vasc Interv Radiol. 2023 Dec;34(12):2242-2246.e2. doi: 
      10.1016/j.jvir.2023.09.010. Epub 2023 Sep 17.

PMID- 37417005
OWN - NLM
STAT- MEDLINE
DCOM- 20230817
LR  - 20230817
IS  - 2043-6289 (Electronic)
IS  - 0266-7681 (Linking)
VI  - 48
IP  - 8
DP  - 2023 Sep
TI  - The use of artificial intelligence (AI) to enhance academic communication, 
      education and research: a balanced approach.
PG  - 819-822
LID - 10.1177/17531934231185746 [doi]
AB  - Much has been written about the concerns surrounding artificial intelligence 
      (AI). This article looks positively at how AI can enhance communication and 
      academic skills, including teaching and research. The article explains what AI, 
      Generative Pre-trained Transformer (GPT), and chat-GPT are and highlights a few 
      AI-based tools that are currently in use to improve communication and academic 
      skills. It also mentions potential AI problems, such as a lack of 
      personalization, societal biases, and privacy concerns. The future lies in the 
      training of hand surgeons to master the skill of precise communication and 
      academia using AI tools.
FAU - Esplugas, Mireia
AU  - Esplugas M
AUID- ORCID: 0000-0002-6931-1339
AD  - Kaplan Hand Institute Barcelona, Avinguda Reina Elisenda 17, Barcelona, 08034, 
      Spain.
LA  - eng
PT  - Journal Article
DEP - 20230707
PL  - England
TA  - J Hand Surg Eur Vol
JT  - The Journal of hand surgery, European volume
JID - 101315820
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Educational Status
MH  - Communication
MH  - *Surgeons
MH  - Writing
OTO - NOTNLM
OT  - AI-related tools
OT  - Artificial intelligence (AI)
OT  - Large language model (LLM) academics
OT  - research
OT  - risks
EDAT- 2023/07/07 06:42
MHDA- 2023/08/17 06:43
CRDT- 2023/07/07 04:33
PHST- 2023/08/17 06:43 [medline]
PHST- 2023/07/07 06:42 [pubmed]
PHST- 2023/07/07 04:33 [entrez]
AID - 10.1177/17531934231185746 [doi]
PST - ppublish
SO  - J Hand Surg Eur Vol. 2023 Sep;48(8):819-822. doi: 10.1177/17531934231185746. Epub 
      2023 Jul 7.

PMID- 37457604
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - Advancing Artificial Intelligence for Clinical Knowledge Retrieval: A Case Study 
      Using ChatGPT-4 and Link Retrieval Plug-In to Analyze Diabetic Ketoacidosis 
      Guidelines.
PG  - e41916
LID - 10.7759/cureus.41916 [doi]
LID - e41916
AB  - Introduction This case study aimed to enhance the traceability and retrieval 
      accuracy of ChatGPT-4 in medical text by employing a step-by-step systematic 
      approach. The focus was on retrieving clinical answers from three international 
      guidelines on diabetic ketoacidosis (DKA). Methods A systematic methodology was 
      developed to guide the retrieval process. One question was asked per guideline to 
      ensure accuracy and maintain referencing. ChatGPT-4 was utilized to retrieve 
      answers, and the 'Link Reader' plug-in was integrated to facilitate direct access 
      to webpages containing the guidelines. Subsequently, ChatGPT-4 was employed to 
      compile answers while providing citations to the sources. This process was 
      iterated 30 times per question to ensure consistency. In this report, we present 
      our observations regarding the retrieval accuracy, consistency of responses, and 
      the challenges encountered during the process. Results Integrating ChatGPT-4 with 
      the 'Link Reader' plug-in demonstrated notable traceability and retrieval 
      accuracy benefits. The AI model successfully provided relevant and accurate 
      clinical answers based on the analyzed guidelines. Despite occasional challenges 
      with webpage access and minor memory drift, the overall performance of the 
      integrated system was promising. The compilation of the answers was also 
      impressive and held significant promise for further trials. Conclusion The 
      findings of this case study contribute to the utilization of AI text-generation 
      models as valuable tools for medical professionals and researchers. The 
      systematic approach employed in this case study and the integration of the 'Link 
      Reader' plug-in offer a framework for automating medical text synthesis, asking 
      one question at a time before compilation from different sources, which has led 
      to improving AI models' traceability and retrieval accuracy. Further advancements 
      and refinement of AI models and integration with other software utilities hold 
      promise for enhancing the utility and applicability of AI-generated 
      recommendations in medicine and scientific academia. These advancements have the 
      potential to drive significant improvements in everyday medical practice.
CI  - Copyright © 2023, Hamed et al.
FAU - Hamed, Ehab
AU  - Hamed E
AD  - Family Medicine, Qatar University Health Centre, Primary Health Care Corporation, 
      Doha, QAT.
FAU - Sharif, Anna
AU  - Sharif A
AD  - Family Medicine, Primary Health Care Corporation, Doha, QAT.
FAU - Eid, Ahmad
AU  - Eid A
AD  - Family Medicine, Primary Health Care Corporation, Doha, QAT.
FAU - Alfehaidi, Alanoud
AU  - Alfehaidi A
AD  - Family Medicine, Primary Health Care Corporation, Doha, QAT.
FAU - Alberry, Medhat
AU  - Alberry M
AD  - Obstetrics and Gynecology, Weill Cornell Medicine - Qatar, Doha, QAT.
AD  - Fetal and Maternal Medicine, Sidra Medicine, Doha, QAT.
LA  - eng
PT  - Journal Article
DEP - 20230715
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10349539
OTO - NOTNLM
OT  - artificial intelligence in medicine
OT  - chatgpt
OT  - chatgpt-4
OT  - clinical decision support
OT  - clinical decision support system
OT  - clinical decision tool
OT  - generative ai
OT  - large language model
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/17 06:42
MHDA- 2023/07/17 06:43
CRDT- 2023/07/17 04:41
PHST- 2023/07/15 00:00 [accepted]
PHST- 2023/07/17 06:43 [medline]
PHST- 2023/07/17 06:42 [pubmed]
PHST- 2023/07/17 04:41 [entrez]
AID - 10.7759/cureus.41916 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 15;15(7):e41916. doi: 10.7759/cureus.41916. eCollection 2023 
      Jul.

PMID- 37936506
OWN - NLM
STAT- Publisher
LR  - 20231108
IS  - 2157-6580 (Electronic)
IS  - 2157-6564 (Linking)
DP  - 2023 Nov 4
TI  - Using A Google Web Search Analysis to Assess the Utility of ChatGPT in Stem Cell 
      Therapy.
LID - szad074 [pii]
LID - 10.1093/stcltm/szad074 [doi]
AB  - OBJECTIVE: Since its introduction, the use of ChatGPT has increased significantly 
      for medically related purposes. However, current research has not captured its 
      applications in providing information on stem cell therapy. To address this gap, 
      the present study compared the effectiveness of ChatGPT to Google in answering 
      medical questions related to stem cell therapy. METHODS: The search term "stem 
      cell therapy" was used to perform a Google web search, and the top 20 frequently 
      asked questions along with answers were recorded together with relevant website 
      sources. Of these questions, the top 10 questions were separately entered into 
      ChatGPT, and the answers and the sources were recorded. Then, the following 
      statement was entered into ChatGPT: "Do a Google search with the search term 
      'stem cell therapy' and record 20 common questions related to the search term." 
      After obtaining these questions, each question was separately entered into 
      ChatGPT for an answer and source. RESULTS: A majority of the top 20 questions 
      provided by Google were related to fact, whereas a majority of the questions 
      provided by ChatGPT were related to policy. The answer sources used by Google 
      were mostly drawn from medical practice, while those used by ChatGPT were mostly 
      drawn from academic information. CONCLUSION: Compared to Google, ChatGPT exhibits 
      stronger capabilities in promoting awareness of stem cell therapy. ChatGPT has 
      the ability to eliminate misleading information by providing accurate and 
      reliable answers. However, the responses provided by ChatGPT are still general in 
      nature and cannot substitute academic sources for providing specialized 
      knowledge.
CI  - © The Author(s) 2023. Published by Oxford University Press.
FAU - Chen, Long
AU  - Chen L
AD  - Arthritis Clinic and Research Center, Peking University People's Hospital, Peking 
      University, Beijing, People's Republic of China.
FAU - Li, Hui
AU  - Li H
AD  - Arthritis Clinic and Research Center, Peking University People's Hospital, Peking 
      University, Beijing, People's Republic of China.
FAU - Su, Yiqi
AU  - Su Y
AD  - Arthritis Clinic and Research Center, Peking University People's Hospital, Peking 
      University, Beijing, People's Republic of China.
FAU - Yang, Zhen
AU  - Yang Z
AD  - Arthritis Clinic and Research Center, Peking University People's Hospital, Peking 
      University, Beijing, People's Republic of China.
FAU - He, Zihao
AU  - He Z
AD  - Arthritis Clinic and Research Center, Peking University People's Hospital, Peking 
      University, Beijing, People's Republic of China.
FAU - Wang, Du
AU  - Wang D
AD  - Arthritis Clinic and Research Center, Peking University People's Hospital, Peking 
      University, Beijing, People's Republic of China.
FAU - Li, Jiao Jiao
AU  - Li JJ
AD  - School of Biomedical Engineering, Faculty of Engineering & IT, University of 
      Technology Sydney, Sydney, NSW, Australia.
FAU - Xing, Dan
AU  - Xing D
AUID- ORCID: 0000-0001-6966-3134
AD  - Arthritis Clinic and Research Center, Peking University People's Hospital, Peking 
      University, Beijing, People's Republic of China.
LA  - eng
GR  - 81973606/National Natural Science Foundations of China/
PT  - Journal Article
DEP - 20231104
PL  - England
TA  - Stem Cells Transl Med
JT  - Stem cells translational medicine
JID - 101578022
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Google
OT  - stem cell therapy
OT  - utility
OT  - web search
EDAT- 2023/11/08 06:43
MHDA- 2023/11/08 06:43
CRDT- 2023/11/08 03:44
PHST- 2023/06/05 00:00 [received]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/11/08 06:43 [medline]
PHST- 2023/11/08 06:43 [pubmed]
PHST- 2023/11/08 03:44 [entrez]
AID - 7344723 [pii]
AID - 10.1093/stcltm/szad074 [doi]
PST - aheadofprint
SO  - Stem Cells Transl Med. 2023 Nov 4:szad074. doi: 10.1093/stcltm/szad074.

PMID- 38076944
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
DP  - 2023 Nov 27
TI  - ChatGPT Influence on Medical Decision-Making, Bias, and Equity: A Randomized 
      Study of Clinicians Evaluating Clinical Vignettes.
LID - 2023.11.24.23298844 [pii]
LID - 10.1101/2023.11.24.23298844 [doi]
AB  - In a randomized, pre-post intervention study, we evaluated the influence of a 
      large language model (LLM) generative AI system on accuracy of physician 
      decision-making and bias in healthcare. 50 US-licensed physicians reviewed a 
      video clinical vignette, featuring actors representing different demographics (a 
      White male or a Black female) with chest pain. Participants were asked to answer 
      clinical questions around triage, risk, and treatment based on these vignettes, 
      then asked to reconsider after receiving advice generated by ChatGPT+ (GPT4). The 
      primary outcome was the accuracy of clinical decisions based on pre-established 
      evidence-based guidelines. Results showed that physicians are willing to change 
      their initial clinical impressions given AI assistance, and that this led to a 
      significant improvement in clinical decision-making accuracy in a chest pain 
      evaluation scenario without introducing or exacerbating existing race or gender 
      biases. A survey of physician participants indicates that the majority expect LLM 
      tools to play a significant role in clinical decision making.
FAU - Goh, Ethan
AU  - Goh E
AUID- ORCID: 0009-0001-7491-4257
FAU - Bunning, Bryan
AU  - Bunning B
AUID- ORCID: 0000-0003-3092-0038
FAU - Khoong, Elaine
AU  - Khoong E
AUID- ORCID: 0000-0002-2514-3572
FAU - Gallo, Robert
AU  - Gallo R
AUID- ORCID: 0000-0002-2601-0173
FAU - Milstein, Arnold
AU  - Milstein A
AUID- ORCID: 0000-0002-2794-2378
FAU - Centola, Damon
AU  - Centola D
AUID- ORCID: 0000-0002-8084-2333
FAU - Chen, Jonathan H
AU  - Chen JH
AUID- ORCID: 0000-0002-4387-8740
LA  - eng
PT  - Preprint
DEP - 20231127
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10705632
EDAT- 2023/12/11 12:43
MHDA- 2023/12/11 12:44
CRDT- 2023/12/11 06:21
PHST- 2023/12/11 12:44 [medline]
PHST- 2023/12/11 12:43 [pubmed]
PHST- 2023/12/11 06:21 [entrez]
AID - 2023.11.24.23298844 [pii]
AID - 10.1101/2023.11.24.23298844 [doi]
PST - epublish
SO  - medRxiv. 2023 Nov 27:2023.11.24.23298844. doi: 10.1101/2023.11.24.23298844. 
      Preprint.

PMID- 36883768
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230403
LR  - 20230403
IS  - 2724-6442 (Electronic)
IS  - 2724-6051 (Linking)
VI  - 75
IP  - 2
DP  - 2023 Apr
TI  - Generative Pre-training Transformer Chat (ChatGPT) in the scientific community: 
      the train has left the station.
PG  - 131-133
LID - 10.23736/S2724-6051.23.05326-0 [doi]
FAU - Checcucci, Enrico
AU  - Checcucci E
AD  - Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, 
      Italy - checcu.e@hotmail.it.
FAU - Verri, Paolo
AU  - Verri P
AD  - Division of Urology, Department of Oncology, University of Turin, San Luigi 
      Gonzaga Hospital, Orbassano, Turin, Italy.
AD  - Department of Urology, Puigvert Foundation, Autonomous University of Barcelona, 
      Barcelona, Spain.
FAU - Amparore, Daniele
AU  - Amparore D
AD  - Division of Urology, Department of Oncology, University of Turin, San Luigi 
      Gonzaga Hospital, Orbassano, Turin, Italy.
FAU - Cacciamani, Giovanni E
AU  - Cacciamani GE
AD  - USC Institute of Urology and Catherine, Joseph Aresty Department of Urology, Keck 
      School of Medicine, University of Southern California, Los Angeles, CA, USA.
AD  - AI Center at USC Urology, USC Institute of Urology, University of Southern 
      California, Los Angeles, CA, USA.
AD  - Department of Radiology, University of Southern California, Los Angeles, CA, USA.
AD  - Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of 
      Urology, Catherine and Joseph Aresty Department of Urology, Keck School of 
      Medicine, University of Southern California, Los Angeles, CA, USA.
AD  - Norris Comprehensive Cancer Center, Institute of Urology, Keck School of Medicine 
      of the University of Southern California, Los Angeles, CA, USA.
FAU - Fiori, Cristian
AU  - Fiori C
AD  - Division of Urology, Department of Oncology, University of Turin, San Luigi 
      Gonzaga Hospital, Orbassano, Turin, Italy.
FAU - Breda, Alberto
AU  - Breda A
AD  - Department of Urology, Puigvert Foundation, Autonomous University of Barcelona, 
      Barcelona, Spain.
FAU - Porpiglia, Francesco
AU  - Porpiglia F
AD  - Division of Urology, Department of Oncology, University of Turin, San Luigi 
      Gonzaga Hospital, Orbassano, Turin, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230308
PL  - Italy
TA  - Minerva Urol Nephrol
JT  - Minerva urology and nephrology
JID - 101777299
SB  - IM
EDAT- 2023/03/09 06:00
MHDA- 2023/03/09 06:01
CRDT- 2023/03/08 08:20
PHST- 2023/03/09 06:01 [medline]
PHST- 2023/03/09 06:00 [pubmed]
PHST- 2023/03/08 08:20 [entrez]
AID - S2724-6051.23.05326-0 [pii]
AID - 10.23736/S2724-6051.23.05326-0 [doi]
PST - ppublish
SO  - Minerva Urol Nephrol. 2023 Apr;75(2):131-133. doi: 
      10.23736/S2724-6051.23.05326-0. Epub 2023 Mar 8.

PMID- 37389659
OWN - NLM
STAT- Publisher
LR  - 20230630
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jun 30
TI  - Enhancing Awareness and Self-diagnosis of Obstructive Sleep Apnea Using 
      AI-Powered Chatbots: The Role of ChatGPT in Revolutionizing Healthcare.
LID - 10.1007/s10439-023-03298-8 [doi]
AB  - Since OpenAI (San Francisco, CA) released its generative AI chatbot, ChatGPT, we 
      are on the cusp of technological transformation. The tool is capable of 
      generating text according to the input that the user adds to it. Due to its 
      ability to imitate human speech tone while extracting encyclopedic knowledge, 
      ChatGPT can be a platform for personalized patient interaction. Thus, it has the 
      potential to revolutionize the healthcare system. Our study aims to evaluate how 
      ChatGPT can answer the queries of patients suffering from obstructive sleep apnea 
      and aid in self-diagnosis. By analyzing symptoms and guiding patients' behavior 
      toward prevention, ChatGPT can play a major role in avoiding serious health 
      repercussions that develop in the later course of obstructive sleep apnea.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Bilal, Maham
AU  - Bilal M
AUID- ORCID: 0009-0009-6094-2239
AD  - Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan. 
      mahambilal33@gmail.com.
FAU - Jamil, Yumna
AU  - Jamil Y
AD  - Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan.
FAU - Rana, Dua
AU  - Rana D
AD  - Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan.
FAU - Shah, Hussain Haider
AU  - Shah HH
AD  - Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan.
LA  - eng
PT  - Letter
DEP - 20230630
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Healthcare
OT  - Obstructive sleep apnea
OT  - Patient interaction
OT  - Self-diagnosis
EDAT- 2023/06/30 13:11
MHDA- 2023/06/30 13:11
CRDT- 2023/06/30 11:06
PHST- 2023/06/16 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/06/30 13:11 [medline]
PHST- 2023/06/30 13:11 [pubmed]
PHST- 2023/06/30 11:06 [entrez]
AID - 10.1007/s10439-023-03298-8 [pii]
AID - 10.1007/s10439-023-03298-8 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jun 30. doi: 10.1007/s10439-023-03298-8.

PMID- 37202644
OWN - NLM
STAT- MEDLINE
DCOM- 20230809
LR  - 20230809
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 9
DP  - 2023 Sep
TI  - The Double-Edged Sword of AI in Biomedical Engineering: ChatGPT's Controversial 
      Impact on Research and Collaboration Paradigms.
PG  - 1904-1905
LID - 10.1007/s10439-023-03237-7 [doi]
AB  - As artificial intelligence (AI) permeates various fields, ChatGPT emerges as a 
      contentious force in biomedical engineering, stirring both excitement and 
      concern. This letter explores the disruptive potential and controversial 
      implications of ChatGPT on research, collaboration, and the future of the field. 
      By presenting thought-provoking questions and confronting divisive issues, we aim 
      to spark a lively debate on the responsible integration of AI technology in 
      biomedical engineering while safeguarding the essence of human expertise.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India. ppray@cus.ac.in.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, Kolkata, India.
LA  - eng
PT  - Letter
DEP - 20230518
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Biomedical Engineering
MH  - *Artificial Intelligence
MH  - Bioengineering
MH  - Technology
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Biomedical engineering
OT  - ChatGPT
OT  - Collaboration
OT  - Controversy
OT  - Research
EDAT- 2023/05/19 01:04
MHDA- 2023/08/09 06:43
CRDT- 2023/05/18 23:27
PHST- 2023/05/04 00:00 [received]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/05/19 01:04 [pubmed]
PHST- 2023/05/18 23:27 [entrez]
AID - 10.1007/s10439-023-03237-7 [pii]
AID - 10.1007/s10439-023-03237-7 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Sep;51(9):1904-1905. doi: 10.1007/s10439-023-03237-7. Epub 
      2023 May 18.

PMID- 37182055
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230516
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial 
      Intelligence Hallucination in Scientific Writing Through ChatGPT References.
PG  - e37432
LID - 10.7759/cureus.37432 [doi]
LID - e37432
AB  - Background Chatbots are computer programs that use artificial intelligence (AI) 
      and natural language processing (NLP) to simulate conversations with humans. One 
      such chatbot is ChatGPT, which uses the third-generation generative pre-trained 
      transformer (GPT-3) developed by OpenAI. ChatGPT has been praised for its ability 
      to generate text, but concerns have been raised about its accuracy and precision 
      in generating data, as well as legal issues related to references. This study 
      aims to investigate the frequency of AI hallucination in research proposals 
      entirely drafted by ChatGPT. Methodology An analytical design was employed to 
      investigate AI hallucination by ChatGPT. A total of 178 references listed by 
      ChatGPT were verified for inclusion in the study. Statistical analysis was 
      performed by five researchers who entered their data into a Google Form, and the 
      final results were represented using pie charts and tables. Results Out of the 
      178 references analyzed, 69 references did not have a Digital Object Identifier 
      (DOI), and 28 references neither turned up on Google search nor had an existing 
      DOI. Three references were listed from books and not research articles. These 
      observations suggest that ChatGPT's ability to generate reliable references for 
      research topics may be limited by the availability of DOI and the accessibility 
      of online articles. Conclusions The study highlights the potential limitations of 
      ChatGPT's ability to generate reliable references for research proposals. AI 
      hallucination is a problem that may negatively impact decision-making and may 
      give rise to ethical and legal problems. Improving the training inputs by 
      including diverse, accurate, and contextually relevant data sets along with 
      frequent updates to the training models could potentially help address these 
      issues. However, until these issues are addressed, researchers using ChatGPT 
      should exercise caution in relying solely on the references generated by the AI 
      chatbot.
CI  - Copyright © 2023, Athaluri et al.
FAU - Athaluri, Sai Anirudh
AU  - Athaluri SA
AD  - Medicine, Rangaraya Medical College, Kakinada, IND.
FAU - Manthena, Sandeep Varma
AU  - Manthena SV
AD  - Medicine, Rangaraya Medical College, Kakinada, IND.
FAU - Kesapragada, V S R Krishna Manoj
AU  - Kesapragada VSRKM
AD  - Medicine, Rangaraya Medical College, Kakinada, IND.
FAU - Yarlagadda, Vineel
AU  - Yarlagadda V
AD  - Medicine, Rangaraya Medical College, Kakinada, IND.
FAU - Dave, Tirth
AU  - Dave T
AD  - Internal Medicine, Bukovinian State Medical University, Chernivtsi, UKR.
FAU - Duddumpudi, Rama Tulasi Siri
AU  - Duddumpudi RTS
AD  - Medicine, Rangaraya Medical College, Kakinada, IND.
LA  - eng
PT  - Journal Article
DEP - 20230411
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10173677
OTO - NOTNLM
OT  - ai hallucination
OT  - artificial intelligence
OT  - chatgpt
OT  - gpt-3
OT  - natural language processing
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/14 19:13
MHDA- 2023/05/14 19:14
CRDT- 2023/05/14 12:26
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/05/14 19:14 [medline]
PHST- 2023/05/14 19:13 [pubmed]
PHST- 2023/05/14 12:26 [entrez]
AID - 10.7759/cureus.37432 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 11;15(4):e37432. doi: 10.7759/cureus.37432. eCollection 2023 
      Apr.

PMID- 37962570
OWN - NLM
STAT- Publisher
LR  - 20231114
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Nov 14
TI  - Is artificial intelligence ready to replace specialist doctors entirely? ENT 
      specialists vs ChatGPT: 1-0, ball at the center.
LID - 10.1007/s00405-023-08321-1 [doi]
AB  - PURPOSE: The purpose of this study is to evaluate ChatGPT's responses to Ear, 
      Nose and Throat (ENT) clinical cases and compare them with the responses of ENT 
      specialists. METHODS: We have hypothesized 10 scenarios, based on ENT daily 
      experience, with the same primary symptom. We have constructed 20 clinical cases, 
      2 for each scenario. We described them to 3 ENT specialists and ChatGPT. The 
      difficulty of the clinical cases was assessed by the 5 ENT authors of this 
      article. The responses of ChatGPT were evaluated by the 5 ENT authors of this 
      article for correctness and consistency with the responses of the 3 ENT experts. 
      To verify the stability of ChatGPT's responses, we conducted the searches, always 
      from the same account, for 5 consecutive days. RESULTS: Among the 20 cases, 8 
      were rated as low complexity, 6 as moderate complexity and 6 as high complexity. 
      The overall mean correctness and consistency score of ChatGPT responses was 3.80 
      (SD 1.02) and 2.89 (SD 1.24), respectively. We did not find a statistically 
      significant difference in the average ChatGPT correctness and coherence score 
      according to case complexity. The total intraclass correlation coefficient (ICC) 
      for the stability of the correctness and consistency of ChatGPT was 0.763 (95% 
      confidence interval [CI] 0.553-0.895) and 0.837 (95% CI 0.689-0.927), 
      respectively. CONCLUSIONS: Our results revealed the potential usefulness of 
      ChatGPT in ENT diagnosis. The instability in responses and the inability to 
      recognise certain clinical elements are its main limitations.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Dallari, Virginia
AU  - Dallari V
AD  - Young Confederation of European ORL-HNS, Y-CEORL-HNS, Dublin, Ireland.
AD  - Unit of Otorhinolaryngology, Head & Neck Department, University of Verona, 
      Piazzale L.A. Scuro 10, 37134, Verona, Italy.
FAU - Sacchetto, Andrea
AU  - Sacchetto A
AUID- ORCID: 0000-0002-0272-6652
AD  - Young Confederation of European ORL-HNS, Y-CEORL-HNS, Dublin, Ireland. 
      andrea.sacchetto89@gmail.com.
AD  - Department of Otolaryngology, Ospedale San Bortolo, AULSS 8 Berica, Vicenza, 
      Italy. andrea.sacchetto89@gmail.com.
FAU - Saetti, Roberto
AU  - Saetti R
AD  - Department of Otolaryngology, Ospedale San Bortolo, AULSS 8 Berica, Vicenza, 
      Italy.
FAU - Calabrese, Luca
AU  - Calabrese L
AD  - Department of Otorhinolaryngology-Head and Neck Surgery, Hospital of Bolzano 
      (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University (PMU), 
      Bolzano-Bozen, Italy.
FAU - Vittadello, Fabio
AU  - Vittadello F
AD  - Explora-Research and Statistical Analysis, Padua, Italy.
FAU - Gazzini, Luca
AU  - Gazzini L
AD  - Young Confederation of European ORL-HNS, Y-CEORL-HNS, Dublin, Ireland.
AD  - Department of Otorhinolaryngology-Head and Neck Surgery, Hospital of Bolzano 
      (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University (PMU), 
      Bolzano-Bozen, Italy.
LA  - eng
PT  - Journal Article
DEP - 20231114
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Machine learning
OT  - Natural language processing
OT  - Otolaryngology
OT  - Research
EDAT- 2023/11/14 12:42
MHDA- 2023/11/14 12:42
CRDT- 2023/11/14 11:03
PHST- 2023/08/17 00:00 [received]
PHST- 2023/10/27 00:00 [accepted]
PHST- 2023/11/14 12:42 [medline]
PHST- 2023/11/14 12:42 [pubmed]
PHST- 2023/11/14 11:03 [entrez]
AID - 10.1007/s00405-023-08321-1 [pii]
AID - 10.1007/s00405-023-08321-1 [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Nov 14. doi: 10.1007/s00405-023-08321-1.

PMID- 38049299
OWN - NLM
STAT- MEDLINE
DCOM- 20231206
LR  - 20231206
IS  - 1938-2421 (Electronic)
IS  - 0148-4834 (Linking)
VI  - 62
IP  - 12
DP  - 2023 Dec
TI  - ChatGPT for Automated Writing Evaluation in Scholarly Writing Instruction.
PG  - 721-727
LID - 10.3928/01484834-20231006-02 [doi]
AB  - BACKGROUND: Effective strategies for developing scholarly writing skills in 
      postsecondary nursing students are needed. Generative artificial intelligence 
      (GAI) tools, such as ChatGPT, for automated writing evaluation (AWE) hold promise 
      for mitigating challenges associated with scholarly writing instruction in 
      nursing education. This article explores the suitability of ChatGPT for AWE in 
      writing instruction. METHOD: ChatGPT feedback on 42 nursing student texts from 
      the Michigan Corpus of Upper-Level Student Papers was assessed. Assessment 
      criteria were derived from recent AWE research. RESULTS: ChatGPT demonstrated 
      utility as an AWE tool. Its scoring performance demonstrated stricter grading 
      than human raters, related feedback to macro-level writing features, and 
      supported multiple submissions and learner autonomy. CONCLUSION: Despite concerns 
      surrounding GAI in academia, educators can accelerate the feedback process 
      without increasing their workload, and students can receive individualized 
      feedback by incorporating AWE provided by ChatGPT into the writing process. [J 
      Nurs Educ. 2023;62(12):721-727.].
FAU - Parker, Jessica L
AU  - Parker JL
FAU - Becker, Kimberly
AU  - Becker K
FAU - Carroca, Catherine
AU  - Carroca C
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - United States
TA  - J Nurs Educ
JT  - The Journal of nursing education
JID - 7705432
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Education, Nursing
MH  - Feedback
MH  - Writing
MH  - *Students, Nursing
EDAT- 2023/12/05 00:41
MHDA- 2023/12/06 06:42
CRDT- 2023/12/04 21:02
PHST- 2023/12/06 06:42 [medline]
PHST- 2023/12/05 00:41 [pubmed]
PHST- 2023/12/04 21:02 [entrez]
AID - 10.3928/01484834-20231006-02 [doi]
PST - ppublish
SO  - J Nurs Educ. 2023 Dec;62(12):721-727. doi: 10.3928/01484834-20231006-02. Epub 
      2023 Dec 1.

PMID- 37648157
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1544-3450 (Electronic)
IS  - 1086-5802 (Linking)
VI  - 63
IP  - 6
DP  - 2023 Nov-Dec
TI  - Evaluation of community pharmacists' perceptions and willingness to integrate 
      ChatGPT into their pharmacy practice: A study from Jordan.
PG  - 1761-1767.e2
LID - S1544-3191(23)00291-1 [pii]
LID - 10.1016/j.japh.2023.08.020 [doi]
AB  - OBJECTIVES: This study aimed to examine the extent of community pharmacists' 
      awareness of Chat Generative Pretraining Transformer (ChatGPT), their willingness 
      to embark on this new development of artificial intelligence (AI) development, 
      and barriers that face the incorporation of this nonconventional source of 
      information into pharmacy practice. METHODS: A cross-sectional study was 
      conducted among community pharmacists in Jordanian cities between April 26, 2023, 
      and May 10, 2023. Convenience and snowball sampling techniques were used to 
      select study participants owing to resource and time constraints. The 
      questionnaire was distributed by research assistants through popular social media 
      platforms. Logistic regression analysis was used to assess predictors affecting 
      their willingness to use this service in the future. RESULTS: A total of 221 
      community pharmacists participated in the current study (response rate was not 
      calculated because opt-in recruitment strategies were used). Remarkably, nearly 
      half of the pharmacists (n = 107, 48.4%) indicated a willingness to incorporate 
      the ChatGPT into their pharmacy practice. Nearly half of the pharmacists (n = 
      105, 47.5%) demonstrated a high perceived benefit score for ChatGPT, whereas 
      approximately 37% of pharmacists (n = 81) expressed a high concern score about 
      ChatGPT. More than 70% of pharmacists believed that ChatGPT lacked the ability to 
      use human judgment and make complicated ethical judgments in its responses (n = 
      168). Finally, logistics regression analysis showed that pharmacists who had 
      previous experience in using ChatGPT were more willing to integrate ChatGPT in 
      their pharmacy practice than those with no previous experience in using ChatGPT 
      (odds ratio 2.312, P = 0.035). CONCLUSION: Although pharmacists show a 
      willingness to incorporate ChatGPT into their practice, especially those with 
      previous experience, there are major concerns. These mainly revolve around the 
      tool's ability to make human-like judgments and ethical decisions. These findings 
      are crucial for the future development and integration of AI tools in pharmacy 
      practice.
CI  - Copyright © 2023 American Pharmacists Association®. Published by Elsevier Inc. 
      All rights reserved.
FAU - Abu-Farha, Rana
AU  - Abu-Farha R
FAU - Fino, Leen
AU  - Fino L
FAU - Al-Ashwal, Fahmi Y
AU  - Al-Ashwal FY
FAU - Zawiah, Mohammed
AU  - Zawiah M
FAU - Gharaibeh, Lobna
AU  - Gharaibeh L
FAU - Harahsheh, Mea'ad M
AU  - Harahsheh MM
FAU - Darwish Elhajji, Feras
AU  - Darwish Elhajji F
LA  - eng
PT  - Journal Article
DEP - 20230828
PL  - United States
TA  - J Am Pharm Assoc (2003)
JT  - Journal of the American Pharmacists Association : JAPhA
JID - 101176252
SB  - IM
MH  - Humans
MH  - Pharmacists
MH  - Jordan
MH  - Cross-Sectional Studies
MH  - Artificial Intelligence
MH  - *Community Pharmacy Services
MH  - Attitude of Health Personnel
MH  - Professional Role
MH  - *Pharmacy
COIS- Disclosure The authors declare no relevant conflicts of interest or financial 
      relationships.
EDAT- 2023/08/31 00:41
MHDA- 2023/11/27 12:44
CRDT- 2023/08/30 19:24
PHST- 2023/05/21 00:00 [received]
PHST- 2023/08/10 00:00 [revised]
PHST- 2023/08/22 00:00 [accepted]
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/08/31 00:41 [pubmed]
PHST- 2023/08/30 19:24 [entrez]
AID - S1544-3191(23)00291-1 [pii]
AID - 10.1016/j.japh.2023.08.020 [doi]
PST - ppublish
SO  - J Am Pharm Assoc (2003). 2023 Nov-Dec;63(6):1761-1767.e2. doi: 
      10.1016/j.japh.2023.08.020. Epub 2023 Aug 28.

PMID- 37368125
OWN - NLM
STAT- Publisher
LR  - 20230627
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jun 27
TI  - Using AI Tools in Writing Peer Review Reports: Should Academic Journals Embrace 
      the Use of ChatGPT?
LID - 10.1007/s10439-023-03299-7 [doi]
AB  - This letter highlights a pressing issue regarding the absence of established 
      editorial policies for the utilization of AI tools (e.g., ChatGPT) in the peer 
      review process. The increasing adoption of AI tools in academic publishing 
      necessitates the formulation of standardized guidelines to ensure fairness, 
      transparency, and accountability. Without clear editorial policies, there is a 
      threat of compromising the integrity of the peer review process and undermining 
      the credibility of academic publications. Urgent attention is needed to address 
      this gap and establish robust protocols that govern the use of AI tools in peer 
      review.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Garcia, Manuel B
AU  - Garcia MB
AUID- ORCID: 0000-0003-2615-422X
AD  - Educational Innovation and Technology Hub, FEU Institute of Technology, Manila, 
      Philippines. mbgarcia@feutech.edu.ph.
AD  - College of Education, University of the Philippines Diliman, Quezon City, 
      Philippines. mbgarcia@feutech.edu.ph.
LA  - eng
PT  - Letter
DEP - 20230627
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Editorial policies
OT  - Peer review
EDAT- 2023/06/27 13:10
MHDA- 2023/06/27 13:10
CRDT- 2023/06/27 11:10
PHST- 2023/06/18 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/06/27 13:10 [medline]
PHST- 2023/06/27 13:10 [pubmed]
PHST- 2023/06/27 11:10 [entrez]
AID - 10.1007/s10439-023-03299-7 [pii]
AID - 10.1007/s10439-023-03299-7 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jun 27. doi: 10.1007/s10439-023-03299-7.

PMID- 37691497
OWN - NLM
STAT- MEDLINE
DCOM- 20230912
LR  - 20230916
IS  - 1651-2081 (Electronic)
IS  - 1650-1977 (Print)
IS  - 1650-1977 (Linking)
VI  - 55
DP  - 2023 Sep 11
TI  - Use of artificial intelligence large language models as a clinical tool in 
      rehabilitation medicine: a comparative test case.
PG  - jrm13373
LID - 10.2340/jrm.v55.13373 [doi]
LID - 13373
AB  - OBJECTIVE: To explore the potential use of artificial intelligence language 
      models in formulating rehabilitation prescriptions and International 
      Classification of Functioning, Disability and Health (ICF) codes.  Design: 
      Comparative study based on a single case report compared to standard answers from 
      a textbook. SUBJECTS: A stroke case from textbook.  Methods: Chat Generative 
      Pre-Trained Transformer-4 (ChatGPT-4)was used to generate comprehensive medical 
      and rehabilitation prescription information and ICF codes pertaining to the 
      stroke case. This information was compared with standard answers from textbook, 
      and 2 licensed Physical Medicine and Rehabilitation (PMR) clinicians reviewed the 
      artificial intelligence recommendations for further discussion. RESULTS: 
      ChatGPT-4 effectively formulated rehabilitation prescriptions and ICF codes for a 
      typical stroke case, together with a rationale to support its recommendations. 
      This information was generated in seconds. Compared with standard answers, the 
      large language model generated broader and more general prescriptions in terms of 
      medical problems and management plans, rehabilitation problems and management 
      plans, as well as rehabilitation goals. It also demonstrated the ability to 
      propose specified approaches for each rehabilitation therapy. The language model 
      made an error regarding the ICF category for the stroke case, but no mistakes 
      were identified in the ICF codes assigned.  Conclusion: This test case suggests 
      that artificial intelligence language models have potential use in facilitating 
      clinical practice and education in the field of rehabilitation medicine.
FAU - Zhang, Liang
AU  - Zhang L
AD  - Department of Rehabilitation Medicine, Kyorin University School of Medicine, 
      Japan.
FAU - Tashiro, Syoichi
AU  - Tashiro S
AD  - Department of Rehabilitation Medicine, Kyorin University School of Medicine, 
      Japan; Department of Rehabilitation Medicine, Keio University School of Medicine, 
      Japan.
FAU - Mukaino, Masahiko
AU  - Mukaino M
AD  - Department of Rehabilitation Medicine, Hokkaido University Hospital, Japan.
FAU - Yamada, Shin
AU  - Yamada S
AD  - Department of Rehabilitation Medicine, Kyorin University School of Medicine, 
      Japan. yamada-shin@ks.kyorin-u.ac.jp.
LA  - eng
PT  - Case Reports
PT  - Journal Article
DEP - 20230911
PL  - Sweden
TA  - J Rehabil Med
JT  - Journal of rehabilitation medicine
JID - 101088169
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Physical and Rehabilitation Medicine
MH  - Language
MH  - *Stroke
PMC - PMC10501385
COIS- The authors have no conflicts of interest to declare.
EDAT- 2023/09/11 06:43
MHDA- 2023/09/12 06:42
CRDT- 2023/09/11 03:34
PHST- 2023/05/21 00:00 [received]
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/09/12 06:42 [medline]
PHST- 2023/09/11 06:43 [pubmed]
PHST- 2023/09/11 03:34 [entrez]
AID - JRM-55-13373 [pii]
AID - 10.2340/jrm.v55.13373 [doi]
PST - epublish
SO  - J Rehabil Med. 2023 Sep 11;55:jrm13373. doi: 10.2340/jrm.v55.13373.

PMID- 38044929
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 0019-5049 (Print)
IS  - 0976-2817 (Electronic)
IS  - 0019-5049 (Linking)
VI  - 67
IP  - 10
DP  - 2023 Oct
TI  - Role of chat-generative pre-trained transformer (ChatGPT) in anaesthesia: Merits 
      and pitfalls.
PG  - 942-944
LID - 10.4103/ija.ija_504_23 [doi]
FAU - Reddy, Ashwini
AU  - Reddy A
AD  - Department of Anaesthesia and Intensive Care, Postgraduate Institute of Medical 
      Education and Research, Chandigarh, India.
FAU - Patel, Swati
AU  - Patel S
AD  - Department of Anaesthesia and Intensive Care, Postgraduate Institute of Medical 
      Education and Research, Chandigarh, India.
FAU - Barik, Amiya Kumar
AU  - Barik AK
AD  - Department of Anaesthesia and Intensive Care, Postgraduate Institute of Medical 
      Education and Research, Chandigarh, India.
FAU - Gowda, Punith
AU  - Gowda P
AD  - Department of Anaesthesia and Intensive Care, Postgraduate Institute of Medical 
      Education and Research, Chandigarh, India.
LA  - eng
PT  - Journal Article
DEP - 20231016
PL  - India
TA  - Indian J Anaesth
JT  - Indian journal of anaesthesia
JID - 0013243
PMC - PMC10691596
COIS- There are no conflicts of interest.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 04:24
PHST- 2023/05/30 00:00 [received]
PHST- 2023/06/15 00:00 [revised]
PHST- 2023/06/18 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 04:24 [entrez]
AID - IJA-67-942 [pii]
AID - 10.4103/ija.ija_504_23 [doi]
PST - ppublish
SO  - Indian J Anaesth. 2023 Oct;67(10):942-944. doi: 10.4103/ija.ija_504_23. Epub 2023 
      Oct 16.

PMID- 37467871
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231025
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 9
DP  - 2023 Sep
TI  - Exploring the Clinical Translation of Generative Models Like ChatGPT: Promise and 
      Pitfalls in Radiology, From Patients to Population Health.
PG  - 877-885
LID - S1546-1440(23)00516-1 [pii]
LID - 10.1016/j.jacr.2023.07.007 [doi]
AB  - Generative artificial intelligence (AI) tools such as GPT-4, and the chatbot 
      interface ChatGPT, show promise for a variety of applications in radiology and 
      health care. However, like other AI tools, ChatGPT has limitations and potential 
      pitfalls that must be considered before adopting it for teaching, clinical 
      practice, and beyond. We summarize five major emerging use cases for ChatGPT and 
      generative AI in radiology across the levels of increasing data complexity, along 
      with pitfalls associated with each. As the use of AI in health care continues to 
      grow, it is crucial for radiologists (and all physicians) to stay informed and 
      ensure the safe translation of these new technologies.
CI  - Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All 
      rights reserved.
FAU - Doo, Florence X
AU  - Doo FX
AD  - Director of Innovation, University of Maryland Medical Intelligent Imaging Center 
      (UM2ii), Baltimore, Maryland; Member, Committee on Economics in Academic 
      Radiology, under the ACR Commission on Economics. Electronic address: 
      fdoo@som.umaryland.edu.
FAU - Cook, Tessa S
AU  - Cook TS
AD  - Vice Chair for Practice Transformation, Department of Radiology, Perelman School 
      of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; 
      Fellowship Director, Imaging Informatics, and Chief, 3-D and Advanced Imaging, 
      Department of Radiology, Penn Medicine, Philadelphia, Pennsylvania; Chair, 
      Society for Imaging Informatics in Medicine; and Vice Chair, ACR Commission on 
      Patient- and Family-Centered Care; Chair, RAHSR Affinity Group. Electronic 
      address: https://twitter.com/asset25.
FAU - Siegel, Eliot L
AU  - Siegel EL
AD  - Vice Chair, Research Information Systems, University of Maryland, Baltimore, 
      Maryland; Lead, Radiology and Nuclear Medicine Diagnostics, US Department of 
      Veterans Affairs Veterans Integrated Services Network; Chief, Imaging, US 
      Department of Veterans Affairs Maryland Healthcare System; Radiology AI Senior 
      Consultant. Electronic address: https://twitter.com/EliotSiegel.
FAU - Joshi, Anupam
AU  - Joshi A
AD  - Oros Family Professor and Chair, Computer Science and Electrical Engineering, 
      University of Maryland, Baltimore, Maryland; Director, University of Maryland, 
      Baltimore County, Center for Cybersecurity; Director, CyberScholars Program; 
      Associate Editor, IEEE Transactions on Dependable and Secure Computing.
FAU - Parekh, Vishwa
AU  - Parekh V
AD  - Technical Director, University of Maryland Medical Intelligent Imaging (UM2ii) 
      Center, Baltimore, Maryland; Review Editor, Frontiers in Oncology. Electronic 
      address: https://twitter.com/vishwa_parekh.
FAU - Elahi, Ameena
AU  - Elahi A
AD  - University of Pennsylvania, Philadelphia, Pennsylvania; Application Manager, 
      Information Services, Penn Medicine, Philadelphia, Pennsylvania; Informatics 
      Operations Director, RAD-AID International. Electronic address: 
      https://twitter.com/AmeenaElahi.
FAU - Yi, Paul H
AU  - Yi PH
AD  - Director, University of Maryland Medical Intelligent Imaging (UM2ii) Center, 
      Baltimore, Maryland; Vice Chair, Society of Imaging Informatics in Medicine 
      Program Planning Committee; Associate Editor, Radiology: Artificial Intelligence. 
      Electronic address: https://twitter.com/PaulYiMD.
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Radiology
MH  - Radiography
MH  - Radiologists
MH  - *Population Health
OTO - NOTNLM
OT  - ChatGPT
OT  - generative artificial intelligence
OT  - large language models
OT  - limitations
OT  - radiology
EDAT- 2023/07/20 01:07
MHDA- 2023/10/23 00:42
CRDT- 2023/07/19 19:23
PHST- 2023/04/24 00:00 [received]
PHST- 2023/06/22 00:00 [revised]
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/07/20 01:07 [pubmed]
PHST- 2023/07/19 19:23 [entrez]
AID - S1546-1440(23)00516-1 [pii]
AID - 10.1016/j.jacr.2023.07.007 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Sep;20(9):877-885. doi: 10.1016/j.jacr.2023.07.007. Epub 
      2023 Jul 17.

PMID- 38059514
OWN - NLM
STAT- Publisher
LR  - 20231207
IS  - 1619-3997 (Electronic)
IS  - 0300-5577 (Linking)
DP  - 2023 Dec 8
TI  - ChatGPT, artificial intelligence and the Journal of Perinatal Medicine: 
      correspondence.
LID - 10.1515/jpm-2023-0314 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Dr DY Patil Vidhyapeeth, Pune, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20231208
PL  - Germany
TA  - J Perinat Med
JT  - Journal of perinatal medicine
JID - 0361031
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - ethics
OT  - intelligence
EDAT- 2023/12/07 12:42
MHDA- 2023/12/07 12:42
CRDT- 2023/12/07 07:50
PHST- 2023/08/03 00:00 [received]
PHST- 2023/10/05 00:00 [accepted]
PHST- 2023/12/07 12:42 [medline]
PHST- 2023/12/07 12:42 [pubmed]
PHST- 2023/12/07 07:50 [entrez]
AID - jpm-2023-0314 [pii]
AID - 10.1515/jpm-2023-0314 [doi]
PST - aheadofprint
SO  - J Perinat Med. 2023 Dec 8. doi: 10.1515/jpm-2023-0314.

PMID- 37145135
OWN - NLM
STAT- MEDLINE
DCOM- 20230524
LR  - 20230524
IS  - 1437-160X (Electronic)
IS  - 0172-8172 (Linking)
VI  - 43
IP  - 7
DP  - 2023 Jul
TI  - Chat generative pre-trained transformer (ChatGPT): potential implications for 
      rheumatology practice.
PG  - 1379-1380
LID - 10.1007/s00296-023-05340-3 [doi]
FAU - Nune, Arvind
AU  - Nune A
AUID- ORCID: 0000-0002-3849-614X
AD  - Department of Rheumatology and General Medicine, Southport and Ormskirk NHS 
      Trust, Southport, PR8 6PN, UK. Arvind.nune@nhs.net.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AUID- ORCID: 0000-0002-4379-1266
AD  - Department of Trauma and Orthopaedics, Southport and Ormskirk NHS Trust, 
      Southport, PR8 6PN, UK.
FAU - Manzo, Ciro
AU  - Manzo C
AUID- ORCID: 0000-0002-4800-1817
AD  - Rheumatology Outpatient Clinic, Azienda Sanitaria Locale Napoli 3 Sud, Mariano 
      Lauro Hospital, Sant'Agnello, Naples, Italy.
FAU - Barman, Bhupen
AU  - Barman B
AUID- ORCID: 0000-0002-5433-1310
AD  - Department of General Medicine, All India Institute of Medical Sciences, 
      Guwahati, Assam, India.
FAU - Botchu, Rajesh
AU  - Botchu R
AUID- ORCID: 0000-0001-7998-2980
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      B31 2AP, UK.
LA  - eng
PT  - Letter
DEP - 20230505
PL  - Germany
TA  - Rheumatol Int
JT  - Rheumatology international
JID - 8206885
SB  - IM
MH  - Humans
MH  - *Rheumatology
MH  - *Artificial Intelligence
EDAT- 2023/05/05 12:42
MHDA- 2023/05/17 06:42
CRDT- 2023/05/05 11:04
PHST- 2023/04/20 00:00 [received]
PHST- 2023/04/29 00:00 [accepted]
PHST- 2023/05/17 06:42 [medline]
PHST- 2023/05/05 12:42 [pubmed]
PHST- 2023/05/05 11:04 [entrez]
AID - 10.1007/s00296-023-05340-3 [pii]
AID - 10.1007/s00296-023-05340-3 [doi]
PST - ppublish
SO  - Rheumatol Int. 2023 Jul;43(7):1379-1380. doi: 10.1007/s00296-023-05340-3. Epub 
      2023 May 5.

PMID- 37266721
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 11
DP  - 2023 Nov
TI  - The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and 
      Education.
PG  - 2351-2352
LID - 10.1007/s10439-023-03262-6 [doi]
AB  - The nursing field has undergone notable changes over time and is projected to 
      undergo further modifications in the future, owing to the advent of sophisticated 
      technologies and growing healthcare needs. The advent of ChatGPT, an AI-powered 
      language model, is expected to exert a significant influence on the nursing 
      profession, specifically in the domains of patient care and instruction. The 
      present article delves into the ramifications of ChatGPT within the nursing 
      domain and accentuates its capacity and constraints to transform the discipline.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Ahmed, Sirwan Khalid
AU  - Ahmed SK
AUID- ORCID: 0000-0002-8361-0546
AD  - , Kurdistan Region, 46012, Iraq. sirwan.k.ahmed@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230602
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Limitation
OT  - Nursing education
OT  - Nursing practice
EDAT- 2023/06/02 13:16
MHDA- 2023/06/02 13:16
CRDT- 2023/06/02 11:10
PHST- 2023/05/24 00:00 [received]
PHST- 2023/05/26 00:00 [accepted]
PHST- 2023/06/02 13:16 [pubmed]
PHST- 2023/06/02 13:16 [medline]
PHST- 2023/06/02 11:10 [entrez]
AID - 10.1007/s10439-023-03262-6 [pii]
AID - 10.1007/s10439-023-03262-6 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Nov;51(11):2351-2352. doi: 10.1007/s10439-023-03262-6. Epub 
      2023 Jun 2.

PMID- 37377631
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2296-9144 (Electronic)
IS  - 2296-9144 (Linking)
VI  - 10
DP  - 2023
TI  - Developing ChatGPT's Theory of Mind.
PG  - 1189525
LID - 10.3389/frobt.2023.1189525 [doi]
LID - 1189525
FAU - Marchetti, Antonella
AU  - Marchetti A
AD  - Research Unit on Theory of Mind, Department of Psychology, Università Cattolica 
      del Sacro Cuore, Milan, Italy.
AD  - Research Unit on Psychology and Robotics in the Lifespan, Department of 
      Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
FAU - Di Dio, Cinzia
AU  - Di Dio C
AD  - Research Unit on Theory of Mind, Department of Psychology, Università Cattolica 
      del Sacro Cuore, Milan, Italy.
AD  - Research Unit on Psychology and Robotics in the Lifespan, Department of 
      Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
FAU - Cangelosi, Angelo
AU  - Cangelosi A
AD  - Manchester Centre for Robotics and AI, University of Manchester, Manchester, 
      United Kingdom.
FAU - Manzi, Federico
AU  - Manzi F
AD  - Research Unit on Theory of Mind, Department of Psychology, Università Cattolica 
      del Sacro Cuore, Milan, Italy.
AD  - Research Unit on Psychology and Robotics in the Lifespan, Department of 
      Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
FAU - Massaro, Davide
AU  - Massaro D
AD  - Research Unit on Theory of Mind, Department of Psychology, Università Cattolica 
      del Sacro Cuore, Milan, Italy.
AD  - Research Unit on Psychology and Robotics in the Lifespan, Department of 
      Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230530
PL  - Switzerland
TA  - Front Robot AI
JT  - Frontiers in robotics and AI
JID - 101749350
PMC - PMC10292745
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - social cognition
OT  - social perception
OT  - theory of mind
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/06/28 13:09
MHDA- 2023/06/28 13:10
CRDT- 2023/06/28 09:17
PHST- 2023/03/19 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/06/28 13:10 [medline]
PHST- 2023/06/28 13:09 [pubmed]
PHST- 2023/06/28 09:17 [entrez]
AID - 1189525 [pii]
AID - 10.3389/frobt.2023.1189525 [doi]
PST - epublish
SO  - Front Robot AI. 2023 May 30;10:1189525. doi: 10.3389/frobt.2023.1189525. 
      eCollection 2023.

PMID- 37987431
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231124
IS  - 2039-7275 (Print)
IS  - 2039-7283 (Electronic)
IS  - 2039-7275 (Linking)
VI  - 13
IP  - 6
DP  - 2023 Nov 20
TI  - Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency 
      Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine.
PG  - 1460-1487
LID - 10.3390/clinpract13060130 [doi]
AB  - The rapid progress in artificial intelligence, machine learning, and natural 
      language processing has led to increasingly sophisticated large language models 
      (LLMs) for use in healthcare. This study assesses the performance of two LLMs, 
      the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access 
      to medical specialist training in Spain. Our objectives included gauging the 
      model's overall performance, analyzing discrepancies across different medical 
      specialties, discerning between theoretical and practical questions, estimating 
      error proportions, and assessing the hypothetical severity of errors committed by 
      a physician. MATERIAL AND METHODS: We studied the 2022 Spanish MIR examination 
      results after excluding those questions requiring image evaluations or having 
      acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 
      and GPT-3.5 in Spanish and English. Logistic regression models analyzed the 
      relationships between question length, sequence, and performance. We also 
      analyzed the 23 questions with images, using GPT-4's new image analysis 
      capability. RESULTS: GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 
      0.001). English translations had a slightly enhanced performance. GPT-4 scored 
      26.1% of the questions with images in English. The results were worse when the 
      questions were in Spanish, 13.0%, although the differences were not statistically 
      significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct 
      response rate in several areas, and the Pharmacology, Critical Care, and 
      Infectious Diseases specialties showed lower performance. The error analysis 
      revealed that while a 13.2% error rate existed, the gravest categories, such as 
      "error requiring intervention to sustain life" and "error resulting in death", 
      had a 0% rate. CONCLUSIONS: GPT-4 performs robustly on the Spanish MIR 
      examination, with varying capabilities to discriminate knowledge across 
      specialties. While the model's high success rate is commendable, understanding 
      the error severity is critical, especially when considering AI's potential role 
      in real-world medical practice and its implications for patient safety.
FAU - Guillen-Grima, Francisco
AU  - Guillen-Grima F
AUID- ORCID: 0000-0001-9749-8076
AD  - Department of Health Sciences, Public University of Navarra, 31008 Pamplona, 
      Spain.
AD  - Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain.
AD  - Department of Preventive Medicine, Clinica Universidad de Navarra, 31008 
      Pamplona, Spain.
AD  - CIBER in Epidemiology and Public Health (CIBERESP), Institute of Health Carlos 
      III, 46980 Madrid, Spain.
FAU - Guillen-Aguinaga, Sara
AU  - Guillen-Aguinaga S
AUID- ORCID: 0000-0003-4748-9520
AD  - Department of Health Sciences, Public University of Navarra, 31008 Pamplona, 
      Spain.
FAU - Guillen-Aguinaga, Laura
AU  - Guillen-Aguinaga L
AD  - Department of Health Sciences, Public University of Navarra, 31008 Pamplona, 
      Spain.
AD  - Department of Nursing, Kystad Helse-og Velferdssenter, 7026 Trondheim, Norway.
FAU - Alas-Brun, Rosa
AU  - Alas-Brun R
AD  - Department of Health Sciences, Public University of Navarra, 31008 Pamplona, 
      Spain.
FAU - Onambele, Luc
AU  - Onambele L
AD  - School of Health Sciences, Catholic University of Central Africa, Yaoundé 1100, 
      Cameroon.
FAU - Ortega, Wilfrido
AU  - Ortega W
AD  - Department of Surgery, Medical and Social Sciences, University of Alcala de 
      Henares, 28871 Alcalá de Henares, Spain.
FAU - Montejo, Rocio
AU  - Montejo R
AD  - Department of Obstetrics and Gynecology, Institute of Clinical Sciences, 
      University of Gothenburg, 413 46 Gothenburg, Sweden.
AD  - Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, 413 46 
      Gothenburg, Sweden.
FAU - Aguinaga-Ontoso, Enrique
AU  - Aguinaga-Ontoso E
AUID- ORCID: 0000-0002-7994-3559
AD  - Department of Sociosanitary Sciences, University of Murcia, 30100 Murcia, Spain.
FAU - Barach, Paul
AU  - Barach P
AUID- ORCID: 0000-0002-7906-698X
AD  - Jefferson College of Population Health, Philadelphia, PA 19107, USA.
AD  - School of Medicine, Thomas Jefferson University, Philadelphia, PA 19107, USA.
AD  - Interdisciplinary Research Institute for Health Law and Science, Sigmund Freud 
      University, 1020 Vienna, Austria.
AD  - Department of Surgery, Imperial College, London SW7 2AZ, UK.
FAU - Aguinaga-Ontoso, Ines
AU  - Aguinaga-Ontoso I
AUID- ORCID: 0000-0002-2882-930X
AD  - Department of Health Sciences, Public University of Navarra, 31008 Pamplona, 
      Spain.
AD  - Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - Switzerland
TA  - Clin Pract
JT  - Clinics and practice
JID - 101563282
PMC - PMC10660543
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-3.5
OT  - GPT-4
OT  - artificial intelligence
OT  - image
OT  - large language model
OT  - machine learning
OT  - medical education
OT  - patient safety
OT  - quality of care
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/21 12:43
MHDA- 2023/11/21 12:44
CRDT- 2023/11/21 06:29
PHST- 2023/09/26 00:00 [received]
PHST- 2023/11/15 00:00 [revised]
PHST- 2023/11/16 00:00 [accepted]
PHST- 2023/11/21 12:44 [medline]
PHST- 2023/11/21 12:43 [pubmed]
PHST- 2023/11/21 06:29 [entrez]
AID - clinpract13060130 [pii]
AID - clinpract-13-00130 [pii]
AID - 10.3390/clinpract13060130 [doi]
PST - epublish
SO  - Clin Pract. 2023 Nov 20;13(6):1460-1487. doi: 10.3390/clinpract13060130.

PMID- 37338549
OWN - NLM
STAT- MEDLINE
DCOM- 20230815
LR  - 20230927
IS  - 1432-1238 (Electronic)
IS  - 0342-4642 (Linking)
VI  - 49
IP  - 8
DP  - 2023 Aug
TI  - Assessing the usefulness of a large language model to query and summarize 
      unstructured medical notes in intensive care.
PG  - 1018-1020
LID - 10.1007/s00134-023-07128-2 [doi]
FAU - Madden, Michael G
AU  - Madden MG
AD  - School Computer Science, University of Galway, Galway, Ireland.
FAU - McNicholas, Bairbre A
AU  - McNicholas BA
AD  - Anaesthesia and Intensive Care Medicine, School of Medicine, University of 
      Galway, Galway, Ireland.
AD  - Department of Anaesthesia and Intensive Care Medicine, Galway University 
      Hospitals, SAOLTA University Health Care Group, Galway, Ireland.
FAU - Laffey, John G
AU  - Laffey JG
AUID- ORCID: 0000-0002-1246-9573
AD  - Anaesthesia and Intensive Care Medicine, School of Medicine, University of 
      Galway, Galway, Ireland. john.laffey@universityofgalway.ie.
AD  - Department of Anaesthesia and Intensive Care Medicine, Galway University 
      Hospitals, SAOLTA University Health Care Group, Galway, Ireland. 
      john.laffey@universityofgalway.ie.
LA  - eng
PT  - Letter
DEP - 20230620
PL  - United States
TA  - Intensive Care Med
JT  - Intensive care medicine
JID - 7704851
SB  - IM
MH  - Humans
MH  - *Critical Care
MH  - *Electronic Health Records
EDAT- 2023/06/20 13:10
MHDA- 2023/08/15 06:42
CRDT- 2023/06/20 11:04
PHST- 2023/04/30 00:00 [received]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/08/15 06:42 [medline]
PHST- 2023/06/20 13:10 [pubmed]
PHST- 2023/06/20 11:04 [entrez]
AID - 10.1007/s00134-023-07128-2 [pii]
AID - 10.1007/s00134-023-07128-2 [doi]
PST - ppublish
SO  - Intensive Care Med. 2023 Aug;49(8):1018-1020. doi: 10.1007/s00134-023-07128-2. 
      Epub 2023 Jun 20.

PMID- 37444647
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 13
DP  - 2023 Jun 21
TI  - ChatGPT and the Future of Digital Health: A Study on Healthcare Workers' 
      Perceptions and Expectations.
LID - 10.3390/healthcare11131812 [doi]
LID - 1812
AB  - This study aimed to assess the knowledge, attitudes, and intended practices of 
      healthcare workers (HCWs) in Saudi Arabia towards ChatGPT, an artificial 
      intelligence (AI) Chatbot, within the first three months after its launch. We 
      also aimed to identify potential barriers to AI Chatbot adoption among healthcare 
      professionals. A cross-sectional survey was conducted among 1057 HCWs in Saudi 
      Arabia, distributed electronically via social media channels from 21 February to 
      6 March 2023. The survey evaluated HCWs' familiarity with ChatGPT-3.5, their 
      satisfaction, intended future use, and perceived usefulness in healthcare 
      practice. Of the respondents, 18.4% had used ChatGPT for healthcare purposes, 
      while 84.1% of non-users expressed interest in utilizing AI Chatbots in the 
      future. Most participants (75.1%) were comfortable with incorporating ChatGPT 
      into their healthcare practice. HCWs perceived the Chatbot to be useful in 
      various aspects of healthcare, such as medical decision-making (39.5%), patient 
      and family support (44.7%), medical literature appraisal (48.5%), and medical 
      research assistance (65.9%). A majority (76.7%) believed ChatGPT could positively 
      impact the future of healthcare systems. Nevertheless, concerns about credibility 
      and the source of information provided by AI Chatbots (46.9%) were identified as 
      the main barriers. Although HCWs recognize ChatGPT as a valuable addition to 
      digital health in the early stages of adoption, addressing concerns regarding 
      accuracy, reliability, and medicolegal implications is crucial. Therefore, due to 
      their unreliability, the current forms of ChatGPT and other Chatbots should not 
      be used for diagnostic or treatment purposes without human expert oversight. 
      Ensuring the trustworthiness and dependability of AI Chatbots is essential for 
      successful implementation in healthcare settings. Future research should focus on 
      evaluating the clinical outcomes of ChatGPT and benchmarking its performance 
      against other AI Chatbots.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AUID- ORCID: 0000-0002-4389-9322
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
AD  - Evidence-Based Health Care & Knowledge Translation Research Chair, King Saud 
      University, Riyadh 11587, Saudi Arabia.
FAU - Aljamaan, Fadi
AU  - Aljamaan F
AUID- ORCID: 0000-0001-8404-6652
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
AD  - Critical Care Department, King Saud University Medical City, Riyadh 11411, Saudi 
      Arabia.
FAU - Malki, Khalid H
AU  - Malki KH
AUID- ORCID: 0000-0002-5986-7843
AD  - Research Chair of Voice, Swallowing, and Communication Disorders, ENT Department, 
      College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
FAU - Alhasan, Khalid
AU  - Alhasan K
AUID- ORCID: 0000-0002-4291-8536
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
AD  - Solid Organ Transplant Center of Excellence, King Faisal Specialist Hospital and 
      Research Center, Riyadh 11564, Saudi Arabia.
FAU - Altamimi, Ibraheem
AU  - Altamimi I
AUID- ORCID: 0000-0002-5149-0877
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
FAU - Aljarbou, Razan
AU  - Aljarbou R
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
FAU - Bazuhair, Faisal
AU  - Bazuhair F
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
FAU - Alsubaihin, Abdulmajeed
AU  - Alsubaihin A
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
FAU - Abdulmajeed, Naif
AU  - Abdulmajeed N
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
AD  - Pediatric Nephrology Department, Prince Sultan Military Medical City, Riyadh 
      12233, Saudi Arabia.
FAU - Alshahrani, Fatimah S
AU  - Alshahrani FS
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
AD  - Division of Infectious Diseases, Department of Internal Medicine, College of 
      Medicine, King Saud University, Riyadh 11451, Saudi Arabia.
FAU - Temsah, Reem
AU  - Temsah R
AD  - College of Pharmacy, Alfaisal University, Riyadh 11533, Saudi Arabia.
FAU - Alshahrani, Turki
AU  - Alshahrani T
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
FAU - Al-Eyadhy, Lama
AU  - Al-Eyadhy L
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
FAU - Alkhateeb, Serin Mohammed
AU  - Alkhateeb SM
AD  - College of Medicine, Jordan University of Science and Technology, Irbid 22110, 
      Jordan.
FAU - Saddik, Basema
AU  - Saddik B
AUID- ORCID: 0000-0002-4682-5927
AD  - Sharjah Institute of Medical Research, University of Sharjah, Sharjah 27272, 
      United Arab Emirates.
AD  - Department of Community and Family Medicine, College of Medicine, University of 
      Sharjah, Sharjah 27272, United Arab Emirates.
AD  - School of Population Health, Faculty of Medicine & Health, UNSW Sydney, Sydney, 
      NSW 2052, Australia.
FAU - Halwani, Rabih
AU  - Halwani R
AD  - Sharjah Institute of Medical Research, University of Sharjah, Sharjah 27272, 
      United Arab Emirates.
AD  - Department of Clinical Sciences, College of Medicine, University of Sharjah, 
      Sharjah 27272, United Arab Emirates.
FAU - Jamal, Amr
AU  - Jamal A
AUID- ORCID: 0000-0002-4051-6592
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
AD  - Evidence-Based Health Care & Knowledge Translation Research Chair, King Saud 
      University, Riyadh 11587, Saudi Arabia.
AD  - Department of Family and Community Medicine, King Saud University Medical City, 
      Riyadh 11411, Saudi Arabia.
FAU - Al-Tawfiq, Jaffar A
AU  - Al-Tawfiq JA
AUID- ORCID: 0000-0002-5752-2235
AD  - Specialty Internal Medicine and Quality Department, Johns Hopkins Aramco 
      Healthcare, Dhahran 34465, Saudi Arabia.
AD  - Infectious Disease Division, Department of Medicine, Indiana University School of 
      Medicine, Indianapolis, IN 46202, USA.
AD  - Infectious Disease Division, Department of Medicine, Johns Hopkins University 
      School of Medicine, Baltimore, MD 21218, USA.
FAU - Al-Eyadhy, Ayman
AU  - Al-Eyadhy A
AUID- ORCID: 0000-0002-6051-9125
AD  - College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
AD  - Pediatric Department, King Saud University Medical City, King Saud University, 
      Riyadh 11411, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20230621
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10340744
OTO - NOTNLM
OT  - AI chatbots
OT  - ChatGPT
OT  - artificial intelligence
OT  - credibility
OT  - healthcare workers
OT  - medicolegal implications
OT  - perception
COIS- All authors declare no conflict of interest.
EDAT- 2023/07/14 13:07
MHDA- 2023/07/14 13:08
CRDT- 2023/07/14 01:07
PHST- 2023/04/25 00:00 [received]
PHST- 2023/06/14 00:00 [revised]
PHST- 2023/06/19 00:00 [accepted]
PHST- 2023/07/14 13:08 [medline]
PHST- 2023/07/14 13:07 [pubmed]
PHST- 2023/07/14 01:07 [entrez]
AID - healthcare11131812 [pii]
AID - healthcare-11-01812 [pii]
AID - 10.3390/healthcare11131812 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Jun 21;11(13):1812. doi: 10.3390/healthcare11131812.

PMID- 37668714
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231106
IS  - 1432-069X (Electronic)
IS  - 0340-3696 (Linking)
VI  - 315
IP  - 10
DP  - 2023 Dec
TI  - Assessing ChatGPT responses to common patient queries regarding basal cell 
      carcinoma.
PG  - 2979-2981
LID - 10.1007/s00403-023-02705-3 [doi]
FAU - Trager, Megan H
AU  - Trager MH
AUID- ORCID: 0000-0002-7330-1627
AD  - Department of Dermatology, Columbia University Irving Medical Center, Herbert 
      Irving Pavilion, 12th Floor, New York, NY, 10032, USA.
FAU - Queen, Dawn
AU  - Queen D
AUID- ORCID: 0000-0001-5665-102X
AD  - Department of Dermatology, Columbia University Irving Medical Center, Herbert 
      Irving Pavilion, 12th Floor, New York, NY, 10032, USA.
FAU - Bordone, Lindsey A
AU  - Bordone LA
AUID- ORCID: 0000-0001-8700-890X
AD  - Department of Dermatology, Columbia University Irving Medical Center, Herbert 
      Irving Pavilion, 12th Floor, New York, NY, 10032, USA.
FAU - Geskin, Larisa J
AU  - Geskin LJ
AUID- ORCID: 0000-0001-7348-2571
AD  - Department of Dermatology, Columbia University Irving Medical Center, Herbert 
      Irving Pavilion, 12th Floor, New York, NY, 10032, USA.
FAU - Samie, Faramarz H
AU  - Samie FH
AUID- ORCID: 0000-0003-0595-1457
AD  - Department of Dermatology, Columbia University Irving Medical Center, Herbert 
      Irving Pavilion, 12th Floor, New York, NY, 10032, USA. fs2614@cumc.columbia.edu.
LA  - eng
PT  - Letter
DEP - 20230905
PL  - Germany
TA  - Arch Dermatol Res
JT  - Archives of dermatological research
JID - 8000462
SB  - IM
MH  - Humans
MH  - *Carcinoma, Basal Cell/diagnosis/epidemiology
MH  - *Skin Neoplasms/diagnosis
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Basal cell carcinoma
OT  - ChatGPT
EDAT- 2023/09/05 12:42
MHDA- 2023/11/01 12:42
CRDT- 2023/09/05 11:11
PHST- 2023/05/08 00:00 [received]
PHST- 2023/08/13 00:00 [accepted]
PHST- 2023/07/28 00:00 [revised]
PHST- 2023/11/01 12:42 [medline]
PHST- 2023/09/05 12:42 [pubmed]
PHST- 2023/09/05 11:11 [entrez]
AID - 10.1007/s00403-023-02705-3 [pii]
AID - 10.1007/s00403-023-02705-3 [doi]
PST - ppublish
SO  - Arch Dermatol Res. 2023 Dec;315(10):2979-2981. doi: 10.1007/s00403-023-02705-3. 
      Epub 2023 Sep 5.

PMID- 37927543
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231107
IS  - 2398-8835 (Electronic)
IS  - 2398-8835 (Linking)
VI  - 6
IP  - 11
DP  - 2023 Nov
TI  - The power of ChatGPT in revolutionizing rural healthcare delivery.
PG  - e1684
LID - 10.1002/hsr2.1684 [doi]
LID - e1684
FAU - Ahmed, Sirwan Khalid
AU  - Ahmed SK
AUID- ORCID: 0000-0002-8361-0546
AD  - Department of Adult Nursing, College of Nursing University of Raparin Rania Iraq.
AD  - Ministry of Health General Directorate of Health-Raparin Rania Iraq.
FAU - Hussein, Safin
AU  - Hussein S
AUID- ORCID: 0000-0001-8513-2907
AD  - Department of Biology, College of Science University of Raparin Rania Iraq.
FAU - Aziz, Tahir Abdullah
AU  - Aziz TA
AUID- ORCID: 0000-0003-4447-7460
AD  - Ministry of Health General Directorate of Health-Raparin Rania Iraq.
FAU - Chakraborty, Sandip
AU  - Chakraborty S
AUID- ORCID: 0000-0002-2792-3281
AD  - Department of Veterinary Microbiology College of Veterinary Sciences and Animal 
      Husbandry R. K. Nagar West Tripura India.
FAU - Islam, Md Rabiul
AU  - Islam MR
AUID- ORCID: 0000-0003-2820-3144
AD  - School of Pharmacy BRAC University Dhaka Bangladesh.
FAU - Dhama, Kuldeep
AU  - Dhama K
AUID- ORCID: 0000-0001-7469-4752
AD  - Division of Pathology ICAR-Indian Veterinary Research Institute Bareilly Uttar 
      Pradesh India.
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - United States
TA  - Health Sci Rep
JT  - Health science reports
JID - 101728855
PMC - PMC10620374
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - healthcare services
OT  - mental health
OT  - rural regions
OT  - telemedicine
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/06 06:42
MHDA- 2023/11/06 06:43
CRDT- 2023/11/06 04:20
PHST- 2023/07/01 00:00 [received]
PHST- 2023/10/18 00:00 [revised]
PHST- 2023/10/19 00:00 [accepted]
PHST- 2023/11/06 06:43 [medline]
PHST- 2023/11/06 06:42 [pubmed]
PHST- 2023/11/06 04:20 [entrez]
AID - HSR21684 [pii]
AID - 10.1002/hsr2.1684 [doi]
PST - epublish
SO  - Health Sci Rep. 2023 Nov 1;6(11):e1684. doi: 10.1002/hsr2.1684. eCollection 2023 
      Nov.

PMID- 37548379
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230816
IS  - 1520-5126 (Electronic)
IS  - 0002-7863 (Linking)
VI  - 145
IP  - 32
DP  - 2023 Aug 16
TI  - ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis.
PG  - 18048-18062
LID - 10.1021/jacs.3c05819 [doi]
AB  - We use prompt engineering to guide ChatGPT in the automation of text mining of 
      metal-organic framework (MOF) synthesis conditions from diverse formats and 
      styles of the scientific literature. This effectively mitigates ChatGPT's 
      tendency to hallucinate information, an issue that previously made the use of 
      large language models (LLMs) in scientific fields challenging. Our approach 
      involves the development of a workflow implementing three different processes for 
      text mining, programmed by ChatGPT itself. All of them enable parsing, searching, 
      filtering, classification, summarization, and data unification with different 
      trade-offs among labor, speed, and accuracy. We deploy this system to extract 
      26 257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced 
      from peer-reviewed research articles. This process incorporates our ChemPrompt 
      Engineering strategy to instruct ChatGPT in text mining, resulting in impressive 
      precision, recall, and F1 scores of 90-99%. Furthermore, with the data set built 
      by text mining, we constructed a machine-learning model with over 87% accuracy in 
      predicting MOF experimental crystallization outcomes and preliminarily 
      identifying important factors in MOF crystallization. We also developed a 
      reliable data-grounded MOF chatbot to answer questions about chemical reactions 
      and synthesis procedures. Given that the process of using ChatGPT reliably mines 
      and tabulates diverse MOF synthesis information in a unified format while using 
      only narrative language requiring no coding expertise, we anticipate that our 
      ChatGPT Chemistry Assistant will be very useful across various other chemistry 
      subdisciplines.
FAU - Zheng, Zhiling
AU  - Zheng Z
AUID- ORCID: 0000-0001-6090-2258
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
FAU - Zhang, Oufan
AU  - Zhang O
FAU - Borgs, Christian
AU  - Borgs C
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
AD  - Department of Electrical Engineering and Computer Sciences, University of 
      California, Berkeley, California 94720, United States.
FAU - Chayes, Jennifer T
AU  - Chayes JT
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
AD  - Department of Electrical Engineering and Computer Sciences, University of 
      California, Berkeley, California 94720, United States.
AD  - Department of Mathematics, University of California, Berkeley, California 94720, 
      United States.
AD  - Department of Statistics, University of California, Berkeley, California 94720, 
      United States.
AD  - School of Information, University of California, Berkeley, California 94720, 
      United States.
FAU - Yaghi, Omar M
AU  - Yaghi OM
AUID- ORCID: 0000-0002-5611-3325
AD  - Department of Chemistry, University of California, Berkeley, California 94720, 
      United States.
AD  - Kavli Energy Nanoscience Institute, University of California, Berkeley, 
      California 94720, United States.
AD  - Bakar Institute of Digital Materials for the Planet, College of Computing, Data 
      Science, and Society, University of California, Berkeley, California 94720, 
      United States.
AD  - KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy 
      Applications, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi 
      Arabia.
LA  - eng
PT  - Journal Article
DEP - 20230807
PL  - United States
TA  - J Am Chem Soc
JT  - Journal of the American Chemical Society
JID - 7503056
SB  - IM
EDAT- 2023/08/07 13:10
MHDA- 2023/08/07 13:11
CRDT- 2023/08/07 08:03
PHST- 2023/08/07 13:11 [medline]
PHST- 2023/08/07 13:10 [pubmed]
PHST- 2023/08/07 08:03 [entrez]
AID - 10.1021/jacs.3c05819 [doi]
PST - ppublish
SO  - J Am Chem Soc. 2023 Aug 16;145(32):18048-18062. doi: 10.1021/jacs.3c05819. Epub 
      2023 Aug 7.

PMID- 37517980
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20230920
IS  - 1479-666X (Print)
IS  - 1479-666X (Linking)
VI  - 21
IP  - 5
DP  - 2023 Oct
TI  - Assessing ChatGPT's ability to pass the FRCS orthopaedic part A exam: A critical 
      analysis.
PG  - 263-266
LID - S1479-666X(23)00076-8 [pii]
LID - 10.1016/j.surge.2023.07.001 [doi]
AB  - AI technology has made significant advancements in recent years, with the notable 
      development of ChatGPT in November 2022. Users have observed evidence of 
      deductive reasoning, logical thinking, and coherent thought in ChatGPT's 
      responses. This study aimed to determine if ChatGPT has the capability to pass 
      the Orthopaedic Fellow of the Royal College of Surgeons (FRCS Orth) Part A exam. 
      METHODS: To assess ChatGPT4's ability to pass the Orthopaedic FRCS Orth Part A 
      exam, a study was conducted using 240 mock FRCS Orth Part A questions. The study 
      evaluated the accuracy of ChatGPT's answers and the response time for each 
      question. Descriptive statistics were employed to analyse the chatbot's 
      performance. RESULTS: The evaluation revealed that ChatGPT4 achieved an overall 
      score of 67.5% on Part A of the exam. However, ChatGPT4 did not meet the overall 
      pass mark required for the FRCS Orth Part A exam. CONCLUSION: This study 
      demonstrates that ChatGPT was unable to pass the FRCS Orthopaedic examination. 
      Several factors contributed to this outcome, including the lack of critical or 
      high-order thinking abilities, limited clinical expertise, and the inability to 
      meet the rigorous requirements of the exam.
CI  - Copyright © 2023 Royal College of Surgeons of Edinburgh (Scottish charity number 
      SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. 
      All rights reserved.
FAU - Saad, Ahmed
AU  - Saad A
AD  - Department of Orthopedics, Royal Orthopedic Hospital, Birmingham, UK. Electronic 
      address: Ahmed.saad3@nhs.net.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AD  - Department of Orthopedics, Southport and Ormskirk Hospital NHS Trust, Southport, 
      UK. Electronic address: kartikp31@hotmail.com.
FAU - Kurisunkal, Vineet
AU  - Kurisunkal V
AD  - Department of Orthopedic Oncology, Royal Orthopedic Hospital, Birmingham, UK. 
      Electronic address: vineetkurisunkal@gmail.com.
FAU - Botchu, Rajesh
AU  - Botchu R
AD  - Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, 
      UK. Electronic address: drbrajesh@yahoo.com.
LA  - eng
PT  - Journal Article
DEP - 20230728
PL  - Scotland
TA  - Surgeon
JT  - The surgeon : journal of the Royal Colleges of Surgeons of Edinburgh and Ireland
JID - 101168329
SB  - IM
MH  - Humans
MH  - *Orthopedics
MH  - Physical Examination
MH  - *Surgeons
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ChatGPT
OT  - FRCS orthopaedics
OT  - Medical exams
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/07/31 00:41
MHDA- 2023/09/20 06:42
CRDT- 2023/07/30 21:57
PHST- 2023/07/01 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/07/31 00:41 [pubmed]
PHST- 2023/07/30 21:57 [entrez]
AID - S1479-666X(23)00076-8 [pii]
AID - 10.1016/j.surge.2023.07.001 [doi]
PST - ppublish
SO  - Surgeon. 2023 Oct;21(5):263-266. doi: 10.1016/j.surge.2023.07.001. Epub 2023 Jul 
      28.

PMID- 37101311
OWN - NLM
STAT- MEDLINE
DCOM- 20230719
LR  - 20230719
IS  - 1440-1800 (Electronic)
IS  - 1320-7881 (Linking)
VI  - 30
IP  - 3
DP  - 2023 Jul
TI  - Will ChatGPT undermine ethical values in nursing education, research, and 
      practice?
PG  - e12556
LID - 10.1111/nin.12556 [doi]
FAU - Abdulai, Abdul-Fatawu
AU  - Abdulai AF
AUID- ORCID: 0000-0002-9395-8642
AD  - School of Nursing, University of British Columbia, Vancouver, British Columbia, 
      Canada.
FAU - Hung, Lillian
AU  - Hung L
AD  - School of Nursing, University of British Columbia, Vancouver, British Columbia, 
      Canada.
LA  - eng
PT  - Journal Article
DEP - 20230426
PL  - Australia
TA  - Nurs Inq
JT  - Nursing inquiry
JID - 9505881
MH  - Humans
MH  - *Education, Nursing/ethics
MH  - *Artificial Intelligence
MH  - *Nursing Research/ethics
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - ethics and values
OT  - nursing
EDAT- 2023/04/27 00:42
MHDA- 2023/07/19 06:43
CRDT- 2023/04/26 23:52
PHST- 2023/04/03 00:00 [received]
PHST- 2023/04/05 00:00 [accepted]
PHST- 2023/07/19 06:43 [medline]
PHST- 2023/04/27 00:42 [pubmed]
PHST- 2023/04/26 23:52 [entrez]
AID - 10.1111/nin.12556 [doi]
PST - ppublish
SO  - Nurs Inq. 2023 Jul;30(3):e12556. doi: 10.1111/nin.12556. Epub 2023 Apr 26.

PMID- 37493985
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230729
IS  - 2197-1153 (Print)
IS  - 2197-1153 (Electronic)
IS  - 2197-1153 (Linking)
VI  - 10
IP  - 1
DP  - 2023 Jul 26
TI  - Artificial intelligence and ChatGPT in Orthopaedics and sports medicine.
PG  - 74
LID - 10.1186/s40634-023-00642-8 [doi]
LID - 74
AB  - Artificial intelligence (AI) is looked upon nowadays as the potential major 
      catalyst for the fourth industrial revolution. In the last decade, AI use in 
      Orthopaedics increased approximately tenfold. Artificial intelligence helps with 
      tracking activities, evaluating diagnostic images, predicting injury risk, and 
      several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an 
      AI-chatbot, represents an extremely controversial topic in the academic 
      community. The aim of this review article is to simplify the concept of AI and 
      study the extent of AI use in Orthopaedics and sports medicine literature. 
      Additionally, the article will also evaluate the role of ChatGPT in scientific 
      research and publications.Level of evidence: Level V, letter to review.
CI  - © 2023. This is a U.S. Government work and not under copyright protection in the 
      US; foreign copyright protection may apply.
FAU - Fayed, Aly M
AU  - Fayed AM
AUID- ORCID: 0000-0002-3905-9662
AD  - Department of Orthopaedics and Rehabilitation, University of Iowa Hospitals and 
      Clinics, Iowa City, IA, USA. a.m.alyfayed@gmail.com.
FAU - Mansur, Nacime Salomao Barbachan
AU  - Mansur NSB
AD  - Department of Orthopaedics and Rehabilitation, University of Iowa Hospitals and 
      Clinics, Iowa City, IA, USA.
FAU - de Carvalho, Kepler Alencar
AU  - de Carvalho KA
AD  - Department of Orthopaedics and Rehabilitation, University of Iowa Hospitals and 
      Clinics, Iowa City, IA, USA.
FAU - Behrens, Andrew
AU  - Behrens A
AD  - Department of Orthopaedics and Rehabilitation, University of Iowa Hospitals and 
      Clinics, Iowa City, IA, USA.
FAU - D'Hooghe, Pieter
AU  - D'Hooghe P
AD  - Aspetar Orthopedic and Sports Medicine Hospital, Doha, Qatar.
FAU - de Cesar Netto, Cesar
AU  - de Cesar Netto C
AD  - Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230726
PL  - Germany
TA  - J Exp Orthop
JT  - Journal of experimental orthopaedics
JID - 101653750
PMC - PMC10371934
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Distance mapping
OT  - Machine learning
OT  - Statistical algorithm
COIS- The authors declare that they have no competing interests.
EDAT- 2023/07/26 13:08
MHDA- 2023/07/26 13:09
CRDT- 2023/07/26 11:22
PHST- 2023/06/13 00:00 [received]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/07/26 13:09 [medline]
PHST- 2023/07/26 13:08 [pubmed]
PHST- 2023/07/26 11:22 [entrez]
AID - 10.1186/s40634-023-00642-8 [pii]
AID - 642 [pii]
AID - 10.1186/s40634-023-00642-8 [doi]
PST - epublish
SO  - J Exp Orthop. 2023 Jul 26;10(1):74. doi: 10.1186/s40634-023-00642-8.

PMID- 37207953
OWN - NLM
STAT- MEDLINE
DCOM- 20230822
LR  - 20230830
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
VI  - 89
IP  - 3
DP  - 2023 Sep
TI  - The utility of ChatGPT in generating patient-facing and clinical responses for 
      melanoma.
PG  - 602-604
LID - S0190-9622(23)00908-8 [pii]
LID - 10.1016/j.jaad.2023.05.024 [doi]
FAU - Young, Jade N
AU  - Young JN
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York.
FAU - Ross O'Hagan
AU  - Ross O'Hagan
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York.
FAU - Poplausky, Dina
AU  - Poplausky D
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York.
FAU - Levoska, Melissa A
AU  - Levoska MA
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York.
FAU - Gulati, Nicholas
AU  - Gulati N
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York.
FAU - Ungar, Benjamin
AU  - Ungar B
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York.
FAU - Ungar, Jonathan
AU  - Ungar J
AD  - Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New 
      York. Electronic address: jonathan.ungar@mountsinai.org.
LA  - eng
PT  - Journal Article
DEP - 20230518
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
MH  - Humans
MH  - *Melanoma/diagnosis
MH  - Syndrome
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - language learning model
OT  - melanoma
COIS- Conflicts of interest None disclosed.
EDAT- 2023/05/20 09:42
MHDA- 2023/08/22 06:42
CRDT- 2023/05/19 19:27
PHST- 2023/04/06 00:00 [received]
PHST- 2023/04/26 00:00 [revised]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/08/22 06:42 [medline]
PHST- 2023/05/20 09:42 [pubmed]
PHST- 2023/05/19 19:27 [entrez]
AID - S0190-9622(23)00908-8 [pii]
AID - 10.1016/j.jaad.2023.05.024 [doi]
PST - ppublish
SO  - J Am Acad Dermatol. 2023 Sep;89(3):602-604. doi: 10.1016/j.jaad.2023.05.024. Epub 
      2023 May 18.

PMID- 37052880
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231030
LR  - 20231030
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
VI  - 74
IP  - 4
DP  - 2023 Nov
TI  - Appropriateness of Recommendations Provided by ChatGPT to Interventional 
      Radiologists.
PG  - 758-763
LID - 10.1177/08465371231170133 [doi]
FAU - Barat, Maxime
AU  - Barat M
AD  - Department of Radiology, Hopital Cochin, AP-HP, Université Paris Cité, Paris, 
      France.
FAU - Soyer, Philippe
AU  - Soyer P
AUID- ORCID: 0000-0002-5055-1682
AD  - Department of Radiology, Hopital Cochin, AP-HP, Université Paris Cité, Paris, 
      France.
FAU - Dohan, Anthony
AU  - Dohan A
AD  - Department of Radiology, Hopital Cochin, AP-HP, Université Paris Cité, Paris, 
      France.
LA  - eng
PT  - Letter
DEP - 20230413
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - generative pre-trained transformer
OT  - interventional radiology
OT  - radiology
COIS- Declaration of Conflicting InterestsThe author(s) declared no potential conflicts 
      of interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/04/14 06:00
MHDA- 2023/04/14 06:01
CRDT- 2023/04/13 11:30
PHST- 2023/04/14 06:01 [medline]
PHST- 2023/04/14 06:00 [pubmed]
PHST- 2023/04/13 11:30 [entrez]
AID - 10.1177/08465371231170133 [doi]
PST - ppublish
SO  - Can Assoc Radiol J. 2023 Nov;74(4):758-763. doi: 10.1177/08465371231170133. Epub 
      2023 Apr 13.

PMID- 37352054
OWN - NLM
STAT- MEDLINE
DCOM- 20230626
LR  - 20230927
IS  - 1536-5964 (Electronic)
IS  - 0025-7974 (Print)
IS  - 0025-7974 (Linking)
VI  - 102
IP  - 25
DP  - 2023 Jun 23
TI  - Assessing ChatGPT's capacity for clinical decision support in pediatrics: A 
      comparative study with pediatricians using KIDMAP of Rasch analysis.
PG  - e34068
LID - 10.1097/MD.0000000000034068 [doi]
LID - e34068
AB  - BACKGROUND: The application of large language models in clinical decision support 
      (CDS) is an area that warrants further investigation. ChatGPT, a prominent large 
      language models developed by OpenAI, has shown promising performance across 
      various domains. However, there is limited research evaluating its use 
      specifically in pediatric clinical decision-making. This study aimed to assess 
      ChatGPT's potential as a CDS tool in pediatrics by evCDSaluating its performance 
      on 8 common clinical symptom prompts. Study objectives were to answer the 2 
      research questions: the ChatGPT's overall grade in a range from A (high) to E 
      (low) compared to a normal sample and the difference in assessment of ChatGPT 
      between 2 pediatricians. METHODS: We compared ChatGPT's responses to 8 items 
      related to clinical symptoms commonly encountered by pediatricians. Two 
      pediatricians independently assessed the answers provided by ChatGPT in an 
      open-ended format. The scoring system ranged from 0 to 100, which was then 
      transformed into 5 ordinal categories. We simulated 300 virtual students with a 
      normal distribution to provide scores on items based on Rasch rating scale model 
      and their difficulties in a range between -2 to 2.5 logits. Two visual 
      presentations (Wright map and KIDMAP) were generated to answer the 2 research 
      questions outlined in the objectives of the study. RESULTS: The 2 pediatricians' 
      assessments indicated that ChatGPT's overall performance corresponded to a grade 
      of C in a range from A to E, with average scores of -0.89 logits and 0.90 logits 
      (=log odds), respectively. The assessments revealed a significant difference in 
      performance between the 2 pediatricians (P < .05), with scores of -0.89 (SE = 
      0.37) and 0.90 (SE = 0.41) in log odds units (logits in Rasch analysis). 
      CONCLUSION: This study demonstrates the feasibility of utilizing ChatGPT as a CDS 
      tool for patients presenting with common pediatric symptoms. The findings suggest 
      that ChatGPT has the potential to enhance clinical workflow and aid in 
      responsible clinical decision-making. Further exploration and refinement of 
      ChatGPT's capabilities in pediatric care can potentially contribute to improved 
      healthcare outcomes and patient management.
CI  - Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Kao, Hsu-Ju
AU  - Kao HJ
AD  - Department of Internal Medicine, Chi Mei Medical Center, Chiali, Taiwan.
FAU - Chien, Tsair-Wei
AU  - Chien TW
AD  - Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan.
FAU - Wang, Wen-Chung
AU  - Wang WC
AD  - The Education University of Hong Kong, Hong Kong, China.
FAU - Chou, Willy
AU  - Chou W
AUID- ORCID: 0000-0002-1132-9341
AD  - Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, 
      Tainan, Taiwan.
AD  - Department of Physical Medicine and Rehabilitation, Chung San Medical University 
      Hospital, Taichung, Taiwan.
FAU - Chow, Julie Chi
AU  - Chow JC
AUID- ORCID: 0000-0003-3150-4917
AD  - Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan.
AD  - Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical 
      University, Taipei, Taiwan.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Medicine (Baltimore)
JT  - Medicine
JID - 2985248R
SB  - IM
MH  - Humans
MH  - Child
MH  - *Decision Support Systems, Clinical
MH  - Pediatricians
MH  - Delivery of Health Care
MH  - Software
MH  - *Pediatrics
PMC - PMC10289633
COIS- The authors have no funding and conflicts of interest to disclose.
EDAT- 2023/06/23 19:11
MHDA- 2023/06/26 06:42
CRDT- 2023/06/23 12:33
PHST- 2023/06/26 06:42 [medline]
PHST- 2023/06/23 19:11 [pubmed]
PHST- 2023/06/23 12:33 [entrez]
AID - 00005792-202306230-00035 [pii]
AID - 10.1097/MD.0000000000034068 [doi]
PST - ppublish
SO  - Medicine (Baltimore). 2023 Jun 23;102(25):e34068. doi: 
      10.1097/MD.0000000000034068.

PMID- 37323348
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230619
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - Genital and Extragenital Lichen Sclerosus et Atrophicus: A Case Series Written 
      Using ChatGPT.
PG  - e38987
LID - 10.7759/cureus.38987 [doi]
LID - e38987
AB  - Background Lichen sclerosus et atrophicus (LSEA) is a chronic inflammatory 
      dermatosis of genital and extragenital sites with a prevalence ranging from 9% in 
      prepubertal patients to 50% in postmenopausal patients. Chat generative 
      pre-trained transformer (ChatGPT) is an artificial intelligence tool designed to 
      assist humans based on supervised and reinforcement techniques. In this study, we 
      aimed to evaluate the characteristics of patients with LSEA using ChatGPT. 
      Methods In this retrospective study, we included all patients who presented to 
      the outpatient dermatology department during 2017-2022 at a tertiary care 
      teaching hospital in South India. Information regarding demographic data, 
      characteristics of LSEA, comorbidities, and associated autoimmune disorders was 
      gathered using a medical chart review. Following data analysis and drafting of 
      the manuscript, the utility of ChatGPT-3 and ChatGPT-4 in finalizing the draft 
      was assessed. Results Of 20 patients diagnosed with LSEA, 16 (80%) and four (20%) 
      patients were females and males, respectively. Of them, 50% of female patients 
      had attained menopause. While 65% of patients had genital LSEA, 30% of patients 
      had extragenital LSEA only, and 5% of patients had both genital and extragenital 
      LSEA. Furthermore, four (20%) patients were prepubertal children. Of four male 
      patients, two (50%) were younger than 18 years of age, and one patient was 
      diagnosed with balanitis xerotica obliterans. The commonest associated features 
      in LSEA included joint involvement (30%), hypertension (25%), and anemia (15%). 
      Rare concomitant disorders included psoriasis, asthma, and basal cell carcinoma 
      over the nose. Conclusions LSEA may be confused with other various dermatoses, 
      such as morphea, vitiligo, and lichen planus. A high index of suspicion is 
      required, especially in children, to diagnose it early and intervene to prevent 
      further complications. Its relationship with autoimmune disorders and 
      comorbidities warrants further large-scale studies. ChatGPT was unreliable in the 
      literature search due to the provision of non-existent citations. ChatGPT-4 was 
      better than ChatGPT-3 since it provided few true publications. ChatGPT was used 
      in this study to summarize the articles identified by the authors during the 
      literature search and to correct grammatical errors in the final draft of the 
      manuscript.
CI  - Copyright © 2023, P et al.
FAU - P, Pratibha J
AU  - P PJ
AD  - Department of Dermatology, St. John's Medical College, Bangalore, IND.
FAU - Prasad, Shruthi S
AU  - Prasad SS
AD  - Department of Dermatology, St. John's Medical College, Bangalore, IND.
FAU - Manohar, Naveen
AU  - Manohar N
AD  - Department of Dermatology, Belagavi Institute of Medical Sciences, Belagavi, IND.
LA  - eng
PT  - Journal Article
DEP - 20230513
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10261872
OTO - NOTNLM
OT  - artificial intelligence
OT  - autoimmune
OT  - chatgpt
OT  - depigmentation
OT  - extragenital
OT  - genital
OT  - lichen sclerosus et atrophicus
OT  - lsea
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/16 06:42
MHDA- 2023/06/16 06:43
CRDT- 2023/06/16 03:57
PHST- 2023/05/13 00:00 [accepted]
PHST- 2023/06/16 06:43 [medline]
PHST- 2023/06/16 06:42 [pubmed]
PHST- 2023/06/16 03:57 [entrez]
AID - 10.7759/cureus.38987 [doi]
PST - epublish
SO  - Cureus. 2023 May 13;15(5):e38987. doi: 10.7759/cureus.38987. eCollection 2023 
      May.

PMID- 37812998
OWN - NLM
STAT- MEDLINE
DCOM- 20231107
LR  - 20231107
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 89
DP  - 2023 Nov
TI  - Appraising the performance of ChatGPT in psychiatry using 100 clinical case 
      vignettes.
PG  - 103770
LID - S1876-2018(23)00326-X [pii]
LID - 10.1016/j.ajp.2023.103770 [doi]
AB  - BACKGROUND: ChatGPT has emerged as the most advanced and rapidly developing large 
      language chatbot system. With its immense potential ranging from answering a 
      simple query to cracking highly competitive medical exams, ChatGPT continues to 
      impress the scientists and researchers worldwide giving room for more discussions 
      regarding its utility in various fields. One such field of attention is 
      Psychiatry. With suboptimal diagnosis and treatment, assuring mental health and 
      well-being is a challenge in many countries, particularly developing nations. To 
      this regard, we conducted an evaluation to assess the performance of ChatGPT 3.5 
      in Psychiatry using clinical cases to provide evidence-based information 
      regarding the implication of ChatGPT 3.5 in enhancing mental health and 
      well-being. METHODS: ChatGPT 3.5 was used in this experimental study to initiate 
      the conversations and collect responses to clinical vignettes in Psychiatry. 
      Using 100 clinical case vignettes, the replies were assessed by expert faculties 
      from the Department of Psychiatry. There were 100 different psychiatric illnesses 
      represented in the cases. We recorded and assessed the initial ChatGPT 3.5 
      responses. The evaluation was conducted using the objective of questions that 
      were put forth at the conclusion of the case, and the aim of the questions was 
      divided into 10 categories. The grading was completed by taking the mean value of 
      the scores provided by the evaluators. Graphs and tables were used to represent 
      the grades. RESULTS: The evaluation report suggests that ChatGPT 3.5 fared 
      extremely well in Psychiatry by receiving "Grade A" ratings in 61 out of 100 
      cases, "Grade B" ratings in 31, and "Grade C" ratings in 8. Majority of the 
      queries were concerned with the management strategies, which were followed by 
      diagnosis, differential diagnosis, assessment, investigation, counselling, 
      clinical reasoning, ethical reasoning, prognosis, and request acceptance. ChatGPT 
      3.5 performed extremely well, especially in generating management strategies 
      followed by diagnoses for different psychiatric conditions. There were no 
      responses which were graded "D" indicating that there were no errors in the 
      diagnosis or response for clinical care. Only a few discrepancies and additional 
      details were missed in a few responses that received a "Grade C" CONCLUSION: It 
      is evident from our study that ChatGPT 3.5 has appreciable knowledge and 
      interpretation skills in Psychiatry. Thus, ChatGPT 3.5 undoubtedly has the 
      potential to transform the field of Medicine and we emphasize its utility in 
      Psychiatry through the finding of our study. However, for any AI model to be 
      successful, assuring the reliability, validation of information, proper 
      guidelines and implementation framework are necessary.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Franco D'Souza, Russell
AU  - Franco D'Souza R
AD  - Professor of Organizational Psychological Medicine, International Institute of 
      Organisational Psychological Medicine, 71 Cleeland Street, Dandenong Victoria, 
      Melbourne, 3175 Australia.
FAU - Amanullah, Shabbir
AU  - Amanullah S
AD  - Division of Geriatric Psychiatry, Queen's University, 752 King Street West, 
      Postal Bag 603 Kingston, ON K7L7X3.
FAU - Mathew, Mary
AU  - Mathew M
AD  - Department of Pathology, Kasturba Medical College, Manipal Academy of Higher 
      Education, Tiger Circle Road, Madhav Nagar, Manipal, Karnataka 576104.
FAU - Surapaneni, Krishna Mohan
AU  - Surapaneni KM
AD  - Department of Biochemistry, Panimalar Medical College Hospital & Research 
      Institute, Varadharajapuram, Poonamallee, Chennai - 600 123, Tamil Nadu, India; 
      Departments of Medical Education, Molecular Virology, Research, Clinical Skills & 
      Simulation, Panimalar Medical College Hospital & Research Institute, 
      Varadharajapuram, Poonamallee, Chennai - 600 123, Tamil Nadu, India. Electronic 
      address: krishnamohan.surapaneni@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - Reproducibility of Results
MH  - *Psychiatry
MH  - Diagnosis, Differential
MH  - *Mental Disorders/diagnosis/drug therapy
MH  - Communication
OTO - NOTNLM
OT  - “Artificial Intelligence”, “ChatGPT”
OT  - “Mental health”
OT  - “Mental well-being”
OT  - “Psychiatry”
COIS- Declaration of Competing Interest All the authors declare that, there is No 
      Conflict of Interest associated with this study.
EDAT- 2023/10/10 00:42
MHDA- 2023/11/07 06:46
CRDT- 2023/10/09 18:38
PHST- 2023/05/20 00:00 [received]
PHST- 2023/09/13 00:00 [revised]
PHST- 2023/09/18 00:00 [accepted]
PHST- 2023/11/07 06:46 [medline]
PHST- 2023/10/10 00:42 [pubmed]
PHST- 2023/10/09 18:38 [entrez]
AID - S1876-2018(23)00326-X [pii]
AID - 10.1016/j.ajp.2023.103770 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Nov;89:103770. doi: 10.1016/j.ajp.2023.103770. Epub 2023 
      Sep 20.

PMID- 37484787
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230725
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - ChatGPT's Ability to Assess Quality and Readability of Online Medical 
      Information: Evidence From a Cross-Sectional Study.
PG  - e42214
LID - 10.7759/cureus.42214 [doi]
LID - e42214
AB  - Introduction Artificial Intelligence (AI) platforms have gained widespread 
      attention for their distinct ability to generate automated responses to various 
      prompts. However, its role in assessing the quality and readability of a provided 
      text remains unclear. Thus, the purpose of this study is to evaluate the 
      proficiency of the conversational generative pre-trained transformer (ChatGPT) in 
      utilizing the DISCERN tool to evaluate the quality of online content regarding 
      shock wave therapy for erectile dysfunction. Methods Websites were generated 
      using a Google search of "shock wave therapy for erectile dysfunction" with 
      location filters disabled. Readability was analyzed using Readable software 
      (Readable.com, Horsham, United Kingdom). Quality was assessed independently by 
      three reviewers using the DISCERN tool. The same plain text files collected were 
      inputted into ChatGPT to determine whether they produced comparable metrics for 
      readability and quality. Results The study results revealed a notable disparity 
      between ChatGPT's readability assessment and that obtained from a reliable tool, 
      Readable.com (p<0.05). This indicates a lack of alignment between ChatGPT's 
      algorithm and that of established tools, such as Readable.com. Similarly, the 
      DISCERN score generated by ChatGPT differed significantly from the scores 
      generated manually by human evaluators (p<0.05), suggesting that ChatGPT may not 
      be capable of accurately identifying poor-quality information sources regarding 
      shock wave therapy as a treatment for erectile dysfunction. Conclusion ChatGPT's 
      evaluation of the quality and readability of online text regarding shockwave 
      therapy for erectile dysfunction differs from that of human raters and trusted 
      tools. Therefore, ChatGPT's current capabilities were not sufficient for reliably 
      assessing the quality and readability of textual content. Further research is 
      needed to elucidate the role of AI in the objective evaluation of online medical 
      content in other fields. Continued development in AI and incorporation of tools 
      such as DISCERN into AI software may enhance the way patients navigate the web in 
      search of high-quality medical content in the future.
CI  - Copyright © 2023, Golan et al.
FAU - Golan, Roei
AU  - Golan R
AD  - Department of Clinical Sciences, Florida State University College of Medicine, 
      Tallahassee, USA.
FAU - Ripps, Sarah J
AU  - Ripps SJ
AD  - Department of Clinical Sciences, Florida State University College of Medicine, 
      Tallahassee, USA.
FAU - Reddy, Raghuram
AU  - Reddy R
AD  - Herbert Wertheim College of Medicine, Florida International University, Miami, 
      USA.
FAU - Loloi, Justin
AU  - Loloi J
AD  - Department of Urology, Montefiore Medical Center, Bronx, USA.
FAU - Bernstein, Ari P
AU  - Bernstein AP
AD  - Department of Urology, New York University Langone Health, New York, USA.
FAU - Connelly, Zachary M
AU  - Connelly ZM
AD  - Department of Surgery, Louisiana State University Health Shreveport, Shreveport, 
      USA.
FAU - Golan, Noa S
AU  - Golan NS
AD  - Department of Psychology, University of Florida, Gainesville, USA.
FAU - Ramasamy, Ranjith
AU  - Ramasamy R
AD  - Department of Urology, Desai Sethi Urology Institute, Miami, USA.
LA  - eng
PT  - Journal Article
DEP - 20230720
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10362474
OTO - NOTNLM
OT  - artificial intelligence in medicine
OT  - chatgpt
OT  - healthcare ai and robotics
OT  - online medical information
OT  - readability
OT  - shock wave therapy
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/24 06:42
MHDA- 2023/07/24 06:43
CRDT- 2023/07/24 04:48
PHST- 2023/05/16 00:00 [received]
PHST- 2023/07/20 00:00 [accepted]
PHST- 2023/07/24 06:43 [medline]
PHST- 2023/07/24 06:42 [pubmed]
PHST- 2023/07/24 04:48 [entrez]
AID - 10.7759/cureus.42214 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 20;15(7):e42214. doi: 10.7759/cureus.42214. eCollection 2023 
      Jul.

PMID- 37968109
OWN - NLM
STAT- Publisher
LR  - 20231115
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
DP  - 2023 Nov 15
TI  - Artificial intelligence and allergic rhinitis: does ChatGPT increase or impair 
      the knowledge?
LID - fdad219 [pii]
LID - 10.1093/pubmed/fdad219 [doi]
AB  - BACKGROUND: Optimal management of allergic rhinitis requires patient education 
      with easy access to accurate information. However, previous online platforms have 
      provided misleading information. The demand for online medical information 
      continues to grow, especially with the introduction of advanced chatbots like 
      ChatGPT. METHODS: This study aimed to evaluate the quality of information 
      provided by ChatGPT regarding allergic rhinitis. A Likert scale was used to 
      assess the accuracy of responses, ranging from 1 to 5. Four authors independently 
      rated the responses from a healthcare professional's perspective. RESULTS: A 
      total of 20 questions covering various aspects of allergic rhinitis were asked. 
      Among the answers, eight received a score of 5 (no inaccuracies), five received a 
      score of 4 (minor non-harmful inaccuracies), six received a score of 3 
      (potentially misinterpretable inaccuracies) and one answer had a score of 2 
      (minor potentially harmful inaccuracies). CONCLUSIONS: The variability in 
      accuracy scores highlights the need for caution when relying solely on chatbots 
      like ChatGPT for medical advice. Patients should consult qualified healthcare 
      professionals and use online sources as a supplement. While ChatGPT has 
      advantages in medical information delivery, its use should be approached with 
      caution. ChatGPT can be useful for patient education but cannot replace 
      healthcare professionals.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Faculty 
      of Public Health. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Høj, Simon
AU  - Høj S
AUID- ORCID: 0000-0003-1576-3679
AD  - Steno Diabetes Center Copenhagen, Copenhagen University Hospital, Herlev 2730, 
      Denmark.
AD  - Department of Dermatology, Venereology, and Wound Healing Centre, Copenhagen 
      University Hospital-Bispebjerg, Copenhagen 2400, Denmark.
AD  - Department of Public Health, Environment, Occupation, and Health, Aarhus 
      University, Aarhus 8000, Denmark.
FAU - Thomsen, Simon F
AU  - Thomsen SF
AD  - Department of Dermatology, Venereology, and Wound Healing Centre, Copenhagen 
      University Hospital-Bispebjerg, Copenhagen 2400, Denmark.
AD  - Department of Biomedical Sciences, University of Copenhagen, Copenhagen 2200, 
      Denmark.
FAU - Meteran, Hanieh
AU  - Meteran H
AD  - Department of Internal Medicine, Section of Endocrinology, Copenhagen University 
      Hospital-Hvidovre, Hvidovre 2650, Denmark.
FAU - Sigsgaard, Torben
AU  - Sigsgaard T
AD  - Department of Public Health, Environment, Occupation, and Health, Aarhus 
      University, Aarhus 8000, Denmark.
FAU - Meteran, Howraman
AU  - Meteran H
AUID- ORCID: 0000-0001-5994-8064
AD  - Department of Public Health, Environment, Occupation, and Health, Aarhus 
      University, Aarhus 8000, Denmark.
AD  - Department of Internal Medicine, Respiratory Medicine Section, Copenhagen 
      University Hospital-Hvidovre, Hvidovre 2650, Denmark.
AD  - Department of Respiratory Medicine, Zealand University Hospital Roskilde-Næstved, 
      Næstved 4700, Denmark.
LA  - eng
PT  - Journal Article
DEP - 20231115
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - allergy
OT  - digital health
OT  - rhinitis
EDAT- 2023/11/16 00:43
MHDA- 2023/11/16 00:43
CRDT- 2023/11/15 21:23
PHST- 2023/05/22 00:00 [received]
PHST- 2023/09/14 00:00 [revised]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/11/16 00:43 [medline]
PHST- 2023/11/16 00:43 [pubmed]
PHST- 2023/11/15 21:23 [entrez]
AID - 7423000 [pii]
AID - 10.1093/pubmed/fdad219 [doi]
PST - aheadofprint
SO  - J Public Health (Oxf). 2023 Nov 15:fdad219. doi: 10.1093/pubmed/fdad219.

PMID- 38066714
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 1552-4833 (Electronic)
IS  - 1552-4825 (Linking)
DP  - 2023 Dec 8
TI  - Genetic counselors' utilization of ChatGPT in professional practice: A 
      cross-sectional study.
LID - 10.1002/ajmg.a.63493 [doi]
AB  - PURPOSE: The precision medicine era has seen increased utilization of artificial 
      intelligence (AI) in the field of genetics. We sought to explore the ways that 
      genetic counselors (GCs) currently use the publicly accessible AI tool Chat 
      Generative Pre-trained Transformer (ChatGPT) in their work. METHODS: GCs in North 
      America were surveyed about how ChatGPT is used in different aspects of their 
      work. Descriptive statistics were reported through frequencies and means. 
      RESULTS: Of 118 GCs who completed the survey, 33.8% (40) reported using ChatGPT 
      in their work; 47.5% (19) use it in clinical practice, 35% (14) use it in 
      education, and 32.5% (13) use it in research. Most GCs (62.7%; 74) felt that it 
      saves time on administrative tasks but the majority (82.2%; 97) felt that a 
      paramount challenge was the risk of obtaining incorrect information. The majority 
      of GCs not using ChatGPT (58.9%; 46) felt it was not necessary for their work. 
      CONCLUSION: A considerable number of GCs in the field are using ChatGPT in 
      different ways, but it is primarily helpful with tasks that involve writing. It 
      has potential to streamline workflow issues encountered in clinical genetics, but 
      practitioners need to be informed and uniformly trained about its limitations.
CI  - © 2023 Wiley Periodicals LLC.
FAU - Ahimaz, Priyanka
AU  - Ahimaz P
AUID- ORCID: 0000-0001-5112-1599
AD  - Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, 
      Columbia University, New York, New York, USA.
AD  - Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia 
      University, New York, New York, USA.
FAU - Bergner, Amanda L
AU  - Bergner AL
AD  - Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, 
      Columbia University, New York, New York, USA.
AD  - Department of Genetics and Development, Vagelos College of Physicians and 
      Surgeons, Columbia University, New York, New York, USA.
AD  - Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia 
      University, New York, New York, USA.
FAU - Florido, Michelle E
AU  - Florido ME
AD  - Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, 
      Columbia University, New York, New York, USA.
AD  - Department of Genetics and Development, Vagelos College of Physicians and 
      Surgeons, Columbia University, New York, New York, USA.
FAU - Harkavy, Nina
AU  - Harkavy N
AD  - Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, 
      Columbia University, New York, New York, USA.
AD  - Department of Obstetrics and Gynecology, Vagelos College of Physicians and 
      Surgeons, Columbia University, New York, New York, USA.
FAU - Bhattacharyya, Sriya
AU  - Bhattacharyya S
AD  - Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, 
      Columbia University, New York, New York, USA.
AD  - Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia 
      University, New York, New York, USA.
LA  - eng
PT  - Journal Article
DEP - 20231208
PL  - United States
TA  - Am J Med Genet A
JT  - American journal of medical genetics. Part A
JID - 101235741
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - clinical genetics
OT  - genetic counselors
EDAT- 2023/12/09 10:46
MHDA- 2023/12/09 10:46
CRDT- 2023/12/09 00:23
PHST- 2023/11/21 00:00 [revised]
PHST- 2023/11/15 00:00 [received]
PHST- 2023/11/22 00:00 [accepted]
PHST- 2023/12/09 10:46 [medline]
PHST- 2023/12/09 10:46 [pubmed]
PHST- 2023/12/09 00:23 [entrez]
AID - 10.1002/ajmg.a.63493 [doi]
PST - aheadofprint
SO  - Am J Med Genet A. 2023 Dec 8. doi: 10.1002/ajmg.a.63493.

PMID- 37604703
OWN - NLM
STAT- Publisher
LR  - 20230821
IS  - 1471-0528 (Electronic)
IS  - 1470-0328 (Linking)
DP  - 2023 Aug 21
TI  - Performance of ChatGPT in medical examinations: A systematic review and a 
      meta-analysis.
LID - 10.1111/1471-0528.17641 [doi]
FAU - Levin, Gabriel
AU  - Levin G
AUID- ORCID: 0000-0003-1282-5379
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Quebec, Quebec City, Canada.
AD  - Factuly of Medicine, Department of Gynecologic Oncology, Hadassah Medical Center, 
      Hebrew University Jerusalem, Jerusalem, Israel.
FAU - Horesh, Nir
AU  - Horesh N
AD  - Ellen Leifer Shulman and Steven Shulman Digestive Disease Center, Cleveland 
      Clinic Florida, Florida, Weston, USA.
FAU - Brezinov, Yoav
AU  - Brezinov Y
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Quebec, Quebec City, Canada.
FAU - Meyer, Raanan
AU  - Meyer R
AD  - Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics and 
      Gynecology, Cedars Sinai Medical Center, California, Los Angeles, USA.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - England
TA  - BJOG
JT  - BJOG : an international journal of obstetrics and gynaecology
JID - 100935741
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligenc
OT  - education
OT  - examination
OT  - meta analysis
EDAT- 2023/08/22 00:42
MHDA- 2023/08/22 00:42
CRDT- 2023/08/21 21:53
PHST- 2023/08/04 00:00 [revised]
PHST- 2023/06/14 00:00 [received]
PHST- 2023/08/05 00:00 [accepted]
PHST- 2023/08/22 00:42 [medline]
PHST- 2023/08/22 00:42 [pubmed]
PHST- 2023/08/21 21:53 [entrez]
AID - 10.1111/1471-0528.17641 [doi]
PST - aheadofprint
SO  - BJOG. 2023 Aug 21. doi: 10.1111/1471-0528.17641.

PMID- 37151080
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230908
LR  - 20230921
IS  - 0036-9330 (Print)
IS  - 0036-9330 (Linking)
VI  - 68
IP  - 3
DP  - 2023 Aug
TI  - ChatGPT in academic publishing: An ally or an adversary?
PG  - 129-130
LID - 10.1177/00369330231174231 [doi]
FAU - Ariyaratne, Sisith
AU  - Ariyaratne S
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      UK.
FAU - Botchu, Rajesh
AU  - Botchu R
AUID- ORCID: 0000-0001-7998-2980
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      UK.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AUID- ORCID: 0000-0002-4379-1266
AD  - Department of Orthopaedics, Southport and Ormskirk NHS Trust, Southport, UK.
LA  - eng
PT  - Letter
DEP - 20230507
PL  - Scotland
TA  - Scott Med J
JT  - Scottish medical journal
JID - 2983335R
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbots
OT  - algorithms
OT  - artificial intelligence and writing
OT  - machine learning
OT  - scientific writing
EDAT- 2023/05/08 06:42
MHDA- 2023/05/08 06:43
CRDT- 2023/05/08 03:41
PHST- 2023/05/08 06:43 [medline]
PHST- 2023/05/08 06:42 [pubmed]
PHST- 2023/05/08 03:41 [entrez]
AID - 10.1177/00369330231174231 [doi]
PST - ppublish
SO  - Scott Med J. 2023 Aug;68(3):129-130. doi: 10.1177/00369330231174231. Epub 2023 
      May 7.

PMID- 37757713
OWN - NLM
STAT- MEDLINE
DCOM- 20231110
LR  - 20231110
IS  - 1532-2793 (Electronic)
IS  - 0260-6917 (Linking)
VI  - 131
DP  - 2023 Dec
TI  - A holistic approach to remote patient monitoring, fueled by ChatGPT and Metaverse 
      technology: The future of nursing education.
PG  - 105972
LID - S0260-6917(23)00266-6 [pii]
LID - 10.1016/j.nedt.2023.105972 [doi]
AB  - ChatGPT and Metaverse are contemporary artificial intelligence tools that are 
      increasingly being used in healthcare professional training, particularly for 
      remote patient monitoring. These technologies offer immersive and personalized 
      learning experiences for nurses, improving their skills and confidence in 
      managing remote patient care. ChatGPT can create simulated patient interactions 
      that mimic real-life scenarios, while the Metaverse can provide virtual reality 
      simulations and scenarios for nurses to practice and learn in a safe and 
      controlled environment. The unification of ChatGPT and Metaverse technology in 
      nursing education can enrich the learning experience and equip nurses with the 
      necessary skills for remote patient monitoring, ultimately leading to improved 
      patient outcomes and quality of care.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Sharma, Manik
AU  - Sharma M
AD  - Department of CSA, DAV University Jalandhar, India. Electronic address: 
      manik10143@davuniversity.org.
FAU - Sharma, Samriti
AU  - Sharma S
AD  - Department of CS, GNDU Amritsar, India.
LA  - eng
PT  - Journal Article
DEP - 20230912
PL  - Scotland
TA  - Nurse Educ Today
JT  - Nurse education today
JID - 8511379
MH  - Humans
MH  - Artificial Intelligence
MH  - *Education, Nursing
MH  - Learning
MH  - *Virtual Reality
MH  - Delivery of Health Care
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Immersive learning
OT  - Metaverse
OT  - Nurse training
OT  - Remote patient monitoring
COIS- Declaration of competing interest No potential conflict of interest relevant to 
      this article was reported.
EDAT- 2023/09/28 00:42
MHDA- 2023/11/10 06:45
CRDT- 2023/09/27 18:09
PHST- 2023/03/25 00:00 [received]
PHST- 2023/08/18 00:00 [revised]
PHST- 2023/09/11 00:00 [accepted]
PHST- 2023/11/10 06:45 [medline]
PHST- 2023/09/28 00:42 [pubmed]
PHST- 2023/09/27 18:09 [entrez]
AID - S0260-6917(23)00266-6 [pii]
AID - 10.1016/j.nedt.2023.105972 [doi]
PST - ppublish
SO  - Nurse Educ Today. 2023 Dec;131:105972. doi: 10.1016/j.nedt.2023.105972. Epub 2023 
      Sep 12.

PMID- 37043716
OWN - NLM
STAT- MEDLINE
DCOM- 20230427
LR  - 20230929
IS  - 1538-9855 (Electronic)
IS  - 0363-3624 (Linking)
VI  - 48
IP  - 3
DP  - 2023 May-Jun 01
TI  - The ChatGPT Storm and What Faculty Can Do.
PG  - 119-124
LID - 10.1097/NNE.0000000000001390 [doi]
AB  - BACKGROUND: ChatGPT, an artificial intelligence-driven, pretrained, deep learning 
      language model, can generate natural language text in response to a given query. 
      Its rapid growth has led to concerns about ethical use in academia. PROBLEM: The 
      exponential rise in the popularity of ChatGPT, and concerns of academic integrity 
      with its use, has raised concerns among faculty for how to best address this 
      issue. APPROACH: Faculty should understand the potential benefits and limitations 
      of ChatGPT and create assignments that emphasize self-reflection, critical 
      thinking, problem solving, and independent learning. Students must be taught how 
      to critically evaluate information and how to make informed decisions. 
      CONCLUSIONS: ChatGPT has the potential to revolutionize nursing education. 
      However, it is critical for faculty to be familiar with its capabilities and 
      limitations in order to foster effective, yet ethical and responsible 
      utilization, while preparing students for the dynamic, rapidly advancing 
      technological landscape in nursing and health care.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Sun, Grace H
AU  - Sun GH
AD  - Associate Professor (Dr Sun), Department of Advanced Practice Registered Nurse 
      Studies, and Associate Professor (Dr Hoelscher), Department of Leadership 
      Studies-Program Director MSN Informatics, Texas Tech University Health Sciences 
      Center School of Nursing, Lubbock.
FAU - Hoelscher, Stephanie H
AU  - Hoelscher SH
LA  - eng
PT  - Journal Article
DEP - 20230227
PL  - United States
TA  - Nurse Educ
JT  - Nurse educator
JID - 7701902
MH  - Humans
MH  - Artificial Intelligence
MH  - Nursing Education Research
MH  - *Education, Nursing
MH  - Faculty, Nursing
MH  - *Students, Nursing
COIS- The authors declare no conflicts of interest.
EDAT- 2023/04/13 06:00
MHDA- 2023/04/27 06:42
CRDT- 2023/04/12 16:12
PHST- 2023/04/27 06:42 [medline]
PHST- 2023/04/13 06:00 [pubmed]
PHST- 2023/04/12 16:12 [entrez]
AID - 00006223-202305000-00001 [pii]
AID - 10.1097/NNE.0000000000001390 [doi]
PST - ppublish
SO  - Nurse Educ. 2023 May-Jun 01;48(3):119-124. doi: 10.1097/NNE.0000000000001390. 
      Epub 2023 Feb 27.

PMID- 36915398
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230315
IS  - 2752-4191 (Electronic)
IS  - 2752-4191 (Linking)
VI  - 3
IP  - 2
DP  - 2023 Mar
TI  - ChatGPT: the next frontier in academic writing for cardiologists or a pandora's 
      box of ethical dilemmas.
PG  - oead007
LID - 10.1093/ehjopen/oead007 [doi]
LID - oead007
FAU - Marchandot, Benjamin
AU  - Marchandot B
AUID- ORCID: 0000-0001-7034-1466
AD  - Division of Cardiovascular Medicine, Nouvel Hôpital Civil, Strasbourg University 
      Hospital, 1 place de l'Hôpital, 67000 Strasbourg, France.
FAU - Matsushita, Kensuke
AU  - Matsushita K
AUID- ORCID: 0000-0002-3722-2921
AD  - Division of Cardiovascular Medicine, Nouvel Hôpital Civil, Strasbourg University 
      Hospital, 1 place de l'Hôpital, 67000 Strasbourg, France.
AD  - UMR 1260, INSERM (French National Institute of Health and Medical Research), 
      Regenerative Nanomedicine, FMTS, 1 rue Eugene Boeckel, Strasbourg 67000, France.
FAU - Carmona, Adrien
AU  - Carmona A
AD  - Division of Cardiovascular Medicine, Nouvel Hôpital Civil, Strasbourg University 
      Hospital, 1 place de l'Hôpital, 67000 Strasbourg, France.
FAU - Trimaille, Antonin
AU  - Trimaille A
AD  - Division of Cardiovascular Medicine, Nouvel Hôpital Civil, Strasbourg University 
      Hospital, 1 place de l'Hôpital, 67000 Strasbourg, France.
AD  - UMR 1260, INSERM (French National Institute of Health and Medical Research), 
      Regenerative Nanomedicine, FMTS, 1 rue Eugene Boeckel, Strasbourg 67000, France.
FAU - Morel, Olivier
AU  - Morel O
AD  - Division of Cardiovascular Medicine, Nouvel Hôpital Civil, Strasbourg University 
      Hospital, 1 place de l'Hôpital, 67000 Strasbourg, France.
AD  - UMR 1260, INSERM (French National Institute of Health and Medical Research), 
      Regenerative Nanomedicine, FMTS, 1 rue Eugene Boeckel, Strasbourg 67000, France.
LA  - eng
PT  - Journal Article
DEP - 20230213
PL  - England
TA  - Eur Heart J Open
JT  - European heart journal open
JID - 9918282081406676
PMC - PMC10006694
OTO - NOTNLM
OT  - Academic Writing
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Ethics
COIS- Conflict of interest: The authors declare that there is no conflict of interest 
      regarding the publication of this article. Prof Olivier Morel (O.M) received 
      institutional research grants from Fondation Coeur et Vaisseaux, AstraZeneca and 
      Boehringher Ingelheim. The manuscript has been read and approved for submission 
      by all authors. The authors take responsibility for all aspects of the 
      reliability and freedom from bias of the data presented and their discussed 
      interpretation.
EDAT- 2023/03/15 06:00
MHDA- 2023/03/15 06:01
CRDT- 2023/03/14 02:06
PHST- 2023/01/27 00:00 [received]
PHST- 2023/02/02 00:00 [revised]
PHST- 2023/03/14 02:06 [entrez]
PHST- 2023/03/15 06:00 [pubmed]
PHST- 2023/03/15 06:01 [medline]
AID - oead007 [pii]
AID - 10.1093/ehjopen/oead007 [doi]
PST - epublish
SO  - Eur Heart J Open. 2023 Feb 13;3(2):oead007. doi: 10.1093/ehjopen/oead007. 
      eCollection 2023 Mar.

PMID- 36916887
OWN - NLM
STAT- Publisher
LR  - 20230928
IS  - 1935-9780 (Electronic)
IS  - 1935-9772 (Linking)
DP  - 2023 Mar 14
TI  - The rise of ChatGPT: Exploring its potential in medical education.
LID - 10.1002/ase.2270 [doi]
AB  - The integration of artificial intelligence (AI) into medical education has the 
      potential to revolutionize the way students learn about biomedical sciences. 
      Large language models, such as ChatGPT, can serve as virtual teaching assistants, 
      providing students with detailed and relevant information and perhaps eventually 
      interactive simulations. ChatGPT has the potential to increase student engagement 
      and enhance student learning, though research is needed to confirm this. The 
      challenges and limitations of ChatGPT must also be considered, including ethical 
      issues and potentially harmful effects. It is crucial for medical educators to 
      keep pace with technology's rapidly changing landscape and consider the 
      implications for curriculum design, assessment strategies, and teaching methods. 
      Continued research and evaluation are necessary to ensure the optimal integration 
      of AI-based learning tools into medical education.
CI  - © 2023 American Association for Anatomy.
FAU - Lee, Hyunsu
AU  - Lee H
AUID- ORCID: 0000-0003-3631-3028
AD  - Department of Medical Informatics, School of Medicine, Keimyung University, #223, 
      1095, Dalgubeoldae-ro, Dalseo-gu, Daegu, Republic of Korea.
LA  - eng
PT  - Journal Article
DEP - 20230314
PL  - United States
TA  - Anat Sci Educ
JT  - Anatomical sciences education
JID - 101392205
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - medical education
OT  - natural language processing
OT  - virtual teaching assistant
EDAT- 2023/03/15 06:00
MHDA- 2023/03/15 06:00
CRDT- 2023/03/14 09:53
PHST- 2023/03/08 00:00 [revised]
PHST- 2023/02/07 00:00 [received]
PHST- 2023/03/09 00:00 [accepted]
PHST- 2023/03/15 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
PHST- 2023/03/14 09:53 [entrez]
AID - 10.1002/ase.2270 [doi]
PST - aheadofprint
SO  - Anat Sci Educ. 2023 Mar 14. doi: 10.1002/ase.2270.

PMID- 37843604
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1248-9204 (Electronic)
IS  - 1248-9204 (Linking)
VI  - 27
IP  - 6
DP  - 2023 Dec
TI  - AI's deep dive into complex pediatric inguinal hernia issues: a challenge to 
      traditional guidelines?
PG  - 1587-1599
LID - 10.1007/s10029-023-02900-1 [doi]
AB  - OBJECTIVE: This study utilized ChatGPT, an artificial intelligence program based 
      on large language models, to explore controversial issues in pediatric inguinal 
      hernia surgery and compare its responses with the guidelines of the European 
      Association of Pediatric Surgeons (EUPSA). METHODS: Six contentious issues raised 
      by EUPSA were submitted to ChatGPT 4.0 for analysis, for which two independent 
      responses were generated for each issue. These generated answers were 
      subsequently compared with systematic reviews and guidelines. To ensure content 
      accuracy and reliability, a content analysis was conducted, and expert 
      evaluations were solicited for validation. Content analysis evaluated the 
      consistency or discrepancy between ChatGPT 4.0's responses and the guidelines. An 
      expert scoring method assess the quality, reliability, and applicability of 
      responses. The TF-IDF model tested the stability and consistency of the two 
      responses. RESULTS: The responses generated by ChatGPT 4.0 were mostly consistent 
      with the guidelines. However, some differences and contradictions were noted. The 
      average quality score was 3.33, reliability score was 2.75, and applicability 
      score was 3.46 (out of 5). The average similarity between the two responses was 
      0.72 (out of 1), Content analysis and expert ratings yielded consistent 
      conclusions, enhancing the credibility of our research. CONCLUSION: ChatGPT can 
      provide valuable responses to clinical questions, but it has limitations and 
      requires further improvement. It is recommended to combine ChatGPT with other 
      reliable data sources to improve clinical practice and decision-making.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag France SAS, 
      part of Springer Nature.
FAU - Wang, G
AU  - Wang G
AUID- ORCID: 0000-0002-8224-7706
AD  - Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical 
      University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
AD  - Department of Pediatrics, Children's Hospital, Chongqing Medical University, 
      Chongqing, People's Republic of China.
AD  - Department of Pediatric General Surgery, Chongqing Maternal and Child Health 
      Hospital, Chongqing Medical University, Chongqing, People's Republic of China.
FAU - Liu, Q
AU  - Liu Q
AD  - Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical 
      University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
AD  - Department of Fetus and Pediatrics, Chongqing Health Center for Women and 
      Children, Chongqing, People's Republic of China.
FAU - Chen, G
AU  - Chen G
AD  - Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical 
      University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
AD  - Department of Fetus and Pediatrics, Chongqing Health Center for Women and 
      Children, Chongqing, People's Republic of China.
FAU - Xia, B
AU  - Xia B
AD  - Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical 
      University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
AD  - Department of Fetus and Pediatrics, Chongqing Health Center for Women and 
      Children, Chongqing, People's Republic of China.
FAU - Zeng, D
AU  - Zeng D
AD  - Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical 
      University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
AD  - Department of Fetus and Pediatrics, Chongqing Health Center for Women and 
      Children, Chongqing, People's Republic of China.
FAU - Chen, G
AU  - Chen G
AD  - Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical 
      University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China. 
      38096550@qq.com.
AD  - Department of Fetus and Pediatrics, Chongqing Health Center for Women and 
      Children, Chongqing, People's Republic of China. 38096550@qq.com.
AD  - Department of Pediatric General Surgery, Chongqing Maternal and Child Health 
      Hospital, Chongqing Medical University, Chongqing, People's Republic of China. 
      38096550@qq.com.
AD  - Department of Obstetrics and Gynecology, Chongqing Health Center for Women and 
      Children, Women and Children's Hospital of Chongqing Medical University, 120 
      Longshan Rd., Chongqing, 401147, People's Republic of China. 38096550@qq.com.
FAU - Guo, C
AU  - Guo C
AD  - Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical 
      University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China. 
      guochunbao@foxmail.com.
AD  - Department of Fetus and Pediatrics, Chongqing Health Center for Women and 
      Children, Chongqing, People's Republic of China. guochunbao@foxmail.com.
AD  - Department of Pediatric General Surgery, Chongqing Maternal and Child Health 
      Hospital, Chongqing Medical University, Chongqing, People's Republic of China. 
      guochunbao@foxmail.com.
LA  - eng
GR  - No: 30973440/the National Natural Science Foundation of China/
GR  - 30770950/the National Natural Science Foundation of China/
GR  - No. YBRP-2021XX/the Youth Basic Research Project from the Ministry of Key 
      Laboratory of Child Development and Disorders/
GR  - cstc2020jcyj-msxmX0326/the Chongqing Natural Science Foundation/
GR  - CSTB2022NSCQ-MSX0819/the Chongqing Natural Science Foundation/
PT  - Journal Article
DEP - 20231016
PL  - France
TA  - Hernia
JT  - Hernia : the journal of hernias and abdominal wall surgery
JID - 9715168
SB  - IM
MH  - Humans
MH  - Child
MH  - *Artificial Intelligence
MH  - *Hernia, Inguinal/surgery
MH  - Reproducibility of Results
MH  - Herniorrhaphy
MH  - Research Design
OTO - NOTNLM
OT  - ChatGPT/ChatGPT 4.0
OT  - Controversial issues
OT  - Guidelines
OT  - Inguinal hernia
OT  - Systematic reviews
EDAT- 2023/10/16 12:42
MHDA- 2023/12/07 12:42
CRDT- 2023/10/16 11:05
PHST- 2023/07/04 00:00 [received]
PHST- 2023/09/19 00:00 [accepted]
PHST- 2023/12/07 12:42 [medline]
PHST- 2023/10/16 12:42 [pubmed]
PHST- 2023/10/16 11:05 [entrez]
AID - 10.1007/s10029-023-02900-1 [pii]
AID - 10.1007/s10029-023-02900-1 [doi]
PST - ppublish
SO  - Hernia. 2023 Dec;27(6):1587-1599. doi: 10.1007/s10029-023-02900-1. Epub 2023 Oct 
      16.

PMID- 37462242
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230925
LR  - 20230925
IS  - 1469-0756 (Electronic)
IS  - 0032-5473 (Linking)
VI  - 99
IP  - 1176
DP  - 2023 Sep 21
TI  - ChatGPT.
PG  - 1047-1048
LID - 10.1093/postmj/qgad056 [doi]
AB  - An algorithm is a process or set of rules to be followed, especially by a 
      computer.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Postgraduate Medical Journal. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Welsby, Philip
AU  - Welsby P
AD  - Faculty of Medicine, University of Hong Kong.
FAU - Cheung, Bernard M Y
AU  - Cheung BMY
AD  - Faculty of Medicine, University of Hong Kong.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Postgrad Med J
JT  - Postgraduate medical journal
JID - 0234135
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - generative pre-trained transformer
EDAT- 2023/07/18 13:10
MHDA- 2023/09/25 06:42
CRDT- 2023/07/18 07:32
PHST- 2023/06/15 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/07/18 13:10 [pubmed]
PHST- 2023/07/18 07:32 [entrez]
AID - 7224397 [pii]
AID - 10.1093/postmj/qgad056 [doi]
PST - ppublish
SO  - Postgrad Med J. 2023 Sep 21;99(1176):1047-1048. doi: 10.1093/postmj/qgad056.

PMID- 37207982
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231025
IS  - 1469-0691 (Electronic)
IS  - 1198-743X (Linking)
VI  - 29
IP  - 8
DP  - 2023 Aug
TI  - A study on the performance of ChatGPT in infectious diseases clinical 
      consultation.
PG  - 1088-1089
LID - S1198-743X(23)00241-0 [pii]
LID - 10.1016/j.cmi.2023.05.017 [doi]
FAU - Sarink, Maarten J
AU  - Sarink MJ
AD  - Department of Medical Microbiology and Infectious Diseases, Erasmus University 
      Medical Center, Rotterdam, The Netherlands.
FAU - Bakker, Ingrid L
AU  - Bakker IL
AD  - Department of Medical Microbiology and Infectious Diseases, Erasmus University 
      Medical Center, Rotterdam, The Netherlands.
FAU - Anas, Adam A
AU  - Anas AA
AD  - Department of Medical Microbiology and Infectious Diseases, Erasmus University 
      Medical Center, Rotterdam, The Netherlands.
FAU - Yusuf, Erlangga
AU  - Yusuf E
AD  - Department of Medical Microbiology and Infectious Diseases, Erasmus University 
      Medical Center, Rotterdam, The Netherlands. Electronic address: 
      angga.yusuf@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230518
PL  - England
TA  - Clin Microbiol Infect
JT  - Clinical microbiology and infection : the official publication of the European 
      Society of Clinical Microbiology and Infectious Diseases
JID - 9516420
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Clinical consultation
OT  - Clinical microbiology
OT  - Infectious disease
EDAT- 2023/05/20 09:42
MHDA- 2023/05/20 09:43
CRDT- 2023/05/19 19:28
PHST- 2023/03/26 00:00 [received]
PHST- 2023/05/10 00:00 [revised]
PHST- 2023/05/11 00:00 [accepted]
PHST- 2023/05/20 09:43 [medline]
PHST- 2023/05/20 09:42 [pubmed]
PHST- 2023/05/19 19:28 [entrez]
AID - S1198-743X(23)00241-0 [pii]
AID - 10.1016/j.cmi.2023.05.017 [doi]
PST - ppublish
SO  - Clin Microbiol Infect. 2023 Aug;29(8):1088-1089. doi: 10.1016/j.cmi.2023.05.017. 
      Epub 2023 May 18.

PMID- 37115531
OWN - NLM
STAT- MEDLINE
DCOM- 20230607
LR  - 20230612
IS  - 2168-6114 (Electronic)
IS  - 2168-6106 (Linking)
VI  - 183
IP  - 6
DP  - 2023 Jun 1
TI  - How Chatbots and Large Language Model Artificial Intelligence Systems Will 
      Reshape Modern Medicine: Fountain of Creativity or Pandora's Box?
PG  - 596-597
LID - 10.1001/jamainternmed.2023.1835 [doi]
FAU - Li, Ron
AU  - Li R
AD  - Division of Hospital Medicine, Stanford University School of Medicine, Stanford, 
      California.
AD  - Stanford Center for Biomedical Informatics Research, Stanford University School 
      of Medicine, Stanford, California.
FAU - Kumar, Andre
AU  - Kumar A
AD  - Division of Hospital Medicine, Stanford University School of Medicine, Stanford, 
      California.
FAU - Chen, Jonathan H
AU  - Chen JH
AD  - Division of Hospital Medicine, Stanford University School of Medicine, Stanford, 
      California.
AD  - Stanford Center for Biomedical Informatics Research, Stanford University School 
      of Medicine, Stanford, California.
AD  - Clinical Excellence Research Center, Stanford University School of Medicine, 
      Stanford, California.
LA  - eng
PT  - Comment
PT  - Journal Article
PL  - United States
TA  - JAMA Intern Med
JT  - JAMA internal medicine
JID - 101589534
SB  - IM
CON - JAMA Intern Med. 2023 Jun 1;183(6):589-596. PMID: 37115527
MH  - Humans
MH  - *Artificial Intelligence
EDAT- 2023/04/28 12:43
MHDA- 2023/06/07 06:42
CRDT- 2023/04/28 11:33
PHST- 2023/06/07 06:42 [medline]
PHST- 2023/04/28 12:43 [pubmed]
PHST- 2023/04/28 11:33 [entrez]
AID - 2804310 [pii]
AID - 10.1001/jamainternmed.2023.1835 [doi]
PST - ppublish
SO  - JAMA Intern Med. 2023 Jun 1;183(6):596-597. doi: 10.1001/jamainternmed.2023.1835.

PMID- 37088277
OWN - NLM
STAT- MEDLINE
DCOM- 20230724
LR  - 20230724
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Linking)
VI  - 229
IP  - 2
DP  - 2023 Aug
TI  - ChatGPT outscored human candidates in a virtual objective structured clinical 
      examination in obstetrics and gynecology.
PG  - 172.e1-172.e12
LID - S0002-9378(23)00251-X [pii]
LID - 10.1016/j.ajog.2023.04.020 [doi]
AB  - BACKGROUND: Natural language processing is a form of artificial intelligence that 
      allows human users to interface with a machine without using complex codes. The 
      ability of natural language processing systems, such as ChatGPT, to successfully 
      engage with healthcare systems requiring fluid reasoning, specialist data 
      interpretation, and empathetic communication in an unfamiliar and evolving 
      environment is poorly studied. This study investigated whether the ChatGPT 
      interface could engage with and complete a mock objective structured clinical 
      examination simulating assessment for membership of the Royal College of 
      Obstetricians and Gynaecologists. OBJECTIVE: This study aimed to determine 
      whether ChatGPT, without additional training, would achieve a score at least 
      equivalent to that achieved by human candidates who sat for virtual objective 
      structured clinical examinations in Singapore. STUDY DESIGN: This study was 
      conducted in 2 phases. In the first phase, a total of 7 structured discussion 
      questions were selected from 2 historical cohorts (cohorts A and B) of objective 
      structured clinical examination questions. ChatGPT was examined using these 
      questions and responses recorded in a script. Of note, 2 human candidates (acting 
      as anonymizers) were examined on the same questions using videoconferencing, and 
      their responses were transcribed verbatim into written scripts. The 3 sets of 
      response scripts were mixed, and each set was allocated to 1 of 3 human actors. 
      In the second phase, actors were used to presenting these scripts to examiners in 
      response to the same examination questions. These responses were blind scored by 
      14 qualified examiners. ChatGPT scores were unblinded and compared with 
      historical human candidate performance scores. RESULTS: The average score given 
      to ChatGPT by 14 examiners was 77.2%. The average historical human score (n=26 
      candidates) was 73.7 %. ChatGPT demonstrated sizable performance improvements 
      over the average human candidate in several subject domains. The median time 
      taken for ChatGPT to complete each station was 2.54 minutes, well before the 10 
      minutes allowed. CONCLUSION: ChatGPT generated factually accurate and 
      contextually relevant structured discussion answers to complex and evolving 
      clinical questions based on unfamiliar settings within a very short period. 
      ChatGPT outperformed human candidates in several knowledge areas. Not all 
      examiners were able to discern between human and ChatGPT responses. Our data 
      highlight the emergent ability of natural language processing models to 
      demonstrate fluid reasoning in unfamiliar environments and successfully compete 
      with human candidates that have undergone extensive specialist training.
CI  - Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
FAU - Li, Sarah W
AU  - Li SW
AD  - Department of Obstetrics and Gynaecology, National University Hospital, 
      Singapore, Singapore.
FAU - Kemp, Matthew W
AU  - Kemp MW
AD  - Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, 
      National University of Singapore, Singapore, Singapore; Centre for Perinatal and 
      Neonatal Medicine, Tohoku University Hospital, Sendai, Japan; Department of 
      Obstetrics and Gynaecology, University of Western Australia, Perth, Australia; 
      Women and Infants Research Foundation, King Edward Memorial Hospital, Subiaco, 
      Western Australia, Australia.
FAU - Logan, Susan J S
AU  - Logan SJS
AD  - Department of Obstetrics and Gynaecology, National University Hospital, 
      Singapore, Singapore.
FAU - Dimri, Pooja Sharma
AU  - Dimri PS
AD  - Department of Obstetrics and Gynaecology, National University Hospital, 
      Singapore, Singapore.
FAU - Singh, Navkaran
AU  - Singh N
AD  - Department of Obstetrics and Gynaecology, National University Hospital, 
      Singapore, Singapore.
FAU - Mattar, Citra N Z
AU  - Mattar CNZ
AD  - Department of Obstetrics and Gynaecology, National University Hospital, 
      Singapore, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin 
      School of Medicine, National University of Singapore, Singapore, Singapore.
FAU - Dashraath, Pradip
AU  - Dashraath P
AD  - Department of Obstetrics and Gynaecology, National University Hospital, 
      Singapore, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin 
      School of Medicine, National University of Singapore, Singapore, Singapore.
FAU - Ramlal, Harshaana
AU  - Ramlal H
AD  - Department of Obstetrics and Gynaecology, National University Hospital, 
      Singapore, Singapore.
FAU - Mahyuddin, Aniza P
AU  - Mahyuddin AP
AD  - Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, 
      National University of Singapore, Singapore, Singapore.
FAU - Kanayan, Suren
AU  - Kanayan S
AD  - Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, 
      National University of Singapore, Singapore, Singapore.
FAU - Carter, Sean W D
AU  - Carter SWD
AD  - Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, 
      National University of Singapore, Singapore, Singapore.
FAU - Thain, Serene P T
AU  - Thain SPT
AD  - Department of Maternal-Fetal Medicine, Division of Obstetrics and Gynaecology, KK 
      Women's and Children's Hospital, Singapore, Singapore.
FAU - Fee, Erin L
AU  - Fee EL
AD  - Department of Obstetrics and Gynaecology, University of Western Australia, Perth, 
      Australia.
FAU - Illanes, Sebastian E
AU  - Illanes SE
AD  - Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, 
      National University of Singapore, Singapore, Singapore; Department of Obstetrics 
      and Gynecology, and Reproductive Biology Laboratory, Universidad de Los Andes, 
      Santiago, Chile; Reproductive Biology Program, Center for Biomedical Research and 
      Innovation, Universidad de los Andes, Santiago, Chile; Center of Interventional 
      Medicine for Precision and Advanced Cellular Therapy (IMPACT), Santiago, Chile; 
      Faculty of Medicine, Department of Obstetrics and Gynecology, Universidad de los 
      Andes, Santiago, Chile.
FAU - Choolani, Mahesh A
AU  - Choolani MA
AD  - Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, 
      National University of Singapore, Singapore, Singapore. Electronic address: 
      obgmac@nus.edu.sg.
CN  - National University of Singapore Obstetrics and Gynecology Artificial 
      Intelligence (NUS OBGYN-AI) Collaborative Group
LA  - eng
PT  - Journal Article
DEP - 20230422
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
MH  - Humans
MH  - *Gynecology/education
MH  - *Obstetrics/education
MH  - Artificial Intelligence
MH  - Clinical Competence
MH  - Educational Measurement
OTO - NOTNLM
OT  - Chat Generative Pre-trained Transformer
OT  - artificial intelligence
OT  - objective structured clinical examination
OT  - obstetrics and gynecology
OT  - postgraduate specialty training
OT  - reasoning
FIR - Rauff, Mary
IR  - Rauff M
FIR - Biswas, Arijit
IR  - Biswas A
FIR - Low, Jeffrey J H
IR  - Low JJH
FIR - Ng, Joseph S
IR  - Ng JS
FIR - Gosavi, Arundhati T
IR  - Gosavi AT
FIR - Huang, Zhongwei
IR  - Huang Z
FIR - Rajendran, Preethi
IR  - Rajendran P
FIR - Ng, Kwok Weng Roy
IR  - Ng KWR
FIR - Wong, Jeslyn J L
IR  - Wong JJL
FIR - Chan, Grace M F
IR  - Chan GMF
FIR - Lee, Kelvin Zhi Xing
IR  - Lee KZX
FIR - Yap, Jeannie J Y
IR  - Yap JJY
FIR - Ahmad, Nurulhuda
IR  - Ahmad N
FIR - Shanmugam, Shwetha
IR  - Shanmugam S
FIR - Kathirvel, Rajeswari
IR  - Kathirvel R
FIR - Mathur, Manisha
IR  - Mathur M
FIR - Lim, Whui Whui
IR  - Lim WW
FIR - Lim, Min Yu
IR  - Lim MY
EDAT- 2023/04/24 00:41
MHDA- 2023/07/24 06:42
CRDT- 2023/04/23 19:30
PHST- 2023/03/21 00:00 [received]
PHST- 2023/04/17 00:00 [revised]
PHST- 2023/04/18 00:00 [accepted]
PHST- 2023/07/24 06:42 [medline]
PHST- 2023/04/24 00:41 [pubmed]
PHST- 2023/04/23 19:30 [entrez]
AID - S0002-9378(23)00251-X [pii]
AID - 10.1016/j.ajog.2023.04.020 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol. 2023 Aug;229(2):172.e1-172.e12. doi: 
      10.1016/j.ajog.2023.04.020. Epub 2023 Apr 22.

PMID- 38062632
OWN - NLM
STAT- Publisher
LR  - 20231208
IS  - 1619-3997 (Electronic)
IS  - 0300-5577 (Linking)
DP  - 2023 Dec 11
TI  - Re: to the Letter to the Editor: "ChatGPT and artificial intelligence in the 
      Journal of Perinatal Medicine".
LID - 10.1515/jpm-2023-0380 [doi]
FAU - Grünebaum, Amos
AU  - Grünebaum A
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, 
      USA.
FAU - Chervenak, Frank A
AU  - Chervenak FA
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, 
      USA.
FAU - Dudenhausen, Joachim
AU  - Dudenhausen J
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, 
      USA.
AD  - Department of Obstetrics, Charité - University Medicine Berlin, Berlin, Germany.
LA  - eng
PT  - Letter
DEP - 20231211
PL  - Germany
TA  - J Perinat Med
JT  - Journal of perinatal medicine
JID - 0361031
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Internet
OT  - computers
EDAT- 2023/12/08 06:42
MHDA- 2023/12/08 06:42
CRDT- 2023/12/08 00:43
PHST- 2023/09/13 00:00 [received]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/12/08 06:42 [medline]
PHST- 2023/12/08 06:42 [pubmed]
PHST- 2023/12/08 00:43 [entrez]
AID - jpm-2023-0380 [pii]
AID - 10.1515/jpm-2023-0380 [doi]
PST - aheadofprint
SO  - J Perinat Med. 2023 Dec 11. doi: 10.1515/jpm-2023-0380.

PMID- 37210281
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230724
LR  - 20230724
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
VI  - 131
IP  - 2
DP  - 2023 Aug
TI  - Performance of ChatGPT on a primary FRCA multiple choice question bank.
PG  - e34-e35
LID - S0007-0912(23)00200-3 [pii]
LID - 10.1016/j.bja.2023.04.025 [doi]
FAU - Birkett, Liam
AU  - Birkett L
AD  - Royal Free Hospital, London, UK. Electronic address: liam.birkett@nhs.net.
FAU - Fowler, Thomas
AU  - Fowler T
AD  - Queen's Hospital, London, UK.
FAU - Pullen, Simon
AU  - Pullen S
AD  - Princess Alexandra Hospital, Essex, UK.
LA  - eng
PT  - Letter
DEP - 20230518
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - assessments
OT  - postgraduate examinations
OT  - primary FRCA
EDAT- 2023/05/21 01:05
MHDA- 2023/05/21 01:06
CRDT- 2023/05/20 21:59
PHST- 2023/04/08 00:00 [received]
PHST- 2023/04/19 00:00 [revised]
PHST- 2023/04/21 00:00 [accepted]
PHST- 2023/05/21 01:06 [medline]
PHST- 2023/05/21 01:05 [pubmed]
PHST- 2023/05/20 21:59 [entrez]
AID - S0007-0912(23)00200-3 [pii]
AID - 10.1016/j.bja.2023.04.025 [doi]
PST - ppublish
SO  - Br J Anaesth. 2023 Aug;131(2):e34-e35. doi: 10.1016/j.bja.2023.04.025. Epub 2023 
      May 18.

PMID- 37779749
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT in Medical Education and Research: A Boon or a Bane?
PG  - e44316
LID - 10.7759/cureus.44316 [doi]
LID - e44316
AB  - Science fiction literature and films no longer focus on artificial intelligence. 
      In contrast to all other aspects of life, medical education and clinical patient 
      care have been progressing slowly. Recently, a lot of text from the internet was 
      used to construct and train chatbots, especially ChatGPT. The language model 
      ChatGPT, created by OpenAI, has emerged as a useful resource for medical research 
      and education. It has proven to be a useful tool for researchers, students, and 
      medical professionals because of its capacity to produce human-like answers to 
      challenging medical queries. However, using ChatGPT also has significant 
      drawbacks. The possibility of erroneous or biased information being spread, which 
      could have negative effects on patient care, is one of the key worries. Moreover, 
      the overreliance on technology in medical education could also lead to a decline 
      in critical thinking and clinical decision-making skills. Overall, ChatGPT has 
      the potential to be a boon to medical education and research, but its use must be 
      accompanied by caution and critical evaluation.
CI  - Copyright © 2023, Jeyaraman et al.
FAU - Jeyaraman, Madhan
AU  - Jeyaraman M
AD  - Orthopaedics, South Texas Orthopaedic Research Institute (STORI), Laredo, USA.
AD  - Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research 
      Institute, Chennai, IND.
FAU - K, Shanmuga Priya
AU  - K SP
AD  - Pulmonology, Faculty of Medicine, Sri Lalithambigai Medical College and Hospital, 
      Dr MGR Educational and Research Institute, Chennai, IND.
FAU - Jeyaraman, Naveen
AU  - Jeyaraman N
AD  - Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research 
      Institute, Chennai, IND.
FAU - Nallakumarasamy, Arulkumar
AU  - Nallakumarasamy A
AD  - Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research 
      Institute, Chennai, IND.
FAU - Yadav, Sankalp
AU  - Yadav S
AD  - Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND.
FAU - Bondili, Suresh K
AU  - Bondili SK
AD  - Medical Oncology, Kauvery Hospital, Chennai, IND.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230829
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10536401
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - artificial intelligence
OT  - chatgpt
OT  - medical education
OT  - open ai
OT  - research
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/02 06:42
MHDA- 2023/10/02 06:43
CRDT- 2023/10/02 04:17
PHST- 2023/08/29 00:00 [accepted]
PHST- 2023/10/02 06:43 [medline]
PHST- 2023/10/02 06:42 [pubmed]
PHST- 2023/10/02 04:17 [entrez]
AID - 10.7759/cureus.44316 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 29;15(8):e44316. doi: 10.7759/cureus.44316. eCollection 2023 
      Aug.

PMID- 37071280
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230615
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 7
DP  - 2023 Jul
TI  - Will ChatGPT/GPT-4 be a Lighthouse to Guide Spinal Surgeons?
PG  - 1362-1365
LID - 10.1007/s10439-023-03206-0 [doi]
AB  - The advent of artificial intelligence (AI), particularly ChatGPT/GPT-4, has led 
      to advancements in various fields, including healthcare. This study explores the 
      prospective role of ChatGPT/GPT-4 in various facets of spinal surgical practice, 
      especially in supporting spinal surgeons during the perioperative management of 
      endoscopic spinal surgery for patients with lumbar disc herniation. The AI-driven 
      chatbot can facilitate communication between spinal surgeons, patients, and their 
      relatives, streamline the collection and analysis of patient data, and contribute 
      to the surgical planning process. Furthermore, ChatGPT/GPT-4 may enhance 
      intraoperative support by providing real-time surgical navigation information and 
      physiological parameter monitoring, as well as aiding in postoperative 
      rehabilitation guidance. However, the appropriate and supervised use of 
      ChatGPT/GPT-4 is essential, considering the potential risks associated with data 
      security and privacy. The study concludes that ChatGPT/GPT-4 can serve as a 
      valuable lighthouse for spinal surgeons if used correctly and responsibly.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - He, Yongbin
AU  - He Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
FAU - Tang, Haifeng
AU  - Tang H
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
FAU - Wang, Dongxue
AU  - Wang D
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
FAU - Gu, Shuqin
AU  - Gu S
AD  - Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC, USA.
FAU - Ni, Guoxin
AU  - Ni G
AD  - Department of Rehabilitation Medicine, The First Affiliated Hospital of Xiamen 
      University, Xiamen, China. nigx@xmu.edu.cn.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Spine Surgery, Tianjin Huanhu Hospital, Graduate School of Tianjin 
      Medical University, Tianjin, China. wuhaiyang2021@tmu.edu.cn.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      NC, USA. wuhaiyang2021@tmu.edu.cn.
LA  - eng
GR  - 81871848/National Natural Science Foundation of China Project/
PT  - Letter
DEP - 20230418
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Surgeons
MH  - Neurosurgical Procedures
MH  - Software
MH  - *Surgery, Computer-Assisted
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Assistant
OT  - Endoscopic spinal surgery
OT  - GPT-4
OT  - Spinal surgeon
EDAT- 2023/04/19 06:00
MHDA- 2023/06/15 06:42
CRDT- 2023/04/18 11:23
PHST- 2023/04/10 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/04/19 06:00 [pubmed]
PHST- 2023/04/18 11:23 [entrez]
AID - 10.1007/s10439-023-03206-0 [pii]
AID - 10.1007/s10439-023-03206-0 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jul;51(7):1362-1365. doi: 10.1007/s10439-023-03206-0. Epub 
      2023 Apr 18.

PMID- 37059815
OWN - NLM
STAT- MEDLINE
DCOM- 20230418
LR  - 20230418
IS  - 1437-2320 (Electronic)
IS  - 0344-5607 (Linking)
VI  - 46
IP  - 1
DP  - 2023 Apr 14
TI  - The role of an open artificial intelligence platform in modern neurosurgical 
      education: a preliminary study.
PG  - 86
LID - 10.1007/s10143-023-01998-2 [doi]
AB  - The use of artificial intelligence in neurosurgical education has been growing in 
      recent times. ChatGPT, a free and easily accessible language model, has been 
      gaining popularity as an alternative education method. It is necessary to explore 
      the potential of this program in neurosurgery education and to evaluate its 
      reliability. This study aimed to show the reliability of ChatGPT by asking 
      various questions to the chat engine, how it can contribute to neurosurgery 
      education by preparing case reports or questions, and its contributions when 
      writing academic articles. The results of the study showed that while ChatGPT 
      provided intriguing and interesting responses, it should not be considered a 
      dependable source of information. The absence of citations for scientific queries 
      raises doubts about the credibility of the answers provided. Therefore, it is not 
      advisable to solely rely on ChatGPT as an educational resource. With further 
      updates and more specific prompts, it may be possible to improve its accuracy. In 
      conclusion, while ChatGPT has potential as an educational tool, its reliability 
      needs to be further evaluated and improved before it can be widely adopted in 
      neurosurgical education.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Sevgi, Umut Tan
AU  - Sevgi UT
AUID- ORCID: 0000-0003-3875-859X
AD  - Department of Neurosurgery, University of Health Sciences, Tepecik Training and 
      Research Hospital, Izmir, Turkey.
AD  - Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy 
      Laboratory, Istanbul, Turkey.
FAU - Erol, Gökberk
AU  - Erol G
AUID- ORCID: 0000-0001-6651-5486
AD  - Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy 
      Laboratory, Istanbul, Turkey.
AD  - Department of Neurosurgery, Faculty of Medicine, Gazi University, Ankara, Turkey.
FAU - Doğruel, Yücel
AU  - Doğruel Y
AUID- ORCID: 0000-0003-4314-5579
AD  - Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy 
      Laboratory, Istanbul, Turkey.
AD  - The Neurosurgical Atlas, Carmel, IN, USA.
FAU - Sönmez, Osman Fikret
AU  - Sönmez OF
AUID- ORCID: 0000-0003-1050-1645
AD  - Department of Neurosurgery, University of Health Sciences, Tepecik Training and 
      Research Hospital, Izmir, Turkey.
FAU - Tubbs, Richard Shane
AU  - Tubbs RS
AUID- ORCID: 0000-0003-1317-1047
AD  - Department of Neurosurgery, Tulane Center for Clinical Neurosciences, Tulane 
      University School of Medicine, New Orleans, LA, USA.
AD  - Department of Anatomical Sciences, St. George's University, St. George's, West 
      Indies, Grenada.
AD  - Department of Structural and Cellular Biology, Tulane University School of 
      Medicine, New Orleans, LA, USA.
AD  - Department of Neurosurgery and Ochsner Neuroscience Institute, Ochsner Health 
      System, New Orleans, LA, USA.
AD  - Department of Neurology, Tulane University School of Medicine, New Orleans, LA, 
      USA.
FAU - Güngor, Abuzer
AU  - Güngor A
AUID- ORCID: 0000-0002-2792-7610
AD  - Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy 
      Laboratory, Istanbul, Turkey. abuzergungor@gmail.com.
AD  - Department of Neurosurgery, University of Health Sciences, Bakirkoy Research and 
      Training Hospital for Neurology, Neurosurgery and Psychiatry, Istanbul, Turkey. 
      abuzergungor@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230414
PL  - Germany
TA  - Neurosurg Rev
JT  - Neurosurgical review
JID - 7908181
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - Language
MH  - *Neurosurgery
MH  - Neurosurgical Procedures
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Neurosurgical education
OT  - Scientific article
EDAT- 2023/04/15 06:00
MHDA- 2023/04/18 06:42
CRDT- 2023/04/14 23:18
PHST- 2023/02/09 00:00 [received]
PHST- 2023/04/08 00:00 [accepted]
PHST- 2023/02/09 00:00 [revised]
PHST- 2023/04/18 06:42 [medline]
PHST- 2023/04/14 23:18 [entrez]
PHST- 2023/04/15 06:00 [pubmed]
AID - 10.1007/s10143-023-01998-2 [pii]
AID - 10.1007/s10143-023-01998-2 [doi]
PST - epublish
SO  - Neurosurg Rev. 2023 Apr 14;46(1):86. doi: 10.1007/s10143-023-01998-2.

PMID- 37252576
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230531
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - A Case Study Demonstrating Applications of ChatGPT in the Clinical Management of 
      Treatment-Resistant Schizophrenia.
PG  - e38166
LID - 10.7759/cureus.38166 [doi]
LID - e38166
AB  - Chat Generative Pre-trained Transformer, also known as ChatGPT, is a new 
      artificial intelligence (AI) program that responds to user inquiry with discourse 
      resembling human language. The range of ChatGPT capabilities caught the interest 
      of the medical world after it demonstrated its ability to pass medical boards 
      examinations. In this case report, we present the clinical treatment of a 
      22-year-old male diagnosed with treatment-resistant schizophrenia (TRS) and 
      compare the medical management suggested by ChatGPT to current standards of care 
      in order to assess the program's ability to identify the disorder, evaluate 
      potential medical and psychiatric work-up, and develop a treatment plan 
      addressing the distinct nuances of our patient. In our inquiry with ChatGPT, we 
      found that it can accurately identify our patient as having TRS and order 
      appropriate tests to methodically rule out alternative causes of acute psychosis. 
      Furthermore, the AI program suggests pharmacologic treatment options including 
      clozapine with adjuvant medications, and nonpharmacologic treatment options 
      including electroconvulsive therapy (ECT), repetitive transcranial magnetic 
      stimulation (rTMS), and psychotherapy which align with current standards of care. 
      Lastly, ChatGPT provides a comprehensive list of side effects associated with 
      antipsychotics and mood stabilizers used to treat TRS. We found both potential 
      for and limitations in the clinical application of ChatGPT to assist in the 
      assessment and management of complex medical conditions. Overall, ChatGPT may 
      serve as a powerful tool to organize medical data in a meaningful and palatable 
      format for medical professionals to reference during patient care.
CI  - Copyright © 2023, Galido et al.
FAU - Galido, Pearl Valentine
AU  - Galido PV
AD  - Medicine, College of Osteopathic Medicine of the Pacific, Western University of 
      Health Sciences, Pomona, USA.
FAU - Butala, Saloni
AU  - Butala S
AD  - Medicine, College of Osteopathic Medicine of the Pacific, Western University of 
      Health Sciences, Pomona, USA.
FAU - Chakerian, Meg
AU  - Chakerian M
AD  - Medicine, College of Osteopathic Medicine of the Pacific, Western University of 
      Health Sciences, Pomona, USA.
FAU - Agustines, Davin
AU  - Agustines D
AD  - Psychiatry, Olive View-University of California Los Angeles Medical Center, 
      Sylmar, USA.
LA  - eng
PT  - Case Reports
DEP - 20230426
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10219639
OTO - NOTNLM
OT  - antipsychotics
OT  - artificial intelligence in medicine
OT  - chatgpt
OT  - psychosis
OT  - standard of care
OT  - treatment-resistant schizophrenia
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/30 13:07
MHDA- 2023/05/30 13:08
CRDT- 2023/05/30 11:55
PHST- 2023/04/24 00:00 [accepted]
PHST- 2023/05/30 13:08 [medline]
PHST- 2023/05/30 13:07 [pubmed]
PHST- 2023/05/30 11:55 [entrez]
AID - 10.7759/cureus.38166 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 26;15(4):e38166. doi: 10.7759/cureus.38166. eCollection 2023 
      Apr.

PMID- 38016014
OWN - NLM
STAT- Publisher
LR  - 20231128
IS  - 1536-4798 (Electronic)
IS  - 0277-3740 (Linking)
DP  - 2023 Nov 28
TI  - Quality and Agreement With Scientific Consensus of ChatGPT Information Regarding 
      Corneal Transplantation and Fuchs Dystrophy.
LID - 10.1097/ICO.0000000000003439 [doi]
AB  - PURPOSE: ChatGPT is a commonly used source of information by patients and 
      clinicians. However, it can be prone to error and requires validation. We sought 
      to assess the quality and accuracy of information regarding corneal 
      transplantation and Fuchs dystrophy from 2 iterations of ChatGPT, and whether its 
      answers improve over time. METHODS: A total of 10 corneal specialists 
      collaborated to assess responses of the algorithm to 10 commonly asked questions 
      related to endothelial keratoplasty and Fuchs dystrophy. These questions were 
      asked from both ChatGPT-3.5 and its newer generation, GPT-4. Assessments tested 
      quality, safety, accuracy, and bias of information. Chi-squared, Fisher exact 
      tests, and regression analyses were conducted. RESULTS: We analyzed 180 valid 
      responses. On a 1 (A+) to 5 (F) scale, the average score given by all specialists 
      across questions was 2.5 for ChatGPT-3.5 and 1.4 for GPT-4, a significant 
      improvement (P < 0.0001). Most responses by both ChatGPT-3.5 (61%) and GPT-4 
      (89%) used correct facts, a proportion that significantly improved across 
      iterations (P < 0.00001). Approximately a third (35%) of responses from 
      ChatGPT-3.5 were considered against the scientific consensus, a notable rate of 
      error that decreased to only 5% of answers from GPT-4 (P < 0.00001). CONCLUSIONS: 
      The quality of responses in ChatGPT significantly improved between versions 3.5 
      and 4, and the odds of providing information against the scientific consensus 
      decreased. However, the technology is still capable of producing inaccurate 
      statements. Corneal specialists are uniquely positioned to assist users to 
      discern the veracity and application of such information.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Barclay, Kayson S
AU  - Barclay KS
AD  - Morgan State University, Baltimore, MD.
FAU - You, Jane Y
AU  - You JY
AD  - Harvard Medical School, Boston, MA.
FAU - Coleman, Michael J
AU  - Coleman MJ
AD  - Cataract and Laser Institute, Mishawaka, IN.
FAU - Mathews, Priya M
AU  - Mathews PM
AD  - Center for Sight, Sarasota, FL.
FAU - Ray, Vincent L
AU  - Ray VL
AD  - Fremont Eye Care Physicians, Fremont, CA.
FAU - Riaz, Kamran M
AU  - Riaz KM
AD  - Dean McGee Eye Institute, Oklahoma City, OK.
FAU - De Rojas, Joaquin O
AU  - De Rojas JO
AD  - Center for Sight, Sarasota, FL.
FAU - Wang, Aaron S
AU  - Wang AS
AD  - Glaucoma Cataract Consultants, Pittsburgh, PA.
FAU - Watson, Shelly H
AU  - Watson SH
AD  - Northern Virginia Ophthalmology Associates, Falls Church, VA.
FAU - Koo, Ellen H
AU  - Koo EH
AD  - Bascom Palmer Eye Institute, Miami, FL; and.
FAU - Eghrari, Allen O
AU  - Eghrari AO
AUID- ORCID: 0000-0003-2798-038
AD  - Wilmer Eye Institute at Johns Hopkins, Baltimore, MD.
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - United States
TA  - Cornea
JT  - Cornea
JID - 8216186
SB  - IM
COIS- The authors have no funding or conflicts of interest to disclose.
EDAT- 2023/11/28 18:42
MHDA- 2023/11/28 18:42
CRDT- 2023/11/28 14:53
PHST- 2023/06/07 00:00 [received]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/11/28 18:42 [medline]
PHST- 2023/11/28 18:42 [pubmed]
PHST- 2023/11/28 14:53 [entrez]
AID - 00003226-990000000-00430 [pii]
AID - 10.1097/ICO.0000000000003439 [doi]
PST - aheadofprint
SO  - Cornea. 2023 Nov 28. doi: 10.1097/ICO.0000000000003439.

PMID- 37900984
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2169-7574 (Print)
IS  - 2169-7574 (Electronic)
IS  - 2169-7574 (Linking)
VI  - 11
IP  - 10
DP  - 2023 Oct
TI  - Utilization of ChatGPT-4 in Plastic and Reconstructive Surgery: A Narrative 
      Review.
PG  - e5305
LID - 10.1097/GOX.0000000000005305 [doi]
LID - e5305
AB  - BACKGROUND: ChatGPT-4 (Chat Generative Pre-Trained Transformer) has demonstrated 
      remarkable capabilities in natural language processing and understanding, making 
      it a promising tool for various medical domains. This article presents a 
      comprehensive overview of the potential applications of ChatGPT-4, a cutting-edge 
      language model developed by OpenAI, in the field of plastic and reconstructive 
      surgery. METHODS: After conducting a thorough literature review, we discovered 
      pertinent articles that explore the application of ChatGPT-4 in plastic surgery. 
      By examining these findings and integrating the information with our personal 
      experience using ChatGPT-4 in the field of plastic surgery, we have produced an 
      all-encompassing narrative review. RESULTS: The narrative review focuses on three 
      main areas: clinical applications, research applications, and medical education. 
      In the clinical realm, ChatGPT-4 has the potential to streamline documentation 
      processes, improve communication, and enhance personalized patient care. It can 
      assist in generating accurate and comprehensive progress notes, operative notes, 
      surgical consent forms, on-call schedules, and consultation reports. However, it 
      is important to note that ChatGPT-4 should be used as a supportive tool and 
      should not replace human doctors. CONCLUSIONS: The potential applications of 
      ChatGPT-4 in plastic and reconstructive surgery are vast and promising. This 
      technology has the potential to revolutionize documentation, research, and 
      medical education in the field. However, it is crucial to integrate this tool 
      responsibly, considering its limitations and ensuring that human expertise 
      remains paramount.
CI  - Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf 
      of The American Society of Plastic Surgeons.
FAU - Aljindan, Fahad K
AU  - Aljindan FK
AD  - From the Department of Plastic Surgery, King Abdullah Medical City, Makkah, Saudi 
      Arabia.
FAU - Shawosh, Mohammed H
AU  - Shawosh MH
AD  - Division of Plastic Surgery, Department of Surgery, King Fahad Armed Forces 
      Hospital, Jeddah, Saudi Arabia.
FAU - Altamimi, Lamees
AU  - Altamimi L
AD  - From the Department of Plastic Surgery, King Abdullah Medical City, Makkah, Saudi 
      Arabia.
FAU - Arif, Sultan
AU  - Arif S
AD  - Department of Plastic Surgery and Burn Unit, Security Force Hospital, Riyadh, 
      Saudi Arabia.
AD  - Facharzt Plastic Surgery and Aesthetic Surgery, Riyadh, Saudi Arabia.
FAU - Mortada, Hatan
AU  - Mortada H
AD  - Division of Plastic Surgery, Department of Surgery, King Saud University Medical 
      City, King Saud University, Riyadh, Saudi Arabia.
AD  - Department of Plastic Surgery & Burn Unit, King Saud Medical City, Riyadh, Saudi 
      Arabia.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - United States
TA  - Plast Reconstr Surg Glob Open
JT  - Plastic and reconstructive surgery. Global open
JID - 101622231
PMC - PMC10602496
COIS- The authors have no financial interest to declare in relation to the content of 
      this article.
EDAT- 2023/10/30 06:46
MHDA- 2023/10/30 06:47
CRDT- 2023/10/30 04:43
PHST- 2023/05/27 00:00 [received]
PHST- 2023/08/11 00:00 [accepted]
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/10/30 06:46 [pubmed]
PHST- 2023/10/30 04:43 [entrez]
AID - 10.1097/GOX.0000000000005305 [doi]
PST - epublish
SO  - Plast Reconstr Surg Glob Open. 2023 Oct 26;11(10):e5305. doi: 
      10.1097/GOX.0000000000005305. eCollection 2023 Oct.

PMID- 37387114
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20230703
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 305
DP  - 2023 Jun 29
TI  - The Pros and Cons of Using ChatGPT in Medical Education: A Scoping Review.
PG  - 644-647
LID - 10.3233/SHTI230580 [doi]
AB  - This scoping review explores the advantages and disadvantages of using ChatGPT in 
      medical education. We searched PubMed, Google Scholar, Medline, Scopus, and 
      Science Direct to identify relevant studies. Two reviewers independently 
      conducted study selection and data extraction, followed by a narrative synthesis. 
      Out of 197 references, 25 studies met the eligibility criteria. The primary 
      applications of ChatGPT in medical education include automated scoring, teaching 
      assistance, personalized learning, research assistance, quick access to 
      information, generating case scenarios and exam questions, content creation for 
      learning facilitation, and language translation. We also discuss the challenges 
      and limitations of using ChatGPT in medical education, such as its inability to 
      reason beyond existing knowledge, generation of incorrect information, bias, 
      potential undermining of students' critical thinking skills, and ethical 
      concerns. These concerns include using ChatGPT for exam and assignment cheating 
      by students and researchers, as well as issues related to patients' privacy.
FAU - Mohammad, Bushra
AU  - Mohammad B
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
FAU - Supti, Turjana
AU  - Supti T
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
FAU - Alzubaidi, Mahmood
AU  - Alzubaidi M
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
FAU - Shah, Hurmat
AU  - Shah H
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
FAU - Alam, Tanvir
AU  - Alam T
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
FAU - Shah, Zubair
AU  - Shah Z
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
FAU - Househ, Mowafa
AU  - Househ M
AD  - College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - Humans
MH  - *Education, Medical
MH  - Eligibility Determination
MH  - Knowledge
MH  - Learning
MH  - MEDLINE
OTO - NOTNLM
OT  - ChatGPT
OT  - Chatbot
OT  - Medical Education
OT  - Open AI
EDAT- 2023/06/30 06:42
MHDA- 2023/07/03 06:41
CRDT- 2023/06/30 04:24
PHST- 2023/07/03 06:41 [medline]
PHST- 2023/06/30 06:42 [pubmed]
PHST- 2023/06/30 04:24 [entrez]
AID - SHTI230580 [pii]
AID - 10.3233/SHTI230580 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 Jun 29;305:644-647. doi: 10.3233/SHTI230580.

PMID- 37991499
OWN - NLM
STAT- Publisher
LR  - 20231122
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Nov 22
TI  - How ChatGPT works: a mini review.
LID - 10.1007/s00405-023-08337-7 [doi]
AB  - OBJECTIVE: This paper offers a mini-review of OpenAI's language model, ChatGPT, 
      detailing its mechanisms, applications in healthcare, and comparisons with other 
      large language models (LLMs). METHODS: The underlying technology of ChatGPT is 
      outlined, focusing on its neural network architecture, training process, and the 
      role of key elements such as input embedding, encoder, decoder, attention 
      mechanism, and output projection. The advancements in GPT-4, including its 
      capacity for internet connection and the integration of plugins for enhanced 
      functionality are discussed. RESULTS: ChatGPT can generate creative, coherent, 
      and contextually relevant sentences, making it a valuable tool in healthcare for 
      patient engagement, medical education, and clinical decision support. Yet, like 
      other LLMs, it has limitations, including a lack of common sense knowledge, a 
      propensity for hallucination of facts, a restricted context window, and potential 
      privacy concerns. CONCLUSION: Despite the limitations, LLMs like ChatGPT offer 
      transformative possibilities for healthcare. With ongoing research in model 
      interpretability, common-sense reasoning, and handling of longer context windows, 
      their potential is vast. It is crucial for healthcare professionals to remain 
      informed about these technologies and consider their ethical integration into 
      practice.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Briganti, Giovanni
AU  - Briganti G
AUID- ORCID: 0000-0002-4038-3363
AD  - Chair of AI and Digital Medicine, Department of Neuroscience, Faculty of 
      Medicine, University of Mons, Avenue du Champs de Mars 6, B7000, Mons, Belgium. 
      giovanni.briganti@hotmail.com.
AD  - Department of Clinical Science, Faculty of Medicine, University of Liège, 
      Quartier Hôpital, Avenue Hippocrate 13, B4000, Liege, Belgium. 
      giovanni.briganti@hotmail.com.
AD  - Faculty of Medicine, Université Libre de Bruxelles, Route de Lennik 808, B1070, 
      Brussels, Belgium. giovanni.briganti@hotmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - Artificial
OT  - ChatGPT
OT  - Chatbot
OT  - GPT
OT  - Head Neck
OT  - Medicine
OT  - Otolaryngology
OT  - Surgery
EDAT- 2023/11/22 12:45
MHDA- 2023/11/22 12:45
CRDT- 2023/11/22 11:03
PHST- 2023/08/05 00:00 [received]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/11/22 12:45 [medline]
PHST- 2023/11/22 12:45 [pubmed]
PHST- 2023/11/22 11:03 [entrez]
AID - 10.1007/s00405-023-08337-7 [pii]
AID - 10.1007/s00405-023-08337-7 [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Nov 22. doi: 10.1007/s00405-023-08337-7.

PMID- 36753318
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Feb 8
TI  - How Does ChatGPT Perform on the United States Medical Licensing Examination? The 
      Implications of Large Language Models for Medical Education and Knowledge 
      Assessment.
PG  - e45312
LID - 10.2196/45312 [doi]
LID - e45312
AB  - BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 
      175-billion-parameter natural language processing model that can generate 
      conversation-style responses to user input. OBJECTIVE: This study aimed to 
      evaluate the performance of ChatGPT on questions within the scope of the United 
      States Medical Licensing Examination Step 1 and Step 2 exams, as well as to 
      analyze responses for user interpretability. METHODS: We used 2 sets of 
      multiple-choice questions to evaluate ChatGPT's performance, each with questions 
      pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a 
      commonly used question bank for medical students, which also provides statistics 
      on question difficulty and the performance on an exam relative to the user base. 
      The second set was the National Board of Medical Examiners (NBME) free 120 
      questions. ChatGPT's performance was compared to 2 other large language models, 
      GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated 
      across 3 qualitative metrics: logical justification of the answer selected, 
      presence of information internal to the question, and presence of information 
      external to the question. RESULTS: Of the 4 data sets, AMBOSS-Step1, 
      AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies 
      of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. 
      ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and 
      GPT-3 performed similarly to random chance. The model demonstrated a significant 
      decrease in performance as question difficulty increased (P=.01) within the 
      AMBOSS-Step1 data set. We found that logical justification for ChatGPT's answer 
      selection was present in 100% of outputs of the NBME data sets. Internal 
      information to the question was present in 96.8% (183/189) of all questions. The 
      presence of information external to the question was 44.5% and 27% lower for 
      incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001) and 
      NBME-Free-Step2 (P=.001) data sets, respectively. CONCLUSIONS: ChatGPT marks a 
      significant improvement in natural language processing models on the tasks of 
      medical question answering. By performing at a greater than 60% threshold on the 
      NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a 
      passing score for a third-year medical student. Additionally, we highlight 
      ChatGPT's capacity to provide logic and informational context across the majority 
      of answers. These facts taken together make a compelling case for the potential 
      applications of ChatGPT as an interactive medical education tool to support 
      learning.
CI  - ©Aidan Gilson, Conrad W Safranek, Thomas Huang, Vimig Socrates, Ling Chi, Richard 
      Andrew Taylor, David Chartash. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 08.02.2023.
FAU - Gilson, Aidan
AU  - Gilson A
AUID- ORCID: 0000-0002-4770-4705
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - Department of Emergency Medicine, Yale University School of Medicine, New Haven, 
      CT, United States.
FAU - Safranek, Conrad W
AU  - Safranek CW
AUID- ORCID: 0000-0003-1985-9432
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
FAU - Huang, Thomas
AU  - Huang T
AUID- ORCID: 0000-0001-9056-7016
AD  - Department of Emergency Medicine, Yale University School of Medicine, New Haven, 
      CT, United States.
FAU - Socrates, Vimig
AU  - Socrates V
AUID- ORCID: 0000-0001-7955-9875
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - Program of Computational Biology and Bioinformatics, Yale University, New Haven, 
      CT, United States.
FAU - Chi, Ling
AU  - Chi L
AUID- ORCID: 0000-0002-8270-9245
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
FAU - Taylor, Richard Andrew
AU  - Taylor RA
AUID- ORCID: 0000-0002-9082-6644
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - Department of Emergency Medicine, Yale University School of Medicine, New Haven, 
      CT, United States.
FAU - Chartash, David
AU  - Chartash D
AUID- ORCID: 0000-0002-0265-330X
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - School of Medicine, University College Dublin, National University of Ireland, 
      Dublin, Dublin, Ireland.
LA  - eng
GR  - T35 DK104689/DK/NIDDK NIH HHS/United States
GR  - UL1 TR001863/TR/NCATS NIH HHS/United States
PT  - Journal Article
DEP - 20230208
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
CIN - Med Clin (Barc). 2023 Oct 27;161(8):363-364. PMID: 37438191
PMC - PMC9947764
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT
OT  - MedQA
OT  - NLP
OT  - artificial intelligence
OT  - chatbot
OT  - conversational agent
OT  - education technology
OT  - generative pre-trained transformer
OT  - machine learning
OT  - medical education
OT  - natural language processing
COIS- Conflicts of Interest: None declared.
EDAT- 2023/02/09 06:00
MHDA- 2023/02/09 06:01
CRDT- 2023/02/08 11:53
PHST- 2022/12/23 00:00 [received]
PHST- 2023/01/29 00:00 [accepted]
PHST- 2023/01/27 00:00 [revised]
PHST- 2023/02/08 11:53 [entrez]
PHST- 2023/02/09 06:00 [pubmed]
PHST- 2023/02/09 06:01 [medline]
AID - v9i1e45312 [pii]
AID - 10.2196/45312 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Feb 8;9:e45312. doi: 10.2196/45312.

PMID- 37529703
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230803
IS  - 1663-9812 (Print)
IS  - 1663-9812 (Electronic)
IS  - 1663-9812 (Linking)
VI  - 14
DP  - 2023
TI  - Future implications of ChatGPT in pharmaceutical industry: drug discovery and 
      development.
PG  - 1194216
LID - 10.3389/fphar.2023.1194216 [doi]
LID - 1194216
FAU - Zhao, Ailin
AU  - Zhao A
AD  - Department of Hematology, West China Hospital, Sichuan University, Chengdu, 
      Sichuan, China.
FAU - Wu, Yijun
AU  - Wu Y
AD  - Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - Switzerland
TA  - Front Pharmacol
JT  - Frontiers in pharmacology
JID - 101548923
PMC - PMC10390092
OTO - NOTNLM
OT  - ChatGPT
OT  - academic writing
OT  - artificial intelligence
OT  - clinical drug
OT  - drug discovsry
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/08/02 06:43
MHDA- 2023/08/02 06:44
CRDT- 2023/08/02 04:01
PHST- 2023/03/26 00:00 [received]
PHST- 2023/06/08 00:00 [accepted]
PHST- 2023/08/02 06:44 [medline]
PHST- 2023/08/02 06:43 [pubmed]
PHST- 2023/08/02 04:01 [entrez]
AID - 1194216 [pii]
AID - 10.3389/fphar.2023.1194216 [doi]
PST - epublish
SO  - Front Pharmacol. 2023 Jul 17;14:1194216. doi: 10.3389/fphar.2023.1194216. 
      eCollection 2023.

PMID- 37229911
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230615
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 85
DP  - 2023 Jul
TI  - What do psychiatry researchers feel about ChatGPT? A study based on Natural 
      Language Processing techniques.
PG  - 103626
LID - S1876-2018(23)00181-8 [pii]
LID - 10.1016/j.ajp.2023.103626 [doi]
FAU - Praveen, S V
AU  - Praveen SV
AD  - Xavier Institute of Entrepreneurship and Management, Bangalore, Karnataka, India. 
      Electronic address: praveenscissci@gmail.com.
FAU - Lohia, Radhika
AU  - Lohia R
AD  - Xavier Institute of Entrepreneurship and Management, Bangalore, Karnataka, India.
LA  - eng
PT  - Letter
DEP - 20230514
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - *Natural Language Processing
MH  - *Emotions
OTO - NOTNLM
OT  - ChatGPT
OT  - Ethics
OT  - Natural Language Processing
OT  - Psychiatry
OT  - Research
COIS- Declaration of Competing Interest None.
EDAT- 2023/05/26 01:05
MHDA- 2023/06/15 06:42
CRDT- 2023/05/25 18:04
PHST- 2023/04/12 00:00 [received]
PHST- 2023/05/11 00:00 [revised]
PHST- 2023/05/12 00:00 [accepted]
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/05/26 01:05 [pubmed]
PHST- 2023/05/25 18:04 [entrez]
AID - S1876-2018(23)00181-8 [pii]
AID - 10.1016/j.ajp.2023.103626 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Jul;85:103626. doi: 10.1016/j.ajp.2023.103626. Epub 2023 
      May 14.

PMID- 37195756
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230603
IS  - 2292-9495 (Electronic)
IS  - 2292-9495 (Linking)
VI  - 10
DP  - 2023 May 17
TI  - User Intentions to Use ChatGPT for Self-Diagnosis and Health-Related Purposes: 
      Cross-sectional Survey Study.
PG  - e47564
LID - 10.2196/47564 [doi]
LID - e47564
AB  - BACKGROUND: With the rapid advancement of artificial intelligence (AI) 
      technologies, AI-powered chatbots, such as Chat Generative Pretrained Transformer 
      (ChatGPT), have emerged as potential tools for various applications, including 
      health care. However, ChatGPT is not specifically designed for health care 
      purposes, and its use for self-diagnosis raises concerns regarding its adoption's 
      potential risks and benefits. Users are increasingly inclined to use ChatGPT for 
      self-diagnosis, necessitating a deeper understanding of the factors driving this 
      trend. OBJECTIVE: This study aims to investigate the factors influencing users' 
      perception of decision-making processes and intentions to use ChatGPT for 
      self-diagnosis and to explore the implications of these findings for the safe and 
      effective integration of AI chatbots in health care. METHODS: A cross-sectional 
      survey design was used, and data were collected from 607 participants. The 
      relationships between performance expectancy, risk-reward appraisal, 
      decision-making, and intention to use ChatGPT for self-diagnosis were analyzed 
      using partial least squares structural equation modeling (PLS-SEM). RESULTS: Most 
      respondents were willing to use ChatGPT for self-diagnosis (n=476, 78.4%). The 
      model demonstrated satisfactory explanatory power, accounting for 52.4% of the 
      variance in decision-making and 38.1% in the intent to use ChatGPT for 
      self-diagnosis. The results supported all 3 hypotheses: The higher performance 
      expectancy of ChatGPT (β=.547, 95% CI 0.474-0.620) and positive risk-reward 
      appraisals (β=.245, 95% CI 0.161-0.325) were positively associated with the 
      improved perception of decision-making outcomes among users, and enhanced 
      perception of decision-making processes involving ChatGPT positively impacted 
      users' intentions to use the technology for self-diagnosis (β=.565, 95% CI 
      0.498-0.628). CONCLUSIONS: Our research investigated factors influencing users' 
      intentions to use ChatGPT for self-diagnosis and health-related purposes. Even 
      though the technology is not specifically designed for health care, people are 
      inclined to use ChatGPT in health care contexts. Instead of solely focusing on 
      discouraging its use for health care purposes, we advocate for improving the 
      technology and adapting it for suitable health care applications. Our study 
      highlights the importance of collaboration among AI developers, health care 
      providers, and policy makers in ensuring AI chatbots' safe and responsible use in 
      health care. By understanding users' expectations and decision-making processes, 
      we can develop AI chatbots, such as ChatGPT, that are tailored to human needs, 
      providing reliable and verified health information sources. This approach not 
      only enhances health care accessibility but also improves health literacy and 
      awareness. As the field of AI chatbots in health care continues to evolve, future 
      research should explore the long-term effects of using AI chatbots for 
      self-diagnosis and investigate their potential integration with other digital 
      health interventions to optimize patient care and outcomes. In doing so, we can 
      ensure that AI chatbots, including ChatGPT, are designed and implemented to 
      safeguard users' well-being and support positive health outcomes in health care 
      settings.
CI  - ©Yeganeh Shahsavar, Avishek Choudhury. Originally published in JMIR Human Factors 
      (https://humanfactors.jmir.org), 17.05.2023.
FAU - Shahsavar, Yeganeh
AU  - Shahsavar Y
AUID- ORCID: 0000-0003-3422-7257
AD  - Industrial and Management Systems Engineering, Benjamin M Statler College of 
      Engineering and Mineral Resources, West Virginia University, Morgantown, WV, 
      United States.
FAU - Choudhury, Avishek
AU  - Choudhury A
AUID- ORCID: 0000-0002-5342-0709
AD  - Industrial and Management Systems Engineering, Benjamin M Statler College of 
      Engineering and Mineral Resources, West Virginia University, Morgantown, WV, 
      United States.
LA  - eng
PT  - Journal Article
DEP - 20230517
PL  - Canada
TA  - JMIR Hum Factors
JT  - JMIR human factors
JID - 101666561
PMC - PMC10233444
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - behavioral intention
OT  - chatbots
OT  - decision-making
OT  - health care
OT  - human factors
OT  - integrated diagnostics
OT  - integration
OT  - policy
OT  - self-diagnosis
OT  - use
OT  - users
OT  - willingness
COIS- Conflicts of Interest: None declared.
EDAT- 2023/05/17 13:09
MHDA- 2023/05/17 13:10
CRDT- 2023/05/17 11:54
PHST- 2023/03/24 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/04/24 00:00 [revised]
PHST- 2023/05/17 13:10 [medline]
PHST- 2023/05/17 13:09 [pubmed]
PHST- 2023/05/17 11:54 [entrez]
AID - v10i1e47564 [pii]
AID - 10.2196/47564 [doi]
PST - epublish
SO  - JMIR Hum Factors. 2023 May 17;10:e47564. doi: 10.2196/47564.

PMID- 37693092
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2472-7245 (Electronic)
IS  - 2472-7245 (Linking)
VI  - 8
IP  - 3
DP  - 2023 Jul-Sep
TI  - Evaluating ChatGPT Performance on the Orthopaedic In-Training Examination.
LID - 10.2106/JBJS.OA.23.00056 [doi]
LID - e23.00056
AB  - BACKGROUND: Artificial intelligence (AI) holds potential in improving medical 
      education and healthcare delivery. ChatGPT is a state-of-the-art natural language 
      processing AI model which has shown impressive capabilities, scoring in the top 
      percentiles on numerous standardized examinations, including the Uniform Bar Exam 
      and Scholastic Aptitude Test. The goal of this study was to evaluate ChatGPT 
      performance on the Orthopaedic In-Training Examination (OITE), an assessment of 
      medical knowledge for orthopedic residents. METHODS: OITE 2020, 2021, and 2022 
      questions without images were inputted into ChatGPT version 3.5 and version 4 
      (GPT-4) with zero prompting. The performance of ChatGPT was evaluated as a 
      percentage of correct responses and compared with the national average of 
      orthopedic surgery residents at each postgraduate year (PGY) level. ChatGPT was 
      asked to provide a source for its answer, which was categorized as being a 
      journal article, book, or website, and if the source could be verified. Impact 
      factor for the journal cited was also recorded. RESULTS: ChatGPT answered 196 of 
      360 answers correctly (54.3%), corresponding to a PGY-1 level. ChatGPT cited a 
      verifiable source in 47.2% of questions, with an average median journal impact 
      factor of 5.4. GPT-4 answered 265 of 360 questions correctly (73.6%), 
      corresponding to the average performance of a PGY-5 and exceeding the 
      corresponding passing score for the American Board of Orthopaedic Surgery Part I 
      Examination of 67%. GPT-4 cited a verifiable source in 87.9% of questions, with 
      an average median journal impact factor of 5.2. CONCLUSIONS: ChatGPT performed 
      above the average PGY-1 level and GPT-4 performed better than the average PGY-5 
      level, showing major improvement. Further investigation is needed to determine 
      how successive versions of ChatGPT would perform and how to optimize this 
      technology to improve medical education. CLINICAL RELEVANCE: AI has the potential 
      to aid in medical education and healthcare delivery.
CI  - Copyright © 2023 The Authors. Published by The Journal of Bone and Joint Surgery, 
      Incorporated. All rights reserved.
FAU - Kung, Justin E
AU  - Kung JE
AUID- ORCID: 0000-0003-2536-5076
AD  - Department of Orthopedic Surgery, Prisma Health-Midlands University of South 
      Carolina, Columbia, South Carolina.
FAU - Marshall, Christopher
AU  - Marshall C
AUID- ORCID: 0009-0003-3212-0656
AD  - University of South Carolina School of Medicine, Columbia, South Carolina.
FAU - Gauthier, Chase
AU  - Gauthier C
AUID- ORCID: 0009-0004-4738-8468
AD  - Department of Orthopedic Surgery, Prisma Health-Midlands University of South 
      Carolina, Columbia, South Carolina.
FAU - Gonzalez, Tyler A
AU  - Gonzalez TA
AUID- ORCID: 0000-0002-3210-8097
AD  - Department of Orthopedic Surgery, Prisma Health-Midlands University of South 
      Carolina, Columbia, South Carolina.
FAU - Jackson, J Benjamin 3rd
AU  - Jackson JB 3rd
AD  - Department of Orthopedic Surgery, Prisma Health-Midlands University of South 
      Carolina, Columbia, South Carolina.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230908
PL  - United States
TA  - JB JS Open Access
JT  - JB & JS open access
JID - 101726219
PMC - PMC10484364
EDAT- 2023/09/11 06:43
MHDA- 2023/09/11 06:44
CRDT- 2023/09/11 04:47
PHST- 2023/09/11 06:44 [medline]
PHST- 2023/09/11 06:43 [pubmed]
PHST- 2023/09/11 04:47 [entrez]
AID - JBJSOA-D-23-00056 [pii]
AID - 10.2106/JBJS.OA.23.00056 [doi]
PST - epublish
SO  - JB JS Open Access. 2023 Sep 8;8(3):e23.00056. doi: 10.2106/JBJS.OA.23.00056. 
      eCollection 2023 Jul-Sep.

PMID- 37305993
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 2042-8189 (Electronic)
IS  - 1478-2715 (Linking)
VI  - 53
IP  - 2
DP  - 2023 Jun
TI  - ChatGPT, artificial intelligence and scientific writing: What authors, peer 
      reviewers and editors should know.
PG  - 90-93
LID - 10.1177/14782715231181023 [doi]
FAU - Misra, Durga Prasanna
AU  - Misra DP
AUID- ORCID: 0000-0002-5035-7396
AD  - Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate 
      Institute of Medical Sciences, Lucknow, India.
FAU - Chandwar, Kunal
AU  - Chandwar K
AUID- ORCID: 0000-0002-0499-0641
AD  - Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate 
      Institute of Medical Sciences, Lucknow, India.
LA  - eng
PT  - Editorial
DEP - 20230612
PL  - England
TA  - J R Coll Physicians Edinb
JT  - The journal of the Royal College of Physicians of Edinburgh
JID - 101144324
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Peer Review, Research
MH  - Publishing
MH  - Writing
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - information science
OT  - peer review
OT  - publications
OT  - scientific publication
EDAT- 2023/06/12 06:42
MHDA- 2023/07/17 06:42
CRDT- 2023/06/12 05:25
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/06/12 06:42 [pubmed]
PHST- 2023/06/12 05:25 [entrez]
AID - 10.1177/14782715231181023 [doi]
PST - ppublish
SO  - J R Coll Physicians Edinb. 2023 Jun;53(2):90-93. doi: 10.1177/14782715231181023. 
      Epub 2023 Jun 12.

PMID- 37948112
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231127
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Nov 10
TI  - ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study.
PG  - e49877
LID - 10.2196/49877 [doi]
LID - e49877
AB  - BACKGROUND: The transition to clinical clerkships can be difficult for medical 
      students, as it requires the synthesis and application of preclinical information 
      into diagnostic and therapeutic decisions. ChatGPT-a generative language model 
      with many medical applications due to its creativity, memory, and accuracy-can 
      help students in this transition. OBJECTIVE: This paper models ChatGPT 3.5's 
      ability to perform interactive clinical simulations and shows this tool's benefit 
      to medical education. METHODS: Simulation starting prompts were refined using 
      ChatGPT 3.5 in Google Chrome. Starting prompts were selected based on assessment 
      format, stepwise progression of simulation events and questions, free-response 
      question type, responsiveness to user inputs, postscenario feedback, and medical 
      accuracy of the feedback. The chosen scenarios were advanced cardiac life support 
      and medical intensive care (for sepsis and pneumonia). RESULTS: Two starting 
      prompts were chosen. Prompt 1 was developed through 3 test simulations and used 
      successfully in 2 simulations. Prompt 2 was developed through 10 additional test 
      simulations and used successfully in 1 simulation. CONCLUSIONS: ChatGPT is 
      capable of creating simulations for early clinical education. These simulations 
      let students practice novel parts of the clinical curriculum, such as forming 
      independent diagnostic and therapeutic impressions over an entire patient 
      encounter. Furthermore, the simulations can adapt to user inputs in a way that 
      replicates real life more accurately than premade question bank clinical 
      vignettes. Finally, ChatGPT can create potentially unlimited free simulations 
      with specific feedback, which increases access for medical students with lower 
      socioeconomic status and underresourced medical schools. However, no tool is 
      perfect, and ChatGPT is no exception; there are concerns about simulation 
      accuracy and replicability that need to be addressed to further optimize 
      ChatGPT's performance as an educational resource.
CI  - ©Riley Scherr, Faris F Halaseh, Aidin Spina, Saman Andalib, Ronald Rivera. 
      Originally published in JMIR Medical Education (https://mededu.jmir.org), 
      10.11.2023.
FAU - Scherr, Riley
AU  - Scherr R
AUID- ORCID: 0009-0008-4796-4081
AD  - Irvine School of Medicine, University of California, Irvine, CA, United States.
FAU - Halaseh, Faris F
AU  - Halaseh FF
AUID- ORCID: 0000-0003-0569-1915
AD  - Irvine School of Medicine, University of California, Irvine, CA, United States.
FAU - Spina, Aidin
AU  - Spina A
AUID- ORCID: 0000-0003-3994-9646
AD  - Irvine School of Medicine, University of California, Irvine, CA, United States.
FAU - Andalib, Saman
AU  - Andalib S
AUID- ORCID: 0009-0002-3281-8256
AD  - Irvine School of Medicine, University of California, Irvine, CA, United States.
FAU - Rivera, Ronald
AU  - Rivera R
AUID- ORCID: 0000-0002-2221-9877
AD  - Department of Emergency Medicine, Irvine School of Medicine, University of 
      California, Irvine, CA, United States.
LA  - eng
PT  - Journal Article
DEP - 20231110
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10674152
OTO - NOTNLM
OT  - AI
OT  - AI in medical education
OT  - ChatGPT
OT  - artificial intelligence
OT  - clinical education
OT  - curriculum
OT  - generative
OT  - medical education
OT  - medical school simulations
OT  - preclinical curriculum
OT  - simulation
OT  - simulations
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/10 12:44
MHDA- 2023/11/10 12:45
CRDT- 2023/11/10 11:54
PHST- 2023/06/14 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/08/30 00:00 [revised]
PHST- 2023/11/10 12:45 [medline]
PHST- 2023/11/10 12:44 [pubmed]
PHST- 2023/11/10 11:54 [entrez]
AID - v9i1e49877 [pii]
AID - 10.2196/49877 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Nov 10;9:e49877. doi: 10.2196/49877.

PMID- 37707707
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231116
IS  - 2193-8245 (Print)
IS  - 2193-6528 (Electronic)
VI  - 12
IP  - 6
DP  - 2023 Dec
TI  - The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case 
      Reports.
PG  - 3121-3132
LID - 10.1007/s40123-023-00805-x [doi]
AB  - INTRODUCTION: The purpose of this study was to evaluate the capabilities of large 
      language models such as Chat Generative Pretrained Transformer (ChatGPT) to 
      diagnose glaucoma based on specific clinical case descriptions with comparison to 
      the performance of senior ophthalmology resident trainees. METHODS: We selected 
      11 cases with primary and secondary glaucoma from a publicly accessible online 
      database of case reports. A total of four cases had primary glaucoma including 
      open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven 
      cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion 
      glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, 
      and inflammatory glaucoma. We input the text of each case detail into ChatGPT and 
      asked for provisional and differential diagnoses. We then presented the details 
      of 11 cases to three senior ophthalmology residents and recorded their 
      provisional and differential diagnoses. We finally evaluated the responses based 
      on the correct diagnoses and evaluated agreements. RESULTS: The provisional 
      diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three 
      ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight 
      (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, 
      and third ophthalmology residents were 9, 7, and 7, respectively. CONCLUSIONS: 
      The accuracy of ChatGPT in diagnosing patients with primary and secondary 
      glaucoma, using specific case examples, was similar or better than senior 
      ophthalmology residents. With further development, ChatGPT may have the potential 
      to be used in clinical care settings, such as primary care offices, for triaging 
      and in eye care clinical practices to provide objective and quick diagnoses of 
      patients with glaucoma.
CI  - © 2023. The Author(s).
FAU - Delsoz, Mohammad
AU  - Delsoz M
AD  - Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee 
      Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA.
FAU - Raja, Hina
AU  - Raja H
AD  - Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee 
      Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA.
FAU - Madadi, Yeganeh
AU  - Madadi Y
AD  - Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee 
      Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA.
FAU - Tang, Anthony A
AU  - Tang AA
AD  - Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee 
      Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA.
FAU - Wirostko, Barbara M
AU  - Wirostko BM
AD  - John Moran Eye Center, University of Utah, Salt Lake City, UT, USA.
FAU - Kahook, Malik Y
AU  - Kahook MY
AD  - Department of Ophthalmology, University of Colorado School of Medicine, Aurora, 
      CO, USA.
FAU - Yousefi, Siamak
AU  - Yousefi S
AUID- ORCID: 0000-0001-8633-5730
AD  - Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee 
      Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA. 
      siamak.yousefi@uthsc.edu.
AD  - Department of Genetics, Genomics, and Informatics, University of Tennessee Health 
      Science Center, Memphis, TN, USA. siamak.yousefi@uthsc.edu.
LA  - eng
GR  - R01 EY033005/EY/NEI NIH HHS/United States
GR  - R01EY033005/EY/NEI NIH HHS/United States
GR  - R21EY031725/EY/NEI NIH HHS/United States
PT  - Journal Article
DEP - 20230914
PL  - England
TA  - Ophthalmol Ther
JT  - Ophthalmology and therapy
JID - 101634502
PMC - PMC10640454
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ChatGPT
OT  - Differential diagnosis
OT  - Glaucoma
OT  - Large language models (LLM)
OT  - Provisional diagnosis
COIS- Mohammad Delsoz, Hina Raja, Yeganeh Madadi, Anthony A. Tang, and Malik Y. Kahook 
      have nothing to disclose. Barbara M. Wirostko: Works for MyEyes LLC, and provides 
      consultation for Qlaris Bio and iCare. Siamak Yousefi: Received prototype 
      instruments from Remidio, M&S Technologies, and Visrtucal Fields. He gives 
      consultations to the InsihgtAEye and Enolink.
EDAT- 2023/09/14 12:42
MHDA- 2023/09/14 12:43
CRDT- 2023/09/14 11:12
PHST- 2023/08/01 00:00 [received]
PHST- 2023/08/29 00:00 [accepted]
PHST- 2023/09/14 12:43 [medline]
PHST- 2023/09/14 12:42 [pubmed]
PHST- 2023/09/14 11:12 [entrez]
AID - 10.1007/s40123-023-00805-x [pii]
AID - 805 [pii]
AID - 10.1007/s40123-023-00805-x [doi]
PST - ppublish
SO  - Ophthalmol Ther. 2023 Dec;12(6):3121-3132. doi: 10.1007/s40123-023-00805-x. Epub 
      2023 Sep 14.

PMID- 37676187
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231102
IS  - 1943-4723 (Electronic)
IS  - 0002-8177 (Linking)
VI  - 154
IP  - 11
DP  - 2023 Nov
TI  - The performance of artificial intelligence language models in board-style dental 
      knowledge assessment: A preliminary study on ChatGPT.
PG  - 970-974
LID - S0002-8177(23)00458-0 [pii]
LID - 10.1016/j.adaj.2023.07.016 [doi]
AB  - BACKGROUND: Although Chat Generative Pre-trained Transformer (ChatGPT) (OpenAI) 
      may be an appealing educational resource for students, the chatbot responses can 
      be subject to misinformation. This study was designed to evaluate the performance 
      of ChatGPT on a board-style multiple-choice dental knowledge assessment to gauge 
      its capacity to output accurate dental content and in turn the risk of 
      misinformation associated with use of the chatbot as an educational resource by 
      dental students. METHODS: ChatGPT3.5 and ChatGPT4 were asked questions obtained 
      from 3 different sources: INBDE Bootcamp, ITDOnline, and a list of board-style 
      questions provided by the Joint Commission on National Dental Examinations. 
      Image-based questions were excluded, as ChatGPT only takes text-based inputs. The 
      mean performance across 3 trials was reported for each model. RESULTS: ChatGPT3.5 
      and ChatGPT4 answered 61.3% and 76.9% of the questions correctly on average, 
      respectively. A 2-tailed t test was used to compare 2 independent sample means, 
      and a 2-tailed χ(2) test was used to compare 2 sample proportions. A P value less 
      than .05 was considered to be statistically significant. CONCLUSION: ChatGPT3.5 
      did not perform sufficiently well on the board-style knowledge assessment. 
      ChatGPT4, however, displayed a competent ability to output accurate dental 
      content. Future research should evaluate the proficiency of emerging models of 
      ChatGPT in dentistry to assess its evolving role in dental education. PRACTICAL 
      IMPLICATIONS: Although ChatGPT showed an impressive ability to output accurate 
      dental content, our findings should encourage dental students to incorporate 
      ChatGPT to supplement their existing learning program instead of using it as 
      their primary learning resource.
CI  - Copyright © 2023 American Dental Association. Published by Elsevier Inc. All 
      rights reserved.
FAU - Danesh, Arman
AU  - Danesh A
FAU - Pazouki, Hirad
AU  - Pazouki H
FAU - Danesh, Kasra
AU  - Danesh K
FAU - Danesh, Farzad
AU  - Danesh F
FAU - Danesh, Arsalan
AU  - Danesh A
LA  - eng
PT  - Journal Article
DEP - 20230905
PL  - England
TA  - J Am Dent Assoc
JT  - Journal of the American Dental Association (1939)
JID - 7503060
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Language
MH  - Educational Status
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Integrated National Board Dental Examination
OT  - dental board examination
OT  - dental education
OT  - dentistry
EDAT- 2023/09/07 12:42
MHDA- 2023/10/30 06:47
CRDT- 2023/09/07 10:33
PHST- 2023/06/30 00:00 [received]
PHST- 2023/07/27 00:00 [revised]
PHST- 2023/07/29 00:00 [accepted]
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/09/07 12:42 [pubmed]
PHST- 2023/09/07 10:33 [entrez]
AID - S0002-8177(23)00458-0 [pii]
AID - 10.1016/j.adaj.2023.07.016 [doi]
PST - ppublish
SO  - J Am Dent Assoc. 2023 Nov;154(11):970-974. doi: 10.1016/j.adaj.2023.07.016. Epub 
      2023 Sep 5.

PMID- 37560946
OWN - NLM
STAT- Publisher
LR  - 20230816
IS  - 2192-5682 (Print)
IS  - 2192-5682 (Linking)
DP  - 2023 Aug 10
TI  - ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of 
      Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review.
PG  - 21925682231195783
LID - 10.1177/21925682231195783 [doi]
AB  - STUDY DESIGN: Comparative Analysis and Narrative Review. OBJECTIVE: To assess and 
      compare ChatGPT's responses to the clinical questions and recommendations 
      proposed by The 2011 North American Spine Society (NASS) Clinical Guideline for 
      the Diagnosis and Treatment of Degenerative Lumbar Spinal Stenosis (LSS). We 
      explore the advantages and disadvantages of ChatGPT's responses through an 
      updated literature review on spinal stenosis. METHODS: We prompted ChatGPT with 
      questions from the NASS Evidence-based Clinical Guidelines for LSS and compared 
      its generated responses with the recommendations provided by the guidelines. A 
      review of the literature was performed via PubMed, OVID, and Cochrane on the 
      diagnosis and treatment of lumbar spinal stenosis between January 2012 and April 
      2023. RESULTS: 14 questions proposed by the NASS guidelines for LSS were uploaded 
      into ChatGPT and directly compared to the responses offered by NASS. Three 
      questions were on the definition and history of LSS, one on diagnostic tests, 
      seven on non-surgical interventions and three on surgical interventions. The 
      review process found 40 articles that were selected for inclusion that helped 
      corroborate or contradict the responses that were generated by ChatGPT. 
      CONCLUSIONS: ChatGPT's responses were similar to findings in the current 
      literature on LSS. These results demonstrate the potential for implementing 
      ChatGPT into the spine surgeon's workplace as a means of supporting the 
      decision-making process for LSS diagnosis and treatment. However, our narrative 
      summary only provides a limited literature review and additional research is 
      needed to standardize our findings as means of validating ChatGPT's use in the 
      clinical space.
FAU - Rajjoub, Rami
AU  - Rajjoub R
AUID- ORCID: 0009-0005-2990-7874
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
FAU - Arroyave, Juan Sebastian
AU  - Arroyave JS
AUID- ORCID: 0009-0003-9480-0657
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
FAU - Zaidat, Bashar
AU  - Zaidat B
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
FAU - Ahmed, Wasil
AU  - Ahmed W
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
FAU - Mejia, Mateo Restrepo
AU  - Mejia MR
AUID- ORCID: 0009-0003-0457-3308
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
FAU - Tang, Justin
AU  - Tang J
AUID- ORCID: 0000-0003-4544-3262
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
FAU - Kim, Jun S
AU  - Kim JS
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
FAU - Cho, Samuel K
AU  - Cho SK
AD  - Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230810
PL  - England
TA  - Global Spine J
JT  - Global spine journal
JID - 101596156
OTO - NOTNLM
OT  - artificial intelligence
OT  - clinical guidelines
OT  - decision-making
OT  - large language models
OT  - lumbar stenosis
OT  - spine
EDAT- 2023/08/10 12:41
MHDA- 2023/08/10 12:41
CRDT- 2023/08/10 09:12
PHST- 2023/08/10 12:41 [pubmed]
PHST- 2023/08/10 12:41 [medline]
PHST- 2023/08/10 09:12 [entrez]
AID - 10.1177/21925682231195783 [doi]
PST - aheadofprint
SO  - Global Spine J. 2023 Aug 10:21925682231195783. doi: 10.1177/21925682231195783.

PMID- 37352529
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231212
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 10
DP  - 2023 Oct 1
TI  - ChatGPT encounters multiple opportunities and challenges in neurosurgery.
PG  - 2886-2891
LID - 10.1097/JS9.0000000000000571 [doi]
AB  - BACKGROUND: ChatGPT, powered by the GPT model and Transformer architecture, has 
      demonstrated remarkable performance in the domains of medicine and healthcare, 
      providing customized and informative responses. In our study, we investigated the 
      potential of ChatGPT in the field of neurosurgery, focusing on its applications 
      at the patient, neurosurgery student/resident, and neurosurgeon levels. METHOD: 
      The authors conducted inquiries with ChatGPT from the viewpoints of patients, 
      neurosurgery students/residents, and neurosurgeons, covering a range of topics, 
      such as disease diagnosis, treatment options, prognosis, rehabilitation, and 
      patient care. The authors also explored concepts related to neurosurgery, 
      including fundamental principles and clinical aspects, as well as tools and 
      techniques to enhance the skills of neurosurgery students/residents. 
      Additionally, the authors examined disease-specific medical interventions and the 
      decision-making processes involved in clinical practice. RESULTS: The authors 
      received individual responses from ChatGPT, but they tended to be shallow and 
      repetitive, lacking depth and personalization. Furthermore, ChatGPT may struggle 
      to discern a patient's emotional state, hindering the establishment of rapport 
      and the delivery of appropriate care. The language used in the medical field is 
      influenced by technical and cultural factors, and biases in the training data can 
      result in skewed or inaccurate responses. Additionally, ChatGPT's limitations 
      include the inability to conduct physical examinations or interpret diagnostic 
      images, potentially overlooking complex details and individual nuances in each 
      patient's case. Moreover, its absence in the surgical setting limits its 
      practical utility. CONCLUSION: Although ChatGPT is a powerful language model, it 
      cannot substitute for the expertise and experience of trained medical 
      professionals. It lacks the capability to perform physical examinations, make 
      diagnoses, administer treatments, establish trust, provide emotional support, and 
      assist in the recovery process. Moreover, the implementation of Artificial 
      Intelligence in healthcare necessitates careful consideration of legal and 
      ethical concerns. While recognizing the potential of ChatGPT, additional training 
      with comprehensive data is necessary to fully maximize its capabilities.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Kuang, Yi-Rui
AU  - Kuang YR
AD  - Department of Spine Surgery, The First Affiliated Hospital, Hengyang medical 
      school, University of South China, Hengyang, China.
AD  - Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 
      China.
AD  - National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, 
      Central South University, Changsha, China.
FAU - Zou, Ming-Xiang
AU  - Zou MX
AD  - Department of Spine Surgery, The First Affiliated Hospital, Hengyang medical 
      school, University of South China, Hengyang, China.
FAU - Niu, Hua-Qing
AU  - Niu HQ
AD  - Department of Ophthalmology, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, China.
FAU - Zheng, Bo-Yv
AU  - Zheng BY
AD  - Department of Orthopedics Surgery, General Hospital of the Central Theater 
      Command, Wuhan, China.
FAU - Zhang, Tao-Lan
AU  - Zhang TL
AD  - Department of Spine Surgery, The First Affiliated Hospital, Hengyang medical 
      school, University of South China, Hengyang, China.
AD  - Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, 
      University of South China, Hengyang, China.
FAU - Zheng, Bo-Wen
AU  - Zheng BW
AD  - Department of Musculoskeletal Tumor Center, People's Hospital, Peking University, 
      Beijing Key Laboratory of Musculoskeletal Tumor. Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20231001
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *Neurosurgery
MH  - Artificial Intelligence
MH  - Neurosurgical Procedures
MH  - Health Facilities
PMC - PMC10583932
COIS- The authors declare that they have no competing interests.
EDAT- 2023/06/23 19:11
MHDA- 2023/10/23 00:44
CRDT- 2023/06/23 17:03
PHST- 2023/06/06 00:00 [received]
PHST- 2023/06/10 00:00 [accepted]
PHST- 2023/10/23 00:44 [medline]
PHST- 2023/06/23 19:11 [pubmed]
PHST- 2023/06/23 17:03 [entrez]
AID - 01279778-202310000-00001 [pii]
AID - IJS-D-23-01093 [pii]
AID - 10.1097/JS9.0000000000000571 [doi]
PST - epublish
SO  - Int J Surg. 2023 Oct 1;109(10):2886-2891. doi: 10.1097/JS9.0000000000000571.

PMID- 37314848
OWN - NLM
STAT- MEDLINE
DCOM- 20230616
LR  - 20230714
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Jun 14
TI  - Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey 
      Analysis.
PG  - e47184
LID - 10.2196/47184 [doi]
LID - e47184
AB  - BACKGROUND: ChatGPT (Chat Generative Pre-trained Transformer) has gained 
      popularity for its ability to generate human-like responses. It is essential to 
      note that overreliance or blind trust in ChatGPT, especially in high-stakes 
      decision-making contexts, can have severe consequences. Similarly, lacking trust 
      in the technology can lead to underuse, resulting in missed opportunities. 
      OBJECTIVE: This study investigated the impact of users' trust in ChatGPT on their 
      intent and actual use of the technology. Four hypotheses were tested: (1) users' 
      intent to use ChatGPT increases with their trust in the technology; (2) the 
      actual use of ChatGPT increases with users' intent to use the technology; (3) the 
      actual use of ChatGPT increases with users' trust in the technology; and (4) 
      users' intent to use ChatGPT can partially mediate the effect of trust in the 
      technology on its actual use. METHODS: This study distributed a web-based survey 
      to adults in the United States who actively use ChatGPT (version 3.5) at least 
      once a month between February 2023 through March 2023. The survey responses were 
      used to develop 2 latent constructs: Trust and Intent to Use, with Actual Use 
      being the outcome variable. The study used partial least squares structural 
      equation modeling to evaluate and test the structural model and hypotheses. 
      RESULTS: In the study, 607 respondents completed the survey. The primary uses of 
      ChatGPT were for information gathering (n=219, 36.1%), entertainment (n=203, 
      33.4%), and problem-solving (n=135, 22.2%), with a smaller number using it for 
      health-related queries (n=44, 7.2%) and other activities (n=6, 1%). Our model 
      explained 50.5% and 9.8% of the variance in Intent to Use and Actual Use, 
      respectively, with path coefficients of 0.711 and 0.221 for Trust on Intent to 
      Use and Actual Use, respectively. The bootstrapped results failed to reject all 4 
      null hypotheses, with Trust having a significant direct effect on both Intent to 
      Use (β=0.711, 95% CI 0.656-0.764) and Actual Use (β=0.302, 95% CI 0.229-0.374). 
      The indirect effect of Trust on Actual Use, partially mediated by Intent to Use, 
      was also significant (β=0.113, 95% CI 0.001-0.227). CONCLUSIONS: Our results 
      suggest that trust is critical to users' adoption of ChatGPT. It remains crucial 
      to highlight that ChatGPT was not initially designed for health care 
      applications. Therefore, an overreliance on it for health-related advice could 
      potentially lead to misinformation and subsequent health risks. Efforts must be 
      focused on improving the ChatGPT's ability to distinguish between queries that it 
      can safely handle and those that should be redirected to human experts (health 
      care professionals). Although risks are associated with excessive trust in 
      artificial intelligence-driven chatbots such as ChatGPT, the potential risks can 
      be reduced by advocating for shared accountability and fostering collaboration 
      between developers, subject matter experts, and human factors researchers.
CI  - ©Avishek Choudhury, Hamid Shamszare. Originally published in the Journal of 
      Medical Internet Research (https://www.jmir.org), 14.06.2023.
FAU - Choudhury, Avishek
AU  - Choudhury A
AUID- ORCID: 0000-0002-5342-0709
AD  - Industrial and Management Systems Engineering, Benjamin M. Statler College of 
      Engineering and Mineral Resources, West Virginia University, Morgantown, WV, 
      United States.
FAU - Shamszare, Hamid
AU  - Shamszare H
AUID- ORCID: 0009-0007-7786-3452
AD  - Industrial and Management Systems Engineering, Benjamin M. Statler College of 
      Engineering and Mineral Resources, West Virginia University, Morgantown, WV, 
      United States.
LA  - eng
PT  - Journal Article
DEP - 20230614
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Adult
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Trust
MH  - Health Personnel
MH  - Intention
MH  - Surveys and Questionnaires
PMC - PMC10337387
OTO - NOTNLM
OT  - AI policy
OT  - ChatGPT
OT  - adoption
OT  - artificial intelligence
OT  - behavioral intention
OT  - chatbot
OT  - human factors
OT  - intent
OT  - shared accountability
OT  - survey
OT  - technology adoption
OT  - trust
OT  - trust in AI
COIS- Conflicts of Interest: None declared.
EDAT- 2023/06/14 13:07
MHDA- 2023/06/16 06:42
CRDT- 2023/06/14 11:53
PHST- 2023/03/10 00:00 [received]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/04/19 00:00 [revised]
PHST- 2023/06/16 06:42 [medline]
PHST- 2023/06/14 13:07 [pubmed]
PHST- 2023/06/14 11:53 [entrez]
AID - v25i1e47184 [pii]
AID - 10.2196/47184 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Jun 14;25:e47184. doi: 10.2196/47184.

PMID- 37267643
OWN - NLM
STAT- MEDLINE
DCOM- 20230612
LR  - 20231116
IS  - 1532-2793 (Electronic)
IS  - 0260-6917 (Linking)
VI  - 127
DP  - 2023 Aug
TI  - What is ChatGPT and what do we do with it? Implications of the age of AI for 
      nursing and midwifery practice and education: An editorial.
PG  - 105835
LID - S0260-6917(23)00129-6 [pii]
LID - 10.1016/j.nedt.2023.105835 [doi]
FAU - Irwin, Pauletta
AU  - Irwin P
AD  - Charles Sturt University, School of Nursing Paramedicine and Healthcare Sciences, 
      Faculty of Science and Health, Australia. Electronic address: pirwin@csu.edu.au.
FAU - Jones, Donovan
AU  - Jones D
AD  - Charles Sturt University, School of Nursing Paramedicine and Healthcare Sciences, 
      Faculty of Science and Health, Australia; University of Newcastle, School of 
      Medicine and Public Health, College of Health Medicine and Wellbeing, Australia.
FAU - Fealy, Shanna
AU  - Fealy S
AD  - Charles Sturt University, School of Nursing Paramedicine and Healthcare Sciences, 
      Faculty of Science and Health, Australia; University of Newcastle, School of 
      Medicine and Public Health, College of Health Medicine and Wellbeing, Australia.
LA  - eng
PT  - Editorial
DEP - 20230530
PL  - Scotland
TA  - Nurse Educ Today
JT  - Nurse education today
JID - 8511379
MH  - Female
MH  - Humans
MH  - Pregnancy
MH  - Artificial Intelligence
MH  - Educational Status
MH  - *Midwifery
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Midwifery
OT  - Nursing
COIS- Declaration of competing interest All listed authors declare that they have no 
      conflicts of interest.
EDAT- 2023/06/02 19:13
MHDA- 2023/06/12 06:43
CRDT- 2023/06/02 18:00
PHST- 2023/02/08 00:00 [received]
PHST- 2023/04/10 00:00 [revised]
PHST- 2023/04/23 00:00 [accepted]
PHST- 2023/06/12 06:43 [medline]
PHST- 2023/06/02 19:13 [pubmed]
PHST- 2023/06/02 18:00 [entrez]
AID - S0260-6917(23)00129-6 [pii]
AID - 10.1016/j.nedt.2023.105835 [doi]
PST - ppublish
SO  - Nurse Educ Today. 2023 Aug;127:105835. doi: 10.1016/j.nedt.2023.105835. Epub 2023 
      May 30.

PMID- 37129631
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231101
IS  - 1435-702X (Electronic)
IS  - 0721-832X (Linking)
VI  - 261
IP  - 10
DP  - 2023 Oct
TI  - Artificial intelligence-based ChatGPT chatbot responses for patient and parent 
      questions on vernal keratoconjunctivitis.
PG  - 3041-3043
LID - 10.1007/s00417-023-06078-1 [doi]
FAU - Rasmussen, Marie Louise Roed
AU  - Rasmussen MLR
AUID- ORCID: 0000-0002-9392-8697
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, DK-2600, 
      Glostrup, Denmark. marie.louise.roed.rasmussen@regionh.dk.
AD  - Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 
      marie.louise.roed.rasmussen@regionh.dk.
FAU - Larsen, Ann-Cathrine
AU  - Larsen AC
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, DK-2600, 
      Glostrup, Denmark.
FAU - Subhi, Yousif
AU  - Subhi Y
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, DK-2600, 
      Glostrup, Denmark.
AD  - Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
FAU - Potapenko, Ivan
AU  - Potapenko I
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, DK-2600, 
      Glostrup, Denmark.
LA  - eng
PT  - Journal Article
DEP - 20230502
PL  - Germany
TA  - Graefes Arch Clin Exp Ophthalmol
JT  - Graefe's archive for clinical and experimental ophthalmology = Albrecht von 
      Graefes Archiv fur klinische und experimentelle Ophthalmologie
JID - 8205248
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Conjunctivitis, Allergic/diagnosis
OTO - NOTNLM
OT  - ChatGPT
OT  - Information accuracy
OT  - Patient information
OT  - Vernal keratoconjunctivitis
EDAT- 2023/05/02 12:44
MHDA- 2023/10/23 12:41
CRDT- 2023/05/02 11:05
PHST- 2023/03/06 00:00 [received]
PHST- 2023/04/15 00:00 [accepted]
PHST- 2023/04/11 00:00 [revised]
PHST- 2023/10/23 12:41 [medline]
PHST- 2023/05/02 12:44 [pubmed]
PHST- 2023/05/02 11:05 [entrez]
AID - 10.1007/s00417-023-06078-1 [pii]
AID - 10.1007/s00417-023-06078-1 [doi]
PST - ppublish
SO  - Graefes Arch Clin Exp Ophthalmol. 2023 Oct;261(10):3041-3043. doi: 
      10.1007/s00417-023-06078-1. Epub 2023 May 2.

PMID- 37775381
OWN - NLM
STAT- In-Process
LR  - 20231212
IS  - 0219-3108 (Electronic)
IS  - 1015-9584 (Linking)
VI  - 46
IP  - 12
DP  - 2023 Dec
TI  - Can ChatGPT pass China's national medical licensing examination?
PG  - 6112-6113
LID - S1015-9584(23)01505-1 [pii]
LID - 10.1016/j.asjsur.2023.09.089 [doi]
FAU - Shang, Luxiang
AU  - Shang L
AD  - Department of Cardiology, The First Affiliated Hospital of Shandong First Medical 
      University, Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health 
      Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, Shandong, 
      250014, China.
FAU - Xue, Mingyue
AU  - Xue M
AD  - Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Canada.
FAU - Hou, Yinglong
AU  - Hou Y
AD  - Department of Cardiology, The First Affiliated Hospital of Shandong First Medical 
      University, Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health 
      Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, Shandong, 
      250014, China. Electronic address: yinglonghou@hotmail.com.
FAU - Tang, Baopeng
AU  - Tang B
AD  - Department of Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac 
      Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang 
      Medical University, Urumqi, Xinjiang, 830054, China. Electronic address: 
      tangbaopeng1111@163.com.
LA  - eng
PT  - Letter
DEP - 20230927
PL  - Netherlands
TA  - Asian J Surg
JT  - Asian journal of surgery
JID - 8900600
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Medical education
OT  - National Medical Licensing Examination
COIS- Declaration of competing interest All authors report no conflict of interest.
EDAT- 2023/09/30 09:42
MHDA- 2023/09/30 09:42
CRDT- 2023/09/29 21:54
PHST- 2023/08/27 00:00 [received]
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/09/30 09:42 [pubmed]
PHST- 2023/09/30 09:42 [medline]
PHST- 2023/09/29 21:54 [entrez]
AID - S1015-9584(23)01505-1 [pii]
AID - 10.1016/j.asjsur.2023.09.089 [doi]
PST - ppublish
SO  - Asian J Surg. 2023 Dec;46(12):6112-6113. doi: 10.1016/j.asjsur.2023.09.089. Epub 
      2023 Sep 27.

PMID- 37887080
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231029
IS  - 2039-7275 (Print)
IS  - 2039-7283 (Electronic)
IS  - 2039-7275 (Linking)
VI  - 13
IP  - 5
DP  - 2023 Sep 26
TI  - AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat.
PG  - 1160-1172
LID - 10.3390/clinpract13050104 [doi]
AB  - Patients with chronic kidney disease (CKD) necessitate specialized renal diets to 
      prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive 
      assessment of food components is pivotal, yet burdensome for healthcare 
      providers. With evolving artificial intelligence (AI) technology, models such as 
      ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and 
      assisting professionals. To gauge the efficacy of different AI models in 
      discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, 
      ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, 
      curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input 
      into each model. These items were characterized by their potassium (149 items) 
      and phosphorus (91 items) content. Each model was tasked to categorize the items 
      into high or low potassium and high phosphorus content. The results were 
      juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The 
      concordance between repeated sessions was also evaluated to assess model 
      consistency. Among the models tested, ChatGPT 4 displayed superior performance in 
      identifying potassium content, correctly classifying 81% of the foods. It 
      accurately discerned 60% of low potassium and 99% of high potassium foods. In 
      comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat 
      models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus 
      content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and 
      Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while 
      ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse 
      range of accuracy in discerning potassium and phosphorus content in foods 
      suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement 
      over its predecessor, especially in detecting potassium content. The Bard AI 
      model exhibited exceptional precision for phosphorus identification. This study 
      underscores the potential of AI models as efficient tools in renal dietary 
      planning, though refinements are warranted for optimal utility.
FAU - Qarajeh, Ahmad
AU  - Qarajeh A
AUID- ORCID: 0000-0003-2457-9435
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Faculty of Medicine, University of Jordan, Amman 11942, Jordan.
FAU - Tangpanithandee, Supawit
AU  - Tangpanithandee S
AUID- ORCID: 0000-0001-6103-2338
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, 
      Mahidol University, Samut Prakan 10540, Thailand.
FAU - Thongprayoon, Charat
AU  - Thongprayoon C
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Suppadungsuk, Supawadee
AU  - Suppadungsuk S
AUID- ORCID: 0000-0003-1597-2411
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, 
      Mahidol University, Samut Prakan 10540, Thailand.
FAU - Krisanapan, Pajaree
AU  - Krisanapan P
AUID- ORCID: 0000-0002-2888-881X
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Department of Internal Medicine, Faculty of Medicine, Thammasat University, 
      Pathum Thani 12120, Thailand.
FAU - Aiumtrakul, Noppawit
AU  - Aiumtrakul N
AUID- ORCID: 0000-0003-0479-7785
AD  - Department of Medicine, John A. Burns School of Medicine, University of Hawaii, 
      Honolulu, HI 96813, USA.
FAU - Garcia Valencia, Oscar A
AU  - Garcia Valencia OA
AUID- ORCID: 0000-0003-0186-9448
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Miao, Jing
AU  - Miao J
AUID- ORCID: 0000-0003-0642-9740
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Qureshi, Fawad
AU  - Qureshi F
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Cheungpasitporn, Wisit
AU  - Cheungpasitporn W
AUID- ORCID: 0000-0001-9954-9711
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
LA  - eng
PT  - Journal Article
DEP - 20230926
PL  - Switzerland
TA  - Clin Pract
JT  - Clinics and practice
JID - 101563282
PMC - PMC10605499
OTO - NOTNLM
OT  - AI technology
OT  - Bard AI
OT  - Bing Chat
OT  - CKD
OT  - ChatGPT
OT  - chronic kidney disease
OT  - dietary planning
OT  - efficacy evaluation
OT  - food assessment
OT  - healthcare providers
OT  - hyperkalemia
OT  - hyperphosphatemia
OT  - phosphorus content
OT  - potassium content
OT  - renal diets
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/27 12:44
MHDA- 2023/10/27 12:45
CRDT- 2023/10/27 06:52
PHST- 2023/08/27 00:00 [received]
PHST- 2023/09/15 00:00 [revised]
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/10/27 12:45 [medline]
PHST- 2023/10/27 12:44 [pubmed]
PHST- 2023/10/27 06:52 [entrez]
AID - clinpract13050104 [pii]
AID - clinpract-13-00104 [pii]
AID - 10.3390/clinpract13050104 [doi]
PST - epublish
SO  - Clin Pract. 2023 Sep 26;13(5):1160-1172. doi: 10.3390/clinpract13050104.

PMID- 37577545
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
DP  - 2023 Aug 1
TI  - From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and 
      Biomedical Knowledge Graphs.
LID - rs.3.rs-3185632 [pii]
LID - 10.21203/rs.3.rs-3185632/v1 [doi]
AB  - PURPOSE: Large Language Models (LLMs) have shown exceptional performance in 
      various natural language processing tasks, benefiting from their language 
      generation capabilities and ability to acquire knowledge from unstructured text. 
      However, in the biomedical domain, LLMs face limitations that lead to inaccurate 
      and inconsistent answers. Knowledge Graphs (KGs) have emerged as valuable 
      resources for organizing structured information. Biomedical Knowledge Graphs 
      (BKGs) have gained significant attention for managing diverse and large-scale 
      biomedical knowledge. The objective of this study is to assess and compare the 
      capabilities of ChatGPT and existing BKGs in question-answering, biomedical 
      knowledge discovery, and reasoning tasks within the biomedical domain. METHODS: 
      We conducted a series of experiments to assess the performance of ChatGPT and the 
      BKGs in various aspects of querying existing biomedical knowledge, knowledge 
      discovery, and knowledge reasoning. Firstly, we tasked ChatGPT with answering 
      questions sourced from the "Alternative Medicine" sub-category of Yahoo! Answers 
      and recorded the responses. Additionally, we queried BKG to retrieve the relevant 
      knowledge records corresponding to the questions and assessed them manually. In 
      another experiment, we formulated a prediction scenario to assess ChatGPT's 
      ability to suggest potential drug/dietary supplement repurposing candidates. 
      Simultaneously, we utilized BKG to perform link prediction for the same task. The 
      outcomes of ChatGPT and BKG were compared and analyzed. Furthermore, we evaluated 
      ChatGPT and BKG's capabilities in establishing associations between pairs of 
      proposed entities. This evaluation aimed to assess their reasoning abilities and 
      the extent to which they can infer connections within the knowledge domain. 
      RESULTS: The results indicate that ChatGPT with GPT-4.0 outperforms both GPT-3.5 
      and BKGs in providing existing information. However, BKGs demonstrate higher 
      reliability in terms of information accuracy. ChatGPT exhibits limitations in 
      performing novel discoveries and reasoning, particularly in establishing 
      structured links between entities compared to BKGs. CONCLUSIONS: To address the 
      limitations observed, future research should focus on integrating LLMs and BKGs 
      to leverage the strengths of both approaches. Such integration would optimize 
      task performance and mitigate potential risks, leading to advancements in 
      knowledge within the biomedical field and contributing to the overall well-being 
      of individuals.
FAU - Hou, Yu
AU  - Hou Y
AD  - University of Minnesota.
FAU - Yeung, Jeremy
AU  - Yeung J
AD  - University of Minnesota.
FAU - Xu, Hua
AU  - Xu H
AD  - Yale University.
FAU - Su, Chang
AU  - Su C
AD  - Temple University.
FAU - Wang, Fei
AU  - Wang F
AD  - Weill Cornell Medicine.
FAU - Zhang, Rui
AU  - Zhang R
AD  - University of Minnesota.
LA  - eng
GR  - R01 AG078154/AG/NIA NIH HHS/United States
PT  - Preprint
DEP - 20230801
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10418534
OTO - NOTNLM
OT  - Biomedical Knowledge Graph (BKG)
OT  - ChatGPT
OT  - Large Language Models (LLMs)
COIS- Declarations Competing interests The authors declare that they have no competing 
      interests.
EDAT- 2023/08/14 06:41
MHDA- 2023/08/14 06:42
CRDT- 2023/08/14 05:01
PHST- 2023/08/14 06:41 [pubmed]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/14 05:01 [entrez]
AID - rs.3.rs-3185632 [pii]
AID - 10.21203/rs.3.rs-3185632/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 Aug 1:rs.3.rs-3185632. doi: 10.21203/rs.3.rs-3185632/v1. Preprint.

PMID- 37553552
OWN - NLM
STAT- MEDLINE
DCOM- 20231027
LR  - 20231027
IS  - 1433-7347 (Electronic)
IS  - 0942-2056 (Print)
IS  - 0942-2056 (Linking)
VI  - 31
IP  - 11
DP  - 2023 Nov
TI  - Exploring the potential of ChatGPT as a supplementary tool for providing 
      orthopaedic information.
PG  - 5190-5198
LID - 10.1007/s00167-023-07529-2 [doi]
AB  - PURPOSE: To investigate the potential use of large language models (LLMs) in 
      orthopaedics by presenting queries pertinent to anterior cruciate ligament (ACL) 
      surgery to generative pre-trained transformer (ChatGPT, specifically using its 
      GPT-4 model of March 14th 2023). Additionally, this study aimed to evaluate the 
      depth of the LLM's knowledge and investigate its adaptability to different user 
      groups. It was hypothesized that the ChatGPT would be able to adapt to different 
      target groups due to its strong language understanding and processing 
      capabilities. METHODS: ChatGPT was presented with 20 questions and response was 
      requested for two distinct target audiences: patients and non-orthopaedic medical 
      doctors. Two board-certified orthopaedic sports medicine surgeons and two expert 
      orthopaedic sports medicine surgeons independently evaluated the responses 
      generated by ChatGPT. Mean correctness, completeness, and adaptability to the 
      target audiences (patients and non-orthopaedic medical doctors) were determined. 
      A three-point response scale facilitated nuanced assessment. RESULTS: ChatGPT 
      exhibited fair accuracy, with average correctness scores of 1.69 and 1.66 (on a 
      scale from 0, incorrect, 1, partially correct, to 2, correct) for patients and 
      medical doctors, respectively. Three of the 20 questions (15.0%) were deemed 
      incorrect by any of the four orthopaedic sports medicine surgeon assessors. 
      Moreover, overall completeness was calculated to be 1.51 and 1.64 for patients 
      and medical doctors, respectively, while overall adaptiveness was determined to 
      be 1.75 and 1.73 for patients and doctors, respectively. CONCLUSION: Overall, 
      ChatGPT was successful in generating correct responses in approximately 65% of 
      the cases related to ACL surgery. The findings of this study imply that LLMs 
      offer potential as a supplementary tool for acquiring orthopaedic knowledge. 
      However, although ChatGPT can provide guidance and effectively adapt to diverse 
      target audiences, it cannot supplant the expertise of orthopaedic sports medicine 
      surgeons in diagnostic and treatment planning endeavours due to its limited 
      understanding of orthopaedic domains and its potential for erroneous responses. 
      LEVEL OF EVIDENCE: V.
CI  - © 2023. The Author(s).
FAU - Kaarre, Janina
AU  - Kaarre J
AUID- ORCID: 0000-0003-2559-8283
AD  - Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, 
      University of Pittsburgh, Pittsburgh, USA. janina.kaarre@gu.se.
AD  - Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, 
      University of Gothenburg, Göteborgsvägen 31, 431 80, Mölndal, Sweden. 
      janina.kaarre@gu.se.
FAU - Feldt, Robert
AU  - Feldt R
AD  - Department of Computer Science and Engineering, Chalmers University of 
      Technology, Gothenburg, Sweden.
FAU - Keeling, Laura E
AU  - Keeling LE
AD  - Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, 
      University of Pittsburgh, Pittsburgh, USA.
FAU - Dadoo, Sahil
AU  - Dadoo S
AD  - Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, 
      University of Pittsburgh, Pittsburgh, USA.
FAU - Zsidai, Bálint
AU  - Zsidai B
AD  - Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, 
      University of Gothenburg, Göteborgsvägen 31, 431 80, Mölndal, Sweden.
FAU - Hughes, Jonathan D
AU  - Hughes JD
AD  - Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, 
      University of Pittsburgh, Pittsburgh, USA.
FAU - Samuelsson, Kristian
AU  - Samuelsson K
AD  - Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, 
      University of Gothenburg, Göteborgsvägen 31, 431 80, Mölndal, Sweden.
AD  - Department of Orthopaedics, Sahlgrenska University Hospital, Mölndal, Sweden.
FAU - Musahl, Volker
AU  - Musahl V
AD  - Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, 
      University of Pittsburgh, Pittsburgh, USA.
LA  - eng
PT  - Journal Article
DEP - 20230808
PL  - Germany
TA  - Knee Surg Sports Traumatol Arthrosc
JT  - Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
JID - 9314730
SB  - IM
MH  - Humans
MH  - *Orthopedics
MH  - *Orthopedic Procedures
MH  - *Orthopedic Surgeons
MH  - Anterior Cruciate Ligament
MH  - Language
PMC - PMC10598178
OTO - NOTNLM
OT  - ACL
OT  - Anterior cruciate ligament
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Correctness
OT  - Large language models
COIS- Robert Feldt is CTO and founder of a software consultancy company (Accelerandium 
      AB). Volker Musahl reports educational grants, consulting fees, and speaking fees 
      from Smith & Nephew plc, educational grants from Arthrex and DePuy/Synthes, is a 
      board member of the International Society of Arthroscopy, Knee Surgery and 
      Orthopaedic Sports Medicine (ISAKOS), and deputy editor-in-chief of Knee Surgery, 
      Sports Traumatology, Arthroscopy (KSSTA). Volker Musahl also has a patent, U.S. 
      Patent No. 9,949,684, issued on April 24, 2018, to the University of Pittsburgh. 
      Kristian Samuelsson is a member of the Board of Directors of Getinge AB (publ).
EDAT- 2023/08/09 01:05
MHDA- 2023/10/27 06:42
CRDT- 2023/08/08 23:30
PHST- 2023/04/19 00:00 [received]
PHST- 2023/07/26 00:00 [accepted]
PHST- 2023/10/27 06:42 [medline]
PHST- 2023/08/09 01:05 [pubmed]
PHST- 2023/08/08 23:30 [entrez]
AID - 10.1007/s00167-023-07529-2 [pii]
AID - 7529 [pii]
AID - 10.1007/s00167-023-07529-2 [doi]
PST - ppublish
SO  - Knee Surg Sports Traumatol Arthrosc. 2023 Nov;31(11):5190-5198. doi: 
      10.1007/s00167-023-07529-2. Epub 2023 Aug 8.

PMID- 37438164
OWN - NLM
STAT- Publisher
LR  - 20230712
IS  - 1097-6841 (Electronic)
IS  - 0022-3913 (Linking)
DP  - 2023 Jul 10
TI  - How much can we rely on artificial intelligence chatbots such as the ChatGPT 
      software program to assist with scientific writing?
LID - S0022-3913(23)00371-2 [pii]
LID - 10.1016/j.prosdent.2023.05.023 [doi]
AB  - STATEMENT OF PROBLEM: Use of the ChatGPT software program by authors raises many 
      questions, primarily regarding egregious issues such as plagiarism. Nevertheless, 
      little is known about the extent to which artificial intelligence (AI) models can 
      produce high-quality research publications and advance and shape the direction of 
      a research topic. PURPOSE: The purpose of this study was to determine how well 
      the ChatGPT software program, a writing tool powered by AI, could respond to 
      questions about scientific or research writing and generate accurate references 
      with academic examples. MATERIAL AND METHODS: Questions were made for the ChatGPT 
      software program to locate an abstract containing a particular keyword in the 
      Journal of Prosthetic Dentistry (JPD). Then, whether the resulting articles 
      existed or were published was determined. Questions were made for the algorithm 5 
      times to locate 5 JPD articles containing 2 specific keywords, bringing the total 
      number of articles to 25. The process was repeated twice, each time with a 
      different set of keywords, and the ChatGPT software program provided a total of 
      75 articles. The search was conducted at various times between April 1 and 4, 
      2023. Finally, 2 authors independently searched the JPD website and Google 
      Scholar to determine whether the articles provided by the ChatGPT software 
      program existed. RESULTS: When the author tested the ChatGPT software program's 
      ability to locate articles in the JPD and Google Scholar using a set of keywords, 
      the results did not match the papers that the ChatGPT software program had 
      generated with the help of the AI tool. Consequently, all 75 articles provided by 
      the ChatGPT software program were not accurately located in the JPD or Google 
      Scholar databases and had to be added manually to ensure the accuracy of the 
      relevant references. CONCLUSIONS: Researchers and academic scholars must be 
      cautious when using the ChatGPT software program because AI-generated content 
      cannot provide or analyze the same information as an author or researcher. In 
      addition, the results indicated that writing credit or references to such content 
      or references in prestigious academic journals is not yet appropriate. At this 
      time, scientific writing is only valid when performed manually by researchers.
CI  - Copyright © 2023 Editorial Council for The Journal of Prosthetic Dentistry. 
      Published by Elsevier Inc. All rights reserved.
FAU - Dashti, Mahmood
AU  - Dashti M
AD  - Researcher, School of Dentistry, Shahid Beheshti University of Medical Sciences, 
      Tehran, Iran. Electronic address: Dashti.mahmood72@gmail.com.
FAU - Londono, Jimmy
AU  - Londono J
AD  - Professor and Director of the Prosthodontics Residency Program and the Ronald 
      Goldstein Center for Esthetics and Implant Dentistry, The Dental College of 
      Georgia at Augusta University, Augusta, Ga.
FAU - Ghasemi, Shohreh
AU  - Ghasemi S
AD  - Adjunct Assistant Professor, Department of Oral and Maxillofacial Surgery, The 
      Dental College of Georgia at Augusta University, Augusta, Ga.
FAU - Moghaddasi, Negar
AU  - Moghaddasi N
AD  - Researcher, College of Dental Medicine, Western University of Health Sciences, 
      Pomona, Calif.
LA  - eng
PT  - Journal Article
DEP - 20230710
PL  - United States
TA  - J Prosthet Dent
JT  - The Journal of prosthetic dentistry
JID - 0376364
SB  - IM
EDAT- 2023/07/13 01:06
MHDA- 2023/07/13 01:06
CRDT- 2023/07/12 21:58
PHST- 2023/04/17 00:00 [received]
PHST- 2023/05/22 00:00 [revised]
PHST- 2023/05/24 00:00 [accepted]
PHST- 2023/07/13 01:06 [medline]
PHST- 2023/07/13 01:06 [pubmed]
PHST- 2023/07/12 21:58 [entrez]
AID - S0022-3913(23)00371-2 [pii]
AID - 10.1016/j.prosdent.2023.05.023 [doi]
PST - aheadofprint
SO  - J Prosthet Dent. 2023 Jul 10:S0022-3913(23)00371-2. doi: 
      10.1016/j.prosdent.2023.05.023.

PMID- 37031907
OWN - NLM
STAT- MEDLINE
DCOM- 20230501
LR  - 20230505
IS  - 1879-0542 (Electronic)
IS  - 0165-2478 (Linking)
VI  - 256-257
DP  - 2023 Apr-May
TI  - Science fact vs science fiction: A ChatGPT immunological review experiment gone 
      awry.
PG  - 42-47
LID - S0165-2478(23)00053-6 [pii]
LID - 10.1016/j.imlet.2023.04.002 [doi]
AB  - Artificial intelligence (AI) has made great progress in recent years. The latest 
      chatbot to make a splash is ChatGPT. To see if this type of AI could also be 
      helpful in creating an immunological review article, I put a planned review on 
      different classes of small RNAs during murine B cell development to the test. 
      Although the general wording sounded very polished and convincing, ChatGPT 
      encountered great difficulties when asked for details and references and made 
      many incorrect statements, leading me to conclude that this kind of AI is not 
      (yet?) suitable for assisting in the writing of scientific articles.
CI  - Copyright © 2023. Published by Elsevier B.V.
FAU - Wittmann, Jürgen
AU  - Wittmann J
AD  - Division of Molecular Immunology, Department of Internal Medicine III, 
      Nikolaus-Fiebiger-Center of Molecular Medicine (NFZ), 
      Friedrich-Alexander-Universität Erlangen-Nürnberg, Glückstraße 6, Erlangen 
      D-91054, Germany. Electronic address: juergen.wittmann@uk-erlangen.de.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230407
PL  - Netherlands
TA  - Immunol Lett
JT  - Immunology letters
JID - 7910006
RN  - 63231-63-0 (RNA)
SB  - IM
MH  - Animals
MH  - Mice
MH  - *Artificial Intelligence
MH  - *Lymphocyte Activation
MH  - RNA
MH  - Software
OTO - NOTNLM
OT  - ChatGPT
OT  - Chatbot
OT  - Human intervention
OT  - Manuscript preparation
OT  - MicroRNAs
OT  - References
EDAT- 2023/04/10 06:00
MHDA- 2023/05/01 06:42
CRDT- 2023/04/09 19:26
PHST- 2023/01/31 00:00 [received]
PHST- 2023/03/23 00:00 [revised]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/10 06:00 [pubmed]
PHST- 2023/04/09 19:26 [entrez]
AID - S0165-2478(23)00053-6 [pii]
AID - 10.1016/j.imlet.2023.04.002 [doi]
PST - ppublish
SO  - Immunol Lett. 2023 Apr-May;256-257:42-47. doi: 10.1016/j.imlet.2023.04.002. Epub 
      2023 Apr 7.

PMID- 37091303
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230425
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Impact of ChatGPT on medical chatbots as a disruptive technology.
PG  - 1166014
LID - 10.3389/frai.2023.1166014 [doi]
LID - 1166014
FAU - Chow, James C L
AU  - Chow JCL
AD  - Radiation Medicine Program, Princess Margaret Cancer Centre, University Health 
      Network, Toronto, ON, Canada.
AD  - Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.
FAU - Sanders, Leslie
AU  - Sanders L
AD  - Department of Humanities, York University, Toronto, ON, Canada.
FAU - Li, Kay
AU  - Li K
AD  - Department of English, University of Toronto, Toronto, ON, Canada.
LA  - eng
PT  - Journal Article
DEP - 20230405
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10113434
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - big data
OT  - disruptive technology
OT  - healthcare
OT  - knowledge base
OT  - medical chatbot
OT  - nature language processing
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/04/24 06:41
MHDA- 2023/04/24 06:42
CRDT- 2023/04/24 03:54
PHST- 2023/02/14 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/04/24 06:42 [medline]
PHST- 2023/04/24 06:41 [pubmed]
PHST- 2023/04/24 03:54 [entrez]
AID - 10.3389/frai.2023.1166014 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Apr 5;6:1166014. doi: 10.3389/frai.2023.1166014. 
      eCollection 2023.

PMID- 36907556
OWN - NLM
STAT- Publisher
LR  - 20230920
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
DP  - 2023 Mar 11
TI  - Consulting ChatGPT: Ethical dilemmas in language model artificial intelligence.
LID - S0190-9622(23)00364-X [pii]
LID - 10.1016/j.jaad.2023.02.052 [doi]
FAU - Beltrami, Eric J
AU  - Beltrami EJ
AD  - University of Connecticut School of Medicine, Farmington, CT.
FAU - Grant-Kels, Jane Margaret
AU  - Grant-Kels JM
AD  - Department of Dermatology, University of Connecticut Health Center, Farmington, 
      CT; Department of Dermatology, University of Florida, Gainesville, FL. Electronic 
      address: grant@uchc.edu.
LA  - eng
PT  - Letter
DEP - 20230311
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
CIN - J Am Acad Dermatol. 2023 Oct;89(4):e157-e158. PMID: 37263382
OTO - NOTNLM
OT  - ChatGPT
OT  - accessibility to dermatology
OT  - artificial intelligence
OT  - beneficence
OT  - clinical practice
OT  - ethics
OT  - justice
OT  - language models
OT  - nonmaleficence
COIS- Conflicts of interest Dr Grant-Kels is a stockholder and medical advisor for 
      DermaSensor. This company’s specific technology is not discussed in this 
      submission. Author Beltrami has no conflicts of interest to declare.
EDAT- 2023/03/13 06:00
MHDA- 2023/03/13 06:00
CRDT- 2023/03/12 20:32
PHST- 2023/01/12 00:00 [received]
PHST- 2023/02/14 00:00 [revised]
PHST- 2023/02/28 00:00 [accepted]
PHST- 2023/03/13 06:00 [pubmed]
PHST- 2023/03/13 06:00 [medline]
PHST- 2023/03/12 20:32 [entrez]
AID - S0190-9622(23)00364-X [pii]
AID - 10.1016/j.jaad.2023.02.052 [doi]
PST - aheadofprint
SO  - J Am Acad Dermatol. 2023 Mar 11:S0190-9622(23)00364-X. doi: 
      10.1016/j.jaad.2023.02.052.

PMID- 37289368
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 11
DP  - 2023 Nov
TI  - Leveraging ChatGPT for Human Behavior Assessment: Potential Implications for 
      Mental Health Care.
PG  - 2362-2364
LID - 10.1007/s10439-023-03269-z [doi]
AB  - This letter explores the capability of AI, specifically OpenAI's ChatGPT, in 
      interpreting human behavior and its potential implications for mental health 
      care. Data were collected from the Reddit forum "AmItheAsshole" (AITA) to assess 
      the congruence between AI's verdict and the collective human opinion on this 
      platform. AITA, with its vast range of interpersonal situations, provides rich 
      insights into human behavioral evaluation and perception. Two key research 
      questions were addressed: the degree of alignment between ChatGPT's judgment and 
      collective verdicts of Redditors, and the consistency of ChatGPT in evaluating 
      the same AITA post repeatedly. The results exhibited a promising level of 
      agreement between ChatGPT and human verdicts. It also demonstrated high 
      consistency across repeated evaluations of the same posts. These findings hint at 
      the significant potential of AI in mental health care provision, underscoring the 
      importance of continued research and development in this field.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Haman, Michael
AU  - Haman M
AUID- ORCID: 0000-0001-5772-2045
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Kamýcká 129, 165 00, Praha-Suchdol, Czech Republic. 
      haman@pef.czu.cz.
FAU - Školník, Milan
AU  - Školník M
AUID- ORCID: 0000-0002-0672-219X
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Kamýcká 129, 165 00, Praha-Suchdol, Czech Republic.
FAU - Šubrt, Tomáš
AU  - Šubrt T
AUID- ORCID: 0000-0002-4437-1431
AD  - Department of Systems Engineering, Faculty of Economics and Management, Czech 
      University of Life Sciences Prague, Kamýcká 129, 165 00, Praha-Suchdol, Czech 
      Republic.
LA  - eng
PT  - Letter
DEP - 20230608
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
EDAT- 2023/06/08 13:07
MHDA- 2023/06/08 13:07
CRDT- 2023/06/08 11:12
PHST- 2023/05/27 00:00 [received]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/06/08 13:07 [pubmed]
PHST- 2023/06/08 13:07 [medline]
PHST- 2023/06/08 11:12 [entrez]
AID - 10.1007/s10439-023-03269-z [pii]
AID - 10.1007/s10439-023-03269-z [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Nov;51(11):2362-2364. doi: 10.1007/s10439-023-03269-z. Epub 
      2023 Jun 8.

PMID- 36934624
OWN - NLM
STAT- MEDLINE
DCOM- 20230414
LR  - 20231031
IS  - 1532-2793 (Electronic)
IS  - 0260-6917 (Linking)
VI  - 125
DP  - 2023 Jun
TI  - Chatting or cheating? The impacts of ChatGPT and other artificial intelligence 
      language models on nurse education.
PG  - 105796
LID - S0260-6917(23)00090-4 [pii]
LID - 10.1016/j.nedt.2023.105796 [doi]
FAU - Choi, Edmond Pui Hang
AU  - Choi EPH
AD  - School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong 
      Kong. Electronic address: h0714919@connect.hku.hk.
FAU - Lee, Jung Jae
AU  - Lee JJ
AD  - School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong 
      Kong.
FAU - Ho, Mu-Hsing
AU  - Ho MH
AD  - School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong 
      Kong.
FAU - Kwok, Jojo Yan Yan
AU  - Kwok JYY
AD  - School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong 
      Kong.
FAU - Lok, Kris Yuet Wan
AU  - Lok KYW
AD  - School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong 
      Kong.
LA  - eng
PT  - Journal Article
DEP - 20230315
PL  - Scotland
TA  - Nurse Educ Today
JT  - Nurse education today
JID - 8511379
MH  - Humans
MH  - *Artificial Intelligence
MH  - Educational Status
MH  - Language
MH  - *Education, Nursing
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Nurse Education
OT  - Nursing
COIS- Declaration of competing interest We declare that there are no conflicts of 
      interest related to this manuscript, and we take full responsibility for its 
      completion and attest to its validity.
EDAT- 2023/03/20 06:00
MHDA- 2023/04/14 06:41
CRDT- 2023/03/19 19:06
PHST- 2023/02/20 00:00 [received]
PHST- 2023/03/02 00:00 [revised]
PHST- 2023/03/09 00:00 [accepted]
PHST- 2023/04/14 06:41 [medline]
PHST- 2023/03/20 06:00 [pubmed]
PHST- 2023/03/19 19:06 [entrez]
AID - S0260-6917(23)00090-4 [pii]
AID - 10.1016/j.nedt.2023.105796 [doi]
PST - ppublish
SO  - Nurse Educ Today. 2023 Jun;125:105796. doi: 10.1016/j.nedt.2023.105796. Epub 2023 
      Mar 15.

PMID- 37832828
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 2468-7855 (Electronic)
IS  - 2468-7855 (Linking)
VI  - 125
IP  - 2
DP  - 2023 Oct 11
TI  - Virtual surgical planning in orthognathic surgery and ChatGPT-4: how artificial 
      intelligence can optimize patient care.
PG  - 101655
LID - S2468-7855(23)00276-8 [pii]
LID - 10.1016/j.jormas.2023.101655 [doi]
FAU - Santana, Lucas Alves da Mota
AU  - Santana LADM
AD  - School of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, Brazil; 
      School of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil. 
      Electronic address: lucassantana.pat@gmail.com.
FAU - Floresta, Lara Góis
AU  - Floresta LG
AD  - School of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil.
FAU - Alves, Êmilly Victória Maciel
AU  - Alves ÊVM
AD  - School of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil.
FAU - Barbosa, Breno Ferreira
AU  - Barbosa BF
AD  - School of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, Brazil.
FAU - Borges, Lysandro Pinto
AU  - Borges LP
AD  - Schoolof Pharmacy, Federal University of Sergipe (UFS), Aracaju, SE, Brazil.
FAU - Barreto, Marina Dos Santos
AU  - Barreto MDS
AD  - Schoolof Pharmacy, Federal University of Sergipe (UFS), Aracaju, SE, Brazil.
FAU - Santos, Ronaldy Santana
AU  - Santos RS
AD  - Schoolof Pharmacy, Federal University of Sergipe (UFS), Aracaju, SE, Brazil.
FAU - Silva, Deise Maria Rego Rodrigues
AU  - Silva DMRR
AD  - Schoolof Pharmacy, Federal University of Sergipe (UFS), Aracaju, SE, Brazil.
FAU - Palanch Repeke, Carlos Eduardo
AU  - Palanch Repeke CE
AD  - School of Dentistry, Federal University of Sergipe (UFS), Lagarto, SE, Brazil.
FAU - Brasileiro, Bernardo Ferreira
AU  - Brasileiro BF
AD  - School of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, Brazil.
FAU - Trento, Cleverson Luciano
AU  - Trento CL
AD  - School of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, Brazil.
LA  - eng
PT  - Letter
DEP - 20231011
PL  - France
TA  - J Stomatol Oral Maxillofac Surg
JT  - Journal of stomatology, oral and maxillofacial surgery
JID - 101701089
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Orthognathic surgery
OT  - Virtual surgical planning
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interestsor personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/10/14 10:41
MHDA- 2023/10/14 10:41
CRDT- 2023/10/13 19:27
PHST- 2023/09/28 00:00 [received]
PHST- 2023/10/10 00:00 [accepted]
PHST- 2023/10/14 10:41 [pubmed]
PHST- 2023/10/14 10:41 [medline]
PHST- 2023/10/13 19:27 [entrez]
AID - S2468-7855(23)00276-8 [pii]
AID - 10.1016/j.jormas.2023.101655 [doi]
PST - aheadofprint
SO  - J Stomatol Oral Maxillofac Surg. 2023 Oct 11;125(2):101655. doi: 
      10.1016/j.jormas.2023.101655.

PMID- 37869501
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2297-1769 (Print)
IS  - 2297-1769 (Electronic)
IS  - 2297-1769 (Linking)
VI  - 10
DP  - 2023
TI  - ChatGPT and scientific papers in veterinary neurology; is the genie out of the 
      bottle?
PG  - 1272755
LID - 10.3389/fvets.2023.1272755 [doi]
LID - 1272755
FAU - Abani, Samira
AU  - Abani S
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
AD  - Centre for Systems Neuroscience, University of Veterinary Medicine Hannover, 
      Hannover, Germany.
FAU - Volk, Holger Andreas
AU  - Volk HA
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
AD  - Centre for Systems Neuroscience, University of Veterinary Medicine Hannover, 
      Hannover, Germany.
FAU - De Decker, Steven
AU  - De Decker S
AD  - Department of Veterinary Clinical Science and Services, Royal Veterinary College, 
      University of London, London, United Kingdom.
FAU - Fenn, Joe
AU  - Fenn J
AD  - Department of Veterinary Clinical Science and Services, Royal Veterinary College, 
      University of London, London, United Kingdom.
FAU - Rusbridge, Clare
AU  - Rusbridge C
AD  - Faculty of Health and Medical Sciences, School of Veterinary Medicine, University 
      of Surrey, Guildford, United Kingdom.
FAU - Charalambous, Marios
AU  - Charalambous M
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
FAU - Goncalves, Rita
AU  - Goncalves R
AD  - Department of Veterinary Science, Small Animal Teaching Hospital, University of 
      Liverpool, Neston, United Kingdom.
FAU - Gutierrez-Quintana, Rodrigo
AU  - Gutierrez-Quintana R
AD  - Small Animal Hospital, School of Biodiversity, One Health and Veterinary 
      Medicine, University of Glasgow, Glasgow, United Kingdom.
FAU - Loderstedt, Shenja
AU  - Loderstedt S
AD  - Department of Small Animal Medicine, Leipzig University, Leipzig, Germany.
FAU - Flegel, Thomas
AU  - Flegel T
AD  - Department of Small Animal Medicine, Leipzig University, Leipzig, Germany.
FAU - Ros, Carlos
AU  - Ros C
AD  - Memvet Referral Veterinary Center, Palma de Mallorca, Spain.
FAU - von Klopmann, Thilo
AU  - von Klopmann T
AD  - Department of Neurology, Small Animal Clinic Hofheim, Hofheim, Germany.
FAU - Schenk, Henning Christian
AU  - Schenk HC
AD  - Department of Neurology, Lüneburg Small Animal Clinic, Lüneburg, Germany.
FAU - Kornberg, Marion
AU  - Kornberg M
AD  - AniCura Small Animal Clinic, Trier, Germany.
FAU - Meyerhoff, Nina
AU  - Meyerhoff N
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
FAU - Tipold, Andrea
AU  - Tipold A
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
AD  - Centre for Systems Neuroscience, University of Veterinary Medicine Hannover, 
      Hannover, Germany.
FAU - Nessler, Jasmin Nicole
AU  - Nessler JN
AD  - Department of Small Animal Medicine and Surgery, University of Veterinary 
      Medicine Hannover, Hannover, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231005
PL  - Switzerland
TA  - Front Vet Sci
JT  - Frontiers in veterinary science
JID - 101666658
PMC - PMC10585059
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence (AI)
OT  - ethics
OT  - generative AI
OT  - integrity
OT  - machine learning
OT  - plagiarism
OT  - scientific writing
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest. The author(s) declared that they were an editorial board 
      member of Frontiers, at the time of submission. This had no impact on the peer 
      review process and the final decision.
EDAT- 2023/10/23 06:46
MHDA- 2023/10/23 06:47
CRDT- 2023/10/23 04:54
PHST- 2023/08/04 00:00 [received]
PHST- 2023/09/18 00:00 [accepted]
PHST- 2023/10/23 06:47 [medline]
PHST- 2023/10/23 06:46 [pubmed]
PHST- 2023/10/23 04:54 [entrez]
AID - 10.3389/fvets.2023.1272755 [doi]
PST - epublish
SO  - Front Vet Sci. 2023 Oct 5;10:1272755. doi: 10.3389/fvets.2023.1272755. 
      eCollection 2023.

PMID- 37855875
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231121
LR  - 20231121
IS  - 1432-119X (Electronic)
IS  - 0948-6143 (Linking)
VI  - 160
IP  - 5
DP  - 2023 Nov
TI  - A note to our authors: Histochemistry and Cell Biology implements guidelines for 
      the use of large language models (including ChatGPT).
PG  - 375
LID - 10.1007/s00418-023-02245-x [doi]
FAU - Roth, Jürgen
AU  - Roth J
AD  - University of Zurich, CH-8091, Zurich, Switzerland. hcb.editor@bluewin.ch.
LA  - eng
PT  - Editorial
PL  - Germany
TA  - Histochem Cell Biol
JT  - Histochemistry and cell biology
JID - 9506663
SB  - IM
EDAT- 2023/10/19 12:48
MHDA- 2023/11/06 06:42
CRDT- 2023/10/19 11:12
PHST- 2023/11/06 06:42 [medline]
PHST- 2023/10/19 12:48 [pubmed]
PHST- 2023/10/19 11:12 [entrez]
AID - 10.1007/s00418-023-02245-x [pii]
AID - 10.1007/s00418-023-02245-x [doi]
PST - ppublish
SO  - Histochem Cell Biol. 2023 Nov;160(5):375. doi: 10.1007/s00418-023-02245-x.

PMID- 37392002
OWN - NLM
STAT- Publisher
LR  - 20230701
IS  - 1741-2854 (Electronic)
IS  - 0020-7640 (Linking)
DP  - 2023 Jun 30
TI  - ChatGPT and social psychiatry: A commentary on the article 'Old dog, new tricks? 
      Exploring the potential functionalities of ChatGPT in supporting educational 
      methods in social psychiatry'.
PG  - 207640231178488
LID - 10.1177/00207640231178488 [doi]
FAU - Torales, Julio
AU  - Torales J
AUID- ORCID: 0000-0003-3277-7036
AD  - Department of Medical Psychology, School of Medical Sciences, National University 
      of Asunción, San Lorenzo, Paraguay.
AD  - Department of Psychiatry, School of Medical Sciences, National University of 
      Asunción, San Lorenzo, Paraguay.
FAU - O'Higgins, Marcelo
AU  - O'Higgins M
AD  - Department of Psychiatry, School of Medical Sciences, National University of 
      Asunción, San Lorenzo, Paraguay.
LA  - eng
PT  - Editorial
DEP - 20230630
PL  - England
TA  - Int J Soc Psychiatry
JT  - The International journal of social psychiatry
JID - 0374726
SB  - IM
EDAT- 2023/07/01 11:42
MHDA- 2023/07/01 11:42
CRDT- 2023/07/01 02:33
PHST- 2023/07/01 11:42 [medline]
PHST- 2023/07/01 11:42 [pubmed]
PHST- 2023/07/01 02:33 [entrez]
AID - 10.1177/00207640231178488 [doi]
PST - aheadofprint
SO  - Int J Soc Psychiatry. 2023 Jun 30:207640231178488. doi: 
      10.1177/00207640231178488.

PMID- 37528548
OWN - NLM
STAT- MEDLINE
DCOM- 20230905
LR  - 20230919
IS  - 1326-5377 (Electronic)
IS  - 0025-729X (Linking)
VI  - 219
IP  - 5
DP  - 2023 Sep 4
TI  - This too shall pass: the performance of ChatGPT-3.5, ChatGPT-4 and New Bing in an 
      Australian medical licensing examination.
PG  - 237
LID - 10.5694/mja2.52061 [doi]
FAU - Kleinig, Oliver
AU  - Kleinig O
AUID- ORCID: 0000-0003-3320-4424
AD  - University of Adelaide, Adelaide, SA.
FAU - Gao, Christina
AU  - Gao C
AUID- ORCID: 0009-0005-0033-3352
AD  - University of Adelaide, Adelaide, SA.
FAU - Bacchi, Stephen
AU  - Bacchi S
AUID- ORCID: 0000-0001-5130-8628
AD  - Royal Adelaide Hospital, Adelaide, SA.
LA  - eng
PT  - Letter
DEP - 20230801
PL  - Australia
TA  - Med J Aust
JT  - The Medical journal of Australia
JID - 0400714
SB  - IM
MH  - Australia
MH  - *Licensure
MH  - *Artificial Intelligence
MH  - Educational Measurement
OTO - NOTNLM
OT  - Artificial intelligence
EDAT- 2023/08/02 06:43
MHDA- 2023/09/05 06:42
CRDT- 2023/08/02 00:52
PHST- 2023/06/28 00:00 [revised]
PHST- 2023/03/21 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/09/05 06:42 [medline]
PHST- 2023/08/02 06:43 [pubmed]
PHST- 2023/08/02 00:52 [entrez]
AID - 10.5694/mja2.52061 [doi]
PST - ppublish
SO  - Med J Aust. 2023 Sep 4;219(5):237. doi: 10.5694/mja2.52061. Epub 2023 Aug 1.

PMID- 37487184
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - Ethics Education for Healthcare Professionals in the Era of ChatGPT and Other 
      Large Language Models: Do We Still Need It?
PG  - 17-27
LID - 10.1080/15265161.2023.2233358 [doi]
AB  - In this paper, we contend with whether we still need traditional ethics education 
      as part of healthcare professional training given the abilities of chatGPT 
      (generative pre-trained transformer) and other large language models (LLM). We 
      reflect on common programmatic goals to assess the current strengths and 
      limitations of LLMs in helping to build ethics competencies among future 
      clinicians. Through an actual case analysis, we highlight areas in which chatGPT 
      and other LLMs are conducive to common bioethics education goals. We also comment 
      on where such technologies remain an imperfect substitute for human-led ethics 
      teaching and learning. Finally, we conclude that the relative strengths of 
      chatGPT warrant its consideration as a teaching and learning tool in ethics 
      education in ways that account for current limitations and build in flexibility 
      as the technology evolves.
FAU - Rahimzadeh, Vasiliki
AU  - Rahimzadeh V
AUID- ORCID: 0000-0003-3537-7601
AD  - Baylor College of Medicine.
FAU - Kostick-Quenet, Kristin
AU  - Kostick-Quenet K
AUID- ORCID: 0000-0003-2510-0174
AD  - Baylor College of Medicine.
FAU - Blumenthal Barby, Jennifer
AU  - Blumenthal Barby J
AUID- ORCID: 0000-0003-1054-619X
AD  - Baylor College of Medicine.
FAU - McGuire, Amy L
AU  - McGuire AL
AUID- ORCID: 0000-0002-7819-519X
AD  - Baylor College of Medicine.
LA  - eng
PT  - Journal Article
DEP - 20230724
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CIN - Am J Bioeth. 2023 Oct;23(10):63-65. PMID: 37812097
CIN - Am J Bioeth. 2023 Oct;23(10):86-88. PMID: 37812108
CIN - Am J Bioeth. 2023 Oct;23(10):68-70. PMID: 37812111
CIN - Am J Bioeth. 2023 Oct;23(10):42-44. PMID: 37812114
CIN - Am J Bioeth. 2023 Oct;23(10):58-60. PMID: 37812118
CIN - Am J Bioeth. 2023 Oct;23(10):89-91. PMID: 37812124
CIN - Am J Bioeth. 2023 Oct;23(10):1-5. PMID: 37831940
OTO - NOTNLM
OT  - bioethics
OT  - chatGPT
OT  - healthcare professionals
OT  - medical education
EDAT- 2023/07/24 19:10
MHDA- 2023/07/24 19:10
CRDT- 2023/07/24 16:33
PHST- 2023/07/24 19:10 [pubmed]
PHST- 2023/07/24 19:10 [medline]
PHST- 2023/07/24 16:33 [entrez]
AID - 10.1080/15265161.2023.2233358 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):17-27. doi: 10.1080/15265161.2023.2233358. Epub 2023 
      Jul 24.

PMID- 38056130
OWN - NLM
STAT- Publisher
LR  - 20231206
IS  - 1872-7123 (Electronic)
IS  - 0165-1781 (Linking)
VI  - 331
DP  - 2023 Dec 2
TI  - Assessing the potential of ChatGPT for psychodynamic formulations in psychiatry: 
      An exploratory study.
PG  - 115655
LID - S0165-1781(23)00605-4 [pii]
LID - 10.1016/j.psychres.2023.115655 [doi]
AB  - Although there were several attempts to apply ChatGPT (Generative Pre-Trained 
      Transformer) to medicine, little is known about therapeutic applications in 
      psychiatry. In this exploratory study, we aimed to evaluate the characteristics 
      and appropriateness of the psychodynamic formulations created by ChatGPT. Along 
      with a case selected from the psychoanalytic literature, input prompts were 
      designed to include different levels of background knowledge. These included 
      naïve prompts, keywords created by ChatGPT, keywords created by psychiatrists, 
      and psychodynamic concepts from the literature. The psychodynamic formulations 
      generated from the different prompts were evaluated by five psychiatrists from 
      different institutions. We next conducted further tests in which instructions on 
      the use of different psychodynamic models were added to the input prompts. The 
      models used were ego psychology, self-psychology, and object relations. The 
      results from naïve prompts and psychodynamic concepts were rated as appropriate 
      by most raters. The psychodynamic concept prompt output was rated the highest. 
      Interrater agreement was statistically significant. The results from the tests 
      using instructions in different psychoanalytic theories were also rated as 
      appropriate by most raters. They included key elements of the psychodynamic 
      formulation and suggested interpretations similar to the literature. These 
      findings suggest potential of ChatGPT for use in psychiatry.
CI  - Copyright © 2023. Published by Elsevier B.V.
FAU - Hwang, Gyubeom
AU  - Hwang G
AD  - Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, 
      Republic of Korea; Department of Medical Sciences, Graduate School of Ajou 
      University, Suwon, Republic of Korea.
FAU - Lee, Dong Yun
AU  - Lee DY
AD  - Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, 
      Republic of Korea; Department of Medical Sciences, Graduate School of Ajou 
      University, Suwon, Republic of Korea.
FAU - Seol, Soobeen
AU  - Seol S
AD  - Department of Biomedical Sciences, Ajou University Graduate School of Medicine, 
      Suwon, Republic of Korea.
FAU - Jung, Jaeoh
AU  - Jung J
AD  - Department of Child and Adolescent Psychiatry, Seoul Metropolitan Eunpyeong 
      Hospital, Seoul, Republic of Korea.
FAU - Choi, Yeonkyu
AU  - Choi Y
AD  - Armed Forces Yangju Hospital, Yang-ju, Republic of Korea.
FAU - Her, Eun Sil
AU  - Her ES
AD  - Ajou Big Tree Psychiatric Clinic, Suwon, Republic of Korea.
FAU - An, Min Ho
AU  - An MH
AD  - Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, 
      Republic of Korea; Department of Medical Sciences, Graduate School of Ajou 
      University, Suwon, Republic of Korea.
FAU - Park, Rae Woong
AU  - Park RW
AD  - Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, 
      Republic of Korea; Department of Medical Sciences, Graduate School of Ajou 
      University, Suwon, Republic of Korea; Department of Biomedical Sciences, Ajou 
      University Graduate School of Medicine, Suwon, Republic of Korea. Electronic 
      address: veritas@ajou.ac.kr.
LA  - eng
PT  - Journal Article
DEP - 20231202
PL  - Ireland
TA  - Psychiatry Res
JT  - Psychiatry research
JID - 7911385
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Psychiatry
OT  - Psychoanalysis
OT  - Psychodynamic formulations
COIS- Declaration of Competing Interest All authors declare no competing interests.
EDAT- 2023/12/07 00:42
MHDA- 2023/12/07 00:42
CRDT- 2023/12/06 18:07
PHST- 2023/07/21 00:00 [received]
PHST- 2023/11/27 00:00 [revised]
PHST- 2023/12/01 00:00 [accepted]
PHST- 2023/12/07 00:42 [medline]
PHST- 2023/12/07 00:42 [pubmed]
PHST- 2023/12/06 18:07 [entrez]
AID - S0165-1781(23)00605-4 [pii]
AID - 10.1016/j.psychres.2023.115655 [doi]
PST - aheadofprint
SO  - Psychiatry Res. 2023 Dec 2;331:115655. doi: 10.1016/j.psychres.2023.115655.

PMID- 37709536
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1535-5667 (Electronic)
IS  - 0161-5505 (Print)
IS  - 0161-5505 (Linking)
VI  - 64
IP  - 12
DP  - 2023 Dec 1
TI  - ChatGPT: Can You Prepare My Patients for [(18)F]FDG PET/CT and Explain My 
      Reports?
PG  - 1876-1879
LID - 10.2967/jnumed.123.266114 [doi]
AB  - We evaluated whether the artificial intelligence chatbot ChatGPT can adequately 
      answer patient questions related to [(18)F]FDG PET/CT in common clinical 
      indications before and after scanning. Methods: Thirteen questions regarding 
      [(18)F]FDG PET/CT were submitted to ChatGPT. ChatGPT was also asked to explain 6 
      PET/CT reports (lung cancer, Hodgkin lymphoma) and answer 6 follow-up questions 
      (e.g., on tumor stage or recommended treatment). To be rated "useful" or 
      "appropriate," a response had to be adequate by the standards of the nuclear 
      medicine staff. Inconsistency was assessed by regenerating responses. Results: 
      Responses were rated "appropriate" for 92% of 25 tasks and "useful" for 96%. 
      Considerable inconsistencies were found between regenerated responses for 16% of 
      tasks. Responses to 83% of sensitive questions (e.g., staging/treatment options) 
      were rated "empathetic." Conclusion: ChatGPT might adequately substitute for 
      advice given to patients by nuclear medicine staff in the investigated settings. 
      Improving the consistency of ChatGPT would further increase reliability.
CI  - © 2023 by the Society of Nuclear Medicine and Molecular Imaging.
FAU - Rogasch, Julian M M
AU  - Rogasch JMM
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany; julian.rogasch@charite.de.
AD  - Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Berlin, Germany.
FAU - Metzger, Giulia
AU  - Metzger G
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
FAU - Preisler, Martina
AU  - Preisler M
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany; and.
FAU - Galler, Markus
AU  - Galler M
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
FAU - Thiele, Felix
AU  - Thiele F
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
FAU - Brenner, Winfried
AU  - Brenner W
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
FAU - Feldhaus, Felix
AU  - Feldhaus F
AD  - Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
FAU - Wetz, Christoph
AU  - Wetz C
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
FAU - Amthauer, Holger
AU  - Amthauer H
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
FAU - Furth, Christian
AU  - Furth C
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
FAU - Schatka, Imke
AU  - Schatka I
AD  - Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, 
      Germany.
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - United States
TA  - J Nucl Med
JT  - Journal of nuclear medicine : official publication, Society of Nuclear Medicine
JID - 0217410
RN  - 0Z5B2CJX4D (Fluorodeoxyglucose F18)
RN  - 0 (Radiopharmaceuticals)
SB  - IM
MH  - Humans
MH  - *Positron Emission Tomography Computed Tomography
MH  - *Fluorodeoxyglucose F18
MH  - Radiopharmaceuticals
MH  - Artificial Intelligence
MH  - Reproducibility of Results
PMC - PMC10690125
OTO - NOTNLM
OT  - FDG PET/CT
OT  - GPT-4
OT  - artificial intelligence
OT  - chatbot
OT  - patient communication
EDAT- 2023/09/15 00:41
MHDA- 2023/12/04 12:43
CRDT- 2023/09/14 21:23
PHST- 2023/06/02 00:00 [received]
PHST- 2023/08/22 00:00 [revised]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/09/15 00:41 [pubmed]
PHST- 2023/09/14 21:23 [entrez]
AID - jnumed.123.266114 [pii]
AID - 266114 [pii]
AID - 10.2967/jnumed.123.266114 [doi]
PST - epublish
SO  - J Nucl Med. 2023 Dec 1;64(12):1876-1879. doi: 10.2967/jnumed.123.266114.

PMID- 37678271
OWN - NLM
STAT- Publisher
LR  - 20230907
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Sep 4
TI  - Auxiliary use of ChatGPT in surgical diagnosis and treatment.
LID - 10.1097/JS9.0000000000000686 [doi]
AB  - ChatGPT can be used as an auxiliary tool in surgical diagnosis and treatment in 
      several ways. One of the most incredible values of using ChatGPT is its ability 
      to quickly process and handle large amounts of data and provide relatively 
      accurate information to healthcare workers. Due to its high accuracy and ability 
      to process big data, ChatGPT has been widely used in the healthcare industry for 
      tasks such as assisting medical diagnosis, giving predictions of some diseases, 
      and analyzing some medical cases. Surgical diagnosis and treatment can serve as 
      an auxiliary tool to help healthcare professionals. Process large amounts of 
      medical data, provide real-time guidance and feedback, and increase healthcare's 
      overall speed and quality. Although it has great acceptance, it still faces 
      issues such as ethics, patient privacy, data security, law, trustworthiness, and 
      accuracy. This study aimed to explore the auxiliary use of ChatGPT in surgical 
      diagnosis and treatment.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Au, Kahei
AU  - Au K
AD  - School of Medicine, Jinan University.
FAU - Yang, Wah
AU  - Yang W
AD  - Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of 
      Jinan University, Guangzhou, Guangdong Province, People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230904
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/09/08 00:41
MHDA- 2023/09/08 00:41
CRDT- 2023/09/07 18:53
PHST- 2023/05/24 00:00 [received]
PHST- 2023/08/09 00:00 [accepted]
PHST- 2023/09/08 00:41 [medline]
PHST- 2023/09/08 00:41 [pubmed]
PHST- 2023/09/07 18:53 [entrez]
AID - 01279778-990000000-00583 [pii]
AID - 10.1097/JS9.0000000000000686 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Sep 4. doi: 10.1097/JS9.0000000000000686.

PMID- 36809511
OWN - NLM
STAT- MEDLINE
DCOM- 20230330
LR  - 20230504
IS  - 1433-7347 (Electronic)
IS  - 0942-2056 (Linking)
VI  - 31
IP  - 4
DP  - 2023 Apr
TI  - Artificial intelligence bot ChatGPT in medical research: the potential game 
      changer as a double-edged sword.
PG  - 1187-1189
LID - 10.1007/s00167-023-07355-6 [doi]
FAU - Dahmen, Jari
AU  - Dahmen J
AUID- ORCID: 0000-0002-6849-1008
AD  - Department of Orthopaedic Surgery and Sports Medicine, Amsterdam Movement 
      Sciences, Amsterdam UMC, Location AMC, University of Amsterdam, Meibergdreef 9, 
      Amsterdam, The Netherlands. j.dahmen@amsterdamumc.nl.
AD  - Academic Center for Evidence Based Sports Medicine (ACES), Amsterdam, The 
      Netherlands. j.dahmen@amsterdamumc.nl.
AD  - Amsterdam Collaboration for Health and Safety in Sports (ACHSS), International 
      Olympic Committee (IOC) Research Center Amsterdam UMC, Amsterdam, The 
      Netherlands. j.dahmen@amsterdamumc.nl.
FAU - Kayaalp, M Enes
AU  - Kayaalp ME
AD  - Department for Orthopaedics and Traumatology, Istanbul Kartal Dr. Lutfi Kirdar 
      Training and Research Hospital, Istanbul, Turkey.
FAU - Ollivier, Matthieu
AU  - Ollivier M
AD  - Aix Marseille Univ, CNRS, ISM, Inst Movement Sci, Marseille, France.
FAU - Pareek, Ayoosh
AU  - Pareek A
AD  - Sports Medicine and Shoulder Service, Department of Orthopedic Surgery, Hospital 
      for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
FAU - Hirschmann, Michael T
AU  - Hirschmann MT
AD  - Department of Orthopedic Surgery and Traumatology, Head Knee Surgery and DKF Head 
      of Research, Kantonsspital Baselland, Bruderholz, 4101, Bottmingen, Switzerland.
FAU - Karlsson, Jon
AU  - Karlsson J
AD  - Department for Orthopaedics, Sahlgrenska University Hospital, Institute of 
      Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, 
      Sweden.
FAU - Winkler, Philipp W
AU  - Winkler PW
AD  - Department of Orthopaedics and Traumatology, Kepler University Hospital Linz, 
      Linz, Austria.
LA  - eng
PT  - Editorial
DEP - 20230221
PL  - Germany
TA  - Knee Surg Sports Traumatol Arthrosc
JT  - Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
JID - 9314730
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Biomedical Research
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Natural language processing
OT  - Plagiarism
EDAT- 2023/02/23 06:00
MHDA- 2023/03/30 06:11
CRDT- 2023/02/22 13:51
PHST- 2023/02/05 00:00 [received]
PHST- 2023/02/10 00:00 [accepted]
PHST- 2023/03/30 06:11 [medline]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/02/22 13:51 [entrez]
AID - 10.1007/s00167-023-07355-6 [pii]
AID - 10.1007/s00167-023-07355-6 [doi]
PST - ppublish
SO  - Knee Surg Sports Traumatol Arthrosc. 2023 Apr;31(4):1187-1189. doi: 
      10.1007/s00167-023-07355-6. Epub 2023 Feb 21.

PMID- 38038222
OWN - NLM
STAT- Publisher
LR  - 20231201
IS  - 1930-7837 (Electronic)
IS  - 0022-0337 (Linking)
DP  - 2023 Dec 1
TI  - Use of ChatGPT as an educational tool to support generating structured concept 
      map for tobacco cessation (Pilot study).
LID - 10.1002/jdd.13430 [doi]
FAU - Omar, Hanan
AU  - Omar H
AUID- ORCID: 0000-0002-4144-7170
AD  - ResearchMissouri School of Dentistry & Oral Health A.T. Still University 800 West 
      Jefferson Street, Kirksville, Missouri, US.
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - United States
TA  - J Dent Educ
JT  - Journal of dental education
JID - 8000150
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Concept maps
OT  - Critical thinking
EDAT- 2023/12/01 12:42
MHDA- 2023/12/01 12:42
CRDT- 2023/12/01 07:03
PHST- 2023/10/24 00:00 [revised]
PHST- 2023/05/30 00:00 [received]
PHST- 2023/11/03 00:00 [accepted]
PHST- 2023/12/01 12:42 [medline]
PHST- 2023/12/01 12:42 [pubmed]
PHST- 2023/12/01 07:03 [entrez]
AID - 10.1002/jdd.13430 [doi]
PST - aheadofprint
SO  - J Dent Educ. 2023 Dec 1. doi: 10.1002/jdd.13430.

PMID- 37493845
OWN - NLM
STAT- MEDLINE
DCOM- 20230905
LR  - 20230912
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
VI  - 280
IP  - 10
DP  - 2023 Oct
TI  - "Vertigo, likely peripheral": the dizzying rise of ChatGPT.
PG  - 4687-4689
LID - 10.1007/s00405-023-08135-1 [doi]
AB  - PURPOSE: The use of artificial intelligence (AI) in medical decision-making has 
      once again come to the forefront with the prevalence of Natural Language 
      Processing (NLP). In this exploratory article, we tested one such model, ChatGPT, 
      for its ability to identify vestibular causes of dizziness METHODS: Eight 
      hypothetical scenarios were presented to ChatGPT, which included varying clinical 
      pictures and types of prompts. The responses given by ChatGPT were evaluated for 
      coherence, clarity, consistency, accuracy, appropriateness, and recognition of 
      limitations. ChatGPT provided coherent and logical responses. RESULTS: The model 
      accurately provided differentials for both vestibular and non-vestibular causes 
      of dizziness, with the correct diagnosis presented first in six of the cases, 
      with important limitations CONCLUSION: Being an AI tool, ChatGPT lacks the 
      ability to process certain nuances in clinical decision making, in both 
      identifying atypical dizziness, as well as in recommending further examination 
      steps to elucidate a clearer diagnosis. We believe that AI will continue to forge 
      ahead in the medical field. Merging the immense knowledge base of AI programming 
      with the nuances of clinical assessment and knowledge integration will surely 
      enhance patient care in the years to come.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Chee, Jeremy
AU  - Chee J
AUID- ORCID: 0000-0002-4702-7951
AD  - Department of Otolaryngology-Head and Neck Surgery, National University Hospital, 
      1E Kent Ridge Rd, Singapore, 119228, Singapore. cheejeremy@gmail.com.
FAU - Kwa, Eunice Dawn
AU  - Kwa ED
AD  - Department of Otolaryngology-Head and Neck Surgery, National University Hospital, 
      1E Kent Ridge Rd, Singapore, 119228, Singapore.
AD  - Department of Otolaryngology-Head and Neck Surgery, National University of 
      Singapore, Singapore, Singapore.
FAU - Goh, Xueying
AU  - Goh X
AD  - Department of Otolaryngology-Head and Neck Surgery, National University Hospital, 
      1E Kent Ridge Rd, Singapore, 119228, Singapore.
AD  - Department of Otolaryngology-Head and Neck Surgery, National University of 
      Singapore, Singapore, Singapore.
LA  - eng
PT  - Journal Article
DEP - 20230726
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
MH  - Humans
MH  - *Dizziness/diagnosis/etiology
MH  - Artificial Intelligence
MH  - Vertigo
MH  - Clinical Decision-Making
MH  - *Vestibule, Labyrinth
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Dizziness
OT  - Natural language processing
OT  - Vertigo
EDAT- 2023/07/26 13:07
MHDA- 2023/09/05 06:42
CRDT- 2023/07/26 11:15
PHST- 2023/06/17 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/09/05 06:42 [medline]
PHST- 2023/07/26 13:07 [pubmed]
PHST- 2023/07/26 11:15 [entrez]
AID - 10.1007/s00405-023-08135-1 [pii]
AID - 10.1007/s00405-023-08135-1 [doi]
PST - ppublish
SO  - Eur Arch Otorhinolaryngol. 2023 Oct;280(10):4687-4689. doi: 
      10.1007/s00405-023-08135-1. Epub 2023 Jul 26.

PMID- 37385685
OWN - NLM
STAT- MEDLINE
DCOM- 20230712
LR  - 20230722
IS  - 1975-5937 (Electronic)
IS  - 1975-5937 (Linking)
VI  - 20
DP  - 2023
TI  - Comparing ChatGPT’s ability to rate the degree of stereotypes and the consistency 
      of stereotype attribution with those of medical students in New Zealand in 
      developing a similarity rating test: a methodological study.
PG  - 17
LID - 10.3352/jeehp.2023.20.17 [doi]
LID - 17
AB  - Learning about one’s implicit bias is crucial for improving one’s cultural 
      competency and thereby reducing health inequity. To evaluate bias among medical 
      students following a previously developed cultural training program targeting New 
      Zealand Māori, we developed a text-based, self-evaluation tool called the 
      Similarity Rating Test (SRT). The development process of the SRT was 
      resource-intensive, limiting its generalizability and applicability. Here, we 
      explored the potential of ChatGPT, an automated chatbot, to assist in the 
      development process of the SRT by comparing ChatGPT’s and students’ evaluations 
      of the SRT. Despite results showing non-significant equivalence and difference 
      between ChatGPT’s and students’ ratings, ChatGPT’s ratings were more consistent 
      than students’ ratings. The consistency rate was higher for non-stereotypical 
      than for stereotypical statements, regardless of rater type. Further studies are 
      warranted to validate ChatGPT’s potential for assisting in SRT development for 
      implementation in medical education and evaluation of ethnic stereotypes and 
      related topics.
FAU - Lin, Chao-Cheng
AU  - Lin CC
AD  - Department of Psychological Medicine, Dunedin School of Medicine, the University 
      of Otago, Dunedin, New Zealand.
AD  - Department of Psychiatry, National Taiwan University College of Medicine, Taipei, 
      Taiwan.
FAU - Akuhata-Huntington, Zaine
AU  - Akuhata-Huntington Z
AD  - Kōhatu Centre for Hauora Māori, Dunedin School of Medicine, the University of 
      Otago, Dunedin, New Zealand.
FAU - Hsu, Che-Wei
AU  - Hsu CW
AD  - Department of Psychological Medicine, Dunedin School of Medicine, the University 
      of Otago, Dunedin, New Zealand.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20230612
PL  - Korea (South)
TA  - J Educ Eval Health Prof
JT  - Journal of educational evaluation for health professions
JID - 101490061
SB  - IM
MH  - Humans
MH  - *Cultural Competency/education/psychology
MH  - *Education, Medical/methods
MH  - *Maori People
MH  - New Zealand
MH  - *Students, Medical/psychology
MH  - *Artificial Intelligence
MH  - Stereotyping
MH  - *Bias, Implicit
PMC - PMC10356547
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Cultural competency
OT  - Implicit bias
OT  - Medical education
OT  - New Zealand
COIS- Conflict of interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/06/30 01:06
MHDA- 2023/07/03 06:41
CRDT- 2023/06/29 20:53
PHST- 2023/05/16 00:00 [received]
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/07/03 06:41 [medline]
PHST- 2023/06/30 01:06 [pubmed]
PHST- 2023/06/29 20:53 [entrez]
AID - jeehp.2023.20.17 [pii]
AID - jeehp-20-17 [pii]
AID - 10.3352/jeehp.2023.20.17 [doi]
PST - ppublish
SO  - J Educ Eval Health Prof. 2023;20:17. doi: 10.3352/jeehp.2023.20.17. Epub 2023 Jun 
      12.

PMID- 37181697
OWN - NLM
STAT- MEDLINE
DCOM- 20230921
LR  - 20230921
IS  - 2296-2565 (Electronic)
IS  - 2296-2565 (Linking)
VI  - 11
DP  - 2023
TI  - ChatGPT and the rise of large language models: the new AI-driven infodemic threat 
      in public health.
PG  - 1166120
LID - 10.3389/fpubh.2023.1166120 [doi]
LID - 1166120
AB  - Large Language Models (LLMs) have recently gathered attention with the release of 
      ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, 
      we retrace the evolution of LLMs to understand the revolution brought by ChatGPT 
      in the artificial intelligence (AI) field. The opportunities offered by LLMs in 
      supporting scientific research are multiple and various models have already been 
      tested in Natural Language Processing (NLP) tasks in this domain. The impact of 
      ChatGPT has been huge for the general public and the research community, with 
      many authors using the chatbot to write part of their articles and some papers 
      even listing ChatGPT as an author. Alarming ethical and practical challenges 
      emerge from the use of LLMs, particularly in the medical field for the potential 
      impact on public health. Infodemic is a trending topic in public health and the 
      ability of LLMs to rapidly produce vast amounts of text could leverage 
      misinformation spread at an unprecedented scale, this could create an "AI-driven 
      infodemic," a novel public health threat. Policies to contrast this phenomenon 
      need to be rapidly elaborated, the inability to accurately detect 
      artificial-intelligence-produced text is an unresolved issue.
CI  - Copyright © 2023 De Angelis, Baglivo, Arzilli, Privitera, Ferragina, Tozzi and 
      Rizzo.
FAU - De Angelis, Luigi
AU  - De Angelis L
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa, Italy.
FAU - Baglivo, Francesco
AU  - Baglivo F
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa, Italy.
FAU - Arzilli, Guglielmo
AU  - Arzilli G
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa, Italy.
FAU - Privitera, Gaetano Pierpaolo
AU  - Privitera GP
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa, Italy.
AD  - Training Office, National Institute of Health, Rome, Italy.
FAU - Ferragina, Paolo
AU  - Ferragina P
AD  - Department of Computer Science, University of Pisa, Pisa, Italy.
FAU - Tozzi, Alberto Eugenio
AU  - Tozzi AE
AD  - Fetal, Neonatal and Cardiologic Science Research Area, Predictive and Preventive 
      Medicine Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
FAU - Rizzo, Caterina
AU  - Rizzo C
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230425
PL  - Switzerland
TA  - Front Public Health
JT  - Frontiers in public health
JID - 101616579
SB  - IM
MH  - *Artificial Intelligence
MH  - *Infodemic
MH  - Public Health
MH  - Language
MH  - Natural Language Processing
PMC - PMC10166793
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - infodemic
OT  - large language models
OT  - natural language processing
OT  - public health
COIS- AT received honoraria for education activities on digital health and 
      immunizations from Roche, AstraZeneca, Novartis, Merck, MSD, Pfizer, Sanofi. The 
      remaining authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/05/14 19:13
MHDA- 2023/05/16 06:42
CRDT- 2023/05/14 12:22
PHST- 2023/02/14 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/05/14 19:13 [pubmed]
PHST- 2023/05/16 06:42 [medline]
PHST- 2023/05/14 12:22 [entrez]
AID - 10.3389/fpubh.2023.1166120 [doi]
PST - epublish
SO  - Front Public Health. 2023 Apr 25;11:1166120. doi: 10.3389/fpubh.2023.1166120. 
      eCollection 2023.

PMID- 37576290
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230815
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT's influence on customer experience in digital marketing: Investigating 
      the moderating roles.
PG  - e18770
LID - 10.1016/j.heliyon.2023.e18770 [doi]
LID - e18770
AB  - ChatGPT is an artificial intelligence model intended for conversational purposes 
      that has grown in popularity in digital marketing, offering organizations a vital 
      tool for engaging with clients and enhancing their marketing efforts. The main 
      objective of this research is to investigate the impact of ChatGPT on the 
      customer experience in digital marketing. Additionally, the study intends to 
      investigate the moderating impacts of business type and technology familiarity 
      and comfort on the customer experience. Furthermore, the research explores the 
      moderating roles of gender, age, and education level. The data for this study 
      were collected electronically from 394 clients who have interacted with ChatGPT 
      in digital marketing using an open-access questionnaire. The results support the 
      significance of the moderating role of (Familiarity and Comfort with Technology, 
      Business Type, Age, and Education level on the relation between customer 
      experience with ChatGPT and overall satisfaction with digital marketing, while 
      Gender is not supported. This article's findings are intended to contribute to 
      the current literature on the use of conversational AI models in digital 
      marketing and customer experience, providing insights and recommendations for 
      future research.
CI  - © 2023 The Author.
FAU - Abdelkader, Osama Ahmed
AU  - Abdelkader OA
AD  - Marketing Department, College of Business Administration, Imam Abdulrahman Bin 
      Faisal University, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20230802
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10415881
OTO - NOTNLM
OT  - ChatGPT
OT  - Chatbots
OT  - Consumer behavior
OT  - Digital marketing
OT  - Marketing
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/08/14 06:42
MHDA- 2023/08/14 06:43
CRDT- 2023/08/14 04:40
PHST- 2023/04/30 00:00 [received]
PHST- 2023/07/26 00:00 [revised]
PHST- 2023/07/27 00:00 [accepted]
PHST- 2023/08/14 06:43 [medline]
PHST- 2023/08/14 06:42 [pubmed]
PHST- 2023/08/14 04:40 [entrez]
AID - S2405-8440(23)05978-9 [pii]
AID - e18770 [pii]
AID - 10.1016/j.heliyon.2023.e18770 [doi]
PST - epublish
SO  - Heliyon. 2023 Aug 2;9(8):e18770. doi: 10.1016/j.heliyon.2023.e18770. eCollection 
      2023 Aug.

PMID- 37519406
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2052-2975 (Print)
IS  - 2052-2975 (Electronic)
IS  - 2052-2975 (Linking)
VI  - 54
DP  - 2023 Sep
TI  - ChatGPT in infectious diseases: A practical evaluation and future considerations.
PG  - 101166
LID - 10.1016/j.nmni.2023.101166 [doi]
LID - 101166
FAU - Kaneda, Yudai
AU  - Kaneda Y
AD  - School of Medicine, Hokkaido University, Hokkaido, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230720
PL  - England
TA  - New Microbes New Infect
JT  - New microbes and new infections
JID - 101624750
PMC - PMC10382662
OTO - NOTNLM
OT  - ChatGPT
OT  - Infectious diseases
OT  - The Japanese association for infectious diseases
COIS- None.
EDAT- 2023/07/31 06:42
MHDA- 2023/07/31 06:43
CRDT- 2023/07/31 04:36
PHST- 2023/06/26 00:00 [received]
PHST- 2023/07/13 00:00 [revised]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/07/31 06:43 [medline]
PHST- 2023/07/31 06:42 [pubmed]
PHST- 2023/07/31 04:36 [entrez]
AID - S2052-2975(23)00085-9 [pii]
AID - 101166 [pii]
AID - 10.1016/j.nmni.2023.101166 [doi]
PST - epublish
SO  - New Microbes New Infect. 2023 Jul 20;54:101166. doi: 10.1016/j.nmni.2023.101166. 
      eCollection 2023 Sep.

PMID- 36834073
OWN - NLM
STAT- MEDLINE
DCOM- 20230228
LR  - 20231117
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 20
IP  - 4
DP  - 2023 Feb 15
TI  - Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative 
      Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief 
      Complaints: A Pilot Study.
LID - 10.3390/ijerph20043378 [doi]
LID - 3378
AB  - The diagnostic accuracy of differential diagnoses generated by artificial 
      intelligence (AI) chatbots, including the generative pretrained transformer 3 
      (GPT-3) chatbot (ChatGPT-3) is unknown. This study evaluated the accuracy of 
      differential-diagnosis lists generated by ChatGPT-3 for clinical vignettes with 
      common chief complaints. General internal medicine physicians created clinical 
      cases, correct diagnoses, and five differential diagnoses for ten common chief 
      complaints. The rate of correct diagnosis by ChatGPT-3 within the ten 
      differential-diagnosis lists was 28/30 (93.3%). The rate of correct diagnosis by 
      physicians was still superior to that by ChatGPT-3 within the five 
      differential-diagnosis lists (98.3% vs. 83.3%, p = 0.03). The rate of correct 
      diagnosis by physicians was also superior to that by ChatGPT-3 in the top 
      diagnosis (53.3% vs. 93.3%, p < 0.001). The rate of consistent differential 
      diagnoses among physicians within the ten differential-diagnosis lists generated 
      by ChatGPT-3 was 62/88 (70.5%). In summary, this study demonstrates the high 
      diagnostic accuracy of differential-diagnosis lists generated by ChatGPT-3 for 
      clinical cases with common chief complaints. This suggests that AI chatbots such 
      as ChatGPT-3 can generate a well-differentiated diagnosis list for common chief 
      complaints. However, the order of these lists can be improved in the future.
FAU - Hirosawa, Takanobu
AU  - Hirosawa T
AUID- ORCID: 0000-0002-3573-8203
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi 321-0293, Japan.
FAU - Harada, Yukinori
AU  - Harada Y
AUID- ORCID: 0000-0001-6042-7397
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi 321-0293, Japan.
FAU - Yokose, Masashi
AU  - Yokose M
AUID- ORCID: 0000-0002-2685-0658
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi 321-0293, Japan.
FAU - Sakamoto, Tetsu
AU  - Sakamoto T
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi 321-0293, Japan.
FAU - Kawamura, Ren
AU  - Kawamura R
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi 321-0293, Japan.
FAU - Shimizu, Taro
AU  - Shimizu T
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi 321-0293, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230215
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Diagnosis, Differential
MH  - Pilot Projects
MH  - Software
MH  - *General Practitioners
PMC - PMC9967747
OTO - NOTNLM
OT  - AI chatbot
OT  - artificial intelligence
OT  - clinical decision support
OT  - diagnosis
OT  - diagnostic accuracy
OT  - generative pretrained transformers
OT  - natural language processing
COIS- The authors declare no conflict of interest.
EDAT- 2023/02/26 06:00
MHDA- 2023/03/03 06:00
CRDT- 2023/02/25 02:28
PHST- 2023/01/25 00:00 [received]
PHST- 2023/02/09 00:00 [revised]
PHST- 2023/02/13 00:00 [accepted]
PHST- 2023/02/25 02:28 [entrez]
PHST- 2023/02/26 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
AID - ijerph20043378 [pii]
AID - ijerph-20-03378 [pii]
AID - 10.3390/ijerph20043378 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2023 Feb 15;20(4):3378. doi: 
      10.3390/ijerph20043378.

PMID- 37263382
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20230920
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
VI  - 89
IP  - 4
DP  - 2023 Oct
TI  - The complex ethics of applying ChatGPT and language model artificial intelligence 
      in dermatology.
PG  - e157-e158
LID - S0190-9622(23)00993-3 [pii]
LID - 10.1016/j.jaad.2023.05.054 [doi]
FAU - Ferreira, Alana Luna
AU  - Ferreira AL
AD  - Department of Dermatology, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania.
FAU - Lipoff, Jules B
AU  - Lipoff JB
AD  - Department of Dermatology, Lewis Katz School of Medicine, Temple University, 
      Philadelphia, Pennsylvania. Electronic address: jules.lipoff@temple.edu.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230530
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
CON - J Am Acad Dermatol. 2023 Mar 11;:null. PMID: 36907556
CIN - J Am Acad Dermatol. 2023 Oct;89(4):e159-e160. PMID: 37268021
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Dermatology
OTO - NOTNLM
OT  - ChatGPT
OT  - accessibility to dermatology
OT  - artificial intelligence
OT  - beneficence
OT  - clinical practice
OT  - ethics
OT  - justice
OT  - language models
OT  - nonmaleficence
COIS- Conflicts of interest None disclosed.
EDAT- 2023/06/02 01:07
MHDA- 2023/09/20 06:42
CRDT- 2023/06/01 19:23
PHST- 2023/03/28 00:00 [received]
PHST- 2023/05/02 00:00 [revised]
PHST- 2023/05/06 00:00 [accepted]
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/06/02 01:07 [pubmed]
PHST- 2023/06/01 19:23 [entrez]
AID - S0190-9622(23)00993-3 [pii]
AID - 10.1016/j.jaad.2023.05.054 [doi]
PST - ppublish
SO  - J Am Acad Dermatol. 2023 Oct;89(4):e157-e158. doi: 10.1016/j.jaad.2023.05.054. 
      Epub 2023 May 30.

PMID- 37625909
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231103
LR  - 20231103
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
VI  - 131
IP  - 5
DP  - 2023 Nov
TI  - ChatGPT risk of fabrication in literature searches. Comment on Br J Anaesth 2023; 
      131: e29-e30.
PG  - e172-e173
LID - S0007-0912(23)00424-5 [pii]
LID - 10.1016/j.bja.2023.07.024 [doi]
FAU - Arrivé, Lionel
AU  - Arrivé L
AD  - Department of Radiology, Saint-Antoine Hospital, Assistance Publique - Hôpitaux 
      de Paris (APHP) and Sorbonne University, Paris, France. Electronic address: 
      lionel.arrive@aphp.fr.
FAU - Minssen, Lise
AU  - Minssen L
AD  - Department of Radiology, Saint-Antoine Hospital, Assistance Publique - Hôpitaux 
      de Paris (APHP) and Sorbonne University, Paris, France.
FAU - Ali, Amal
AU  - Ali A
AD  - Department of Radiology, Saint-Antoine Hospital, Assistance Publique - Hôpitaux 
      de Paris (APHP) and Sorbonne University, Paris, France.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230823
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
CON - Br J Anaesth. 2023 Jul;131(1):e29-e30. PMID: 37183102
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - bibliography
OT  - citations
OT  - generative pretrained transformer
OT  - large language models
EDAT- 2023/08/26 05:42
MHDA- 2023/10/27 06:43
CRDT- 2023/08/25 21:56
PHST- 2023/06/17 00:00 [received]
PHST- 2023/07/14 00:00 [revised]
PHST- 2023/07/30 00:00 [accepted]
PHST- 2023/10/27 06:43 [medline]
PHST- 2023/08/26 05:42 [pubmed]
PHST- 2023/08/25 21:56 [entrez]
AID - S0007-0912(23)00424-5 [pii]
AID - 10.1016/j.bja.2023.07.024 [doi]
PST - ppublish
SO  - Br J Anaesth. 2023 Nov;131(5):e172-e173. doi: 10.1016/j.bja.2023.07.024. Epub 
      2023 Aug 23.

PMID- 37939645
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 87
DP  - 2023 Dec
TI  - Integrating artificial intelligence in orthognathic surgery: A case study of 
      ChatGPT's role in enhancing physician-patient consultations for dentofacial 
      deformities.
PG  - 405-407
LID - S1748-6815(23)00667-8 [pii]
LID - 10.1016/j.bjps.2023.10.097 [doi]
AB  - Orthognathic surgery, a multidisciplinary treatment for dentofacial deformities, 
      presents complex preoperative preparations and follow-up procedures. This study 
      aims to evaluate the effectiveness of ChatGPT-4, an artificial intelligence-based 
      language model, as a supportive tool during patient consultations for 
      orthognathic surgery. Nine critical questions that candidates for orthognathic 
      surgery should ask during a consultation were identified and posed to ChatGPT-4. 
      The responses were subsequently assessed by experienced plastic surgeons and 
      collaborating orthodontists at a leading orthognathic surgery center. Evaluations 
      focused on the accuracy, clarity, and comprehensibility of the information 
      provided. ChatGPT-4 generated comprehensive, clear, and accurate responses, 
      offering critical technical information to guide patients through the 
      complexities of orthognathic surgery. However, it consistently underscored the 
      necessity of individualized responses and emphasized that specialized medical 
      consultation is crucial for treatment and follow-up plans. While ChatGPT-4 shows 
      promise as a reliable informational resource, it cannot fully replace the nuanced 
      physician-patient relationship, particularly in situations requiring emotional 
      intelligence and specialized expertise. ChatGPT-4 can serve as an informative and 
      guiding assistant during the consultation process for orthognathic surgery, 
      although it cannot substitute for direct medical consultation. This tool could be 
      an asset for both patients and physicians in managing the intricate treatment 
      process of orthognathic surgery.
CI  - Copyright © 2023 British Association of Plastic, Reconstructive and Aesthetic 
      Surgeons. Published by Elsevier Ltd. All rights reserved.
FAU - Alten, Aytaç
AU  - Alten A
AD  - Istanbul University, Istanbul Faculty of Medicine, Plastic Reconstructive and 
      Aesthetic Surgery Department, İstanbul Tıp Fakültesi Çapa - Fatih, Istanbul, 
      Türkiye. Electronic address: altenaytac@istanbul.edu.tr.
FAU - Gündeş, Elif
AU  - Gündeş E
AD  - Istanbul University, Istanbul Faculty of Medicine, Plastic Reconstructive and 
      Aesthetic Surgery Department, İstanbul Tıp Fakültesi Çapa - Fatih, Istanbul, 
      Türkiye. Electronic address: elifgundes@istanbul.edu.tr.
FAU - Tuncer, Eren
AU  - Tuncer E
AD  - Başakşehir Çam and Sakura State Hospital Plastic Reconstructive and Aesthetic 
      Surgery Department, G-434 Street No: 2L, Başakşehir Neighborhood, Başakşehir, 
      Istanbul, Türkiye. Electronic address: erentuncr@gmail.com.
FAU - Kozanoğlu, Erol
AU  - Kozanoğlu E
AD  - Istanbul University, Istanbul Faculty of Medicine, Plastic Reconstructive and 
      Aesthetic Surgery Department, İstanbul Tıp Fakültesi Çapa - Fatih, Istanbul, 
      Türkiye. Electronic address: erol.kozanoglu@istanbul.edu.tr.
FAU - Akalın, Bora Edim
AU  - Akalın BE
AD  - Istanbul University, Istanbul Faculty of Medicine, Plastic Reconstructive and 
      Aesthetic Surgery Department, İstanbul Tıp Fakültesi Çapa - Fatih, Istanbul, 
      Türkiye. Electronic address: bora.akalin@istanbul.edu.tr.
FAU - Emekli, Ufuk
AU  - Emekli U
AD  - Istanbul University, Istanbul Faculty of Medicine, Plastic Reconstructive and 
      Aesthetic Surgery Department, İstanbul Tıp Fakültesi Çapa - Fatih, Istanbul, 
      Türkiye. Electronic address: ufukemekli@yahoo.com.
LA  - eng
PT  - Journal Article
DEP - 20231018
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Orthognathic Surgery
MH  - *Dentofacial Deformities
MH  - Referral and Consultation
MH  - *Surgeons
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ChatGPT
OT  - Dentofacial deformity
OT  - Orthognathic surgery
OT  - Patient consultation
COIS- Declaration of Competing Interest None declared.
EDAT- 2023/11/09 00:42
MHDA- 2023/12/05 12:44
CRDT- 2023/11/08 18:11
PHST- 2023/09/14 00:00 [received]
PHST- 2023/10/10 00:00 [revised]
PHST- 2023/10/15 00:00 [accepted]
PHST- 2023/12/05 12:44 [medline]
PHST- 2023/11/09 00:42 [pubmed]
PHST- 2023/11/08 18:11 [entrez]
AID - S1748-6815(23)00667-8 [pii]
AID - 10.1016/j.bjps.2023.10.097 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Dec;87:405-407. doi: 
      10.1016/j.bjps.2023.10.097. Epub 2023 Oct 18.

PMID- 37735339
OWN - NLM
STAT- Publisher
LR  - 20230922
IS  - 1534-6285 (Electronic)
IS  - 1527-2737 (Linking)
DP  - 2023 Sep 22
TI  - Exploring the Ethical, Legal, and Social Implications of ChatGPT in Urology.
LID - 10.1007/s11934-023-01185-2 [doi]
AB  - PURPOSE OF THE REVIEW: ChatGPT is programmed to generate responses based on 
      pattern recognition. With this vast popularity and exponential growth, the 
      question arises of moral issues, security and legitimacy. In this review article, 
      we aim to analyze the ethical and legal implications of using ChatGPT in Urology 
      and explore potential solutions addressing these concerns. RECENT FINDINGS: There 
      are many potential applications of ChatGPT in urology, and the extent to which it 
      might improve healthcare may cause a profound shift in the way we deliver our 
      services to patients and the overall healthcare system. This encompasses 
      diagnosis and treatment planning, clinical workflow, patient education, 
      augmenting consultations, and urological research. The ethical and legal 
      considerations include patient autonomy and informed consent, privacy and 
      confidentiality, bias and fairness, human oversight and accountability, trust and 
      transparency, liability and malpractice, intellectual property rights, and 
      regulatory framework. The application of ChatGPT in urology has shown great 
      potential to improve patient care and assist urologists in various aspects of 
      clinical practice, research, and education. Complying with data security and 
      privacy regulations, and ensuring human oversight and accountability are some 
      potential solutions to these legal and ethical concerns. Overall, the benefits 
      and risks of using ChatGPT in urology must be weighed carefully, and a cautious 
      approach must be taken to ensure that its use aligns with human values and 
      advances patient care ethically and responsibly.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Adhikari, Kinju
AU  - Adhikari K
AD  - Department of Urology, HCG Cancer Centre, Bangaluru, India.
FAU - Naik, Nithesh
AU  - Naik N
AD  - Department of Mechanical and Industrial Engineering, Manipal Institute of 
      Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
FAU - Hameed, Bm Zeeshan
AU  - Hameed BZ
AD  - Department of Urology, Father Muller Medical College, Mangalore, Karnataka, 
      India.
FAU - Raghunath, S K
AU  - Raghunath SK
AD  - Department of Urology, HCG Cancer Centre, Bangaluru, India.
FAU - Somani, Bhaskar K
AU  - Somani BK
AD  - Department of Urology, University Hospital Southampton NHS Trust, Southampton, 
      SO16 6YD, UK. bhaskarsomani@yahoo.com.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230922
PL  - United States
TA  - Curr Urol Rep
JT  - Current urology reports
JID - 100900943
SB  - IM
OTO - NOTNLM
OT  - AI-based tools
OT  - ChatGPT
OT  - Ethical implications.
OT  - Legal implications
OT  - Social implications
OT  - Urology
EDAT- 2023/09/22 06:42
MHDA- 2023/09/22 06:42
CRDT- 2023/09/22 00:23
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/09/22 06:42 [medline]
PHST- 2023/09/22 06:42 [pubmed]
PHST- 2023/09/22 00:23 [entrez]
AID - 10.1007/s11934-023-01185-2 [pii]
AID - 10.1007/s11934-023-01185-2 [doi]
PST - aheadofprint
SO  - Curr Urol Rep. 2023 Sep 22. doi: 10.1007/s11934-023-01185-2.

PMID- 37967485
OWN - NLM
STAT- MEDLINE
DCOM- 20231201
LR  - 20231212
IS  - 1532-8171 (Electronic)
IS  - 0735-6757 (Linking)
VI  - 75
DP  - 2024 Jan
TI  - Performance of Google bard and ChatGPT in mass casualty incidents triage.
PG  - 72-78
LID - S0735-6757(23)00576-4 [pii]
LID - 10.1016/j.ajem.2023.10.034 [doi]
AB  - AIM: The objective of our research is to evaluate and compare the performance of 
      ChatGPT, Google Bard, and medical students in performing START triage during mass 
      casualty situations. METHOD: We conducted a cross-sectional analysis to compare 
      ChatGPT, Google Bard, and medical students in mass casualty incident (MCI) triage 
      using the Simple Triage And Rapid Treatment (START) method. A validated 
      questionnaire with 15 diverse MCI scenarios was used to assess triage accuracy 
      and content analysis in four categories: "Walking wounded," "Respiration," 
      "Perfusion," and "Mental Status." Statistical analysis compared the results. 
      RESULT: Google Bard demonstrated a notably higher accuracy of 60%, while ChatGPT 
      achieved an accuracy of 26.67% (p = 0.002). Comparatively, medical students 
      performed at an accuracy rate of 64.3% in a previous study. However, there was no 
      significant difference observed between Google Bard and medical students 
      (p = 0.211). Qualitative content analysis of 'walking-wounded', 'respiration', 
      'perfusion', and 'mental status' indicated that Google Bard outperformed ChatGPT. 
      CONCLUSION: Google Bard was found to be superior to ChatGPT in correctly 
      performing mass casualty incident triage. Google Bard achieved an accuracy of 
      60%, while chatGPT only achieved an accuracy of 26.67%. This difference was 
      statistically significant (p = 0.002).
CI  - Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Gan, Rick Kye
AU  - Gan RK
AD  - Unit for Research in Emergency and Disaster, Faculty of Medicine and Health 
      Sciences, University of Oviedo, Oviedo 33006, Spain. Electronic address: 
      ganrick@uniovi.es.
FAU - Ogbodo, Jude Chukwuebuka
AU  - Ogbodo JC
AD  - Unit for Research in Emergency and Disaster, Faculty of Medicine and Health 
      Sciences, University of Oviedo, Oviedo 33006, Spain; Department of Primary Care 
      and Population Health, Medical School, University of Nicosia, Nicosia 2408, 
      Cyprus.
FAU - Wee, Yong Zheng
AU  - Wee YZ
AD  - Faculty of Computing & Informatics, Multimedia University, 63100 Cyberjaya, 
      Selangor, Malaysia.
FAU - Gan, Ann Zee
AU  - Gan AZ
AD  - Tenghilan Health Clinic, Tuaran 89208, Sabah, Malaysia; Hospital Universiti Sains 
      Malaysia, 16150 Kota Bharu, Malaysia.
FAU - González, Pedro Arcos
AU  - González PA
AD  - Unit for Research in Emergency and Disaster, Faculty of Medicine and Health 
      Sciences, University of Oviedo, Oviedo 33006, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231029
PL  - United States
TA  - Am J Emerg Med
JT  - The American journal of emergency medicine
JID - 8309942
SB  - IM
MH  - Humans
MH  - *Triage/methods
MH  - *Mass Casualty Incidents
MH  - Cross-Sectional Studies
MH  - Search Engine
MH  - Computer Simulation
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Disaster medicine
OT  - Mass casualty incident
OT  - Triage
COIS- Declaration of Competing Interest All authors declared no conflict of interest.
EDAT- 2023/11/15 18:42
MHDA- 2023/12/01 06:43
CRDT- 2023/11/15 18:00
PHST- 2023/08/20 00:00 [received]
PHST- 2023/10/03 00:00 [revised]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/12/01 06:43 [medline]
PHST- 2023/11/15 18:42 [pubmed]
PHST- 2023/11/15 18:00 [entrez]
AID - S0735-6757(23)00576-4 [pii]
AID - 10.1016/j.ajem.2023.10.034 [doi]
PST - ppublish
SO  - Am J Emerg Med. 2024 Jan;75:72-78. doi: 10.1016/j.ajem.2023.10.034. Epub 2023 Oct 
      29.

PMID- 37521213
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230801
IS  - 2229-5178 (Print)
IS  - 2249-5673 (Electronic)
IS  - 2229-5178 (Linking)
VI  - 14
IP  - 4
DP  - 2023 Jul-Aug
TI  - Using ChatGPT for Writing Articles for Patients' Education for Dermatological 
      Diseases: A Pilot Study.
PG  - 482-486
LID - 10.4103/idoj.idoj_72_23 [doi]
AB  - BACKGROUND: Patients' education is a vital strategy for understanding a disease 
      by patients and proper management of the condition. Physicians and academicians 
      frequently make customized education materials for their patients. An artificial 
      intelligence (AI)-based writer can help them write an article. Chat Generative 
      Pre-Trained Transformer (ChatGPT) is a conversational language model developed by 
      OpenAI (openai.com). The model can generate human-like responses. OBJECTIVE: We 
      aimed to evaluate the generated text from ChatGPT for its suitability in 
      patients' education. MATERIALS AND METHODS: We asked the ChatGPT to list common 
      dermatological diseases. It provided a list of 14 diseases. We used the disease 
      names to converse with the application with disease-specific input (e.g., write a 
      patient education guide on acne). The text was copied for checking the number of 
      words, readability, and text similarity by software. The text's accuracy was 
      checked by a dermatologist following the structure of observed learning outcomes 
      (SOLO) taxonomy. For the readability ease score, we compared the observed value 
      with a score of 30. For the similarity index, we compared the observed value with 
      15% and tested it with a one-sample t-test. RESULTS: The ChatGPT generated a 
      paragraph of text of 377.43 ± 60.85 words for a patient education guide on skin 
      diseases. The average text reading ease score was 46.94 ± 8.23 (P < 0.0001), and 
      it indicates that this level of text can easily be understood by a high-school 
      student to a newly joined college student. The text similarity index was higher 
      (27.07 ± 11.46%, P = 0.002) than the expected limit of 15%. The text had a 
      "relational" level of accuracy according to the SOLO taxonomy. CONCLUSION: In its 
      current form, ChatGPT can generate a paragraph of text for patients' educational 
      purposes that can be easily understood. However, the similarity index is high. 
      Hence, doctors should be cautious when using the text generated by ChatGPT and 
      must check for text similarity before using it.
CI  - Copyright: © 2023 Indian Dermatology Online Journal.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Department of Physiology, All India Institute of Medical Sciences, Deoghar, 
      Jharkhand, India.
FAU - Mondal, Shaikat
AU  - Mondal S
AD  - Department of Physiology, Raiganj Government Medical College and Hospital, West 
      Bengal, India.
FAU - Podder, Indrashis
AU  - Podder I
AD  - Department of Dermatology, College of Medicine and Sagore Dutta Hospital, 
      Kolkata, West Bengal, India.
LA  - eng
PT  - Journal Article
DEP - 20230628
PL  - India
TA  - Indian Dermatol Online J
JT  - Indian dermatology online journal
JID - 101586880
PMC - PMC10373821
OTO - NOTNLM
OT  - Article
OT  - ChatGPT
OT  - artificial intelligence
OT  - dermatologists
OT  - software
COIS- There are no conflicts of interest.
EDAT- 2023/07/31 06:43
MHDA- 2023/07/31 06:44
CRDT- 2023/07/31 05:04
PHST- 2023/01/24 00:00 [received]
PHST- 2023/04/09 00:00 [revised]
PHST- 2023/04/16 00:00 [accepted]
PHST- 2023/07/31 06:44 [medline]
PHST- 2023/07/31 06:43 [pubmed]
PHST- 2023/07/31 05:04 [entrez]
AID - IDOJ-14-482 [pii]
AID - 10.4103/idoj.idoj_72_23 [doi]
PST - epublish
SO  - Indian Dermatol Online J. 2023 Jun 28;14(4):482-486. doi: 
      10.4103/idoj.idoj_72_23. eCollection 2023 Jul-Aug.

PMID- 37155932
OWN - NLM
STAT- MEDLINE
DCOM- 20230929
LR  - 20231003
IS  - 1437-4331 (Electronic)
IS  - 1434-6621 (Linking)
VI  - 61
IP  - 11
DP  - 2023 Oct 26
TI  - AI, diabetes and getting lost in translation: a multilingual evaluation of Bing 
      with ChatGPT focused in HbA(1c).
PG  - e222-e224
LID - 10.1515/cclm-2023-0295 [doi]
FAU - Barallat, Jaume
AU  - Barallat J
AUID- ORCID: 0000-0003-3493-5958
AD  - Biochemistry Department, LCMN, Germans Trias i Pujol University Hospital 
      Badalona, Spain.
FAU - Gómez, Carolina
AU  - Gómez C
AD  - Biochemistry Department, LCMN, Germans Trias i Pujol University Hospital 
      Badalona, Spain.
FAU - Sancho-Cerro, Ana
AU  - Sancho-Cerro A
AD  - Biochemistry Department, LCMN, Germans Trias i Pujol University Hospital 
      Badalona, Spain.
LA  - eng
PT  - Letter
DEP - 20230509
PL  - Germany
TA  - Clin Chem Lab Med
JT  - Clinical chemistry and laboratory medicine
JID - 9806306
SB  - IM
MH  - Humans
MH  - *Diabetes Mellitus/diagnosis
MH  - Artificial Intelligence
OTO - NOTNLM
OT  - Bing
OT  - ChatGPT
OT  - HbA1c
OT  - artificial intelligence
OT  - diabetes
OT  - evaluation of new products
EDAT- 2023/05/08 18:42
MHDA- 2023/09/29 06:44
CRDT- 2023/05/08 15:52
PHST- 2023/03/21 00:00 [received]
PHST- 2023/04/28 00:00 [accepted]
PHST- 2023/09/29 06:44 [medline]
PHST- 2023/05/08 18:42 [pubmed]
PHST- 2023/05/08 15:52 [entrez]
AID - cclm-2023-0295 [pii]
AID - 10.1515/cclm-2023-0295 [doi]
PST - epublish
SO  - Clin Chem Lab Med. 2023 May 9;61(11):e222-e224. doi: 10.1515/cclm-2023-0295. 
      Print 2023 Oct 26.

PMID- 37871487
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 87
DP  - 2023 Dec
TI  - AI did not pass Finnish plastic surgery written board examination.
PG  - 172-179
LID - S1748-6815(23)00598-3 [pii]
LID - 10.1016/j.bjps.2023.10.059 [doi]
FAU - Kelloniemi, Minna
AU  - Kelloniemi M
AD  - Department of Plastic Surgery, Tampere University Hospital, Tampere, Finland.
FAU - Koljonen, Virve
AU  - Koljonen V
AD  - Department of Plastic Surgery, Helsinki University and Helsinki University 
      Hospital, Helsinki, Finland. Electronic address: virve.koljonen@hus.fi.
LA  - eng
PT  - Journal Article
DEP - 20231014
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
MH  - Humans
MH  - United States
MH  - *Surgery, Plastic/education
MH  - Finland
MH  - *Internship and Residency
MH  - Education, Medical, Graduate
MH  - *Plastic Surgery Procedures
MH  - Educational Measurement
OTO - NOTNLM
OT  - AI
OT  - Board examination
OT  - Large language model
OT  - Medical education
OT  - Plastic surgery
OT  - Surgery
EDAT- 2023/10/24 00:41
MHDA- 2023/12/05 12:42
CRDT- 2023/10/23 18:07
PHST- 2023/08/23 00:00 [received]
PHST- 2023/09/16 00:00 [revised]
PHST- 2023/10/08 00:00 [accepted]
PHST- 2023/12/05 12:42 [medline]
PHST- 2023/10/24 00:41 [pubmed]
PHST- 2023/10/23 18:07 [entrez]
AID - S1748-6815(23)00598-3 [pii]
AID - 10.1016/j.bjps.2023.10.059 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Dec;87:172-179. doi: 
      10.1016/j.bjps.2023.10.059. Epub 2023 Oct 14.

PMID- 37999815
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1435-2451 (Electronic)
IS  - 1435-2443 (Linking)
VI  - 408
IP  - 1
DP  - 2023 Nov 24
TI  - Delving into New Frontiers: assessing ChatGPT's proficiency in revealing 
      uncharted dimensions of general surgery and pinpointing innovations for future 
      advancements.
PG  - 446
LID - 10.1007/s00423-023-03173-z [doi]
AB  - PURPOSE: The advent of artificial intelligence (AI) has significantly influenced 
      various medical domains, including general surgery. This research aims to assess 
      ChatGPT, an AI language model, in its ability to shed light on the historical 
      facets of general surgery and pinpoint opportunities for innovation. METHODS: A 
      series of 7 pertinent questions on field of general surgery was posed to ChatGPT. 
      The AI-generated responses were meticulously examined for their relevance, 
      accuracy, and novelty. Additionally, the study explored the AI's ability to 
      recognize knowledge gaps and propose inventive solutions. Expert general surgeons 
      and general surgical residents possessing comprehensive research experience 
      assessed ChatGPT's answers by comparing them to established guidelines and 
      existing literature. RESULTS: ChatGPT presented information that was relevant and 
      accurate, albeit superficial. However, it exhibited convergent thinking and was 
      unable to produce truly groundbreaking ideas to transform general surgery. 
      Instead, it pointed to current popular trends with significant potential for 
      further development. It failed to provide references when prompted and even 
      created references that could not be verified in exhibiting databases. 
      CONCLUSION: While ChatGPT demonstrated a comprehensive understanding of existing 
      general surgical knowledge and the capacity to generate relevant, evidence-based 
      material, it displayed limitations in producing truly groundbreaking concepts or 
      discoveries beyond current knowledge. These results highlight the necessity of 
      enhancing AI-driven models to facilitate the emergence of new insights and 
      promote synergistic, human-AI partnerships for expediting advancements within the 
      general surgery domain.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Lim, Bryan
AU  - Lim B
AUID- ORCID: 0009-0007-9647-5180
AD  - Department of Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia. 
      lim.bryan58@gmail.com.
AD  - Central Clinical School at Monash University, The Alfred Centre, 99 Commercial 
      Rd, Melbourne, VIC, 3004, Australia. lim.bryan58@gmail.com.
FAU - Seth, Ishith
AU  - Seth I
AD  - Department of Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
AD  - Central Clinical School at Monash University, The Alfred Centre, 99 Commercial 
      Rd, Melbourne, VIC, 3004, Australia.
AD  - Department of Surgery, Bendigo Hospital, Victoria, 3550, Australia.
FAU - Dooreemeah, Dilshad
AU  - Dooreemeah D
AD  - Department of Surgery, Bendigo Hospital, Victoria, 3550, Australia.
FAU - Lee, Chun Hin Angus
AU  - Lee CHA
AD  - Department of Surgery, Bendigo Hospital, Victoria, 3550, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231124
PL  - Germany
TA  - Langenbecks Arch Surg
JT  - Langenbeck's archives of surgery
JID - 9808285
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Surgeons
MH  - Databases, Factual
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - General surgery
OT  - Innovation
EDAT- 2023/11/24 12:41
MHDA- 2023/11/27 12:42
CRDT- 2023/11/24 11:08
PHST- 2023/05/05 00:00 [received]
PHST- 2023/11/07 00:00 [accepted]
PHST- 2023/11/27 12:42 [medline]
PHST- 2023/11/24 12:41 [pubmed]
PHST- 2023/11/24 11:08 [entrez]
AID - 10.1007/s00423-023-03173-z [pii]
AID - 10.1007/s00423-023-03173-z [doi]
PST - epublish
SO  - Langenbecks Arch Surg. 2023 Nov 24;408(1):446. doi: 10.1007/s00423-023-03173-z.

PMID- 37632558
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231129
IS  - 1433-8726 (Electronic)
IS  - 0724-4983 (Linking)
VI  - 41
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT and most frequent urological diseases: analysing the quality of 
      information and potential risks for patients.
PG  - 3149-3153
LID - 10.1007/s00345-023-04563-0 [doi]
AB  - PURPOSE: Artificial intelligence (AI) is a set of systems or combinations of 
      algorithms, which mimic human intelligence. ChatGPT is software with artificial 
      intelligence which was recently developed by OpenAI. One of its potential uses 
      could be to consult the information about pathologies and treatments. Our 
      objective was to assess the quality of the information provided by AI like 
      ChatGPT and establish if it is a secure source of information for patients. 
      METHODS: Questions about bladder cancer, prostate cancer, renal cancer, benign 
      prostatic hypertrophy (BPH), and urinary stones were queried through ChatGPT 4.0. 
      Two urologists analysed the responses provided by ChatGPT using DISCERN 
      questionary and a brief instrument for evaluating the quality of informed consent 
      documents. RESULTS: The overall information provided in all pathologies was 
      well-balanced. In each pathology was explained its anatomical location, affected 
      population and a description of the symptoms. It concluded with the established 
      risk factors and possible treatment. All treatment answers had a moderate quality 
      score with DISCERN (3 of 5 points). The answers about surgical options contain 
      the recovery time, type of anaesthesia, and potential complications. After 
      analysing all the responses related to each disease, all pathologies except BPH 
      achieved a DISCERN score of 4. CONCLUSIONS: ChatGPT information should be used 
      with caution since the chatbot does not disclose the sources of information and 
      may contain bias even with simple questions related to the basics of urologic 
      diseases.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Szczesniewski, Juliusz Jan
AU  - Szczesniewski JJ
AUID- ORCID: 0000-0001-8690-5692
AD  - Department of Urology, Getafe University Hospital, Carretera Madrid-Toledo km 
      12,500 Getafe, 28905, Madrid, Spain. juliusz.szcz@gmail.com.
AD  - Faculty of Medicine, University of Salamanca, C. Alfonso X el Sabio, s/n, 37007, 
      Salamanca, Spain. juliusz.szcz@gmail.com.
FAU - Tellez Fouz, Carlos
AU  - Tellez Fouz C
AD  - Department of Urology, Getafe University Hospital, Carretera Madrid-Toledo km 
      12,500 Getafe, 28905, Madrid, Spain.
FAU - Ramos Alba, Alejandra
AU  - Ramos Alba A
AD  - DXC Technology, C. de José Echegaray, Las Rozas, 28232, Madrid, Spain.
AD  - Rey Juan Carlos University, P. de los Artilleros 38, 28032, Madrid, Spain.
FAU - Diaz Goizueta, Francisco Javier
AU  - Diaz Goizueta FJ
AD  - Department of Urology, Getafe University Hospital, Carretera Madrid-Toledo km 
      12,500 Getafe, 28905, Madrid, Spain.
FAU - García Tello, Ana
AU  - García Tello A
AD  - Department of Urology, Getafe University Hospital, Carretera Madrid-Toledo km 
      12,500 Getafe, 28905, Madrid, Spain.
FAU - Llanes González, Luis
AU  - Llanes González L
AD  - Department of Urology, Getafe University Hospital, Carretera Madrid-Toledo km 
      12,500 Getafe, 28905, Madrid, Spain.
AD  - Francisco de Vitoria University, Carretera Pozuelo a Majadahonda, Km 1.800, 
      28223, Madrid, Spain.
LA  - eng
PT  - Journal Article
DEP - 20230826
PL  - Germany
TA  - World J Urol
JT  - World journal of urology
JID - 8307716
SB  - IM
CIN - World J Urol. 2023 Sep 26;:. PMID: 37750962
CIN - World J Urol. 2023 Sep 26;:. PMID: 37752273
MH  - Male
MH  - Humans
MH  - Artificial Intelligence
MH  - *Prostatic Hyperplasia
MH  - *Urologic Diseases
MH  - *Urinary Calculi
MH  - *Kidney Neoplasms
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Patient information
OT  - Quality of information
OT  - Urology
EDAT- 2023/08/27 05:42
MHDA- 2023/11/09 06:42
CRDT- 2023/08/26 11:04
PHST- 2023/04/16 00:00 [received]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/11/09 06:42 [medline]
PHST- 2023/08/27 05:42 [pubmed]
PHST- 2023/08/26 11:04 [entrez]
AID - 10.1007/s00345-023-04563-0 [pii]
AID - 10.1007/s00345-023-04563-0 [doi]
PST - ppublish
SO  - World J Urol. 2023 Nov;41(11):3149-3153. doi: 10.1007/s00345-023-04563-0. Epub 
      2023 Aug 26.

PMID- 37566454
OWN - NLM
STAT- MEDLINE
DCOM- 20230828
LR  - 20231106
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Aug 11
TI  - Ethical Considerations of Using ChatGPT in Health Care.
PG  - e48009
LID - 10.2196/48009 [doi]
LID - e48009
AB  - ChatGPT has promising applications in health care, but potential ethical issues 
      need to be addressed proactively to prevent harm. ChatGPT presents potential 
      ethical challenges from legal, humanistic, algorithmic, and informational 
      perspectives. Legal ethics concerns arise from the unclear allocation of 
      responsibility when patient harm occurs and from potential breaches of patient 
      privacy due to data collection. Clear rules and legal boundaries are needed to 
      properly allocate liability and protect users. Humanistic ethics concerns arise 
      from the potential disruption of the physician-patient relationship, humanistic 
      care, and issues of integrity. Overreliance on artificial intelligence (AI) can 
      undermine compassion and erode trust. Transparency and disclosure of AI-generated 
      content are critical to maintaining integrity. Algorithmic ethics raise concerns 
      about algorithmic bias, responsibility, transparency and explainability, as well 
      as validation and evaluation. Information ethics include data bias, validity, and 
      effectiveness. Biased training data can lead to biased output, and overreliance 
      on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring 
      the accuracy, reliability, and validity of ChatGPT-generated content requires 
      rigorous validation and ongoing updates based on clinical practice. To navigate 
      the evolving ethical landscape of AI, AI in health care must adhere to the 
      strictest ethical standards. Through comprehensive ethical guidelines, health 
      care professionals can ensure the responsible use of ChatGPT, promote accurate 
      and reliable information exchange, protect patient privacy, and empower patients 
      to make informed decisions about their health care.
CI  - ©Changyu Wang, Siru Liu, Hao Yang, Jiulin Guo, Yuxuan Wu, Jialin Liu. Originally 
      published in the Journal of Medical Internet Research (https://www.jmir.org), 
      11.08.2023.
FAU - Wang, Changyu
AU  - Wang C
AUID- ORCID: 0000-0003-4548-331X
AD  - Department of Medical Informatics, West China Medical School, Sichuan University, 
      Chengdu, China.
AD  - West China College of Stomatology, Sichuan University, Chengdu, China.
FAU - Liu, Siru
AU  - Liu S
AUID- ORCID: 0000-0002-5003-5354
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, TN, United States.
FAU - Yang, Hao
AU  - Yang H
AUID- ORCID: 0000-0002-3505-9403
AD  - Information Center, West China Hospital, Sichuan University, Chengdu, China.
FAU - Guo, Jiulin
AU  - Guo J
AUID- ORCID: 0009-0006-5995-8290
AD  - Information Center, West China Hospital, Sichuan University, Chengdu, China.
FAU - Wu, Yuxuan
AU  - Wu Y
AUID- ORCID: 0000-0003-1333-4627
AD  - Department of Medical Informatics, West China Medical School, Sichuan University, 
      Chengdu, China.
FAU - Liu, Jialin
AU  - Liu J
AUID- ORCID: 0000-0002-1369-4625
AD  - Department of Medical Informatics, West China Medical School, Sichuan University, 
      Chengdu, China.
AD  - Information Center, West China Hospital, Sichuan University, Chengdu, China.
AD  - Department of Otolaryngology-Head and Neck Surgery, West China Hospital, Sichuan 
      University, Chengdu, China.
LA  - eng
PT  - Journal Article
DEP - 20230811
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - Data Collection
MH  - *Disclosure
MH  - Patient Compliance
PMC - PMC10457697
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - algorithm
OT  - artificial intelligence
OT  - artificial intelligence development
OT  - development
OT  - ethics
OT  - health care
OT  - large language models
OT  - patient privacy
OT  - patient safety
OT  - privacy
OT  - safety
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/11 12:42
MHDA- 2023/08/28 06:42
CRDT- 2023/08/11 11:53
PHST- 2023/04/07 00:00 [received]
PHST- 2023/07/25 00:00 [accepted]
PHST- 2023/07/05 00:00 [revised]
PHST- 2023/08/28 06:42 [medline]
PHST- 2023/08/11 12:42 [pubmed]
PHST- 2023/08/11 11:53 [entrez]
AID - v25i1e48009 [pii]
AID - 10.2196/48009 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Aug 11;25:e48009. doi: 10.2196/48009.

PMID- 37545428
OWN - NLM
STAT- Publisher
LR  - 20230807
IS  - 1545-1569 (Electronic)
IS  - 1055-6656 (Linking)
DP  - 2023 Aug 7
TI  - Dr. ChatGPT: Utilizing Artificial Intelligence in Surgical Education.
PG  - 10556656231193966
LID - 10.1177/10556656231193966 [doi]
AB  - OBJECTIVE: This study sought to explore the unexamined capabilities of ChatGPT in 
      describing the surgical steps of a specialized operation, the Fisher cleft lip 
      repair. DESIGN: A chat log within ChatGPT was created to generate the procedural 
      steps of a cleft lip repair utilizing the Fisher technique. A board certified 
      craniomaxillofacial (CMF) surgeon then wrote the Fisher repair in his own words 
      blinded to the ChatGPT response. Using both responses, a voluntary survey 
      questionnaire was distributed to residents of plastic and reconstructive surgery 
      (PRS), general surgery (GS), internal medicine (IM), and medical students at our 
      institution in a blinded study. SETTING: Authors collected information from 
      residents (PRS, GS, IM) and medical students at one institution. MAIN OUTCOME 
      MEASURES: Primary outcome measures included understanding, preference, and author 
      identification of the procedural prompts. RESULTS: Results show PRS residents 
      were able to detect more inaccuracies of the ChatGPT response as well as prefer 
      the CMF surgeon's prompt in performing the surgery. Residents with less expertise 
      in the procedure not only failed to detect who wrote what procedure, but 
      preferred the ChatGPT response in explaining the concept and chose it to perform 
      the surgery. CONCLUSIONS: In applications to surgical education, ChatGPT was 
      found to be effective in generating easy to understand procedural steps that can 
      be followed by medical personnel of all specialties. However, it does not have 
      expert capabilities to provide the minute detail of measurements and specific 
      anatomy required to perform medical procedures.
FAU - Lebhar, Michael S
AU  - Lebhar MS
AUID- ORCID: 0000-0001-6560-3329
AD  - Division of Plastic and Reconstructive Surgery, University of Mississippi Medical 
      Center, Jackson, MS, USA.
FAU - Velazquez, Alexander
AU  - Velazquez A
AD  - University of Mississippi School of Medicine, Jackson, MS, USA.
FAU - Goza, Shelby
AU  - Goza S
AD  - University of Mississippi School of Medicine, Jackson, MS, USA.
FAU - Hoppe, Ian C
AU  - Hoppe IC
AUID- ORCID: 0000-0001-8332-2280
AD  - Division of Plastic and Reconstructive Surgery, University of Mississippi Medical 
      Center, Jackson, MS, USA.
LA  - eng
PT  - Journal Article
DEP - 20230807
PL  - United States
TA  - Cleft Palate Craniofac J
JT  - The Cleft palate-craniofacial journal : official publication of the American 
      Cleft Palate-Craniofacial Association
JID - 9102566
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - craniofacial surgery
OT  - surgical technique
EDAT- 2023/08/07 06:41
MHDA- 2023/08/07 06:41
CRDT- 2023/08/07 04:04
PHST- 2023/08/07 06:41 [medline]
PHST- 2023/08/07 06:41 [pubmed]
PHST- 2023/08/07 04:04 [entrez]
AID - 10.1177/10556656231193966 [doi]
PST - aheadofprint
SO  - Cleft Palate Craniofac J. 2023 Aug 7:10556656231193966. doi: 
      10.1177/10556656231193966.

PMID- 37528247
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231018
IS  - 1573-2584 (Electronic)
IS  - 0301-1623 (Linking)
VI  - 55
IP  - 11
DP  - 2023 Nov
TI  - The utility of the ChatGPT artificial intelligence tool for patient education and 
      enquiry in robotic radical prostatectomy.
PG  - 2717-2732
LID - 10.1007/s11255-023-03729-4 [doi]
AB  - OBJECTIVES: To assess the ChatGPT (Open AI) artificial intelligence platform's 
      utility and accuracy as a patient education tool in robotic-assisted radical 
      prostatectomy. MATERIALS AND METHODS: The ChatGPT 3.5 interface ( 
      https://chat.openai.com/chat ) was interrogated and asked the 14 questions 
      related to the frequency of complications of robotic-assisted radical 
      prostatectomy as listed on British Association of Urological Surgeons (BAUS) 
      patient information leaflet "Robotic-Assisted Laparoscopic Radical 
      Prostatectomy)" published July 2021. The AI's responses to each question were 
      tabulated, and compared alongside the official figures quoted from the BAUS 
      information leaflet. A global assessment of the AI's response was also made for 
      accuracy and relevance to patients. RESULTS: Of the 14 questions asked, 11/14 
      (78.6%) of ChatGPT's quoted figures were considered concordant and comparable to 
      those quoted by the BAUS patient information leaflet. 13/14 (92.9%) of ChatGPT's 
      responses were globally assessed to contain accurate information, appropriate, 
      and pertinent to a patient's potential enquiry. CONCLUSION: ChatGPT is a powerful 
      tool and is able to generate accurate and helpful information for patients 
      seeking information regarding urological procedures and their potential 
      complications. It is not, however, infallible, and urologists should remain 
      vigilant in their patient interactions to any potential misinformation a patient 
      may have gathered from it in their personal research prior to surgery.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature B.V.
FAU - Gabriel, Joseph
AU  - Gabriel J
AD  - Department of Urology, University Hospitals Sussex NHS Foundation Trust, Princess 
      Royal Hospital, Haywards Heath, UK. joseph.gabriel@nhs.net.
FAU - Shafik, Lidia
AU  - Shafik L
AD  - Surrey and Sussex Healthcare NHS Trust, Redhill, UK.
FAU - Alanbuki, Ammar
AU  - Alanbuki A
AD  - Department of Urology, University Hospitals Sussex NHS Foundation Trust, Princess 
      Royal Hospital, Haywards Heath, UK.
FAU - Larner, Tim
AU  - Larner T
AD  - Department of Urology, University Hospitals Sussex NHS Foundation Trust, Princess 
      Royal Hospital, Haywards Heath, UK.
LA  - eng
PT  - Journal Article
DEP - 20230802
PL  - Netherlands
TA  - Int Urol Nephrol
JT  - International urology and nephrology
JID - 0262521
SB  - IM
MH  - Male
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Robotic Surgical Procedures
MH  - Patient Education as Topic
MH  - Prostatectomy
MH  - Urologists
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Consent
OT  - Patient education
OT  - Robotic prostatectomy
EDAT- 2023/08/02 01:07
MHDA- 2023/10/09 06:42
CRDT- 2023/08/01 23:29
PHST- 2023/06/27 00:00 [received]
PHST- 2023/07/25 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/08/02 01:07 [pubmed]
PHST- 2023/08/01 23:29 [entrez]
AID - 10.1007/s11255-023-03729-4 [pii]
AID - 10.1007/s11255-023-03729-4 [doi]
PST - ppublish
SO  - Int Urol Nephrol. 2023 Nov;55(11):2717-2732. doi: 10.1007/s11255-023-03729-4. 
      Epub 2023 Aug 2.

PMID- 37794649
OWN - NLM
STAT- Publisher
LR  - 20231005
IS  - 1601-0825 (Electronic)
IS  - 1354-523X (Linking)
DP  - 2023 Oct 4
TI  - How ChatGPT performs in Oral Medicine: The case of oral potentially malignant 
      disorders.
LID - 10.1111/odi.14750 [doi]
FAU - Diniz-Freitas, M
AU  - Diniz-Freitas M
AUID- ORCID: 0000-0001-5349-1091
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - Rivas-Mundiña, B
AU  - Rivas-Mundiña B
AUID- ORCID: 0000-0003-4976-395X
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - García-Iglesias, J R
AU  - García-Iglesias JR
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - García-Mato, E
AU  - García-Mato E
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - Diz-Dios, P
AU  - Diz-Dios P
AUID- ORCID: 0000-0002-1483-401X
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231004
PL  - Denmark
TA  - Oral Dis
JT  - Oral diseases
JID - 9508565
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - oral leukoplakia
OT  - oral potentially malignant disorders
EDAT- 2023/10/05 06:44
MHDA- 2023/10/05 06:44
CRDT- 2023/10/05 01:13
PHST- 2023/08/15 00:00 [revised]
PHST- 2023/04/17 00:00 [received]
PHST- 2023/09/13 00:00 [accepted]
PHST- 2023/10/05 06:44 [medline]
PHST- 2023/10/05 06:44 [pubmed]
PHST- 2023/10/05 01:13 [entrez]
AID - 10.1111/odi.14750 [doi]
PST - aheadofprint
SO  - Oral Dis. 2023 Oct 4. doi: 10.1111/odi.14750.

PMID- 37727444
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230921
IS  - 2162-2531 (Print)
IS  - 2162-2531 (Electronic)
IS  - 2162-2531 (Linking)
VI  - 33
DP  - 2023 Sep 12
TI  - Can artificial intelligence-strengthened ChatGPT or other large language models 
      transform nucleic acid research?
PG  - 205-207
LID - 10.1016/j.omtn.2023.06.019 [doi]
FAU - Chatterjee, Srijan
AU  - Chatterjee S
AD  - Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon 
      Sacred Heart Hospital, Chuncheon-si, Gangwon-do 24252, Republic of Korea.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 
      756020, India.
FAU - Lee, Sang-Soo
AU  - Lee SS
AD  - Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon 
      Sacred Heart Hospital, Chuncheon-si, Gangwon-do 24252, Republic of Korea.
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal 700126, India.
LA  - eng
PT  - News
DEP - 20230712
PL  - United States
TA  - Mol Ther Nucleic Acids
JT  - Molecular therapy. Nucleic acids
JID - 101581621
PMC - PMC10505907
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - large language models
OT  - nucleic acid
COIS- The authors declare no competing interests.
EDAT- 2023/09/20 06:42
MHDA- 2023/09/20 06:43
CRDT- 2023/09/20 03:50
PHST- 2023/09/20 06:43 [medline]
PHST- 2023/09/20 06:42 [pubmed]
PHST- 2023/09/20 03:50 [entrez]
AID - S2162-2531(23)00169-5 [pii]
AID - 10.1016/j.omtn.2023.06.019 [doi]
PST - epublish
SO  - Mol Ther Nucleic Acids. 2023 Jul 12;33:205-207. doi: 10.1016/j.omtn.2023.06.019. 
      eCollection 2023 Sep 12.

PMID- 37285018
OWN - NLM
STAT- MEDLINE
DCOM- 20230731
LR  - 20231116
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Print)
IS  - 0937-4477 (Linking)
VI  - 280
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT's quiz skills in different otolaryngology subspecialties: an analysis of 
      2576 single-choice and multiple-choice board certification preparation questions.
PG  - 4271-4278
LID - 10.1007/s00405-023-08051-4 [doi]
AB  - PURPOSE: With the increasing adoption of artificial intelligence (AI) in various 
      domains, including healthcare, there is growing acceptance and interest in 
      consulting AI models to provide medical information and advice. This study aimed 
      to evaluate the accuracy of ChatGPT's responses to practice quiz questions 
      designed for otolaryngology board certification and decipher potential 
      performance disparities across different otolaryngology subspecialties. METHODS: 
      A dataset covering 15 otolaryngology subspecialties was collected from an online 
      learning platform funded by the German Society of Oto-Rhino-Laryngology, Head and 
      Neck Surgery, designed for board certification examination preparation. These 
      questions were entered into ChatGPT, with its responses being analyzed for 
      accuracy and variance in performance. RESULTS: The dataset included 2576 
      questions (479 multiple-choice and 2097 single-choice), of which 57% (n = 1475) 
      were answered correctly by ChatGPT. An in-depth analysis of question style 
      revealed that single-choice questions were associated with a significantly higher 
      rate (p < 0.001) of correct responses (n = 1313; 63%) compared to multiple-choice 
      questions (n = 162; 34%). Stratified by question categories, ChatGPT yielded the 
      highest rate of correct responses (n = 151; 72%) in the field of allergology, 
      whereas 7 out of 10 questions (n = 65; 71%) on legal otolaryngology aspects were 
      answered incorrectly. CONCLUSION: The study reveals ChatGPT's potential as a 
      supplementary tool for otolaryngology board certification preparation. However, 
      its propensity for errors in certain otolaryngology areas calls for further 
      refinement. Future research should address these limitations to improve ChatGPT's 
      educational use. An approach, with expert collaboration, is recommended for the 
      reliable and accurate integration of such AI models.
CI  - © 2023. The Author(s).
FAU - Hoch, Cosima C
AU  - Hoch CC
AUID- ORCID: 0000-0002-3875-7389
AD  - Department of Otolaryngology, Head and Neck Surgery, School of Medicine, 
      Technical University of Munich (TUM), Ismaningerstrasse 22, 81675, Munich, 
      Germany. cosima.chiara.hoch@tum.de.
FAU - Wollenberg, Barbara
AU  - Wollenberg B
AD  - Department of Otolaryngology, Head and Neck Surgery, School of Medicine, 
      Technical University of Munich (TUM), Ismaningerstrasse 22, 81675, Munich, 
      Germany.
FAU - Lüers, Jan-Christoffer
AU  - Lüers JC
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty, 
      University of Cologne, 50937, Cologne, Germany.
FAU - Knoedler, Samuel
AU  - Knoedler S
AD  - Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical 
      School, Boston, MA, 02152, USA.
AD  - Department of Plastic Surgery and Hand Surgery, Klinikum Rechts Der Isar, 
      Technical University of Munich, Munich, Germany.
FAU - Knoedler, Leonard
AU  - Knoedler L
AD  - Division of Plastic and Reconstructive Surgery, Massachusetts General Hospital, 
      Harvard Medical School, Boston, MA, 02115, USA.
FAU - Frank, Konstantin
AU  - Frank K
AD  - Ocean Clinic, Marbella, Spain.
FAU - Cotofana, Sebastian
AU  - Cotofana S
AD  - Department of Dermatology, Erasmus Hospital, Rotterdam, The Netherlands.
AD  - Centre for Cutaneous Research, Blizard Institute, Queen Mary University of 
      London, London, UK.
FAU - Alfertshofer, Michael
AU  - Alfertshofer M
AD  - Division of Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilians-University 
      Munich, Munich, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230607
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
CIN - Eur Arch Otorhinolaryngol. 2023 Nov 13;:. PMID: 37955694
MH  - Humans
MH  - *Artificial Intelligence
MH  - Certification
MH  - Educational Status
MH  - *Otolaryngology
MH  - Referral and Consultation
PMC - PMC10382366
OTO - NOTNLM
OT  - AI
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Multiple-choice
OT  - Otolaryngology quiz
OT  - Single-choice
COIS- The authors have no relevant financial or non-financial interests to disclose. 
      Jan-Christoffer Lüers, M.D., Ph.D. is the developer and owner of the online 
      learning platform.
EDAT- 2023/06/07 13:10
MHDA- 2023/07/31 06:42
CRDT- 2023/06/07 11:13
PHST- 2023/05/23 00:00 [received]
PHST- 2023/05/26 00:00 [accepted]
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/06/07 13:10 [pubmed]
PHST- 2023/06/07 11:13 [entrez]
AID - 10.1007/s00405-023-08051-4 [pii]
AID - 8051 [pii]
AID - 10.1007/s00405-023-08051-4 [doi]
PST - ppublish
SO  - Eur Arch Otorhinolaryngol. 2023 Sep;280(9):4271-4278. doi: 
      10.1007/s00405-023-08051-4. Epub 2023 Jun 7.

PMID- 37768724
OWN - NLM
STAT- Publisher
LR  - 20231015
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Sep 28
TI  - Performance of ChatGPT on the Peruvian National Licensing Medical Examination: 
      Cross-Sectional Study.
PG  - e48039
LID - 10.2196/48039 [doi]
LID - e48039
AB  - BACKGROUND: ChatGPT has shown impressive performance in national medical 
      licensing examinations, such as the United States Medical Licensing Examination 
      (USMLE), even passing it with expert-level performance. However, there is a lack 
      of research on its performance in low-income countries' national licensing 
      medical examinations. In Peru, where almost one out of three examinees fails the 
      national licensing medical examination, ChatGPT has the potential to enhance 
      medical education. OBJECTIVE: We aimed to assess the accuracy of ChatGPT using 
      GPT-3.5 and GPT-4 on the Peruvian National Licensing Medical Examination (Examen 
      Nacional de Medicina [ENAM]). Additionally, we sought to identify factors 
      associated with incorrect answers provided by ChatGPT. METHODS: We used the ENAM 
      2022 data set, which consisted of 180 multiple-choice questions, to evaluate the 
      performance of ChatGPT. Various prompts were used, and accuracy was evaluated. 
      The performance of ChatGPT was compared to that of a sample of 1025 examinees. 
      Factors such as question type, Peruvian-specific knowledge, discrimination, 
      difficulty, quality of questions, and subject were analyzed to determine their 
      influence on incorrect answers. Questions that received incorrect answers 
      underwent a three-step process involving different prompts to explore the 
      potential impact of adding roles and context on ChatGPT's accuracy. RESULTS: 
      GPT-4 achieved an accuracy of 86% on the ENAM, followed by GPT-3.5 with 77%. The 
      accuracy obtained by the 1025 examinees was 55%. There was a fair agreement 
      (κ=0.38) between GPT-3.5 and GPT-4. Moderate-to-high-difficulty questions were 
      associated with incorrect answers in the crude and adjusted model for GPT-3.5 
      (odds ratio [OR] 6.6, 95% CI 2.73-15.95) and GPT-4 (OR 33.23, 95% CI 4.3-257.12). 
      After reinputting questions that received incorrect answers, GPT-3.5 went from 41 
      (100%) to 12 (29%) incorrect answers, and GPT-4 from 25 (100%) to 4 (16%). 
      CONCLUSIONS: Our study found that ChatGPT (GPT-3.5 and GPT-4) can achieve 
      expert-level performance on the ENAM, outperforming most of our examinees. We 
      found fair agreement between both GPT-3.5 and GPT-4. Incorrect answers were 
      associated with the difficulty of questions, which may resemble human 
      performance. Furthermore, by reinputting questions that initially received 
      incorrect answers with different prompts containing additional roles and context, 
      ChatGPT achieved improved accuracy.
CI  - ©Javier A Flores-Cohaila, Abigaíl García-Vicente, Sonia F Vizcarra-Jiménez, 
      Janith P De la Cruz-Galán, Jesús D Gutiérrez-Arratia, Blanca Geraldine Quiroga 
      Torres, Alvaro Taype-Rondan. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 28.09.2023.
FAU - Flores-Cohaila, Javier A
AU  - Flores-Cohaila JA
AUID- ORCID: 0000-0002-9169-2094
AD  - Academic Department, USAMEDIC, Lima, Peru.
AD  - Facultad de Ciencias de la Salud, Carrera de Medicina, Universidad Científica del 
      Sur, Lima, Peru.
FAU - García-Vicente, Abigaíl
AU  - García-Vicente A
AUID- ORCID: 0000-0003-3679-4239
AD  - School of Medicine, Universidad Nacional de Piura, Piura, Peru.
AD  - Comité Permanente Académico, Sociedad Científica Médico Estudiantil Peruana, 
      Lima, Peru.
FAU - Vizcarra-Jiménez, Sonia F
AU  - Vizcarra-Jiménez SF
AUID- ORCID: 0000-0002-8030-4092
AD  - Comité Permanente Académico, Sociedad Científica Médico Estudiantil Peruana, 
      Lima, Peru.
AD  - Centro de Investigación de Estudiantes de Medicina, Tacna, Peru.
FAU - De la Cruz-Galán, Janith P
AU  - De la Cruz-Galán JP
AUID- ORCID: 0000-0001-7261-9038
AD  - Comité Permanente Académico, Sociedad Científica Médico Estudiantil Peruana, 
      Lima, Peru.
AD  - School of Medicine, Universidad de San Martin de Porres - Filial Norte, Chiclayo, 
      Peru.
FAU - Gutiérrez-Arratia, Jesús D
AU  - Gutiérrez-Arratia JD
AUID- ORCID: 0000-0003-4082-5966
AD  - Academic Department, USAMEDIC, Lima, Peru.
FAU - Quiroga Torres, Blanca Geraldine
AU  - Quiroga Torres BG
AUID- ORCID: 0000-0002-1141-1062
AD  - Comité Permanente Académico, Sociedad Científica Médico Estudiantil Peruana, 
      Lima, Peru.
FAU - Taype-Rondan, Alvaro
AU  - Taype-Rondan A
AUID- ORCID: 0000-0001-8758-0463
AD  - Unidad de Investigación Para la Generación y Síntesis de Evidencias en Salud, 
      Vicerrectorado de Investigación, Universidad San Ignacio de Loyola, Lima, Peru.
AD  - EviSalud - Evidencias en Salud, Lima, Peru.
LA  - eng
PT  - Journal Article
DEP - 20230928
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10570896
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - ENAM
OT  - Examen Nacional de Medicina
OT  - Peru
OT  - artificial intelligence
OT  - assessment
OT  - generative pre-trained transformer
OT  - learning model
OT  - licensing examination
OT  - medical education
OT  - medical examination
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/28 12:42
MHDA- 2023/09/28 12:42
CRDT- 2023/09/28 11:54
PHST- 2023/04/09 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/06/16 00:00 [revised]
PHST- 2023/09/28 12:42 [medline]
PHST- 2023/09/28 12:42 [pubmed]
PHST- 2023/09/28 11:54 [entrez]
AID - v9i1e48039 [pii]
AID - 10.2196/48039 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Sep 28;9:e48039. doi: 10.2196/48039.

PMID- 37310252
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230620
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 5
DP  - 2023 Jun
TI  - How AI Responds to Common Lung Cancer Questions: ChatGPT vs Google Bard.
PG  - e230922
LID - 10.1148/radiol.230922 [doi]
AB  - Background The recent release of large language models (LLMs) for public use, 
      such as ChatGPT and Google Bard, has opened up a multitude of potential benefits 
      as well as challenges. Purpose To evaluate and compare the accuracy and 
      consistency of responses generated by publicly available ChatGPT-3.5 and Google 
      Bard to non-expert questions related to lung cancer prevention, screening, and 
      terminology commonly used in radiology reports based on the recommendation of 
      Lung Imaging Reporting and Data System (Lung-RADS) v2022 from American College of 
      Radiology and Fleischner society. Materials and Methods Forty of the exact same 
      questions were created and presented to ChatGPT-3.5 and Google Bard experimental 
      version as well as Bing and Google search engines by three different authors of 
      this paper. Each answer was reviewed by two radiologists for accuracy. Responses 
      were scored as correct, partially correct, incorrect, or unanswered. Consistency 
      was also evaluated among the answers. Here, consistency was defined as the 
      agreement between the three answers provided by ChatGPT-3.5, Google Bard 
      experimental version, Bing, and Google search engines regardless of whether the 
      concept conveyed was correct or incorrect. The accuracy among different tools 
      were evaluated using Stata. Results ChatGPT-3.5 answered 120 questions with 85 
      (70.8%) correct, 14 (11.7%) partially correct, and 21 (17.5%) incorrect. Google 
      Bard did not answer 23 (19.1%) questions. Among the 97 questions answered by 
      Google Bard, 62 (51.7%) were correct, 11 (9.2%) were partially correct, and 24 
      (20%) were incorrect. Bing answered 120 questions with 74 (61.7%) correct, 13 
      (10.8%) partially correct, and 33 (27.5%) incorrect. Google search engine 
      answered 120 questions with 66 (55%) correct, 27 (22.5%) partially correct, and 
      27 (22.5%) incorrect. The ChatGPT-3.5 is more likely to provide correct or 
      partially answer than Google Bard, approximately by 1.5 folds (OR = 1.55, P = 
      0.004). ChatGPT-3.5 and Google search engine were more likely to be consistent 
      than Google Bard by approximately 7 and 29 folds (OR = 6.65, P = 0.002 for 
      ChatGPT and OR = 28.83, P = 0.002 for Google search engine, respectively). 
      Conclusion Although ChatGPT-3.5 had a higher accuracy in comparison with the 
      other tools, neither ChatGPT nor Google Bard, Bing and Google search engines 
      answered all questions correctly and with 100% consistency.
FAU - Rahsepar, Amir Ali
AU  - Rahsepar AA
AUID- ORCID: 0000-0002-6228-4673
AD  - Department of Radiological Sciences, Division of Cardiothoracic Imaging, David 
      Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
FAU - Tavakoli, Neda
AU  - Tavakoli N
AUID- ORCID: 0000-0002-1541-5917
AD  - School of Computational Science and Engineering, Georgia Institute of Technology, 
      Atlanta, Georgia, USA.
FAU - Kim, Grace Hyun J
AU  - Kim GHJ
AUID- ORCID: 0000-0003-1225-3489
AD  - Center for Computer Vision and Imaging Biomarkers, Dept of Radiological Sciences 
      David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
AD  - Dept of Biostatistics Fielding School of Public at UCLA, Los Angeles, CA, USA.
FAU - Hassani, Cameron
AU  - Hassani C
AUID- ORCID: 0000-0002-2831-5384
AD  - Department of Radiological Sciences, Division of Cardiothoracic Imaging, David 
      Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
FAU - Abtin, Fereidoun
AU  - Abtin F
AUID- ORCID: 0000-0003-2927-0883
AD  - Department of Radiological Sciences, Division of Cardiothoracic Imaging, David 
      Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
FAU - Bedayat, Arash
AU  - Bedayat A
AUID- ORCID: 0000-0001-8769-0224
AD  - Department of Radiological Sciences, Division of Cardiothoracic Imaging, David 
      Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
MH  - Humans
MH  - *Lung Neoplasms/diagnostic imaging
MH  - Search Engine
MH  - Tomography, X-Ray Computed
MH  - Language
MH  - Artificial Intelligence
EDAT- 2023/06/13 13:12
MHDA- 2023/06/15 06:42
CRDT- 2023/06/13 10:05
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/06/13 13:12 [pubmed]
PHST- 2023/06/13 10:05 [entrez]
AID - 10.1148/radiol.230922 [doi]
PST - ppublish
SO  - Radiology. 2023 Jun;307(5):e230922. doi: 10.1148/radiol.230922.

PMID- 37330210
OWN - NLM
STAT- MEDLINE
DCOM- 20230929
LR  - 20230929
IS  - 1535-7732 (Electronic)
IS  - 1051-0443 (Linking)
VI  - 34
IP  - 10
DP  - 2023 Oct
TI  - Evaluation of an Artificial Intelligence Chatbot for Delivery of IR Patient 
      Education Material: A Comparison with Societal Website Content.
PG  - 1760-1768.e32
LID - S1051-0443(23)00423-2 [pii]
LID - 10.1016/j.jvir.2023.05.037 [doi]
AB  - PURPOSE: To assess the accuracy, completeness, and readability of patient 
      educational material produced by a machine learning model and compare the output 
      to that provided by a societal website. MATERIALS AND METHODS: Content from the 
      Society of Interventional Radiology Patient Center website was retrieved, 
      categorized, and organized into discrete questions. These questions were entered 
      into the ChatGPT platform, and the output was analyzed for word and sentence 
      counts, readability using multiple validated scales, factual correctness, and 
      suitability for patient education using the Patient Education Materials 
      Assessment Tool for Printable Materials (PEMAT-P) instrument. RESULTS: A total of 
      21,154 words were analyzed, including 7,917 words from the website and 13,377 
      words representing the total output of the ChatGPT platform across 22 text 
      passages. Compared to the societal website, output from the ChatGPT platform was 
      longer and more difficult to read on 4 of 5 readability scales. The ChatGPT 
      output was incorrect for 12 (11.5%) of 104 questions. When reviewed using the 
      PEMAT-P tool, the ChatGPT content scored lower than the website material. Content 
      from both the website and ChatGPT were significantly above the recommended fifth 
      or sixth grade level for patient education, with a mean Flesch-Kincaid grade 
      level of 11.1 (±1.3) for the website and 11.9 (±1.6) for the ChatGPT content. 
      CONCLUSIONS: The ChatGPT platform may produce incomplete or inaccurate patient 
      educational content, and providers should be familiar with the limitations of the 
      system in its current form. Opportunities may exist to fine-tune existing large 
      language models, which could be optimized for the delivery of patient educational 
      content.
CI  - Copyright © 2023 SIR. Published by Elsevier Inc. All rights reserved.
FAU - McCarthy, Colin J
AU  - McCarthy CJ
AD  - Division of Vascular and Interventional Radiology, Beth Israel Deaconess Medical 
      Center, Harvard Medical School, Boston, Massachusetts. Electronic address: 
      cmccar11@bidmc.harvard.edu.
FAU - Berkowitz, Seth
AU  - Berkowitz S
AD  - Division of Vascular and Interventional Radiology, Beth Israel Deaconess Medical 
      Center, Harvard Medical School, Boston, Massachusetts.
FAU - Ramalingam, Vijay
AU  - Ramalingam V
AD  - Division of Vascular and Interventional Radiology, Beth Israel Deaconess Medical 
      Center, Harvard Medical School, Boston, Massachusetts.
FAU - Ahmed, Muneeb
AU  - Ahmed M
AD  - Division of Vascular and Interventional Radiology, Beth Israel Deaconess Medical 
      Center, Harvard Medical School, Boston, Massachusetts.
LA  - eng
PT  - Journal Article
DEP - 20230616
PL  - United States
TA  - J Vasc Interv Radiol
JT  - Journal of vascular and interventional radiology : JVIR
JID - 9203369
SB  - IM
MH  - Humans
MH  - *Health Literacy
MH  - Artificial Intelligence
MH  - Patient Education as Topic
MH  - Software
MH  - Societies, Medical
EDAT- 2023/06/18 01:07
MHDA- 2023/09/29 06:44
CRDT- 2023/06/17 19:18
PHST- 2023/02/01 00:00 [received]
PHST- 2023/04/05 00:00 [revised]
PHST- 2023/05/08 00:00 [accepted]
PHST- 2023/09/29 06:44 [medline]
PHST- 2023/06/18 01:07 [pubmed]
PHST- 2023/06/17 19:18 [entrez]
AID - S1051-0443(23)00423-2 [pii]
AID - 10.1016/j.jvir.2023.05.037 [doi]
PST - ppublish
SO  - J Vasc Interv Radiol. 2023 Oct;34(10):1760-1768.e32. doi: 
      10.1016/j.jvir.2023.05.037. Epub 2023 Jun 16.

PMID- 37303347
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230613
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - Exploring ChatGPT's Potential in Facilitating Adaptation of Clinical Guidelines: 
      A Case Study of Diabetic Ketoacidosis Guidelines.
PG  - e38784
LID - 10.7759/cureus.38784 [doi]
LID - e38784
AB  - Background This study aimed to evaluate the efficacy of ChatGPT, an advanced 
      natural language processing model, in adapting and synthesizing clinical 
      guidelines for diabetic ketoacidosis (DKA) by comparing and contrasting different 
      guideline sources. Methodology We employed a comprehensive comparison approach 
      and examined three reputable guideline sources: Diabetes Canada Clinical Practice 
      Guidelines Expert Committee (2018), Emergency Management of Hyperglycaemia in 
      Primary Care, and Joint British Diabetes Societies (JBDS) 02 The Management of 
      Diabetic Ketoacidosis in Adults. Data extraction focused on diagnostic criteria, 
      risk factors, signs and symptoms, investigations, and treatment recommendations. 
      We compared the synthesized guidelines generated by ChatGPT and identified any 
      misreporting or non-reporting errors. Results ChatGPT was capable of generating a 
      comprehensive table comparing the guidelines. However, multiple recurrent errors, 
      including misreporting and non-reporting errors, were identified, rendering the 
      results unreliable. Additionally, inconsistencies were observed in the repeated 
      reporting of data. The study highlights the limitations of using ChatGPT for the 
      adaptation of clinical guidelines without expert human intervention. Conclusions 
      Although ChatGPT demonstrates the potential for the synthesis of clinical 
      guidelines, the presence of multiple recurrent errors and inconsistencies 
      underscores the need for expert human intervention and validation. Future 
      research should focus on improving the accuracy and reliability of ChatGPT, as 
      well as exploring its potential applications in other areas of clinical practice 
      and guideline development.
CI  - Copyright © 2023, Hamed et al.
FAU - Hamed, Ehab
AU  - Hamed E
AD  - Qatar University Health Center, Primary Health Care Corporation, Doha, QAT.
FAU - Eid, Ahmad
AU  - Eid A
AD  - Umm Slal Health Center, Primary Health Care Corporation, Doha, QAT.
FAU - Alberry, Medhat
AU  - Alberry M
AD  - Fetal Medicine, Sidra Medicine, Doha, QAT.
LA  - eng
PT  - Journal Article
DEP - 20230509
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10249915
OTO - NOTNLM
OT  - ai chatbot
OT  - artificial intelligence
OT  - chatgpt
OT  - clinical guidelines
OT  - evidence-based medicine
OT  - evidence-based recommendations
OT  - healthcare management
OT  - healthcare technology
OT  - medical informatics
OT  - prompt design
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/12 06:42
MHDA- 2023/06/12 06:43
CRDT- 2023/06/12 03:53
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/06/12 06:43 [medline]
PHST- 2023/06/12 06:42 [pubmed]
PHST- 2023/06/12 03:53 [entrez]
AID - 10.7759/cureus.38784 [doi]
PST - epublish
SO  - Cureus. 2023 May 9;15(5):e38784. doi: 10.7759/cureus.38784. eCollection 2023 May.

PMID- 37198001
OWN - NLM
STAT- MEDLINE
DCOM- 20230705
LR  - 20230828
IS  - 1460-9584 (Electronic)
IS  - 1268-7731 (Linking)
VI  - 29
IP  - 5
DP  - 2023 Jul
TI  - Foot and Ankle Surgery declares use of generative artificial intelligence like 
      Chat Generative Pre-trained Transformer (ChatGPT) for scientific publications.
PG  - 385-386
LID - S1268-7731(23)00080-2 [pii]
LID - 10.1016/j.fas.2023.05.002 [doi]
FAU - Richter, Martinus
AU  - Richter M
AD  - Department for Foot and Ankle Surgery Nuremberg and Rummelsberg, Location 
      Hospital Rummelsberg, Rummelsberg 71, 90592 Schwarzenbruck, Germany. Electronic 
      address: martinus.richter@sana.de.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230511
PL  - France
TA  - Foot Ankle Surg
JT  - Foot and ankle surgery : official journal of the European Society of Foot and 
      Ankle Surgeons
JID - 9609647
SB  - IM
CON - Cureus. 2023 Feb 4;15(2):e34616. PMID: 36895547
MH  - Humans
MH  - *Ankle
MH  - *Artificial Intelligence
MH  - Endoscopy
MH  - Lower Extremity
MH  - Publications
EDAT- 2023/05/18 01:07
MHDA- 2023/07/05 06:42
CRDT- 2023/05/17 21:56
PHST- 2023/07/05 06:42 [medline]
PHST- 2023/05/18 01:07 [pubmed]
PHST- 2023/05/17 21:56 [entrez]
AID - S1268-7731(23)00080-2 [pii]
AID - 10.1016/j.fas.2023.05.002 [doi]
PST - ppublish
SO  - Foot Ankle Surg. 2023 Jul;29(5):385-386. doi: 10.1016/j.fas.2023.05.002. Epub 
      2023 May 11.

PMID- 37976385
OWN - NLM
STAT- Publisher
LR  - 20231117
IS  - 1940-5480 (Electronic)
IS  - 1067-151X (Linking)
DP  - 2023 Nov 17
TI  - ChatGPT's Ability to Assist with Clinical Documentation: A Randomized Controlled 
      Trial.
LID - 10.5435/JAAOS-D-23-00474 [doi]
AB  - INTRODUCTION: Clinical documentation is a critical aspect of health care that 
      enables healthcare providers to communicate effectively with each other and 
      maintain accurate patient care records. Artificial intelligence tools, such as 
      chatbots and virtual assistants, have the potential to assist healthcare 
      providers in clinical documentation. ChatGPT is an artificial intelligence 
      conversational model that generates human-like responses to text-based prompts. 
      In this study, we sought to investigate ChatGPT's ability to assist with writing 
      a history of present illness based on standardized patient histories. METHODS: A 
      blinded, randomized controlled study was conducted to compare the use of typing, 
      dictation, and ChatGPT as tools to document history of present illness (HPI) of 
      standardized patient histories. Eleven study participants, consisting of medical 
      students, orthopaedic surgery residents, and attending surgeons, completed three 
      HPIs using a different documentation technique for each one. Participants were 
      randomized into cohorts based on the type of documentation technique. 
      Participants were asked to interview standardized patients and document the 
      patient's history of present illness using their assigned method. RESULTS: 
      ChatGPT was found to be intermediate for speed; dictation was fastest, but 
      produced markedly longer and higher quality patient histories based on Physician 
      Documentation Quality Instrument score compared with dictation and typing. 
      However, ChatGPT included erroneous information in 36% of the documents. Poor 
      agreement existed on the quality of patient histories between reviewers. 
      DISCUSSION: Our study suggests that ChatGPT has the potential to improve clinical 
      documentation by producing more comprehensive and organized HPIs. ChatGPT can 
      generate longer and more detailed documentation compared with typing or dictation 
      documentation methods. However, additional studies are needed to investigate and 
      address concerns regarding privacy, bias, and accuracy of information.
CI  - Copyright © 2023 by the American Academy of Orthopaedic Surgeons.
FAU - Baker, Hayden P
AU  - Baker HP
AUID- ORCID: 0000-0002-9306-7006
AD  - From the Department of Orthopaedic Surgery, University of Chicago, Chicago, IL.
FAU - Dwyer, Emma
AU  - Dwyer E
FAU - Kalidoss, Senthooran
AU  - Kalidoss S
FAU - Hynes, Kelly
AU  - Hynes K
FAU - Wolf, Jennifer
AU  - Wolf J
FAU - Strelzow, Jason A
AU  - Strelzow JA
LA  - eng
PT  - Journal Article
DEP - 20231117
PL  - United States
TA  - J Am Acad Orthop Surg
JT  - The Journal of the American Academy of Orthopaedic Surgeons
JID - 9417468
SB  - IM
EDAT- 2023/11/17 18:42
MHDA- 2023/11/17 18:42
CRDT- 2023/11/17 14:23
PHST- 2023/05/23 00:00 [received]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/11/17 18:42 [medline]
PHST- 2023/11/17 18:42 [pubmed]
PHST- 2023/11/17 14:23 [entrez]
AID - 00124635-990000000-00834 [pii]
AID - 10.5435/JAAOS-D-23-00474 [doi]
PST - aheadofprint
SO  - J Am Acad Orthop Surg. 2023 Nov 17. doi: 10.5435/JAAOS-D-23-00474.

PMID- 37404248
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2666-3287 (Electronic)
IS  - 2666-3287 (Linking)
VI  - 12
DP  - 2023 Sep
TI  - ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous 
      neoplasms.
PG  - 168-170
LID - 10.1016/j.jdin.2023.06.002 [doi]
FAU - O'Hern, Keegan
AU  - O'Hern K
AD  - Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
FAU - Yang, Eilene
AU  - Yang E
AD  - Mayo Clinic Alix School of Medicine, Rochester, Minnesota.
FAU - Vidal, Nahid Y
AU  - Vidal NY
AD  - Division of Dermatologic Surgery, Mayo Clinic, Rochester, Minnesota.
LA  - eng
PT  - Journal Article
DEP - 20230609
PL  - United States
TA  - JAAD Int
JT  - JAAD international
JID - 101774762
PMC - PMC10316650
OTO - NOTNLM
OT  - ChatGPT
OT  - Mohs surgery
OT  - appropriate use criteria
OT  - artificial intelligence
OT  - dermatologic surgery
OT  - wide local excision
COIS- None disclosed.
EDAT- 2023/07/05 13:05
MHDA- 2023/07/05 13:06
CRDT- 2023/07/05 10:34
PHST- 2023/07/05 13:06 [medline]
PHST- 2023/07/05 13:05 [pubmed]
PHST- 2023/07/05 10:34 [entrez]
AID - S2666-3287(23)00089-5 [pii]
AID - 10.1016/j.jdin.2023.06.002 [doi]
PST - epublish
SO  - JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. eCollection 
      2023 Sep.

PMID- 37742349
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231106
IS  - 1878-5883 (Electronic)
IS  - 0022-510X (Linking)
VI  - 453
DP  - 2023 Oct 15
TI  - Invoking AI for diagnosis: Art at the cutting edge of science.
PG  - 120803
LID - S0022-510X(23)00264-2 [pii]
LID - 10.1016/j.jns.2023.120803 [doi]
FAU - Vinny, Pulikottil Wilson
AU  - Vinny PW
AD  - Armed Forces Medical College, Pune, Maharashtra PIN: 411040, India. Electronic 
      address: vinnywilson@gmail.com.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230916
PL  - Netherlands
TA  - J Neurol Sci
JT  - Journal of the neurological sciences
JID - 0375403
SB  - IM
CON - J Neurol Sci. 2023 Oct 15;453:120804. PMID: 37741773
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Diagnosis
OT  - GPT-4
OT  - Large Language Model (LLM)
OT  - Neurology
OT  - Training
EDAT- 2023/09/24 18:41
MHDA- 2023/09/24 18:42
CRDT- 2023/09/24 18:00
PHST- 2023/09/03 00:00 [received]
PHST- 2023/09/09 00:00 [accepted]
PHST- 2023/09/24 18:42 [medline]
PHST- 2023/09/24 18:41 [pubmed]
PHST- 2023/09/24 18:00 [entrez]
AID - S0022-510X(23)00264-2 [pii]
AID - 10.1016/j.jns.2023.120803 [doi]
PST - ppublish
SO  - J Neurol Sci. 2023 Oct 15;453:120803. doi: 10.1016/j.jns.2023.120803. Epub 2023 
      Sep 16.

PMID- 37025464
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230424
IS  - 2052-2975 (Print)
IS  - 2052-2975 (Electronic)
IS  - 2052-2975 (Linking)
VI  - 53
DP  - 2023 Jun
TI  - Are the issues pointed out by ChatGPT can be applied to Japan? - Examining the 
      reasons behind high COVID-19 excess deaths in Japan.
PG  - 101116
LID - 10.1016/j.nmni.2023.101116 [doi]
LID - 101116
FAU - Kaneda, Yudai
AU  - Kaneda Y
AD  - School of Medicine, Hokkaido University, Hokkaido, Japan.
FAU - Tsubokura, Masaharu
AU  - Tsubokura M
AD  - Department of Radiation Health Management, Fukushima Medical University School of 
      Medicine, Fukushima, Japan.
FAU - Ozaki, Akihiko
AU  - Ozaki A
AD  - Department of Breast and Thyroid Surgery, Jyoban Hospital of Tokiwa Foundation, 
      Fukushima, Japan.
FAU - Saito, Hiroaki
AU  - Saito H
AD  - Department of Internal Medicine, Soma Central Hospital, Fukushima, Japan.
FAU - Tanimoto, Tetsuya
AU  - Tanimoto T
AD  - Department of Internal Medicine, Jyoban Hospital of Tokiwa Foundation, Fukushima, 
      Japan.
LA  - eng
PT  - Journal Article
DEP - 20230329
PL  - England
TA  - New Microbes New Infect
JT  - New microbes and new infections
JID - 101624750
PMC - PMC10052853
OTO - NOTNLM
OT  - COVID-19
OT  - ChatGPT
OT  - Excess death
OT  - Japan
COIS- Dr Ozaki reported personal fees from Medical Network Systems Inc. and 
      10.13039/501100004095Kyowa Kirin co. ltd. outside the submitted work. Dr Tanimoto 
      reported personal fees from Medical Network Systems Inc. and Bionics co. ltd., 
      outside the submitted work. No other disclosures were reported.
EDAT- 2023/04/08 06:00
MHDA- 2023/04/08 06:01
CRDT- 2023/04/07 02:30
PHST- 2023/03/06 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/04/08 06:01 [medline]
PHST- 2023/04/07 02:30 [entrez]
PHST- 2023/04/08 06:00 [pubmed]
AID - S2052-2975(23)00035-5 [pii]
AID - 101116 [pii]
AID - 10.1016/j.nmni.2023.101116 [doi]
PST - ppublish
SO  - New Microbes New Infect. 2023 Jun;53:101116. doi: 10.1016/j.nmni.2023.101116. 
      Epub 2023 Mar 29.

PMID- 37651381
OWN - NLM
STAT- MEDLINE
DCOM- 20230904
LR  - 20230905
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 18
IP  - 8
DP  - 2023
TI  - ChatGPT- versus human-generated answers to frequently asked questions about 
      diabetes: A Turing test-inspired survey among employees of a Danish diabetes 
      center.
PG  - e0290773
LID - 10.1371/journal.pone.0290773 [doi]
LID - e0290773
AB  - Large language models have received enormous attention recently with some studies 
      demonstrating their potential clinical value, despite not being trained 
      specifically for this domain. We aimed to investigate whether ChatGPT, a language 
      model optimized for dialogue, can answer frequently asked questions about 
      diabetes. We conducted a closed e-survey among employees of a large Danish 
      diabetes center. The study design was inspired by the Turing test and 
      non-inferiority trials. Our survey included ten questions with two answers each. 
      One of these was written by a human expert, while the other was generated by 
      ChatGPT. Participants had the task to identify the ChatGPT-generated answer. Data 
      was analyzed at the question-level using logistic regression with robust variance 
      estimation with clustering at participant level. In secondary analyses, we 
      investigated the effect of participant characteristics on the outcome. A 55% 
      non-inferiority margin was pre-defined based on precision simulations and had 
      been published as part of the study protocol before data collection began. Among 
      311 invited individuals, 183 participated in the survey (59% response rate). 64% 
      had heard of ChatGPT before, and 19% had tried it. Overall, participants could 
      identify ChatGPT-generated answers 59.5% (95% CI: 57.0, 62.0) of the time, which 
      was outside of the non-inferiority zone. Among participant characteristics, 
      previous ChatGPT use had the strongest association with the outcome (odds ratio: 
      1.52 (1.16, 2.00), p = 0.003). Previous users answered 67.4% (61.7, 72.7) of the 
      questions correctly, versus non-users' 57.6% (54.9, 60.3). Participants could 
      distinguish between ChatGPT-generated and human-written answers somewhat better 
      than flipping a fair coin, which was against our initial hypothesis. Rigorously 
      planned studies are needed to elucidate the risks and benefits of integrating 
      such technologies in routine clinical practice.
CI  - Copyright: © 2023 Hulman et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Hulman, Adam
AU  - Hulman A
AUID- ORCID: 0000-0002-3969-1000
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
AD  - Department of Public Health, Aarhus University, Aarhus, Denmark.
FAU - Dollerup, Ole Lindgård
AU  - Dollerup OL
AUID- ORCID: 0000-0001-6924-7114
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
FAU - Mortensen, Jesper Friis
AU  - Mortensen JF
AUID- ORCID: 0000-0001-7683-0906
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
AD  - Department of Public Health, Aarhus University, Aarhus, Denmark.
FAU - Fenech, Matthew E
AU  - Fenech ME
AUID- ORCID: 0000-0001-9970-0215
AD  - Una Health GmbH, Hamburg, Germany.
FAU - Norman, Kasper
AU  - Norman K
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
FAU - Støvring, Henrik
AU  - Støvring H
AUID- ORCID: 0000-0002-5821-2351
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
AD  - Department of Public Health, University of Southern Denmark, Odense, Denmark.
FAU - Hansen, Troels Krarup
AU  - Hansen TK
AUID- ORCID: 0000-0002-0708-2868
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
AD  - Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230831
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Humans
MH  - Data Collection
MH  - *Diabetes Mellitus/diagnosis/epidemiology
MH  - Cluster Analysis
MH  - Language
MH  - Denmark/epidemiology
PMC - PMC10470899
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/31 18:42
MHDA- 2023/09/04 06:42
CRDT- 2023/08/31 13:35
PHST- 2023/05/02 00:00 [received]
PHST- 2023/08/16 00:00 [accepted]
PHST- 2023/09/04 06:42 [medline]
PHST- 2023/08/31 18:42 [pubmed]
PHST- 2023/08/31 13:35 [entrez]
AID - PONE-D-23-12539 [pii]
AID - 10.1371/journal.pone.0290773 [doi]
PST - epublish
SO  - PLoS One. 2023 Aug 31;18(8):e0290773. doi: 10.1371/journal.pone.0290773. 
      eCollection 2023.

PMID- 37917165
OWN - NLM
STAT- Publisher
LR  - 20231102
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Nov 2
TI  - Artificial intelligence chatbots as sources of patient education material for 
      obstructive sleep apnoea: ChatGPT versus Google Bard.
LID - 10.1007/s00405-023-08319-9 [doi]
AB  - PURPOSE: To perform the first head-to-head comparative evaluation of patient 
      education material for obstructive sleep apnoea generated by two artificial 
      intelligence chatbots, ChatGPT and its primary rival Google Bard. METHODS: Fifty 
      frequently asked questions on obstructive sleep apnoea in English were extracted 
      from the patient information webpages of four major sleep organizations and 
      categorized as input prompts. ChatGPT and Google Bard responses were selected and 
      independently rated using the Patient Education Materials Assessment 
      Tool-Printable (PEMAT-P) Auto-Scoring Form by two otolaryngologists, with a 
      Fellowship of the Royal College of Surgeons (FRCS) and a special interest in 
      sleep medicine and surgery. Responses were subjectively screened for any 
      incorrect or dangerous information as a secondary outcome. The Flesch-Kincaid 
      Calculator was used to evaluate the readability of responses for both ChatGPT and 
      Google Bard. RESULTS: A total of 46 questions were curated and categorized into 
      three domains: condition (n = 14), investigation (n = 9) and treatment (n = 23). 
      Understandability scores for ChatGPT versus Google Bard on the various domains 
      were as follows: condition 90.86% vs.76.32% (p < 0.001); investigation 89.94% vs. 
      71.67% (p < 0.001); treatment 90.78% vs.73.74% (p < 0.001). Actionability scores 
      for ChatGPT versus Google Bard on the various domains were as follows: condition 
      77.14% vs. 51.43% (p < 0.001); investigation 72.22% vs. 54.44% (p = 0.05); 
      treatment 73.04% vs. 54.78% (p = 0.002). The mean Flesch-Kincaid Grade Level for 
      ChatGPT was 9.0 and Google Bard was 5.9. No incorrect or dangerous information 
      was identified in any of the generated responses from both ChatGPT and Google 
      Bard. CONCLUSION: Evaluation of ChatGPT and Google Bard patient education 
      material for OSA indicates the former to offer superior information across 
      several domains.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Cheong, Ryan Chin Taw
AU  - Cheong RCT
AUID- ORCID: 0000-0001-7846-8699
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal Marsden NHS Foundation 
      Trust, Fulham Road, London, SW3 6JJ, UK. ryan.cheong@nhs.net.
FAU - Unadkat, Samit
AU  - Unadkat S
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal National ENT and 
      Eastman Dental Hospitals, University College London Hospitals NHS Foundation 
      Trust, London, UK.
FAU - Mcneillis, Venkata
AU  - Mcneillis V
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal National ENT and 
      Eastman Dental Hospitals, University College London Hospitals NHS Foundation 
      Trust, London, UK.
FAU - Williamson, Andrew
AU  - Williamson A
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal Marsden NHS Foundation 
      Trust, Fulham Road, London, SW3 6JJ, UK.
FAU - Joseph, Jonathan
AU  - Joseph J
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal National ENT and 
      Eastman Dental Hospitals, University College London Hospitals NHS Foundation 
      Trust, London, UK.
FAU - Randhawa, Premjit
AU  - Randhawa P
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal National ENT and 
      Eastman Dental Hospitals, University College London Hospitals NHS Foundation 
      Trust, London, UK.
FAU - Andrews, Peter
AU  - Andrews P
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal National ENT and 
      Eastman Dental Hospitals, University College London Hospitals NHS Foundation 
      Trust, London, UK.
FAU - Paleri, Vinidh
AU  - Paleri V
AD  - Otolaryngology-Head and Neck Surgery Department, The Royal Marsden NHS Foundation 
      Trust, Fulham Road, London, SW3 6JJ, UK.
LA  - eng
PT  - Journal Article
DEP - 20231102
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Google Bard
OT  - Large language models
OT  - Obstructive sleep apnoea
OT  - Patient education material
EDAT- 2023/11/02 18:43
MHDA- 2023/11/02 18:43
CRDT- 2023/11/02 12:03
PHST- 2023/07/22 00:00 [received]
PHST- 2023/10/26 00:00 [accepted]
PHST- 2023/11/02 18:43 [medline]
PHST- 2023/11/02 18:43 [pubmed]
PHST- 2023/11/02 12:03 [entrez]
AID - 10.1007/s00405-023-08319-9 [pii]
AID - 10.1007/s00405-023-08319-9 [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Nov 2. doi: 10.1007/s00405-023-08319-9.

PMID- 37509379
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230731
IS  - 2072-6694 (Print)
IS  - 2072-6694 (Electronic)
IS  - 2072-6694 (Linking)
VI  - 15
IP  - 14
DP  - 2023 Jul 22
TI  - Enhancing Triage Efficiency and Accuracy in Emergency Rooms for Patients with 
      Metastatic Prostate Cancer: A Retrospective Analysis of Artificial 
      Intelligence-Assisted Triage Using ChatGPT 4.0.
LID - 10.3390/cancers15143717 [doi]
LID - 3717
AB  - BACKGROUND: Accurate and efficient triage is crucial for prioritizing care and 
      managing resources in emergency rooms. This study investigates the effectiveness 
      of ChatGPT, an advanced artificial intelligence system, in assisting health 
      providers with decision-making for patients presenting with metastatic prostate 
      cancer, focusing on the potential to improve both patient outcomes and resource 
      allocation. METHODS: Clinical data from patients with metastatic prostate cancer 
      who presented to the emergency room between 1 May 2022 and 30 April 2023 were 
      retrospectively collected. The primary outcome was the sensitivity and 
      specificity of ChatGPT in determining whether a patient required admission or 
      discharge. The secondary outcomes included the agreement between ChatGPT and 
      emergency medicine physicians, the comprehensiveness of diagnoses, the accuracy 
      of treatment plans proposed by both parties, and the length of medical decision 
      making. RESULTS: Of the 147 patients screened, 56 met the inclusion criteria. 
      ChatGPT had a sensitivity of 95.7% in determining admission and a specificity of 
      18.2% in discharging patients. In 87.5% of cases, ChatGPT made the same primary 
      diagnoses as physicians, with more accurate terminology use (42.9% vs. 21.4%, p = 
      0.02) and more comprehensive diagnostic lists (median number of diagnoses: 3 vs. 
      2, p < 0.001). Emergency Severity Index scores calculated by ChatGPT were not 
      associated with admission (p = 0.12), hospital stay length (p = 0.91) or ICU 
      admission (p = 0.54). Despite shorter mean word count (169 ± 66 vs. 272 ± 105, p 
      < 0.001), ChatGPT was more likely to give additional treatment recommendations 
      than physicians (94.3% vs. 73.5%, p < 0.001). CONCLUSIONS: Our 
      hypothesis-generating data demonstrated that ChatGPT is associated with a high 
      sensitivity in determining the admission of patients with metastatic prostate 
      cancer in the emergency room. It also provides accurate and comprehensive 
      diagnoses. These findings suggest that ChatGPT has the potential to assist health 
      providers in improving patient triage in emergency settings, and may enhance both 
      efficiency and quality of care provided by the physicians.
FAU - Gebrael, Georges
AU  - Gebrael G
AUID- ORCID: 0000-0002-4085-6415
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Sahu, Kamal Kant
AU  - Sahu KK
AUID- ORCID: 0000-0002-0382-6882
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Chigarira, Beverly
AU  - Chigarira B
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Tripathi, Nishita
AU  - Tripathi N
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Mathew Thomas, Vinay
AU  - Mathew Thomas V
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Sayegh, Nicolas
AU  - Sayegh N
AUID- ORCID: 0000-0003-1924-8148
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Maughan, Benjamin L
AU  - Maughan BL
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Agarwal, Neeraj
AU  - Agarwal N
AUID- ORCID: 0000-0003-1076-0428
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Swami, Umang
AU  - Swami U
AUID- ORCID: 0000-0003-3518-0411
AD  - Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake 
      City, UT 84112, USA.
FAU - Li, Haoran
AU  - Li H
AD  - Division of Medical Oncology, University of Kansas Cancer Center, Westwood, KS 
      66205, USA.
LA  - eng
PT  - Journal Article
DEP - 20230722
PL  - Switzerland
TA  - Cancers (Basel)
JT  - Cancers
JID - 101526829
PMC - PMC10378202
OTO - NOTNLM
OT  - ChatGPT 4.0
OT  - artificial intelligence
OT  - emergency room
OT  - medical decision-making
OT  - metastatic prostate cancer
OT  - triage
COIS- Neeraj Agarwal (lifetime): consultancy to Astellas, AstraZeneca, Aveo, Bayer, 
      Bristol Myers Squibb, Calithera, Clovis, Eisai, Eli Lilly, EMD Serono, Exelixis, 
      Foundation Medicine, Genentech, Gilead, Janssen, Merck, MEI Pharma, Nektar, 
      Novartis, Pfizer, Pharmacyclics, and Seattle Genetics outside the submitted work. 
      Research funding to Neeraj Agarwal’s institution: Astellas, Astra Zeneca, 
      Bavarian Nordic, Bayer, Bristol Myers Squibb, Calithera, Celldex, Clovis, Eisai, 
      Eli Lilly, EMD Serono, Exelixis, Genentech, Gilead, Glaxo Smith Kline, 
      Immunomedics, Janssen, Medivation, Merck, Nektar, New Link Genetics, Novartis, 
      Pfizer, Prometheus, Rexahn, Roche, Sanofi, Seattle Genetics, Takeda, and Tracon. 
      Umang Swami: consultancy to Astellas, Exelixis, Seattle Genetics, Imvax, Sanofi, 
      AstraZeneca and Gilead and research funding to institute from Janssen, Exelixis 
      and Astellas/Seattle Genetics. Benjamin Maughan: Consultancy to Abbive, Pfizer, 
      AVEO oncology, Janssen, Astellas, Bristol-Myers Squibb, Clovis, Tempus, Merck, 
      Exelixis, Bayer Oncology, Lilly, Sanofi, Telix and Peloton Therapeutics and 
      research funding to institute from Exelixis, Bavarian-Nordic, Clovis and 
      Bristol-Myers Squibb. The rest of authors declare no conflict of interest.
EDAT- 2023/07/29 11:44
MHDA- 2023/07/29 11:45
CRDT- 2023/07/29 01:11
PHST- 2023/06/27 00:00 [received]
PHST- 2023/07/19 00:00 [revised]
PHST- 2023/07/20 00:00 [accepted]
PHST- 2023/07/29 11:45 [medline]
PHST- 2023/07/29 11:44 [pubmed]
PHST- 2023/07/29 01:11 [entrez]
AID - cancers15143717 [pii]
AID - cancers-15-03717 [pii]
AID - 10.3390/cancers15143717 [doi]
PST - epublish
SO  - Cancers (Basel). 2023 Jul 22;15(14):3717. doi: 10.3390/cancers15143717.

PMID- 37788080
OWN - NLM
STAT- Publisher
LR  - 20231003
IS  - 1708-8305 (Electronic)
IS  - 1195-1982 (Linking)
DP  - 2023 Oct 3
TI  - Artificial intelligence models for pre-travel consultation and advice: yea or 
      nay?
LID - taad124 [pii]
LID - 10.1093/jtm/taad124 [doi]
AB  - We assessed the accuracy of advice provided by ChatGPT for pre-travel 
      counselling. ChatGPT appears to be complementary to the physical pre-travel 
      consultation, giving appropriate advice and raising awareness. However, the 
      advice lacked specificity, and travel physicians can help to fine-tune the advice 
      provided to an individual's specific travel needs.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      International Society of Travel Medicine.
FAU - Ngiam, Jinghao Nicholas
AU  - Ngiam JN
AD  - Division of Infectious Diseases, Department of Medicine, National University 
      Health System, Singapore.
FAU - Koh, Matthew Chung Yi
AU  - Koh MCY
AD  - Division of Infectious Diseases, Department of Medicine, National University 
      Health System, Singapore.
FAU - Lye, Priscillia
AU  - Lye P
AD  - Division of Infectious Diseases, Department of Medicine, National University 
      Health System, Singapore.
FAU - Liong, Tze Sian
AU  - Liong TS
AD  - Department of Medicine, National University Health System Singapore.
FAU - Salada, Brenda Mae Alferez
AU  - Salada BMA
AD  - Division of Infectious Diseases, Department of Medicine, National University 
      Health System, Singapore.
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
FAU - Tambyah, Paul Anantharajah
AU  - Tambyah PA
AD  - Division of Infectious Diseases, Department of Medicine, National University 
      Health System, Singapore.
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
AD  - Infectious Diseases Translational Research Programme, Department of Medicine, 
      Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
FAU - Ling, Jolene Oon Ee
AU  - Ling JOE
AD  - Division of Infectious Diseases, Department of Medicine, National University 
      Health System, Singapore.
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
LA  - eng
PT  - Journal Article
DEP - 20231003
PL  - England
TA  - J Travel Med
JT  - Journal of travel medicine
JID - 9434456
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - pre-travel consultation
OT  - vaccination
EDAT- 2023/10/03 12:44
MHDA- 2023/10/03 12:44
CRDT- 2023/10/03 11:55
PHST- 2023/08/21 00:00 [received]
PHST- 2023/09/15 00:00 [revised]
PHST- 2023/09/27 00:00 [accepted]
PHST- 2023/10/03 12:44 [medline]
PHST- 2023/10/03 12:44 [pubmed]
PHST- 2023/10/03 11:55 [entrez]
AID - 7288181 [pii]
AID - 10.1093/jtm/taad124 [doi]
PST - aheadofprint
SO  - J Travel Med. 2023 Oct 3:taad124. doi: 10.1093/jtm/taad124.

PMID- 37541814
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20231003
IS  - 1878-4372 (Electronic)
IS  - 1360-1385 (Linking)
VI  - 28
IP  - 10
DP  - 2023 Oct
TI  - Plant blindness and diversity in AI language models.
PG  - 1095-1097
LID - S1360-1385(23)00207-8 [pii]
LID - 10.1016/j.tplants.2023.06.016 [doi]
AB  - Large language models (LLMs) will benefit science by accelerating task 
      performance. We explored whether answers generated by ChatGPT (generative 
      pretrained transformer) to questions of biology are sufficiently diverse. 'Plant 
      awareness' in ChatGPT answers was found to be highly variable, illustrating the 
      importance of scientists being involved in validating the data and methods used 
      to train artificial intelligence (AI) models.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Geitmann, Anja
AU  - Geitmann A
AD  - Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, 
      QC, Canada. Electronic address: geitmann.aes@mcgill.ca.
FAU - Bidhendi, Amir J
AU  - Bidhendi AJ
AD  - Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, 
      QC, Canada.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230802
PL  - England
TA  - Trends Plant Sci
JT  - Trends in plant science
JID - 9890299
SB  - IM
MH  - *Artificial Intelligence
MH  - *Language
MH  - Blindness
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence models
OT  - diversity
OT  - large language models
OT  - plant blindness
COIS- Declaration of interests The authors declare no conflicts of interest.
EDAT- 2023/08/05 05:42
MHDA- 2023/09/18 12:44
CRDT- 2023/08/04 21:53
PHST- 2023/05/23 00:00 [received]
PHST- 2023/06/13 00:00 [revised]
PHST- 2023/06/20 00:00 [accepted]
PHST- 2023/09/18 12:44 [medline]
PHST- 2023/08/05 05:42 [pubmed]
PHST- 2023/08/04 21:53 [entrez]
AID - S1360-1385(23)00207-8 [pii]
AID - 10.1016/j.tplants.2023.06.016 [doi]
PST - ppublish
SO  - Trends Plant Sci. 2023 Oct;28(10):1095-1097. doi: 10.1016/j.tplants.2023.06.016. 
      Epub 2023 Aug 2.

PMID- 36945641
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
DP  - 2023 Mar 8
TI  - Empowering Beginners in Bioinformatics with ChatGPT.
LID - 2023.03.07.531414 [pii]
LID - 10.1101/2023.03.07.531414 [doi]
AB  - The impressive conversational and programming abilities of ChatGPT make it an 
      attractive tool for facilitating the education of bioinformatics data analysis 
      for beginners. In this study, we proposed an iterative model to fine-tune 
      instructions for guiding a ChatGPT in generating code for bioinformatics data 
      analysis tasks. We demonstrated the feasibility of the model by applying it to 
      various bioinformatics topics. Additionally, we discussed practical 
      considerations and limitations regarding the use of the model in chatbot-aided 
      bioinformatics education.
FAU - Shue, Evelyn
AU  - Shue E
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV, USA.
FAU - Liu, Li
AU  - Liu L
AD  - College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
AD  - Biodesign Institute, Arizona State University, Tempe, AZ, USA.
FAU - Li, Bingxin
AU  - Li B
AD  - Finance Department, John Chambers College of Business and Economics, West 
      Virginia University, Morgantown, WV, USA.
FAU - Feng, Zifeng
AU  - Feng Z
AD  - Department of Economics and Finance, The University of Texas at El Paso, El Paso, 
      TX, USA.
FAU - Li, Xin
AU  - Li X
AD  - Lane Department of Computer Science and Electrical Engineering, West Virginia 
      University, Morgantown, WV, USA.
FAU - Hu, Gangqing
AU  - Hu G
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV, USA.
LA  - eng
GR  - P20 GM103434/GM/NIGMS NIH HHS/United States
GR  - P20 GM121322/GM/NIGMS NIH HHS/United States
GR  - R01 LM013438/LM/NLM NIH HHS/United States
GR  - U54 GM104942/GM/NIGMS NIH HHS/United States
PT  - Preprint
DEP - 20230308
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
UIN - This article has been published with doi: 10.15302/J-QB-023-0327
CIN - Quant Biol. 2023 Jun;11(2):204-206. PMID: 37900935
PMC - PMC10028953
EDAT- 2023/03/23 06:00
MHDA- 2023/03/23 06:01
CRDT- 2023/03/22 01:58
PHST- 2023/03/23 06:00 [pubmed]
PHST- 2023/03/23 06:01 [medline]
PHST- 2023/03/22 01:58 [entrez]
AID - 2023.03.07.531414 [pii]
AID - 10.1101/2023.03.07.531414 [doi]
PST - epublish
SO  - bioRxiv. 2023 Mar 8:2023.03.07.531414. doi: 10.1101/2023.03.07.531414. Preprint.

PMID- 36775769
OWN - NLM
STAT- MEDLINE
DCOM- 20230331
LR  - 20230525
IS  - 1538-7836 (Electronic)
IS  - 1538-7836 (Linking)
VI  - 21
IP  - 4
DP  - 2023 Apr
TI  - Evaluation of OpenAI's large language model as a new tool for writing papers in 
      the field of thrombosis and hemostasis.
PG  - 1055-1058
LID - S1538-7836(23)00039-9 [pii]
LID - 10.1016/j.jtha.2023.01.011 [doi]
FAU - Klang, Eyal
AU  - Klang E
AD  - Sami Sagol AI Hub, ARC, Sheba Medical Center, Ramat Gan, Israel; Sackler School 
      of Medicine, Tel Aviv University, Tel Aviv, Israel; The Sheba Talpiot Medical 
      Leadership Program, Sheba Medical Center, Ramat Gan, Israel.
FAU - Levy-Mendelovich, Sarina
AU  - Levy-Mendelovich S
AD  - Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; The Sheba 
      Talpiot Medical Leadership Program, Sheba Medical Center, Ramat Gan, Israel; 
      National Hemophilia Center, Sheba Medical Center, Ramat Gan, Israel; Amalia Biron 
      Research Institute of Thrombosis and Hemostasis, Sheba Medical Center, Ramat Gan, 
      Israel. Electronic address: Sarina.Levy@sheba.health.gov.il.
LA  - eng
PT  - Letter
DEP - 20230120
PL  - England
TA  - J Thromb Haemost
JT  - Journal of thrombosis and haemostasis : JTH
JID - 101170508
SB  - IM
MH  - Humans
MH  - *Hemostasis
MH  - Language
MH  - *Thrombosis
MH  - Writing
COIS- Declaration of competing interests There are no competing interests to disclose.
EDAT- 2023/02/13 06:00
MHDA- 2023/03/31 06:41
CRDT- 2023/02/12 22:04
PHST- 2022/12/25 00:00 [received]
PHST- 2023/01/04 00:00 [revised]
PHST- 2023/01/11 00:00 [accepted]
PHST- 2023/03/31 06:41 [medline]
PHST- 2023/02/13 06:00 [pubmed]
PHST- 2023/02/12 22:04 [entrez]
AID - S1538-7836(23)00039-9 [pii]
AID - 10.1016/j.jtha.2023.01.011 [doi]
PST - ppublish
SO  - J Thromb Haemost. 2023 Apr;21(4):1055-1058. doi: 10.1016/j.jtha.2023.01.011. Epub 
      2023 Jan 20.

PMID- 37529789
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230803
IS  - 2452-2473 (Print)
IS  - 2452-2473 (Electronic)
IS  - 2452-2473 (Linking)
VI  - 23
IP  - 3
DP  - 2023 Jul-Sep
TI  - Performance of emergency triage prediction of an open access natural language 
      processing based chatbot application (ChatGPT): A preliminary, scenario-based 
      cross-sectional study.
PG  - 156-161
LID - 10.4103/tjem.tjem_79_23 [doi]
AB  - OBJECTIVES: Artificial intelligence companies have been increasing their 
      initiatives recently to improve the results of chatbots, which are software 
      programs that can converse with a human in natural language. The role of chatbots 
      in health care is deemed worthy of research. OpenAI's ChatGPT is a supervised and 
      empowered machine learning-based chatbot. The aim of this study was to determine 
      the performance of ChatGPT in emergency medicine (EM) triage prediction. METHODS: 
      This was a preliminary, cross-sectional study conducted with case scenarios 
      generated by the researchers based on the emergency severity index (ESI) handbook 
      v4 cases. Two independent EM specialists who were experts in the ESI triage scale 
      determined the triage categories for each case. A third independent EM specialist 
      was consulted as arbiter, if necessary. Consensus results for each case scenario 
      were assumed as the reference triage category. Subsequently, each case scenario 
      was queried with ChatGPT and the answer was recorded as the index triage 
      category. Inconsistent classifications between the ChatGPT and reference category 
      were defined as over-triage (false positive) or under-triage (false negative). 
      RESULTS: Fifty case scenarios were assessed in the study. Reliability analysis 
      showed a fair agreement between EM specialists and ChatGPT (Cohen's Kappa: 
      0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged 
      by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, 
      one of which matched the expert consensus. It had an overall sensitivity of 57.1% 
      (95% confidence interval [CI]: 34-78.2), specificity of 34.5% (95% CI: 
      17.9-54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8-57.8), 
      negative predictive value (NPV) of 52.6 (95% CI: 28.9-75.6), and an F1 score of 
      0.461. In high acuity cases (ESI-1 and ESI-2), ChatGPT showed a sensitivity of 
      76.2% (95% CI: 52.8-91.8), specificity of 93.1% (95% CI: 77.2-99.2), PPV of 88.9% 
      (95% CI: 65.3-98.6), NPV of 84.4 (95% CI: 67.2-94.7), and an F1 score of 0.821. 
      The receiver operating characteristic curve showed an area under the curve of 
      0.846 (95% CI: 0.724-0.969, P < 0.001) for high acuity cases. CONCLUSION: The 
      performance of ChatGPT was best when predicting high acuity cases (ESI-1 and 
      ESI-2). It may be useful when determining the cases requiring critical care. When 
      trained with more medical knowledge, ChatGPT may be more accurate for other 
      triage category predictions.
CI  - Copyright: © 2023 Turkish Journal of Emergency Medicine.
FAU - Sarbay, İbrahim
AU  - Sarbay İ
AUID- ORCID: 0000-0001-8804-2501
AD  - Department of Emergency Medicine, Keşan State Hospital, Edirne, Turkey.
FAU - Berikol, Göksu Bozdereli
AU  - Berikol GB
AUID- ORCID: 0000-0002-4529-3578
AD  - Department of Emergency Medicine, Bakırköy Dr. Sadi Konuk Training and Research 
      Hospital, İstanbul, Turkey.
FAU - Özturan, İbrahim Ulaş
AU  - Özturan İU
AUID- ORCID: 0000-0002-1364-5292
AD  - Department of Emergency Medicine, Kocaeli University, Faculty of Medicine, 
      Kocaeli, Turkey.
AD  - Department of Medical Education, Acibadem University, Institute of Health 
      Sciences, Istanbul, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230626
PL  - India
TA  - Turk J Emerg Med
JT  - Turkish journal of emergency medicine
JID - 101681782
PMC - PMC10389099
OTO - NOTNLM
OT  - ChatGPT
OT  - Chatbot
OT  - emergency severity index
OT  - triage
COIS- None Declared.
EDAT- 2023/08/02 06:43
MHDA- 2023/08/02 06:44
CRDT- 2023/08/02 04:03
PHST- 2023/03/18 00:00 [received]
PHST- 2023/04/13 00:00 [revised]
PHST- 2023/05/24 00:00 [accepted]
PHST- 2023/08/02 06:44 [medline]
PHST- 2023/08/02 06:43 [pubmed]
PHST- 2023/08/02 04:03 [entrez]
AID - TJEM-23-156 [pii]
AID - 10.4103/tjem.tjem_79_23 [doi]
PST - epublish
SO  - Turk J Emerg Med. 2023 Jun 26;23(3):156-161. doi: 10.4103/tjem.tjem_79_23. 
      eCollection 2023 Jul-Sep.

PMID- 37636841
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230829
IS  - 0972-5229 (Print)
IS  - 1998-359X (Electronic)
IS  - 0972-5229 (Linking)
VI  - 27
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT: Friend or Foe?-Utility in Trauma Triage.
PG  - 563-566
LID - 10.5005/jp-journals-10071-24498 [doi]
AB  - Artificial intelligence (AI) has been growing rapidly in the health and 
      production industry. The most recent AI program that has caught everyone's eye 
      is, Chat Generative Pre-trained Transformer (ChatGPT). It is a program based on 
      Open AI and was released in November 2022 to the public. It is a chatbot that 
      helps with analyzing and learning data from the net, based on human literature 
      and data. HOW TO CITE THIS ARTICLE: Jacob J. ChatGPT: Friend or Foe?-Utility in 
      Trauma Triage. Indian J Crit Care Med 2023;27(8):563-566.
CI  - Copyright © 2023; The Author(s).
FAU - Jacob, Jerry
AU  - Jacob J
AUID- ORCID: 0000-0003-3816-189X
AD  - Department of Emergency Medicine, M S Ramaiah Medical College, Bengaluru, 
      Karnataka, India.
LA  - eng
PT  - Journal Article
PL  - India
TA  - Indian J Crit Care Med
JT  - Indian journal of critical care medicine : peer-reviewed, official publication of 
      Indian Society of Critical Care Medicine
JID - 101208863
PMC - PMC10452764
OTO - NOTNLM
OT  - Comorbidities
OT  - Intensive care specialist
OT  - Treatment guideline
COIS- Source of support: Nil Conflict of interest: None
EDAT- 2023/08/28 06:43
MHDA- 2023/08/28 06:44
CRDT- 2023/08/28 04:40
PHST- 2023/06/06 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/08/28 06:44 [medline]
PHST- 2023/08/28 06:43 [pubmed]
PHST- 2023/08/28 04:40 [entrez]
AID - 10.5005/jp-journals-10071-24498 [doi]
PST - ppublish
SO  - Indian J Crit Care Med. 2023 Aug;27(8):563-566. doi: 
      10.5005/jp-journals-10071-24498.

PMID- 37440646
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230718
LR  - 20230718
IS  - 1095-9203 (Electronic)
IS  - 0036-8075 (Linking)
VI  - 381
IP  - 6654
DP  - 2023 Jul 14
TI  - Experimental evidence on the productivity effects of generative artificial 
      intelligence.
PG  - 187-192
LID - 10.1126/science.adh2586 [doi]
AB  - We examined the productivity effects of a generative artificial intelligence (AI) 
      technology, the assistive chatbot ChatGPT, in the context of midlevel 
      professional writing tasks. In a preregistered online experiment, we assigned 
      occupation-specific, incentivized writing tasks to 453 college-educated 
      professionals and randomly exposed half of them to ChatGPT. Our results show that 
      ChatGPT substantially raised productivity: The average time taken decreased by 
      40% and output quality rose by 18%. Inequality between workers decreased, and 
      concern and excitement about AI temporarily rose. Workers exposed to ChatGPT 
      during the experiment were 2 times as likely to report using it in their real job 
      2 weeks after the experiment and 1.6 times as likely 2 months after the 
      experiment.
FAU - Noy, Shakked
AU  - Noy S
AUID- ORCID: 0000-0002-7814-5184
AD  - Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, 
      USA.
FAU - Zhang, Whitney
AU  - Zhang W
AUID- ORCID: 0000-0001-7399-0066
AD  - Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230713
PL  - United States
TA  - Science
JT  - Science (New York, N.Y.)
JID - 0404511
SB  - IM
EDAT- 2023/07/13 19:15
MHDA- 2023/07/17 06:42
CRDT- 2023/07/13 14:03
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/07/13 19:15 [pubmed]
PHST- 2023/07/13 14:03 [entrez]
AID - 10.1126/science.adh2586 [doi]
PST - ppublish
SO  - Science. 2023 Jul 14;381(6654):187-192. doi: 10.1126/science.adh2586. Epub 2023 
      Jul 13.

PMID- 37976093
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231204
IS  - 2562-0959 (Electronic)
IS  - 2562-0959 (Linking)
VI  - 6
DP  - 2023 Nov 17
TI  - Evaluation of ChatGPT Dermatology Responses to Common Patient Queries.
PG  - e49280
LID - 10.2196/49280 [doi]
LID - e49280
FAU - Ferreira, Alana L
AU  - Ferreira AL
AUID- ORCID: 0000-0001-6494-9598
AD  - Department of Dermatology, Perelman School of Medicine, University of 
      Pennsylvania, Philadelphia, PA, United States.
FAU - Chu, Brian
AU  - Chu B
AUID- ORCID: 0000-0002-0803-5529
AD  - Department of Dermatology, Perelman School of Medicine, University of 
      Pennsylvania, Philadelphia, PA, United States.
FAU - Grant-Kels, Jane M
AU  - Grant-Kels JM
AUID- ORCID: 0000-0002-1844-8415
AD  - Department of Dermatology, University of Connecticut Health Center, Farmington, 
      CT, United States.
AD  - Department of Dermatology, University of Florida, Gainesville, FL, United States.
FAU - Ogunleye, Temitayo
AU  - Ogunleye T
AUID- ORCID: 0000-0002-9465-347X
AD  - Department of Dermatology, Perelman School of Medicine, University of 
      Pennsylvania, Philadelphia, PA, United States.
FAU - Lipoff, Jules B
AU  - Lipoff JB
AUID- ORCID: 0000-0002-0557-4260
AD  - Department of Dermatology, Lewis Katz School of Medicine, Temple University, 
      Philadelphia, PA, United States.
LA  - eng
PT  - Journal Article
DEP - 20231117
PL  - Canada
TA  - JMIR Dermatol
JT  - JMIR dermatology
JID - 101770607
PMC - PMC10692871
OTO - NOTNLM
OT  - AI
OT  - AI tool
OT  - ChatGPT
OT  - GPT-4
OT  - artificial intelligence
OT  - dermatologist
OT  - dermatology
OT  - information resource
OT  - medical advice
OT  - patient queries
OT  - response evaluation
OT  - skin
OT  - skin condition
OT  - tool
COIS- Conflicts of Interest: JMGK is a medical advisor to Dermasensor. The authors have 
      no further interests to declare.
EDAT- 2023/11/17 15:29
MHDA- 2023/11/17 15:30
CRDT- 2023/11/17 11:54
PHST- 2023/05/23 00:00 [received]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/10/24 00:00 [revised]
PHST- 2023/11/17 15:30 [medline]
PHST- 2023/11/17 15:29 [pubmed]
PHST- 2023/11/17 11:54 [entrez]
AID - v6i1e49280 [pii]
AID - 10.2196/49280 [doi]
PST - epublish
SO  - JMIR Dermatol. 2023 Nov 17;6:e49280. doi: 10.2196/49280.

PMID- 37387096
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20230703
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 305
DP  - 2023 Jun 29
TI  - Training ChatGPT Models in Assisting Urologists in Daily Practice.
PG  - 576-579
LID - 10.3233/SHTI230562 [doi]
AB  - Artificial Intelligence (AI) has shown the ability to enhance the accuracy and 
      efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans 
      through text, over the internet. It is trained with machine learning algorithms, 
      using large datasets. In this study, we compare the performance of using a 
      ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in 
      obtaining accurate, valid medical information. The API was accessed through a 
      Python script that was applied specifically for this study based on 2023 EAU 
      guidelines in PDF format. This custom-trained model leads to providing doctors 
      with more precise, prompt answers about specific urologic subjects, thus helping 
      them, ultimately, providing better patient care.
FAU - Manolitsis, Ioannis
AU  - Manolitsis I
AD  - Second Department of Urology, National and Kapodistrian University of Athens, 
      Sismanogleio General Hospital, Athens, Greece.
FAU - Feretzakis, Georgios
AU  - Feretzakis G
AD  - School of Science and Technology, Hellenic Open University, Patras, Greece.
AD  - Sismanogleio General Hospital, Department of Quality Control, Research and 
      Continuing Education, Marousi, Greece.
FAU - Tzelves, Lazaros
AU  - Tzelves L
AD  - Second Department of Urology, National and Kapodistrian University of Athens, 
      Sismanogleio General Hospital, Athens, Greece.
FAU - Kalles, Dimitris
AU  - Kalles D
AD  - School of Science and Technology, Hellenic Open University, Patras, Greece.
FAU - Katsimperis, Stamatis
AU  - Katsimperis S
AD  - Second Department of Urology, National and Kapodistrian University of Athens, 
      Sismanogleio General Hospital, Athens, Greece.
FAU - Angelopoulos, Panagiotis
AU  - Angelopoulos P
AD  - Second Department of Urology, National and Kapodistrian University of Athens, 
      Sismanogleio General Hospital, Athens, Greece.
FAU - Anastasiou, Athanasios
AU  - Anastasiou A
AD  - National Technical University of Athens (NTUA).
FAU - Koutsouris, Dimitrios
AU  - Koutsouris D
AD  - National Technical University of Athens (NTUA).
FAU - Kosmidis, Thanos
AU  - Kosmidis T
AD  - CareAcross Ltd, London, United Kingdom.
FAU - Verykios, Vassilios S
AU  - Verykios VS
AD  - School of Science and Technology, Hellenic Open University, Patras, Greece.
FAU - Skolarikos, Andreas
AU  - Skolarikos A
AD  - Second Department of Urology, National and Kapodistrian University of Athens, 
      Sismanogleio General Hospital, Athens, Greece.
FAU - Varkarakis, Ioannis
AU  - Varkarakis I
AD  - Second Department of Urology, National and Kapodistrian University of Athens, 
      Sismanogleio General Hospital, Athens, Greece.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - Humans
MH  - *Urologists
MH  - Artificial Intelligence
MH  - *Physicians
MH  - Algorithms
MH  - Culture
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Large Language Models
OT  - Urology
EDAT- 2023/06/30 06:42
MHDA- 2023/07/03 06:41
CRDT- 2023/06/30 04:24
PHST- 2023/07/03 06:41 [medline]
PHST- 2023/06/30 06:42 [pubmed]
PHST- 2023/06/30 04:24 [entrez]
AID - SHTI230562 [pii]
AID - 10.3233/SHTI230562 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 Jun 29;305:576-579. doi: 10.3233/SHTI230562.

PMID- 37809128
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231010
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Predicting Future Pandemics and Formulating Prevention Strategies: The Role of 
      ChatGPT.
PG  - e44825
LID - 10.7759/cureus.44825 [doi]
LID - e44825
AB  - The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 
      late 2019 and its subsequent worldwide spread has emphasized the urgent need for 
      better approaches for predicting and managing infectious disease outbreaks. One 
      potential instrument in this effort is artificial intelligence (AI), and in 
      specific, language models like ChatGPT (Chat Generative Pre-trained Transformer). 
      In the present study, to explore how ChatGPT could predict future pandemics and 
      give suggestions about the prevention strategy, our research team chatted with 
      ChatGPT with several questions on July 12, 2023. Based on our conversation, we 
      can conclude that AI is not a substitute for human expertise, but an adjunct to 
      support early prediction, prevention, and management of future pandemics.
CI  - Copyright © 2023, Jana et al.
FAU - Jana, Pradip K
AU  - Jana PK
AD  - Regional Virus Research and Diagnostic Laboratory, Indian Council of Medical 
      Research, National Institute of Cholera and Enteric Diseases, Kolkata, IND.
FAU - Majumdar, Agniva
AU  - Majumdar A
AD  - Regional Virus Research and Diagnostic Laboratory, Indian Council of Medical 
      Research, National Institute of Cholera and Enteric Diseases, Kolkata, IND.
FAU - Dutta, Shanta
AU  - Dutta S
AD  - Bacteriology, Indian Council of Medical Research, National Institute of Cholera 
      and Enteric Diseases, Kolkata, IND.
LA  - eng
PT  - Editorial
DEP - 20230907
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10559259
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - artificial intelligence
OT  - chatbot
OT  - chatgpt
OT  - machine learning
OT  - outbreak
OT  - pandemic
COIS- Responses from the ChatGPT Language Model are directly copied and pasted in Table 
      1 of our manuscript.
EDAT- 2023/10/09 06:42
MHDA- 2023/10/09 06:43
CRDT- 2023/10/09 05:52
PHST- 2023/09/07 00:00 [accepted]
PHST- 2023/10/09 06:43 [medline]
PHST- 2023/10/09 06:42 [pubmed]
PHST- 2023/10/09 05:52 [entrez]
AID - 10.7759/cureus.44825 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 7;15(9):e44825. doi: 10.7759/cureus.44825. eCollection 2023 Sep.

PMID- 37528607
OWN - NLM
STAT- Publisher
LR  - 20230808
IS  - 2042-1818 (Electronic)
IS  - 0025-8024 (Linking)
DP  - 2023 Aug 1
TI  - ChatGPT: Forensic, legal, and ethical issues.
PG  - 258024231191829
LID - 10.1177/00258024231191829 [doi]
AB  - Artificial intelligence (AI) refers to a group of technologies that enable people 
      to perform a variety of activities, including observing, comprehending, analysing 
      and translating data, among other things. Nowadays, practically every school of 
      thought is interested in AI. One such innovation, a chatbot by the name of 
      ChatGPT (Chat Generative Pre-Trained Transformer), launched by OpenAI recently, 
      has taken the internet by storm. It had one million users within 1 week of its 
      launch. The present communication explores the practicability and versatility of 
      the ChatGPT in forensic examinations and scenarios, and also addresses the 
      ethical and legal issues surrounding its usage. The observations suggest that the 
      said technology, in its current form, has limited relevance in the realm of 
      forensic science and the law. Only human critical thinking, expertise, and 
      practical experience can provide the information and competencies needed in the 
      realms of forensics, research, clinical and legal practices. Thus, the ChatGPT 
      should be used with utmost caution in the disciplines of medicine, forensic 
      science and the law, irrespective of its many positive attributes.
FAU - Guleria, Ankita
AU  - Guleria A
AD  - Department of Anthropology, Panjab University, Chandigarh, India. RINGGOLD: 29750
FAU - Krishan, Kewal
AU  - Krishan K
AUID- ORCID: 0000-0001-5321-0958
AD  - Department of Anthropology, Panjab University, Chandigarh, India. RINGGOLD: 29750
FAU - Sharma, Vishal
AU  - Sharma V
AD  - Institute of Forensic Science and Criminology, Panjab University, Chandigarh, 
      India. RINGGOLD: 29750
FAU - Kanchan, Tanuj
AU  - Kanchan T
AUID- ORCID: 0000-0003-0346-1075
AD  - Department of Forensic Medicine and Toxicology, All India Institute of Medical 
      Sciences, Jodhpur, India.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - England
TA  - Med Sci Law
JT  - Medicine, science, and the law
JID - 0400721
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - crime investigation
OT  - ethics
OT  - forensic science
OT  - law
EDAT- 2023/08/02 06:43
MHDA- 2023/08/02 06:43
CRDT- 2023/08/02 01:53
PHST- 2023/08/02 06:43 [pubmed]
PHST- 2023/08/02 06:43 [medline]
PHST- 2023/08/02 01:53 [entrez]
AID - 10.1177/00258024231191829 [doi]
PST - aheadofprint
SO  - Med Sci Law. 2023 Aug 1:258024231191829. doi: 10.1177/00258024231191829.

PMID- 38073698
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2307-8960 (Print)
IS  - 2307-8960 (Electronic)
IS  - 2307-8960 (Linking)
VI  - 11
IP  - 32
DP  - 2023 Nov 16
TI  - Potential and limitations of ChatGPT and generative artificial intelligence in 
      medical safety education.
PG  - 7935-7939
LID - 10.12998/wjcc.v11.i32.7935 [doi]
AB  - The primary objectives of medical safety education are to provide the public with 
      essential knowledge about medications and to foster a scientific approach to drug 
      usage. The era of using artificial intelligence to revolutionize medical safety 
      education has already dawned, and ChatGPT and other generative artificial 
      intelligence models have immense potential in this domain. Notably, they offer a 
      wealth of knowledge, anonymity, continuous availability, and personalized 
      services. However, the practical implementation of generative artificial 
      intelligence models such as ChatGPT in medical safety education still faces 
      several challenges, including concerns about the accuracy of information, legal 
      responsibilities, and ethical obligations. Moving forward, it is crucial to 
      intelligently upgrade ChatGPT by leveraging the strengths of existing medical 
      practices. This task involves further integrating the model with real-life 
      scenarios and proactively addressing ethical and security issues with the 
      ultimate goal of providing the public with comprehensive, convenient, efficient, 
      and personalized medical services.
CI  - ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights 
      reserved.
FAU - Wang, Xin
AU  - Wang X
AD  - School of Education, Tianjin University, Tianjin 300350, China.
FAU - Liu, Xin-Qiao
AU  - Liu XQ
AD  - School of Education, Tianjin University, Tianjin 300350, China. 
      xinqiaoliu@pku.edu.cn.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - World J Clin Cases
JT  - World journal of clinical cases
JID - 101618806
PMC - PMC10698415
OTO - NOTNLM
OT  - ChatGPT
OT  - Generative artificial intelligence
OT  - Limitation
OT  - Medical safety education
OT  - Potential
COIS- Conflict-of-interest statement: The authors declare no conflict of interests.
EDAT- 2023/12/11 06:45
MHDA- 2023/12/11 06:46
CRDT- 2023/12/11 05:35
PHST- 2023/08/07 00:00 [received]
PHST- 2023/09/21 00:00 [revised]
PHST- 2023/11/02 00:00 [accepted]
PHST- 2023/12/11 06:46 [medline]
PHST- 2023/12/11 06:45 [pubmed]
PHST- 2023/12/11 05:35 [entrez]
AID - 10.12998/wjcc.v11.i32.7935 [doi]
PST - ppublish
SO  - World J Clin Cases. 2023 Nov 16;11(32):7935-7939. doi: 
      10.12998/wjcc.v11.i32.7935.

PMID- 37705958
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230916
IS  - 2589-0042 (Electronic)
IS  - 2589-0042 (Linking)
VI  - 26
IP  - 9
DP  - 2023 Sep 15
TI  - A descriptive study based on the comparison of ChatGPT and evidence-based 
      neurosurgeons.
PG  - 107590
LID - 10.1016/j.isci.2023.107590 [doi]
LID - 107590
AB  - ChatGPT is an artificial intelligence product developed by OpenAI. This study 
      aims to investigate whether ChatGPT can respond in accordance with evidence-based 
      medicine in neurosurgery. We generated 50 neurosurgical questions covering 
      neurosurgical diseases. Each question was posed three times to GPT-3.5 and 
      GPT-4.0. We also recruited three neurosurgeons with high, middle, and low 
      seniority to respond to questions. The results were analyzed regarding ChatGPT's 
      overall performance score, mean scores by the items' specialty classification, 
      and question type. In conclusion, GPT-3.5's ability to respond in accordance with 
      evidence-based medicine was comparable to that of neurosurgeons with low 
      seniority, and GPT-4.0's ability was comparable to that of neurosurgeons with 
      high seniority. Although ChatGPT is yet to be comparable to a neurosurgeon with 
      high seniority, future upgrades could enhance its performance and abilities.
CI  - © 2023 The Authors.
FAU - Liu, Jiayu
AU  - Liu J
AD  - Department of Neurosurgery, the First Medical Centre, Chinese PLA General 
      Hospital, Beijing 100853, China.
FAU - Zheng, Jiqi
AU  - Zheng J
AD  - School of Health Humanities, Peking University, Beijing 100191, China.
FAU - Cai, Xintian
AU  - Cai X
AD  - Department of Graduate School, Xinjiang Medical University, Urumqi 830001, China.
FAU - Wu, Dongdong
AU  - Wu D
AD  - Department of Information, Daping Hospital, Army Medical University, Chongqing 
      400042, China.
FAU - Yin, Chengliang
AU  - Yin C
AD  - Faculty of Medicine, Macau University of Science and Technology, Macau 999078, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20230809
PL  - United States
TA  - iScience
JT  - iScience
JID - 101724038
PMC - PMC10495632
OTO - NOTNLM
OT  - Artificial intelligence applications
OT  - Health informatics
OT  - Neurology
OT  - Neurosurgery
COIS- The authors declare no competing interests.
EDAT- 2023/09/14 06:42
MHDA- 2023/09/14 06:43
CRDT- 2023/09/14 04:10
PHST- 2023/05/31 00:00 [received]
PHST- 2023/06/21 00:00 [revised]
PHST- 2023/08/04 00:00 [accepted]
PHST- 2023/09/14 06:43 [medline]
PHST- 2023/09/14 06:42 [pubmed]
PHST- 2023/09/14 04:10 [entrez]
AID - S2589-0042(23)01667-X [pii]
AID - 107590 [pii]
AID - 10.1016/j.isci.2023.107590 [doi]
PST - epublish
SO  - iScience. 2023 Aug 9;26(9):107590. doi: 10.1016/j.isci.2023.107590. eCollection 
      2023 Sep 15.

PMID- 36808255
OWN - NLM
STAT- MEDLINE
DCOM- 20230316
LR  - 20230928
IS  - 2515-5091 (Electronic)
IS  - 2515-5091 (Linking)
VI  - 7
IP  - 2
DP  - 2023 Mar 1
TI  - Artificial intelligence chatbots will revolutionize how cancer patients access 
      information: ChatGPT represents a paradigm-shift.
LID - 10.1093/jncics/pkad010 [doi]
LID - pkad010
AB  - On November 30, 2022, OpenAI enabled public access to ChatGPT, a next-generation 
      artificial intelligence with a highly sophisticated ability to write, solve 
      coding issues, and answer questions. This communication draws attention to the 
      prospect that ChatGPT and its successors will become important virtual assistants 
      to patients and health-care providers. In our assessments, ranging from answering 
      basic fact-based questions to responding to complex clinical questions, ChatGPT 
      demonstrated a remarkable ability to formulate interpretable responses, which 
      appeared to minimize the likelihood of alarm compared with Google's feature 
      snippet. Arguably, the ChatGPT use case presents an urgent need for regulators 
      and health-care professionals to be involved in developing standards for minimum 
      quality and to raise patient awareness of current limitations of emerging 
      artificial intelligence assistants. This commentary aims to raise awareness at 
      the tipping point of a paradigm shift.
CI  - © The Author(s) 2023. Published by Oxford University Press.
FAU - Hopkins, Ashley M
AU  - Hopkins AM
AUID- ORCID: 0000-0001-7652-4378
AD  - College of Medicine and Public Health, Flinders University, Adelaide, South 
      Australia, Australia.
FAU - Logan, Jessica M
AU  - Logan JM
AUID- ORCID: 0000-0003-4046-6908
AD  - Clinical and Health Sciences, University of South Australia, Adelaide, South 
      Australia, Australia.
FAU - Kichenadasse, Ganessan
AU  - Kichenadasse G
AUID- ORCID: 0000-0001-9923-5149
AD  - College of Medicine and Public Health, Flinders University, Adelaide, South 
      Australia, Australia.
AD  - Flinders Centre for Innovation in Cancer, Department of Medical Oncology, 
      Flinders Medical Centre, Adelaide, South Australia, Australia.
FAU - Sorich, Michael J
AU  - Sorich MJ
AD  - College of Medicine and Public Health, Flinders University, Adelaide, South 
      Australia, Australia.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - JNCI Cancer Spectr
JT  - JNCI cancer spectrum
JID - 101721827
SB  - IM
CIN - JNCI Cancer Spectr. 2023 Aug 31;7(5):. PMID: 37656938
MH  - Humans
MH  - *Artificial Intelligence
MH  - Communication
MH  - Health Personnel
MH  - Probability
MH  - *Neoplasms/therapy
PMC - PMC10013638
EDAT- 2023/02/23 06:00
MHDA- 2023/03/17 06:00
CRDT- 2023/02/22 12:43
PHST- 2023/01/10 00:00 [received]
PHST- 2023/02/06 00:00 [revised]
PHST- 2023/02/13 00:00 [accepted]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/03/17 06:00 [medline]
PHST- 2023/02/22 12:43 [entrez]
AID - 7049531 [pii]
AID - pkad010 [pii]
AID - 10.1093/jncics/pkad010 [doi]
PST - ppublish
SO  - JNCI Cancer Spectr. 2023 Mar 1;7(2):pkad010. doi: 10.1093/jncics/pkad010.

PMID- 37210278
OWN - NLM
STAT- MEDLINE
DCOM- 20231024
LR  - 20231025
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
VI  - 131
IP  - 2
DP  - 2023 Aug
TI  - Assessment of ChatGPT success with specialty medical knowledge using 
      anaesthesiology board examination practice questions.
PG  - e31-e34
LID - S0007-0912(23)00192-7 [pii]
LID - 10.1016/j.bja.2023.04.017 [doi]
FAU - Shay, Denys
AU  - Shay D
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel 
      Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Center for 
      Anesthesia Research Excellence, Beth Israel Deaconess Medical Center, Harvard 
      Medical School, Boston, MA, USA.
FAU - Kumar, Bhawesh
AU  - Kumar B
AD  - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA.
FAU - Bellamy, David
AU  - Bellamy D
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 
      Boston, MA, USA.
FAU - Palepu, Anil
AU  - Palepu A
AD  - Harvard-MIT Health Sciences and Technology, Massachusetts Institute of 
      Technology, Cambridge, MA, USA.
FAU - Dershwitz, Mark
AU  - Dershwitz M
AD  - Department of Anesthesiology & Perioperative Medicine, University of 
      Massachusetts Chan Medical School, Worcester, MA, USA.
FAU - Walz, Jens M
AU  - Walz JM
AD  - Department of Anesthesiology & Perioperative Medicine, University of 
      Massachusetts Chan Medical School, Worcester, MA, USA.
FAU - Schaefer, Maximilian S
AU  - Schaefer MS
AD  - Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess 
      Medical Center, Harvard Medical School, Boston, MA, USA; Center for Anesthesia 
      Research Excellence, Beth Israel Deaconess Medical Center, Harvard Medical 
      School, Boston, MA, USA; Department of Anesthesiology, Dusseldorf University 
      Hospital, Dusseldorf, Germany.
FAU - Beam, Andrew
AU  - Beam A
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA. Electronic address: andrew_beam@hms.harvard.edu.
LA  - eng
PT  - Letter
DEP - 20230518
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
MH  - Humans
MH  - *Anesthesiology
MH  - *Artificial Intelligence
MH  - Academic Performance
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - board examination
OT  - large language models
OT  - medical knowledge
OT  - multiple choice questions
OT  - specialty qualifications
EDAT- 2023/05/21 01:05
MHDA- 2023/10/23 00:44
CRDT- 2023/05/20 21:59
PHST- 2023/04/13 00:00 [received]
PHST- 2023/04/14 00:00 [accepted]
PHST- 2023/10/23 00:44 [medline]
PHST- 2023/05/21 01:05 [pubmed]
PHST- 2023/05/20 21:59 [entrez]
AID - S0007-0912(23)00192-7 [pii]
AID - 10.1016/j.bja.2023.04.017 [doi]
PST - ppublish
SO  - Br J Anaesth. 2023 Aug;131(2):e31-e34. doi: 10.1016/j.bja.2023.04.017. Epub 2023 
      May 18.

PMID- 38036007
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1600-0641 (Electronic)
IS  - 0168-8278 (Linking)
DP  - 2023 Nov 28
TI  - Preliminary Fatty Liver Disease Grading Using General-Purpose Online Large 
      Language Models: ChatGPT-4 or Bard?
LID - S0168-8278(23)05293-5 [pii]
LID - 10.1016/j.jhep.2023.11.017 [doi]
FAU - Zhang, Yiwen
AU  - Zhang Y
AD  - Department of Endocrinology and Metabolic Hepatology, The Affiliated Hospital of 
      Qingdao University, Qingdao, China.
FAU - Liu, Hanyun
AU  - Liu H
AD  - Department of Infectious Disease and Hepatology, The Affiliated Hospital of 
      Qingdao University, Qingdao, China.
FAU - Sheng, Bin
AU  - Sheng B
AD  - Department of Computer Science and Engineering, Shanghai Jiao Tong University, 
      Shanghai, China.
FAU - Tham, Yih Chung
AU  - Tham YC
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 
      Singapore.
FAU - Ji, Hongwei
AU  - Ji H
AD  - Tsinghua Medicine, Tsinghua University, Beijing, China; Department of Internal 
      Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China. Electronic 
      address: hongweijicn@gmail.com.
LA  - eng
PT  - Letter
DEP - 20231128
PL  - Netherlands
TA  - J Hepatol
JT  - Journal of hepatology
JID - 8503886
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - Fatty Liver Disease
OT  - Large Language Models
OT  - Pathology
COIS- Conflicts of Interest None.
EDAT- 2023/12/01 00:43
MHDA- 2023/12/01 00:43
CRDT- 2023/11/30 21:53
PHST- 2023/11/13 00:00 [received]
PHST- 2023/11/17 00:00 [accepted]
PHST- 2023/12/01 00:43 [medline]
PHST- 2023/12/01 00:43 [pubmed]
PHST- 2023/11/30 21:53 [entrez]
AID - S0168-8278(23)05293-5 [pii]
AID - 10.1016/j.jhep.2023.11.017 [doi]
PST - aheadofprint
SO  - J Hepatol. 2023 Nov 28:S0168-8278(23)05293-5. doi: 10.1016/j.jhep.2023.11.017.

PMID- 37349180
OWN - NLM
STAT- Publisher
LR  - 20230622
IS  - 2405-4569 (Electronic)
IS  - 2405-4569 (Linking)
DP  - 2023 Jun 20
TI  - The Long but Necessary Journey Towards Optimization of the Cause-Effect 
      Relationship Between Input and Output for Accountable Use of ChatGPT for Academic 
      Purposes.
LID - S2405-4569(23)00142-6 [pii]
LID - 10.1016/j.euf.2023.06.002 [doi]
AB  - The use of appropriate inputs can significantly improve the outputs obtained from 
      ChatGPT for academic research.
CI  - Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All 
      rights reserved.
FAU - Piazza, Pietro
AU  - Piazza P
AD  - Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, 
      Italy. Electronic address: pietropiazza1209@gmail.com.
FAU - Checcucci, Enrico
AU  - Checcucci E
AD  - Department of Surgery, FPO-IRCCS Candiolo Cancer Institute, Turin, Italy.
FAU - Puliatti, Stefano
AU  - Puliatti S
AD  - Department of Urology, University of Modena, and Reggio Emilia, Modena, Italy.
FAU - Belenchòn, Inés Rivero
AU  - Belenchòn IR
AD  - Urology and Nephrology Department, Virgen del Rocío University Hospital, Seville, 
      Spain.
FAU - Veccia, Alessandro
AU  - Veccia A
AD  - Department of Urology, University of Verona, Azienda Ospedaliera Universitaria 
      Integrata, Verona, Italy.
FAU - Rivas, Juan Gomez
AU  - Rivas JG
AD  - Department of Urology, Hospital Clinico San Carlos, Madrid, Spain.
FAU - Taratkin, Mark
AU  - Taratkin M
AD  - Institute for Urology and Reproductive Health, Sechenov University, Moscow, 
      Russia.
FAU - Kowalewski, Karl-Friedrich
AU  - Kowalewski KF
AD  - Department of Urology and Urosurgery, University Medical Center Mannheim, Medical 
      Faculty Mannheim at Heidelberg University, Mannheim, Germany.
FAU - Rodler, Severin
AU  - Rodler S
AD  - Department of Urology, University Hospital LMU Munich, Munich, Germany.
FAU - Cacciamani, Giovanni Enrico
AU  - Cacciamani GE
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 
      AI Center at USC Urology, USC Institute of Urology, University of Southern 
      California, Los Angeles, CA, USA.
CN  - Uro-Technology, Social Media Working Group of the Young Academic Urologists 
      Working Party of the European Association of Urology
LA  - eng
PT  - Editorial
DEP - 20230620
PL  - Netherlands
TA  - Eur Urol Focus
JT  - European urology focus
JID - 101665661
SB  - IM
EDAT- 2023/06/23 01:10
MHDA- 2023/06/23 01:10
CRDT- 2023/06/22 21:54
PHST- 2023/04/17 00:00 [received]
PHST- 2023/05/22 00:00 [revised]
PHST- 2023/06/09 00:00 [accepted]
PHST- 2023/06/23 01:10 [medline]
PHST- 2023/06/23 01:10 [pubmed]
PHST- 2023/06/22 21:54 [entrez]
AID - S2405-4569(23)00142-6 [pii]
AID - 10.1016/j.euf.2023.06.002 [doi]
PST - aheadofprint
SO  - Eur Urol Focus. 2023 Jun 20:S2405-4569(23)00142-6. doi: 
      10.1016/j.euf.2023.06.002.

PMID- 37257586
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230810
IS  - 2589-9333 (Electronic)
IS  - 2589-9333 (Linking)
VI  - 5
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT: a pioneering approach to complex prenatal differential diagnosis.
PG  - 101029
LID - S2589-9333(23)00171-4 [pii]
LID - 10.1016/j.ajogmf.2023.101029 [doi]
AB  - This commentary examines how ChatGPT can assist healthcare teams in the prenatal 
      diagnosis of rare and complex cases by creating a differential diagnoses based on 
      deidentified clinical findings, while also acknowledging its limitations.
CI  - Copyright © 2023. Published by Elsevier Inc.
FAU - Suhag, Anju
AU  - Suhag A
AD  - Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU 
      Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of 
      Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr 
      Chavez). Electronic address: anju.suhag@nyulangone.org.
FAU - Kidd, Jennifer
AU  - Kidd J
AD  - Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU 
      Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of 
      Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr 
      Chavez).
FAU - McGath, Meghan
AU  - McGath M
AD  - Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU 
      Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of 
      Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr 
      Chavez); Department of Clinical Genetics, NYU Langone Hospital-Long Island, 
      Mineola, NY (Mses McGath and Cacace, and Dr Monteleone).
FAU - Rajesh, Raeshmma
AU  - Rajesh R
AD  - Department of Obstetrics and Gynecology, Richmond University Medical Center, 
      Staten Island, NY (Dr Rajesh).
FAU - Gelfinbein, Joseph
AU  - Gelfinbein J
AD  - NYU Long Island School of Medicine, Mineola, NY (Mr Gelfinbein).
FAU - Cacace, Nicole
AU  - Cacace N
AD  - Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU 
      Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of 
      Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr 
      Chavez); Department of Clinical Genetics, NYU Langone Hospital-Long Island, 
      Mineola, NY (Mses McGath and Cacace, and Dr Monteleone).
FAU - Monteleone, Berrin
AU  - Monteleone B
AD  - Department of Clinical Genetics, NYU Langone Hospital-Long Island, Mineola, NY 
      (Mses McGath and Cacace, and Dr Monteleone).
FAU - Chavez, Martin R
AU  - Chavez MR
AD  - Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU 
      Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of 
      Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr 
      Chavez).
LA  - eng
PT  - Journal Article
DEP - 20230529
PL  - United States
TA  - Am J Obstet Gynecol MFM
JT  - American journal of obstetrics & gynecology MFM
JID - 101746609
SB  - IM
MH  - Humans
MH  - Female
MH  - Pregnancy
MH  - Diagnosis, Differential
MH  - *Patient Care Team
MH  - *Prenatal Diagnosis
OTO - NOTNLM
OT  - ChatGPT-4
OT  - artificial intelligence
OT  - complex prenatal disorders
OT  - generative pre-trained transformer language model
OT  - human phenotype ontology
OT  - neonatal genetic disorders
OT  - online mendelian inheritance in man
OT  - prenatal diagnosis
EDAT- 2023/06/01 01:08
MHDA- 2023/08/07 06:41
CRDT- 2023/05/31 19:25
PHST- 2023/05/01 00:00 [received]
PHST- 2023/05/19 00:00 [accepted]
PHST- 2023/08/07 06:41 [medline]
PHST- 2023/06/01 01:08 [pubmed]
PHST- 2023/05/31 19:25 [entrez]
AID - S2589-9333(23)00171-4 [pii]
AID - 10.1016/j.ajogmf.2023.101029 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol MFM. 2023 Aug;5(8):101029. doi: 10.1016/j.ajogmf.2023.101029. 
      Epub 2023 May 29.

PMID- 37994939
OWN - NLM
STAT- Publisher
LR  - 20231123
IS  - 1432-1920 (Electronic)
IS  - 0028-3940 (Linking)
DP  - 2023 Nov 23
TI  - Accuracy of ChatGPT generated diagnosis from patient's medical history and 
      imaging findings in neuroradiology cases.
LID - 10.1007/s00234-023-03252-4 [doi]
AB  - PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer 
      (ChatGPT), an artificial intelligence text generation model based on the GPT-4 
      architecture, has been demonstrated in various fields; however, its potential 
      applications in neuroradiology remain unexplored. This study aimed to evaluate 
      the diagnostic performance of GPT-4 based ChatGPT in neuroradiology. METHODS: We 
      collected 100 consecutive "Case of the Week" cases from the American Journal of 
      Neuroradiology between October 2021 and September 2023. ChatGPT generated a 
      diagnosis from patient's medical history and imaging findings for each case. Then 
      the diagnostic accuracy rate was determined using the published ground truth. 
      Each case was categorized by anatomical location (brain, spine, and head & neck), 
      and brain cases were further divided into central nervous system (CNS) tumor and 
      non-CNS tumor groups. Fisher's exact test was conducted to compare the accuracy 
      rates among the three anatomical locations, as well as between the CNS tumor and 
      non-CNS tumor groups. RESULTS: ChatGPT achieved a diagnostic accuracy rate of 50% 
      (50/100 cases). There were no significant differences between the accuracy rates 
      of the three anatomical locations (p = 0.89). The accuracy rate was significantly 
      lower for the CNS tumor group compared to the non-CNS tumor group in the brain 
      cases (16% [3/19] vs. 62% [36/58], p < 0.001). CONCLUSION: This study 
      demonstrated the diagnostic performance of ChatGPT in neuroradiology. ChatGPT's 
      diagnostic accuracy varied depending on disease etiologies, and its diagnostic 
      accuracy was significantly lower in CNS tumors compared to non-CNS tumors.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Horiuchi, Daisuke
AU  - Horiuchi D
AUID- ORCID: 0000-0002-8929-8098
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Tatekawa, Hiroyuki
AU  - Tatekawa H
AUID- ORCID: 0000-0002-8050-4895
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Shimono, Taro
AU  - Shimono T
AUID- ORCID: 0000-0001-5847-6587
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Walston, Shannon L
AU  - Walston SL
AUID- ORCID: 0000-0002-7268-8313
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Takita, Hirotaka
AU  - Takita H
AUID- ORCID: 0000-0003-2860-4503
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Matsushita, Shu
AU  - Matsushita S
AUID- ORCID: 0000-0003-1132-9736
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Oura, Tatsushi
AU  - Oura T
AUID- ORCID: 0000-0002-0930-1032
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Mitsuyama, Yasuhito
AU  - Mitsuyama Y
AUID- ORCID: 0000-0001-7979-2300
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Miki, Yukio
AU  - Miki Y
AUID- ORCID: 0000-0003-0621-0044
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Ueda, Daiju
AU  - Ueda D
AUID- ORCID: 0000-0002-3878-3616
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan. ai.labo.ocu@gmail.com.
AD  - Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan 
      University, Osaka, Japan. ai.labo.ocu@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231123
PL  - Germany
TA  - Neuroradiology
JT  - Neuroradiology
JID - 1302751
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chat Generative Pre-trained Transformer (ChatGPT)
OT  - Generative Pre-trained Transformer (GPT)-4
OT  - Large language models
EDAT- 2023/11/23 12:43
MHDA- 2023/11/23 12:43
CRDT- 2023/11/23 11:07
PHST- 2023/10/02 00:00 [received]
PHST- 2023/11/13 00:00 [accepted]
PHST- 2023/11/23 12:43 [medline]
PHST- 2023/11/23 12:43 [pubmed]
PHST- 2023/11/23 11:07 [entrez]
AID - 10.1007/s00234-023-03252-4 [pii]
AID - 10.1007/s00234-023-03252-4 [doi]
PST - aheadofprint
SO  - Neuroradiology. 2023 Nov 23. doi: 10.1007/s00234-023-03252-4.

PMID- 37719492
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230919
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in 
      Neurosurgery: A Systematic Review of Literature and Conceptual Framework.
PG  - e43502
LID - 10.7759/cureus.43502 [doi]
LID - e43502
AB  - The objective of this study is to explore the use of ChatGPT (Chat-Generative 
      Pre-Trained Transformer) in neurosurgery and its potential impact on the field. 
      The authors aim to discuss, through a systematic review of current literature, 
      how this rising new artificial intelligence (AI) technology may prove to be a 
      useful tool in the future, weighing its potential benefits and limitations. The 
      authors conducted a comprehensive and systematic literature review of the use of 
      ChatGPT and its applications in healthcare and different neurosurgery topics. 
      Through a systematic review of the literature, with a search strategy using the 
      databases such as PubMed, Google Scholar, and Embase, we analyzed the advantages 
      and limitations of using ChatGPT in neurosurgery and evaluated its potential 
      impact. ChatGPT has demonstrated promising results in various applications, such 
      as natural language processing, language translation, and text summarization. In 
      neurosurgery, ChatGPT can assist in different areas such as surgical planning, 
      image recognition, medical diagnosis, patient care, and scientific production. A 
      total of 128 articles were retrieved from databases, where the final 22 articles 
      were included for thorough analysis. The studies reviewed demonstrate the 
      potential of AI and deep learning (DL), through language models such as ChatGPT, 
      to improve the accuracy and efficiency of neurosurgical procedures, as well as 
      diagnosis, treatment, and patient outcomes across various medical specialties, 
      including neurosurgery. There are, however, limitations to its use, including the 
      need for large datasets and the potential for errors in the output, which most 
      authors concur will need human verification for the final application. Our search 
      demonstrated the potential that ChatGPT holds for the present and future, in 
      accordance with the studies' authors' findings herein analyzed and expert 
      opinions. Further research and development are required to fully understand its 
      capabilities and limitations. AI technology can serve as a useful tool to augment 
      human intelligence; however, it is essential to use it in a responsible and 
      ethical manner.
CI  - Copyright © 2023, Roman et al.
FAU - Roman, Alex
AU  - Roman A
AD  - Neurological Surgery, Cleveland Clinic Abu Dhabi, Abu Dhabi, ARE.
FAU - Al-Sharif, Lubna
AU  - Al-Sharif L
AD  - Physiology, Pharmacology and Toxicology, An-Najah National University, Nablus, 
      PSE.
FAU - Al Gharyani, Mohamed
AU  - Al Gharyani M
AD  - Neurosurgery, University of Benghazi, Benghazi, LBY.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230815
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10500385
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - artificial intelligence
OT  - chatgpt
OT  - deep learning
OT  - neurosurgery
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:43
CRDT- 2023/09/18 04:22
PHST- 2023/08/15 00:00 [accepted]
PHST- 2023/09/18 06:43 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:22 [entrez]
AID - 10.7759/cureus.43502 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 15;15(8):e43502. doi: 10.7759/cureus.43502. eCollection 2023 
      Aug.

PMID- 37516804
OWN - NLM
STAT- Publisher
LR  - 20230922
IS  - 1476-5608 (Electronic)
IS  - 1365-7852 (Linking)
DP  - 2023 Jul 29
TI  - Quality of information and appropriateness of ChatGPT outputs for urology 
      patients.
LID - 10.1038/s41391-023-00705-y [doi]
AB  - BACKGROUND: The proportion of health-related searches on the internet is 
      continuously growing. ChatGPT, a natural language processing (NLP) tool created 
      by OpenAI, has been gaining increasing user attention and can potentially be used 
      as a source for obtaining information related to health concerns. This study aims 
      to analyze the quality and appropriateness of ChatGPT's responses to Urology case 
      studies compared to those of a urologist. METHODS: Data from 100 patient case 
      studies, comprising patient demographics, medical history, and urologic 
      complaints, were sequentially inputted into ChatGPT, one by one. A question was 
      posed to determine the most likely diagnosis, suggested examinations, and 
      treatment options. The responses generated by ChatGPT were then compared to those 
      provided by a board-certified urologist who was blinded to ChatGPT's responses 
      and graded on a 5-point Likert scale based on accuracy, comprehensiveness, and 
      clarity as criterias for appropriateness. The quality of information was graded 
      based on the section 2 of the DISCERN tool and readability assessments were 
      performed using the Flesch Reading Ease (FRE) and Flesch-Kincaid Reading Grade 
      Level (FKGL) formulas. RESULTS: 52% of all responses were deemed appropriate. 
      ChatGPT provided more appropriate responses for non-oncology conditions (58.5%) 
      compared to oncology (52.6%) and emergency urology cases (11.1%) (p = 0.03). The 
      median score of the DISCERN tool was 15 (IQR = 5.3) corresponding to a quality 
      score of poor. The ChatGPT responses demonstrated a college graduate reading 
      level, as indicated by the median FRE score of 18 (IQR = 21) and the median FKGL 
      score of 15.8 (IQR = 3). CONCLUSIONS: ChatGPT serves as an interactive tool for 
      providing medical information online, offering the possibility of enhancing 
      health outcomes and patient satisfaction. Nevertheless, the insufficient 
      appropriateness and poor quality of the responses on Urology cases emphasizes the 
      importance of thorough evaluation and use of NLP-generated outputs when 
      addressing health-related concerns.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature Limited.
FAU - Cocci, Andrea
AU  - Cocci A
AUID- ORCID: 0000-0003-0138-6294
AD  - Urology Section, University of Florence, Florence, Italy. cocci.andrea@gmail.com.
FAU - Pezzoli, Marta
AU  - Pezzoli M
AUID- ORCID: 0009-0007-7168-5499
AD  - Urology Section, University of Florence, Florence, Italy.
FAU - Lo Re, Mattia
AU  - Lo Re M
AD  - Urology Section, University of Florence, Florence, Italy.
FAU - Russo, Giorgio Ivan
AU  - Russo GI
AUID- ORCID: 0000-0003-4687-7353
AD  - Urology Section, University of Catania, Catania, Italy.
FAU - Asmundo, Maria Giovanna
AU  - Asmundo MG
AD  - Urology Section, University of Catania, Catania, Italy.
FAU - Fode, Mikkel
AU  - Fode M
AUID- ORCID: 0000-0002-8963-4581
AD  - Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
AD  - Department of Urology, Copenhagen University Hospital, Herlev and Gentofte 
      Hospital, Copenhagen, Denmark.
FAU - Cacciamani, Giovanni
AU  - Cacciamani G
AD  - Institute of Urology, Keck School of Medicine, University of Southern California 
      (USC), Los Angeles, CA, USA.
FAU - Cimino, Sebastiano
AU  - Cimino S
AD  - Urology Section, University of Catania, Catania, Italy.
FAU - Minervini, Andrea
AU  - Minervini A
AD  - Urology Section, University of Florence, Florence, Italy.
FAU - Durukan, Emil
AU  - Durukan E
AUID- ORCID: 0000-0001-9221-8944
AD  - Department of Urology, Copenhagen University Hospital, Herlev and Gentofte 
      Hospital, Copenhagen, Denmark.
LA  - eng
PT  - Journal Article
DEP - 20230729
PL  - England
TA  - Prostate Cancer Prostatic Dis
JT  - Prostate cancer and prostatic diseases
JID - 9815755
SB  - IM
EDAT- 2023/07/30 01:06
MHDA- 2023/07/30 01:06
CRDT- 2023/07/29 23:21
PHST- 2023/05/30 00:00 [received]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/06/22 00:00 [revised]
PHST- 2023/07/30 01:06 [pubmed]
PHST- 2023/07/30 01:06 [medline]
PHST- 2023/07/29 23:21 [entrez]
AID - 10.1038/s41391-023-00705-y [pii]
AID - 10.1038/s41391-023-00705-y [doi]
PST - aheadofprint
SO  - Prostate Cancer Prostatic Dis. 2023 Jul 29. doi: 10.1038/s41391-023-00705-y.

PMID- 37303324
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230613
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - Proof of Concept: Using ChatGPT to Teach Emergency Physicians How to Break Bad 
      News.
PG  - e38755
LID - 10.7759/cureus.38755 [doi]
LID - e38755
AB  - Background Breaking bad news is an essential skill for practicing physicians, 
      particularly in the field of emergency medicine (EM). Patient-physician 
      communication teaching has previously relied on standardized patient scenarios 
      and objective structured clinical examination formats. The novel use of 
      artificial intelligence (AI) chatbot technology, such as Chat Generative 
      Pre-trained Transformer (ChatGPT), may provide an alternative role in graduate 
      medical education in this area. As a proof of concept, the author demonstrates 
      how providing detailed prompts to the AI chatbot can facilitate the design of a 
      realistic clinical scenario, enable active roleplay, and deliver effective 
      feedback to physician trainees. Methods ChatGPT-3.5 language model was utilized 
      to assist in the roleplay of breaking bad news. A detailed input prompt was 
      designed to outline rules of play and grading assessment via a standardized 
      scale. User inputs (physician role), chatbot outputs (patient role) and 
      ChatGPT-generated feedback were recorded. Results ChatGPT set up a realistic 
      training scenario on breaking bad news based on the initial prompt. Active 
      roleplay as a patient in an emergency department setting was accomplished, and 
      clear feedback was provided to the user through the application of the Setting 
      up, Perception, Invitation, Knowledge, Emotions with Empathy, and Strategy or 
      Summary (SPIKES) framework for breaking bad news. Conclusion The novel use of AI 
      chatbot technology to assist educators is abundant with potential. ChatGPT was 
      able to design an appropriate scenario, provide a means for simulated 
      patient-physician roleplay, and deliver real-time feedback to the physician user. 
      Future studies are required to expand use to a targeted group of EM physician 
      trainees and provide best practice guidelines for AI use in graduate medical 
      education.
CI  - Copyright © 2023, Webb et al.
FAU - Webb, Jeremy J
AU  - Webb JJ
AD  - Emergency Medicine, LewisGale Medical Center, Salem, USA.
AD  - School of Medicine, Edward Via College of Osteopathic Medicine, Blacksburg, USA.
LA  - eng
PT  - Journal Article
DEP - 20230509
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10250131
OTO - NOTNLM
OT  - artificial intelligence
OT  - breaking bad news
OT  - chatbot
OT  - chatgpt
OT  - machine learning
OT  - medical education
COIS- This research was supported (in whole or in part) by Hospital Corporation of 
      America (HCA) Healthcare and/or an HCA Healthcare-affiliated entity. The views 
      expressed in this manuscript represent those of the author and do not necessarily 
      represent the official views of HCA Healthcare or any of its affiliated entities.
EDAT- 2023/06/12 06:42
MHDA- 2023/06/12 06:43
CRDT- 2023/06/12 03:53
PHST- 2023/05/08 00:00 [accepted]
PHST- 2023/06/12 06:43 [medline]
PHST- 2023/06/12 06:42 [pubmed]
PHST- 2023/06/12 03:53 [entrez]
AID - 10.7759/cureus.38755 [doi]
PST - epublish
SO  - Cureus. 2023 May 9;15(5):e38755. doi: 10.7759/cureus.38755. eCollection 2023 May.

PMID- 37675304
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230908
IS  - 2305-5839 (Print)
IS  - 2305-5847 (Electronic)
IS  - 2305-5839 (Linking)
VI  - 11
IP  - 10
DP  - 2023 Aug 30
TI  - New era after ChatGPT in ophthalmology: advances from data-based decision support 
      to patient-centered generative artificial intelligence.
PG  - 337
LID - 10.21037/atm-23-1598 [doi]
LID - 337
FAU - Choi, Joon Yul
AU  - Choi JY
AD  - Department of Biomedical Engineering, Yonsei University, Wonju, South Korea.
FAU - Yoo, Tae Keun
AU  - Yoo TK
AD  - B&VIIT Eye Center, Seoul, South Korea.
AD  - VISUWORKS, Seoul, South Korea.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230630
PL  - China
TA  - Ann Transl Med
JT  - Annals of translational medicine
JID - 101617978
CON - Ann Transl Med. 2023 Mar 15;11(5):219. PMID: 37007552
PMC - PMC10477620
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - generative artificial intelligence (generative AI)
OT  - ophthalmology
COIS- Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure 
      form (available at 
      https://atm.amegroups.com/article/view/10.21037/atm-23-1598/coif). TKY reported 
      that he served as a speaker for an academic lecture in VUNO and Hangil Eye 
      Hospital, and served as a lecturer for a commercial conference held by the Korea 
      Association of Intelligence Wellcare Industries (KIWI). TKY is an employee of 
      B&VIIT Eye Center and VISUWORKS. He received a salary as part of the standard 
      compensation package. He also received research grants for refractive surgery 
      from Carl Zeiss Meditec AG. The research grants did not affect this manuscript. 
      The other author has no conflicts of interest to declare.
EDAT- 2023/09/07 06:42
MHDA- 2023/09/07 06:43
CRDT- 2023/09/07 04:19
PHST- 2023/05/07 00:00 [received]
PHST- 2023/06/28 00:00 [accepted]
PHST- 2023/09/07 06:43 [medline]
PHST- 2023/09/07 06:42 [pubmed]
PHST- 2023/09/07 04:19 [entrez]
AID - atm-11-10-337 [pii]
AID - 10.21037/atm-23-1598 [doi]
PST - ppublish
SO  - Ann Transl Med. 2023 Aug 30;11(10):337. doi: 10.21037/atm-23-1598. Epub 2023 Jun 
      30.

PMID- 37889368
OWN - NLM
STAT- MEDLINE
DCOM- 20231130
LR  - 20231130
IS  - 1708-0428 (Electronic)
IS  - 0960-8923 (Linking)
VI  - 33
IP  - 12
DP  - 2023 Dec
TI  - Bariatric Evaluation Through AI: a Survey of Expert Opinions Versus ChatGPT-4 
      (BETA-SEOV).
PG  - 3971-3980
LID - 10.1007/s11695-023-06903-w [doi]
AB  - BACKGROUND: Recent advancements in artificial intelligence, such as OpenAI's 
      ChatGPT-4, are revolutionizing various sectors, including healthcare. This study 
      investigates the use of ChatGPT-4 in identifying suitable candidates for 
      bariatric surgery and providing surgical recommendations to improve 
      decision-making in obesity treatment amid the global obesity epidemic. METHODS: 
      We devised ten patient scenarios, thoughtfully encompassing a spectrum that spans 
      from uncomplicated cases to more complex ones. Our objective was to delve into 
      the decision-making process regarding the recommendation of bariatric surgery. 
      From July 29th to August 10th, 2023, we conducted a voluntary online survey 
      involving thirty prominent bariatric surgeons, ensuring that there was no 
      predetermined bias in the selection of a specific type of bariatric surgery. This 
      survey was designed to collect their insights on these scenarios and gain a 
      deeper understanding of their professional experience and background in the field 
      of bariatric surgery. Additionally, we consulted ChatGPT-4 in two separate 
      conversations to evaluate its alignment with expert opinions on bariatric surgery 
      options. RESULTS: In 40% of the scenarios, disparities were identified between 
      the two conversations with ChatGPT-4. It matched expert opinions in 30% of cases. 
      Differences were noted in cases like gastrointestinal metaplasia and gastric 
      adenocarcinoma, but there was alignment with conditions like endometriosis and 
      GERD. CONCLUSION: The evaluation of ChatGPT-4's role in determining bariatric 
      surgery suitability uncovered both potential and shortcomings. Its alignment with 
      experts was inconsistent, and it often overlooked key factors, emphasizing human 
      expertise's value. Its current use requires caution, and further refinement is 
      needed for clinical application.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Jazi, Amir Hossein Davarpanah
AU  - Jazi AHD
AD  - Department of Surgery, Minimally Invasive Surgery Research Center, Division of 
      Minimally Invasive and Bariatric Surgery, School of Medicine, Rasool-E Akram 
      Hospital, Iran University of Medical Sciences, Niyaesh Avenue, Sattar Khan 
      Street, Tehran, Iran.
FAU - Mahjoubi, Mohammad
AU  - Mahjoubi M
AD  - Clinical Research Development Center, Najafabad Branch, Islamic Azad University, 
      Najafabad, Iran.
FAU - Shahabi, Shahab
AU  - Shahabi S
AUID- ORCID: 0000-0002-8415-2567
AD  - Department of Surgery, Minimally Invasive Surgery Research Center, Division of 
      Minimally Invasive and Bariatric Surgery, School of Medicine, Rasool-E Akram 
      Hospital, Iran University of Medical Sciences, Niyaesh Avenue, Sattar Khan 
      Street, Tehran, Iran. shshahabi@yahoo.com.
FAU - Alqahtani, Aayed R
AU  - Alqahtani AR
AD  - New Medical Center, Riyadh, Saudi Arabia.
FAU - Haddad, Ashraf
AU  - Haddad A
AD  - Gastrointestinal Metabolic and Bariatric Center, GBMC M, Jordan Hospital, Amman, 
      Jordan.
FAU - Pazouki, Abdolreza
AU  - Pazouki A
AD  - Department of Surgery, Minimally Invasive Surgery Research Center, Division of 
      Minimally Invasive and Bariatric Surgery, School of Medicine, Rasool-E Akram 
      Hospital, Iran University of Medical Sciences, Niyaesh Avenue, Sattar Khan 
      Street, Tehran, Iran.
FAU - Prasad, Arun
AU  - Prasad A
AD  - GI, Bariatric and Robotic Surgery Apollo Hospital, New Delhi, India.
FAU - Safadi, Bassem Y
AU  - Safadi BY
AD  - Surgical Services Aman Hospital, Doha, Qatar.
FAU - Chiappetta, Sonja
AU  - Chiappetta S
AD  - Bariatric and Metabolic Surgery Unit, Ospedale Evangelico Betania, Naples, Italy.
FAU - Taskin, Halit Eren
AU  - Taskin HE
AD  - Department of General Surgery, Cerrahpaşa Medical Faculty, Istanbul University 
      Cerrahpaşa, Istanbul, Turkey.
FAU - Billy, Helmuth Thorlakur
AU  - Billy HT
AD  - Ventura Advanced Surgical Associates, Ventura, CA, USA.
FAU - Kasama, Kazunori
AU  - Kasama K
AD  - Weight Loss and Metabolic Surgery Center, Yotsuya Medical Cube, Tokyo, Japan.
FAU - Mahawar, Kamal
AU  - Mahawar K
AD  - Sunderland Royal Hospital, Sunderland, UK.
FAU - Gawdat, Khaled
AU  - Gawdat K
AD  - Ain Shams University Faculty of Medicine Department of General Surgery, Cairo, 
      Egypt.
FAU - Rheinwalt, Karl Peter
AU  - Rheinwalt KP
AD  - Department of Bariatric, Metabolic and Plastic Surgery, St. Franziskus Hospital, 
      Cologne, Germany.
FAU - Miller, Karl A
AU  - Miller KA
AD  - Dubai London Hospital, Dubai, UAE.
FAU - Kow, Lilian
AU  - Kow L
AD  - Department GI Surgery Flinders, University South Australia, Adelaide, Australia.
FAU - Neto, Manoel Galvao
AU  - Neto MG
AD  - Mohak Bariatric and Robotic Center, Indore, India.
FAU - Yang, Wah
AU  - Yang W
AD  - The First Affiliated Hospital of Jinan University, Guangzhou, China.
FAU - Palermo, Mariano
AU  - Palermo M
AD  - Gastrointestinal and Bariatric Surgery, University of Buenos Aires, Buenos Aires, 
      Argentina.
FAU - Ghanem, Omar M
AU  - Ghanem OM
AD  - Mayo Clinic, Rochester, MN, USA.
FAU - Lainas, Panagiotis
AU  - Lainas P
AD  - Department of Digestive and Bariatric Surgery, Metropolitan Hospital, HEAL 
      Academy, Athens, Greece.
FAU - Peterli, Ralph
AU  - Peterli R
AD  - Deputy Head of Visceral Surgery and Head of Bariatric-Metabolic Surgery Clarunis, 
      Department of Visceral Surgery, University Centre for Gastrointestinal and Liver 
      Diseases St. Clara Hospital and University Hospital Basel, 4002, Basel, 
      Switzerland.
FAU - Kassir, Radwan
AU  - Kassir R
AD  - Digestive Surgery Unit, University Hospital of La Réunion -Félix Guyon Hospital, 
      Saint-Denis, La Réunion, France.
FAU - Puy, Ramon Vilallonga
AU  - Puy RV
AD  - Head Endocrine-Metabolic and Bariatric Surgery Unit, Vall Hebron Barcelona 
      Hospital Campus, Pg. De La Vall d'hebron, 119-129, 08035, Barcelona, Spain.
FAU - Da Silva Ribeiro, Rui José
AU  - Da Silva Ribeiro RJ
AD  - General Surgery Department, Multidisciplinary Center for Obesity Treatment - 
      Hospital Lusíadas Amadora, Amadora, Portugal.
FAU - Verboonen, Sergio
AU  - Verboonen S
AD  - Obesity Goodbye Center, Tijuana, Mexico.
FAU - Pintar, Tadeja
AU  - Pintar T
AD  - UMC Ljubljana, Department of Abdominal Surgery and Medical Faculty, Ljubljana, 
      Slovenia.
FAU - Shabbir, Asim
AU  - Shabbir A
AD  - National University of Singapore, Singapore, Singapore.
FAU - Musella, Mario
AU  - Musella M
AD  - Advanced Biomedical Sciences Department, "Federico II" University, Naples, Italy.
FAU - Kermansaravi, Mohammad
AU  - Kermansaravi M
AD  - Department of Surgery, Minimally Invasive Surgery Research Center, Division of 
      Minimally Invasive and Bariatric Surgery, School of Medicine, Rasool-E Akram 
      Hospital, Iran University of Medical Sciences, Niyaesh Avenue, Sattar Khan 
      Street, Tehran, Iran.
LA  - eng
PT  - Journal Article
DEP - 20231027
PL  - United States
TA  - Obes Surg
JT  - Obesity surgery
JID - 9106714
SB  - IM
MH  - Female
MH  - Humans
MH  - Expert Testimony
MH  - Artificial Intelligence
MH  - *Obesity, Morbid/surgery
MH  - *Bariatrics
MH  - Obesity
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Bariatric Evaluation
OT  - Bariatric Surgery
OT  - ChatGPT
OT  - Health Literacy
OT  - Language Learning Models
EDAT- 2023/10/27 12:42
MHDA- 2023/11/30 06:43
CRDT- 2023/10/27 11:10
PHST- 2023/08/22 00:00 [received]
PHST- 2023/10/11 00:00 [accepted]
PHST- 2023/10/08 00:00 [revised]
PHST- 2023/11/30 06:43 [medline]
PHST- 2023/10/27 12:42 [pubmed]
PHST- 2023/10/27 11:10 [entrez]
AID - 10.1007/s11695-023-06903-w [pii]
AID - 10.1007/s11695-023-06903-w [doi]
PST - ppublish
SO  - Obes Surg. 2023 Dec;33(12):3971-3980. doi: 10.1007/s11695-023-06903-w. Epub 2023 
      Oct 27.

PMID- 36924907
OWN - NLM
STAT- MEDLINE
DCOM- 20230605
LR  - 20230828
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Linking)
VI  - 228
IP  - 6
DP  - 2023 Jun
TI  - The exciting potential for ChatGPT in obstetrics and gynecology.
PG  - 696-705
LID - S0002-9378(23)00154-0 [pii]
LID - 10.1016/j.ajog.2023.03.009 [doi]
AB  - Natural language processing-the branch of artificial intelligence concerned with 
      the interaction between computers and human language-has advanced markedly in 
      recent years with the introduction of sophisticated deep-learning models. 
      Improved performance in natural language processing tasks, such as text and 
      speech processing, have fueled impressive demonstrations of these models' 
      capabilities. Perhaps no demonstration has been more impactful to date than the 
      introduction of the publicly available online chatbot ChatGPT in November 2022 by 
      OpenAI, which is based on a natural language processing model known as a 
      Generative Pretrained Transformer. Through a series of questions posed by the 
      authors about obstetrics and gynecology to ChatGPT as prompts, we evaluated the 
      model's ability to handle clinical-related queries. Its answers demonstrated that 
      in its current form, ChatGPT can be valuable for users who want preliminary 
      information about virtually any topic in the field. Because its educational role 
      is still being defined, we must recognize its limitations. Although answers were 
      generally eloquent, informed, and lacked a significant degree of mistakes or 
      misinformation, we also observed evidence of its weaknesses. A significant 
      drawback is that the data on which the model has been trained are apparently not 
      readily updated. The specific model that was assessed here, seems to not reliably 
      (if at all) source data from after 2021. Users of ChatGPT who expect data to be 
      more up to date need to be aware of this drawback. An inability to cite sources 
      or to truly understand what the user is asking suggests that it has the 
      capability to mislead. Responsible use of models like ChatGPT will be important 
      for ensuring that they work to help but not harm users seeking information on 
      obstetrics and gynecology.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Grünebaum, Amos
AU  - Grünebaum A
AD  - Department of Obstetrics and Gynecology, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell, Lenox Hill Hospital, New York, NY. Electronic 
      address: agrunebaum@northwell.edu.
FAU - Chervenak, Joseph
AU  - Chervenak J
AD  - Department of Obstetrics and Gynecology, Albert Einstein College of Medicine, 
      Montefiore Hospital, Bronx, NY.
FAU - Pollet, Susan L
AU  - Pollet SL
AD  - Department of Obstetrics and Gynecology, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell, Lenox Hill Hospital, New York, NY.
FAU - Katz, Adi
AU  - Katz A
AD  - Department of Obstetrics and Gynecology, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell, Lenox Hill Hospital, New York, NY.
FAU - Chervenak, Frank A
AU  - Chervenak FA
AD  - Department of Obstetrics and Gynecology, Donald and Barbara Zucker School of 
      Medicine at Hofstra/Northwell, Lenox Hill Hospital, New York, NY.
LA  - eng
PT  - Journal Article
DEP - 20230315
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
CIN - Am J Obstet Gynecol. 2023 Sep;229(3):356-357. PMID: 37031761
MH  - Female
MH  - Pregnancy
MH  - Humans
MH  - *Gynecology
MH  - Artificial Intelligence
MH  - *Obstetrics
MH  - Awareness
MH  - Educational Status
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - cesarean delivery
OT  - chatbots
OT  - ethics
OT  - gynecology
OT  - home birth
OT  - informed consent
OT  - maternal-fetal medicine
OT  - obstetrics
OT  - oncology
OT  - preeclampsia
OT  - prematurity
OT  - preterm birth
OT  - progesterone
OT  - reproductive medicine
OT  - short cervix
OT  - vaginal progesterone
EDAT- 2023/03/17 06:00
MHDA- 2023/06/05 06:42
CRDT- 2023/03/16 20:33
PHST- 2023/02/10 00:00 [received]
PHST- 2023/03/02 00:00 [revised]
PHST- 2023/03/02 00:00 [accepted]
PHST- 2023/06/05 06:42 [medline]
PHST- 2023/03/17 06:00 [pubmed]
PHST- 2023/03/16 20:33 [entrez]
AID - S0002-9378(23)00154-0 [pii]
AID - 10.1016/j.ajog.2023.03.009 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol. 2023 Jun;228(6):696-705. doi: 10.1016/j.ajog.2023.03.009. 
      Epub 2023 Mar 15.

PMID- 37071282
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230615
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 7
DP  - 2023 Jul
TI  - Curious Questions About Covid-19 Pandemic with ChatGPT: Answers and 
      Recommendations.
PG  - 1371-1373
LID - 10.1007/s10439-023-03209-x [doi]
AB  - This article aims to answer frequently asked questions about the Covid-19 
      pandemic using ChatGPT and contribute to the spread of accurate information about 
      the pandemic. The article provides general information about the ways Covid-19 is 
      spread, symptoms, diagnosis, treatment, vaccines and pandemic management. It also 
      provides advice on infection control, vaccination campaigns and emergency 
      preparedness.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Tekinay, Osman Nuri
AU  - Tekinay ON
AUID- ORCID: 0000-0002-3668-2336
AD  - Department of Business Administration, Institute of Graduate Studies, Yalova 
      University, Yalova, Turkey. www.osmannuritekinay@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230418
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *COVID-19
MH  - Pandemics/prevention & control
MH  - Infection Control
PMC - PMC10112300
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Chatbots
OT  - Covid-19
COIS- No benefits in any form have been or will be received from a commercial party 
      related directly or indirectly to the subject of this manuscript. The author 
      declare no conflict of interest.
EDAT- 2023/04/19 06:00
MHDA- 2023/06/15 06:42
CRDT- 2023/04/18 11:23
PHST- 2023/04/11 00:00 [received]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/04/19 06:00 [pubmed]
PHST- 2023/04/18 11:23 [entrez]
AID - 10.1007/s10439-023-03209-x [pii]
AID - 3209 [pii]
AID - 10.1007/s10439-023-03209-x [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jul;51(7):1371-1373. doi: 10.1007/s10439-023-03209-x. Epub 
      2023 Apr 18.

PMID- 37356806
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231113
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 10
DP  - 2023 Oct
TI  - Evaluating GPT as an Adjunct for Radiologic Decision Making: GPT-4 Versus GPT-3.5 
      in a Breast Imaging Pilot.
PG  - 990-997
LID - S1546-1440(23)00394-0 [pii]
LID - 10.1016/j.jacr.2023.05.003 [doi]
AB  - OBJECTIVE: Despite rising popularity and performance, studies evaluating the use 
      of large language models for clinical decision support are lacking. Here, we 
      evaluate ChatGPT (Generative Pre-trained Transformer)-3.5 and GPT-4's (OpenAI, 
      San Francisco, California) capacity for clinical decision support in radiology 
      via the identification of appropriate imaging services for two important clinical 
      presentations: breast cancer screening and breast pain. METHODS: We compared 
      ChatGPT's responses to the ACR Appropriateness Criteria for breast pain and 
      breast cancer screening. Our prompt formats included an open-ended (OE) and a 
      select all that apply (SATA) format. Scoring criteria evaluated whether proposed 
      imaging modalities were in accordance with ACR guidelines. Three replicate 
      entries were conducted for each prompt, and the average of these was used to 
      determine final scores. RESULTS: Both ChatGPT-3.5 and ChatGPT-4 achieved an 
      average OE score of 1.830 (out of 2) for breast cancer screening prompts. 
      ChatGPT-3.5 achieved a SATA average percentage correct of 88.9%, compared with 
      ChatGPT-4's average percentage correct of 98.4% for breast cancer screening 
      prompts. For breast pain, ChatGPT-3.5 achieved an average OE score of 1.125 (out 
      of 2) and a SATA average percentage correct of 58.3%, as compared with an average 
      OE score of 1.666 (out of 2) and a SATA average percentage correct of 77.7%. 
      DISCUSSION: Our results demonstrate the eventual feasibility of using large 
      language models like ChatGPT for radiologic decision making, with the potential 
      to improve clinical workflow and responsible use of radiology services. More use 
      cases and greater accuracy are necessary to evaluate and implement such tools.
CI  - Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All 
      rights reserved.
FAU - Rao, Arya
AU  - Rao A
AD  - Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in 
      Healthcare, Innovation in Operations Research Center, Massachusetts General 
      Hospital, Boston, Massachusetts.
FAU - Kim, John
AU  - Kim J
AD  - Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in 
      Healthcare, Innovation in Operations Research Center, Massachusetts General 
      Hospital, Boston, Massachusetts.
FAU - Kamineni, Meghana
AU  - Kamineni M
AD  - Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in 
      Healthcare, Innovation in Operations Research Center, Massachusetts General 
      Hospital, Boston, Massachusetts.
FAU - Pang, Michael
AU  - Pang M
AD  - Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in 
      Healthcare, Innovation in Operations Research Center, Massachusetts General 
      Hospital, Boston, Massachusetts.
FAU - Lie, Winston
AU  - Lie W
AD  - Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in 
      Healthcare, Innovation in Operations Research Center, Massachusetts General 
      Hospital, Boston, Massachusetts.
FAU - Dreyer, Keith J
AU  - Dreyer KJ
AD  - Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in 
      Healthcare, Innovation in Operations Research Center, Massachusetts General 
      Hospital, Boston, Massachusetts; Department of Radiology, Massachusetts General 
      Hospital, Boston, Massachusetts; and Chief Data Science Officer and Chief Imaging 
      Information Officer for Mass General Brigham, Boston, Massachusetts.
FAU - Succi, Marc D
AU  - Succi MD
AD  - Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in 
      Healthcare, Innovation in Operations Research Center and Associate Chair of 
      Innovation & Commercialization, Mass General Brigham Enterprise Radiology; 
      Executive Director, MESH Incubator. Massachusetts General Hospital, Boston, 
      Massachusetts; and Department of Radiology, Massachusetts General Hospital, 
      Boston, Massachusetts. Electronic address: msucci@mgh.harvard.edu.
LA  - eng
GR  - T32 GM007753/GM/NIGMS NIH HHS/United States
GR  - T32 GM144273/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20230621
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
RN  - 76931-93-6 (N-hydroxysuccinimide S-acetylthioacetate)
SB  - IM
UOF - medRxiv. 2023 Feb 07;:. PMID: 36798292
MH  - Humans
MH  - Female
MH  - *Mastodynia
MH  - *Radiology
MH  - *Breast Neoplasms/diagnostic imaging
MH  - Decision Making
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - breast imaging
OT  - clinical decision making
OT  - clinical decision support
EDAT- 2023/06/26 00:41
MHDA- 2023/11/03 06:44
CRDT- 2023/06/25 19:20
PHST- 2023/04/26 00:00 [received]
PHST- 2023/05/16 00:00 [revised]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/11/03 06:44 [medline]
PHST- 2023/06/26 00:41 [pubmed]
PHST- 2023/06/25 19:20 [entrez]
AID - S1546-1440(23)00394-0 [pii]
AID - 10.1016/j.jacr.2023.05.003 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Oct;20(10):990-997. doi: 10.1016/j.jacr.2023.05.003. Epub 
      2023 Jun 21.

PMID- 37158147
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20230918
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 10
DP  - 2023 Sep 14
TI  - Evaluating Chatbot Efficacy for Answering Frequently Asked Questions in Plastic 
      Surgery: A ChatGPT Case Study Focused on Breast Augmentation.
PG  - 1126-1135
LID - 10.1093/asj/sjad140 [doi]
AB  - BACKGROUND: The integration of artificial intelligence (AI) and machine learning 
      (ML) technologies into healthcare is transforming patient-practitioner 
      interaction and could offer an additional platform for patient education and 
      support. OBJECTIVES: This study investigated whether ChatGPT-4 could provide safe 
      and up-to-date medical information about breast augmentation that is comparable 
      to other patient information sources. METHODS: ChatGPT-4 was asked to generate 6 
      commonly asked questions regarding breast augmentation and respond to them. Its 
      responses were qualitatively evaluated by a panel of specialist plastic and 
      reconstructive surgeons and reconciled with a literature search of 2 large 
      medical databases for accuracy, informativeness, and accessibility. RESULTS: 
      ChatGPT-4 provided well-structured, grammatically accurate, and comprehensive 
      responses to the questions posed; however, it was limited in providing 
      personalized advice and sometimes generated inappropriate or outdated references. 
      ChatGPT consistently encouraged engagement with a specialist for specific 
      information. CONCLUSIONS: Although ChatGPT-4 showed promise as an adjunct tool in 
      patient education regarding breast augmentation, there are areas requiring 
      improvement. Additional advancements and software engineering are needed to 
      enhance the reliability and applicability of AI-driven chatbots in patient 
      education and support systems.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of The 
      Aesthetic Society. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
FAU - Cox, Aram
AU  - Cox A
FAU - Xie, Yi
AU  - Xie Y
FAU - Bulloch, Gabriella
AU  - Bulloch G
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
FAU - Rozen, Warren M
AU  - Rozen WM
FAU - Ross, Richard J
AU  - Ross RJ
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CIN - Aesthet Surg J. 2023 Sep 14;43(10):1136-1138. PMID: 37287210
MH  - Humans
MH  - *Surgery, Plastic
MH  - Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Mammaplasty
MH  - Software
EDAT- 2023/05/09 06:42
MHDA- 2023/09/18 12:42
CRDT- 2023/05/09 05:33
PHST- 2023/09/18 12:42 [medline]
PHST- 2023/05/09 06:42 [pubmed]
PHST- 2023/05/09 05:33 [entrez]
AID - 7157259 [pii]
AID - 10.1093/asj/sjad140 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Sep 14;43(10):1126-1135. doi: 10.1093/asj/sjad140.

PMID- 37891083
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1535-6302 (Electronic)
IS  - 0363-0188 (Linking)
DP  - 2023 Oct 20
TI  - Analysis of ChatGPT publications in radiology: Literature so far.
LID - S0363-0188(23)00166-4 [pii]
LID - 10.1067/j.cpradiol.2023.10.013 [doi]
AB  - OBJECTIVE: To perform a detailed qualitative and quantitative analysis of the 
      published literature on ChatGPT and radiology in the nine months since its public 
      release, detailing the scope of the work in the short timeframe. METHODS: A 
      systematic literature search was carried out of the MEDLINE, EMBASE databases 
      through August 15, 2023 for articles that were focused on ChatGPT and 
      imaging/radiology. Articles were classified into original research and 
      reviews/perspectives. Quantitative analysis was carried out by two experienced 
      radiologists using objective scoring systems for evaluating original and 
      non-original research. RESULTS: 51 articles were published involving ChatGPT and 
      radiology/imaging dating from 26 Jan 2023 to the last article published on 14 Aug 
      2023. 23 articles were original research while the rest included 
      reviews/perspectives or brief communications. For quantitative analysis scored by 
      two readers, we included 23 original research and 17 non-original research 
      articles (after excluding 11 letters as responses to previous articles). Mean 
      score for original research was 3.20 out of 5 (across five questions), while mean 
      score for non-original research was 1.17 out of 2 (across six questions). Mean 
      score grading performance of ChatGPT in original research was 3.20 out of five 
      (across two questions). DISCUSSION: While it is early days for ChatGPT and its 
      impact in radiology, there has already been a plethora of articles talking about 
      the multifaceted nature of the tool and how it can impact every aspect of 
      radiology from patient education, pre-authorization, protocol selection, 
      generating differentials, to structuring radiology reports. Most articles show 
      impressive performance of ChatGPT which can only improve with more research and 
      improvements in the tool itself. There have also been several articles which have 
      highlighted the limitations of ChatGPT in its current iteration, which will allow 
      radiologists and researchers to improve these areas.
CI  - Copyright © 2023. Published by Elsevier Inc.
FAU - Bera, Kaustav
AU  - Bera K
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 11000 
      Euclid Avenue, Cleveland, OH, 44106, USA. Electronic address: kxb413@case.edu.
FAU - O'Connor, Gregory
AU  - O'Connor G
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 11000 
      Euclid Avenue, Cleveland, OH, 44106, USA.
FAU - Jiang, Sirui
AU  - Jiang S
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 11000 
      Euclid Avenue, Cleveland, OH, 44106, USA.
FAU - Tirumani, Sree Harsha
AU  - Tirumani SH
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 11000 
      Euclid Avenue, Cleveland, OH, 44106, USA.
FAU - Ramaiya, Nikhil
AU  - Ramaiya N
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 11000 
      Euclid Avenue, Cleveland, OH, 44106, USA.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - United States
TA  - Curr Probl Diagn Radiol
JT  - Current problems in diagnostic radiology
JID - 7607123
SB  - IM
EDAT- 2023/10/28 11:42
MHDA- 2023/10/28 11:42
CRDT- 2023/10/27 22:57
PHST- 2023/09/19 00:00 [received]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/10/28 11:42 [medline]
PHST- 2023/10/28 11:42 [pubmed]
PHST- 2023/10/27 22:57 [entrez]
AID - S0363-0188(23)00166-4 [pii]
AID - 10.1067/j.cpradiol.2023.10.013 [doi]
PST - aheadofprint
SO  - Curr Probl Diagn Radiol. 2023 Oct 20:S0363-0188(23)00166-4. doi: 
      10.1067/j.cpradiol.2023.10.013.

PMID- 37969495
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231117
IS  - 2666-6383 (Electronic)
IS  - 2666-6383 (Linking)
VI  - 7
IP  - 6
DP  - 2023 Nov
TI  - Breaking barriers: can ChatGPT compete with a shoulder and elbow specialist in 
      diagnosis and management?
PG  - 2534-2541
LID - 10.1016/j.jseint.2023.07.018 [doi]
AB  - BACKGROUND: ChatGPT is an artificial intelligence (AI) language processing model 
      that uses deep learning to generate human-like responses to natural language 
      inputs. Its potential use in health care has raised questions and several studies 
      have assessed its effectiveness in writing articles, clinical reasoning, and 
      solving complex questions. This study aims to investigate ChatGPT's capabilities 
      and implications in diagnosing and managing patients with new shoulder and elbow 
      complaints in a private clinical setting to provide insights into its potential 
      use as a diagnostic tool for patients and a first consultation resource for 
      primary physicians. METHODS: In a private clinical setting, patients were 
      assessed by ChatGPT after being seen by a shoulder and elbow specialist for 
      shoulder and elbow symptoms. To be assessed by the AI model, a research fellow 
      filled out a standardized form (including age, gender, major comorbidities, 
      symptoms and the localization, natural history, and duration, any associated 
      symptoms or movement deficit, aggravating/relieving factors, and x-ray/imaging 
      report if present). This form was submitted through the ChatGPT portal and the AI 
      model was asked for a diagnosis and best management modality. RESULTS: A total of 
      29 patients with 15 males and 14 females, were included in this study. The AI 
      model was able to correctly choose the diagnosis and management in 93% (27/29) 
      and 83% (24/29) of the patients, respectively. Furthermore, of the remaining 24 
      patients that were managed correctly, ChatGPT did not specify the appropriate 
      management in 6 patients and chose only one management in 5 patients, where both 
      were applicable and dependent on the patient's choice. Therefore, 55% of 
      ChatGPT's management was poor. CONCLUSION: ChatGPT made a worthy opponent; 
      however, it will not be able to replace in its current form a shoulder and elbow 
      specialist in diagnosing and treating patients for many reasons such as 
      misdiagnosis, poor management, lack of empathy and interactions with patients, 
      its dependence on magnetic resonance imaging reports, and its lack of new 
      knowledge.
CI  - © 2023 The Authors.
FAU - Daher, Mohammad
AU  - Daher M
AD  - Hotel Dieu de France, Beirut, Lebanon.
FAU - Koa, Jonathan
AU  - Koa J
AD  - Rothman Institute/Thomas Jefferson Medical Center, Philadelphia, PA, USA.
FAU - Boufadel, Peter
AU  - Boufadel P
AD  - Rothman Institute/Thomas Jefferson Medical Center, Philadelphia, PA, USA.
FAU - Singh, Jaspal
AU  - Singh J
AD  - Rothman Institute/Thomas Jefferson Medical Center, Philadelphia, PA, USA.
FAU - Fares, Mohamad Y
AU  - Fares MY
AD  - Rothman Institute/Thomas Jefferson Medical Center, Philadelphia, PA, USA.
FAU - Abboud, Joseph A
AU  - Abboud JA
AD  - Rothman Institute/Thomas Jefferson Medical Center, Philadelphia, PA, USA.
LA  - eng
PT  - Journal Article
DEP - 20230904
PL  - United States
TA  - JSES Int
JT  - JSES international
JID - 101763461
PMC - PMC10638599
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Diagnosis and management
OT  - Empathy
OT  - Orthopedics
OT  - Shoulder and elbow surgery
EDAT- 2023/11/16 06:45
MHDA- 2023/11/16 06:46
CRDT- 2023/11/16 04:22
PHST- 2023/11/16 06:46 [medline]
PHST- 2023/11/16 06:45 [pubmed]
PHST- 2023/11/16 04:22 [entrez]
AID - S2666-6383(23)00212-8 [pii]
AID - 10.1016/j.jseint.2023.07.018 [doi]
PST - epublish
SO  - JSES Int. 2023 Sep 4;7(6):2534-2541. doi: 10.1016/j.jseint.2023.07.018. 
      eCollection 2023 Nov.

PMID- 37122982
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230502
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Enhancing Expert Panel Discussions in Pediatric Palliative Care: Innovative 
      Scenario Development and Summarization With ChatGPT-4.
PG  - e38249
LID - 10.7759/cureus.38249 [doi]
LID - e38249
AB  - This study presents a novel approach to enhance expert panel discussions in a 
      medical conference through the use of ChatGPT-4 (Generative Pre-trained 
      Transformer version 4), a recently launched powerful artificial intelligence (AI) 
      language model. We report on ChatGPT-4's ability to optimize and summarize the 
      medical conference panel recommendations of the first Pan-Arab Pediatric 
      Palliative Critical Care Hybrid Conference, held in Riyadh, Saudi 
      Arabia. ChatGPT-4 was incorporated into the discussions in two sequential phases: 
      first, scenarios were optimized by the AI model to stimulate in-depth 
      conversations; second, the model identified, summarized, and contrasted key 
      themes from the panel and audience discussions. The results suggest that 
      ChatGPT-4 effectively facilitated complex do-not-resuscitate (DNR) conflict 
      resolution by summarizing key themes such as effective communication, 
      collaboration, patient and family-centered care, trust, and ethical 
      considerations. The inclusion of ChatGPT-4 in pediatric palliative care panel 
      discussions demonstrated potential benefits for enhancing critical thinking among 
      medical professionals. Further research is warranted to validate and broaden 
      these insights across various settings and cultures.
CI  - Copyright © 2023, Almazyad et al.
FAU - Almazyad, Mohammed
AU  - Almazyad M
AD  - Pediatric Intensive Care Unit, Pediatric Department, College of Medicine, King 
      Saud University, Riyadh, SAU.
FAU - Aljofan, Fahad
AU  - Aljofan F
AD  - Pediatric Intensive Care Unit, Pediatric Department, King Faisal Specialist 
      Hospital & Research Centre, Riyadh, SAU.
FAU - Abouammoh, Noura A
AU  - Abouammoh NA
AD  - Department of Family and Community Medicine, College of Medicine, King Saud 
      University, Riyadh, SAU.
FAU - Muaygil, Ruaim
AU  - Muaygil R
AD  - Medical Education Department, College of Medicine, King Saud University, Riyadh, 
      SAU.
FAU - Malki, Khalid H
AU  - Malki KH
AD  - Department of Otolaryngology, College of Medicine, King Saud University, Riyadh, 
      SAU.
FAU - Aljamaan, Fadi
AU  - Aljamaan F
AD  - Critical Care Department, College of Medicine, King Saud University, Riyadh, SAU.
FAU - Alturki, Abdullah
AU  - Alturki A
AD  - Pediatric Intensive Care Unit, Pediatric Department, King Faisal Specialist 
      Hospital & Research Centre, Riyadh, SAU.
FAU - Alayed, Tareq
AU  - Alayed T
AD  - Pediatric Critical Care Medicine, King Faisal Specialist Hospital & Research 
      Centre, Riyadh, SAU.
FAU - Alshehri, Saleh S
AU  - Alshehri SS
AD  - Pediatric Intensive Care Unit, King Saud Medical City, Riyadh, SAU.
FAU - Alrbiaan, Abdullah
AU  - Alrbiaan A
AD  - Critical Care Department, King Faisal Specialist Hospital & Research Centre, 
      Riyadh, SAU.
FAU - Alsatrawi, Mohammed
AU  - Alsatrawi M
AD  - Pediatric Critical Care Medicine, King Saud University Medical City, Riyadh, SAU.
FAU - Temsah, Hazar A
AU  - Temsah HA
AD  - Biomedical Engineering Department, Faculty of Electrical and Computer 
      Engineering, Beirut Arab University, Beirut, LBN.
FAU - Alsohime, Fahad
AU  - Alsohime F
AD  - Pediatric Critical Care Department, King Saud University, Riyadh, SAU.
FAU - Alhaboob, Ali A
AU  - Alhaboob AA
AD  - Department of Pediatrics, King Saud University, Riyadh, SAU.
FAU - Alabdulhafid, Majed
AU  - Alabdulhafid M
AD  - Pediatric Intensive Care Unit, Pediatric Department, College of Medicine, King 
      Saud University, Riyadh, SAU.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Department of Family and Community Medicine, King Saud University, Riyadh, SAU.
FAU - Alhasan, Khalid
AU  - Alhasan K
AD  - Department of Pediatric Nephrology, King Saud University, Riyadh, SAU.
FAU - Al-Eyadhy, Ayman
AU  - Al-Eyadhy A
AD  - Pediatric Intensive Care Unit, Pediatric Department, College of Medicine, King 
      Saud University, Riyadh, SAU.
AD  - Pediatric Intensive Care Unit, King Saud University Medical City, Riyadh, SAU.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Intensive Care Unit, Pediatric Department, King Saud University Medical 
      City, Riyadh, SAU.
AD  - Pediatric Intensive Care Unit, Pediatric Department, College of Medicine, King 
      Saud University, Riyadh, SAU.
LA  - eng
PT  - Journal Article
DEP - 20230428
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10143975
OTO - NOTNLM
OT  - artificial intelligence chatgpt-4
OT  - chat generative pre-trained transformer
OT  - do-not-resuscitate (dnr) conflicts discussion
OT  - ethical considerations
OT  - expert panel discussions
OT  - human-ai scenario development
OT  - medical conferences
OT  - patient and family-centered care
OT  - pediatric palliative critical care
OT  - picu multidisciplinary approach
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/01 06:42
MHDA- 2023/05/01 06:43
CRDT- 2023/05/01 03:25
PHST- 2023/04/28 00:00 [accepted]
PHST- 2023/05/01 06:43 [medline]
PHST- 2023/05/01 06:42 [pubmed]
PHST- 2023/05/01 03:25 [entrez]
AID - 10.7759/cureus.38249 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 28;15(4):e38249. doi: 10.7759/cureus.38249. eCollection 2023 
      Apr.

PMID- 37898341
OWN - NLM
STAT- Publisher
LR  - 20231028
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
DP  - 2023 Oct 26
TI  - Analysis of ChatGPT generated differential diagnoses in response to physical exam 
      findings for benign and malignant cutaneous neoplasms.
LID - S0190-9622(23)03044-X [pii]
LID - 10.1016/j.jaad.2023.10.040 [doi]
FAU - Rundle, Chandler W
AU  - Rundle CW
AD  - Department of Dermatology, Duke University Hospital, Durham, NC.
FAU - Szeto, Mindy D
AU  - Szeto MD
AD  - Department of Dermatology, University of Colorado Anschutz Medical Campus, 
      Aurora, CO.
FAU - Presley, Colby L
AU  - Presley CL
AD  - Division of Dermatology, Lehigh Valley Network, Allentown, PA.
FAU - Shahwan, Kathryn T
AU  - Shahwan KT
AD  - Department of Dermatology, The Ohio State University Medical Center, Columbus, 
      OH; Department of Internal Medicine, University of North Dakota Medical School, 
      Grand Forks, ND; Department of Dermatology, Altru Health System, Grand Forks, ND. 
      Electronic address: kathryn.shahwan@osumc.edu.
FAU - Carr, David R
AU  - Carr DR
AD  - Department of Dermatology, The Ohio State University Medical Center, Columbus, 
      OH.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - cutaneous oncology
OT  - large language models
OT  - machine learning
OT  - neoplastic dermatology
EDAT- 2023/10/29 06:46
MHDA- 2023/10/29 06:46
CRDT- 2023/10/28 19:29
PHST- 2023/09/10 00:00 [received]
PHST- 2023/10/08 00:00 [revised]
PHST- 2023/10/21 00:00 [accepted]
PHST- 2023/10/29 06:46 [medline]
PHST- 2023/10/29 06:46 [pubmed]
PHST- 2023/10/28 19:29 [entrez]
AID - S0190-9622(23)03044-X [pii]
AID - 10.1016/j.jaad.2023.10.040 [doi]
PST - aheadofprint
SO  - J Am Acad Dermatol. 2023 Oct 26:S0190-9622(23)03044-X. doi: 
      10.1016/j.jaad.2023.10.040.

PMID- 37294147
OWN - NLM
STAT- MEDLINE
DCOM- 20230811
LR  - 20230811
IS  - 1728-7731 (Electronic)
IS  - 1726-4901 (Linking)
VI  - 86
IP  - 8
DP  - 2023 Aug 1
TI  - ChatGPT failed Taiwan's Family Medicine Board Exam.
PG  - 762-766
LID - 10.1097/JCMA.0000000000000946 [doi]
AB  - BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT), OpenAI Limited 
      Partnership, San Francisco, CA, USA is an artificial intelligence language model 
      gaining popularity because of its large database and ability to interpret and 
      respond to various queries. Although it has been tested by researchers in 
      different fields, its performance varies depending on the domain. We aimed to 
      further test its ability in the medical field. METHODS: We used questions from 
      Taiwan's 2022 Family Medicine Board Exam, which combined both Chinese and English 
      and covered various question types, including reverse questions and 
      multiple-choice questions, and mainly focused on general medical knowledge. We 
      pasted each question into ChatGPT and recorded its response, comparing it to the 
      correct answer provided by the exam board. We used SAS 9.4 (Cary, North Carolina, 
      USA) and Excel to calculate the accuracy rates for each question type. RESULTS: 
      ChatGPT answered 52 questions out of 125 correctly, with an accuracy rate of 
      41.6%. The questions' length did not affect the accuracy rates. These were 45.5%, 
      33.3%, 58.3%, 50.0%, and 43.5% for negative-phrase questions, multiple-choice 
      questions, mutually exclusive options, case scenario questions, and Taiwan's 
      local policy-related questions, with no statistical difference observed. 
      CONCLUSION: ChatGPT's accuracy rate was not good enough for Taiwan's Family 
      Medicine Board Exam. Possible reasons include the difficulty level of the 
      specialist exam and the relatively weak database of traditional Chinese language 
      resources. However, ChatGPT performed acceptably in negative-phrase questions, 
      mutually exclusive questions, and case scenario questions, and it can be a 
      helpful tool for learning and exam preparation. Future research can explore ways 
      to improve ChatGPT's accuracy rate for specialized exams and other domains.
CI  - Copyright © 2023, the Chinese Medical Association.
FAU - Weng, Tzu-Ling
AU  - Weng TL
AD  - Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, 
      Taiwan, ROC.
AD  - Institute of Public Health, School of Medicine, National Yang Ming Chiao Tung 
      University, Taipei, Taiwan, ROC.
FAU - Wang, Ying-Mei
AU  - Wang YM
AD  - Department of Medical Education and Research, Taipei Veterans General Hospital 
      Hsinchu Branch, Hsinchu, Taiwan, ROC.
AD  - Department of Pharmacy, Taipei Veterans General Hospital Hsinchu Branch, Hsinchu, 
      Taiwan, ROC.
AD  - School of Medicine, National Tsing Hua University, Hsinchu, Taiwan, ROC.
FAU - Chang, Samuel
AU  - Chang S
AD  - School of Medicine, Taipei Medical University, Taipei, Taiwan, ROC.
FAU - Chen, Tzeng-Ji
AU  - Chen TJ
AD  - Department of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, 
      Hsinchu, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, 
      ROC.
AD  - Department of Post-Baccalaureate Medicine, National Chung Hsing University, 
      Taichung, Taiwan, ROC.
FAU - Hwang, Shinn-Jang
AU  - Hwang SJ
AD  - Department of Family Medicine, En Chu Kong Hospital, New Taipei City, Taiwan, 
      ROC.
AD  - Department of Family Medicine, National Yang Ming Chiao Tung University, School 
      of Medicine, Taipei, Taiwan, ROC.
LA  - eng
PT  - Journal Article
DEP - 20230609
PL  - Netherlands
TA  - J Chin Med Assoc
JT  - Journal of the Chinese Medical Association : JCMA
JID - 101174817
SB  - IM
CIN - J Chin Med Assoc. 2023 Aug 1;86(8):775. PMID: 37314310
MH  - *Artificial Intelligence
MH  - Databases, Factual
MH  - *Family Practice
MH  - Taiwan
MH  - *Academic Performance
COIS- Conflicts of interest: Dr. Tzeng-Ji Chen and Dr. Shinn-Jang Hwang, editorial 
      board members at the Journal of the Chinese Medical Association , have no roles 
      in the peer review process or decision to publish this article. The other authors 
      declare that they have no conflicts of interest related to the subject matter or 
      materials discussed in this article.
EDAT- 2023/06/09 13:42
MHDA- 2023/08/11 06:42
CRDT- 2023/06/09 09:33
PHST- 2023/08/11 06:42 [medline]
PHST- 2023/06/09 13:42 [pubmed]
PHST- 2023/06/09 09:33 [entrez]
AID - 02118582-990000000-00224 [pii]
AID - 10.1097/JCMA.0000000000000946 [doi]
PST - ppublish
SO  - J Chin Med Assoc. 2023 Aug 1;86(8):762-766. doi: 10.1097/JCMA.0000000000000946. 
      Epub 2023 Jun 9.

PMID- 37227901
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20231127
IS  - 1728-7731 (Electronic)
IS  - 1726-4901 (Linking)
VI  - 86
IP  - 7
DP  - 2023 Jul 1
TI  - Performance of ChatGPT on the pharmacist licensing examination in Taiwan.
PG  - 653-658
LID - 10.1097/JCMA.0000000000000942 [doi]
AB  - BACKGROUND: ChatGPT is an artificial intelligence model trained for 
      conversations. ChatGPT has been widely applied in general medical education and 
      cardiology, but its application in pharmacy has been lacking. This study examined 
      the accuracy of ChatGPT on the Taiwanese Pharmacist Licensing Examination and 
      investigated its potential role in pharmacy education. METHODS: ChatGPT was used 
      on the first Taiwanese Pharmacist Licensing Examination in 2023 in Mandarin and 
      English. The questions were entered manually one by one. Graphical questions, 
      chemical formulae, and tables were excluded. Textual questions were scored 
      according to the number of correct answers. Chart question scores were determined 
      by multiplying the number and the correct rate of text questions. This study was 
      conducted from March 5 to March 10, 2023, by using ChatGPT 3.5. RESULTS: The 
      correct rate of ChatGPT in Chinese and English questions was 54.4% and 56.9% in 
      the first stage, and 53.8% and 67.6% in the second stage. On the Chinese test, 
      only pharmacology and pharmacochemistry sections received passing scores. The 
      English test scores were higher than the Chinese test scores across all subjects 
      and were significantly higher in dispensing pharmacy and clinical pharmacy as 
      well as therapeutics. CONCLUSION: ChatGPT 3.5 failed the Taiwanese Pharmacist 
      Licensing Examination. Although it is not able to pass the examination, it can be 
      improved quickly through deep learning. It reminds us that we should not only use 
      multiple-choice questions to assess a pharmacist's ability, but also use more 
      variety of evaluations in the future. Pharmacy education should be changed in 
      line with the examination, and students must be able to use AI technology for 
      self-learning. More importantly, we need to help students develop humanistic 
      qualities and strengthen their ability to interact with patients, so that they 
      can become warm-hearted healthcare professionals.
CI  - Copyright © 2023, the Chinese Medical Association.
FAU - Wang, Ying-Mei
AU  - Wang YM
AD  - Department of Medical Education and Research, Taipei Veterans General Hospital 
      Hsinchu Branch, Hsinchu, Taiwan, ROC.
AD  - Department of Pharmacy, Taipei Veterans General Hospital Hsinchu Branch, Hsinchu, 
      Taiwan, ROC.
AD  - School of Medicine, National Tsing Hua University, Hsinchu, Taiwan, ROC.
FAU - Shen, Hung-Wei
AU  - Shen HW
AD  - Department of Pharmacy, Taipei Veterans General Hospital Hsinchu Branch, Hsinchu, 
      Taiwan, ROC.
FAU - Chen, Tzeng-Ji
AU  - Chen TJ
AD  - Department of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, 
      Hsinchu, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, 
      ROC.
AD  - Department of Post-Baccalaureate Medicine, National Chung Hsing University, 
      Taichung, Taiwan, ROC.
LA  - eng
PT  - Journal Article
DEP - 20230705
PL  - Netherlands
TA  - J Chin Med Assoc
JT  - Journal of the Chinese Medical Association : JCMA
JID - 101174817
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Asian People
MH  - Health Personnel
MH  - Pharmacists/standards
MH  - Taiwan
MH  - *Pharmacy/standards
MH  - *Education, Pharmacy/methods
MH  - *Licensure
COIS- Conflicts of interest: Dr. Tzeng-Ji Chen, an editorial board member at Journal of 
      the Chinese Medical Association, had no role in the peer review process of or 
      decision to publish this article. The other authors declare that they have no 
      conflicts of interest related to the subject matter or materials discussed in 
      this article.
EDAT- 2023/05/25 19:12
MHDA- 2023/07/17 06:42
CRDT- 2023/05/25 12:45
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/05/25 19:12 [pubmed]
PHST- 2023/05/25 12:45 [entrez]
AID - 02118582-202307000-00007 [pii]
AID - 10.1097/JCMA.0000000000000942 [doi]
PST - ppublish
SO  - J Chin Med Assoc. 2023 Jul 1;86(7):653-658. doi: 10.1097/JCMA.0000000000000942. 
      Epub 2023 Jul 5.

PMID- 37833847
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1460-2350 (Electronic)
IS  - 0268-1161 (Linking)
VI  - 38
IP  - 12
DP  - 2023 Dec 4
TI  - AI language models in human reproduction research: exploring ChatGPT's potential 
      to assist academic writing.
PG  - 2281-2288
LID - 10.1093/humrep/dead207 [doi]
AB  - Artificial intelligence (AI)-driven language models have the potential to serve 
      as an educational tool, facilitate clinical decision-making, and support research 
      and academic writing. The benefits of their use are yet to be evaluated and 
      concerns have been raised regarding the accuracy, transparency, and ethical 
      implications of using this AI technology in academic publishing. At the moment, 
      Chat Generative Pre-trained Transformer (ChatGPT) is one of the most powerful and 
      widely debated AI language models. Here, we discuss its feasibility to answer 
      scientific questions, identify relevant literature, and assist writing in the 
      field of human reproduction. With consideration of the scarcity of data on this 
      topic, we assessed the feasibility of ChatGPT in academic writing, using data 
      from six meta-analyses published in a leading journal of human reproduction. The 
      text generated by ChatGPT was evaluated and compared to the original text by 
      blinded reviewers. While ChatGPT can produce high-quality text and summarize 
      information efficiently, its current ability to interpret data and answer 
      scientific questions is limited, and it cannot be relied upon for a literature 
      search or accurate source citation due to the potential spread of incomplete or 
      false information. We advocate for open discussions within the reproductive 
      medicine research community to explore the advantages and disadvantages of 
      implementing this AI technology. Researchers and reviewers should be informed 
      about AI language models, and we encourage authors to transparently disclose 
      their use.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of European 
      Society of Human Reproduction and Embryology. All rights reserved. For 
      permissions, please email: journals.permissions@oup.com.
FAU - Semrl, N
AU  - Semrl N
AUID- ORCID: 0000-0003-0199-605X
AD  - Department of Obstetrics and Gynecology, Medical University of Graz, Graz, 
      Austria.
FAU - Feigl, S
AU  - Feigl S
AD  - Department of Obstetrics and Gynecology, Medical University of Graz, Graz, 
      Austria.
FAU - Taumberger, N
AU  - Taumberger N
AUID- ORCID: 0000-0002-8969-7064
AD  - Department of Obstetrics and Gynecology, Medical University of Graz, Graz, 
      Austria.
FAU - Bracic, T
AU  - Bracic T
AD  - Department of Obstetrics and Gynecology, Medical University of Graz, Graz, 
      Austria.
FAU - Fluhr, H
AU  - Fluhr H
AD  - Department of Obstetrics and Gynecology, Medical University of Graz, Graz, 
      Austria.
FAU - Blockeel, C
AU  - Blockeel C
AD  - Centre for Reproductive Medicine, Universitair Ziekenhuis Brussel (UZ Brussel), 
      Brussels, Belgium.
FAU - Kollmann, M
AU  - Kollmann M
AUID- ORCID: 0000-0001-8528-4759
AD  - Department of Obstetrics and Gynecology, Medical University of Graz, Graz, 
      Austria.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Hum Reprod
JT  - Human reproduction (Oxford, England)
JID - 8701199
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Language
MH  - Clinical Decision-Making
MH  - Reproduction
MH  - Writing
OTO - NOTNLM
OT  - ChatGPT
OT  - academic writing
OT  - artificial intelligence
OT  - language models
OT  - reproductive medicine
EDAT- 2023/10/14 10:46
MHDA- 2023/12/05 12:42
CRDT- 2023/10/14 00:52
PHST- 2023/05/10 00:00 [received]
PHST- 2023/09/06 00:00 [revised]
PHST- 2023/12/05 12:42 [medline]
PHST- 2023/10/14 10:46 [pubmed]
PHST- 2023/10/14 00:52 [entrez]
AID - 7313666 [pii]
AID - 10.1093/humrep/dead207 [doi]
PST - ppublish
SO  - Hum Reprod. 2023 Dec 4;38(12):2281-2288. doi: 10.1093/humrep/dead207.

PMID- 37315798
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20230920
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
VI  - 89
IP  - 4
DP  - 2023 Oct
TI  - ChatGPT for healthcare providers and patients: Practical implications within 
      dermatology.
PG  - 870-871
LID - S0190-9622(23)01106-4 [pii]
LID - 10.1016/j.jaad.2023.05.081 [doi]
FAU - Jin, Joy Q
AU  - Jin JQ
AD  - School of Medicine, University of California San Francisco, San Francisco, 
      California; Department of Dermatology, University of California San Francisco, 
      San Francisco, California. Electronic address: joy.jin@ucsf.edu.
FAU - Dobry, Allison S
AU  - Dobry AS
AD  - Department of Dermatology, University of California San Francisco, San Francisco, 
      California.
LA  - eng
PT  - Journal Article
DEP - 20230612
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
MH  - Humans
MH  - *Dermatology
MH  - Health Personnel
MH  - Patients
OTO - NOTNLM
OT  - ChatGPT
OT  - administrative support
OT  - artificial intelligence
OT  - clinical decision-making
OT  - clinical research
OT  - dermatology
OT  - health literacy
OT  - healthcare providers
OT  - innovation
OT  - large language models
OT  - medical education
OT  - patients
OT  - technology
COIS- Conflicts of interest None disclosed.
EDAT- 2023/06/15 01:08
MHDA- 2023/09/20 06:42
CRDT- 2023/06/14 19:16
PHST- 2023/03/02 00:00 [received]
PHST- 2023/05/09 00:00 [revised]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/06/15 01:08 [pubmed]
PHST- 2023/06/14 19:16 [entrez]
AID - S0190-9622(23)01106-4 [pii]
AID - 10.1016/j.jaad.2023.05.081 [doi]
PST - ppublish
SO  - J Am Acad Dermatol. 2023 Oct;89(4):870-871. doi: 10.1016/j.jaad.2023.05.081. Epub 
      2023 Jun 12.

PMID- 37829968
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT Conquers the Saudi Medical Licensing Exam: Exploring the Accuracy of 
      Artificial Intelligence in Medical Knowledge Assessment and Implications for 
      Modern Medical Education.
PG  - e45043
LID - 10.7759/cureus.45043 [doi]
LID - e45043
AB  - Background The application of artificial intelligence (AI) in education is 
      undergoing rapid advancements, with models such as ChatGPT-4 showing potential in 
      medical education. This study aims to evaluate the proficiency of ChatGPT-4 in 
      answering Saudi Medical Licensing Exam (SMLE) questions. Methodology A dataset of 
      220 questions across four medical disciplines was used. The model was trained 
      using a specific code to answer the questions accurately, and its performance was 
      assessed using key performance indicators, difficulty level, and exam sections. 
      Results ChatGPT-4 demonstrated an overall accuracy of 88.6%. It showed high 
      proficiency with Easy and Average questions, but accuracy decreased for Hard 
      questions. Performance was consistent across all disciplines, indicating a broad 
      knowledge base. However, an error analysis revealed areas for further refinement, 
      particularly with category (Option) A questions across all sections. Conclusions 
      This study underscores the potential of ChatGPT-4 as an AI-assisted tool in 
      medical education, demonstrating high proficiency in answering SMLE questions. 
      Future research is recommended to expand the scope of training and evaluation as 
      well as to enhance the model's performance on complex clinical questions.
CI  - Copyright © 2023, Aljindan et al.
FAU - Aljindan, Fahad K
AU  - Aljindan FK
AD  - Department of Plastic Surgery, King Abdullah Medical City, Makkah, SAU.
FAU - Al Qurashi, Abdullah A
AU  - Al Qurashi AA
AD  - College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, 
      Jeddah, SAU.
FAU - Albalawi, Ibrahim Abdullah S
AU  - Albalawi IAS
AD  - College of Medicine, Tabuk University for Health Sciences, Tabuk, SAU.
FAU - Alanazi, Abeer Mohammed M
AU  - Alanazi AMM
AD  - Department of Pediatrics, Faculty of Medicine, University of Tabuk, Tabuk, SAU.
FAU - Aljuhani, Hussam Abdulkhaliq M
AU  - Aljuhani HAM
AD  - Faculty of Medicine, Ibn Sina National College, Jeddah, SAU.
FAU - Falah Almutairi, Faisal
AU  - Falah Almutairi F
AD  - College of Medicine, Unaizah College of Medicine and Medical Sciences, Qassim 
      University, Unaizah, SAU.
FAU - Aldamigh, Omar A
AU  - Aldamigh OA
AD  - College of Medicine, King Faisal University, Dammam, SAU.
FAU - Halawani, Ibrahim R
AU  - Halawani IR
AD  - Faculty of Medicine, King Abdulaziz University, Jeddah, SAU.
FAU - K Zino Alarki, Subhi M
AU  - K Zino Alarki SM
AD  - Department of Surgery, King Khalid University Hospital, Riyadh, SAU.
LA  - eng
PT  - Journal Article
DEP - 20230911
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10566535
OTO - NOTNLM
OT  - ai in healthcare
OT  - artificial intelligence
OT  - chatgpt-4
OT  - medical education
OT  - saudi medical licensing exam
OT  - smle
OT  - standardized medical exam
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/13 06:46
MHDA- 2023/10/13 06:47
CRDT- 2023/10/13 04:13
PHST- 2023/09/11 00:00 [accepted]
PHST- 2023/10/13 06:47 [medline]
PHST- 2023/10/13 06:46 [pubmed]
PHST- 2023/10/13 04:13 [entrez]
AID - 10.7759/cureus.45043 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 11;15(9):e45043. doi: 10.7759/cureus.45043. eCollection 2023 
      Sep.

PMID- 38050503
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231206
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT's Potential in Enhancing Physician Efficiency: A Japanese Case Study.
PG  - e48235
LID - 10.7759/cureus.48235 [doi]
LID - e48235
AB  - Artificial intelligence (AI), particularly ChatGPT, developed by OpenAI (San 
      Francisco, CA, USA), is making significant strides in the medical field. In a 
      simulated case study, a 66-year-old Japanese female patient's dialogue with a 
      physician was transcribed and inputted into ChatGPT to assess its efficacy in 
      drafting medical records, formulating differential diagnoses, and establishing 
      treatment plans. The results showed a high similarity between the medical 
      summaries generated by ChatGPT and those of the attending physician. This 
      suggests that ChatGPT has the potential to assist physicians in clinical 
      reasoning and reduce the administrative burden, allowing them to spend more time 
      with patients. However, there are limitations, such as the system's reliance on 
      linguistic data and occasional inaccuracies. Despite its potential, the ethical 
      implications of using patient data and the risk of AI replacing clinicians 
      emphasize the need for continuous evaluation, rigorous oversight, and the 
      establishment of comprehensive guidelines. As AI continues to integrate into 
      healthcare, it is crucial for physicians to ensure that technology complements, 
      rather than replaces, human expertise, with the primary focus remaining on 
      delivering high-quality patient care.
CI  - Copyright © 2023, Kaneda et al.
FAU - Kaneda, Yudai
AU  - Kaneda Y
AD  - Epidemiology and Public Health, School of Medicine, Hokkaido University, 
      Hokkaido, JPN.
FAU - Takita, Morihito
AU  - Takita M
AD  - Internal Medicine, Medical Governance Research Institute, Tokyo, JPN.
FAU - Hamaki, Tamae
AU  - Hamaki T
AD  - Internal Medicine, Accessible Rail Medical Services Tetsuikai, Navitas Clinic 
      Shinjuku, Tokyo, JPN.
FAU - Ozaki, Akihiko
AU  - Ozaki A
AD  - Breast and Thyroid Surgery, Jyoban Hospital of Tokiwa Foundation, Fukushima, JPN.
FAU - Tanimoto, Tetsuya
AU  - Tanimoto T
AD  - Internal Medicine, Accessible Rail Medical Services Tetsuikai, Navitas Clinic 
      Kawasaki, Kanagawa, JPN.
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10693924
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - chatgpt
OT  - differential diagnoses
OT  - japan
OT  - medical records
COIS- The authors have declared financial relationships, which are detailed in the next 
      section.
EDAT- 2023/12/05 12:42
MHDA- 2023/12/05 12:43
CRDT- 2023/12/05 03:47
PHST- 2023/11/03 00:00 [accepted]
PHST- 2023/12/05 12:43 [medline]
PHST- 2023/12/05 12:42 [pubmed]
PHST- 2023/12/05 03:47 [entrez]
AID - 10.7759/cureus.48235 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 3;15(11):e48235. doi: 10.7759/cureus.48235. eCollection 2023 
      Nov.

PMID- 37974429
OWN - NLM
STAT- Publisher
LR  - 20231117
IS  - 1724-6016 (Electronic)
IS  - 1120-6721 (Linking)
DP  - 2023 Nov 16
TI  - Leveraging ChatGPT for ophthalmic education: A critical appraisal.
PG  - 11206721231215862
LID - 10.1177/11206721231215862 [doi]
AB  - In recent years, the advent of artificial intelligence (AI) has transformed many 
      sectors, including medical education. This editorial critically appraises the 
      integration of ChatGPT, a state-of-the-art AI language model, into ophthalmic 
      education, focusing on its potential, limitations, and ethical considerations. 
      The application of ChatGPT in teaching and training ophthalmologists presents an 
      innovative method to offer real-time, customized learning experiences. Through a 
      systematic analysis of both experimental and clinical data, this editorial 
      examines how ChatGPT enhances engagement, understanding, and retention of complex 
      ophthalmological concepts. The study also evaluates the efficacy of ChatGPT in 
      simulating patient interactions and clinical scenarios, which can foster improved 
      diagnostic and interpersonal skills. Despite the promising advantages, concerns 
      regarding reliability, lack of personal touch, and potential biases in the 
      AI-generated content are scrutinized. Ethical considerations concerning data 
      privacy and potential misuse are also explored. The findings underline the need 
      for carefully designed integration, continuous evaluation, and adherence to 
      ethical guidelines to maximize benefits while mitigating risks. By shedding light 
      on these multifaceted aspects, this paper contributes to the ongoing discourse on 
      the incorporation of AI in medical education, offering valuable insights and 
      guidance for educators, practitioners, and policymakers aiming to leverage modern 
      technology for enhancing ophthalmic education.
FAU - Gurnani, Bharat
AU  - Gurnani B
AUID- ORCID: 0000-0003-0848-5172
AD  - Cataract, Cornea, Trauma, External Diseases, Ocular Surface and Refractive 
      Services, ASG Eye Hospital, Jodhpur, Rajasthan, India.
AD  - Sadguru Netra Chikitsalya, Shri Sadguru Seva Sangh Trust, Chitrakoot, Madhya 
      Pradesh, India.
FAU - Kaur, Kirandeep
AU  - Kaur K
AUID- ORCID: 0000-0002-0951-7415
AD  - Cataract, Pediatric Ophthalmology and Strabismus, ASG Eye Hospital, Jodhpur, 
      Rajasthan, India.
AD  - Children Eye Care Centre, Sadguru Netra Chikitsalya, Shri Sadguru Seva Sangh 
      Trust, Chitrakoot, Madhya Pradesh, India.
LA  - eng
PT  - Editorial
DEP - 20231116
PL  - United States
TA  - Eur J Ophthalmol
JT  - European journal of ophthalmology
JID - 9110772
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - ophthalmology education
COIS- Declaration of conflicting interestsThe author(s) declared no potential conflicts 
      of interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/11/17 15:24
MHDA- 2023/11/17 15:24
CRDT- 2023/11/17 03:42
PHST- 2023/11/17 15:24 [medline]
PHST- 2023/11/17 15:24 [pubmed]
PHST- 2023/11/17 03:42 [entrez]
AID - 10.1177/11206721231215862 [doi]
PST - aheadofprint
SO  - Eur J Ophthalmol. 2023 Nov 16:11206721231215862. doi: 10.1177/11206721231215862.

PMID- 37809155
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231018
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT Surpasses 1000 Publications on PubMed: Envisioning the Road Ahead.
PG  - e44769
LID - 10.7759/cureus.44769 [doi]
LID - e44769
AB  - The exponential growth of ChatGPT in medical literature, amassing over 1000 
      PubMed citations by August 2023, underscores a pivotal juncture in the 
      convergence of artificial intelligence (AI) and healthcare. This remarkable rise 
      not only showcases its potential to revolutionize medical academia but also 
      indicates its impending influence on patient care and healthcare systems. 
      Notwithstanding this enthusiasm, one-third of these citations are editorials or 
      commentaries, stressing a gap in empirical research. Alongside its potential, 
      there are concerns about ChatGPT becoming a "Weapon of Mass Deception" and the 
      need for rigorous evaluations to counter inaccuracies. The World Association of 
      Medical Editors has released guidelines emphasizing that AI tools should not be 
      manuscript co-authors and advocates for clear disclosures in AI-assisted academic 
      works. Interestingly, ChatGPT achieved its citation milestone within nine months, 
      compared to Google's 14 years. As Large Language Models (LLMs), like ChatGPT, 
      become more integral in healthcare, issues surrounding data protection, patient 
      privacy, and ethical implications gain prominence. As the future of LLM research 
      unfolds, key areas of interest include its efficacy in clinical settings, its 
      role in telemedicine, and its potential in medical education. The journey ahead 
      necessitates a harmonious partnership between the medical community and AI 
      developers, emphasizing both technological advancements and ethical 
      considerations.
CI  - Copyright © 2023, Temsah et al.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Intensive Care Unit, King Saud University, Riyadh, SAU.
FAU - Altamimi, Ibraheem
AU  - Altamimi I
AD  - College of Medicine, King Saud University, Riyadh, SAU.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Family and Community Medicine, King Saud University, Riyadh, SAU.
FAU - Alhasan, Khalid
AU  - Alhasan K
AD  - Pediatric Nephrology, King Saud University, Riyadh, SAU.
FAU - Al-Eyadhy, Ayman
AU  - Al-Eyadhy A
AD  - Pediatrics, King Saud University, Riyadh, SAU.
AD  - Pediatric Intensive Care Unit, King Saud University Medical City, Riyadh, SAU.
LA  - eng
PT  - Editorial
DEP - 20230906
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10557088
OTO - NOTNLM
OT  - ai chatbot
OT  - artificial intelligence (ai)
OT  - chatgpt
OT  - ethical implications
OT  - llms in healthcare
OT  - medical education
OT  - published in pubmed journal
OT  - pubmed
OT  - randomized controlled trial (rct)
OT  - telemedicine
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/09 06:41
MHDA- 2023/10/09 06:42
CRDT- 2023/10/09 05:52
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/10/09 06:41 [pubmed]
PHST- 2023/10/09 05:52 [entrez]
AID - 10.7759/cureus.44769 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 6;15(9):e44769. doi: 10.7759/cureus.44769. eCollection 2023 Sep.

PMID- 37274428
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230606
IS  - 1178-2390 (Print)
IS  - 1178-2390 (Electronic)
IS  - 1178-2390 (Linking)
VI  - 16
DP  - 2023
TI  - Using ChatGPT in Medical Research: Current Status and Future Directions.
PG  - 1513-1520
LID - 10.2147/JMDH.S413470 [doi]
AB  - OBJECTIVE: This review aims to evaluate the current evidence on the use of the 
      Generative Pre-trained Transformer (ChatGPT) in medical research, including but 
      not limited to treatment, diagnosis, or medication provision. METHODS: This 
      review follows the Preferred Reporting Items for Systematic Reviews and 
      Meta-Analyses (PRISMA) guidelines. We searched Google Scholar, Web of Science, 
      PubMed, and Medline to identify studies published between 2022 and 2023 that 
      aimed to utilize ChatGPT in medical research. All identified references were 
      stored in EndNote. RESULTS: We initially identified 114 articles, out of which 
      six studies met the inclusion and exclusion criteria for full-text screening. 
      Among the six studies, two focused on drug development (33.33%), two on 
      literature review writing (33.33%), and one each on medical report improvement, 
      provision of medical information, improving research conduct, data analysis, and 
      personalized medicine (16.67% each). CONCLUSION: ChatGPT has the potential to 
      revolutionize medical research in various ways. However, its accuracy, 
      originality, academic integrity, and ethical issues must be thoroughly discussed 
      and improved before its widespread implementation in clinical research and 
      medical practice.
CI  - © 2023 Ruksakulpiwat et al.
FAU - Ruksakulpiwat, Suebsarn
AU  - Ruksakulpiwat S
AUID- ORCID: 0000-0003-2168-5195
AD  - Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, 
      Thailand.
FAU - Kumar, Ayanesh
AU  - Kumar A
AD  - School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
FAU - Ajibade, Anuoluwapo
AU  - Ajibade A
AUID- ORCID: 0000-0003-4915-5698
AD  - College of Art and Science, Department of Anthropology, Case Western Reserve 
      University, Cleveland, OH, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230530
PL  - New Zealand
TA  - J Multidiscip Healthc
JT  - Journal of multidisciplinary healthcare
JID - 101512691
PMC - PMC10239248
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - medical research
OT  - review
COIS- The authors report no conflicts of interest in this work.
EDAT- 2023/06/05 13:04
MHDA- 2023/06/05 13:05
CRDT- 2023/06/05 12:02
PHST- 2023/03/28 00:00 [received]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2023/06/05 13:05 [medline]
PHST- 2023/06/05 13:04 [pubmed]
PHST- 2023/06/05 12:02 [entrez]
AID - 413470 [pii]
AID - 10.2147/JMDH.S413470 [doi]
PST - epublish
SO  - J Multidiscip Healthc. 2023 May 30;16:1513-1520. doi: 10.2147/JMDH.S413470. 
      eCollection 2023.

PMID- 37603843
OWN - NLM
STAT- Publisher
LR  - 20230821
IS  - 1873-1953 (Electronic)
IS  - 1474-5151 (Linking)
DP  - 2023 Aug 21
TI  - Using ChatGPT and Google Bard to improve the readability of written patient 
      information: A proof-of-concept.
LID - zvad087 [pii]
LID - 10.1093/eurjcn/zvad087 [doi]
AB  - Patient information materials often tend to be written at a reading level that is 
      too advanced for patients. In this proof-of-concept study, we used ChatGPT and 
      Google Bard to reduce the reading level of three selected patient information 
      sections from scientific journals. ChatGPT successfully improved readability. 
      However, it could not achieve the recommended sixth-grade reading level. Bard 
      reached the reading level of sixth-graders, but oversimplified the texts by 
      omitting up to 83% of the content. Despite the present limitations, developers of 
      patient information are encouraged to employ large language models, preferably 
      ChatGPT, to optimize their materials.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      European Society of Cardiology.
FAU - Moons, Philip
AU  - Moons P
AUID- ORCID: 0000-0002-8609-4516
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven - University of 
      Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens 
      backe 1, 413 46 Gothenburg, Sweden.
AD  - Department of Paediatrics and Child Health, University of Cape Town, Klipfontein 
      Rd, Rondebosch, 7700 Cape Town, South Africa.
FAU - Van Bulck, Liesbet
AU  - Van Bulck L
AUID- ORCID: 0000-0001-8975-4455
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven - University of 
      Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Research Foundation Flanders (FWO), Leuvenseweg 38, 1000 Brussels, Belgium.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - England
TA  - Eur J Cardiovasc Nurs
JT  - European journal of cardiovascular nursing
JID - 101128793
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - chatbot
OT  - language models
OT  - natural language processing
OT  - nurses
OT  - nursing
OT  - patient education
OT  - patient information
OT  - reading level
EDAT- 2023/08/21 18:42
MHDA- 2023/08/21 18:42
CRDT- 2023/08/21 16:33
PHST- 2023/07/14 00:00 [received]
PHST- 2023/08/16 00:00 [revised]
PHST- 2023/08/18 00:00 [accepted]
PHST- 2023/08/21 18:42 [medline]
PHST- 2023/08/21 18:42 [pubmed]
PHST- 2023/08/21 16:33 [entrez]
AID - 7246857 [pii]
AID - 10.1093/eurjcn/zvad087 [doi]
PST - aheadofprint
SO  - Eur J Cardiovasc Nurs. 2023 Aug 21:zvad087. doi: 10.1093/eurjcn/zvad087.

PMID- 36701446
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230127
LR  - 20230202
IS  - 1095-9203 (Electronic)
IS  - 0036-8075 (Linking)
VI  - 379
IP  - 6630
DP  - 2023 Jan 27
TI  - ChatGPT is fun, but not an author.
PG  - 313
LID - 10.1126/science.adg7879 [doi]
AB  - In less than 2 months, the artificial intelligence (AI) program ChatGPT has 
      become a cultural sensation. It is freely accessible through a web portal created 
      by the tool's developer, OpenAI. The program-which automatically creates text 
      based on written prompts-is so popular that it's likely to be "at capacity right 
      now" if you attempt to use it. When you do get through, ChatGPT provides endless 
      entertainment. I asked it to rewrite the first scene of the classic American play 
      Death of a Salesman, but to feature Princess Elsa from the animated movie Frozen 
      as the main character instead of Willy Loman. The output was an amusing 
      conversation in which Elsa-who has come home from a tough day of selling-is told 
      by her son Happy, "Come on, Mom. You're Elsa from Frozen. You have ice powers and 
      you're a queen. You're unstoppable." Mash-ups like this are certainly fun, but 
      there are serious implications for generative AI programs like ChatGPT in science 
      and academia.
FAU - Thorp, H Holden
AU  - Thorp HH
AD  - H. Holden Thorp Editor-in-Chief, Science journals.
LA  - eng
PT  - Editorial
DEP - 20230126
PL  - United States
TA  - Science
JT  - Science (New York, N.Y.)
JID - 0404511
SB  - IM
EDAT- 2023/01/27 06:00
MHDA- 2023/01/27 06:01
CRDT- 2023/01/26 14:03
PHST- 2023/01/26 14:03 [entrez]
PHST- 2023/01/27 06:00 [pubmed]
PHST- 2023/01/27 06:01 [medline]
AID - 10.1126/science.adg7879 [doi]
PST - ppublish
SO  - Science. 2023 Jan 27;379(6630):313. doi: 10.1126/science.adg7879. Epub 2023 Jan 
      26.

PMID- 37779171
OWN - NLM
STAT- MEDLINE
DCOM- 20231003
LR  - 20231121
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 1
TI  - Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments.
PG  - 16492
LID - 10.1038/s41598-023-43436-9 [doi]
LID - 16492
AB  - The United States Medical Licensing Examination (USMLE) has been a subject of 
      performance study for artificial intelligence (AI) models. However, their 
      performance on questions involving USMLE soft skills remains unexplored. This 
      study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving 
      communication skills, ethics, empathy, and professionalism. We used 80 
      USMLE-style questions involving soft skills, taken from the USMLE website and the 
      AMBOSS question bank. A follow-up query was used to assess the models' 
      consistency. The performance of the AI models was compared to that of previous 
      AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to 
      ChatGPT's 62.5%. GPT-4 showed more confidence, not revising any responses, while 
      ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 
      was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, 
      showed capacity for empathy, indicating AI's potential to meet the complex 
      interpersonal, ethical, and professional demands intrinsic to the practice of 
      medicine.
CI  - © 2023. Springer Nature Limited.
FAU - Brin, Dana
AU  - Brin D
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel. 
      dannabrin@gmail.com.
AD  - Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel. dannabrin@gmail.com.
FAU - Sorin, Vera
AU  - Sorin V
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel.
AD  - Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
FAU - Vaid, Akhil
AU  - Vaid A
AD  - The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine 
      at Mount Sinai, New York, NY, USA.
FAU - Soroush, Ali
AU  - Soroush A
AD  - Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at 
      Mount Sinai, New York, NY, USA.
FAU - Glicksberg, Benjamin S
AU  - Glicksberg BS
AD  - Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount 
      Sinai, New York, NY, USA.
FAU - Charney, Alexander W
AU  - Charney AW
AD  - The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine 
      at Mount Sinai, New York, NY, USA.
FAU - Nadkarni, Girish
AU  - Nadkarni G
AD  - Division of Data-Driven and Digital Medicine (D3M), The Charles Bronfman 
      Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA.
FAU - Klang, Eyal
AU  - Klang E
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel.
AD  - Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
LA  - eng
PT  - Journal Article
DEP - 20231001
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - *Artificial Intelligence
MH  - *Medicine
MH  - Empathy
MH  - Mental Processes
PMC - PMC10543445
COIS- The authors declare no competing interests.
EDAT- 2023/10/02 00:41
MHDA- 2023/10/03 06:47
CRDT- 2023/10/01 23:09
PHST- 2023/07/26 00:00 [received]
PHST- 2023/09/23 00:00 [accepted]
PHST- 2023/10/03 06:47 [medline]
PHST- 2023/10/02 00:41 [pubmed]
PHST- 2023/10/01 23:09 [entrez]
AID - 10.1038/s41598-023-43436-9 [pii]
AID - 43436 [pii]
AID - 10.1038/s41598-023-43436-9 [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 1;13(1):16492. doi: 10.1038/s41598-023-43436-9.

PMID- 37679532
OWN - NLM
STAT- Publisher
LR  - 20231009
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
VI  - 280
IP  - 11
DP  - 2023 Nov
TI  - Assessing the accuracy of ChatGPT references in head and neck and ENT 
      disciplines.
PG  - 5129-5133
LID - 10.1007/s00405-023-08205-4 [doi]
AB  - PURPOSE: ChatGPT has gained popularity as a web application since its release in 
      2022. While artificial intelligence (AI) systems' potential in scientific writing 
      is widely discussed, their reliability in reviewing literature and providing 
      accurate references remains unexplored. This study examines the reliability of 
      references generated by ChatGPT language models in the Head and Neck field. 
      METHODS: Twenty clinical questions were generated across different Head and Neck 
      disciplines, to prompt ChatGPT versions 3.5 and 4.0 to produce texts on the 
      assigned topics. The generated references were categorized as "true," 
      "erroneous," or "inexistent" based on congruence with existing records in 
      scientific databases. RESULTS: ChatGPT 4.0 outperformed version 3.5 in terms of 
      reference reliability. However, both versions displayed a tendency to provide 
      erroneous/non-existent references. CONCLUSIONS: It is crucial to address this 
      challenge to maintain the reliability of scientific literature. Journals and 
      institutions should establish strategies and good-practice principles in the 
      evolving landscape of AI-assisted scientific writing.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Frosolini, Andrea
AU  - Frosolini A
AUID- ORCID: 0000-0003-1347-4013
AD  - Department of Maxillo-Facial Surgery, Policlinico Le Scotte, University of Siena, 
      Siena, Italy. andreafrosolini@gmail.com.
FAU - Franz, Leonardo
AU  - Franz L
AUID- ORCID: 0000-0002-2306-4088
AD  - Phoniatris and Audiology Unit, Department of Neuroscience DNS, University of 
      Padova, Treviso, Italy.
AD  - Artificial Intelligence in Medicine and Innovation in Clinical Research and 
      Methodology (PhD Program), Department of Clinical and Experimental Sciences, 
      University of Brescia, Brescia, Italy.
FAU - Benedetti, Simone
AU  - Benedetti S
AUID- ORCID: 0000-0002-8997-5335
AD  - Department of Maxillo-Facial Surgery, Policlinico Le Scotte, University of Siena, 
      Siena, Italy.
FAU - Vaira, Luigi Angelo
AU  - Vaira LA
AUID- ORCID: 0000-0002-7789-145X
AD  - Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and 
      Pharmacy, University of Sassari, Sassari, Italy.
AD  - PhD School of Biomedical Sciences, Department of Biomedical Sciences, University 
      of Sassari, Sassari, Italy.
FAU - de Filippis, Cosimo
AU  - de Filippis C
AUID- ORCID: 0000-0002-2491-7783
AD  - Phoniatris and Audiology Unit, Department of Neuroscience DNS, University of 
      Padova, Treviso, Italy.
FAU - Gennaro, Paolo
AU  - Gennaro P
AUID- ORCID: 0000-0002-1603-3697
AD  - Department of Maxillo-Facial Surgery, Policlinico Le Scotte, University of Siena, 
      Siena, Italy.
FAU - Marioni, Gino
AU  - Marioni G
AUID- ORCID: 0000-0001-8751-0588
AD  - Phoniatris and Audiology Unit, Department of Neuroscience DNS, University of 
      Padova, Treviso, Italy.
FAU - Gabriele, Guido
AU  - Gabriele G
AUID- ORCID: 0000-0002-1028-7493
AD  - Department of Maxillo-Facial Surgery, Policlinico Le Scotte, University of Siena, 
      Siena, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230908
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - Artificial intelligence
OT  - Chat-GPT
OT  - Head and neck surgery
OT  - Maxillofacial
EDAT- 2023/09/08 00:41
MHDA- 2023/09/08 00:41
CRDT- 2023/09/07 23:36
PHST- 2023/07/21 00:00 [received]
PHST- 2023/08/19 00:00 [accepted]
PHST- 2023/09/08 00:41 [pubmed]
PHST- 2023/09/08 00:41 [medline]
PHST- 2023/09/07 23:36 [entrez]
AID - 10.1007/s00405-023-08205-4 [pii]
AID - 10.1007/s00405-023-08205-4 [doi]
PST - ppublish
SO  - Eur Arch Otorhinolaryngol. 2023 Nov;280(11):5129-5133. doi: 
      10.1007/s00405-023-08205-4. Epub 2023 Sep 8.

PMID- 36798998
OWN - NLM
STAT- MEDLINE
DCOM- 20230222
LR  - 20230222
IS  - 2047-2986 (Electronic)
IS  - 2047-2978 (Print)
IS  - 2047-2978 (Linking)
VI  - 13
DP  - 2023 Feb 17
TI  - Can ChatGPT draft a research article? An example of population-level vaccine 
      effectiveness analysis.
PG  - 01003
LID - 10.7189/jogh.13.01003 [doi]
LID - 01003
AB  - We reflect on our experiences of using Generative Pre-trained Transformer 
      ChatGPT, a chatbot launched by OpenAI in November 2022, to draft a research 
      article. We aim to demonstrate how ChatGPT could help researchers to accelerate 
      drafting their papers. We created a simulated data set of 100 000 health care 
      workers with varying ages, Body Mass Index (BMI), and risk profiles. Simulation 
      data allow analysts to test statistical analysis techniques, such as 
      machine-learning based approaches, without compromising patient privacy. 
      Infections were simulated with a randomized probability of hospitalisation. A 
      subset of these fictitious people was vaccinated with a fictional vaccine that 
      reduced this probability of hospitalisation after infection. We then used ChatGPT 
      to help us decide how to handle the simulated data in order to determine vaccine 
      effectiveness and draft a related research paper. AI-based language models in 
      data analysis and scientific writing are an area of growing interest, and this 
      exemplar analysis aims to contribute to the understanding of how ChatGPT can be 
      used to facilitate these tasks.
CI  - Copyright © 2023 by the Journal of Global Health. All rights reserved.
FAU - Macdonald, Calum
AU  - Macdonald C
FAU - Adeloye, Davies
AU  - Adeloye D
FAU - Sheikh, Aziz
AU  - Sheikh A
FAU - Rudan, Igor
AU  - Rudan I
LA  - eng
PT  - Editorial
DEP - 20230217
PL  - Scotland
TA  - J Glob Health
JT  - Journal of global health
JID - 101578780
SB  - IM
MH  - Humans
MH  - *Vaccine Efficacy
MH  - Computer Simulation
MH  - *Software
MH  - Confidentiality
MH  - Health Personnel
PMC - PMC9936200
EDAT- 2023/02/18 06:00
MHDA- 2023/02/22 06:00
CRDT- 2023/02/17 02:42
PHST- 2023/02/17 02:42 [entrez]
PHST- 2023/02/18 06:00 [pubmed]
PHST- 2023/02/22 06:00 [medline]
AID - jogh-13-01003 [pii]
AID - 10.7189/jogh.13.01003 [doi]
PST - epublish
SO  - J Glob Health. 2023 Feb 17;13:01003. doi: 10.7189/jogh.13.01003.

PMID- 37273063
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 11
DP  - 2023 Nov
TI  - ChatGPTs' Journey in Medical Revolution: A Potential Panacea or a Hidden 
      Pathogen?
PG  - 2356-2358
LID - 10.1007/s10439-023-03264-4 [doi]
AB  - At the fascinating intersection of artificial intelligence and medicine, ChatGPT 
      morphs into a compact, personal digital physician. With a simple click, it 
      furnishes an abundance of health-related information, initial medical 
      consultations, and a plethora of disease management recommendations. Moreover, it 
      stands at the ready to provide immediate mental health assistance in times of 
      psychological distress. Yet, each innovation carries inherent challenges. As we 
      embrace the conveniences proffered by ChatGPT, it is imperative that we grapple 
      with associated issues such as data privacy, risk of misdiagnosis, complexities 
      in human-machine interaction, and particular situations that elude its 
      understanding. Let's probe further into this intriguing world, brimming with 
      contention and prospects, and observe how ChatGPT traverses the landscape of 
      digital health, uncovering the potential it holds for the future evolution of 
      medical practice.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Yang, Jing
AU  - Yang J
AUID- ORCID: 0000-0002-8132-8395
AD  - Department of Computer System and Technology, Faculty of Computer Science and 
      Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia. 
      s2147529@siswa.um.edu.my.
LA  - eng
PT  - Letter
DEP - 20230605
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Digital health
OT  - Revolution
EDAT- 2023/06/05 13:04
MHDA- 2023/06/05 13:04
CRDT- 2023/06/05 11:08
PHST- 2023/05/29 00:00 [received]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/06/05 13:04 [pubmed]
PHST- 2023/06/05 13:04 [medline]
PHST- 2023/06/05 11:08 [entrez]
AID - 10.1007/s10439-023-03264-4 [pii]
AID - 10.1007/s10439-023-03264-4 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Nov;51(11):2356-2358. doi: 10.1007/s10439-023-03264-4. Epub 
      2023 Jun 5.

PMID- 37103921
OWN - NLM
STAT- MEDLINE
DCOM- 20230612
LR  - 20231116
IS  - 2168-619X (Electronic)
IS  - 2168-6181 (Print)
IS  - 2168-6181 (Linking)
VI  - 149
IP  - 6
DP  - 2023 Jun 1
TI  - Comparison Between ChatGPT and Google Search as Sources of Postoperative Patient 
      Instructions.
PG  - 556-558
LID - 10.1001/jamaoto.2023.0704 [doi]
FAU - Ayoub, Noel F
AU  - Ayoub NF
AD  - Department of Otolaryngology-Head and Neck Surgery, Stanford University School of 
      Medicine, Stanford, California.
FAU - Lee, Yu-Jin
AU  - Lee YJ
AD  - Department of Otolaryngology-Head and Neck Surgery, Stanford University School of 
      Medicine, Stanford, California.
FAU - Grimm, David
AU  - Grimm D
AD  - Department of Otolaryngology-Head and Neck Surgery, Stanford University School of 
      Medicine, Stanford, California.
FAU - Balakrishnan, Karthik
AU  - Balakrishnan K
AD  - Department of Otolaryngology-Head and Neck Surgery, Stanford University School of 
      Medicine, Stanford, California.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - JAMA Otolaryngol Head Neck Surg
JT  - JAMA otolaryngology-- head & neck surgery
JID - 101589542
SB  - IM
MH  - Humans
MH  - *Search Engine
MH  - *Health Literacy
PMC - PMC10141286
OAB - This qualitative study rates the level of understandability, actionability, and 
      procedure-specific content in postoperative instructions generated from ChatGPT, 
      Google Search, and Stanford University.
OABL- eng
COIS- Conflict of Interest Disclosures: Dr Balakrishnan reported receiving royalties 
      from Springer Inc outside the submitted work. No other disclosures were reported.
EDAT- 2023/04/27 12:41
MHDA- 2023/06/12 06:42
PMCR- 2024/04/27
CRDT- 2023/04/27 11:33
PHST- 2024/04/27 00:00 [pmc-release]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/04/27 12:41 [pubmed]
PHST- 2023/04/27 11:33 [entrez]
AID - 2804300 [pii]
AID - old230004 [pii]
AID - 10.1001/jamaoto.2023.0704 [doi]
PST - ppublish
SO  - JAMA Otolaryngol Head Neck Surg. 2023 Jun 1;149(6):556-558. doi: 
      10.1001/jamaoto.2023.0704.

PMID- 37167941
OWN - NLM
STAT- MEDLINE
DCOM- 20230515
LR  - 20230515
IS  - 1175-8716 (Electronic)
IS  - 0028-8446 (Linking)
VI  - 136
IP  - 1575
DP  - 2023 May 12
TI  - The impact of AI and ChatGPT on research reporting.
PG  - 60-64
AB  - ChatGPT and the newest GPT-4 are AI language models developed by OpenAI that have 
      gained attention for their potential applications in biomedical research 
      reporting. The models can assist researchers in various stages of writing 
      scientific articles, including literature search, outlining, writing different 
      sections, formatting, and translation. The use of ChatGPT or GPT-4 in research 
      reporting has the potential to speed up the writing process, but its limitations, 
      such as incorrect answers and biases, should also be considered. There is ongoing 
      debate over the issue of AI authorship in scientific papers, with some publishers 
      allowing it to be listed as a contributor in the acknowledgements section, while 
      others do not allow it to be listed as an author. The use of ChatGPT or GPT-4 in 
      research reporting is a recent development, and further studies and discussions 
      are needed to determine their potential and limitations in this field.
CI  - © PMA.
FAU - Mojadeddi, Zubair M
AU  - Mojadeddi ZM
AD  - Center for Perioperative Optimization, Department of Surgery, Herlev and Gentofte 
      Hospital, University of Copenhagen, Copenhagen, Denmark.
FAU - Rosenberg, Jacob
AU  - Rosenberg J
AD  - Center for Perioperative Optimization, Department of Surgery, Herlev and Gentofte 
      Hospital, University of Copenhagen, Copenhagen, Denmark; Department of Clinical 
      Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 
      Denmark.
LA  - eng
PT  - Journal Article
DEP - 20230512
PL  - New Zealand
TA  - N Z Med J
JT  - The New Zealand medical journal
JID - 0401067
SB  - IM
MH  - Humans
MH  - New Zealand
MH  - *Authorship
MH  - *Biomedical Research
MH  - Research Personnel
MH  - Artificial Intelligence
COIS- Nil
EDAT- 2023/05/12 01:07
MHDA- 2023/05/15 06:42
CRDT- 2023/05/11 18:13
PHST- 2023/05/15 06:42 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 18:13 [entrez]
PST - epublish
SO  - N Z Med J. 2023 May 12;136(1575):60-64.

PMID- 37952568
OWN - NLM
STAT- Publisher
LR  - 20231203
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
DP  - 2023 Nov 11
TI  - ChatRx: ChatGPT's potential to educate patients on medication adverse effects.
LID - S0190-9622(23)03199-7 [pii]
LID - 10.1016/j.jaad.2023.11.008 [doi]
FAU - Chen, Annie I
AU  - Chen AI
AD  - Department of Dermatology, University of Pittsburgh School of Medicine, 
      Pittsburgh, Pennsylvania.
FAU - Ferris, Laura K
AU  - Ferris LK
AD  - Department of Dermatology, University of Pittsburgh School of Medicine, 
      Pittsburgh, Pennsylvania.
FAU - Nambudiri, Vinod E
AU  - Nambudiri VE
AD  - Department of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts; 
      Harvard Medical School, Boston, Massachusetts.
FAU - Piette, Evan W
AU  - Piette EW
AD  - Department of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts; 
      Harvard Medical School, Boston, Massachusetts. Electronic address: 
      epiette@bwh.harvard.edu.
LA  - eng
PT  - Journal Article
DEP - 20231111
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - clinical research
OT  - dermatology
OT  - drug side effects
OT  - general dermatology
OT  - innovation
OT  - large language models
OT  - medical dermatology
OT  - medical education
OT  - patients
OT  - technology
COIS- Conflicts of interest None disclosed.
EDAT- 2023/11/13 00:42
MHDA- 2023/11/13 00:42
CRDT- 2023/11/12 19:12
PHST- 2023/08/24 00:00 [received]
PHST- 2023/10/23 00:00 [revised]
PHST- 2023/11/08 00:00 [accepted]
PHST- 2023/11/13 00:42 [pubmed]
PHST- 2023/11/13 00:42 [medline]
PHST- 2023/11/12 19:12 [entrez]
AID - S0190-9622(23)03199-7 [pii]
AID - 10.1016/j.jaad.2023.11.008 [doi]
PST - aheadofprint
SO  - J Am Acad Dermatol. 2023 Nov 11:S0190-9622(23)03199-7. doi: 
      10.1016/j.jaad.2023.11.008.

PMID- 37378043
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2095-4689 (Print)
IS  - 2095-4697 (Electronic)
IS  - 2095-4689 (Linking)
VI  - 11
IP  - 2
DP  - 2023 Jun
TI  - Empowering beginners in bioinformatics with ChatGPT.
PG  - 105-108
LID - 10.15302/j-qb-023-0327 [doi]
AB  - The impressive conversational and programming abilities of ChatGPT make it an 
      attractive tool for facilitating the education of bioinformatics data analysis 
      for beginners. In this study, we proposed an iterative model to fine-tune 
      instructions for guiding a chatbot in generating code for bioinformatics data 
      analysis tasks. We demonstrated the feasibility of the model by applying it to 
      various bioinformatics topics. Additionally, we discussed practical 
      considerations and limitations regarding the use of the model in chatbot-aided 
      bioinformatics education.
FAU - Shue, Evelyn
AU  - Shue E
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV 26506, USA.
FAU - Liu, Li
AU  - Liu L
AD  - College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
AD  - Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.
FAU - Li, Bingxin
AU  - Li B
AD  - Finance Department, John Chambers College of Business and Economics, West 
      Virginia University, Morgantown, WV 26506, USA.
FAU - Feng, Zifeng
AU  - Feng Z
AD  - Department of Economics and Finance, The University of Texas at El Paso, El Paso, 
      TX 79902, USA.
FAU - Li, Xin
AU  - Li X
AD  - Lane Department of Computer Science and Electrical Engineering, West Virginia 
      University, Morgantown, WV 26506, USA.
FAU - Hu, Gangqing
AU  - Hu G
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV 26506, USA.
LA  - eng
GR  - P20 GM103434/GM/NIGMS NIH HHS/United States
GR  - P20 GM121322/GM/NIGMS NIH HHS/United States
GR  - R01 LM013438/LM/NLM NIH HHS/United States
GR  - U54 GM104942/GM/NIGMS NIH HHS/United States
PT  - Journal Article
DEP - 20230331
PL  - China
TA  - Quant Biol
JT  - Quantitative biology (Beijing, China)
JID - 101639642
PMC - PMC10299548
MID - NIHMS1890079
OTO - NOTNLM
OT  - ChatGPT
OT  - bioinformatics
OT  - education
OT  - scientific data analysis
COIS- COMPLIANCE WITH ETHICS GUIDELINES Evelyn Shue, Li Liu, Bingxin Li, Zifeng Feng, 
      Xin Li and Gangqing Hu declare that they have no conflict of interest. This 
      article is a perspective article and does not contain any studies with human or 
      animal subjects performed by any of the authors.
EDAT- 2023/06/28 13:08
MHDA- 2023/06/28 13:09
CRDT- 2023/06/28 09:27
PHST- 2023/06/28 13:09 [medline]
PHST- 2023/06/28 13:08 [pubmed]
PHST- 2023/06/28 09:27 [entrez]
AID - 10.15302/j-qb-023-0327 [doi]
PST - ppublish
SO  - Quant Biol. 2023 Jun;11(2):105-108. doi: 10.15302/j-qb-023-0327. Epub 2023 Mar 
      31.

PMID- 37549499
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1872-8243 (Electronic)
IS  - 1386-5056 (Linking)
VI  - 177
DP  - 2023 Sep
TI  - Performance and exploration of ChatGPT in medical examination, records and 
      education in Chinese: Pave the way for medical AI.
PG  - 105173
LID - S1386-5056(23)00191-0 [pii]
LID - 10.1016/j.ijmedinf.2023.105173 [doi]
AB  - BACKGROUND: Although chat generative pre-trained transformer (ChatGPT) has made 
      several successful attempts in the medical field, most notably in answering 
      medical questions in English, no studies have evaluated ChatGPT's performance in 
      a Chinese context for a medical task. OBJECTIVE: The aim of this study was to 
      evaluate ChatGPT's ability to understand medical knowledge in Chinese, as well as 
      its potential to serve as an electronic health infrastructure for medical 
      development, by evaluating its performance in medical examinations, records, and 
      education. METHOD: The Chinese (CNMLE) and English (ENMLE) datasets of the China 
      National Medical Licensing Examination and the Chinese dataset (NEEPM) of the 
      China National Entrance Examination for Postgraduate Clinical Medicine 
      Comprehensive Ability were used to evaluate the performance of ChatGPT (GPT-3.5 
      and GPT-4). We assessed answer accuracy, verbal fluency, and the classification 
      of incorrect responses owing to hallucinations on multiple occasions. In 
      addition, we tested ChatGPT's performance on discharge summaries and group 
      learning in a Chinese context on a small scale. RESULTS: The accuracy of GPT-3.5 
      in CNMLE, ENMLE, and NEEPM was 56% (56/100), 76% (76/100), and 62% (62/100), 
      respectively, compared to that of GPT-4, which was of 84% (84/100), 86% (86/100), 
      and 82% (82/100). The verbal fluency of all the ChatGPT responses exceeded 95%. 
      Among the GPT-3.5 incorrect responses, the proportions of open-domain 
      hallucinations were 66 % (29/44), 54 % (14/24), and 63 % (24/38), whereas 
      close-domain hallucinations accounted for 34 % (15/44), 46 % (14/24), and 37 % 
      (14/38), respectively. By contrast, GPT-4 open-domain hallucinations accounted 
      for 56% (9/16), 43% (6/14), and 83% (15/18), while close-domain hallucinations 
      accounted for 44% (7/16), 57% (8/14), and 17% (3/18), respectively. In the 
      discharge summary, ChatGPT demonstrated logical coherence, however GPT-3.5 could 
      not fulfill the quality requirements, while GPT-4 met the qualification of 60% 
      (6/10). In group learning, the verbal fluency and interaction satisfaction with 
      ChatGPT were 100% (10/10). CONCLUSION: ChatGPT based on GPT-4 is at par with 
      Chinese medical practitioners who passed the CNMLE and at the standard required 
      for admission to clinical medical graduate programs in China. The GPT-4 shows 
      promising potential for discharge summarization and group learning. Additionally, 
      it shows high verbal fluency, resulting in a positive human-computer interaction 
      experience. GPT-4 significantly improves multiple capabilities and reduces 
      hallucinations compared to the previous GPT-3.5 model, with a particular leap 
      forward in the Chinese comprehension capability of medical tasks. Artificial 
      intelligence (AI) systems face the challenges of hallucinations, legal risks, and 
      ethical issues. However, we discovered ChatGPT's potential to promote medical 
      development as an electronic health infrastructure, paving the way for Medical AI 
      to become necessary.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Wang, Hongyan
AU  - Wang H
AD  - Department of Pain Management, Xuanwu Hospital, Capital Medical University.
FAU - Wu, WeiZhen
AU  - Wu W
AD  - Department of Anesthesia, China-Japan Union Hospital of Jilin University.
FAU - Dou, Zhi
AU  - Dou Z
AD  - Department of Pain Management, Xuanwu Hospital, Capital Medical University.
FAU - He, Liangliang
AU  - He L
AD  - Department of Pain Management, Xuanwu Hospital, Capital Medical University.
FAU - Yang, Liqiang
AU  - Yang L
AD  - Department of Pain Management, Xuanwu Hospital, Capital Medical University. 
      Electronic address: yangliqiangxwpain@outlook.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230804
PL  - Ireland
TA  - Int J Med Inform
JT  - International journal of medical informatics
JID - 9711057
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - China
MH  - *Clinical Medicine
MH  - Electronics
MH  - Hallucinations
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/08/08 00:42
MHDA- 2023/08/14 06:43
CRDT- 2023/08/07 18:03
PHST- 2023/03/01 00:00 [received]
PHST- 2023/07/01 00:00 [revised]
PHST- 2023/07/08 00:00 [accepted]
PHST- 2023/08/14 06:43 [medline]
PHST- 2023/08/08 00:42 [pubmed]
PHST- 2023/08/07 18:03 [entrez]
AID - S1386-5056(23)00191-0 [pii]
AID - 10.1016/j.ijmedinf.2023.105173 [doi]
PST - ppublish
SO  - Int J Med Inform. 2023 Sep;177:105173. doi: 10.1016/j.ijmedinf.2023.105173. Epub 
      2023 Aug 4.

PMID- 37833131
OWN - NLM
STAT- MEDLINE
DCOM- 20231113
LR  - 20231113
IS  - 0968-0004 (Print)
IS  - 0968-0004 (Linking)
VI  - 48
IP  - 12
DP  - 2023 Dec
TI  - AI interprets the Central Dogma and Genetic Code.
PG  - 1014-1018
LID - S0968-0004(23)00230-X [pii]
LID - 10.1016/j.tibs.2023.09.004 [doi]
AB  - Generative artificial intelligence (AI) is a burgeoning field with widespread 
      applications, including in science. Here, we explore two paradigms that provide 
      insight into the capabilities and limitations of Chat Generative Pre-trained 
      Transformer (ChatGPT): its ability to (i) define a core biological concept (the 
      Central Dogma of molecular biology); and (ii) interpret the genetic code.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Ille, Alexander M
AU  - Ille AM
AD  - School of Graduate Studies, Rutgers University, Newark, NJ, USA. Electronic 
      address: mai86@gsbs.rutgers.edu.
FAU - Mathews, Michael B
AU  - Mathews MB
AD  - School of Graduate Studies, Rutgers University, Newark, NJ, USA; Department of 
      Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA. Electronic address: 
      mathews@njms.rutgers.edu.
LA  - eng
PT  - Journal Article
DEP - 20231011
PL  - England
TA  - Trends Biochem Sci
JT  - Trends in biochemical sciences
JID - 7610674
SB  - IM
MH  - *Artificial Intelligence
MH  - *Genetic Code
MH  - Molecular Biology
OTO - NOTNLM
OT  - Central Dogma
OT  - ChatGPT
OT  - Sequence Hypothesis
OT  - artificial intelligence
OT  - large language models
OT  - natural language processing
COIS- Declaration of interests No interests are declared.
EDAT- 2023/10/14 10:43
MHDA- 2023/11/13 06:42
CRDT- 2023/10/13 21:59
PHST- 2023/06/19 00:00 [received]
PHST- 2023/09/01 00:00 [revised]
PHST- 2023/09/13 00:00 [accepted]
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/10/14 10:43 [pubmed]
PHST- 2023/10/13 21:59 [entrez]
AID - S0968-0004(23)00230-X [pii]
AID - 10.1016/j.tibs.2023.09.004 [doi]
PST - ppublish
SO  - Trends Biochem Sci. 2023 Dec;48(12):1014-1018. doi: 10.1016/j.tibs.2023.09.004. 
      Epub 2023 Oct 11.

PMID- 36819954
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230224
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - ChatGPT Output Regarding Compulsory Vaccination and COVID-19 Vaccine Conspiracy: 
      A Descriptive Study at the Outset of a Paradigm Shift in Online Search for 
      Information.
PG  - e35029
LID - 10.7759/cureus.35029 [doi]
LID - e35029
AB  - BACKGROUND: Being on the verge of a revolutionary approach to gathering 
      information, ChatGPT (an artificial intelligence (AI)-based language model 
      developed by OpenAI, and capable of producing human-like text) could be the prime 
      motive of a paradigm shift on how humans will acquire information. Despite the 
      concerns related to the use of such a promising tool in relation to the future of 
      the quality of education, this technology will soon be incorporated into web 
      search engines mandating the need to evaluate the output of such a tool. Previous 
      studies showed that dependence on some sources of online information (e.g., 
      social media platforms) was associated with higher rates of vaccination 
      hesitancy. Therefore, the aim of the current study was to describe the output of 
      ChatGPT regarding coronavirus disease 2019 (COVID-19) vaccine conspiracy beliefs. 
      and compulsory vaccination. METHODS: The current descriptive study was conducted 
      on January 14, 2023 using the ChatGPT from OpenAI (OpenAI, L.L.C., San Francisco, 
      CA, USA). The output was evaluated by two authors and the degree of agreement 
      regarding the correctness, clarity, conciseness, and bias was evaluated using 
      Cohen's kappa. RESULTS: The ChatGPT responses were dismissive of conspiratorial 
      ideas about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) origins 
      labeling it as non-credible and lacking scientific evidence. Additionally, 
      ChatGPT responses were totally against COVID-19 vaccine conspiracy statements. 
      Regarding compulsory vaccination, ChatGPT responses were neutral citing the 
      following as advantages of this strategy: protecting public health, maintaining 
      herd immunity, reducing the spread of disease, cost-effectiveness, and legal 
      obligation, and on the other hand, it cited the following as disadvantages of 
      compulsory vaccination: ethical and legal concerns, mistrust and resistance, 
      logistical challenges, and limited resources and knowledge. CONCLUSIONS: The 
      current study showed that ChatGPT could be a source of information to challenge 
      COVID-19 vaccine conspiracies. For compulsory vaccination, ChatGPT resonated with 
      the divided opinion in the scientific community toward such a strategy; 
      nevertheless, it detailed the pros and cons of this approach. As it currently 
      stands, the judicious use of ChatGPT could be utilized as a user-friendly source 
      of COVID-19 vaccine information that could challenge conspiracy ideas with clear, 
      concise, and non-biased content. However, ChatGPT content cannot be used as an 
      alternative to the original reliable sources of vaccine information (e.g., the 
      World Health Organization [WHO] and the Centers for Disease Control and 
      Prevention [CDC]).
CI  - Copyright © 2023, Sallam et al.
FAU - Sallam, Malik
AU  - Sallam M
AD  - Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, 
      University of Jordan, Amman, JOR.
AD  - Department of Clinical Laboratories and Forensic Medicine, Jordan University 
      Hospital, Amman, JOR.
FAU - Salim, Nesreen A
AU  - Salim NA
AD  - Department of Prosthodontics, Jordan University Hospital, Amman, JOR.
FAU - Al-Tammemi, Ala'a B
AU  - Al-Tammemi AB
AD  - Infectious Disease, Applied Science Research Center, Applied Science Private 
      University, Amman, JOR.
AD  - Migration Health Division, International Organization for Migration (IOM) The UN 
      Migration Agency, Amman, JOR.
FAU - Barakat, Muna
AU  - Barakat M
AD  - School of Pharmacy, Applied Science Private University, Amman, JOR.
FAU - Fayyad, Diaa
AU  - Fayyad D
AD  - Department of Biological Sciences, Faculty of Science, Yarmouk University, Irbid, 
      JOR.
FAU - Hallit, Souheil
AU  - Hallit S
AD  - Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik 
      (USEK), Jounieh, LBN.
FAU - Harapan, Harapan
AU  - Harapan H
AD  - Department of Epidemiology and Public Health, Syiah Kuala University, Banda Aceh, 
      IDN.
FAU - Hallit, Rabih
AU  - Hallit R
AD  - Department of Infectious Disease, School of Medicine and Medical Sciences, Holy 
      Spirit University of Kaslik (USEK), Jounieh, LBN.
FAU - Mahafzah, Azmi
AU  - Mahafzah A
AD  - Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, 
      The University of Jordan, Amman, JOR.
LA  - eng
PT  - Journal Article
DEP - 20230215
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC9931398
OTO - NOTNLM
OT  - artificial intelligence in medicine
OT  - covid-19 vaccine
OT  - machine learning
OT  - mandatory vaccination
OT  - vaccine promotion
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/02/24 06:00
MHDA- 2023/02/24 06:01
CRDT- 2023/02/23 10:09
PHST- 2023/02/15 00:00 [accepted]
PHST- 2023/02/23 10:09 [entrez]
PHST- 2023/02/24 06:00 [pubmed]
PHST- 2023/02/24 06:01 [medline]
AID - 10.7759/cureus.35029 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 15;15(2):e35029. doi: 10.7759/cureus.35029. eCollection 2023 
      Feb.

PMID- 37908959
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231102
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Evaluating ChatGPT-3.5 and Claude-2 in Answering and Explaining Conceptual 
      Medical Physiology Multiple-Choice Questions.
PG  - e46222
LID - 10.7759/cureus.46222 [doi]
LID - e46222
AB  - Background Generative artificial intelligence (AI) systems such as ChatGPT-3.5 
      and Claude-2 may assist in explaining complex medical science topics. A few 
      studies have shown that AI can solve complicated physiology problems that require 
      critical thinking and analysis. However, further studies are required to validate 
      the effectiveness of AI in answering conceptual multiple-choice questions (MCQs) 
      in human physiology. Objective This study aimed to evaluate and compare the 
      proficiency of ChatGPT-3.5 and Claude-2 in answering and explaining a curated set 
      of MCQs in medical physiology. Methods In this cross-sectional study, a set of 55 
      MCQs from 10 competencies of medical physiology was purposefully constructed that 
      required comprehension, problem-solving, and analytical skills to solve them. The 
      MCQs and a structured prompt for response generation were presented to 
      ChatGPT-3.5 and Claude-2. The explanations provided by both AI systems were 
      documented in an Excel spreadsheet. All three authors subjected these 
      explanations to a rating process using a scale of 0 to 3. A rating of 0 was 
      assigned to an incorrect, 1 to a partially correct, 2 to a correct explanation 
      with some aspects missing, and 3 to a perfectly correct explanation. Both AI 
      models were evaluated for their ability to choose the correct answer (option) and 
      provide clear and comprehensive explanations of the MCQs. The Mann-Whitney U test 
      was used to compare AI responses. The Fleiss multi-rater kappa (κ) was used to 
      determine the score agreement among the three raters. The statistical 
      significance level was decided at P ≤ 0.05. Results Claude-2 answered 40 MCQs 
      correctly, which was significantly higher than the 26 correct responses from 
      ChatGPT-3.5. The rating distribution for the explanations generated by Claude-2 
      was significantly higher than that of ChatGPT-3.5. The κ values were 0.804 and 
      0.818 for Claude-2 and ChatGPT-3.5, respectively. Conclusion In terms of 
      answering and elucidating conceptual MCQs in medical physiology, Claude-2 
      surpassed ChatGPT-3.5. However, accessing Claude-2 from India requires the use of 
      a virtual private network, which may raise security concerns.
CI  - Copyright © 2023, Agarwal et al.
FAU - Agarwal, Mayank
AU  - Agarwal M
AD  - Physiology, All India Institute of Medical Sciences, Raebareli, IND.
FAU - Goswami, Ayan
AU  - Goswami A
AD  - Physiology, Santiniketan Medical College, Bolpur, IND.
FAU - Sharma, Priyanka
AU  - Sharma P
AD  - Physiology, School of Medical Sciences & Research, Sharda University, Greater 
      Noida, IND.
LA  - eng
PT  - Journal Article
DEP - 20230929
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10613833
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - claude
OT  - large language models
OT  - medical education
OT  - multiple choice questions
OT  - physiology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/01 06:43
MHDA- 2023/11/01 06:44
CRDT- 2023/11/01 04:04
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/11/01 06:44 [medline]
PHST- 2023/11/01 06:43 [pubmed]
PHST- 2023/11/01 04:04 [entrez]
AID - 10.7759/cureus.46222 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 29;15(9):e46222. doi: 10.7759/cureus.46222. eCollection 2023 
      Sep.

PMID- 37792149
OWN - NLM
STAT- Publisher
LR  - 20231004
IS  - 1867-108X (Electronic)
IS  - 1867-1071 (Linking)
DP  - 2023 Oct 4
TI  - Performance evaluation of ChatGPT, GPT-4, and Bard on the official board 
      examination of the Japan Radiology Society.
LID - 10.1007/s11604-023-01491-2 [doi]
AB  - PURPOSE: Herein, we assessed the accuracy of large language models (LLMs) in 
      generating responses to questions in clinical radiology practice. We compared the 
      performance of ChatGPT, GPT-4, and Google Bard using questions from the Japan 
      Radiology Board Examination (JRBE). MATERIALS AND METHODS: In total, 103 
      questions from the JRBE 2022 were used with permission from the Japan 
      Radiological Society. These questions were categorized by pattern, required level 
      of thinking, and topic. McNemar's test was used to compare the proportion of 
      correct responses between the LLMs. Fisher's exact test was used to assess the 
      performance of GPT-4 for each topic category. RESULTS: ChatGPT, GPT-4, and Google 
      Bard correctly answered 40.8% (42 of 103), 65.0% (67 of 103), and 38.8% (40 of 
      103) of the questions, respectively. GPT-4 significantly outperformed ChatGPT by 
      24.2% (p < 0.001) and Google Bard by 26.2% (p < 0.001). In the categorical 
      analysis by level of thinking, GPT-4 correctly answered 79.7% of the lower-order 
      questions, which was significantly higher than ChatGPT or Google Bard 
      (p < 0.001). The categorical analysis by question pattern revealed GPT-4's 
      superiority over ChatGPT (67.4% vs. 46.5%, p = 0.004) and Google Bard (39.5%, 
      p < 0.001) in the single-answer questions. The categorical analysis by topic 
      revealed that GPT-4 outperformed ChatGPT (40%, p = 0.013) and Google Bard (26.7%, 
      p = 0.004). No significant differences were observed between the LLMs in the 
      categories not mentioned above. The performance of GPT-4 was significantly better 
      in nuclear medicine (93.3%) than in diagnostic radiology (55.8%; p < 0.001). 
      GPT-4 also performed better on lower-order questions than on higher-order 
      questions (79.7% vs. 45.5%, p < 0.001). CONCLUSION: ChatGPTplus based on GPT-4 
      scored 65% when answering Japanese questions from the JRBE, outperforming ChatGPT 
      and Google Bard. This highlights the potential of using LLMs to address advanced 
      clinical questions in the field of radiology in Japan.
CI  - © 2023. The Author(s).
FAU - Toyama, Yoshitaka
AU  - Toyama Y
AUID- ORCID: 0000-0003-0027-9681
AD  - Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, 
      Aoba-Ku, Sendai, 980-8575, Japan. ytoyama0818@gmail.com.
FAU - Harigai, Ayaka
AU  - Harigai A
AD  - Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, 
      Aoba-Ku, Sendai, 980-8575, Japan.
AD  - Department of Radiology, Tohoku Medical and Pharmaceutical University, Sendai, 
      Japan.
AD  - Department of Diagnostic Radiology, Tohoku University Graduate School of 
      Medicine, Sendai, Japan.
FAU - Abe, Mirei
AU  - Abe M
AD  - Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, 
      Aoba-Ku, Sendai, 980-8575, Japan.
FAU - Nagano, Mitsutoshi
AU  - Nagano M
AD  - School of Medicine, Tohoku University, Sendai, Japan.
FAU - Kawabata, Masahiro
AU  - Kawabata M
AD  - Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, 
      Aoba-Ku, Sendai, 980-8575, Japan.
FAU - Seki, Yasuhiro
AU  - Seki Y
AD  - Department of Radiation Oncology, Tohoku University Hospital, Sendai, Japan.
FAU - Takase, Kei
AU  - Takase K
AD  - Department of Diagnostic Radiology, Tohoku University Graduate School of 
      Medicine, Sendai, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231004
PL  - Japan
TA  - Jpn J Radiol
JT  - Japanese journal of radiology
JID - 101490689
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - GPT-4
OT  - Japan Radiology Society
EDAT- 2023/10/04 12:42
MHDA- 2023/10/04 12:42
CRDT- 2023/10/04 11:10
PHST- 2023/08/25 00:00 [received]
PHST- 2023/09/12 00:00 [accepted]
PHST- 2023/10/04 12:42 [medline]
PHST- 2023/10/04 12:42 [pubmed]
PHST- 2023/10/04 11:10 [entrez]
AID - 10.1007/s11604-023-01491-2 [pii]
AID - 10.1007/s11604-023-01491-2 [doi]
PST - aheadofprint
SO  - Jpn J Radiol. 2023 Oct 4. doi: 10.1007/s11604-023-01491-2.

PMID- 37692649
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - The Use of AI in Diagnosing Diseases and Providing Management Plans: A 
      Consultation on Cardiovascular Disorders With ChatGPT.
PG  - e43106
LID - 10.7759/cureus.43106 [doi]
LID - e43106
AB  - BACKGROUND: Cardiovascular diseases (CVDs) have remained the leading causes of 
      death worldwide and substantially contribute to loss of health and excess health 
      system costs. According to WHO, cardiovascular diseases (CVDs) take an estimated 
      17.9 million lives each year. One of the reasons for an immensely high fatality 
      in CVDs is lack of efficient diagnosis and prompt treatment. Timely recognition 
      and management are crucial to minimize mortality. In the advancing world, AI 
      (artificial intelligence) and machine learning technologies continue to progress, 
      this advancement has opened new avenues for innovative approaches in the field of 
      medicine. Despite the rapid development in the field of AI, there is a limited 
      understanding of the potential benefits among clinicians and medical 
      practitioners. METHODS: In this study, we aimed to investigate the potential that 
      the AI language model holds to assist health practitioners in the diagnosis and 
      treatment of cardiovascular disorders. We asked Chat Generative Pre-trained 
      Transformer (ChatGPT) 10 hypothetical questions simulating clinical consultation. 
      The responses given by ChatGPT were accessed for its accuracy and accessibility 
      by a team of medical specialists and cardiologists with extensive experience in 
      managing cardiovascular disorders.  Result: Out of the 10 clinical scenarios 
      inserted in ChatGPT, eight were perfectly diagnosed, however, the other two 
      answers given by ChatGPT were not entirely incorrect since those conditions were 
      associated with the actual diagnosis. Furthermore, the management plans and the 
      treatment protocols that were given by ChatGPT were in line with the literature 
      and current medical knowledge. The exact drug names and regimens were not 
      provided but a general guideline that was given by this AI tool is definitely 
      beneficial for junior doctors in getting an idea on how to proceed or refresh 
      their previous knowledge. CONCLUSION: ChatGPT is a valuable resource in the field 
      of medicine. Its comprehensive and properly organized response in an 
      understandable language has made it an effective and efficient tool to be used. 
      However, it is crucial to note that its limitations, such as the need for all 
      associated and typical signs, symptoms, and physical examination findings, and 
      its inability to personalize treatments need to be acknowledged.
CI  - Copyright © 2023, Rizwan et al.
FAU - Rizwan, Ayesha
AU  - Rizwan A
AD  - Medicine and Surgery, Islamic International Medical College, Islamabad, PAK.
FAU - Sadiq, Tahira
AU  - Sadiq T
AD  - Community Medicine, Islamic International Medical College, Islamabad, PAK.
LA  - eng
PT  - Journal Article
DEP - 20230807
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10483170
OTO - NOTNLM
OT  - ai and robotics in healthcare
OT  - ai consultation
OT  - artificial intelligence chatgpt-4
OT  - cardiovascular disorders
OT  - diagnosis and management
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/11 06:43
MHDA- 2023/09/11 06:44
CRDT- 2023/09/11 04:41
PHST- 2023/08/07 00:00 [accepted]
PHST- 2023/09/11 06:44 [medline]
PHST- 2023/09/11 06:43 [pubmed]
PHST- 2023/09/11 04:41 [entrez]
AID - 10.7759/cureus.43106 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 7;15(8):e43106. doi: 10.7759/cureus.43106. eCollection 2023 Aug.

PMID- 37476297
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - The Potential Usefulness of ChatGPT in Oral and Maxillofacial Radiology.
PG  - e42133
LID - 10.7759/cureus.42133 [doi]
LID - e42133
AB  - Aim This study aimed to evaluate the potential usefulness of Chat Generated 
      Pre-Trained Transformer-3 (ChatGPT-3) in oral and maxillofacial radiology for 
      report writing by identifying radiographic anatomical landmarks and learning 
      about oral and maxillofacial pathologies and their radiographic features. The 
      study also aimed to evaluate the performance of ChatGPT-3 and its usage in oral 
      and maxillofacial radiology training. Materials and methods A questionnaire 
      consisting of 80 questions was queried on the OpenAI app ChatGPT-3. The questions 
      were stratified based on three categories. The categorization was based on random 
      anatomical landmarks, oral and maxillofacial pathologies, and the radiographic 
      features of some of these pathologies. One oral and maxillofacial radiologist 
      evaluated queries that were answered by the ChatGPT-3 model and rated them on a 
      4-point, modified Likert scale. The post-survey analysis for the performance of 
      ChatGPT-3 was based on the Strengths, Weaknesses, Opportunities, and Threats 
      (SWOT) analysis, its application in oral and maxillofacial radiology training, 
      and its recommended use. Results In order of efficiency, Chat GPT-3 gave 
      100% accuracy in describing radiographic landmarks. However, the content of the 
      oral and maxillofacial pathologies was limited to major or characteristic 
      radiographic features. The mean scores for the queries related to the anatomic 
      landmarks, oral and maxillofacial pathologies, and radiographic features of the 
      oral and maxillofacial pathologies were 3.94, 3.85, and 3.96, respectively. 
      However, the median and mode scores were 4 and were similar to all categories. 
      The data for the oral and maxillofacial pathologies when the questions were not 
      specifically included in the format of the introduction of the pathology, causes, 
      symptoms, and treatment. Out of two abbreviations, one was not answered 
      correctly. Conclusion The study showed that ChatGPT-3 is efficient in describing 
      the pathology, characteristic radiographic features, and describing anatomical 
      landmarks. ChatGPT-3 can be used as an adjunct when an oral radiologist needs 
      additional information on any pathology, however, it cannot be the mainstay for 
      reference. ChatGPT-3 is less detail-oriented, and the data has a risk of 
      infodemics and the possibility of medical errors. However, Chat GPT-3 can be an 
      excellent tool in helping the community in increasing the knowledge and awareness 
      of various pathologies and decreasing the anxiety of the patients while dental 
      healthcare professionals formulate an appropriate treatment plan.
CI  - Copyright © 2023, Mago et al.
FAU - Mago, Jyoti
AU  - Mago J
AD  - Oral and Maxillofacial Radiology, University of Nevada, Las Vegas (UNLV), Las 
      Vegas, USA.
FAU - Sharma, Manoj
AU  - Sharma M
AD  - Public Health, University of Nevada, Las Vegas (UNLV), Las Vegas, USA.
LA  - eng
PT  - Journal Article
DEP - 20230719
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10355343
OTO - NOTNLM
OT  - chatgpt
OT  - open-ai
OT  - oral and maxillofacial radiology
OT  - pathology
OT  - radiographic features
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/21 06:42
MHDA- 2023/07/21 06:43
CRDT- 2023/07/21 04:03
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/07/21 06:43 [medline]
PHST- 2023/07/21 06:42 [pubmed]
PHST- 2023/07/21 04:03 [entrez]
AID - 10.7759/cureus.42133 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 19;15(7):e42133. doi: 10.7759/cureus.42133. eCollection 2023 
      Jul.

PMID- 38010917
OWN - NLM
STAT- Publisher
LR  - 20231127
IS  - 1557-9077 (Electronic)
IS  - 1050-7256 (Linking)
DP  - 2023 Nov 27
TI  - Evaluating ChatGPT responses on thyroid nodules for patient education.
LID - 10.1089/thy.2023.0491 [doi]
AB  - BACKGROUND: ChatGPT, an artificial intelligence (AI) chatbot, is the fastest 
      growing consumer application in history. Given recent trends identifying 
      increasing patient use of Internet sources for self-education, we seek to 
      evaluate the quality of ChatGPT-generated responses for patient education on 
      thyroid nodules. METHODS: ChatGPT was queried 4 times with 30 identical 
      questions. Queries differed by initial chatbot prompting: no prompting, 
      patient-friendly prompting, 8th-grade level prompting, and prompting for 
      references. Answers were scored on a hierarchical score: incorrect, partially 
      correct, correct, or correct with references. Proportions of responses at 
      incremental score thresholds were compared by prompt type using chi-squared 
      analysis. Flesch-Kincaid grade level was calculated for each answer. The 
      relationship between prompt type and grade level was assessed using analysis of 
      variance. References provided within ChatGPT answers were totaled and analyzed 
      for veracity. RESULTS: Across all prompts (n=120 questions), 83 answers (69.2%) 
      were at least correct. Proportions of responses that were at least partially 
      correct (p=0.795) and correct (p=0.402) did not differ by prompt; responses that 
      were correct with references did (p<0.0001). Responses from 8th-grade level 
      prompting were the lowest mean grade level (13.43 ± 2.86) and were significantly 
      lower than no prompting (14.97 ± 2.01, p=0.01) and prompting for references 
      (16.43 ± 2.05, p<0.0001). Prompting for references generated 80/80 (100%) of 
      referenced publications within answers. Seventy references (87.5%) were 
      legitimate citations, and 58/80 (72.5%) provided accurately reported information 
      from the referenced publications. CONCLUSION: ChatGPT overall provides 
      appropriate answers to most questions on thyroid nodules regardless of prompting. 
      Despite targeted prompting strategies, ChatGPT reliably generates responses 
      corresponding to grade levels well-above accepted recommendations for presenting 
      medical information to patients. Significant rates of AI hallucination may 
      preclude clinicians from recommending the current version of ChatGPT as an 
      educational tool for patients at this time.
FAU - Campbell, Daniel J
AU  - Campbell DJ
AD  - Thomas Jefferson University - Center City Campus, 6559, Otolaryngology - Head & 
      Neck Surgery, 925 Chestnut St, Floor 6, Philadelphia, Pennsylvania, United 
      States, 19107-5084; djc024@jefferson.edu.
FAU - Estephan, Leonard E
AU  - Estephan LE
AD  - Thomas Jefferson University - Center City Campus, 6559, Otolaryngology - Head & 
      Neck Surgery , Philadelphia, Pennsylvania, United States; 
      Leonard.estephan@jefferson.edu.
FAU - Sina, Elliott
AU  - Sina E
AD  - Thomas Jefferson University - Center City Campus, 6559, Otolaryngology - Head & 
      Neck Surgery , Philadelphia, Pennsylvania, United States; 
      Elliott.sina@students.jefferson.edu.
FAU - Mastrolonardo, Eric V
AU  - Mastrolonardo EV
AD  - Thomas Jefferson University - Center City Campus, 6559, Otolaryngology - Head & 
      Neck Surgery, Philadelphia, Pennsylvania, United States; 
      Eric.mastrolonardo@jefferson.edu.
FAU - Alapati, Rahul
AU  - Alapati R
AD  - Thomas Jefferson University - Center City Campus, 6559, Philadelphia, 
      Pennsylvania, United States; ralapati98@gmail.com.
FAU - Amin, Dev R
AU  - Amin DR
AD  - Thomas Jefferson University - Center City Campus, 6559, Otolaryngology - Head & 
      Neck Surgery, Philadelphia, Pennsylvania, United States; dev.amin@jefferson.edu.
FAU - Cottrill, Elizabeth
AU  - Cottrill E
AD  - Thomas Jefferson University, Otolaryngology - Head and Neck Surgery, 
      Philadelphia, Pennsylvania, United States; Elizabeth.Cottrill@jefferson.edu.
LA  - eng
PT  - Journal Article
DEP - 20231127
PL  - United States
TA  - Thyroid
JT  - Thyroid : official journal of the American Thyroid Association
JID - 9104317
SB  - IM
EDAT- 2023/11/27 18:44
MHDA- 2023/11/27 18:44
CRDT- 2023/11/27 12:33
PHST- 2023/11/27 18:44 [medline]
PHST- 2023/11/27 18:44 [pubmed]
PHST- 2023/11/27 12:33 [entrez]
AID - 10.1089/thy.2023.0491 [doi]
PST - aheadofprint
SO  - Thyroid. 2023 Nov 27. doi: 10.1089/thy.2023.0491.

PMID- 37716431
OWN - NLM
STAT- Publisher
LR  - 20231025
IS  - 2468-6530 (Electronic)
IS  - 2468-6530 (Linking)
DP  - 2023 Sep 15
TI  - The Use of Large Language Models to Generate Education Materials about Uveitis.
LID - S2468-6530(23)00449-9 [pii]
LID - 10.1016/j.oret.2023.09.008 [doi]
AB  - OBJECTIVE: To assess large language models in generating readable uveitis 
      information and in improving the readability of online health information. 
      DESIGN: Evaluation of technology. SUBJECTS: Not applicable. METHODS: ChatGPT and 
      Bard were asked the following prompts: (prompt A) "considering that the average 
      American reads at a 6th grade level, using the Flesch-Kincaid Grade Level (FKGL) 
      formula, can you write patient-targeted health information on uveitis of around 
      6th grade level?" and (prompt B) "can you write patient-targeted health 
      information on uveitis that is easy to understand by an average American?" 
      Additionally, ChatGPT and Bard were asked the following prompt from the 
      first-page results of Google when the term "uveitis" was searched: "Considering 
      that the average American reads at a 6th grade level, using the FKGL formula, can 
      you rewrite the following text to 6th grade level: [insert text]." The 
      readability of each response was analyzed and compared using several metrics 
      described below. MAIN OUTCOME MEASURES: The FKGL is a highly validated 
      readability assessment tool that assigns a grade level to a given text, the total 
      number of words, sentences, syllables, and complex words. Complex words were 
      defined as those with > 2 syllables. RESULTS: ChatGPT and Bard generated 
      responses with lower FKGL scores (i.e., easier to understand) in response to 
      prompt A compared with prompt B. This was only significant for ChatGPT (P < 
      0.0001). The mean FKGL of responses to ChatGPT (6.3 ± 1.2) was significantly 
      lower (P < 0.0001) than Bard 10.5 ± 0.8. ChatGPT responses also contained less 
      complex words than Bard (P < 0.0001). Online health information on uveitis had a 
      mean grade level of 11.0 ± 1.4. ChatGPT lowered the FKGL to 8.0 ± 1.0 (P < 
      0.0001) when asked to rewrite the content. Bard was not able to do so (mean FKGL 
      of 11.1 ± 1.6). CONCLUSIONS: ChatGPT can aid clinicians in producing 
      easier-to-understand health information on uveitis for patients compared with 
      already-existing content. It can also help with reducing the difficulty of the 
      language used for uveitis health information targeted for patients. FINANCIAL 
      DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes 
      and Disclosures at the end of this article.
CI  - Copyright © 2023 American Academy of Ophthalmology. Published by Elsevier Inc. 
      All rights reserved.
FAU - Kianian, Reza
AU  - Kianian R
AD  - Stein Eye Institute, Department of Ophthalmology, David Geffen School of 
      Medicine, University of California Los Angeles, Los Angeles, California.
FAU - Sun, Deyu
AU  - Sun D
AD  - Stein Eye Institute, Department of Ophthalmology, David Geffen School of 
      Medicine, University of California Los Angeles, Los Angeles, California.
FAU - Crowell, Eric L
AU  - Crowell EL
AD  - Mitchel and Shannon Wong Eye Institute, Dell Medical School at the University of 
      Texas at Austin, Austin, Texas.
FAU - Tsui, Edmund
AU  - Tsui E
AD  - Stein Eye Institute, Department of Ophthalmology, David Geffen School of 
      Medicine, University of California Los Angeles, Los Angeles, California. 
      Electronic address: ETsui@mednet.ucla.edu.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - United States
TA  - Ophthalmol Retina
JT  - Ophthalmology. Retina
JID - 101695048
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bard
OT  - ChatGPT
OT  - Consumer health information
OT  - Readability
OT  - Uveitis
EDAT- 2023/09/17 00:41
MHDA- 2023/09/17 00:41
CRDT- 2023/09/16 19:13
PHST- 2023/07/16 00:00 [received]
PHST- 2023/09/08 00:00 [revised]
PHST- 2023/09/08 00:00 [accepted]
PHST- 2023/09/17 00:41 [pubmed]
PHST- 2023/09/17 00:41 [medline]
PHST- 2023/09/16 19:13 [entrez]
AID - S2468-6530(23)00449-9 [pii]
AID - 10.1016/j.oret.2023.09.008 [doi]
PST - aheadofprint
SO  - Ophthalmol Retina. 2023 Sep 15:S2468-6530(23)00449-9. doi: 
      10.1016/j.oret.2023.09.008.

PMID- 37123797
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230502
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - A Case Report on Ground-Level Alternobaric Vertigo Due to Eustachian Tube 
      Dysfunction With the Assistance of Conversational Generative Pre-trained 
      Transformer (ChatGPT).
PG  - e36830
LID - 10.7759/cureus.36830 [doi]
LID - e36830
AB  - Alternatenobaric vertigo (ABV) develops when the middle ear pressure (MEP) is not 
      equal at the same height in the sea or the air. This is possible when the 
      altitude changes. Eustachian tube dysfunction (ETD) is a common cause of ABV. In 
      this case report, we discuss a patient who experienced repeated bouts of 
      ground-level alternobaric vertigo (GLABV) due to ETD. We also discuss how 
      Conversational Generative Pre-trained Transformer (ChatGPT) might be used in the 
      creation of this case report. A 41-year-old male patient complained of vertigo at 
      ground level on several occasions. His medical history included chronic 
      sinusitis, nasal congestion, and laryngopharyngeal reflux (LPR). During the 
      physical exam, his tympanic membranes were dull and moved less. Tympanometry 
      showed that he had an asymmetric type A and that both of his middle ears had 
      negative pressure. The results of the audiometry test were normal, and the 
      laryngoscopy revealed LPR. The patient was found to have GLABV because of ETD, 
      and different treatment options, such as Eustachian tube catheterization (ETC), 
      were thought about. This case study demonstrates how ChatGPT can be used to 
      assist with medical documentation and the treatment of GLABV caused by ETD. Even 
      though ChatGPT did not provide specific diagnostic or treatment recommendations 
      for the patient's condition, it did assist the doctor in determining what was 
      wrong and how to treat it while writing the case report. It also aided the doctor 
      in writing the case report by allowing them to discuss it. The use of artificial 
      intelligence (AI) tools such as ChatGPT has the potential to improve the accuracy 
      and speed of medical documentation, thereby streamlining clinical workflows and 
      improving patient care. Nonetheless, it is critical to consider the ethical 
      implications of using AI in clinical practice This case study emphasizes the 
      importance of understanding that ETD is a common cause of GLABV and how ChatGPT 
      can aid in the diagnosis and treatment of this condition. More research is needed 
      to fully understand how long-term AI interventions in medicine work and how 
      reliable they are.
CI  - Copyright © 2023, Kim et al.
FAU - Kim, Hee-Young
AU  - Kim HY
AD  - Center for Executive and Continuing Professional Education, Harvard T.H. Chan 
      School of Public Health, Boston, USA.
AD  - Department of Otolaryngology - Head and Neck Surgery, Samsung Seoul Hospital, 
      Seoul, KOR.
AD  - Otolaryngology, Kim ENT Clinic, Seoul, KOR.
LA  - eng
PT  - Case Reports
DEP - 20230328
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10140002
OTO - NOTNLM
OT  - alternobaric vertigo
OT  - artificial intelligence
OT  - chatgpt
OT  - eustachian tube catheterization
OT  - eustachian tube dysfunction
OT  - ground-level alternobaric vertigo
OT  - laryngopharyngeal reflux
OT  - middle ear pressure
OT  - tympanometry
OT  -  laryngoscopy
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/01 06:41
MHDA- 2023/05/01 06:42
CRDT- 2023/05/01 03:34
PHST- 2023/03/28 00:00 [accepted]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/05/01 06:41 [pubmed]
PHST- 2023/05/01 03:34 [entrez]
AID - 10.7759/cureus.36830 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 28;15(3):e36830. doi: 10.7759/cureus.36830. eCollection 2023 
      Mar.

PMID- 37423350
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231113
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT-4 Assistance in Optimizing Emergency Department Radiology Referrals and 
      Imaging Selection.
PG  - 998-1003
LID - S1546-1440(23)00484-2 [pii]
LID - 10.1016/j.jacr.2023.06.009 [doi]
AB  - PURPOSE: The quality of radiology referrals influences patient management and 
      imaging interpretation by radiologists. The aim of this study was to evaluate 
      ChatGPT-4 as a decision support tool for selecting imaging examinations and 
      generating radiology referrals in the emergency department (ED). METHODS: Five 
      consecutive clinical notes from the ED were retrospectively extracted, for each 
      of the following pathologies: pulmonary embolism, obstructing kidney stones, 
      acute appendicitis, diverticulitis, small bowel obstruction, acute cholecystitis, 
      acute hip fracture, and testicular torsion. A total of 40 cases were included. 
      These notes were entered into ChatGPT-4, requesting recommendations on the most 
      appropriate imaging examinations and protocols. The chatbot was also asked to 
      generate radiology referrals. Two independent radiologists graded the referral on 
      a scale ranging from 1 to 5 for clarity, clinical relevance, and differential 
      diagnosis. The chatbot's imaging recommendations were compared with the ACR 
      Appropriateness Criteria (AC) and with the examinations performed in the ED. 
      Agreement between readers was assessed using linear weighted Cohen's κ 
      coefficient. RESULTS: ChatGPT-4's imaging recommendations aligned with the ACR AC 
      and ED examinations in all cases. Protocol discrepancies between ChatGPT and the 
      ACR AC were observed in two cases (5%). ChatGPT-4-generated referrals received 
      mean scores of 4.6 and 4.8 for clarity, 4.5 and 4.4 for clinical relevance, and 
      4.9 from both reviewers for differential diagnosis. Agreement between readers was 
      moderate for clinical relevance and clarity and substantial for differential 
      diagnosis grading. CONCLUSIONS: ChatGPT-4 has shown potential in aiding imaging 
      study selection for select clinical cases. As a complementary tool, large 
      language models may improve radiology referral quality. Radiologists should stay 
      informed about this technology and be mindful of potential challenges and risks.
CI  - Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All 
      rights reserved.
FAU - Barash, Yiftach
AU  - Barash Y
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; 
      DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel. Electronic 
      address: yibarash@gmail.com.
FAU - Klang, Eyal
AU  - Klang E
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; Head, 
      Sami Sagol AI Hub, ARC, Chaim Sheba Medical Center, Tel Hashomer, Israel; 
      DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel.
FAU - Konen, Eli
AU  - Konen E
AD  - Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; Head, 
      Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel.
FAU - Sorin, Vera
AU  - Sorin V
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, 
      Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; 
      DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230707
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
MH  - Humans
MH  - Retrospective Studies
MH  - Radiography
MH  - *Radiology
MH  - Emergency Service, Hospital
MH  - *Hip Fractures
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Large language models
OT  - radiology
OT  - referrals
EDAT- 2023/07/10 00:41
MHDA- 2023/11/03 06:44
CRDT- 2023/07/09 19:24
PHST- 2023/05/09 00:00 [received]
PHST- 2023/06/19 00:00 [revised]
PHST- 2023/06/29 00:00 [accepted]
PHST- 2023/11/03 06:44 [medline]
PHST- 2023/07/10 00:41 [pubmed]
PHST- 2023/07/09 19:24 [entrez]
AID - S1546-1440(23)00484-2 [pii]
AID - 10.1016/j.jacr.2023.06.009 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Oct;20(10):998-1003. doi: 10.1016/j.jacr.2023.06.009. Epub 
      2023 Jul 7.

PMID- 37601325
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230823
IS  - 1178-2390 (Print)
IS  - 1178-2390 (Electronic)
IS  - 1178-2390 (Linking)
VI  - 16
DP  - 2023
TI  - Impact of ChatGPT on Teleconsultants in Healthcare: Perceptions of Healthcare 
      Experts in Saudi Arabia.
PG  - 2309-2321
LID - 10.2147/JMDH.S419847 [doi]
AB  - PURPOSE: This study aims to investigate the impact of ChatGPT on teleconsultants 
      in managing their operations and services. METHODS: A qualitative approach with 
      focus groups is adopted in this study. A total of 54 participants with varying 
      degrees of experience using AI such as ChatGPT in healthcare, including 11 
      physicians, 24 nurses, eight dieticians, six pharmacists, and five 
      physiotherapists providing teleconsultations participated in this study. RESULTS: 
      Twelve themes including informational support, diagnostic assistance, 
      communication, enhancing efficiency, cost and time saving, personalizing care, 
      multilingual support, assisting in medical research, decision-making, 
      documentation, continuing education, and enhanced team collaboration reflecting 
      positive impact were identified from the data analysis of seven focus groups. In 
      addition, six themes including misdiagnosis and errors, issues in personalized 
      care, ethical and legal issues, limited medical context/knowledge, communication 
      challenges, and increased dependency reflecting negative impact were identified. 
      CONCLUSION: Although ChatGPT has several advantages for teleconsultants in the 
      healthcare sector, it is associated with ethical issues.
CI  - © 2023 Alanzi.
FAU - Alanzi, Turki M
AU  - Alanzi TM
AUID- ORCID: 0000-0001-6598-1274
AD  - Health Information Management and Technology Department, College of Public 
      Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20230814
PL  - New Zealand
TA  - J Multidiscip Healthc
JT  - Journal of multidisciplinary healthcare
JID - 101512691
PMC - PMC10438433
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - decision-making
OT  - healthcare
OT  - information
OT  - teleconsultants
COIS- The authors report no conflicts of interest in this work.
EDAT- 2023/08/21 06:43
MHDA- 2023/08/21 06:44
CRDT- 2023/08/21 04:59
PHST- 2023/05/04 00:00 [received]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/08/21 06:44 [medline]
PHST- 2023/08/21 06:43 [pubmed]
PHST- 2023/08/21 04:59 [entrez]
AID - 419847 [pii]
AID - 10.2147/JMDH.S419847 [doi]
PST - epublish
SO  - J Multidiscip Healthc. 2023 Aug 14;16:2309-2321. doi: 10.2147/JMDH.S419847. 
      eCollection 2023.

PMID- 37992892
OWN - NLM
STAT- Publisher
LR  - 20231202
IS  - 0003-4509 (Print)
IS  - 0003-4509 (Linking)
DP  - 2023 Nov 20
TI  - Assessing the applicability and appropriateness of ChatGPT in answering clinical 
      pharmacy questions.
LID - S0003-4509(23)00140-2 [pii]
LID - 10.1016/j.pharma.2023.11.001 [doi]
AB  - OBJECTIVES: Clinical pharmacists rely on different scientific references to 
      ensure appropriate, safe, and cost-effective drug use. Tools based on artificial 
      intelligence (AI) such as ChatGPT (Generative Pre-trained Transformer) could 
      offer valuable support. The objective of this study was to assess ChatGPT's 
      capacity to correctly respond to clinical pharmacy questions asked by healthcare 
      professionals in our university hospital. MATERIAL AND METHODS: ChatGPT's 
      capacity to respond correctly to the last 100 consecutive questions recorded in 
      our clinical pharmacy database was assessed. Questions were copied from our 
      FileMaker Pro database and pasted into ChatGPT March 14 version online platform. 
      The generated answers were then copied verbatim into an Excel file. Two blinded 
      clinical pharmacists reviewed all the questions and the answers given by the 
      software. In case of disagreements, a third blinded pharmacist intervened to 
      decide. RESULTS: Documentation-related issues (n=36) and drug administration mode 
      (n=30) were preponderantly recorded. Among 69 applicable questions, the rate of 
      correct answers varied from 30 to 57.1% depending on questions type with a global 
      rate of 44.9%. Regarding inappropriate answers (n=38), 20 were incorrect, 18 gave 
      no answers and 8 were incomplete with 8 answers belonging to 2 different 
      categories. No better answers than the pharmacists were observed. CONCLUSIONS: 
      ChatGPT demonstrated a mitigated performance in answering clinical pharmacy 
      questions. It should not replace human expertise as a high rate of inappropriate 
      answers was highlighted. Future studies should focus on the optimization of 
      ChatGPT for specific clinical pharmacy questions and explore the potential 
      benefits and limitations of integrating this technology into clinical practice.
CI  - Copyright © 2023 Académie Nationale de Pharmacie. Published by Elsevier Masson 
      SAS. All rights reserved.
FAU - Fournier, A
AU  - Fournier A
AD  - Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, 
      Switzerland.
FAU - Fallet, C
AU  - Fallet C
AD  - Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, 
      Switzerland.
FAU - Sadeghipour, F
AU  - Sadeghipour F
AD  - Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, 
      Switzerland; School of Pharmaceutical Sciences, University of Geneva, University 
      of Lausanne, Geneva, Switzerland; Center for Research and Innovation in Clinical 
      Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, 
      Lausanne, Switzerland.
FAU - Perrottet, N
AU  - Perrottet N
AD  - Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, 
      Switzerland; School of Pharmaceutical Sciences, University of Geneva, University 
      of Lausanne, Geneva, Switzerland. Electronic address: Nancy.Perrottet@chuv.ch.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - France
TA  - Ann Pharm Fr
JT  - Annales pharmaceutiques francaises
JID - 2985176R
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Clinical pharmacy
OT  - Grands modèles de langage
OT  - Healthcare professionals’ issues
OT  - Intelligence artificielle
OT  - Large language models
OT  - Pharmacie clinique
OT  - Questions fréquentes des professionnels de la santé
EDAT- 2023/11/23 00:42
MHDA- 2023/11/23 00:42
CRDT- 2023/11/22 19:29
PHST- 2023/08/28 00:00 [received]
PHST- 2023/11/16 00:00 [revised]
PHST- 2023/11/16 00:00 [accepted]
PHST- 2023/11/23 00:42 [pubmed]
PHST- 2023/11/23 00:42 [medline]
PHST- 2023/11/22 19:29 [entrez]
AID - S0003-4509(23)00140-2 [pii]
AID - 10.1016/j.pharma.2023.11.001 [doi]
PST - aheadofprint
SO  - Ann Pharm Fr. 2023 Nov 20:S0003-4509(23)00140-2. doi: 
      10.1016/j.pharma.2023.11.001.

PMID- 37546795
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Jul 27
TI  - Interdisciplinary Inquiry via PanelGPT: Application to Explore Chatbot 
      Application in Sports Rehabilitation.
LID - 2023.07.23.23292452 [pii]
LID - 10.1101/2023.07.23.23292452 [doi]
AB  - BACKGROUND: ChatGPT showcases exceptional conversational capabilities and 
      extensive cross-disciplinary knowledge. In addition, it possesses the ability to 
      perform multiple roles within a single chat session. This unique 
      multi-role-playing feature positions ChatGPT as a promising tool to explore 
      interdisciplinary subjects. OBJECTIVE: The study intended to guide ChatGPT for 
      interdisciplinary exploration through simulated panel discussions. As a 
      proof-of-concept, we employed this method to evaluate the advantages and 
      challenges of using chatbots in sports rehabilitation. METHODS: We proposed a 
      model termed PanelGPT to explore ChatGPTs' knowledge graph on interdisciplinary 
      topics through simulated panel discussions. Applied to "chatbots in sports 
      rehabilitation", ChatGPT role-played both the moderator and panelists, which 
      included a physiotherapist, psychologist, nutritionist, AI expert, and an 
      athlete. We act as the audience posed questions to the panel, with ChatGPT acting 
      as both the panelists for responses and the moderator for hosting the discussion. 
      We performed the simulation using the ChatGPT-4 model and evaluated the responses 
      with existing literature and human expertise. RESULTS: Each simulation mimicked a 
      real-life panel discussion: The moderator introduced the panel and posed 
      opening/closing questions, to which all panelists responded. The experts engaged 
      with each other to address inquiries from the audience, primarily from their 
      respective fields of expertise. By tackling questions related to education, 
      physiotherapy, physiology, nutrition, and ethical consideration, the discussion 
      highlighted benefits such as 24/7 support, personalized advice, automated 
      tracking, and reminders. It also emphasized the importance of user education and 
      identified challenges such as limited interaction modes, inaccuracies in 
      emotion-related advice, assurance on data privacy and security, transparency in 
      data handling, and fairness in model training. The panelists reached a consensus 
      that chatbots are designed to assist, not replace, human healthcare professionals 
      in the rehabilitation process. CONCLUSIONS: Compared to a typical conversation 
      with ChatGPT, the multi-perspective approach of PanelGPT facilitates a 
      comprehensive understanding of an interdisciplinary topic by integrating insights 
      from experts with complementary knowledge. Beyond addressing the exemplified 
      topic of chatbots in sports rehabilitation, the model can be adapted to tackle a 
      wide array of interdisciplinary topics within educational, research, and 
      healthcare settings.
FAU - McBee, Joseph C
AU  - McBee JC
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV 26506, USA.
AD  - Department of Chemical and Biomedical Engineering, West Virginia University, 
      Morgantown, WV 26506, USA.
FAU - Han, Daniel Y
AU  - Han DY
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV 26506, USA.
FAU - Liu, Li
AU  - Liu L
AD  - College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
AD  - Biodesign Institute, Arizona State University, Tempe, AZ, 85281 USA.
FAU - Ma, Leah
AU  - Ma L
AD  - College of Health, Education, and Human Services, Wright State University, 
      Dayton, OH 45345, USA.
FAU - Adjeroh, Donald A
AU  - Adjeroh DA
AD  - Lane Department of Computer Science & Electrical Engineering, West Virginia 
      University, Morgantown, WV 26506, USA.
FAU - Xu, Dong
AU  - Xu D
AD  - Department of Electrical Engineer and Computer Science, Christopher S. Bond Life 
      Sciences Center, University of Missouri, Columbia, MO65211, USA.
FAU - Hu, Gangqing
AU  - Hu G
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV 26506, USA.
LA  - eng
GR  - R01 LM013392/LM/NLM NIH HHS/United States
GR  - U54 GM104942/GM/NIGMS NIH HHS/United States
GR  - P20 GM121322/GM/NIGMS NIH HHS/United States
GR  - P20 GM103434/GM/NIGMS NIH HHS/United States
GR  - R01 LM013438/LM/NLM NIH HHS/United States
PT  - Preprint
DEP - 20230727
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10402232
OTO - NOTNLM
OT  - ChatGPT
OT  - chatbots
OT  - interdisciplinary inquiry
OT  - multi-role-playing
OT  - sports medicine
COIS- Competing Interests The Authors declare no Competing Financial or Non-Financial 
      Interests
EDAT- 2023/08/07 06:41
MHDA- 2023/08/07 06:42
CRDT- 2023/08/07 04:43
PHST- 2023/08/07 06:41 [pubmed]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/08/07 04:43 [entrez]
AID - 2023.07.23.23292452 [pii]
AID - 10.1101/2023.07.23.23292452 [doi]
PST - epublish
SO  - medRxiv. 2023 Jul 27:2023.07.23.23292452. doi: 10.1101/2023.07.23.23292452. 
      Preprint.

PMID- 37840252
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231120
IS  - 1975-5937 (Electronic)
IS  - 1975-5937 (Linking)
VI  - 20
DP  - 2023
TI  - Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in 
      medical education in Serbia: a descriptive study.
PG  - 28
LID - 10.3352/jeehp.2023.20.28 [doi]
LID - 28
AB  - PURPOSE: This study aimed to assess the performance of ChatGPT (GPT-3.5 and 
      GPT-4) as a study tool in solving biostatistical problems and to identify any 
      potential drawbacks that might arise from using ChatGPT in medical education, 
      particularly in solving practical biostatistical problems. METHODS: ChatGPT was 
      tested to evaluate its ability to solve biostatistical problems from the Handbook 
      of Medical Statistics by Peacock and Peacock in this descriptive study. Tables 
      from the problems were transformed into textual questions. Ten biostatistical 
      problems were randomly chosen and used as text-based input for conversation with 
      ChatGPT (versions 3.5 and 4). RESULTS: GPT-3.5 solved 5 practical problems in the 
      first attempt, related to categorical data, cross-sectional study, measuring 
      reliability, probability properties, and the t-test. GPT-3.5 failed to provide 
      correct answers regarding analysis of variance, the chi-square test, and sample 
      size within 3 attempts. GPT-4 also solved a task related to the confidence 
      interval in the first attempt and solved all questions within 3 attempts, with 
      precise guidance and monitoring. CONCLUSION: The assessment of both versions of 
      ChatGPT performance in 10 biostatistical problems revealed that GPT-3.5 and 4’s 
      performance was below average, with correct response rates of 5 and 6 out of 10 
      on the first attempt. GPT-4 succeeded in providing all correct answers within 3 
      attempts. These findings indicate that students must be aware that this tool, 
      even when providing and calculating different statistical analyses, can be wrong, 
      and they should be aware of ChatGPT’s limitations and be careful when 
      incorporating this model into medical education.
FAU - Ignjatović, Aleksandra
AU  - Ignjatović A
AD  - Department of Medical Statistics and Informatics, Faculty of Medicine, University 
      of Niš, Niš, Serbia.
FAU - Stevanović, Lazar
AU  - Stevanović L
AD  - Faculty of Medicine, University of Niš, Niš, Serbia.
LA  - eng
PT  - Journal Article
DEP - 20231016
PL  - Korea (South)
TA  - J Educ Eval Health Prof
JT  - Journal of educational evaluation for health professions
JID - 101490061
SB  - IM
MH  - Humans
MH  - Cross-Sectional Studies
MH  - Reproducibility of Results
MH  - *Education, Medical
MH  - Awareness
MH  - Communication
PMC - PMC10646144
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Biostatistics
OT  - Medical education
OT  - Reproducibility of results
COIS- Conflict of interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/10/16 06:48
MHDA- 2023/11/01 12:45
CRDT- 2023/10/16 03:20
PHST- 2023/09/21 00:00 [received]
PHST- 2023/10/10 00:00 [accepted]
PHST- 2023/11/01 12:45 [medline]
PHST- 2023/10/16 06:48 [pubmed]
PHST- 2023/10/16 03:20 [entrez]
AID - jeehp.2023.20.28 [pii]
AID - jeehp-20-28 [pii]
AID - 10.3352/jeehp.2023.20.28 [doi]
PST - ppublish
SO  - J Educ Eval Health Prof. 2023;20:28. doi: 10.3352/jeehp.2023.20.28. Epub 2023 Oct 
      16.

PMID- 37659658
OWN - NLM
STAT- Publisher
LR  - 20231022
IS  - 1879-0887 (Electronic)
IS  - 0167-8140 (Linking)
VI  - 188
DP  - 2023 Nov
TI  - Potential of ChatGPT in facilitating research in radiation oncology?
PG  - 109894
LID - S0167-8140(23)89788-2 [pii]
LID - 10.1016/j.radonc.2023.109894 [doi]
AB  - PURPOSE: To evaluate the potential of the artificial intelligence (AI) chatbot 
      ChatGPT in supporting young clinical scientists with scientific tasks in radio 
      oncological research. MATERIALS AND METHODS: Seven scientific tasks were to be 
      completed in 3 h by 8 radiation oncologists with different scientific experience 
      working at a university hospital: creation of a scientific synopsis, creation of 
      a research question and corresponding clinical trial hypotheses, writing of the 
      first paragraph of a manuscript introduction, clinical trial sample size 
      calculation, and clinical data analyses (multivariate analysis, boxplot and 
      survival curve). No participant had prior experience with an AI chatbot. All 
      participants were instructed in ChatGPT v3.5 and its use was provided for all 
      tasks. Answers were scored independently by two blinded experts. The subjective 
      value of ChatGPT was rated by each participant. Data were analyzed with 
      regression-, t-test and Spearman correlation (p < 0.05). RESULTS: Participants 
      completed tasks 1-3 with an average score of 50% and 4-7 with 56%. Scientific 
      experience, number of original publications and of first/last authorships showed 
      a positive correlation with overall scoring (p = 0.01-0.04). Participants with 
      little to moderate scientific experience scored ChatGPT to be more helpful in 
      solving tasks 4-7 compared to more experienced participants (p = 0.04), with 
      simultaneously presenting lower scorings (p = 0.03). CONCLUSIONS: ChatGPT did not 
      compensate for differences in scientific experience of young clinical scientists, 
      with less experienced researchers believing false AI-generated scientific 
      results.
CI  - Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.
FAU - Guckenberger, Matthias
AU  - Guckenberger M
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Andratschke, Nicolaus
AU  - Andratschke N
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Ahmadsei, Maiwand
AU  - Ahmadsei M
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Christ, Sebastian Matthias
AU  - Christ SM
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Heusel, Astrid Elisabeth
AU  - Heusel AE
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Kamal, Sandeep
AU  - Kamal S
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Kroese, Tiuri Ewout
AU  - Kroese TE
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Looman, Esmée Lauren
AU  - Looman EL
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Reichl, Sabrina
AU  - Reichl S
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Vlaskou Badra, Eugenia
AU  - Vlaskou Badra E
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - von der Grün, Jens
AU  - von der Grün J
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Willmann, Jonas
AU  - Willmann J
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Tanadini-Lang, Stephanie
AU  - Tanadini-Lang S
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland.
FAU - Mayinger, Michael
AU  - Mayinger M
AD  - Department of Radiation Oncology, University Hospital of Zurich, University of 
      Zurich, Zurich, Switzerland. Electronic address: michael.mayinger@usz.ch.
LA  - eng
PT  - Journal Article
DEP - 20230901
PL  - Ireland
TA  - Radiother Oncol
JT  - Radiotherapy and oncology : journal of the European Society for Therapeutic 
      Radiology and Oncology
JID - 8407192
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Radiation oncology
COIS- Declaration of Competing Interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: [Dr. Andratschke reports personal fees from AstraZeneca, personal fees 
      from Debiopharm, grants, personal fees and nonfinancial support from ViewRay, 
      grants and personal fees from Brainlab, outside the submitted work. Dr. 
      Tanadini-Lang reports that her husband works at Varian Medical Systems. The other 
      authors report no conflicts of interest.].
EDAT- 2023/09/03 00:41
MHDA- 2023/09/03 00:41
CRDT- 2023/09/02 19:27
PHST- 2023/05/14 00:00 [received]
PHST- 2023/08/20 00:00 [revised]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/09/03 00:41 [pubmed]
PHST- 2023/09/03 00:41 [medline]
PHST- 2023/09/02 19:27 [entrez]
AID - S0167-8140(23)89788-2 [pii]
AID - 10.1016/j.radonc.2023.109894 [doi]
PST - ppublish
SO  - Radiother Oncol. 2023 Nov;188:109894. doi: 10.1016/j.radonc.2023.109894. Epub 
      2023 Sep 1.

PMID- 38015597
OWN - NLM
STAT- MEDLINE
DCOM- 20231129
LR  - 20231129
IS  - 1438-8871 (Electronic)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Nov 28
TI  - Security Implications of AI Chatbots in Health Care.
PG  - e47551
LID - 10.2196/47551 [doi]
AB  - Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer 
      programs that use AI and natural language processing to understand customer 
      questions and generate natural, fluid, dialogue-like responses to their inputs. 
      ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool 
      on the internet. AI chatbots have the potential to improve patient care and 
      public health. However, they are trained on massive amounts of people's data, 
      which may include sensitive patient data and business information. The increased 
      use of chatbots introduces data security issues, which should be handled yet 
      remain understudied. This paper aims to identify the most important security 
      problems of AI chatbots and propose guidelines for protecting sensitive health 
      information. It explores the impact of using ChatGPT in health care. It also 
      identifies the principal security risks of ChatGPT and suggests key 
      considerations for security risk mitigation. It concludes by discussing the 
      policy implications of using AI chatbots in health care.
CI  - ©Jingquan Li. Originally published in the Journal of Medical Internet Research 
      (https://www.jmir.org), 28.11.2023.
FAU - Li, Jingquan
AU  - Li J
AUID- ORCID: 0000-0002-9654-1770
AD  - Hofstra University, Hempstead, NY, United States.
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Software
MH  - Natural Language Processing
MH  - Commerce
MH  - Delivery of Health Care
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - HIPAA
OT  - artificial intelligence
OT  - care
OT  - chatbot
OT  - computer program
OT  - data security
OT  - guidelines
OT  - health information
OT  - improvement
OT  - natural language processing
OT  - patient care
OT  - policy
OT  - privacy
OT  - risk
OT  - security
OT  - tool
EDAT- 2023/11/28 12:42
MHDA- 2023/11/29 06:42
CRDT- 2023/11/28 11:53
PHST- 2023/03/24 00:00 [received]
PHST- 2023/11/20 00:00 [accepted]
PHST- 2023/08/30 00:00 [revised]
PHST- 2023/11/29 06:42 [medline]
PHST- 2023/11/28 12:42 [pubmed]
PHST- 2023/11/28 11:53 [entrez]
AID - v25i1e47551 [pii]
AID - 10.2196/47551 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Nov 28;25:e47551. doi: 10.2196/47551.

PMID- 37456381
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - Snakebite Advice and Counseling From Artificial Intelligence: An Acute Venomous 
      Snakebite Consultation With ChatGPT.
PG  - e40351
LID - 10.7759/cureus.40351 [doi]
LID - e40351
AB  - BACKGROUND: Snakebites, particularly from venomous species, present a significant 
      global public health challenge. Access to accurate and timely information 
      regarding snakebite prevention, recognition, and management is crucial for 
      minimizing morbidity and mortality. Artificial intelligence (AI) language models, 
      such as ChatGPT (Chat Generative Pre-trained Transformer), have the potential to 
      revolutionize the dissemination of medical information and improve patient 
      education and satisfaction. METHODS: This study aimed to explore the utility of 
      ChatGPT, an advanced language model, in simulating acute venomous snakebite 
      consultations. Nine hypothetical questions based on comprehensive snakebite 
      management guidelines were posed to ChatGPT, and the responses were evaluated by 
      clinical toxicologists and emergency medicine physicians. RESULTS: ChatGPT 
      provided accurate and informative responses related to the immediate management 
      of snakebites, the urgency of seeking medical attention, symptoms, and health 
      issues following venomous snakebites, the role of antivenom, misconceptions about 
      snakebites, recovery, pain management, and prevention strategies. The model 
      highlighted the importance of seeking professional medical care and adhering to 
      healthcare practitioners' advice. However, some limitations were identified, 
      including outdated knowledge, lack of personalization, and inability to consider 
      regional variations and individual characteristics. CONCLUSION: ChatGPT 
      demonstrated proficiency in generating intelligible and well-informed responses 
      related to venomous snakebites. It offers accessible and real-time advice, making 
      it a valuable resource for preliminary information, education, and triage support 
      in remote or underserved areas. While acknowledging its limitations, such as the 
      need for up-to-date information and personalized advice, ChatGPT can serve as a 
      supplementary source of information to complement professional medical 
      consultation and enhance patient education. Future research should focus on 
      addressing the identified limitations and establishing region-specific guidelines 
      for snakebite management.
CI  - Copyright © 2023, Altamimi et al.
FAU - Altamimi, Ibraheem
AU  - Altamimi I
AD  - College of Medicine, King Saud University, Riyadh, SAU.
FAU - Altamimi, Abdullah
AU  - Altamimi A
AD  - Pediatric Emergency and Toxicology Department, King Fahd Medical City, Riyadh, 
      SAU.
FAU - Alhumimidi, Abdullah S
AU  - Alhumimidi AS
AD  - College of Medicine, King Saud University, Riyadh, SAU.
FAU - Altamimi, Abdulaziz
AU  - Altamimi A
AD  - College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, 
      Riyadh, SAU.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Intensive Care Unit, Pediatric Department, King Saud University Medical 
      City, Riyadh, SAU.
LA  - eng
PT  - Journal Article
DEP - 20230613
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10339276
OTO - NOTNLM
OT  - acute venomous snakebite
OT  - ai and machine learning
OT  - ai chatbot
OT  - artificial intelligence and education
OT  - artificial intelligence in healthcare
OT  - chatgpt
OT  - emergency medicine
OT  - medical artificial intelligence
OT  - snake-bite
OT  - toxicology and envenomation
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/17 06:42
MHDA- 2023/07/17 06:43
CRDT- 2023/07/17 04:23
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/07/17 06:43 [medline]
PHST- 2023/07/17 06:42 [pubmed]
PHST- 2023/07/17 04:23 [entrez]
AID - 10.7759/cureus.40351 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 13;15(6):e40351. doi: 10.7759/cureus.40351. eCollection 2023 
      Jun.

PMID- 37197105
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230519
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Evaluation of ChatGPT's Capabilities in Medical Report Generation.
PG  - e37589
LID - 10.7759/cureus.37589 [doi]
LID - e37589
AB  - The growing demand for efficient healthcare delivery has intensified the need for 
      technological innovations that facilitate medical professionals' decision-making 
      processes. In this study, we investigate ChatGPT (OpenAI Incorporated, Mission 
      District, San Francisco, United States), a state-of-the-art language model based 
      on the GPT-4 architecture, as an effective tool for assisting healthcare 
      professionals in writing medical reports based on real patient laboratory 
      results. By leveraging ChatGPT's extraordinary performance across multiple 
      medical domains, including lab result diagnostics and medical literature 
      analysis, we aimed to streamline and enhance the medical report generation 
      process. The generated case report presents a 31-year-old male patient with no 
      significant past medical history who visited a clinic to establish care and seek 
      evaluation for abdominal pain. Following routine laboratory tests, including a 
      complete blood count, comprehensive metabolic panel, and a Helicobacter pylori 
      breath test, ChatGPT provided tailored recommendations addressing identified 
      concerns and abnormalities. These included lifestyle modifications, such as 
      dietary changes, weight management, and avoiding trigger foods or behaviors; 
      alongside medical treatment options, the patient was advised to consult a 
      gastroenterologist for further evaluation and potential advanced treatment 
      options. The organization and structure of this case study are derived from 
      ChatGPT's output, using patient's actual physical information and lab results as 
      input, without any prior knowledge. Ultimately, we will compare the generated 
      report with suggestions from an online doctor consultation system to demonstrate 
      the precision and reliability of ChatGPT's recommendations. Through this 
      comparison, we aim to show that ChatGPT can produce coherent, comprehensive, and 
      clinically relevant medical reports with a relatively high degree of accuracy and 
      consistency.
CI  - Copyright © 2023, Zhou et al.
FAU - Zhou, Zeyu
AU  - Zhou Z
AD  - Computer Science, Georgia Institute of Technology, Atlanta, USA.
LA  - eng
PT  - Case Reports
DEP - 20230414
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10184716
OTO - NOTNLM
OT  - chatgpt
OT  - epigastric
OT  - epigastric pain
OT  - gerd
OT  - medical examination
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/18 01:07
MHDA- 2023/05/18 01:08
CRDT- 2023/05/17 19:55
PHST- 2023/04/14 00:00 [accepted]
PHST- 2023/05/18 01:08 [medline]
PHST- 2023/05/18 01:07 [pubmed]
PHST- 2023/05/17 19:55 [entrez]
AID - 10.7759/cureus.37589 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 14;15(4):e37589. doi: 10.7759/cureus.37589. eCollection 2023 
      Apr.

PMID- 37771867
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231003
IS  - 2222-0682 (Print)
IS  - 2222-0682 (Electronic)
IS  - 2222-0682 (Linking)
VI  - 13
IP  - 4
DP  - 2023 Sep 20
TI  - ChatGPT in action: Harnessing artificial intelligence potential and addressing 
      ethical challenges in medicine, education, and scientific research.
PG  - 170-178
LID - 10.5662/wjm.v13.i4.170 [doi]
AB  - Artificial intelligence (AI) tools, like OpenAI's Chat Generative Pre-trained 
      Transformer (ChatGPT), hold considerable potential in healthcare, academia, and 
      diverse industries. Evidence demonstrates its capability at a medical student 
      level in standardized tests, suggesting utility in medical education, radiology 
      reporting, genetics research, data optimization, and drafting repetitive texts 
      such as discharge summaries. Nevertheless, these tools should augment, not 
      supplant, human expertise. Despite promising applications, ChatGPT confronts 
      limitations, including critical thinking tasks and generating false references, 
      necessitating stringent cross-verification. Ensuing concerns, such as potential 
      misuse, bias, blind trust, and privacy, underscore the need for transparency, 
      accountability, and clear policies. Evaluations of AI-generated content and 
      preservation of academic integrity are critical. With responsible use, AI can 
      significantly improve healthcare, academia, and industry without compromising 
      integrity and research quality. For effective and ethical AI deployment, 
      collaboration amongst AI developers, researchers, educators, and policymakers is 
      vital. The development of domain-specific tools, guidelines, regulations, and the 
      facilitation of public dialogue must underpin these endeavors to responsibly 
      harness AI's potential.
CI  - ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights 
      reserved.
FAU - Jeyaraman, Madhan
AU  - Jeyaraman M
AD  - Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational 
      and Research Institute, Chennai 600077, Tamil Nadu, India. 
      madhanjeyaraman@gmail.com.
FAU - Ramasubramanian, Swaminathan
AU  - Ramasubramanian S
AD  - Department of General Medicine, Government Medical College, Omandurar Government 
      Estate, Chennai 600018, Tamil Nadu, India.
FAU - Balaji, Sangeetha
AU  - Balaji S
AD  - Department of General Medicine, Government Medical College, Omandurar Government 
      Estate, Chennai 600018, Tamil Nadu, India.
FAU - Jeyaraman, Naveen
AU  - Jeyaraman N
AD  - Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational 
      and Research Institute, Chennai 600077, Tamil Nadu, India.
FAU - Nallakumarasamy, Arulkumar
AU  - Nallakumarasamy A
AD  - Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational 
      and Research Institute, Chennai 600077, Tamil Nadu, India.
FAU - Sharma, Shilpa
AU  - Sharma S
AD  - Department of Paediatric Surgery, All India Institute of Medical Sciences, Delhi 
      110029, New Delhi, India.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230920
PL  - United States
TA  - World J Methodol
JT  - World journal of methodology
JID - 101628739
PMC - PMC10523250
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Education
OT  - Open artificial intelligence
OT  - Research
COIS- Conflict-of-interest statement: The authors declare no competing interests.
EDAT- 2023/09/29 06:44
MHDA- 2023/09/29 06:45
CRDT- 2023/09/29 03:57
PHST- 2023/05/02 00:00 [received]
PHST- 2023/06/29 00:00 [revised]
PHST- 2023/07/24 00:00 [accepted]
PHST- 2023/09/29 06:45 [medline]
PHST- 2023/09/29 06:44 [pubmed]
PHST- 2023/09/29 03:57 [entrez]
AID - 10.5662/wjm.v13.i4.170 [doi]
PST - epublish
SO  - World J Methodol. 2023 Sep 20;13(4):170-178. doi: 10.5662/wjm.v13.i4.170. 
      eCollection 2023 Sep 20.

PMID- 37823044
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2666-3287 (Electronic)
IS  - 2666-3287 (Linking)
VI  - 13
DP  - 2023 Dec
TI  - ChatGPT, large language models, and artificial intelligence in medicine and 
      health care: A primer for clinicians and researchers.
PG  - 168-169
LID - 10.1016/j.jdin.2023.07.011 [doi]
FAU - Kantor, Jonathan
AU  - Kantor J
AD  - Department of Dermatology, Center for Global Health, and Center for Clinical 
      Epidemiology and Biostatistics, Perelman School of Medicine, University of 
      Pennsylvania, Philadelphia, Pennsylvania; Florida Center for Dermatology, St 
      Augustine, Florida; and Alchemy Labs, Oxford, UK.
LA  - eng
PT  - Editorial
DEP - 20230729
PL  - United States
TA  - JAAD Int
JT  - JAAD international
JID - 101774762
PMC - PMC10562174
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Google Bard
OT  - Microsoft Bing
OT  - annotation
OT  - artificial intelligence
OT  - large language models
OT  - medical education
OT  - practice management
COIS- None disclosed.
EDAT- 2023/10/12 06:43
MHDA- 2023/10/12 06:44
CRDT- 2023/10/12 04:19
PHST- 2023/10/12 06:44 [medline]
PHST- 2023/10/12 06:43 [pubmed]
PHST- 2023/10/12 04:19 [entrez]
AID - S2666-3287(23)00118-9 [pii]
AID - 10.1016/j.jdin.2023.07.011 [doi]
PST - epublish
SO  - JAAD Int. 2023 Jul 29;13:168-169. doi: 10.1016/j.jdin.2023.07.011. eCollection 
      2023 Dec.

PMID- 37942394
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231110
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - How Efficient Is ChatGPT in Accessing Accurate and Quality Health-Related 
      Information?
PG  - e46662
LID - 10.7759/cureus.46662 [doi]
LID - e46662
AB  - Background and objective The field of artificial intelligence (AI) is advancing 
      at a rapid pace, impacting all aspects of human life. Chat Generative Pre-trained 
      Transformer (ChatGPT), which represents one of AI's most recent and remarkable 
      achievements, has garnered significant attention and popularity in the academic 
      community. ChatGPT, a language model-based chatbot developed by OpenAI, responds 
      quickly and provides answers to the questions put to it. This chatbot has the 
      ability to gather content from a variety of sources on the internet. However, its 
      success in providing correct information has not yet been comprehensively 
      analyzed. In light of this, this study aimed to engage in a comparative content 
      analysis of health-related information provided by ChatGPT and a few selected 
      websites. Methods We performed a qualitative analysis of data obtained from 
      various information sources by using the DISCERN score and the Journal of 
      the American Medical Association (JAMA) benchmark criteria. In addition, 
      readability levels of the content were measured by using the Flesch-Kincaid grade 
      level, Gunning Fog Index, and Simple Measure of Gobbledygook (SMOG) index. 
      Results Based on our findings, there was no statistically significant difference 
      between the websites and ChatGPT in DISCERN scores. However, the JAMA score was 
      statistically significantly higher for websites. With regard to the 
      Flesch-Kincaid grade level, Gunning Fog Index, and SMOG index values, the data 
      obtained from the websites had higher readability. Conclusion Although AI is 
      starting to play a significant role in our everyday lives, it has yet to surpass 
      traditional methods of accessing information in terms of readability and 
      reliability.
CI  - Copyright © 2023, Ulusoy et al.
FAU - Ulusoy, Ibrahim
AU  - Ulusoy I
AD  - Department of Orthopaedics and Traumatology, Selahaddin Eyyübi State Hospital, 
      Diyarbakır, TUR.
FAU - Yılmaz, Mehmet
AU  - Yılmaz M
AD  - Department of Orthopaedics and Traumatology, Gaziantep 25 Aralık State Hospital, 
      Gaziantep, TUR.
FAU - Kıvrak, Aybars
AU  - Kıvrak A
AD  - Department of Orthopaedics and Traumatology, Avrupa Hospital, Adana, TUR.
LA  - eng
PT  - Journal Article
DEP - 20231007
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10628365
OTO - NOTNLM
OT  - chatgpt
OT  - discern
OT  - health information
OT  - jama
OT  - website
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/09 06:42
MHDA- 2023/11/09 06:43
CRDT- 2023/11/09 04:28
PHST- 2023/10/07 00:00 [accepted]
PHST- 2023/11/09 06:43 [medline]
PHST- 2023/11/09 06:42 [pubmed]
PHST- 2023/11/09 04:28 [entrez]
AID - 10.7759/cureus.46662 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 7;15(10):e46662. doi: 10.7759/cureus.46662. eCollection 2023 
      Oct.

PMID- 37931432
OWN - NLM
STAT- MEDLINE
DCOM- 20231124
LR  - 20231124
IS  - 1872-8243 (Electronic)
IS  - 1386-5056 (Linking)
VI  - 180
DP  - 2023 Dec
TI  - Influence on the accuracy in ChatGPT: Differences in the amount of information 
      per medical field.
PG  - 105283
LID - S1386-5056(23)00301-5 [pii]
LID - 10.1016/j.ijmedinf.2023.105283 [doi]
AB  - OBJECTIVES: Although ChatGPT was not developed for medical use, there is growing 
      interest in its use in medical fields. Understanding its capabilities and 
      precautions for its use in the medical field is an urgent matter. We hypothesized 
      that differences in the amounts of information published in different medical 
      fields would be proportionate to the amounts of training ChatGPT receives in 
      those fields, and hence its accuracy in providing answers. STUDY DESIGN: A 
      non-clinical experimental study. METHODS: We administered the Japanese National 
      Medical Examination to GPT-3.5 and GPT-4 to examine the rates of accuracy and 
      consistency in their responses. We counted the total number of documents in the 
      Web of Science Core Collection per medical field and assessed the relationship 
      with ChatGPT's accuracy. We also performed multivariate-adjusted models to 
      investigate the risk factors for incorrect answers. RESULTS: For GPT-4, we 
      confirmed an accuracy rate of 81.0 % and a consistency rate of 88.8 % on the 
      exam; both showed improvement compared to those for GPT-3.5. A positive 
      correlation was observed between the accuracy rate and consistency rate 
      (R = 0.51, P < 0.001). The number of documents per medical field was 
      significantly correlated with the accuracy rate in that medical field (R = 0.44, 
      P < 0.05), with relatively few publications being an independent risk factor for 
      incorrect answers. CONCLUSIONS: Checking consistency may help identify incorrect 
      answers when using ChatGPT. Users should be aware that the accuracy of the 
      answers by ChatGPT may decrease when it is asked about topics with limited 
      published information, such as new drugs and diseases.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Haze, Tatsuya
AU  - Haze T
AD  - Department of Medical Science and Cardiorenal Medicine, Yokohama City University 
      Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and 
      Hypertension, Yokohama City University Medical Center, Yokohama, Japan; YCU 
      Center for Novel and Exploratory Clinical Trials (Y-NEXT), Yokohama City 
      University Hospital, Yokohama, Japan. Electronic address: 
      haze.tat.bg@yokohama-cu.ac.jp.
FAU - Kawano, Rina
AU  - Kawano R
AD  - Department of Medical Science and Cardiorenal Medicine, Yokohama City University 
      Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and 
      Hypertension, Yokohama City University Medical Center, Yokohama, Japan.
FAU - Takase, Hajime
AU  - Takase H
AD  - YCU Center for Novel and Exploratory Clinical Trials (Y-NEXT), Yokohama City 
      University Hospital, Yokohama, Japan; Department of Neurosurgery, Yokohama City 
      University Graduate School of Medicine, Yokohama, Japan.
FAU - Suzuki, Shota
AU  - Suzuki S
AD  - Department of Medical Science and Cardiorenal Medicine, Yokohama City University 
      Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and 
      Hypertension, Yokohama City University Medical Center, Yokohama, Japan.
FAU - Hirawa, Nobuhito
AU  - Hirawa N
AD  - Department of Medical Science and Cardiorenal Medicine, Yokohama City University 
      Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and 
      Hypertension, Yokohama City University Medical Center, Yokohama, Japan; Clinical 
      Education and Training Center, Yokohama City University Medical Center, Yokohama, 
      Japan.
FAU - Tamura, Kouichi
AU  - Tamura K
AD  - Department of Medical Science and Cardiorenal Medicine, Yokohama City University 
      Graduate School of Medicine, Yokohama, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - Ireland
TA  - Int J Med Inform
JT  - International journal of medical informatics
JID - 9711057
SB  - IM
MH  - Humans
MH  - Risk Factors
MH  - *Artificial Intelligence
MH  - Data Accuracy
OTO - NOTNLM
OT  - Accuracy
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Consistency
OT  - Risk factor
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/11/07 00:42
MHDA- 2023/11/20 06:55
CRDT- 2023/11/06 18:06
PHST- 2023/06/08 00:00 [received]
PHST- 2023/10/30 00:00 [revised]
PHST- 2023/10/31 00:00 [accepted]
PHST- 2023/11/20 06:55 [medline]
PHST- 2023/11/07 00:42 [pubmed]
PHST- 2023/11/06 18:06 [entrez]
AID - S1386-5056(23)00301-5 [pii]
AID - 10.1016/j.ijmedinf.2023.105283 [doi]
PST - ppublish
SO  - Int J Med Inform. 2023 Dec;180:105283. doi: 10.1016/j.ijmedinf.2023.105283. Epub 
      2023 Nov 3.

PMID- 37565917
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20231108
IS  - 1536-5964 (Electronic)
IS  - 0025-7974 (Print)
IS  - 0025-7974 (Linking)
VI  - 102
IP  - 32
DP  - 2023 Aug 11
TI  - ChatGPT performance in the medical specialty exam: An observational study.
PG  - e34673
LID - 10.1097/MD.0000000000034673 [doi]
LID - e34673
AB  - In our study, we aimed to evaluate the success of ChatGPT by determining its 
      performance in the last 5 medical specialty exams (MSE) conducted and its ranking 
      among the candidates of that year, and to determine its potential use in 
      healthcare services. Publicly available MSE questions and answer keys from the 
      last 5 years were scanned, a total of 1177 questions were included in the study, 
      all questions were asked to the ChatGPT (OpenAI; San Francisco, CA) GPT-3.5 
      series, which is the March 23, 2023 version. The average score and rank that 
      ChatGPT would receive if it had entered the exam that year were determined. 
      Questions were categorized as short question group, long question group, single 
      select multiple-choice questions, and multi-select multiple-choice questions. The 
      lowest success proportion was determined as 54.3%, and the highest success 
      proportion was 70.9% correct answer percentage. It achieved a sufficient result 
      as 1787th out of 22,214 people in its most successful exam, and 4428th out of 
      21,476 participants in its least successful one. No statistically significant 
      difference was found between the correct answers it gave to clinical and basic 
      science questions (P: .66). ChatGPT statistically significantly answered a higher 
      proportion of questions correctly in the short questions group compared to the 
      long questions group (P = .03), and in the single select multiple choice 
      questions group compared to the multi-select multiple choice questions group (P < 
      .001). ChatGPT has been successful in the MSE, a challenging exam for doctors in 
      our country. However, it is a fact that ChatGPT is still behind the expert in the 
      field for now, and what will happen with program developments in the future is a 
      matter of curiosity for all of us.
CI  - Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Oztermeli, Ayse Dilara
AU  - Oztermeli AD
AUID- ORCID: 0000-0003-1820-2877
AD  - Emergency Medicine Department, Gebze Fatih State Hospital, Kocaeli, Turkey.
FAU - Oztermeli, Ahmet
AU  - Oztermeli A
AD  - Orthopaedics and Traumatology Department, Gebze Fatih State Hospital, Kocaeli, 
      Turkey.
LA  - eng
PT  - Journal Article
PT  - Observational Study
PL  - United States
TA  - Medicine (Baltimore)
JT  - Medicine
JID - 2985248R
SB  - IM
MH  - Humans
MH  - *Population Health
MH  - *Medicine
MH  - *Physicians
MH  - Program Development
PMC - PMC10419419
COIS- The authors have no funding and conflicts of interest to disclose.
EDAT- 2023/08/11 12:42
MHDA- 2023/08/14 06:42
CRDT- 2023/08/11 10:13
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/11 12:42 [pubmed]
PHST- 2023/08/11 10:13 [entrez]
AID - 00005792-202308110-00076 [pii]
AID - 10.1097/MD.0000000000034673 [doi]
PST - ppublish
SO  - Medicine (Baltimore). 2023 Aug 11;102(32):e34673. doi: 
      10.1097/MD.0000000000034673.

PMID- 37566815
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20231109
IS  - 1536-3708 (Electronic)
IS  - 0148-7043 (Linking)
VI  - 91
IP  - 3
DP  - 2023 Sep 1
TI  - Distinguishing Authentic Voices in the Age of ChatGPT: Comparing AI-Generated and 
      Applicant-Written Personal Statements for Plastic Surgery Residency Application.
PG  - 324-325
LID - 10.1097/SAP.0000000000003653 [doi]
AB  - BACKGROUND: ChatGPT, a generative artificial intelligence model, may be used by 
      future applicants in the plastic surgery residency match. METHODS: Ten personal 
      statements (5 generated by ChatGPT, 5 written by applicants) were rated by 10 
      reviewers, blinded to the source of the essay. RESULTS: A total of a 100 
      evaluations were collected. There was no significant difference in ratings for 
      readability, originality, authenticity, and overall quality (all P > 0.05) when 
      comparing computer-generated and applicant essays. CONCLUSION: Personal 
      statements prepared by ChatGPT are indistinguishable from essays written by 
      actual applicants. This finding suggests that the current plastic surgery 
      application format be reevaluated to better aid in holistic evaluation of 
      students.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Patel, Viren
AU  - Patel V
AD  - From the Department of Plastic Surgery, Cleveland Clinic, Cleveland, OH.
FAU - Deleonibus, Anthony
AU  - Deleonibus A
FAU - Wells, Michael W
AU  - Wells MW
FAU - Bernard, Steven L
AU  - Bernard SL
FAU - Schwarz, Graham S
AU  - Schwarz GS
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Ann Plast Surg
JT  - Annals of plastic surgery
JID - 7805336
SB  - IM
EIN - Ann Plast Surg. 2023 Nov 8;:null. PMID: 37937846
MH  - Humans
MH  - *Internship and Residency
MH  - *Surgery, Plastic/education
MH  - Artificial Intelligence
MH  - Writing
MH  - *Students, Medical
COIS- Conflicts of interest and sources of funding: none declared.
EDAT- 2023/08/11 18:42
MHDA- 2023/08/14 06:41
CRDT- 2023/08/11 16:42
PHST- 2023/08/14 06:41 [medline]
PHST- 2023/08/11 18:42 [pubmed]
PHST- 2023/08/11 16:42 [entrez]
AID - 00000637-202309000-00002 [pii]
AID - 10.1097/SAP.0000000000003653 [doi]
PST - ppublish
SO  - Ann Plast Surg. 2023 Sep 1;91(3):324-325. doi: 10.1097/SAP.0000000000003653.

PMID- 38020160
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2296-858X (Print)
IS  - 2296-858X (Electronic)
IS  - 2296-858X (Linking)
VI  - 10
DP  - 2023
TI  - Artificial intelligence in global health equity: an evaluation and discussion on 
      the application of ChatGPT, in the Chinese National Medical Licensing 
      Examination.
PG  - 1237432
LID - 10.3389/fmed.2023.1237432 [doi]
LID - 1237432
AB  - BACKGROUND: The demand for healthcare is increasing globally, with notable 
      disparities in access to resources, especially in Asia, Africa, and Latin 
      America. The rapid development of Artificial Intelligence (AI) technologies, such 
      as OpenAI's ChatGPT, has shown promise in revolutionizing healthcare. However, 
      potential challenges, including the need for specialized medical training, 
      privacy concerns, and language bias, require attention. METHODS: To assess the 
      applicability and limitations of ChatGPT in Chinese and English settings, we 
      designed an experiment evaluating its performance in the 2022 National Medical 
      Licensing Examination (NMLE) in China. For a standardized evaluation, we used the 
      comprehensive written part of the NMLE, translated into English by a bilingual 
      expert. All questions were input into ChatGPT, which provided answers and reasons 
      for choosing them. Responses were evaluated for "information quality" using the 
      Likert scale. RESULTS: ChatGPT demonstrated a correct response rate of 81.25% for 
      Chinese and 86.25% for English questions. Logistic regression analysis showed 
      that neither the difficulty nor the subject matter of the questions was a 
      significant factor in AI errors. The Brier Scores, indicating predictive 
      accuracy, were 0.19 for Chinese and 0.14 for English, indicating good predictive 
      performance. The average quality score for English responses was excellent (4.43 
      point), slightly higher than for Chinese (4.34 point). CONCLUSION: While AI 
      language models like ChatGPT show promise for global healthcare, language bias is 
      a key challenge. Ensuring that such technologies are robustly trained and 
      sensitive to multiple languages and cultures is vital. Further research into AI's 
      role in healthcare, particularly in areas with limited resources, is warranted.
CI  - Copyright © 2023 Tong, Guan, Chen, Huang, Zhong, Zhang and Zhang.
FAU - Tong, Wenting
AU  - Tong W
AD  - Department of Pharmacy, Gannan Healthcare Vocational College, Ganzhou, Jiangxi, 
      China.
FAU - Guan, Yongfu
AU  - Guan Y
AD  - Department of Rehabilitation and Elderly Care, Gannan Healthcare Vocational 
      College, Ganzhou, Jiangxi, China.
FAU - Chen, Jinping
AU  - Chen J
AD  - Department of Rehabilitation and Elderly Care, Gannan Healthcare Vocational 
      College, Ganzhou, Jiangxi, China.
FAU - Huang, Xixuan
AU  - Huang X
AD  - Department of Mathematics, Xiamen University, Xiamen, Fujian, China.
FAU - Zhong, Yuting
AU  - Zhong Y
AD  - Department of Anesthesiology, Gannan Medical University, Jiangxi, China.
FAU - Zhang, Changrong
AU  - Zhang C
AD  - Department of Chinese Medicine, Affiliated Hospital of Qinghai University, 
      Xining, Qinghai, China.
FAU - Zhang, Hui
AU  - Zhang H
AD  - Department of Rehabilitation and Elderly Care, Gannan Healthcare Vocational 
      College, Ganzhou, Jiangxi, China.
AD  - Chair of Endocrinology and Medical Sexology (ENDOSEX), Department of Experimental 
      Medicine, University of Rome Tor Vergata, Rome, Italy.
LA  - eng
PT  - Journal Article
DEP - 20231019
PL  - Switzerland
TA  - Front Med (Lausanne)
JT  - Frontiers in medicine
JID - 101648047
PMC - PMC10656681
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - equity
OT  - global healthcare
OT  - language bias
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/29 18:41
MHDA- 2023/11/29 18:42
CRDT- 2023/11/29 14:40
PHST- 2023/06/09 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/11/29 18:42 [medline]
PHST- 2023/11/29 18:41 [pubmed]
PHST- 2023/11/29 14:40 [entrez]
AID - 10.3389/fmed.2023.1237432 [doi]
PST - epublish
SO  - Front Med (Lausanne). 2023 Oct 19;10:1237432. doi: 10.3389/fmed.2023.1237432. 
      eCollection 2023.

PMID- 37699647
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1535-5675 (Electronic)
IS  - 0091-4916 (Linking)
VI  - 51
IP  - 4
DP  - 2023 Dec 5
TI  - ChatGPT and Patient Information in Nuclear Medicine: GPT-3.5 Versus GPT-4.
PG  - 307-313
LID - 10.2967/jnmt.123.266151 [doi]
AB  - The GPT-3.5-powered ChatGPT was released in late November 2022 powered by the 
      generative pretrained transformer (GPT) version 3.5. It has emerged as a readily 
      accessible source of patient information ahead of medical procedures. Although 
      ChatGPT has purported benefits for supporting patient education and information, 
      actual capability has not been evaluated. Moreover, the March 2023 emergence of 
      paid subscription access to GPT-4 promises further enhanced capabilities 
      requiring evaluation. Methods: ChatGPT was used to generate patient information 
      sheets suitable for gaining informed consent for 7 common procedures in nuclear 
      medicine. Responses were generated independently for both GPT-3.5 and GPT-4 
      architectures. Specific procedures were selected that had a long-standing history 
      of use to avoid any bias associated with the September 2021 learning cutoff that 
      constrains both GPT-3.5 and GPT-4 architectures. Each information sheet was 
      independently evaluated by 3 expert assessors and ranked on the basis of 
      accuracy, appropriateness, currency, and fitness for purpose. Results: ChatGPT 
      powered by GPT-3.5 provided patient information that was appropriate in terms of 
      being patient-facing but lacked accuracy and currency and omitted important 
      information. GPT-3.5 produced patient information deemed not fit for the purpose. 
      GPT-4 provided patient information enhanced across appropriateness, accuracy, and 
      currency, despite some omission of information. GPT-4 produced patient 
      information that was largely fit for the purpose. Conclusion: Although ChatGPT 
      powered by GPT-3.5 is accessible and provides plausible patient information, 
      inaccuracies and omissions present a risk to patients and informed consent. 
      Conversely, GPT-4 is more accurate and fit for the purpose but, at the time of 
      writing, was available only through a paid subscription.
CI  - © 2023 by the Society of Nuclear Medicine and Molecular Imaging.
FAU - Currie, Geoff
AU  - Currie G
AD  - School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, 
      New South Wales, Australia; gcurrie@csu.edu.au.
FAU - Robbie, Stephanie
AU  - Robbie S
AD  - Queensland X-Ray, St. Andrews Hospital, Toowoomba, Queensland, Australia; and.
FAU - Tually, Peter
AU  - Tually P
AD  - School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, 
      New South Wales, Australia.
AD  - Telemed Health, Kalgoorlie, Western Australia, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231205
PL  - United States
TA  - J Nucl Med Technol
JT  - Journal of nuclear medicine technology
JID - 0430303
SB  - IM
MH  - Humans
MH  - *Nuclear Medicine
MH  - Radionuclide Imaging
MH  - Learning
MH  - Salaries and Fringe Benefits
MH  - Writing
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-4
OT  - generative AI
OT  - language model
OT  - patient education
EDAT- 2023/09/13 00:41
MHDA- 2023/12/07 12:42
CRDT- 2023/09/12 20:33
PHST- 2023/06/08 00:00 [received]
PHST- 2023/07/13 00:00 [revised]
PHST- 2023/12/07 12:42 [medline]
PHST- 2023/09/13 00:41 [pubmed]
PHST- 2023/09/12 20:33 [entrez]
AID - jnmt.123.266151 [pii]
AID - 10.2967/jnmt.123.266151 [doi]
PST - epublish
SO  - J Nucl Med Technol. 2023 Dec 5;51(4):307-313. doi: 10.2967/jnmt.123.266151.

PMID- 38000671
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 1878-8769 (Electronic)
IS  - 1878-8750 (Linking)
DP  - 2023 Nov 22
TI  - Information Quality and Readability: ChatGPT's Responses to the Most Common 
      Questions About Spinal Cord Injury.
LID - S1878-8750(23)01625-X [pii]
LID - 10.1016/j.wneu.2023.11.062 [doi]
AB  - OBJECTIVE: This study aimed to assess the quality, readability, and comprehension 
      of texts generated by ChatGPT in response to commonly asked questions about 
      spinal cord injury (SCI). METHODS: The study utilized Google Trends to identify 
      the most frequently searched keywords related to SCI. The identified keywords 
      were sequentially inputted into ChatGPT, and the resulting responses were 
      assessed for quality using the Ensuring Quality Information for Patients (EQIP) 
      tool. The readability of the texts was analyzed using the Flesch-Kincaid grade 
      level and the Flesch-Kincaid reading ease parameters. RESULTS: The mean EQIP 
      score of the texts was determined to be 43.02 ± 6.37, the Flesch-Kincaid reading 
      ease score to be 26.24 ± 13.81, and the Flesch-Kincaid grade level was determined 
      to be 14.84 ± 1.79. The analysis revealed significant concerns regarding the 
      quality of texts generated by ChatGPT, indicating serious problems with 
      readability and comprehension. The mean EQIP score was low, suggesting a need for 
      improvement in the accuracy and reliability of the information provided. The 
      Flesch-Kincaid grade level indicated a high linguistic complexity, requiring a 
      level of education equivalent to approximately 14 to 15 years of formal education 
      for comprehension. CONCLUSIONS: The results of this study show heightened 
      complexity in ChatGPT-generated SCI texts, surpassing optimal health 
      communication readability. ChatGPT currently cannot substitute comprehensive 
      medical consultations. Enhancing text quality could be attainable through 
      dependence on credible sources, the establishment of a scientific board, and 
      collaboration with expert teams. Addressing these concerns could improve text 
      accessibility, empowering patients and facilitating informed decision-making in 
      SCI.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Temel, Mustafa Hüseyin
AU  - Temel MH
AD  - Physical Medicine and Rehabilitation Clinic, Üsküdar State Hospital, İstanbul, 
      Turkey. Electronic address: mhuseyintemel@gmail.com.
FAU - Erden, Yakup
AU  - Erden Y
AD  - Physical Medicine and Rehabilitation Clinic, İzzet Baysal Physical Medicine and 
      Rehabilitation Training and Research Hospital, Bolu, Turkey.
FAU - Bağcıer, Fatih
AU  - Bağcıer F
AD  - Physical Medicine and Rehabilitation Clinic, Başakşehir Çam and Sakura City 
      Hospital, İstanbul, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - United States
TA  - World Neurosurg
JT  - World neurosurgery
JID - 101528275
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Comprehension
OT  - Quality assessment
OT  - Readability
OT  - Spinal cord injury
EDAT- 2023/11/25 07:42
MHDA- 2023/11/25 07:42
CRDT- 2023/11/24 19:29
PHST- 2023/10/29 00:00 [received]
PHST- 2023/11/14 00:00 [revised]
PHST- 2023/11/15 00:00 [accepted]
PHST- 2023/11/25 07:42 [pubmed]
PHST- 2023/11/25 07:42 [medline]
PHST- 2023/11/24 19:29 [entrez]
AID - S1878-8750(23)01625-X [pii]
AID - 10.1016/j.wneu.2023.11.062 [doi]
PST - aheadofprint
SO  - World Neurosurg. 2023 Nov 22:S1878-8750(23)01625-X. doi: 
      10.1016/j.wneu.2023.11.062.

PMID- 37256656
OWN - NLM
STAT- MEDLINE
DCOM- 20230602
LR  - 20230707
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 May 31
TI  - Open Science and Software Assistance: Commentary on "Artificial Intelligence Can 
      Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora's 
      Box Has Been Opened".
PG  - e49323
LID - 10.2196/49323 [doi]
LID - e49323
AB  - Májovský and colleagues have investigated the important issue of ChatGPT being 
      used for the complete generation of scientific works, including fake data and 
      tables. The issues behind why ChatGPT poses a significant concern to research 
      reach far beyond the model itself. Once again, the lack of reproducibility and 
      visibility of scientific works creates an environment where fraudulent or 
      inaccurate work can thrive. What are some of the ways in which we can handle this 
      new situation?
CI  - ©Pedro L Ballester. Originally published in the Journal of Medical Internet 
      Research (https://www.jmir.org), 31.05.2023.
FAU - Ballester, Pedro L
AU  - Ballester PL
AUID- ORCID: 0000-0001-9928-2527
AD  - Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230531
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
CON - J Med Internet Res. 2023 May 31;25:e46924. PMID: 37256685
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Software
PMC - PMC10267777
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - open science
OT  - reproducibility
OT  - software assistance
COIS- Conflicts of Interest: None declared.
EDAT- 2023/05/31 13:11
MHDA- 2023/06/02 06:42
CRDT- 2023/05/31 11:53
PHST- 2023/05/24 00:00 [received]
PHST- 2023/05/24 00:00 [accepted]
PHST- 2023/06/02 06:42 [medline]
PHST- 2023/05/31 13:11 [pubmed]
PHST- 2023/05/31 11:53 [entrez]
AID - v25i1e49323 [pii]
AID - 10.2196/49323 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 May 31;25:e49323. doi: 10.2196/49323.

PMID- 37575206
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230815
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Using ChatGPT to navigate ambivalent and contradictory research findings on 
      artificial intelligence.
PG  - 1195797
LID - 10.3389/frai.2023.1195797 [doi]
LID - 1195797
AB  - With the rapid development and integration of AI in various domains, 
      understanding the nuances of AI research has become critical for policymakers, 
      researchers, and practitioners. However, the results are vast and diverse and 
      even can be contradictory or ambivalent, presenting a significant challenge for 
      individuals seeking to grasp and synthesize the findings. This perspective paper 
      discusses the ambivalent and contradictory research findings in the literature on 
      artificial intelligence (AI) and explores whether ChatGPT can be used to navigate 
      and make sense of the AI literature.
CI  - Copyright © 2023 Sohail, Madsen, Himeur and Ashraf.
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AD  - Department of Computer Science and Engineering, School of Engineering Sciences 
      and Technology, Jamia Hamdard, New Delhi, India.
FAU - Madsen, Dag Øivind
AU  - Madsen DØ
AD  - USN School of Business, University of South-Eastern Norway, Hønefoss, Norway.
FAU - Himeur, Yassine
AU  - Himeur Y
AD  - College of Engineering and Information Technology, University of Dubai, Dubai, 
      United Arab Emirates.
FAU - Ashraf, Maheen
AU  - Ashraf M
AD  - Department of Computer Science and Engineering, School of Engineering Sciences 
      and Technology, Jamia Hamdard, New Delhi, India.
LA  - eng
PT  - Journal Article
DEP - 20230727
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10413582
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - perspective
OT  - reaching consensus
OT  - research findings
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/08/14 06:41
MHDA- 2023/08/14 06:42
CRDT- 2023/08/14 04:25
PHST- 2023/05/25 00:00 [received]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/14 06:41 [pubmed]
PHST- 2023/08/14 04:25 [entrez]
AID - 10.3389/frai.2023.1195797 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Jul 27;6:1195797. doi: 10.3389/frai.2023.1195797. 
      eCollection 2023.

PMID- 37353657
OWN - NLM
STAT- MEDLINE
DCOM- 20231122
LR  - 20231122
IS  - 1873-4626 (Electronic)
IS  - 1091-255X (Linking)
VI  - 27
IP  - 11
DP  - 2023 Nov
TI  - Sugarbaker Versus Keyhole Repair for Parastomal Hernia: Results of an Artificial 
      Intelligence Large Language Model Post Hoc Analysis.
PG  - 2567-2570
LID - 10.1007/s11605-023-05749-y [doi]
FAU - Fleming, Andrew M
AU  - Fleming AM
AUID- ORCID: 0000-0001-5421-9014
AD  - Department of Surgery, The University of Tennessee Health Science Center, 
      Memphis, TN, USA. aflemin8@uthsc.edu.
AD  - Department of Surgery, St. Jude Children's Research Hospital, Memphis, TN, USA. 
      aflemin8@uthsc.edu.
FAU - Phillips, Alisa L
AU  - Phillips AL
AD  - Department of Surgery, The University of Tennessee Health Science Center, 
      Memphis, TN, USA.
FAU - Drake, Justin A
AU  - Drake JA
AD  - Division of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, 
      USA.
FAU - Murphy, Andrew J
AU  - Murphy AJ
AD  - Department of Surgery, The University of Tennessee Health Science Center, 
      Memphis, TN, USA.
AD  - Department of Surgery, St. Jude Children's Research Hospital, Memphis, TN, USA.
FAU - Yakoub, Danny
AU  - Yakoub D
AD  - Division of Surgical Oncology, Augusta University Medical Center, Augusta, GA, 
      USA.
FAU - Shibata, David
AU  - Shibata D
AD  - Department of Surgery, The University of Tennessee Health Science Center, 
      Memphis, TN, USA.
FAU - Wood, Elizabeth H
AU  - Wood EH
AD  - Department of Surgery, The University of Tennessee Health Science Center, 
      Memphis, TN, USA.
LA  - eng
PT  - Journal Article
DEP - 20230623
PL  - United States
TA  - J Gastrointest Surg
JT  - Journal of gastrointestinal surgery : official journal of the Society for Surgery 
      of the Alimentary Tract
JID - 9706084
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Incisional Hernia/surgery
MH  - *Hernia, Ventral/surgery
MH  - Language
MH  - Herniorrhaphy/methods
MH  - Surgical Mesh/adverse effects
MH  - *Surgical Stomas/adverse effects
MH  - *Laparoscopy/methods
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Parastomal Hernia repair
OT  - Systematic Review
EDAT- 2023/06/24 11:42
MHDA- 2023/11/22 06:43
CRDT- 2023/06/23 23:26
PHST- 2023/05/05 00:00 [received]
PHST- 2023/06/03 00:00 [accepted]
PHST- 2023/11/22 06:43 [medline]
PHST- 2023/06/24 11:42 [pubmed]
PHST- 2023/06/23 23:26 [entrez]
AID - 10.1007/s11605-023-05749-y [pii]
AID - 10.1007/s11605-023-05749-y [doi]
PST - ppublish
SO  - J Gastrointest Surg. 2023 Nov;27(11):2567-2570. doi: 10.1007/s11605-023-05749-y. 
      Epub 2023 Jun 23.

PMID- 37257860
OWN - NLM
STAT- MEDLINE
DCOM- 20230605
LR  - 20230605
IS  - 2049-4408 (Electronic)
IS  - 2049-4394 (Linking)
VI  - 105-B
IP  - 6
DP  - 2023 Jun 1
TI  - What's all the chatter about?
PG  - 587-589
LID - 10.1302/0301-620X.105B6.BJJ-2023-0156 [doi]
AB  - The OpenAI chatbot ChatGPT is an artificial intelligence (AI) application that 
      uses state-of-the-art language processing AI. It can perform a vast number of 
      tasks, from writing poetry and explaining complex quantum mechanics, to 
      translating language and writing research articles with a human-like 
      understanding and legitimacy. Since its initial release to the public in November 
      2022, ChatGPT has garnered considerable attention due to its ability to mimic the 
      patterns of human language, and it has attracted billion-dollar investments from 
      Microsoft and PricewaterhouseCoopers. The scope of ChatGPT and other large 
      language models appears infinite, but there are several important limitations. 
      This editorial provides an introduction to the basic functionality of ChatGPT and 
      other large language models, their current applications and limitations, and the 
      associated implications for clinical practice and research.
CI  - © 2023 The British Editorial Society of Bone & Joint Surgery.
FAU - Kunze, Kyle N
AU  - Kunze KN
AUID- ORCID: 0000-0002-0363-3482
AD  - Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New 
      York, USA.
FAU - Jang, Seong J
AU  - Jang SJ
AUID- ORCID: 0000-0002-9967-9476
AD  - Weill Cornell Medical College, New York, New York, USA.
FAU - Fullerton, Mark A
AU  - Fullerton MA
AD  - The Bone & Joint Journal , London, UK.
FAU - Vigdorchik, Jonathan M
AU  - Vigdorchik JM
AUID- ORCID: 0000-0003-0308-9648
AD  - Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New 
      York, USA.
AD  - Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, 
      New York, New York, USA.
FAU - Haddad, Fares S
AU  - Haddad FS
AD  - The Bone & Joint Journal , London, UK.
AD  - University College London Hospitals, and The NIHR Biomedical Research Centre at 
      UCLH, London, UK.
AD  - Princess Grace Hospital, London, UK.
LA  - eng
PT  - Editorial
DEP - 20230601
PL  - England
TA  - Bone Joint J
JT  - The bone & joint journal
JID - 101599229
SB  - IM
MH  - *Artificial Intelligence
MH  - Language
COIS- J. M. Vigdorchik reports royalties or licenses from Corin and Depuy Synthes, 
      consulting fees from Stryker, Zimmer Biomet, and Depuy Synthes, patents planned, 
      issued or pending from Ortho AI, and stock or stock options from Corin, 
      Intellijoint, Ortho AI, and T3 Medical, none of which are related to this 
      article. F. S. Haddad reports board membership of The Bone & Joint Journal and 
      the Annals of the Royal College of Surgeons; consultancy for Smith & Nephew, 
      Corin, MatOrtho, and Stryker; payment for lectures, including service on 
      speakers’ bureaus, for Smith & Nephew and Stryker; and royalties paid by Smith & 
      Nephew, MatOrtho, Corin, and Stryker, all of which are also unrelated to this 
      article.
EDAT- 2023/06/01 01:08
MHDA- 2023/06/02 06:42
CRDT- 2023/05/31 20:03
PHST- 2023/06/02 06:42 [medline]
PHST- 2023/06/01 01:08 [pubmed]
PHST- 2023/05/31 20:03 [entrez]
AID - BJJ-2023-0156 [pii]
AID - 10.1302/0301-620X.105B6.BJJ-2023-0156 [doi]
PST - epublish
SO  - Bone Joint J. 2023 Jun 1;105-B(6):587-589. doi: 
      10.1302/0301-620X.105B6.BJJ-2023-0156.

PMID- 37996275
OWN - NLM
STAT- Publisher
LR  - 20231123
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
DP  - 2023 Nov 22
TI  - Could ChatGPT-4 pass an anaesthesiology board examination? Follow-up assessment 
      of a comprehensive set of board examination practice questions.
LID - S0007-0912(23)00580-9 [pii]
LID - 10.1016/j.bja.2023.10.025 [doi]
FAU - Shay, Denys
AU  - Shay D
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA; The CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, 
      USA; Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel 
      Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Center for 
      Anesthesia Research Excellence (CARE), Beth Israel Deaconess Medical Center, 
      Harvard Medical School, Boston, MA, USA.
FAU - Kumar, Bhawesh
AU  - Kumar B
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA.
FAU - Redaelli, Simone
AU  - Redaelli S
AD  - Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess 
      Medical Center, Harvard Medical School, Boston, MA, USA; Center for Anesthesia 
      Research Excellence (CARE), Beth Israel Deaconess Medical Center, Harvard Medical 
      School, Boston, MA, USA; School of Medicine and Surgery, University of 
      Milano-Bicocca, Milan, Italy.
FAU - von Wedel, Dario
AU  - von Wedel D
AD  - Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess 
      Medical Center, Harvard Medical School, Boston, MA, USA; Center for Anesthesia 
      Research Excellence (CARE), Beth Israel Deaconess Medical Center, Harvard Medical 
      School, Boston, MA, USA.
FAU - Liu, Manqing
AU  - Liu M
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA; The CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, 
      USA.
FAU - Dershwitz, Mark
AU  - Dershwitz M
AD  - Department of Anesthesiology & Perioperative Medicine, University of 
      Massachusetts Chan Medical School, Worcester, MA, USA.
FAU - Schaefer, Maximilian S
AU  - Schaefer MS
AD  - Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess 
      Medical Center, Harvard Medical School, Boston, MA, USA; Center for Anesthesia 
      Research Excellence (CARE), Beth Israel Deaconess Medical Center, Harvard Medical 
      School, Boston, MA, USA; Department of Anesthesiology, Duesseldorf University 
      Hospital, Duesseldorf, Germany.
FAU - Beam, Andrew
AU  - Beam A
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA; The CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, 
      USA. Electronic address: andrew_beam@hms.harvard.edu.
LA  - eng
PT  - Letter
DEP - 20231122
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-4
OT  - artificial intelligence
OT  - board examination
OT  - large language models
OT  - medical knowledge
OT  - multiple-choice questions
OT  - specialty qualifications
EDAT- 2023/11/24 00:42
MHDA- 2023/11/24 00:42
CRDT- 2023/11/23 21:59
PHST- 2023/10/02 00:00 [received]
PHST- 2023/10/20 00:00 [revised]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/11/24 00:42 [medline]
PHST- 2023/11/24 00:42 [pubmed]
PHST- 2023/11/23 21:59 [entrez]
AID - S0007-0912(23)00580-9 [pii]
AID - 10.1016/j.bja.2023.10.025 [doi]
PST - aheadofprint
SO  - Br J Anaesth. 2023 Nov 22:S0007-0912(23)00580-9. doi: 10.1016/j.bja.2023.10.025.

PMID- 37573231
OWN - NLM
STAT- MEDLINE
DCOM- 20230829
LR  - 20230829
IS  - 1773-0597 (Electronic)
IS  - 0181-5512 (Linking)
VI  - 46
IP  - 7
DP  - 2023 Sep
TI  - Evaluating the potential of ChatGPT-4 in ophthalmology: The good, the bad and the 
      ugly.
PG  - 697-705
LID - S0181-5512(23)00341-8 [pii]
LID - 10.1016/j.jfo.2023.07.001 [doi]
AB  - There is growing interest nowadays for artificial intelligence (AI) in all 
      medical fields. Beyond the direct medical application of AI to medical data, 
      generative AI such as "pre-trained transformer" (GPT) could significantly change 
      the ophthalmology landscape, opening up new avenues for enhancing precision, 
      productivity, and patient outcomes. At present, ChatGPT-4 has been investigated 
      in various ways in ophthalmology for research, medical education, and support for 
      clinical decisions purposes. This article intends to demonstrate the application 
      of ChatGPT-4 within the field of ophthalmology by employing a 'mise en abime' 
      approach. While we explore its potential to enhance the future of ophthalmology 
      care, we will also carefully outline its current limitations and potential risks.
CI  - Copyright © 2023 Elsevier Masson SAS. All rights reserved.
FAU - Khanna, R K
AU  - Khanna RK
AD  - Service d'ophtalmologie, hôpital universitaire Bretonneau, Inserm 1253 iBrain, 
      Tours, France. Electronic address: raoul.khanna@univ-tours.fr.
FAU - Ducloyer, J-B
AU  - Ducloyer JB
AD  - Service d'ophtalmologie, Nantes université, CHU de Nantes, 44000 Nantes, France.
FAU - Hage, A
AU  - Hage A
AD  - Service d'ophtalmologie, CHNO 15-20, Paris, France.
FAU - Rezkallah, A
AU  - Rezkallah A
AD  - Service d'ophtalmologie, hôpital de la Croix Rousse, Lyon, France.
FAU - Durbant, E
AU  - Durbant E
AD  - Cabinet d'ophtalmologie, Paris, France.
FAU - Bigoteau, M
AU  - Bigoteau M
AD  - Service d'ophtalmologie, hôpital Jacques-Cœur, Bourges, France.
FAU - Mouchel, R
AU  - Mouchel R
AD  - Centre ophtalmologique Kléber, Clinique du parc, Lyon, France; Centre 
      ophtalmologique du Grand Lac, Aix-les-Bains, France.
FAU - Guillon-Rolf, R
AU  - Guillon-Rolf R
AD  - Service d'ophtalmologie, Fondation Adolphe-de-Rothschild, Paris, France.
FAU - Le, L
AU  - Le L
AD  - Cabinet d'ophtalmologie, Chartres, France.
FAU - Tahiri, R
AU  - Tahiri R
AD  - Service de chirurgie ambulatoire, centre hospitalier d'Avranches-Granville, 849, 
      rue des Menneries, 50400 Granville, France.
FAU - Chammas, J
AU  - Chammas J
AD  - Service d'ophtalmologie, CHU Robert-Debré, Reims, France; Centre ophtalmologique 
      du Rhin, Strasbourg, France.
FAU - Baudouin, C
AU  - Baudouin C
AD  - Service d'ophtalmologie, CHNO 15-20, Sorbonne Université, Inserm, CNRS, Institut 
      de la Vision, IHU FOReSIGHT, Paris, France.
LA  - eng
PT  - Journal Article
DEP - 20230810
PL  - France
TA  - J Fr Ophtalmol
JT  - Journal francais d'ophtalmologie
JID - 7804128
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Ophthalmology
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT-4
OT  - Chatbot
OT  - Intelligence artificielle
OT  - Ophtalmologie
OT  - Ophthalmology
EDAT- 2023/08/13 00:43
MHDA- 2023/08/29 12:45
CRDT- 2023/08/12 22:06
PHST- 2023/06/12 00:00 [received]
PHST- 2023/07/10 00:00 [revised]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/08/29 12:45 [medline]
PHST- 2023/08/13 00:43 [pubmed]
PHST- 2023/08/12 22:06 [entrez]
AID - S0181-5512(23)00341-8 [pii]
AID - 10.1016/j.jfo.2023.07.001 [doi]
PST - ppublish
SO  - J Fr Ophtalmol. 2023 Sep;46(7):697-705. doi: 10.1016/j.jfo.2023.07.001. Epub 2023 
      Aug 10.

PMID- 37671567
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20231012
IS  - 1530-0374 (Electronic)
IS  - 1072-3714 (Linking)
VI  - 30
IP  - 10
DP  - 2023 Oct 1
TI  - Appropriateness of premature ovarian insufficiency recommendations provided by 
      ChatGPT.
PG  - 1033-1037
LID - 10.1097/GME.0000000000002246 [doi]
AB  - OBJECTIVE: This study aimed to evaluate the appropriateness of ChatGPT's 
      responses to frequently asked queries regarding the prevention and treatment of 
      premature ovarian insufficiency (POI). METHODS: A set of 25 questions covering 
      topics related to disease risk factors, screening, symptoms, treatment, and 
      treatment adverse effects were posed to ChatGPT, and the responses were evaluated 
      by two experienced obstetrician/gynecologists. RESULTS: Of the responses given by 
      ChatGPT, 19 of 25 (76%) were deemed appropriate, five (20%) were rated as 
      inappropriate, and the remaining one (4%) was considered unreliable. There was no 
      question that ChatGPT answered completely incorrectly; answers that were 
      considered inappropriate included partially correct information. CONCLUSIONS: The 
      study's findings suggest that ChatGPT provides patients with mostly accurate 
      information about the POI but might present significantly inaccurate information. 
      However, it is important to note that further research is needed to validate the 
      use of artificial intelligence chatbots in the context of reproductive health, 
      especially for individuals who may be highly impacted by the emotional and 
      psychological effects of POI. In addition, given the complexity and individuality 
      of medical care, AI chatbots should not be considered a replacement for medical 
      professionals but rather a supplementary tool that can help patients access 
      reliable information and support. Overall, this study contributes to the growing 
      body of research on AI chatbots and their potential use in reproductive health, 
      highlighting both the benefits and limitations of these technologies.
CI  - Copyright © 2023 by The Menopause Society.
FAU - Sütcüoğlu, Bengü Mutlu
AU  - Sütcüoğlu BM
AD  - From the Department of Obstetrics and Gynaecology, Lokman Hekim University, 
      Ankara, Turkey.
FAU - Güler, Melike
AU  - Güler M
LA  - eng
PT  - Journal Article
DEP - 20230905
PL  - United States
TA  - Menopause
JT  - Menopause (New York, N.Y.)
JID - 9433353
SB  - IM
MH  - Humans
MH  - Female
MH  - Artificial Intelligence
MH  - *Menopause, Premature
MH  - *Primary Ovarian Insufficiency/drug therapy
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - Emotions
COIS- Financial disclosure/conflicts of interest: None reported.
EDAT- 2023/09/06 06:42
MHDA- 2023/09/25 06:42
CRDT- 2023/09/06 05:26
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/09/06 06:42 [pubmed]
PHST- 2023/09/06 05:26 [entrez]
AID - 00042192-202310000-00008 [pii]
AID - 10.1097/GME.0000000000002246 [doi]
PST - ppublish
SO  - Menopause. 2023 Oct 1;30(10):1033-1037. doi: 10.1097/GME.0000000000002246. Epub 
      2023 Sep 5.

PMID- 37515033
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230801
IS  - 2076-393X (Print)
IS  - 2076-393X (Electronic)
IS  - 2076-393X (Linking)
VI  - 11
IP  - 7
DP  - 2023 Jul 7
TI  - Artificial Intelligence and Public Health: Evaluating ChatGPT Responses to 
      Vaccination Myths and Misconceptions.
LID - 10.3390/vaccines11071217 [doi]
LID - 1217
AB  - Artificial intelligence (AI) tools, such as ChatGPT, are the subject of intense 
      debate regarding their possible applications in contexts such as health care. 
      This study evaluates the Correctness, Clarity, and Exhaustiveness of the answers 
      provided by ChatGPT on the topic of vaccination. The World Health Organization's 
      11 "myths and misconceptions" about vaccinations were administered to both the 
      free (GPT-3.5) and paid version (GPT-4.0) of ChatGPT. The AI tool's responses 
      were evaluated qualitatively and quantitatively, in reference to those myth and 
      misconceptions provided by WHO, independently by two expert Raters. The agreement 
      between the Raters was significant for both versions (p of K < 0.05). Overall, 
      ChatGPT responses were easy to understand and 85.4% accurate although one of the 
      questions was misinterpreted. Qualitatively, the GPT-4.0 responses were superior 
      to the GPT-3.5 responses in terms of Correctness, Clarity, and Exhaustiveness (Δ 
      = 5.6%, 17.9%, 9.3%, respectively). The study shows that, if appropriately 
      questioned, AI tools can represent a useful aid in the health care field. 
      However, when consulted by non-expert users, without the support of expert 
      medical advice, these tools are not free from the risk of eliciting misleading 
      responses. Moreover, given the existing social divide in information access, the 
      improved accuracy of answers from the paid version raises further ethical issues.
FAU - Deiana, Giovanna
AU  - Deiana G
AUID- ORCID: 0000-0003-1151-6830
AD  - Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy.
AD  - Department of Medical, Surgical and Experimental Sciences, University Hospital of 
      Sassari, 07100 Sassari, Italy.
FAU - Dettori, Marco
AU  - Dettori M
AUID- ORCID: 0000-0002-4901-2067
AD  - Department of Medical, Surgical and Experimental Sciences, University Hospital of 
      Sassari, 07100 Sassari, Italy.
AD  - Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 
      Sassari, Italy.
AD  - Department of Restorative, Pediatric and Preventive Dentistry, University of 
      Bern, 3012 Bern, Switzerland.
FAU - Arghittu, Antonella
AU  - Arghittu A
AUID- ORCID: 0000-0001-9257-1422
AD  - Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 
      Sassari, Italy.
FAU - Azara, Antonio
AU  - Azara A
AUID- ORCID: 0000-0001-9089-0817
AD  - Department of Medical, Surgical and Experimental Sciences, University Hospital of 
      Sassari, 07100 Sassari, Italy.
AD  - Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 
      Sassari, Italy.
FAU - Gabutti, Giovanni
AU  - Gabutti G
AUID- ORCID: 0000-0002-7259-4923
AD  - Working Group "Vaccines and Immunization Policies", Italian Society of Hygiene, 
      Preventive Medicine and Public Health, 16030 Cogorno, Italy.
FAU - Castiglia, Paolo
AU  - Castiglia P
AUID- ORCID: 0000-0002-0972-4828
AD  - Department of Medical, Surgical and Experimental Sciences, University Hospital of 
      Sassari, 07100 Sassari, Italy.
AD  - Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 
      Sassari, Italy.
AD  - Working Group "Vaccines and Immunization Policies", Italian Society of Hygiene, 
      Preventive Medicine and Public Health, 16030 Cogorno, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230707
PL  - Switzerland
TA  - Vaccines (Basel)
JT  - Vaccines
JID - 101629355
PMC - PMC10386180
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - immunization
OT  - myths and misconceptions
OT  - public health
OT  - vaccines
COIS- The authors declare no conflict of interest directly relating to this manuscript. 
      GG declares outside this paper having received personal fees for advisory board 
      membership and consultancy from Emergent BioSolutions, the GSK group of 
      companies, Merck Sharp & Dohme, Pfizer, Sanofi Pasteur Italy, Moderna and 
      Seqirus, as well as personal fees for lectures from Merck Sharp & Dohme, Moderna, 
      Pfizer, and Seqirus. PC declares outside this paper having received personal fees 
      for advisory board membership and travel expenses for lectures from the GSK group 
      of companies, Merck Sharp & Dohme, Pfizer, Sanofi Pasteur Italy, Moderna and 
      Seqirus.
EDAT- 2023/07/29 11:49
MHDA- 2023/07/29 11:50
CRDT- 2023/07/29 01:44
PHST- 2023/06/01 00:00 [received]
PHST- 2023/07/04 00:00 [revised]
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/07/29 11:50 [medline]
PHST- 2023/07/29 11:49 [pubmed]
PHST- 2023/07/29 01:44 [entrez]
AID - vaccines11071217 [pii]
AID - vaccines-11-01217 [pii]
AID - 10.3390/vaccines11071217 [doi]
PST - epublish
SO  - Vaccines (Basel). 2023 Jul 7;11(7):1217. doi: 10.3390/vaccines11071217.

PMID- 37891086
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1535-6302 (Electronic)
IS  - 0363-0188 (Linking)
DP  - 2023 Oct 20
TI  - Assessing appropriate responses to ACR urologic imaging scenarios using ChatGPT 
      and Bard.
LID - S0363-0188(23)00175-5 [pii]
LID - 10.1067/j.cpradiol.2023.10.022 [doi]
AB  - Artificial intelligence (AI) has recently become a trending tool and topic 
      regarding productivity especially with publicly available free services such as 
      ChatGPT and Bard. In this report, we investigate if two widely available chatbots 
      chatGPT and Bard, are able to show consistent accurate responses for the best 
      imaging modality for urologic clinical situations and if they are in line with 
      American College of Radiology (ACR) Appropriateness Criteria (AC). All clinical 
      scenarios provided by the ACR were inputted into ChatGPT and Bard with result 
      compared to the ACR AC and recorded. Both chatbots had an appropriate imaging 
      modality rate of of 62% and no significant difference in proportion of correct 
      imaging modality was found overall between the two services (p>0.05). The results 
      of our study found that both ChatGPT and Bard are similar in their ability to 
      suggest the most appropriate imaging modality in a variety of urologic scenarios 
      based on ACR AC criteria. Nonetheless, both chatbots lack consistent accuracy and 
      further development is necessary for implementation in clinical settings. For 
      proper use of these AI services in clinical decision making, further developments 
      are needed to improve the workflow of physicians.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Doddi, Sishir
AU  - Doddi S
AD  - University of Toledo College of Medicine, Toledo, OH, United States. Electronic 
      address: sishir.doddi@utoledo.edu.
FAU - Hibshman, Taryn
AU  - Hibshman T
AD  - University of Toledo College of Medicine, Toledo, OH, United States.
FAU - Salichs, Oscar
AU  - Salichs O
AD  - University of Toledo College of Medicine, Toledo, OH, United States.
FAU - Bera, Kaustav
AU  - Bera K
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH.
FAU - Tippareddy, Charit
AU  - Tippareddy C
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH.
FAU - Ramaiya, Nikhil
AU  - Ramaiya N
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH.
FAU - Tirumani, Sree Harsha
AU  - Tirumani SH
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - United States
TA  - Curr Probl Diagn Radiol
JT  - Current problems in diagnostic radiology
JID - 7607123
SB  - IM
EDAT- 2023/10/28 11:42
MHDA- 2023/10/28 11:42
CRDT- 2023/10/27 22:57
PHST- 2023/10/04 00:00 [received]
PHST- 2023/10/18 00:00 [revised]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/10/28 11:42 [medline]
PHST- 2023/10/28 11:42 [pubmed]
PHST- 2023/10/27 22:57 [entrez]
AID - S0363-0188(23)00175-5 [pii]
AID - 10.1067/j.cpradiol.2023.10.022 [doi]
PST - aheadofprint
SO  - Curr Probl Diagn Radiol. 2023 Oct 20:S0363-0188(23)00175-5. doi: 
      10.1067/j.cpradiol.2023.10.022.

PMID- 37603142
OWN - NLM
STAT- MEDLINE
DCOM- 20231110
LR  - 20231120
IS  - 1970-9366 (Electronic)
IS  - 1828-0447 (Linking)
VI  - 18
IP  - 8
DP  - 2023 Nov
TI  - Performance of an artificial intelligence-based chatbot when acting as EMS 
      dispatcher in a cardiac arrest scenario.
PG  - 2449-2452
LID - 10.1007/s11739-023-03399-1 [doi]
FAU - Birkun, Alexei
AU  - Birkun A
AUID- ORCID: 0000-0002-2789-9760
AD  - Department of General Surgery, Anaesthesiology, Resuscitation and Emergency 
      Medicine, Medical Academy Named After S. I. Georgievsky of V. I. Vernadsky 
      Crimean Federal University, Simferopol, Russian Federation. 
      birkunalexei@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230821
PL  - Italy
TA  - Intern Emerg Med
JT  - Internal and emergency medicine
JID - 101263418
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Heart Arrest/therapy
MH  - *Cardiopulmonary Resuscitation
MH  - *Out-of-Hospital Cardiac Arrest/therapy
MH  - *Emergency Medical Services
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Cardiopulmonary resuscitation
OT  - Chatbot
OT  - GPT-4
OT  - Large language model
OT  - T-CPR
EDAT- 2023/08/21 12:42
MHDA- 2023/11/10 06:45
CRDT- 2023/08/21 11:07
PHST- 2023/06/21 00:00 [received]
PHST- 2023/08/10 00:00 [accepted]
PHST- 2023/11/10 06:45 [medline]
PHST- 2023/08/21 12:42 [pubmed]
PHST- 2023/08/21 11:07 [entrez]
AID - 10.1007/s11739-023-03399-1 [pii]
AID - 10.1007/s11739-023-03399-1 [doi]
PST - ppublish
SO  - Intern Emerg Med. 2023 Nov;18(8):2449-2452. doi: 10.1007/s11739-023-03399-1. Epub 
      2023 Aug 21.

PMID- 37994523
OWN - NLM
STAT- Publisher
LR  - 20231123
IS  - 1538-9855 (Electronic)
IS  - 0363-3624 (Linking)
DP  - 2023 Nov 22
TI  - Reliability and Quality of the Nursing Care Planning Texts Generated by ChatGPT.
LID - 10.1097/NNE.0000000000001566 [doi]
AB  - BACKGROUND: The research on ChatGPT-generated nursing care planning texts is 
      critical for enhancing nursing education through innovative and accessible 
      learning methods, improving reliability and quality. PURPOSE: The aim of the 
      study was to examine the quality, authenticity, and reliability of the nursing 
      care planning texts produced using ChatGPT. METHODS: The study sample comprised 
      40 texts generated by ChatGPT selected nursing diagnoses that were included in 
      NANDA 2021-2023. The texts were evaluated by using a descriptive criteria form 
      and the DISCERN tool to evaluate health information. RESULTS: DISCERN total 
      average score of the texts was 45.93 ± 4.72. All texts had a moderate level of 
      reliability and 97.5% of them provided moderate quality subscale score of 
      information. A statistically significant relationship was found among the number 
      of accessible references, reliability (r = 0.408), and quality subscale score (r 
      = 0.379) of the texts (P < .05). CONCLUSION: ChatGPT-generated texts exhibited 
      moderate reliability, quality of nursing care information, and overall quality 
      despite low similarity rates.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Dağcı, Mahmut
AU  - Dağcı M
AD  - Department of Nursing, Bezmialem Vakif University, Faculty of Health Sciences, 
      Istanbul, Turkey.
FAU - Çam, Funda
AU  - Çam F
FAU - Dost, Ayşe
AU  - Dost A
AUID- ORCID: 0000-0002-4651-7254
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - United States
TA  - Nurse Educ
JT  - Nurse educator
JID - 7701902
COIS- The authors declare no conflicts of interest with respect to the authorship 
      and/or publication of this article.
EDAT- 2023/11/23 06:42
MHDA- 2023/11/23 06:42
CRDT- 2023/11/23 04:28
PHST- 2023/11/23 06:42 [medline]
PHST- 2023/11/23 06:42 [pubmed]
PHST- 2023/11/23 04:28 [entrez]
AID - 00006223-990000000-00373 [pii]
AID - 10.1097/NNE.0000000000001566 [doi]
PST - aheadofprint
SO  - Nurse Educ. 2023 Nov 22. doi: 10.1097/NNE.0000000000001566.

PMID- 37459740
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1532-818X (Electronic)
IS  - 0196-0709 (Linking)
VI  - 44
IP  - 6
DP  - 2023 Nov-Dec
TI  - ChatGPT in head and neck scientific writing: A precautionary anecdote.
PG  - 103980
LID - S0196-0709(23)00194-1 [pii]
LID - 10.1016/j.amjoto.2023.103980 [doi]
AB  - PURPOSE: To evaluate the accuracy of ChatGPT references in scientific writing 
      relevant to head and neck surgery. MATERIALS AND METHODS: Five commonly 
      researched keywords relevant to head and neck surgery were selected 
      (osteoradionecrosis of the jaws, oral cancer, adjuvant therapy for oral cancer, 
      TORS, and free flap reconstruction in oral cancer). The AI chatbot was then asked 
      to provide ten complete citations for each of the keywords. Two independent 
      authors reviewed the results for accuracy and assigned each article a numerical 
      score based on pre-selected criteria. RESULTS: Among 50 total references provided 
      by ChatGPT, only five (10 %) were found to have the correct title, journal, 
      authors, year of publication, and DOI. Merely 14 % of the presented references 
      had correct DOI. References regarding free flap reconstruction for oral cancer 
      were the least accurate from all the five categories, with no correct DOI. 
      Complete inter-rater agreement was noted while evaluating the citations. 
      CONCLUSION: Only 10 % of the articles provided by ChatGPT, relevant to head and 
      neck surgery, were correct. A high degree of academic hallucination was noted.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Wu, Robin T
AU  - Wu RT
AD  - Department of Plastic and Reconstruction Surgery, Chang Gung Memorial Hospital, 
      Linkou, Taiwan; Division of Plastic and Reconstructive Surgery, Department of 
      Surgery, Stanford University Hospital, Stanford, CA, USA.
FAU - Dang, Rushil R
AU  - Dang RR
AD  - Department of Plastic and Reconstruction Surgery, Chang Gung Memorial Hospital, 
      Linkou, Taiwan; Former fellow, Maxillofacial Oncology and Reconstructive Surgery, 
      Department of Oral and Maxillofacial Surgery, Boston Medical Center, Boston, MA, 
      USA. Electronic address: rushilrd@bu.edu.
LA  - eng
PT  - Journal Article
DEP - 20230706
PL  - United States
TA  - Am J Otolaryngol
JT  - American journal of otolaryngology
JID - 8000029
SB  - IM
MH  - Humans
MH  - *Head
MH  - Neck
MH  - *Mouth Neoplasms
MH  - Combined Modality Therapy
MH  - Writing
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Head and neck surgery
OT  - Oral cancer
COIS- Declaration of competing interest The authors have no conflict of interest to 
      declare.
EDAT- 2023/07/17 19:08
MHDA- 2023/07/17 19:09
CRDT- 2023/07/17 18:03
PHST- 2023/06/06 00:00 [received]
PHST- 2023/07/04 00:00 [accepted]
PHST- 2023/07/17 19:09 [medline]
PHST- 2023/07/17 19:08 [pubmed]
PHST- 2023/07/17 18:03 [entrez]
AID - S0196-0709(23)00194-1 [pii]
AID - 10.1016/j.amjoto.2023.103980 [doi]
PST - ppublish
SO  - Am J Otolaryngol. 2023 Nov-Dec;44(6):103980. doi: 10.1016/j.amjoto.2023.103980. 
      Epub 2023 Jul 6.

PMID- 37434733
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230918
IS  - 2055-2076 (Print)
IS  - 2055-2076 (Electronic)
IS  - 2055-2076 (Linking)
VI  - 9
DP  - 2023 Jan-Dec
TI  - ChatGPT's potential role in non-English-speaking outpatient clinic settings.
PG  - 20552076231184091
LID - 10.1177/20552076231184091 [doi]
LID - 20552076231184091
AB  - Researchers recently utilized ChatGPT as a tool for composing clinic letters, 
      highlighting its ability to generate accurate and empathetic communications. Here 
      we demonstrated the potential application of ChatGPT as a medical assistant in 
      Mandarin Chinese-speaking outpatient clinics, aiming to improve patient 
      satisfaction in high-patient volume settings. ChatGPT achieved an average score 
      of 72.4% in the Chinese Medical Licensing Examination's Clinical Knowledge 
      section, ranking within the top 20th percentile. It also demonstrated its 
      potential for clinical communication in non-English speaking environments. Our 
      study suggests that ChatGPT could serve as an interface between physicians and 
      patients in Chinese-speaking outpatient settings, possibly extending to other 
      languages. However, further optimization is required, including training on 
      medical-specific datasets, rigorous testing, privacy compliance, integration with 
      existing systems, user-friendly interfaces, and the development of guidelines for 
      medical professionals. Controlled clinical trials and regulatory approval are 
      necessary before widespread implementation. As chatbots' integration into medical 
      practice becomes more feasible, rigorous early investigations and pilot studies 
      can help mitigate potential risks.
CI  - © The Author(s) 2023.
FAU - Zhu, Zhoule
AU  - Zhu Z
AD  - Department of Neurosurgery, Zhejiang University School of Medicine Second 
      Affiliated Hospital, Hangzhou, Zhejiang, China. RINGGOLD: 89681
AD  - Clinical Research Center for Neurological Diseases of Zhejiang Province, 
      Hangzhou, Zhejiang, China.
FAU - Ying, Yuqi
AU  - Ying Y
AD  - Department of Neurosurgery, Zhejiang University School of Medicine Second 
      Affiliated Hospital, Hangzhou, Zhejiang, China. RINGGOLD: 89681
AD  - Clinical Research Center for Neurological Diseases of Zhejiang Province, 
      Hangzhou, Zhejiang, China.
FAU - Zhu, Junming
AU  - Zhu J
AD  - Department of Neurosurgery, Zhejiang University School of Medicine Second 
      Affiliated Hospital, Hangzhou, Zhejiang, China. RINGGOLD: 89681
AD  - Clinical Research Center for Neurological Diseases of Zhejiang Province, 
      Hangzhou, Zhejiang, China.
FAU - Wu, Hemmings
AU  - Wu H
AUID- ORCID: 0000-0001-9665-2617
AD  - Department of Neurosurgery, Zhejiang University School of Medicine Second 
      Affiliated Hospital, Hangzhou, Zhejiang, China. RINGGOLD: 89681
AD  - Clinical Research Center for Neurological Diseases of Zhejiang Province, 
      Hangzhou, Zhejiang, China.
LA  - eng
PT  - Journal Article
DEP - 20230626
PL  - United States
TA  - Digit Health
JT  - Digital health
JID - 101690863
PMC - PMC10331772
OTO - NOTNLM
OT  - Artificial intelligence
OT  - digital health
OT  - general medicine
OT  - health communications
COIS- The author(s) declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/07/12 06:42
MHDA- 2023/07/12 06:43
CRDT- 2023/07/12 03:46
PHST- 2023/04/26 00:00 [received]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/07/12 06:42 [pubmed]
PHST- 2023/07/12 06:43 [medline]
PHST- 2023/07/12 03:46 [entrez]
AID - 10.1177_20552076231184091 [pii]
AID - 10.1177/20552076231184091 [doi]
PST - epublish
SO  - Digit Health. 2023 Jun 26;9:20552076231184091. doi: 10.1177/20552076231184091. 
      eCollection 2023 Jan-Dec.

PMID- 37115245
OWN - NLM
STAT- MEDLINE
DCOM- 20230605
LR  - 20230605
IS  - 2193-6226 (Electronic)
IS  - 2193-6218 (Linking)
VI  - 118
IP  - 5
DP  - 2023 Jun
TI  - [Artificial intelligence: How will ChatGPT and other AI applications change our 
      everyday medical practice?].
PG  - 366-371
LID - 10.1007/s00063-023-01019-6 [doi]
AB  - BACKGROUND: With the free provision of the chat robot "ChatGPT" by the company 
      OpenAI in November 2022, an application of artificial intelligence (AI) became 
      tangible for everyone. OBJECTIVES: An explanation of the basic functionality of 
      large language models (LLM) is given, followed by a presentation of application 
      options of ChatGPT in medicine, and an outlook and discussion of possible dangers 
      of AI applications. METHODS: Problem solving with ChatGPT using concrete 
      examples. Analysis and discussion of the available scientific literature. 
      RESULTS: There has been a significant increase in the use of AI applications in 
      scientific work, especially in scientific writing. Wide application of LLM in 
      writing medical documentation is conceivable. Technical functionality allows the 
      use of AI applications as a diagnostic support system. There is a risk of 
      spreading and entrenching inaccuracies and bias through application of LLM. 
      Regulation of this new technology is pending. CONCLUSION: AI applications such as 
      ChatGPT have the potential to permanently change everyday medical practice. An 
      examination of this technology and evaluation of opportunities and risks is 
      warranted.
CI  - © 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, 
      ein Teil von Springer Nature.
FAU - Sonntagbauer, Michael
AU  - Sonntagbauer M
AD  - Klinik für Intensivmedizin, Universitätsklinikum Hamburg-Eppendorf, O 10, Raum 
      02.5.062.1, Martinistr. 52, 20251, Hamburg, Deutschland. m.sonntagbauer@uke.de.
FAU - Haar, Markus
AU  - Haar M
AD  - Klinik für Intensivmedizin, Universitätsklinikum Hamburg-Eppendorf, O 10, Raum 
      02.5.062.1, Martinistr. 52, 20251, Hamburg, Deutschland.
FAU - Kluge, Stefan
AU  - Kluge S
AD  - Klinik für Intensivmedizin, Universitätsklinikum Hamburg-Eppendorf, O 10, Raum 
      02.5.062.1, Martinistr. 52, 20251, Hamburg, Deutschland.
LA  - ger
PT  - English Abstract
PT  - Journal Article
PT  - Review
TT  - Künstliche Intelligenz: Wie werden ChatGPT und andere KI-Anwendungen unseren 
      ärztlichen Alltag verändern?
DEP - 20230428
PL  - Germany
TA  - Med Klin Intensivmed Notfmed
JT  - Medizinische Klinik, Intensivmedizin und Notfallmedizin
JID - 101575086
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Documentation
MH  - *Medicine
OTO - NOTNLM
OT  - Big data
OT  - Diagnostic support systems
OT  - Digitization in medicine
OT  - Large language models
OT  - Machine learning
EDAT- 2023/04/28 12:42
MHDA- 2023/06/05 06:42
CRDT- 2023/04/28 11:05
PHST- 2023/04/17 00:00 [received]
PHST- 2023/04/19 00:00 [accepted]
PHST- 2023/06/05 06:42 [medline]
PHST- 2023/04/28 12:42 [pubmed]
PHST- 2023/04/28 11:05 [entrez]
AID - 10.1007/s00063-023-01019-6 [pii]
AID - 10.1007/s00063-023-01019-6 [doi]
PST - ppublish
SO  - Med Klin Intensivmed Notfmed. 2023 Jun;118(5):366-371. doi: 
      10.1007/s00063-023-01019-6. Epub 2023 Apr 28.

PMID- 37309705
OWN - NLM
STAT- Publisher
LR  - 20230916
IS  - 1555-9823 (Electronic)
IS  - 0003-1348 (Linking)
DP  - 2023 Jun 13
TI  - The Utilization of ChatGPT in Reshaping Future Medical Education and Learning 
      Perspectives: A Curse or a Blessing?
PG  - 31348231180950
LID - 10.1177/00031348231180950 [doi]
AB  - BACKGROUND: ChatGPT has substantial potential to revolutionize medical education. 
      We aim to assess how medical students and laypeople evaluate information produced 
      by ChatGPT compared to an evidence-based resource on the diagnosis and management 
      of 5 common surgical conditions. METHODS: A 60-question anonymous online survey 
      was distributed to third- and fourth-year U.S. medical students and laypeople to 
      evaluate articles produced by ChatGPT and an evidence-based source on clarity, 
      relevance, reliability, validity, organization, and comprehensiveness. 
      Participants received 2 blinded articles, 1 from each source, for each surgical 
      condition. Paired-sample t-tests were used to compare ratings between the 2 
      sources. RESULTS: Of 56 survey participants, 50.9% (n = 28) were U.S. medical 
      students and 49.1% (n = 27) were from the general population. Medical students 
      reported that ChatGPT articles displayed significantly more clarity 
      (appendicitis: 4.39 vs 3.89, P = .020; diverticulitis: 4.54 vs 3.68, P < .001; 
      SBO 4.43 vs 3.79, P = .003; GI bleed: 4.36 vs 3.93, P = .020) and better 
      organization (diverticulitis: 4.36 vs 3.68, P = .021; SBO: 4.39 vs 3.82, P = 
      .033) than the evidence-based source. However, for all 5 conditions, medical 
      students found evidence-based passages to be more comprehensive than ChatGPT 
      articles (cholecystitis: 4.04 vs 3.36, P = .009; appendicitis: 4.07 vs 3.36, P = 
      .015; diverticulitis: 4.07 vs 3.36, P = .015; small bowel obstruction: 4.11 vs 
      3.54, P = .030; upper GI bleed: 4.11 vs 3.29, P = .003). CONCLUSION: Medical 
      students perceived ChatGPT articles to be clearer and better organized than 
      evidence-based sources on the pathogenesis, diagnosis, and management of 5 common 
      surgical pathologies. However, evidence-based articles were rated as 
      significantly more comprehensive.
FAU - Breeding, Tessa
AU  - Breeding T
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Martinez, Brian
AU  - Martinez B
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Patel, Heli
AU  - Patel H
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Nasef, Hazem
AU  - Nasef H
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Arif, Hasan
AU  - Arif H
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Nakayama, Don
AU  - Nakayama D
AD  - Mercer University School of Medicine, Columbus, GA, USA.
AD  - Department of Pediatric Surgery, Piedmont Columbus Regional Hospital, Piedmont, 
      GA, USA. RINGGOLD: 602096
FAU - Elkbuli, Adel
AU  - Elkbuli A
AD  - Department of Surgery, Division of Trauma and Surgical Critical Care, Orlando 
      Regional Medical Center, Orlando, FL, USA. RINGGOLD: 25105
AD  - Department of Surgical Education, Orlando Regional Medical Center, Orlando, FL, 
      USA. RINGGOLD: 25105
LA  - eng
PT  - Journal Article
DEP - 20230613
PL  - United States
TA  - Am Surg
JT  - The American surgeon
JID - 0370522
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - common surgical conditions
OT  - laypeople
OT  - medical education
OT  - medical students
EDAT- 2023/06/13 06:42
MHDA- 2023/06/13 06:42
CRDT- 2023/06/13 05:27
PHST- 2023/06/13 06:42 [pubmed]
PHST- 2023/06/13 06:42 [medline]
PHST- 2023/06/13 05:27 [entrez]
AID - 10.1177/00031348231180950 [doi]
PST - aheadofprint
SO  - Am Surg. 2023 Jun 13:31348231180950. doi: 10.1177/00031348231180950.

PMID- 37269717
OWN - NLM
STAT- MEDLINE
DCOM- 20230616
LR  - 20230628
IS  - 1873-1244 (Electronic)
IS  - 0899-9007 (Linking)
VI  - 112
DP  - 2023 Aug
TI  - The credibility of dietary advice formulated by ChatGPT: Robo-diets for people 
      with food allergies.
PG  - 112076
LID - S0899-9007(23)00105-3 [pii]
LID - 10.1016/j.nut.2023.112076 [doi]
AB  - The introduction of ChatGPT has sparked enormous public interest in large 
      language (deep-learning) models, which have been sophisticated enough to perform 
      well on a variety of tasks. One way people are using these models is to construct 
      diets. The prompts often include food restrictions that are an obligatory part of 
      everyday life for millions of people worldwide. The aim of this study was to 
      investigate the safety and accuracy of 56 diets, constructed for hypothetical 
      individuals who are allergic to food allergens. Four levels, corresponding to the 
      "baseline" ability of ChatGPT without prompting for specifics, as well as its 
      ability to prepare appropriate diets when an individual has an adverse food 
      reaction to two allergens or solicits a low-calorie diet, were defined. Findings 
      from our study demonstrated that ChatGPT, although generally accurate, has the 
      potential to produce harmful diets. More common errors involve inaccuracies in 
      portions or calories of food, meals, or diets. We discuss here how the accuracy 
      of large language models could be increased and the trade-offs involved. We 
      propose that prompting for elimination diets can serve as one way to assess 
      differences between such models.
CI  - Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
FAU - Niszczota, Paweł
AU  - Niszczota P
AD  - Institute of International Business and Economics, Poznań University of Economics 
      and Business, Poznań, Poland; Humans & AI Laboratory (HAI Lab), Poznań University 
      of Economics and Business, Poznań, Poland.
FAU - Rybicka, Iga
AU  - Rybicka I
AD  - Humans & AI Laboratory (HAI Lab), Poznań University of Economics and Business, 
      Poznań, Poland; Institute of Quality Science, Poznań University of Economics and 
      Business, Poznań, Poland. Electronic address: iga.rybicka@ue.poznan.pl.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230511
PL  - United States
TA  - Nutrition
JT  - Nutrition (Burbank, Los Angeles County, Calif.)
JID - 8802712
RN  - 0 (Allergens)
SB  - IM
MH  - Humans
MH  - *Diet
MH  - *Food Hypersensitivity
MH  - Energy Intake
MH  - Allergens
MH  - Meals
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Dietary advice
OT  - Food allergy
OT  - Nutrition
OT  - Robo-diet
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/06/04 01:08
MHDA- 2023/06/16 06:42
CRDT- 2023/06/03 18:06
PHST- 2023/02/15 00:00 [received]
PHST- 2023/04/07 00:00 [revised]
PHST- 2023/05/08 00:00 [accepted]
PHST- 2023/06/16 06:42 [medline]
PHST- 2023/06/04 01:08 [pubmed]
PHST- 2023/06/03 18:06 [entrez]
AID - S0899-9007(23)00105-3 [pii]
AID - 10.1016/j.nut.2023.112076 [doi]
PST - ppublish
SO  - Nutrition. 2023 Aug;112:112076. doi: 10.1016/j.nut.2023.112076. Epub 2023 May 11.

PMID- 36950398
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1682-024X (Print)
IS  - 1681-715X (Electronic)
IS  - 1681-715X (Linking)
VI  - 39
IP  - 2
DP  - 2023 Mar-Apr
TI  - ChatGPT - Reshaping medical education and clinical management.
PG  - 605-607
LID - 10.12669/pjms.39.2.7653 [doi]
AB  - Artificial Intelligence is no more the talk of the fiction read in novels or seen 
      in movies. It has been making inroads slowly and gradually in medical education 
      and clinical management of patients apart from all other walks of life. Recently, 
      chatbots particularly ChatGPT, were developed and trained, using a huge amount of 
      textual data from the internet. This has made a significant impact on our 
      approach in medical science. Though there are benefits of this new technology, a 
      lot of caution is required for its use.
CI  - Copyright: © Pakistan Journal of Medical Sciences.
FAU - Khan, Rehan Ahmed
AU  - Khan RA
AD  - Rehan Ahmed Khan, MBBS, FCPS, FRCS, MHPE, PhD (Medical Education), Dean Riphah 
      Institute of Assessment, HOD & Prof. of Surgery, Islamic International Medical 
      College, Riphah International University.
FAU - Jawaid, Masood
AU  - Jawaid M
AD  - Masood Jawaid, MBBS, MCPS, MRCS, FCPS, MHPE, Director Medical Affairs, PharmEvo 
      (Pvt) Ltd.
FAU - Khan, Aymen Rehan
AU  - Khan AR
AD  - Aymen Rehan Khan, Final year student, Department of English, Foundation 
      University, Rawalpindi.
FAU - Sajjad, Madiha
AU  - Sajjad M
AD  - Madiha Sajjad, MBBS, FCPS, MHPE, Prof. of Pathology, Islamic International 
      Medical College, Riphah International University.
LA  - eng
PT  - Journal Article
PL  - Pakistan
TA  - Pak J Med Sci
JT  - Pakistan journal of medical sciences
JID - 100913117
PMC - PMC10025693
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Education
OT  - NLP
OT  - Open AI
OT  - clinical management
OT  - medical education
EDAT- 2023/03/24 06:00
MHDA- 2023/03/24 06:01
CRDT- 2023/03/23 02:26
PHST- 2023/02/04 00:00 [received]
PHST- 2023/02/06 00:00 [revised]
PHST- 2023/02/07 00:00 [accepted]
PHST- 2023/03/23 02:26 [entrez]
PHST- 2023/03/24 06:00 [pubmed]
PHST- 2023/03/24 06:01 [medline]
AID - PJMS-39-605 [pii]
AID - 10.12669/pjms.39.2.7653 [doi]
PST - ppublish
SO  - Pak J Med Sci. 2023 Mar-Apr;39(2):605-607. doi: 10.12669/pjms.39.2.7653.

PMID- 37040823
OWN - NLM
STAT- MEDLINE
DCOM- 20230612
LR  - 20231116
IS  - 1532-8406 (Electronic)
IS  - 0883-5403 (Linking)
VI  - 38
IP  - 7
DP  - 2023 Jul
TI  - Using a Google Web Search Analysis to Assess the Utility of ChatGPT in Total 
      Joint Arthroplasty.
PG  - 1195-1202
LID - S0883-5403(23)00352-2 [pii]
LID - 10.1016/j.arth.2023.04.007 [doi]
AB  - BACKGROUND: Rapid technological advancements have laid the foundations for the 
      use of artificial intelligence in medicine. The promise of machine learning (ML) 
      lies in its potential ability to improve treatment decision making, predict 
      adverse outcomes, and streamline the management of perioperative healthcare. In 
      an increasing consumer-focused health care model, unprecedented access to 
      information may extend to patients using ChatGPT to gain insight into medical 
      questions. The main objective of our study was to replicate a patient's internet 
      search in order to assess the appropriateness of ChatGPT, a novel machine 
      learning tool released in 2022 that provides dialogue responses to queries, in 
      comparison to Google Web Search, the most widely used search engine in the United 
      States today, as a resource for patients for online health information. For the 2 
      different search engines, we compared i) the most frequently asked questions 
      (FAQs) associated with total knee arthroplasty (TKA) and total hip arthroplasty 
      (THA) by question type and topic; ii) the answers to the most frequently asked 
      questions; as well as iii) the FAQs yielding a numerical response. METHODS: A 
      Google web search was performed with the following search terms: "total knee 
      replacement" and "total hip replacement." These terms were individually entered 
      and the first 10 FAQs were extracted along with the source of the associated 
      website for each question. The following statements were inputted into ChatGPT: 
      1) "Perform a google search with the search term 'total knee replacement' and 
      record the 10 most FAQs related to the search term" as well as 2) "Perform a 
      google search with the search term 'total hip replacement' and record the 10 most 
      FAQs related to the search term." A Google web search was repeated with the same 
      search terms to identify the first 10 FAQs that included a numerical response for 
      both "total knee replacement" and "total hip replacement." These questions were 
      then inputted into ChatGPT and the questions and answers were recorded. RESULTS: 
      There were 5 of 20 (25%) questions that were similar when performing a Google web 
      search and a search of ChatGPT for all search terms. Of the 20 questions asked 
      for the Google Web Search, 13 of 20 were provided by commercial websites. For 
      ChatGPT, 15 of 20 (75%) questions were answered by government websites, with the 
      most frequent one being PubMed. In terms of numerical questions, 11 of 20 (55%) 
      of the most FAQs provided different responses between a Google web search and 
      ChatGPT. CONCLUSION: A comparison of the FAQs by a Google web search with 
      attempted replication by ChatGPT revealed heterogenous questions and responses 
      for open and discrete questions. ChatGPT should remain a trending use as a 
      potential resource to patients that needs further corroboration until its ability 
      to provide credible information is verified and concordant with the goals of the 
      physician and the patient alike.
CI  - Copyright © 2023. Published by Elsevier Inc.
FAU - Dubin, Jeremy A
AU  - Dubin JA
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Bains, Sandeep S
AU  - Bains SS
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Chen, Zhongming
AU  - Chen Z
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Hameed, Daniel
AU  - Hameed D
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Nace, James
AU  - Nace J
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Mont, Michael A
AU  - Mont MA
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Delanois, Ronald E
AU  - Delanois RE
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
LA  - eng
PT  - Journal Article
DEP - 20230410
PL  - United States
TA  - J Arthroplasty
JT  - The Journal of arthroplasty
JID - 8703515
SB  - IM
CIN - J Arthroplasty. 2023 Sep;38(9):e18. PMID: 37573082
CIN - J Arthroplasty. 2023 Sep;38(9):e19-e20. PMID: 37573083
CIN - J Arthroplasty. 2023 Sep;38(9):e21. PMID: 37573084
MH  - Humans
MH  - Search Engine
MH  - Artificial Intelligence
MH  - *Arthroplasty, Replacement, Knee
MH  - *Arthroplasty, Replacement, Hip
OTO - NOTNLM
OT  - ChatGPT
OT  - google
OT  - total joint arthroplasty
OT  - utility
OT  - web search
EDAT- 2023/04/12 06:00
MHDA- 2023/06/12 06:42
CRDT- 2023/04/11 19:14
PHST- 2023/02/07 00:00 [received]
PHST- 2023/03/22 00:00 [revised]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/04/12 06:00 [pubmed]
PHST- 2023/04/11 19:14 [entrez]
AID - S0883-5403(23)00352-2 [pii]
AID - 10.1016/j.arth.2023.04.007 [doi]
PST - ppublish
SO  - J Arthroplasty. 2023 Jul;38(7):1195-1202. doi: 10.1016/j.arth.2023.04.007. Epub 
      2023 Apr 10.

PMID- 37944140
OWN - NLM
STAT- Publisher
LR  - 20231109
IS  - 1873-233X (Electronic)
IS  - 0029-7844 (Linking)
DP  - 2023 Nov 9
TI  - The Genie Is Out of the Bottle: What ChatGPT Can and Cannot Do for Medical 
      Professionals.
LID - 10.1097/AOG.0000000000005446 [doi]
AB  - ChatGPT is a cutting-edge artificial intelligence technology that was released 
      for public use in November 2022. Its rapid adoption has raised questions about 
      capabilities, limitations, and risks. This article presents an overview of 
      ChatGPT, and it highlights the current state of this technology for the medical 
      field. The article seeks to provide a balanced perspective on what the model can 
      and cannot do in three specific domains: clinical practice, research, and medical 
      education. It also provides suggestions on how to optimize the use of this tool.
CI  - Copyright © 2023 by the American College of Obstetricians and Gynecologists. 
      Published by Wolters Kluwer Health, Inc. All rights reserved.
FAU - Morales-Ramirez, Pedro
AU  - Morales-Ramirez P
AD  - University of Missouri-Kansas City School of Medicine, Kansas City, Missouri.
FAU - Mishek, Henry
AU  - Mishek H
FAU - Dasgupta, Arhita
AU  - Dasgupta A
LA  - eng
PT  - Journal Article
DEP - 20231109
PL  - United States
TA  - Obstet Gynecol
JT  - Obstetrics and gynecology
JID - 0401101
SB  - IM
COIS- Financial Disclosure The authors did not report any potential conflicts of 
      interest.
EDAT- 2023/11/09 18:42
MHDA- 2023/11/09 18:42
CRDT- 2023/11/09 17:03
PHST- 2023/08/24 00:00 [received]
PHST- 2023/10/12 00:00 [accepted]
PHST- 2023/11/09 18:42 [medline]
PHST- 2023/11/09 18:42 [pubmed]
PHST- 2023/11/09 17:03 [entrez]
AID - 00006250-990000000-00955 [pii]
AID - 10.1097/AOG.0000000000005446 [doi]
PST - aheadofprint
SO  - Obstet Gynecol. 2023 Nov 9. doi: 10.1097/AOG.0000000000005446.

PMID- 37095665
OWN - NLM
STAT- MEDLINE
DCOM- 20230824
LR  - 20230824
IS  - 1546-3141 (Electronic)
IS  - 0361-803X (Linking)
VI  - 221
IP  - 3
DP  - 2023 Sep
TI  - Potential Use Cases for ChatGPT in Radiology Reporting.
PG  - 373-376
LID - 10.2214/AJR.23.29198 [doi]
AB  - Large language models (LLMs) such as ChatGPT are advanced artificial intelligence 
      models that are designed to process and understand human language. LLMs have the 
      potential to improve radiology reporting and patient engagement by automating 
      generation of the clinical history and impression of a radiology report, creating 
      layperson reports, and providing patients with pertinent questions and answers 
      about findings in radiology reports. However, LLMs are error prone, and human 
      oversight is needed to reduce the risk of patient harm.
FAU - Elkassem, Asser Abou
AU  - Elkassem AA
AD  - Department of Radiology, University of Alabama at Birmingham, JTN 452, 619 19th 
      St S, Birmingham, AL 35249-6830.
FAU - Smith, Andrew D
AU  - Smith AD
AD  - Department of Radiology, University of Alabama at Birmingham, JTN 452, 619 19th 
      St S, Birmingham, AL 35249-6830.
LA  - eng
PT  - Journal Article
DEP - 20230419
PL  - United States
TA  - AJR Am J Roentgenol
JT  - AJR. American journal of roentgenology
JID - 7708173
SB  - IM
CIN - AJR Am J Roentgenol. 2023 Sep;221(3):403. PMID: 37406203
MH  - Humans
MH  - *Artificial Intelligence
MH  - Patient Participation
MH  - *Radiology
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - health care
OT  - large language models
OT  - radiology reports
EDAT- 2023/04/25 06:42
MHDA- 2023/08/24 06:42
CRDT- 2023/04/25 01:01
PHST- 2023/08/24 06:42 [medline]
PHST- 2023/04/25 06:42 [pubmed]
PHST- 2023/04/25 01:01 [entrez]
AID - 10.2214/AJR.23.29198 [doi]
PST - ppublish
SO  - AJR Am J Roentgenol. 2023 Sep;221(3):373-376. doi: 10.2214/AJR.23.29198. Epub 
      2023 Apr 19.

PMID- 37609022
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230928
IS  - 0019-5413 (Print)
IS  - 1998-3727 (Electronic)
IS  - 0019-5413 (Linking)
VI  - 57
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT-4: Transforming Medical Education and Addressing Clinical Exposure 
      Challenges in the Post-pandemic Era.
PG  - 1527-1544
LID - 10.1007/s43465-023-00967-7 [doi]
AB  - BACKGROUND: The COVID-19 pandemic has affected medical education, constraining 
      clinical exposure and posing unprecedented challenges for students and junior 
      doctors. This research explores the potential of artificial intelligence (AI), 
      specifically the ChatGPT-4 language model, to transform medical education and 
      address the deficiencies in clinical exposure during the post-pandemic era. 
      RESEARCH QUESTIONS/PURPOSE: What is the potential of AI large language models in 
      delivering safe and coherent medical advice to junior doctors for clinical 
      orthopaedic scenarios? PATIENTS AND METHODS: A series of diverse orthopaedic 
      questions was presented to ChatGPT-4, from general medicine to highly specialised 
      fields. The questions were based on a variety of common orthopaedic presentations 
      including neck of femur fracture, compartment syndrome, pulmonary embolism, and a 
      motor vehicle accident. A validated questionnaire (Likert Scale) was implemented 
      to evaluate the answers produced by ChatGPT-4. RESULTS: Our results indicate that 
      ChatGPT-4 exhibits exceptional proficiency in delivering accurate and coherent 
      medical advice. Its intuitive interface, accessibility, and sophisticated 
      algorithm render it an ideal supplementary tool for medical students and junior 
      doctors. Despite certain limitations, such as its inability to fully address 
      highly specialised areas, this study highlights the potential of AI and ChatGPT-4 
      to revolutionise medical education and fill the clinical exposure void generated 
      by the pandemic. Future research should concentrate on the practical application 
      of ChatGPT-4 in real-world medical environments and its integration with other 
      emerging technologies to optimise its influence on the education and training of 
      healthcare professionals. CONCLUSIONS: ChatGPT-4's integration into orthopaedic 
      education and practice can mitigate pandemic-related experience gaps, promoting 
      self-directed, personalised learning and decision-making support for interns and 
      residents. Future advancements may address limitations to enhance healthcare 
      professionals' learning and expertise. LEVEL OF EVIDENCE: Level III 
      evidence-observational study.
CI  - © Crown 2023.
FAU - Lower, Kirk
AU  - Lower K
AUID- ORCID: 0000-0001-6722-3426
AD  - Department of Orthopaedics, St George Hospital, Gray Street, Sydney, NSW 2000 
      Australia. GRID: grid.416398.1. ISNI: 0000 0004 0417 5393
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Faculty of Science, Medicine, and Health, Central Clinical School at Monash 
      University, The Alfred Centre, 99 Commercial Rd, Melbourne, VIC 3004 Australia. 
      GRID: grid.1002.3. ISNI: 0000 0004 1936 7857
AD  - Department of Orthopaedic Surgery, Peninsula Health, Melbourne, VIC 3004 
      Australia. GRID: grid.466993.7. ISNI: 0000 0004 0436 2893
FAU - Lim, Bryan
AU  - Lim B
AD  - Faculty of Science, Medicine, and Health, Central Clinical School at Monash 
      University, The Alfred Centre, 99 Commercial Rd, Melbourne, VIC 3004 Australia. 
      GRID: grid.1002.3. ISNI: 0000 0004 1936 7857
AD  - Department of Orthopaedic Surgery, Peninsula Health, Melbourne, VIC 3004 
      Australia. GRID: grid.466993.7. ISNI: 0000 0004 0436 2893
FAU - Seth, Nimish
AU  - Seth N
AD  - Department of Orthopaedic Surgery, Peninsula Health, Melbourne, VIC 3004 
      Australia. GRID: grid.466993.7. ISNI: 0000 0004 0436 2893
LA  - eng
PT  - Journal Article
DEP - 20230810
PL  - Switzerland
TA  - Indian J Orthop
JT  - Indian journal of orthopaedics
JID - 0137736
PMC - PMC10442004
COIS- Conflict of InterestThe authors declare no conflict of interest.
EDAT- 2023/08/23 06:42
MHDA- 2023/08/23 06:43
PMCR- 2024/08/10
CRDT- 2023/08/23 03:59
PHST- 2023/04/29 00:00 [received]
PHST- 2023/07/27 00:00 [accepted]
PHST- 2024/08/10 00:00 [pmc-release]
PHST- 2023/08/23 06:43 [medline]
PHST- 2023/08/23 06:42 [pubmed]
PHST- 2023/08/23 03:59 [entrez]
AID - 967 [pii]
AID - 10.1007/s43465-023-00967-7 [doi]
PST - epublish
SO  - Indian J Orthop. 2023 Aug 10;57(9):1527-1544. doi: 10.1007/s43465-023-00967-7. 
      eCollection 2023 Sep.

PMID- 37957964
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231119
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 21
DP  - 2023 Oct 25
TI  - Comment on Sallam, M. ChatGPT Utility in Healthcare Education, Research, and 
      Practice: Systematic Review on the Promising Perspectives and Valid Concerns. 
      Healthcare 2023, 11, 887.
LID - 10.3390/healthcare11212819 [doi]
LID - 2819
AB  - We read with great interest and applaud the recently published review paper 
      regarding ChatGPT and its implications in research and education [...].
FAU - Moreno, Emilio
AU  - Moreno E
AD  - Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, 
      Monterrey 64460, Mexico.
FAU - Alvarez-Lozada, Luis Adrian
AU  - Alvarez-Lozada LA
AD  - Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, 
      Monterrey 64460, Mexico.
FAU - Arrambide-Garza, Francisco Javier
AU  - Arrambide-Garza FJ
AD  - Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, 
      Monterrey 64460, Mexico.
FAU - Quiroga-Garza, Alejandro
AU  - Quiroga-Garza A
AUID- ORCID: 0000-0002-5398-247X
AD  - Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, 
      Monterrey 64460, Mexico.
FAU - Elizondo-Omaña, Rodrigo Enrique
AU  - Elizondo-Omaña RE
AD  - Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, 
      Monterrey 64460, Mexico.
LA  - eng
PT  - Journal Article
DEP - 20231025
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
CON - Healthcare (Basel). 11:887.
PMC - PMC10650921
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/14 06:43
MHDA- 2023/11/14 06:44
CRDT- 2023/11/14 02:05
PHST- 2023/07/20 00:00 [received]
PHST- 2023/10/13 00:00 [revised]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/11/14 06:44 [medline]
PHST- 2023/11/14 06:43 [pubmed]
PHST- 2023/11/14 02:05 [entrez]
AID - healthcare11212819 [pii]
AID - healthcare-11-02819 [pii]
AID - 10.3390/healthcare11212819 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Oct 25;11(21):2819. doi: 10.3390/healthcare11212819.

PMID- 37474421
OWN - NLM
STAT- MEDLINE
DCOM- 20230825
LR  - 20230828
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
VI  - 131
IP  - 3
DP  - 2023 Sep
TI  - Large language models in anaesthesiology: use of ChatGPT for American Society of 
      Anesthesiologists physical status classification.
PG  - e73-e75
LID - S0007-0912(23)00358-6 [pii]
LID - 10.1016/j.bja.2023.06.052 [doi]
FAU - Lim, Daniel Y Z
AU  - Lim DYZ
AD  - Department of Gastroenterology and Hepatology, Singapore General Hospital, 
      Singapore; Duke-NUS Medical School, Singapore.
FAU - Ke, Yu He
AU  - Ke YH
AD  - Department of Anaesthesiology and Perioperative Medicine, Singapore General 
      Hospital, Singapore.
FAU - Sng, Gerald G R
AU  - Sng GGR
AD  - Department of Endocrinology, Singapore General Hospital, Singapore.
FAU - Tung, Joshua Y M
AU  - Tung JYM
AD  - Department of Urology, Singapore General Hospital, Singapore.
FAU - Chai, Jia X
AU  - Chai JX
AD  - Department of Anaesthesiology and Perioperative Medicine, Singapore General 
      Hospital, Singapore.
FAU - Abdullah, Hairil R
AU  - Abdullah HR
AD  - Duke-NUS Medical School, Singapore; Department of Anaesthesiology and 
      Perioperative Medicine, Singapore General Hospital, Singapore. Electronic 
      address: hairil.rizal.abdullah@singhealth.com.sg.
LA  - eng
PT  - Letter
DEP - 20230718
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
MH  - Humans
MH  - United States
MH  - *Anesthesiology
MH  - Anesthesiologists
OTO - NOTNLM
OT  - American Society of Anesthesiologists physical status
OT  - ChatGPT
OT  - GPT
OT  - artificial intelligence
OT  - large language models
OT  - machine learning
EDAT- 2023/07/21 01:12
MHDA- 2023/08/25 06:42
CRDT- 2023/07/20 21:59
PHST- 2023/05/16 00:00 [received]
PHST- 2023/06/02 00:00 [revised]
PHST- 2023/06/09 00:00 [accepted]
PHST- 2023/08/25 06:42 [medline]
PHST- 2023/07/21 01:12 [pubmed]
PHST- 2023/07/20 21:59 [entrez]
AID - S0007-0912(23)00358-6 [pii]
AID - 10.1016/j.bja.2023.06.052 [doi]
PST - ppublish
SO  - Br J Anaesth. 2023 Sep;131(3):e73-e75. doi: 10.1016/j.bja.2023.06.052. Epub 2023 
      Jul 18.

PMID- 37701430
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230915
IS  - 2949-9127 (Electronic)
IS  - 2949-9127 (Linking)
VI  - 1
DP  - 2023
TI  - The potential of chatbots in chronic venous disease patient management.
LID - 100019 [pii]
LID - 10.1016/j.jvsvi.2023.100019 [doi]
AB  - OBJECTIVE: Health care providers and recipients have been using artificial 
      intelligence and its subfields, such as natural language processing and machine 
      learning technologies, in the form of search engines to obtain medical 
      information for some time now. Although a search engine returns a ranked list of 
      webpages in response to a query and allows the user to obtain information from 
      those links directly, ChatGPT has elevated the interface between humans with 
      artificial intelligence by attempting to provide relevant information in a 
      human-like textual conversation. This technology is being adopted rapidly and has 
      enormous potential to impact various aspects of health care, including patient 
      education, research, scientific writing, pre-visit/post-visit queries, 
      documentation assistance, and more. The objective of this study is to assess 
      whether chatbots could assist with answering patient questions and electronic 
      health record inbox management. METHODS: We devised two questionnaires: (1) 
      administrative and non-complex medical questions (based on actual inbox 
      questions); and (2) complex medical questions on the topic of chronic venous 
      disease. We graded the performance of publicly available chatbots regarding their 
      potential to assist with electronic health record inbox management. The study was 
      graded by an internist and a vascular medicine specialist independently. RESULTS: 
      On administrative and non-complex medical questions, ChatGPT 4.0 performed better 
      than ChatGPT 3.5. ChatGPT 4.0 received a grade of 1 on all the questions: 20 of 
      20 (100%). ChatGPT 3.5 received a grade of 1 on 14 of 20 questions (70%), grade 2 
      on 4 of 16 questions (20%), grade 3 on 0 questions (0%), and grade 4 on 2/20 
      questions (10%). On complex medical questions, ChatGPT 4.0 performed the best. 
      ChatGPT 4.0 received a grade of 1 on 15 of 20 questions (75%), grade 2 on 2 of 20 
      questions (10%), grade 3 on 2 of 20 questions (10%), and grade 4 on 1 of 20 
      questions (5%). ChatGPT 3.5 received a grade of 1 on 9 of 20 questions (45%), 
      grade 2 on 4 of 20 questions (20%), grade 3 on 4 of 20 questions (20%), and grade 
      4 on 3 of 20 questions (15%). Clinical Camel received a grade of 1 on 0 of 20 
      questions (0%), grade 2 on 5 of 20 questions (25%), grade 3 on 5 of 20 questions 
      (25%), and grade 4 on 10 of 20 questions (50%). CONCLUSIONS: Based on our 
      interactions with ChatGPT regarding the topic of chronic venous disease, it is 
      plausible that in the future, this technology may be used to assist with 
      electronic health record inbox management and offload medical staff. However, for 
      this technology to receive regulatory approval to be used for that purpose, it 
      will require extensive supervised training by subject experts, have guardrails to 
      prevent "hallucinations" and maintain confidentiality, and prove that it can 
      perform at a level comparable to (if not better than) humans. (JVS-Vascular 
      Insights 2023;1:100019.).
FAU - Athavale, Anand
AU  - Athavale A
AD  - Division of Vascular Surgery, Stanford University School of Medicine, Palo Alto.
FAU - Baier, Jonathan
AU  - Baier J
AD  - NextNext LLC, Lovettsville.
FAU - Ross, Elsie
AU  - Ross E
AD  - Division of Vascular Surgery, Stanford University School of Medicine, Palo Alto.
FAU - Fukaya, Eri
AU  - Fukaya E
AD  - Division of Vascular Surgery, Stanford University School of Medicine, Palo Alto.
LA  - eng
GR  - K01 HL148639/HL/NHLBI NIH HHS/United States
PT  - Journal Article
DEP - 20230619
PL  - United States
TA  - JVS Vasc Insights
JT  - JVS-vascular insights
JID - 9918663888006676
PMC - PMC10497234
MID - NIHMS1925830
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT 3.5
OT  - ChatGPT 4.0
OT  - Chronic venous disease
OT  - Clinical Camel
OT  - Electronic health record inbox management
OT  - Generative AI
COIS- Author conflict of interest: none.
EDAT- 2023/09/13 06:42
MHDA- 2023/09/13 06:43
CRDT- 2023/09/13 04:05
PHST- 2023/09/13 06:43 [medline]
PHST- 2023/09/13 06:42 [pubmed]
PHST- 2023/09/13 04:05 [entrez]
AID - 100019 [pii]
AID - 10.1016/j.jvsvi.2023.100019 [doi]
PST - ppublish
SO  - JVS Vasc Insights. 2023;1:100019. doi: 10.1016/j.jvsvi.2023.100019. Epub 2023 Jun 
      19.

PMID- 36786388
OWN - NLM
STAT- MEDLINE
DCOM- 20230529
LR  - 20230919
IS  - 1751-7915 (Electronic)
IS  - 1751-7915 (Linking)
VI  - 16
IP  - 6
DP  - 2023 Jun
TI  - Scientific novelty beyond the experiment.
PG  - 1131-1173
LID - 10.1111/1751-7915.14222 [doi]
AB  - Practical experiments drive important scientific discoveries in biology, but 
      theory-based research studies also contribute novel-sometimes 
      paradigm-changing-findings. Here, we appraise the roles of theory-based 
      approaches focusing on the experiment-dominated wet-biology research areas of 
      microbial growth and survival, cell physiology, host-pathogen interactions, and 
      competitive or symbiotic interactions. Additional examples relate to analyses of 
      genome-sequence data, climate change and planetary health, habitability, and 
      astrobiology. We assess the importance of thought at each step of the research 
      process; the roles of natural philosophy, and inconsistencies in logic and 
      language, as drivers of scientific progress; the value of thought experiments; 
      the use and limitations of artificial intelligence technologies, including their 
      potential for interdisciplinary and transdisciplinary research; and other 
      instances when theory is the most-direct and most-scientifically robust route to 
      scientific novelty including the development of techniques for practical 
      experimentation or fieldwork. We highlight the intrinsic need for human 
      engagement in scientific innovation, an issue pertinent to the ongoing 
      controversy over papers authored using/authored by artificial intelligence (such 
      as the large language model/chatbot ChatGPT). Other issues discussed are the way 
      in which aspects of language can bias thinking towards the spatial rather than 
      the temporal (and how this biased thinking can lead to skewed scientific 
      terminology); receptivity to research that is non-mainstream; and the importance 
      of theory-based science in education and epistemology. Whereas we briefly 
      highlight classic works (those by Oakes Ames, Francis H.C. Crick and James D. 
      Watson, Charles R. Darwin, Albert Einstein, James E. Lovelock, Lynn Margulis, 
      Gilbert Ryle, Erwin R.J.A. Schrödinger, Alan M. Turing, and others), the focus is 
      on microbiology studies that are more-recent, discussing these in the context of 
      the scientific process and the types of scientific novelty that they represent. 
      These include several studies carried out during the 2020 to 2022 lockdowns of 
      the COVID-19 pandemic when access to research laboratories was disallowed (or 
      limited). We interviewed the authors of some of the featured microbiology-related 
      papers and-although we ourselves are involved in laboratory experiments and 
      practical fieldwork-also drew from our own research experiences showing that such 
      studies can not only produce new scientific findings but can also transcend 
      barriers between disciplines, act counter to scientific reductionism, integrate 
      biological data across different timescales and levels of complexity, and 
      circumvent constraints imposed by practical techniques. In relation to urgent 
      research needs, we believe that climate change and other global challenges may 
      require approaches beyond the experiment.
CI  - © 2023 The Authors. Microbial Biotechnology published by Applied Microbiology 
      International and John Wiley & Sons Ltd.
FAU - Hallsworth, John E
AU  - Hallsworth JE
AUID- ORCID: 0000-0001-6797-9362
AD  - Institute for Global Food Security, School of Biological Sciences, Queen's 
      University Belfast, Belfast, UK.
FAU - Udaondo, Zulema
AU  - Udaondo Z
AUID- ORCID: 0000-0003-3445-6842
AD  - Department of Biomedical Informatics, University of Arkansas for Medical 
      Sciences, Little Rock, Arkansas, USA.
FAU - Pedrós-Alió, Carlos
AU  - Pedrós-Alió C
AUID- ORCID: 0000-0003-1009-4277
AD  - Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, 
      Spain.
FAU - Höfer, Juan
AU  - Höfer J
AUID- ORCID: 0000-0002-5887-4929
AD  - Escuela de Ciencias del Mar, Pontificia Universidad Católica de Valparaíso, 
      Valparaíso, Chile.
FAU - Benison, Kathleen C
AU  - Benison KC
AUID- ORCID: 0000-0001-6104-2333
AD  - Department of Geology and Geography, West Virginia University, Morgantown, West 
      Virginia, USA.
FAU - Lloyd, Karen G
AU  - Lloyd KG
AUID- ORCID: 0000-0003-0914-6375
AD  - Microbiology Department, University of Tennessee, Knoxville, Tennessee, USA.
FAU - Cordero, Radamés J B
AU  - Cordero RJB
AUID- ORCID: 0000-0002-3026-7094
AD  - Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg 
      School of Public Health, Baltimore, Maryland, USA.
FAU - de Campos, Claudia B L
AU  - de Campos CBL
AUID- ORCID: 0000-0002-1836-0915
AD  - Institute of Science and Technology, Universidade Federal de Sao Paulo (UNIFESP), 
      São José dos Campos, SP, Brazil.
FAU - Yakimov, Michail M
AU  - Yakimov MM
AUID- ORCID: 0000-0003-1418-363X
AD  - Institute of Polar Sciences, ISP-CNR, Messina, Italy.
FAU - Amils, Ricardo
AU  - Amils R
AD  - Department of Molecular Biology, Centro de Biología Molecular Severo Ochoa 
      (CSIC-UAM), Nicolás Cabrera n° 1, Universidad Autónoma de Madrid, Madrid, Spain.
AD  - Department of Planetology and Habitability, Centro de Astrobiología (INTA-CSIC), 
      Torrejón de Ardoz, Spain.
LA  - eng
PT  - Journal Article
DEP - 20230214
PL  - United States
TA  - Microb Biotechnol
JT  - Microbial biotechnology
JID - 101316335
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Pandemics
MH  - *COVID-19
MH  - Communicable Disease Control
MH  - Philosophy
PMC - PMC10221578
COIS- The authors declare that they have no conflict of interest.
EDAT- 2023/02/15 06:00
MHDA- 2023/05/29 06:41
CRDT- 2023/02/14 07:13
PHST- 2023/01/09 00:00 [revised]
PHST- 2022/10/20 00:00 [received]
PHST- 2023/01/11 00:00 [accepted]
PHST- 2023/05/29 06:41 [medline]
PHST- 2023/02/15 06:00 [pubmed]
PHST- 2023/02/14 07:13 [entrez]
AID - MBT214222 [pii]
AID - 10.1111/1751-7915.14222 [doi]
PST - ppublish
SO  - Microb Biotechnol. 2023 Jun;16(6):1131-1173. doi: 10.1111/1751-7915.14222. Epub 
      2023 Feb 14.

PMID- 37950660
OWN - NLM
STAT- Publisher
LR  - 20231111
IS  - 1840-2445 (Electronic)
IS  - 1840-0132 (Linking)
VI  - 21
IP  - 1
DP  - 2024 Feb 1
TI  - ChatGPT's contributions to the evolution of neurosurgical practice and education: 
      a systematic review of benefits, concerns and limitations.
LID - 10.17392/1661-23 [doi]
AB  - Aim This study provides a comprehensive review of the current literature on the 
      use of ChatGPT, a generative Artificial Intelligence (AI) tool, in neurosurgery. 
      The study examines potential benefits and limitations of ChatGPT in neurosurgical 
      practice and education. Methods The study involved a systematic review of the 
      current literature on the use of AI in neurosurgery, with a focus on ChatGPT. The 
      Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 
      guidelines were followed to ensure a comprehensive and transparent review 
      process. Thirteen studies met the inclusion criteria and were included in the 
      final analysis. The data extracted from the included studies were analysed and 
      synthesized to provide an overview of the current state of research on the use of 
      ChatGPT in neurosurgery. Results The ChatGPT showed a potential to complement and 
      enhance neurosurgical practice. However, there are risks and limitations 
      associated with its use, including question format limitations, validation 
      challenges, and algorithmic bias. The study highlights the importance of 
      validating machine-generated content for accuracy and addressing ethical concerns 
      associated with AI technologies. The study also identifies potential benefits of 
      ChatGPT, such as providing personalized treatment plans, supporting surgical 
      planning and navigation, and enhancing large data processing efficiency and 
      accuracy. Conclusion The integration of AI technologies into neurosurgery should 
      be approached with caution and careful consideration of ethical and validation 
      issues. Continued research and development of AI tools in neurosurgery can help 
      us further understand their potential benefits and limitations.
CI  - Copyright© by the Medical Assotiation of Zenica-Doboj Canton.
FAU - Bečulić, Hakija
AU  - Bečulić H
AD  - Department of Neurosurgery, Cantonal Hospital Zenica, Zenica, Bosnia and 
      Herzegovina.
AD  - Department of Anatomy, School of Medicine, University of Zenica, Zenica, Bosnia 
      and Herzegovina.
FAU - Begagić, Emir
AU  - Begagić E
AD  - Deparment of General Medicine, School of Medicine, University of Zenica, Zenica, 
      Bosnia and Herzegovina.
FAU - Skomorac, Rasim
AU  - Skomorac R
AD  - Department of Neurosurgery, Cantonal Hospital Zenica, Zenica, Bosnia and 
      Herzegovina.
AD  - Deparment of Surgery, School of Medicine, University of Zenica, Zenica, Bosnia 
      and Herzegovina.
FAU - Mašović, Anes
AU  - Mašović A
AD  - Department of Neurosurgery, Cantonal Hospital Zenica, Zenica, Bosnia and 
      Herzegovina.
FAU - Selimović, Edin
AU  - Selimović E
AD  - Deparment of Surgery, School of Medicine, University of Zenica, Zenica, Bosnia 
      and Herzegovina.
FAU - Pojskić, Mirza
AU  - Pojskić M
AD  - Department of Neurosurgery, University of Marburg, Marburg, Germany.
LA  - eng
PT  - Journal Article
DEP - 20240201
PL  - Bosnia and Herzegovina
TA  - Med Glas (Zenica)
JT  - Medicinski glasnik : official publication of the Medical Association of 
      Zenica-Doboj Canton, Bosnia and Herzegovina
JID - 101250177
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - decision support systems
OT  - ethics
OT  - machine learning
EDAT- 2023/11/11 20:50
MHDA- 2023/11/11 20:50
CRDT- 2023/11/11 07:33
PHST- 2023/09/29 00:00 [received]
PHST- 2023/10/12 00:00 [revised]
PHST- 2023/10/14 00:00 [accepted]
PHST- 2023/11/11 20:50 [medline]
PHST- 2023/11/11 20:50 [pubmed]
PHST- 2023/11/11 07:33 [entrez]
AID - 10.17392/1661-23 [doi]
PST - aheadofprint
SO  - Med Glas (Zenica). 2024 Feb 1;21(1). doi: 10.17392/1661-23.

PMID- 37458761
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231115
IS  - 1432-0711 (Electronic)
IS  - 0932-0067 (Print)
IS  - 0932-0067 (Linking)
VI  - 308
IP  - 6
DP  - 2023 Dec
TI  - Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board 
      decision-making in primary breast cancer cases.
PG  - 1831-1844
LID - 10.1007/s00404-023-07130-5 [doi]
AB  - BACKGROUND: As the available information about breast cancer is growing every 
      day, the decision-making process for the therapy is getting more complex. ChatGPT 
      as a transformer-based language model possesses the ability to write scientific 
      articles and pass medical exams. But is it able to support the multidisciplinary 
      tumor board (MDT) in the planning of the therapy of patients with breast cancer? 
      MATERIAL AND METHODS: We performed a pilot study on 10 consecutive cases of 
      breast cancer patients discussed in MDT at our department in January 2023. 
      Included were patients with a primary diagnosis of early breast cancer. The 
      recommendation of MDT was compared with the recommendation of the ChatGPT for 
      particular patients and the clinical score of the agreement was calculated. 
      RESULTS: Results showed that ChatGPT provided mostly general answers regarding 
      chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody 
      therapy. It was able to identify risk factors for hereditary breast cancer and 
      point out the elderly patient indicated for chemotherapy to evaluate the 
      cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 
      2 + (FISH negative) as in need of therapy with an antibody and called endocrine 
      therapy "hormonal treatment". CONCLUSIONS: Support of artificial intelligence by 
      finding individualized and personalized therapy for our patients in the time of 
      rapidly expanding amount of information is looking for the ways in the clinical 
      routine. ChatGPT has the potential to find its spot in clinical medicine, but the 
      current version is not able to provide specific recommendations for the therapy 
      of patients with primary breast cancer.
CI  - © 2023. The Author(s).
FAU - Lukac, Stefan
AU  - Lukac S
AUID- ORCID: 0000-0002-9336-2267
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany. stefanlukacjr@gmail.com.
FAU - Dayan, Davut
AU  - Dayan D
AUID- ORCID: 0000-0003-1589-2016
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Fink, Visnja
AU  - Fink V
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Leinert, Elena
AU  - Leinert E
AUID- ORCID: 0000-0001-5795-3417
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Hartkopf, Andreas
AU  - Hartkopf A
AUID- ORCID: 0000-0003-1227-1118
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Veselinovic, Kristina
AU  - Veselinovic K
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Janni, Wolfgang
AU  - Janni W
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Rack, Brigitte
AU  - Rack B
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Pfister, Kerstin
AU  - Pfister K
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Heitmeir, Benedikt
AU  - Heitmeir B
AUID- ORCID: 0000-0003-2989-5234
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
FAU - Ebner, Florian
AU  - Ebner F
AUID- ORCID: 0000-0003-2066-5344
AD  - Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 
      43, 89075, Ulm, Germany.
AD  - Gynäkologische Gemeinschaftspraxis Freising & Moosburg, Munich, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - Germany
TA  - Arch Gynecol Obstet
JT  - Archives of gynecology and obstetrics
JID - 8710213
RN  - 0 (Antibodies)
SB  - IM
MH  - Aged
MH  - Humans
MH  - Female
MH  - *Breast Neoplasms/therapy
MH  - Artificial Intelligence
MH  - Pilot Projects
MH  - Oncogenes
MH  - Antibodies
PMC - PMC10579162
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Breast cancer
OT  - ChatGPT
OT  - Multidisciplinary tumor board
COIS- All authors confirm that they have no conflict of interest regarding this 
      paper.The authors have no relevant financial or non-financial interests to 
      disclose.
EDAT- 2023/07/17 15:09
MHDA- 2023/10/23 00:42
CRDT- 2023/07/17 11:04
PHST- 2023/04/03 00:00 [received]
PHST- 2023/06/27 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/07/17 15:09 [pubmed]
PHST- 2023/07/17 11:04 [entrez]
AID - 10.1007/s00404-023-07130-5 [pii]
AID - 7130 [pii]
AID - 10.1007/s00404-023-07130-5 [doi]
PST - ppublish
SO  - Arch Gynecol Obstet. 2023 Dec;308(6):1831-1844. doi: 10.1007/s00404-023-07130-5. 
      Epub 2023 Jul 17.

PMID- 37314466
OWN - NLM
STAT- Publisher
LR  - 20230614
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Jun 14
TI  - Evaluation of the Artificial Intelligence Chatbot on Breast Reconstruction and 
      Its Efficacy in Surgical Research: A Case Study.
LID - 10.1007/s00266-023-03443-7 [doi]
AB  - BACKGROUND: ChatGPT is an open-source artificial intelligence (AI) chatbot that 
      uses deep learning to produce human-like text dialog. Its potential applications 
      in the scientific community are vast; however, its efficacy on performing 
      comprehensive literature searches, data analysis and report writing in aesthetic 
      plastic surgery topics remains unknown. This study aims to evaluate both the 
      accuracy and comprehensiveness of ChatGPT's responses to assess its suitability 
      for use in aesthetic plastic surgery research. METHODS: Six questions were 
      prompted to ChatGPT on post-mastectomy breast reconstruction. First two questions 
      focused on the current evidence and options for breast reconstruction 
      post-mastectomy, and remaining four questions focused specifically on autologous 
      breast reconstruction. Using the Likert framework, the responses provided by 
      ChatGPT were qualitatively assessed for accuracy and information content by two 
      specialist plastic surgeons with extensive experience in the field. RESULTS: 
      ChatGPT provided relevant, accurate information; however, it lacked depth. It 
      could provide no more than a superficial overview in response to more esoteric 
      questions and generated incorrect references. It created non-existent references, 
      cited wrong journal and date, which poses a significant challenge in maintaining 
      academic integrity and caution of its use in academia. CONCLUSION: While ChatGPT 
      demonstrated proficiency in summarizing existing knowledge, it created fictitious 
      references which poses a significant concern of its use in academia and 
      healthcare. Caution should be exercised in interpreting its responses in the 
      aesthetic plastic surgical field and should only be used for such with sufficient 
      oversight. LEVEL OF EVIDENCE IV: This journal requires that authors assign a 
      level of evidence to each article. For a full description of these Evidence-Based 
      Medicine ratings, please refer to the Table of Contents or the online 
      Instructions to Authors www.springer.com/00266 .
CI  - © 2023. Crown.
FAU - Xie, Yi
AU  - Xie Y
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia. ishithseth1@gmail.com.
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia. 
      ishithseth1@gmail.com.
FAU - Rozen, Warren M
AU  - Rozen WM
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, 
      Australia.
AD  - Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
LA  - eng
PT  - Journal Article
DEP - 20230614
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Breast reconstruction
OT  - ChatGPT
OT  - Chatbot
EDAT- 2023/06/14 13:07
MHDA- 2023/06/14 13:07
CRDT- 2023/06/14 11:05
PHST- 2023/03/06 00:00 [received]
PHST- 2023/05/27 00:00 [accepted]
PHST- 2023/06/14 13:07 [medline]
PHST- 2023/06/14 13:07 [pubmed]
PHST- 2023/06/14 11:05 [entrez]
AID - 10.1007/s00266-023-03443-7 [pii]
AID - 10.1007/s00266-023-03443-7 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Jun 14. doi: 10.1007/s00266-023-03443-7.

PMID- 37276372
OWN - NLM
STAT- MEDLINE
DCOM- 20230622
LR  - 20230625
IS  - 2352-0787 (Electronic)
IS  - 2352-0779 (Linking)
VI  - 10
IP  - 4
DP  - 2023 Jul
TI  - New Artificial Intelligence ChatGPT Performs Poorly on the 2022 Self-assessment 
      Study Program for Urology.
PG  - 409-415
LID - 10.1097/UPJ.0000000000000406 [doi]
AB  - INTRODUCTION: Large language models have demonstrated impressive capabilities, 
      but application to medicine remains unclear. We seek to evaluate the use of 
      ChatGPT on the American Urological Association Self-assessment Study Program as 
      an educational adjunct for urology trainees and practicing physicians. METHODS: 
      One hundred fifty questions from the 2022 Self-assessment Study Program exam were 
      screened, and those containing visual assets (n=15) were removed. The remaining 
      items were encoded as open ended or multiple choice. ChatGPT's output was coded 
      as correct, incorrect, or indeterminate; if indeterminate, responses were 
      regenerated up to 2 times. Concordance, quality, and accuracy were ascertained by 
      3 independent researchers and reviewed by 2 physician adjudicators. A new session 
      was started for each entry to avoid crossover learning. RESULTS: ChatGPT was 
      correct on 36/135 (26.7%) open-ended and 38/135 (28.2%) multiple-choice 
      questions. Indeterminate responses were generated in 40 (29.6%) and 4 (3.0%), 
      respectively. Of the correct responses, 24/36 (66.7%) and 36/38 (94.7%) were on 
      initial output, 8 (22.2%) and 1 (2.6%) on second output, and 4 (11.1%) and 1 
      (2.6%) on final output, respectively. Although regeneration decreased 
      indeterminate responses, proportion of correct responses did not increase. For 
      open-ended and multiple-choice questions, ChatGPT provided consistent 
      justifications for incorrect answers and remained concordant between correct and 
      incorrect answers. CONCLUSIONS: ChatGPT previously demonstrated promise on 
      medical licensing exams; however, application to the 2022 Self-assessment Study 
      Program was not demonstrated. Performance improved with multiple-choice over 
      open-ended questions. More importantly were the persistent justifications for 
      incorrect responses-left unchecked, utilization of ChatGPT in medicine may 
      facilitate medical misinformation.
FAU - Huynh, Linda My
AU  - Huynh LM
AUID- ORCID: 0000-0002-8655-8587
AD  - MD/PhD Scholars Program, University of Nebraska Medical Center, Omaha, Nebraska.
FAU - Bonebrake, Benjamin T
AU  - Bonebrake BT
AD  - College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska.
FAU - Schultis, Kaitlyn
AU  - Schultis K
AD  - College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska.
FAU - Quach, Alan
AU  - Quach A
AD  - Division of Urology, University of Nebraska Medical Center, Omaha, Nebraska.
FAU - Deibert, Christopher M
AU  - Deibert CM
AD  - Division of Urology, University of Nebraska Medical Center, Omaha, Nebraska.
LA  - eng
PT  - Journal Article
DEP - 20230605
PL  - United States
TA  - Urol Pract
JT  - Urology practice
JID - 101635343
SB  - IM
MH  - *Urology
MH  - Artificial Intelligence
MH  - Self-Assessment
MH  - *Medicine
MH  - Educational Status
OTO - NOTNLM
OT  - artificial intelligence
OT  - medical informatics applications
OT  - urology
EDAT- 2023/06/05 19:10
MHDA- 2023/06/22 06:42
CRDT- 2023/06/05 15:03
PHST- 2023/06/22 06:42 [medline]
PHST- 2023/06/05 19:10 [pubmed]
PHST- 2023/06/05 15:03 [entrez]
AID - 10.1097/UPJ.0000000000000406 [doi]
PST - ppublish
SO  - Urol Pract. 2023 Jul;10(4):409-415. doi: 10.1097/UPJ.0000000000000406. Epub 2023 
      Jun 5.

PMID- 37724135
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231102
IS  - 1179-1578 (Print)
IS  - 1179-1578 (Electronic)
IS  - 1179-1578 (Linking)
VI  - 16
DP  - 2023
TI  - A Cogitation on the ChatGPT Craze from the Perspective of Psychological Algorithm 
      Aversion and Appreciation.
PG  - 3837-3844
LID - 10.2147/PRBM.S430936 [doi]
AB  - In recent times, ChatGPT has garnered significant interest from the public, 
      sparking a range of reactions that encompass both aversion and appreciation. This 
      paper delves into the paradoxical attitudes of individuals towards ChatGPT, 
      highlighting the simultaneous existence of algorithmic aversion and appreciation. 
      A comprehensive analysis is conducted from the vantage points of psychology and 
      algorithmic decision-making, exploring the underlying causes of these conflicting 
      attitudes from three dimensions: self-performance, task types, and individual 
      factors. Subsequently, strategies to reconcile these opposing psychological 
      stances are proposed, delineated into two categories: flexible coping and 
      inflexible coping. In light of the ongoing advancements in artificial 
      intelligence, this paper posits recommendations for the attitudes and actions 
      that individuals ought to adopt in the face of artificial intelligence. 
      Regardless of whether one exhibits algorithm aversion or appreciation, the paper 
      underscores that coexisting with algorithms is an inescapable reality in the age 
      of artificial intelligence, necessitating the preservation of human advantages.
CI  - © 2023 Yu.
FAU - Yu, Hao
AU  - Yu H
AUID- ORCID: 0009-0002-5223-4055
AD  - Faculty of Education, Shaanxi Normal University, Xi'an, Shaanxi, People's 
      Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - New Zealand
TA  - Psychol Res Behav Manag
JT  - Psychology research and behavior management
JID - 101514563
PMC - PMC10505389
OTO - NOTNLM
OT  - ChatGPT
OT  - algorithm appreciation
OT  - algorithm aversion
OT  - algorithmic decision-making
OT  - artificial intelligence
OT  - contradictory psychology
COIS- The author reports no conflicts of interest in this work.
EDAT- 2023/09/19 12:42
MHDA- 2023/09/19 12:43
CRDT- 2023/09/19 03:38
PHST- 2023/07/17 00:00 [received]
PHST- 2023/09/08 00:00 [accepted]
PHST- 2023/09/19 12:43 [medline]
PHST- 2023/09/19 12:42 [pubmed]
PHST- 2023/09/19 03:38 [entrez]
AID - 430936 [pii]
AID - 10.2147/PRBM.S430936 [doi]
PST - epublish
SO  - Psychol Res Behav Manag. 2023 Sep 13;16:3837-3844. doi: 10.2147/PRBM.S430936. 
      eCollection 2023.

PMID- 37469634
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230721
IS  - 2477-4073 (Electronic)
IS  - 2528-181X (Print)
IS  - 2477-4073 (Linking)
VI  - 9
IP  - 1
DP  - 2023
TI  - Exploring the future of nursing: Insights from the ChatGPT model.
PG  - 1-5
LID - 10.33546/bnj.2551 [doi]
AB  - This editorial aims to provide a comprehensive overview of the future of nursing 
      through the lens of the ChatGPT model, a state-of-the-art language processing 
      artificial intelligence (AI) developed by OpenAI. The edited chat transcripts 
      with ChatGPT may offer key trends and developments in nursing, such as the 
      increasing use of technology and digital tools, the integration of AI, and the 
      use of robotics in patient care, as well as insights into the implications of 
      these changes for nurses, patients, and the healthcare system as a whole. 
      However, the future of nursing is continuously evolving. Therefore, nurses need 
      to keep abreast of the latest developments and adapt to them while remaining 
      committed to providing quality care.
CI  - © The Author(s) 2023.
FAU - Gunawan, Joko
AU  - Gunawan J
AUID- ORCID: 0000-0001-6694-8679
AD  - Belitung Raya Foundation, Manggar, East Belitung, Bangka Belitung, Indonesia.
LA  - eng
PT  - Editorial
DEP - 20230212
PL  - Indonesia
TA  - Belitung Nurs J
JT  - Belitung nursing journal
JID - 101685037
PMC - PMC10353608
OTO - NOTNLM
OT  - ChatGPT
OT  - OpenAI
OT  - artificial intelligence
OT  - future of nursing
OT  - robotics
OT  - state-of-the-art language processing
COIS- None.
EDAT- 2023/07/20 06:42
MHDA- 2023/07/20 06:43
CRDT- 2023/07/20 04:01
PHST- 2023/01/19 00:00 [received]
PHST- 2023/01/20 00:00 [revised]
PHST- 2023/01/20 00:00 [accepted]
PHST- 2023/07/20 06:43 [medline]
PHST- 2023/07/20 06:42 [pubmed]
PHST- 2023/07/20 04:01 [entrez]
AID - BNJ-9-1-001 [pii]
AID - 10.33546/bnj.2551 [doi]
PST - epublish
SO  - Belitung Nurs J. 2023 Feb 12;9(1):1-5. doi: 10.33546/bnj.2551. eCollection 2023.

PMID- 37432530
OWN - NLM
STAT- Publisher
LR  - 20230711
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 11
TI  - Use of ChatGPT on Taiwan's Examination for Medical Doctors.
LID - 10.1007/s10439-023-03308-9 [doi]
AB  - The study evaluates the performance of OpenAI's GPT-3 model on answering medical 
      exam questions from Staged Senior Professional and Technical Examinations 
      Regulations for Medical Doctors in the field of internal medicine. The study used 
      the official API to connect the questionnaire with the ChatGPT model, and the 
      results showed that the AI model performed reasonably well, with the highest 
      score of 8/13 in chest medicine. However, the overall performance of the AI model 
      was limited, with only chest medicine scoring more than 60. ChatGPT scored 
      relatively high in Chest medicine, Gastroenterology, and general medicine. One of 
      the limitations of the study is the use of non-English text, which may affect the 
      model's performance as the model is primarily trained on English text.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Kao, Yung-Shuo
AU  - Kao YS
AD  - Department of Radiation Oncology, Taoyuan General Hospital, Ministry of Health 
      and Welfare, Taoyüan, Taiwan.
FAU - Chuang, Wei-Kai
AU  - Chuang WK
AD  - Department of Radiation Oncology, Shuang Ho Hospital, Taipei Medical University, 
      New Taipei, Taiwan.
FAU - Yang, Jen
AU  - Yang J
AUID- ORCID: 0000-0002-7165-2140
AD  - Department of Medical Imaging, National Cheng Kung University Hospital, College 
      of Medicine, National Cheng Kung University, Tainan, Taiwan. 
      aldosteroneyang@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230711
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Deep learning
OT  - Medical exam
EDAT- 2023/07/11 13:10
MHDA- 2023/07/11 13:10
CRDT- 2023/07/11 11:09
PHST- 2023/06/24 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/07/11 13:10 [medline]
PHST- 2023/07/11 13:10 [pubmed]
PHST- 2023/07/11 11:09 [entrez]
AID - 10.1007/s10439-023-03308-9 [pii]
AID - 10.1007/s10439-023-03308-9 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 11. doi: 10.1007/s10439-023-03308-9.

PMID- 37387301
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20230703
IS  - 1120-9763 (Print)
IS  - 1120-9763 (Linking)
VI  - 47
IP  - 3
DP  - 2023 May-Jun
TI  - [ChatGPT in scientific research: a guide to informed use].
PG  - 203-207
LID - 10.19191/EP23.3.A639.051 [doi]
AB  - Using ChatGPT in scientific research offers revolutionary opportunities thanks to 
      its natural language interaction capabilities and production of coherent and 
      sophisticated text.Artificial intelligence can automate activities such as 
      information synthesis and schematization, improving scientific communication and 
      computer code writing.However, the lack of a complete understanding of context, 
      the risk of spreading misleading information, and the possibility of plagiarism 
      represent some of the biggest limitations in the current use of this 
      technology.The role of human experience remains fundamental for in-depth 
      understanding of context, exercising critical thinking, and ensuring respect for 
      the ethical principles of scientific research.A responsible and aware use of 
      tools such as ChatGPT can offer great benefits to the scientific community, but 
      it is essential to remember that these tools are only a support and cannot 
      replace human judgment and experience.
FAU - Ferrante, Gianluigi
AU  - Ferrante G
AD  - SSD epidemiologia screening, AOU Città della salute e della scienza di Torino, 
      CPO Piemonte, Torino; gianluigi.ferrante@iss.it.
FAU - Lanera, Corrado
AU  - Lanera C
AD  - Unità di biostatistica, epidemiologia e sanità pubblica, Università degli studi 
      di Padova.
LA  - ita
PT  - English Abstract
PT  - Journal Article
TT  - ChatGPT nella ricerca scientifica: guida all’uso consapevole.
PL  - Italy
TA  - Epidemiol Prev
JT  - Epidemiologia e prevenzione
JID - 8902507
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Italy
MH  - *Communication
MH  - Exercise
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGpt
OT  - Ethical implications
OT  - Large language models
OT  - Scientific research
EDAT- 2023/06/30 13:11
MHDA- 2023/07/03 06:41
CRDT- 2023/06/30 06:14
PHST- 2023/07/03 06:41 [medline]
PHST- 2023/06/30 13:11 [pubmed]
PHST- 2023/06/30 06:14 [entrez]
AID - 10.19191/EP23.3.A639.051 [doi]
PST - ppublish
SO  - Epidemiol Prev. 2023 May-Jun;47(3):203-207. doi: 10.19191/EP23.3.A639.051.

PMID- 37456464
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - Implications of ChatGPT in Public Health Dentistry: A Systematic Review.
PG  - e40367
LID - 10.7759/cureus.40367 [doi]
LID - e40367
AB  - An artificial intelligence (AI) program called ChatGPT that generates text in 
      response to typed commands has proven to be highly popular, as evidenced by the 
      fact that OpenAI makes it available online. The goal of the present investigation 
      was to investigate ChatGPT's potential applications as an outstanding instance of 
      large language models (LLMs) in the fields of public dental health schooling, 
      writing for academic use, research in public dental health, and clinical practice 
      in public dental health based on the available data. Importantly, the goals of 
      the current review included locating any drawbacks and issues that might be 
      connected to using ChatGPT in the previously mentioned contexts in healthcare 
      settings. Using search phrases including chatGPT, implications, artificial 
      intelligence (AI), public health dentistry, public health, practice in public 
      health dentistry, education in public health dentistry, academic writing in 
      public health dentistry, etc., a thorough search was carried out on the Pubmed 
      database, the Embase database, the Ovid database, the Global Health database, 
      PsycINFO, and the Web of Science. The dates of publication were not restricted. 
      Systematic searches were carried out for all publications according to inclusion 
      and exclusion criteria between March 31, 2018, and March 31, 2023. Eighty-four 
      papers were obtained through a literature search using search terms. Sixteen 
      similar and duplicate papers were excluded and 68 distinct articles were 
      initially selected. Thirty-three articles were excluded after reviewing abstracts 
      and titles. Thirty-five papers were selected, for which full text was managed. 
      Four extra papers were found manually from references. Thirty-nine articles with 
      full texts were eligible for the study. Eighteen inadequate articles are excluded 
      from the final 21 studies that were finally selected for systemic review. 
      According to previously published studies, ChatGPT has demonstrated its 
      effectiveness in helping scholars with the authoring of scientific research and 
      dental studies. If the right structures are created, ChatGPT can offer suitable 
      responses and more time to concentrate on the phase of experimentation for 
      scientists. Risks include prejudice in the training data, undervaluing human 
      skills, the possibility of fraud in science, as well as legal and reproducibility 
      concerns. It was concluded that practice considering ChatGPT's potential 
      significance, the research's uniqueness, and the premise-the activity of the 
      human brain-remains. While there is no question about the superiority of 
      incorporating ChatGPT into the practice of public health dentistry, it does not, 
      in any way, take the place of a dentist since clinical practice involves more 
      than just making diagnoses; it also involves relating to clinical findings and 
      providing individualized patient care. Even though AI can be useful in a number 
      of ways, a dentist must ultimately make the decision because dentistry is a field 
      that involves several disciplines.
CI  - Copyright © 2023, Tiwari et al.
FAU - Tiwari, Anushree
AU  - Tiwari A
AD  - Clinical Quality and Value, American Academy of Orthopaedic Surgeons, Rosemont, 
      USA.
FAU - Kumar, Amit
AU  - Kumar A
AD  - Department of Dentistry, All India Institute of Medical Sciences, Patna, IND.
FAU - Jain, Shailesh
AU  - Jain S
AD  - Department of Prosthodontics and Crown and Bridge, School of Dental Sciences, 
      Sharda University, Greater Noida, IND.
FAU - Dhull, Kanika S
AU  - Dhull KS
AD  - Department of Pedodontics and Preventive Dentistry, Kalinga Institute of Dental 
      Sciences (KIIT) Deemed to be University, Bhubaneswar, IND.
FAU - Sajjanar, Arunkumar
AU  - Sajjanar A
AD  - Department of Pediatrics and Preventive Dentistry, Swargiya Dadasaheb Kalmegh 
      Smruti Dental College and Hospital, Nagpur, IND.
FAU - Puthenkandathil, Rahul
AU  - Puthenkandathil R
AD  - Department of Prosthodontics and Crown and Bridge, AB Shetty Memorial Institute 
      of Dental Sciences (ABSMIDS) Nitte (Deemed to be University), Mangalore, IND.
FAU - Paiwal, Kapil
AU  - Paiwal K
AD  - Department of Oral and Maxillofacial Pathology, Daswani Dental College and 
      Research Center, Kota, IND.
FAU - Singh, Ramanpal
AU  - Singh R
AD  - Oral Medicine and Radiology, New Horizon Dental College and Research Institute, 
      Bilaspur, IND.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230613
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10340128
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - dentist
OT  - implications
OT  - public health dentistry
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/17 06:42
MHDA- 2023/07/17 06:43
CRDT- 2023/07/17 04:23
PHST- 2023/05/04 00:00 [received]
PHST- 2023/06/10 00:00 [accepted]
PHST- 2023/07/17 06:43 [medline]
PHST- 2023/07/17 06:42 [pubmed]
PHST- 2023/07/17 04:23 [entrez]
AID - 10.7759/cureus.40367 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 13;15(6):e40367. doi: 10.7759/cureus.40367. eCollection 2023 
      Jun.

PMID- 38050835
OWN - NLM
STAT- Publisher
LR  - 20231205
IS  - 1545-0813 (Electronic)
IS  - 1059-924X (Linking)
DP  - 2023 Dec 5
TI  - The Potential of AI and ChatGPT in Improving Agricultural Injury and Illness 
      Surveillance Programming and Dissemination.
PG  - 1-5
LID - 10.1080/1059924X.2023.2284959 [doi]
AB  - Generative Artificial Intelligence (AI) provides unprecedented opportunities to 
      improve injury surveillance systems in many ways, including the curation and 
      publication of information related to agricultural injuries and illnesses. This 
      editorial explores the feasibility and implication of ChatGPT integration in an 
      international sentinel agricultural injury surveillance system, AgInjuryNews, 
      highlighting that AI integration may enhance workflows by reducing human and 
      financial resources and increasing outputs. In the coming years, text intensive 
      natural language reports in AgInjuryNews and similar systems could be a rich 
      source for data for ChatGPT or other more customized and fine-tuned LLMs. By 
      harnessing the capabilities of AI and NLP, teams could potentially streamline the 
      process of data analysis, report generation, and public dissemination, ultimately 
      contributing to improved agricultural injury prevention efforts, well beyond any 
      manually driven efforts.
FAU - Weichelt, Bryan P
AU  - Weichelt BP
AUID- ORCID: 0000-0001-7444-4374
AD  - National Children's Center for Rural and Agricultural Health and Safety; National 
      Farm Medicine Center, Marshfield Clinic Research Institute, Marshfield, WI, USA.
FAU - Pilz, Matthew
AU  - Pilz M
AD  - National Children's Center for Rural and Agricultural Health and Safety; National 
      Farm Medicine Center, Marshfield Clinic Research Institute, Marshfield, WI, USA.
FAU - Burke, Richard
AU  - Burke R
AD  - National Children's Center for Rural and Agricultural Health and Safety; National 
      Farm Medicine Center, Marshfield Clinic Research Institute, Marshfield, WI, USA.
FAU - Puthoff, David
AU  - Puthoff D
AD  - Office of Research and Sponsored Programs, Marshfield Clinic Research Institute, 
      Marshfield, WI, USA.
FAU - Namkoong, Kang
AU  - Namkoong K
AD  - Department of Communication, University of Maryland, College Park, MD, USA.
LA  - eng
PT  - Journal Article
DEP - 20231205
PL  - England
TA  - J Agromedicine
JT  - Journal of agromedicine
JID - 9421530
SB  - IM
OTO - NOTNLM
OT  - Agricultural injury
OT  - ChatGPT
OT  - artificial intelligence
OT  - surveillance
EDAT- 2023/12/05 17:44
MHDA- 2023/12/05 17:44
CRDT- 2023/12/05 06:36
PHST- 2023/12/05 17:44 [medline]
PHST- 2023/12/05 17:44 [pubmed]
PHST- 2023/12/05 06:36 [entrez]
AID - 10.1080/1059924X.2023.2284959 [doi]
PST - aheadofprint
SO  - J Agromedicine. 2023 Dec 5:1-5. doi: 10.1080/1059924X.2023.2284959.

PMID- 37945282
OWN - NLM
STAT- MEDLINE
DCOM- 20231118
LR  - 20231118
IS  - 2053-3624 (Print)
IS  - 2053-3624 (Electronic)
IS  - 2053-3624 (Linking)
VI  - 10
IP  - 2
DP  - 2023 Nov
TI  - Heart-to-heart with ChatGPT: the impact of patients consulting AI for 
      cardiovascular health advice.
LID - 10.1136/openhrt-2023-002455 [doi]
LID - e002455
AB  - OBJECTIVES: The advent of conversational artificial intelligence (AI) systems 
      employing large language models such as ChatGPT has sparked public, professional 
      and academic debates on the capabilities of such technologies. This mixed-methods 
      study sets out to review and systematically explore the capabilities of ChatGPT 
      to adequately provide health advice to patients when prompted regarding four 
      topics from the field of cardiovascular diseases. METHODS: As of 30 May 2023, 528 
      items on PubMed contained the term ChatGPT in their title and/or abstract, with 
      258 being classified as journal articles and included in our thematic 
      state-of-the-art review. For the experimental part, we systematically developed 
      and assessed 123 prompts across the four topics based on three classes of users 
      and two languages. Medical and communications experts scored ChatGPT's responses 
      according to the 4Cs of language model evaluation proposed in this article: 
      correct, concise, comprehensive and comprehensible. RESULTS: The articles 
      reviewed were fairly evenly distributed across discussing how ChatGPT could be 
      used for medical publishing, in clinical practice and for education of medical 
      personnel and/or patients. Quantitatively and qualitatively assessing the 
      capability of ChatGPT on the 123 prompts demonstrated that, while the responses 
      generally received above-average scores, they occupy a spectrum from the concise 
      and correct via the absurd to what only can be described as hazardously incorrect 
      and incomplete. Prompts formulated at higher levels of health literacy generally 
      yielded higher-quality answers. Counterintuitively, responses in a lower-resource 
      language were often of higher quality. CONCLUSIONS: The results emphasise the 
      relationship between prompt and response quality and hint at potentially 
      concerning futures in personalised medicine. The widespread use of large language 
      models for health advice might amplify existing health inequalities and will 
      increase the pressure on healthcare systems by providing easy access to many 
      seemingly likely differential diagnoses and recommendations for seeing a doctor 
      for even harmless ailments.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Lautrup, Anton Danholt
AU  - Lautrup AD
AD  - Department of Mathematics and Computer Science, University of Southern Denmark, 
      Odense, Denmark.
FAU - Hyrup, Tobias
AU  - Hyrup T
AD  - Department of Mathematics and Computer Science, University of Southern Denmark, 
      Odense, Denmark.
FAU - Schneider-Kamp, Anna
AU  - Schneider-Kamp A
AD  - Department of Business and Management, University of Southern Denmark Faculty of 
      Business and Social Sciences, Odense, Denmark.
FAU - Dahl, Marie
AU  - Dahl M
AD  - Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
FAU - Lindholt, Jes Sanddal
AU  - Lindholt JS
AD  - Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
FAU - Schneider-Kamp, Peter
AU  - Schneider-Kamp P
AUID- ORCID: 0000-0003-4000-5570
AD  - Department of Mathematics and Computer Science, University of Southern Denmark, 
      Odense, Denmark petersk@sdu.dk.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - England
TA  - Open Heart
JT  - Open heart
JID - 101631219
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Cardiovascular Diseases/diagnosis/therapy
MH  - Heart
MH  - Patients
MH  - Referral and Consultation
PMC - PMC10649823
OTO - NOTNLM
OT  - computer simulation
OT  - myocardial infarction
OT  - systematic reviews as topic
COIS- Competing interests: None declared.
EDAT- 2023/11/10 00:44
MHDA- 2023/11/13 06:42
CRDT- 2023/11/09 21:02
PHST- 2023/08/15 00:00 [received]
PHST- 2023/10/26 00:00 [accepted]
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/11/10 00:44 [pubmed]
PHST- 2023/11/09 21:02 [entrez]
AID - openhrt-2023-002455 [pii]
AID - 10.1136/openhrt-2023-002455 [doi]
PST - ppublish
SO  - Open Heart. 2023 Nov;10(2):e002455. doi: 10.1136/openhrt-2023-002455.

PMID- 37937071
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231109
IS  - 2632-1297 (Electronic)
IS  - 2632-1297 (Linking)
VI  - 5
IP  - 6
DP  - 2023
TI  - To ChatGPT or not to ChatGPT: the use of artificial intelligence in writing 
      scientific papers.
PG  - fcad266
LID - 10.1093/braincomms/fcad266 [doi]
LID - fcad266
AB  - Our Scientific Editor discusses the current use of artificial intelligence in 
      writing academic papers and reports the updated guidelines for Brain 
      Communications on the use of this tool in scientific writing.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      Guarantors of Brain.
FAU - Marescotti, Manuela
AU  - Marescotti M
AUID- ORCID: 0000-0003-3398-3560
AD  - Edinburgh, UK.
LA  - eng
PT  - Editorial
DEP - 20231101
PL  - England
TA  - Brain Commun
JT  - Brain communications
JID - 101755125
PMC - PMC10627518
COIS- The author reports no competing interests.
EDAT- 2023/11/08 06:43
MHDA- 2023/11/08 06:44
CRDT- 2023/11/08 04:00
PHST- 2023/10/06 00:00 [received]
PHST- 2023/10/06 00:00 [revised]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/11/08 06:44 [medline]
PHST- 2023/11/08 06:43 [pubmed]
PHST- 2023/11/08 04:00 [entrez]
AID - fcad266 [pii]
AID - 10.1093/braincomms/fcad266 [doi]
PST - epublish
SO  - Brain Commun. 2023 Nov 1;5(6):fcad266. doi: 10.1093/braincomms/fcad266. 
      eCollection 2023.

PMID- 37548259
OWN - NLM
STAT- MEDLINE
DCOM- 20230808
LR  - 20230828
IS  - 2001-1326 (Electronic)
IS  - 2001-1326 (Linking)
VI  - 13
IP  - 8
DP  - 2023 Aug
TI  - A regulatory challenge for natural language processing (NLP)-based tools such as 
      ChatGPT to be legally used for healthcare decisions. Where are we now?
PG  - e1362
LID - 10.1002/ctm2.1362 [doi]
LID - e1362
FAU - Baumgartner, Christian
AU  - Baumgartner C
AUID- ORCID: 0000-0002-3763-5195
AD  - Institute of Health Care Engineering with European Testing Center of Medical 
      Devices, Graz University of Technology, Graz, Austria.
FAU - Baumgartner, Daniela
AU  - Baumgartner D
AUID- ORCID: 0000-0001-8141-2426
AD  - Clinical Division of Pediatric Cardiology, Department of Pediatrics and 
      Adolescent Medicine, Medical University of Graz, Graz, Austria.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Clin Transl Med
JT  - Clinical and translational medicine
JID - 101597971
SB  - IM
MH  - *Natural Language Processing
MH  - *Artificial Intelligence
MH  - *Clinical Decision-Making
PMC - PMC10405238
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - healthcare decisions
OT  - natural language processing (NLP)
OT  - regulatory approval
OT  - software as medical device (SaMD)
COIS- The authors declare that they have no conflict of interest.
EDAT- 2023/08/07 13:10
MHDA- 2023/08/08 06:42
CRDT- 2023/08/07 06:54
PHST- 2023/07/24 00:00 [received]
PHST- 2023/07/29 00:00 [accepted]
PHST- 2023/08/08 06:42 [medline]
PHST- 2023/08/07 13:10 [pubmed]
PHST- 2023/08/07 06:54 [entrez]
AID - CTM21362 [pii]
AID - 10.1002/ctm2.1362 [doi]
PST - ppublish
SO  - Clin Transl Med. 2023 Aug;13(8):e1362. doi: 10.1002/ctm2.1362.

PMID- 37679503
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231119
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Sep 7
TI  - Fabrication and errors in the bibliographic citations generated by ChatGPT.
PG  - 14045
LID - 10.1038/s41598-023-41032-5 [doi]
LID - 14045
AB  - Although chatbots such as ChatGPT can facilitate cost-effective text generation 
      and editing, factually incorrect responses (hallucinations) limit their utility. 
      This study evaluates one particular type of hallucination: fabricated 
      bibliographic citations that do not represent actual scholarly works. We used 
      ChatGPT-3.5 and ChatGPT-4 to produce short literature reviews on 42 
      multidisciplinary topics, compiling data on the 636 bibliographic citations 
      (references) found in the 84 papers. We then searched multiple databases and 
      websites to determine the prevalence of fabricated citations, to identify errors 
      in the citations to non-fabricated papers, and to evaluate adherence to APA 
      citation format. Within this set of documents, 55% of the GPT-3.5 citations but 
      just 18% of the GPT-4 citations are fabricated. Likewise, 43% of the real 
      (non-fabricated) GPT-3.5 citations but just 24% of the real GPT-4 citations 
      include substantive citation errors. Although GPT-4 is a major improvement over 
      GPT-3.5, problems remain.
CI  - © 2023. Springer Nature Limited.
FAU - Walters, William H
AU  - Walters WH
AD  - Mary Alice & Tom O'Malley Library, Manhattan College, Riverdale, NY, USA. 
      william.walters@manhattan.edu.
FAU - Wilder, Esther Isabelle
AU  - Wilder EI
AD  - Department of Sociology, Lehman College, The City University of New York, Bronx, 
      NY, USA.
AD  - Doctoral Program in Sociology, CUNY Graduate Center, The City University of New 
      York, New York, NY, USA.
LA  - eng
PT  - Journal Article
DEP - 20230907
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
PMC - PMC10484980
COIS- The authors declare no competing interests.
EDAT- 2023/09/08 00:42
MHDA- 2023/09/08 00:43
CRDT- 2023/09/07 23:32
PHST- 2023/07/11 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/09/08 00:43 [medline]
PHST- 2023/09/08 00:42 [pubmed]
PHST- 2023/09/07 23:32 [entrez]
AID - 10.1038/s41598-023-41032-5 [pii]
AID - 41032 [pii]
AID - 10.1038/s41598-023-41032-5 [doi]
PST - epublish
SO  - Sci Rep. 2023 Sep 7;13(1):14045. doi: 10.1038/s41598-023-41032-5.

PMID- 37332004
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231111
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 12
DP  - 2023 Dec
TI  - Success Through Simplicity: What Other Artificial Intelligence Applications in 
      Medicine Should Learn from History and ChatGPT.
PG  - 2657-2658
LID - 10.1007/s10439-023-03287-x [doi]
AB  - Many artificial intelligence (AI) algorithms have been developed for medical 
      practice, but few have led to clinically used products. The recent hype of 
      ChatGPT shows us that simple, user-friendly interfaces are one major factor in 
      the applications' popularity. The majority of AI-based applications in clinical 
      practice are still far from simple-to-use applications with user-friendly 
      interfaces. Therefore, simplifying operations is one key to AI-based medical 
      applications' success.
CI  - © 2023. The Author(s).
FAU - Sedaghat, Sam
AU  - Sedaghat S
AUID- ORCID: 0000-0003-2804-3718
AD  - Department of Diagnostic and Interventional Radiology, University Hospital of 
      Heidelberg, Heidelberg, Germany. samsedaghat1@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230618
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Artificial Intelligence
MH  - *Algorithms
PMC - PMC10632240
OTO - NOTNLM
OT  - AI
OT  - Application
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Future
OT  - Medicine
COIS- Author declares no conflict of interest.
EDAT- 2023/06/19 00:42
MHDA- 2023/11/09 06:42
CRDT- 2023/06/18 23:35
PHST- 2023/06/07 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/11/09 06:42 [medline]
PHST- 2023/06/19 00:42 [pubmed]
PHST- 2023/06/18 23:35 [entrez]
AID - 10.1007/s10439-023-03287-x [pii]
AID - 3287 [pii]
AID - 10.1007/s10439-023-03287-x [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Dec;51(12):2657-2658. doi: 10.1007/s10439-023-03287-x. Epub 
      2023 Jun 18.

PMID- 37716835
OWN - NLM
STAT- Publisher
LR  - 20230916
IS  - 2588-9311 (Electronic)
IS  - 2588-9311 (Linking)
DP  - 2023 Sep 14
TI  - Would Uro_Chat, a Newly Developed Generative Artificial Intelligence Large 
      Language Model, Have Successfully Passed the In-Service Assessment Questions of 
      the European Board of Urology in 2022?
LID - S2588-9311(23)00178-5 [pii]
LID - 10.1016/j.euo.2023.08.013 [doi]
FAU - May, Matthias
AU  - May M
AD  - Department of Urology, St. Elisabeth Hospital Straubing, Brothers of Mercy 
      Hospital, Straubing, Germany. Electronic address: 
      matthias.may@klinikum-straubing.de.
FAU - Körner-Riffard, Katharina
AU  - Körner-Riffard K
AD  - Department of Urology, Caritas St. Josef Medical Centre, University of 
      Regensburg, Regensburg, Germany.
FAU - Marszalek, Martin
AU  - Marszalek M
AD  - Department of Urology and Andrology, Klinik Donaustadt, Vienna, Austria.
FAU - Eredics, Klaus
AU  - Eredics K
AD  - Department of Urology and Andrology, Klinik Donaustadt, Vienna, Austria.
LA  - eng
PT  - Letter
DEP - 20230914
PL  - Netherlands
TA  - Eur Urol Oncol
JT  - European urology oncology
JID - 101724904
SB  - IM
EDAT- 2023/09/17 00:41
MHDA- 2023/09/17 00:41
CRDT- 2023/09/16 21:54
PHST- 2023/08/18 00:00 [received]
PHST- 2023/08/29 00:00 [accepted]
PHST- 2023/09/17 00:41 [medline]
PHST- 2023/09/17 00:41 [pubmed]
PHST- 2023/09/16 21:54 [entrez]
AID - S2588-9311(23)00178-5 [pii]
AID - 10.1016/j.euo.2023.08.013 [doi]
PST - aheadofprint
SO  - Eur Urol Oncol. 2023 Sep 14:S2588-9311(23)00178-5. doi: 
      10.1016/j.euo.2023.08.013.

PMID- 37962920
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2562-0959 (Electronic)
IS  - 2562-0959 (Linking)
VI  - 6
DP  - 2023 Nov 14
TI  - Assessing the Accuracy and Comprehensiveness of ChatGPT in Offering Clinical 
      Guidance for Atopic Dermatitis and Acne Vulgaris.
PG  - e50409
LID - 10.2196/50409 [doi]
LID - e50409
FAU - Lakdawala, Nehal
AU  - Lakdawala N
AUID- ORCID: 0009-0003-0814-4121
AD  - University of Connecticut School of Medicine, Farmington, CT, United States.
FAU - Channa, Leelakrishna
AU  - Channa L
AUID- ORCID: 0009-0002-0538-9636
AD  - University of Connecticut School of Medicine, Farmington, CT, United States.
FAU - Gronbeck, Christian
AU  - Gronbeck C
AUID- ORCID: 0000-0002-5146-9904
AD  - Department of Dermatology, University of Connecticut Health Center, Farmington, 
      CT, United States.
FAU - Lakdawala, Nikita
AU  - Lakdawala N
AUID- ORCID: 0000-0002-8122-6145
AD  - The Ronald O. Perelman Department of Dermatology, New York University, New York, 
      NY, United States.
FAU - Weston, Gillian
AU  - Weston G
AUID- ORCID: 0000-0002-9358-0707
AD  - Department of Dermatology, University of Connecticut Health Center, Farmington, 
      CT, United States.
FAU - Sloan, Brett
AU  - Sloan B
AUID- ORCID: 0000-0002-7508-3184
AD  - Department of Dermatology, University of Connecticut Health Center, Farmington, 
      CT, United States.
FAU - Feng, Hao
AU  - Feng H
AUID- ORCID: 0009-0008-4099-4886
AD  - Department of Dermatology, University of Connecticut Health Center, Farmington, 
      CT, United States.
LA  - eng
PT  - Journal Article
DEP - 20231114
PL  - Canada
TA  - JMIR Dermatol
JT  - JMIR dermatology
JID - 101770607
PMC - PMC10685272
OTO - NOTNLM
OT  - ChatGPT
OT  - NLP
OT  - acne
OT  - acne vulgaris
OT  - advise
OT  - answer
OT  - answers
OT  - artificial intelligence
OT  - atopic dermatitis
OT  - automated
OT  - chatbot
OT  - chatbots
OT  - clinical guidance
OT  - computer generated
OT  - conversational agent
OT  - conversational agents
OT  - counsel
OT  - counseling
OT  - dermatitis
OT  - dermatologic
OT  - dermatological
OT  - dermatology
OT  - guidance
OT  - natural language processing
OT  - recommendation
OT  - recommendations
OT  - response
OT  - responses
OT  - skin
COIS- Conflicts of Interest: HF is a consultant for Cytrellis Biosystems, Inc and 
      Soliton, Inc. The other authors declare no conflicts of interest.
EDAT- 2023/11/14 12:42
MHDA- 2023/11/14 12:43
CRDT- 2023/11/14 11:53
PHST- 2023/06/29 00:00 [received]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/09/13 00:00 [revised]
PHST- 2023/11/14 12:43 [medline]
PHST- 2023/11/14 12:42 [pubmed]
PHST- 2023/11/14 11:53 [entrez]
AID - v6i1e50409 [pii]
AID - 10.2196/50409 [doi]
PST - epublish
SO  - JMIR Dermatol. 2023 Nov 14;6:e50409. doi: 10.2196/50409.

PMID- 37410015
OWN - NLM
STAT- MEDLINE
DCOM- 20230821
LR  - 20230908
IS  - 2352-0787 (Electronic)
IS  - 2352-0779 (Linking)
VI  - 10
IP  - 5
DP  - 2023 Sep
TI  - Bridging the Gap Between Urological Research and Patient Understanding: The Role 
      of Large Language Models in Automated Generation of Layperson's Summaries.
PG  - 436-443
LID - 10.1097/UPJ.0000000000000428 [doi]
AB  - INTRODUCTION: This study assessed ChatGPT's ability to generate readable, 
      accurate, and clear layperson summaries of urological studies, and compared the 
      performance of ChatGPT-generated summaries with original abstracts and 
      author-written patient summaries to determine its effectiveness as a potential 
      solution for creating accessible medical literature for the public. METHODS: 
      Articles from the top 5 ranked urology journals were selected. A ChatGPT prompt 
      was developed following guidelines to maximize readability, accuracy, and 
      clarity, minimizing variability. Readability scores and grade-level indicators 
      were calculated for the ChatGPT summaries, original abstracts, and patient 
      summaries. Two MD physicians independently rated the accuracy and clarity of the 
      ChatGPT-generated layperson summaries. Statistical analyses were conducted to 
      compare readability scores. Cohen's κ coefficient was used to assess interrater 
      reliability for correctness and clarity evaluations. RESULTS: A total of 256 
      journal articles were included. The ChatGPT-generated summaries were created with 
      an average time of 17.5 (SD 15.0) seconds. The readability scores of the 
      ChatGPT-generated summaries were significantly better than the original 
      abstracts, with Global Readability Score 54.8 (12.3) vs 29.8 (18.5), Flesch 
      Kincade Reading Ease 54.8 (12.3) vs 29.8 (18.5), Flesch Kincaid Grade Level 10.4 
      (2.2) vs 13.5 (4.0), Gunning Fog Score 12.9 (2.6) vs 16.6 (4.1), Smog Index 9.1 
      (2.0) vs 12.0 (3.0), Coleman Liau Index 12.9 (2.1) vs 14.9 (3.7), and Automated 
      Readability Index 11.1 (2.5) vs 12.0 (5.7; P < .0001 for all except Automated 
      Readability Index, which was P = .037). The correctness rate of ChatGPT outputs 
      was >85% across all categories assessed, with interrater agreement (Cohen's κ) 
      between 2 independent physician reviewers ranging from 0.76-0.95. CONCLUSIONS: 
      ChatGPT can create accurate summaries of scientific abstracts for patients, with 
      well-crafted prompts enhancing user-friendliness. Although the summaries are 
      satisfactory, expert verification is necessary for improved accuracy.
FAU - Eppler, Michael B
AU  - Eppler MB
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Ganjavi, Conner
AU  - Ganjavi C
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Knudsen, J Everett
AU  - Knudsen JE
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Davis, Ryan J
AU  - Davis RJ
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Ayo-Ajibola, Oluwatobiloba
AU  - Ayo-Ajibola O
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Desai, Aditya
AU  - Desai A
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Storino Ramacciotti, Lorenzo
AU  - Storino Ramacciotti L
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Chen, Andrew
AU  - Chen A
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - De Castro Abreu, Andre
AU  - De Castro Abreu A
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Desai, Mihir M
AU  - Desai MM
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Gill, Inderbir S
AU  - Gill IS
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
FAU - Cacciamani, Giovanni E
AU  - Cacciamani GE
AD  - USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - Artificial Intelligence Center at USC Urology, USC Institute of Urology, 
      University of Southern California, Los Angeles, California.
LA  - eng
PT  - Journal Article
DEP - 20230705
PL  - United States
TA  - Urol Pract
JT  - Urology practice
JID - 101635343
SB  - IM
CIN - Urol Pract. 2023 Sep;10(5):444. PMID: 37410018
CIN - Urol Pract. 2023 Sep;10(5):443-444. PMID: 37410020
MH  - Humans
MH  - *Health Literacy
MH  - Reproducibility of Results
MH  - *Urology
MH  - Comprehension
MH  - Language
OTO - NOTNLM
OT  - artificial intelligence
OT  - communication
OT  - comprehension
OT  - patient education as topic
OT  - urology
EDAT- 2023/07/06 13:14
MHDA- 2023/08/21 06:42
CRDT- 2023/07/06 10:33
PHST- 2023/08/21 06:42 [medline]
PHST- 2023/07/06 13:14 [pubmed]
PHST- 2023/07/06 10:33 [entrez]
AID - 10.1097/UPJ.0000000000000428 [doi]
PST - ppublish
SO  - Urol Pract. 2023 Sep;10(5):436-443. doi: 10.1097/UPJ.0000000000000428. Epub 2023 
      Jul 5.

PMID- 37612880
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1440-1819 (Electronic)
IS  - 1323-1316 (Linking)
VI  - 77
IP  - 11
DP  - 2023 Nov
TI  - The now and future of ChatGPT and GPT in psychiatry.
PG  - 592-596
LID - 10.1111/pcn.13588 [doi]
AB  - ChatGPT has sparked extensive discussions within the healthcare community since 
      its November 2022 release. However, potential applications in the field of 
      psychiatry have received limited attention. Deep learning has proven beneficial 
      to psychiatry, and GPT is a powerful deep learning-based language model with 
      immense potential for this field. Despite the convenience of ChatGPT, this 
      advanced chatbot currently has limited practical applications in psychiatry. It 
      may be used to support psychiatrists in routine tasks such as completing medical 
      records, facilitating communications between clinicians and with patients, 
      polishing academic writings and presentations, and programming and performing 
      analyses for research. The current training and application of ChatGPT require 
      using appropriate prompts to maximize appropriate outputs and minimize 
      deleterious inaccuracies and phantom errors. Moreover, future GPT advances that 
      incorporate empathy, emotion recognition, personality assessment, and detection 
      of mental health warning signs are essential for its effective integration into 
      psychiatric care. In the near future, developing a fully-automated psychotherapy 
      system trained for expert communication (such as psychotherapy verbatim) is 
      conceivable by building on foundational GPT technology. This dream system should 
      integrate practical 'real world' inputs and friendly AI user and patient 
      interfaces via clinically validated algorithms, voice comprehension/generation 
      modules, and emotion discrimination algorithms based on facial expressions and 
      physiological inputs from wearable devices. In addition to the technology 
      challenges, we believe it is critical to establish generally accepted ethical 
      standards for applying ChatGPT-related tools in all mental healthcare 
      environments, including telemedicine and academic/training settings.
CI  - © 2023 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley 
      & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.
FAU - Cheng, Szu-Wei
AU  - Cheng SW
AUID- ORCID: 0000-0002-4460-5517
AD  - College of Medicine, China Medical University, Taichung, Taiwan.
AD  - Mind-Body Interface Laboratory (MBI-Lab) and Department of Psychiatry, China 
      Medical University Hospital, Taichung, Taiwan.
FAU - Chang, Chung-Wen
AU  - Chang CW
AD  - Department of Information Management, Chia-Nan University of Pharmacy & Science, 
      Tainan, Taiwan.
FAU - Chang, Wan-Jung
AU  - Chang WJ
AUID- ORCID: 0000-0002-7478-7315
AD  - Department of Electronic Engineering, Southern Taiwan University of Science and 
      Technology, Tainan, Taiwan.
AD  - Internet of Things Laboratory (IOT Lab), Medical and Intelligent Technology 
      Research Center (MIT Center), Southern Taiwan University of Science and 
      Technology, Tainan, Taiwan.
FAU - Wang, Hao-Wei
AU  - Wang HW
AD  - Department of Family Studies and Child Development, Shih Chien University, 
      Taipei, Taiwan.
FAU - Liang, Chih-Sung
AU  - Liang CS
AUID- ORCID: 0000-0003-1138-5586
AD  - Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National 
      Defense Medical Center, Taipei, Taiwan.
FAU - Kishimoto, Taishiro
AU  - Kishimoto T
AUID- ORCID: 0000-0003-0557-8648
AD  - Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio 
      University School of Medicine, Tokyo, Japan.
FAU - Chang, Jane Pei-Chen
AU  - Chang JP
AUID- ORCID: 0000-0001-5582-0928
AD  - College of Medicine, China Medical University, Taichung, Taiwan.
AD  - Mind-Body Interface Laboratory (MBI-Lab) and Department of Psychiatry, China 
      Medical University Hospital, Taichung, Taiwan.
FAU - Kuo, John S
AU  - Kuo JS
AUID- ORCID: 0000-0001-6809-4806
AD  - Neuroscience and Brain Disease Center, China Medical University, Taichung, 
      Taiwan.
AD  - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 
      Taiwan.
FAU - Su, Kuan-Pin
AU  - Su KP
AUID- ORCID: 0000-0002-4501-2502
AD  - College of Medicine, China Medical University, Taichung, Taiwan.
AD  - Mind-Body Interface Laboratory (MBI-Lab) and Department of Psychiatry, China 
      Medical University Hospital, Taichung, Taiwan.
AD  - Neuroscience and Brain Disease Center, China Medical University, Taichung, 
      Taiwan.
AD  - An-Nan Hospital, China Medical University, Tainan, Taiwan.
LA  - eng
GR  - 106-227/China Medical University Hospital/
GR  - 109-102/China Medical University Hospital/
GR  - 109-244/China Medical University Hospital/
GR  - 110-124/China Medical University Hospital/
GR  - 111-245/China Medical University Hospital/
GR  - 112-086/China Medical University Hospital/
GR  - 112-097/China Medical University Hospital/
GR  - 112-109/China Medical University Hospital/
GR  - 112-232/China Medical University Hospital/
GR  - DMR-106-101/China Medical University Hospital/
GR  - DMR-HHC-109-11/China Medical University Hospital/
GR  - HHC-109-12/China Medical University Hospital/
GR  - HHC-110-10/China Medical University Hospital/
GR  - HHC-111-8/China Medical University Hospital/
GR  - 110-N-17/China Medical University, Taiwan/
GR  - 1110-SR-73/China Medical University, Taiwan/
GR  - CMU 110-AWARD-02/China Medical University, Taiwan/
GR  - CMRC-CMA-2/Ministry of Education (MOE), Taiwan/
GR  - 110-2314-B-039-029-MY3/National Science and Technology Council/
GR  - 110-2320-B-039-047-MY3/National Science and Technology Council/
GR  - 110-2320-B-039-048-MY2/National Science and Technology Council/
GR  - 110-2321-B-006-004/National Science and Technology Council/
GR  - 110-2811-B-039-507/National Science and Technology Council/
GR  - 110-2813-C-039-327-B/National Science and Technology Council/
GR  - 111-2321-B-006-008/National Science and Technology Council/
GR  - MOST 109-2320-B-038-057-MY3/National Science and Technology Council/
GR  - NSTC 111-2314-B-039 -072 -MY3/National Science and Technology Council/
GR  - NSTC 111-2314-B-039-041-MY3/National Science and Technology Council/
GR  - 109-40/Tainan Municipal An-Nan Hospital/
GR  - 110-13/Tainan Municipal An-Nan Hospital/
GR  - 110-26/Tainan Municipal An-Nan Hospital/
GR  - 110-44/Tainan Municipal An-Nan Hospital/
GR  - 110-45/Tainan Municipal An-Nan Hospital/
GR  - 111-27/Tainan Municipal An-Nan Hospital/
GR  - 111-28/Tainan Municipal An-Nan Hospital/
GR  - 111-47/Tainan Municipal An-Nan Hospital/
GR  - 111-48/Tainan Municipal An-Nan Hospital/
GR  - 111-52/Tainan Municipal An-Nan Hospital/
GR  - ANHRF 109-31/Tainan Municipal An-Nan Hospital/
PT  - Journal Article
PT  - Review
DEP - 20230911
PL  - Australia
TA  - Psychiatry Clin Neurosci
JT  - Psychiatry and clinical neurosciences
JID - 9513551
SB  - IM
MH  - Humans
MH  - *Psychiatry
MH  - Algorithms
MH  - Emotions
MH  - Empathy
MH  - Language
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT
OT  - artificial intelligence
OT  - deep learning
OT  - informatics and telecommunications in psychiatry
EDAT- 2023/08/24 06:43
MHDA- 2023/11/02 12:45
CRDT- 2023/08/24 00:52
PHST- 2023/08/11 00:00 [revised]
PHST- 2023/04/26 00:00 [received]
PHST- 2023/08/16 00:00 [accepted]
PHST- 2023/11/02 12:45 [medline]
PHST- 2023/08/24 06:43 [pubmed]
PHST- 2023/08/24 00:52 [entrez]
AID - 10.1111/pcn.13588 [doi]
PST - ppublish
SO  - Psychiatry Clin Neurosci. 2023 Nov;77(11):592-596. doi: 10.1111/pcn.13588. Epub 
      2023 Sep 11.

PMID- 37467806
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1527-9995 (Electronic)
IS  - 0090-4295 (Linking)
VI  - 180
DP  - 2023 Oct
TI  - Caution! AI Bot Has Entered the Patient Chat: ChatGPT Has Limitations in 
      Providing Accurate Urologic Healthcare Advice.
PG  - 278-284
LID - S0090-4295(23)00597-6 [pii]
LID - 10.1016/j.urology.2023.07.010 [doi]
AB  - OBJECTIVE: To conduct the first study examining the accuracy of ChatGPT, an 
      artificial intelligence (AI) chatbot, derived patient counseling responses based 
      on clinical care guidelines in urology using a validated questionnaire. METHODS: 
      We asked ChatGPT a set of 13 urological guideline-based questions three times. 
      Answers were evaluated for appropriateness and using Brief DISCERN (BD), a 
      validated healthcare information assessment questionnaire. Data analysis included 
      descriptive statistics and Student's t test (SAS Studio). RESULTS: 60% (115/195) 
      of ChatGPT responses were deemed appropriate. Variability existed between 
      responses to the same prompt, with 25% of the 13 question sets having discordant 
      appropriateness designations. The average BD score was 16.8 ± 3.59. Only 7 (54%) 
      of 13 topics and 21 (54%) of 39 responses met the BD cut-off score of ≥16 to 
      denote good-quality content. Appropriateness was associated with higher overall 
      and Relevance domain scores (both P < .01). The lowest BD domain scores were for 
      Source categories, since ChatGPT does not provide references by default. With 
      prompting, 92.3% had ≥1 incorrect, misinterpreted, or nonfunctional citations. 
      CONCLUSION: While ChatGPT provides appropriate responses to urological questions 
      more than half of the time, it misinterprets clinical care guidelines, dismisses 
      important contextual information, conceals its sources, and provides 
      inappropriate references. Chatbot models hold great promise, but users should be 
      cautious when interpreting healthcare-related advice from existing AI models. 
      Additional training and modifications are needed before these AI models will be 
      ready for reliable use by patients and providers.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Whiles, Bristol B
AU  - Whiles BB
AD  - Department of Urology, University of Florida, Gainesville, FL. Electronic 
      address: bristolwhiles@gmail.com.
FAU - Bird, Vincent G
AU  - Bird VG
AD  - Department of Urology, University of Florida, Gainesville, FL.
FAU - Canales, Benjamin K
AU  - Canales BK
AD  - Department of Urology, University of Florida, Gainesville, FL.
FAU - DiBianco, John M
AU  - DiBianco JM
AD  - Department of Urology, University of Florida, Gainesville, FL.
FAU - Terry, Russell S
AU  - Terry RS
AD  - Department of Urology, University of Florida, Gainesville, FL.
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - United States
TA  - Urology
JT  - Urology
JID - 0366151
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Urology
MH  - Software
MH  - Data Analysis
MH  - Health Facilities
COIS- Declaration of Competing Interest The authors have no conflict of interest to 
      declare.
EDAT- 2023/07/20 01:07
MHDA- 2023/10/23 00:44
CRDT- 2023/07/19 19:22
PHST- 2023/04/25 00:00 [received]
PHST- 2023/06/20 00:00 [revised]
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/10/23 00:44 [medline]
PHST- 2023/07/20 01:07 [pubmed]
PHST- 2023/07/19 19:22 [entrez]
AID - S0090-4295(23)00597-6 [pii]
AID - 10.1016/j.urology.2023.07.010 [doi]
PST - ppublish
SO  - Urology. 2023 Oct;180:278-284. doi: 10.1016/j.urology.2023.07.010. Epub 2023 Jul 
      17.

PMID- 37041067
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 1468-2060 (Electronic)
IS  - 0003-4967 (Print)
IS  - 0003-4967 (Linking)
VI  - 82
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT: when artificial intelligence replaces the rheumatologist in medical 
      writing.
PG  - 1015-1017
LID - 10.1136/ard-2023-223936 [doi]
AB  - In this editorial we discuss the place of artificial intelligence (AI) in the 
      writing of scientific articles and especially editorials. We asked chatGPT « to 
      write an editorial for Annals of Rheumatic Diseases about how AI may replace the 
      rheumatologist in editorial writing ». chatGPT's response is diplomatic and 
      describes AI as a tool to help the rheumatologist but not replace him. AI is 
      already used in medicine, especially in image analysis, but the domains are 
      infinite and it is possible that AI could quickly help or replace rheumatologists 
      in the writing of scientific articles. We discuss the ethical aspects and the 
      future role of rheumatologists.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Verhoeven, Frank
AU  - Verhoeven F
AUID- ORCID: 0000-0003-2708-2918
AD  - Rheumatology, CHU Besancon, Besancon, France.
AD  - EA 4267 PEPITE, Université de Franche-Comté, Besancon, France.
FAU - Wendling, Daniel
AU  - Wendling D
AUID- ORCID: 0000-0002-4687-5780
AD  - Rheumatology, CHU Besancon, Besancon, France.
AD  - EA4266 EPILAB, Université de Franche-Comté, Besancon, France.
FAU - Prati, Clément
AU  - Prati C
AD  - Rheumatology, CHU Besancon, Besancon, France cprati@chu-besancon.fr.
AD  - EA 4267 PEPITE, Université de Franche-Comté, Besancon, France.
LA  - eng
PT  - Journal Article
DEP - 20230411
PL  - England
TA  - Ann Rheum Dis
JT  - Annals of the rheumatic diseases
JID - 0372355
SB  - IM
MH  - Humans
MH  - Male
MH  - Rheumatologists
MH  - Artificial Intelligence
MH  - *Medical Writing
MH  - *Rheumatic Diseases
PMC - PMC10359572
OTO - NOTNLM
OT  - Health services research
OT  - Patient Care Team
OT  - Qualitative research
OT  - Social work
COIS- Competing interests: None declared.
EDAT- 2023/04/12 06:00
MHDA- 2023/07/17 06:42
CRDT- 2023/04/11 21:12
PHST- 2023/01/25 00:00 [received]
PHST- 2023/03/06 00:00 [accepted]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/04/12 06:00 [pubmed]
PHST- 2023/04/11 21:12 [entrez]
AID - ard-2023-223936 [pii]
AID - 10.1136/ard-2023-223936 [doi]
PST - ppublish
SO  - Ann Rheum Dis. 2023 Aug;82(8):1015-1017. doi: 10.1136/ard-2023-223936. Epub 2023 
      Apr 11.

PMID- 37671207
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230907
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Performance of Large Language Models (ChatGPT, Bing Search, and Google Bard) in 
      Solving Case Vignettes in Physiology.
PG  - e42972
LID - 10.7759/cureus.42972 [doi]
LID - e42972
AB  - Background Large language models (LLMs) have emerged as powerful tools capable of 
      processing and generating human-like text. These LLMs, such as ChatGPT (OpenAI 
      Incorporated, Mission District, San Francisco, United States), Google Bard 
      (Alphabet Inc., CA, US), and Microsoft Bing (Microsoft Corporation, WA, US), have 
      been applied across various domains, demonstrating their potential to assist in 
      solving complex tasks and improving information accessibility. However, their 
      application in solving case vignettes in physiology has not been explored. This 
      study aimed to assess the performance of three LLMs, namely, ChatGPT (3.5; free 
      research version), Google Bard (Experiment), and Microsoft Bing (precise), in 
      answering cases vignettes in Physiology. Methods This cross-sectional study was 
      conducted in July 2023. A total of 77 case vignettes in physiology were prepared 
      by two physiologists and were validated by two other content experts. These cases 
      were presented to each LLM, and their responses were collected. Two physiologists 
      independently rated the answers provided by the LLMs based on their accuracy. The 
      ratings were measured on a scale from 0 to 4 according to the structure of the 
      observed learning outcome (pre-structural = 0, uni-structural = 1, 
      multi-structural = 2, relational = 3, extended-abstract). The scores among the 
      LLMs were compared by Friedman's test and inter-observer agreement was checked by 
      the intraclass correlation coefficient (ICC). Results The overall scores for 
      ChatGPT, Bing, and Bard in the study, with a total of 77 cases, were found to be 
      3.19±0.3, 2.15±0.6, and 2.91±0.5, respectively, p<0.0001. Hence, ChatGPT 3.5 
      (free version) obtained the highest score, Bing (Precise) had the lowest score, 
      and Bard (Experiment) fell in between the two in terms of performance. The 
      average ICC values for ChatGPT, Bing, and Bard were 0.858 (95% CI: 0.777 to 0.91, 
      p<0.0001), 0.975 (95% CI: 0.961 to 0.984, p<0.0001), and 0.964 (95% CI: 0.944 to 
      0.977, p<0.0001), respectively. Conclusion ChatGPT outperformed Bard and Bing in 
      answering case vignettes in physiology. Hence, students and teachers may think 
      about choosing LLMs for their educational purposes accordingly for case-based 
      learning in physiology. Further exploration of their capabilities is needed for 
      adopting those in medical education and support for clinical decision-making.
CI  - Copyright © 2023, Dhanvijay et al.
FAU - Dhanvijay, Anup Kumar D
AU  - Dhanvijay AKD
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Pinjar, Mohammed Jaffer
AU  - Pinjar MJ
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Dhokane, Nitin
AU  - Dhokane N
AD  - Physiology, Government Medical College, Sindhudurg, Oros, IND.
FAU - Sorte, Smita R
AU  - Sorte SR
AD  - Physiology, All India Institute of Medical Sciences, Nagpur, Nagpur, IND.
FAU - Kumari, Amita
AU  - Kumari A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
LA  - eng
PT  - Journal Article
DEP - 20230804
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10475852
OTO - NOTNLM
OT  - artificial intelligence
OT  - bard
OT  - bing
OT  - case vignette
OT  - chatgpt
OT  - language model
OT  - medical education
OT  - physiology
OT  - problem-based learning
OT  - self-directed learning (sdl)
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/06 06:42
MHDA- 2023/09/06 06:43
CRDT- 2023/09/06 03:55
PHST- 2023/08/04 00:00 [accepted]
PHST- 2023/09/06 06:43 [medline]
PHST- 2023/09/06 06:42 [pubmed]
PHST- 2023/09/06 03:55 [entrez]
AID - 10.7759/cureus.42972 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 4;15(8):e42972. doi: 10.7759/cureus.42972. eCollection 2023 Aug.

PMID- 37519497
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230801
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - Analysing the Applicability of ChatGPT, Bard, and Bing to Generate 
      Reasoning-Based Multiple-Choice Questions in Medical Physiology.
PG  - e40977
LID - 10.7759/cureus.40977 [doi]
LID - e40977
AB  - Background Artificial intelligence (AI) is evolving in the medical education 
      system. ChatGPT, Google Bard, and Microsoft Bing are AI-based models that can 
      solve problems in medical education. However, the applicability of AI to create 
      reasoning-based multiple-choice questions (MCQs) in the field of medical 
      physiology is yet to be explored. Objective We aimed to assess and compare the 
      applicability of ChatGPT, Bard, and Bing in generating reasoning-based MCQs for 
      MBBS (Bachelor of Medicine, Bachelor of Surgery) undergraduate students on the 
      subject of physiology. Methods The National Medical Commission of India has 
      developed an 11-module physiology curriculum with various competencies. Two 
      physiologists independently chose a competency from each module. The third 
      physiologist prompted all three AIs to generate five MCQs for each chosen 
      competency. The two physiologists who provided the competencies rated the MCQs 
      generated by the AIs on a scale of 0-3 for validity, difficulty, and reasoning 
      ability required to answer them. We analyzed the average of the two scores using 
      the Kruskal-Wallis test to compare the distribution across the total and 
      module-wise responses, followed by a post-hoc test for pairwise comparisons. We 
      used Cohen's Kappa (Κ) to assess the agreement in scores between the two raters. 
      We expressed the data as a median with an interquartile range. We determined 
      their statistical significance by a p-value <0.05. Results ChatGPT and Bard 
      generated 110 MCQs for the chosen competencies. However, Bing provided only 100 
      MCQs as it failed to generate them for two competencies. The validity of the MCQs 
      was rated as 3 (3-3) for ChatGPT, 3 (1.5-3) for Bard, and 3 (1.5-3) for Bing, 
      showing a significant difference (p<0.001) among the models. The difficulty of 
      the MCQs was rated as 1 (0-1) for ChatGPT, 1 (1-2) for Bard, and 1 (1-2) for 
      Bing, with a significant difference (p=0.006). The required reasoning ability to 
      answer the MCQs was rated as 1 (1-2) for ChatGPT, 1 (1-2) for Bard, and 1 (1-2) 
      for Bing, with no significant difference (p=0.235). K was ≥ 0.8 for all three 
      parameters across all three AI models. Conclusion AI still needs to evolve to 
      generate reasoning-based MCQs in medical physiology. ChatGPT, Bard, and Bing 
      showed certain limitations. Bing generated significantly least valid MCQs, while 
      ChatGPT generated significantly least difficult MCQs.
CI  - Copyright © 2023, Agarwal et al.
FAU - Agarwal, Mayank
AU  - Agarwal M
AD  - Physiology, All India Institute of Medical Sciences, Raebareli, IND.
FAU - Sharma, Priyanka
AU  - Sharma P
AD  - Physiology, School of Medical Sciences and Research, Sharda University, Greater 
      Noida, IND.
FAU - Goswami, Ayan
AU  - Goswami A
AD  - Physiology, Santiniketan Medical College, Bolpur, IND.
LA  - eng
PT  - Journal Article
DEP - 20230626
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10372539
OTO - NOTNLM
OT  - artificial intelligence
OT  - educational technology
OT  - examination questions
OT  - medical education
OT  - physiology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/31 06:42
MHDA- 2023/07/31 06:43
CRDT- 2023/07/31 04:38
PHST- 2023/06/26 00:00 [accepted]
PHST- 2023/07/31 06:43 [medline]
PHST- 2023/07/31 06:42 [pubmed]
PHST- 2023/07/31 04:38 [entrez]
AID - 10.7759/cureus.40977 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 26;15(6):e40977. doi: 10.7759/cureus.40977. eCollection 2023 
      Jun.

PMID- 37975899
OWN - NLM
STAT- Publisher
LR  - 20231117
IS  - 1432-0711 (Electronic)
IS  - 0932-0067 (Linking)
DP  - 2023 Nov 17
TI  - Will I soon be out of my job? Quality and guideline conformity of ChatGPT therapy 
      suggestions to patient inquiries with gynecologic symptoms in a palliative 
      setting.
LID - 10.1007/s00404-023-07272-6 [doi]
AB  - PURPOSE: The market and application possibilities for artificial intelligence are 
      currently growing at high speed and are increasingly finding their way into 
      gynecology. While the medical side is highly represented in the current 
      literature, the patient's perspective is still lagging behind. Therefore, the aim 
      of this study was to evaluate the recommendations of ChatGPT regarding patient 
      inquiries about the possible therapy of gynecological leading symptoms in a 
      palliative situation by experts. METHODS: Case vignettes were constructed for 10 
      common concomitant symptoms in gynecologic oncology tumors in a palliative 
      setting, and patient queries regarding therapy of these symptoms were generated 
      as prompts for ChatGPT. Five experts in palliative care and gynecologic oncology 
      evaluated the responses with respect to guideline adherence and applicability and 
      identified advantages and disadvantages. RESULTS: The overall rating of ChatGPT 
      responses averaged 4.1 (5 = strongly agree; 1 = strongly disagree). The experts 
      saw an average guideline conformity of the therapy recommendations with a value 
      of 4.0. ChatGPT sometimes omits relevant therapies and does not provide an 
      individual assessment of the suggested therapies, but does indicate that a 
      physician consultation is additionally necessary. CONCLUSIONS: Language models, 
      such as ChatGPT, can provide valid and largely guideline-compliant therapy 
      recommendations in their freely available and thus in principle accessible 
      version for our patients. For a complete therapy recommendation, an evaluation of 
      the therapies, their individual adjustment as well as a filtering of possible 
      wrong recommendations, a medical expert's opinion remains indispensable.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Braun, Eva-Marie
AU  - Braun EM
AUID- ORCID: 0000-0002-7621-8482
AD  - Center for Integrative Oncology, Die Filderklinik, Im Haberschlai 7, 70794, 
      Filderstadt-Bonlanden, Germany. braun.em@web.de.
FAU - Juhasz-Böss, Ingolf
AU  - Juhasz-Böss I
AD  - Department of Gynecology, University Medical Center Freiburg, Hugstetter Straße 
      55, 79106, Freiburg, Germany.
FAU - Solomayer, Erich-Franz
AU  - Solomayer EF
AD  - Department of Gynecology, Obstetrics and Reproductive Medicine, Saarland 
      University Hospital, Kirrberger Straße, Building 9, 66421, Homburg, Germany.
FAU - Truhn, Daniel
AU  - Truhn D
AD  - Department of Diagnostic and Interventional Radiology, University Hospital 
      Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
FAU - Keller, Christiane
AU  - Keller C
AD  - Center for Palliative Medicine and Pediatric Pain Therapy, Saarland University 
      Hospital, Kirrberger Straße, Building 69, 66421, Homburg, Germany.
FAU - Heinrich, Vanessa
AU  - Heinrich V
AD  - Department of Radiation Oncology, University Hospital Tübingen, Crona Kliniken, 
      Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
FAU - Braun, Benedikt Johannes
AU  - Braun BJ
AD  - Department of Trauma and Reconstructive Surgery at the Eberhard Karls University 
      Tübingen, BG Unfallklinik Tübingen, Schnarrenbergstrasse 95, 72076, Tübingen, 
      Germany.
LA  - eng
PT  - Journal Article
DEP - 20231117
PL  - Germany
TA  - Arch Gynecol Obstet
JT  - Archives of gynecology and obstetrics
JID - 8710213
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Gynecologic oncology
OT  - Language model
OT  - Palliative care
EDAT- 2023/11/17 15:29
MHDA- 2023/11/17 15:29
CRDT- 2023/11/17 11:04
PHST- 2023/08/13 00:00 [received]
PHST- 2023/10/15 00:00 [accepted]
PHST- 2023/11/17 15:29 [medline]
PHST- 2023/11/17 15:29 [pubmed]
PHST- 2023/11/17 11:04 [entrez]
AID - 10.1007/s00404-023-07272-6 [pii]
AID - 10.1007/s00404-023-07272-6 [doi]
PST - aheadofprint
SO  - Arch Gynecol Obstet. 2023 Nov 17. doi: 10.1007/s00404-023-07272-6.

PMID- 37968408
OWN - NLM
STAT- Publisher
LR  - 20231115
IS  - 1432-5195 (Electronic)
IS  - 0341-2695 (Linking)
DP  - 2023 Nov 15
TI  - Does Google's Bard Chatbot perform better than ChatGPT on the European hand 
      surgery exam?
LID - 10.1007/s00264-023-06034-y [doi]
AB  - PURPOSE: According to a previous research, the chatbot ChatGPT® V3.5 was unable 
      to pass the first part of the European Board of Hand Surgery (EBHS) diploma 
      examination. This study aimed to investigate whether Google's chatbot Bard® would 
      have superior performance compared to ChatGPT on the EBHS diploma examination. 
      METHODS: Chatbots were asked to answer 18 EBHS multiple choice questions (MCQs) 
      published in the Journal of Hand Surgery (European Volume) in five trials (A1 to 
      A5). After A3, chatbots received correct answers, and after A4, incorrect 
      answers. Consequently, their ability to modify their response was measured and 
      compared. RESULTS: Bard® scored 3/18 (A1), 1/18 (A2), 4/18 (A3) and 2/18 (A4 and 
      A5). The average percentage of correct answers was 61.1% for A1, 62.2% for A2, 
      64.4% for A3, 65.6% for A4, 63.3% for A5 and 63.3% for all trials combined. 
      Agreement was moderate from A1 to A5 (kappa = 0.62 (IC95% = [0.51; 0.73])) as 
      well as from A1 to A3 (kappa = 0.60 (IC95% = [0.47; 0.74])). The formulation of 
      Bard® responses was homogeneous, but its learning capacity is still developing. 
      CONCLUSIONS: The main hypothesis of our study was not proved since Bard did not 
      score significantly higher than ChatGPT when answering the MCQs of the EBHS 
      diploma exam. In conclusion, neither ChatGPT® nor Bard®, in their current 
      versions, can pass the first part of the EBHS diploma exam.
CI  - © 2023. The Author(s) under exclusive licence to SICOT aisbl.
FAU - Thibaut, Goetsch
AU  - Thibaut G
AD  - Department of Public Health, Strasbourg University Hospital, FMTS, GMRC, 1 avenue 
      de l'hôpital, 67000, Strasbourg cedex, France.
FAU - Dabbagh, Armaghan
AU  - Dabbagh A
AD  - Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
FAU - Liverneaux, Philippe
AU  - Liverneaux P
AUID- ORCID: 0000-0002-5509-8995
AD  - ICube CNRS UMR7357, Strasbourg University, 2-4 rue Boussingault, 67000, 
      Strasbourg, France. philippe.liverneaux@chru-strasbourg.fr.
AD  - Department of Hand Surgery, Strasbourg University Hospitals, FMTS, 1 avenue 
      Molière, 67200, Strasbourg, France. philippe.liverneaux@chru-strasbourg.fr.
LA  - eng
PT  - Journal Article
DEP - 20231115
PL  - Germany
TA  - Int Orthop
JT  - International orthopaedics
JID - 7705431
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bard
OT  - ChatGPT
OT  - Chatbot
OT  - Hand Surgery
OT  - Multiple-choice question
EDAT- 2023/11/16 00:42
MHDA- 2023/11/16 00:42
CRDT- 2023/11/15 23:56
PHST- 2023/10/07 00:00 [received]
PHST- 2023/11/01 00:00 [accepted]
PHST- 2023/11/16 00:42 [medline]
PHST- 2023/11/16 00:42 [pubmed]
PHST- 2023/11/15 23:56 [entrez]
AID - 10.1007/s00264-023-06034-y [pii]
AID - 10.1007/s00264-023-06034-y [doi]
PST - aheadofprint
SO  - Int Orthop. 2023 Nov 15. doi: 10.1007/s00264-023-06034-y.

PMID- 37908933
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231102
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Utilizing ChatGPT-4 for Providing Information on Periodontal Disease to Patients: 
      A DISCERN Quality Analysis.
PG  - e46213
LID - 10.7759/cureus.46213 [doi]
LID - e46213
AB  - BACKGROUND: Due to their ability to mimic human responses, anthropomorphic 
      entities such as ChatGPT have a higher likelihood of gaining people's trust. This 
      study aimed to evaluate the quality of information generated by ChatGPT-4, as an 
      artificial intelligence (AI) chatbot, on periodontal disease (PD) using the 
      DISCERN instrument. METHODS: Using Google Bard, the topics related to PD that had 
      the highest search volume according to Google Trends were identified. An 
      interactive dialogue was created by placing the topics in the standard question 
      pattern. As a patient with PD, detailed information was requested from ChatGPT-4 
      regarding the relevant topics. The 'regenerate response' feature was not 
      employed, and the initial response generated by ChatGPT-4 was carefully 
      considered for each topic as new prompts in the form of questions were entered. 
      The response to each question was independently assessed and rated by two 
      experienced raters using the DISCERN instrument. RESULTS: Based on the total 
      DISCERN scores, the qualities of the responses generated by ChatGPT-4 were 
      'good', except for the two responses that rater-2 scored as 'fair'. It was also 
      observed that the 'treatment choices' section of both raters had significantly 
      fewer scores than the other sections. In both weighted kappa and Krippendorff 
      alpha measures, the strength of agreement varied from 'substantial' to 
      'almost-perfect', and the correlation between values was statistically 
      significant. CONCLUSION: Despite some limitations in providing complete treatment 
      choice information according to the DISCERN instrument, it is considered valuable 
      for PD patients seeking information, as it consistently offered accurate guidance 
      in the majority of responses.
CI  - Copyright © 2023, Alan et al.
FAU - Alan, Raif
AU  - Alan R
AD  - Periodontology, Faculty of Dentistry, Canakkale Onsekiz Mart University, 
      Canakkale, TUR.
FAU - Alan, Betül Melek
AU  - Alan BM
AD  - General Dentistry, Private Practice, Canakkale, TUR.
LA  - eng
PT  - Journal Article
DEP - 20230929
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10613831
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatbot
OT  - health information management
OT  - oral health
OT  - periodontal disease
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/01 06:43
MHDA- 2023/11/01 06:44
CRDT- 2023/11/01 04:04
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/11/01 06:44 [medline]
PHST- 2023/11/01 06:43 [pubmed]
PHST- 2023/11/01 04:04 [entrez]
AID - 10.7759/cureus.46213 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 29;15(9):e46213. doi: 10.7759/cureus.46213. eCollection 2023 
      Sep.

PMID- 37882183
OWN - NLM
STAT- Publisher
LR  - 20231026
IS  - 1938-2405 (Electronic)
IS  - 0191-3913 (Linking)
DP  - 2023 Oct 25
TI  - Assessment of the Responses of the Artificial Intelligence-based Chatbot 
      ChatGPT-4 to Frequently Asked Questions About Amblyopia and Childhood Myopia.
PG  - 1-4
LID - 10.3928/01913913-20231005-02 [doi]
AB  - PURPOSE: To assess the responses of the ChatGPT-4, the forerunner artificial 
      intelligence-based chatbot, to frequently asked questions regarding two common 
      pediatric ophthalmologic disorders, amblyopia and childhood myopia. METHODS: 
      Twenty-seven questions about amblyopia and 28 questions about childhood myopia 
      were asked of the ChatGPT twice (totally 110 questions). The responses were 
      evaluated by two pediatric ophthalmologists as acceptable, incomplete, or 
      unacceptable. RESULTS: There was remarkable agreement (96.4%) between the two 
      pediatric ophthalmologists on their assessment of the responses. Acceptable 
      responses were provided by the ChatGPT to 93 of 110 (84.6%) questions in total 
      (44 of 54 [81.5%] for amblyopia and 49 of 56 [87.5%] questions for childhood 
      myopia). Seven of 54 (12.9%) responses to questions on amblyopia were graded as 
      incomplete compared to 4 of 56 (7.1%) of questions on childhood myopia. The 
      ChatGPT gave inappropriate responses to three questions about amblyopia (5.6%) 
      and childhood myopia (5.4%). The most noticeable inappropriate responses were 
      related to the definition of reverse amblyopia and the threshold of refractive 
      error for prescription of spectacles to children with myopia. CONCLUSIONS: The 
      ChatGPT has the potential to serve as an adjunct informational tool for pediatric 
      ophthalmology patients and their caregivers by demonstrating a relatively good 
      performance in answering 84.6% of the most frequently asked questions about 
      amblyopia and childhood myopia. [J Pediatr Ophthalmol Strabismus. 
      20XX;X(X):XXX-XXX.].
FAU - Nikdel, Mojgan
AU  - Nikdel M
FAU - Ghadimi, Hadi
AU  - Ghadimi H
FAU - Tavakoli, Mehdi
AU  - Tavakoli M
FAU - Suh, Donny W
AU  - Suh DW
LA  - eng
PT  - Journal Article
DEP - 20231025
PL  - United States
TA  - J Pediatr Ophthalmol Strabismus
JT  - Journal of pediatric ophthalmology and strabismus
JID - 7901143
SB  - IM
EDAT- 2023/10/26 12:42
MHDA- 2023/10/26 12:42
CRDT- 2023/10/26 06:47
PHST- 2023/10/26 12:42 [medline]
PHST- 2023/10/26 12:42 [pubmed]
PHST- 2023/10/26 06:47 [entrez]
AID - 10.3928/01913913-20231005-02 [doi]
PST - aheadofprint
SO  - J Pediatr Ophthalmol Strabismus. 2023 Oct 25:1-4. doi: 
      10.3928/01913913-20231005-02.

PMID- 37965416
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231116
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Ectopic Pituitary Neuroendocrine Tumor: A Case Report Written With the Help of 
      ChatGPT.
PG  - e46999
LID - 10.7759/cureus.46999 [doi]
LID - e46999
AB  - ChatGPT (Chat Generative Pre-trained Transformer) is a chat robot program 
      developed by OpenAI (OpenAI Incorporated, Mission District, San Francisco, USA). 
      It is a natural-language processing tool driven by artificial intelligence 
      technology. It can complete tasks such as writing emails, video scripts, 
      copywriting, translation, coding, and writing papers. At present, several studies 
      have been conducted on the use of this tool for academic writing, so we report a 
      rare case of suprasellar ectopic pituitary neuroendocrine tumor (PitNET) 
      secreting growth hormones, in a female 53-year-old patient with facial changes 
      and hypertrophy of her hands and feet for six years. When asking questions on 
      unusual topics, ChatGPT can provide answers but not specific ones, and similar to 
      most machine learning solutions, verifying the correctness of information is a 
      problem. Question asked from ChatGPT: Summarize ectopic pituitary neuroendocrine 
      tumor. "Suprasellar heterotopic pituitary neuroendocrine tumors refer to tumors 
      formed by the heterotopic growth of pituitary neuroendocrine tumor cells in the 
      sellar region other than the pituitary glands. This tumor is common in 
      middle-aged women, and it often manifests as headache, visual impairment, and 
      endocrine dysfunction. Diagnosis is usually made by combining history, physical 
      examination, blood tests, and imaging. Treatment is primarily performed through 
      surgical resection of the tumor, radiotherapy, and drug therapy, and the 
      prognosis is mostly good." The content in quotation marks is generated by 
      ChatGPT.
CI  - Copyright © 2023, Li et al.
FAU - Li, Jun
AU  - Li J
AD  - Neurosurgery, Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical 
      Medical College of Fujian Medical University, Fuzhou, CHN.
FAU - Zhong, Jiansheng
AU  - Zhong J
AD  - Neurosurgery, Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical 
      Medical College of Fujian Medical University, Fuzhou, CHN.
FAU - Li, Ziqi
AU  - Li Z
AD  - Neurosurgery, Department of Neurosurgery, Oriental Hospital Affiliated to Xiamen 
      University, Fuzhou, CHN.
FAU - Xiao, Yong
AU  - Xiao Y
AD  - Neurosurgery, Central Institute for Mental Health, University of Heidelberg, 
      Heidelberg, DEU.
FAU - Wang, Shousen
AU  - Wang S
AD  - Neurosurgery, Department of Neurosurgery, Oriental Hospital Affiliated to Xiamen 
      University, Fuzhou, CHN.
LA  - eng
PT  - Case Reports
DEP - 20231013
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10641033
OTO - NOTNLM
OT  - artificial intelligence
OT  - brain tumor
OT  - chatgpt
OT  - ectopic pituitary neuroendocrine tumor
OT  - neuro-oncology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/15 06:42
MHDA- 2023/11/15 06:43
CRDT- 2023/11/15 04:26
PHST- 2023/10/12 00:00 [accepted]
PHST- 2023/11/15 06:43 [medline]
PHST- 2023/11/15 06:42 [pubmed]
PHST- 2023/11/15 04:26 [entrez]
AID - 10.7759/cureus.46999 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 13;15(10):e46999. doi: 10.7759/cureus.46999. eCollection 2023 
      Oct.

PMID- 37629452
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230829
IS  - 2077-0383 (Print)
IS  - 2077-0383 (Electronic)
IS  - 2077-0383 (Linking)
VI  - 12
IP  - 16
DP  - 2023 Aug 20
TI  - Comparing Meta-Analyses with ChatGPT in the Evaluation of the Effectiveness and 
      Tolerance of Systemic Therapies in Moderate-to-Severe Plaque Psoriasis.
LID - 10.3390/jcm12165410 [doi]
LID - 5410
AB  - BACKGROUND: Meta-analyses (MAs) and network meta-analyses (NMAs) are high-quality 
      studies for assessing drug efficacy, but they are time-consuming and may be 
      affected by biases. The capacity of artificial intelligence to aggregate huge 
      amounts of information is emerging as particularly interesting for processing the 
      volume of information needed to generate MAs. In this study, we analyzed whether 
      the chatbot ChatGPT is able to summarize information in a useful fashion for 
      providers and patients in a way that matches up with the results of MAs/NMAs. 
      METHODS: We included 16 studies (13 NMAs and 3 MAs) that evaluate biologics (n = 
      6) and both biologic and systemic treatment (n = 10) for moderate-to-severe 
      psoriasis, published between January 2021 and May 2023. RESULTS: The conclusions 
      of the MAs/NMAs were compared to ChatGPT's answers to queries about the molecules 
      evaluated in the selected MAs/NMAs. The reproducibility between the results of 
      ChatGPT and the MAs/NMAs was random regarding drug safety. Regarding efficacy, 
      ChatGPT reached the same conclusion as 5 out of the 16 studies (four out of four 
      studies when three molecules were compared), gave acceptable answers in 7 out of 
      16 studies, and was inconclusive in 4 out of 16 studies. CONCLUSIONS: ChatGPT can 
      generate conclusions that are similar to MAs when the efficacy of fewer drugs is 
      compared but is still unable to summarize information in a way that matches up to 
      the results of MAs/NMAs when more than three molecules are compared.
FAU - Lam Hoai, Xuân-Lan
AU  - Lam Hoai XL
AD  - Department of Dermatology, St Pierre-Brugmann-HUDERF University Hospitals, 
      Université Libre de Bruxelles, 1050 Brussels, Belgium.
FAU - Simonart, Thierry
AU  - Simonart T
AD  - Department of Dermatology, Delta Hospital, CHIREC, Université Libre de Bruxelles, 
      1050 Brussels, Belgium.
LA  - eng
PT  - Journal Article
DEP - 20230820
PL  - Switzerland
TA  - J Clin Med
JT  - Journal of clinical medicine
JID - 101606588
PMC - PMC10455399
OTO - NOTNLM
OT  - artificial intelligence
OT  - meta-analysis
OT  - psoriasis
COIS- The authors declare no conflict of interest.
EDAT- 2023/08/26 10:43
MHDA- 2023/08/26 10:44
CRDT- 2023/08/26 01:19
PHST- 2023/07/28 00:00 [received]
PHST- 2023/08/16 00:00 [revised]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/08/26 10:44 [medline]
PHST- 2023/08/26 10:43 [pubmed]
PHST- 2023/08/26 01:19 [entrez]
AID - jcm12165410 [pii]
AID - jcm-12-05410 [pii]
AID - 10.3390/jcm12165410 [doi]
PST - epublish
SO  - J Clin Med. 2023 Aug 20;12(16):5410. doi: 10.3390/jcm12165410.

PMID- 37550893
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1600-0579 (Electronic)
IS  - 1396-5883 (Linking)
DP  - 2023 Aug 7
TI  - ChatGPT-A double-edged sword for healthcare education? Implications for 
      assessments of dental students.
LID - 10.1111/eje.12937 [doi]
AB  - INTRODUCTION: Open-source generative artificial intelligence (AI) applications 
      are fast-transforming access to information and allow students to prepare 
      assignments and offer quite accurate responses to a wide range of exam questions 
      which are routinely used in assessments of students across the board including 
      undergraduate dental students. This study aims to evaluate the performance of 
      Chat Generative Pre-trained Transformer (ChatGPT), a generative AI-based 
      application, on a wide range of assessments used in contemporary healthcare 
      education and discusses the implications for undergraduate dental education. 
      MATERIALS AND METHODS: This was an exploratory study investigating the accuracy 
      of ChatGPT to attempt a range of recognised assessments in healthcare education 
      curricula. A total of 50 independent items encompassing 50 different learning 
      outcomes (n = 10 per item) were developed by the research team. These included 10 
      separate items based on each of the five commonly used question formats including 
      multiple-choice questions (MCQs); short-answer questions (SAQs); short essay 
      questions (SEQs); single true/false questions; and fill in the blanks items. Chat 
      GPT was used to attempt each of these 50 questions. In addition, ChatGPT was used 
      to generate reflective reports based on multisource feedback; research 
      methodology; and critical appraisal of the literature. RESULTS: ChatGPT 
      application provided accurate responses to majority of knowledge-based 
      assessments based on MCQs, SAQs, SEQs, true/false and fill in the blanks items. 
      However, it was only able to answer text-based questions and did not allow 
      processing of questions based on images. Responses generated to written 
      assignments were also satisfactory apart from those for critical appraisal of 
      literature. Word count was the key limitation observed in outputs generated by 
      the free version of ChatGPT. CONCLUSION: Notwithstanding their current 
      limitations, generative AI-based applications have the potential to revolutionise 
      virtual learning. Instead of treating it as a threat, healthcare educators need 
      to adapt teaching and assessments in medical and dental education to the benefits 
      of the learners while mitigating against dishonest use of AI-based technology.
CI  - © 2023 The Authors. European Journal of Dental Education published by John Wiley 
      & Sons Ltd.
FAU - Ali, Kamran
AU  - Ali K
AD  - College of Dental Medicine QU Health, Qatar University, Doha, Qatar.
FAU - Barhom, Noha
AU  - Barhom N
AD  - College of Dental Medicine QU Health, Qatar University, Doha, Qatar.
FAU - Tamimi, Faleh
AU  - Tamimi F
AD  - College of Dental Medicine QU Health, Qatar University, Doha, Qatar.
FAU - Duggal, Monty
AU  - Duggal M
AD  - College of Dental Medicine QU Health, Qatar University, Doha, Qatar.
LA  - eng
GR  - Open access funding was provided by the Qatar National Library/
PT  - Journal Article
DEP - 20230807
PL  - England
TA  - Eur J Dent Educ
JT  - European journal of dental education : official journal of the Association for 
      Dental Education in Europe
JID - 9712132
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - dental education
OT  - education technology
OT  - machine learning
EDAT- 2023/08/08 06:42
MHDA- 2023/08/08 06:42
CRDT- 2023/08/08 00:52
PHST- 2023/07/08 00:00 [revised]
PHST- 2023/03/01 00:00 [received]
PHST- 2023/07/24 00:00 [accepted]
PHST- 2023/08/08 06:42 [pubmed]
PHST- 2023/08/08 06:42 [medline]
PHST- 2023/08/08 00:52 [entrez]
AID - 10.1111/eje.12937 [doi]
PST - aheadofprint
SO  - Eur J Dent Educ. 2023 Aug 7. doi: 10.1111/eje.12937.

PMID- 37707884
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231113
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Sep 14
TI  - The Potential and Concerns of Using AI in Scientific Research: ChatGPT 
      Performance Evaluation.
PG  - e47049
LID - 10.2196/47049 [doi]
LID - e47049
AB  - BACKGROUND: Artificial intelligence (AI) has many applications in various aspects 
      of our daily life, including health, criminal, education, civil, business, and 
      liability law. One aspect of AI that has gained significant attention is natural 
      language processing (NLP), which refers to the ability of computers to understand 
      and generate human language. OBJECTIVE: This study aims to examine the potential 
      for, and concerns of, using AI in scientific research. For this purpose, 
      high-impact research articles were generated by analyzing the quality of reports 
      generated by ChatGPT and assessing the application's impact on the research 
      framework, data analysis, and the literature review. The study also explored 
      concerns around ownership and the integrity of research when using AI-generated 
      text. METHODS: A total of 4 articles were generated using ChatGPT, and thereafter 
      evaluated by 23 reviewers. The researchers developed an evaluation form to assess 
      the quality of the articles generated. Additionally, 50 abstracts were generated 
      using ChatGPT and their quality was evaluated. The data were subjected to ANOVA 
      and thematic analysis to analyze the qualitative data provided by the reviewers. 
      RESULTS: When using detailed prompts and providing the context of the study, 
      ChatGPT would generate high-quality research that could be published in 
      high-impact journals. However, ChatGPT had a minor impact on developing the 
      research framework and data analysis. The primary area needing improvement was 
      the development of the literature review. Moreover, reviewers expressed concerns 
      around ownership and the integrity of the research when using AI-generated text. 
      Nonetheless, ChatGPT has a strong potential to increase human productivity in 
      research and can be used in academic writing. CONCLUSIONS: AI-generated text has 
      the potential to improve the quality of high-impact research articles. The 
      findings of this study suggest that decision makers and researchers should focus 
      more on the methodology part of the research, which includes research design, 
      developing research tools, and analyzing data in depth, to draw strong 
      theoretical and practical implications, thereby establishing a revolution in 
      scientific research in the era of AI. The practical implications of this study 
      can be used in different fields such as medical education to deliver materials to 
      develop the basic competencies for both medicine students and faculty members.
CI  - ©Zuheir N Khlaif, Allam Mousa, Muayad Kamal Hattab, Jamil Itmazi, Amjad A Hassan, 
      Mageswaran Sanmugam, Abedalkarim Ayyoub. Originally published in JMIR Medical 
      Education (https://mededu.jmir.org), 14.09.2023.
FAU - Khlaif, Zuheir N
AU  - Khlaif ZN
AUID- ORCID: 0000-0002-7354-7512
AD  - Faculty of Humanities and Educational Sciences, An-Najah National University, 
      Nablus, Occupied Palestinian Territory.
FAU - Mousa, Allam
AU  - Mousa A
AUID- ORCID: 0000-0001-6413-1622
AD  - Artificial Intelligence and Virtual Reality Research Center, Department of 
      Electrical and Computer Engineering, An Najah National University, Nablus, 
      Occupied Palestinian Territory.
FAU - Hattab, Muayad Kamal
AU  - Hattab MK
AUID- ORCID: 0000-0002-1096-1839
AD  - Faculty of Law and Political Sciences, An-Najah National University, Nablus, 
      Occupied Palestinian Territory.
FAU - Itmazi, Jamil
AU  - Itmazi J
AUID- ORCID: 0000-0002-4206-7989
AD  - Department of Information Technology, College of Engineering and Information 
      Technology, Palestine Ahliya University, Bethlahem, Occupied Palestinian 
      Territory.
FAU - Hassan, Amjad A
AU  - Hassan AA
AUID- ORCID: 0009-0009-6917-4782
AD  - Faculty of Law and Political Sciences, An-Najah National University, Nablus, 
      Occupied Palestinian Territory.
FAU - Sanmugam, Mageswaran
AU  - Sanmugam M
AUID- ORCID: 0000-0003-3313-4462
AD  - Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, 
      Penang, Malaysia.
FAU - Ayyoub, Abedalkarim
AU  - Ayyoub A
AUID- ORCID: 0000-0001-9111-4465
AD  - Faculty of Humanities and Educational Sciences, An-Najah National University, 
      Nablus, Occupied Palestinian Territory.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10636627
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - research ethics
OT  - scientific research
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/14 12:42
MHDA- 2023/09/14 12:43
CRDT- 2023/09/14 11:53
PHST- 2023/03/06 00:00 [received]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/06/04 00:00 [revised]
PHST- 2023/09/14 12:43 [medline]
PHST- 2023/09/14 12:42 [pubmed]
PHST- 2023/09/14 11:53 [entrez]
AID - v9i1e47049 [pii]
AID - 10.2196/47049 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Sep 14;9:e47049. doi: 10.2196/47049.

PMID- 37339728
OWN - NLM
STAT- MEDLINE
DCOM- 20230926
LR  - 20231011
IS  - 1879-1891 (Electronic)
IS  - 0002-9394 (Linking)
VI  - 254
DP  - 2023 Oct
TI  - Performance of Generative Large Language Models on Ophthalmology Board-Style 
      Questions.
PG  - 141-149
LID - S0002-9394(23)00230-1 [pii]
LID - 10.1016/j.ajo.2023.05.024 [doi]
AB  - PURPOSE: To investigate the ability of generative artificial intelligence models 
      to answer ophthalmology board-style questions. DESIGN: Experimental study. 
      METHODS: This study evaluated 3 large language models (LLMs) with chat 
      interfaces, Bing Chat (Microsoft) and ChatGPT 3.5 and 4.0 (OpenAI), using 250 
      questions from the Basic Science and Clinical Science Self-Assessment Program. 
      Although ChatGPT is trained on information last updated in 2021, Bing Chat 
      incorporates a more recently indexed internet search to generate its answers. 
      Performance was compared with human respondents. Questions were categorized by 
      complexity and patient care phase, and instances of information fabrication or 
      nonlogical reasoning were documented. MAIN OUTCOME MEASURES: Primary outcome was 
      response accuracy. Secondary outcomes were performance in question subcategories 
      and hallucination frequency. RESULTS: Human respondents had an average accuracy 
      of 72.2%. ChatGPT-3.5 scored the lowest (58.8%), whereas ChatGPT-4.0 (71.6%) and 
      Bing Chat (71.2%) performed comparably. ChatGPT-4.0 excelled in workup-type 
      questions (odds ratio [OR], 3.89, 95% CI, 1.19-14.73, P = .03) compared with 
      diagnostic questions, but struggled with image interpretation (OR, 0.14, 95% CI, 
      0.05-0.33, P < .01) when compared with single-step reasoning questions. Against 
      single-step questions, Bing Chat also faced difficulties with image 
      interpretation (OR, 0.18, 95% CI, 0.08-0.44, P < .01) and multi-step reasoning 
      (OR, 0.30, 95% CI, 0.11-0.84, P = .02). ChatGPT-3.5 had the highest rate of 
      hallucinations and nonlogical reasoning (42.4%), followed by ChatGPT-4.0 (18.0%) 
      and Bing Chat (25.6%). CONCLUSIONS: LLMs (particularly ChatGPT-4.0 and Bing Chat) 
      can perform similarly with human respondents answering questions from the Basic 
      Science and Clinical Science Self-Assessment Program. The frequency of 
      hallucinations and nonlogical reasoning suggests room for improvement in the 
      performance of conversational agents in the medical domain.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Cai, Louis Z
AU  - Cai LZ
AD  - From the Bascom Palmer Eye Institute, Miami, Florida, USA (L.Z.C., A.S., J.S.Y., 
      N.Y., C.A.). Electronic address: louiezcai@gmail.com.
FAU - Shaheen, Abdulla
AU  - Shaheen A
AD  - From the Bascom Palmer Eye Institute, Miami, Florida, USA (L.Z.C., A.S., J.S.Y., 
      N.Y., C.A.).
FAU - Jin, Andrew
AU  - Jin A
AD  - Yale Eye Center, New Haven, Connecticut, USA (A.J.).
FAU - Fukui, Riya
AU  - Fukui R
AD  - Houston Rehabilitation Group, Houston, Texas, USA (R.F.).
FAU - Yi, Jonathan S
AU  - Yi JS
AD  - From the Bascom Palmer Eye Institute, Miami, Florida, USA (L.Z.C., A.S., J.S.Y., 
      N.Y., C.A.).
FAU - Yannuzzi, Nicolas
AU  - Yannuzzi N
AD  - From the Bascom Palmer Eye Institute, Miami, Florida, USA (L.Z.C., A.S., J.S.Y., 
      N.Y., C.A.).
FAU - Alabiad, Chrisfouad
AU  - Alabiad C
AD  - From the Bascom Palmer Eye Institute, Miami, Florida, USA (L.Z.C., A.S., J.S.Y., 
      N.Y., C.A.).
LA  - eng
PT  - Journal Article
DEP - 20230618
PL  - United States
TA  - Am J Ophthalmol
JT  - American journal of ophthalmology
JID - 0370500
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Ophthalmology
MH  - Language
MH  - Hallucinations/diagnosis
MH  - Internet
EDAT- 2023/06/21 01:07
MHDA- 2023/09/26 13:43
CRDT- 2023/06/20 19:11
PHST- 2023/03/31 00:00 [received]
PHST- 2023/05/27 00:00 [revised]
PHST- 2023/05/28 00:00 [accepted]
PHST- 2023/09/26 13:43 [medline]
PHST- 2023/06/21 01:07 [pubmed]
PHST- 2023/06/20 19:11 [entrez]
AID - S0002-9394(23)00230-1 [pii]
AID - 10.1016/j.ajo.2023.05.024 [doi]
PST - ppublish
SO  - Am J Ophthalmol. 2023 Oct;254:141-149. doi: 10.1016/j.ajo.2023.05.024. Epub 2023 
      Jun 18.

PMID- 38073946
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - Potential Use of ChatGPT for Patient Information in Periodontology: A Descriptive 
      Pilot Study.
PG  - e48518
LID - 10.7759/cureus.48518 [doi]
LID - e48518
AB  - Objectives The aim of this study is to evaluate the accuracy and completeness of 
      the answers given by Chat Generative Pre-trained Transformer (ChatGPT) (OpenAI 
      OpCo, LLC, San Francisco, CA), to the most frequently asked questions on 
      different topics in the field of periodontology. Methods The 10 most frequently 
      asked questions by patients about seven different topics (periodontal diseases, 
      peri-implant diseases, tooth sensitivity, gingival recessions, halitosis, dental 
      implants, and periodontal surgery) in periodontology were created by ChatGPT. To 
      obtain responses, a set of 70 questions was submitted to ChatGPT, with an 
      allocation of 10 questions per subject. The responses that were documented were 
      assessed using two distinct Likert scales by professionals specializing in the 
      subject of periodontology. The accuracy of the responses was rated on a Likert 
      scale ranging from one to six, while the completeness of the responses was rated 
      on a scale ranging from one to three. Results The median accuracy score for all 
      responses was six, while the completeness score was two. The mean scores for 
      accuracy and completeness were 5.50 ± 0.23 and 2.34 ± 0.24, respectively. It was 
      observed that ChatGPT's responses to the most frequently asked questions by 
      patients for information purposes in periodontology were at least "nearly 
      completely correct" in terms of accuracy and "adequate" in terms of completeness. 
      There was a statistically significant difference between subjects in terms of 
      accuracy and completeness (P<0.05). The highest and lowest accuracy scores were 
      peri-implant diseases and gingival recession, respectively, while the highest and 
      lowest completeness scores were gingival recession and dental implants, 
      respectively. Conclusions The utilization of large language models has become 
      increasingly prevalent, extending its applicability to patients within the 
      healthcare domain. While ChatGPT may not offer absolute precision and 
      comprehensive results without expert supervision, it is apparent that those 
      within the field of periodontology can utilize it as an informational resource, 
      albeit acknowledging the potential for inaccuracies.
CI  - Copyright © 2023, Babayiğit et al.
FAU - Babayiğit, Osman
AU  - Babayiğit O
AD  - Department of Periodontology, Necmettin Erbakan University, Faculty of Dentistry, 
      Konya, TUR.
FAU - Tastan Eroglu, Zeynep
AU  - Tastan Eroglu Z
AD  - Department of Periodontology, Necmettin Erbakan University, Faculty of Dentistry, 
      Konya, TUR.
FAU - Ozkan Sen, Dilek
AU  - Ozkan Sen D
AD  - Department of Periodontology, Necmettin Erbakan University, Faculty of Dentistry, 
      Konya, TUR.
FAU - Ucan Yarkac, Fatma
AU  - Ucan Yarkac F
AD  - Department of Periodontology, Necmettin Erbakan University, Faculty of Dentistry, 
      Konya, TUR.
LA  - eng
PT  - Journal Article
DEP - 20231108
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10708896
OTO - NOTNLM
OT  - artificial intelligence in dentistry
OT  - chat generative pre-trained transformer
OT  - chatgpt
OT  - dental care
OT  - large language models (llms)
OT  - oral medicine and periodontology
OT  - patient information
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/11 06:45
MHDA- 2023/12/11 06:46
CRDT- 2023/12/11 05:37
PHST- 2023/11/08 00:00 [accepted]
PHST- 2023/12/11 06:46 [medline]
PHST- 2023/12/11 06:45 [pubmed]
PHST- 2023/12/11 05:37 [entrez]
AID - 10.7759/cureus.48518 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 8;15(11):e48518. doi: 10.7759/cureus.48518. eCollection 2023 
      Nov.

PMID- 37667724
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230906
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Assessing the Performance of GPT-3.5 and GPT-4 on the 2023 Japanese Nursing 
      Examination.
PG  - e42924
LID - 10.7759/cureus.42924 [doi]
LID - e42924
AB  - Purpose The purpose of this study was to evaluate the changes in capabilities 
      between the Generative Pre-trained Transformer (GPT)-3.5 and GPT-4 versions of 
      the large-scale language model ChatGPT within a Japanese medical context. Methods 
      The study involved ChatGPT versions 3.5 and 4 responding to questions from the 
      112th Japanese National Nursing Examination (JNNE). The study comprised three 
      analyses: correct answer rate and score rate calculations, comparisons between 
      GPT-3.5 and GPT-4, and comparisons of correct answer rates for conversation 
      questions. Results ChatGPT versions 3.5 and 4 responded to 237 out of 238 
      Japanese questions from the 112th JNNE. While GPT-3.5 achieved an overall 
      accuracy rate of 59.9%, failing to meet the passing standards in compulsory and 
      general/scenario-based questions, scoring 58.0% and 58.3%, respectively, GPT-4 
      had an accuracy rate of 79.7%, satisfying the passing standards by scoring 90.0% 
      and 77.7%, respectively. For each problem type, GPT-4 showed a higher accuracy 
      rate than GPT-3.5. Specifically, the accuracy rates for compulsory questions 
      improved from 58.0% with GPT-3.5 to 90.0% with GPT-4. For general questions, the 
      rates went from 64.6% with GPT-3.5 to 75.6% with GPT-4. In scenario-based 
      questions, the accuracy rates improved substantially from 51.7% with GPT-3.5 to 
      80.0% with GPT-4. For conversation questions, GPT-3.5 had an accuracy rate of 
      73.3% and GPT-4 had an accuracy rate of 93.3%. Conclusions The GPT-4 version of 
      ChatGPT displayed performance sufficient to pass the JNNE, significantly 
      improving from GPT-3.5. This suggests specialized medical training could make 
      such models beneficial in Japanese clinical settings, aiding decision-making. 
      However, user awareness and training are crucial, given potential inaccuracies in 
      ChatGPT's responses. Hence, responsible usage with an understanding of its 
      capabilities and limitations is vital to best support healthcare professionals 
      and patients.
CI  - Copyright © 2023, Kaneda et al.
FAU - Kaneda, Yudai
AU  - Kaneda Y
AD  - College of Medicine, Hokkaido University, Hokkaido, JPN.
FAU - Takahashi, Ryo
AU  - Takahashi R
AD  - Department of Rehabilitation Medicine, Sonodakai Joint Replacement Center 
      Hospital, Tokyo, JPN.
FAU - Kaneda, Uiri
AU  - Kaneda U
AD  - Department of Foreign Languages, Dokkyo University, Saitama, JPN.
FAU - Akashima, Shiori
AU  - Akashima S
AD  - Department of Obstetrics and Gynecology, Shonan Kamakura General Hospital, 
      Kanagawa, JPN.
FAU - Okita, Haruna
AU  - Okita H
AD  - College of Medicine, Tokyo Women's Medical University, Tokyo, JPN.
FAU - Misaki, Sadaya
AU  - Misaki S
AD  - Department of Rehabilitation Medicine, Sonoda Daiichi Hospital, Tokyo, JPN.
FAU - Yamashiro, Akimi
AU  - Yamashiro A
AD  - Department of Nutrition Science, Shokei Gakuin University, Miyagi, JPN.
FAU - Ozaki, Akihiko
AU  - Ozaki A
AD  - Department of Breast and Thyroid Surgery, Jyoban Hospital of Tokiwa Foundation, 
      Fukushima, JPN.
FAU - Tanimoto, Tetsuya
AU  - Tanimoto T
AD  - Department of Internal Medicine, Navitas Clinic, Kanagawa, JPN.
LA  - eng
PT  - Journal Article
DEP - 20230803
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10475149
OTO - NOTNLM
OT  - ai & robotics healthcare
OT  - chatgpt
OT  - chatgpt-3.5
OT  - chatgpt-4
OT  - clinical applications
OT  - gpt-3.5
OT  - gpt-4
OT  - japan
OT  - national nursing examination
COIS- The authors have declared financial relationships, which are detailed in the next 
      section.
EDAT- 2023/09/05 06:42
MHDA- 2023/09/05 06:43
CRDT- 2023/09/05 03:35
PHST- 2023/08/03 00:00 [accepted]
PHST- 2023/09/05 06:43 [medline]
PHST- 2023/09/05 06:42 [pubmed]
PHST- 2023/09/05 03:35 [entrez]
AID - 10.7759/cureus.42924 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 3;15(8):e42924. doi: 10.7759/cureus.42924. eCollection 2023 Aug.

PMID- 38029273
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2052-4374 (Print)
IS  - 2052-4374 (Electronic)
IS  - 2052-4374 (Linking)
VI  - 35
DP  - 2023
TI  - The use of ChatGPT in occupational medicine: opportunities and threats.
PG  - e42
LID - 10.35371/aoem.2023.35.e42 [doi]
LID - e42
AB  - ChatGPT has the potential to revolutionize occupational medicine by providing a 
      powerful tool for analyzing data, improving communication, and increasing 
      efficiency. It can help identify patterns and trends in workplace health and 
      safety, act as a virtual assistant for workers, employers, and occupational 
      health professionals, and automate certain tasks. However, caution is required 
      due to ethical concerns, the need to maintain confidentiality, and the risk of 
      inconsistent or inaccurate results. ChatGPT cannot replace the crucial role of 
      the occupational health professional in the medical surveillance of workers and 
      the analysis of data on workers' health.
CI  - Copyright © 2023 Korean Society of Occupational & Environmental Medicine.
FAU - Sridi, Chayma
AU  - Sridi C
AUID- ORCID: 0000-0003-1718-0952
AD  - Department of Occupational Medicine, Sahloul University Hospital, Sousse, 
      Tunisia.
FAU - Brigui, Salem
AU  - Brigui S
AUID- ORCID: 0009-0004-2627-7734
AD  - Hospital of Tela, Kasserine, Tunisia.
LA  - eng
PT  - Journal Article
DEP - 20231023
PL  - Korea (South)
TA  - Ann Occup Environ Med
JT  - Annals of occupational and environmental medicine
JID - 101609244
PMC - PMC10654530
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Occupational medicine
OT  - Workplace
COIS- Competing interests: The authors declare that they have no competing interests.
EDAT- 2023/11/29 18:41
MHDA- 2023/11/29 18:42
CRDT- 2023/11/29 17:23
PHST- 2023/08/07 00:00 [received]
PHST- 2023/09/30 00:00 [revised]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/11/29 18:42 [medline]
PHST- 2023/11/29 18:41 [pubmed]
PHST- 2023/11/29 17:23 [entrez]
AID - 10.35371/aoem.2023.35.e42 [doi]
PST - epublish
SO  - Ann Occup Environ Med. 2023 Oct 23;35:e42. doi: 10.35371/aoem.2023.35.e42. 
      eCollection 2023.

PMID- 38049136
OWN - NLM
STAT- MEDLINE
DCOM- 20231206
LR  - 20231206
IS  - 2208-7958 (Electronic)
VI  - 52
IP  - 12
DP  - 2023 Dec
TI  - Universal precautions required: Artificial intelligence takes on the Australian 
      Medical Council's trial examination.
PG  - 863-865
LID - 10.31128/AJGP-02-23-6708 [doi]
AB  - BACKGROUND AND OBJECTIVES: The potential of artificial intelligence in medical 
      practice is increasingly being investigated. This study aimed to examine OpenAI's 
      ChatGPT in answering medical multiple choice questions (MCQ) in an Australian 
      context. METHOD: We provided MCQs from the Australian Medical Council's (AMC) 
      medical licencing practice examination to ChatGPT. The chatbot's responses 
      were graded using AMC's online portal. This experiment was repeated twice. 
      RESULTS: ChatGPT was moderately accurate in answering the questions, achieving a 
      score of 29/50. It was able to generate answer explanations to most questions 
      (45/50). The chatbot was moderately consistent, providing the same overall answer 
      to 40 of the 50 questions between trial runs. DISCUSSION: The moderate accuracy 
      of ChatGPT demonstrates potential risks for both patients and physicians using 
      this tool. Further research is required to create more accurate models and to 
      critically appraise such models.
FAU - Kleinig, Oliver
AU  - Kleinig O
AD  - MBBS III, Faculty of Health and Medical Sciences, The University of Adelaide, 
      Adelaide, SA; Royal Adelaide Hospital, Adelaide, SA.
FAU - Kovoor, Joshua G
AU  - Kovoor JG
AD  - MS, Surgical Resident Medical Officer, Department of General Surgery, Queen 
      Elizabeth Hospital, Woodville South, SA.
FAU - Gupta, Aashray K
AU  - Gupta AK
AD  - MBBS, MS, Cardiothoracic Registrar, Department of Cardiothoracic Surgery, Gold 
      Coast University Hospital, Gold Coast, QLD.
FAU - Bacchi, Stephen
AU  - Bacchi S
AD  - MBBS, PhD, Neurology Registrar, Department of Neurology, Royal Adelaide Hospital, 
      Adelaide, SA; Neurology Registrar, Department of Neurology, Flinders University, 
      Bedford Park, SA.
LA  - eng
PT  - Journal Article
PL  - Australia
TA  - Aust J Gen Pract
JT  - Australian journal of general practice
JID - 101718099
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Universal Precautions
MH  - Australia
MH  - *Physicians
EDAT- 2023/12/05 00:42
MHDA- 2023/12/06 06:42
CRDT- 2023/12/04 19:53
PHST- 2023/12/06 06:42 [medline]
PHST- 2023/12/05 00:42 [pubmed]
PHST- 2023/12/04 19:53 [entrez]
AID - 10.31128/AJGP-02-23-6708 [doi]
PST - ppublish
SO  - Aust J Gen Pract. 2023 Dec;52(12):863-865. doi: 10.31128/AJGP-02-23-6708.

PMID- 37811040
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231018
IS  - 2049-0801 (Print)
IS  - 2049-0801 (Electronic)
IS  - 2049-0801 (Linking)
VI  - 85
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT and artificial hallucinations in stem cell research: assessing the 
      accuracy of generated references - a preliminary study.
PG  - 5275-5278
LID - 10.1097/MS9.0000000000001228 [doi]
AB  - Stem cell research has the transformative potential to revolutionize medicine. 
      Language models like ChatGPT, which use artificial intelligence (AI) and natural 
      language processing, generate human-like text that can aid researchers. However, 
      it is vital to ensure the accuracy and reliability of AI-generated references. 
      This study assesses Chat Generative Pre-Trained Transformer (ChatGPT)'s utility 
      in stem cell research and evaluates the accuracy of its references. Of the 86 
      references analyzed, 15.12% were fabricated and 9.30% were erroneous. These 
      errors were due to limitations such as no real-time internet access and reliance 
      on preexisting data. Artificial hallucinations were also observed, where the text 
      seems plausible but deviates from fact. Monitoring, diverse training, and 
      expanding knowledge cut-off can help to reduce fabricated references and 
      hallucinations. Researchers must verify references and consider the limitations 
      of AI models. Further research is needed to enhance the accuracy of such language 
      models. Despite these challenges, ChatGPT has the potential to be a valuable tool 
      for stem cell research. It can help researchers to stay up-to-date on the latest 
      developments in the field and to find relevant information.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Sharun, Khan
AU  - Sharun K
AD  - Division of Surgery.
FAU - Banu, S Amitha
AU  - Banu SA
AD  - Division of Surgery.
FAU - Pawde, Abhijit M
AU  - Pawde AM
AD  - Division of Surgery.
FAU - Kumar, Rohit
AU  - Kumar R
AD  - Division of Surgery.
FAU - Akash, Shopnil
AU  - Akash S
AD  - Department of Pharmacy, Faculty of Allied Health Science, Daffodil International 
      University, Daffodil Smart City, Ashulia, Savar, Dhaka, Bangladesh.
FAU - Dhama, Kuldeep
AU  - Dhama K
AD  - Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, 
      Bareilly, Uttar Pradesh, India.
FAU - Pal, Amar
AU  - Pal A
AD  - Division of Surgery.
LA  - eng
PT  - Journal Article
DEP - 20230901
PL  - England
TA  - Ann Med Surg (Lond)
JT  - Annals of medicine and surgery (2012)
JID - 101616869
PMC - PMC10553015
OTO - NOTNLM
OT  - artificial intelligence
OT  - erroneous references
OT  - fabricated references
OT  - limitations
OT  - natural language processing
OT  - reliable knowledge
COIS- The authors declare that they have no conflicts of interest.Sponsorships or 
      competing interests that may be relevant to content are disclosed at the end of 
      this article.
EDAT- 2023/10/09 12:42
MHDA- 2023/10/09 12:43
CRDT- 2023/10/09 06:12
PHST- 2023/06/26 00:00 [received]
PHST- 2023/08/12 00:00 [accepted]
PHST- 2023/10/09 12:43 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 06:12 [entrez]
AID - AMSU-D-23-01385 [pii]
AID - 10.1097/MS9.0000000000001228 [doi]
PST - epublish
SO  - Ann Med Surg (Lond). 2023 Sep 1;85(10):5275-5278. doi: 
      10.1097/MS9.0000000000001228. eCollection 2023 Oct.

PMID- 37100871
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230830
IS  - 2398-6352 (Electronic)
IS  - 2398-6352 (Linking)
VI  - 6
IP  - 1
DP  - 2023 Apr 26
TI  - Comparing scientific abstracts generated by ChatGPT to real abstracts with 
      detectors and blinded human reviewers.
PG  - 75
LID - 10.1038/s41746-023-00819-6 [doi]
LID - 75
AB  - Large language models such as ChatGPT can produce increasingly realistic text, 
      with unknown information on the accuracy and integrity of using these models in 
      scientific writing. We gathered fifth research abstracts from five high-impact 
      factor medical journals and asked ChatGPT to generate research abstracts based on 
      their titles and journals. Most generated abstracts were detected using an AI 
      output detector, 'GPT-2 Output Detector', with % 'fake' scores (higher meaning 
      more likely to be generated) of median [interquartile range] of 99.98% 'fake' 
      [12.73%, 99.98%] compared with median 0.02% [IQR 0.02%, 0.09%] for the original 
      abstracts. The AUROC of the AI output detector was 0.94. Generated abstracts 
      scored lower than original abstracts when run through a plagiarism detector 
      website and iThenticate (higher scores meaning more matching text found). When 
      given a mixture of original and general abstracts, blinded human reviewers 
      correctly identified 68% of generated abstracts as being generated by ChatGPT, 
      but incorrectly identified 14% of original abstracts as being generated. 
      Reviewers indicated that it was surprisingly difficult to differentiate between 
      the two, though abstracts they suspected were generated were vaguer and more 
      formulaic. ChatGPT writes believable scientific abstracts, though with completely 
      generated data. Depending on publisher-specific guidelines, AI output detectors 
      may serve as an editorial tool to help maintain scientific standards. The 
      boundaries of ethical and acceptable use of large language models to help 
      scientific writing are still being discussed, and different journals and 
      conferences are adopting varying policies.
CI  - © 2023. The Author(s).
FAU - Gao, Catherine A
AU  - Gao CA
AUID- ORCID: 0000-0001-5576-3943
AD  - Division of Pulmonary and Critical Care, Department of Medicine, Northwestern 
      University Feinberg School of Medicine, Chicago, IL, USA. 
      catherine.gao@northwestern.edu.
FAU - Howard, Frederick M
AU  - Howard FM
AUID- ORCID: 0000-0002-6039-1141
AD  - Section of Hematology/Oncology, Department of Medicine, University of Chicago, 
      Chicago, IL, USA.
FAU - Markov, Nikolay S
AU  - Markov NS
AUID- ORCID: 0000-0002-3659-4387
AD  - Division of Pulmonary and Critical Care, Department of Medicine, Northwestern 
      University Feinberg School of Medicine, Chicago, IL, USA.
FAU - Dyer, Emma C
AU  - Dyer EC
AUID- ORCID: 0000-0002-5818-3589
AD  - Section of Hematology/Oncology, Department of Medicine, University of Chicago, 
      Chicago, IL, USA.
FAU - Ramesh, Siddhi
AU  - Ramesh S
AD  - Section of Hematology/Oncology, Department of Medicine, University of Chicago, 
      Chicago, IL, USA.
FAU - Luo, Yuan
AU  - Luo Y
AUID- ORCID: 0000-0003-0195-7456
AD  - Division of Health and Biomedical Informatics, Department of Preventive Medicine, 
      Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
FAU - Pearson, Alexander T
AU  - Pearson AT
AD  - Section of Hematology/Oncology, Department of Medicine, University of Chicago, 
      Chicago, IL, USA.
LA  - eng
GR  - 2022YIA-6675470300/American Society of Clinical Oncology (ASCO)/
GR  - 2022YIA-6675470300/Breast Cancer Research Foundation (BCRF)/
GR  - U01TR003528/U.S. Department of Health & Human Services | NIH | National Center 
      for Advancing Translational Sciences (NCATS)/
GR  - R01LM013337/U.S. Department of Health & Human Services | NIH | U.S. National 
      Library of Medicine (NLM)/
GR  - U01-CA243075/U.S. Department of Health & Human Services | NIH | National Cancer 
      Institute (NCI)/
GR  - R56-DE030958/U.S. Department of Health & Human Services | NIH | National 
      Institute of Dental and Craniofacial Research (NIDCR)/
PT  - Journal Article
DEP - 20230426
PL  - England
TA  - NPJ Digit Med
JT  - NPJ digital medicine
JID - 101731738
CIN - J Am Acad Dermatol. 2023 Sep;89(3):e127-e129. PMID: 37179029
PMC - PMC10133283
COIS- A.T.P. reports no competing interests for this work, and reports personal fees 
      from Prelude Therapeutics Advisory Board, Elevar Advisory Board, AbbVie 
      consulting, Ayala Advisory Board, and Privo Therapeutics, all outside of 
      submitted work. The remaining authors declare no competing interests.
EDAT- 2023/04/27 00:42
MHDA- 2023/04/27 00:43
CRDT- 2023/04/26 23:21
PHST- 2023/01/01 00:00 [received]
PHST- 2023/03/30 00:00 [accepted]
PHST- 2023/04/27 00:43 [medline]
PHST- 2023/04/27 00:42 [pubmed]
PHST- 2023/04/26 23:21 [entrez]
AID - 10.1038/s41746-023-00819-6 [pii]
AID - 819 [pii]
AID - 10.1038/s41746-023-00819-6 [doi]
PST - epublish
SO  - NPJ Digit Med. 2023 Apr 26;6(1):75. doi: 10.1038/s41746-023-00819-6.

PMID- 37017291
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231026
IS  - 1708-8240 (Electronic)
IS  - 1496-4155 (Linking)
VI  - 35
IP  - 7
DP  - 2023 Oct
TI  - Implications of large language models such as ChatGPT for dental medicine.
PG  - 1098-1102
LID - 10.1111/jerd.13046 [doi]
AB  - OBJECTIVE: This article provides an overview of the implications of ChatGPT and 
      other large language models (LLMs) for dental medicine. OVERVIEW: ChatGPT, a LLM 
      trained on massive amounts of textual data, is adept at fulfilling various 
      language-related tasks. Despite its impressive capabilities, ChatGPT has serious 
      limitations, such as occasionally giving incorrect answers, producing nonsensical 
      content, and presenting misinformation as fact. Dental practitioners, assistants, 
      and hygienists are not likely to be significantly impacted by LLMs. However, LLMs 
      could affect the work of administrative personnel and the provision of dental 
      telemedicine. LLMs offer potential for clinical decision support, text 
      summarization, efficient writing, and multilingual communication. As more people 
      seek health information from LLMs, it is crucial to safeguard against inaccurate, 
      outdated, and biased responses to health-related queries. LLMs pose challenges 
      for patient data confidentiality and cybersecurity that must be tackled. In 
      dental education, LLMs present fewer challenges than in other academic fields. 
      LLMs can enhance academic writing fluency, but acceptable usage boundaries in 
      science need to be established. CONCLUSIONS: While LLMs such as ChatGPT may have 
      various useful applications in dental medicine, they come with risks of malicious 
      use and serious limitations, including the potential for misinformation. CLINICAL 
      SIGNIFICANCE: Along with the potential benefits of using LLMs as an additional 
      tool in dental medicine, it is crucial to carefully consider the limitations and 
      potential risks inherent in such artificial intelligence technologies.
CI  - © 2023 The Authors. Journal of Esthetic and Restorative Dentistry published by 
      Wiley Periodicals LLC.
FAU - Eggmann, Florin
AU  - Eggmann F
AUID- ORCID: 0000-0001-6185-1480
AD  - Department of Preventive and Restorative Sciences, Penn Dental Medicine, Robert 
      Schattner Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
AD  - Department of Periodontology, Endodontology, and Cariology, University Center for 
      Dental Medicine Basel UZB, University of Basel, Basel, Switzerland.
FAU - Weiger, Roland
AU  - Weiger R
AD  - Department of Periodontology, Endodontology, and Cariology, University Center for 
      Dental Medicine Basel UZB, University of Basel, Basel, Switzerland.
FAU - Zitzmann, Nicola U
AU  - Zitzmann NU
AD  - Department of Reconstructive Dentistry, University Center for Dental Medicine 
      Basel UZB, University of Basel, Basel, Switzerland.
FAU - Blatz, Markus B
AU  - Blatz MB
AUID- ORCID: 0000-0001-6341-7047
AD  - Department of Preventive and Restorative Sciences, Penn Dental Medicine, Robert 
      Schattner Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
LA  - eng
GR  - Freiwillige Akademische Gesellschaft/
GR  - Gottfried und Julia Bangerter-Rhyner-Stiftung/
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20230405
PL  - England
TA  - J Esthet Restor Dent
JT  - Journal of esthetic and restorative dentistry : official publication of the 
      American Academy of Esthetic Dentistry ... [et al.]
JID - 101096515
RN  - 621BVT9M36 (Fenbendazole)
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Dentists
MH  - Professional Role
MH  - Language
MH  - Fenbendazole
OTO - NOTNLM
OT  - artificial intelligence
OT  - dental care
OT  - dental education
OT  - evidence-based dentistry
OT  - machine learning
EDAT- 2023/04/06 06:00
MHDA- 2023/10/23 00:43
CRDT- 2023/04/05 07:43
PHST- 2023/03/25 00:00 [revised]
PHST- 2023/03/07 00:00 [received]
PHST- 2023/03/28 00:00 [accepted]
PHST- 2023/10/23 00:43 [medline]
PHST- 2023/04/06 06:00 [pubmed]
PHST- 2023/04/05 07:43 [entrez]
AID - 10.1111/jerd.13046 [doi]
PST - ppublish
SO  - J Esthet Restor Dent. 2023 Oct;35(7):1098-1102. doi: 10.1111/jerd.13046. Epub 
      2023 Apr 5.

PMID- 37515957
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1532-2793 (Electronic)
IS  - 0260-6917 (Linking)
VI  - 129
DP  - 2023 Oct
TI  - Revolutionizing nursing education through Ai integration: A reflection on the 
      disruptive impact of ChatGPT.
PG  - 105916
LID - S0260-6917(23)00210-1 [pii]
LID - 10.1016/j.nedt.2023.105916 [doi]
AB  - Artificial intelligence (AI) is driving global change. An AI language model like 
      ChatGPT could revolutionize the delivery of nursing education in the future. 
      ChatGPT is an AI-enabled text generator that has garnered significant attention 
      due to its ability to engage in conversations and answer questions. Nurse 
      educators play a crucial role in preparing nursing students for a 
      technology-integrated healthcare system, and the emergence of ChatGPT presents 
      both opportunities and challenges. While the technology has limitations and 
      potential biases, it also has the potential to benefit students by facilitating 
      learning, improving digital literacy, and encouraging critical thinking about AI 
      integration in healthcare. Nurse educators can incorporate ChatGPT into their 
      curriculum through formative or summative assessments and should prioritize 
      faculty development to understand and use AI technologies effectively. 
      Collaboration between educational institutions, regulatory bodies, and educators 
      is crucial to establish provincial and national competencies and frameworks that 
      reflect the increasing importance of AI in nursing education and practice. It is 
      paramount that nurses and nurse educators be open to AI-enabled innovations as 
      well as continue to critically think about their potential value to advance the 
      profession so nurses are better prepared to lead the digital future.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Castonguay, Alexandre
AU  - Castonguay A
AD  - University of Montreal, Nursing Faculty, Marguerite-d'Youville Campus, C.P. 6128 
      succ. Centre-ville, Montréal, Québec H3C 3J7, Canada. Electronic address: 
      alexandre.castonguay.2@umontreal.ca.
FAU - Farthing, Pamela
AU  - Farthing P
AD  - Saskatchewan Polytechnic, Faculty, School of Nursing, SCBScN, Canada. Electronic 
      address: pamela.farthing@saskpolytech.ca.
FAU - Davies, Shauna
AU  - Davies S
AD  - University of Regina, Canada. Electronic address: shauna.davies@uregina.ca.
FAU - Vogelsang, Laura
AU  - Vogelsang L
AD  - University of Lethbridge, Faculty of Health Sciences, Canada. Electronic address: 
      laura.vogelsang@uleth.ca.
FAU - Kleib, Manal
AU  - Kleib M
AD  - University of Alberta, Faculty of Nursing, Canada. Electronic address: 
      manal.kleib@ualberta.ca.
FAU - Risling, Tracie
AU  - Risling T
AD  - University of Calgary, Faculty of Nursing, Canada. Electronic address: 
      tracie.risling@ucalgary.ca.
FAU - Green, Nadia
AU  - Green N
AD  - University of Alberta, Faculty of Nursing, Canada. Electronic address: 
      ntgreen@ualberta.ca.
LA  - eng
PT  - Journal Article
DEP - 20230718
PL  - Scotland
TA  - Nurse Educ Today
JT  - Nurse education today
JID - 8511379
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Education, Nursing
MH  - Curriculum
MH  - Delivery of Health Care
MH  - Learning
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Authorship
OT  - Health knowledge, attitudes, practice
OT  - Nursing education research
OT  - Nursing informatics
OT  - Nursing students
OT  - Social responsibility
COIS- Declaration of competing interest The authors declare that they have no 
      conflicting interests.
EDAT- 2023/07/30 01:06
MHDA- 2023/08/14 06:42
CRDT- 2023/07/29 18:06
PHST- 2023/03/03 00:00 [received]
PHST- 2023/06/06 00:00 [revised]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/07/30 01:06 [pubmed]
PHST- 2023/07/29 18:06 [entrez]
AID - S0260-6917(23)00210-1 [pii]
AID - 10.1016/j.nedt.2023.105916 [doi]
PST - ppublish
SO  - Nurse Educ Today. 2023 Oct;129:105916. doi: 10.1016/j.nedt.2023.105916. Epub 2023 
      Jul 18.

PMID- 37261590
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 11
DP  - 2023 Nov
TI  - Realization of Green 5G Cellular Network Role in Medical Applications: Use of 
      ChatGPT-AI.
PG  - 2337-2339
LID - 10.1007/s10439-023-03257-3 [doi]
AB  - Wireless communication in medical applications improves patient monitoring, care 
      coordination, early disease detection, and patient empowerment. It improves 
      healthcare and patient outcomes. The design and configuration of a solar-powered 
      emergency battery backup system for 5G telecommunication base stations, including 
      medical applications, may vary depending on local climate, power requirements, 
      and resources. In this connection, uninterrupted power supply to the base 
      stations become crucial. The author utilizes the ChatGPT-AI features and prepared 
      this comprehensive letter for realizing the role of sustainable practices towards 
      climatic changes.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Janamala, Varaprasad
AU  - Janamala V
AUID- ORCID: 0000-0001-9716-5458
AD  - Department of Electrical and Electronics Engineering, School of Engineering and 
      Technology, CHRIST (Deemed to Be University), Bangalore, Karnataka, 560074, 
      India. varaprasad.janamala@christuniversity.in.
FAU - Ram, Inkollu Sai
AU  - Ram IS
AUID- ORCID: 0000-0003-2597-818X
AD  - Department of Electrical and Electronics Engineering, Dhanekula Institute of 
      Engineering & Technology, Vijayawada, Andhra Pradesh, 521139, India.
FAU - Daram, Suresh Babu
AU  - Daram SB
AUID- ORCID: 0000-0001-5304-8620
AD  - Department of Electrical and Electronics Engineering, Mohan Babu University (Erst 
      while Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh, 517102, 
      India.
LA  - eng
PT  - Letter
DEP - 20230601
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
PMC - PMC10234226
OTO - NOTNLM
OT  - 5G cellular networks
OT  - ChatGPT
OT  - Green technologies
OT  - Medical research
EDAT- 2023/06/01 13:10
MHDA- 2023/06/01 13:10
CRDT- 2023/06/01 11:12
PHST- 2023/05/24 00:00 [received]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/06/01 13:10 [pubmed]
PHST- 2023/06/01 13:10 [medline]
PHST- 2023/06/01 11:12 [entrez]
AID - 10.1007/s10439-023-03257-3 [pii]
AID - 3257 [pii]
AID - 10.1007/s10439-023-03257-3 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Nov;51(11):2337-2339. doi: 10.1007/s10439-023-03257-3. Epub 
      2023 Jun 1.

PMID- 37790062
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - A Radiation Oncology Board Exam of ChatGPT.
PG  - e44541
LID - 10.7759/cureus.44541 [doi]
LID - e44541
AB  - As artificial intelligence (AI) models improve and become widely integrated into 
      healthcare systems, healthcare providers must understand the strengths and 
      limitations of AI tools to realize the full spectrum of potential patient-care 
      benefits. However, most providers have a poor understanding of AI, leading to 
      distrust and poor adoption of this emerging technology. To bridge this divide, 
      this editorial presents a novel view of ChatGPT's current capabilities in the 
      medical field of radiation oncology. By replicating the format of the oral 
      qualification exam required for radiation oncology board certification, we 
      demonstrate ChatGPT's ability to analyze a commonly encountered patient case, 
      make diagnostic decisions, and integrate information to generate treatment 
      recommendations. Through this simulation, we highlight ChatGPT's strengths and 
      limitations in replicating human decision-making in clinical radiation oncology, 
      while providing an accessible resource to educate radiation oncologists on the 
      capabilities of AI chatbots.
CI  - Copyright © 2023, Barbour et al.
FAU - Barbour, Andrew B
AU  - Barbour AB
AD  - Radiation Oncology, University of Washington - Fred Hutchinson Cancer Center, 
      Seattle, USA.
FAU - Barbour, T Aleksandr
AU  - Barbour TA
AD  - Starlink, SpaceX, Redmond, USA.
LA  - eng
PT  - Editorial
DEP - 20230901
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10544698
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - ai in medicine
OT  - ai tools
OT  - radiation oncology
OT  - radiotherapy
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/04 06:44
MHDA- 2023/10/04 06:45
CRDT- 2023/10/04 03:58
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/10/04 06:45 [medline]
PHST- 2023/10/04 06:44 [pubmed]
PHST- 2023/10/04 03:58 [entrez]
AID - 10.7759/cureus.44541 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 1;15(9):e44541. doi: 10.7759/cureus.44541. eCollection 2023 Sep.

PMID- 37958050
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231122
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 21
DP  - 2023 Nov 6
TI  - The Role of ChatGPT in the Advancement of Diagnosis, Management, and Prognosis of 
      Cardiovascular and Cerebrovascular Disease.
LID - 10.3390/healthcare11212906 [doi]
LID - 2906
AB  - Cardiovascular and cerebrovascular disease incidence has risen mainly due to poor 
      control of preventable risk factors and still constitutes a significant financial 
      and health burden worldwide. ChatGPT is an artificial intelligence language-based 
      model developed by OpenAI. Due to the model's unique cognitive capabilities 
      beyond data processing and the production of high-quality text, there has been a 
      surge of research interest concerning its role in the scientific community and 
      contemporary clinical practice. To fully exploit ChatGPT's potential benefits and 
      reduce its possible misuse, extreme caution must be taken to ensure its 
      implications ethically and equitably. In this narrative review, we explore the 
      language model's possible applications and limitations while emphasizing its 
      potential value for diagnosing, managing, and prognosis of cardiovascular and 
      cerebrovascular disease.
FAU - Chlorogiannis, David-Dimitris
AU  - Chlorogiannis DD
AD  - Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA.
FAU - Apostolos, Anastasios
AU  - Apostolos A
AUID- ORCID: 0000-0003-2616-7952
AD  - First Department of Cardiology, School of Medicine, National Kapodistrian 
      University of Athens, Hippokrateion General Hospital of Athens, 115 27 Athens, 
      Greece.
FAU - Chlorogiannis, Anargyros
AU  - Chlorogiannis A
AD  - Department of Health Economics, Policy and Management, Karolinska Institutet, 171 
      77 Stockholm, Sweden.
FAU - Palaiodimos, Leonidas
AU  - Palaiodimos L
AUID- ORCID: 0000-0003-4682-5991
AD  - Division of Hospital Medicine, Jacobi Medical Center, NYC H+H, Albert Einstein 
      College of Medicine, New York, NY 10461, USA.
FAU - Giannakoulas, George
AU  - Giannakoulas G
AUID- ORCID: 0000-0001-7491-6319
AD  - Department of Cardiology, AHEPA University Hospital, Aristotle University of 
      Thessaloniki, 541 24 Thessaloniki, Greece.
FAU - Pargaonkar, Sumant
AU  - Pargaonkar S
AUID- ORCID: 0000-0002-2568-2664
AD  - Division of Hospital Medicine, Jacobi Medical Center, NYC H+H, Albert Einstein 
      College of Medicine, New York, NY 10461, USA.
FAU - Xesfingi, Sofia
AU  - Xesfingi S
AD  - Department of Economics, University of Piraeus, 185 34 Piraeus, Greece.
FAU - Kokkinidis, Damianos G
AU  - Kokkinidis DG
AD  - Section of Cardiovascular Medicine, Yale University School of Medicine, New 
      Haven, CT 06510, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231106
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10648908
OTO - NOTNLM
OT  - cardiovascular disease
OT  - cerebrovascular disease
OT  - chatGPT
OT  - diagnosis
OT  - language-based models
OT  - myocardial infarction
OT  - stroke
OT  - treatment
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/14 06:43
MHDA- 2023/11/14 06:44
CRDT- 2023/11/14 02:05
PHST- 2023/10/03 00:00 [received]
PHST- 2023/10/24 00:00 [revised]
PHST- 2023/11/04 00:00 [accepted]
PHST- 2023/11/14 06:44 [medline]
PHST- 2023/11/14 06:43 [pubmed]
PHST- 2023/11/14 02:05 [entrez]
AID - healthcare11212906 [pii]
AID - healthcare-11-02906 [pii]
AID - 10.3390/healthcare11212906 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Nov 6;11(21):2906. doi: 10.3390/healthcare11212906.

PMID- 36960444
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230501
IS  - 2666-3899 (Electronic)
IS  - 2666-3899 (Linking)
VI  - 4
IP  - 3
DP  - 2023 Mar 10
TI  - Is using ChatGPT cheating, plagiarism, both, neither, or forward thinking?
PG  - 100694
LID - 10.1016/j.patter.2023.100694 [doi]
LID - 100694
AB  - The recent emergence of ChatGPT has led to multiple considerations and 
      discussions regarding the ethics and usage of AI. In particular, the potential 
      exploitation in the educational realm must be considered, future-proofing 
      curriculum for the inevitable wave of AI-assisted assignments. Here, Brent Anders 
      discusses some of the key issues and concerns.
CI  - © 2023 The Author(s).
FAU - Anders, Brent A
AU  - Anders BA
AD  - Office of Institutional Research and Assessment and the Center for Teaching and 
      Learning, American University of Armenia, Yerevan, Armenia.
LA  - eng
PT  - News
DEP - 20230228
PL  - United States
TA  - Patterns (N Y)
JT  - Patterns (New York, N.Y.)
JID - 101767765
CIN - Patterns (N Y). 2023 Apr 14;4(4):100731. PMID: 37123441
PMC - PMC10028419
COIS- The author declares no competing interests.
EDAT- 2023/03/25 06:00
MHDA- 2023/03/25 06:01
CRDT- 2023/03/24 02:31
PHST- 2023/03/24 02:31 [entrez]
PHST- 2023/03/25 06:00 [pubmed]
PHST- 2023/03/25 06:01 [medline]
AID - S2666-3899(23)00025-9 [pii]
AID - 100694 [pii]
AID - 10.1016/j.patter.2023.100694 [doi]
PST - epublish
SO  - Patterns (N Y). 2023 Feb 28;4(3):100694. doi: 10.1016/j.patter.2023.100694. 
      eCollection 2023 Mar 10.

PMID- 37726551
OWN - NLM
STAT- MEDLINE
DCOM- 20231122
LR  - 20231122
IS  - 1473-1150 (Electronic)
IS  - 1470-269X (Linking)
VI  - 23
IP  - 6
DP  - 2023 Nov
TI  - Using ChatGPT to predict the future of personalized medicine.
PG  - 178-184
LID - 10.1038/s41397-023-00316-9 [doi]
AB  - Personalized medicine is a novel frontier in health care that is based on each 
      person's unique genetic makeup. It represents an exciting opportunity to improve 
      the future of individualized health care for all individuals. Pharmacogenomics, 
      as the main part of personalized medicine, aims to optimize and create a more 
      targeted treatment approach based on genetic variations in drug response. It is 
      predicted that future treatments will be algorithm-based instead of 
      evidence-based that will consider a patient's genetic, transcriptomic, proteomic, 
      epigenetic, and lifestyle factors resulting in individualized medication. A 
      generative pretrained transformer (GPT) is an artificial intelligence (AI) tool 
      that generates language resembling human-like writing enabling users to engage in 
      a manner that is practically identical to speaking with a human being. GPT's 
      predictive algorithms can respond to questions that have never been addressed. 
      Chat Generative Pretrained Transformer (ChatGPT) is an AI chatbot's advanced with 
      conversational capabilities. In the present study, questions were asked from 
      ChatGPT about the future of personalized medicine and pharmacogenomics. ChatGPT 
      predicted both to be a promising approach with a bright future that holds great 
      promises in improving patient outcomes and transforming the field of medicine. 
      But it still has several limitations that need to be solved.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature Limited.
FAU - Patrinos, George P
AU  - Patrinos GP
AUID- ORCID: 0000-0002-0519-7776
AD  - Department of Pharmacy, School of Health Sciences, University of Patras, 26504, 
      Patras, Greece.
AD  - Department of Genetics and Genomics, College of Medicine and Health Sciences, 
      United Arab Emirates University, Al Ain, P.O. Box 15551, Abu Dhabi, UAE.
AD  - United Arab Emirates University, Zayed Center for Health Sciences, Al Ain, P.O. 
      Box 15551, Abu Dhabi, UAE.
FAU - Sarhangi, Negar
AU  - Sarhangi N
AD  - Personalized Medicine Research Center, Endocrinology and Metabolism Clinical 
      Sciences Institute, Tehran University of Medical Sciences, 1411713119, Tehran, 
      Iran.
FAU - Sarrami, Behnaz
AU  - Sarrami B
AD  - Missouri Pharmacogenomics Consulting, St. Mo, 63011, St. Louis, MO, USA.
FAU - Khodayari, Nazli
AU  - Khodayari N
AD  - Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, 
      University of Florida, Gainesville, 32610-0225, FL, USA.
FAU - Larijani, Bagher
AU  - Larijani B
AD  - Endocrinology and Metabolism Research Center, Endocrinology and Metabolism 
      Clinical Sciences Institute, Tehran University of Medical Sciences, 1411713119, 
      Tehran, Iran.
FAU - Hasanzad, Mandana
AU  - Hasanzad M
AUID- ORCID: 0000-0002-0538-1135
AD  - Personalized Medicine Research Center, Endocrinology and Metabolism Clinical 
      Sciences Institute, Tehran University of Medical Sciences, 1411713119, Tehran, 
      Iran. mandanahasanzad@yahoo.com.
AD  - Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad 
      University, 193951459, Tehran, Iran. mandanahasanzad@yahoo.com.
LA  - eng
PT  - Journal Article
DEP - 20230919
PL  - United States
TA  - Pharmacogenomics J
JT  - The pharmacogenomics journal
JID - 101083949
RN  - EC 2.6.1.2 (Alanine Transaminase)
SB  - IM
MH  - Humans
MH  - *Precision Medicine
MH  - *Artificial Intelligence
MH  - Proteomics
MH  - Pharmacogenetics
MH  - Alanine Transaminase
EDAT- 2023/09/20 00:43
MHDA- 2023/11/22 06:43
CRDT- 2023/09/19 23:36
PHST- 2023/04/10 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/11/22 06:43 [medline]
PHST- 2023/09/20 00:43 [pubmed]
PHST- 2023/09/19 23:36 [entrez]
AID - 10.1038/s41397-023-00316-9 [pii]
AID - 10.1038/s41397-023-00316-9 [doi]
PST - ppublish
SO  - Pharmacogenomics J. 2023 Nov;23(6):178-184. doi: 10.1038/s41397-023-00316-9. Epub 
      2023 Sep 19.

PMID- 38028967
OWN - NLM
STAT- MEDLINE
DCOM- 20231201
LR  - 20231201
IS  - 1947-6108 (Electronic)
IS  - 1947-6094 (Print)
IS  - 1947-6108 (Linking)
VI  - 19
IP  - 5
DP  - 2023
TI  - ChatGPT and Artificial Intelligence in Hospital Level Research: Potential, 
      Precautions, and Prospects.
PG  - 77-84
LID - 10.14797/mdcvj.1290 [doi]
AB  - Rapid advancements in artificial intelligence (AI) have revolutionized numerous 
      sectors, including medical research. Among the various AI tools, OpenAI's 
      ChatGPT, a state-of-the-art language model, has demonstrated immense potential in 
      aiding and enhancing research processes. This review explores the application of 
      ChatGPT in medical hospital level research, focusing on its capabilities for 
      academic writing assistance, data analytics, statistics handling, and code 
      generation. Notably, it delves into the model's ability to streamline tasks, 
      support decision making, and improve patient interaction. However, the article 
      also underscores the importance of exercising caution while dealing with 
      sensitive healthcare data and highlights the limitations of ChatGPT, such as its 
      potential for erroneous outputs and biases. Furthermore, the review discusses the 
      ethical considerations that arise with AI use in health care, including data 
      privacy, AI interpretability, and the risk of AI-induced disparities. The article 
      culminates by envisioning the future of AI in medical research, emphasizing the 
      need for robust regulatory frameworks and guidelines that balance the potential 
      of AI with ethical considerations. As AI continues to evolve, it holds promising 
      potential to augment medical research in a manner that is ethical, equitable, and 
      patient-centric.
CI  - Copyright: © 2023 The Author(s).
FAU - Arshad, Hassaan B
AU  - Arshad HB
AUID- ORCID: 0000-0002-2690-1646
AD  - Houston Methodist DeBakey Heart & Vascular Center, Houston, Texas, US.
FAU - Butt, Sara A
AU  - Butt SA
AD  - Houston Methodist Research Institute, Houston, Texas, US.
FAU - Khan, Safi U
AU  - Khan SU
AUID- ORCID: 0000-0003-1559-6911
AD  - Houston Methodist DeBakey Heart & Vascular Center, Houston, Texas, US.
FAU - Javed, Zulqarnain
AU  - Javed Z
AUID- ORCID: 0000-0003-4321-4394
AD  - Houston Methodist Research Institute, Houston, Texas, US.
FAU - Nasir, Khurram
AU  - Nasir K
AUID- ORCID: 0000-0001-5376-2269
AD  - Houston Methodist DeBakey Heart & Vascular Center, Houston, Texas, US.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231116
PL  - United States
TA  - Methodist Debakey Cardiovasc J
JT  - Methodist DeBakey cardiovascular journal
JID - 101508600
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Hospitals
MH  - *Biomedical Research
MH  - Exercise
MH  - Writing
PMC - PMC10655767
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - data analytics
OT  - medical research
OT  - outcomes research
COIS- The authors have no competing interests to declare.
EDAT- 2023/11/29 18:43
MHDA- 2023/12/01 06:44
CRDT- 2023/11/29 17:19
PHST- 2023/09/12 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/12/01 06:44 [medline]
PHST- 2023/11/29 18:43 [pubmed]
PHST- 2023/11/29 17:19 [entrez]
AID - 10.14797/mdcvj.1290 [doi]
PST - epublish
SO  - Methodist Debakey Cardiovasc J. 2023 Nov 16;19(5):77-84. doi: 
      10.14797/mdcvj.1290. eCollection 2023.

PMID- 37907728
OWN - NLM
STAT- Publisher
LR  - 20231101
IS  - 1432-0460 (Electronic)
IS  - 0179-051X (Linking)
DP  - 2023 Oct 31
TI  - Using ChatGPT to Generate Research Ideas in Dysphagia: A Pilot Study.
LID - 10.1007/s00455-023-10623-9 [doi]
AB  - Current research in dysphagia faces challenges due to the rapid growth of 
      scientific literature and the interdisciplinary nature of the field. To address 
      this, the study evaluates ChatGPT, an AI language model, as a supplementary 
      resource to assist clinicians and researchers in generating research ideas for 
      dysphagia, utilizing recent advancements in natural language processing and 
      machine learning. The research ideas were generated through ChatGPT's command to 
      explore diverse aspects of dysphagia. A web-based survey was conducted, 45 
      dysphagia experts were asked to rank each study on a scale of 1 to 5 according to 
      feasibility, novelty, clinical implications, and relevance to current practice. A 
      total of 26 experts (58%) completed the survey. The mean (± sd) rankings of 
      research ideas were 4.03 (± 0.17) for feasibility, 3.5 (± 0.17) for potential 
      impact on the field, 3.84 (± 0.12) for clinical relevance, and 3.08 (± 0.36) for 
      novelty and innovation. Results of this study suggest that ChatGPT offers a 
      promising approach to generating research ideas in dysphagia. While its current 
      capability to generate innovative ideas appears limited, it can serve as a 
      supplementary resource for researchers.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Nachalon, Yuval
AU  - Nachalon Y
AUID- ORCID: 0000-0001-6202-4647
AD  - Department of Otolaryngology, Head and Neck Surgery and Maxillofacial Surgery, 
      Tel-Aviv Sourasky Medical Center, 6 Weizman Street, 6423906, Tel-Aviv, Israel. 
      yuval.nachalon@gmail.com.
AD  - Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. 
      yuval.nachalon@gmail.com.
FAU - Broer, Maya
AU  - Broer M
AD  - Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
FAU - Nativ-Zeltzer, Nogah
AU  - Nativ-Zeltzer N
AD  - Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
LA  - eng
PT  - Journal Article
DEP - 20231031
PL  - United States
TA  - Dysphagia
JT  - Dysphagia
JID - 8610856
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Clinical relevance
OT  - Dysphagia
OT  - Machine learning
OT  - Natural language processing (NLP)
OT  - Research ideas
EDAT- 2023/11/01 06:43
MHDA- 2023/11/01 06:43
CRDT- 2023/11/01 00:49
PHST- 2023/07/18 00:00 [received]
PHST- 2023/09/20 00:00 [accepted]
PHST- 2023/11/01 06:43 [medline]
PHST- 2023/11/01 06:43 [pubmed]
PHST- 2023/11/01 00:49 [entrez]
AID - 10.1007/s00455-023-10623-9 [pii]
AID - 10.1007/s00455-023-10623-9 [doi]
PST - aheadofprint
SO  - Dysphagia. 2023 Oct 31. doi: 10.1007/s00455-023-10623-9.

PMID- 37746411
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230926
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0's 
      Accuracy with the American Journal of Neuroradiology's (AJNR) "Case of the 
      Month".
PG  - e43958
LID - 10.7759/cureus.43958 [doi]
LID - e43958
AB  - The advent of artificial intelligence (AI), particularly large language models 
      (LLMs) such as ChatGPT 4.0, holds significant potential in healthcare, 
      specifically in radiology. This study examined the accuracy of ChatGPT 4.0 (July 
      20, 2023, version) in solving diagnostic quizzes from the American Journal of 
      Neuroradiology's (AJNR) "Case of the Month." We evaluated the diagnostic accuracy 
      of ChatGPT 4.0 when provided with a patient's history and imaging findings weekly 
      over four weeks, using 140 cases from the AJNR "Case of the Month" portal (from 
      November 2011 to July 2023). The overall diagnostic accuracy was found to be 
      57.86% (81 out of 140 cases). The diagnostic performance varied across brain, 
      head and neck, and spine subgroups, with accuracy rates of 54.65%, 67.65%, and 
      55.0%, respectively. These findings suggest that AI models such as ChatGPT 4.0 
      could serve as useful adjuncts in radiological diagnostics, thus potentially 
      enhancing patient care and revolutionizing medical education.
CI  - Copyright © 2023, Suthar et al.
FAU - Suthar, Pokhraj P
AU  - Suthar PP
AD  - Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical 
      Center, Chicago, USA.
FAU - Kounsal, Avin
AU  - Kounsal A
AD  - Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical 
      Center, Chicago, USA.
FAU - Chhetri, Lavanya
AU  - Chhetri L
AD  - Department of Clinical Nutrition, Rush University Medical Center, Chicago, USA.
FAU - Saini, Divya
AU  - Saini D
AD  - Department of Public Health, Johns Hopkins Bloomberg School of Public Health, 
      Baltimore, USA.
FAU - Dua, Sumeet G
AU  - Dua SG
AD  - Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical 
      Center, Chicago, USA.
LA  - eng
PT  - Journal Article
DEP - 20230823
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10516448
OTO - NOTNLM
OT  - accuracy
OT  - artificial intelligence in radiology
OT  - chat gpt
OT  - large language models (llms)
OT  - neuroradiology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/25 06:42
MHDA- 2023/09/25 06:43
CRDT- 2023/09/25 05:15
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/09/25 06:43 [medline]
PHST- 2023/09/25 06:42 [pubmed]
PHST- 2023/09/25 05:15 [entrez]
AID - 10.7759/cureus.43958 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 23;15(8):e43958. doi: 10.7759/cureus.43958. eCollection 2023 
      Aug.

PMID- 37698823
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231113
IS  - 2193-8245 (Print)
IS  - 2193-6528 (Electronic)
VI  - 12
IP  - 6
DP  - 2023 Dec
TI  - Artificial Intelligence-Based ChatGPT Responses for Patient Questions on Optic 
      Disc Drusen.
PG  - 3109-3119
LID - 10.1007/s40123-023-00800-2 [doi]
AB  - INTRODUCTION: Optic disc drusen (ODD) are acellular deposits in the optic nerve 
      head, which are most often benign and asymptomatic. Patients may develop visual 
      field defects and be at increased risk of ischemic co-morbidities. As ODD can be 
      difficult to distinguish from papilledema, patients are at risk of unnecessary 
      clinical workups. Patient information is a key aspect of ODD management. In this 
      study, we explored the accuracy of ChatGPT responses for typical patient 
      questions on ODD. METHODS: Two content experts reached consensus on 20 typical 
      patient questions. We retrieved five separate responses for each question from 
      ChatGPT, totaling 100 responses. Each response was evaluated on a 5-point 
      Likert-scale on accuracy by each content expert in an individual fashion. 
      RESULTS: The two experts were in fair/substantial agreement in the evaluation of 
      responses (Cronbach's alpha: 0.64). Of the 100 responses, 17 were relevant and 
      without any inaccuracies, 78 were relevant and with inaccuracies not being 
      harmful, and five were relevant and with inaccuracies potentially harmful. The 
      lowest accuracy scores were obtained for questions dealing with treatment and 
      prognosis. CONCLUSIONS: ChatGPT often provides relevant answers for patient 
      questions on ODD, but inaccuracies become potentially harmful when questions deal 
      with treatment and prognosis.
CI  - © 2023. The Author(s).
FAU - Potapenko, Ivan
AU  - Potapenko I
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, 2600, 
      Glostrup, Denmark.
FAU - Malmqvist, Lasse
AU  - Malmqvist L
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, 2600, 
      Glostrup, Denmark.
FAU - Subhi, Yousif
AU  - Subhi Y
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, 2600, 
      Glostrup, Denmark.
AD  - Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
FAU - Hamann, Steffen
AU  - Hamann S
AUID- ORCID: 0000-0003-4318-2716
AD  - Department of Ophthalmology, Rigshospitalet, Valdemar Hansens Vej 3, 2600, 
      Glostrup, Denmark. steffen.ellitsgaard.hamann@regionh.dk.
AD  - Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 
      steffen.ellitsgaard.hamann@regionh.dk.
LA  - eng
PT  - Journal Article
DEP - 20230912
PL  - England
TA  - Ophthalmol Ther
JT  - Ophthalmology and therapy
JID - 101634502
PMC - PMC10640407
OTO - NOTNLM
OT  - Accuracy
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Optic disc drusen
OT  - Patient information
COIS- Yousif Subhi has received speakers fees from Bayer and Roche, not related to this 
      work. Ivan Potapenko, Lasse Malmqvist, and Steffen Hamann have nothing to 
      disclose.
EDAT- 2023/09/12 12:42
MHDA- 2023/09/12 12:43
CRDT- 2023/09/12 11:24
PHST- 2023/06/09 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/09/12 12:43 [medline]
PHST- 2023/09/12 12:42 [pubmed]
PHST- 2023/09/12 11:24 [entrez]
AID - 10.1007/s40123-023-00800-2 [pii]
AID - 800 [pii]
AID - 10.1007/s40123-023-00800-2 [doi]
PST - ppublish
SO  - Ophthalmol Ther. 2023 Dec;12(6):3109-3119. doi: 10.1007/s40123-023-00800-2. Epub 
      2023 Sep 12.

PMID- 37073200
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230421
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Mitigating the Burden of Severe Pediatric Respiratory Viruses in the 
      Post-COVID-19 Era: ChatGPT Insights and Recommendations.
PG  - e36263
LID - 10.7759/cureus.36263 [doi]
LID - e36263
AB  - In the current post-pandemic era, the rapid spread of respiratory viruses among 
      children and infants resulted in hospitals and pediatric intensive care units 
      (PICUs) becoming overwhelmed. Healthcare providers around the world faced a 
      significant challenge from the outbreak of respiratory viruses like respiratory 
      syncytial virus (RSV), metapneumovirus, and influenza viruses. The chatbot 
      generative pre-trained transformer, ChatGPT, which was launched by OpenAI in 
      November 2022, had both positive and negative aspects in medical writing. Still, 
      it has the potential to generate mitigation suggestions that could be rapidly 
      implemented. We describe the generated suggestion from ChatGPT on 27 Feb 2023 in 
      response to the question "What's your advice for the pediatric intensivists?" We 
      as human authors and healthcare providers, do agree with and supplement with 
      references these suggestions of ChatGPT. We also advocate that artificial 
      intelligence (AI)-enabled chatbots could be utilized in seeking a vigilant and 
      robust healthcare system to rapidly adapt to changing respiratory viruses 
      circulating around the seasons, but AI-generated suggestions need experts to 
      validate them, and further research is warranted.
CI  - Copyright © 2023, Alhasan et al.
FAU - Alhasan, Khalid
AU  - Alhasan K
AD  - Department of Pediatrics, King Saud University, Riyadh, SAU.
AD  - Department of Kidney and Pancreas Transplant, King Faisal Specialist Hospital and 
      Research Center, Riyadh, SAU.
FAU - Al-Tawfiq, Jaffar
AU  - Al-Tawfiq J
AD  - Department of Specialty Internal Medicine and Quality, Johns Hopkins Aramco 
      Healthcare, Dhahran, SAU.
AD  - Department of Medicine, Indiana University School of Medicine, Indianapolis, USA.
AD  - Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, 
      USA.
FAU - Aljamaan, Fadi
AU  - Aljamaan F
AD  - Department of Critical Care, King Saud University, Riyadh, SAU.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Department of Family and Community Medicine, King Saud University, Riyadh, SAU.
FAU - Al-Eyadhy, Ayman
AU  - Al-Eyadhy A
AD  - Department of Pediatrics, King Saud University, Riyadh, SAU.
AD  - Department of Pediatrics, King Saud University Medical City, Riyadh, SAU.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Department of Pediatrics, King Saud University, Riyadh, SAU.
LA  - eng
PT  - Editorial
DEP - 20230316
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10105647
OTO - NOTNLM
OT  - ai chatbot
OT  - ai language model
OT  - chatgpt
OT  - human metapneumovirus (hmpv)
OT  - influenza vaccine
OT  - pediatric intensive care unit(picu)
OT  - post-covid sequelae
OT  - respiratory syncytial virus (rsv)
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/19 06:41
MHDA- 2023/04/19 06:42
CRDT- 2023/04/19 01:44
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/04/19 06:42 [medline]
PHST- 2023/04/19 06:41 [pubmed]
PHST- 2023/04/19 01:44 [entrez]
AID - 10.7759/cureus.36263 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 16;15(3):e36263. doi: 10.7759/cureus.36263. eCollection 2023 
      Mar.

PMID- 37890504
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1439-3999 (Electronic)
IS  - 0023-2165 (Linking)
DP  - 2023 Oct 27
TI  - Assessment of ChatGPT in the Prehospital Management of Ophthalmological 
      Emergencies - An Analysis of 10 Fictional Case Vignettes.
LID - 10.1055/a-2149-0447 [doi]
AB  - BACKGROUND: The artificial intelligence (AI)-based platform ChatGPT (Chat 
      Generative Pre-Trained Transformer, OpenAI LP, San Francisco, CA, USA) has gained 
      impressive popularity in recent months. Its performance on case vignettes of 
      general medical (non-ophthalmological) emergencies has been assessed - with very 
      encouraging results. The purpose of this study was to assess the performance of 
      ChatGPT on ophthalmological emergency case vignettes in terms of the main outcome 
      measures triage accuracy, appropriateness of recommended prehospital measures, 
      and overall potential to inflict harm to the user/patient. METHODS: We wrote ten 
      short, fictional case vignettes describing different acute ophthalmological 
      symptoms. Each vignette was entered into ChatGPT five times with the same wording 
      and following a standardized interaction pathway. The answers were analyzed 
      following a systematic approach. RESULTS: We observed a triage accuracy of 93.6%. 
      Most answers contained only appropriate recommendations for prehospital measures. 
      However, an overall potential to inflict harm to users/patients was present in 
      32% of answers. CONCLUSION: ChatGPT should presently not be used as a stand-alone 
      primary source of information about acute ophthalmological symptoms. As AI 
      continues to evolve, its safety and efficacy in the prehospital management of 
      ophthalmological emergencies has to be reassessed regularly.
CI  - Thieme. All rights reserved.
FAU - Knebel, Dominik
AU  - Knebel D
AD  - Department of Ophthalmology, University Hospital, Ludwigs-Maximilians-Universität 
      München, München, Germany.
FAU - Priglinger, Siegfried
AU  - Priglinger S
AD  - Department of Ophthalmology, University Hospital, Ludwigs-Maximilians-Universität 
      München, München, Germany.
FAU - Scherer, Nicolas
AU  - Scherer N
AD  - Department of Ophthalmology, University Hospital, Ludwigs-Maximilians-Universität 
      München, München, Germany.
FAU - Klaas, Julian
AU  - Klaas J
AD  - Department of Ophthalmology, University Hospital, Ludwigs-Maximilians-Universität 
      München, München, Germany.
FAU - Siedlecki, Jakob
AU  - Siedlecki J
AUID- ORCID: 0000-0002-0279-4823
AD  - Department of Ophthalmology, University Hospital, Ludwigs-Maximilians-Universität 
      München, München, Germany.
FAU - Schworm, Benedikt
AU  - Schworm B
AD  - Department of Ophthalmology, University Hospital, Ludwigs-Maximilians-Universität 
      München, München, Germany.
LA  - eng
PT  - Journal Article
TT  - ChatGPT in der präklinischen Versorgung augenärztlicher Notfälle – eine 
      Untersuchung von 10 fiktiven Fallvignetten.
DEP - 20231027
PL  - Germany
TA  - Klin Monbl Augenheilkd
JT  - Klinische Monatsblatter fur Augenheilkunde
JID - 0014133
SB  - IM
COIS- The authors declare that they have no conflict of interest.
EDAT- 2023/10/28 11:42
MHDA- 2023/10/28 11:42
CRDT- 2023/10/27 19:07
PHST- 2023/10/28 11:42 [medline]
PHST- 2023/10/28 11:42 [pubmed]
PHST- 2023/10/27 19:07 [entrez]
AID - 10.1055/a-2149-0447 [doi]
PST - aheadofprint
SO  - Klin Monbl Augenheilkd. 2023 Oct 27. doi: 10.1055/a-2149-0447.

PMID- 37645039
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231127
IS  - 2331-8422 (Electronic)
IS  - 2331-8422 (Linking)
DP  - 2023 Oct 19
TI  - ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug 
      Development with Chatbots.
LID - arXiv:2308.06920v2
AB  - The birth of ChatGPT, a cutting-edge language model-based chatbot developed by 
      OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role 
      in rigorous scientific research is not clear yet. This paper vividly showcases 
      its innovative application within the field of drug discovery. Focused 
      specifically on developing anti-cocaine addiction drugs, the study employs GPT-4 
      as a virtual guide, offering strategic and methodological insights to researchers 
      working on generative models for drug candidates. The primary objective is to 
      generate optimal drug-like molecules with desired properties. By leveraging the 
      capabilities of ChatGPT, the study introduces a novel approach to the drug 
      discovery process. This symbiotic partnership between AI and researchers 
      transforms how drug development is approached. Chatbots become facilitators, 
      steering researchers towards innovative methodologies and productive paths for 
      creating effective drug candidates. This research sheds light on the 
      collaborative synergy between human expertise and AI assistance, wherein 
      ChatGPT's cognitive abilities enhance the design and development of potential 
      pharmaceutical solutions. This paper not only explores the integration of 
      advanced AI in drug discovery but also reimagines the landscape by advocating for 
      AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation.
FAU - Wang, Rui
AU  - Wang R
AD  - Department of Mathematics,Michigan State University, MI 48824, USA.
FAU - Feng, Hongsong
AU  - Feng H
AD  - Department of Mathematics,Michigan State University, MI 48824, USA.
FAU - Wei, Guo-Wei
AU  - Wei GW
AD  - Department of Mathematics,Michigan State University, MI 48824, USA.
AD  - Department of Electrical and Computer Engineering, Michigan State University, MI 
      48824, USA.
AD  - Department of Biochemistry and Molecular Biology, Michigan State University, MI 
      48824, USA.
LA  - eng
PT  - Preprint
DEP - 20231019
PL  - United States
TA  - ArXiv
JT  - ArXiv
JID - 101759493
UIN - J Chem Inf Model. 2023 Nov 13;:. PMID: 37956228
PMC - PMC10462169
OTO - NOTNLM
OT  - AutoEncoder
OT  - ChatGPT
OT  - Cocaine Addition
OT  - Drug Discovery
OT  - Langevin Equation
COIS- Competing interests The authors declare no competing interests.
EDAT- 2023/08/30 06:47
MHDA- 2023/08/30 06:48
CRDT- 2023/08/30 03:46
PHST- 2023/08/30 06:47 [pubmed]
PHST- 2023/08/30 06:48 [medline]
PHST- 2023/08/30 03:46 [entrez]
AID - 2308.06920 [pii]
PST - epublish
SO  - ArXiv. 2023 Oct 19:arXiv:2308.06920v2. Preprint.

PMID- 37540292
OWN - NLM
STAT- Publisher
LR  - 20230804
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Aug 4
TI  - A Promising Start and Not a Panacea: ChatGPT's Early Impact and Potential in 
      Medical Science and Biomedical Engineering Research.
LID - 10.1007/s10439-023-03335-6 [doi]
AB  - The advent of artificial intelligence (AI) has catalyzed a revolutionary 
      transformation across various industries, including healthcare. Medical 
      applications of ChatGPT, a powerful language model based on the generative 
      pre-trained transformer (GPT) architecture, encompass the creation of 
      conversational agents capable of accessing and generating medical information 
      from multiple sources and formats. This study investigates the research trends of 
      large language models such as ChatGPT, GPT 4, and Google Bard, comparing their 
      publication trends with early COVID-19 research. The findings underscore the 
      current prominence of AI research and its potential implications in biomedical 
      engineering. A search of the Scopus database on July 23, 2023, yielded 1,096 
      articles related to ChatGPT, with approximately 26% being medical 
      science-related. Keywords related to artificial intelligence, natural language 
      processing (NLP), LLM, and generative AI dominate ChatGPT research, while a 
      focused representation of medical science research emerges, with emphasis on 
      biomedical research and engineering. This analysis serves as a call to action for 
      researchers, healthcare professionals, and policymakers to recognize and harness 
      AI's potential in healthcare, particularly in the realm of biomedical research.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AUID- ORCID: 0000-0002-5944-7371
AD  - Department of Computer Science and Engineering, School of Engineering Sciences 
      and Technology, Jamia Hamdard, New Delhi, 110062, India. 
      shahabssohail@jamiahamdard.ac.in.
LA  - eng
PT  - Letter
DEP - 20230804
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Biomedical engineering
OT  - Chat GPT
OT  - Generative AI
OT  - Google Bard
OT  - LLM
EDAT- 2023/08/04 13:11
MHDA- 2023/08/04 13:11
CRDT- 2023/08/04 11:06
PHST- 2023/07/23 00:00 [received]
PHST- 2023/07/26 00:00 [accepted]
PHST- 2023/08/04 13:11 [medline]
PHST- 2023/08/04 13:11 [pubmed]
PHST- 2023/08/04 11:06 [entrez]
AID - 10.1007/s10439-023-03335-6 [pii]
AID - 10.1007/s10439-023-03335-6 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Aug 4. doi: 10.1007/s10439-023-03335-6.

PMID- 38015610
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231128
IS  - 2291-9694 (Print)
VI  - 11
DP  - 2023 Nov 28
TI  - Applications of the Natural Language Processing Tool ChatGPT in Clinical 
      Practice: Comparative Study and Augmented Systematic Review.
PG  - e48933
LID - 10.2196/48933 [doi]
AB  - BACKGROUND: This research integrates a comparative analysis of the performance of 
      human researchers and OpenAI's ChatGPT in systematic review tasks and describes 
      an assessment of the application of natural language processing (NLP) models in 
      clinical practice through a review of 5 studies. OBJECTIVE: This study aimed to 
      evaluate the reliability between ChatGPT and human researchers in extracting key 
      information from clinical articles, and to investigate the practical use of NLP 
      in clinical settings as evidenced by selected studies. METHODS: The study design 
      comprised a systematic review of clinical articles executed independently by 
      human researchers and ChatGPT. The level of agreement between and within raters 
      for parameter extraction was assessed using the Fleiss and Cohen κ statistics. 
      RESULTS: The comparative analysis revealed a high degree of concordance between 
      ChatGPT and human researchers for most parameters, with less agreement for study 
      design, clinical task, and clinical implementation. The review identified 5 
      significant studies that demonstrated the diverse applications of NLP in clinical 
      settings. These studies' findings highlight the potential of NLP to improve 
      clinical efficiency and patient outcomes in various contexts, from enhancing 
      allergy detection and classification to improving quality metrics in 
      psychotherapy treatments for veterans with posttraumatic stress disorder. 
      CONCLUSIONS: Our findings underscore the potential of NLP models, including 
      ChatGPT, in performing systematic reviews and other clinical tasks. Despite 
      certain limitations, NLP models present a promising avenue for enhancing health 
      care efficiency and accuracy. Future studies must focus on broadening the range 
      of clinical applications and exploring the ethical considerations of implementing 
      NLP applications in health care settings.
CI  - ©Nikolas Schopow, Georg Osterhoff, David Baur. Originally published in JMIR 
      Medical Informatics (https://medinform.jmir.org), 28.11.2023.
FAU - Schopow, Nikolas
AU  - Schopow N
AUID- ORCID: 0000-0002-6754-5692
AD  - Department for Orthopedics, Trauma Surgery and Plastic Surgery, University 
      Hospital Leipzig, Leipzig, Germany.
FAU - Osterhoff, Georg
AU  - Osterhoff G
AUID- ORCID: 0000-0001-5051-0998
AD  - Department for Orthopedics, Trauma Surgery and Plastic Surgery, University 
      Hospital Leipzig, Leipzig, Germany.
FAU - Baur, David
AU  - Baur D
AUID- ORCID: 0000-0001-8602-617X
AD  - Department for Orthopedics, Trauma Surgery and Plastic Surgery, University 
      Hospital Leipzig, Leipzig, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - Canada
TA  - JMIR Med Inform
JT  - JMIR medical informatics
JID - 101645109
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-3
OT  - GPT-4
OT  - NLP
OT  - artificial intelligence
OT  - clinical decision support systems
OT  - clinical practice
OT  - extract
OT  - extraction
OT  - health care
OT  - healthcare
OT  - language model
OT  - large language models
OT  - machine learning
OT  - natural language processing
OT  - review methodology
OT  - review methods
OT  - systematic
OT  - systematic review
OT  - text
OT  - unstructured
EDAT- 2023/11/28 12:42
MHDA- 2023/11/28 12:43
CRDT- 2023/11/28 11:54
PHST- 2023/05/11 00:00 [received]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/06/20 00:00 [revised]
PHST- 2023/11/28 12:43 [medline]
PHST- 2023/11/28 12:42 [pubmed]
PHST- 2023/11/28 11:54 [entrez]
AID - v11i1e48933 [pii]
AID - 10.2196/48933 [doi]
PST - epublish
SO  - JMIR Med Inform. 2023 Nov 28;11:e48933. doi: 10.2196/48933.

PMID- 37263772
OWN - NLM
STAT- Publisher
LR  - 20230601
IS  - 2047-9956 (Print)
IS  - 2047-9956 (Linking)
DP  - 2023 Jun 1
TI  - Performance and risks of ChatGPT used in drug information: an exploratory 
      real-world analysis.
LID - ejhpharm-2023-003750 [pii]
LID - 10.1136/ejhpharm-2023-003750 [doi]
AB  - OBJECTIVES: To investigate the performance and risk associated with the usage of 
      Chat Generative Pre-trained Transformer (ChatGPT) to answer drug-related 
      questions. METHODS: A sample of 50 drug-related questions were consecutively 
      collected and entered in the artificial intelligence software application 
      ChatGPT. Answers were documented and rated in a standardised consensus process by 
      six senior hospital pharmacists in the domains content (correct, incomplete, 
      false), patient management (possible, insufficient, not possible) and risk (no 
      risk, low risk, high risk). As reference, answers were researched in adherence to 
      the German guideline of drug information and stratified in four categories 
      according to the sources used. In addition, the reproducibility of ChatGPT's 
      answers was analysed by entering three questions at different timepoints 
      repeatedly (day 1, day 2, week 2, week 3). RESULTS: Overall, only 13 of 50 
      answers provided correct content and had enough information to initiate 
      management with no risk of patient harm. The majority of answers were either 
      false (38%, n=19) or had partly correct content (36%, n=18) and no references 
      were provided. A high risk of patient harm was likely in 26% (n=13) of the cases 
      and risk was judged low for 28% (n=14) of the cases. In all high-risk cases, 
      actions could have been initiated based on the provided information. The answers 
      of ChatGPT varied over time when entered repeatedly and only three out of 12 
      answers were identical, showing no reproducibility to low reproducibility. 
      CONCLUSION: In a real-world sample of 50 drug-related questions, ChatGPT answered 
      the majority of questions wrong or partly wrong. The use of artificial 
      intelligence applications in drug information is not possible as long as barriers 
      like wrong content, missing references and reproducibility remain.
CI  - © European Association of Hospital Pharmacists 2023. No commercial re-use. See 
      rights and permissions. Published by BMJ.
FAU - Morath, Benedict
AU  - Morath B
AUID- ORCID: 0000-0003-1926-0585
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany 
      benedict.morath@med.uni-heidelberg.de.
FAU - Chiriac, Ute
AU  - Chiriac U
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany.
FAU - Jaszkowski, Elena
AU  - Jaszkowski E
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany.
FAU - Deiß, Carolin
AU  - Deiß C
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany.
FAU - Nürnberg, Hannah
AU  - Nürnberg H
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany.
FAU - Hörth, Katrin
AU  - Hörth K
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany.
FAU - Hoppe-Tichy, Torsten
AU  - Hoppe-Tichy T
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany.
FAU - Green, Kim
AU  - Green K
AD  - Hospital Pharmacy, Heidelberg University Hospital, Heidelberg, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230601
PL  - England
TA  - Eur J Hosp Pharm
JT  - European journal of hospital pharmacy : science and practice
JID - 101578294
SB  - IM
OTO - NOTNLM
OT  - EVIDENCE-BASED MEDICINE
OT  - HEALTH SERVICES ADMINISTRATION
OT  - JOURNALISM, MEDICAL
OT  - Medical Informatics
OT  - PHARMACY SERVICE, HOSPITAL
COIS- Competing interests: None declared.
EDAT- 2023/06/02 01:07
MHDA- 2023/06/02 01:07
CRDT- 2023/06/01 21:38
PHST- 2023/03/02 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/06/02 01:07 [medline]
PHST- 2023/06/02 01:07 [pubmed]
PHST- 2023/06/01 21:38 [entrez]
AID - ejhpharm-2023-003750 [pii]
AID - 10.1136/ejhpharm-2023-003750 [doi]
PST - aheadofprint
SO  - Eur J Hosp Pharm. 2023 Jun 1:ejhpharm-2023-003750. doi: 
      10.1136/ejhpharm-2023-003750.

PMID- 37851468
OWN - NLM
STAT- Publisher
LR  - 20231103
IS  - 1555-905X (Electronic)
IS  - 1555-9041 (Linking)
DP  - 2023 Oct 18
TI  - Performance of ChatGPT on Nephrology Test Questions.
LID - 10.2215/CJN.0000000000000330 [doi]
AB  - BACKGROUND: ChatGPT is a novel tool that allows people to engage in conversations 
      with an advanced machine learning model. ChatGPT's performance in the US Medical 
      Licensing Examination is comparable with a successful candidate's performance. 
      However, its performance in the nephrology field remains undetermined. This study 
      assessed ChatGPT's capabilities in answering nephrology test questions. METHODS: 
      Questions sourced from Nephrology Self-Assessment Program and Kidney 
      Self-Assessment Program were used, each with multiple-choice single-answer 
      questions. Questions containing visual elements were excluded. Each question bank 
      was run twice using GPT-3.5 and GPT-4. Total accuracy rate, defined as the 
      percentage of correct answers obtained by ChatGPT in either the first or second 
      run, and the total concordance, defined as the percentage of identical answers 
      provided by ChatGPT during both runs, regardless of their correctness, were used 
      to assess its performance. RESULTS: A comprehensive assessment was conducted on a 
      set of 975 questions, comprising 508 questions from Nephrology Self-Assessment 
      Program and 467 from Kidney Self-Assessment Program. GPT-3.5 resulted in a total 
      accuracy rate of 51%. Notably, the employment of Nephrology Self-Assessment 
      Program yielded a higher accuracy rate compared with Kidney Self-Assessment 
      Program (58% versus 44%; P < 0.001). The total concordance rate across all 
      questions was 78%, with correct answers exhibiting a higher concordance rate 
      (84%) compared with incorrect answers (73%) ( P < 0.001). When examining various 
      nephrology subfields, the total accuracy rates were relatively lower in 
      electrolyte and acid-base disorder, glomerular disease, and kidney-related bone 
      and stone disorders. The total accuracy rate of GPT-4's response was 74%, higher 
      than GPT-3.5 ( P < 0.001) but remained below the passing threshold and average 
      scores of nephrology examinees (77%). CONCLUSIONS: ChatGPT exhibited limitations 
      regarding accuracy and repeatability when addressing nephrology-related 
      questions. Variations in performance were evident across various subfields.
CI  - Copyright © 2023 by the American Society of Nephrology.
FAU - Miao, Jing
AU  - Miao J
AUID- ORCID: 0000-0003-0642-9740
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, Minnesota.
FAU - Thongprayoon, Charat
AU  - Thongprayoon C
AUID- ORCID: 0000-0002-8313-3604
FAU - Garcia Valencia, Oscar A
AU  - Garcia Valencia OA
AUID- ORCID: 0000-0003-0186-9448
FAU - Krisanapan, Pajaree
AU  - Krisanapan P
AUID- ORCID: 0000-0002-2888-881
FAU - Sheikh, Mohammad S
AU  - Sheikh MS
AUID- ORCID: 0009-0006-9388-8505
FAU - Davis, Paul W
AU  - Davis PW
AUID- ORCID: 0000-0003-4637-3198
FAU - Mekraksakit, Poemlarp
AU  - Mekraksakit P
AUID- ORCID: 0000-0002-2127-2529
FAU - Suarez, Maria Gonzalez
AU  - Suarez MG
AUID- ORCID: 0000-0002-8930-4611
FAU - Craici, Iasmina M
AU  - Craici IM
FAU - Cheungpasitporn, Wisit
AU  - Cheungpasitporn W
AUID- ORCID: 0000-0001-9954-9711
LA  - eng
PT  - Journal Article
DEP - 20231018
PL  - United States
TA  - Clin J Am Soc Nephrol
JT  - Clinical journal of the American Society of Nephrology : CJASN
JID - 101271570
SB  - IM
EDAT- 2023/10/18 12:43
MHDA- 2023/10/18 12:43
CRDT- 2023/10/18 11:34
PHST- 2023/06/01 00:00 [received]
PHST- 2023/10/12 00:00 [accepted]
PHST- 2023/10/18 12:43 [pubmed]
PHST- 2023/10/18 12:43 [medline]
PHST- 2023/10/18 11:34 [entrez]
AID - 01277230-990000000-00266 [pii]
AID - 10.2215/CJN.0000000000000330 [doi]
PST - aheadofprint
SO  - Clin J Am Soc Nephrol. 2023 Oct 18. doi: 10.2215/CJN.0000000000000330.

PMID- 37606629
OWN - NLM
STAT- Publisher
LR  - 20230822
IS  - 1549-960X (Electronic)
IS  - 1549-9596 (Linking)
DP  - 2023 Aug 22
TI  - ChatGPT Generated Content and Similarity Index in Chemistry†.
LID - 10.1021/acs.jcim.3c01110 [doi]
AB  - This study aims to verify similarity index of ChatGPT generated content in 
      chemistry. Twenty subsubjects of chemistry based on controlled vocabulary tools, 
      such as Dewey Decimal Classification, Sears List, and LCSH have been considered. 
      The similarity index has been checked using iThenticate, Urkund, and Turnitin. 
      Surprisingly, the matching percentage is relatively low.
FAU - Kirtania, Deep Kumar
AU  - Kirtania DK
AUID- ORCID: 0000-0002-5319-4705
AD  - Bankura Sammilani College, Bankura, West Bengal 722102, India.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
SB  - IM
EDAT- 2023/08/22 13:42
MHDA- 2023/08/22 13:42
CRDT- 2023/08/22 10:55
PHST- 2023/08/22 13:42 [medline]
PHST- 2023/08/22 13:42 [pubmed]
PHST- 2023/08/22 10:55 [entrez]
AID - 10.1021/acs.jcim.3c01110 [doi]
PST - aheadofprint
SO  - J Chem Inf Model. 2023 Aug 22. doi: 10.1021/acs.jcim.3c01110.

PMID- 37998447
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231126
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 22
DP  - 2023 Nov 13
TI  - Reply to Moreno et al. Comment on "Sallam, M. ChatGPT Utility in Healthcare 
      Education, Research, and Practice: Systematic Review on the Promising 
      Perspectives and Valid Concerns. Healthcare 2023, 11, 887".
LID - 10.3390/healthcare11222955 [doi]
LID - 2955
AB  - I would like to thank the authors for their commentary on the publication 
      "ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic 
      Review on the Promising Perspectives and Valid Concerns" [...].
FAU - Sallam, Malik
AU  - Sallam M
AUID- ORCID: 0000-0002-0165-9670
AD  - Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, 
      The University of Jordan, Amman 11942, Jordan.
AD  - Department of Clinical Laboratories and Forensic Medicine, Jordan University 
      Hospital, Amman 11942, Jordan.
LA  - eng
PT  - Journal Article
DEP - 20231113
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
CIN - Healthcare (Basel). 11:2819.
PMC - PMC10671560
COIS- The author declares no conflict of interest.
EDAT- 2023/11/24 12:42
MHDA- 2023/11/24 12:43
CRDT- 2023/11/24 09:36
PHST- 2023/08/30 00:00 [received]
PHST- 2023/10/20 00:00 [revised]
PHST- 2023/10/26 00:00 [accepted]
PHST- 2023/11/24 12:43 [medline]
PHST- 2023/11/24 12:42 [pubmed]
PHST- 2023/11/24 09:36 [entrez]
AID - healthcare11222955 [pii]
AID - healthcare-11-02955 [pii]
AID - 10.3390/healthcare11222955 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Nov 13;11(22):2955. doi: 10.3390/healthcare11222955.

PMID- 36872153
OWN - NLM
STAT- MEDLINE
DCOM- 20230417
LR  - 20230520
IS  - 1471-5007 (Electronic)
IS  - 1471-4922 (Linking)
VI  - 39
IP  - 5
DP  - 2023 May
TI  - Are ChatGPT and other pretrained language models good parasitologists?
PG  - 314-316
LID - S1471-4922(23)00039-9 [pii]
LID - 10.1016/j.pt.2023.02.006 [doi]
AB  - Large language models, such as ChatGPT, will have far-reaching impacts on 
      parasitology, including on students. Authentic experiences gained during 
      students' training are absent from these models. This is not a weakness of the 
      models but rather an opportunity benefiting parasitology at large.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Šlapeta, Jan
AU  - Šlapeta J
AD  - Sydney School of Veterinary Science, Faculty of Science, The University of 
      Sydney, Sydney, New South Wales 2006, Australia; The University of Sydney 
      Institute for Infectious Diseases, University of Sydney, Sydney, New South Wales 
      2006, Australia. Electronic address: jan.slapeta@sydney.edu.au.
LA  - eng
PT  - Journal Article
DEP - 20230303
PL  - England
TA  - Trends Parasitol
JT  - Trends in parasitology
JID - 100966034
SB  - IM
MH  - Humans
MH  - *Language
OTO - NOTNLM
OT  - artificial intelligence
OT  - education
OT  - examination
OT  - generative pretrained transformer
OT  - study
COIS- Declaration of interests The author declares no competing interests.
EDAT- 2023/03/06 06:00
MHDA- 2023/04/17 06:41
CRDT- 2023/03/05 22:05
PHST- 2023/02/02 00:00 [received]
PHST- 2023/02/15 00:00 [revised]
PHST- 2023/02/16 00:00 [accepted]
PHST- 2023/04/17 06:41 [medline]
PHST- 2023/03/06 06:00 [pubmed]
PHST- 2023/03/05 22:05 [entrez]
AID - S1471-4922(23)00039-9 [pii]
AID - 10.1016/j.pt.2023.02.006 [doi]
PST - ppublish
SO  - Trends Parasitol. 2023 May;39(5):314-316. doi: 10.1016/j.pt.2023.02.006. Epub 
      2023 Mar 3.

PMID- 37888068
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231029
IS  - 2075-4426 (Print)
IS  - 2075-4426 (Electronic)
IS  - 2075-4426 (Linking)
VI  - 13
IP  - 10
DP  - 2023 Sep 30
TI  - Navigating the Landscape of Personalized Medicine: The Relevance of ChatGPT, 
      BingChat, and Bard AI in Nephrology Literature Searches.
LID - 10.3390/jpm13101457 [doi]
LID - 1457
AB  - BACKGROUND AND OBJECTIVES: Literature reviews are foundational to understanding 
      medical evidence. With AI tools like ChatGPT, Bing Chat and Bard AI emerging as 
      potential aids in this domain, this study aimed to individually assess their 
      citation accuracy within Nephrology, comparing their performance in providing 
      precise. MATERIALS AND METHODS: We generated the prompt to solicit 20 references 
      in Vancouver style in each 12 Nephrology topics, using ChatGPT, Bing Chat and 
      Bard. We verified the existence and accuracy of the provided references using 
      PubMed, Google Scholar, and Web of Science. We categorized the validity of the 
      references from the AI chatbot into (1) incomplete, (2) fabricated, (3) 
      inaccurate, and (4) accurate. RESULTS: A total of 199 (83%), 158 (66%) and 112 
      (47%) unique references were provided from ChatGPT, Bing Chat and Bard, 
      respectively. ChatGPT provided 76 (38%) accurate, 82 (41%) inaccurate, 32 (16%) 
      fabricated and 9 (5%) incomplete references. Bing Chat provided 47 (30%) 
      accurate, 77 (49%) inaccurate, 21 (13%) fabricated and 13 (8%) incomplete 
      references. In contrast, Bard provided 3 (3%) accurate, 26 (23%) inaccurate, 71 
      (63%) fabricated and 12 (11%) incomplete references. The most common error type 
      across platforms was incorrect DOIs. CONCLUSIONS: In the field of medicine, the 
      necessity for faultless adherence to research integrity is highlighted, asserting 
      that even small errors cannot be tolerated. The outcomes of this investigation 
      draw attention to inconsistent citation accuracy across the different AI tools 
      evaluated. Despite some promising results, the discrepancies identified call for 
      a cautious and rigorous vetting of AI-sourced references in medicine. Such 
      chatbots, before becoming standard tools, need substantial refinements to assure 
      unwavering precision in their outputs.
FAU - Aiumtrakul, Noppawit
AU  - Aiumtrakul N
AUID- ORCID: 0000-0003-0479-7785
AD  - Department of Medicine, John A. Burns School of Medicine, University of Hawaii, 
      Honolulu, HI 96813, USA.
FAU - Thongprayoon, Charat
AU  - Thongprayoon C
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Suppadungsuk, Supawadee
AU  - Suppadungsuk S
AUID- ORCID: 0000-0003-1597-2411
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, 
      Mahidol University, Samut Prakan 10540, Thailand.
FAU - Krisanapan, Pajaree
AU  - Krisanapan P
AUID- ORCID: 0000-0002-2888-881X
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
AD  - Department of Internal Medicine, Faculty of Medicine, Thammasat University, 
      Pathum Thani 12120, Thailand.
FAU - Miao, Jing
AU  - Miao J
AUID- ORCID: 0000-0003-0642-9740
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Qureshi, Fawad
AU  - Qureshi F
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Cheungpasitporn, Wisit
AU  - Cheungpasitporn W
AUID- ORCID: 0000-0001-9954-9711
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
LA  - eng
PT  - Journal Article
DEP - 20230930
PL  - Switzerland
TA  - J Pers Med
JT  - Journal of personalized medicine
JID - 101602269
PMC - PMC10608326
OTO - NOTNLM
OT  - Bard AI
OT  - Bing Chat
OT  - ChatGPT
OT  - accuracy
OT  - literature review
OT  - nephrology references
OT  - personalized medicine
OT  - precision medicine
COIS- The authors declare no conflict of interest. All authors had access to the data 
      and played essential roles in writing of the manuscript.
EDAT- 2023/10/27 12:43
MHDA- 2023/10/27 12:44
CRDT- 2023/10/27 07:09
PHST- 2023/09/10 00:00 [received]
PHST- 2023/09/29 00:00 [revised]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/10/27 12:44 [medline]
PHST- 2023/10/27 12:43 [pubmed]
PHST- 2023/10/27 07:09 [entrez]
AID - jpm13101457 [pii]
AID - jpm-13-01457 [pii]
AID - 10.3390/jpm13101457 [doi]
PST - epublish
SO  - J Pers Med. 2023 Sep 30;13(10):1457. doi: 10.3390/jpm13101457.

PMID- 37699796
OWN - NLM
STAT- Publisher
LR  - 20230920
IS  - 1758-1001 (Electronic)
IS  - 0004-5632 (Linking)
DP  - 2023 Sep 20
TI  - Can artificial intelligence replace biochemists? A study comparing interpretation 
      of thyroid function test results by ChatGPT and Google Bard to practising 
      biochemists.
PG  - 45632231203473
LID - 10.1177/00045632231203473 [doi]
AB  - BACKGROUND: Public awareness of artificial intelligence (AI) is increasing and 
      this novel technology is being used for a range of everyday tasks and more 
      specialist clinical applications. On a background of increasing waits for GP 
      appointments alongside patient access to laboratory test results through the NHS 
      app, this study aimed to assess the accuracy and safety of two AI tools, ChatGPT 
      and Google Bard, in providing interpretation of thyroid function test results as 
      if posed by laboratory scientists or patients. METHODS: Fifteen fictional cases 
      were presented to a team of clinicians and clinical scientists to produce a 
      consensus opinion. The cases were then presented to ChatGPT and Google Bard as 
      though from healthcare providers and from patients. The responses were 
      categorized as correct, partially correct or incorrect compared to consensus 
      opinion and the advice assessed for safety to patients. RESULTS: Of the 15 cases 
      presented, ChatGPT and Google Bard correctly interpreted only 33.3% and 20.0% of 
      cases, respectively. When queries were posed as a patient, 66.7% of ChatGPT 
      responses were safe compared to 60.0% of Google Bard responses. Both AI tools 
      were able to identify primary hypothyroidism and hyperthyroidism but failed to 
      identify subclinical presentations, non-thyroidal illness or secondary 
      hypothyroidism. CONCLUSIONS: This study has demonstrated that AI tools do not 
      currently have the capacity to generate consistently correct interpretation and 
      safe advice to patients and should not be used as an alternative to a 
      consultation with a qualified medical professional. Available AI in its current 
      form cannot replace human clinical knowledge in this scenario.
FAU - Stevenson, Emma
AU  - Stevenson E
AUID- ORCID: 0000-0002-9509-8826
AD  - Clinical Biochemistry, Gloucestershire Hospitals NHS Foundation Trust, 
      Cheltenham, UK. RINGGOLD: 2379
FAU - Walsh, Chelsey
AU  - Walsh C
AD  - Clinical Biochemistry, Gloucestershire Hospitals NHS Foundation Trust, 
      Cheltenham, UK. RINGGOLD: 2379
FAU - Hibberd, Luke
AU  - Hibberd L
AUID- ORCID: 0009-0006-5388-4921
AD  - Clinical Biochemistry, Gloucestershire Hospitals NHS Foundation Trust, 
      Cheltenham, UK. RINGGOLD: 2379
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - England
TA  - Ann Clin Biochem
JT  - Annals of clinical biochemistry
JID - 0324055
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Google Bard
OT  - chatbot
OT  - result interpretation
OT  - thyroid
EDAT- 2023/09/13 00:42
MHDA- 2023/09/13 00:42
CRDT- 2023/09/12 22:13
PHST- 2023/09/13 00:42 [pubmed]
PHST- 2023/09/13 00:42 [medline]
PHST- 2023/09/12 22:13 [entrez]
AID - 10.1177/00045632231203473 [doi]
PST - aheadofprint
SO  - Ann Clin Biochem. 2023 Sep 20:45632231203473. doi: 10.1177/00045632231203473.

PMID- 37061037
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230926
IS  - 2468-7855 (Electronic)
IS  - 2468-7855 (Linking)
VI  - 124
IP  - 5
DP  - 2023 Oct
TI  - Can ChatGPT be used in oral and maxillofacial surgery?
PG  - 101471
LID - S2468-7855(23)00093-9 [pii]
LID - 10.1016/j.jormas.2023.101471 [doi]
AB  - OBJECTIVE: The aim of this study is to assess the usability of the information 
      generated by ChatGPT in oral and maxillofacial surgery. This assessment will have 
      two components: Firstly, to measure the quality of patient information provided, 
      and secondly, to measure the quality of educational information in this field. 
      MATERIALS AND METHODS: Commonly asked questions by patients about oral and 
      maxillofacial surgical procedures and technical questions for training purposes 
      were selected to be posed to ChatGPT. The questions were divided into two 
      categories, consisting of 60 Patient Questions and 60 Technical Questions. The 
      specific topics covered were Impacted Teeth, Dental Implants, Temporomandibular 
      Joint Diseases, and Orthognathic Surgery. ChatGPT provided responses to these 
      questions on February 23, 2023. The answers were evaluated by oral and 
      maxillofacial surgeons using a modified global quality scale. RESULTS: The study 
      involved 33 participating surgeons. The mean score for the answers to the Patient 
      Questions was 4.62±0.78, while the mean score for answers to the Technical 
      Questions was 3.1 ± 1.49. The difference in scores between the two question 
      categories was found to be statistically significant (P<0.001). There was no 
      significant difference between the quality of the answers given to the questions 
      across the different topics (P>0.05). CONCLUSIONS: ChatGPT has significant 
      potential as a tool for patient information in oral and maxillofacial surgery. 
      However, its use in training may not be completely safe at present. Surgeons 
      should exercise caution when using ChatGPT and consider it as a supplement to 
      their clinical knowledge and experience.
CI  - Copyright © 2023 Elsevier Masson SAS. All rights reserved.
FAU - Balel, Yunus
AU  - Balel Y
AD  - Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Tokat 
      Gaziosmanpasa University, Tokat, Turkey. Electronic address: 
      yunusbalel@hotmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230413
PL  - France
TA  - J Stomatol Oral Maxillofac Surg
JT  - Journal of stomatology, oral and maxillofacial surgery
JID - 101701089
SB  - IM
CIN - J Stomatol Oral Maxillofac Surg. 2023 Oct;124(5):101492. PMID: 37149261
MH  - Humans
MH  - *Surgery, Oral
MH  - *Temporomandibular Joint Disorders
MH  - *Tooth, Impacted
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chatbot
OT  - Patient information
COIS- Declaration of Competing Interest The author declares that there is no conflict 
      of interest.
EDAT- 2023/04/16 06:00
MHDA- 2023/09/25 06:42
CRDT- 2023/04/15 19:26
PHST- 2023/04/03 00:00 [received]
PHST- 2023/04/05 00:00 [revised]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/04/16 06:00 [pubmed]
PHST- 2023/04/15 19:26 [entrez]
AID - S2468-7855(23)00093-9 [pii]
AID - 10.1016/j.jormas.2023.101471 [doi]
PST - ppublish
SO  - J Stomatol Oral Maxillofac Surg. 2023 Oct;124(5):101471. doi: 
      10.1016/j.jormas.2023.101471. Epub 2023 Apr 13.

PMID- 37922198
OWN - NLM
STAT- MEDLINE
DCOM- 20231106
LR  - 20231107
IS  - 2050-084X (Electronic)
IS  - 2050-084X (Linking)
VI  - 12
DP  - 2023 Nov 3
TI  - ChatGPT identifies gender disparities in scientific peer review.
LID - 10.7554/eLife.90230 [doi]
LID - RP90230
AB  - The peer review process is a critical step in ensuring the quality of scientific 
      research. However, its subjectivity has raised concerns. To investigate this 
      issue, I examined over 500 publicly available peer review reports from 200 
      published neuroscience papers in 2022-2023. OpenAI's generative artificial 
      intelligence ChatGPT was used to analyze language use in these reports, which 
      demonstrated superior performance compared to traditional lexicon- and rule-based 
      language models. As expected, most reviews for these published papers were seen 
      as favorable by ChatGPT (89.8% of reviews), and language use was mostly polite 
      (99.8% of reviews). However, this analysis also demonstrated high levels of 
      variability in how each reviewer scored the same paper, indicating the presence 
      of subjectivity in the peer review process. The results further revealed that 
      female first authors received less polite reviews than their male peers, 
      indicating a gender bias in reviewing. In addition, published papers with a 
      female senior author received more favorable reviews than papers with a male 
      senior author, for which I discuss potential causes. Together, this study 
      highlights the potential of generative artificial intelligence in performing 
      natural language processing of specialized scientific texts. As a proof of 
      concept, I show that ChatGPT can identify areas of concern in scientific peer 
      review, underscoring the importance of transparent peer review in studying 
      equitability in scientific publishing.
CI  - © 2023, Verharen.
FAU - Verharen, Jeroen P H
AU  - Verharen JPH
AUID- ORCID: 0000-0001-7582-802X
AD  - Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, 
      University of California, Berkeley, Berkeley, United States.
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - England
TA  - Elife
JT  - eLife
JID - 101579614
SB  - IM
UOF - doi: 10.1101/2023.07.18.549552
UOF - doi: 10.7554/eLife.90230.1
UOF - doi: 10.7554/eLife.90230.2
MH  - Female
MH  - Male
MH  - Humans
MH  - *Publishing
MH  - *Artificial Intelligence
MH  - Sexism
MH  - Peer Review
MH  - Research Report
PMC - PMC10624422
OAB - Peer review is a vital step in ensuring the quality and accuracy of scientific 
      research before publication. Experts assess research manuscripts, advise journal 
      editors on publishing them, and provide authors with recommendations for 
      improvement. But some scientists have raised concerns about potential biases and 
      subjectivity in the peer review process. Author attributes, such as gender, 
      reputation, or how prestigious their institution is, may subconsciously influence 
      reviewers’ scores. Studying peer review to identify potential biases is 
      challenging. The language reviewers use is very technical, and some of their 
      commentary may be subjective and vary from reviewer to reviewer. The emergence of 
      OpenAI’s ChatGPT, which uses machine learning to process large amounts of 
      information, may provide a new tool to analyze peer review for signs of bias. 
      Verharen demonstrated that ChatGPT can be used to analyze peer review reports and 
      found potential indications of gender bias in scientific publishing. In the 
      experiments, Verharen asked ChatGPT to analyze more than 500 reviews of 200 
      neuroscience studies published in the scientific journal Nature Communications 
      over the past year. The experiments found no evidence that institutional 
      reputation influenced reviews. Yet, female first authors were more likely to 
      receive impolite comments from reviewers. Female senior authors were more likely 
      to receive higher review scores, which may indicate they had to clear a higher 
      bar for publication. The experiments indicate that ChatGPT could be used to 
      analyze peer review for fairness. Verharen suggests that reviewers might apply 
      this tool to ensure their reviews are polite and accurate reflections of their 
      opinions. Scientists or publishers might also use it for large-scale analyses of 
      peer review in individual journals or in scientific publishing more widely. 
      Journals might also use ChatGPT to assess the impact of bias-prevention 
      interventions on review fairness.
OABL- eng
OTO - NOTNLM
OT  - artificial intelligence
OT  - meta-science
OT  - natural language processing
OT  - neuroscience
OT  - none
OT  - peer review
COIS- JV No competing interests declared
EDAT- 2023/11/03 18:43
MHDA- 2023/11/06 06:43
CRDT- 2023/11/03 12:52
PHST- 2023/11/06 06:43 [medline]
PHST- 2023/11/03 18:43 [pubmed]
PHST- 2023/11/03 12:52 [entrez]
AID - 90230 [pii]
AID - 10.7554/eLife.90230 [doi]
PST - epublish
SO  - Elife. 2023 Nov 3;12:RP90230. doi: 10.7554/eLife.90230.

PMID- 38029791
OWN - NLM
STAT- Publisher
LR  - 20231129
IS  - 1098-8793 (Electronic)
IS  - 0736-6825 (Linking)
DP  - 2023 Nov 29
TI  - ChatGPT and Rhinoplasty Recovery: An Exploration of AI's Role in Post-Operative 
      Guidance.
LID - 10.1055/a-2219-4901 [doi]
AB  - The potential applications of artificial intelligence (AI) in healthcare have 
      garnered significant interest in recent years. This study presents the first 
      published exploration of ChatGPT, an AI language model, as a tool for providing 
      post-operative guidance during rhinoplasty recovery. The primary objective was to 
      shed light on the role of ChatGPT in augmenting patient care during the critical 
      post-operative phase. Using the Rhinobase database, standardized questions were 
      formulated to evaluate AI-generated responses addressing pain management, 
      swelling, bruising, and potential asymmetries. Results demonstrated that ChatGPT 
      has the potential to enhance patient education and alleviate emotional distress 
      by providing general information and reassurance during the recovery process. 
      However, the study emphasized that AI should not replace personalized advice from 
      qualified healthcare professionals. This pioneering investigation offers valuable 
      insights into the integration of AI and human expertise, paving the way for 
      optimized post-rhinoplasty recovery care.
CI  - Thieme. All rights reserved.
FAU - Capelleras, Marta
AU  - Capelleras M
AD  - OTORHINOLARYNGOLOGY, Ege University, Izmir, Turkey.
AD  - OTORHINOLARYNGOLOGY, Hospital Universitario de Bellvitge, L'Hospitalet de 
      Llobregat, Spain.
FAU - Soto-Galindo, Germán Armando
AU  - Soto-Galindo GA
AD  - Department of Otolaryngology, Dr Jose Eleuterio Gonzalez University Hospital, 
      Monterrey, Mexico.
FAU - Cruellas, Marc
AU  - Cruellas M
AD  - OTORHINOLARYNGOLOGY, Hospital Universitari de Bellvitge, L'Hospitalet de 
      Llobregat, Spain.
FAU - Apaydin, Fazil
AU  - Apaydin F
AD  - OTORHINOLARYNGOLOGY, EGE UNIVERSITY, IZMIR, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20231129
PL  - United States
TA  - Facial Plast Surg
JT  - Facial plastic surgery : FPS
JID - 8405303
COIS- The authors declare that they have no conflict of interest.
EDAT- 2023/11/30 00:42
MHDA- 2023/11/30 00:42
CRDT- 2023/11/29 19:13
PHST- 2023/11/30 00:42 [medline]
PHST- 2023/11/30 00:42 [pubmed]
PHST- 2023/11/29 19:13 [entrez]
AID - 10.1055/a-2219-4901 [doi]
PST - aheadofprint
SO  - Facial Plast Surg. 2023 Nov 29. doi: 10.1055/a-2219-4901.

PMID- 37809772
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231018
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 9
DP  - 2023 Sep
TI  - The impact of Artificial Intelligence in academia: Views of Turkish academics on 
      ChatGPT.
PG  - e19688
LID - 10.1016/j.heliyon.2023.e19688 [doi]
LID - e19688
AB  - In the past decade, Artificial Intelligence (AI) and machine learning 
      technologies have become increasingly prevalent in the academic world. This 
      growing trend has led to debates about the impact of these technologies on 
      academia. The purpose of this article is to examine the impact of ChatGPT, an AI 
      and machine learning technology, in the academic field and to determine 
      academics' perceptions of it. To achieve this goal, in-depth interviews were 
      conducted with 10 academics, and their views on the subject were analyzed. It is 
      seen that academics believe that ChatGPT will play a helpful role as a tool in 
      scientific research and educational processes and can serve as an inspiration for 
      new topics and research areas. Despite these advantages, academics also have 
      ethical concerns, such as plagiarism and misinformation. The study found that 
      ChatGPT is viewed positively as a useful tool in scientific research and 
      education, but ethical concerns such as plagiarism and misinformation need to be 
      addressed.
CI  - © 2023 The Authors.
FAU - Livberber, Tuba
AU  - Livberber T
AD  - Department of Journalism, Faculty of Communication, University of Akdeniz, 
      Antalya, Turkey.
FAU - Ayvaz, Süheyla
AU  - Ayvaz S
AD  - Department of Advertising, Faculty of Communication, University of Selcuk, Konya, 
      Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230901
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10558923
OTO - NOTNLM
OT  - Academia
OT  - Artificial intelligence
OT  - Human-AI collaboration
OT  - Learning & teaching
OT  - Machine learning
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/10/09 06:41
MHDA- 2023/10/09 06:42
CRDT- 2023/10/09 05:57
PHST- 2023/06/06 00:00 [received]
PHST- 2023/08/15 00:00 [revised]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/10/09 06:41 [pubmed]
PHST- 2023/10/09 05:57 [entrez]
AID - S2405-8440(23)06896-2 [pii]
AID - e19688 [pii]
AID - 10.1016/j.heliyon.2023.e19688 [doi]
PST - epublish
SO  - Heliyon. 2023 Sep 1;9(9):e19688. doi: 10.1016/j.heliyon.2023.e19688. eCollection 
      2023 Sep.

PMID- 37674966
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230908
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Integrating ChatGPT in Medical Education: Adapting Curricula to Cultivate 
      Competent Physicians for the AI Era.
PG  - e43036
LID - 10.7759/cureus.43036 [doi]
LID - e43036
AB  - The rapid advancements in artificial intelligence (AI) language models, 
      particularly ChatGPT (OpenAI, San Francisco, California, United States), 
      necessitate the adaptation of medical education curricula to cultivate competent 
      physicians in the AI era. In this editorial, we discuss short-term solutions and 
      long-term adaptations for integrating ChatGPT into medical education. We 
      recommend promoting digital literacy, developing critical thinking skills, and 
      emphasizing evidence-based relevance as quick fixes. Long-term adaptations 
      include focusing on the human factor, interprofessional collaboration, continuous 
      professional development, and research and evaluation. By implementing these 
      changes, medical educators can optimize medical education for the AI era, 
      ensuring students are well prepared for a technologically advanced future in 
      healthcare.
CI  - Copyright © 2023, Jamal et al.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Family and Community Medicine, King Saud University, Riyadh, SAU.
FAU - Solaiman, Mona
AU  - Solaiman M
AD  - Medical Education, King Saud University, Riyadh, SAU.
FAU - Alhasan, Khalid
AU  - Alhasan K
AD  - Pediatric Nephrology, King Saud University, Riyadh, SAU.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Intensive Care Unit, King Saud University, Riyadh, SAU.
FAU - Sayed, Gary
AU  - Sayed G
AD  - Medical Education, California State University, Carson, USA.
LA  - eng
PT  - Editorial
DEP - 20230806
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10479954
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - ai in medical education
OT  - artificial intelligence
OT  - chatgpt
OT  - critical thinking
OT  - curriculum adaptation
OT  - digital literacy
OT  - interprofessional collaboration
OT  - long-term solutions
OT  - rapid solutions
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/07 06:43
MHDA- 2023/09/07 06:44
CRDT- 2023/09/07 04:12
PHST- 2023/08/06 00:00 [accepted]
PHST- 2023/09/07 06:44 [medline]
PHST- 2023/09/07 06:43 [pubmed]
PHST- 2023/09/07 04:12 [entrez]
AID - 10.7759/cureus.43036 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 6;15(8):e43036. doi: 10.7759/cureus.43036. eCollection 2023 Aug.

PMID- 37736448
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230923
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Large Language Models in Hematology Case Solving: A Comparative Study of 
      ChatGPT-3.5, Google Bard, and Microsoft Bing.
PG  - e43861
LID - 10.7759/cureus.43861 [doi]
LID - e43861
AB  - Background Large language models (LLMs), such as ChatGPT-3.5, Google Bard, and 
      Microsoft Bing, have shown promising capabilities in various natural language 
      processing (NLP) tasks. However, their performance and accuracy in solving 
      domain-specific questions, particularly in the field of hematology, have not been 
      extensively investigated. Objective This study aimed to explore the capability of 
      LLMs, namely, ChatGPT-3.5, Google Bard, and Microsoft Bing (Precise), in solving 
      hematology-related cases and comparing their performance. Methods This was a 
      cross-sectional study conducted in the Department of Physiology and Pathology, 
      All India Institute of Medical Sciences, Deoghar, Jharkhand, India. We curated a 
      set of 50 cases on hematology covering a range of topics and complexities. The 
      dataset included queries related to blood disorders, hematologic malignancies, 
      laboratory test parameters, calculations, and treatment options. Each case and 
      related question was prepared with a set of correct answers to compare with. We 
      utilized ChatGPT-3.5, Google Bard Experiment, and Microsoft Bing (Precise) for 
      question-answering tasks. The answers were checked by two physiologists and one 
      pathologist. They rated the answers on a rating scale from one to five. The 
      average score of the three models was compared by Friedman's test with Dunn's 
      post-hoc test. The performance of the LLMs was compared with a median of 2.5 by a 
      one-sample median test as the curriculum from which the questions were curated 
      has a 50% pass grade. Results The scores among the three LLMs were significantly 
      different (p-value < 0.0001) with the highest score by ChatGPT (3.15±1.19), 
      followed by Bard (2.23±1.17) and Bing (1.98±1.01). The score of ChatGPT was 
      significantly higher than 50% (p-value = 0.0004), Bard's score was close to 50% 
      (p-value = 0.38), and Bing's score was significantly lower than the pass score 
      (p-value = 0.0015). Conclusion The LLMs reveal significant differences in solving 
      case vignettes in hematology. ChatGPT exhibited the highest score, followed by 
      Google Bard and Microsoft Bing. The observed performance trends suggest that 
      ChatGPT holds promising potential in the medical domain. However, none of the 
      models was capable of answering all questions accurately. Further research and 
      optimization of language models can offer valuable contributions to healthcare 
      and medical education applications.
CI  - Copyright © 2023, Kumari et al.
FAU - Kumari, Amita
AU  - Kumari A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Kumari, Anita
AU  - Kumari A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Singh, Amita
AU  - Singh A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Singh, Sanjeet K
AU  - Singh SK
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Juhi, Ayesha
AU  - Juhi A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Dhanvijay, Anup Kumar D
AU  - Dhanvijay AKD
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Pinjar, Mohammed Jaffer
AU  - Pinjar MJ
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10511207
OTO - NOTNLM
OT  - ai and robotics in healthcare
OT  - chatgpt
OT  - google bard
OT  - hematologic diseases
OT  - hematology
OT  - microsoft bing
OT  - natural language processing
OT  - pathologists
OT  - pathology
OT  - search engine
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/22 06:42
MHDA- 2023/09/22 06:43
CRDT- 2023/09/22 03:46
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/09/22 06:43 [medline]
PHST- 2023/09/22 06:42 [pubmed]
PHST- 2023/09/22 03:46 [entrez]
AID - 10.7759/cureus.43861 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 21;15(8):e43861. doi: 10.7759/cureus.43861. eCollection 2023 
      Aug.

PMID- 37615976
OWN - NLM
STAT- Publisher
LR  - 20231019
IS  - 2374-2445 (Electronic)
IS  - 2374-2437 (Print)
IS  - 2374-2437 (Linking)
VI  - 9
IP  - 10
DP  - 2023 Oct 1
TI  - Use of Artificial Intelligence Chatbots for Cancer Treatment Information.
PG  - 1459-1462
LID - 10.1001/jamaoncol.2023.2954 [doi]
FAU - Chen, Shan
AU  - Chen S
AD  - Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard 
      Medical School, Boston, Massachusetts.
FAU - Kann, Benjamin H
AU  - Kann BH
AD  - Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard 
      Medical School, Boston, Massachusetts.
FAU - Foote, Michael B
AU  - Foote MB
AD  - Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New 
      York.
FAU - Aerts, Hugo J W L
AU  - Aerts HJWL
AD  - Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard 
      Medical School, Boston, Massachusetts.
FAU - Savova, Guergana K
AU  - Savova GK
AD  - Computational Health Informatics Program, Boston Children's Hospital, Boston, 
      Massachusetts.
FAU - Mak, Raymond H
AU  - Mak RH
AD  - Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard 
      Medical School, Boston, Massachusetts.
FAU - Bitterman, Danielle S
AU  - Bitterman DS
AD  - Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard 
      Medical School, Boston, Massachusetts.
LA  - eng
PT  - Comment
PT  - Journal Article
PL  - United States
TA  - JAMA Oncol
JT  - JAMA oncology
JID - 101652861
SB  - IM
CON - JAMA Oncol. 2023 Oct 1;9(10):1341-1342. PMID: 37615950
PMC - PMC10450584
OAB - This survey study examines the performance of a large language model chatbot in 
      providing cancer treatment recommendations that are concordant with National 
      Comprehensive Cancer Network guidelines.
OABL- eng
COIS- Conflict of Interest Disclosures: Dr Aerts reported receiving personal fees from 
      Onc.AI, Sphera, LLC, and Bristol Myers Squibb outside the submitted work. Dr Mak 
      reported receiving personal fees from ViewRay, AstraZeneca, Novartis, Varian 
      Medical Systems, and Sio Capital outside the submitted work. Dr Bitterman 
      reported serving as an associate editor for HemOnc.org and her institution 
      received support for research from the American Association for Cancer Research 
      outside the submitted work. No other disclosures were reported.
EDAT- 2023/08/24 13:43
MHDA- 2023/08/24 13:43
CRDT- 2023/08/24 11:45
PHST- 2023/08/24 13:43 [pubmed]
PHST- 2023/08/24 13:43 [medline]
PHST- 2023/08/24 11:45 [entrez]
AID - 2808731 [pii]
AID - cld230013 [pii]
AID - 10.1001/jamaoncol.2023.2954 [doi]
PST - ppublish
SO  - JAMA Oncol. 2023 Oct 1;9(10):1459-1462. doi: 10.1001/jamaoncol.2023.2954.

PMID- 38054196
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231207
IS  - 2666-3287 (Electronic)
IS  - 2666-3287 (Linking)
VI  - 14
DP  - 2024 Mar
TI  - Best practices for implementing ChatGPT, large language models, and artificial 
      intelligence in qualitative and survey-based research.
PG  - 22-23
LID - 10.1016/j.jdin.2023.10.001 [doi]
FAU - Kantor, Jonathan
AU  - Kantor J
AD  - Department of Dermatology, Center for Global Health, and Center for Clinical 
      Epidemiology and Biostatistics, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania.
AD  - Florida Center for Dermatology, St Augustine, Florida.
LA  - eng
PT  - Editorial
DEP - 20231008
PL  - United States
TA  - JAAD Int
JT  - JAAD international
JID - 101774762
PMC - PMC10694559
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT-4
OT  - Google Bard
OT  - LLM
OT  - Microsoft Bing
OT  - NLP
OT  - QDA
OT  - annotation
OT  - artificial intelligence
OT  - factor analysis
OT  - focus group
OT  - large language models
OT  - medical education
OT  - natural language processing
OT  - practice management
OT  - prompt engineering
OT  - qualitative data analysis software
OT  - qualitative research
COIS- None disclosed.
EDAT- 2023/12/06 06:42
MHDA- 2023/12/06 06:43
CRDT- 2023/12/06 04:20
PHST- 2023/12/06 06:43 [medline]
PHST- 2023/12/06 06:42 [pubmed]
PHST- 2023/12/06 04:20 [entrez]
AID - S2666-3287(23)00157-8 [pii]
AID - 10.1016/j.jdin.2023.10.001 [doi]
PST - epublish
SO  - JAAD Int. 2023 Oct 8;14:22-23. doi: 10.1016/j.jdin.2023.10.001. eCollection 2024 
      Mar.

PMID- 37270457
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 11
DP  - 2023 Nov
TI  - Response of ChatGPT for Humanoid Robots Role in Improving Healthcare and Patient 
      Outcomes.
PG  - 2359-2361
LID - 10.1007/s10439-023-03267-1 [doi]
AB  - Humanoid robotics is characterized by constant developments, which are supported 
      by several research facilities across the world. Humanoid robots are used in many 
      different industries. In this setting, this letter, written by people, makes use 
      of ChatGPT answers to examine how humanoid robots might be used in the medical 
      industry, particularly in light of the COVID-19 pandemic and in future. Although 
      humanoid robots can help with certain jobs, it is important to recognize the 
      indispensable importance of human healthcare professionals who have knowledge, 
      empathy, and the capacity for critical judgment. Although humanoid robots can 
      complement healthcare initiatives, they shouldn't be viewed as a full-fledged 
      replacement for human care.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Janamla, Varaprasad
AU  - Janamla V
AUID- ORCID: 0000-0001-9716-5458
AD  - Department of Electrical and Electronics Engineering, School of Engineering and 
      Technology, CHRIST (Deemed to Be University), Bangalore, Karnataka, 560074, 
      India. varaprasad.janamala@christuniversity.in.
FAU - Daram, Suresh Babu
AU  - Daram SB
AD  - Department of Electrical and Electronics Engineering, Mohan Babu University (Erst 
      while Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh, 517102, 
      India.
FAU - Rajesh, Patil
AU  - Rajesh P
AD  - Department of Electrical and Electronics Engineering, SVERIS College of 
      Engineering, Pamdharpur, Solarpur, Maharashtra, 413304, India.
FAU - Kumari, C H Nagaraja
AU  - Kumari CHN
AD  - Department of Electrical and Electronics Engineering, Guru Nanak Institute of 
      Technology, Ibrahimpatnam, Telangana, 501506, India.
LA  - eng
PT  - Letter
DEP - 20230603
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
PMC - PMC10239274
OTO - NOTNLM
OT  - ChatGPT
OT  - Humanoid robotics
OT  - Medical field
OT  - Patient outcomes
COIS- The authors declare that they have no conflict of interest.
EDAT- 2023/06/04 01:08
MHDA- 2023/06/04 01:08
CRDT- 2023/06/03 23:04
PHST- 2023/05/28 00:00 [received]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/06/04 01:08 [pubmed]
PHST- 2023/06/04 01:08 [medline]
PHST- 2023/06/03 23:04 [entrez]
AID - 10.1007/s10439-023-03267-1 [pii]
AID - 3267 [pii]
AID - 10.1007/s10439-023-03267-1 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Nov;51(11):2359-2361. doi: 10.1007/s10439-023-03267-1. Epub 
      2023 Jun 3.

PMID- 37062612
OWN - NLM
STAT- MEDLINE
DCOM- 20230515
LR  - 20230515
IS  - 1768-3122 (Electronic)
IS  - 0248-8663 (Linking)
VI  - 44
IP  - 5
DP  - 2023 May
TI  - [Artificial intelligence and internal medicine: The example of hydroxychloroquine 
      according to ChatGPT].
PG  - 218-226
LID - S0248-8663(23)00121-2 [pii]
LID - 10.1016/j.revmed.2023.03.017 [doi]
AB  - Artificial intelligence (AI) using deep learning is revolutionizing several 
      fields, including medicine, with a wide range of applications. Available since 
      the end of 2022, ChatGPT is a conversational AI or "chatbot", using artificial 
      intelligence to dialogue with its users in all fields. Through the example of 
      hydroxychloroquine (HCQ), we discuss its use for patients, clinicians, or 
      researchers, and discuss its performance and limitations, particularly in 
      relation to algorithmic bias. If AI tools using deep learning do not dispense 
      with the expertise and experience of a clinician (at least, for the moment), they 
      have a potential to improve or simplify our daily practice.
CI  - Copyright © 2023 Société Nationale Française de Médecine Interne (SNFMI). 
      Published by Elsevier Masson SAS. All rights reserved.
FAU - Nguyen, Y
AU  - Nguyen Y
AD  - Service de médecine interne, hôpital Cochin, AP-HP centre, Université Paris cité, 
      75014 Paris, France; Centre de recherche en épidémiologie et statistiques 
      (CRESS), unité Inserm 1153, Université de Paris cité, Paris, France. Electronic 
      address: yann.nguyen2@aphp.fr.
FAU - Costedoat-Chalumeau, N
AU  - Costedoat-Chalumeau N
AD  - Service de médecine interne, hôpital Cochin, AP-HP centre, Université Paris cité, 
      75014 Paris, France; Centre de recherche en épidémiologie et statistiques 
      (CRESS), unité Inserm 1153, Université de Paris cité, Paris, France.
LA  - fre
PT  - English Abstract
PT  - Journal Article
TT  - Les intelligences artificielles conversationnelles en médecine interne : 
      l’exemple de l’hydroxychloroquine selon ChatGPT.
DEP - 20230414
PL  - France
TA  - Rev Med Interne
JT  - La Revue de medecine interne
JID - 8101383
RN  - 4QWG6N8QKH (Hydroxychloroquine)
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Hydroxychloroquine/therapeutic use
MH  - Internal Medicine
MH  - Software
MH  - Communication
OTO - NOTNLM
OT  - Artifical intelligence
OT  - ChatGPT
OT  - Hydroxychloroquine
OT  - Intelligence artificielle
OT  - Lupus érythémateux systémique
OT  - Systemic lupus erythematosus
EDAT- 2023/04/17 06:00
MHDA- 2023/05/15 06:42
CRDT- 2023/04/16 21:57
PHST- 2023/01/18 00:00 [received]
PHST- 2023/03/22 00:00 [revised]
PHST- 2023/03/31 00:00 [accepted]
PHST- 2023/05/15 06:42 [medline]
PHST- 2023/04/17 06:00 [pubmed]
PHST- 2023/04/16 21:57 [entrez]
AID - S0248-8663(23)00121-2 [pii]
AID - 10.1016/j.revmed.2023.03.017 [doi]
PST - ppublish
SO  - Rev Med Interne. 2023 May;44(5):218-226. doi: 10.1016/j.revmed.2023.03.017. Epub 
      2023 Apr 14.

PMID- 38027458
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2473-0114 (Electronic)
IS  - 2473-0114 (Linking)
VI  - 8
IP  - 4
DP  - 2023 Oct
TI  - Evaluating the Quality and Usability of Artificial Intelligence-Generated 
      Responses to Common Patient Questions in Foot and Ankle Surgery.
PG  - 24730114231209919
LID - 10.1177/24730114231209919 [doi]
LID - 24730114231209919
AB  - BACKGROUND: Artificial intelligence (AI) platforms, such as ChatGPT, have become 
      increasingly popular outlets for the consumption and distribution of health 
      care-related advice. Because of a lack of regulation and oversight, the 
      reliability of health care-related responses has become a topic of controversy in 
      the medical community. To date, no study has explored the quality of AI-derived 
      information as it relates to common foot and ankle pathologies. This study aims 
      to assess the quality and educational benefit of ChatGPT responses to common foot 
      and ankle-related questions. METHODS: ChatGPT was asked a series of 5 questions, 
      including "What is the optimal treatment for ankle arthritis?" "How should I 
      decide on ankle arthroplasty versus ankle arthrodesis?" "Do I need surgery for 
      Jones fracture?" "How can I prevent Charcot arthropathy?" and "Do I need to see a 
      doctor for my ankle sprain?" Five responses (1 per each question) were included 
      after applying the exclusion criteria. The content was graded using DISCERN (a 
      well-validated informational analysis tool) and AIRM (a self-designed tool for 
      exercise evaluation). RESULTS: Health care professionals graded the 
      ChatGPT-generated responses as bottom tier 4.5% of the time, middle tier 27.3% of 
      the time, and top tier 68.2% of the time. CONCLUSION: Although ChatGPT and other 
      related AI platforms have become a popular means for medical information 
      distribution, the educational value of the AI-generated responses related to foot 
      and ankle pathologies was variable. With 4.5% of responses receiving a 
      bottom-tier rating, 27.3% of responses receiving a middle-tier rating, and 68.2% 
      of responses receiving a top-tier rating, health care professionals should be 
      aware of the high viewership of variable-quality content easily accessible on 
      ChatGPT. LEVEL OF EVIDENCE: Level III, cross sectional study.
CI  - © The Author(s) 2023.
FAU - Anastasio, Albert Thomas
AU  - Anastasio AT
AUID- ORCID: 0000-0001-5817-3826
AD  - Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, 
      USA.
FAU - Mills, Frederic Baker 4th
AU  - Mills FB 4th
AD  - Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, 
      USA.
FAU - Karavan, Mark P Jr
AU  - Karavan MP Jr
AUID- ORCID: 0000-0002-8954-5861
AD  - Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, 
      USA.
FAU - Adams, Samuel B Jr
AU  - Adams SB Jr
AD  - Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - United States
TA  - Foot Ankle Orthop
JT  - Foot & ankle orthopaedics
JID - 101752333
PMC - PMC10666700
OTO - NOTNLM
OT  - Charcot arthropathy
OT  - ChatGPT
OT  - Jones fracture
OT  - ankle arthritis
OT  - ankle arthroplasty
OT  - ankle sprain
OT  - artificial intelligence
OT  - education
COIS- The author(s) declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article. ICMJE forms for all 
      authors are available online.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 17:05
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 17:05 [entrez]
AID - 10.1177_24730114231209919 [pii]
AID - 10.1177/24730114231209919 [doi]
PST - epublish
SO  - Foot Ankle Orthop. 2023 Nov 22;8(4):24730114231209919. doi: 
      10.1177/24730114231209919. eCollection 2023 Oct.

PMID- 37795257
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231006
IS  - 2631-4797 (Electronic)
IS  - 2631-4797 (Linking)
VI  - 5
DP  - 2023
TI  - Comparing the Efficacy of Large Language Models ChatGPT, BARD, and Bing AI in 
      Providing Information on Rhinoplasty: An Observational Study.
PG  - ojad084
LID - 10.1093/asjof/ojad084 [doi]
LID - ojad084
AB  - BACKGROUND: Large language models (LLMs) are emerging artificial intelligence 
      (AI) technologies refining research and healthcare. However, the impact of these 
      models on presurgical planning and education remains under-explored. OBJECTIVES: 
      This study aims to assess 3 prominent LLMs-Google's AI BARD (Mountain View, CA), 
      Bing AI (Microsoft, Redmond, WA), and ChatGPT-3.5 (Open AI, San Francisco, CA) in 
      providing safe medical information for rhinoplasty. METHODS: Six questions 
      regarding rhinoplasty were prompted to ChatGPT, BARD, and Bing AI. A Likert scale 
      was used to evaluate these responses by a panel of Specialist Plastic and 
      Reconstructive Surgeons with extensive experience in rhinoplasty. To measure 
      reliability, the Flesch Reading Ease Score, the Flesch-Kincaid Grade Level, and 
      the Coleman-Liau Index were used. The modified DISCERN score was chosen as the 
      criterion for assessing suitability and reliability. A t test was performed to 
      calculate the difference between the LLMs, and a double-sided P-value <.05 was 
      considered statistically significant. RESULTS: In terms of reliability, BARD and 
      ChatGPT demonstrated a significantly (P < .05) greater Flesch Reading Ease Score 
      of 47.47 (±15.32) and 37.68 (±12.96), Flesch-Kincaid Grade Level of 9.7 (±3.12) 
      and 10.15 (±1.84), and a Coleman-Liau Index of 10.83 (±2.14) and 12.17 (±1.17) 
      than Bing AI. In terms of suitability, BARD (46.3 ± 2.8) demonstrated a 
      significantly greater DISCERN score than ChatGPT and Bing AI. In terms of Likert 
      score, ChatGPT and BARD demonstrated similar scores and were greater than Bing 
      AI. CONCLUSIONS: BARD delivered the most succinct and comprehensible information, 
      followed by ChatGPT and Bing AI. Although these models demonstrate potential, 
      challenges regarding their depth and specificity remain. Therefore, future 
      research should aim to augment LLM performance through the integration of 
      specialized databases and expert knowledge, while also refining their algorithms.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of The 
      Aesthetic Society.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
FAU - Lim, Bryan
AU  - Lim B
FAU - Xie, Yi
AU  - Xie Y
FAU - Cevik, Jevan
AU  - Cevik J
FAU - Rozen, Warren M
AU  - Rozen WM
FAU - Ross, Richard J
AU  - Ross RJ
FAU - Lee, Mathew
AU  - Lee M
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - England
TA  - Aesthet Surg J Open Forum
JT  - Aesthetic surgery journal. Open forum
JID - 101771713
PMC - PMC10547367
EDAT- 2023/10/05 06:44
MHDA- 2023/10/05 06:45
CRDT- 2023/10/05 04:08
PHST- 2023/10/05 06:45 [medline]
PHST- 2023/10/05 06:44 [pubmed]
PHST- 2023/10/05 04:08 [entrez]
AID - ojad084 [pii]
AID - 10.1093/asjof/ojad084 [doi]
PST - epublish
SO  - Aesthet Surg J Open Forum. 2023 Sep 14;5:ojad084. doi: 10.1093/asjof/ojad084. 
      eCollection 2023.

PMID- 37090396
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230425
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Currarino Syndrome Presenting in Adulthood: A Rare Case.
PG  - e36497
LID - 10.7759/cureus.36497 [doi]
LID - e36497
AB  - Currarino syndrome or Currarino triad is a complex condition consisting of 
      congenital anomalies. The triad consists of anterior sacral mass (meningocele, 
      teratoma or dermoid/epidermoid cyst), sacral bone defect (hypoplasia, agenesis ), 
      anorectal malformation/congenital anorectal stenosis. This condition is named 
      after Dr Guido Currarino, an Italian-American paediatric radiologist, who first 
      described it in 1975. This needs a multidisciplinary treatment approach. We 
      describe a case of successfully managed young adult with Currarino syndrome. The 
      latest artificial intelligence tool, Chat Generative Pre-Trained Transformer 
      (ChatGPT), helped to write this case report.
CI  - Copyright © 2023, Verma et al.
FAU - Verma, Abhijit
AU  - Verma A
AD  - Department of Neurosurgery, NIMS University, Jaipur, IND.
FAU - Attry, Sanjeev
AU  - Attry S
AD  - Department of Neurosurgery, NIMS University, Jaipur, IND.
FAU - Haldenia, Kulbhushan
AU  - Haldenia K
AD  - Surgical Gastroenterology, NIMS University, Jaipur, IND.
FAU - Gupta, Vijay K
AU  - Gupta VK
AD  - Department of Neurosurgery, NIMS University, Jaipur, IND.
FAU - Bansal, Puneet
AU  - Bansal P
AD  - Department of Neurosurgery, NIMS University, Jaipur, IND.
LA  - eng
PT  - Case Reports
DEP - 20230321
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10119035
OTO - NOTNLM
OT  - anorectal anomaly
OT  - anterior sacral meningocoele
OT  - chatgpt
OT  - currarino syndrome
OT  - currarino triad
OT  - sacrococcygeal agenesis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/24 06:41
MHDA- 2023/04/24 06:42
CRDT- 2023/04/24 03:40
PHST- 2023/03/21 00:00 [accepted]
PHST- 2023/04/24 06:42 [medline]
PHST- 2023/04/24 06:41 [pubmed]
PHST- 2023/04/24 03:40 [entrez]
AID - 10.7759/cureus.36497 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 21;15(3):e36497. doi: 10.7759/cureus.36497. eCollection 2023 
      Mar.

PMID- 37410934
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231213
IS  - 1572-0241 (Electronic)
IS  - 0002-9270 (Linking)
VI  - 118
IP  - 12
DP  - 2023 Dec 1
TI  - Evaluation of the Potential Utility of an Artificial Intelligence Chatbot in 
      Gastroesophageal Reflux Disease Management.
PG  - 2276-2279
LID - 10.14309/ajg.0000000000002397 [doi]
AB  - INTRODUCTION: Artificial intelligence chatbots could serve as an information 
      resource for patients and a tool for clinicians. Their ability to respond 
      appropriately to questions regarding gastroesophageal reflux disease is unknown. 
      METHODS: Twenty-three prompts regarding gastroesophageal reflux disease 
      management were submitted to ChatGPT, and responses were rated by 3 
      gastroenterologists and 8 patients. RESULTS: ChatGPT provided largely appropriate 
      responses (91.3%), although with some inappropriateness (8.7%) and inconsistency. 
      Most responses (78.3%) contained at least some specific guidance. Patients 
      considered this a useful tool (100%). DISCUSSION: ChatGPT's performance 
      demonstrates the potential for this technology in health care, although also its 
      limitations in its current state.
CI  - Copyright © 2023 by The American College of Gastroenterology.
FAU - Henson, Jacqueline B
AU  - Henson JB
AUID- ORCID: 0000-0003-3488-6615
AD  - Division of Gastroenterology, Department of Medicine, Duke University School of 
      Medicine, Durham, North Carolina, USA.
FAU - Glissen Brown, Jeremy R
AU  - Glissen Brown JR
AD  - Division of Gastroenterology, Department of Medicine, Duke University School of 
      Medicine, Durham, North Carolina, USA.
FAU - Lee, Joshua P
AU  - Lee JP
AD  - Division of Gastroenterology, Department of Medicine, Duke University School of 
      Medicine, Durham, North Carolina, USA.
FAU - Patel, Amit
AU  - Patel A
AD  - Division of Gastroenterology, Department of Medicine, Duke University School of 
      Medicine, Durham, North Carolina, USA.
AD  - Division of Gastroenterology, Durham Veterans Affairs Medical Center, Durham, 
      North Carolina, USA.
FAU - Leiman, David A
AU  - Leiman DA
AD  - Division of Gastroenterology, Department of Medicine, Duke University School of 
      Medicine, Durham, North Carolina, USA.
AD  - Duke Clinical Research Institute, Durham, North Carolina, USA.
LA  - eng
GR  - T32 DK007568/DK/NIDDK NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20230710
PL  - United States
TA  - Am J Gastroenterol
JT  - The American journal of gastroenterology
JID - 0421030
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - Software
MH  - *Gastroenterologists
MH  - *Gastroesophageal Reflux/diagnosis/therapy
EDAT- 2023/07/06 19:12
MHDA- 2023/12/04 12:42
CRDT- 2023/07/06 15:43
PHST- 2023/03/30 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/07/06 19:12 [pubmed]
PHST- 2023/07/06 15:43 [entrez]
AID - 00000434-202312000-00031 [pii]
AID - 10.14309/ajg.0000000000002397 [doi]
PST - ppublish
SO  - Am J Gastroenterol. 2023 Dec 1;118(12):2276-2279. doi: 
      10.14309/ajg.0000000000002397. Epub 2023 Jul 10.

PMID- 37823708
OWN - NLM
STAT- Publisher
LR  - 20231012
IS  - 1537-453X (Electronic)
IS  - 0277-3732 (Linking)
DP  - 2023 Oct 12
TI  - Physician Assessment of ChatGPT and Bing Answers to American Cancer Society's 
      Questions to Ask About Your Cancer.
LID - 10.1097/COC.0000000000001050 [doi]
AB  - OBJECTIVES: Artificial intelligence (AI) chatbots are a new, publicly available 
      tool for patients to access health care-related information with unknown 
      reliability related to cancer-related questions. This study assesses the quality 
      of responses to common questions for patients with cancer. METHODS: From February 
      to March 2023, we queried chat generative pretrained transformer (ChatGPT) from 
      OpenAI and Bing AI from Microsoft questions from the American Cancer Society's 
      recommended "Questions to Ask About Your Cancer" customized for all stages of 
      breast, colon, lung, and prostate cancer. Questions were, in addition, grouped by 
      type (prognosis, treatment, or miscellaneous). The quality of AI chatbot 
      responses was assessed by an expert panel using the validated DISCERN criteria. 
      RESULTS: Of the 117 questions presented to ChatGPT and Bing, the average score 
      for all questions were 3.9 and 3.2, respectively (P < 0.001) and the overall 
      DISCERN scores were 4.1 and 4.4, respectively. By disease site, the average score 
      for ChatGPT and Bing, respectively, were 3.9 and 3.6 for prostate cancer (P = 
      0.02), 3.7 and 3.3 for lung cancer (P < 0.001), 4.1 and 2.9 for breast cancer (P 
      < 0.001), and 3.8 and 3.0 for colorectal cancer (P < 0.001). By type of question, 
      the average score for ChatGPT and Bing, respectively, were 3.6 and 3.4 for 
      prognostic questions (P = 0.12), 3.9 and 3.1 for treatment questions (P < 0.001), 
      and 4.2 and 3.3 for miscellaneous questions (P = 0.001). For 3 responses (3%) by 
      ChatGPT and 18 responses (15%) by Bing, at least one panelist rated them as 
      having serious or extensive shortcomings. CONCLUSIONS: AI chatbots provide 
      multiple opportunities for innovating health care. This analysis suggests a 
      critical need, particularly around cancer prognostication, for continual 
      refinement to limit misleading counseling, confusion, and emotional distress to 
      patients and families.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Janopaul-Naylor, James R
AU  - Janopaul-Naylor JR
AD  - Department of Radiation Oncology, Emory University.
AD  - Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center.
FAU - Koo, Andee
AU  - Koo A
AD  - Department of Radiation Oncology, Emory University.
FAU - Qian, David C
AU  - Qian DC
AD  - Department of Radiation Oncology, Emory University.
FAU - McCall, Neal S
AU  - McCall NS
AD  - Department of Radiation Oncology, Emory University.
FAU - Liu, Yuan
AU  - Liu Y
AD  - Department of Biostatistics and Bioinformatics, Rollins School of Public Health, 
      Emory University.
FAU - Patel, Sagar A
AU  - Patel SA
AD  - Department of Radiation Oncology, Emory University.
LA  - eng
PT  - Journal Article
DEP - 20231012
PL  - United States
TA  - Am J Clin Oncol
JT  - American journal of clinical oncology
JID - 8207754
SB  - IM
COIS- The authors declare no conflicts of interest.
EDAT- 2023/10/12 12:44
MHDA- 2023/10/12 12:44
CRDT- 2023/10/12 09:14
PHST- 2023/10/12 12:44 [medline]
PHST- 2023/10/12 12:44 [pubmed]
PHST- 2023/10/12 09:14 [entrez]
AID - 00000421-990000000-00137 [pii]
AID - 10.1097/COC.0000000000001050 [doi]
PST - aheadofprint
SO  - Am J Clin Oncol. 2023 Oct 12. doi: 10.1097/COC.0000000000001050.

PMID- 37405455
OWN - NLM
STAT- Publisher
LR  - 20231011
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Jul 5
TI  - Exploring the potential of Chat-GPT as a supportive tool for sialendoscopy 
      clinical decision making and patient information support.
LID - 10.1007/s00405-023-08104-8 [doi]
AB  - INTRODUCTION: Sialendoscopy has emerged in the last decades as a groundbreaking 
      technique, offering a minimally invasive approach for exploring and managing 
      salivary gland disorders. More recently, the advent of chatbots, powered by 
      advanced natural processing language and artificial intelligence algorithms, has 
      revolutionized the way healthcare professionals and patients access and analyze 
      medical information and potentially will support soon the clinical 
      decision-making process. MATERIALS AND METHODS: A prospective, cross-sectional 
      study was designed to assess the level of agreement between Chat-GPT and 10 
      expert sialendoscopists aiming the capabilities of Chat-GPT to further improve 
      the management of salivary gland disorders. RESULTS: The mean level of agreement 
      was 3.4 (SD: 0.69; Min: 2, Max: 4) for Chat-GPT's answers while it was 4.1 (SD: 
      0.56; Min: 3, Max: 5) for the group of EESS (p < 0.015). The overall Wilcoxon 
      signed-rank test yielded a significance level of p < 0.026 when comparing the 
      level of agreement between Chat-GPT and EESS. The mean number of therapeutic 
      alternatives suggested by Chat-GPT was 3.33 (SD: 1.2; Min: 2, Max: 5), while it 
      was 2.6 (SD: 0.51; Min: 2, Max: 3) for the group of EESS; p = 0.286 (95% 
      CI - 0.385 to 1.320). CONCLUSION: Chat-GPT represents a promising tool in the 
      clinical decision-making process within the salivary gland clinic, particularly 
      for patients who are candidates for sialendoscopy treatment. Additionally, it 
      serves as a valuable source of information for patients. However, further 
      development is necessary to enhance the reliability of these tools and ensure 
      their safety and optimal use in the clinical setting.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Chiesa-Estomba, Carlos M
AU  - Chiesa-Estomba CM
AUID- ORCID: 0000-0001-9454-9464
AD  - Department of Otorhinolaryngology, Donostia University Hospital, Biodonostia 
      Research Institute, Osakidetza, 20014, San Sebastian, Spain. 
      chiesaestomba86@gmail.com.
AD  - Otorhinolaryngology Department, Faculty of Medicine, Deusto University, Bilbo, 
      Spain. chiesaestomba86@gmail.com.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain. chiesaestomba86@gmail.com.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain. 
      chiesaestomba86@gmail.com.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France. 
      chiesaestomba86@gmail.com.
AD  - Young Confederation of European Otorhinolaryngology, Head and Neck Surgery, 
      Vienna, Austria. chiesaestomba86@gmail.com.
FAU - Lechien, Jerome R
AU  - Lechien JR
AD  - Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology 
      and Head and Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health 
      Sciences and Technology, University of Mons, Mons, Belgium.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Vaira, Luigi A
AU  - Vaira LA
AD  - Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and 
      Pharmacy, University of Sassari, Sassari, Italy.
AD  - Biomedical Sciences Department, School of Biomedical Sciences, University of 
      Sassari, Sassari, Italy.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Brunet, Aina
AU  - Brunet A
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
FAU - Cammaroto, Giovanni
AU  - Cammaroto G
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Department of Head-Neck Surgery, Otolaryngology, Head-Neck and Oral Surgery Unit, 
      Morgagni Pierantoni Hospital, 47121, Forlì, Italy.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Mayo-Yanez, Miguel
AU  - Mayo-Yanez M
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Otorhinolaryngology, Head and Neck Surgery Department, Complexo Hospitalario 
      Universitario A Coruña (CHUAC), 15006, A Coruña, Galicia, Spain.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Sanchez-Barrueco, Alvaro
AU  - Sanchez-Barrueco A
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - ENT and Cervicofacial Surgery Department, Hospital Universitario Fundación 
      Jiménez Díaz, Madrid, Spain.
AD  - Otorhinolaryngology Department, Faculty of Medicine, Universidad Alfonso X el 
      Sabio, Madrid, Spain.
FAU - Saga-Gutierrez, Carlos
AU  - Saga-Gutierrez C
AD  - Department of Otorhinolaryngology, Donostia University Hospital, Biodonostia 
      Research Institute, Osakidetza, 20014, San Sebastian, Spain.
AD  - Otorhinolaryngology Department, Faculty of Medicine, Deusto University, Bilbo, 
      Spain.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
LA  - eng
PT  - Journal Article
DEP - 20230705
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
EIN - Eur Arch Otorhinolaryngol. 2023 Oct 11;:. PMID: 37819549
OTO - NOTNLM
OT  - Chat-GPT
OT  - Chatbot
OT  - Gland
OT  - Salivary
OT  - Sialendoscopy
EDAT- 2023/07/05 13:05
MHDA- 2023/07/05 13:05
CRDT- 2023/07/05 11:05
PHST- 2023/06/19 00:00 [received]
PHST- 2023/06/29 00:00 [accepted]
PHST- 2023/07/05 13:05 [pubmed]
PHST- 2023/07/05 13:05 [medline]
PHST- 2023/07/05 11:05 [entrez]
AID - 10.1007/s00405-023-08104-8 [pii]
AID - 10.1007/s00405-023-08104-8 [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Jul 5. doi: 10.1007/s00405-023-08104-8.

PMID- 37395691
OWN - NLM
STAT- Publisher
LR  - 20231012
IS  - 1097-685X (Electronic)
IS  - 0022-5223 (Linking)
VI  - 166
IP  - 5
DP  - 2023 Nov
TI  - A conversation with ChatGPT.
PG  - e465-e466
LID - S0022-5223(23)00511-1 [pii]
LID - 10.1016/j.jtcvs.2023.05.038 [doi]
FAU - Young, Andrew M
AU  - Young AM
AD  - Department of Surgery, University of Virginia School of Medicine, 
      Charlottesville, Va.
FAU - Teman, Nicholas R
AU  - Teman NR
AD  - Department of Surgery, University of Virginia School of Medicine, 
      Charlottesville, Va.
LA  - eng
PT  - Letter
DEP - 20230703
PL  - United States
TA  - J Thorac Cardiovasc Surg
JT  - The Journal of thoracic and cardiovascular surgery
JID - 0376343
SB  - IM
EDAT- 2023/07/03 13:05
MHDA- 2023/07/03 13:05
CRDT- 2023/07/03 10:29
PHST- 2023/05/24 00:00 [received]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/07/03 13:05 [pubmed]
PHST- 2023/07/03 13:05 [medline]
PHST- 2023/07/03 10:29 [entrez]
AID - S0022-5223(23)00511-1 [pii]
AID - 10.1016/j.jtcvs.2023.05.038 [doi]
PST - ppublish
SO  - J Thorac Cardiovasc Surg. 2023 Nov;166(5):e465-e466. doi: 
      10.1016/j.jtcvs.2023.05.038. Epub 2023 Jul 3.

PMID- 37097229
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230427
LR  - 20230508
IS  - 0807-7096 (Electronic)
IS  - 0029-2001 (Linking)
VI  - 143
IP  - 6
DP  - 2023 Apr 25
TI  - From ELIZA to ChatGPT.
LID - 10.4045/tidsskr.23.0279 [doi]
FAU - Brean, Are
AU  - Brean A
AUID- ORCID: 0000-0001-5683-5099
LA  - eng
LA  - nor
PT  - Journal Article
TT  - Fra Eliza til ChatGPT.
DEP - 20230424
PL  - Norway
TA  - Tidsskr Nor Laegeforen
JT  - Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny 
      raekke
JID - 0413423
SB  - IM
EDAT- 2023/04/25 17:42
MHDA- 2023/04/25 17:43
CRDT- 2023/04/25 10:42
PHST- 2023/04/25 17:43 [medline]
PHST- 2023/04/25 17:42 [pubmed]
PHST- 2023/04/25 10:42 [entrez]
AID - 23-0279 [pii]
AID - 10.4045/tidsskr.23.0279 [doi]
PST - epublish
SO  - Tidsskr Nor Laegeforen. 2023 Apr 24;143(6). doi: 10.4045/tidsskr.23.0279. Print 
      2023 Apr 25.

PMID- 37578819
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230831
IS  - 1929-073X (Print)
IS  - 1929-073X (Electronic)
IS  - 1929-073X (Linking)
VI  - 12
DP  - 2023 Aug 14
TI  - Appropriateness and Comprehensiveness of Using ChatGPT for Perioperative Patient 
      Education in Thoracic Surgery in Different Language Contexts: Survey Study.
PG  - e46900
LID - 10.2196/46900 [doi]
LID - e46900
AB  - BACKGROUND: ChatGPT, a dialogue-based artificial intelligence language model, has 
      shown promise in assisting clinical workflows and patient-clinician 
      communication. However, there is a lack of feasibility assessments regarding its 
      use for perioperative patient education in thoracic surgery. OBJECTIVE: This 
      study aimed to assess the appropriateness and comprehensiveness of using ChatGPT 
      for perioperative patient education in thoracic surgery in both English and 
      Chinese contexts. METHODS: This pilot study was conducted in February 2023. A 
      total of 37 questions focused on perioperative patient education in thoracic 
      surgery were created based on guidelines and clinical experience. Two sets of 
      inquiries were made to ChatGPT for each question, one in English and the other in 
      Chinese. The responses generated by ChatGPT were evaluated separately by 
      experienced thoracic surgical clinicians for appropriateness and 
      comprehensiveness based on a hypothetical draft response to a patient's question 
      on the electronic information platform. For a response to be qualified, it 
      required at least 80% of reviewers to deem it appropriate and 50% to deem it 
      comprehensive. Statistical analyses were performed using the unpaired chi-square 
      test or Fisher exact test, with a significance level set at P<.05. RESULTS: The 
      set of 37 commonly asked questions covered topics such as disease information, 
      diagnostic procedures, perioperative complications, treatment measures, disease 
      prevention, and perioperative care considerations. In both the English and 
      Chinese contexts, 34 (92%) out of 37 responses were qualified in terms of both 
      appropriateness and comprehensiveness. The remaining 3 (8%) responses were 
      unqualified in these 2 contexts. The unqualified responses primarily involved the 
      diagnosis of disease symptoms and surgical-related complications symptoms. The 
      reasons for determining the responses as unqualified were similar in both 
      contexts. There was no statistically significant difference (34/37, 92% vs 34/37, 
      92%; P=.99) in the qualification rate between the 2 language sets. CONCLUSIONS: 
      This pilot study demonstrates the potential feasibility of using ChatGPT for 
      perioperative patient education in thoracic surgery in both English and Chinese 
      contexts. ChatGPT is expected to enhance patient satisfaction, reduce anxiety, 
      and improve compliance during the perioperative period. In the future, there will 
      be remarkable potential application for using artificial intelligence, in 
      conjunction with human review, for patient education and health consultation 
      after patients have provided their informed consent.
CI  - ©Chen-ye Shao, Hui Li, Xiao-long Liu, Chang Li, Li-qin Yang, Yue-juan Zhang, Jing 
      Luo, Jun Zhao. Originally published in the Interactive Journal of Medical 
      Research (https://www.i-jmr.org/), 14.08.2023.
FAU - Shao, Chen-Ye
AU  - Shao CY
AUID- ORCID: 0009-0006-7288-816X
AD  - Department of Thoracic Surgery, The First Affiliated Hospital of Soochow 
      University, Suzhou, China.
FAU - Li, Hui
AU  - Li H
AUID- ORCID: 0000-0002-6190-6266
AD  - Department of Obstetrics and Gynecology, The First Affiliated Hospital of Soochow 
      University, Suzhou, China.
FAU - Liu, Xiao-Long
AU  - Liu XL
AUID- ORCID: 0000-0002-3969-1684
AD  - Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing 
      University, Nanjing, China.
FAU - Li, Chang
AU  - Li C
AUID- ORCID: 0000-0002-9469-7173
AD  - Department of Thoracic Surgery, The First Affiliated Hospital of Soochow 
      University, Suzhou, China.
FAU - Yang, Li-Qin
AU  - Yang LQ
AUID- ORCID: 0009-0007-2765-0977
AD  - Department of Thoracic Surgery, The First Affiliated Hospital of Soochow 
      University, Suzhou, China.
FAU - Zhang, Yue-Juan
AU  - Zhang YJ
AUID- ORCID: 0009-0006-9280-9393
AD  - Department of Thoracic Surgery, The First Affiliated Hospital of Soochow 
      University, Suzhou, China.
FAU - Luo, Jing
AU  - Luo J
AUID- ORCID: 0000-0002-7400-7462
AD  - Department of Thoracic Surgery, The First Affiliated Hospital of Soochow 
      University, Suzhou, China.
FAU - Zhao, Jun
AU  - Zhao J
AUID- ORCID: 0000-0001-8903-8205
AD  - Department of Thoracic Surgery, The First Affiliated Hospital of Soochow 
      University, Suzhou, China.
LA  - eng
PT  - Journal Article
DEP - 20230814
PL  - Canada
TA  - Interact J Med Res
JT  - Interactive journal of medical research
JID - 101598421
PMC - PMC10463083
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Generative Pre-trained Transformer
OT  - artificial intelligence
OT  - clinical workflow
OT  - communication
OT  - education
OT  - evaluation
OT  - feasibility
OT  - language
OT  - language model
OT  - patient
OT  - patient education
OT  - surgery
OT  - thoracic
OT  - thoracic surgery
OT  - workflow
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/14 12:42
MHDA- 2023/08/14 12:43
CRDT- 2023/08/14 11:52
PHST- 2023/03/01 00:00 [received]
PHST- 2023/07/27 00:00 [accepted]
PHST- 2023/07/22 00:00 [revised]
PHST- 2023/08/14 12:43 [medline]
PHST- 2023/08/14 12:42 [pubmed]
PHST- 2023/08/14 11:52 [entrez]
AID - v12i1e46900 [pii]
AID - 10.2196/46900 [doi]
PST - epublish
SO  - Interact J Med Res. 2023 Aug 14;12:e46900. doi: 10.2196/46900.

PMID- 37399112
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230821
LR  - 20230830
IS  - 1748-880X (Electronic)
IS  - 0007-1285 (Print)
IS  - 0007-1285 (Linking)
VI  - 96
IP  - 1149
DP  - 2023 Sep
TI  - ChatGPT and radiologists: comment.
PG  - 20230442
LID - 10.1259/bjr.20230442 [doi]
LID - 20230442
FAU - Kleebayoon, Aamnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Adjunct professor, Chandigarh University, Punjab, India.
AD  - Adjunct professor, Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230629
PL  - England
TA  - Br J Radiol
JT  - The British journal of radiology
JID - 0373125
SB  - IM
CON - Br J Radiol. 2023 Aug;96(1148):20230203. PMID: 37183840
PMC - PMC10461268
EDAT- 2023/07/03 19:08
MHDA- 2023/08/21 06:42
PMCR- 2024/09/01
CRDT- 2023/07/03 12:33
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/08/21 06:42 [medline]
PHST- 2023/07/03 19:08 [pubmed]
PHST- 2023/07/03 12:33 [entrez]
AID - 10.1259/bjr.20230442 [doi]
PST - ppublish
SO  - Br J Radiol. 2023 Sep;96(1149):20230442. doi: 10.1259/bjr.20230442. Epub 2023 Jun 
      29.

PMID- 37095802
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230426
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Long-Term Survival of Patients With Glioblastoma of the Pineal Gland: A 
      ChatGPT-Assisted, Updated Case of a Multimodal Treatment Strategy Resulting in 
      Extremely Long Overall Survival at a Site With Historically Poor Outcomes.
PG  - e36590
LID - 10.7759/cureus.36590 [doi]
LID - e36590
AB  - We present an updated case report of a patient with glioblastoma isolated to the 
      pineal gland with an overall survival greater than five years and no progression 
      of focal central nervous system (CNS) deficits since initial presentation. The 
      patient underwent radiotherapy up to 60 Gy with concurrent and adjuvant 
      temozolomide with the use of non-standard treatment volumes that included the 
      ventricular system. The utilization of ventricular irradiation as well as the 
      addition of bevacizumab at disease recurrence may have encouraged this unusually 
      long survival by preventing/delaying leptomeningeal spread. We also present an 
      updated review of the literature, which shows a median survival of six months, 
      reinforcing the patients atypical disease trajectory. Finally, we utilize 
      OpenAI's language model ChatGPT to aid in synthesizing this manuscript. In doing 
      so, we demonstrate that ChatGPT is apt at creating concise summaries of relevant 
      literature and topic subjects, however its output is often repetitive with 
      similar sentence/paragraph structure, less than ideal grammar and poor syntax 
      requiring editing. Thus, in its current iteration, ChatGPT is a helpful aid that 
      cuts down on the time spent in data acquisition and processing but is not a 
      replacement for human input in the creation of quality medical literature.
CI  - Copyright © 2023, Cunningham et al.
FAU - Cunningham, Andrew R
AU  - Cunningham AR
AD  - Department of Radiation Oncology, Brody School of Medicine, East Carolina 
      University, Greenville, USA.
FAU - Behm, Hayley E
AU  - Behm HE
AD  - Department of Radiation Oncology, Brody School of Medicine, East Carolina 
      University, Greenville, USA.
FAU - Ju, Andrew
AU  - Ju A
AD  - Department of Radiation Oncology, Brody School of Medicine, East Carolina 
      University, Greenville, USA.
FAU - Peach, Matthew S
AU  - Peach MS
AD  - Department of Radiation Oncology, Brody School of Medicine, East Carolina 
      University, Greenville, USA.
LA  - eng
PT  - Case Reports
DEP - 20230323
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10122505
OTO - NOTNLM
OT  - bevacizumab
OT  - chatgpt
OT  - glioblastoma
OT  - leptomeningeal spread
OT  - pineal gland
OT  - radiotherapy
OT  - temozolomide
OT  - ventricular radiation
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/25 06:42
MHDA- 2023/04/25 06:43
CRDT- 2023/04/25 02:04
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/04/25 06:43 [medline]
PHST- 2023/04/25 06:42 [pubmed]
PHST- 2023/04/25 02:04 [entrez]
AID - 10.7759/cureus.36590 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 23;15(3):e36590. doi: 10.7759/cureus.36590. eCollection 2023 
      Mar.

PMID- 37531256
OWN - NLM
STAT- MEDLINE
DCOM- 20231010
LR  - 20231018
IS  - 1460-2423 (Electronic)
IS  - 0959-6658 (Linking)
VI  - 33
IP  - 8
DP  - 2023 Oct 6
TI  - Can ChatGPT pass Glycobiology?
PG  - 606-614
LID - 10.1093/glycob/cwad064 [doi]
AB  - The release of text-generating applications based on interactive Large Language 
      Models (LLMs) in late 2022 triggered an unprecedented and ever-growing interest 
      worldwide. The almost instantaneous success of LLMs stimulated lively discussions 
      in public media and in academic fora alike not only on the value and potentials 
      of such tools in all areas of knowledge and information acquisition and 
      distribution but also on the dangers posed by their uncontrolled and 
      indiscriminate use. This conversation is now particularly active in the higher 
      education sector, where LLMs are seen as a potential threat to academic integrity 
      at all levels, from facilitating cheating by students in assignments to 
      plagiarizing academic writing in the case of researchers and administrators. 
      Within this framework, we are interested in testing the boundaries of the LLM 
      ChatGPT (www.openai.com) in areas of our scientific interest and expertise and in 
      analyzing the results from different perspectives, i.e. of a final year BSc 
      student, of a research scientist, and of a lecturer in higher education. To this 
      end, in this paper, we present and discuss a systematic evaluation on how ChatGPT 
      addresses progressively complex scientific writing tasks and exam-type questions 
      in Carbohydrate Chemistry and Glycobiology. The results of this project allowed 
      us to gain insight on: (i) the strengths and limitations of the ChatGPT model to 
      provide relevant and (most importantly) correct scientific information, (ii) the 
      format(s) and complexity of the query required to obtain the desired output, and 
      (iii) strategies to integrate LLMs in teaching and learning.
CI  - © The Author(s) 2023. Published by Oxford University Press. All rights reserved. 
      For permissions, please e-mail: journals.permissions@oup.com.
FAU - Williams, Devin Ormsby
AU  - Williams DO
AD  - Department of Chemistry.
FAU - Fadda, Elisa
AU  - Fadda E
AD  - Department of Chemistry.
AD  - Hamilton Institute, Maynooth University, Maynooth, co. Kildare, Ireland.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Glycobiology
JT  - Glycobiology
JID - 9104124
SB  - IM
MH  - Humans
MH  - *Glycomics/education
MH  - *Artificial Intelligence
MH  - Writing
MH  - *Educational Measurement
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Glycobiology
OT  - LLM
OT  - higher education
EDAT- 2023/08/02 19:15
MHDA- 2023/10/09 06:41
CRDT- 2023/08/02 12:42
PHST- 2023/05/25 00:00 [received]
PHST- 2023/06/30 00:00 [revised]
PHST- 2023/07/12 00:00 [accepted]
PHST- 2023/10/09 06:41 [medline]
PHST- 2023/08/02 19:15 [pubmed]
PHST- 2023/08/02 12:42 [entrez]
AID - 7235670 [pii]
AID - 10.1093/glycob/cwad064 [doi]
PST - ppublish
SO  - Glycobiology. 2023 Oct 6;33(8):606-614. doi: 10.1093/glycob/cwad064.

PMID- 37361298
OWN - NLM
STAT- Publisher
LR  - 20230928
IS  - 2198-1833 (Print)
IS  - 1613-2238 (Electronic)
IS  - 0943-1853 (Linking)
DP  - 2023 May 26
TI  - A step-by-step researcher's guide to the use of an AI-based transformer in 
      epidemiology: an exploratory analysis of ChatGPT using the STROBE checklist for 
      observational studies.
PG  - 1-36
LID - 10.1007/s10389-023-01936-y [doi]
AB  - OBJECTIVE: This study aims at investigating how AI-based transformers can support 
      researchers in designing and conducting an epidemiological study. To accomplish 
      this, we used ChatGPT to reformulate the STROBE recommendations into a list of 
      questions to be answered by the transformer itself. We then qualitatively 
      evaluated the coherence and relevance of the transformer's outputs. STUDY DESIGN: 
      Descriptive study. METHODS: We first chose a study to be used as a basis for the 
      simulation. We then used ChatGPT to transform each STROBE checklist's item into 
      specific prompts. Each answer to the respective prompt was evaluated by 
      independent researchers in terms of coherence and relevance. RESULTS: The mean 
      scores assigned to each prompt were heterogeneous. On average, for the coherence 
      domain, the overall mean score was 3.6 out of 5.0, and for relevance it was 3.3 
      out of 5.0. The lowest scores were assigned to items belonging to the Methods 
      section of the checklist. CONCLUSIONS: ChatGPT can be considered as a valuable 
      support for researchers in conducting an epidemiological study, following 
      internationally recognized guidelines and standards. It is crucial for the users 
      to have knowledge on the subject and a critical mindset when evaluating the 
      outputs. The potential benefits of AI in scientific research and publishing are 
      undeniable, but it is crucial to address the risks, and the ethical and legal 
      consequences associated with its use.
CI  - © The Author(s) 2023.
FAU - Sanmarchi, Francesco
AU  - Sanmarchi F
AUID- ORCID: 0000-0001-8288-0563
AD  - Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum - 
      University of Bologna, Via San Giacomo 12, 40126 Bologna, Italy. GRID: 
      grid.6292.f. ISNI: 0000 0004 1757 1758
FAU - Bucci, Andrea
AU  - Bucci A
AD  - Department of Economics and Law, University of Macerata, Macerata, Italy. GRID: 
      grid.8042.e. ISNI: 0000 0001 2188 0260
FAU - Nuzzolese, Andrea Giovanni
AU  - Nuzzolese AG
AD  - STLab, Institute for Cognitive Sciences and Technologies (ISTC)-CNR, Roma, Italy. 
      GRID: grid.428479.4. ISNI: 0000 0001 2297 9633
FAU - Carullo, Gherardo
AU  - Carullo G
AD  - Department of Italian and Supranational Public Law, University of Milan, Milan, 
      Italy. GRID: grid.4708.b. ISNI: 0000 0004 1757 2822
FAU - Toscano, Fabrizio
AU  - Toscano F
AD  - Montefiore Medical Center, Bronx, NY USA. GRID: grid.240283.f. ISNI: 0000 0001 
      2152 0791
FAU - Nante, Nicola
AU  - Nante N
AD  - Present Address: Department of Molecular and Developmental Medicine, University 
      of Siena, Siena, Italy. GRID: grid.9024.f. ISNI: 0000 0004 1757 4641
FAU - Golinelli, Davide
AU  - Golinelli D
AD  - Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum - 
      University of Bologna, Via San Giacomo 12, 40126 Bologna, Italy. GRID: 
      grid.6292.f. ISNI: 0000 0004 1757 1758
AD  - Present Address: Department of Molecular and Developmental Medicine, University 
      of Siena, Siena, Italy. GRID: grid.9024.f. ISNI: 0000 0004 1757 4641
LA  - eng
PT  - Journal Article
DEP - 20230526
PL  - Germany
TA  - Z Gesundh Wiss
JT  - Zeitschrift fur Gesundheitswissenschaften = Journal of public health
JID - 9425271
PMC - PMC10215032
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Epidemiology
OT  - Ethics
OT  - Legal
OT  - Methodology
OT  - Public Health
OT  - STROBE
OT  - Scientific research
OT  - Transformers
COIS- Competing interestsThe authors report no conflict of interest.
EDAT- 2023/06/26 19:07
MHDA- 2023/06/26 19:07
CRDT- 2023/06/26 12:50
PHST- 2023/02/16 00:00 [received]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/06/26 19:07 [pubmed]
PHST- 2023/06/26 19:07 [medline]
PHST- 2023/06/26 12:50 [entrez]
AID - 1936 [pii]
AID - 10.1007/s10389-023-01936-y [doi]
PST - aheadofprint
SO  - Z Gesundh Wiss. 2023 May 26:1-36. doi: 10.1007/s10389-023-01936-y.

PMID- 38042556
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1532-8481 (Electronic)
IS  - 8755-7223 (Linking)
VI  - 49
DP  - 2023 Nov-Dec
TI  - What does ChatGPT advise about predatory publishing?
PG  - 188-189
LID - S8755-7223(23)00119-9 [pii]
LID - 10.1016/j.profnurs.2023.08.002 [doi]
AB  - The debate surrounding "predatory publishing" continues to be unable to find 
      entirely effective solutions to dealing with this problem, despite fervent 
      efforts by many academics and policy makers around the world. Given this 
      situation, we were interested in appreciating whether ChatGPT would be able to 
      offer insight and solutions, to complement current human-based efforts.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Tsigaris, Panagiotis
AU  - Tsigaris P
AD  - 805 TRU Way, Department of Economics, Thompson Rivers University, Kamloops, 
      British Columbia V2C 0C8, Canada. Electronic address: ptsigaris@tru.ca.
FAU - Kendall, Graham
AU  - Kendall G
AD  - University of Nottingham (UK and Malaysia), University Park, Nottingham NG7 2RD, 
      UK. Electronic address: graham.kendall@nottingham.edu.my.
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Ikenobe 3011-2, Kagawa-ken 761-0799, Japan. Electronic address: 
      jaimetex@yahoo.com.
LA  - eng
PT  - Letter
DEP - 20230819
PL  - United States
TA  - J Prof Nurs
JT  - Journal of professional nursing : official journal of the American Association of 
      Colleges of Nursing
JID - 8511298
SB  - IM
MH  - *Publishing
MH  - *Predatory Journals as Topic
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - Ethics
OT  - Safelists (whitelists) and watchlists (blacklists)
OT  - Transparency
OT  - Trust
COIS- Declaration of competing interest The authors, who declare no conflicts of 
      interest, contributed equally to the conceptual design, writing, editing, and 
      take responsibility for the content of the paper (i.e., the four ICMJE clauses 
      for authorship). The authors declare that the free version of ChatGPT was used 
      via the first author's account.
EDAT- 2023/12/03 00:42
MHDA- 2023/12/04 12:43
CRDT- 2023/12/02 20:59
PHST- 2023/05/30 00:00 [received]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/12/03 00:42 [pubmed]
PHST- 2023/12/02 20:59 [entrez]
AID - S8755-7223(23)00119-9 [pii]
AID - 10.1016/j.profnurs.2023.08.002 [doi]
PST - ppublish
SO  - J Prof Nurs. 2023 Nov-Dec;49:188-189. doi: 10.1016/j.profnurs.2023.08.002. Epub 
      2023 Aug 19.

PMID- 38009943
OWN - NLM
STAT- Publisher
LR  - 20231127
IS  - 1748-3115 (Electronic)
IS  - 1748-3107 (Linking)
DP  - 2023 Nov 27
TI  - ChatGPT and dyslexia: correspondence.
PG  - 1-2
LID - 10.1080/17483107.2023.2287162 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Laos.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Research Center, Chandigarh University, Mohali, India.
LA  - eng
PT  - Journal Article
DEP - 20231127
PL  - England
TA  - Disabil Rehabil Assist Technol
JT  - Disability and rehabilitation. Assistive technology
JID - 101255937
SB  - IM
EDAT- 2023/11/27 12:44
MHDA- 2023/11/27 12:44
CRDT- 2023/11/27 09:04
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/11/27 12:44 [pubmed]
PHST- 2023/11/27 09:04 [entrez]
AID - 10.1080/17483107.2023.2287162 [doi]
PST - aheadofprint
SO  - Disabil Rehabil Assist Technol. 2023 Nov 27:1-2. doi: 
      10.1080/17483107.2023.2287162.

PMID- 37661540
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 2095-9281 (Electronic)
IS  - 2095-9273 (Linking)
VI  - 68
IP  - 19
DP  - 2023 Oct 15
TI  - Can ChatGPT replace scientists?
PG  - 2128-2131
LID - S2095-9273(23)00560-1 [pii]
LID - 10.1016/j.scib.2023.08.032 [doi]
FAU - Song, Paul
AU  - Song P
AD  - Space Science Laboratory, Department of Physics and Applied Physics, University 
      of Massachusetts, Lowell MA 01852, USA.
FAU - Wang, Chi
AU  - Wang C
AD  - National Space Science Center, Chinese Academy of Sciences, Beijing 100190, 
      China. Electronic address: cw@nssc.ac.cn.
LA  - eng
PT  - Journal Article
DEP - 20230818
PL  - Netherlands
TA  - Sci Bull (Beijing)
JT  - Science bulletin
JID - 101655530
SB  - IM
COIS- Conflict of interest The authors declare that they have no conflict of interest.
EDAT- 2023/09/04 06:43
MHDA- 2023/09/04 06:43
CRDT- 2023/09/04 01:00
PHST- 2023/09/04 06:43 [pubmed]
PHST- 2023/09/04 06:43 [medline]
PHST- 2023/09/04 01:00 [entrez]
AID - S2095-9273(23)00560-1 [pii]
AID - 10.1016/j.scib.2023.08.032 [doi]
PST - ppublish
SO  - Sci Bull (Beijing). 2023 Oct 15;68(19):2128-2131. doi: 
      10.1016/j.scib.2023.08.032. Epub 2023 Aug 18.

PMID- 36853242
OWN - NLM
STAT- MEDLINE
DCOM- 20230406
LR  - 20230406
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT: Temptations of Progress.
PG  - 6-8
LID - 10.1080/15265161.2023.2180110 [doi]
FAU - Doshi, Rushabh H
AU  - Doshi RH
AUID- ORCID: 0000-0002-2692-8195
AD  - Yale School of Medicine.
AD  - Yale New Haven Hospital.
FAU - Bajaj, Simar S
AU  - Bajaj SS
AD  - Harvard University.
FAU - Krumholz, Harlan M
AU  - Krumholz HM
AD  - Yale School of Medicine.
AD  - Yale New Haven Hospital.
AD  - Yale School of Public Health.
LA  - eng
PT  - Journal Article
DEP - 20230228
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
EDAT- 2023/03/01 06:00
MHDA- 2023/04/05 06:42
CRDT- 2023/02/28 11:03
PHST- 2023/04/05 06:42 [medline]
PHST- 2023/03/01 06:00 [pubmed]
PHST- 2023/02/28 11:03 [entrez]
AID - 10.1080/15265161.2023.2180110 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Apr;23(4):6-8. doi: 10.1080/15265161.2023.2180110. Epub 2023 
      Feb 28.

PMID- 37971538
OWN - NLM
STAT- MEDLINE
DCOM- 20231130
LR  - 20231130
IS  - 1432-069X (Electronic)
IS  - 0340-3696 (Linking)
VI  - 316
IP  - 1
DP  - 2023 Nov 16
TI  - Can ChatGPT be my Dermatologist?
PG  - 2
LID - 10.1007/s00403-023-02762-8 [doi]
FAU - Rau, Akash
AU  - Rau A
AD  - Michigan State University College of Human Medicine, 15 Michigan St NE, Grand 
      Rapids, MI, 49503, USA. rauakash@msu.edu.
FAU - Kerr, Holly
AU  - Kerr H
AD  - Department of Dermatology, Henry Ford Hospital, Detroit, MI, USA.
LA  - eng
PT  - Letter
DEP - 20231116
PL  - Germany
TA  - Arch Dermatol Res
JT  - Archives of dermatological research
JID - 8000462
SB  - IM
MH  - Humans
MH  - *Dermatologists
MH  - *Artificial Intelligence
EDAT- 2023/11/17 15:27
MHDA- 2023/11/27 12:43
CRDT- 2023/11/16 11:04
PHST- 2023/08/04 00:00 [received]
PHST- 2023/09/16 00:00 [accepted]
PHST- 2023/08/04 00:00 [revised]
PHST- 2023/11/27 12:43 [medline]
PHST- 2023/11/17 15:27 [pubmed]
PHST- 2023/11/16 11:04 [entrez]
AID - 10.1007/s00403-023-02762-8 [pii]
AID - 10.1007/s00403-023-02762-8 [doi]
PST - epublish
SO  - Arch Dermatol Res. 2023 Nov 16;316(1):2. doi: 10.1007/s00403-023-02762-8.

PMID- 37828162
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231101
LR  - 20231113
IS  - 1476-5373 (Electronic)
IS  - 0007-0610 (Linking)
VI  - 235
IP  - 7
DP  - 2023 Oct
TI  - ChatGPT fails in ORE.
PG  - 448-449
LID - 10.1038/s41415-023-6395-3 [doi]
FAU - Kurian, N
AU  - Kurian N
AD  - Christian Dental College, Ludhiana, Punjab, India. nirmal36@gmail.com.
FAU - Varghese, V
AU  - Varghese V
AD  - Christian Dental College, Ludhiana, Punjab, India. 
      vinaya.varghese@cmcludhiana.in.
FAU - Wadhwa, S
AU  - Wadhwa S
AD  - Christian Dental College, Ludhiana, Punjab, India. samikshawadhwa120@gmail.com.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20231013
PL  - England
TA  - Br Dent J
JT  - British dental journal
JID - 7513219
SB  - IM
CON - Br Dent J. 2023 Sep;235(5):295. PMID: 37684439
EDAT- 2023/10/13 00:43
MHDA- 2023/10/13 00:44
CRDT- 2023/10/12 23:29
PHST- 2023/09/10 00:00 [received]
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/10/13 00:44 [medline]
PHST- 2023/10/13 00:43 [pubmed]
PHST- 2023/10/12 23:29 [entrez]
AID - 10.1038/s41415-023-6395-3 [pii]
AID - 10.1038/s41415-023-6395-3 [doi]
PST - ppublish
SO  - Br Dent J. 2023 Oct;235(7):448-449. doi: 10.1038/s41415-023-6395-3. Epub 2023 Oct 
      13.

PMID- 37407734
OWN - NLM
STAT- Publisher
LR  - 20230816
IS  - 1476-5438 (Electronic)
IS  - 1018-4813 (Linking)
DP  - 2023 Jul 5
TI  - Can ChatGPT understand genetics?
LID - 10.1038/s41431-023-01419-4 [doi]
FAU - Emmert-Streib, Frank
AU  - Emmert-Streib F
AUID- ORCID: 0000-0003-0745-5641
AD  - Predictive Society and Data Analytics Lab, Faculty of Information Technology and 
      Communication Sciences, Tampere University, Tampere, Finland. 
      v@bio-complexity.com.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230705
PL  - England
TA  - Eur J Hum Genet
JT  - European journal of human genetics : EJHG
JID - 9302235
SB  - IM
CON - Eur J Hum Genet. 2023 May 29;:. PMID: 37246194
EDAT- 2023/07/06 01:08
MHDA- 2023/07/06 01:08
CRDT- 2023/07/05 23:30
PHST- 2023/06/08 00:00 [received]
PHST- 2023/06/19 00:00 [accepted]
PHST- 2023/07/06 01:08 [pubmed]
PHST- 2023/07/06 01:08 [medline]
PHST- 2023/07/05 23:30 [entrez]
AID - 10.1038/s41431-023-01419-4 [pii]
AID - 10.1038/s41431-023-01419-4 [doi]
PST - aheadofprint
SO  - Eur J Hum Genet. 2023 Jul 5. doi: 10.1038/s41431-023-01419-4.

PMID- 37058699
OWN - NLM
STAT- MEDLINE
DCOM- 20230418
LR  - 20230418
IS  - 1538-9782 (Electronic)
IS  - 0887-6274 (Linking)
VI  - 37
IP  - 3
DP  - 2023 May-Jun 01
TI  - Authorship and ChatGPT.
PG  - 109-110
LID - 10.1097/NUR.0000000000000750 [doi]
FAU - Fulton, Janet S
AU  - Fulton JS
AD  - Author Affiliation: Centennial Endowed Professor in Innovation in Nursing 
      Education, Indiana University School of Nursing,600 Barnhill Drive, Indianapolis, 
      IN 46202.
LA  - eng
PT  - Editorial
PL  - United States
TA  - Clin Nurse Spec
JT  - Clinical nurse specialist CNS
JID - 8709115
MH  - *Authorship
MH  - *Publishing
COIS- No conflict of interest to declare.
EDAT- 2023/04/15 06:00
MHDA- 2023/04/18 06:42
CRDT- 2023/04/14 16:38
PHST- 2023/04/18 06:42 [medline]
PHST- 2023/04/14 16:38 [entrez]
PHST- 2023/04/15 06:00 [pubmed]
AID - 00002800-202305000-00002 [pii]
AID - 10.1097/NUR.0000000000000750 [doi]
PST - ppublish
SO  - Clin Nurse Spec. 2023 May-Jun 01;37(3):109-110. doi: 
      10.1097/NUR.0000000000000750.

PMID- 37949663
OWN - NLM
STAT- Publisher
LR  - 20231203
IS  - 1365-2125 (Electronic)
IS  - 0306-5251 (Linking)
DP  - 2023 Nov 10
TI  - Clinical decision-making in benzodiazepine deprescribing by healthcare providers 
      vs. AI-assisted approach.
LID - 10.1111/bcp.15963 [doi]
AB  - AIMS: The aim of this study was to compare the clinical decision-making for 
      benzodiazepine deprescribing between a healthcare provider (HCP) and an 
      artificial intelligence (AI) chatbot GPT4 (ChatGPT-4). METHODS: We analysed 
      real-world data from a Croatian cohort of community-dwelling benzodiazepine 
      patients (n = 154) within the EuroAgeism H2020 ESR 7 project. HCPs evaluated the 
      data using pre-established deprescribing criteria to assess benzodiazepine 
      discontinuation potential. The research team devised and tested AI prompts to 
      ensure consistency with HCP judgements. An independent researcher employed 
      ChatGPT-4 with predetermined prompts to simulate clinical decisions for each 
      patient case. Data derived from human-HCP and ChatGPT-4 decisions were compared 
      for agreement rates and Cohen's kappa. RESULTS: Both HPC and ChatGPT identified 
      patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), 
      showing an agreement rate of 95% (κ = .200, P = .012). Agreement on four 
      deprescribing criteria ranged from 74.7% to 91.3% (lack of indication κ = .352, 
      P < .001; prolonged use κ = .088, P = .280; safety concerns κ = .123, P = .006; 
      incorrect dosage κ = .264, P = .001). Important limitations of GPT-4 responses 
      were identified, including 22.1% ambiguous outputs, generic answers and 
      inaccuracies, posing inappropriate decision-making risks. CONCLUSIONS: While 
      AI-HCP agreement is substantial, sole AI reliance poses a risk for unsuitable 
      clinical decision-making. This study's findings reveal both strengths and areas 
      for enhancement of ChatGPT-4 in the deprescribing recommendations within a 
      real-world sample. Our study underscores the need for additional research on 
      chatbot functionality in patient therapy decision-making, further fostering the 
      advancement of AI for optimal performance.
CI  - © 2023 British Pharmacological Society.
FAU - Bužančić, Iva
AU  - Bužančić I
AD  - Center for Applied Pharmacy, Faculty of Pharmacy and Biochemistry, University of 
      Zagreb, Zagreb, Croatia.
AD  - City Pharmacy Zagreb, Zagreb, Croatia.
FAU - Belec, Dora
AU  - Belec D
AD  - Center for Applied Pharmacy, Faculty of Pharmacy and Biochemistry, University of 
      Zagreb, Zagreb, Croatia.
FAU - Držaić, Margita
AU  - Držaić M
AD  - Center for Applied Pharmacy, Faculty of Pharmacy and Biochemistry, University of 
      Zagreb, Zagreb, Croatia.
AD  - City Pharmacy Zagreb, Zagreb, Croatia.
FAU - Kummer, Ingrid
AU  - Kummer I
AD  - Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec 
      Králové, Charles University, Hradec Králové, Czech Republic.
FAU - Brkić, Jovana
AU  - Brkić J
AD  - Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec 
      Králové, Charles University, Hradec Králové, Czech Republic.
AD  - Department of Social Pharmacy and Pharmaceutical Legislation, Faculty of 
      Pharmacy, University of Belgrade, Belgrade, Serbia.
FAU - Fialová, Daniela
AU  - Fialová D
AD  - Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec 
      Králové, Charles University, Hradec Králové, Czech Republic.
AD  - Department of Geriatrics and Gerontology, 1st Faculty of Medicine in Prague, 
      Charles University, Prague, Czech Republic.
FAU - Ortner Hadžiabdić, Maja
AU  - Ortner Hadžiabdić M
AUID- ORCID: 0000-0003-1578-9764
AD  - Center for Applied Pharmacy, Faculty of Pharmacy and Biochemistry, University of 
      Zagreb, Zagreb, Croatia.
LA  - eng
GR  - MSCF-ITN-764632/Marie Skłodowska-Curie Foundation/
GR  - CZ.02.1.01/0.0/0.0/18_069/0010046/InoMed/
GR  - SVV 260 551/European Horizon 2020 I-CARE4OLD/
GR  - START/MED/093 EN.02.2.69/0.0/0.0/19_073/0016935/European Horizon 2020 I-CARE4OLD/
GR  - 965341/European Horizon 2020 I-CARE4OLD/
GR  - Faculty of Pharmacy, Charles University/
PT  - Journal Article
DEP - 20231110
PL  - England
TA  - Br J Clin Pharmacol
JT  - British journal of clinical pharmacology
JID - 7503323
SB  - IM
OTO - NOTNLM
OT  - ChatGPT-4
OT  - artificial intelligence (AI)
OT  - benzodiazepines
OT  - chatbot
OT  - deprescribing
EDAT- 2023/11/11 11:43
MHDA- 2023/11/11 11:43
CRDT- 2023/11/10 21:40
PHST- 2023/10/26 00:00 [revised]
PHST- 2023/08/05 00:00 [received]
PHST- 2023/10/29 00:00 [accepted]
PHST- 2023/11/11 11:43 [pubmed]
PHST- 2023/11/11 11:43 [medline]
PHST- 2023/11/10 21:40 [entrez]
AID - 10.1111/bcp.15963 [doi]
PST - aheadofprint
SO  - Br J Clin Pharmacol. 2023 Nov 10. doi: 10.1111/bcp.15963.

PMID- 37643186
OWN - NLM
STAT- MEDLINE
DCOM- 20230831
LR  - 20230904
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 18
IP  - 8
DP  - 2023
TI  - ChatGPT versus human in generating medical graduate exam multiple choice 
      questions-A multinational prospective study (Hong Kong S.A.R., Singapore, 
      Ireland, and the United Kingdom).
PG  - e0290691
LID - 10.1371/journal.pone.0290691 [doi]
LID - e0290691
AB  - INTRODUCTION: Large language models, in particular ChatGPT, have showcased 
      remarkable language processing capabilities. Given the substantial workload of 
      university medical staff, this study aims to assess the quality of 
      multiple-choice questions (MCQs) produced by ChatGPT for use in graduate medical 
      examinations, compared to questions written by university professoriate staffs 
      based on standard medical textbooks. METHODS: 50 MCQs were generated by ChatGPT 
      with reference to two standard undergraduate medical textbooks (Harrison's, and 
      Bailey & Love's). Another 50 MCQs were drafted by two university professoriate 
      staff using the same medical textbooks. All 100 MCQ were individually numbered, 
      randomized and sent to five independent international assessors for MCQ quality 
      assessment using a standardized assessment score on five assessment domains, 
      namely, appropriateness of the question, clarity and specificity, relevance, 
      discriminative power of alternatives, and suitability for medical graduate 
      examination. RESULTS: The total time required for ChatGPT to create the 50 
      questions was 20 minutes 25 seconds, while it took two human examiners a total of 
      211 minutes 33 seconds to draft the 50 questions. When a comparison of the mean 
      score was made between the questions constructed by A.I. with those drafted by 
      humans, only in the relevance domain that the A.I. was inferior to humans (A.I.: 
      7.56 +/- 0.94 vs human: 7.88 +/- 0.52; p = 0.04). There was no significant 
      difference in question quality between questions drafted by A.I. versus humans, 
      in the total assessment score as well as in other domains. Questions generated by 
      A.I. yielded a wider range of scores, while those created by humans were 
      consistent and within a narrower range. CONCLUSION: ChatGPT has the potential to 
      generate comparable-quality MCQs for medical graduate examinations within a 
      significantly shorter time.
CI  - Copyright: © 2023 Cheung et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Cheung, Billy Ho Hung
AU  - Cheung BHH
AUID- ORCID: 0000-0002-8843-7893
AD  - L.K.S. Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong S.A.R.
FAU - Lau, Gary Kui Kai
AU  - Lau GKK
AD  - L.K.S. Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong S.A.R.
FAU - Wong, Gordon Tin Chun
AU  - Wong GTC
AD  - L.K.S. Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong S.A.R.
FAU - Lee, Elaine Yuen Phin
AU  - Lee EYP
AD  - L.K.S. Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong S.A.R.
FAU - Kulkarni, Dhananjay
AU  - Kulkarni D
AD  - Department of Surgery, University of Edinburgh, Edinburgh, United Kingdom.
FAU - Seow, Choon Sheong
AU  - Seow CS
AD  - Department of Surgery, National University Cancer Institute Singapore, Singapore, 
      Singapore.
FAU - Wong, Ruby
AU  - Wong R
AUID- ORCID: 0000-0002-0847-8514
AD  - Department of Surgery, University of Galway, Galway, Ireland.
FAU - Co, Michael Tiong-Hong
AU  - Co MT
AUID- ORCID: 0000-0002-1705-051X
AD  - L.K.S. Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong S.A.R.
LA  - eng
PT  - Journal Article
DEP - 20230829
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Humans
MH  - Hong Kong
MH  - Ireland
MH  - Prospective Studies
MH  - Singapore
MH  - United Kingdom
MH  - *Artificial Intelligence
MH  - *Education, Medical, Graduate
MH  - *Educational Measurement/methods
PMC - PMC10464959
COIS- No.
EDAT- 2023/08/29 18:42
MHDA- 2023/08/31 06:42
CRDT- 2023/08/29 13:34
PHST- 2023/06/05 00:00 [received]
PHST- 2023/08/15 00:00 [accepted]
PHST- 2023/08/31 06:42 [medline]
PHST- 2023/08/29 18:42 [pubmed]
PHST- 2023/08/29 13:34 [entrez]
AID - PONE-D-23-14620 [pii]
AID - 10.1371/journal.pone.0290691 [doi]
PST - epublish
SO  - PLoS One. 2023 Aug 29;18(8):e0290691. doi: 10.1371/journal.pone.0290691. 
      eCollection 2023.

PMID- 37306853
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 1179-1942 (Electronic)
IS  - 0114-5916 (Linking)
VI  - 46
IP  - 8
DP  - 2023 Aug
TI  - Future of ChatGPT in Pharmacovigilance.
PG  - 711-713
LID - 10.1007/s40264-023-01315-2 [doi]
FAU - Wang, Hanyin
AU  - Wang H
AD  - Department of Preventive Medicine, Feinberg School of Medicine, Northwestern 
      University, 750 N. Lake Shore Drive, 11-189, Chicago, IL, 60611, USA.
FAU - Ding, Yanyi Jenny
AU  - Ding YJ
AD  - Department of Preventive Medicine, Feinberg School of Medicine, Northwestern 
      University, 750 N. Lake Shore Drive, 11-189, Chicago, IL, 60611, USA.
FAU - Luo, Yuan
AU  - Luo Y
AUID- ORCID: 0000-0003-0195-7456
AD  - Department of Preventive Medicine, Feinberg School of Medicine, Northwestern 
      University, 750 N. Lake Shore Drive, 11-189, Chicago, IL, 60611, USA. 
      Yuan.Luo@Northwestern.edu.
LA  - eng
GR  - U01TR003528/GF/NIH HHS/United States
GR  - R01LM013337/GF/NIH HHS/United States
PT  - Editorial
DEP - 20230612
PL  - New Zealand
TA  - Drug Saf
JT  - Drug safety
JID - 9002928
SB  - IM
MH  - Humans
MH  - *Pharmacovigilance
MH  - *Artificial Intelligence
EDAT- 2023/06/12 13:07
MHDA- 2023/07/17 06:42
CRDT- 2023/06/12 11:10
PHST- 2023/04/27 00:00 [accepted]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/06/12 13:07 [pubmed]
PHST- 2023/06/12 11:10 [entrez]
AID - 10.1007/s40264-023-01315-2 [pii]
AID - 10.1007/s40264-023-01315-2 [doi]
PST - ppublish
SO  - Drug Saf. 2023 Aug;46(8):711-713. doi: 10.1007/s40264-023-01315-2. Epub 2023 Jun 
      12.

PMID- 37398259
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Jun 12
TI  - From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and 
      Biomedical Knowledge Graphs.
LID - 2023.06.09.23291208 [pii]
LID - 10.1101/2023.06.09.23291208 [doi]
AB  - Large Language Models (LLMs) have demonstrated exceptional performance in various 
      natural language processing tasks, utilizing their language generation 
      capabilities and knowledge acquisition potential from unstructured text. However, 
      when applied to the biomedical domain, LLMs encounter limitations, resulting in 
      erroneous and inconsistent answers. Knowledge Graphs (KGs) have emerged as 
      valuable resources for structured information representation and organization. 
      Specifically, Biomedical Knowledge Graphs (BKGs) have attracted significant 
      interest in managing large-scale and heterogeneous biomedical knowledge. This 
      study evaluates the capabilities of ChatGPT and existing BKGs in question 
      answering, knowledge discovery, and reasoning. Results indicate that while 
      ChatGPT with GPT-4.0 surpasses both GPT-3.5 and BKGs in providing existing 
      information, BKGs demonstrate superior information reliability. Additionally, 
      ChatGPT exhibits limitations in performing novel discoveries and reasoning, 
      particularly in establishing structured links between entities compared to BKGs. 
      To overcome these limitations, future research should focus on integrating LLMs 
      and BKGs to leverage their respective strengths. Such an integrated approach 
      would optimize task performance and mitigate potential risks, thereby advancing 
      knowledge in the biomedical field and contributing to overall well-being.
FAU - Hou, Yu
AU  - Hou Y
AD  - Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
FAU - Yeung, Jeremy
AU  - Yeung J
AD  - Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
FAU - Xu, Hua
AU  - Xu H
AD  - Section of Biomedical Informatics and Data Science, Yale University, New Haven, 
      Connecticut, USA.
FAU - Su, Chang
AU  - Su C
AD  - Department of Health Service Administration and Policy, Temple University, 
      Philadelphia, PA, USA.
FAU - Wang, Fei
AU  - Wang F
AD  - Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 
      USA.
FAU - Zhang, Rui
AU  - Zhang R
AD  - Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
LA  - eng
GR  - R01 AG078154/AG/NIA NIH HHS/United States
GR  - R01 AT009457/AT/NCCIH NIH HHS/United States
PT  - Preprint
DEP - 20230612
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10312889
EDAT- 2023/07/03 13:05
MHDA- 2023/07/03 13:06
CRDT- 2023/07/03 11:44
PHST- 2023/07/03 13:05 [pubmed]
PHST- 2023/07/03 13:06 [medline]
PHST- 2023/07/03 11:44 [entrez]
AID - 2023.06.09.23291208 [pii]
AID - 10.1101/2023.06.09.23291208 [doi]
PST - epublish
SO  - medRxiv. 2023 Jun 12:2023.06.09.23291208. doi: 10.1101/2023.06.09.23291208. 
      Preprint.

PMID- 38042580
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1532-8473 (Electronic)
IS  - 1089-9472 (Linking)
VI  - 38
IP  - 6
DP  - 2023 Dec
TI  - ChatGPT and Artificial Intelligence.
PG  - 840
LID - S1089-9472(23)00878-X [pii]
LID - 10.1016/j.jopan.2023.07.005 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Ajitgarh, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20231130
PL  - United States
TA  - J Perianesth Nurs
JT  - Journal of perianesthesia nursing : official journal of the American Society of 
      PeriAnesthesia Nurses
JID - 9610507
COIS- Declaration of Competing Interest None to report.
EDAT- 2023/12/03 00:42
MHDA- 2023/12/04 12:41
CRDT- 2023/12/02 21:01
PHST- 2023/04/25 00:00 [received]
PHST- 2023/07/16 00:00 [accepted]
PHST- 2023/12/04 12:41 [medline]
PHST- 2023/12/03 00:42 [pubmed]
PHST- 2023/12/02 21:01 [entrez]
AID - S1089-9472(23)00878-X [pii]
AID - 10.1016/j.jopan.2023.07.005 [doi]
PST - ppublish
SO  - J Perianesth Nurs. 2023 Dec;38(6):840. doi: 10.1016/j.jopan.2023.07.005. Epub 
      2023 Nov 30.

PMID- 36754723
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230228
LR  - 20230228
IS  - 2589-7500 (Electronic)
IS  - 2589-7500 (Linking)
VI  - 5
IP  - 3
DP  - 2023 Mar
TI  - ChatGPT: friend or foe?
PG  - e102
LID - S2589-7500(23)00023-7 [pii]
LID - 10.1016/S2589-7500(23)00023-7 [doi]
FAU - The Lancet Digital Health
AU  - The Lancet Digital Health
LA  - eng
PT  - Editorial
DEP - 20230206
PL  - England
TA  - Lancet Digit Health
JT  - The Lancet. Digital health
JID - 101751302
SB  - IM
EDAT- 2023/02/09 06:00
MHDA- 2023/02/09 06:01
CRDT- 2023/02/08 22:01
PHST- 2023/02/09 06:00 [pubmed]
PHST- 2023/02/09 06:01 [medline]
PHST- 2023/02/08 22:01 [entrez]
AID - S2589-7500(23)00023-7 [pii]
AID - 10.1016/S2589-7500(23)00023-7 [doi]
PST - ppublish
SO  - Lancet Digit Health. 2023 Mar;5(3):e102. doi: 10.1016/S2589-7500(23)00023-7. Epub 
      2023 Feb 6.

PMID- 37890505
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1439-3999 (Electronic)
IS  - 0023-2165 (Linking)
DP  - 2023 Oct 27
TI  - [ChatGPT in Ophthalmology - A Report].
LID - 10.1055/a-2142-2910 [doi]
FAU - Herwig-Carl, Martina C
AU  - Herwig-Carl MC
AUID- ORCID: 0000-0002-5943-7675
AD  - Klinik für Augenheilkunde, Universitätsklinikum Bonn, Deutschland.
LA  - ger
PT  - Journal Article
TT  - ChatGPT in der Augenheilkunde – ein Erfahrungsbericht.
DEP - 20231027
PL  - Germany
TA  - Klin Monbl Augenheilkd
JT  - Klinische Monatsblatter fur Augenheilkunde
JID - 0014133
SB  - IM
COIS- Glaxo Smith Kline (Teilnahme an Advisory Board, Referent). In Bezug auf diese 
      Arbeit besteht kein Interessenkonflikt.
EDAT- 2023/10/28 11:42
MHDA- 2023/10/28 11:42
CRDT- 2023/10/27 19:07
PHST- 2023/10/28 11:42 [medline]
PHST- 2023/10/28 11:42 [pubmed]
PHST- 2023/10/27 19:07 [entrez]
AID - 10.1055/a-2142-2910 [doi]
PST - aheadofprint
SO  - Klin Monbl Augenheilkd. 2023 Oct 27. doi: 10.1055/a-2142-2910.

PMID- 37337114
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230706
LR  - 20230718
IS  - 1755-4349 (Electronic)
IS  - 1755-4330 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - ChatGPT saves the day.
PG  - 890-891
LID - 10.1038/s41557-023-01253-7 [doi]
FAU - Francl, Michelle
AU  - Francl M
AD  - Department of Chemistry, Bryn Mawr College, Bryn Mawr, PA, USA. 
      mfrancl@brynmawr.edu.
AD  - Vatican Observatory, Vatican City, Vatican City State. mfrancl@brynmawr.edu.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Nat Chem
JT  - Nature chemistry
JID - 101499734
SB  - IM
EDAT- 2023/06/20 01:09
MHDA- 2023/06/20 01:10
CRDT- 2023/06/19 23:28
PHST- 2023/06/20 01:10 [medline]
PHST- 2023/06/20 01:09 [pubmed]
PHST- 2023/06/19 23:28 [entrez]
AID - 10.1038/s41557-023-01253-7 [pii]
AID - 10.1038/s41557-023-01253-7 [doi]
PST - ppublish
SO  - Nat Chem. 2023 Jul;15(7):890-891. doi: 10.1038/s41557-023-01253-7.

PMID- 37083166
OWN - NLM
STAT- MEDLINE
DCOM- 20230530
LR  - 20230606
IS  - 1437-4331 (Electronic)
IS  - 1434-6621 (Linking)
VI  - 61
IP  - 7
DP  - 2023 Jun 27
TI  - Potentials and pitfalls of ChatGPT and natural-language artificial intelligence 
      models for the understanding of laboratory medicine test results. An assessment 
      by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 
      Working Group on Artificial Intelligence (WG-AI).
PG  - 1158-1166
LID - 10.1515/cclm-2023-0355 [doi]
AB  - OBJECTIVES: ChatGPT, a tool based on natural language processing (NLP), is on 
      everyone's mind, and several potential applications in healthcare have been 
      already proposed. However, since the ability of this tool to interpret laboratory 
      test results has not yet been tested, the EFLM Working group on Artificial 
      Intelligence (WG-AI) has set itself the task of closing this gap with a 
      systematic approach. METHODS: WG-AI members generated 10 simulated laboratory 
      reports of common parameters, which were then passed to ChatGPT for 
      interpretation, according to reference intervals (RI) and units, using an 
      optimized prompt. The results were subsequently evaluated independently by all 
      WG-AI members with respect to relevance, correctness, helpfulness and safety. 
      RESULTS: ChatGPT recognized all laboratory tests, it could detect if they 
      deviated from the RI and gave a test-by-test as well as an overall 
      interpretation. The interpretations were rather superficial, not always correct, 
      and, only in some cases, judged coherently. The magnitude of the deviation from 
      the RI seldom plays a role in the interpretation of laboratory tests, and 
      artificial intelligence (AI) did not make any meaningful suggestion regarding 
      follow-up diagnostics or further procedures in general. CONCLUSIONS: ChatGPT in 
      its current form, being not specifically trained on medical data or laboratory 
      data in particular, may only be considered a tool capable of interpreting a 
      laboratory report on a test-by-test basis at best, but not on the interpretation 
      of an overall diagnostic picture. Future generations of similar AIs with medical 
      ground truth training data might surely revolutionize current processes in 
      healthcare, despite this implementation is not ready yet.
CI  - © 2023 Walter de Gruyter GmbH, Berlin/Boston.
FAU - Cadamuro, Janne
AU  - Cadamuro J
AUID- ORCID: 0000-0002-6200-9831
AD  - Department of Laboratory Medicine, Paracelsus Medical University Salzburg, 
      Salzburg, Austria.
FAU - Cabitza, Federico
AU  - Cabitza F
AD  - DISCo, Università degli Studi di Milano-Bicocca, Milano, Italy.
AD  - IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
FAU - Debeljak, Zeljko
AU  - Debeljak Z
AD  - Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, Osijek, 
      Croatia.
AD  - Clinical Institute of Laboratory Diagnostics, University Hospital Center Osijek, 
      Osijek, Croatia.
FAU - De Bruyne, Sander
AU  - De Bruyne S
AD  - Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium.
FAU - Frans, Glynis
AU  - Frans G
AUID- ORCID: 0000-0001-8528-5719
AD  - Department of Laboratory Medicine, University Hospitals Leuven, KU Leuven, 
      Leuven, Belgium.
FAU - Perez, Salomon Martin
AU  - Perez SM
AD  - Unidad de Bioquímica Clínica, Hospital Universitario Virgen Macarena, Sevilla, 
      Spain.
FAU - Ozdemir, Habib
AU  - Ozdemir H
AUID- ORCID: 0000-0002-1267-7233
AD  - Department of Medical Biochemistry, Faculty of Medicine, Manisa Celal Bayar 
      University, Manisa, Türkiye.
FAU - Tolios, Alexander
AU  - Tolios A
AD  - Department of Transfusion Medicine and Cell Therapy, Medical University of 
      Vienna, Vienna, Austria.
FAU - Carobene, Anna
AU  - Carobene A
AD  - IRCCS San Raffaele Scientific Institute, Milan, Italy.
FAU - Padoan, Andrea
AU  - Padoan A
AUID- ORCID: 0000-0003-1284-7885
AD  - Department of Medicine (DIMED), University of Padova, Padova, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230424
PL  - Germany
TA  - Clin Chem Lab Med
JT  - Clinical chemistry and laboratory medicine
JID - 9806306
SB  - IM
CIN - Clin Chem Lab Med. 2023 Apr 25;61(7):1131-1132. PMID: 37092365
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Chemistry, Clinical
MH  - Laboratories
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - laboratory tests
OT  - natural language processing
EDAT- 2023/04/21 12:42
MHDA- 2023/05/30 06:42
CRDT- 2023/04/21 07:13
PHST- 2023/04/07 00:00 [received]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/05/30 06:42 [medline]
PHST- 2023/04/21 12:42 [pubmed]
PHST- 2023/04/21 07:13 [entrez]
AID - cclm-2023-0355 [pii]
AID - 10.1515/cclm-2023-0355 [doi]
PST - epublish
SO  - Clin Chem Lab Med. 2023 Apr 24;61(7):1158-1166. doi: 10.1515/cclm-2023-0355. 
      Print 2023 Jun 27.

PMID- 37619604
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231204
LR  - 20231206
IS  - 1559-7776 (Electronic)
IS  - 1559-7768 (Linking)
VI  - 34
IP  - 4
DP  - 2023 Dec 15
TI  - ChatGPT Is Trending: Trust but Verify.
PG  - 280-286
LID - 10.4037/aacnacc2023129 [doi]
FAU - Harrington, Linda
AU  - Harrington L
AD  - Linda Harrington is an Independent Consultant, Health Informatics and Digital 
      Strategy, and Adjunct Faculty at Texas Christian University, 2800 South 
      University Drive, Fort Worth, TX 76109 (linda.harrington@gmail.com).
LA  - eng
PT  - Journal Article
PL  - United States
TA  - AACN Adv Crit Care
JT  - AACN advanced critical care
JID - 101269322
EDAT- 2023/08/25 00:41
MHDA- 2023/08/25 00:42
CRDT- 2023/08/24 19:03
PHST- 2023/08/25 00:42 [medline]
PHST- 2023/08/25 00:41 [pubmed]
PHST- 2023/08/24 19:03 [entrez]
AID - 32172 [pii]
AID - 10.4037/aacnacc2023129 [doi]
PST - ppublish
SO  - AACN Adv Crit Care. 2023 Dec 15;34(4):280-286. doi: 10.4037/aacnacc2023129.

PMID- 37550592
OWN - NLM
STAT- MEDLINE
DCOM- 20230928
LR  - 20231016
IS  - 1432-5195 (Electronic)
IS  - 0341-2695 (Linking)
VI  - 47
IP  - 10
DP  - 2023 Oct
TI  - Orthopaedics and ChatGPT: correspondence.
PG  - 2625
LID - 10.1007/s00264-023-05909-4 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - , Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Dr. D.Y. Patil Vidyapeeth, Pune, India.
AD  - Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230807
PL  - Germany
TA  - Int Orthop
JT  - International orthopaedics
JID - 7705431
SB  - IM
CON - Int Orthop. 2023 Aug;47(8):1887-1893. PMID: 37428231
MH  - Humans
MH  - *Orthopedics
MH  - *Orthopedic Procedures
EDAT- 2023/08/08 00:42
MHDA- 2023/09/28 06:43
CRDT- 2023/08/07 23:34
PHST- 2023/07/20 00:00 [received]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/09/28 06:43 [medline]
PHST- 2023/08/08 00:42 [pubmed]
PHST- 2023/08/07 23:34 [entrez]
AID - 10.1007/s00264-023-05909-4 [pii]
AID - 10.1007/s00264-023-05909-4 [doi]
PST - ppublish
SO  - Int Orthop. 2023 Oct;47(10):2625. doi: 10.1007/s00264-023-05909-4. Epub 2023 Aug 
      7.

PMID- 37343680
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1527-9995 (Electronic)
IS  - 0090-4295 (Linking)
VI  - 179
DP  - 2023 Sep
TI  - ChatGPT Wrote This Article.
PG  - 1-4
LID - S0090-4295(23)00500-9 [pii]
LID - 10.1016/j.urology.2023.03.061 [doi]
FAU - Kaplan-Marans, Elie
AU  - Kaplan-Marans E
AD  - Maimonides Medical Center, Division of Urology, New York, NY. Electronic address: 
      Ekaplan@maimonidesmed.org.
FAU - Khurgin, Jacob
AU  - Khurgin J
AD  - Maimonides Medical Center, Division of Urology, New York, NY.
LA  - eng
PT  - Letter
DEP - 20230619
PL  - United States
TA  - Urology
JT  - Urology
JID - 0366151
SB  - IM
COIS- Declaration of Competing Interest The authors have no conflict of interest to 
      declare.
EDAT- 2023/06/22 01:07
MHDA- 2023/06/22 01:08
CRDT- 2023/06/21 19:14
PHST- 2023/02/10 00:00 [received]
PHST- 2023/03/15 00:00 [revised]
PHST- 2023/03/29 00:00 [accepted]
PHST- 2023/06/22 01:08 [medline]
PHST- 2023/06/22 01:07 [pubmed]
PHST- 2023/06/21 19:14 [entrez]
AID - S0090-4295(23)00500-9 [pii]
AID - 10.1016/j.urology.2023.03.061 [doi]
PST - ppublish
SO  - Urology. 2023 Sep;179:1-4. doi: 10.1016/j.urology.2023.03.061. Epub 2023 Jun 19.

PMID- 37307661
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230822
LR  - 20230822
IS  - 1873-7862 (Electronic)
IS  - 0924-977X (Linking)
VI  - 74
DP  - 2023 Sep
TI  - Towards a ChatGPT psychopathology: Comment.
PG  - 77
LID - S0924-977X(23)00104-9 [pii]
LID - 10.1016/j.euroneuro.2023.05.003 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Adjunct professor, Joesph Ayobabalola 
      University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230610
PL  - Netherlands
TA  - Eur Neuropsychopharmacol
JT  - European neuropsychopharmacology : the journal of the European College of 
      Neuropsychopharmacology
JID - 9111390
SB  - IM
COIS- Declaration of Competing Interest None.
EDAT- 2023/06/12 19:12
MHDA- 2023/06/12 19:13
CRDT- 2023/06/12 18:03
PHST- 2023/05/08 00:00 [received]
PHST- 2023/05/14 00:00 [revised]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/06/12 19:13 [medline]
PHST- 2023/06/12 19:12 [pubmed]
PHST- 2023/06/12 18:03 [entrez]
AID - S0924-977X(23)00104-9 [pii]
AID - 10.1016/j.euroneuro.2023.05.003 [doi]
PST - ppublish
SO  - Eur Neuropsychopharmacol. 2023 Sep;74:77. doi: 10.1016/j.euroneuro.2023.05.003. 
      Epub 2023 Jun 10.

PMID- 36656495
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230307
LR  - 20230517
IS  - 1572-8633 (Electronic)
IS  - 1386-7423 (Linking)
VI  - 26
IP  - 1
DP  - 2023 Mar
TI  - ChatGPT: evolution or revolution?
PG  - 1-2
LID - 10.1007/s11019-023-10136-0 [doi]
FAU - Gordijn, Bert
AU  - Gordijn B
AD  - Institute of Ethics, Dublin City University, Dublin, Ireland. 
      bert.gordijn@dcu.ie.
FAU - Have, Henk Ten
AU  - Have HT
AD  - Duquesne University, Pittsburgh, USA.
AD  - Anahuac University, Mexico City, Mexico.
LA  - eng
PT  - Editorial
PL  - Netherlands
TA  - Med Health Care Philos
JT  - Medicine, health care, and philosophy
JID - 9815900
SB  - IM
EDAT- 2023/01/20 06:00
MHDA- 2023/01/20 06:01
CRDT- 2023/01/19 11:24
PHST- 2023/01/20 06:00 [pubmed]
PHST- 2023/01/20 06:01 [medline]
PHST- 2023/01/19 11:24 [entrez]
AID - 10.1007/s11019-023-10136-0 [pii]
AID - 10.1007/s11019-023-10136-0 [doi]
PST - ppublish
SO  - Med Health Care Philos. 2023 Mar;26(1):1-2. doi: 10.1007/s11019-023-10136-0.

PMID- 37538021
OWN - NLM
STAT- Publisher
LR  - 20231114
IS  - 1365-2850 (Electronic)
IS  - 1351-0126 (Linking)
DP  - 2023 Aug 4
TI  - Could artificial intelligence write mental health nursing care plans?
LID - 10.1111/jpm.12965 [doi]
AB  - WHAT IS KNOWN ON THE SUBJECT?: Artificial intelligence (AI) is freely available, 
      responds to very basic text input (such as a question) and can now create a wide 
      range of outputs, communicating in many languages or art forms. AI platforms like 
      OpenAI's ChatGPT can now create passages of text that could be used to create 
      plans of care for people with mental health needs. As such, AI output can be 
      difficult to distinguish from human-output, and there is a risk that its use 
      could go unnoticed. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Whilst it is 
      known that AI can produce text or pass pre-registration health-profession exams, 
      it is not known if AI can produce meaningful results for care delivery. We asked 
      ChatGPT basic questions about a fictitious person who presents with self-harm and 
      then evaluated the quality of the output. We found that the output could look 
      reasonable to laypersons but there were significant errors and ethical issues. 
      There are potential harms to people in care if AI is used without an expert 
      correcting or removing these errors. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: We 
      suggest that there is a risk that AI use could cause harm if it was used in 
      direct care delivery. There is a lack of policy and research to safeguard people 
      receiving care - and this needs to be in place before AI should be used in this 
      way. Key aspects of the role of a mental health nurse are relational and AI use 
      may diminish mental health nurses' ability to provide safe care in its current 
      form. Many aspects of mental health recovery are linked to relationships and 
      social engagement, however AI is not able to provide this and may push the people 
      who are in most need of help further away from services that assist recovery. 
      ABSTRACT: Background Artificial intelligence (AI) is being increasingly used and 
      discussed in care contexts. ChatGPT has gained significant attention in popular 
      and scientific literature although how ChatGPT can be used in care-delivery is 
      not yet known. Aims To use artificial intelligence (ChatGPT) to create a mental 
      health nursing care plan and evaluate the quality of the output against the 
      authors' clinical experience and existing guidance. Materials & Methods Basic 
      text commands were input into ChatGPT about a fictitious person called 'Emily' 
      who presents with self-injurious behaviour. The output from ChatGPT was then 
      evaluated against the authors' clinical experience and current (national) care 
      guidance. Results ChatGPT was able to provide a care plan that incorporated some 
      principles of dialectical behaviour therapy, but the output had significant 
      errors and limitations and thus there is a reasonable likelihood of harm if used 
      in this way. Discussion AI use is increasing in direct-care contexts through the 
      use of chatbots or other means. However, AI can inhibit clinician to 
      care-recipient engagement, 'recycle' existing stigma, and introduce error, which 
      may thus diminish the ability for care to uphold personhood and therefore lead to 
      significant avoidable harms. Conclusion Use of AI in this context should be 
      avoided until a point where policy and guidance can safeguard the wellbeing of 
      care recipients and the sophistication of AI output has increased. Given 
      ChatGPT's ability to provide superficially reasonable outputs there is a risk 
      that errors may go unnoticed and thus increase the likelihood of patient harms. 
      Further research evaluating AI output is needed to consider how AI may be used 
      safely in care delivery.
CI  - © 2023 The Authors. Journal of Psychiatric and Mental Health Nursing published by 
      John Wiley & Sons Ltd.
FAU - Woodnutt, Samuel
AU  - Woodnutt S
AUID- ORCID: 0000-0001-6821-3158
AD  - School of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
FAU - Allen, Chris
AU  - Allen C
AD  - School of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
FAU - Snowden, Jasmine
AU  - Snowden J
AUID- ORCID: 0000-0001-5290-4587
AD  - School of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
FAU - Flynn, Matt
AU  - Flynn M
AUID- ORCID: 0009-0005-7354-7490
AD  - School of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
FAU - Hall, Simon
AU  - Hall S
AD  - School of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
FAU - Libberton, Paula
AU  - Libberton P
AD  - School of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
FAU - Purvis, Francesca
AU  - Purvis F
AD  - School of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
LA  - eng
PT  - Journal Article
DEP - 20230804
PL  - England
TA  - J Psychiatr Ment Health Nurs
JT  - Journal of psychiatric and mental health nursing
JID - 9439514
OTO - NOTNLM
OT  - art of nursing
OT  - nursing role
OT  - quality of care
OT  - self-harm
OT  - therapeutic relationships
EDAT- 2023/08/04 06:43
MHDA- 2023/08/04 06:43
CRDT- 2023/08/04 03:30
PHST- 2023/07/14 00:00 [revised]
PHST- 2023/04/05 00:00 [received]
PHST- 2023/07/23 00:00 [accepted]
PHST- 2023/08/04 06:43 [pubmed]
PHST- 2023/08/04 06:43 [medline]
PHST- 2023/08/04 03:30 [entrez]
AID - 10.1111/jpm.12965 [doi]
PST - aheadofprint
SO  - J Psychiatr Ment Health Nurs. 2023 Aug 4. doi: 10.1111/jpm.12965.

PMID- 37703484
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1546-3141 (Electronic)
IS  - 0361-803X (Linking)
VI  - 221
IP  - 5
DP  - 2023 Nov
TI  - Improving Accuracy in ChatGPT.
PG  - 705
LID - 10.2214/AJR.23.29868 [doi]
FAU - Elek, Alperen
AU  - Elek A
AD  - Ege University Faculty of Medicine, Izmir, Turkey, alperenelek13@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - United States
TA  - AJR Am J Roentgenol
JT  - AJR. American journal of roentgenology
JID - 7708173
SB  - IM
EDAT- 2023/09/13 18:41
MHDA- 2023/09/13 18:41
CRDT- 2023/09/13 15:42
PHST- 2023/09/13 18:41 [pubmed]
PHST- 2023/09/13 18:41 [medline]
PHST- 2023/09/13 15:42 [entrez]
AID - 10.2214/AJR.23.29868 [doi]
PST - ppublish
SO  - AJR Am J Roentgenol. 2023 Nov;221(5):705. doi: 10.2214/AJR.23.29868. Epub 2023 
      Sep 13.

PMID- 37641909
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230905
IS  - 2234-7550 (Print)
IS  - 2234-5930 (Electronic)
IS  - 1225-1585 (Linking)
VI  - 49
IP  - 4
DP  - 2023 Aug 31
TI  - ChatGPT and scientific paper.
PG  - 239-240
LID - 10.5125/jkaoms.2023.49.4.239 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Dr. D. Y. Patil Vidhyapeeth, Pune, India.
AD  - Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
PL  - Korea (South)
TA  - J Korean Assoc Oral Maxillofac Surg
JT  - Journal of the Korean Association of Oral and Maxillofacial Surgeons
JID - 101580242
PMC - PMC10466016
COIS- Conflict of Interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/08/29 06:43
MHDA- 2023/08/29 06:44
CRDT- 2023/08/29 04:32
PHST- 2023/08/29 06:44 [medline]
PHST- 2023/08/29 06:43 [pubmed]
PHST- 2023/08/29 04:32 [entrez]
AID - jkaoms.2023.49.4.239 [pii]
AID - jkaoms-49-4-239 [pii]
AID - 10.5125/jkaoms.2023.49.4.239 [doi]
PST - ppublish
SO  - J Korean Assoc Oral Maxillofac Surg. 2023 Aug 31;49(4):239-240. doi: 
      10.5125/jkaoms.2023.49.4.239.

PMID- 37165022
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
VI  - 47
IP  - 5
DP  - 2023 Oct
TI  - When ChatGPT Meets Plastic Surgeons.
PG  - 2190-2193
LID - 10.1007/s00266-023-03372-5 [doi]
FAU - Kang, Yuanbo
AU  - Kang Y
AD  - Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese 
      Academy of Medical Sciences, Shuaifuyuan 1#, Dongcheng District, Beijing, 100730, 
      People's Republic of China.
AD  - Peking Union Medical College, Chinese Academy of Medical Sciences, Dongdan 
      Santiao 9#, Dongcheng District, Beijing, 100730, People's Republic of China.
FAU - Xia, Zenan
AU  - Xia Z
AD  - Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese 
      Academy of Medical Sciences, Shuaifuyuan 1#, Dongcheng District, Beijing, 100730, 
      People's Republic of China.
FAU - Zhu, Lin
AU  - Zhu L
AD  - Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese 
      Academy of Medical Sciences, Shuaifuyuan 1#, Dongcheng District, Beijing, 100730, 
      People's Republic of China. pumchzhulin@126.com.
LA  - eng
PT  - Letter
DEP - 20230510
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EDAT- 2023/05/11 00:41
MHDA- 2023/05/11 00:42
CRDT- 2023/05/10 23:18
PHST- 2023/03/21 00:00 [received]
PHST- 2023/04/23 00:00 [accepted]
PHST- 2023/05/11 00:42 [medline]
PHST- 2023/05/11 00:41 [pubmed]
PHST- 2023/05/10 23:18 [entrez]
AID - 10.1007/s00266-023-03372-5 [pii]
AID - 10.1007/s00266-023-03372-5 [doi]
PST - ppublish
SO  - Aesthetic Plast Surg. 2023 Oct;47(5):2190-2193. doi: 10.1007/s00266-023-03372-5. 
      Epub 2023 May 10.

PMID- 37147231
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230522
LR  - 20230522
IS  - 1872-7654 (Electronic)
IS  - 0301-2115 (Linking)
VI  - 285
DP  - 2023 Jun
TI  - ChatGPT for guidance during unexpected labour: Comment.
PG  - 219
LID - S0301-2115(23)00175-6 [pii]
LID - 10.1016/j.ejogrb.2023.04.027 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230429
PL  - Ireland
TA  - Eur J Obstet Gynecol Reprod Biol
JT  - European journal of obstetrics, gynecology, and reproductive biology
JID - 0375672
SB  - IM
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/05/06 09:42
MHDA- 2023/05/06 09:43
CRDT- 2023/05/05 22:39
PHST- 2023/04/25 00:00 [received]
PHST- 2023/04/26 00:00 [accepted]
PHST- 2023/05/06 09:43 [medline]
PHST- 2023/05/06 09:42 [pubmed]
PHST- 2023/05/05 22:39 [entrez]
AID - S0301-2115(23)00175-6 [pii]
AID - 10.1016/j.ejogrb.2023.04.027 [doi]
PST - ppublish
SO  - Eur J Obstet Gynecol Reprod Biol. 2023 Jun;285:219. doi: 
      10.1016/j.ejogrb.2023.04.027. Epub 2023 Apr 29.

PMID- 37093697
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230511
LR  - 20230803
IS  - 1536-5026 (Print)
IS  - 1536-5026 (Linking)
VI  - 44
IP  - 3
DP  - 2023 May-Jun 01
TI  - ChatGPT: Disruptive Educational Technology.
PG  - 198-199
LID - 10.1097/01.NEP.0000000000001129 [doi]
FAU - Frith, Karen H
AU  - Frith KH
AD  - About the Author Karen H. Frith, PhD, RN, NEA-BC, CNE, is dean and professor, The 
      University of Alabama in Huntsville College of Nursing. Contact her at 
      karen.frith@uah.edu.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Nurs Educ Perspect
JT  - Nursing education perspectives
JID - 101140025
COIS- The author declares no conflict of interest.
EDAT- 2023/04/24 18:42
MHDA- 2023/04/26 06:42
CRDT- 2023/04/24 12:26
PHST- 2023/04/26 06:42 [medline]
PHST- 2023/04/24 18:42 [pubmed]
PHST- 2023/04/24 12:26 [entrez]
AID - 00024776-202305000-00020 [pii]
AID - 10.1097/01.NEP.0000000000001129 [doi]
PST - ppublish
SO  - Nurs Educ Perspect. 2023 May-Jun 01;44(3):198-199. doi: 
      10.1097/01.NEP.0000000000001129.

PMID- 36702491
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230131
LR  - 20230202
IS  - 1756-1833 (Electronic)
IS  - 0959-8138 (Linking)
VI  - 380
DP  - 2023 Jan 26
TI  - Sixty seconds on . . . ChatGPT.
PG  - 205
LID - 10.1136/bmj.p205 [doi]
FAU - Looi, Mun-Keat
AU  - Looi MK
AD  - The BMJ.
LA  - eng
PT  - Journal Article
DEP - 20230126
PL  - England
TA  - BMJ
JT  - BMJ (Clinical research ed.)
JID - 8900488
SB  - IM
EDAT- 2023/01/27 06:00
MHDA- 2023/01/31 06:00
CRDT- 2023/01/26 20:33
PHST- 2023/01/26 20:33 [entrez]
PHST- 2023/01/27 06:00 [pubmed]
PHST- 2023/01/31 06:00 [medline]
AID - 10.1136/bmj.p205 [doi]
PST - epublish
SO  - BMJ. 2023 Jan 26;380:205. doi: 10.1136/bmj.p205.

PMID- 37428357
OWN - NLM
STAT- Publisher
LR  - 20230710
IS  - 2196-8837 (Electronic)
IS  - 2196-8837 (Linking)
DP  - 2023 Jul 10
TI  - Psychometric Properties and Assessment of Knowledge, Attitude, and Practice 
      Towards ChatGPT in Pharmacy Practice and Education: a Study Protocol.
LID - 10.1007/s40615-023-01696-1 [doi]
AB  - ChatGPT represents an advanced conversational artificial intelligence (AI), 
      providing a powerful tool for generating human-like responses that could change 
      pharmacy prospects. This protocol aims to describe the development, validation, 
      and utilization of a tool to assess the knowledge, attitude, and practice towards 
      ChatGPT (KAP-C) in pharmacy practice and education. The development and 
      validation process of the KAP-C tool will include a comprehensive literature 
      search to identify relevant constructs, content validation by a panel of experts 
      for items relevancy using content validity index (CVI) and face validation by 
      sample participants for items clarity using face validity index (FVI), 
      readability and difficulty index using the Flesch-Kincaid Readability Test, 
      Gunning Fog Index, or Simple Measure of Gobbledygook (SMOG), assessment of 
      reliability using internal consistency (Cronbach's alpha), and exploratory factor 
      analysis (EFA) to determine the underlying factor structures (eigenvalues, scree 
      plot analysis, factor loadings, and varimax). The second phase will utilize the 
      validated KAP-C tool to conduct KAP surveys among pharmacists and pharmacy 
      students in selected low- and middle-income countries (LMICs) (Nigeria, Pakistan, 
      and Yemen). The final data will be analyzed descriptively using frequencies, 
      percentages, mean (standard deviation) or median (interquartile range), and 
      inferential statistics like Chi-square or regression analyses using IBM SPSS 
      version 28. A p<0.05 will be considered statistically significant. ChatGPT holds 
      the potential to revolutionize pharmacy practice and education. This study will 
      highlight the psychometric properties of the KAP-C tool that assesses the 
      knowledge, attitude, and practice towards ChatGPT in pharmacy practice and 
      education. The findings will contribute to the potential ethical integration of 
      ChatGPT into pharmacy practice and education in LMICs, serve as a reference to 
      other economies, and provide valuable evidence for leveraging AI advancements in 
      pharmacy.
CI  - © 2023. W. Montague Cobb-NMA Health Institute.
FAU - Mohammed, Mustapha
AU  - Mohammed M
AUID- ORCID: 0000-0002-5021-1610
AD  - Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmaceutical 
      Sciences, Ahmadu Bello University, Zaria, Kaduna, Nigeria. 
      mohammedmmrx@gmail.com.
AD  - School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Pulau 
      Pinang, Malaysia. mohammedmmrx@gmail.com.
AD  - Vice President for Medical and Health Science Office, QU Health, Qatar 
      University, Doha, Qatar. mohammedmmrx@gmail.com.
FAU - Kumar, Narendar
AU  - Kumar N
AUID- ORCID: 0000-0002-2648-3915
AD  - Department of Pharmacy Practice, Faculty of Pharmacy, University of Sindh 
      Jamshoro, Sindh, Pakistan.
FAU - Zawiah, Mohammed
AU  - Zawiah M
AUID- ORCID: 0000-0002-3325-1442
AD  - School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Pulau 
      Pinang, Malaysia.
AD  - Department of Pharmacy Practice, Faculty of Clinical Pharmacy, Hodeidah 
      University, Al Hodeidah, Yemen.
FAU - Al-Ashwal, Fahmi Y
AU  - Al-Ashwal FY
AUID- ORCID: 0000-0003-2076-0771
AD  - Department of Pharmacy, Al-Maarif University College, Anbar, 31001, Iraq.
FAU - Bala, Auwal Adam
AU  - Bala AA
AUID- ORCID: 0000-0003-0402-9639
AD  - Department of Pharmacology, College of Medicine and Health Sciences, Federal 
      University Dutse, Dutse, Jigawa, Nigeria.
FAU - Lawal, Basira Kankia
AU  - Lawal BK
AUID- ORCID: 0000-0003-1975-0648
AD  - Department of Clinical Pharmacy and Pharmacy Management, Kaduna State University, 
      Kaduna, Nigeria.
FAU - Wada, Abubakar Sadiq
AU  - Wada AS
AUID- ORCID: 0000-0001-8221-1159
AD  - Department of Pharmacology and Therapeutics, Bayero University, Kano, Nigeria.
FAU - Halboup, Abdulsalam
AU  - Halboup A
AUID- ORCID: 0000-0002-9026-5656
AD  - School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Pulau 
      Pinang, Malaysia.
AD  - Department of Clinical Pharmacy and Pharmacy Practice, University of Science and 
      Technology, Sana'a, Yemen.
FAU - Muhammad, Surajuddeen
AU  - Muhammad S
AUID- ORCID: 0000-0001-8567-0748
AD  - Faculty of Veterinary Medicine, Ahmadu Bello University, Zaria, Kaduna, Nigeria.
FAU - Ahmad, Rabbiya
AU  - Ahmad R
AUID- ORCID: 0000-0003-0648-6825
AD  - School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Pulau 
      Pinang, Malaysia.
FAU - Sha'aban, Abubakar
AU  - Sha'aban A
AUID- ORCID: 0000-0002-5491-9851
AD  - Division of Population Medicine, School of Medicine, Cardiff University, Heath 
      Park, Cardiff, CF14 4YS, Wales, UK.
LA  - eng
PT  - Journal Article
DEP - 20230710
PL  - Switzerland
TA  - J Racial Ethn Health Disparities
JT  - Journal of racial and ethnic health disparities
JID - 101628476
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Attitude
OT  - ChatGPT
OT  - Knowledge
OT  - Pharmacy
OT  - Practice
EDAT- 2023/07/10 13:05
MHDA- 2023/07/10 13:05
CRDT- 2023/07/10 11:13
PHST- 2023/04/28 00:00 [received]
PHST- 2023/06/21 00:00 [accepted]
PHST- 2023/06/17 00:00 [revised]
PHST- 2023/07/10 13:05 [medline]
PHST- 2023/07/10 13:05 [pubmed]
PHST- 2023/07/10 11:13 [entrez]
AID - 10.1007/s40615-023-01696-1 [pii]
AID - 10.1007/s40615-023-01696-1 [doi]
PST - aheadofprint
SO  - J Racial Ethn Health Disparities. 2023 Jul 10. doi: 10.1007/s40615-023-01696-1.

PMID- 37212874
OWN - NLM
STAT- MEDLINE
DCOM- 20231024
LR  - 20231024
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
VI  - 47
IP  - 5
DP  - 2023 Oct
TI  - Rhinoplasty Consultation with ChatGPT.
PG  - 2203-2204
LID - 10.1007/s00266-023-03394-z [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Mohali, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230522
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
CON - Aesthetic Plast Surg. 2023 Oct;47(5):1985-1993. PMID: 37095384
CIN - Aesthetic Plast Surg. 2023 Oct;47(5):2205-2206. PMID: 37253840
MH  - Humans
MH  - *Rhinoplasty
MH  - *Artificial Intelligence
MH  - Referral and Consultation
EDAT- 2023/05/22 13:04
MHDA- 2023/10/23 00:41
CRDT- 2023/05/22 11:05
PHST- 2023/04/28 00:00 [received]
PHST- 2023/04/29 00:00 [accepted]
PHST- 2023/10/23 00:41 [medline]
PHST- 2023/05/22 13:04 [pubmed]
PHST- 2023/05/22 11:05 [entrez]
AID - 10.1007/s00266-023-03394-z [pii]
AID - 10.1007/s00266-023-03394-z [doi]
PST - ppublish
SO  - Aesthetic Plast Surg. 2023 Oct;47(5):2203-2204. doi: 10.1007/s00266-023-03394-z. 
      Epub 2023 May 22.

PMID- 37235742
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230717
LR  - 20230718
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 8
DP  - 2023 Jul 15
TI  - ChatGPT and the Honor System.
PG  - 938-940
LID - 10.1093/asj/sjad163 [doi]
FAU - Cress, Phaedra E
AU  - Cress PE
AUID- ORCID: 0000-0002-6217-2604
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
EDAT- 2023/05/26 19:14
MHDA- 2023/05/26 19:15
CRDT- 2023/05/26 14:53
PHST- 2023/05/26 19:15 [medline]
PHST- 2023/05/26 19:14 [pubmed]
PHST- 2023/05/26 14:53 [entrez]
AID - 7180807 [pii]
AID - 10.1093/asj/sjad163 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jul 15;43(8):938-940. doi: 10.1093/asj/sjad163.

PMID- 36918736
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230324
LR  - 20230324
IS  - 1546-170X (Electronic)
IS  - 1078-8956 (Linking)
VI  - 29
IP  - 3
DP  - 2023 Mar
TI  - Will ChatGPT transform healthcare?
PG  - 505-506
LID - 10.1038/s41591-023-02289-5 [doi]
LA  - eng
PT  - Editorial
PL  - United States
TA  - Nat Med
JT  - Nature medicine
JID - 9502015
SB  - IM
EDAT- 2023/03/16 06:00
MHDA- 2023/03/16 06:01
CRDT- 2023/03/15 01:05
PHST- 2023/03/16 06:00 [pubmed]
PHST- 2023/03/16 06:01 [medline]
PHST- 2023/03/15 01:05 [entrez]
AID - 10.1038/s41591-023-02289-5 [pii]
AID - 10.1038/s41591-023-02289-5 [doi]
PST - ppublish
SO  - Nat Med. 2023 Mar;29(3):505-506. doi: 10.1038/s41591-023-02289-5.

PMID- 37043291
OWN - NLM
STAT- MEDLINE
DCOM- 20231211
LR  - 20231211
IS  - 1538-9855 (Electronic)
IS  - 0363-3624 (Linking)
VI  - 49
IP  - 1
DP  - 2024 Jan-Feb 01
TI  - Working Smarter Using ChatGPT.
PG  - E35
LID - 10.1097/NNE.0000000000001424 [doi]
FAU - Evans, Jennifer
AU  - Evans J
AUID- ORCID: 0000-0002-4314-9214
AD  - By Jennifer Evans , DNP, RN, NC-BC, College of Nursing and Health Professions, 
      University of Southern Indiana, Evansville, jennifer.evans@usi.edu .
LA  - eng
PT  - Journal Article
DEP - 20230410
PL  - United States
TA  - Nurse Educ
JT  - Nurse educator
JID - 7701902
MH  - Humans
MH  - *Nursing Education Research
COIS- The authors declare no conflict of interest.
EDAT- 2023/04/13 06:00
MHDA- 2023/12/11 12:42
CRDT- 2023/04/12 11:54
PHST- 2023/12/11 12:42 [medline]
PHST- 2023/04/13 06:00 [pubmed]
PHST- 2023/04/12 11:54 [entrez]
AID - 00006223-990000000-00216 [pii]
AID - 10.1097/NNE.0000000000001424 [doi]
PST - ppublish
SO  - Nurse Educ. 2024 Jan-Feb 01;49(1):E35. doi: 10.1097/NNE.0000000000001424. Epub 
      2023 Apr 10.

PMID- 37758604
OWN - NLM
STAT- Publisher
LR  - 20230927
IS  - 1535-6302 (Electronic)
IS  - 0363-0188 (Linking)
DP  - 2023 Aug 30
TI  - Radiology Reading Room for the Future: Harnessing the Power of Large Language 
      Models Like ChatGPT.
LID - S0363-0188(23)00133-0 [pii]
LID - 10.1067/j.cpradiol.2023.08.018 [doi]
AB  - Radiology has usually been the field of medicine that has been at the forefront 
      of technological advances, often being the first to wholeheartedly embrace them. 
      Whether it's from digitization to cloud side architecture, radiology has led the 
      way for adopting the latest advances. With the advent of large language models 
      (LLMs), especially with the unprecedented explosion of freely available ChatGPT, 
      time is ripe for radiology and radiologists to find novel ways to use the 
      technology to improve their workflow. Towards this, we believe these LLMs have a 
      key role in the radiology reading room not only to expedite processes, simplify 
      mundane and archaic tasks, but also to increase the radiologist's and radiologist 
      trainee's knowledge base at a far faster pace. In this article, we discuss some 
      of the ways we believe ChatGPT, and the likes can be harnessed in the reading 
      room.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Tippareddy, Charit
AU  - Tippareddy C
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH.
FAU - Jiang, Sirui
AU  - Jiang S
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH.
FAU - Bera, Kaustav
AU  - Bera K
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH. Electronic address: kxb413@case.edu.
FAU - Ramaiya, Nikhil
AU  - Ramaiya N
AD  - Department of Radiology, University Hospitals Cleveland Medical Center, 
      Cleveland, OH.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230830
PL  - United States
TA  - Curr Probl Diagn Radiol
JT  - Current problems in diagnostic radiology
JID - 7607123
SB  - IM
EDAT- 2023/09/28 00:42
MHDA- 2023/09/28 00:42
CRDT- 2023/09/27 21:56
PHST- 2023/08/09 00:00 [received]
PHST- 2023/08/28 00:00 [revised]
PHST- 2023/08/28 00:00 [accepted]
PHST- 2023/09/28 00:42 [medline]
PHST- 2023/09/28 00:42 [pubmed]
PHST- 2023/09/27 21:56 [entrez]
AID - S0363-0188(23)00133-0 [pii]
AID - 10.1067/j.cpradiol.2023.08.018 [doi]
PST - aheadofprint
SO  - Curr Probl Diagn Radiol. 2023 Aug 30:S0363-0188(23)00133-0. doi: 
      10.1067/j.cpradiol.2023.08.018.

PMID- 37598727
OWN - NLM
STAT- MEDLINE
DCOM- 20231106
LR  - 20231106
IS  - 1879-8519 (Electronic)
IS  - 1879-8500 (Linking)
VI  - 13
IP  - 6
DP  - 2023 Nov-Dec
TI  - Unlocking the Power of ChatGPT, Artificial Intelligence, and Large Language 
      Models: Practical Suggestions for Radiation Oncologists.
PG  - e484-e490
LID - S1879-8500(23)00213-8 [pii]
LID - 10.1016/j.prro.2023.06.011 [doi]
AB  - Recent advances in artificial intelligence (AI), such as generative AI and large 
      language models (LLMs), have generated significant excitement about the potential 
      of AI to revolutionize our lives, work, and interaction with technology. This 
      article explores the practical applications of LLMs, particularly ChatGPT, in the 
      field of radiation oncology. We offer a guide on how radiation oncologists can 
      interact with LLMs like ChatGPT in their routine clinical and administrative 
      tasks, highlighting potential use cases of the present and future. We also 
      highlight limitations and ethical considerations, including the current state of 
      LLMs in decision making, protection of sensitive data, and the important role of 
      human review of AI-generated content.
CI  - Copyright © 2023 American Society for Radiation Oncology. Published by Elsevier 
      Inc. All rights reserved.
FAU - Waters, Michael R
AU  - Waters MR
AD  - Department of Radiation Oncology, Washington University School of Medicine, St. 
      Louis, Missouri.
FAU - Aneja, Sanjay
AU  - Aneja S
AD  - Department of Radiation Oncology, Yale School of Medicine, New Haven, 
      Connecticut.
FAU - Hong, Julian C
AU  - Hong JC
AD  - Department of Radiation Oncology and Bakar Computational Health Sciences 
      Institute, University of California, San Francisco, San Francisco, California. 
      Electronic address: Julian.Hong@ucsf.edu.
LA  - eng
PT  - Journal Article
DEP - 20230819
PL  - United States
TA  - Pract Radiat Oncol
JT  - Practical radiation oncology
JID - 101558279
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Radiation Oncologists
MH  - Language
MH  - *Radiation Oncology
EDAT- 2023/08/21 00:41
MHDA- 2023/11/06 06:42
CRDT- 2023/08/20 19:23
PHST- 2023/06/21 00:00 [received]
PHST- 2023/06/28 00:00 [revised]
PHST- 2023/06/29 00:00 [accepted]
PHST- 2023/11/06 06:42 [medline]
PHST- 2023/08/21 00:41 [pubmed]
PHST- 2023/08/20 19:23 [entrez]
AID - S1879-8500(23)00213-8 [pii]
AID - 10.1016/j.prro.2023.06.011 [doi]
PST - ppublish
SO  - Pract Radiat Oncol. 2023 Nov-Dec;13(6):e484-e490. doi: 
      10.1016/j.prro.2023.06.011. Epub 2023 Aug 19.

PMID- 37277096
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231018
IS  - 2468-6530 (Electronic)
IS  - 2468-6530 (Linking)
VI  - 7
IP  - 10
DP  - 2023 Oct
TI  - Appropriateness and Readability of ChatGPT-4-Generated Responses for Surgical 
      Treatment of Retinal Diseases.
PG  - 862-868
LID - S2468-6530(23)00246-4 [pii]
LID - 10.1016/j.oret.2023.05.022 [doi]
AB  - OBJECTIVE: To evaluate the appropriateness and readability of the medical 
      knowledge provided by ChatGPT-4, an artificial intelligence-powered 
      conversational search engine, regarding common vitreoretinal surgeries for 
      retinal detachments (RDs), macular holes (MHs), and epiretinal membranes (ERMs). 
      DESIGN: Retrospective cross-sectional study. SUBJECTS: This study did not involve 
      any human participants. METHODS: We created lists of common questions about the 
      definition, prevalence, visual impact, diagnostic methods, surgical and 
      nonsurgical treatment options, postoperative information, surgery-related 
      complications, and visual prognosis of RD, MH, and ERM, and asked each question 3 
      times on the online ChatGPT-4 platform. The data for this cross-sectional study 
      were recorded on April 25, 2023. Two independent retina specialists graded the 
      appropriateness of the responses. Readability was assessed using Readable, an 
      online readability tool. MAIN OUTCOME MEASURES: The "appropriateness" and 
      "readability" of the answers generated by ChatGPT-4 bot. RESULTS: Responses were 
      consistently appropriate in 84.6% (33/39), 92% (23/25), and 91.7% (22/24) of the 
      questions related to RD, MH, and ERM, respectively. Answers were inappropriate at 
      least once in 5.1% (2/39), 8% (2/25), and 8.3% (2/24) of the respective 
      questions. The average Flesch Kincaid Grade Level and Flesch Reading Ease Score 
      were 14.1 ± 2.6 and 32.3 ± 10.8 for RD, 14 ± 1.3 and 34.4 ± 7.7 for MH, and 14.8 
      ± 1.3 and 28.1 ± 7.5 for ERM. These scores indicate that the answers are 
      difficult or very difficult to read for the average lay person and college 
      graduation would be required to understand the material. CONCLUSIONS: Most of the 
      answers provided by ChatGPT-4 were consistently appropriate. However, ChatGPT and 
      other natural language models in their current form are not a source of factual 
      information. Improving the credibility and readability of responses, especially 
      in specialized fields, such as medicine, is a critical focus of research. 
      Patients, physicians, and laypersons should be advised of the limitations of 
      these tools for eye- and health-related counseling. FINANCIAL DISCLOSURE(S): 
      Proprietary or commercial disclosure may be found in the Footnotes and 
      Disclosures at the end of this article.
CI  - Copyright © 2023 American Academy of Ophthalmology. Published by Elsevier Inc. 
      All rights reserved.
FAU - Momenaei, Bita
AU  - Momenaei B
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Wakabayashi, Taku
AU  - Wakabayashi T
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Shahlaee, Abtin
AU  - Shahlaee A
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Durrani, Asad F
AU  - Durrani AF
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Pandit, Saagar A
AU  - Pandit SA
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Wang, Kristine
AU  - Wang K
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Mansour, Hana A
AU  - Mansour HA
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Abishek, Robert M
AU  - Abishek RM
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Xu, David
AU  - Xu D
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Sridhar, Jayanth
AU  - Sridhar J
AD  - Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, 
      Miami, Florida.
FAU - Yonekawa, Yoshihiro
AU  - Yonekawa Y
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania.
FAU - Kuriyan, Ajay E
AU  - Kuriyan AE
AD  - Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, 
      Philadelphia, Pennsylvania. Electronic address: ajay.kuriyan@gmail.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230603
PL  - United States
TA  - Ophthalmol Retina
JT  - Ophthalmology. Retina
JID - 101695048
SB  - IM
CIN - Ophthalmol Retina. 2023 Oct;7(10):e15. PMID: 37379882
MH  - Humans
MH  - *Health Literacy
MH  - Comprehension
MH  - Cross-Sectional Studies
MH  - Artificial Intelligence
MH  - Retrospective Studies
MH  - *Retinal Diseases/surgery
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Readability
OT  - Retina
OT  - Vitrectomy
EDAT- 2023/06/06 01:11
MHDA- 2023/10/09 06:41
CRDT- 2023/06/05 19:24
PHST- 2023/03/28 00:00 [received]
PHST- 2023/05/26 00:00 [revised]
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/10/09 06:41 [medline]
PHST- 2023/06/06 01:11 [pubmed]
PHST- 2023/06/05 19:24 [entrez]
AID - S2468-6530(23)00246-4 [pii]
AID - 10.1016/j.oret.2023.05.022 [doi]
PST - ppublish
SO  - Ophthalmol Retina. 2023 Oct;7(10):862-868. doi: 10.1016/j.oret.2023.05.022. Epub 
      2023 Jun 3.

PMID- 37278630
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230608
LR  - 20231204
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 5
DP  - 2023 Jun
TI  - ChatGPT in Medical Publications.
PG  - e231188
LID - 10.1148/radiol.231188 [doi]
FAU - Darzidehkalani, Erfan
AU  - Darzidehkalani E
AUID- ORCID: 0000-0002-6003-0119
AD  - CSAIL, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 
      02139.
LA  - eng
PT  - Letter
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
EDAT- 2023/06/06 13:09
MHDA- 2023/06/06 13:10
CRDT- 2023/06/06 10:03
PHST- 2023/06/06 13:10 [medline]
PHST- 2023/06/06 13:09 [pubmed]
PHST- 2023/06/06 10:03 [entrez]
AID - 10.1148/radiol.231188 [doi]
PST - ppublish
SO  - Radiology. 2023 Jun;307(5):e231188. doi: 10.1148/radiol.231188.

PMID- 37684439
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230911
LR  - 20231113
IS  - 1476-5373 (Electronic)
IS  - 0007-0610 (Linking)
VI  - 235
IP  - 5
DP  - 2023 Sep
TI  - ChatGPT passes anatomy exam.
PG  - 295
LID - 10.1038/s41415-023-6286-7 [doi]
FAU - Mehrabanian, M
AU  - Mehrabanian M
AD  - Alumni of University of Debrecen, Debrecen, Hungary. m.mehrabanian@gmail.com.
FAU - Zariat, Y
AU  - Zariat Y
AD  - Alumni of University of Debrecen, Debrecen, Hungary. younes.zariat@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230908
PL  - England
TA  - Br Dent J
JT  - British dental journal
JID - 7513219
SB  - IM
CIN - Br Dent J. 2023 Oct;235(7):448-449. PMID: 37828162
EDAT- 2023/09/09 10:41
MHDA- 2023/09/09 10:42
CRDT- 2023/09/08 23:31
PHST- 2023/07/28 00:00 [received]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/09/09 10:42 [medline]
PHST- 2023/09/09 10:41 [pubmed]
PHST- 2023/09/08 23:31 [entrez]
AID - 10.1038/s41415-023-6286-7 [pii]
AID - 10.1038/s41415-023-6286-7 [doi]
PST - ppublish
SO  - Br Dent J. 2023 Sep;235(5):295. doi: 10.1038/s41415-023-6286-7. Epub 2023 Sep 8.

PMID- 37442056
OWN - NLM
STAT- MEDLINE
DCOM- 20230718
LR  - 20230718
IS  - 1879-0593 (Electronic)
IS  - 1368-8375 (Linking)
VI  - 144
DP  - 2023 Sep
TI  - ChatGPT: Empowering dentistry with future possibilities.
PG  - 106496
LID - S1368-8375(23)00192-6 [pii]
LID - 10.1016/j.oraloncology.2023.106496 [doi]
FAU - Vimalraj, Selvaraj
AU  - Vimalraj S
AD  - Department of Prosthodontics, Saveetha Dental College and Hospital, Saveetha 
      Institute of Medical and Technical Sciences (SIMATS), Saveetha University, 
      Chennai 600 077, Tamil Nadu, India. Electronic address: vimalr50@gmail.com.
FAU - Sekaran, Saravanan
AU  - Sekaran S
AD  - Department of Prosthodontics, Saveetha Dental College and Hospital, Saveetha 
      Institute of Medical and Technical Sciences (SIMATS), Saveetha University, 
      Chennai 600 077, Tamil Nadu, India. Electronic address: ranklopg@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230711
PL  - England
TA  - Oral Oncol
JT  - Oral oncology
JID - 9709118
SB  - IM
MH  - Humans
MH  - *Power, Psychological
MH  - *Dentistry
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/07/14 13:08
MHDA- 2023/07/18 13:10
CRDT- 2023/07/13 18:10
PHST- 2023/07/01 00:00 [received]
PHST- 2023/07/04 00:00 [accepted]
PHST- 2023/07/18 13:10 [medline]
PHST- 2023/07/14 13:08 [pubmed]
PHST- 2023/07/13 18:10 [entrez]
AID - S1368-8375(23)00192-6 [pii]
AID - 10.1016/j.oraloncology.2023.106496 [doi]
PST - ppublish
SO  - Oral Oncol. 2023 Sep;144:106496. doi: 10.1016/j.oraloncology.2023.106496. Epub 
      2023 Jul 11.

PMID- 37220943
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230525
LR  - 20230530
IS  - 1756-1833 (Electronic)
IS  - 0959-8138 (Linking)
VI  - 381
DP  - 2023 May 23
TI  - ChatGPT: effective writing is succinct.
PG  - 1125
LID - 10.1136/bmj.p1125 [doi]
FAU - Kovoor, Joshua G
AU  - Kovoor JG
AD  - University of Adelaide, Adelaide, South Australia, Australia.
FAU - Gupta, Aashray K
AU  - Gupta AK
AD  - University of Adelaide, Adelaide, South Australia, Australia.
FAU - Bacchi, Stephen
AU  - Bacchi S
AD  - University of Adelaide, Adelaide, South Australia, Australia.
LA  - eng
PT  - Letter
DEP - 20230523
PL  - England
TA  - BMJ
JT  - BMJ (Clinical research ed.)
JID - 8900488
SB  - IM
COIS- Competing interests: None declared.
EDAT- 2023/05/24 01:06
MHDA- 2023/05/24 01:07
CRDT- 2023/05/23 20:43
PHST- 2023/05/24 01:07 [medline]
PHST- 2023/05/24 01:06 [pubmed]
PHST- 2023/05/23 20:43 [entrez]
AID - 10.1136/bmj.p1125 [doi]
PST - epublish
SO  - BMJ. 2023 May 23;381:1125. doi: 10.1136/bmj.p1125.

PMID- 38088452
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 1522-9645 (Electronic)
IS  - 0195-668X (Linking)
DP  - 2023 Dec 13
TI  - ChatGPT hallucinating: can it get any more humanlike?
LID - ehad766 [pii]
LID - 10.1093/eurheartj/ehad766 [doi]
FAU - Siontis, Konstantinos C
AU  - Siontis KC
AUID- ORCID: 0000-0002-6658-0971
AD  - Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, 
      Rochester, MN 55905, USA.
FAU - Attia, Zachi I
AU  - Attia ZI
AD  - Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, 
      Rochester, MN 55905, USA.
FAU - Asirvatham, Samuel J
AU  - Asirvatham SJ
AD  - Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, 
      Rochester, MN 55905, USA.
FAU - Friedman, Paul A
AU  - Friedman PA
AUID- ORCID: 0000-0001-5052-2948
AD  - Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, 
      Rochester, MN 55905, USA.
LA  - eng
PT  - Journal Article
DEP - 20231213
PL  - England
TA  - Eur Heart J
JT  - European heart journal
JID - 8006263
SB  - IM
EDAT- 2023/12/13 12:43
MHDA- 2023/12/13 12:43
CRDT- 2023/12/13 08:59
PHST- 2023/12/13 12:43 [medline]
PHST- 2023/12/13 12:43 [pubmed]
PHST- 2023/12/13 08:59 [entrez]
AID - 7471676 [pii]
AID - 10.1093/eurheartj/ehad766 [doi]
PST - aheadofprint
SO  - Eur Heart J. 2023 Dec 13:ehad766. doi: 10.1093/eurheartj/ehad766.

PMID- 37501529
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1533-4031 (Electronic)
IS  - 1072-4109 (Linking)
VI  - 31
IP  - 1
DP  - 2024 Jan 1
TI  - Application of ChatGPT in Routine Diagnostic Pathology: Promises, Pitfalls, and 
      Potential Future Directions.
PG  - 15-21
LID - 10.1097/PAP.0000000000000406 [doi]
AB  - Large Language Models are forms of artificial intelligence that use deep learning 
      algorithms to decipher large amounts of text and exhibit strong capabilities like 
      question answering and translation. Recently, an influx of Large Language Models 
      has emerged in the medical and academic discussion, given their potential 
      widespread application to improve patient care and provider workflow. One 
      application that has gained notable recognition in the literature is ChatGPT, 
      which is a natural language processing "chatbot" technology developed by the 
      artificial intelligence development software company OpenAI. It learns from large 
      amounts of text data to generate automated responses to inquiries in seconds. In 
      health care and academia, chatbot systems like ChatGPT have gained much 
      recognition recently, given their potential to become functional, reliable 
      virtual assistants. However, much research is required to determine the accuracy, 
      validity, and ethical concerns of the integration of ChatGPT and other chatbots 
      into everyday practice. One such field where little information and research on 
      the matter currently exists is pathology. Herein, we present a literature review 
      of pertinent articles regarding the current status and understanding of ChatGPT 
      and its potential application in routine diagnostic pathology. In this review, we 
      address the promises, possible pitfalls, and future potential of this 
      application. We provide examples of actual conversations conducted with the 
      chatbot technology that mimic hypothetical but practical diagnostic pathology 
      scenarios that may be encountered in routine clinical practice. On the basis of 
      this experience, we observe that ChatGPT and other chatbots already have a 
      remarkable ability to distill and summarize, within seconds, vast amounts of 
      publicly available data and information to assist in laying a foundation of 
      knowledge on a specific topic. We emphasize that, at this time, any use of such 
      knowledge at the patient care level in clinical medicine must be carefully vetted 
      through established sources of medical information and expertise. We suggest and 
      anticipate that with the ever-expanding knowledge base required to reliably 
      practice personalized, precision anatomic pathology, improved technologies like 
      future versions of ChatGPT (and other chatbots) enabled by expanded access to 
      reliable, diverse data, might serve as a key ally to the diagnostician. Such 
      technology has real potential to further empower the time-honored paradigm of 
      histopathologic diagnoses based on the integrative cognitive assessment of 
      clinical, gross, and microscopic findings and ancillary immunohistochemical and 
      molecular studies at a time of exploding biomedical knowledge.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Schukow, Casey
AU  - Schukow C
AD  - Department of Pathology, Corewell Health, Royal Oak, MI.
FAU - Smith, Steven Christopher
AU  - Smith SC
AD  - Department of Pathology, Virginia Commonwealth University School of Medicine, 
      Massey Cancer Center, and Richmond Veterans Affairs Medical Center, Richmond, VA.
FAU - Landgrebe, Eric
AU  - Landgrebe E
AD  - Cornell University College of Engineering, Ithaca, NY.
FAU - Parasuraman, Surya
AU  - Parasuraman S
AD  - Department of Biological Sciences, University of Toledo, Toledo, OH.
FAU - Folaranmi, Olaleke Oluwasegun
AU  - Folaranmi OO
AD  - Department of Anatomic Pathology, University of Ilorin Teaching Hospital, Ilorin, 
      Nigeria.
FAU - Paner, Gladell P
AU  - Paner GP
AD  - Departments of Pathology and Surgery (Urology), University of Chicago, Chicago, 
      IL.
FAU - Amin, Mahul B
AU  - Amin MB
AD  - Department of Pathology and Laboratory Medicine, University of Tennessee Health 
      Science Center, Memphis, TN.
AD  - Department of Urology, University of Southern California Keck School of Medicine, 
      Los Angeles, CA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230727
PL  - United States
TA  - Adv Anat Pathol
JT  - Advances in anatomic pathology
JID - 9435676
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Algorithms
MH  - Communication
COIS- The authors have no conflicts of interest to disclose.
EDAT- 2023/07/28 06:43
MHDA- 2023/12/07 12:43
CRDT- 2023/07/28 02:53
PHST- 2023/12/07 12:43 [medline]
PHST- 2023/07/28 06:43 [pubmed]
PHST- 2023/07/28 02:53 [entrez]
AID - 00125480-990000000-00063 [pii]
AID - 10.1097/PAP.0000000000000406 [doi]
PST - ppublish
SO  - Adv Anat Pathol. 2024 Jan 1;31(1):15-21. doi: 10.1097/PAP.0000000000000406. Epub 
      2023 Jul 27.

PMID- 37537126
OWN - NLM
STAT- MEDLINE
DCOM- 20230829
LR  - 20230829
IS  - 1773-0597 (Electronic)
IS  - 0181-5512 (Linking)
VI  - 46
IP  - 7
DP  - 2023 Sep
TI  - Success of ChatGPT, an AI language model, in taking the French language version 
      of the European Board of Ophthalmology examination: A novel approach to medical 
      knowledge assessment.
PG  - 706-711
LID - S0181-5512(23)00305-4 [pii]
LID - 10.1016/j.jfo.2023.05.006 [doi]
AB  - PURPOSE: The purpose of this study was to evaluate the performance of ChatGPT, a 
      cutting-edge artificial intelligence (AI) language model developed by OpenAI, in 
      successfully completing the French language version of the European Board of 
      Ophthalmology (EBO) examination and to assess its potential role in medical 
      education and knowledge assessment. METHODS: ChatGPT, based on the GPT-4 
      architecture, was exposed to a series of EBO examination questions in French, 
      covering various aspects of ophthalmology. The AI's performance was evaluated by 
      comparing its responses with the correct answers provided by ophthalmology 
      experts. Additionally, the study assessed the time taken by ChatGPT to answer 
      each question as a measure of efficiency. RESULTS: ChatGPT achieved a 91% success 
      rate on the EBO examination, demonstrating a high level of competency in 
      ophthalmology knowledge and application. The AI provided correct answers across 
      all question categories, indicating a strong understanding of basic sciences, 
      clinical knowledge, and clinical management. The AI model also answered the 
      questions rapidly, taking only a fraction of the time needed by human 
      test-takers. CONCLUSION: ChatGPT's performance on the French language version of 
      the EBO examination demonstrates its potential to be a valuable tool in medical 
      education and knowledge assessment. Further research is needed to explore optimal 
      ways to implement AI language models in medical education and to address the 
      associated ethical and practical concerns.
CI  - Copyright © 2023 The Author(s). Published by Elsevier Masson SAS.. All rights 
      reserved.
FAU - Panthier, C
AU  - Panthier C
AD  - Department of Ophthalmology, Rothschild Foundation Hospital, 25, rue Manin, 75019 
      Paris, France; Center of Expertise and Research in Optics for Vision (CEROV), 
      Paris, France.
FAU - Gatinel, D
AU  - Gatinel D
AD  - Department of Ophthalmology, Rothschild Foundation Hospital, 25, rue Manin, 75019 
      Paris, France; Center of Expertise and Research in Optics for Vision (CEROV), 
      Paris, France. Electronic address: gatinel@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - France
TA  - J Fr Ophtalmol
JT  - Journal francais d'ophtalmologie
JID - 7804128
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Ophthalmology
MH  - Language
OTO - NOTNLM
OT  - AI applications
OT  - Apprentissage par la machine
OT  - Apprentissage profond
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Deep learning
OT  - Entraînement sur base de données
OT  - Ethics in AI
OT  - European Board of Ophthalmology
OT  - Examen médical
OT  - Generative AI
OT  - Générateur de texte
OT  - Génération d’IA
OT  - Human-like interaction
OT  - Intelligence artificielle
OT  - Language model
OT  - Machine learning
OT  - Medical examination
OT  - Modèle conversationnel
OT  - Natural language processing
OT  - OpenAI
OT  - Ophtalmologie
OT  - Ophthalmology
OT  - Simulation d’interaction humaine
OT  - Text generation
OT  - Training dataset
OT  - Transformateur d’architecture
OT  - Transformer architecture
OT  - Éthique en IA
EDAT- 2023/08/04 01:08
MHDA- 2023/08/29 12:43
CRDT- 2023/08/03 21:57
PHST- 2023/03/27 00:00 [received]
PHST- 2023/05/27 00:00 [revised]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/08/29 12:43 [medline]
PHST- 2023/08/04 01:08 [pubmed]
PHST- 2023/08/03 21:57 [entrez]
AID - S0181-5512(23)00305-4 [pii]
AID - 10.1016/j.jfo.2023.05.006 [doi]
PST - ppublish
SO  - J Fr Ophtalmol. 2023 Sep;46(7):706-711. doi: 10.1016/j.jfo.2023.05.006. Epub 2023 
      Aug 1.

PMID- 38065864
OWN - NLM
STAT- In-Process
LR  - 20231208
IS  - 1536-5964 (Electronic)
IS  - 0025-7974 (Print)
IS  - 0025-7974 (Linking)
VI  - 102
IP  - 49
DP  - 2023 Dec 8
TI  - Evaluating cluster analysis techniques in ChatGPT versus R-language with 
      visualizations of author collaborations and keyword cooccurrences on articles in 
      the Journal of Medicine (Baltimore) 2023: Bibliometric analysis.
PG  - e36154
LID - 10.1097/MD.0000000000036154 [doi]
LID - e36154
AB  - BACKGROUND: Analyses of author collaborations and keyword co-occurrences are 
      frequently used in bibliographic research. However, no studies have introduced a 
      straightforward yet effective approach, such as utilizing ChatGPT with Code 
      Interpreter (ChatGPT_CI) or the R language, for creating cluster-oriented 
      networks. This research aims to compare cluster analysis methods in ChatGPT_CI 
      and R, visualize country-specific author collaborations, and then demonstrate the 
      most effective approach. METHODS: The research focused on articles and review 
      pieces from Medicine (Baltimore) published in 2023. By August 20, 2023, we had 
      gathered metadata for 1976 articles using the Web of Science core collections. 
      The efficiency and effectiveness of cluster displays between ChatGPT_CI and R 
      were compared by evaluating their time consumption. The best method was then 
      employed to present a series of visualizations of country-specific author 
      collaborations, rooted in social network and cluster analyses. Visualization 
      techniques incorporating network charts, chord diagrams, circle bar plots, circle 
      packing plots, heat dendrograms, dendrograms, and word clouds were demonstrated. 
      We further highlighted the research profiles of 2 prolific authors using timeline 
      visuals. RESULTS: The research findings include that (1) the most active 
      contributors were China, Nanjing Medical University (China), the Medical School 
      Department, and Dr Chou from Taiwan when considering countries, institutions, 
      departments, and individual authors, respectively; (2) the highest cited articles 
      originated from Medicine (Baltimore) accounting for 4.53%: New England Journal of 
      Medicine, PLOS ONE, LANCET, and The Journal of the American Medical Association, 
      with respective contributions of 3.25%, 2.7%, 2.52%, and 1.54%; (3) visual 
      cluster analysis in R proved to be more efficient and effective than ChatGPT_CI, 
      reducing the time taken from 1 hour to just 3 minutes; (4) 7 cluster-focused 
      networks were crafted using R on a custom platform; and (5) the research 
      trajectories of 2 prominent authors (Dr Brin from the United States and Dr Chow 
      from Taiwan) and articles themes in Medicine 2023 were depicted using timeline 
      visuals. CONCLUSIONS: This research highlighted the efficient and effective 
      methods for conducting cluster analyses of author collaborations using R. For 
      future related studies, such as keyword co-occurrence analysis, R is recommended 
      as a viable alternative for bibliographic research.
CI  - Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Cheng, Yung-Ze
AU  - Cheng YZ
AD  - Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
FAU - Lai, Tzu-Han
AU  - Lai TH
AD  - Grade Two in Senior High School, National Tainan Second Senior High School, 
      Tainan, Taiwan.
FAU - Chien, Tsair-Wei
AU  - Chien TW
AD  - Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan.
FAU - Chou, Willy
AU  - Chou W
AUID- ORCID: 0000-0002-1132-9341
AD  - Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, 
      Tainan, Taiwan.
AD  - Department of Physical Medicine and Rehabilitation, Chung San Medical University 
      Hospital, Taichung, Taiwan.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Medicine (Baltimore)
JT  - Medicine
JID - 2985248R
SB  - IM
PMC - PMC10713138
COIS- The authors have no funding and conflicts of interest to disclose.
EDAT- 2023/12/09 05:42
MHDA- 2023/12/09 05:42
CRDT- 2023/12/08 22:33
PHST- 2023/12/09 05:42 [medline]
PHST- 2023/12/09 05:42 [pubmed]
PHST- 2023/12/08 22:33 [entrez]
AID - 00005792-202312080-00022 [pii]
AID - 10.1097/MD.0000000000036154 [doi]
PST - ppublish
SO  - Medicine (Baltimore). 2023 Dec 8;102(49):e36154. doi: 
      10.1097/MD.0000000000036154.

PMID- 37356702
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231121
IS  - 1873-6246 (Electronic)
IS  - 0301-0511 (Linking)
VI  - 181
DP  - 2023 Jul
TI  - Editorial: Generative artificial intelligence as a plagiarism problem.
PG  - 108621
LID - S0301-0511(23)00138-2 [pii]
LID - 10.1016/j.biopsycho.2023.108621 [doi]
AB  - There is increasing concern and consternation about generative artificial 
      intelligence (AI) programs and its potential impact on academia. This editorial 
      addresses the potential impact of such programs on scientific publishing as it 
      relates to the journal Biological Psychology. Using chatGPT as an example, it 
      makes the case that a prime concern is its implications for facilitating 
      plagiarism. It briefly outlines what is known about the algorithm of the GPT text 
      model, and also the implications of its chatGPT front end, on being able to 
      establish appropriate credit for ideas in text that it outputs. It is concluded 
      that, at least for Biological Psychology, the expectation is that authors will be 
      transparent about AI usage, will declare when AI is the source of an idea, and 
      will redouble efforts to seek out and cite prior claims to ideas in the published 
      literature when AI is involved.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Dien, Joseph
AU  - Dien J
AD  - Department of Human Development and Quantitative Methodology, University of 
      Maryland, 3304 Benjamin Building, College Park, MD 20742, USA. Electronic 
      address: jdien07@mac.com.
LA  - eng
PT  - Editorial
DEP - 20230624
PL  - Netherlands
TA  - Biol Psychol
JT  - Biological psychology
JID - 0375566
SB  - IM
MH  - Humans
MH  - *Plagiarism
MH  - *Artificial Intelligence
MH  - Publishing
MH  - Algorithms
OTO - NOTNLM
OT  - Academic misconduct
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Large language models
OT  - Plagiarism
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/06/26 00:41
MHDA- 2023/07/17 06:42
CRDT- 2023/06/25 19:16
PHST- 2023/05/26 00:00 [received]
PHST- 2023/06/20 00:00 [revised]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/06/26 00:41 [pubmed]
PHST- 2023/06/25 19:16 [entrez]
AID - S0301-0511(23)00138-2 [pii]
AID - 10.1016/j.biopsycho.2023.108621 [doi]
PST - ppublish
SO  - Biol Psychol. 2023 Jul;181:108621. doi: 10.1016/j.biopsycho.2023.108621. Epub 
      2023 Jun 24.

PMID- 37943569
OWN - NLM
STAT- Publisher
LR  - 20231112
IS  - 2168-6173 (Electronic)
IS  - 2168-6165 (Print)
IS  - 2168-6165 (Linking)
DP  - 2023 Nov 9
TI  - Large Language Model Advanced Data Analysis Abuse to Create a Fake Data Set in 
      Medical Research.
LID - 10.1001/jamaophthalmol.2023.5162 [doi]
LID - e235162
FAU - Taloni, Andrea
AU  - Taloni A
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Scorcia, Vincenzo
AU  - Scorcia V
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Giannaccare, Giuseppe
AU  - Giannaccare G
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
AD  - Eye Clinic, Department of Surgical Sciences, University of Cagliari, Cagliari, 
      Italy.
LA  - eng
PT  - Journal Article
DEP - 20231109
PL  - United States
TA  - JAMA Ophthalmol
JT  - JAMA ophthalmology
JID - 101589539
SB  - IM
PMC - PMC10636646
OAB - This quality improvement study evaluates the ability of GPT-4 Advanced Data 
      Analysis to create a fake data set that can be used for the purpose of scientific 
      research.
OABL- eng
COIS- Conflict of Interest Disclosures: None reported.
EDAT- 2023/11/09 12:42
MHDA- 2023/11/09 12:42
PMCR- 2024/11/09
CRDT- 2023/11/09 11:34
PHST- 2024/11/09 00:00 [pmc-release]
PHST- 2023/11/09 12:42 [medline]
PHST- 2023/11/09 12:42 [pubmed]
PHST- 2023/11/09 11:34 [entrez]
AID - 2811505 [pii]
AID - eld230008 [pii]
AID - 10.1001/jamaophthalmol.2023.5162 [doi]
PST - aheadofprint
SO  - JAMA Ophthalmol. 2023 Nov 9:e235162. doi: 10.1001/jamaophthalmol.2023.5162.

PMID- 37806436
OWN - NLM
STAT- Publisher
LR  - 20231031
IS  - 2212-2672 (Print)
IS  - 2212-2672 (Linking)
DP  - 2023 Oct 6
TI  - Is ChatGPT Really Helpful for Nutrition and Dietetics?
LID - S2212-2672(23)01593-9 [pii]
LID - 10.1016/j.jand.2023.10.003 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India.
LA  - eng
PT  - Letter
DEP - 20231006
PL  - United States
TA  - J Acad Nutr Diet
JT  - Journal of the Academy of Nutrition and Dietetics
JID - 101573920
SB  - IM
EDAT- 2023/10/09 00:41
MHDA- 2023/10/09 00:41
CRDT- 2023/10/08 19:27
PHST- 2023/08/10 00:00 [received]
PHST- 2023/10/03 00:00 [accepted]
PHST- 2023/10/09 00:41 [pubmed]
PHST- 2023/10/09 00:41 [medline]
PHST- 2023/10/08 19:27 [entrez]
AID - S2212-2672(23)01593-9 [pii]
AID - 10.1016/j.jand.2023.10.003 [doi]
PST - aheadofprint
SO  - J Acad Nutr Diet. 2023 Oct 6:S2212-2672(23)01593-9. doi: 
      10.1016/j.jand.2023.10.003.

PMID- 37332007
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231129
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 12
DP  - 2023 Dec
TI  - Is It Feasible to Reduce Academic Stress in Net-Zero Energy Buildings? Reaction 
      from ChatGPT.
PG  - 2654-2656
LID - 10.1007/s10439-023-03286-y [doi]
AB  - We may lessen the detrimental effects of global warming on human thought 
      processes by reducing greenhouse gas emissions, encouraging sustainability, and 
      giving adaption measures top priority. The purpose of the letter is to draw 
      attention to the necessity of net-zero energy buildings (NZEB) in academic 
      institutions in order to reduce academic stress, promote well-being, and improve 
      cognitive functions. While some levels of stress might be advantageous, excessive 
      and mismanaged stress can be detrimental to students' well-being. To foster a 
      healthy academic atmosphere, it is essential to offer resources, support 
      networks, and stress-reduction techniques. As human authors, we thoroughly edited 
      ChatGPT's responses to create this letter.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Rani, P Sobha
AU  - Rani PS
AUID- ORCID: 0000-0002-5095-5994
AD  - Department of Electrical and Electronics Engineering, Lakireddy Bali Reddy 
      College of Engineering (Autonomous), Jawaharlal Nehru Technological University, 
      Kakinada (JNTUK), East Godavari, Kakinada, Andhra Pradesh, 533003, India. 
      sobhareveru@gmail.com.
FAU - Rani, K Radha
AU  - Rani KR
AD  - Department of Electrical and Electronics Engineering, R.V.R & J.C. College of 
      Engineering, Chowdavaram, Guntur, Andhra Pradesh, India.
FAU - Daram, Suresh Babu
AU  - Daram SB
AD  - Department of Electrical and Electronics Engineering, Mohan Babu University (Erst 
      while Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh, 517102, 
      India.
FAU - Angadi, Ravi V
AU  - Angadi RV
AD  - Department of Electrical and Electronics Engineering, School of Engineering, 
      Presidency University, Bangalore, Karnataka, 560064, India.
LA  - eng
PT  - Letter
DEP - 20230618
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
RN  - 0 (Greenhouse Gases)
SB  - IM
MH  - Humans
MH  - *Greenhouse Gases
MH  - Global Warming
MH  - Universities
OTO - NOTNLM
OT  - Academic stress
OT  - ChatGPT
OT  - Global warming
OT  - Net-zero energy buildings
OT  - Sustainability
EDAT- 2023/06/19 00:42
MHDA- 2023/11/09 06:42
CRDT- 2023/06/18 23:35
PHST- 2023/06/07 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/11/09 06:42 [medline]
PHST- 2023/06/19 00:42 [pubmed]
PHST- 2023/06/18 23:35 [entrez]
AID - 10.1007/s10439-023-03286-y [pii]
AID - 10.1007/s10439-023-03286-y [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Dec;51(12):2654-2656. doi: 10.1007/s10439-023-03286-y. Epub 
      2023 Jun 18.

PMID- 37804010
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1681-7168 (Electronic)
IS  - 1022-386X (Linking)
VI  - 33
IP  - 10
DP  - 2023 Oct
TI  - AI at Doorstep: ChatGPT and Academia.
PG  - 1085-1086
LID - 10.29271/jcpsp.2023.10.1085 [doi]
AB  - Null.
FAU - Khan, Sikandar Hayat
AU  - Khan SH
AD  - Department of Pathology, Combined Military Hospital, Multan, Pakistan.
LA  - eng
PT  - Editorial
PL  - Pakistan
TA  - J Coll Physicians Surg Pak
JT  - Journal of the College of Physicians and Surgeons--Pakistan : JCPSP
JID - 9606447
SB  - IM
EDAT- 2023/10/07 11:42
MHDA- 2023/10/07 11:43
CRDT- 2023/10/07 03:51
PHST- 2023/07/06 00:00 [received]
PHST- 2023/08/26 00:00 [accepted]
PHST- 2023/10/07 11:43 [medline]
PHST- 2023/10/07 11:42 [pubmed]
PHST- 2023/10/07 03:51 [entrez]
AID - 040579197 [pii]
AID - 10.29271/jcpsp.2023.10.1085 [doi]
PST - ppublish
SO  - J Coll Physicians Surg Pak. 2023 Oct;33(10):1085-1086. doi: 
      10.29271/jcpsp.2023.10.1085.

PMID- 37801243
OWN - NLM
STAT- Publisher
LR  - 20231006
IS  - 1556-2891 (Electronic)
IS  - 1547-769X (Linking)
DP  - 2023 Oct 6
TI  - ChatGPT and forensic science: comment.
LID - 10.1007/s12024-023-00731-1 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - , Phonhong, Lao People's Democratic Republic. hinpetchdaung@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Ajeetgarh, India.
AD  - Saveetha medical college, Chennai, India.
LA  - eng
PT  - Letter
DEP - 20231006
PL  - United States
TA  - Forensic Sci Med Pathol
JT  - Forensic science, medicine, and pathology
JID - 101236111
SB  - IM
EDAT- 2023/10/06 12:43
MHDA- 2023/10/06 12:43
CRDT- 2023/10/06 11:11
PHST- 2023/10/02 00:00 [accepted]
PHST- 2023/10/06 12:43 [medline]
PHST- 2023/10/06 12:43 [pubmed]
PHST- 2023/10/06 11:11 [entrez]
AID - 10.1007/s12024-023-00731-1 [pii]
AID - 10.1007/s12024-023-00731-1 [doi]
PST - aheadofprint
SO  - Forensic Sci Med Pathol. 2023 Oct 6. doi: 10.1007/s12024-023-00731-1.

PMID- 37799739
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231026
IS  - 2233-7822 (Print)
IS  - 2233-7830 (Electronic)
IS  - 2233-7822 (Linking)
VI  - 53
IP  - 3
DP  - 2023 Sep
TI  - Correspondence: ChatGPT and scientific writing.
PG  - 267-268
LID - 10.5624/isd.20230145 [doi]
FAU - Mungmunpuntipantip, Rujittika
AU  - Mungmunpuntipantip R
AUID- ORCID: 0000-0003-0078-7897
AD  - Private Academic Consultant, Bangkok, Thailand.
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Research Center, Chandigarh University, Punjab, India.
AD  - Department of Biological Science, Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230904
PL  - Korea (South)
TA  - Imaging Sci Dent
JT  - Imaging science in dentistry
JID - 101559249
CON - Imaging Sci Dent. 2023 Jun;53(2):175-176. PMID: 37405199
PMC - PMC10548157
COIS- Conflicts of Interest: None
EDAT- 2023/10/06 06:43
MHDA- 2023/10/06 06:44
CRDT- 2023/10/06 03:56
PHST- 2023/07/06 00:00 [received]
PHST- 2023/07/22 00:00 [revised]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/10/06 06:44 [medline]
PHST- 2023/10/06 06:43 [pubmed]
PHST- 2023/10/06 03:56 [entrez]
AID - 10.5624/isd.20230145 [doi]
PST - ppublish
SO  - Imaging Sci Dent. 2023 Sep;53(3):267-268. doi: 10.5624/isd.20230145. Epub 2023 
      Sep 4.

PMID- 37032108
OWN - NLM
STAT- MEDLINE
DCOM- 20230707
LR  - 20230718
IS  - 1349-9092 (Electronic)
IS  - 0917-5040 (Print)
IS  - 0917-5040 (Linking)
VI  - 33
IP  - 7
DP  - 2023 Jul 5
TI  - Should We Acknowledge ChatGPT as an Author?
PG  - 333-334
LID - 10.2188/jea.JE20230078 [doi]
FAU - Goto, Atsushi
AU  - Goto A
AUID- ORCID: 0000-0003-0669-654X
AD  - Department of Public Health, School of Medicine, Yokohama City University.
AD  - Department of Health Data Science, Graduate School of Data Science, Yokohama City 
      University.
FAU - Katanoda, Kota
AU  - Katanoda K
AUID- ORCID: 0000-0001-8687-1269
AD  - Division of Surveillance and Policy Evaluation, National Cancer Center Institute 
      for Cancer Control.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230531
PL  - Japan
TA  - J Epidemiol
JT  - Journal of epidemiology
JID - 9607688
SB  - IM
CON - J Epidemiol. 2023 Jul 5;33(7):381-382. PMID: 37032109
MH  - Japan
MH  - *Authorship
MH  - *Artificial Intelligence
PMC - PMC10257990
COIS- Conflicts of interest: None declared.
EDAT- 2023/04/10 06:00
MHDA- 2023/07/07 06:42
CRDT- 2023/04/09 21:13
PHST- 2023/07/07 06:42 [medline]
PHST- 2023/04/10 06:00 [pubmed]
PHST- 2023/04/09 21:13 [entrez]
AID - JE20230078 [pii]
AID - 10.2188/jea.JE20230078 [doi]
PST - ppublish
SO  - J Epidemiol. 2023 Jul 5;33(7):333-334. doi: 10.2188/jea.JE20230078. Epub 2023 May 
      31.

PMID- 37688659
OWN - NLM
STAT- Publisher
LR  - 20230909
IS  - 1556-2891 (Electronic)
IS  - 1547-769X (Linking)
DP  - 2023 Sep 9
TI  - ChatGPT's thoughts: comment.
LID - 10.1007/s12024-023-00712-4 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - , Samraong, Cambodia. amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Dr DY Patil Vidhyapeeth, Pune, Pimpri-Chinchwad, India.
AD  - Joesph Ayo Babalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230909
PL  - United States
TA  - Forensic Sci Med Pathol
JT  - Forensic science, medicine, and pathology
JID - 101236111
SB  - IM
EDAT- 2023/09/09 21:42
MHDA- 2023/09/09 21:42
CRDT- 2023/09/09 11:06
PHST- 2023/08/29 00:00 [accepted]
PHST- 2023/09/09 21:42 [medline]
PHST- 2023/09/09 21:42 [pubmed]
PHST- 2023/09/09 11:06 [entrez]
AID - 10.1007/s12024-023-00712-4 [pii]
AID - 10.1007/s12024-023-00712-4 [doi]
PST - aheadofprint
SO  - Forensic Sci Med Pathol. 2023 Sep 9. doi: 10.1007/s12024-023-00712-4.

PMID- 37583636
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1735-7020 (Print)
IS  - 2008-238X (Electronic)
IS  - 1735-7020 (Linking)
VI  - 18
IP  - 2
DP  - 2023 Apr-Jun
TI  - The Potential Breakthroughs with ChatGPT in Parasitology.
PG  - 275-278
LID - 10.18502/ijpa.v18i2.13197 [doi]
FAU - Oanh, Nguyen Kim
AU  - Oanh NK
AD  - Department of Parasitology and Tropical Medicine, Gyeongsang National University 
      College of Medicine, Jinju 52727, Republic of Korea.
AD  - Department of Convergence Medical Science, Gyeongsang National University, Jinju 
      52727, Republic of Korea.
FAU - Na, Byoung-Kuk
AU  - Na BK
AD  - Department of Parasitology and Tropical Medicine, Gyeongsang National University 
      College of Medicine, Jinju 52727, Republic of Korea.
AD  - Department of Convergence Medical Science, Gyeongsang National University, Jinju 
      52727, Republic of Korea.
FAU - Yoo, Won Gi
AU  - Yoo WG
AD  - Department of Parasitology and Tropical Medicine, Gyeongsang National University 
      College of Medicine, Jinju 52727, Republic of Korea.
AD  - Department of Convergence Medical Science, Gyeongsang National University, Jinju 
      52727, Republic of Korea.
LA  - eng
PT  - Journal Article
PL  - Iran
TA  - Iran J Parasitol
JT  - Iranian journal of parasitology
JID - 101464309
PMC - PMC10423911
COIS- Conflict of Interest Non-declared.
EDAT- 2023/08/16 06:43
MHDA- 2023/08/16 06:44
CRDT- 2023/08/16 03:49
PHST- 2023/05/14 00:00 [received]
PHST- 2023/05/26 00:00 [accepted]
PHST- 2023/08/16 06:44 [medline]
PHST- 2023/08/16 06:43 [pubmed]
PHST- 2023/08/16 03:49 [entrez]
AID - IJPA-18-275 [pii]
AID - 10.18502/ijpa.v18i2.13197 [doi]
PST - ppublish
SO  - Iran J Parasitol. 2023 Apr-Jun;18(2):275-278. doi: 10.18502/ijpa.v18i2.13197.

PMID- 37102458
OWN - NLM
STAT- MEDLINE
DCOM- 20230621
LR  - 20230621
IS  - 1468-3083 (Electronic)
IS  - 0926-9959 (Linking)
VI  - 37
IP  - 7
DP  - 2023 Jul
TI  - Potential applications of ChatGPT in dermatology.
PG  - e941-e942
LID - 10.1111/jdv.19152 [doi]
FAU - Kluger, Nicolas
AU  - Kluger N
AUID- ORCID: 0000-0002-5225-8316
AD  - Department of Dermatology, Allergology and Venereology, Helsinki University 
      Hospital and University of Helsinki, Helsinki, Finland.
LA  - eng
PT  - Letter
DEP - 20230508
PL  - England
TA  - J Eur Acad Dermatol Venereol
JT  - Journal of the European Academy of Dermatology and Venereology : JEADV
JID - 9216037
SB  - IM
MH  - Humans
MH  - *Dermatology
MH  - Artificial Intelligence
EDAT- 2023/04/27 12:42
MHDA- 2023/06/14 06:42
CRDT- 2023/04/27 07:33
PHST- 2023/03/19 00:00 [received]
PHST- 2023/04/26 00:00 [accepted]
PHST- 2023/06/14 06:42 [medline]
PHST- 2023/04/27 12:42 [pubmed]
PHST- 2023/04/27 07:33 [entrez]
AID - 10.1111/jdv.19152 [doi]
PST - ppublish
SO  - J Eur Acad Dermatol Venereol. 2023 Jul;37(7):e941-e942. doi: 10.1111/jdv.19152. 
      Epub 2023 May 8.

PMID- 38057467
OWN - NLM
STAT- Publisher
LR  - 20231206
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Dec 5
TI  - Daily briefing: Happy birthday, ChatGPT!
LID - 10.1038/d41586-023-03878-7 [doi]
FAU - Krämer, Katrina
AU  - Krämer K
LA  - eng
PT  - News
DEP - 20231205
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
EDAT- 2023/12/07 00:42
MHDA- 2023/12/07 00:42
CRDT- 2023/12/06 23:38
PHST- 2023/12/07 00:42 [medline]
PHST- 2023/12/07 00:42 [pubmed]
PHST- 2023/12/06 23:38 [entrez]
AID - 10.1038/d41586-023-03878-7 [pii]
AID - 10.1038/d41586-023-03878-7 [doi]
PST - aheadofprint
SO  - Nature. 2023 Dec 5. doi: 10.1038/d41586-023-03878-7.

PMID- 37852648
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1535-5675 (Electronic)
IS  - 0091-4916 (Linking)
VI  - 51
IP  - 4
DP  - 2023 Dec 5
TI  - ChatGPT in Nuclear Medicine Education.
PG  - 344
LID - 10.2967/jnmt.123.266334 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
LA  - eng
PT  - Letter
DEP - 20231205
PL  - United States
TA  - J Nucl Med Technol
JT  - Journal of nuclear medicine technology
JID - 0430303
SB  - IM
MH  - *Nuclear Medicine
MH  - Radionuclide Imaging
EDAT- 2023/10/19 00:44
MHDA- 2023/12/07 12:43
CRDT- 2023/10/18 20:43
PHST- 2023/07/12 00:00 [received]
PHST- 2023/07/13 00:00 [revised]
PHST- 2023/12/07 12:43 [medline]
PHST- 2023/10/19 00:44 [pubmed]
PHST- 2023/10/18 20:43 [entrez]
AID - jnmt.123.266334 [pii]
AID - 10.2967/jnmt.123.266334 [doi]
PST - epublish
SO  - J Nucl Med Technol. 2023 Dec 5;51(4):344. doi: 10.2967/jnmt.123.266334.

PMID- 37703485
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1546-3141 (Electronic)
IS  - 0361-803X (Linking)
VI  - 221
IP  - 5
DP  - 2023 Nov
TI  - Reply to "Improving Accuracy in ChatGPT".
PG  - 705-706
LID - 10.2214/AJR.23.29949 [doi]
FAU - Cao, Jennie J
AU  - Cao JJ
AD  - Stanford University School of Medicine, Stanford, CA.
FAU - Kamaya, Aya
AU  - Kamaya A
AD  - Stanford University School of Medicine, Stanford, CA.
FAU - Tse, Justin R
AU  - Tse JR
AD  - Stanford University School of Medicine, Stanford, CA, jrtse@stanford.edu.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - United States
TA  - AJR Am J Roentgenol
JT  - AJR. American journal of roentgenology
JID - 7708173
SB  - IM
EDAT- 2023/09/13 18:41
MHDA- 2023/09/13 18:41
CRDT- 2023/09/13 15:42
PHST- 2023/09/13 18:41 [pubmed]
PHST- 2023/09/13 18:41 [medline]
PHST- 2023/09/13 15:42 [entrez]
AID - 10.2214/AJR.23.29949 [doi]
PST - ppublish
SO  - AJR Am J Roentgenol. 2023 Nov;221(5):705-706. doi: 10.2214/AJR.23.29949. Epub 
      2023 Sep 13.

PMID- 36749034
OWN - NLM
STAT- Publisher
LR  - 20230214
IS  - 1935-9780 (Electronic)
IS  - 1935-9772 (Linking)
DP  - 2023 Feb 7
TI  - Initial impressions of ChatGPT for anatomy education.
LID - 10.1002/ase.2261 [doi]
FAU - Mogali, Sreenivasulu Reddy
AU  - Mogali SR
AUID- ORCID: 0000-0002-0981-5607
AD  - Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, 
      Singapore.
LA  - eng
PT  - Letter
DEP - 20230207
PL  - United States
TA  - Anat Sci Educ
JT  - Anatomical sciences education
JID - 101392205
SB  - IM
EDAT- 2023/02/08 06:00
MHDA- 2023/02/08 06:00
CRDT- 2023/02/07 08:53
PHST- 2023/02/02 00:00 [revised]
PHST- 2023/01/19 00:00 [received]
PHST- 2023/02/03 00:00 [accepted]
PHST- 2023/02/08 06:00 [pubmed]
PHST- 2023/02/08 06:00 [medline]
PHST- 2023/02/07 08:53 [entrez]
AID - 10.1002/ase.2261 [doi]
PST - aheadofprint
SO  - Anat Sci Educ. 2023 Feb 7. doi: 10.1002/ase.2261.

PMID- 37222278
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20231201
IS  - 1546-3141 (Electronic)
IS  - 0361-803X (Linking)
VI  - 221
IP  - 4
DP  - 2023 Oct
TI  - Accuracy of Information Provided by ChatGPT Regarding Liver Cancer Surveillance 
      and Diagnosis.
PG  - 556-559
LID - 10.2214/AJR.23.29493 [doi]
FAU - Cao, Jennie J
AU  - Cao JJ
AD  - Stanford University School of Medicine, Stanford, CA.
FAU - Kwon, Daniel H
AU  - Kwon DH
AD  - University of California, San Francisco School of Medicine, San Francisco, CA.
FAU - Ghaziani, Tara T
AU  - Ghaziani TT
AD  - Stanford University School of Medicine, Stanford, CA.
FAU - Kwo, Paul
AU  - Kwo P
AD  - Stanford University School of Medicine, Stanford, CA.
FAU - Tse, Gary
AU  - Tse G
AD  - University of California, Los Angeles David Geffen School of Medicine, Los 
      Angeles, CA.
FAU - Kesselman, Andrew
AU  - Kesselman A
AD  - Stanford University School of Medicine, Stanford, CA.
FAU - Kamaya, Aya
AU  - Kamaya A
AD  - Stanford University School of Medicine, Stanford, CA.
FAU - Tse, Justin R
AU  - Tse JR
AD  - Stanford University School of Medicine, Stanford, CA, jrtse@stanford.edu.
LA  - eng
PT  - Journal Article
DEP - 20230524
PL  - United States
TA  - AJR Am J Roentgenol
JT  - AJR. American journal of roentgenology
JID - 7708173
SB  - IM
MH  - Humans
MH  - *Liver Neoplasms/diagnostic imaging
MH  - *Carcinoma, Hepatocellular
OAB - ChatGPT did not reliably provide accurate information to 20 questions about liver 
      cancer surveillance and diagnosis, as assessed by six physicians who actively 
      diagnose and/or treat liver cancer. Answers deemed inaccurate commonly related to 
      questions on specific LI-RADS categories and included contradictory or falsely 
      reassuring, if not wrong, information.
OABL- eng
EDAT- 2023/05/24 13:08
MHDA- 2023/09/25 06:42
CRDT- 2023/05/24 07:03
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/05/24 13:08 [pubmed]
PHST- 2023/05/24 07:03 [entrez]
AID - 10.2214/AJR.23.29493 [doi]
PST - ppublish
SO  - AJR Am J Roentgenol. 2023 Oct;221(4):556-559. doi: 10.2214/AJR.23.29493. Epub 
      2023 May 24.

PMID- 38034065
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231202
IS  - 2473-974X (Electronic)
IS  - 2473-974X (Linking)
VI  - 7
IP  - 4
DP  - 2023 Oct-Dec
TI  - Assessment of Artificial Intelligence Performance on the Otolaryngology Residency 
      In-Service Exam.
PG  - e98
LID - 10.1002/oto2.98 [doi]
LID - e98
AB  - OBJECTIVES: This study seeks to determine the potential use and reliability of a 
      large language learning model for answering questions in a sub-specialized area 
      of medicine, specifically practice exam questions in otolaryngology-head and neck 
      surgery and assess its current efficacy for surgical trainees and learners. STUDY 
      DESIGN AND SETTING: All available questions from a public, paid-access question 
      bank were manually input through ChatGPT. METHODS: Outputs from ChatGPT were 
      compared against the benchmark of the answers and explanations from the question 
      bank. Questions were assessed in 2 domains: accuracy and comprehensiveness of 
      explanations. RESULTS: Overall, our study demonstrates a ChatGPT correct answer 
      rate of 53% and a correct explanation rate of 54%. We find that with increasing 
      difficulty of questions there is a decreasing rate of answer and explanation 
      accuracy. CONCLUSION: Currently, artificial intelligence-driven learning 
      platforms are not robust enough to be reliable medical education resources to 
      assist learners in sub-specialty specific patient decision making scenarios.
CI  - © 2023 The Authors. OTO Open published by Wiley Periodicals LLC on behalf of 
      American Academy of Otolaryngology–Head and Neck Surgery Foundation.
FAU - Mahajan, Arushi P
AU  - Mahajan AP
AUID- ORCID: 0009-0007-6948-0689
AD  - University of Michigan Medical School Ann Arbor Michigan USA.
FAU - Shabet, Christina L
AU  - Shabet CL
AD  - University of Michigan Medical School Ann Arbor Michigan USA.
FAU - Smith, Joshua
AU  - Smith J
AD  - Department of Otolaryngology-Head and Neck Surgery University of Michigan School 
      of Medicine Ann Arbor Michigan USA.
FAU - Rudy, Shannon F
AU  - Rudy SF
AD  - Department of Otolaryngology-Head and Neck Surgery University of Michigan School 
      of Medicine Ann Arbor Michigan USA.
FAU - Kupfer, Robbi A
AU  - Kupfer RA
AD  - Department of Otolaryngology-Head and Neck Surgery University of Michigan School 
      of Medicine Ann Arbor Michigan USA.
FAU - Bohm, Lauren A
AU  - Bohm LA
AUID- ORCID: 0000-0003-3775-8424
AD  - Department of Otolaryngology-Head and Neck Surgery University of Michigan School 
      of Medicine Ann Arbor Michigan USA.
LA  - eng
PT  - Journal Article
DEP - 20231129
PL  - United States
TA  - OTO Open
JT  - OTO open
JID - 101717942
PMC - PMC10687376
OTO - NOTNLM
OT  - BoardVitals
OT  - ChatGPT
OT  - artificial intelligence
OT  - in‐service exams
OT  - large language models
OT  - otolaryngology residency training
COIS- None.
EDAT- 2023/11/30 18:45
MHDA- 2023/11/30 18:46
CRDT- 2023/11/30 17:28
PHST- 2023/08/09 00:00 [received]
PHST- 2023/09/19 00:00 [revised]
PHST- 2023/11/04 00:00 [accepted]
PHST- 2023/11/30 18:46 [medline]
PHST- 2023/11/30 18:45 [pubmed]
PHST- 2023/11/30 17:28 [entrez]
AID - OTO298 [pii]
AID - 10.1002/oto2.98 [doi]
PST - epublish
SO  - OTO Open. 2023 Nov 29;7(4):e98. doi: 10.1002/oto2.98. eCollection 2023 Oct-Dec.

PMID- 37354792
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230818
LR  - 20230818
IS  - 1873-491X (Electronic)
IS  - 0020-7489 (Linking)
VI  - 145
DP  - 2023 Sep
TI  - Can ChatGPT pass a nursing exam?
PG  - 104522
LID - S0020-7489(23)00087-1 [pii]
LID - 10.1016/j.ijnurstu.2023.104522 [doi]
FAU - Allen, Chris
AU  - Allen C
AD  - University of Southampton, School of Health Sciences, United Kingdom of Great 
      Britain and Northern Ireland. Electronic address: c.e.w.allen@soton.ac.uk.
FAU - Woodnutt, Samuel
AU  - Woodnutt S
AD  - University of Southampton, School of Health Sciences, United Kingdom of Great 
      Britain and Northern Ireland.
LA  - eng
PT  - Editorial
DEP - 20230510
PL  - England
TA  - Int J Nurs Stud
JT  - International journal of nursing studies
JID - 0400675
SB  - IM
COIS- Declaration of Competing Interest None.
EDAT- 2023/06/25 01:08
MHDA- 2023/06/25 01:09
CRDT- 2023/06/24 18:07
PHST- 2023/04/12 00:00 [received]
PHST- 2023/04/19 00:00 [revised]
PHST- 2023/04/26 00:00 [accepted]
PHST- 2023/06/25 01:09 [medline]
PHST- 2023/06/25 01:08 [pubmed]
PHST- 2023/06/24 18:07 [entrez]
AID - S0020-7489(23)00087-1 [pii]
AID - 10.1016/j.ijnurstu.2023.104522 [doi]
PST - ppublish
SO  - Int J Nurs Stud. 2023 Sep;145:104522. doi: 10.1016/j.ijnurstu.2023.104522. Epub 
      2023 May 10.

PMID- 37144055
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230507
IS  - 2757-8038 (Electronic)
IS  - 2757-8038 (Linking)
VI  - 24
IP  - 2
DP  - 2023 Mar
TI  - AI and Psychiatry: The ChatGPT Perspective.
PG  - 41-42
LID - 10.5152/alphapsychiatry.2023.010223 [doi]
FAU - Okan, Çalıyurt
AU  - Okan Ç
AD  - Department of Psychiatry, Trakya University School of Medicine, Edirne, Turkey.
LA  - eng
PT  - Editorial
DEP - 20230301
PL  - Turkey
TA  - Alpha Psychiatry
JT  - Alpha psychiatry
JID - 9918284277106676
PMC - PMC10151963
EDAT- 2023/05/05 06:42
MHDA- 2023/05/05 06:43
CRDT- 2023/05/05 03:56
PHST- 2023/05/05 06:43 [medline]
PHST- 2023/05/05 06:42 [pubmed]
PHST- 2023/05/05 03:56 [entrez]
AID - ap-24-2-41 [pii]
AID - 10.5152/alphapsychiatry.2023.010223 [doi]
PST - epublish
SO  - Alpha Psychiatry. 2023 Mar 1;24(2):41-42. doi: 
      10.5152/alphapsychiatry.2023.010223. eCollection 2023 Mar.

PMID- 37539925
OWN - NLM
STAT- Publisher
LR  - 20230817
IS  - 1930-7837 (Electronic)
IS  - 0022-0337 (Linking)
DP  - 2023 Aug 4
TI  - Introducing ClinicGPT: A custom large language model for institutional dental 
      clinics.
LID - 10.1002/jdd.13348 [doi]
FAU - Zhou, Kevin Xiang
AU  - Zhou KX
AUID- ORCID: 0000-0003-0332-9947
AD  - Schulich School of Medicine and Dentistry, University of Western Ontario, London, 
      Ontario, Canada.
LA  - eng
PT  - Journal Article
DEP - 20230804
PL  - United States
TA  - J Dent Educ
JT  - Journal of dental education
JID - 8000150
SB  - IM
OTO - NOTNLM
OT  - advanced education general dentistry
OT  - computer simulation
OT  - computer-assisted instruction
OT  - dental specialties
OT  - education
OT  - educational technology′′parent
OT  - flipped classroom
OT  - information technology
OT  - learning management systems
OT  - massive open online courses
OT  - oral and maxillofacial surgery
OT  - teaching
OT  - teaching materials
OT  - technology
EDAT- 2023/08/04 13:09
MHDA- 2023/08/04 13:09
CRDT- 2023/08/04 07:53
PHST- 2023/06/19 00:00 [revised]
PHST- 2023/05/05 00:00 [received]
PHST- 2023/07/22 00:00 [accepted]
PHST- 2023/08/04 13:09 [pubmed]
PHST- 2023/08/04 13:09 [medline]
PHST- 2023/08/04 07:53 [entrez]
AID - 10.1002/jdd.13348 [doi]
PST - aheadofprint
SO  - J Dent Educ. 2023 Aug 4. doi: 10.1002/jdd.13348.

PMID- 38060628
OWN - NLM
STAT- Publisher
LR  - 20231207
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Dec 7
TI  - Evaluation of ChatGPT's Acne Advice.
LID - llad434 [pii]
LID - 10.1093/ced/llad434 [doi]
FAU - Li, Kai
AU  - Li K
AUID- ORCID: 0000-0001-8557-166X
AD  - Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, 
      Canada.
FAU - Hsu, Jeffrey T S
AU  - Hsu JTS
AD  - Department of Dermatology, College of Medicine, University of Illinois Chicago, 
      Chicago, IL, USA.
AD  - Oak Dermatology, Itasca, IL, USA.
FAU - Li, Monica K
AU  - Li MK
AD  - Department of Dermatology and Skin Science, University of British Columbia, 
      Vancouver, BC, Canada.
AD  - Vancouver Skin MD, Vancouver, BC, Canada.
LA  - eng
PT  - Journal Article
DEP - 20231207
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
EDAT- 2023/12/07 18:42
MHDA- 2023/12/07 18:42
CRDT- 2023/12/07 14:07
PHST- 2023/11/28 00:00 [received]
PHST- 2023/12/05 00:00 [accepted]
PHST- 2023/12/07 18:42 [medline]
PHST- 2023/12/07 18:42 [pubmed]
PHST- 2023/12/07 14:07 [entrez]
AID - 7462150 [pii]
AID - 10.1093/ced/llad434 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Dec 7:llad434. doi: 10.1093/ced/llad434.

PMID- 37722221
OWN - NLM
STAT- Publisher
LR  - 20230918
IS  - 1532-818X (Electronic)
IS  - 0196-0709 (Linking)
VI  - 45
IP  - 1
DP  - 2023 Sep 15
TI  - Role of ChatGPT in clinical otolaryngology.
PG  - 104042
LID - S0196-0709(23)00256-9 [pii]
LID - 10.1016/j.amjoto.2023.104042 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230915
PL  - United States
TA  - Am J Otolaryngol
JT  - American journal of otolaryngology
JID - 8000029
SB  - IM
COIS- Declaration of competing interest None.
EDAT- 2023/09/19 00:42
MHDA- 2023/09/19 00:42
CRDT- 2023/09/18 18:03
PHST- 2023/04/26 00:00 [received]
PHST- 2023/04/29 00:00 [accepted]
PHST- 2023/09/19 00:42 [medline]
PHST- 2023/09/19 00:42 [pubmed]
PHST- 2023/09/18 18:03 [entrez]
AID - S0196-0709(23)00256-9 [pii]
AID - 10.1016/j.amjoto.2023.104042 [doi]
PST - aheadofprint
SO  - Am J Otolaryngol. 2023 Sep 15;45(1):104042. doi: 10.1016/j.amjoto.2023.104042.

PMID- 37194805
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230518
LR  - 20230831
IS  - 1806-9282 (Electronic)
IS  - 0104-4230 (Print)
IS  - 0104-4230 (Linking)
VI  - 69
IP  - 4
DP  - 2023
TI  - How may ChatGPT impact medical teaching?
PG  - e20230282
LID - S0104-42302023000400100 [pii]
LID - 10.1590/1806-9282.20230282 [doi]
LID - e20230282
FAU - Yoshinari Júnior, Gerson Hiroshi
AU  - Yoshinari Júnior GH
AUID- ORCID: 0000-0001-5837-5743
AD  - Faculdade de Medicina de Itajubá - Itajubá (MG), Brazil.
FAU - Vitorino, Luciano Magalhães
AU  - Vitorino LM
AUID- ORCID: 0000-0003-1023-8488
AD  - Faculdade de Medicina de Itajubá - Itajubá (MG), Brazil.
LA  - eng
PT  - Editorial
DEP - 20230515
PL  - Brazil
TA  - Rev Assoc Med Bras (1992)
JT  - Revista da Associacao Medica Brasileira (1992)
JID - 9308586
SB  - IM
CIN - Rev Assoc Med Bras (1992). 2023 Aug 25;69(8):e20230593. PMID: 37646736
PMC - PMC10185047
COIS- Conflicts of interest: the authors declare there is no conflicts of interest.
EDAT- 2023/05/17 13:10
MHDA- 2023/05/17 13:11
CRDT- 2023/05/17 07:24
PHST- 2023/03/07 00:00 [received]
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/05/17 13:11 [medline]
PHST- 2023/05/17 13:10 [pubmed]
PHST- 2023/05/17 07:24 [entrez]
AID - S0104-42302023000400100 [pii]
AID - 10.1590/1806-9282.20230282 [doi]
PST - epublish
SO  - Rev Assoc Med Bras (1992). 2023 May 15;69(4):e20230282. doi: 
      10.1590/1806-9282.20230282. eCollection 2023.

PMID- 37154989
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230809
LR  - 20230809
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 9
DP  - 2023 Sep
TI  - Passing is Great: Can ChatGPT Conduct USMLE Exams?
PG  - 1885-1886
LID - 10.1007/s10439-023-03224-y [doi]
FAU - Biswas, Som
AU  - Biswas S
AUID- ORCID: 0000-0002-4038-5844
AD  - Department of Pediatric Radiology, Le Bonheur Children's Hospital, The University 
      of Tennessee Health Sciences Center, Memphis, 38103, USA. ssbinmemphis@gmail.com.
LA  - eng
PT  - Editorial
DEP - 20230508
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
EDAT- 2023/05/08 13:41
MHDA- 2023/08/09 06:43
CRDT- 2023/05/08 11:18
PHST- 2023/04/25 00:00 [received]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/05/08 13:41 [pubmed]
PHST- 2023/05/08 11:18 [entrez]
AID - 10.1007/s10439-023-03224-y [pii]
AID - 10.1007/s10439-023-03224-y [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Sep;51(9):1885-1886. doi: 10.1007/s10439-023-03224-y. Epub 
      2023 May 8.

PMID- 36773836
OWN - NLM
STAT- MEDLINE
DCOM- 20230331
LR  - 20230407
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
VI  - 185
DP  - 2023 Apr
TI  - Exploring ChatGPT for information of cardiopulmonary resuscitation.
PG  - 109729
LID - S0300-9572(23)00042-4 [pii]
LID - 10.1016/j.resuscitation.2023.109729 [doi]
FAU - Ahn, Chiwon
AU  - Ahn C
AD  - Department of Emergency Medicine, College of Medicine, Chung-Ang University, 
      Seoul, South Korea.
LA  - eng
PT  - Letter
DEP - 20230210
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
MH  - Humans
MH  - *Cardiopulmonary Resuscitation
MH  - *Heart Arrest/therapy
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/02/12 06:00
MHDA- 2023/03/31 06:41
CRDT- 2023/02/11 19:27
PHST- 2023/02/02 00:00 [received]
PHST- 2023/02/03 00:00 [accepted]
PHST- 2023/03/31 06:41 [medline]
PHST- 2023/02/12 06:00 [pubmed]
PHST- 2023/02/11 19:27 [entrez]
AID - S0300-9572(23)00042-4 [pii]
AID - 10.1016/j.resuscitation.2023.109729 [doi]
PST - ppublish
SO  - Resuscitation. 2023 Apr;185:109729. doi: 10.1016/j.resuscitation.2023.109729. 
      Epub 2023 Feb 10.

PMID- 38065605
OWN - NLM
STAT- In-Process
LR  - 20231208
IS  - 1473-4893 (Electronic)
IS  - 1470-2118 (Linking)
VI  - 23
IP  - 6
DP  - 2023 Nov
TI  - Addressing obesity and homelessness via ChatGPT.
PG  - 647
LID - 10.7861/clinmed.Let.23.6.3 [doi]
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AD  - School of Engineering Sciences & Technology, Jamia Hamdard, New Delhi, India.
LA  - eng
PT  - Letter
PL  - England
TA  - Clin Med (Lond)
JT  - Clinical medicine (London, England)
JID - 101092853
SB  - IM
EDAT- 2023/12/09 05:43
MHDA- 2023/12/09 05:43
CRDT- 2023/12/08 20:43
PHST- 2023/12/09 05:43 [medline]
PHST- 2023/12/09 05:43 [pubmed]
PHST- 2023/12/08 20:43 [entrez]
AID - 23/6/647 [pii]
AID - 10.7861/clinmed.Let.23.6.3 [doi]
PST - ppublish
SO  - Clin Med (Lond). 2023 Nov;23(6):647. doi: 10.7861/clinmed.Let.23.6.3.

PMID- 38048210
OWN - NLM
STAT- Publisher
LR  - 20231204
IS  - 1555-905X (Electronic)
IS  - 1555-9041 (Linking)
DP  - 2023 Dec 1
TI  - Will ChatGPT Be the Next Nephrologist?
LID - 10.2215/CJN.0000000000000378 [doi]
FAU - Maursetter, Laura
AU  - Maursetter L
AUID- ORCID: 0000-0002-1624-6908
AD  - University of Wisconsin-Madison School of Medicine and Public Health, Madison, 
      Wisconsin.
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - United States
TA  - Clin J Am Soc Nephrol
JT  - Clinical journal of the American Society of Nephrology : CJASN
JID - 101271570
SB  - IM
EDAT- 2023/12/04 19:41
MHDA- 2023/12/04 19:41
CRDT- 2023/12/04 12:32
PHST- 2023/12/04 19:41 [medline]
PHST- 2023/12/04 19:41 [pubmed]
PHST- 2023/12/04 12:32 [entrez]
AID - 01277230-990000000-00301 [pii]
AID - 10.2215/CJN.0000000000000378 [doi]
PST - aheadofprint
SO  - Clin J Am Soc Nephrol. 2023 Dec 1. doi: 10.2215/CJN.0000000000000378.

PMID- 37697967
OWN - NLM
STAT- Publisher
LR  - 20230912
IS  - 1748-3115 (Electronic)
IS  - 1748-3107 (Linking)
DP  - 2023 Sep 12
TI  - Can ChatGPT empower people with dyslexia?
PG  - 1-2
LID - 10.1080/17483107.2023.2256805 [doi]
FAU - Botchu, Bhuvan
AU  - Botchu B
AD  - Solihull School, Solihull, UK.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AD  - Department of Orthopaedics, Southport and Ormskirk Hospital NHS Trust, Southport, 
      UK.
FAU - Botchu, Rajesh
AU  - Botchu R
AD  - Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, 
      UK.
LA  - eng
PT  - Journal Article
DEP - 20230912
PL  - England
TA  - Disabil Rehabil Assist Technol
JT  - Disability and rehabilitation. Assistive technology
JID - 101255937
SB  - IM
EDAT- 2023/09/12 06:41
MHDA- 2023/09/12 06:41
CRDT- 2023/09/12 04:33
PHST- 2023/09/12 06:41 [medline]
PHST- 2023/09/12 06:41 [pubmed]
PHST- 2023/09/12 04:33 [entrez]
AID - 10.1080/17483107.2023.2256805 [doi]
PST - aheadofprint
SO  - Disabil Rehabil Assist Technol. 2023 Sep 12:1-2. doi: 
      10.1080/17483107.2023.2256805.

PMID- 37403421
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230817
IS  - 2578-5745 (Electronic)
IS  - 2578-5745 (Linking)
VI  - 5
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT, authorship, and medical publishing.
PG  - 419
LID - 10.1002/acr2.11583 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Punjab, India and Joesph Ayo Babalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230704
PL  - United States
TA  - ACR Open Rheumatol
JT  - ACR open rheumatology
JID - 101740025
PMC - PMC10425586
EDAT- 2023/07/05 06:42
MHDA- 2023/07/05 06:43
CRDT- 2023/07/05 02:08
PHST- 2023/04/25 00:00 [received]
PHST- 2023/06/18 00:00 [accepted]
PHST- 2023/07/05 06:43 [medline]
PHST- 2023/07/05 06:42 [pubmed]
PHST- 2023/07/05 02:08 [entrez]
AID - ACR211583 [pii]
AID - 10.1002/acr2.11583 [doi]
PST - ppublish
SO  - ACR Open Rheumatol. 2023 Aug;5(8):419. doi: 10.1002/acr2.11583. Epub 2023 Jul 4.

PMID- 37074456
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230505
LR  - 20230510
IS  - 1432-1998 (Electronic)
IS  - 0301-0449 (Linking)
VI  - 53
IP  - 5
DP  - 2023 May
TI  - ChatGPT: an editor's perspective.
PG  - 816-817
LID - 10.1007/s00247-023-05668-9 [doi]
FAU - Offiah, Amaka C
AU  - Offiah AC
AUID- ORCID: 0000-0001-8991-5036
AD  - Department of Oncology and Metabolism, University of Sheffield, Sheffield 
      Children's NHS Foundation Trust, Western Bank, Room 3, Damer Street Building, 
      Sheffield, S10 2TH, UK. a.offiah@sheffield.ac.uk.
AD  - Department of Radiology, Sheffield Children's NHS Foundation Trust, Sheffield, 
      UK. a.offiah@sheffield.ac.uk.
FAU - Khanna, Geetika
AU  - Khanna G
AD  - Department of Radiology & Imaging Sciences, Emory University and Children's 
      Healthcare of Atlanta, Atlanta, GA, USA.
LA  - eng
PT  - Editorial
PL  - Germany
TA  - Pediatr Radiol
JT  - Pediatric radiology
JID - 0365332
SB  - IM
EDAT- 2023/04/19 12:41
MHDA- 2023/04/19 12:42
CRDT- 2023/04/19 11:15
PHST- 2023/04/19 12:42 [medline]
PHST- 2023/04/19 12:41 [pubmed]
PHST- 2023/04/19 11:15 [entrez]
AID - 10.1007/s00247-023-05668-9 [pii]
AID - 10.1007/s00247-023-05668-9 [doi]
PST - ppublish
SO  - Pediatr Radiol. 2023 May;53(5):816-817. doi: 10.1007/s00247-023-05668-9.

PMID- 37668870
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231130
IS  - 1996-9805 (Electronic)
IS  - 1818-6300 (Linking)
VI  - 24
IP  - 5
DP  - 2023 Oct
TI  - ChatGPT and its use in paediatric dentistry.
PG  - 675-676
LID - 10.1007/s40368-023-00838-7 [doi]
FAU - Maltarollo, T F H
AU  - Maltarollo TFH
AD  - Department of Preventive and Restorative Dentistry, School of Dentistry, São 
      Paulo State University (Unesp), Araçatuba, Brazil.
FAU - Shitsuka, C
AU  - Shitsuka C
AUID- ORCID: 0000-0002-9813-0457
AD  - Department of Pediatric Dentistry, School of Dentistry, Faculdades Metropolitanas 
      Unidas, São Paulo, Brazil. cashitsuka@gmail.com.
FAU - Sivieri-Araújo, G
AU  - Sivieri-Araújo G
AD  - Department of Preventive and Restorative Dentistry, School of Dentistry, São 
      Paulo State University (Unesp), Araçatuba, Brazil.
LA  - eng
PT  - Letter
DEP - 20230905
PL  - England
TA  - Eur Arch Paediatr Dent
JT  - European archives of paediatric dentistry : official journal of the European 
      Academy of Paediatric Dentistry
JID - 101277157
SB  - IM
MH  - Humans
MH  - *Pediatric Dentistry
MH  - Artificial Intelligence
EDAT- 2023/09/05 12:42
MHDA- 2023/10/27 06:42
CRDT- 2023/09/05 11:18
PHST- 2023/08/01 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/10/27 06:42 [medline]
PHST- 2023/09/05 12:42 [pubmed]
PHST- 2023/09/05 11:18 [entrez]
AID - 10.1007/s40368-023-00838-7 [pii]
AID - 10.1007/s40368-023-00838-7 [doi]
PST - ppublish
SO  - Eur Arch Paediatr Dent. 2023 Oct;24(5):675-676. doi: 10.1007/s40368-023-00838-7. 
      Epub 2023 Sep 5.

PMID- 37657969
OWN - NLM
STAT- MEDLINE
DCOM- 20231124
LR  - 20231128
IS  - 1365-229X (Electronic)
IS  - 0009-9260 (Linking)
VI  - 78
IP  - 11
DP  - 2023 Nov
TI  - Empowering radiology: the transformative role of ChatGPT.
PG  - 851-855
LID - S0009-9260(23)00336-7 [pii]
LID - 10.1016/j.crad.2023.08.006 [doi]
FAU - Perera Molligoda Arachchige, A S
AU  - Perera Molligoda Arachchige AS
AD  - Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, 
      Italy; Department of Radiology, Istituto Clinico Humanitas, Milan, Italy. 
      Electronic address: aroshshavinda.pereramolligodaarachchige@st.hunimed.eu.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - England
TA  - Clin Radiol
JT  - Clinical radiology
JID - 1306016
SB  - IM
CIN - Clin Radiol. 2023 Dec;78(12):e1091. PMID: 37805305
MH  - Humans
MH  - Radiography
MH  - *Radiology
MH  - Power, Psychological
EDAT- 2023/09/02 05:42
MHDA- 2023/11/24 06:43
CRDT- 2023/09/01 21:57
PHST- 2023/07/09 00:00 [received]
PHST- 2023/08/14 00:00 [revised]
PHST- 2023/08/16 00:00 [accepted]
PHST- 2023/11/24 06:43 [medline]
PHST- 2023/09/02 05:42 [pubmed]
PHST- 2023/09/01 21:57 [entrez]
AID - S0009-9260(23)00336-7 [pii]
AID - 10.1016/j.crad.2023.08.006 [doi]
PST - ppublish
SO  - Clin Radiol. 2023 Nov;78(11):851-855. doi: 10.1016/j.crad.2023.08.006. Epub 2023 
      Aug 22.

PMID- 37349234
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230725
LR  - 20230726
IS  - 1532-8171 (Electronic)
IS  - 0735-6757 (Linking)
VI  - 70
DP  - 2023 Aug
TI  - ChatGPT and future of research: Comment.
PG  - 190
LID - S0735-6757(23)00323-6 [pii]
LID - 10.1016/j.ajem.2023.06.025 [doi]
FAU - Mungmunpuntipantip, Rujittika
AU  - Mungmunpuntipantip R
AD  - Private Academic Consultant, Bangkok, Thailand. Electronic address: 
      rujittika@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230614
PL  - United States
TA  - Am J Emerg Med
JT  - The American journal of emergency medicine
JID - 8309942
SB  - IM
CON - Am J Emerg Med. 2023 Aug;70:81-83. PMID: 37229893
CIN - Am J Emerg Med. 2023 Aug;70:191. PMID: 37380584
COIS- Declaration of Competing Interest None.
EDAT- 2023/06/23 01:10
MHDA- 2023/06/23 01:11
CRDT- 2023/06/22 21:56
PHST- 2023/05/26 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/06/23 01:11 [medline]
PHST- 2023/06/23 01:10 [pubmed]
PHST- 2023/06/22 21:56 [entrez]
AID - S0735-6757(23)00323-6 [pii]
AID - 10.1016/j.ajem.2023.06.025 [doi]
PST - ppublish
SO  - Am J Emerg Med. 2023 Aug;70:190. doi: 10.1016/j.ajem.2023.06.025. Epub 2023 Jun 
      14.

PMID- 37127438
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230703
LR  - 20230703
IS  - 1472-6491 (Electronic)
IS  - 1472-6483 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Jul
TI  - ChatGPT and scientific publications: friend or foe?
PG  - 1-2
LID - S1472-6483(23)00217-1 [pii]
LID - 10.1016/j.rbmo.2023.04.007 [doi]
FAU - Macklon, Nick
AU  - Macklon N
AD  - Co-Chief Editors; RBMO. Electronic address: Nick.Macklon@londonwomensclinic.com.
FAU - Garcia-Velasco, Juan
AU  - Garcia-Velasco J
AD  - Co-Chief Editors; RBMO.
LA  - eng
PT  - Editorial
DEP - 20230419
PL  - Netherlands
TA  - Reprod Biomed Online
JT  - Reproductive biomedicine online
JID - 101122473
SB  - IM
EDAT- 2023/05/02 00:42
MHDA- 2023/05/02 00:43
CRDT- 2023/05/01 21:55
PHST- 2023/05/02 00:43 [medline]
PHST- 2023/05/02 00:42 [pubmed]
PHST- 2023/05/01 21:55 [entrez]
AID - S1472-6483(23)00217-1 [pii]
AID - 10.1016/j.rbmo.2023.04.007 [doi]
PST - ppublish
SO  - Reprod Biomed Online. 2023 Jul;47(1):1-2. doi: 10.1016/j.rbmo.2023.04.007. Epub 
      2023 Apr 19.

PMID- 37074116
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230421
LR  - 20231024
IS  - 2573-7732 (Electronic)
IS  - 2573-7732 (Linking)
VI  - 7
IP  - 4
DP  - 2023 Apr 1
TI  - I Haven't Been Replaced by ChatGPT.
PG  - 286-287
LID - 10.4049/immunohorizons.2300024 [doi]
FAU - Kaplan, Mark H
AU  - Kaplan MH
AD  - ImmunoHorizons On Twitter @statfourwork.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Immunohorizons
JT  - ImmunoHorizons
JID - 101708159
SB  - IM
PMC - PMC10579842
EDAT- 2023/04/19 12:42
MHDA- 2023/04/19 12:43
CRDT- 2023/04/19 09:42
PHST- 2023/04/19 12:43 [medline]
PHST- 2023/04/19 12:42 [pubmed]
PHST- 2023/04/19 09:42 [entrez]
AID - 263630 [pii]
AID - immunohorizons_2300024 [pii]
AID - 10.4049/immunohorizons.2300024 [doi]
PST - ppublish
SO  - Immunohorizons. 2023 Apr 1;7(4):286-287. doi: 10.4049/immunohorizons.2300024.

PMID- 37578830
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Aug 14
TI  - The Role of Large Language Models in Medical Education: Applications and 
      Implications.
PG  - e50945
LID - 10.2196/50945 [doi]
LID - e50945
AB  - Large language models (LLMs) such as ChatGPT have sparked extensive discourse 
      within the medical education community, spurring both excitement and 
      apprehension. Written from the perspective of medical students, this editorial 
      offers insights gleaned through immersive interactions with ChatGPT, 
      contextualized by ongoing research into the imminent role of LLMs in health care. 
      Three distinct positive use cases for ChatGPT were identified: facilitating 
      differential diagnosis brainstorming, providing interactive practice cases, and 
      aiding in multiple-choice question review. These use cases can effectively help 
      students learn foundational medical knowledge during the preclinical curriculum 
      while reinforcing the learning of core Entrustable Professional Activities. 
      Simultaneously, we highlight key limitations of LLMs in medical education, 
      including their insufficient ability to teach the integration of contextual and 
      external information, comprehend sensory and nonverbal cues, cultivate rapport 
      and interpersonal interaction, and align with overarching medical education and 
      patient care goals. Through interacting with LLMs to augment learning during 
      medical school, students can gain an understanding of their strengths and 
      weaknesses. This understanding will be pivotal as we navigate a health care 
      landscape increasingly intertwined with LLMs and artificial intelligence.
CI  - ©Conrad W Safranek, Anne Elizabeth Sidamon-Eristoff, Aidan Gilson, David 
      Chartash. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 14.08.2023.
FAU - Safranek, Conrad W
AU  - Safranek CW
AUID- ORCID: 0000-0003-1985-9432
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
FAU - Sidamon-Eristoff, Anne Elizabeth
AU  - Sidamon-Eristoff AE
AUID- ORCID: 0000-0001-7422-9703
AD  - Yale University School of Medicine, New Haven, CT, United States.
FAU - Gilson, Aidan
AU  - Gilson A
AUID- ORCID: 0000-0002-4770-4705
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
FAU - Chartash, David
AU  - Chartash D
AUID- ORCID: 0000-0002-0265-330X
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - School of Medicine, University College Dublin, National University of Ireland, 
      Dublin, Ireland.
LA  - eng
GR  - T32 GM136651/GM/NIGMS NIH HHS/United States
GR  - T35 DK104689/DK/NIDDK NIH HHS/United States
GR  - T35 HL007649/HL/NHLBI NIH HHS/United States
PT  - Editorial
DEP - 20230814
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10463084
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - LLM
OT  - artificial intelligence in health care
OT  - autoethnography
OT  - large language models
OT  - medical education
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/14 12:43
MHDA- 2023/08/14 12:44
CRDT- 2023/08/14 11:53
PHST- 2023/07/17 00:00 [received]
PHST- 2023/07/26 00:00 [accepted]
PHST- 2023/07/26 00:00 [revised]
PHST- 2023/08/14 12:44 [medline]
PHST- 2023/08/14 12:43 [pubmed]
PHST- 2023/08/14 11:53 [entrez]
AID - v9i1e50945 [pii]
AID - 10.2196/50945 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Aug 14;9:e50945. doi: 10.2196/50945.

PMID- 37846274
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - The Significance of Artificial Intelligence Platforms in Anatomy Education: An 
      Experience With ChatGPT and Google Bard.
PG  - e45301
LID - 10.7759/cureus.45301 [doi]
LID - e45301
AB  - This study evaluated the use of two large language models (LLMs), ChatGPT and 
      Google Bard, in anatomy education. The models were asked to answer questions, 
      generate multiple-choice questions, and write articles on anatomy topics. The 
      results showed that the models were able to perform these tasks with varying 
      degrees of accuracy. ChatGPT and Google Bard did not differ significantly in 
      terms of answering questions. Both models were able to generate multiple-choice 
      questions with a high degree of accuracy. However, the performance of the models 
      in article writing was not yet at a sufficient level. The study also found that 
      the use of LLMs in medical education requires caution. This is because LLMs are 
      still under development and they can sometimes generate inaccurate or misleading 
      information. It is important to carefully evaluate the output of LLMs before 
      using them in educational settings. Overall, the study found that LLMs have the 
      potential to be valuable tools for anatomy education. However, more research is 
      needed to improve the accuracy of the models and to better understand how they 
      can be used effectively in educational settings.
CI  - Copyright © 2023, Ilgaz et al.
FAU - Ilgaz, Hasan B
AU  - Ilgaz HB
AD  - Anatomy, Hacettepe University Faculty of Medicine, Ankara, TUR.
FAU - Çelik, Zehra
AU  - Çelik Z
AD  - Anatomy, Hacettepe University Faculty of Medicine, Ankara, TUR.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10576957
OTO - NOTNLM
OT  - anatomy education
OT  - artificial intelligence
OT  - chatgpt
OT  - google bard
OT  - large language models
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/17 06:42
MHDA- 2023/10/17 06:43
CRDT- 2023/10/17 03:50
PHST- 2023/09/15 00:00 [accepted]
PHST- 2023/10/17 06:43 [medline]
PHST- 2023/10/17 06:42 [pubmed]
PHST- 2023/10/17 03:50 [entrez]
AID - 10.7759/cureus.45301 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 15;15(9):e45301. doi: 10.7759/cureus.45301. eCollection 2023 
      Sep.

PMID- 37956228
OWN - NLM
STAT- MEDLINE
DCOM- 20231128
LR  - 20231128
IS  - 1549-960X (Electronic)
IS  - 1549-9596 (Linking)
VI  - 63
IP  - 22
DP  - 2023 Nov 27
TI  - ChatGPT in Drug Discovery: A Case Study on Anticocaine Addiction Drug Development 
      with Chatbots.
PG  - 7189-7209
LID - 10.1021/acs.jcim.3c01429 [doi]
AB  - The birth of ChatGPT, a cutting-edge language model-based chatbot developed by 
      OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role 
      in rigorous scientific research is not clear yet. This paper vividly showcases 
      its innovative application within the field of drug discovery. Focused 
      specifically on developing anticocaine addiction drugs, the study employs GPT-4 
      as a virtual guide, offering strategic and methodological insights to researchers 
      working on generative models for drug candidates. The primary objective is to 
      generate optimal drug-like molecules with desired properties. By leveraging the 
      capabilities of ChatGPT, the study introduces a novel approach to the drug 
      discovery process. This symbiotic partnership between AI and researchers 
      transforms how drug development is approached. Chatbots become facilitators, 
      steering researchers toward innovative methodologies and productive paths for 
      creating effective drug candidates. This research sheds light on the 
      collaborative synergy between human expertise and AI assistance, wherein 
      ChatGPT's cognitive abilities enhance the design and development of 
      pharmaceutical solutions. This paper not only explores the integration of 
      advanced AI in drug discovery but also reimagines the landscape by advocating for 
      AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation.
FAU - Wang, Rui
AU  - Wang R
AUID- ORCID: 0000-0002-7402-6372
AD  - Department of Mathematics, Michigan State University, East Lansing, Michigan 
      48824, United States.
FAU - Feng, Hongsong
AU  - Feng H
AUID- ORCID: 0000-0001-8039-3059
AD  - Department of Mathematics, Michigan State University, East Lansing, Michigan 
      48824, United States.
FAU - Wei, Guo-Wei
AU  - Wei GW
AUID- ORCID: 0000-0002-5781-2937
AD  - Department of Mathematics, Michigan State University, East Lansing, Michigan 
      48824, United States.
AD  - Department of Biochemistry and Molecular Biology, Michigan State University, East 
      Lansing, Michigan 48824, United States.
AD  - Department of Electrical and Computer Engineering, Michigan State University, 
      East Lansing, Michigan 48824, United States.
LA  - eng
PT  - Journal Article
DEP - 20231113
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
SB  - IM
UOF - ArXiv. 2023 Oct 19;:. PMID: 37645039
MH  - Humans
MH  - *Drug Development
MH  - Drug Discovery
MH  - *Substance-Related Disorders
MH  - Language
MH  - Research Personnel
EDAT- 2023/11/13 18:41
MHDA- 2023/11/28 06:42
CRDT- 2023/11/13 14:06
PHST- 2023/11/28 06:42 [medline]
PHST- 2023/11/13 18:41 [pubmed]
PHST- 2023/11/13 14:06 [entrez]
AID - 10.1021/acs.jcim.3c01429 [doi]
PST - ppublish
SO  - J Chem Inf Model. 2023 Nov 27;63(22):7189-7209. doi: 10.1021/acs.jcim.3c01429. 
      Epub 2023 Nov 13.

PMID- 37920024
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231103
IS  - 1551-0018 (Electronic)
IS  - 1547-1063 (Linking)
VI  - 20
IP  - 9
DP  - 2023 Aug 17
TI  - Investigating difficulties and enhancing understanding in linear algebra: 
      Leveraging SageMath and ChatGPT for (orthogonal) diagonalization and singular 
      value decomposition.
PG  - 16551-16595
LID - 10.3934/mbe.2023738 [doi]
AB  - We explored some common challenges faced by undergraduate students when studying 
      linear algebra, particularly when dealing with algorithmic thinking skills 
      required for topics such as matrix factorization, focusing on (orthogonal) 
      diagonalization and singular value decomposition (SVD). To address these 
      challenges, we introduced SageMath, a Python-based open-source computer algebra 
      system, as a supportive tool for students performing computational tasks despite 
      its static output nature. We further examined the potential of dynamic ChatGPT, 
      an AI-based chatbot, by requesting examples or problem-solving assistance related 
      to (orthogonal) diagonalization or the SVD of a specific matrix. By reinforcing 
      essential concepts in linear algebra and enhancing computational skills through 
      effective practice, mastering these topics can become more accessible while 
      minimizing mistakes. Although static in nature, SageMath proved valuable for 
      confirming calculations and handling tedious computations because of its 
      easy-to-understand syntax and accurate solutions. However, although dynamic 
      ChatGPT may not be fully reliable for solving linear algebra problems, the errors 
      it produces can serve as a valuable resource for improving critical thinking 
      skills.
FAU - Karjanto, Natanael
AU  - Karjanto N
AD  - Department of Mathematics, University College, Natural Science Campus, 
      Sungkyunkwan University, Suwon 16419, Republic of Korea.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Math Biosci Eng
JT  - Mathematical biosciences and engineering : MBE
JID - 101197794
SB  - IM
OTO - NOTNLM
OT  - (orthogonal) diagonalization
OT  - AI-based chatbot
OT  - ChatGPT
OT  - SageMath
OT  - algorithmic thinking skills
OT  - computer algebra system
OT  - linear algebra
OT  - singular value decomposition
EDAT- 2023/11/03 06:44
MHDA- 2023/11/03 06:45
CRDT- 2023/11/03 03:43
PHST- 2023/11/03 06:45 [medline]
PHST- 2023/11/03 06:44 [pubmed]
PHST- 2023/11/03 03:43 [entrez]
AID - 10.3934/mbe.2023738 [doi]
PST - ppublish
SO  - Math Biosci Eng. 2023 Aug 17;20(9):16551-16595. doi: 10.3934/mbe.2023738.

PMID- 37903836
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231102
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 30
TI  - A large-scale comparison of human-written versus ChatGPT-generated essays.
PG  - 18617
LID - 10.1038/s41598-023-45644-9 [doi]
LID - 18617
AB  - ChatGPT and similar generative AI models have attracted hundreds of millions of 
      users and have become part of the public discourse. Many believe that such models 
      will disrupt society and lead to significant changes in the education system and 
      information generation. So far, this belief is based on either colloquial 
      evidence or benchmarks from the owners of the models-both lack scientific rigor. 
      We systematically assess the quality of AI-generated content through a 
      large-scale study comparing human-written versus ChatGPT-generated argumentative 
      student essays. We use essays that were rated by a large number of human experts 
      (teachers). We augment the analysis by considering a set of linguistic 
      characteristics of the generated essays. Our results demonstrate that ChatGPT 
      generates essays that are rated higher regarding quality than human-written 
      essays. The writing style of the AI models exhibits linguistic characteristics 
      that are different from those of the human-written essays. Since the technology 
      is readily available, we believe that educators must act immediately. We must 
      re-invent homework and develop teaching concepts that utilize these AI models in 
      the same way as math utilizes the calculator: teach the general concepts first 
      and then use AI tools to free up time for other learning objectives.
CI  - © 2023. The Author(s).
FAU - Herbold, Steffen
AU  - Herbold S
AD  - Faculty of Computer Science and Mathematics, University of Passau, Passau, 
      Germany. steffen.herbold@uni-passau.de.
FAU - Hautli-Janisz, Annette
AU  - Hautli-Janisz A
AD  - Faculty of Computer Science and Mathematics, University of Passau, Passau, 
      Germany.
FAU - Heuer, Ute
AU  - Heuer U
AD  - Faculty of Computer Science and Mathematics, University of Passau, Passau, 
      Germany.
FAU - Kikteva, Zlata
AU  - Kikteva Z
AD  - Faculty of Computer Science and Mathematics, University of Passau, Passau, 
      Germany.
FAU - Trautsch, Alexander
AU  - Trautsch A
AD  - Faculty of Computer Science and Mathematics, University of Passau, Passau, 
      Germany.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
PMC - PMC10616290
COIS- The authors declare no competing interests.
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:43
CRDT- 2023/10/31 00:24
PHST- 2023/06/01 00:00 [received]
PHST- 2023/10/22 00:00 [accepted]
PHST- 2023/10/31 06:43 [medline]
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 00:24 [entrez]
AID - 10.1038/s41598-023-45644-9 [pii]
AID - 45644 [pii]
AID - 10.1038/s41598-023-45644-9 [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 30;13(1):18617. doi: 10.1038/s41598-023-45644-9.

PMID- 37648742
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20231119
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Aug 30
TI  - Performance of ChatGPT, human radiologists, and context-aware ChatGPT in 
      identifying AO codes from radiology reports.
PG  - 14215
LID - 10.1038/s41598-023-41512-8 [doi]
LID - 14215
AB  - While radiologists can describe a fracture's morphology and complexity with ease, 
      the translation into classification systems such as the Arbeitsgemeinschaft 
      Osteosynthesefragen (AO) Fracture and Dislocation Classification Compendium is 
      more challenging. We tested the performance of generic chatbots and chatbots 
      aware of specific knowledge of the AO classification provided by a vector-index 
      and compared it to human readers. In the 100 radiological reports we created 
      based on random AO codes, chatbots provided AO codes significantly faster than 
      humans (mean 3.2 s per case vs. 50 s per case, p < .001) though not reaching 
      human performance (max. chatbot performance of 86% correct full AO codes vs. 95% 
      in human readers). In general, chatbots based on GPT 4 outperformed the ones 
      based on GPT 3.5-Turbo. Further, we found that providing specific knowledge 
      substantially enhances the chatbot's performance and consistency as the 
      context-aware chatbot based on GPT 4 provided 71% consistent correct full AO 
      codes for the compared to the 2% consistent correct full AO codes for the generic 
      ChatGPT 4. This provides evidence, that refining and providing specific context 
      to ChatGPT will be the next essential step in harnessing its power.
CI  - © 2023. Springer Nature Limited.
FAU - Russe, Maximilian F
AU  - Russe MF
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher 
      Str. 64, 79106, Freiburg, Germany. maximilian.russe@uniklinik-freiburg.de.
FAU - Fink, Anna
AU  - Fink A
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher 
      Str. 64, 79106, Freiburg, Germany.
FAU - Ngo, Helen
AU  - Ngo H
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher 
      Str. 64, 79106, Freiburg, Germany.
FAU - Tran, Hien
AU  - Tran H
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher 
      Str. 64, 79106, Freiburg, Germany.
FAU - Bamberg, Fabian
AU  - Bamberg F
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher 
      Str. 64, 79106, Freiburg, Germany.
FAU - Reisert, Marco
AU  - Reisert M
AD  - Department of Stereotactic and Functional Neurosurgery, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, 
      Germany.
AD  - Medical Physics, Department of Diagnostic and Interventional Radiology, Medical 
      Center - University of Freiburg, Faculty of Medicine, University of Freiburg, 
      Freiburg, Germany.
FAU - Rau, Alexander
AU  - Rau A
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher 
      Str. 64, 79106, Freiburg, Germany.
AD  - Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of 
      Medicine, University of Freiburg, Freiburg, Germany.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230830
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
RN  - 0 (Drugs, Generic)
SB  - IM
MH  - Humans
MH  - Awareness
MH  - Drugs, Generic
MH  - *Fractures, Bone
MH  - Radiologists
MH  - *Radiology
PMC - PMC10468502
COIS- The authors declare no competing interests.
EDAT- 2023/08/31 00:41
MHDA- 2023/09/01 06:42
CRDT- 2023/08/30 23:20
PHST- 2023/05/15 00:00 [received]
PHST- 2023/08/28 00:00 [accepted]
PHST- 2023/09/01 06:42 [medline]
PHST- 2023/08/31 00:41 [pubmed]
PHST- 2023/08/30 23:20 [entrez]
AID - 10.1038/s41598-023-41512-8 [pii]
AID - 41512 [pii]
AID - 10.1038/s41598-023-41512-8 [doi]
PST - epublish
SO  - Sci Rep. 2023 Aug 30;13(1):14215. doi: 10.1038/s41598-023-41512-8.

PMID- 37949772
OWN - NLM
STAT- Publisher
LR  - 20231110
IS  - 2603-6479 (Electronic)
IS  - 2603-6479 (Linking)
DP  - 2023 Nov 8
TI  - [Considerations for using ChatGPT in medical practice].
LID - S2603-6479(23)00054-4 [pii]
LID - 10.1016/j.jhqr.2023.09.007 [doi]
FAU - Iglesias-Puzas, A
AU  - Iglesias-Puzas A
AD  - Servicio de Dermatología, Hospital Universitario Clínico San Carlos, Universidad 
      Complutense, Madrid, España. Electronic address: alvaroigpu@gmail.com.
FAU - Conde-Taboada, A
AU  - Conde-Taboada A
AD  - Servicio de Dermatología, Hospital Universitario Clínico San Carlos, Universidad 
      Complutense, Madrid, España.
FAU - López-Bran, E
AU  - López-Bran E
AD  - Servicio de Dermatología, Hospital Universitario Clínico San Carlos, Universidad 
      Complutense, Madrid, España.
LA  - spa
PT  - Letter
TT  - Consideraciones sobre el uso de ChatGPT en la práctica médica.
DEP - 20231108
PL  - Spain
TA  - J Healthc Qual Res
JT  - Journal of healthcare quality research
JID - 101735273
SB  - IM
EDAT- 2023/11/11 11:43
MHDA- 2023/11/11 11:43
CRDT- 2023/11/10 21:58
PHST- 2023/04/18 00:00 [received]
PHST- 2023/05/03 00:00 [revised]
PHST- 2023/09/26 00:00 [accepted]
PHST- 2023/11/11 11:43 [medline]
PHST- 2023/11/11 11:43 [pubmed]
PHST- 2023/11/10 21:58 [entrez]
AID - S2603-6479(23)00054-4 [pii]
AID - 10.1016/j.jhqr.2023.09.007 [doi]
PST - aheadofprint
SO  - J Healthc Qual Res. 2023 Nov 8:S2603-6479(23)00054-4. doi: 
      10.1016/j.jhqr.2023.09.007.

PMID- 37743152
OWN - NLM
STAT- Publisher
LR  - 20230924
IS  - 2603-6479 (Electronic)
IS  - 2603-6479 (Linking)
DP  - 2023 Sep 22
TI  - [Considerations for using ChatGPT in medical practice].
LID - S2603-6479(23)00049-0 [pii]
LID - 10.1016/j.jhqr.2023.09.002 [doi]
FAU - Iglesias-Puzas, Á
AU  - Iglesias-Puzas Á
AD  - Servicio de Dermatología, Hospital Universitario Clínico San Carlos, Universidad 
      Complutense, Madrid, España. Electronic address: alvaroigpu@gmail.com.
FAU - Conde-Taboada, A
AU  - Conde-Taboada A
AD  - Servicio de Dermatología, Hospital Universitario Clínico San Carlos, Universidad 
      Complutense, Madrid, España.
FAU - López-Bran, E
AU  - López-Bran E
AD  - Servicio de Dermatología, Hospital Universitario Clínico San Carlos, Universidad 
      Complutense, Madrid, España.
LA  - spa
PT  - Letter
TT  - Consideraciones sobre el uso de ChatGPT en la práctica médica.
DEP - 20230922
PL  - Spain
TA  - J Healthc Qual Res
JT  - Journal of healthcare quality research
JID - 101735273
SB  - IM
EDAT- 2023/09/25 00:42
MHDA- 2023/09/25 00:42
CRDT- 2023/09/24 21:55
PHST- 2023/08/22 00:00 [received]
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/09/25 00:42 [medline]
PHST- 2023/09/25 00:42 [pubmed]
PHST- 2023/09/24 21:55 [entrez]
AID - S2603-6479(23)00049-0 [pii]
AID - 10.1016/j.jhqr.2023.09.002 [doi]
PST - aheadofprint
SO  - J Healthc Qual Res. 2023 Sep 22:S2603-6479(23)00049-0. doi: 
      10.1016/j.jhqr.2023.09.002.

PMID- 37677075
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1550-9397 (Electronic)
IS  - 1550-9389 (Print)
IS  - 1550-9389 (Linking)
VI  - 19
IP  - 12
DP  - 2023 Dec 1
TI  - ChatGPT for patient education: an evolving investigation.
PG  - 2135-2136
LID - 10.5664/jcsm.10808 [doi]
FAU - Campbell, Daniel J
AU  - Campbell DJ
AD  - Department of Otolaryngology-Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
FAU - Estephan, Leonard E
AU  - Estephan LE
AD  - Department of Otolaryngology-Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - J Clin Sleep Med
JT  - Journal of clinical sleep medicine : JCSM : official publication of the American 
      Academy of Sleep Medicine
JID - 101231977
SB  - IM
MH  - Humans
MH  - *Patient Education as Topic
MH  - Artificial Intelligence
PMC - PMC10692945
COIS- All authors have seen and approved the manuscript. Institution where work was 
      performed: Department of Otolaryngology–Head and Neck Surgery, Thomas Jefferson 
      University Hospitals. This research did not receive any specific grant from 
      funding agencies in the public, commercial, or not-for-profit sectors. The 
      authors report no conflicts of interest.
EDAT- 2023/09/07 18:42
MHDA- 2023/12/04 12:42
PMCR- 2024/12/01
CRDT- 2023/09/07 15:02
PHST- 2024/12/01 00:00 [pmc-release]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/09/07 18:42 [pubmed]
PHST- 2023/09/07 15:02 [entrez]
AID - jcsm.10808 [pii]
AID - JC2300484 [pii]
AID - 10.5664/jcsm.10808 [doi]
PST - ppublish
SO  - J Clin Sleep Med. 2023 Dec 1;19(12):2135-2136. doi: 10.5664/jcsm.10808.

PMID- 37485080
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230725
IS  - 2666-6758 (Electronic)
IS  - 2666-6758 (Linking)
VI  - 4
IP  - 4
DP  - 2023 Jul 10
TI  - Public aversion against ChatGPT in creative fields?
PG  - 100449
LID - 10.1016/j.xinn.2023.100449 [doi]
LID - 100449
FAU - Chu, Yueying
AU  - Chu Y
AD  - Center for Psychological Sciences, Zhejiang University, Hangzhou 310063, China.
FAU - Liu, Peng
AU  - Liu P
AD  - Center for Psychological Sciences, Zhejiang University, Hangzhou 310063, China.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230526
PL  - United States
TA  - Innovation (Camb)
JT  - Innovation (Cambridge (Mass.))
JID - 101771342
PMC - PMC10362514
COIS- The authors declare no competing interests.
EDAT- 2023/07/24 06:43
MHDA- 2023/07/24 06:44
CRDT- 2023/07/24 04:53
PHST- 2023/03/29 00:00 [received]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/07/24 06:44 [medline]
PHST- 2023/07/24 06:43 [pubmed]
PHST- 2023/07/24 04:53 [entrez]
AID - S2666-6758(23)00077-2 [pii]
AID - 100449 [pii]
AID - 10.1016/j.xinn.2023.100449 [doi]
PST - epublish
SO  - Innovation (Camb). 2023 May 26;4(4):100449. doi: 10.1016/j.xinn.2023.100449. 
      eCollection 2023 Jul 10.

PMID- 37207910
OWN - NLM
STAT- Publisher
LR  - 20230604
IS  - 1528-0012 (Electronic)
IS  - 0016-5085 (Linking)
DP  - 2023 May 18
TI  - ChatGPT and Patient Questions About Colonoscopy: Comment.
LID - S0016-5085(23)00734-5 [pii]
LID - 10.1053/j.gastro.2023.05.012 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230518
PL  - United States
TA  - Gastroenterology
JT  - Gastroenterology
JID - 0374630
SB  - IM
EDAT- 2023/05/20 09:42
MHDA- 2023/05/20 09:42
CRDT- 2023/05/19 19:26
PHST- 2023/05/10 00:00 [received]
PHST- 2023/05/11 00:00 [revised]
PHST- 2023/05/12 00:00 [accepted]
PHST- 2023/05/20 09:42 [pubmed]
PHST- 2023/05/20 09:42 [medline]
PHST- 2023/05/19 19:26 [entrez]
AID - S0016-5085(23)00734-5 [pii]
AID - 10.1053/j.gastro.2023.05.012 [doi]
PST - aheadofprint
SO  - Gastroenterology. 2023 May 18:S0016-5085(23)00734-5. doi: 
      10.1053/j.gastro.2023.05.012.

PMID- 36754724
OWN - NLM
STAT- MEDLINE
DCOM- 20230228
LR  - 20230412
IS  - 2589-7500 (Electronic)
IS  - 2589-7500 (Linking)
VI  - 5
IP  - 3
DP  - 2023 Mar
TI  - ChatGPT: the future of discharge summaries?
PG  - e107-e108
LID - S2589-7500(23)00021-3 [pii]
LID - 10.1016/S2589-7500(23)00021-3 [doi]
FAU - Patel, Sajan B
AU  - Patel SB
AD  - Department of Surgery and Cancer, St Mary's Hospital, London W2 1NY, UK.
FAU - Lam, Kyle
AU  - Lam K
AD  - Department of Surgery and Cancer, St Mary's Hospital, London W2 1NY, UK. 
      Electronic address: k.lam@imperial.ac.uk.
LA  - eng
PT  - Journal Article
DEP - 20230206
PL  - England
TA  - Lancet Digit Health
JT  - The Lancet. Digital health
JID - 101751302
SB  - IM
MH  - Humans
MH  - *Patient Discharge
COIS- We declare no competing interests. Funding and infrastructural support was 
      provided by the UK National Institute for Health and Care Research (NIHR) 
      Imperial Biomedical Research Centre. KL is supported by a NIHR Academic Clinical 
      Fellowship.
EDAT- 2023/02/09 06:00
MHDA- 2023/03/03 06:00
CRDT- 2023/02/08 22:01
PHST- 2022/12/29 00:00 [received]
PHST- 2023/01/24 00:00 [accepted]
PHST- 2023/02/09 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
PHST- 2023/02/08 22:01 [entrez]
AID - S2589-7500(23)00021-3 [pii]
AID - 10.1016/S2589-7500(23)00021-3 [doi]
PST - ppublish
SO  - Lancet Digit Health. 2023 Mar;5(3):e107-e108. doi: 10.1016/S2589-7500(23)00021-3. 
      Epub 2023 Feb 6.

PMID- 37998082
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231126
IS  - 2313-433X (Electronic)
IS  - 2313-433X (Linking)
VI  - 9
IP  - 11
DP  - 2023 Oct 25
TI  - Empowering Deaf-Hearing Communication: Exploring Synergies between Predictive and 
      Generative AI-Based Strategies towards (Portuguese) Sign Language Interpretation.
LID - 10.3390/jimaging9110235 [doi]
LID - 235
AB  - Communication between Deaf and hearing individuals remains a persistent challenge 
      requiring attention to foster inclusivity. Despite notable efforts in the 
      development of digital solutions for sign language recognition (SLR), several 
      issues persist, such as cross-platform interoperability and strategies for 
      tokenizing signs to enable continuous conversations and coherent sentence 
      construction. To address such issues, this paper proposes a non-invasive 
      Portuguese Sign Language (Língua Gestual Portuguesa or LGP) interpretation 
      system-as-a-service, leveraging skeletal posture sequence inference powered by 
      long-short term memory (LSTM) architectures. To address the scarcity of examples 
      during machine learning (ML) model training, dataset augmentation strategies are 
      explored. Additionally, a buffer-based interaction technique is introduced to 
      facilitate LGP terms tokenization. This technique provides real-time feedback to 
      users, allowing them to gauge the time remaining to complete a sign, which aids 
      in the construction of grammatically coherent sentences based on inferred 
      terms/words. To support human-like conditioning rules for interpretation, a large 
      language model (LLM) service is integrated. Experiments reveal that LSTM-based 
      neural networks, trained with 50 LGP terms and subjected to data augmentation, 
      achieved accuracy levels ranging from 80% to 95.6%. Users unanimously reported a 
      high level of intuition when using the buffer-based interaction strategy for 
      terms/words tokenization. Furthermore, tests with an LLM-specifically 
      ChatGPT-demonstrated promising semantic correlation rates in generated sentences, 
      comparable to expected sentences.
FAU - Adão, Telmo
AU  - Adão T
AUID- ORCID: 0000-0002-2727-0014
AD  - Department of Engineering, School of Sciences and Technology, University of 
      Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.
AD  - ALGORITMI Research Centre/LASI, University of Minho, 4800-058 Guimarães, 
      Portugal.
FAU - Oliveira, João
AU  - Oliveira J
AD  - Centro de Computação Gráfica-CCG/zgdv, University of Minho, Campus de Azurém, 
      Edifício 14, 4800-058 Guimarães, Portugal.
FAU - Shahrabadi, Somayeh
AU  - Shahrabadi S
AD  - Centro de Computação Gráfica-CCG/zgdv, University of Minho, Campus de Azurém, 
      Edifício 14, 4800-058 Guimarães, Portugal.
FAU - Jesus, Hugo
AU  - Jesus H
AD  - Centro de Computação Gráfica-CCG/zgdv, University of Minho, Campus de Azurém, 
      Edifício 14, 4800-058 Guimarães, Portugal.
FAU - Fernandes, Marco
AU  - Fernandes M
AD  - Polytechnic Institute of Bragança, School of Communication, Administration and 
      Tourism, Campus do Cruzeiro, 5370-202 Mirandela, Portugal.
FAU - Costa, Ângelo
AU  - Costa Â
AD  - Associação Portuguesa de Surdos (APS), 1600-796 Lisboa, Portugal.
FAU - Ferreira, Vânia
AU  - Ferreira V
AD  - Associação Portuguesa de Surdos (APS), 1600-796 Lisboa, Portugal.
FAU - Gonçalves, Martinho Fradeira
AU  - Gonçalves MF
AD  - Polytechnic Institute of Bragança, School of Communication, Administration and 
      Tourism, Campus do Cruzeiro, 5370-202 Mirandela, Portugal.
FAU - Lopéz, Miguel A Guevara
AU  - Lopéz MAG
AUID- ORCID: 0000-0001-7814-1653
AD  - ALGORITMI Research Centre/LASI, University of Minho, 4800-058 Guimarães, 
      Portugal.
AD  - Instituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, 
      2914-508 Setúbal, Portugal.
FAU - Peres, Emanuel
AU  - Peres E
AUID- ORCID: 0000-0001-5669-7976
AD  - Department of Engineering, School of Sciences and Technology, University of 
      Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.
AD  - Centre for the Research and Technology of Agro-Environmental and Biological 
      Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, 
      Portugal.
AD  - Institute for Innovation, Capacity Building and Sustainability of Agri-Food 
      Production, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, 
      Portugal.
FAU - Magalhães, Luís Gonzaga
AU  - Magalhães LG
AD  - ALGORITMI Research Centre/LASI, University of Minho, 4800-058 Guimarães, 
      Portugal.
LA  - eng
GR  - POCI-01-0247-FEDER-068605/Portugal 2020, under the Competitiveness and 
      Internationalization Operational Program (POCI), and by the European Regional 
      Development Fund (ERDF)/
GR  - C644866286-00000011/Vine&Wine Portugal Project, co-financed by the RRP - Recovery 
      and Resilience Plan and the Eu-ropean NextGeneration EU Funds/
PT  - Journal Article
DEP - 20231025
PL  - Switzerland
TA  - J Imaging
JT  - Journal of imaging
JID - 101698819
PMC - PMC10672430
OTO - NOTNLM
OT  - Portuguese Sign Language
OT  - deaf-hearing communication
OT  - generative pre-trained transformer (GPT)
OT  - inclusion
OT  - large language models (LLM)
OT  - long-short term memory (LSTM)
OT  - machine learning (ML)
OT  - sign language recognition (SLR)
OT  - video-based motion analytics
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/24 12:42
MHDA- 2023/11/24 12:43
CRDT- 2023/11/24 09:33
PHST- 2023/09/16 00:00 [received]
PHST- 2023/10/14 00:00 [revised]
PHST- 2023/10/19 00:00 [accepted]
PHST- 2023/11/24 12:43 [medline]
PHST- 2023/11/24 12:42 [pubmed]
PHST- 2023/11/24 09:33 [entrez]
AID - jimaging9110235 [pii]
AID - jimaging-09-00235 [pii]
AID - 10.3390/jimaging9110235 [doi]
PST - epublish
SO  - J Imaging. 2023 Oct 25;9(11):235. doi: 10.3390/jimaging9110235.

PMID- 37983896
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1601-0825 (Electronic)
IS  - 1354-523X (Linking)
DP  - 2023 Nov 20
TI  - Critical viewpoint on ChatGPT usage in oral medicine.
LID - 10.1111/odi.14818 [doi]
FAU - Sarode, Gargi
AU  - Sarode G
AUID- ORCID: 0000-0003-4628-8866
AD  - Department of Oral Pathology and Microbiology, Dr. D.Y. Patil Dental College and 
      Hospital, Dr.D. Y. Patil Vidyapeeth, Pimpri, Pune, India.
FAU - Sarode, Sachin C
AU  - Sarode SC
AUID- ORCID: 0000-0003-1856-0957
AD  - Department of Oral Pathology and Microbiology, Dr. D.Y. Patil Dental College and 
      Hospital, Dr.D. Y. Patil Vidyapeeth, Pimpri, Pune, India.
AD  - Dr. D. Y. Patil Unitech Society's Dr. D. Y. Patil Institute of Pharmaceutical 
      Sciences and Research, Pimpri, Pune, Maharashtra, India.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - Denmark
TA  - Oral Dis
JT  - Oral diseases
JID - 9508565
EDAT- 2023/11/20 18:44
MHDA- 2023/11/20 18:44
CRDT- 2023/11/20 17:30
PHST- 2023/10/14 00:00 [received]
PHST- 2023/11/11 00:00 [accepted]
PHST- 2023/11/20 18:44 [medline]
PHST- 2023/11/20 18:44 [pubmed]
PHST- 2023/11/20 17:30 [entrez]
AID - 10.1111/odi.14818 [doi]
PST - aheadofprint
SO  - Oral Dis. 2023 Nov 20. doi: 10.1111/odi.14818.

PMID- 37897694
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231107
LR  - 20231201
IS  - 1049-7323 (Print)
IS  - 1049-7323 (Linking)
VI  - 33
IP  - 13
DP  - 2023 Nov
TI  - What Does ChatGPT Mean for Qualitative Health Research?
PG  - 1135-1139
LID - 10.1177/10497323231210816 [doi]
FAU - van Manen, Michael
AU  - van Manen M
AUID- ORCID: 0000-0001-6070-0314
AD  - Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
AD  - John Dossetor Health Ethics Centre, University of Alberta, Edmonton, AB, Canada.
LA  - eng
PT  - Editorial
DEP - 20231028
PL  - United States
TA  - Qual Health Res
JT  - Qualitative health research
JID - 9202144
COIS- Declaration of Conflicting InterestsThe author(s) declared no potential conflicts 
      of interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/10/29 06:42
MHDA- 2023/10/29 06:43
CRDT- 2023/10/28 11:32
PHST- 2023/10/29 06:43 [medline]
PHST- 2023/10/29 06:42 [pubmed]
PHST- 2023/10/28 11:32 [entrez]
AID - 10.1177/10497323231210816 [doi]
PST - ppublish
SO  - Qual Health Res. 2023 Nov;33(13):1135-1139. doi: 10.1177/10497323231210816. Epub 
      2023 Oct 28.

PMID- 37646736
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230831
LR  - 20230905
IS  - 1806-9282 (Electronic)
IS  - 0104-4230 (Print)
IS  - 0104-4230 (Linking)
VI  - 69
IP  - 8
DP  - 2023
TI  - Comment on "How may ChatGPT impact medical teaching?".
PG  - e20230593
LID - S0104-42302023000800305 [pii]
LID - 10.1590/1806-9282.20230593 [doi]
LID - e20230593
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University - Punjab, India.
AD  - Joesph Ayobabalola University - Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230825
PL  - Brazil
TA  - Rev Assoc Med Bras (1992)
JT  - Revista da Associacao Medica Brasileira (1992)
JID - 9308586
SB  - IM
CON - Rev Assoc Med Bras (1992). 2023 May 15;69(4):e20230282. PMID: 37194805
PMC - PMC10453621
COIS- Conflicts of interest: the authors declare there is no conflicts of interest.
EDAT- 2023/08/30 12:42
MHDA- 2023/08/30 12:43
CRDT- 2023/08/30 10:43
PHST- 2023/05/19 00:00 [received]
PHST- 2023/05/21 00:00 [accepted]
PHST- 2023/08/30 12:43 [medline]
PHST- 2023/08/30 12:42 [pubmed]
PHST- 2023/08/30 10:43 [entrez]
AID - S0104-42302023000800305 [pii]
AID - 10.1590/1806-9282.20230593 [doi]
PST - epublish
SO  - Rev Assoc Med Bras (1992). 2023 Aug 25;69(8):e20230593. doi: 
      10.1590/1806-9282.20230593. eCollection 2023.

PMID- 36965923
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230329
LR  - 20230407
IS  - 1532-8473 (Electronic)
IS  - 1089-9472 (Linking)
VI  - 38
IP  - 2
DP  - 2023 Apr
TI  - The Role of ChatGPT in Perianesthesia Nursing.
PG  - 176-177
LID - S1089-9472(23)00066-7 [pii]
LID - 10.1016/j.jopan.2023.02.006 [doi]
FAU - Odom-Forren, Jan
AU  - Odom-Forren J
AD  - College of Nursing, University of Kentucky, Lexington, KY. Electronic address: 
      jan.forren@uky.edu.
LA  - eng
PT  - Comment
PT  - Editorial
PL  - United States
TA  - J Perianesth Nurs
JT  - Journal of perianesthesia nursing : official journal of the American Society of 
      PeriAnesthesia Nurses
JID - 9610507
CON - JAMA. 2023 Feb 28;329(8):637-639. PMID: 36719674
EDAT- 2023/03/26 06:00
MHDA- 2023/03/28 17:16
CRDT- 2023/03/25 22:08
PHST- 2023/02/17 00:00 [received]
PHST- 2023/02/17 00:00 [accepted]
PHST- 2023/03/28 17:16 [medline]
PHST- 2023/03/25 22:08 [entrez]
PHST- 2023/03/26 06:00 [pubmed]
AID - S1089-9472(23)00066-7 [pii]
AID - 10.1016/j.jopan.2023.02.006 [doi]
PST - ppublish
SO  - J Perianesth Nurs. 2023 Apr;38(2):176-177. doi: 10.1016/j.jopan.2023.02.006.

PMID- 36906169
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1555-7162 (Electronic)
IS  - 0002-9343 (Linking)
VI  - 136
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT in Scientific Writing: A Cautionary Tale.
PG  - 725-726.e6
LID - S0002-9343(23)00159-6 [pii]
LID - 10.1016/j.amjmed.2023.02.011 [doi]
FAU - Zheng, Haoyi
AU  - Zheng H
AD  - Saint Francis Hospital and Heart Center, Roslyn, NY. Electronic address: 
      Haoyi.Zheng@chsli.org.
FAU - Zhan, Huichun
AU  - Zhan H
AD  - Department of Medicine, Stony Brook School of Medicine, Stony Brook, NY; Medical 
      Service, Northport VA Medical Center, Northport, NY.
LA  - eng
PT  - Editorial
DEP - 20230310
PL  - United States
TA  - Am J Med
JT  - The American journal of medicine
JID - 0267200
SB  - IM
EDAT- 2023/03/12 06:00
MHDA- 2023/03/12 06:01
CRDT- 2023/03/11 19:30
PHST- 2023/02/14 00:00 [received]
PHST- 2023/02/14 00:00 [accepted]
PHST- 2023/03/12 06:01 [medline]
PHST- 2023/03/12 06:00 [pubmed]
PHST- 2023/03/11 19:30 [entrez]
AID - S0002-9343(23)00159-6 [pii]
AID - 10.1016/j.amjmed.2023.02.011 [doi]
PST - ppublish
SO  - Am J Med. 2023 Aug;136(8):725-726.e6. doi: 10.1016/j.amjmed.2023.02.011. Epub 
      2023 Mar 10.

PMID- 38085847
OWN - NLM
STAT- Publisher
LR  - 20231212
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Dec 12
TI  - Can ChatGPT assist urologists manage overactive bladder?
LID - 10.1097/JS9.0000000000000887 [doi]
FAU - Gao, Xiaoshuai
AU  - Gao X
AD  - Department of Urology and Institute of Urology (Laboratory of Reconstructive 
      Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.
FAU - Feng, Mei-Lin
AU  - Feng ML
AD  - School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, 
      P.R. China.
LA  - eng
PT  - Journal Article
DEP - 20231212
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/12/12 18:42
MHDA- 2023/12/12 18:42
CRDT- 2023/12/12 14:23
PHST- 2023/10/24 00:00 [received]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/12/12 18:42 [medline]
PHST- 2023/12/12 18:42 [pubmed]
PHST- 2023/12/12 14:23 [entrez]
AID - 01279778-990000000-00887 [pii]
AID - 10.1097/JS9.0000000000000887 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Dec 12. doi: 10.1097/JS9.0000000000000887.

PMID- 37968516
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1476-5454 (Electronic)
IS  - 0950-222X (Linking)
DP  - 2023 Nov 15
TI  - ChatGPT to document ocular infectious diseases.
LID - 10.1038/s41433-023-02823-2 [doi]
FAU - Masalkhi, Mouayad
AU  - Masalkhi M
AUID- ORCID: 0000-0003-0811-358X
AD  - University College Dublin School of Medicine, Belfield, Dublin, Ireland. 
      mouayad.masalkhi@ucdconnect.ie.
FAU - Ong, Joshua
AU  - Ong J
AUID- ORCID: 0000-0003-4860-827X
AD  - Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
FAU - Waisberg, Ethan
AU  - Waisberg E
AUID- ORCID: 0000-0001-8999-0212
AD  - Department of Ophthalmology, University of Cambridge, Cambridge, UK.
FAU - Zaman, Nasif
AU  - Zaman N
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
FAU - Sarker, Prithul
AU  - Sarker P
AUID- ORCID: 0000-0002-6290-5484
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
FAU - Lee, Andrew G
AU  - Lee AG
AD  - Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA.
AD  - Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, 
      Houston, TX, USA.
AD  - The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, 
      TX, USA.
AD  - Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell 
      Medicine, New York, NY, USA.
AD  - Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, 
      USA.
AD  - University of Texas MD Anderson Cancer Center, Houston, TX, USA.
AD  - Texas A&M College of Medicine, Bryan, TX, USA.
AD  - Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa 
      City, IA, USA.
FAU - Tavakkoli, Alireza
AU  - Tavakkoli A
AUID- ORCID: 0000-0001-9460-1269
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
LA  - eng
PT  - Journal Article
DEP - 20231115
PL  - England
TA  - Eye (Lond)
JT  - Eye (London, England)
JID - 8703986
SB  - IM
EDAT- 2023/11/16 06:45
MHDA- 2023/11/16 06:45
CRDT- 2023/11/16 00:03
PHST- 2023/10/22 00:00 [received]
PHST- 2023/11/01 00:00 [accepted]
PHST- 2023/10/26 00:00 [revised]
PHST- 2023/11/16 06:45 [medline]
PHST- 2023/11/16 06:45 [pubmed]
PHST- 2023/11/16 00:03 [entrez]
AID - 10.1038/s41433-023-02823-2 [pii]
AID - 10.1038/s41433-023-02823-2 [doi]
PST - aheadofprint
SO  - Eye (Lond). 2023 Nov 15. doi: 10.1038/s41433-023-02823-2.

PMID- 37934157
OWN - NLM
STAT- Publisher
LR  - 20231107
IS  - 1365-2591 (Electronic)
IS  - 0143-2885 (Linking)
DP  - 2023 Nov 7
TI  - ChatGPT, consistency and accuracy of endodontic question.
LID - 10.1111/iej.13997 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Laos.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Ajitgarh, India.
LA  - eng
PT  - Journal Article
DEP - 20231107
PL  - England
TA  - Int Endod J
JT  - International endodontic journal
JID - 8004996
EDAT- 2023/11/07 12:43
MHDA- 2023/11/07 12:43
CRDT- 2023/11/07 10:33
PHST- 2023/10/10 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/11/07 12:43 [medline]
PHST- 2023/11/07 12:43 [pubmed]
PHST- 2023/11/07 10:33 [entrez]
AID - 10.1111/iej.13997 [doi]
PST - aheadofprint
SO  - Int Endod J. 2023 Nov 7. doi: 10.1111/iej.13997.

PMID- 37719916
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0377-1237 (Print)
IS  - 2213-4743 (Electronic)
IS  - 0377-1237 (Linking)
VI  - 79
IP  - 5
DP  - 2023 Sep-Oct
TI  - ChatGPT for scientific community: Boon or bane?
PG  - 498-499
LID - 10.1016/j.mjafi.2023.06.009 [doi]
FAU - Jain, Ankur
AU  - Jain A
AD  - Assistant Professor (Clinical Haematology), Vardhman Mahavir Medical College & 
      Safdarjung Hospital, New Delhi, India.
LA  - eng
PT  - Journal Article
DEP - 20230808
PL  - India
TA  - Med J Armed Forces India
JT  - Medical journal, Armed Forces India
JID - 7602492
PMC - PMC10499628
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:43
PMCR- 2024/09/01
CRDT- 2023/09/18 04:30
PHST- 2023/04/12 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/09/18 06:43 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:30 [entrez]
AID - S0377-1237(23)00104-1 [pii]
AID - 10.1016/j.mjafi.2023.06.009 [doi]
PST - ppublish
SO  - Med J Armed Forces India. 2023 Sep-Oct;79(5):498-499. doi: 
      10.1016/j.mjafi.2023.06.009. Epub 2023 Aug 8.

PMID- 37609604
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230928
IS  - 0970-9134 (Print)
IS  - 0973-7723 (Electronic)
IS  - 0970-9134 (Linking)
VI  - 39
IP  - 5
DP  - 2023 Sep
TI  - The role of ChatGPT in cardiothoracic surgery.
PG  - 562-563
LID - 10.1007/s12055-023-01568-7 [doi]
FAU - Ahmed, Sirwan Khalid
AU  - Ahmed SK
AUID- ORCID: 0000-0002-8361-0546
AD  - Ministry of Health, General Health Directorate of Raparin, Rania, Sulaymaniyah 
      46012 Iraq. GRID: grid.415808.0. ISNI: 0000 0004 1765 5302
FAU - Hussein, Safin
AU  - Hussein S
AUID- ORCID: 0000-0001-8513-2907
AD  - Department of Biology, College of Science, University of Raparin, Rania, 
      Sulaymaniyah, 46012 Iraq. GRID: grid.449870.6. ISNI: 0000 0004 4650 8790
FAU - Essa, Rawand Abdulrahman
AU  - Essa RA
AUID- ORCID: 0000-0002-1957-3488
AD  - Ministry of Health, General Health Directorate of Raparin, Rania, Sulaymaniyah 
      46012 Iraq. GRID: grid.415808.0. ISNI: 0000 0004 1765 5302
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - India
TA  - Indian J Thorac Cardiovasc Surg
JT  - Indian journal of thoracic and cardiovascular surgery
JID - 8700105
PMC - PMC10441939
COIS- Conflict of interestThe authors declare no competing interests.
EDAT- 2023/08/23 06:42
MHDA- 2023/08/23 06:43
PMCR- 2024/09/01
CRDT- 2023/08/23 04:22
PHST- 2023/06/05 00:00 [received]
PHST- 2023/06/19 00:00 [revised]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/08/23 06:43 [medline]
PHST- 2023/08/23 06:42 [pubmed]
PHST- 2023/08/23 04:22 [entrez]
AID - 1568 [pii]
AID - 10.1007/s12055-023-01568-7 [doi]
PST - ppublish
SO  - Indian J Thorac Cardiovasc Surg. 2023 Sep;39(5):562-563. doi: 
      10.1007/s12055-023-01568-7. Epub 2023 Aug 1.

PMID- 37609457
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231107
IS  - 0026-6620 (Print)
IS  - 0026-6620 (Linking)
VI  - 120
IP  - 4
DP  - 2023 Jul-Aug
TI  - The Promise & Peril of Artificial Intelligence: A Conversation with ChatGPT.
PG  - 240-242
FAU - Hagan, John C 3rd
AU  - Hagan JC 3rd
AD  - Editor of Missouri Medicine since 2001 and recent AI user. ChatGPT: AI generated 
      anthropomorphized image.
LA  - eng
PT  - Editorial
PL  - United States
TA  - Mo Med
JT  - Missouri medicine
JID - 0400744
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Communication
PMC - PMC10441258
EDAT- 2023/08/23 06:42
MHDA- 2023/08/23 06:43
CRDT- 2023/08/23 04:16
PHST- 2023/08/23 06:43 [medline]
PHST- 2023/08/23 06:42 [pubmed]
PHST- 2023/08/23 04:16 [entrez]
AID - ms120_p0240 [pii]
PST - ppublish
SO  - Mo Med. 2023 Jul-Aug;120(4):240-242.

PMID- 37423432
OWN - NLM
STAT- Publisher
LR  - 20230724
IS  - 1555-7162 (Electronic)
IS  - 0002-9343 (Linking)
DP  - 2023 Jul 7
TI  - Pharmacogenomics of Statins: A View from ChatGPT.
LID - S0002-9343(23)00430-8 [pii]
LID - 10.1016/j.amjmed.2023.06.014 [doi]
FAU - Alpert, Joseph S
AU  - Alpert JS
AD  - Department of Medicine, University of Arizona, Tucson, Editor in Chief, The 
      American Journal of Medicine. Electronic address: ibaird@amjmed.org.
FAU - Chen, Qin M
AU  - Chen QM
AD  - Department of Pharmacy Practice and Science, University of Arizona College of 
      Pharmacy, Tucson.
LA  - eng
PT  - Editorial
DEP - 20230707
PL  - United States
TA  - Am J Med
JT  - The American journal of medicine
JID - 0267200
SB  - IM
EDAT- 2023/07/10 00:42
MHDA- 2023/07/10 00:42
CRDT- 2023/07/09 19:26
PHST- 2023/06/22 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/07/10 00:42 [pubmed]
PHST- 2023/07/10 00:42 [medline]
PHST- 2023/07/09 19:26 [entrez]
AID - S0002-9343(23)00430-8 [pii]
AID - 10.1016/j.amjmed.2023.06.014 [doi]
PST - aheadofprint
SO  - Am J Med. 2023 Jul 7:S0002-9343(23)00430-8. doi: 10.1016/j.amjmed.2023.06.014.

PMID- 36894409
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230328
LR  - 20230328
IS  - 2589-7500 (Electronic)
IS  - 2589-7500 (Linking)
VI  - 5
IP  - 4
DP  - 2023 Apr
TI  - Using ChatGPT to write patient clinic letters.
PG  - e179-e181
LID - S2589-7500(23)00048-1 [pii]
LID - 10.1016/S2589-7500(23)00048-1 [doi]
FAU - Ali, Stephen R
AU  - Ali SR
AD  - Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of 
      Life Sciences, Swansea University Medical School, Swansea SA2 8PP, UK; Welsh 
      Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK. Electronic 
      address: Stephen.ali@wales.nhs.uk.
FAU - Dobbs, Thomas D
AU  - Dobbs TD
AD  - Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of 
      Life Sciences, Swansea University Medical School, Swansea SA2 8PP, UK; Welsh 
      Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK.
FAU - Hutchings, Hayley A
AU  - Hutchings HA
AD  - Swansea University Medical School, Faculty of Medicine, Health and Life Science, 
      Swansea, UK.
FAU - Whitaker, Iain S
AU  - Whitaker IS
AD  - Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of 
      Life Sciences, Swansea University Medical School, Swansea SA2 8PP, UK; Welsh 
      Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK.
LA  - eng
PT  - Journal Article
DEP - 20230307
PL  - England
TA  - Lancet Digit Health
JT  - The Lancet. Digital health
JID - 101751302
SB  - IM
COIS- We declare no competing interests. SRA and TDD are funded by the Welsh Clinical 
      Academic Training Fellowship. SRA received a Paton Masser grant from the British 
      Association of Plastic, Reconstructive and Aesthetic Surgeons to support this 
      work. ISW is the surgical Specialty Lead for Health and Care Research Wales; the 
      Chief Investigator for the Scar Free Foundation & Health and Care Research Wales 
      Programme of Reconstructive and Regenerative Surgery Research; an associate 
      editor for the Annals of Plastic Surgery; an editorial board member of BMC 
      Medicine and numerous other editorial board roles. Ethical committee approval has 
      been obtained from Swansea University Medical School Research Ethics Subcommittee 
      (2020-0025). The study was performed in accordance with the Declaration of 
      Helsinki.
EDAT- 2023/03/10 06:00
MHDA- 2023/03/10 06:01
CRDT- 2023/03/09 22:07
PHST- 2022/12/22 00:00 [received]
PHST- 2023/02/18 00:00 [revised]
PHST- 2023/02/22 00:00 [accepted]
PHST- 2023/03/10 06:01 [medline]
PHST- 2023/03/10 06:00 [pubmed]
PHST- 2023/03/09 22:07 [entrez]
AID - S2589-7500(23)00048-1 [pii]
AID - 10.1016/S2589-7500(23)00048-1 [doi]
PST - ppublish
SO  - Lancet Digit Health. 2023 Apr;5(4):e179-e181. doi: 10.1016/S2589-7500(23)00048-1. 
      Epub 2023 Mar 7.

PMID- 37900935
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231101
IS  - 2095-4689 (Print)
IS  - 2095-4697 (Electronic)
IS  - 2095-4689 (Linking)
VI  - 11
IP  - 2
DP  - 2023 Jun
TI  - ChatGPT opens a new door for bioinformatics.
PG  - 204-206
LID - 10.15302/j-qb-023-0328 [doi]
FAU - Xu, Dong
AU  - Xu D
AD  - Department of Electrical Engineering and Computer Science, Christopher S. Bond 
      Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
LA  - eng
GR  - R01 LM013392/LM/NLM NIH HHS/United States
PT  - Comment
PT  - Journal Article
PL  - China
TA  - Quant Biol
JT  - Quantitative biology (Beijing, China)
JID - 101639642
CON - bioRxiv. 2023 Mar 08;:. PMID: 36945641
PMC - PMC10609615
MID - NIHMS1890078
COIS- COMPLIANCE WITH ETHICS GUIDELINES The author Dong Xu declares that he has no 
      conflict of interest. This article is a commentary article and does not contain 
      any studies with human or animal subjects performed by any of the authors.
EDAT- 2023/10/30 06:46
MHDA- 2023/10/30 06:47
CRDT- 2023/10/30 04:41
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/10/30 06:46 [pubmed]
PHST- 2023/10/30 04:41 [entrez]
AID - 10.15302/j-qb-023-0328 [doi]
PST - ppublish
SO  - Quant Biol. 2023 Jun;11(2):204-206. doi: 10.15302/j-qb-023-0328.

PMID- 37811166
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0971-3026 (Print)
IS  - 1998-3808 (Electronic)
IS  - 0970-2016 (Linking)
VI  - 33
IP  - 4
DP  - 2023 Oct
TI  - Will ChatGPT Drive Radiology in the Future?
PG  - 436-437
LID - 10.1055/s-0043-1769591 [doi]
FAU - Botchu, Rajesh
AU  - Botchu R
AUID- ORCID: 0000-0001-7998-2980
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      United Kingdom.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AUID- ORCID: 0000-0002-4379-1266
AD  - Department of Orthopaedics, Southport and Ormskirk NHS Trust, Southport, United 
      Kingdom.
LA  - eng
PT  - Editorial
DEP - 20230616
PL  - Germany
TA  - Indian J Radiol Imaging
JT  - The Indian journal of radiology & imaging
JID - 8503873
PMC - PMC10556333
COIS- Conflict of Interest None declared.
EDAT- 2023/10/09 12:43
MHDA- 2023/10/09 12:44
CRDT- 2023/10/09 06:13
PHST- 2023/10/09 12:44 [medline]
PHST- 2023/10/09 12:43 [pubmed]
PHST- 2023/10/09 06:13 [entrez]
AID - IJRI-23-3-2334 [pii]
AID - 10.1055/s-0043-1769591 [doi]
PST - epublish
SO  - Indian J Radiol Imaging. 2023 Jun 16;33(4):436-437. doi: 10.1055/s-0043-1769591. 
      eCollection 2023 Oct.

PMID- 37598184
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231121
IS  - 2754-6993 (Electronic)
IS  - 2754-6993 (Linking)
VI  - 9
IP  - 1
DP  - 2023 Aug 19
TI  - ChatGPT: these are not hallucinations - they're fabrications and falsifications.
PG  - 52
LID - 10.1038/s41537-023-00379-4 [doi]
LID - 52
FAU - Emsley, Robin
AU  - Emsley R
AD  - Editor, Schizophrenia, . rae@sun.ac.za.
LA  - eng
PT  - Editorial
DEP - 20230819
PL  - Germany
TA  - Schizophrenia (Heidelb)
JT  - Schizophrenia (Heidelberg, Germany)
JID - 9918367987006676
PMC - PMC10439949
COIS- The author declares no competing interests.
EDAT- 2023/08/20 00:41
MHDA- 2023/08/20 00:42
CRDT- 2023/08/19 23:17
PHST- 2023/07/17 00:00 [received]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/08/20 00:42 [medline]
PHST- 2023/08/20 00:41 [pubmed]
PHST- 2023/08/19 23:17 [entrez]
AID - 10.1038/s41537-023-00379-4 [pii]
AID - 379 [pii]
AID - 10.1038/s41537-023-00379-4 [doi]
PST - epublish
SO  - Schizophrenia (Heidelb). 2023 Aug 19;9(1):52. doi: 10.1038/s41537-023-00379-4.

PMID- 37566899
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231124
LR  - 20231124
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 11
DP  - 2023 Nov 1
TI  - The vast chasm in ChatGPT assisting in realistic surgery.
PG  - 3724-3726
LID - 10.1097/JS9.0000000000000657 [doi]
FAU - Sun, Xinxin
AU  - Sun X
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
FAU - Wei, Jingbo
AU  - Wei J
AD  - Department of Electrocardiogram, Henan Cancer Hospital, Zhengzhou, Henan, 
      People's Republic of China.
FAU - Wang, Xiaojing
AU  - Wang X
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
FAU - Wang, Bing
AU  - Wang B
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
FAU - Fan, Zhixiang
AU  - Fan Z
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
FAU - Wan, Shi
AU  - Wan S
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
FAU - Zhang, Ke
AU  - Zhang K
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
FAU - Huang, Dongmei
AU  - Huang D
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
FAU - Zhang, Qing
AU  - Zhang Q
AD  - Department of Obstetrics and Gynecology, The Second Affiliated Hospital of 
      Zhengzhou University.
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
PMC - PMC10651257
COIS- The authors declare that they have no conflicts of interest. Sponsorships or 
      competing interests that may be relevant to content are disclosed at the end of 
      this article.
EDAT- 2023/08/11 18:42
MHDA- 2023/08/11 18:43
CRDT- 2023/08/11 17:45
PHST- 2023/07/20 00:00 [received]
PHST- 2023/07/23 00:00 [accepted]
PHST- 2023/08/11 18:43 [medline]
PHST- 2023/08/11 18:42 [pubmed]
PHST- 2023/08/11 17:45 [entrez]
AID - 01279778-990000000-00538 [pii]
AID - IJS-D-23-01530 [pii]
AID - 10.1097/JS9.0000000000000657 [doi]
PST - epublish
SO  - Int J Surg. 2023 Nov 1;109(11):3724-3726. doi: 10.1097/JS9.0000000000000657.

PMID- 37391999
OWN - NLM
STAT- Publisher
LR  - 20230701
IS  - 1741-2854 (Electronic)
IS  - 0020-7640 (Linking)
DP  - 2023 Jun 30
TI  - ChatGPT use in teaching social psychiatry.
PG  - 207640231178481
LID - 10.1177/00207640231178481 [doi]
FAU - Ventriglio, Antonio
AU  - Ventriglio A
AUID- ORCID: 0000-0002-3934-7007
AD  - Department of Clinical and Experimental Medicine, University of Foggia, Italy.
FAU - Ricci, Fabiana
AU  - Ricci F
AD  - Department of Clinical and Experimental Medicine, University of Foggia, Italy.
LA  - eng
PT  - Editorial
DEP - 20230630
PL  - England
TA  - Int J Soc Psychiatry
JT  - The International journal of social psychiatry
JID - 0374726
SB  - IM
EDAT- 2023/07/01 11:42
MHDA- 2023/07/01 11:42
CRDT- 2023/07/01 02:32
PHST- 2023/07/01 11:42 [medline]
PHST- 2023/07/01 11:42 [pubmed]
PHST- 2023/07/01 02:32 [entrez]
AID - 10.1177/00207640231178481 [doi]
PST - aheadofprint
SO  - Int J Soc Psychiatry. 2023 Jun 30:207640231178481. doi: 
      10.1177/00207640231178481.

PMID- 37338818
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1677-6119 (Electronic)
IS  - 1677-5538 (Print)
IS  - 1677-5538 (Linking)
VI  - 49
IP  - 5
DP  - 2023 Sep-Oct
TI  - ChatGPT for medical applications and urological science.
PG  - 652-656
LID - 10.1590/S1677-5538.IBJU.2023.0112 [doi]
FAU - Reis, Leonardo O
AU  - Reis LO
AD  - UroScience e Departamento de Urologia, Faculdade de Ciências Médicas, 
      Universidade Estadual de Campinas - UNICAMP, Campinas, São Paulo, Brasil.
AD  - Departamento de Imunoncologia, Faculdade de Ciências da Vida, Pontifícia 
      Universidade Católica de Campinas, PUC-Campinas, Campinas, São Paulo, Brasil.
LA  - eng
PT  - Letter
PL  - Brazil
TA  - Int Braz J Urol
JT  - International braz j urol : official journal of the Brazilian Society of Urology
JID - 101158091
SB  - IM
PMC - PMC10482461
COIS- None declared.
EDAT- 2023/06/20 13:10
MHDA- 2023/06/20 13:11
CRDT- 2023/06/20 11:20
PHST- 2023/04/02 00:00 [received]
PHST- 2023/04/30 00:00 [accepted]
PHST- 2023/06/20 13:11 [medline]
PHST- 2023/06/20 13:10 [pubmed]
PHST- 2023/06/20 11:20 [entrez]
AID - IBJU20230112 [pii]
AID - S1677-5538.IBJU.2023.0112 [pii]
AID - 10.1590/S1677-5538.IBJU.2023.0112 [doi]
PST - ppublish
SO  - Int Braz J Urol. 2023 Sep-Oct;49(5):652-656. doi: 
      10.1590/S1677-5538.IBJU.2023.0112.

PMID- 37061445
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1873-7560 (Electronic)
IS  - 0302-2838 (Linking)
VI  - 84
IP  - 3
DP  - 2023 Sep
TI  - Is ChatGPT an Evidence-based Doctor?
PG  - 355-356
LID - S0302-2838(23)02717-3 [pii]
LID - 10.1016/j.eururo.2023.03.037 [doi]
FAU - Zhou, Zhonghan
AU  - Zhou Z
AD  - Cheeloo College of Medicine, Shandong University, Jinan; Department of Urology, 
      China Rehabilitation Research Center, Beijing, China; University of Health and 
      Rehabilitation Sciences, Qingdao, China; China Rehabilitation Science Institute, 
      Beijing, China; Beijing Key Laboratory of Neural Injury and Rehabilitation, 
      Beijing, China.
FAU - Wang, Xuesheng
AU  - Wang X
AD  - University of Health and Rehabilitation Sciences, Qingdao, China; China 
      Rehabilitation Science Institute, Beijing, China; Beijing Key Laboratory of 
      Neural Injury and Rehabilitation, Beijing, China; School of Rehabilitation, 
      Capital Medical University, Beijing, China.
FAU - Li, Xunhua
AU  - Li X
AD  - University of Health and Rehabilitation Sciences, Qingdao, China; China 
      Rehabilitation Science Institute, Beijing, China; Beijing Key Laboratory of 
      Neural Injury and Rehabilitation, Beijing, China; School of Rehabilitation, 
      Capital Medical University, Beijing, China.
FAU - Liao, Limin
AU  - Liao L
AD  - Cheeloo College of Medicine, Shandong University, Jinan; Department of Urology, 
      China Rehabilitation Research Center, Beijing, China; University of Health and 
      Rehabilitation Sciences, Qingdao, China; China Rehabilitation Science Institute, 
      Beijing, China; Beijing Key Laboratory of Neural Injury and Rehabilitation, 
      Beijing, China; School of Rehabilitation, Capital Medical University, Beijing, 
      China. Electronic address: lmliao@263.net.
LA  - eng
PT  - Letter
DEP - 20230413
PL  - Switzerland
TA  - Eur Urol
JT  - European urology
JID - 7512719
SB  - IM
EDAT- 2023/04/16 06:00
MHDA- 2023/04/16 06:01
CRDT- 2023/04/15 22:00
PHST- 2023/03/19 00:00 [received]
PHST- 2023/03/31 00:00 [accepted]
PHST- 2023/04/16 06:01 [medline]
PHST- 2023/04/16 06:00 [pubmed]
PHST- 2023/04/15 22:00 [entrez]
AID - S0302-2838(23)02717-3 [pii]
AID - 10.1016/j.eururo.2023.03.037 [doi]
PST - ppublish
SO  - Eur Urol. 2023 Sep;84(3):355-356. doi: 10.1016/j.eururo.2023.03.037. Epub 2023 
      Apr 13.

PMID- 37776670
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1872-8243 (Electronic)
IS  - 1386-5056 (Linking)
VI  - 179
DP  - 2023 Nov
TI  - A comprehensive evaluation of ChatGPT consultation quality for augmentation 
      mammoplasty: A comparative analysis between plastic surgeons and laypersons.
PG  - 105219
LID - S1386-5056(23)00237-X [pii]
LID - 10.1016/j.ijmedinf.2023.105219 [doi]
AB  - OBJECTIVES: ChatGPT has gained significant popularity as a source of healthcare 
      information among the general population. Evaluating the quality of chatbot 
      responses is crucial, requiring comprehensive and qualitative analysis. This 
      study aims to assess the answers provided by ChatGPT during hypothetical breast 
      augmentation consultations across various categories and depths. The evaluation 
      involves the utilization of validated tools and a comparison of scores between 
      plastic surgeons and laypersons. METHODS: A panel consisting of five plastic 
      surgeons and five laypersons evaluated ChatGPT's responses to 25 questions 
      spanning consultation, procedure, recovery, and sentiment categories. The DISCERN 
      and PEMAT tools were employed to assess the responses, while emotional context 
      was examined through ten specific questions. Additionally, readability was 
      measured using the Flesch Reading Ease score. Qualitative analysis was performed 
      to identify the overall strengths and weaknesses. RESULTS: Plastic surgeons 
      generally scored lower than laypersons across most domains. Scores for each 
      evaluation domain varied by category, with the consultation category 
      demonstrating lower scores in terms of DISCERN reliability, information quality, 
      and DISCERN score. Plastic surgeons assigned significantly lower overall quality 
      ratings to the procedure category compared to other question categories. They 
      also gave lower emotion scores in the procedure category compared to laypersons. 
      The depth of the questions did not impact the scoring. CONCLUSIONS: Existing 
      health information evaluation tools may not be entirely suitable for 
      comprehensively evaluating the quality of individual responses generated by 
      ChatGPT. Consequently, the development and implementation of appropriate 
      evaluation tools to assess the appropriateness and quality of AI consultations 
      are necessary.
CI  - Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.
FAU - Yun, Ji Young
AU  - Yun JY
AD  - Department of Plastic and Reconstructive Surgery, Busan Paik Hospital, Inje 
      University School of Medicine, Busan, Republic of Korea.
FAU - Kim, Dong Jin
AU  - Kim DJ
AD  - Department of Plastic Surgery, Asan Medical Center, University of Ulsan College 
      of Medicine, Seoul, Republic of Korea.
FAU - Lee, Nara
AU  - Lee N
AD  - Department of Plastic Surgery, Asan Medical Center, University of Ulsan College 
      of Medicine, Seoul, Republic of Korea.
FAU - Kim, Eun Key
AU  - Kim EK
AD  - Department of Plastic Surgery, Asan Medical Center, University of Ulsan College 
      of Medicine, Seoul, Republic of Korea. Electronic address: nicekek@korea.com.
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - Ireland
TA  - Int J Med Inform
JT  - International journal of medical informatics
JID - 9711057
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Consumer health information
OT  - Health literacy
OT  - Internet
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/10/01 04:44
MHDA- 2023/10/01 04:44
CRDT- 2023/09/30 18:05
PHST- 2023/08/03 00:00 [received]
PHST- 2023/09/05 00:00 [revised]
PHST- 2023/09/16 00:00 [accepted]
PHST- 2023/10/01 04:44 [pubmed]
PHST- 2023/10/01 04:44 [medline]
PHST- 2023/09/30 18:05 [entrez]
AID - S1386-5056(23)00237-X [pii]
AID - 10.1016/j.ijmedinf.2023.105219 [doi]
PST - ppublish
SO  - Int J Med Inform. 2023 Nov;179:105219. doi: 10.1016/j.ijmedinf.2023.105219. Epub 
      2023 Sep 20.

PMID- 37654681
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230902
IS  - 2169-7574 (Print)
IS  - 2169-7574 (Electronic)
IS  - 2169-7574 (Linking)
VI  - 11
IP  - 8
DP  - 2023 Aug
TI  - Evaluation of Artificial Intelligence-generated Responses to Common Plastic 
      Surgery Questions.
PG  - e5226
LID - 10.1097/GOX.0000000000005226 [doi]
LID - e5226
AB  - BACKGROUND: Artificial intelligence (AI) is increasingly used to answer 
      questions, yet the accuracy and validity of current tools are uncertain. In 
      contrast to internet queries, AI generates summary responses as definitive. The 
      internet is rife with inaccuracies, and plastic surgery management guidelines 
      evolve, making verifiable information important. METHODS: We posed 10 questions 
      about breast implant-associated illness, anaplastic large lymphoma, and squamous 
      carcinoma to Bing, using the "more balanced" option, and to ChatGPT. Answers were 
      reviewed by two plastic surgeons for accuracy and fidelity to information on the 
      Food and Drug Administration (FDA) and American Society of Plastic Surgeons 
      (ASPS) websites. We also presented 10 multiple-choice questions from the 2022 
      plastic surgery in-service examination to Bing, using the "more precise" option, 
      and ChatGPT. Questions were repeated three times over consecutive weeks, and 
      answers were evaluated for accuracy and stability. RESULTS: Compared with answers 
      from the FDA and ASPS, Bing and ChatGPT were accurate. Bing answered 10 of the 30 
      multiple-choice questions correctly, nine incorrectly, and did not answer 11. 
      ChatGPT correctly answered 16 and incorrectly answered 14. In both parts, 
      responses from Bing were shorter, less detailed, and referred to verified and 
      unverified sources; ChatGPT did not provide citations. CONCLUSIONS: These AI 
      tools provided accurate information from the FDA and ASPS websites, but neither 
      consistently answered questions requiring nuanced decision-making correctly. 
      Advances in applications to plastic surgery will require algorithms that 
      selectively identify, evaluate, and exclude information to enhance the accuracy, 
      precision, validity, reliability, and utility of AI-generated responses.
CI  - Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf 
      of The American Society of Plastic Surgeons.
FAU - Copeland-Halperin, Libby R
AU  - Copeland-Halperin LR
AD  - From Northwell Health, New York, N.Y.
FAU - O'Brien, Lauren
AU  - O'Brien L
AD  - Michelle Copeland DMD MD PC, New York, N.Y.
FAU - Copeland, Michelle
AU  - Copeland M
AD  - Icahn School of Medicine at Mount Sinai, New York, N.Y.
LA  - eng
PT  - Journal Article
DEP - 20230830
PL  - United States
TA  - Plast Reconstr Surg Glob Open
JT  - Plastic and reconstructive surgery. Global open
JID - 101622231
PMC - PMC10468106
COIS- The authors have no financial interest to declare in relation to the content of 
      this article. Disclosure statements are at the end of this article, following the 
      correspondence information.
EDAT- 2023/09/01 06:43
MHDA- 2023/09/01 06:44
CRDT- 2023/09/01 03:59
PHST- 2023/05/31 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/09/01 06:44 [medline]
PHST- 2023/09/01 06:43 [pubmed]
PHST- 2023/09/01 03:59 [entrez]
AID - 10.1097/GOX.0000000000005226 [doi]
PST - epublish
SO  - Plast Reconstr Surg Glob Open. 2023 Aug 30;11(8):e5226. doi: 
      10.1097/GOX.0000000000005226. eCollection 2023 Aug.

PMID- 38016158
OWN - NLM
STAT- Publisher
LR  - 20231129
IS  - 1530-0374 (Electronic)
IS  - 1072-3714 (Linking)
DP  - 2023 Nov 28
TI  - Premature ovarian insufficiency recommendations and ChatGPT.
LID - 10.1097/GME.0000000000002286 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - United States
TA  - Menopause
JT  - Menopause (New York, N.Y.)
JID - 9433353
SB  - IM
COIS- Financial disclosures/conflicts of interest: None reported.
EDAT- 2023/11/28 18:42
MHDA- 2023/11/28 18:42
CRDT- 2023/11/28 17:12
PHST- 2023/11/28 18:42 [pubmed]
PHST- 2023/11/28 18:42 [medline]
PHST- 2023/11/28 17:12 [entrez]
AID - 00042192-990000000-00265 [pii]
AID - 10.1097/GME.0000000000002286 [doi]
PST - aheadofprint
SO  - Menopause. 2023 Nov 28. doi: 10.1097/GME.0000000000002286.

PMID- 37724353
OWN - NLM
STAT- Publisher
LR  - 20230919
IS  - 1784-973X (Electronic)
IS  - 0001-5385 (Linking)
DP  - 2023 Sep 19
TI  - Prompting ChatGPT to perform an umbrella review.
PG  - 1-2
LID - 10.1080/00015385.2023.2240120 [doi]
FAU - Teperikidis, Eleftherios
AU  - Teperikidis E
AD  - Third Department of Cardiology, Ippokratio General Hospital, Aristotle University 
      of Thessa-loniki, Thessaloniki, Greece.
AD  - Clinical Research Unit, Special Unit for Biomedical Research and Education 
      (SUBRE), School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 
      Greece.
FAU - Boulmpou, Aristi
AU  - Boulmpou A
AD  - Clinical Research Unit, Special Unit for Biomedical Research and Education 
      (SUBRE), School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 
      Greece.
FAU - Papadopoulos, Christodoulos
AU  - Papadopoulos C
AD  - Clinical Research Unit, Special Unit for Biomedical Research and Education 
      (SUBRE), School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 
      Greece.
LA  - eng
PT  - Journal Article
DEP - 20230919
PL  - England
TA  - Acta Cardiol
JT  - Acta cardiologica
JID - 0370570
SB  - IM
EDAT- 2023/09/19 06:42
MHDA- 2023/09/19 06:42
CRDT- 2023/09/19 04:03
PHST- 2023/09/19 06:42 [medline]
PHST- 2023/09/19 06:42 [pubmed]
PHST- 2023/09/19 04:03 [entrez]
AID - 10.1080/00015385.2023.2240120 [doi]
PST - aheadofprint
SO  - Acta Cardiol. 2023 Sep 19:1-2. doi: 10.1080/00015385.2023.2240120.

PMID- 37697493
OWN - NLM
STAT- Publisher
LR  - 20230912
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
DP  - 2023 Sep 11
TI  - Reply to: "Can ChatGPT Truly Overcome Other LLMs?".
PG  - 8465371231201379
LID - 10.1177/08465371231201379 [doi]
FAU - Patil, Nikhil S
AU  - Patil NS
AUID- ORCID: 0000-0003-3929-0482
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, 
      Canada. RINGGOLD: 3710
FAU - Huang, Ryan S
AU  - Huang RS
AUID- ORCID: 0000-0002-3404-5376
AD  - Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. 
      RINGGOLD: 7938
FAU - van der Pol, Christian B
AU  - van der Pol CB
AUID- ORCID: 0000-0002-1718-2619
AD  - Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton 
      Health Sciences, Hamilton, ON, Canada. RINGGOLD: 25453
FAU - Larocque, Natasha
AU  - Larocque N
AUID- ORCID: 0000-0003-0449-9438
AD  - Department of Radiology, McMaster University, Hamilton, ON, Canada. RINGGOLD: 
      3710
LA  - eng
PT  - Letter
DEP - 20230911
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
EDAT- 2023/09/12 06:41
MHDA- 2023/09/12 06:41
CRDT- 2023/09/12 01:02
PHST- 2023/09/12 06:41 [medline]
PHST- 2023/09/12 06:41 [pubmed]
PHST- 2023/09/12 01:02 [entrez]
AID - 10.1177/08465371231201379 [doi]
PST - aheadofprint
SO  - Can Assoc Radiol J. 2023 Sep 11:8465371231201379. doi: 10.1177/08465371231201379.

PMID- 37086240
OWN - NLM
STAT- MEDLINE
DCOM- 20230523
LR  - 20230523
IS  - 1532-8473 (Electronic)
IS  - 1089-9472 (Linking)
VI  - 38
IP  - 3
DP  - 2023 Jun
TI  - Generative Artificial Intelligence and ChatGPT.
PG  - 519-522
LID - S1089-9472(23)00140-5 [pii]
LID - 10.1016/j.jopan.2023.04.001 [doi]
FAU - Byrne, Matthew D
AU  - Byrne MD
AD  - Saint Catherine University, Saint Paul, MN. Electronic address: 
      mdbyrne@stkate.edu.
LA  - eng
PT  - Journal Article
DEP - 20230420
PL  - United States
TA  - J Perianesth Nurs
JT  - Journal of perianesthesia nursing : official journal of the American Society of 
      PeriAnesthesia Nurses
JID - 9610507
MH  - *Artificial Intelligence
EDAT- 2023/04/22 19:42
MHDA- 2023/05/23 06:42
CRDT- 2023/04/22 10:33
PHST- 2023/03/07 00:00 [received]
PHST- 2023/04/01 00:00 [accepted]
PHST- 2023/05/23 06:42 [medline]
PHST- 2023/04/22 19:42 [pubmed]
PHST- 2023/04/22 10:33 [entrez]
AID - S1089-9472(23)00140-5 [pii]
AID - 10.1016/j.jopan.2023.04.001 [doi]
PST - ppublish
SO  - J Perianesth Nurs. 2023 Jun;38(3):519-522. doi: 10.1016/j.jopan.2023.04.001. Epub 
      2023 Apr 20.

PMID- 37017719
OWN - NLM
STAT- MEDLINE
DCOM- 20230505
LR  - 20230510
IS  - 1432-1998 (Electronic)
IS  - 0301-0449 (Linking)
VI  - 53
IP  - 5
DP  - 2023 May
TI  - ChatGPT, a radiologist's perspective.
PG  - 813-815
LID - 10.1007/s00247-023-05656-z [doi]
FAU - Saliba, Thomas
AU  - Saliba T
AUID- ORCID: 0000-0001-6989-9577
AD  - Hopital Universitaire des Enfants Reine Fabiola (HUDERF), Av. Jean Joseph Crocq 
      15, 1020, Brussels, Belgium. thomas.saliba@ulb.be.
FAU - Boitsios, Grammatina
AU  - Boitsios G
AUID- ORCID: 0000-0003-1575-9119
AD  - Hopital Universitaire des Enfants Reine Fabiola (HUDERF), Av. Jean Joseph Crocq 
      15, 1020, Brussels, Belgium.
LA  - eng
PT  - Letter
DEP - 20230405
PL  - Germany
TA  - Pediatr Radiol
JT  - Pediatric radiology
JID - 0365332
SB  - IM
MH  - Humans
MH  - *Radiology
MH  - Radiologists
EDAT- 2023/04/06 06:00
MHDA- 2023/05/05 06:42
CRDT- 2023/04/05 11:13
PHST- 2023/03/06 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/03/13 00:00 [revised]
PHST- 2023/05/05 06:42 [medline]
PHST- 2023/04/06 06:00 [pubmed]
PHST- 2023/04/05 11:13 [entrez]
AID - 10.1007/s00247-023-05656-z [pii]
AID - 10.1007/s00247-023-05656-z [doi]
PST - ppublish
SO  - Pediatr Radiol. 2023 May;53(5):813-815. doi: 10.1007/s00247-023-05656-z. Epub 
      2023 Apr 5.

PMID- 36879536
OWN - NLM
STAT- Publisher
LR  - 20230307
IS  - 1545-5815 (Electronic)
IS  - 0898-9621 (Linking)
DP  - 2023 Mar 6
TI  - Using ChatGPT to conduct a literature review.
PG  - 1-3
LID - 10.1080/08989621.2023.2185514 [doi]
FAU - Haman, Michael
AU  - Haman M
AUID- ORCID: 0000-0001-5772-2045
AD  - Department of Humanities, Czech University of Life Sciences Prague; Prague, Czech 
      Republic.
FAU - Školník, Milan
AU  - Školník M
AUID- ORCID: 0000-0002-0672-219X
AD  - Department of Humanities, Czech University of Life Sciences Prague; Prague, Czech 
      Republic.
LA  - eng
PT  - Journal Article
DEP - 20230306
PL  - United States
TA  - Account Res
JT  - Accountability in research
JID - 9100813
SB  - IM
EDAT- 2023/03/08 06:00
MHDA- 2023/03/08 06:00
CRDT- 2023/03/07 01:33
PHST- 2023/03/07 01:33 [entrez]
PHST- 2023/03/08 06:00 [pubmed]
PHST- 2023/03/08 06:00 [medline]
AID - 10.1080/08989621.2023.2185514 [doi]
PST - aheadofprint
SO  - Account Res. 2023 Mar 6:1-3. doi: 10.1080/08989621.2023.2185514.

PMID- 37871032
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231030
LR  - 20231116
IS  - 1529-4242 (Electronic)
IS  - 0032-1052 (Linking)
VI  - 152
IP  - 5
DP  - 2023 Nov 1
TI  - ChatGPT for the Modern Plastic Surgeon.
PG  - 969e-970e
LID - 10.1097/PRS.0000000000010794 [doi]
FAU - Bogdanovich, Brennan
AU  - Bogdanovich B
AD  - Medical College of Georgia, Augusta University, Augusta, GA.
FAU - Patel, Parth A
AU  - Patel PA
AD  - Medical College of Georgia, Augusta University, Augusta, GA.
FAU - Kavian, Joseph Abraham
AU  - Kavian JA
AD  - Hansjörg Wyss Department of Plastic Surgery, New York University Langone Health, 
      New York, NY.
FAU - Boyd, Carter J
AU  - Boyd CJ
AD  - Hansjörg Wyss Department of Plastic Surgery, New York University Langone Health, 
      New York, NY.
FAU - Rodriguez, Eduardo D
AU  - Rodriguez ED
AD  - Hansjörg Wyss Department of Plastic Surgery, New York University Langone Health, 
      New York, NY.
LA  - eng
PT  - Journal Article
DEP - 20231023
PL  - United States
TA  - Plast Reconstr Surg
JT  - Plastic and reconstructive surgery
JID - 1306050
SB  - IM
EDAT- 2023/10/23 18:42
MHDA- 2023/10/23 18:43
CRDT- 2023/10/23 13:34
PHST- 2023/10/23 18:43 [medline]
PHST- 2023/10/23 18:42 [pubmed]
PHST- 2023/10/23 13:34 [entrez]
AID - 00006534-202311000-00052 [pii]
AID - 10.1097/PRS.0000000000010794 [doi]
PST - ppublish
SO  - Plast Reconstr Surg. 2023 Nov 1;152(5):969e-970e. doi: 
      10.1097/PRS.0000000000010794. Epub 2023 Oct 23.

PMID- 37713498
OWN - NLM
STAT- Publisher
LR  - 20230915
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Sep 14
TI  - Application and challenges of ChatGPT in interventional surgery.
LID - 10.1097/JS9.0000000000000704 [doi]
FAU - Wu, Zhaorong
AU  - Wu Z
AD  - Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong 
      University School of Medicine, Shanghai, China.
FAU - Bian, Linke
AU  - Bian L
AD  - Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong 
      University School of Medicine, Shanghai, China.
FAU - Geng, Haigang
AU  - Geng H
AD  - Department of Gastrointestinal Surgery, Renji Hospital, Shanghai Jiao Tong 
      University School of Medicine, Shanghai, China.
FAU - Zheng, Zhigang
AU  - Zheng Z
AD  - Department of Liver Surgery and Liver Transplantation, School of Medicine, Renji 
      Hospital, Shanghai Jiao Tong University, Shanghai, China.
FAU - Wang, Hongye
AU  - Wang H
AD  - Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong 
      University School of Medicine, Shanghai, China.
FAU - Zhai, Bo
AU  - Zhai B
AD  - Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong 
      University School of Medicine, Shanghai, China.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/09/15 18:43
MHDA- 2023/09/15 18:43
CRDT- 2023/09/15 14:05
PHST- 2023/08/10 00:00 [received]
PHST- 2023/08/12 00:00 [accepted]
PHST- 2023/09/15 18:43 [medline]
PHST- 2023/09/15 18:43 [pubmed]
PHST- 2023/09/15 14:05 [entrez]
AID - 01279778-990000000-00605 [pii]
AID - 10.1097/JS9.0000000000000704 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Sep 14. doi: 10.1097/JS9.0000000000000704.

PMID- 37582903
OWN - NLM
STAT- Publisher
LR  - 20230815
IS  - 1476-5438 (Electronic)
IS  - 1018-4813 (Linking)
DP  - 2023 Aug 15
TI  - Importance of critical thinking to understand ChatGPT.
LID - 10.1038/s41431-023-01443-4 [doi]
FAU - Emmert-Streib, Frank
AU  - Emmert-Streib F
AUID- ORCID: 0000-0003-0745-5641
AD  - Predictive Society and Data Analytics Lab, Faculty of Information Technology and 
      Communication Sciences, Tampere University, Tampere, Finland. 
      v@bio-complexity.com.
LA  - eng
PT  - Letter
DEP - 20230815
PL  - England
TA  - Eur J Hum Genet
JT  - European journal of human genetics : EJHG
JID - 9302235
SB  - IM
EDAT- 2023/08/16 00:42
MHDA- 2023/08/16 00:42
CRDT- 2023/08/15 23:31
PHST- 2023/07/17 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/08/16 00:42 [medline]
PHST- 2023/08/16 00:42 [pubmed]
PHST- 2023/08/15 23:31 [entrez]
AID - 10.1038/s41431-023-01443-4 [pii]
AID - 10.1038/s41431-023-01443-4 [doi]
PST - aheadofprint
SO  - Eur J Hum Genet. 2023 Aug 15. doi: 10.1038/s41431-023-01443-4.

PMID- 37194403
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230921
LR  - 20231007
IS  - 1365-2702 (Electronic)
IS  - 0962-1067 (Linking)
VI  - 32
IP  - 19-20
DP  - 2023 Oct
TI  - ChatGPT and the teaching of contemporary nursing: Comment.
PG  - 7642
LID - 10.1111/jocn.16762 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Mohali, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230516
PL  - England
TA  - J Clin Nurs
JT  - Journal of clinical nursing
JID - 9207302
CON - J Clin Nurs. 2023 Apr 2;:null. PMID: 37004198
EDAT- 2023/05/17 06:42
MHDA- 2023/05/17 06:43
CRDT- 2023/05/17 03:48
PHST- 2023/04/28 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/05/17 06:43 [medline]
PHST- 2023/05/17 06:42 [pubmed]
PHST- 2023/05/17 03:48 [entrez]
AID - 10.1111/jocn.16762 [doi]
PST - ppublish
SO  - J Clin Nurs. 2023 Oct;32(19-20):7642. doi: 10.1111/jocn.16762. Epub 2023 May 16.

PMID- 36882591
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230310
IS  - 2288-8101 (Print)
IS  - 2288-8586 (Electronic)
IS  - 2288-8101 (Linking)
VI  - 45
IP  - 1
DP  - 2023 Mar 8
TI  - Using ChatGPT for language editing in scientific articles.
PG  - 13
LID - 10.1186/s40902-023-00381-x [doi]
LID - 13
FAU - Kim, Seong-Gon
AU  - Kim SG
AD  - Department of Oral and Maxillofacial Surgery, College of Dentistry, 
      Gangneung-Wonju National University, Gangneung, 28644, Republic of Korea. 
      kimsg@gwnu.ac.kr.
LA  - eng
PT  - Editorial
DEP - 20230308
PL  - England
TA  - Maxillofac Plast Reconstr Surg
JT  - Maxillofacial plastic and reconstructive surgery
JID - 101633100
PMC - PMC9992464
COIS- This paper's results and/or discussion are not influenced by any competing 
      interests of authors.
EDAT- 2023/03/08 06:00
MHDA- 2023/03/08 06:01
CRDT- 2023/03/07 23:25
PHST- 2023/03/07 23:25 [entrez]
PHST- 2023/03/08 06:00 [pubmed]
PHST- 2023/03/08 06:01 [medline]
AID - 10.1186/s40902-023-00381-x [pii]
AID - 381 [pii]
AID - 10.1186/s40902-023-00381-x [doi]
PST - epublish
SO  - Maxillofac Plast Reconstr Surg. 2023 Mar 8;45(1):13. doi: 
      10.1186/s40902-023-00381-x.

PMID- 38040124
OWN - NLM
STAT- Publisher
LR  - 20231203
IS  - 1879-0887 (Electronic)
IS  - 0167-8140 (Linking)
VI  - 190
DP  - 2023 Nov 29
TI  - ChatGPT and research in radiation oncology: Correspondence.
PG  - 110037
LID - S0167-8140(23)09344-1 [pii]
LID - 10.1016/j.radonc.2023.110037 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AD  - Private Academic Consultant, Lak52, Phonhong, Lao Democratic People's Republic. 
      Electronic address: hinpetchdaung@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, India; Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Letter
DEP - 20231129
PL  - Ireland
TA  - Radiother Oncol
JT  - Radiotherapy and oncology : journal of the European Society for Therapeutic 
      Radiology and Oncology
JID - 8407192
SB  - IM
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/12/02 00:42
MHDA- 2023/12/02 00:42
CRDT- 2023/12/01 19:27
PHST- 2023/09/03 00:00 [received]
PHST- 2023/09/08 00:00 [revised]
PHST- 2023/11/21 00:00 [accepted]
PHST- 2023/12/02 00:42 [pubmed]
PHST- 2023/12/02 00:42 [medline]
PHST- 2023/12/01 19:27 [entrez]
AID - S0167-8140(23)09344-1 [pii]
AID - 10.1016/j.radonc.2023.110037 [doi]
PST - aheadofprint
SO  - Radiother Oncol. 2023 Nov 29;190:110037. doi: 10.1016/j.radonc.2023.110037.

PMID- 37945111
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231113
LR  - 20231113
IS  - 1998-3646 (Electronic)
IS  - 0255-0857 (Linking)
VI  - 46
DP  - 2023 Nov-Dec
TI  - Clinical knowledge of ChatGPT in medical microbiology.
PG  - 100468
LID - S0255-0857(23)00184-6 [pii]
LID - 10.1016/j.ijmmb.2023.100468 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Dr DY Patil Vidhyapeeth, Pune, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230828
PL  - United States
TA  - Indian J Med Microbiol
JT  - Indian journal of medical microbiology
JID - 8700903
SB  - IM
COIS- Declaration of competing interest Corresponding author ask for waiving for all 
      charge from the journal.
EDAT- 2023/11/10 00:44
MHDA- 2023/11/10 00:45
CRDT- 2023/11/09 20:56
PHST- 2023/08/13 00:00 [received]
PHST- 2023/08/17 00:00 [revised]
PHST- 2023/08/18 00:00 [accepted]
PHST- 2023/11/10 00:45 [medline]
PHST- 2023/11/10 00:44 [pubmed]
PHST- 2023/11/09 20:56 [entrez]
AID - S0255-0857(23)00184-6 [pii]
AID - 10.1016/j.ijmmb.2023.100468 [doi]
PST - ppublish
SO  - Indian J Med Microbiol. 2023 Nov-Dec;46:100468. doi: 10.1016/j.ijmmb.2023.100468. 
      Epub 2023 Aug 28.

PMID- 37647848
OWN - NLM
STAT- MEDLINE
DCOM- 20231003
LR  - 20231011
IS  - 1578-8865 (Electronic)
IS  - 1138-3593 (Linking)
VI  - 49
IP  - 7
DP  - 2023 Oct
TI  - [Artificial intelligence, ChatGPT and primary care].
PG  - 102069
LID - S1138-3593(23)00149-1 [pii]
LID - 10.1016/j.semerg.2023.102069 [doi]
FAU - Eguia, Hans
AU  - Eguia H
AD  - Rudkøbing lægehuset, Dinamarca, Miembro del grupo de trabajo de nuevas 
      tecnologías SEMERGEN, miembro DSAM - Dinamarca. Electronic address: 
      heguia@uoc.edu.
FAU - Sanz García, Javier Francisco
AU  - Sanz García JF
AD  - Conselleria de Sanitat Valenciana, Coordinador del grupo de trabajo de nuevas 
      tecnologías SEMERGEN. Electronic address: https://twitter.com/@javikin84.
LA  - spa
PT  - Editorial
TT  - Inteligencia artificial, ChatGPT y atención primaria.
DEP - 20230828
PL  - Spain
TA  - Semergen
JT  - Semergen
JID - 9610769
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Primary Health Care
EDAT- 2023/08/31 00:42
MHDA- 2023/10/03 06:47
CRDT- 2023/08/30 18:09
PHST- 2023/07/12 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/10/03 06:47 [medline]
PHST- 2023/08/31 00:42 [pubmed]
PHST- 2023/08/30 18:09 [entrez]
AID - S1138-3593(23)00149-1 [pii]
AID - 10.1016/j.semerg.2023.102069 [doi]
PST - ppublish
SO  - Semergen. 2023 Oct;49(7):102069. doi: 10.1016/j.semerg.2023.102069. Epub 2023 Aug 
      28.

PMID- 37562197
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230829
LR  - 20230829
IS  - 2059-7029 (Electronic)
IS  - 2059-7029 (Linking)
VI  - 8
IP  - 4
DP  - 2023 Aug
TI  - Use of ChatGPT as a co-author.
PG  - 101620
LID - S2059-7029(23)00855-4 [pii]
LID - 10.1016/j.esmoop.2023.101620 [doi]
LID - 101620
FAU - Kleebayoon, A
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, V
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joseph Ayo Babalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230808
PL  - England
TA  - ESMO Open
JT  - ESMO open
JID - 101690685
SB  - IM
PMC - PMC10432790
COIS- Disclosure The authors have declared no conflicts of interest.
EDAT- 2023/08/11 00:42
MHDA- 2023/08/11 00:43
CRDT- 2023/08/10 18:06
PHST- 2023/07/14 00:00 [received]
PHST- 2023/07/26 00:00 [accepted]
PHST- 2023/08/11 00:43 [medline]
PHST- 2023/08/11 00:42 [pubmed]
PHST- 2023/08/10 18:06 [entrez]
AID - S2059-7029(23)00855-4 [pii]
AID - 101620 [pii]
AID - 10.1016/j.esmoop.2023.101620 [doi]
PST - ppublish
SO  - ESMO Open. 2023 Aug;8(4):101620. doi: 10.1016/j.esmoop.2023.101620. Epub 2023 Aug 
      8.

PMID- 37534774
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231114
LR  - 20231129
IS  - 1573-2665 (Electronic)
IS  - 0141-8955 (Linking)
VI  - 46
IP  - 6
DP  - 2023 Nov
TI  - Risk and potential of ChatGPT in scientific publishing.
PG  - 1005-1006
LID - 10.1002/jimd.12666 [doi]
FAU - Peters, Verena
AU  - Peters V
AD  - Centre for Paediatric and Adolescent Medicine, Metabolic Center, University 
      Hospital Heidelberg, Heidelberg, Germany.
FAU - Baumgartner, Matthias
AU  - Baumgartner M
AD  - Division of Metabolism and Children's Research Center, University Children's 
      Hospital Zurich, University of Zurich, Zurich, Switzerland.
FAU - Froese, Sean
AU  - Froese S
AD  - Division of Metabolism and Children's Research Center, University Children's 
      Hospital Zurich, University of Zurich, Zurich, Switzerland.
FAU - Morava, Eva
AU  - Morava E
AD  - Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA.
AD  - Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, 
      Minnesota, USA.
FAU - Patterson, Marc
AU  - Patterson M
AD  - Division of Child and Adolescent Neurology, Departments of Neurology, Pediatrics, 
      and Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA.
FAU - Zschocke, Johannes
AU  - Zschocke J
AD  - Institute of Human Genetics, Medical University Innsbruck, Innsbruck, Austria.
FAU - Rahman, Shamima
AU  - Rahman S
AD  - Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, 
      London, UK.
AD  - Metabolic Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, 
      London, UK.
LA  - eng
PT  - Editorial
DEP - 20230817
PL  - United States
TA  - J Inherit Metab Dis
JT  - Journal of inherited metabolic disease
JID - 7910918
SB  - IM
EDAT- 2023/08/03 13:08
MHDA- 2023/08/03 13:09
CRDT- 2023/08/03 07:54
PHST- 2023/08/03 13:09 [medline]
PHST- 2023/08/03 13:08 [pubmed]
PHST- 2023/08/03 07:54 [entrez]
AID - 10.1002/jimd.12666 [doi]
PST - ppublish
SO  - J Inherit Metab Dis. 2023 Nov;46(6):1005-1006. doi: 10.1002/jimd.12666. Epub 2023 
      Aug 17.

PMID- 37087366
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231024
LR  - 20231024
IS  - 1879-730X (Electronic)
IS  - 1879-7296 (Linking)
VI  - 140
IP  - 5
DP  - 2023 Sep
TI  - Sex and the nose: What's it all about, ChatGPT?
PG  - 251-252
LID - S1879-7296(23)00049-2 [pii]
LID - 10.1016/j.anorl.2023.03.008 [doi]
FAU - Mudry, A
AU  - Mudry A
AD  - Department of Otolaryngology, Head & Neck Surgery, Stanford University School of 
      Medicine, avenue de la Gare 6, 1003 Lausanne, Switzerland. Electronic address: 
      albert@oreillemudry.ch.
LA  - eng
PT  - Letter
DEP - 20230420
PL  - France
TA  - Eur Ann Otorhinolaryngol Head Neck Dis
JT  - European annals of otorhinolaryngology, head and neck diseases
JID - 101531465
SB  - IM
EDAT- 2023/04/23 00:41
MHDA- 2023/10/23 12:42
CRDT- 2023/04/22 22:01
PHST- 2023/03/20 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/10/23 12:42 [medline]
PHST- 2023/04/23 00:41 [pubmed]
PHST- 2023/04/22 22:01 [entrez]
AID - S1879-7296(23)00049-2 [pii]
AID - 10.1016/j.anorl.2023.03.008 [doi]
PST - ppublish
SO  - Eur Ann Otorhinolaryngol Head Neck Dis. 2023 Sep;140(5):251-252. doi: 
      10.1016/j.anorl.2023.03.008. Epub 2023 Apr 20.

PMID- 37823416
OWN - NLM
STAT- Publisher
LR  - 20231012
IS  - 1537-6591 (Electronic)
IS  - 1058-4838 (Linking)
DP  - 2023 Oct 12
TI  - Can Chatbot artificial intelligence replace infectious disease physicians in the 
      management of bloodstream infections? A prospective cohort study.
LID - ciad632 [pii]
LID - 10.1093/cid/ciad632 [doi]
AB  - BACKGROUND: The development of chatbot artificial intelligences (AIs) have raised 
      major questions about their use in healthcare. We assessed the quality and safety 
      of the management suggested by ChatGPT-4 in real-life practice for patients with 
      positive blood culture. METHODS: On a four-week period in a tertiary care 
      hospital, data from consecutive infectious diseases (ID) consultations for a 
      first positive blood-culture were prospectively provided to ChatGPT-4. It was 
      requested to propose a comprehensive management plan (suspected/confirmed 
      diagnosis, work-up, antibiotic therapy, source control, follow-up). We compared 
      the management plan suggested by ChatGPT-4 with the plan suggested by ID 
      consultants, based on literature and guidelines. Comparisons were performed by 
      two ID physicians not involved in the patient management. RESULTS: Forty-four 
      cases with a first episode of positive blood-culture were included. ChatGPT-4 
      provided detailed and well-written responses in all cases. AI's diagnoses were 
      identical to those of the consultant in 26 cases (59 %). Suggested diagnostic 
      workups were satisfactory (i.e., no missing important diagnostic tests) in 35 
      (80%) cases, empirical antimicrobial therapies were adequate in 28 (64%) case, 
      and harmful in one (2%) case. Source control plans were inadequate in four (9%) 
      cases. Definitive antibiotic therapies were optimal in 16 patients (36%), and 
      harmful in two (5 %). Overall, management plans were considered optimal in only 
      one patient, as satisfactory in 17 (39%), and as harmful in seven patients (16%). 
      CONCLUSIONS: The use of ChatGPT-4 without consultant input remains hazardous when 
      seeking expert medical advice in 2023, especially for severe infectious diseases.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Infectious Diseases Society of America. All rights reserved. For permissions, 
      please e-mail: journals.permissions@oup.com.
FAU - Maillard, Alexis
AU  - Maillard A
AUID- ORCID: 0000-0002-7165-4103
AD  - Paris Centre University Hospital, Infectious Diseases Stewardship team, AP-HP, 
      Paris, France.
FAU - Micheli, Giulia
AU  - Micheli G
AD  - Paris Centre University Hospital, Infectious Diseases Stewardship team, AP-HP, 
      Paris, France.
AD  - Dipartimento di Sicurezza e Bioetica - Sezione di Malattie Infettive, Università 
      Cattolica del Sacro Cuore, Rome, RM 00168  Italy.
FAU - Lefevre, Leila
AU  - Lefevre L
AD  - Paris Centre University Hospital, Infectious Diseases Stewardship team, AP-HP, 
      Paris, France.
FAU - Guyonnet, Céline
AU  - Guyonnet C
AD  - Paris Centre University Hospital, Microbiology department, AP-HP, Paris, France.
AD  - Université Paris Cité, Institut Cochin, INSERM, U1016, CNRS, UMR8104, Paris, 
      France.
FAU - Poyart, Claire
AU  - Poyart C
AD  - Paris Centre University Hospital, Microbiology department, AP-HP, Paris, France.
AD  - Université Paris Cité, Institut Cochin, INSERM, U1016, CNRS, UMR8104, Paris, 
      France.
FAU - Canouï, Etienne
AU  - Canouï E
AD  - Paris Centre University Hospital, Infectious Diseases Stewardship team, AP-HP, 
      Paris, France.
FAU - Belan, Martin
AU  - Belan M
AD  - Paris Centre University Hospital, Infectious Diseases Stewardship team, AP-HP, 
      Paris, France.
AD  - Université de Paris Cité, Paris, France.
FAU - Charlier, Caroline
AU  - Charlier C
AD  - Paris Centre University Hospital, Infectious Diseases Stewardship team, AP-HP, 
      Paris, France.
AD  - Université de Paris Cité, Paris, France.
AD  - Institut Pasteur, French National Reference Center and WHO Collaborating Center 
      Listeria, Paris, France.
AD  - Institut Pasteur, Biology of Infection Unit, Inserm U1117, Paris, France.
LA  - eng
PT  - Journal Article
DEP - 20231012
PL  - United States
TA  - Clin Infect Dis
JT  - Clinical infectious diseases : an official publication of the Infectious Diseases 
      Society of America
JID - 9203213
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - bacteremia
OT  - evaluation
OT  - infectious diseases
EDAT- 2023/10/12 12:43
MHDA- 2023/10/12 12:43
CRDT- 2023/10/12 06:55
PHST- 2023/05/25 00:00 [received]
PHST- 2023/09/03 00:00 [revised]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/10/12 12:43 [medline]
PHST- 2023/10/12 12:43 [pubmed]
PHST- 2023/10/12 06:55 [entrez]
AID - 7308526 [pii]
AID - 10.1093/cid/ciad632 [doi]
PST - aheadofprint
SO  - Clin Infect Dis. 2023 Oct 12:ciad632. doi: 10.1093/cid/ciad632.

PMID- 37260081
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 2042-6984 (Electronic)
IS  - 2042-6976 (Linking)
VI  - 13
IP  - 12
DP  - 2023 Dec
TI  - GPT-4 accuracy and completeness against International Consensus Statement on 
      Allergy and Rhinology: Rhinosinusitis.
PG  - 2231-2234
LID - 10.1002/alr.23201 [doi]
AB  - GPT-4 is an AI language model that can answer basic questions about rhinologic 
      disease. Vetting is needed before AI models can be safely integrated into 
      otolarygologic patient care.
CI  - © 2023 ARS-AAOA, LLC.
FAU - Yoshiyasu, Yuki
AU  - Yoshiyasu Y
AUID- ORCID: 0000-0002-4127-1048
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Texas Medical 
      Branch, Galveston, Texas, USA.
FAU - Wu, Franklin
AU  - Wu F
AD  - Department of Otolaryngology-Head and Neck Surgery, Houston Methodist Hospital, 
      Houston, Texas, USA.
FAU - Dhanda, Aatin K
AU  - Dhanda AK
AD  - Department of Otolaryngology-Head and Neck Surgery, Houston Methodist Hospital, 
      Houston, Texas, USA.
FAU - Gorelik, Daniel
AU  - Gorelik D
AUID- ORCID: 0000-0001-7998-6949
AD  - Department of Otolaryngology-Head and Neck Surgery, Houston Methodist Hospital, 
      Houston, Texas, USA.
FAU - Takashima, Masayoshi
AU  - Takashima M
AD  - Department of Otolaryngology-Head and Neck Surgery, Houston Methodist Hospital, 
      Houston, Texas, USA.
FAU - Ahmed, Omar G
AU  - Ahmed OG
AD  - Department of Otolaryngology-Head and Neck Surgery, Houston Methodist Hospital, 
      Houston, Texas, USA.
LA  - eng
PT  - Journal Article
DEP - 20230616
PL  - United States
TA  - Int Forum Allergy Rhinol
JT  - International forum of allergy & rhinology
JID - 101550261
SB  - IM
MH  - Humans
MH  - *Rhinitis/diagnosis/therapy
MH  - *Hypersensitivity
MH  - *Sinusitis/diagnosis/therapy
MH  - Consensus
MH  - Chronic Disease
OTO - NOTNLM
OT  - Large language model (LLM)
OT  - chronic rhinosinusitis
OT  - rhinosinusitis
OT  - sinusitis
EDAT- 2023/06/01 13:10
MHDA- 2023/12/04 12:42
CRDT- 2023/06/01 07:33
PHST- 2023/05/14 00:00 [revised]
PHST- 2023/05/02 00:00 [received]
PHST- 2023/05/22 00:00 [accepted]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/06/01 13:10 [pubmed]
PHST- 2023/06/01 07:33 [entrez]
AID - 10.1002/alr.23201 [doi]
PST - ppublish
SO  - Int Forum Allergy Rhinol. 2023 Dec;13(12):2231-2234. doi: 10.1002/alr.23201. Epub 
      2023 Jun 16.

PMID- 37749010
OWN - NLM
STAT- Publisher
LR  - 20230925
IS  - 1873-4898 (Electronic)
IS  - 1477-5131 (Linking)
DP  - 2023 Sep 13
TI  - ChatGPT in answering questions related to pediatric urology: Comment.
LID - S1477-5131(23)00402-3 [pii]
LID - 10.1016/j.jpurol.2023.08.042 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Pune, India; Dr DY Patil Vidhyapeeth, Pune, India; Joesph 
      Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230913
PL  - England
TA  - J Pediatr Urol
JT  - Journal of pediatric urology
JID - 101233150
SB  - IM
COIS- Conflict of interest None.
EDAT- 2023/09/26 00:42
MHDA- 2023/09/26 00:42
CRDT- 2023/09/25 21:55
PHST- 2023/08/24 00:00 [received]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/09/26 00:42 [medline]
PHST- 2023/09/26 00:42 [pubmed]
PHST- 2023/09/25 21:55 [entrez]
AID - S1477-5131(23)00402-3 [pii]
AID - 10.1016/j.jpurol.2023.08.042 [doi]
PST - aheadofprint
SO  - J Pediatr Urol. 2023 Sep 13:S1477-5131(23)00402-3. doi: 
      10.1016/j.jpurol.2023.08.042.

PMID- 37707522
OWN - NLM
STAT- Publisher
LR  - 20230914
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Sep 14
TI  - Is ChatGPT a qualified thoracic surgeon assistant?
LID - 10.1097/JS9.0000000000000745 [doi]
FAU - Dai, Zhangyi
AU  - Dai Z
AD  - Department of Thoracic Surgery, Sichuan University West China Medical Center, 
      Chengdu, China.
FAU - Zhang, Xiaolong
AU  - Zhang X
FAU - Shen, Cheng
AU  - Shen C
FAU - Xing, Yikai
AU  - Xing Y
FAU - Pu, Qiang
AU  - Pu Q
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/09/14 12:42
MHDA- 2023/09/14 12:42
CRDT- 2023/09/14 11:05
PHST- 2023/08/24 00:00 [received]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/09/14 12:42 [medline]
PHST- 2023/09/14 12:42 [pubmed]
PHST- 2023/09/14 11:05 [entrez]
AID - 01279778-990000000-00611 [pii]
AID - 10.1097/JS9.0000000000000745 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Sep 14. doi: 10.1097/JS9.0000000000000745.

PMID- 37223691
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230525
IS  - 1178-6329 (Print)
IS  - 1178-6329 (Electronic)
IS  - 1178-6329 (Linking)
VI  - 16
DP  - 2023
TI  - ChatGPT: A Double-Edged Sword?
PG  - 11786329231174338
LID - 10.1177/11786329231174338 [doi]
LID - 11786329231174338
FAU - Palal, Deepu
AU  - Palal D
AUID- ORCID: 0000-0001-9770-3914
AD  - Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil 
      Vidyapeeth, Pimpri, Pune, Maharashtra, India.
FAU - Ghonge, Swati
AU  - Ghonge S
AD  - Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil 
      Vidyapeeth, Pimpri, Pune, Maharashtra, India.
FAU - Jadav, Vallari
AU  - Jadav V
AUID- ORCID: 0000-0002-2281-3008
AD  - Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil 
      Vidyapeeth, Pimpri, Pune, Maharashtra, India.
FAU - Rathod, Hetal
AU  - Rathod H
AD  - Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil 
      Vidyapeeth, Pimpri, Pune, Maharashtra, India.
LA  - eng
PT  - Journal Article
DEP - 20230519
PL  - United States
TA  - Health Serv Insights
JT  - Health services insights
JID - 101624726
PMC - PMC10201134
COIS- The author(s) declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/05/24 13:08
MHDA- 2023/05/24 13:09
CRDT- 2023/05/24 11:52
PHST- 2023/05/24 13:09 [medline]
PHST- 2023/05/24 13:08 [pubmed]
PHST- 2023/05/24 11:52 [entrez]
AID - 10.1177_11786329231174338 [pii]
AID - 10.1177/11786329231174338 [doi]
PST - epublish
SO  - Health Serv Insights. 2023 May 19;16:11786329231174338. doi: 
      10.1177/11786329231174338. eCollection 2023.

PMID- 37209600
OWN - NLM
STAT- MEDLINE
DCOM- 20230619
LR  - 20231130
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 82
DP  - 2023 Jul
TI  - Utilization of ChatGPT for plastic surgery research: Comment.
PG  - 275
LID - S1748-6815(23)00246-2 [pii]
LID - 10.1016/j.bjps.2023.04.084 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230503
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
CON - J Plast Reconstr Aesthet Surg. 2023 May;80:145-147. PMID: 37023599
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
COIS- Conflict of Interest None.
EDAT- 2023/05/21 01:05
MHDA- 2023/06/19 13:08
CRDT- 2023/05/20 18:08
PHST- 2023/04/25 00:00 [received]
PHST- 2023/04/26 00:00 [accepted]
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/05/21 01:05 [pubmed]
PHST- 2023/05/20 18:08 [entrez]
AID - S1748-6815(23)00246-2 [pii]
AID - 10.1016/j.bjps.2023.04.084 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Jul;82:275. doi: 10.1016/j.bjps.2023.04.084. 
      Epub 2023 May 3.

PMID- 37120746
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230501
LR  - 20230501
IS  - 0017-7768 (Print)
IS  - 0017-7768 (Linking)
VI  - 162
IP  - 4
DP  - 2023 Apr
TI  - [THE 21ST CENTURY INTELLECTUAL REVOLUTION - MAKING HISTORY WITH CHATGPT: IS IT 
      SO?].
PG  - 250-251
FAU - Dvir, Yoad
AU  - Dvir Y
AD  - Microsoft Central.
AD  - Eastern Europe Ltd.
FAU - Dotan, Arad
AU  - Dotan A
AD  - Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, 
      Ramat-Gan, 52621, Israel, Sackler Faculty of Medicine, Tel Aviv University, Tel 
      Aviv, Israel.
FAU - Shoenfeld, Yehuda
AU  - Shoenfeld Y
AD  - Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, 
      Ramat-Gan, 52621, Israel, Sackler Faculty of Medicine, Tel Aviv University, Tel 
      Aviv, Israel.
LA  - heb
PT  - Journal Article
PL  - Israel
TA  - Harefuah
JT  - Harefuah
JID - 0034351
SB  - IM
EDAT- 2023/04/30 12:42
MHDA- 2023/04/30 12:43
CRDT- 2023/04/30 06:23
PHST- 2023/04/30 12:43 [medline]
PHST- 2023/04/30 12:42 [pubmed]
PHST- 2023/04/30 06:23 [entrez]
PST - ppublish
SO  - Harefuah. 2023 Apr;162(4):250-251.

PMID- 37059351
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230522
LR  - 20231208
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
VI  - 187
DP  - 2023 Jun
TI  - Exploring the capabilities of ChatGPT in academic research recommendation.
PG  - 109795
LID - S0300-9572(23)00108-9 [pii]
LID - 10.1016/j.resuscitation.2023.109795 [doi]
FAU - Haman, Michael
AU  - Haman M
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Czech Republic. Electronic address: haman@pef.czu.cz.
FAU - Školník, Milan
AU  - Školník M
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Czech Republic. Electronic address: skolnikm@pef.czu.cz.
LA  - eng
PT  - Letter
DEP - 20230413
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
EDAT- 2023/04/15 06:00
MHDA- 2023/04/15 06:01
CRDT- 2023/04/14 19:28
PHST- 2023/04/03 00:00 [received]
PHST- 2023/04/04 00:00 [accepted]
PHST- 2023/04/15 06:01 [medline]
PHST- 2023/04/15 06:00 [pubmed]
PHST- 2023/04/14 19:28 [entrez]
AID - S0300-9572(23)00108-9 [pii]
AID - 10.1016/j.resuscitation.2023.109795 [doi]
PST - ppublish
SO  - Resuscitation. 2023 Jun;187:109795. doi: 10.1016/j.resuscitation.2023.109795. 
      Epub 2023 Apr 13.

PMID- 36728748
OWN - NLM
STAT- MEDLINE
DCOM- 20230412
LR  - 20230505
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 2
DP  - 2023 Apr
TI  - ChatGPT and the Future of Medical Writing.
PG  - e223312
LID - 10.1148/radiol.223312 [doi]
FAU - Biswas, Som
AU  - Biswas S
AUID- ORCID: 0000-0002-8129-827X
AD  - From the Department of Radiology, Le Bonheur Children's Hospital, University of 
      Tennessee Health Science Center College of Medicine, Memphis, TN 38103.
LA  - eng
PT  - Journal Article
DEP - 20230202
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CIN - Radiology. 2023 Apr;307(2):e230163. PMID: 36700838
CIN - Radiology. 2023 Feb 2;:230171. PMID: 36728749
CIN - Radiology. 2023 May;307(3):e230312. PMID: 36880946
MH  - Humans
MH  - *Medical Writing
MH  - Curriculum
MH  - Language
EDAT- 2023/02/03 06:00
MHDA- 2023/04/12 06:42
CRDT- 2023/02/02 10:07
PHST- 2023/04/12 06:42 [medline]
PHST- 2023/02/03 06:00 [pubmed]
PHST- 2023/02/02 10:07 [entrez]
AID - 10.1148/radiol.223312 [doi]
PST - ppublish
SO  - Radiology. 2023 Apr;307(2):e223312. doi: 10.1148/radiol.223312. Epub 2023 Feb 2.

PMID- 37219697
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230710
LR  - 20231211
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 8
DP  - 2023 Aug
TI  - Evolution of Healthcare with ChatGPT: A Word of Caution.
PG  - 1663-1664
LID - 10.1007/s10439-023-03225-x [doi]
FAU - Sajjad, Mariam
AU  - Sajjad M
AUID- ORCID: 0000-0001-8421-1443
AD  - Dow University of Health Sciences (DUHS), Karachi, Pakistan.
FAU - Saleem, Rida
AU  - Saleem R
AUID- ORCID: 0000-0002-1126-2773
AD  - Dow University of Health Sciences (DUHS), Karachi, Pakistan. 
      ridasaleem1099@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230523
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
EDAT- 2023/05/23 13:05
MHDA- 2023/07/10 06:42
CRDT- 2023/05/23 11:10
PHST- 2023/04/24 00:00 [received]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/05/23 13:05 [pubmed]
PHST- 2023/05/23 11:10 [entrez]
AID - 10.1007/s10439-023-03225-x [pii]
AID - 10.1007/s10439-023-03225-x [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Aug;51(8):1663-1664. doi: 10.1007/s10439-023-03225-x. Epub 
      2023 May 23.

PMID- 37485676
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231205
IS  - 1550-9397 (Electronic)
IS  - 1550-9389 (Print)
IS  - 1550-9389 (Linking)
VI  - 19
IP  - 12
DP  - 2023 Dec 1
TI  - Evaluating ChatGPT responses on obstructive sleep apnea for patient education.
PG  - 1989-1995
LID - 10.5664/jcsm.10728 [doi]
AB  - STUDY OBJECTIVES: We evaluated the quality of ChatGPT responses to questions on 
      obstructive sleep apnea for patient education and assessed how prompting the 
      chatbot influences correctness, estimated grade level, and references of answers. 
      METHODS: ChatGPT was queried 4 times with 24 identical questions. Queries 
      differed by initial prompting: no prompting, patient-friendly prompting, 
      physician-level prompting, and prompting for statistics/references. Answers were 
      scored on a hierarchical scale: incorrect, partially correct, correct, correct 
      with either statistic or referenced citation ("correct+"), or correct with both a 
      statistic and citation ("perfect"). Flesch-Kincaid grade level and citation 
      publication years were recorded for answers. Proportions of responses at 
      incremental score thresholds were compared by prompt type using chi-squared 
      analysis. The relationship between prompt type and grade level was assessed using 
      analysis of variance. RESULTS: Across all prompts (n = 96 questions), 69 answers 
      (71.9%) were at least correct. Proportions of responses that were at least 
      partially correct (P = .387) or correct (P = .453) did not differ by prompt; 
      responses that were at least correct+ (P < .001) or perfect (P < .001) did. 
      Statistics/references prompting provided 74/77 (96.1%) references. Responses from 
      patient-friendly prompting had a lower mean grade level (12.45 ± 2.32) than no 
      prompting (14.15 ± 1.59), physician-level prompting (14.27 ± 2.09), and 
      statistics/references prompting (15.00 ± 2.26) (P < .0001). CONCLUSIONS: ChatGPT 
      overall provides appropriate answers to most questions on obstructive sleep apnea 
      regardless of prompting. While prompting decreases response grade level, all 
      responses remained above accepted recommendations for presenting medical 
      information to patients. Given ChatGPT's rapid implementation, sleep experts may 
      seek to further scrutinize its medical literacy and utility for patients. 
      CITATION: Campbell DJ, Estephan LE, Mastrolonardo EV, Amin DR, Huntley CT, Boon 
      MS. Evaluating ChatGPT responses on obstructive sleep apnea for patient 
      education. J Clin Sleep Med. 2023;19(12):1989-1995.
CI  - © 2023 American Academy of Sleep Medicine.
FAU - Campbell, Daniel J
AU  - Campbell DJ
AD  - Department of Otolaryngology - Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
FAU - Estephan, Leonard E
AU  - Estephan LE
AD  - Department of Otolaryngology - Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
FAU - Mastrolonardo, Eric V
AU  - Mastrolonardo EV
AD  - Department of Otolaryngology - Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
FAU - Amin, Dev R
AU  - Amin DR
AD  - Department of Otolaryngology - Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
FAU - Huntley, Colin T
AU  - Huntley CT
AD  - Department of Otolaryngology - Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
FAU - Boon, Maurits S
AU  - Boon MS
AD  - Department of Otolaryngology - Head and Neck Surgery, Thomas Jefferson University 
      Hospitals, Philadelphia, Pennsylvania.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - J Clin Sleep Med
JT  - Journal of clinical sleep medicine : JCSM : official publication of the American 
      Academy of Sleep Medicine
JID - 101231977
SB  - IM
MH  - Humans
MH  - Patient Education as Topic
MH  - *Sleep Apnea, Obstructive/therapy
MH  - Sleep
MH  - *Physicians
MH  - Software
PMC - PMC10692937
OTO - NOTNLM
OT  - artificial intelligence
OT  - obstructive sleep apnea
OT  - patient education
OT  - sleep surgery
COIS- All authors have seen and approved the manuscript. Work for this study was 
      performed at Department of Otolaryngology – Head and Neck Surgery, Thomas 
      Jefferson University Hospitals. The authors report no conflicts of interest.
EDAT- 2023/07/24 06:42
MHDA- 2023/12/04 12:42
PMCR- 2024/12/01
CRDT- 2023/07/24 05:26
PHST- 2024/12/01 00:00 [pmc-release]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/07/24 06:42 [pubmed]
PHST- 2023/07/24 05:26 [entrez]
AID - jcsm.10728 [pii]
AID - JC2300220 [pii]
AID - 10.5664/jcsm.10728 [doi]
PST - ppublish
SO  - J Clin Sleep Med. 2023 Dec 1;19(12):1989-1995. doi: 10.5664/jcsm.10728.

PMID- 38046759
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - AI-Generated Information for Vascular Patients: Assessing the Standard of 
      Procedure-Specific Information Provided by the ChatGPT AI-Language Model.
PG  - e49764
LID - 10.7759/cureus.49764 [doi]
LID - e49764
AB  - Introduction Ensuring access to high-quality information is paramount to 
      facilitating informed surgical decision-making. The use of the internet to access 
      health-related information is increasing, along with the growing prevalence of AI 
      language models such as ChatGPT. We aim to assess the standard of AI-generated 
      patient-facing information through a qualitative analysis of its readability and 
      quality. Materials and methods We performed a retrospective qualitative analysis 
      of information regarding three common vascular procedures: endovascular aortic 
      repair (EVAR), endovenous laser ablation (EVLA), and femoro-popliteal bypass 
      (FPBP). The ChatGPT responses were compared to patient information leaflets 
      provided by the vascular charity, Circulation Foundation UK. Readability was 
      assessed using four readability scores: the Flesch-Kincaid reading ease (FKRE) 
      score, the Flesch-Kincaid grade level (FKGL), the Gunning fog score (GFS), and 
      the simple measure of gobbledygook (SMOG) index. Quality was assessed using the 
      DISCERN tool by two independent assessors. Results The mean FKRE score was 33.3, 
      compared to 59.1 for the information provided by the Circulation Foundation 
      (SD=14.5, p=0.025) indicating poor readability of AI-generated information. The 
      FFKGL indicated that the expected grade of students likely to read and understand 
      ChatGPT responses was consistently higher than compared to information leaflets 
      at 12.7 vs. 9.4 (SD=1.9, p=0.002). Two metrics measure readability in terms of 
      the number of years of education required to understand a piece of writing: the 
      GFS and SMOG. Both scores indicated that AI-generated answers were less 
      accessible. The GFS for ChatGPT-provided information was 16.7 years versus 12.8 
      years for the leaflets (SD=2.2, p=0.002) and the SMOG index scores were 12.2 and 
      9.4 years for ChatGPT and the patient information leaflets, respectively (SD=1.7, 
      p=0.001). The DISCERN scores were consistently higher in human-generated patient 
      information leaflets compared to AI-generated information across all procedures; 
      the mean score for the information provided by ChatGPT was 50.3 vs. 56.0 for the 
      Circulation Foundation information leaflets (SD=3.38, p<0.001). Conclusion We 
      concluded that AI-generated information about vascular surgical procedures is 
      currently poor in both the readability of text and the quality of information. 
      Patients should be directed to reputable, human-generated information sources 
      from trusted professional bodies to supplement direct education from the 
      clinician during the pre-procedure consultation process.
CI  - Copyright © 2023, Haidar et al.
FAU - Haidar, Omar
AU  - Haidar O
AD  - Vascular Surgery, Lister Hospital, Stevenage, GBR.
FAU - Jaques, Alexander
AU  - Jaques A
AD  - Vascular Surgery, Lister Hospital, Stevenage, GBR.
FAU - McCaughran, Pierre W
AU  - McCaughran PW
AD  - Vascular Surgery, Lister Hospital, Stevenage, GBR.
FAU - Metcalfe, Matthew J
AU  - Metcalfe MJ
AD  - Vascular Surgery, Lister Hospital, Stevenage, GBR.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10691169
OTO - NOTNLM
OT  - ai
OT  - artificial intelligence
OT  - chatgpt
OT  - patient education
OT  - vascular
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 05:08
PHST- 2023/11/30 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 05:08 [entrez]
AID - 10.7759/cureus.49764 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 30;15(11):e49764. doi: 10.7759/cureus.49764. eCollection 2023 
      Nov.

PMID- 37698703
OWN - NLM
STAT- Publisher
LR  - 20230912
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Sep 12
TI  - Validity and reliability of an instrument evaluating the performance of 
      intelligent chatbot: the Artificial Intelligence Performance Instrument (AIPI).
LID - 10.1007/s00405-023-08219-y [doi]
AB  - OBJECTIVES: To evaluate the reliability and validity of the Artificial 
      Intelligence Performance Instrument (AIPI). METHODS: Medical records of patients 
      consulting in otolaryngology were evaluated by physicians and ChatGPT for 
      differential diagnosis, management, and treatment. The ChatGPT performance was 
      rated twice using AIPI within a 7-day period to assess test-retest reliability. 
      Internal consistency was evaluated using Cronbach's α. Internal validity was 
      evaluated by comparing the AIPI scores of the clinical cases rated by ChatGPT and 
      2 blinded practitioners. Convergent validity was measured by comparing the AIPI 
      score with a modified version of the Ottawa Clinical Assessment Tool (OCAT). 
      Interrater reliability was assessed using Kendall's tau. RESULTS: Forty-five 
      patients completed the evaluations (28 females). The AIPI Cronbach's alpha 
      analysis suggested an adequate internal consistency (α = 0.754). The test-retest 
      reliability was moderate-to-strong for items and the total score of AIPI 
      (r(s) = 0.486, p = 0.001). The mean AIPI score of the senior otolaryngologist was 
      significantly higher compared to the score of ChatGPT, supporting adequate 
      internal validity (p = 0.001). Convergent validity reported a moderate and 
      significant correlation between AIPI and modified OCAT (r(s) = 0.319; p = 0.044). 
      The interrater reliability reported significant positive concordance between both 
      otolaryngologists for the patient feature, diagnostic, additional examination, 
      and treatment subscores as well as for the AIPI total score. CONCLUSIONS: AIPI is 
      a valid and reliable instrument in assessing the performance of ChatGPT in ear, 
      nose and throat conditions. Future studies are needed to investigate the 
      usefulness of AIPI in medicine and surgery, and to evaluate the psychometric 
      properties in these fields.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Lechien, Jerome R
AU  - Lechien JR
AUID- ORCID: 0000-0002-0845-0845
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Societies (IFOS), Paris, France. 
      Jerome.Lechien@umons.ac.be.
AD  - Young Confederation of the European Oto-Rhino-Laryngological Head and Neck 
      Surgery Societies (Y-CEORLHNS), Dublin, Ireland. Jerome.Lechien@umons.ac.be.
AD  - Division of Laryngology and Broncho-Esophagology, Department of 
      Otolaryngology-Head Neck Surgery, EpiCURA Hospital, UMONS Research Institute for 
      Health Sciences and Technology, University of Mons (UMons), Mons, Belgium. 
      Jerome.Lechien@umons.ac.be.
AD  - Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne 
      Nouvelle/Paris 3), Department of Otorhinolaryngology and Head and Neck Surgery, 
      Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles 
      Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France. 
      Jerome.Lechien@umons.ac.be.
AD  - Department of Otorhinolaryngology and Head and Neck Surgery, CHU Saint-Pierre, 
      Brussels, Belgium. Jerome.Lechien@umons.ac.be.
AD  - Faculty of Medicine, Department of Human Anatomy and Experimental Oncology, UMONS 
      Research Institute for Health Sciences and Technology, Avenue du Champ de Mars, 
      6, B7000, Mons, Belgium. Jerome.Lechien@umons.ac.be.
FAU - Maniaci, Antonino
AU  - Maniaci A
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Societies (IFOS), Paris, France.
AD  - Department of Medical, Surgical Sciences and Advanced Technologies G.F. 
      Ingrassia, ENT Section, University of Catania, 95123, Catania, Italy.
FAU - Gengler, Isabelle
AU  - Gengler I
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Societies (IFOS), Paris, France.
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Cincinnati 
      Medical Center, Cincinnati, OH, USA.
FAU - Hans, Stephane
AU  - Hans S
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Societies (IFOS), Paris, France.
AD  - Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne 
      Nouvelle/Paris 3), Department of Otorhinolaryngology and Head and Neck Surgery, 
      Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles 
      Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France.
FAU - Chiesa-Estomba, Carlos M
AU  - Chiesa-Estomba CM
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Societies (IFOS), Paris, France.
AD  - Young Confederation of the European Oto-Rhino-Laryngological Head and Neck 
      Surgery Societies (Y-CEORLHNS), Dublin, Ireland.
AD  - Department of Otorhinolaryngology - Head and Neck Surgery, Donostia University 
      Hospital - Biodonostia Research Institute, St. Sebastian, Spain.
FAU - Vaira, Luigi A
AU  - Vaira LA
AD  - Research Committee of Young Otolaryngologists of the International Federation of 
      Otorhinolaryngological Societies (IFOS), Paris, France.
AD  - Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and 
      Pharmacy, University of Sassari, Sassari, Italy.
AD  - Biomedical Science Department, Biomedical Science PhD School, University of 
      Sassari, Sassari, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230912
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
OTO - NOTNLM
OT  - Artificial
OT  - ChatGPT
OT  - Chatbot
OT  - Comparison
OT  - Diagnosis
OT  - GPT
OT  - Head neck
OT  - Instrument
OT  - Intelligence
OT  - Medicine
OT  - Otolaryngology
OT  - Performance
OT  - Surgery
OT  - Tool
OT  - Treatment
EDAT- 2023/09/12 12:42
MHDA- 2023/09/12 12:42
CRDT- 2023/09/12 11:18
PHST- 2023/07/26 00:00 [received]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/09/12 12:42 [medline]
PHST- 2023/09/12 12:42 [pubmed]
PHST- 2023/09/12 11:18 [entrez]
AID - 10.1007/s00405-023-08219-y [pii]
AID - 10.1007/s00405-023-08219-y [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Sep 12. doi: 10.1007/s00405-023-08219-y.

PMID- 37077800
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230421
IS  - 0860-021X (Print)
IS  - 2083-1862 (Electronic)
IS  - 0860-021X (Linking)
VI  - 40
IP  - 2
DP  - 2023 Apr
TI  - From human writing to artificial intelligence generated text: examining the 
      prospects and potential threats of ChatGPT in academic writing.
PG  - 615-622
LID - 10.5114/biolsport.2023.125623 [doi]
AB  - Natural language processing (NLP) has been studied in computing for decades. 
      Recent technological advancements have led to the development of sophisticated 
      artificial intelligence (AI) models, such as Chat Generative Pre-trained 
      Transformer (ChatGPT). These models can perform a range of language tasks and 
      generate human-like responses, which offers exciting prospects for academic 
      efficiency. This manuscript aims at (i) exploring the potential benefits and 
      threats of ChatGPT and other NLP technologies in academic writing and research 
      publications; (ii) highlights the ethical considerations involved in using these 
      tools, and (iii) consider the impact they may have on the authenticity and 
      credibility of academic work. This study involved a literature review of relevant 
      scholarly articles published in peer-reviewed journals indexed in Scopus as 
      quartile 1. The search used keywords such as "ChatGPT," "AI-generated text," 
      "academic writing," and "natural language processing." The analysis was carried 
      out using a quasi-qualitative approach, which involved reading and critically 
      evaluating the sources and identifying relevant data to support the research 
      questions. The study found that ChatGPT and other NLP technologies have the 
      potential to enhance academic writing and research efficiency. However, their use 
      also raises concerns about the impact on the authenticity and credibility of 
      academic work. The study highlights the need for comprehensive discussions on the 
      potential use, threats, and limitations of these tools, emphasizing the 
      importance of ethical and academic principles, with human intelligence and 
      critical thinking at the forefront of the research process. This study highlights 
      the need for comprehensive debates and ethical considerations involved in their 
      use. The study also recommends that academics exercise caution when using these 
      tools and ensure transparency in their use, emphasizing the importance of human 
      intelligence and critical thinking in academic work.
CI  - Copyright © Biology of Sport 2023.
FAU - Dergaa, Ismail
AU  - Dergaa I
AD  - Primary Health Care Corporation (PHCC), Doha, Qatar.
AD  - Research Unit Physical Activity, Sport, and Health, UR18JS01, National 
      Observatory of Sport, Tunis 1003, Tunisia.
AD  - High Institute of Sport and Physical Education, University of Sfax, Sfax, 
      Tunisia.
FAU - Chamari, Karim
AU  - Chamari K
AD  - Aspetar, Orthopaedic and Sports Medicine Hospital, FIFA Medical Centre of 
      Excellence, Doha, Qatar.
FAU - Zmijewski, Piotr
AU  - Zmijewski P
AD  - Jozef Pilsudski University of Physical Education in Warsaw, Warsaw, Poland.
FAU - Ben Saad, Helmi
AU  - Ben Saad H
AD  - University of Sousse, Farhat HACHED hospital, Service of Physiology and 
      Functional Explorations, Sousse, Tunisia.
AD  - University of Sousse, Farhat HACHED hospital, Research Laboratory LR12SP09 «Heart 
      Failure», Sousse, Tunisia.
AD  - University of Sousse, Faculty of Medicine of Sousse, Laboratory of Physiology, 
      Sousse, Tunisia.
LA  - eng
PT  - Journal Article
DEP - 20230315
PL  - Poland
TA  - Biol Sport
JT  - Biology of sport
JID - 8700872
PMC - PMC10108763
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Chatbot
OT  - Deep Learning
OT  - Google Bard
OT  - Higher Education
OT  - LLM
OT  - LLaMA
OT  - Machine Learning
OT  - NLM
OT  - NLP
OT  - Natural Language Processing
OT  - Paperpal
OT  - Peer Review
OT  - QuillBot
OT  - Rayyan
OT  - Research
OT  - Sports Medicine
COIS- We, hereby declare that we have no financial or personal relationships that could 
      potentially influence or bias the content of this paper. Specifically, none of 
      the authors holds any financial interests or conflicts of interest associated 
      with the ChatGPT or NLM technologies discussed in this paper. Furthermore, none 
      of the authors has affiliations with any organizations that might have a 
      financial interest in the research or its outcomes. Moreover, we confirm that we 
      have no personal or professional relationships that could potentially affect the 
      research or its findings. None of the authors has collaborated or consulted with 
      any individuals or organizations that have a financial or other interest in the 
      ChatGPT or NLM technologies. Additionally, we have not received any funding or 
      other types of support from any sources that could influence the research or its 
      findings. We affirm that the research presented in this paper is entirely based 
      on our own analysis and interpretation of the facts/data. We assure that there 
      are no conflicts of interest that could impact the objectivity or integrity of 
      the research. We make this declaration of no conflict of interest to ensure 
      transparency and maintain the credibility of the research manuscript presented.
EDAT- 2023/04/20 06:42
MHDA- 2023/04/20 06:43
CRDT- 2023/04/20 02:28
PHST- 2023/02/26 00:00 [received]
PHST- 2023/03/04 00:00 [revised]
PHST- 2023/03/09 00:00 [accepted]
PHST- 2023/04/20 06:43 [medline]
PHST- 2023/04/20 06:42 [pubmed]
PHST- 2023/04/20 02:28 [entrez]
AID - 125623 [pii]
AID - 10.5114/biolsport.2023.125623 [doi]
PST - ppublish
SO  - Biol Sport. 2023 Apr;40(2):615-622. doi: 10.5114/biolsport.2023.125623. Epub 2023 
      Mar 15.

PMID- 37440293
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230730
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Jul 13
TI  - Variability in Large Language Models' Responses to Medical Licensing and 
      Certification Examinations. Comment on "How Does ChatGPT Perform on the United 
      States Medical Licensing Examination? The Implications of Large Language Models 
      for Medical Education and Knowledge Assessment".
PG  - e48305
LID - 10.2196/48305 [doi]
LID - e48305
FAU - Epstein, Richard H
AU  - Epstein RH
AUID- ORCID: 0000-0001-8466-3845
AD  - Department of Anesthesiology, Perioperative Medicine and Pain Management, 
      University of Miami Miller School of Medicine, Miami, FL, United States.
FAU - Dexter, Franklin
AU  - Dexter F
AUID- ORCID: 0000-0001-5897-2484
AD  - Division of Management Consulting, Department of Anesthesia, University of Iowa, 
      Iowa City, IA, United States.
LA  - eng
PT  - Journal Article
DEP - 20230713
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
CON - JMIR Med Educ. 9:e45312.
CIN - JMIR Med Educ. 9:e50336.
PMC - PMC10375390
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT
OT  - Google Bard
OT  - MedQA
OT  - NLP
OT  - artificial intelligence
OT  - chatbot
OT  - conversational agent
OT  - education technology
OT  - generative pre-trained transformer
OT  - knowledge assessment
OT  - large language models
OT  - machine learning
OT  - medical education
OT  - natural language processing
COIS- Conflicts of Interest: None declared.
EDAT- 2023/07/13 12:31
MHDA- 2023/07/13 12:32
CRDT- 2023/07/13 11:53
PHST- 2023/04/18 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/06/16 00:00 [revised]
PHST- 2023/07/13 12:32 [medline]
PHST- 2023/07/13 12:31 [pubmed]
PHST- 2023/07/13 11:53 [entrez]
AID - v9i1e48305 [pii]
AID - 10.2196/48305 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Jul 13;9:e48305. doi: 10.2196/48305.

PMID- 38044911
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 0019-5049 (Print)
IS  - 0976-2817 (Electronic)
IS  - 0019-5049 (Linking)
VI  - 67
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT in the field of scientific publication.
PG  - 934
LID - 10.4103/ija.ija_585_23 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Research Center, Chandigarh University, Punjab, India.
AD  - Department of Biological Science, Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20231016
PL  - India
TA  - Indian J Anaesth
JT  - Indian journal of anaesthesia
JID - 0013243
PMC - PMC10691606
COIS- There are no conflicts of interest.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 04:24
PHST- 2023/06/19 00:00 [received]
PHST- 2023/08/03 00:00 [revised]
PHST- 2023/08/10 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 04:24 [entrez]
AID - IJA-67-934 [pii]
AID - 10.4103/ija.ija_585_23 [doi]
PST - ppublish
SO  - Indian J Anaesth. 2023 Oct;67(10):934. doi: 10.4103/ija.ija_585_23. Epub 2023 Oct 
      16.

PMID- 38006981
OWN - NLM
STAT- Publisher
LR  - 20231125
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Linking)
DP  - 2023 Nov 23
TI  - Potential for ChatGPT in obstetrics and gynecology: comment.
LID - S0002-9378(23)02052-5 [pii]
LID - 10.1016/j.ajog.2023.11.1238 [doi]
FAU - Wiwanitmkit, Somsri
AU  - Wiwanitmkit S
AD  - Private Academic and Editorial Consultant, Bangkok Thailand.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Adjunct professor, Saveetha medical college, Chennai, India.
LA  - eng
PT  - Letter
DEP - 20231123
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
EDAT- 2023/11/26 07:44
MHDA- 2023/11/26 07:44
CRDT- 2023/11/25 19:27
PHST- 2023/10/05 00:00 [received]
PHST- 2023/11/20 00:00 [revised]
PHST- 2023/11/20 00:00 [accepted]
PHST- 2023/11/26 07:44 [medline]
PHST- 2023/11/26 07:44 [pubmed]
PHST- 2023/11/25 19:27 [entrez]
AID - S0002-9378(23)02052-5 [pii]
AID - 10.1016/j.ajog.2023.11.1238 [doi]
PST - aheadofprint
SO  - Am J Obstet Gynecol. 2023 Nov 23:S0002-9378(23)02052-5. doi: 
      10.1016/j.ajog.2023.11.1238.

PMID- 37950022
OWN - NLM
STAT- Publisher
LR  - 20231110
IS  - 1476-5608 (Electronic)
IS  - 1365-7852 (Linking)
DP  - 2023 Nov 10
TI  - ChatGPT in prostate cancer: myth or reality?
LID - 10.1038/s41391-023-00750-7 [doi]
FAU - Lombardo, Riccardo
AU  - Lombardo R
AUID- ORCID: 0000-0003-2890-3159
AD  - Ospedale Sant'Andrea, Sapienza University of Rome, Rome, Italy. rlombardo@me.com.
FAU - Cicione, Antonio
AU  - Cicione A
AD  - Ospedale Sant'Andrea, Sapienza University of Rome, Rome, Italy.
FAU - Santoro, Giuseppe
AU  - Santoro G
AD  - Ospedale Sant'Andrea, Sapienza University of Rome, Rome, Italy.
FAU - De Nunzio, Cosimo
AU  - De Nunzio C
AUID- ORCID: 0000-0002-2190-512X
AD  - Ospedale Sant'Andrea, Sapienza University of Rome, Rome, Italy.
LA  - eng
PT  - Editorial
DEP - 20231110
PL  - England
TA  - Prostate Cancer Prostatic Dis
JT  - Prostate cancer and prostatic diseases
JID - 9815755
SB  - IM
EDAT- 2023/11/11 11:43
MHDA- 2023/11/11 11:43
CRDT- 2023/11/10 23:38
PHST- 2023/07/16 00:00 [received]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/08/03 00:00 [revised]
PHST- 2023/11/11 11:43 [medline]
PHST- 2023/11/11 11:43 [pubmed]
PHST- 2023/11/10 23:38 [entrez]
AID - 10.1038/s41391-023-00750-7 [pii]
AID - 10.1038/s41391-023-00750-7 [doi]
PST - aheadofprint
SO  - Prostate Cancer Prostatic Dis. 2023 Nov 10. doi: 10.1038/s41391-023-00750-7.

PMID- 37811001
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1865-5025 (Print)
IS  - 1865-5033 (Electronic)
IS  - 1865-5025 (Linking)
VI  - 16
IP  - 4
DP  - 2023 Aug
TI  - Outsourcing Your Faculty Application to ChatGPT: Would this Work? Should this 
      Work?
PG  - 423-426
LID - 10.1007/s12195-023-00777-9 [doi]
FAU - King, Michael R
AU  - King MR
AD  - Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235 
      USA. ROR: https://ror.org/02vm5rt34. GRID: grid.152326.1. ISNI: 0000 0001 2264 
      7217
LA  - eng
PT  - Editorial
DEP - 20230810
PL  - United States
TA  - Cell Mol Bioeng
JT  - Cellular and molecular bioengineering
JID - 101468590
PMC - PMC10550881
EDAT- 2023/10/09 12:42
MHDA- 2023/10/09 12:43
PMCR- 2024/08/10
CRDT- 2023/10/09 06:12
PHST- 2024/08/10 00:00 [pmc-release]
PHST- 2023/10/09 12:43 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 06:12 [entrez]
AID - 777 [pii]
AID - 10.1007/s12195-023-00777-9 [doi]
PST - epublish
SO  - Cell Mol Bioeng. 2023 Aug 10;16(4):423-426. doi: 10.1007/s12195-023-00777-9. 
      eCollection 2023 Aug.

PMID- 37805305
OWN - NLM
STAT- MEDLINE
DCOM- 20231110
LR  - 20231128
IS  - 1365-229X (Electronic)
IS  - 0009-9260 (Linking)
VI  - 78
IP  - 12
DP  - 2023 Dec
TI  - Re: Empowering radiology: the transformative role of ChatGPT.
PG  - e1091
LID - S0009-9260(23)00391-4 [pii]
LID - 10.1016/j.crad.2023.09.002 [doi]
FAU - Daungsupawong, H
AU  - Daungsupawong H
AD  - Private Academic Consultant, Phonhong, Lao Democratic People's Republic. 
      Electronic address: hinpetchdaung@gmail.com.
FAU - Wiwanitkit, V
AU  - Wiwanitkit V
AD  - Chandigarh University, India; Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230922
PL  - England
TA  - Clin Radiol
JT  - Clinical radiology
JID - 1306016
SB  - IM
CON - Clin Radiol. 2023 Nov;78(11):851-855. PMID: 37657969
MH  - Humans
MH  - Radiography
MH  - *Radiology
MH  - Power, Psychological
EDAT- 2023/10/08 02:42
MHDA- 2023/11/10 06:44
CRDT- 2023/10/07 22:00
PHST- 2023/09/02 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/11/10 06:44 [medline]
PHST- 2023/10/08 02:42 [pubmed]
PHST- 2023/10/07 22:00 [entrez]
AID - S0009-9260(23)00391-4 [pii]
AID - 10.1016/j.crad.2023.09.002 [doi]
PST - ppublish
SO  - Clin Radiol. 2023 Dec;78(12):e1091. doi: 10.1016/j.crad.2023.09.002. Epub 2023 
      Sep 22.

PMID- 37797373
OWN - NLM
STAT- MEDLINE
DCOM- 20231106
LR  - 20231130
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 86
DP  - 2023 Nov
TI  - Letter comments on: Google, ChatGPT and breast reconstruction.
PG  - 269-270
LID - S1748-6815(23)00506-5 [pii]
LID - 10.1016/j.bjps.2023.09.012 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230914
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
CON - J Plast Reconstr Aesthet Surg. 2023 Oct;85:488-497. PMID: 37598590
MH  - Humans
MH  - *Search Engine
MH  - *Mammaplasty
COIS- Declaration of Competing Interest None.
EDAT- 2023/10/05 18:42
MHDA- 2023/11/06 06:41
CRDT- 2023/10/05 18:02
PHST- 2023/08/24 00:00 [received]
PHST- 2023/09/07 00:00 [accepted]
PHST- 2023/11/06 06:41 [medline]
PHST- 2023/10/05 18:42 [pubmed]
PHST- 2023/10/05 18:02 [entrez]
AID - S1748-6815(23)00506-5 [pii]
AID - 10.1016/j.bjps.2023.09.012 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Nov;86:269-270. doi: 
      10.1016/j.bjps.2023.09.012. Epub 2023 Sep 14.

PMID- 37731175
OWN - NLM
STAT- Publisher
LR  - 20231004
IS  - 1533-3450 (Electronic)
IS  - 1046-6673 (Linking)
DP  - 2023 Sep 21
TI  - Does ChatGPT Help Us Understand the Medical Literature?
LID - 10.1681/ASN.0000000000000237 [doi]
FAU - Lemley, Kevin V
AU  - Lemley KV
AUID- ORCID: 0000-0001-8342-5458
AD  - Department of Pediatrics, Division of Nephrology, University of Southern 
      California Keck School of Medicine, Los Angeles, California.
LA  - eng
PT  - Journal Article
DEP - 20230921
PL  - United States
TA  - J Am Soc Nephrol
JT  - Journal of the American Society of Nephrology : JASN
JID - 9013836
SB  - IM
EDAT- 2023/09/21 06:42
MHDA- 2023/09/21 06:42
CRDT- 2023/09/21 00:21
PHST- 2023/09/21 06:42 [pubmed]
PHST- 2023/09/21 06:42 [medline]
PHST- 2023/09/21 00:21 [entrez]
AID - 00001751-990000000-00183 [pii]
AID - 10.1681/ASN.0000000000000237 [doi]
PST - aheadofprint
SO  - J Am Soc Nephrol. 2023 Sep 21. doi: 10.1681/ASN.0000000000000237.

PMID- 37662135
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0970-0218 (Print)
IS  - 1998-3581 (Electronic)
IS  - 0970-0218 (Linking)
VI  - 48
IP  - 4
DP  - 2023 Jul-Aug
TI  - Use of ChatGPT in Creating Awareness about Ischemic Stroke.
PG  - 633-635
LID - 10.4103/ijcm.ijcm_381_23 [doi]
FAU - Vora, Neera N
AU  - Vora NN
AD  - Ex. Rotatory Houseman, Medicine Department, GCS Medical College, Ahmedabad, 
      Gujarat, India.
FAU - Doshi, Preyansh K
AU  - Doshi PK
AD  - Ex. Rotatory Houseman, Medicine Department, GCS Medical College, Ahmedabad, 
      Gujarat, India.
LA  - eng
PT  - Journal Article
DEP - 20230714
PL  - India
TA  - Indian J Community Med
JT  - Indian journal of community medicine : official publication of Indian Association 
      of Preventive & Social Medicine
JID - 9315574
PMC - PMC10470564
COIS- There are no conflicts of interest.
EDAT- 2023/09/04 06:43
MHDA- 2023/09/04 06:44
CRDT- 2023/09/04 04:37
PHST- 2023/05/11 00:00 [received]
PHST- 2023/06/16 00:00 [accepted]
PHST- 2023/09/04 06:44 [medline]
PHST- 2023/09/04 06:43 [pubmed]
PHST- 2023/09/04 04:37 [entrez]
AID - IJCM-48-633 [pii]
AID - 10.4103/ijcm.ijcm_381_23 [doi]
PST - ppublish
SO  - Indian J Community Med. 2023 Jul-Aug;48(4):633-635. doi: 
      10.4103/ijcm.ijcm_381_23. Epub 2023 Jul 14.

PMID- 37400048
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231103
LR  - 20231113
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 10
DP  - 2023 Oct
TI  - The Potential for Using ChatGPT to Improve Imaging Appropriateness.
PG  - 988-989
LID - S1546-1440(23)00474-X [pii]
LID - 10.1016/j.jacr.2023.06.005 [doi]
FAU - Varney, Elliot T
AU  - Varney ET
AD  - Department of Radiology, University of Mississippi Medical Center School of 
      Medicine, Jackson, Mississippi. Electronic address: evarney@umc.edu.
FAU - Lee, Christoph I
AU  - Lee CI
AD  - Director of the Northwest Screening and Cancer Outcomes Research Enterprise, 
      Department of Radiology, University of Washington School of Medicine, Seattle, 
      Washington; and Deputy Editor of JACR. Electronic address: 
      https://twitter.com/christophleemd.
LA  - eng
PT  - Editorial
DEP - 20230701
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
EDAT- 2023/07/04 01:05
MHDA- 2023/07/04 01:06
CRDT- 2023/07/03 19:22
PHST- 2023/05/16 00:00 [received]
PHST- 2023/06/03 00:00 [accepted]
PHST- 2023/07/04 01:06 [medline]
PHST- 2023/07/04 01:05 [pubmed]
PHST- 2023/07/03 19:22 [entrez]
AID - S1546-1440(23)00474-X [pii]
AID - 10.1016/j.jacr.2023.06.005 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Oct;20(10):988-989. doi: 10.1016/j.jacr.2023.06.005. Epub 
      2023 Jul 1.

PMID- 37369764
OWN - NLM
STAT- Publisher
LR  - 20230627
IS  - 1476-5454 (Electronic)
IS  - 0950-222X (Linking)
DP  - 2023 Jun 27
TI  - ChatGPT in ophthalmology: the dawn of a new era?
LID - 10.1038/s41433-023-02619-4 [doi]
FAU - Ting, Darren Shu Jeng
AU  - Ting DSJ
AUID- ORCID: 0000-0003-1081-1141
AD  - Birmingham and Midland Eye Centre, Birmingham, UK.
AD  - Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University 
      of Birmingham, Birmingham, UK.
AD  - Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, 
      UK.
FAU - Tan, Ting Fang
AU  - Tan TF
AD  - Artificial Intelligence and Digital Innovation Research Group, Singapore Eye 
      Research Institute, Singapore, Singapore.
AD  - Singapore National Eye Centre, Singapore, Singapore.
FAU - Ting, Daniel Shu Wei
AU  - Ting DSW
AUID- ORCID: 0000-0003-2264-7174
AD  - Artificial Intelligence and Digital Innovation Research Group, Singapore Eye 
      Research Institute, Singapore, Singapore. daniel.ting@duke-nus.edu.sg.
AD  - Singapore National Eye Centre, Singapore, Singapore. daniel.ting@duke-nus.edu.sg.
AD  - Department of Ophthalmology and Visual Sciences, Duke-National University of 
      Singapore Medical School, Singapore, Singapore. daniel.ting@duke-nus.edu.sg.
LA  - eng
GR  - MR/T001674/1/RCUK | Medical Research Council (MRC)/
GR  - NMCR/HSRG/0087/2018/MOH | National Medical Research Council (NMRC)/
GR  - MOH-000655-00/MOH | National Medical Research Council (NMRC)/
GR  - MOH-001014-00/MOH | National Medical Research Council (NMRC)/
PT  - Editorial
DEP - 20230627
PL  - England
TA  - Eye (Lond)
JT  - Eye (London, England)
JID - 8703986
SB  - IM
EDAT- 2023/06/28 01:06
MHDA- 2023/06/28 01:06
CRDT- 2023/06/27 23:23
PHST- 2023/05/14 00:00 [received]
PHST- 2023/06/02 00:00 [accepted]
PHST- 2023/05/22 00:00 [revised]
PHST- 2023/06/28 01:06 [medline]
PHST- 2023/06/28 01:06 [pubmed]
PHST- 2023/06/27 23:23 [entrez]
AID - 10.1038/s41433-023-02619-4 [pii]
AID - 10.1038/s41433-023-02619-4 [doi]
PST - aheadofprint
SO  - Eye (Lond). 2023 Jun 27. doi: 10.1038/s41433-023-02619-4.

PMID- 37331823
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230817
IS  - 2173-5727 (Electronic)
IS  - 2173-5727 (Linking)
VI  - 47
IP  - 8
DP  - 2023 Aug
TI  - Teaching tools in critical care: chatGPT.
PG  - 480-481
LID - S2173-5727(23)00081-4 [pii]
LID - 10.1016/j.medine.2023.04.006 [doi]
FAU - Valiente Fernández, Marcos
AU  - Valiente Fernández M
AD  - Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Madrid, 
      Spain. Electronic address: mvalientefernandez@gmail.com.
FAU - Delgado Moya, Francisco de Paula
AU  - Delgado Moya FP
AD  - Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Madrid, 
      Spain.
FAU - Lesmes González de Aledo, Amanda
AU  - Lesmes González de Aledo A
AD  - Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Madrid, 
      Spain.
FAU - Martín Badía, Isaías
AU  - Martín Badía I
AD  - Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Madrid, 
      Spain.
FAU - Orejón García, Lidia
AU  - Orejón García L
AD  - Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Madrid, 
      Spain.
LA  - eng
PT  - Letter
DEP - 20230616
PL  - Spain
TA  - Med Intensiva (Engl Ed)
JT  - Medicina intensiva
JID - 101717568
SB  - IM
MH  - Humans
MH  - *Critical Care
MH  - Artificial Intelligence
EDAT- 2023/06/19 00:42
MHDA- 2023/08/07 06:42
CRDT- 2023/06/18 21:53
PHST- 2023/03/08 00:00 [received]
PHST- 2023/04/09 00:00 [accepted]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/06/19 00:42 [pubmed]
PHST- 2023/06/18 21:53 [entrez]
AID - S2173-5727(23)00081-4 [pii]
AID - 10.1016/j.medine.2023.04.006 [doi]
PST - ppublish
SO  - Med Intensiva (Engl Ed). 2023 Aug;47(8):480-481. doi: 
      10.1016/j.medine.2023.04.006. Epub 2023 Jun 16.

PMID- 37306599
OWN - NLM
STAT- MEDLINE
DCOM- 20230621
LR  - 20230621
IS  - 1468-3083 (Electronic)
IS  - 0926-9959 (Linking)
VI  - 37
IP  - 7
DP  - 2023 Jul
TI  - Artificial intelligence-ChatGPT and Scientific Publishing.
PG  - 1253-1254
LID - 10.1111/jdv.19176 [doi]
FAU - Ring, Johannes
AU  - Ring J
AD  - Department of Dermatology and Allergy Biederstein, Technical University of 
      Munich, Munich, Germany.
LA  - eng
PT  - Editorial
PL  - England
TA  - J Eur Acad Dermatol Venereol
JT  - Journal of the European Academy of Dermatology and Venereology : JEADV
JID - 9216037
SB  - IM
MH  - *Artificial Intelligence
MH  - *Publishing
EDAT- 2023/06/12 13:07
MHDA- 2023/06/14 06:42
CRDT- 2023/06/12 10:07
PHST- 2023/06/14 06:42 [medline]
PHST- 2023/06/12 13:07 [pubmed]
PHST- 2023/06/12 10:07 [entrez]
AID - 10.1111/jdv.19176 [doi]
PST - ppublish
SO  - J Eur Acad Dermatol Venereol. 2023 Jul;37(7):1253-1254. doi: 10.1111/jdv.19176.

PMID- 37095351
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230518
LR  - 20230518
IS  - 1546-1696 (Electronic)
IS  - 1087-0156 (Linking)
VI  - 41
IP  - 5
DP  - 2023 May
TI  - Drug discovery companies are customizing ChatGPT: here's how.
PG  - 585-586
LID - 10.1038/s41587-023-01788-7 [doi]
FAU - Savage, Neil
AU  - Savage N
AD  - , Lowell, MA, USA.
LA  - eng
PT  - News
PL  - United States
TA  - Nat Biotechnol
JT  - Nature biotechnology
JID - 9604648
SB  - IM
EDAT- 2023/04/25 00:41
MHDA- 2023/04/25 00:42
CRDT- 2023/04/24 11:30
PHST- 2023/04/25 00:42 [medline]
PHST- 2023/04/25 00:41 [pubmed]
PHST- 2023/04/24 11:30 [entrez]
AID - 10.1038/s41587-023-01788-7 [pii]
AID - 10.1038/s41587-023-01788-7 [doi]
PST - ppublish
SO  - Nat Biotechnol. 2023 May;41(5):585-586. doi: 10.1038/s41587-023-01788-7.

PMID- 37036167
OWN - NLM
STAT- MEDLINE
DCOM- 20230601
LR  - 20230606
IS  - 2326-5205 (Electronic)
IS  - 2326-5191 (Linking)
VI  - 75
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT, et al…Artificial Intelligence, Authorship, and Medical Publishing.
PG  - 867-868
LID - 10.1002/art.42497 [doi]
FAU - Solomon, Daniel H
AU  - Solomon DH
AUID- ORCID: 0000-0001-8202-5428
AD  - Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, 
      Cambridge, Massachusetts.
FAU - Allen, Kelli D
AU  - Allen KD
AUID- ORCID: 0000-0002-3886-0677
AD  - University of North Carolina and Durham VA Healthcare System, Chapel Hill, North 
      Carolina.
FAU - Katz, Patricia
AU  - Katz P
AUID- ORCID: 0000-0002-8146-2519
AD  - University of California, San Francisco.
FAU - Sawalha, Amr H
AU  - Sawalha AH
AD  - University of Pittsburgh, Pittsburgh, Pennsylvania.
FAU - Yelin, Ed
AU  - Yelin E
AUID- ORCID: 0000-0002-9229-9980
AD  - University of California, San Francisco.
LA  - eng
PT  - Journal Article
DEP - 20230410
PL  - United States
TA  - Arthritis Rheumatol
JT  - Arthritis & rheumatology (Hoboken, N.J.)
JID - 101623795
SB  - IM
MH  - *Artificial Intelligence
MH  - *Authorship
EDAT- 2023/04/11 06:00
MHDA- 2023/06/01 06:42
CRDT- 2023/04/10 08:42
PHST- 2023/03/07 00:00 [revised]
PHST- 2023/03/01 00:00 [received]
PHST- 2023/03/07 00:00 [accepted]
PHST- 2023/06/01 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 08:42 [entrez]
AID - 10.1002/art.42497 [doi]
PST - ppublish
SO  - Arthritis Rheumatol. 2023 Jun;75(6):867-868. doi: 10.1002/art.42497. Epub 2023 
      Apr 10.

PMID- 38035553
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1423-002X (Electronic)
IS  - 0378-7346 (Linking)
DP  - 2023 Nov 30
TI  - ChatGPT in Obstetrics and Gynecology.
LID - 10.1159/000535538 [doi]
AB  - n/a.
CI  - S. Karger AG, Basel.
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
LA  - eng
PT  - Letter
DEP - 20231130
PL  - Switzerland
TA  - Gynecol Obstet Invest
JT  - Gynecologic and obstetric investigation
JID - 7900587
SB  - IM
EDAT- 2023/12/01 00:43
MHDA- 2023/12/01 00:43
CRDT- 2023/11/30 18:25
PHST- 2023/09/05 00:00 [received]
PHST- 2023/11/26 00:00 [accepted]
PHST- 2023/12/01 00:43 [medline]
PHST- 2023/12/01 00:43 [pubmed]
PHST- 2023/11/30 18:25 [entrez]
AID - 000535538 [pii]
AID - 10.1159/000535538 [doi]
PST - aheadofprint
SO  - Gynecol Obstet Invest. 2023 Nov 30. doi: 10.1159/000535538.

PMID- 37812113
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231103
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT and the Law of the Horse.
PG  - 55-57
LID - 10.1080/15265161.2023.2250279 [doi]
FAU - Cheung, Alexander T M
AU  - Cheung ATM
AUID- ORCID: 0000-0003-0946-3493
AD  - Barrow Neurological Institute.
FAU - Nasir-Moin, Mustafa
AU  - Nasir-Moin M
AD  - Harvard Medical School.
FAU - Oermann, Eric K
AU  - Oermann EK
AD  - New York University.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CON - Am J Bioeth. 2023 Oct;23(10):8-16. PMID: 37440696
MH  - Horses
MH  - Animals
MH  - *Bioethics
EDAT- 2023/10/09 12:42
MHDA- 2023/11/02 12:43
CRDT- 2023/10/09 10:13
PHST- 2023/11/02 12:43 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 10:13 [entrez]
AID - 10.1080/15265161.2023.2250279 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):55-57. doi: 10.1080/15265161.2023.2250279. Epub 2023 
      Oct 9.

PMID- 37573081
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1532-8406 (Electronic)
IS  - 0883-5403 (Linking)
VI  - 38
IP  - 9
DP  - 2023 Sep
TI  - Utility of ChatGPT in Total Joint Arthroplasty.
PG  - e17
LID - S0883-5403(23)00580-6 [pii]
LID - 10.1016/j.arth.2023.05.056 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Adjunct Professor, Chandigarh University, Punjab, India; Adjunct Professor, 
      Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
PL  - United States
TA  - J Arthroplasty
JT  - The Journal of arthroplasty
JID - 8703515
SB  - IM
CIN - J Arthroplasty. 2023 Sep;38(9):e18. PMID: 37573082
EDAT- 2023/08/13 00:43
MHDA- 2023/08/13 00:44
CRDT- 2023/08/12 20:58
PHST- 2023/04/25 00:00 [received]
PHST- 2023/04/30 00:00 [revised]
PHST- 2023/05/04 00:00 [accepted]
PHST- 2023/08/13 00:44 [medline]
PHST- 2023/08/13 00:43 [pubmed]
PHST- 2023/08/12 20:58 [entrez]
AID - S0883-5403(23)00580-6 [pii]
AID - 10.1016/j.arth.2023.05.056 [doi]
PST - ppublish
SO  - J Arthroplasty. 2023 Sep;38(9):e17. doi: 10.1016/j.arth.2023.05.056.

PMID- 37442292
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231128
LR  - 20231128
IS  - 1535-7732 (Electronic)
IS  - 1051-0443 (Linking)
VI  - 34
IP  - 12
DP  - 2023 Dec
TI  - ChatGPT Provides References That Are Real, Inappropriate, or (Most Often) Fake.
PG  - 2240-2242
LID - S1051-0443(23)00503-1 [pii]
LID - 10.1016/j.jvir.2023.07.001 [doi]
FAU - Hoffer, Eric K
AU  - Hoffer EK
AD  - Division of Vascular and of Interventional Radiology, Department of Radiology, 
      Dartmouth Hitchcock Medical Center, One Medical Center Dr., Lebanon, NH 03756. 
      Electronic address: hoffer@hitchcock.org.
LA  - eng
PT  - Letter
DEP - 20230711
PL  - United States
TA  - J Vasc Interv Radiol
JT  - Journal of vascular and interventional radiology : JVIR
JID - 9203369
SB  - IM
EDAT- 2023/07/14 13:07
MHDA- 2023/07/14 13:08
CRDT- 2023/07/13 19:24
PHST- 2023/05/24 00:00 [received]
PHST- 2023/06/26 00:00 [revised]
PHST- 2023/07/06 00:00 [accepted]
PHST- 2023/07/14 13:08 [medline]
PHST- 2023/07/14 13:07 [pubmed]
PHST- 2023/07/13 19:24 [entrez]
AID - S1051-0443(23)00503-1 [pii]
AID - 10.1016/j.jvir.2023.07.001 [doi]
PST - ppublish
SO  - J Vasc Interv Radiol. 2023 Dec;34(12):2240-2242. doi: 10.1016/j.jvir.2023.07.001. 
      Epub 2023 Jul 11.

PMID- 37037752
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230522
LR  - 20230522
IS  - 1872-7654 (Electronic)
IS  - 0301-2115 (Linking)
VI  - 285
DP  - 2023 Jun
TI  - Exploring the use of ChatGPT for guidance during unexpected labour.
PG  - 208-209
LID - S0301-2115(23)00137-9 [pii]
LID - 10.1016/j.ejogrb.2023.04.001 [doi]
FAU - Santo, Daniela S Espírito
AU  - Santo DSE
AD  - Post-Graduate Program in Health Sciences, Faculdade Ciências Médicas de Minas 
      Gerais, Belo Horizonte, Minas Gerais, Brazil.
FAU - Joviano-Santos, Julliane V
AU  - Joviano-Santos JV
AD  - Post-Graduate Program in Health Sciences, Faculdade Ciências Médicas de Minas 
      Gerais, Belo Horizonte, Minas Gerais, Brazil. Electronic address: 
      julliane.santos@cienciasmedicasmg.edu.br.
LA  - eng
PT  - Letter
DEP - 20230405
PL  - Ireland
TA  - Eur J Obstet Gynecol Reprod Biol
JT  - European journal of obstetrics, gynecology, and reproductive biology
JID - 0375672
SB  - IM
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/04/11 06:00
MHDA- 2023/04/11 06:01
CRDT- 2023/04/10 22:00
PHST- 2023/03/07 00:00 [received]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/04/11 06:01 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 22:00 [entrez]
AID - S0301-2115(23)00137-9 [pii]
AID - 10.1016/j.ejogrb.2023.04.001 [doi]
PST - ppublish
SO  - Eur J Obstet Gynecol Reprod Biol. 2023 Jun;285:208-209. doi: 
      10.1016/j.ejogrb.2023.04.001. Epub 2023 Apr 5.

PMID- 36989732
OWN - NLM
STAT- MEDLINE
DCOM- 20230612
LR  - 20231116
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 84
DP  - 2023 Jun
TI  - ChatGPT as Co-author: Are researchers impressed or distressed?
PG  - 103564
LID - S1876-2018(23)00119-3 [pii]
LID - 10.1016/j.ajp.2023.103564 [doi]
FAU - Bhatia, Gayatri
AU  - Bhatia G
AD  - Department of Psychiatry, All India Institute of Medical Sciences, Rajkot, India. 
      Electronic address: Gayatribhatia90@gmail.com.
FAU - Kulkarni, Alok
AU  - Kulkarni A
AD  - Department of Psychiatry, S.S. Institute of Medical Sciences, Davanagere, India. 
      Electronic address: alokvkulkarni@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230320
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - *Emotions
MH  - *Research Personnel
COIS- Conflict of Interest The authors declare no conflict of interest.
EDAT- 2023/03/30 06:00
MHDA- 2023/06/12 06:42
CRDT- 2023/03/29 18:06
PHST- 2023/03/03 00:00 [received]
PHST- 2023/03/14 00:00 [revised]
PHST- 2023/03/17 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/03/30 06:00 [pubmed]
PHST- 2023/03/29 18:06 [entrez]
AID - S1876-2018(23)00119-3 [pii]
AID - 10.1016/j.ajp.2023.103564 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Jun;84:103564. doi: 10.1016/j.ajp.2023.103564. Epub 2023 
      Mar 20.

PMID- 36763148
OWN - NLM
STAT- MEDLINE
DCOM- 20230313
LR  - 20230526
IS  - 2731-703X (Electronic)
IS  - 2731-7021 (Linking)
VI  - 126
IP  - 3
DP  - 2023 Mar
TI  - [ChatGPT : Milestone text AI with game changing potential].
PG  - 252-254
LID - 10.1007/s00113-023-01296-y [doi]
FAU - Krettek, C
AU  - Krettek C
AD  - Unfallchirurgische Klinik, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 
      30625, Hannover, Deutschland. krettek.christian@mh-hannover.de.
LA  - ger
PT  - Journal Article
TT  - ChatGPT : Milestone-Text-KI mit Game-Changer-Potenzial.
DEP - 20230210
PL  - Germany
TA  - Unfallchirurgie (Heidelb)
JT  - Unfallchirurgie (Heidelberg, Germany)
JID - 9918384886306676
SB  - IM
MH  - *Artificial Intelligence
EDAT- 2023/02/11 06:00
MHDA- 2023/03/14 06:00
CRDT- 2023/02/10 11:14
PHST- 2023/01/16 00:00 [accepted]
PHST- 2023/02/11 06:00 [pubmed]
PHST- 2023/03/14 06:00 [medline]
PHST- 2023/02/10 11:14 [entrez]
AID - 10.1007/s00113-023-01296-y [pii]
AID - 10.1007/s00113-023-01296-y [doi]
PST - ppublish
SO  - Unfallchirurgie (Heidelb). 2023 Mar;126(3):252-254. doi: 
      10.1007/s00113-023-01296-y. Epub 2023 Feb 10.

PMID- 36747099
OWN - NLM
STAT- Publisher
LR  - 20230207
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Feb 3
TI  - Daily briefing: Science urgently needs a plan for ChatGPT.
LID - 10.1038/d41586-023-00360-2 [doi]
FAU - Graham, Flora
AU  - Graham F
LA  - eng
PT  - News
DEP - 20230203
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
EDAT- 2023/02/08 06:00
MHDA- 2023/02/08 06:00
CRDT- 2023/02/07 00:05
PHST- 2023/02/07 00:05 [entrez]
PHST- 2023/02/08 06:00 [pubmed]
PHST- 2023/02/08 06:00 [medline]
AID - 10.1038/d41586-023-00360-2 [pii]
AID - 10.1038/d41586-023-00360-2 [doi]
PST - aheadofprint
SO  - Nature. 2023 Feb 3. doi: 10.1038/d41586-023-00360-2.

PMID- 36690769
OWN - NLM
STAT- Publisher
LR  - 20230125
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Jan 20
TI  - Daily briefing: ChatGPT listed as author on research papers.
LID - 10.1038/d41586-023-00188-w [doi]
FAU - Graham, Flora
AU  - Graham F
LA  - eng
PT  - News
DEP - 20230120
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
EDAT- 2023/01/24 06:00
MHDA- 2023/01/24 06:00
CRDT- 2023/01/23 23:21
PHST- 2023/01/24 06:00 [pubmed]
PHST- 2023/01/24 06:00 [medline]
PHST- 2023/01/23 23:21 [entrez]
AID - 10.1038/d41586-023-00188-w [pii]
AID - 10.1038/d41586-023-00188-w [doi]
PST - aheadofprint
SO  - Nature. 2023 Jan 20. doi: 10.1038/d41586-023-00188-w.

PMID- 37994592
OWN - NLM
STAT- Publisher
LR  - 20231123
IS  - 1747-4477 (Electronic)
IS  - 1329-1947 (Linking)
DP  - 2023 Nov 23
TI  - Is the field of endodontics prepared to utilise ChatGPT?
LID - 10.1111/aej.12821 [doi]
FAU - Maltarollo, Thalya Fernanda Horsth
AU  - Maltarollo TFH
AUID- ORCID: 0000-0003-1640-8898
AD  - Department of Preventive and Restorative Dentistry, School of Dentistry, 
      Araçatuba, São Paulo State University (Unesp), Araçatuba, Brazil.
FAU - Strazzi-Sahyon, Henrico Badaoui
AU  - Strazzi-Sahyon HB
AD  - Department of Dental Materials and Prosthodontics, Araçatuba School of Dentistry, 
      São Paulo State University, UNESP, Araçatuba, Brazil.
AD  - Department of Prosthodontics and Periodontology, Bauru School of Dentistry, 
      University of São Paulo, USP, Bauru, Brazil.
FAU - Amaral, Rodrigo Rodrigues
AU  - Amaral RR
AD  - College of Medicine and Dentistry, James Cook University, Douglas, Queensland, 
      Australia.
FAU - Sivieri-Araújo, Gustavo
AU  - Sivieri-Araújo G
AD  - Department of Preventive and Restorative Dentistry, School of Dentistry, 
      Araçatuba, São Paulo State University (Unesp), Araçatuba, Brazil.
LA  - eng
PT  - Letter
DEP - 20231123
PL  - Australia
TA  - Aust Endod J
JT  - Australian endodontic journal : the journal of the Australian Society of 
      Endodontology Inc
JID - 100888004
EDAT- 2023/11/23 06:42
MHDA- 2023/11/23 06:42
CRDT- 2023/11/23 05:36
PHST- 2023/11/08 00:00 [revised]
PHST- 2023/10/27 00:00 [received]
PHST- 2023/11/14 00:00 [accepted]
PHST- 2023/11/23 06:42 [medline]
PHST- 2023/11/23 06:42 [pubmed]
PHST- 2023/11/23 05:36 [entrez]
AID - 10.1111/aej.12821 [doi]
PST - aheadofprint
SO  - Aust Endod J. 2023 Nov 23. doi: 10.1111/aej.12821.

PMID- 37852644
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1535-5675 (Electronic)
IS  - 0091-4916 (Linking)
VI  - 51
IP  - 4
DP  - 2023 Dec 5
TI  - ChatGPT in Nuclear Medicine: Unveiling the Pros and Cons.
PG  - 343-344
LID - 10.2967/jnmt.123.266070 [doi]
FAU - Jaleel, Jasim
AU  - Jaleel J
FAU - Sagar, Sambit
AU  - Sagar S
FAU - Kumar, Rakesh
AU  - Kumar R
LA  - eng
PT  - Letter
DEP - 20231205
PL  - United States
TA  - J Nucl Med Technol
JT  - Journal of nuclear medicine technology
JID - 0430303
SB  - IM
MH  - *Nuclear Medicine
MH  - Radionuclide Imaging
MH  - Surveys and Questionnaires
EDAT- 2023/10/19 00:45
MHDA- 2023/12/07 12:43
CRDT- 2023/10/18 20:43
PHST- 2023/05/25 00:00 [received]
PHST- 2023/07/25 00:00 [revised]
PHST- 2023/12/07 12:43 [medline]
PHST- 2023/10/19 00:45 [pubmed]
PHST- 2023/10/18 20:43 [entrez]
AID - jnmt.123.266070 [pii]
AID - 10.2967/jnmt.123.266070 [doi]
PST - epublish
SO  - J Nucl Med Technol. 2023 Dec 5;51(4):343-344. doi: 10.2967/jnmt.123.266070.

PMID- 37253794
OWN - NLM
STAT- MEDLINE
DCOM- 20230728
LR  - 20230728
IS  - 1759-5053 (Electronic)
IS  - 1759-5045 (Linking)
VI  - 20
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT and large language models in gastroenterology.
PG  - 481-482
LID - 10.1038/s41575-023-00799-8 [doi]
FAU - Sharma, Prateek
AU  - Sharma P
AD  - University of Kansas School of Medicine, Kansas City, MO, USA. psharma@kumc.edu.
AD  - Veteran Affairs Medical Center, Kansas City, MO, USA. psharma@kumc.edu.
FAU - Parasa, Sravanthi
AU  - Parasa S
AD  - Swedish Medical Center, Seattle, WA, USA.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Nat Rev Gastroenterol Hepatol
JT  - Nature reviews. Gastroenterology & hepatology
JID - 101500079
SB  - IM
MH  - Humans
MH  - *Gastroenterology
MH  - Gastrointestinal Tract
EDAT- 2023/05/31 01:09
MHDA- 2023/07/28 06:43
CRDT- 2023/05/30 23:20
PHST- 2023/07/28 06:43 [medline]
PHST- 2023/05/31 01:09 [pubmed]
PHST- 2023/05/30 23:20 [entrez]
AID - 10.1038/s41575-023-00799-8 [pii]
AID - 10.1038/s41575-023-00799-8 [doi]
PST - ppublish
SO  - Nat Rev Gastroenterol Hepatol. 2023 Aug;20(8):481-482. doi: 
      10.1038/s41575-023-00799-8.

PMID- 37036162
OWN - NLM
STAT- MEDLINE
DCOM- 20230529
LR  - 20230602
IS  - 2151-4658 (Electronic)
IS  - 2151-464X (Linking)
VI  - 75
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT, et al…Artificial Intelligence, Authorship, and Medical Publishing.
PG  - 1196-1197
LID - 10.1002/acr.25113 [doi]
FAU - Solomon, Daniel H
AU  - Solomon DH
AUID- ORCID: 0000-0001-8202-5428
AD  - Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, 
      Cambridge, Massachusetts.
FAU - Allen, Kelli D
AU  - Allen KD
AUID- ORCID: 0000-0002-3886-0677
AD  - University of North Carolina and Durham VA Healthcare System, Chapel Hill, North 
      Carolina.
FAU - Katz, Patricia
AU  - Katz P
AUID- ORCID: 0000-0002-8146-2519
AD  - University of California, San Francisco.
FAU - Sawalha, Amr H
AU  - Sawalha AH
AD  - University of Pittsburgh, Pittsburgh, Pennsylvania.
FAU - Yelin, Ed
AU  - Yelin E
AUID- ORCID: 0000-0002-9229-9980
AD  - University of California, San Francisco.
LA  - eng
PT  - Editorial
DEP - 20230410
PL  - United States
TA  - Arthritis Care Res (Hoboken)
JT  - Arthritis care & research
JID - 101518086
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Authorship
EDAT- 2023/04/11 06:00
MHDA- 2023/05/29 06:41
CRDT- 2023/04/10 08:42
PHST- 2023/03/07 00:00 [revised]
PHST- 2023/03/01 00:00 [received]
PHST- 2023/03/07 00:00 [accepted]
PHST- 2023/05/29 06:41 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 08:42 [entrez]
AID - 10.1002/acr.25113 [doi]
PST - ppublish
SO  - Arthritis Care Res (Hoboken). 2023 Jun;75(6):1196-1197. doi: 10.1002/acr.25113. 
      Epub 2023 Apr 10.

PMID- 36888781
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230310
LR  - 20230322
IS  - 1678-9849 (Electronic)
IS  - 0037-8682 (Print)
IS  - 0037-8682 (Linking)
VI  - 56
DP  - 2023
TI  - ChatGPT: the new panacea of the academic world.
PG  - e0060
LID - S0037-86822023000100500 [pii]
LID - 10.1590/0037-8682-0060-2023 [doi]
LID - e0060-2023
FAU - Quintans-Júnior, Lucindo José
AU  - Quintans-Júnior LJ
AUID- ORCID: 0000-0001-5155-938X
AD  - Universidade Federal de Sergipe, Programa de Pós-graduação em Ciências da Saúde, 
      Aracaju, SE, Brasil.
AD  - Universidade Federal de Sergipe, Laboratório de Neurociências e Ensaios 
      Farmacológicos, São Cristóvão, SE, Brasil.
FAU - Gurgel, Ricardo Queiroz
AU  - Gurgel RQ
AUID- ORCID: 0000-0001-9651-3713
AD  - Universidade Federal de Sergipe, Programa de Pós-graduação em Ciências da Saúde, 
      Aracaju, SE, Brasil.
AD  - Universidade Federal de Sergipe, Departamento de Medicina, Aracaju, SE, Brasil.
FAU - Araújo, Adriano Antunes de Souza
AU  - Araújo AAS
AUID- ORCID: 0000-0001-9665-9923
AD  - Universidade Federal de Sergipe, Programa de Pós-graduação em Ciências da Saúde, 
      Aracaju, SE, Brasil.
AD  - Universidade Federal de Sergipe, Laboratório de Ensaios Farmacêuticos e 
      Toxicidade, São Cristóvão, SE, Brasil.
FAU - Correia, Dalmo
AU  - Correia D
AUID- ORCID: 0000-0002-2174-5058
AD  - Universidade Federal de Sergipe, Programa de Pós-graduação em Ciências da Saúde, 
      Aracaju, SE, Brasil.
AD  - Universidade Federal de Sergipe, Departamento de Medicina, Aracaju, SE, Brasil.
FAU - Martins-Filho, Paulo Ricardo
AU  - Martins-Filho PR
AUID- ORCID: 0000-0001-8779-0727
AD  - Universidade Federal de Sergipe, Programa de Pós-graduação em Ciências da Saúde, 
      Aracaju, SE, Brasil.
AD  - Universidade Federal de Sergipe, Laboratório de Patologia Investigativa, Aracaju, 
      SE, Brasil.
LA  - eng
PT  - Editorial
DEP - 20230306
PL  - Brazil
TA  - Rev Soc Bras Med Trop
JT  - Revista da Sociedade Brasileira de Medicina Tropical
JID - 7507456
SB  - IM
PMC - PMC9991106
COIS- Conflicts of interest: The authors declare no conflicts of interest.
EDAT- 2023/03/09 06:00
MHDA- 2023/03/09 06:01
CRDT- 2023/03/08 14:33
PHST- 2023/02/02 00:00 [received]
PHST- 2023/02/16 00:00 [accepted]
PHST- 2023/03/08 14:33 [entrez]
PHST- 2023/03/09 06:00 [pubmed]
PHST- 2023/03/09 06:01 [medline]
AID - S0037-86822023000100500 [pii]
AID - 10.1590/0037-8682-0060-2023 [doi]
PST - epublish
SO  - Rev Soc Bras Med Trop. 2023 Mar 6;56:e0060. doi: 10.1590/0037-8682-0060-2023. 
      eCollection 2023.

PMID- 36882613
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230309
LR  - 20230720
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 615
IP  - 7951
DP  - 2023 Mar
TI  - ChatGPT: not all languages are equal.
PG  - 216
LID - 10.1038/d41586-023-00680-3 [doi]
FAU - Seghier, Mohamed L
AU  - Seghier ML
AUID- ORCID: 0000-0002-1146-8800
LA  - eng
PT  - Comment
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
CON - Nature. 2023 Feb;614(7947):224-226. PMID: 36737653
CON - Nature. 2023 Feb;614(7947):214-216. PMID: 36747115
OTO - NOTNLM
OT  - Communication
OT  - Machine learning
EDAT- 2023/03/08 06:00
MHDA- 2023/03/08 06:01
CRDT- 2023/03/07 23:26
PHST- 2023/03/07 23:26 [entrez]
PHST- 2023/03/08 06:00 [pubmed]
PHST- 2023/03/08 06:01 [medline]
AID - 10.1038/d41586-023-00680-3 [pii]
AID - 10.1038/d41586-023-00680-3 [doi]
PST - ppublish
SO  - Nature. 2023 Mar;615(7951):216. doi: 10.1038/d41586-023-00680-3.

PMID- 37752273
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231109
IS  - 1433-8726 (Electronic)
IS  - 0724-4983 (Linking)
VI  - 41
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT and most frequent urological diseases: comment.
PG  - 3387
LID - 10.1007/s00345-023-04618-2 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Mohali, Punjab, India.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230926
PL  - Germany
TA  - World J Urol
JT  - World journal of urology
JID - 8307716
SB  - IM
CON - World J Urol. 2023 Nov;41(11):3149-3153. PMID: 37632558
MH  - Humans
MH  - *Patients
EDAT- 2023/09/27 00:42
MHDA- 2023/11/09 06:41
CRDT- 2023/09/26 23:30
PHST- 2023/08/31 00:00 [received]
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/11/09 06:41 [medline]
PHST- 2023/09/27 00:42 [pubmed]
PHST- 2023/09/26 23:30 [entrez]
AID - 10.1007/s00345-023-04618-2 [pii]
AID - 10.1007/s00345-023-04618-2 [doi]
PST - ppublish
SO  - World J Urol. 2023 Nov;41(11):3387. doi: 10.1007/s00345-023-04618-2. Epub 2023 
      Sep 26.

PMID- 37750972
OWN - NLM
STAT- Publisher
LR  - 20230926
IS  - 1556-2891 (Electronic)
IS  - 1547-769X (Linking)
DP  - 2023 Sep 26
TI  - ChatGPT and forensic science: a new dawn of investigation.
LID - 10.1007/s12024-023-00723-1 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Department of Computer Applications, Sikkim University, 6th Mile, PO-Tadong, 
      Gangtok, Sikkim, 737102, India. ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20230926
PL  - United States
TA  - Forensic Sci Med Pathol
JT  - Forensic science, medicine, and pathology
JID - 101236111
SB  - IM
EDAT- 2023/09/26 13:42
MHDA- 2023/09/26 13:42
CRDT- 2023/09/26 11:07
PHST- 2023/09/15 00:00 [accepted]
PHST- 2023/09/26 13:42 [medline]
PHST- 2023/09/26 13:42 [pubmed]
PHST- 2023/09/26 11:07 [entrez]
AID - 10.1007/s12024-023-00723-1 [pii]
AID - 10.1007/s12024-023-00723-1 [doi]
PST - aheadofprint
SO  - Forensic Sci Med Pathol. 2023 Sep 26. doi: 10.1007/s12024-023-00723-1.

PMID- 37694835
OWN - NLM
STAT- MEDLINE
DCOM- 20231128
LR  - 20231128
IS  - 1365-2516 (Electronic)
IS  - 1351-8216 (Linking)
VI  - 29
IP  - 6
DP  - 2023 Nov
TI  - How good does ChatGPT answer frequently asked questions about haemophilia?
PG  - 1646-1648
LID - 10.1111/hae.14858 [doi]
FAU - Van de Wyngaert, Caroline
AU  - Van de Wyngaert C
AD  - Division of Hematology, Hemostasis and Thrombosis Unit, Saint-Luc university 
      Hospital, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
FAU - Iarossi, Michael
AU  - Iarossi M
AUID- ORCID: 0000-0002-8873-6340
AD  - Division of Hematology, Hemostasis and Thrombosis Unit, Saint-Luc university 
      Hospital, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
FAU - Hermans, Cedric
AU  - Hermans C
AUID- ORCID: 0000-0001-5429-8437
AD  - Division of Hematology, Hemostasis and Thrombosis Unit, Saint-Luc university 
      Hospital, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
LA  - eng
PT  - Letter
DEP - 20230911
PL  - England
TA  - Haemophilia
JT  - Haemophilia : the official journal of the World Federation of Hemophilia
JID - 9442916
SB  - IM
MH  - Humans
MH  - *Hemophilia A/epidemiology
MH  - *Artificial Intelligence
EDAT- 2023/09/11 12:43
MHDA- 2023/11/14 06:42
CRDT- 2023/09/11 07:24
PHST- 2023/08/17 00:00 [revised]
PHST- 2023/04/25 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/11/14 06:42 [medline]
PHST- 2023/09/11 12:43 [pubmed]
PHST- 2023/09/11 07:24 [entrez]
AID - 10.1111/hae.14858 [doi]
PST - ppublish
SO  - Haemophilia. 2023 Nov;29(6):1646-1648. doi: 10.1111/hae.14858. Epub 2023 Sep 11.

PMID- 37401455
OWN - NLM
STAT- Publisher
LR  - 20230704
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Jul 4
TI  - ChatGPT and Dermatology: friends or foes?
LID - llad227 [pii]
LID - 10.1093/ced/llad227 [doi]
FAU - Potestio, Luca
AU  - Potestio L
AUID- ORCID: 0000-0001-5940-0592
AD  - Section of Dermatology - Department of Clinical Medicine and Surgery, University 
      of Naples Federico II.
FAU - Martora, Fabrizio
AU  - Martora F
AUID- ORCID: 0000-0003-2523-050X
AD  - Section of Dermatology - Department of Clinical Medicine and Surgery, University 
      of Naples Federico II.
FAU - Villani, Alessia
AU  - Villani A
AUID- ORCID: 0000-0001-6430-268X
AD  - Section of Dermatology - Department of Clinical Medicine and Surgery, University 
      of Naples Federico II.
FAU - Ruggiero, Angelo
AU  - Ruggiero A
AUID- ORCID: 0000-0002-4658-7391
AD  - Section of Dermatology - Department of Clinical Medicine and Surgery, University 
      of Naples Federico II.
FAU - Scalvenzi, Massimiliano
AU  - Scalvenzi M
AD  - Section of Dermatology - Department of Clinical Medicine and Surgery, University 
      of Naples Federico II.
FAU - Megna, Matteo
AU  - Megna M
AD  - Section of Dermatology - Department of Clinical Medicine and Surgery, University 
      of Naples Federico II.
LA  - eng
PT  - Journal Article
DEP - 20230704
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - dermatology
OT  - teleservices
EDAT- 2023/07/04 06:42
MHDA- 2023/07/04 06:42
CRDT- 2023/07/04 04:43
PHST- 2023/06/19 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/07/04 06:42 [medline]
PHST- 2023/07/04 06:42 [pubmed]
PHST- 2023/07/04 04:43 [entrez]
AID - 7218704 [pii]
AID - 10.1093/ced/llad227 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Jul 4:llad227. doi: 10.1093/ced/llad227.

PMID- 37310145
OWN - NLM
STAT- MEDLINE
DCOM- 20230831
LR  - 20230909
IS  - 1533-4112 (Electronic)
IS  - 1095-0680 (Linking)
VI  - 39
IP  - 3
DP  - 2023 Sep 1
TI  - ChatGPT on ECT: Can Large Language Models Support Psychoeducation?
PG  - 130-133
LID - 10.1097/YCT.0000000000000941 [doi]
FAU - Lundin, Robert M
AU  - Lundin RM
AD  - From the Barwon Health MHDAS, Change to Improve Mental Health (CHIME), University 
      Hospital Geelong, Geelong, Victoria, Australia.
FAU - Berk, Michael
AU  - Berk M
AD  - Deakin University, Institute for Mental and Physical Health and Clinical 
      Translation (IMPACT).
FAU - Østergaard, Søren Dinesen
AU  - Østergaard SD
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230612
PL  - United States
TA  - J ECT
JT  - The journal of ECT
JID - 9808943
SB  - IM
MH  - Humans
MH  - *Electroconvulsive Therapy
MH  - *Artificial Intelligence
COIS- S.D.Ø. received the 2020 Lundbeck Foundation Young Investigator Prize. S.D.Ø. 
      owns/has owned units of mutual funds with stock tickers DKIGI, IAIMWC, SPIC25KL, 
      and WEKAFKI, and has owned units of exchange traded funds with stock tickers 
      BATE, TRET, QDV5, QDVH, QDVE, SADM, IQQH, USPY, EXH2, 2B76, and EUNL. The 
      remaining authors have no conflicts of interest or financial disclosures to 
      report.
EDAT- 2023/06/13 13:12
MHDA- 2023/08/31 06:41
CRDT- 2023/06/13 09:13
PHST- 2023/08/31 06:41 [medline]
PHST- 2023/06/13 13:12 [pubmed]
PHST- 2023/06/13 09:13 [entrez]
AID - 00124509-202309000-00003 [pii]
AID - 10.1097/YCT.0000000000000941 [doi]
PST - ppublish
SO  - J ECT. 2023 Sep 1;39(3):130-133. doi: 10.1097/YCT.0000000000000941. Epub 2023 Jun 
      12.

PMID- 37253840
OWN - NLM
STAT- MEDLINE
DCOM- 20231024
LR  - 20231024
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
VI  - 47
IP  - 5
DP  - 2023 Oct
TI  - Response to "Rhinoplasty Consultation with ChatGPT" by Kleebayoon et al.
PG  - 2205-2206
LID - 10.1007/s00266-023-03417-9 [doi]
FAU - Xie, Yi
AU  - Xie Y
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia. 
      ishithseth1@gmail.com.
AD  - Faculty of Medicine, Monash University, Melbourne, VIC, 3004, Australia. 
      ishithseth1@gmail.com.
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
AD  - Faculty of Medicine, Monash University, Melbourne, VIC, 3004, Australia.
FAU - Rozen, Warren M
AU  - Rozen WM
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
AD  - Faculty of Medicine, Monash University, Melbourne, VIC, 3004, Australia.
FAU - Ross, Richard J
AU  - Ross RJ
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
FAU - Lee, Mathew
AU  - Lee M
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230530
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
CON - Aesthetic Plast Surg. 2023 Oct;47(5):2203-2204. PMID: 37212874
MH  - Humans
MH  - *Rhinoplasty
MH  - Referral and Consultation
MH  - *Artificial Intelligence
EDAT- 2023/05/31 01:09
MHDA- 2023/10/23 00:43
CRDT- 2023/05/30 23:23
PHST- 2023/05/16 00:00 [received]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2023/10/23 00:43 [medline]
PHST- 2023/05/31 01:09 [pubmed]
PHST- 2023/05/30 23:23 [entrez]
AID - 10.1007/s00266-023-03417-9 [pii]
AID - 10.1007/s00266-023-03417-9 [doi]
PST - ppublish
SO  - Aesthetic Plast Surg. 2023 Oct;47(5):2205-2206. doi: 10.1007/s00266-023-03417-9. 
      Epub 2023 May 30.

PMID- 37162203
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230801
LR  - 20231204
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
VI  - 98
IP  - 8
DP  - 2023 Aug 1
TI  - ChatGPT: A Helpful Tool for Resident Physicians?
PG  - 868-869
LID - 10.1097/ACM.0000000000005250 [doi]
FAU - Munaf, Uzair
AU  - Munaf U
AD  - Physician, Department of Medicine, Dow Medical College, Dow University of Health 
      Sciences, Karachi, Pakistan.
FAU - Ul-Haque, Ibtehaj
AU  - Ul-Haque I
AD  - Physician, Department of Medicine, Dow Medical College, Dow University of Health 
      Sciences, Karachi, Pakistan.
FAU - Arif, Taha Bin
AU  - Arif TB
AUID- ORCID: 0000-0003-4879-560
AD  - Physician, Department of Medicine, Dow Medical College, Dow University of Health 
      Sciences, Karachi, Pakistan; email: tahaarif20@yahoo.com .
LA  - eng
PT  - Letter
DEP - 20230510
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
EDAT- 2023/05/10 12:42
MHDA- 2023/07/27 06:43
CRDT- 2023/05/10 09:03
PHST- 2023/07/27 06:43 [medline]
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 09:03 [entrez]
AID - 00001888-990000000-00448 [pii]
AID - 10.1097/ACM.0000000000005250 [doi]
PST - ppublish
SO  - Acad Med. 2023 Aug 1;98(8):868-869. doi: 10.1097/ACM.0000000000005250. Epub 2023 
      May 10.

PMID- 37150470
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231024
LR  - 20231024
IS  - 1528-0012 (Electronic)
IS  - 0016-5085 (Linking)
VI  - 165
IP  - 2
DP  - 2023 Aug
TI  - ChatGPT Answers Common Patient Questions About Colonoscopy.
PG  - 509-511.e7
LID - S0016-5085(23)00704-7 [pii]
LID - 10.1053/j.gastro.2023.04.033 [doi]
FAU - Lee, Tsung-Chun
AU  - Lee TC
AD  - Division of Gastroenterology and Hepatology, Department of Internal Medicine, 
      Taipei Medical University Shuang Ho Hospital, New Taipei City, Taiwan; Department 
      of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical 
      University, Taipei, Taiwan; TMU Research Center for Digestive Medicine, Taipei 
      Medical University, Taipei, Taiwan.
FAU - Staller, Kyle
AU  - Staller K
AD  - Center for Neurointestinal Health, Massachusetts General Hospital, Boston, 
      Massachusetts; Division of Gastroenterology, Massachusetts General Hospital, 
      Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, 
      Massachusetts.
FAU - Botoman, Vlaicu
AU  - Botoman V
AD  - Center for Neurointestinal Health, Massachusetts General Hospital, Boston, 
      Massachusetts; Division of Gastroenterology, Massachusetts General Hospital, 
      Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, 
      Massachusetts.
FAU - Pathipati, Mythili P
AU  - Pathipati MP
AD  - Center for Neurointestinal Health, Massachusetts General Hospital, Boston, 
      Massachusetts; Division of Gastroenterology, Massachusetts General Hospital, 
      Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, 
      Massachusetts.
FAU - Varma, Sanskriti
AU  - Varma S
AD  - Center for Neurointestinal Health, Massachusetts General Hospital, Boston, 
      Massachusetts; Division of Gastroenterology, Massachusetts General Hospital, 
      Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, 
      Massachusetts.
FAU - Kuo, Braden
AU  - Kuo B
AD  - Center for Neurointestinal Health, Massachusetts General Hospital, Boston, 
      Massachusetts; Division of Gastroenterology, Massachusetts General Hospital, 
      Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, 
      Massachusetts. Electronic address: bkuo@partners.org.
LA  - eng
GR  - K23 DK120945/DK/NIDDK NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20230505
PL  - United States
TA  - Gastroenterology
JT  - Gastroenterology
JID - 0374630
SB  - IM
CIN - Gastroenterology. 2023 Aug;165(2):336-338. PMID: 37321355
EDAT- 2023/05/08 00:41
MHDA- 2023/10/23 12:42
CRDT- 2023/05/07 19:28
PHST- 2023/03/06 00:00 [received]
PHST- 2023/04/19 00:00 [revised]
PHST- 2023/04/30 00:00 [accepted]
PHST- 2023/10/23 12:42 [medline]
PHST- 2023/05/08 00:41 [pubmed]
PHST- 2023/05/07 19:28 [entrez]
AID - S0016-5085(23)00704-7 [pii]
AID - 10.1053/j.gastro.2023.04.033 [doi]
PST - ppublish
SO  - Gastroenterology. 2023 Aug;165(2):509-511.e7. doi: 10.1053/j.gastro.2023.04.033. 
      Epub 2023 May 5.

PMID- 37094207
OWN - NLM
STAT- MEDLINE
DCOM- 20230426
LR  - 20230620
IS  - 1669-9106 (Electronic)
IS  - 0025-7680 (Linking)
VI  - 83
IP  - 2
DP  - 2023
TI  - [Artificial intelligence and chatGPT. Would you read an artificial author?].
PG  - 329-332
FAU - De Vito, Eduardo L
AU  - De Vito EL
AD  - Instituto de Investigaciones Médicas Alfredo Lanari, Buenos Aires, Argentina. 
      E-mail: eldevito@gmail.com.
LA  - spa
PT  - Editorial
TT  - Inteligencia artificial y chatGPT. ¿Usted leería a un autor artificial?
PL  - Argentina
TA  - Medicina (B Aires)
JT  - Medicina
JID - 0204271
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
EDAT- 2023/04/24 18:42
MHDA- 2023/04/26 06:41
CRDT- 2023/04/24 03:43
PHST- 2023/04/26 06:41 [medline]
PHST- 2023/04/24 18:42 [pubmed]
PHST- 2023/04/24 03:43 [entrez]
PST - ppublish
SO  - Medicina (B Aires). 2023;83(2):329-332.

PMID- 37016002
OWN - NLM
STAT- MEDLINE
DCOM- 20230824
LR  - 20231018
IS  - 1530-0447 (Electronic)
IS  - 0031-3998 (Linking)
VI  - 94
IP  - 3
DP  - 2023 Sep
TI  - ChatGPT-coauthored conversation between a pediatrician and a taxi driver.
PG  - 856-857
LID - 10.1038/s41390-023-02571-9 [doi]
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Department, College of Medicine, King Saud University, Riyadh, Saudi 
      Arabia. mtemsah@ksu.edu.sa.
FAU - Al-Eyadhy, Ayman
AU  - Al-Eyadhy A
AD  - Pediatric Department, College of Medicine, King Saud University, Riyadh, Saudi 
      Arabia.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Family and Community Medicine Department, King Saud University, Riyadh, Saudi 
      Arabia.
AD  - Evidence-Based Healthcare and Knowledge Translation Research Chair, King Saud 
      University, Riyadh, Saudi Arabia.
LA  - eng
PT  - Letter
DEP - 20230404
PL  - United States
TA  - Pediatr Res
JT  - Pediatric research
JID - 0100714
SB  - IM
MH  - Humans
MH  - Surveys and Questionnaires
MH  - *Pediatricians
EDAT- 2023/04/05 06:00
MHDA- 2023/08/24 06:42
CRDT- 2023/04/04 23:20
PHST- 2023/02/23 00:00 [received]
PHST- 2023/03/04 00:00 [accepted]
PHST- 2023/08/24 06:42 [medline]
PHST- 2023/04/05 06:00 [pubmed]
PHST- 2023/04/04 23:20 [entrez]
AID - 10.1038/s41390-023-02571-9 [pii]
AID - 10.1038/s41390-023-02571-9 [doi]
PST - ppublish
SO  - Pediatr Res. 2023 Sep;94(3):856-857. doi: 10.1038/s41390-023-02571-9. Epub 2023 
      Apr 4.

PMID- 37004198
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231114
IS  - 1365-2702 (Electronic)
IS  - 0962-1067 (Linking)
VI  - 32
IP  - 21-22
DP  - 2023 Nov
TI  - ChatGPT and the teaching of contemporary nursing: And now professor?
PG  - 7921-7922
LID - 10.1111/jocn.16706 [doi]
FAU - Vitorino, Luciano Magalhães
AU  - Vitorino LM
AUID- ORCID: 0000-0003-1023-8488
AD  - Faculty of Medicine of Itajubá, Rennó Júnior avenue, 368, 37.502-138, Itajubá, 
      Minas Gerais, Brazil.
FAU - Júnior, Gerson Hiroshi Yoshinari
AU  - Júnior GHY
AUID- ORCID: 0000-0001-5837-5743
AD  - Faculty of Medicine of Itajubá, Rennó Júnior avenue, 368, 37.502-138, Itajubá, 
      Minas Gerais, Brazil.
LA  - eng
PT  - Letter
DEP - 20230402
PL  - England
TA  - J Clin Nurs
JT  - Journal of clinical nursing
JID - 9207302
CIN - J Clin Nurs. 2023 Oct;32(19-20):7642. PMID: 37194403
MH  - Humans
MH  - *Students, Nursing
MH  - Teaching
EDAT- 2023/04/03 06:00
MHDA- 2023/10/23 12:43
CRDT- 2023/04/02 14:11
PHST- 2023/03/15 00:00 [received]
PHST- 2023/03/20 00:00 [accepted]
PHST- 2023/10/23 12:43 [medline]
PHST- 2023/04/03 06:00 [pubmed]
PHST- 2023/04/02 14:11 [entrez]
AID - 10.1111/jocn.16706 [doi]
PST - ppublish
SO  - J Clin Nurs. 2023 Nov;32(21-22):7921-7922. doi: 10.1111/jocn.16706. Epub 2023 Apr 
      2.

PMID- 36959062
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230616
LR  - 20230705
IS  - 0035-3787 (Print)
IS  - 0035-3787 (Linking)
VI  - 179
IP  - 6
DP  - 2023 Jun
TI  - Neuro-ChatGPT? Potential threats and certain opportunities.
PG  - 517-519
LID - S0035-3787(23)00879-2 [pii]
LID - 10.1016/j.neurol.2023.02.066 [doi]
FAU - Moisset, X
AU  - Moisset X
AD  - Université Clermont-Auvergne, CHU de Clermont-Ferrand, Inserm, Neuro-Dol, service 
      de neurologie, 58, rue Montalembert, 63000 Clermont-Ferrand, France. Electronic 
      address: xavier.moisset@gmail.com.
FAU - Ciampi de Andrade, D
AU  - Ciampi de Andrade D
AD  - Center for Neuroplasticity and Pain, Health Science and Technology department, 
      Faculty of Medicine, Aalborg University, Aalborg, Denmark.
LA  - eng
PT  - Editorial
DEP - 20230321
PL  - France
TA  - Rev Neurol (Paris)
JT  - Revue neurologique
JID - 2984779R
SB  - IM
EDAT- 2023/03/24 06:00
MHDA- 2023/03/24 06:01
CRDT- 2023/03/23 23:07
PHST- 2023/02/11 00:00 [received]
PHST- 2023/02/22 00:00 [accepted]
PHST- 2023/03/24 06:01 [medline]
PHST- 2023/03/24 06:00 [pubmed]
PHST- 2023/03/23 23:07 [entrez]
AID - S0035-3787(23)00879-2 [pii]
AID - 10.1016/j.neurol.2023.02.066 [doi]
PST - ppublish
SO  - Rev Neurol (Paris). 2023 Jun;179(6):517-519. doi: 10.1016/j.neurol.2023.02.066. 
      Epub 2023 Mar 21.

PMID- 36934593
OWN - NLM
STAT- MEDLINE
DCOM- 20230509
LR  - 20230510
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 83
DP  - 2023 May
TI  - ChatGPT, 'polypsychic' artificial intelligence, and psychiatry in museums.
PG  - 103548
LID - S1876-2018(23)00103-X [pii]
LID - 10.1016/j.ajp.2023.103548 [doi]
FAU - Kahambing, Jan Gresil
AU  - Kahambing JG
AD  - Department of Philosophy and Religious Studies, University of Macau, SAR, China. 
      Electronic address: yc07706@umac.mo.
LA  - eng
PT  - Letter
DEP - 20230315
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - Museums
MH  - *Psychiatry
MH  - *Mental Disorders/diagnosis/therapy
COIS- Conflict of Interest The author declares no conflict of interest.
EDAT- 2023/03/20 06:00
MHDA- 2023/05/09 10:16
CRDT- 2023/03/19 19:04
PHST- 2023/03/03 00:00 [received]
PHST- 2023/03/13 00:00 [revised]
PHST- 2023/03/14 00:00 [accepted]
PHST- 2023/05/09 10:16 [medline]
PHST- 2023/03/20 06:00 [pubmed]
PHST- 2023/03/19 19:04 [entrez]
AID - S1876-2018(23)00103-X [pii]
AID - 10.1016/j.ajp.2023.103548 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 May;83:103548. doi: 10.1016/j.ajp.2023.103548. Epub 2023 
      Mar 15.

PMID- 38069600
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 1747-4477 (Electronic)
IS  - 1329-1947 (Linking)
DP  - 2023 Dec 9
TI  - Correspondence on 'Is the field of endodontics prepared to utilise ChatGPT?'.
LID - 10.1111/aej.12823 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Laos.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha 
      Institute of Medical and Technical Sciences Saveetha University, Chennai, India.
LA  - eng
PT  - Letter
DEP - 20231209
PL  - Australia
TA  - Aust Endod J
JT  - Australian endodontic journal : the journal of the Australian Society of 
      Endodontology Inc
JID - 100888004
EDAT- 2023/12/09 10:44
MHDA- 2023/12/09 10:44
CRDT- 2023/12/09 05:27
PHST- 2023/11/26 00:00 [received]
PHST- 2023/11/29 00:00 [accepted]
PHST- 2023/12/09 10:44 [medline]
PHST- 2023/12/09 10:44 [pubmed]
PHST- 2023/12/09 05:27 [entrez]
AID - 10.1111/aej.12823 [doi]
PST - aheadofprint
SO  - Aust Endod J. 2023 Dec 9. doi: 10.1111/aej.12823.

PMID- 37133539
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230710
LR  - 20231211
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT/GPT-4 and Spinal Surgeons.
PG  - 1657
LID - 10.1007/s10439-023-03223-z [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - , Samraong, Cambodia. amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Mohali, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230503
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
EDAT- 2023/05/03 12:42
MHDA- 2023/05/03 12:43
CRDT- 2023/05/03 11:05
PHST- 2023/04/25 00:00 [received]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/05/03 12:43 [medline]
PHST- 2023/05/03 12:42 [pubmed]
PHST- 2023/05/03 11:05 [entrez]
AID - 10.1007/s10439-023-03223-z [pii]
AID - 10.1007/s10439-023-03223-z [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Aug;51(8):1657. doi: 10.1007/s10439-023-03223-z. Epub 2023 
      May 3.

PMID- 37426542
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2666-3864 (Electronic)
IS  - 2666-3864 (Linking)
VI  - 4
IP  - 6
DP  - 2023 Jun 21
TI  - Distinguishing academic science writing from humans or ChatGPT with over 99% 
      accuracy using off-the-shelf machine learning tools.
LID - 101426 [pii]
LID - 10.1016/j.xcrp.2023.101426 [doi]
AB  - ChatGPT has enabled access to artificial intelligence (AI)-generated writing for 
      the masses, initiating a culture shift in the way people work, learn, and write. 
      The need to discriminate human writing from AI is now both critical and urgent. 
      Addressing this need, we report a method for discriminating text generated by 
      ChatGPT from (human) academic scientists, relying on prevalent and accessible 
      supervised classification methods. The approach uses new features for 
      discriminating (these) humans from AI; as examples, scientists write long 
      paragraphs and have a penchant for equivocal language, frequently using words 
      like "but," "however," and "although." With a set of 20 features, we built a 
      model that assigns the author, as human or AI, at over 99% accuracy. This 
      strategy could be further adapted and developed by others with basic skills in 
      supervised classification, enabling access to many highly accurate and targeted 
      models for detecting AI usage in academic writing and beyond.
FAU - Desaire, Heather
AU  - Desaire H
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
AD  - Lead contact.
FAU - Chua, Aleesa E
AU  - Chua AE
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
FAU - Isom, Madeline
AU  - Isom M
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
FAU - Jarosova, Romana
AU  - Jarosova R
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
FAU - Hua, David
AU  - Hua D
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
LA  - eng
GR  - R35 GM130354/GM/NIGMS NIH HHS/United States
PT  - Journal Article
DEP - 20230607
PL  - United States
TA  - Cell Rep Phys Sci
JT  - Cell reports. Physical science
JID - 101769239
PMC - PMC10328544
MID - NIHMS1911044
COIS- DECLARATION OF INTERESTS The authors declare no competing interests.
EDAT- 2023/07/10 06:42
MHDA- 2023/07/10 06:43
CRDT- 2023/07/10 05:11
PHST- 2023/07/10 06:43 [medline]
PHST- 2023/07/10 06:42 [pubmed]
PHST- 2023/07/10 05:11 [entrez]
AID - 101426 [pii]
AID - 10.1016/j.xcrp.2023.101426 [doi]
PST - ppublish
SO  - Cell Rep Phys Sci. 2023 Jun 21;4(6):101426. doi: 10.1016/j.xcrp.2023.101426. Epub 
      2023 Jun 7.

PMID- 36906947
OWN - NLM
STAT- MEDLINE
DCOM- 20230404
LR  - 20230404
IS  - 1873-5223 (Electronic)
IS  - 1471-5953 (Linking)
VI  - 68
DP  - 2023 Mar
TI  - Is ChatGPT a valid author?
PG  - 103600
LID - S1471-5953(23)00062-8 [pii]
LID - 10.1016/j.nepr.2023.103600 [doi]
AB  - This letter to the editors takes a deeper look at the validity and ethics of 
      authorship of a recently published article in Nurse Education in Practice in 
      which authorship was shared with a chatbox software program, ChatGPT 
      (https://doi.org/10.1016/j.nepr.2022.103537). In particular, a closer assessment 
      is made of the authorship of that article from the established principles of 
      authorship as delineated by the ICMJE.
CI  - Copyright © 2023 Elsevier Ltd. All rights reserved.
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Independent researcher, Ikenobe 3011-2, Kagawa-ken 761-0799, Japan. Electronic 
      address: jaimetex@yahoo.com.
LA  - eng
PT  - Editorial
DEP - 20230307
PL  - Scotland
TA  - Nurse Educ Pract
JT  - Nurse education in practice
JID - 101090848
SB  - IM
MH  - Humans
MH  - *Editorial Policies
MH  - *Authorship
MH  - Publications
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - Authorship principles
OT  - COPE
OT  - Ethics
OT  - ICMJE
OT  - Responsibility
COIS- Conflicts of Interest The author, who declares no conflicts of interest, 
      contributed to the conceptual design, writing, editing, and takes responsibility 
      for the content of the paper (i.e., according to ICMJE recommendations).
EDAT- 2023/03/13 06:00
MHDA- 2023/04/04 06:42
CRDT- 2023/03/12 19:02
PHST- 2023/02/24 00:00 [received]
PHST- 2023/03/06 00:00 [accepted]
PHST- 2023/04/04 06:42 [medline]
PHST- 2023/03/13 06:00 [pubmed]
PHST- 2023/03/12 19:02 [entrez]
AID - S1471-5953(23)00062-8 [pii]
AID - 10.1016/j.nepr.2023.103600 [doi]
PST - ppublish
SO  - Nurse Educ Pract. 2023 Mar;68:103600. doi: 10.1016/j.nepr.2023.103600. Epub 2023 
      Mar 7.

PMID- 37828840
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 2287-285X (Electronic)
IS  - 2287-2728 (Linking)
DP  - 2023 Oct 13
TI  - Comment on "ChatGPT in answering questions regarding cirrhosis and hepatocellular 
      carcinoma.".
LID - 10.3350/cmh.2023.0394 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AD  - Private Academic Consultant, Phonhong, Lao People's Democratic Republic.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Adjunct professor, Chandigarh University, India; Adjunct professor, Joseph 
      Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20231013
PL  - Korea (South)
TA  - Clin Mol Hepatol
JT  - Clinical and molecular hepatology
JID - 101586730
SB  - IM
EDAT- 2023/10/13 06:45
MHDA- 2023/10/13 06:45
CRDT- 2023/10/13 03:25
PHST- 2023/10/05 00:00 [received]
PHST- 2023/10/11 00:00 [accepted]
PHST- 2023/10/13 06:45 [medline]
PHST- 2023/10/13 06:45 [pubmed]
PHST- 2023/10/13 03:25 [entrez]
AID - cmh.2023.0394 [pii]
AID - 10.3350/cmh.2023.0394 [doi]
PST - aheadofprint
SO  - Clin Mol Hepatol. 2023 Oct 13. doi: 10.3350/cmh.2023.0394.

PMID- 37801561
OWN - NLM
STAT- Publisher
LR  - 20231031
IS  - 1527-6473 (Electronic)
IS  - 1527-6465 (Linking)
DP  - 2023 Oct 9
TI  - Reply: ChatGPT and unknown clinical questions about liver transplantation.
LID - 10.1097/LVT.0000000000000282 [doi]
FAU - Akabane, Miho
AU  - Akabane M
AUID- ORCID: 0000-0002-6705-5730
AD  - Department of Surgery, Division of Abdominal Transplant, Stanford University 
      Medical Center, Stanford, California, USA.
FAU - Iwadoh, Kazuhiro
AU  - Iwadoh K
AUID- ORCID: 0000-0003-0508-7830
FAU - Melcher, Marc L
AU  - Melcher ML
AUID- ORCID: 0000-0002-7185-4383
FAU - Sasaki, Kazunari
AU  - Sasaki K
AUID- ORCID: 0000-0003-2514-5778
LA  - eng
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Liver Transpl
JT  - Liver transplantation : official publication of the American Association for the 
      Study of Liver Diseases and the International Liver Transplantation Society
JID - 100909185
SB  - IM
EDAT- 2023/10/06 18:42
MHDA- 2023/10/06 18:42
CRDT- 2023/10/06 14:13
PHST- 2023/09/22 00:00 [received]
PHST- 2023/09/22 00:00 [accepted]
PHST- 2023/10/06 18:42 [pubmed]
PHST- 2023/10/06 18:42 [medline]
PHST- 2023/10/06 14:13 [entrez]
AID - 01445473-990000000-00263 [pii]
AID - 10.1097/LVT.0000000000000282 [doi]
PST - aheadofprint
SO  - Liver Transpl. 2023 Oct 9. doi: 10.1097/LVT.0000000000000282.

PMID- 37738926
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231121
IS  - 1578-1747 (Electronic)
IS  - 0211-139X (Linking)
VI  - 58
IP  - 6
DP  - 2023 Nov-Dec
TI  - [Artificial intelligence in geriatrics: ChatGPT and AI impact].
PG  - 101403
LID - S0211-139X(23)00124-5 [pii]
LID - 10.1016/j.regg.2023.101403 [doi]
FAU - Fontecha-Gómez, B J
AU  - Fontecha-Gómez BJ
AD  - Servicio de Geriatría y Cuidados Paliativos, Consorci Sanitari Integral, 
      L'Hospitalet de Llobregat, Barcelona, España. Electronic address: 
      bfontecha@csi.cat.
FAU - Betancor-Santana, É
AU  - Betancor-Santana É
AD  - Servicio de Geriatría y Cuidados Paliativos, Consorci Sanitari Integral, 
      L'Hospitalet de Llobregat, Barcelona, España.
LA  - spa
PT  - Journal Article
TT  - Inteligencia artificial en geriatría. Impacto de ChatGPT e IA.
DEP - 20230920
PL  - Spain
TA  - Rev Esp Geriatr Gerontol
JT  - Revista espanola de geriatria y gerontologia
JID - 8009022
SB  - IM
MH  - Humans
MH  - Aged
MH  - *Artificial Intelligence
MH  - *Geriatrics
EDAT- 2023/09/23 11:42
MHDA- 2023/11/20 06:54
CRDT- 2023/09/22 18:11
PHST- 2023/07/24 00:00 [received]
PHST- 2023/07/28 00:00 [accepted]
PHST- 2023/11/20 06:54 [medline]
PHST- 2023/09/23 11:42 [pubmed]
PHST- 2023/09/22 18:11 [entrez]
AID - S0211-139X(23)00124-5 [pii]
AID - 10.1016/j.regg.2023.101403 [doi]
PST - ppublish
SO  - Rev Esp Geriatr Gerontol. 2023 Nov-Dec;58(6):101403. doi: 
      10.1016/j.regg.2023.101403. Epub 2023 Sep 20.

PMID- 37660692
OWN - NLM
STAT- Publisher
LR  - 20230903
IS  - 1421-9786 (Electronic)
IS  - 1015-9770 (Linking)
DP  - 2023 Sep 2
TI  - Artificial Intelligence and Cerebrovascular Diseases: ChatGPT model.
LID - 10.1159/000533967 [doi]
FAU - Altunisik, Erman
AU  - Altunisik E
LA  - eng
PT  - Letter
DEP - 20230902
PL  - Switzerland
TA  - Cerebrovasc Dis
JT  - Cerebrovascular diseases (Basel, Switzerland)
JID - 9100851
SB  - IM
EDAT- 2023/09/04 00:41
MHDA- 2023/09/04 00:41
CRDT- 2023/09/03 18:23
PHST- 2023/07/12 00:00 [received]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/09/04 00:41 [medline]
PHST- 2023/09/04 00:41 [pubmed]
PHST- 2023/09/03 18:23 [entrez]
AID - 000533967 [pii]
AID - 10.1159/000533967 [doi]
PST - aheadofprint
SO  - Cerebrovasc Dis. 2023 Sep 2. doi: 10.1159/000533967.

PMID- 37470178
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231006
LR  - 20231020
IS  - 1528-1140 (Electronic)
IS  - 0003-4932 (Linking)
VI  - 278
IP  - 5
DP  - 2023 Nov 1
TI  - Is ChatGPT a Threat to Surgical Workforce Diversity?
PG  - e941-e942
LID - 10.1097/SLA.0000000000006036 [doi]
FAU - Weaver, M Libby
AU  - Weaver ML
AD  - Division of Vascular and Endovascular Surgery, University of Virginia, 
      Charlottesville, VA.
LA  - eng
PT  - Journal Article
DEP - 20230720
PL  - United States
TA  - Ann Surg
JT  - Annals of surgery
JID - 0372354
SB  - IM
COIS- The author reports no conflicts of interest.
EDAT- 2023/07/20 06:42
MHDA- 2023/07/20 06:43
CRDT- 2023/07/20 05:33
PHST- 2023/07/20 06:43 [medline]
PHST- 2023/07/20 06:42 [pubmed]
PHST- 2023/07/20 05:33 [entrez]
AID - 00000658-202311000-00031 [pii]
AID - 10.1097/SLA.0000000000006036 [doi]
PST - ppublish
SO  - Ann Surg. 2023 Nov 1;278(5):e941-e942. doi: 10.1097/SLA.0000000000006036. Epub 
      2023 Jul 20.

PMID- 37162219
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230801
LR  - 20231204
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
VI  - 98
IP  - 8
DP  - 2023 Aug 1
TI  - ChatGPT and the Future of Medical Education.
PG  - 867-868
LID - 10.1097/ACM.0000000000005242 [doi]
FAU - Feng, Songwei
AU  - Feng S
AD  - Tenth-year medical student, Department of Obstetrics and Gynecology, Zhongda 
      Hospital, School of Medicine, Southeast University, Nanjing, China; email: 
      soochowfengsw@163.com .
FAU - Shen, Yang
AU  - Shen Y
AD  - Professor, Department of Obstetrics and Gynecology, Zhongda Hospital, School of 
      Medicine, Southeast University, Nanjing, China.
LA  - eng
PT  - Letter
DEP - 20230510
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
EDAT- 2023/05/10 12:42
MHDA- 2023/07/27 06:42
CRDT- 2023/05/10 09:04
PHST- 2023/07/27 06:42 [medline]
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 09:04 [entrez]
AID - 00001888-990000000-00444 [pii]
AID - 10.1097/ACM.0000000000005242 [doi]
PST - ppublish
SO  - Acad Med. 2023 Aug 1;98(8):867-868. doi: 10.1097/ACM.0000000000005242. Epub 2023 
      May 10.

PMID- 36988908
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230331
LR  - 20230718
IS  - 1096-4673 (Electronic)
IS  - 0161-2840 (Linking)
VI  - 44
IP  - 3
DP  - 2023 Mar
TI  - Grappling with the Implications of ChatGPT for Researchers, Clinicians, and 
      Educators.
PG  - 141-142
LID - 10.1080/01612840.2023.2180982 [doi]
FAU - Thomas, Sandra P
AU  - Thomas SP
AD  - University of Tennessee, Knoxville, Knoxville, Tennessee, USA.
LA  - eng
PT  - Editorial
PL  - England
TA  - Issues Ment Health Nurs
JT  - Issues in mental health nursing
JID - 7907126
EDAT- 2023/03/30 06:00
MHDA- 2023/03/30 06:01
CRDT- 2023/03/29 11:24
PHST- 2023/03/30 06:01 [medline]
PHST- 2023/03/29 11:24 [entrez]
PHST- 2023/03/30 06:00 [pubmed]
AID - 10.1080/01612840.2023.2180982 [doi]
PST - ppublish
SO  - Issues Ment Health Nurs. 2023 Mar;44(3):141-142. doi: 
      10.1080/01612840.2023.2180982.

PMID- 36921837
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230530
LR  - 20230530
IS  - 1556-5653 (Electronic)
IS  - 0015-0282 (Linking)
VI  - 119
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT: rumors of human providers' demise have been greatly exaggerated.
PG  - 930-931
LID - S0015-0282(23)00217-0 [pii]
LID - 10.1016/j.fertnstert.2023.03.010 [doi]
FAU - Alvero, Ruben
AU  - Alvero R
AD  - Fertility and Reproductive Health, Lucille Packard Children's Hospital, 
      Sunnyvale, California; Department of Obstetrics and Gynecology, Stanford 
      University School of Medicine, Stanford, California.
LA  - eng
PT  - Editorial
DEP - 20230313
PL  - United States
TA  - Fertil Steril
JT  - Fertility and sterility
JID - 0372772
SB  - IM
EDAT- 2023/03/16 06:00
MHDA- 2023/03/16 06:01
CRDT- 2023/03/15 20:29
PHST- 2023/03/07 00:00 [received]
PHST- 2023/03/08 00:00 [accepted]
PHST- 2023/03/16 06:01 [medline]
PHST- 2023/03/16 06:00 [pubmed]
PHST- 2023/03/15 20:29 [entrez]
AID - S0015-0282(23)00217-0 [pii]
AID - 10.1016/j.fertnstert.2023.03.010 [doi]
PST - ppublish
SO  - Fertil Steril. 2023 Jun;119(6):930-931. doi: 10.1016/j.fertnstert.2023.03.010. 
      Epub 2023 Mar 13.

PMID- 36517680
OWN - NLM
STAT- Publisher
LR  - 20221214
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2022 Dec 12
TI  - Daily briefing: Will ChatGPT kill the essay assignment?
LID - 10.1038/d41586-022-04437-2 [doi]
FAU - Graham, Flora
AU  - Graham F
LA  - eng
PT  - News
DEP - 20221212
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
EDAT- 2022/12/15 06:00
MHDA- 2022/12/15 06:00
CRDT- 2022/12/14 23:35
PHST- 2022/12/14 23:35 [entrez]
PHST- 2022/12/15 06:00 [pubmed]
PHST- 2022/12/15 06:00 [medline]
AID - 10.1038/d41586-022-04437-2 [pii]
AID - 10.1038/d41586-022-04437-2 [doi]
PST - aheadofprint
SO  - Nature. 2022 Dec 12. doi: 10.1038/d41586-022-04437-2.

PMID- 38028124
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 1178-6329 (Print)
IS  - 1178-6329 (Electronic)
IS  - 1178-6329 (Linking)
VI  - 16
DP  - 2023
TI  - Correspondence to ChatGPT: A Double-Edged Sword?
PG  - 11786329231212857
LID - 10.1177/11786329231212857 [doi]
LID - 11786329231212857
FAU - Azeez, Mohammad Anas
AU  - Azeez MA
AUID- ORCID: 0009-0000-3344-0166
AD  - Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi, 
      India.
FAU - Siddiqui, Zohaib Hasan
AU  - Siddiqui ZH
AD  - Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi, 
      India.
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AD  - Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi, 
      India.
LA  - eng
PT  - Journal Article
DEP - 20231114
PL  - United States
TA  - Health Serv Insights
JT  - Health services insights
JID - 101624726
PMC - PMC10647918
COIS- The author(s) declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/11/29 18:41
MHDA- 2023/11/29 18:42
CRDT- 2023/11/29 17:10
PHST- 2023/11/29 18:42 [medline]
PHST- 2023/11/29 18:41 [pubmed]
PHST- 2023/11/29 17:10 [entrez]
AID - 10.1177_11786329231212857 [pii]
AID - 10.1177/11786329231212857 [doi]
PST - epublish
SO  - Health Serv Insights. 2023 Nov 14;16:11786329231212857. doi: 
      10.1177/11786329231212857. eCollection 2023.

PMID- 37985460
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1398-9995 (Electronic)
IS  - 0105-4538 (Linking)
DP  - 2023 Nov 20
TI  - Statistical advice provided by ChatGPT regarding an accepted article in Allergy.
LID - 10.1111/all.15956 [doi]
FAU - Ordak, Michal
AU  - Ordak M
AD  - Department of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, 
      Medical University of Warsaw, Warsaw, Poland.
FAU - Canonica, Giorgio Walter
AU  - Canonica GW
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Italy.
AD  - Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, 
      Milan, Italy.
FAU - Paoletti, Giovanni
AU  - Paoletti G
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Italy.
AD  - Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, 
      Milan, Italy.
FAU - Brussino, Liusa
AU  - Brussino L
AD  - SCDU Immunologia e Allergologia, AO Ordine Mauriziano di Torino, Torino, Italy.
AD  - Dipartimento di Scienze Mediche, Università degli Studi di Torino, Torino, Italy.
FAU - Carvalho, Daniela
AU  - Carvalho D
AD  - NOVA National School of Public Health, Public Health Research Centre, 
      Universidade NOVA de Lisboa, Lisbon, Portugal.
FAU - Di Bona, Danilo
AU  - Di Bona D
AUID- ORCID: 0000-0002-9728-3806
AD  - Department of Medical and Surgical Sciences, School of Allergology and Clinical 
      Immunology, University of Foggia, Foggia, Italy.
LA  - eng
PT  - Letter
DEP - 20231120
PL  - Denmark
TA  - Allergy
JT  - Allergy
JID - 7804028
SB  - IM
EDAT- 2023/11/21 01:07
MHDA- 2023/11/21 01:07
CRDT- 2023/11/20 23:13
PHST- 2023/11/08 00:00 [revised]
PHST- 2023/10/17 00:00 [received]
PHST- 2023/11/09 00:00 [accepted]
PHST- 2023/11/21 01:07 [medline]
PHST- 2023/11/21 01:07 [pubmed]
PHST- 2023/11/20 23:13 [entrez]
AID - 10.1111/all.15956 [doi]
PST - aheadofprint
SO  - Allergy. 2023 Nov 20. doi: 10.1111/all.15956.

PMID- 37794865
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2634-3916 (Electronic)
IS  - 2634-3916 (Linking)
VI  - 4
IP  - 5
DP  - 2023 Oct
TI  - Does ChatGPT succeed in the European Exam in Core Cardiology?
PG  - 362-363
LID - 10.1093/ehjdh/ztad040 [doi]
FAU - Plummer, Chris
AU  - Plummer C
AUID- ORCID: 0000-0003-4135-3008
AD  - Department of Cardiology, Freeman Hospital, The Newcastle upon Tyne Hospitals NHS 
      Foundation Trust, Freeman Road, High Heaton, Newcastle upon Tyne, NE7 7DN, UK.
AD  - European Society of Cardiology, The European Heart House, 2035 Route des Colles, 
      Les Templiers - CS 80179 Biot, 06903 Sophia Antipolis, France.
AD  - The Union of European Medical Specialists Cardiology Section, 24 Rue de 
      l'Industrie, 1040 Brussels, Belgium.
FAU - Mathysen, Danny
AU  - Mathysen D
AD  - Faculty of Medicine and Health Sciences, University of Antwerp, 
      Universiteitsplein 1, 2610 Wilrijk, Belgium.
FAU - Lawson, Clive
AU  - Lawson C
AD  - Department of Cardiology, Tunbridge Wells Hospital, Tonbridge Road, Pembury, 
      Tunbridge Wells, Kent TN2 4QJ, UK.
LA  - eng
PT  - Journal Article
DEP - 20230716
PL  - England
TA  - Eur Heart J Digit Health
JT  - European heart journal. Digital health
JID - 101778323
PMC - PMC10545492
COIS- Conflict of interest: None declared.
EDAT- 2023/10/05 06:44
MHDA- 2023/10/05 06:45
CRDT- 2023/10/05 03:57
PHST- 2023/10/05 06:45 [medline]
PHST- 2023/10/05 06:44 [pubmed]
PHST- 2023/10/05 03:57 [entrez]
AID - ztad040 [pii]
AID - 10.1093/ehjdh/ztad040 [doi]
PST - epublish
SO  - Eur Heart J Digit Health. 2023 Jul 16;4(5):362-363. doi: 10.1093/ehjdh/ztad040. 
      eCollection 2023 Oct.

PMID- 37726510
OWN - NLM
STAT- Publisher
LR  - 20230919
IS  - 1573-2584 (Electronic)
IS  - 0301-1623 (Linking)
DP  - 2023 Sep 19
TI  - ChatGPT's competence in addressing urolithiasis: myth or reality?
LID - 10.1007/s11255-023-03802-y [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. ppray@cus.acin.
LA  - eng
PT  - Letter
DEP - 20230919
PL  - Netherlands
TA  - Int Urol Nephrol
JT  - International urology and nephrology
JID - 0262521
SB  - IM
EDAT- 2023/09/20 00:43
MHDA- 2023/09/20 00:43
CRDT- 2023/09/19 23:34
PHST- 2023/09/05 00:00 [received]
PHST- 2023/09/09 00:00 [accepted]
PHST- 2023/09/20 00:43 [medline]
PHST- 2023/09/20 00:43 [pubmed]
PHST- 2023/09/19 23:34 [entrez]
AID - 10.1007/s11255-023-03802-y [pii]
AID - 10.1007/s11255-023-03802-y [doi]
PST - aheadofprint
SO  - Int Urol Nephrol. 2023 Sep 19. doi: 10.1007/s11255-023-03802-y.

PMID- 37541729
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230807
IS  - 1701-2163 (Print)
IS  - 1701-2163 (Linking)
VI  - 45
IP  - 8
DP  - 2023 Aug
TI  - Disclosure of Artificial Intelligence/ChatGPT-Generated Manuscripts.
PG  - 543-544
LID - S1701-2163(23)00167-6 [pii]
LID - 10.1016/j.jogc.2023.03.013 [doi]
FAU - Tulandi, Togas
AU  - Tulandi T
AD  - Department of Obstetrics and Gynecology, McGill University, Montreal, QC. 
      Electronic address: togas.tulandi@mcgill.ca.
LA  - eng
PT  - Editorial
PL  - Netherlands
TA  - J Obstet Gynaecol Can
JT  - Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et 
      gynecologie du Canada : JOGC
JID - 101126664
SB  - IM
MH  - Humans
MH  - *Disclosure
MH  - *Artificial Intelligence
EDAT- 2023/08/05 05:42
MHDA- 2023/08/07 06:41
CRDT- 2023/08/04 20:57
PHST- 2023/08/07 06:41 [medline]
PHST- 2023/08/05 05:42 [pubmed]
PHST- 2023/08/04 20:57 [entrez]
AID - S1701-2163(23)00167-6 [pii]
AID - 10.1016/j.jogc.2023.03.013 [doi]
PST - ppublish
SO  - J Obstet Gynaecol Can. 2023 Aug;45(8):543-544. doi: 10.1016/j.jogc.2023.03.013.

PMID- 38078148
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2666-3864 (Electronic)
IS  - 2666-3864 (Linking)
VI  - 4
IP  - 11
DP  - 2023 Nov
TI  - Accurately detecting AI text when ChatGPT is told to write like a chemist.
LID - 101672 [pii]
LID - 10.1016/j.xcrp.2023.101672 [doi]
AB  - Large language models like ChatGPT can generate authentic-seeming text at 
      lightning speed, but many journal publishers reject language models as authors on 
      manuscripts. Thus, a means to accurately distinguish human-generated from 
      artificial intelligence (AI)-generated text is immediately needed. We recently 
      developed an accurate AI text detector for scientific journals and, herein, test 
      its ability in a variety of challenging situations, including on human text from 
      a wide variety of chemistry journals, on AI text from the most advanced publicly 
      available language model (GPT-4), and, most important, on AI text generated using 
      prompts designed to obfuscate AI use. In all cases, AI and human text was 
      assigned with high accuracy. ChatGPT-generated text can be readily detected in 
      chemistry journals; this advance is a fundamental prerequisite for understanding 
      how automated text generation will impact scientific publishing from now into the 
      future.
FAU - Desaire, Heather
AU  - Desaire H
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
AD  - Lead contact.
FAU - Chua, Aleesa E
AU  - Chua AE
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
FAU - Kim, Min-Gyu
AU  - Kim MG
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
FAU - Hua, David
AU  - Hua D
AD  - Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
LA  - eng
GR  - R35 GM130354/GM/NIGMS NIH HHS/United States
PT  - Journal Article
DEP - 20231106
PL  - United States
TA  - Cell Rep Phys Sci
JT  - Cell reports. Physical science
JID - 101769239
PMC - PMC10704924
MID - NIHMS1945523
COIS- DECLARATION OF INTERESTS The authors declare no competing interests.
EDAT- 2023/12/11 12:42
MHDA- 2023/12/11 12:43
CRDT- 2023/12/11 06:40
PHST- 2023/12/11 12:43 [medline]
PHST- 2023/12/11 12:42 [pubmed]
PHST- 2023/12/11 06:40 [entrez]
AID - 101672 [pii]
AID - 10.1016/j.xcrp.2023.101672 [doi]
PST - ppublish
SO  - Cell Rep Phys Sci. 2023 Nov;4(11):101672. doi: 10.1016/j.xcrp.2023.101672. Epub 
      2023 Nov 6.

PMID- 37973516
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1878-4046 (Electronic)
IS  - 1076-6332 (Linking)
DP  - 2023 Nov 14
TI  - Re: Potential Applications and Impact of ChatGPT in Radiology.
LID - S1076-6332(23)00601-3 [pii]
LID - 10.1016/j.acra.2023.10.048 [doi]
FAU - Perera Molligoda Arachchige, Arosh S
AU  - Perera Molligoda Arachchige AS
AD  - Faculty of Medicine, Humanitas University, Milan, Italy (A.S.P.M.A.). Electronic 
      address: aroshshavinda.pereramolligodaarachchige@st.hunimed.eu.
FAU - Biswas, Som
AU  - Biswas S
AD  - Department of Radiology, Le Bonheur Children's Hospital, University of Tennessee 
      Health Science Center College of Medicine, Memphis, Tennessee (S.B.).
LA  - eng
PT  - Letter
DEP - 20231114
PL  - United States
TA  - Acad Radiol
JT  - Academic radiology
JID - 9440159
SB  - IM
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/11/17 15:30
MHDA- 2023/11/17 15:30
CRDT- 2023/11/16 22:01
PHST- 2023/10/20 00:00 [received]
PHST- 2023/10/24 00:00 [revised]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/11/17 15:30 [medline]
PHST- 2023/11/17 15:30 [pubmed]
PHST- 2023/11/16 22:01 [entrez]
AID - S1076-6332(23)00601-3 [pii]
AID - 10.1016/j.acra.2023.10.048 [doi]
PST - aheadofprint
SO  - Acad Radiol. 2023 Nov 14:S1076-6332(23)00601-3. doi: 10.1016/j.acra.2023.10.048.

PMID- 37936329
OWN - NLM
STAT- Publisher
LR  - 20231108
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Nov 4
TI  - Argentine Dermatology & Chat-GPT: Infrequent use & Intermediate stance.
LID - llad376 [pii]
LID - 10.1093/ced/llad376 [doi]
FAU - Ko, Ery A
AU  - Ko EA
AUID- ORCID: 0009-0002-8473-5292
AD  - Department of Dermatology, Hospital Italiano de Buenos Aires, Ciudad Autónoma de 
      Buenos Aires, Argentina.
FAU - Torre, Ana C
AU  - Torre AC
AD  - Department of Dermatology, Hospital Italiano de Buenos Aires, Ciudad Autónoma de 
      Buenos Aires, Argentina.
FAU - Hernandez, Barbara
AU  - Hernandez B
AD  - Department of Dermatology, Hospital Italiano de Buenos Aires, Ciudad Autónoma de 
      Buenos Aires, Argentina.
FAU - Bibiloni, Nuria
AU  - Bibiloni N
AD  - Department of Dermatology, Hospital Italiano de Buenos Aires, Ciudad Autónoma de 
      Buenos Aires, Argentina.
FAU - Covián, Esteban
AU  - Covián E
AD  - Psoriahue, Ciudad Autónoma de Buenos Aires, Argentina.
FAU - Salerni, Gabriel
AU  - Salerni G
AD  - National University of Rosario, Rosario, Argentina. Centenario Provincial 
      Hospital of Rosario, Rosario, Argentina.
FAU - Alonso, Carlos
AU  - Alonso C
AD  - Oncology Institute of Rosario, Rosario, Argentina.
FAU - Ochoa, Ana K
AU  - Ochoa AK
AD  - Faculty of Medical Sciences of the National University of La Plata, La Plata, 
      Argentina. Single Coordinating Center for Ablation and Implantation of the 
      province of Buenos Aires, Argentina.
FAU - Mazzuoccolo, Luis D
AU  - Mazzuoccolo LD
AD  - Department of Dermatology, Hospital Italiano de Buenos Aires, Ciudad Autónoma de 
      Buenos Aires, Argentina.
LA  - eng
PT  - Journal Article
DEP - 20231104
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
EDAT- 2023/11/08 06:43
MHDA- 2023/11/08 06:43
CRDT- 2023/11/08 01:14
PHST- 2023/10/24 00:00 [received]
PHST- 2023/10/26 00:00 [revised]
PHST- 2023/11/03 00:00 [accepted]
PHST- 2023/11/08 06:43 [medline]
PHST- 2023/11/08 06:43 [pubmed]
PHST- 2023/11/08 01:14 [entrez]
AID - 7344684 [pii]
AID - 10.1093/ced/llad376 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Nov 4:llad376. doi: 10.1093/ced/llad376.

PMID- 37888864
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1743-498X (Electronic)
IS  - 1743-4971 (Linking)
DP  - 2023 Oct 27
TI  - Using ChatGPT for early career research scholarship.
PG  - e13694
LID - 10.1111/tct.13694 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India.
LA  - eng
PT  - Letter
DEP - 20231027
PL  - England
TA  - Clin Teach
JT  - The clinical teacher
JID - 101227511
SB  - IM
EDAT- 2023/10/27 12:43
MHDA- 2023/10/27 12:43
CRDT- 2023/10/27 07:54
PHST- 2023/09/19 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/10/27 12:43 [medline]
PHST- 2023/10/27 12:43 [pubmed]
PHST- 2023/10/27 07:54 [entrez]
AID - 10.1111/tct.13694 [doi]
PST - aheadofprint
SO  - Clin Teach. 2023 Oct 27:e13694. doi: 10.1111/tct.13694.

PMID- 37816164
OWN - NLM
STAT- Publisher
LR  - 20231010
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Oct 10
TI  - ChatGPT promotes healthcare: Current applications and potential challenges.
LID - 10.1097/JS9.0000000000000802 [doi]
FAU - Wu, Xuewei
AU  - Wu X
AD  - Department of Radiology, the First Affiliated Hospital of Jinan University, 
      Guangdong, Guangzhou, China.
FAU - Zhang, Bin
AU  - Zhang B
LA  - eng
PT  - Journal Article
DEP - 20231010
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/10/10 18:42
MHDA- 2023/10/10 18:42
CRDT- 2023/10/10 15:32
PHST- 2023/09/10 00:00 [received]
PHST- 2023/09/17 00:00 [accepted]
PHST- 2023/10/10 18:42 [medline]
PHST- 2023/10/10 18:42 [pubmed]
PHST- 2023/10/10 15:32 [entrez]
AID - 01279778-990000000-00716 [pii]
AID - 10.1097/JS9.0000000000000802 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Oct 10. doi: 10.1097/JS9.0000000000000802.

PMID- 37812102
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - The Artificial Third: Utilizing ChatGPT in Mental Health.
PG  - 74-77
LID - 10.1080/15265161.2023.2250297 [doi]
FAU - Tal, Amir
AU  - Tal A
AUID- ORCID: 0000-0002-6099-8904
AD  - Faculty of Medicine, Tel Aviv University.
AD  - "The Artificial Third" Research Community.
FAU - Elyoseph, Zohar
AU  - Elyoseph Z
AUID- ORCID: 0000-0002-5717-4074
AD  - "The Artificial Third" Research Community.
AD  - Department of Educational Psychology and Counseling, Max Stern Yezreel Valley 
      College.
AD  - Department of Brain Sciences, Faculty of Medicine, Imperial College London.
FAU - Haber, Yuval
AU  - Haber Y
AUID- ORCID: 0000-0003-4933-2113
AD  - "The Artificial Third" Research Community.
FAU - Angert, Tal
AU  - Angert T
AUID- ORCID: 0009-0004-0533-3928
AD  - "The Artificial Third" Research Community.
FAU - Gur, Tamar
AU  - Gur T
AUID- ORCID: 0000-0002-5481-1832
AD  - Faculty of Medicine, Tel Aviv University.
AD  - "The Artificial Third" Research Community.
AD  - Department of Psychology, The Hebrew University of Jerusalem.
FAU - Simon, Tomer
AU  - Simon T
AD  - "The Artificial Third" Research Community.
AD  - Microsoft Israel R&D Center.
FAU - Asman, Oren
AU  - Asman O
AUID- ORCID: 0000-0003-2439-6997
AD  - Faculty of Medicine, Tel Aviv University.
AD  - "The Artificial Third" Research Community.
AD  - Sagol School of Neuroscience, Tel Aviv University.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CON - Am J Bioeth. 2023 Oct;23(10):8-16. PMID: 37440696
MH  - Humans
MH  - *Mental Health
MH  - *Bioethics
EDAT- 2023/10/09 12:42
MHDA- 2023/11/01 12:44
CRDT- 2023/10/09 10:13
PHST- 2023/11/01 12:44 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 10:13 [entrez]
AID - 10.1080/15265161.2023.2250297 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):74-77. doi: 10.1080/15265161.2023.2250297. Epub 2023 
      Oct 9.

PMID- 37777313
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231031
IS  - 1532-8473 (Electronic)
IS  - 1089-9472 (Linking)
VI  - 38
IP  - 5
DP  - 2023 Oct
TI  - Dear ChatGPT, Do We Need Perianesthesia Nurses in the PACU?
PG  - 830-831
LID - S1089-9472(23)00877-8 [pii]
LID - 10.1016/j.jopan.2023.07.003 [doi]
FAU - Nilsson, Ulrica
AU  - Nilsson U
AD  - Division of Nursing, Department of Neurobiology, Care Sciences and Society, 
      Karolinska Institutet, Stockholm, Sweden; Perioperative Medicine and Intensive 
      Care, Karolinska University Hospital, Stockholm, Sweden. Electronic address: 
      ulrica.nilsson@ki.se.
LA  - eng
PT  - Editorial
PL  - United States
TA  - J Perianesth Nurs
JT  - Journal of perianesthesia nursing : official journal of the American Society of 
      PeriAnesthesia Nurses
JID - 9610507
MH  - Humans
MH  - *Perioperative Nursing
MH  - *Postanesthesia Nursing
EDAT- 2023/10/01 04:44
MHDA- 2023/10/23 01:18
CRDT- 2023/09/30 20:58
PHST- 2023/05/29 00:00 [received]
PHST- 2023/07/16 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/01 04:44 [pubmed]
PHST- 2023/09/30 20:58 [entrez]
AID - S1089-9472(23)00877-8 [pii]
AID - 10.1016/j.jopan.2023.07.003 [doi]
PST - ppublish
SO  - J Perianesth Nurs. 2023 Oct;38(5):830-831. doi: 10.1016/j.jopan.2023.07.003.

PMID- 37609613
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230928
IS  - 0970-9134 (Print)
IS  - 0973-7723 (Electronic)
IS  - 0970-9134 (Linking)
VI  - 39
IP  - 5
DP  - 2023 Sep
TI  - ChatGPT-interesting responses: not so terrifying yet?
PG  - 557-559
LID - 10.1007/s12055-023-01545-0 [doi]
FAU - Sripad, Sushila
AU  - Sripad S
AUID- ORCID: 0000-0002-0641-7179
AD  - Department of Cardiothoracic and Vascular Surgery, Retired HOD, The Medical 
      College Calcutta, 88 College Streeet, Kolkata, West Bengal 700073 India. GRID: 
      grid.413204.0. ISNI: 0000 0004 1768 2335
AD  - R N Tagore International Institute of Cardiac Sciences, 124 Mukundapur, Kolkata, 
      700099 India.
LA  - eng
PT  - Journal Article
DEP - 20230810
PL  - India
TA  - Indian J Thorac Cardiovasc Surg
JT  - Indian journal of thoracic and cardiovascular surgery
JID - 8700105
PMC - PMC10441978
COIS- Conflict of interestNone.
EDAT- 2023/08/23 06:42
MHDA- 2023/08/23 06:43
PMCR- 2024/09/01
CRDT- 2023/08/23 04:22
PHST- 2023/05/03 00:00 [received]
PHST- 2023/05/19 00:00 [revised]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/08/23 06:43 [medline]
PHST- 2023/08/23 06:42 [pubmed]
PHST- 2023/08/23 04:22 [entrez]
AID - 1545 [pii]
AID - 10.1007/s12055-023-01545-0 [doi]
PST - ppublish
SO  - Indian J Thorac Cardiovasc Surg. 2023 Sep;39(5):557-559. doi: 
      10.1007/s12055-023-01545-0. Epub 2023 Aug 10.

PMID- 37431530
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2008-2746 (Electronic)
IS  - 1735-7497 (Print)
IS  - 1735-7497 (Linking)
VI  - 18
IP  - 3
DP  - 2023
TI  - Can ChatGPT pass the "Iranian Endodontics Specialist Board" exam?
PG  - 192
FAU - Farajollahi, Mehran
AU  - Farajollahi M
AD  - Iranian Center for Endodontic Research, Research Institute of Dental Sciences, 
      School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 
      Iran.
FAU - Modaberi, Ali
AU  - Modaberi A
AD  - Department of Endodontics, School of Dentistry, Shahid Beheshti University of 
      Medical Sciences, Tehran, Iran.
LA  - eng
PT  - Journal Article
PL  - Iran
TA  - Iran Endod J
JT  - Iranian endodontic journal
JID - 101497347
PMC - PMC10329760
EDAT- 2023/07/11 06:42
MHDA- 2023/07/11 06:43
CRDT- 2023/07/11 03:38
PHST- 2023/07/11 06:43 [medline]
PHST- 2023/07/11 06:42 [pubmed]
PHST- 2023/07/11 03:38 [entrez]
AID - IEJ-18-192 [pii]
PST - ppublish
SO  - Iran Endod J. 2023;18(3):192.

PMID- 37369274
OWN - NLM
STAT- Publisher
LR  - 20231022
IS  - 1555-7162 (Electronic)
IS  - 0002-9343 (Linking)
VI  - 136
IP  - 11
DP  - 2023 Nov
TI  - Hallucinations in ChatGPT: A Cautionary Tale for Biomedical Researchers.
PG  - 1059-1060
LID - S0002-9343(23)00401-1 [pii]
LID - 10.1016/j.amjmed.2023.06.012 [doi]
FAU - Goddard, Jerome
AU  - Goddard J
AD  - Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, 
      Mississippi State University, Mississippi State. Electronic address: 
      jgoddard@entomology.mststate.edu.
LA  - eng
PT  - Editorial
DEP - 20230625
PL  - United States
TA  - Am J Med
JT  - The American journal of medicine
JID - 0267200
SB  - IM
EDAT- 2023/06/28 01:06
MHDA- 2023/06/28 01:06
CRDT- 2023/06/27 19:10
PHST- 2023/06/08 00:00 [received]
PHST- 2023/06/09 00:00 [accepted]
PHST- 2023/06/28 01:06 [pubmed]
PHST- 2023/06/28 01:06 [medline]
PHST- 2023/06/27 19:10 [entrez]
AID - S0002-9343(23)00401-1 [pii]
AID - 10.1016/j.amjmed.2023.06.012 [doi]
PST - ppublish
SO  - Am J Med. 2023 Nov;136(11):1059-1060. doi: 10.1016/j.amjmed.2023.06.012. Epub 
      2023 Jun 25.

PMID- 37210166
OWN - NLM
STAT- MEDLINE
DCOM- 20230522
LR  - 20230607
IS  - 1097-6760 (Electronic)
IS  - 0196-0644 (Linking)
VI  - 81
IP  - 6
DP  - 2023 Jun
TI  - The ChatGPT Era: Artificial Intelligence in Emergency Medicine.
PG  - 764-765
LID - S0196-0644(23)00035-5 [pii]
LID - 10.1016/j.annemergmed.2023.01.022 [doi]
FAU - Bradshaw, Jace C
AU  - Bradshaw JC
AD  - Departments of Emergency Medicine and Anesthesiology and Critical Care Medicine, 
      Johns Hopkins University School of Medicine, Baltimore, MD.
LA  - eng
PT  - Letter
PL  - United States
TA  - Ann Emerg Med
JT  - Annals of emergency medicine
JID - 8002646
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Emergency Medicine
EDAT- 2023/05/21 01:05
MHDA- 2023/05/22 06:42
CRDT- 2023/05/20 21:00
PHST- 2023/01/18 00:00 [received]
PHST- 2023/01/23 00:00 [accepted]
PHST- 2023/05/22 06:42 [medline]
PHST- 2023/05/21 01:05 [pubmed]
PHST- 2023/05/20 21:00 [entrez]
AID - S0196-0644(23)00035-5 [pii]
AID - 10.1016/j.annemergmed.2023.01.022 [doi]
PST - ppublish
SO  - Ann Emerg Med. 2023 Jun;81(6):764-765. doi: 10.1016/j.annemergmed.2023.01.022.

PMID- 37186393
OWN - NLM
STAT- MEDLINE
DCOM- 20230613
LR  - 20230624
IS  - 1537-2995 (Electronic)
IS  - 0041-1132 (Linking)
VI  - 63
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT in transfusion medicine: A new frontier for patients?
PG  - 1110-1112
LID - 10.1111/trf.17385 [doi]
FAU - Stephens, Laura D
AU  - Stephens LD
AUID- ORCID: 0000-0002-9101-9326
AD  - Department of Pathology, University of California San Diego, La Jolla, 
      California, USA.
LA  - eng
PT  - Journal Article
DEP - 20230426
PL  - United States
TA  - Transfusion
JT  - Transfusion
JID - 0417360
SB  - IM
MH  - Humans
MH  - *Transfusion Medicine
MH  - Patients
EDAT- 2023/05/15 19:12
MHDA- 2023/06/13 06:42
CRDT- 2023/05/15 14:26
PHST- 2023/03/20 00:00 [revised]
PHST- 2023/02/13 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/06/13 06:42 [medline]
PHST- 2023/05/15 19:12 [pubmed]
PHST- 2023/05/15 14:26 [entrez]
AID - 10.1111/trf.17385 [doi]
PST - ppublish
SO  - Transfusion. 2023 Jun;63(6):1110-1112. doi: 10.1111/trf.17385. Epub 2023 Apr 26.

PMID- 37031760
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230828
LR  - 20230828
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Linking)
VI  - 229
IP  - 3
DP  - 2023 Sep
TI  - ChatGPT: the good, the bad, and the potential.
PG  - 357
LID - S0002-9378(23)00235-1 [pii]
LID - 10.1016/j.ajog.2023.04.005 [doi]
FAU - Chavez, Martin R
AU  - Chavez MR
AD  - Director of Maternal Fetal Medicine and Fetal Surgery Program, Division of 
      Maternal Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone 
      Health, NYU Langone Hospital-Long Island, Mineola, NY; Professor of Obstetrics, 
      Gynecology and Reproductive Medicine, NYU Long Island School of Medicine, 259 
      First Street, Mineola, NY 11501. Electronic address: 
      Martin.Chavez@nyulangone.org.
LA  - eng
PT  - Letter
DEP - 20230407
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
EDAT- 2023/04/10 06:00
MHDA- 2023/04/10 06:01
CRDT- 2023/04/09 19:23
PHST- 2023/03/27 00:00 [received]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/04/10 06:01 [medline]
PHST- 2023/04/10 06:00 [pubmed]
PHST- 2023/04/09 19:23 [entrez]
AID - S0002-9378(23)00235-1 [pii]
AID - 10.1016/j.ajog.2023.04.005 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol. 2023 Sep;229(3):357. doi: 10.1016/j.ajog.2023.04.005. Epub 
      2023 Apr 7.

PMID- 36948928
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230518
LR  - 20230518
IS  - 1943-4723 (Electronic)
IS  - 0002-8177 (Linking)
VI  - 154
IP  - 5
DP  - 2023 May
TI  - ChatGPT: A disruptive innovation or disrupting innovation in academia?
PG  - 361-364
LID - S0002-8177(23)00075-2 [pii]
LID - 10.1016/j.adaj.2023.02.008 [doi]
FAU - Sardana, Divesh
AU  - Sardana D
FAU - Fagan, Timothy R
AU  - Fagan TR
FAU - Wright, John Timothy
AU  - Wright JT
LA  - eng
PT  - Editorial
DEP - 20230321
PL  - England
TA  - J Am Dent Assoc
JT  - Journal of the American Dental Association (1939)
JID - 7503060
SB  - IM
EDAT- 2023/03/23 06:00
MHDA- 2023/05/01 06:42
CRDT- 2023/03/22 23:10
PHST- 2023/02/13 00:00 [received]
PHST- 2023/02/16 00:00 [accepted]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/03/23 06:00 [pubmed]
PHST- 2023/03/22 23:10 [entrez]
AID - S0002-8177(23)00075-2 [pii]
AID - 10.1016/j.adaj.2023.02.008 [doi]
PST - ppublish
SO  - J Am Dent Assoc. 2023 May;154(5):361-364. doi: 10.1016/j.adaj.2023.02.008. Epub 
      2023 Mar 21.

PMID- 36943139
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230329
LR  - 20230329
IS  - 1464-410X (Electronic)
IS  - 1464-4096 (Linking)
VI  - 131
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT: what does it mean for scientific research and publishing?
PG  - 381-382
LID - 10.1111/bju.15995 [doi]
LA  - eng
PT  - News
PL  - England
TA  - BJU Int
JT  - BJU international
JID - 100886721
SB  - IM
EDAT- 2023/03/22 06:00
MHDA- 2023/03/24 06:00
CRDT- 2023/03/21 10:22
PHST- 2023/03/21 10:22 [entrez]
PHST- 2023/03/22 06:00 [pubmed]
PHST- 2023/03/24 06:00 [medline]
AID - 10.1111/bju.15995 [doi]
PST - ppublish
SO  - BJU Int. 2023 Apr;131(4):381-382. doi: 10.1111/bju.15995.

PMID- 37684390
OWN - NLM
STAT- Publisher
LR  - 20230908
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Sep 7
TI  - Daily briefing: What's the endgame of OpenAI, the creator of ChatGPT?
LID - 10.1038/d41586-023-02849-2 [doi]
FAU - Graham, Flora
AU  - Graham F
LA  - eng
PT  - News
DEP - 20230907
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
EDAT- 2023/09/09 10:41
MHDA- 2023/09/09 10:41
CRDT- 2023/09/08 23:29
PHST- 2023/09/09 10:41 [medline]
PHST- 2023/09/09 10:41 [pubmed]
PHST- 2023/09/08 23:29 [entrez]
AID - 10.1038/d41586-023-02849-2 [pii]
AID - 10.1038/d41586-023-02849-2 [doi]
PST - aheadofprint
SO  - Nature. 2023 Sep 7. doi: 10.1038/d41586-023-02849-2.

PMID- 37979199
OWN - NLM
STAT- Publisher
LR  - 20231118
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Nov 18
TI  - Comment on 'Argentine Dermatology & Chat-GPT: Infrequent use & Intermediate 
      stance'.
LID - llad399 [pii]
LID - 10.1093/ced/llad399 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Lao People's Democratic Republic.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Punjab, India.
LA  - eng
PT  - Journal Article
DEP - 20231118
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
EDAT- 2023/11/18 22:52
MHDA- 2023/11/18 22:52
CRDT- 2023/11/18 15:36
PHST- 2023/11/08 00:00 [received]
PHST- 2023/11/16 00:00 [accepted]
PHST- 2023/11/18 22:52 [medline]
PHST- 2023/11/18 22:52 [pubmed]
PHST- 2023/11/18 15:36 [entrez]
AID - 7429031 [pii]
AID - 10.1093/ced/llad399 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Nov 18:llad399. doi: 10.1093/ced/llad399.

PMID- 37812099
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - Why ChatGPT Means Communication Ethics Problems for Bioethics.
PG  - 80-82
LID - 10.1080/15265161.2023.2250278 [doi]
FAU - Barnhart, Andrew J
AU  - Barnhart AJ
AUID- ORCID: 0000-0002-7935-253X
AD  - KU Leuven.
FAU - Barnhart, Jo Ellen M
AU  - Barnhart JEM
AD  - Johns Hopkins University.
FAU - Dierickx, Kris
AU  - Dierickx K
AUID- ORCID: 0000-0002-1016-1145
AD  - KU Leuven.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CON - Am J Bioeth. 2023 Oct;23(10):8-16. PMID: 37440696
MH  - Humans
MH  - *Bioethics
MH  - Ethicists
MH  - Communication
EDAT- 2023/10/09 12:42
MHDA- 2023/11/01 12:42
CRDT- 2023/10/09 10:13
PHST- 2023/11/01 12:42 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 10:13 [entrez]
AID - 10.1080/15265161.2023.2250278 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):80-82. doi: 10.1080/15265161.2023.2250278. Epub 2023 
      Oct 9.

PMID- 37801734
OWN - NLM
STAT- Publisher
LR  - 20231006
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Oct 5
TI  - "Auxiliary use of ChatGPT in surgical diagnosis and treatment.
LID - 10.1097/JS9.0000000000000803 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AD  - Private Academic Consultant, Phonhong, Lao.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Adjunct professor, Chandigarh University, India; Adjunct professor, Joesph 
      Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20231005
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/10/06 18:41
MHDA- 2023/10/06 18:41
CRDT- 2023/10/06 17:53
PHST- 2023/09/11 00:00 [received]
PHST- 2023/09/17 00:00 [accepted]
PHST- 2023/10/06 18:41 [medline]
PHST- 2023/10/06 18:41 [pubmed]
PHST- 2023/10/06 17:53 [entrez]
AID - 01279778-990000000-00691 [pii]
AID - 10.1097/JS9.0000000000000803 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Oct 5. doi: 10.1097/JS9.0000000000000803.

PMID- 37724974
OWN - NLM
STAT- MEDLINE
DCOM- 20230921
LR  - 20231010
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 308
IP  - 3
DP  - 2023 Sep
TI  - Evaluating Diagnostic Performance of ChatGPT in Radiology: Delving into Methods.
PG  - e232082
LID - 10.1148/radiol.232082 [doi]
FAU - Li, David
AU  - Li D
AUID- ORCID: 0000-0001-8422-421X
AD  - Department of Medical Imaging, London Health Sciences Centre, 800 Commissioners 
      Rd E, London, ON, Canada N6A 5W9.
FAU - Gupta, Kartik
AU  - Gupta K
AUID- ORCID: 0000-0002-1310-9895
AD  - Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western 
      University, London, ON, Canada.
FAU - Chong, Jaron
AU  - Chong J
AUID- ORCID: 0000-0002-7416-6105
AD  - Department of Medical Imaging, London Health Sciences Centre, 800 Commissioners 
      Rd E, London, ON, Canada N6A 5W9.
AD  - Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western 
      University, London, ON, Canada.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CON - Radiology. 2023 Jul;308(1):e231040. PMID: 37462501
MH  - Humans
MH  - Radiography
MH  - *Radiology
EDAT- 2023/09/19 17:42
MHDA- 2023/09/21 06:42
CRDT- 2023/09/19 10:02
PHST- 2023/09/21 06:42 [medline]
PHST- 2023/09/19 17:42 [pubmed]
PHST- 2023/09/19 10:02 [entrez]
AID - 10.1148/radiol.232082 [doi]
PST - ppublish
SO  - Radiology. 2023 Sep;308(3):e232082. doi: 10.1148/radiol.232082.

PMID- 37438191
OWN - NLM
STAT- MEDLINE
DCOM- 20231031
LR  - 20231031
IS  - 1578-8989 (Electronic)
IS  - 0025-7753 (Linking)
VI  - 161
IP  - 8
DP  - 2023 Oct 27
TI  - Can ChatGPT be a new educational tool in medicine?
PG  - 363-364
LID - S0025-7753(23)00316-0 [pii]
LID - 10.1016/j.medcli.2023.05.018 [doi]
FAU - Luo, Yunpeng
AU  - Luo Y
AD  - Department of Anesthesiology, Guizhou Provincial People's Hospital, Guiyang, 
      Guizhou Province, China.
FAU - Hu, Na
AU  - Hu N
AD  - Department of Radiology, The Affiliated Hospital of Guizhou Medical University, 
      Guiyang, Guizhou Province, China. Electronic address: 15519164340@163.com.
LA  - eng
LA  - spa
PT  - Comment
PT  - Letter
DEP - 20230710
PL  - Spain
TA  - Med Clin (Barc)
JT  - Medicina clinica
JID - 0376377
SB  - IM
CON - JMIR Med Educ. 2023 Feb 8;9:e45312. PMID: 36753318
MH  - Humans
MH  - *Medicine
MH  - Artificial Intelligence
EDAT- 2023/07/13 01:06
MHDA- 2023/10/23 00:42
CRDT- 2023/07/12 21:59
PHST- 2023/03/15 00:00 [received]
PHST- 2023/05/11 00:00 [revised]
PHST- 2023/05/15 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/07/13 01:06 [pubmed]
PHST- 2023/07/12 21:59 [entrez]
AID - S0025-7753(23)00316-0 [pii]
AID - 10.1016/j.medcli.2023.05.018 [doi]
PST - ppublish
SO  - Med Clin (Barc). 2023 Oct 27;161(8):363-364. doi: 10.1016/j.medcli.2023.05.018. 
      Epub 2023 Jul 10.

PMID- 37422957
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231102
IS  - 2211-0356 (Electronic)
IS  - 2211-0348 (Linking)
VI  - 76
DP  - 2023 Aug
TI  - Is using ChatGPT to help write papers and grants is useful, and ethical?
PG  - 104873
LID - S2211-0348(23)00374-7 [pii]
LID - 10.1016/j.msard.2023.104873 [doi]
FAU - Levy, Michael
AU  - Levy M
AD  - Department of Neurology, Massachusetts General Hospital and Harvard Medical 
      School, Boston, MA, USA. Electronic address: mlevy11@mgh.harvard.edu.
FAU - Yeh, Ann
AU  - Yeh A
AD  - The Hospital for Sick Children, Toronto, M5G 1 × 8, Ontario, Canada.
FAU - Hawkes, Christopher
AU  - Hawkes C
AD  - Queen Mary University of London, Faculty of Medicine and Dentistry, Garrod 
      Building, Turner Street, Whitechapel, E1 2AD, London, United Kingdom.
FAU - Lechner-Scott, Jennette
AU  - Lechner-Scott J
AD  - John Hunter Hospital, Department of Neurology, Newcastle, 2305, Australia.
FAU - Giovannoni, Gavin
AU  - Giovannoni G
AD  - Queen Mary University of London Centre for Neuroscience Surgery and Trauma, 4 
      Newark Street, E1 2AT, London, United Kingdom.
LA  - eng
PT  - Editorial
DEP - 20230705
PL  - Netherlands
TA  - Mult Scler Relat Disord
JT  - Multiple sclerosis and related disorders
JID - 101580247
SB  - IM
EDAT- 2023/07/10 00:41
MHDA- 2023/07/10 00:42
CRDT- 2023/07/09 18:01
PHST- 2023/07/10 00:42 [medline]
PHST- 2023/07/10 00:41 [pubmed]
PHST- 2023/07/09 18:01 [entrez]
AID - S2211-0348(23)00374-7 [pii]
AID - 10.1016/j.msard.2023.104873 [doi]
PST - ppublish
SO  - Mult Scler Relat Disord. 2023 Aug;76:104873. doi: 10.1016/j.msard.2023.104873. 
      Epub 2023 Jul 5.

PMID- 37162206
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230801
LR  - 20231204
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
VI  - 98
IP  - 8
DP  - 2023 Aug 1
TI  - Finding the Place of ChatGPT in Medical Education.
PG  - 867
LID - 10.1097/ACM.0000000000005254 [doi]
FAU - van de Ridder, J M Monica
AU  - van de Ridder JMM
AD  - Adjunct assistant professor, College of Human Medicine, Michigan State 
      University, Grand Rapids, Michigan; email: vanderid@msu.edu ; Twitter: 
      @MvdRidder; ORCID: https://orcid.org/0000-0003-3401-3523 .
FAU - Shoja, Mohammadali M
AU  - Shoja MM
AD  - Associate professor of medical education, Department of Medical Education, Dr. 
      Kiran C. Patel College of Allopathic Medicine, Nova Southeastern University, Fort 
      Lauderdale, Florida; ORCID: https://orcid.org/0000-0001-9668-5558 .
FAU - Rajput, Vijay
AU  - Rajput V
AD  - Professor and chair of medical education, Department of Medical Education, Dr. 
      Kiran C. Patel College of Allopathic Medicine, Nova Southeastern University, Fort 
      Lauderdale, Florida; ORCID: https://orcid.org/0000-0001-5203-5342 .
LA  - eng
PT  - Letter
DEP - 20230510
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
EDAT- 2023/05/10 12:42
MHDA- 2023/07/27 06:42
CRDT- 2023/05/10 09:03
PHST- 2023/07/27 06:42 [medline]
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 09:03 [entrez]
AID - 00001888-990000000-00446 [pii]
AID - 10.1097/ACM.0000000000005254 [doi]
PST - ppublish
SO  - Acad Med. 2023 Aug 1;98(8):867. doi: 10.1097/ACM.0000000000005254. Epub 2023 May 
      10.

PMID- 37119559
OWN - NLM
STAT- Publisher
LR  - 20230429
IS  - 1873-7862 (Electronic)
IS  - 0924-977X (Linking)
VI  - 73
DP  - 2023 Apr 27
TI  - Psychopathology for the twenty-first century: Towards a ChatGPT psychopathology?
PG  - 21-23
LID - S0924-977X(23)00089-5 [pii]
LID - 10.1016/j.euroneuro.2023.04.016 [doi]
FAU - Cuesta, Manuel J
AU  - Cuesta MJ
AD  - Department of Psychiatry, Hospital Universitario de Navarra, Instituto de 
      Investigación Sanitaria de Navarra (IdiSNa), Pamplona, Spain. Electronic address: 
      mcuestaz@cfnavarra.es.
LA  - eng
PT  - Journal Article
DEP - 20230427
PL  - Netherlands
TA  - Eur Neuropsychopharmacol
JT  - European neuropsychopharmacology : the journal of the European College of 
      Neuropsychopharmacology
JID - 9111390
SB  - IM
COIS- Declaration of Competing Interest None.
EDAT- 2023/04/29 19:44
MHDA- 2023/04/29 19:44
CRDT- 2023/04/29 18:00
PHST- 2023/03/24 00:00 [received]
PHST- 2023/04/13 00:00 [revised]
PHST- 2023/04/18 00:00 [accepted]
PHST- 2023/04/29 19:44 [medline]
PHST- 2023/04/29 19:44 [pubmed]
PHST- 2023/04/29 18:00 [entrez]
AID - S0924-977X(23)00089-5 [pii]
AID - 10.1016/j.euroneuro.2023.04.016 [doi]
PST - aheadofprint
SO  - Eur Neuropsychopharmacol. 2023 Apr 27;73:21-23. doi: 
      10.1016/j.euroneuro.2023.04.016.

PMID- 37502196
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231111
IS  - 2471-1403 (Electronic)
IS  - 2471-1403 (Linking)
VI  - 7
IP  - 7
DP  - 2023 Jul
TI  - Generative AI as a Tool for Environmental Health Research Translation.
PG  - e2023GH000875
LID - 10.1029/2023GH000875 [doi]
LID - e2023GH000875
AB  - One valuable application for generative artificial intelligence (AI) is 
      summarizing research studies for non-academic readers. We submitted five articles 
      to Chat Generative Pre-trained Transformer (ChatGPT) for summarization, and asked 
      the article's author to rate the summaries. Higher ratings were assigned to more 
      insight-oriented activities, such as the production of eighth-grade reading level 
      summaries, and summaries highlighting the most important findings and real-world 
      applications. The general summary request was rated lower. For the field of 
      environmental health science, no-cost AI technology such as ChatGPT holds the 
      promise to improve research translation, but it must continue to be improved (or 
      improve itself) from its current capability.
CI  - © 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of 
      American Geophysical Union.
FAU - Anderson, Lauren B
AU  - Anderson LB
AUID- ORCID: 0000-0003-0556-7260
AD  - Christina Lee Brown Envirome Institute School of Medicine University of 
      Louisville Louisville KY USA.
AD  - Department of Urban and Public Affairs College of Arts and Sciences University of 
      Louisville Louisville KY USA.
FAU - Kanneganti, Dhiraj
AU  - Kanneganti D
AD  - Christina Lee Brown Envirome Institute School of Medicine University of 
      Louisville Louisville KY USA.
FAU - Houk, Mary Bentley
AU  - Houk MB
AD  - Christina Lee Brown Envirome Institute School of Medicine University of 
      Louisville Louisville KY USA.
FAU - Holm, Rochelle H
AU  - Holm RH
AUID- ORCID: 0000-0001-8849-1390
AD  - Christina Lee Brown Envirome Institute School of Medicine University of 
      Louisville Louisville KY USA.
FAU - Smith, Ted
AU  - Smith T
AUID- ORCID: 0000-0003-2804-4294
AD  - Christina Lee Brown Envirome Institute School of Medicine University of 
      Louisville Louisville KY USA.
AD  - University of Louisville Superfund Research Center Louisville KY USA.
LA  - eng
GR  - P30 ES030283/ES/NIEHS NIH HHS/United States
GR  - P42 ES023716/ES/NIEHS NIH HHS/United States
PT  - Journal Article
DEP - 20230726
PL  - United States
TA  - Geohealth
JT  - GeoHealth
JID - 101706476
UOF - medRxiv. 2023 Feb 22;:. PMID: 36865187
PMC - PMC10369501
OTO - NOTNLM
OT  - artificial intelligence
OT  - environmental health justice
OT  - plain language summaries
COIS- The authors declare no conflicts of interest relevant to this study.
EDAT- 2023/07/28 06:42
MHDA- 2023/07/28 06:43
CRDT- 2023/07/28 04:12
PHST- 2023/05/31 00:00 [received]
PHST- 2023/06/30 00:00 [revised]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/07/28 06:43 [medline]
PHST- 2023/07/28 06:42 [pubmed]
PHST- 2023/07/28 04:12 [entrez]
AID - GH2456 [pii]
AID - 10.1029/2023GH000875 [doi]
PST - epublish
SO  - Geohealth. 2023 Jul 26;7(7):e2023GH000875. doi: 10.1029/2023GH000875. eCollection 
      2023 Jul.

PMID- 37401180
OWN - NLM
STAT- MEDLINE
DCOM- 20230705
LR  - 20230908
IS  - 0975-5691 (Electronic)
IS  - 0974-8466 (Linking)
VI  - VIII
IP  - 2
DP  - 2023 Apr-Jun
TI  - ChatGPT: Impact of an artificial author on bibliometrics.
PG  - 93-94
LID - 10.20529/IJME.2023.029 [doi]
AB  - The use of Artificial Intelligence (AI) in medical science has been widely 
      discussed and debated. Topol foresaw that AI, particularly deep learning, would 
      be used in a variety of applications, with users ranging from specialty doctors 
      to paramedics [1]. He discussed how deep neural networks (DNNs) of AI can help 
      interpret medical scans, pathology slides, skin lesions, retinal images, 
      electrocardiograms, endoscopy, faces, and vital signs. He has described its 
      application in radiology, pathology, dermatology, ophthalmology, cardiology, 
      mental health, and other fields [1]. Among several other AI applications used in 
      our daily life, the next generation breakthrough, AI model ChatGPT-3 
      (https://chat.openai.com/) was launched on November 30, 2022 by OpenAI, 
      California, which is well-known for its innovations in automated text generation. 
      ChatGPT converses with the user, ascertains the user's needs, and responds 
      accordingly. It can write a poem, a diet plan, recipes, letters, computer 
      programmes, a eulogy, do copy editing, and so on.
FAU - Nagarkar, Shubhada
AU  - Nagarkar S
AD  - Department of Library and Information Science and Centre for Publication Ethics, 
      Savitribai Phule Pune University, Pune, INDIA.
LA  - eng
PT  - Comment
PT  - Editorial
PL  - India
TA  - Indian J Med Ethics
JT  - Indian journal of medical ethics
JID - 101214913
SB  - IM
CON - Nat Med. 2019 Jan;25(1):44-56. PMID: 30617339
MH  - Male
MH  - Humans
MH  - *Artificial Intelligence
MH  - Bibliometrics
MH  - *Medicine
MH  - Mental Health
MH  - Paramedics
EDAT- 2023/07/04 06:42
MHDA- 2023/07/05 06:42
CRDT- 2023/07/04 02:33
PHST- 2023/07/05 06:42 [medline]
PHST- 2023/07/04 06:42 [pubmed]
PHST- 2023/07/04 02:33 [entrez]
AID - 10.20529/IJME.2023.029 [doi]
PST - ppublish
SO  - Indian J Med Ethics. 2023 Apr-Jun;VIII(2):93-94. doi: 10.20529/IJME.2023.029.

PMID- 37720947
OWN - NLM
STAT- Publisher
LR  - 20230918
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Sep 14
TI  - Re: ChatGPT encounters multiple opportunities and challenges in neurosurgery.
LID - 10.1097/JS9.0000000000000749 [doi]
FAU - Yu, Qin-Xin
AU  - Yu QX
AD  - Ningbo Clinical Pathology Diagnosis center, Ningbo City, Zhejiang Province, 
      China.
FAU - Wu, Rui-Cheng
AU  - Wu RC
AD  - Department of Urology, Institute of Urology, West China Hospital, Sichuan 
      University, Chengdu, Sichuan Province, 610041, China.
FAU - Feng, De-Chao
AU  - Feng DC
AD  - Department of Urology, Institute of Urology, West China Hospital, Sichuan 
      University, Chengdu, Sichuan Province, 610041, China.
FAU - Li, Deng-Xiong
AU  - Li DX
AD  - Department of Urology, Institute of Urology, West China Hospital, Sichuan 
      University, Chengdu, Sichuan Province, 610041, China.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:42
CRDT- 2023/09/18 04:49
PHST- 2023/08/24 00:00 [received]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/09/18 06:42 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:49 [entrez]
AID - 01279778-990000000-00612 [pii]
AID - 10.1097/JS9.0000000000000749 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Sep 14. doi: 10.1097/JS9.0000000000000749.

PMID- 37055219
OWN - NLM
STAT- MEDLINE
DCOM- 20230518
LR  - 20230611
IS  - 1535-5667 (Electronic)
IS  - 0161-5505 (Linking)
VI  - 64
IP  - 5
DP  - 2023 May
TI  - An Opinion on ChatGPT in Health Care-Written by Humans Only.
PG  - 701-703
LID - 10.2967/jnumed.123.265687 [doi]
FAU - Kleesiek, Jens
AU  - Kleesiek J
AD  - Institute for AI in Medicine, University Medicine Essen, Essen, Germany; 
      jens.kleesiek@uk-essen.de.
FAU - Wu, Yonghui
AU  - Wu Y
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, Florida; and.
FAU - Stiglic, Gregor
AU  - Stiglic G
AD  - Faculty of Health Sciences, University of Maribor, Maribor, Slovenia.
FAU - Egger, Jan
AU  - Egger J
AD  - Institute for AI in Medicine, University Medicine Essen, Essen, Germany.
FAU - Bian, Jiang
AU  - Bian J
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, Florida; and.
LA  - eng
PT  - Editorial
DEP - 20230413
PL  - United States
TA  - J Nucl Med
JT  - Journal of nuclear medicine : official publication, Society of Nuclear Medicine
JID - 0217410
SB  - IM
MH  - Humans
MH  - *Delivery of Health Care
MH  - *Artificial Intelligence
MH  - *Writing
EDAT- 2023/04/14 06:00
MHDA- 2023/05/01 06:42
CRDT- 2023/04/13 21:42
PHST- 2023/03/07 00:00 [received]
PHST- 2023/03/14 00:00 [revised]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/14 06:00 [pubmed]
PHST- 2023/04/13 21:42 [entrez]
AID - jnumed.123.265687 [pii]
AID - 10.2967/jnumed.123.265687 [doi]
PST - ppublish
SO  - J Nucl Med. 2023 May;64(5):701-703. doi: 10.2967/jnumed.123.265687. Epub 2023 Apr 
      13.

PMID- 37036161
OWN - NLM
STAT- MEDLINE
DCOM- 20230830
LR  - 20230830
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
VI  - 45
IP  - 9
DP  - 2023 Sep
TI  - Harnessing the power of ChatGPT in medical education.
PG  - 1063
LID - 10.1080/0142159X.2023.2198094 [doi]
FAU - Guo, Alyssa A
AU  - Guo AA
AUID- ORCID: 0000-0002-2310-7594
AD  - University of South Carolina School of Medicine Greenville, Greenville, SC, USA.
FAU - Li, Jason
AU  - Li J
AUID- ORCID: 0009-0007-5233-5573
AD  - Greenville Technical College, Greenville, SC, USA.
LA  - eng
PT  - Letter
DEP - 20230410
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
MH  - Humans
MH  - *Education, Medical
MH  - *Artificial Intelligence
EDAT- 2023/04/11 06:00
MHDA- 2023/08/24 06:42
CRDT- 2023/04/10 08:42
PHST- 2023/08/24 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 08:42 [entrez]
AID - 10.1080/0142159X.2023.2198094 [doi]
PST - ppublish
SO  - Med Teach. 2023 Sep;45(9):1063. doi: 10.1080/0142159X.2023.2198094. Epub 2023 Apr 
      10.

PMID- 37029868
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230523
LR  - 20230523
IS  - 1534-4681 (Electronic)
IS  - 1068-9265 (Linking)
VI  - 30
IP  - 6
DP  - 2023 Jun
TI  - A Ghostwriter for the Masses: ChatGPT and the Future of Writing.
PG  - 3170-3173
LID - 10.1245/s10434-023-13436-0 [doi]
FAU - Zimmerman, Asha
AU  - Zimmerman A
AD  - Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA. 
      Asha.M.Zimmerman@hitchcock.org.
LA  - eng
PT  - Editorial
DEP - 20230408
PL  - United States
TA  - Ann Surg Oncol
JT  - Annals of surgical oncology
JID - 9420840
SB  - IM
EDAT- 2023/04/09 06:00
MHDA- 2023/05/15 06:42
CRDT- 2023/04/08 11:18
PHST- 2023/01/30 00:00 [received]
PHST- 2023/02/12 00:00 [accepted]
PHST- 2023/05/15 06:42 [medline]
PHST- 2023/04/09 06:00 [pubmed]
PHST- 2023/04/08 11:18 [entrez]
AID - 10.1245/s10434-023-13436-0 [pii]
AID - 10.1245/s10434-023-13436-0 [doi]
PST - ppublish
SO  - Ann Surg Oncol. 2023 Jun;30(6):3170-3173. doi: 10.1245/s10434-023-13436-0. Epub 
      2023 Apr 8.

PMID- 36797148
OWN - NLM
STAT- MEDLINE
DCOM- 20230407
LR  - 20230407
IS  - 1526-3231 (Electronic)
IS  - 0749-8063 (Linking)
VI  - 39
IP  - 5
DP  - 2023 May
TI  - ChatGPT, An Artificial Intelligence Chatbot, Is Impacting Medical Literature.
PG  - 1121-1122
LID - S0749-8063(23)00033-6 [pii]
LID - 10.1016/j.arthro.2023.01.015 [doi]
FAU - Lubowitz, James H
AU  - Lubowitz JH
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230201
PL  - United States
TA  - Arthroscopy
JT  - Arthroscopy : the journal of arthroscopic & related surgery : official 
      publication of the Arthroscopy Association of North America and the International 
      Arthroscopy Association
JID - 8506498
SB  - IM
CON - Arthroscopy. 2023 May;39(5):1119-1120. PMID: 36774329
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Software
EDAT- 2023/02/17 06:00
MHDA- 2023/04/07 06:41
CRDT- 2023/02/16 22:04
PHST- 2023/01/14 00:00 [received]
PHST- 2023/01/14 00:00 [accepted]
PHST- 2023/04/07 06:41 [medline]
PHST- 2023/02/17 06:00 [pubmed]
PHST- 2023/02/16 22:04 [entrez]
AID - S0749-8063(23)00033-6 [pii]
AID - 10.1016/j.arthro.2023.01.015 [doi]
PST - ppublish
SO  - Arthroscopy. 2023 May;39(5):1121-1122. doi: 10.1016/j.arthro.2023.01.015. Epub 
      2023 Feb 1.

PMID- 37848712
OWN - NLM
STAT- Publisher
LR  - 20231017
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Oct 16
TI  - Daily briefing: ChatGPT use makes a mockery of grant applications.
LID - 10.1038/d41586-023-03254-5 [doi]
FAU - Graham, Flora
AU  - Graham F
LA  - eng
PT  - News
DEP - 20231016
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
EDAT- 2023/10/18 00:42
MHDA- 2023/10/18 00:42
CRDT- 2023/10/17 23:44
PHST- 2023/10/18 00:42 [medline]
PHST- 2023/10/18 00:42 [pubmed]
PHST- 2023/10/17 23:44 [entrez]
AID - 10.1038/d41586-023-03254-5 [pii]
AID - 10.1038/d41586-023-03254-5 [doi]
PST - aheadofprint
SO  - Nature. 2023 Oct 16. doi: 10.1038/d41586-023-03254-5.

PMID- 37799088
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231018
IS  - 2164-554X (Electronic)
IS  - 2164-5515 (Print)
IS  - 2164-5515 (Linking)
VI  - 19
IP  - 2
DP  - 2023 Aug
TI  - ChatGPT and safety of COVID-19 vaccines.
PG  - 2263982
LID - 10.1080/21645515.2023.2263982 [doi]
LID - 2263982
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Lao.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, India.
AD  - Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20231006
PL  - United States
TA  - Hum Vaccin Immunother
JT  - Human vaccines & immunotherapeutics
JID - 101572652
RN  - 0 (COVID-19 Vaccines)
SB  - IM
CON - Hum Vaccin Immunother. 2023 Aug 1;19(2):2235200. PMID: 37660470
MH  - Humans
MH  - *COVID-19/prevention & control
MH  - COVID-19 Vaccines/adverse effects
PMC - PMC10561564
COIS- No potential conflict of interest was reported by the author(s).
EDAT- 2023/10/06 06:42
MHDA- 2023/10/09 06:41
CRDT- 2023/10/06 03:35
PHST- 2023/10/09 06:41 [medline]
PHST- 2023/10/06 06:42 [pubmed]
PHST- 2023/10/06 03:35 [entrez]
AID - 2263982 [pii]
AID - 10.1080/21645515.2023.2263982 [doi]
PST - ppublish
SO  - Hum Vaccin Immunother. 2023 Aug;19(2):2263982. doi: 
      10.1080/21645515.2023.2263982. Epub 2023 Oct 6.

PMID- 37633496
OWN - NLM
STAT- Publisher
LR  - 20230914
IS  - 1528-0012 (Electronic)
IS  - 0016-5085 (Linking)
DP  - 2023 Aug 25
TI  - Comment on "ChatGPT Answers Common Patient Questions About Colonoscopy".
LID - S0016-5085(23)04913-2 [pii]
LID - 10.1053/j.gastro.2023.08.030 [doi]
FAU - Wu, Qiqi
AU  - Wu Q
AD  - Department of Acupuncture and Massage, Wenzhou Central Hospital, Wenzhou City, 
      China.
LA  - eng
PT  - Letter
DEP - 20230825
PL  - United States
TA  - Gastroenterology
JT  - Gastroenterology
JID - 0374630
SB  - IM
EDAT- 2023/08/27 05:43
MHDA- 2023/08/27 05:43
CRDT- 2023/08/26 19:26
PHST- 2023/08/18 00:00 [received]
PHST- 2023/08/22 00:00 [accepted]
PHST- 2023/08/27 05:43 [pubmed]
PHST- 2023/08/27 05:43 [medline]
PHST- 2023/08/26 19:26 [entrez]
AID - S0016-5085(23)04913-2 [pii]
AID - 10.1053/j.gastro.2023.08.030 [doi]
PST - aheadofprint
SO  - Gastroenterology. 2023 Aug 25:S0016-5085(23)04913-2. doi: 
      10.1053/j.gastro.2023.08.030.

PMID- 37377506
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2333-0406 (Print)
IS  - 2333-0406 (Electronic)
IS  - 2333-0406 (Linking)
VI  - 25
IP  - 2
DP  - 2023 Apr-Jun
TI  - Artificial Intelligence and ChatGPT: Bane or Boon for Academic Writing.
PG  - E702
LID - 10.46374/volxxv_issue2_Culp [doi]
LID - E702
FAU - Culp, William C Jr
AU  - Culp WC Jr
AD  - is a Professor of Anesthesiology and Associate Dean of Faculty Affairs & Faculty 
      Development at Baylor Scott & White Health-Temple Medical Center and Baylor 
      College of Medicine-Temple in Temple, TX.
LA  - eng
PT  - Editorial
DEP - 20230401
PL  - United States
TA  - J Educ Perioper Med
JT  - The journal of education in perioperative medicine : JEPM
JID - 101122652
PMC - PMC10291958
COIS- Conflicts of interest: None
EDAT- 2023/06/28 13:09
MHDA- 2023/06/28 13:10
CRDT- 2023/06/28 09:15
PHST- 2023/06/28 13:10 [medline]
PHST- 2023/06/28 13:09 [pubmed]
PHST- 2023/06/28 09:15 [entrez]
AID - 10.46374/volxxv_issue2_Culp [doi]
PST - epublish
SO  - J Educ Perioper Med. 2023 Apr 1;25(2):E702. doi: 10.46374/volxxv_issue2_Culp. 
      eCollection 2023 Apr-Jun.

PMID- 38069557
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 1601-0825 (Electronic)
IS  - 1354-523X (Linking)
DP  - 2023 Dec 8
TI  - ChatGPT in oral medicine: A crossroads for educators, students, clinicians, and 
      patients.
LID - 10.1111/odi.14833 [doi]
FAU - Diniz-Freitas, Márcio
AU  - Diniz-Freitas M
AUID- ORCID: 0000-0001-5349-1091
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - Rivas-Mundiña, Berta
AU  - Rivas-Mundiña B
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - García-Iglesias, José Ramón
AU  - García-Iglesias JR
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - García-Mato, Eliane
AU  - García-Mato E
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
FAU - Diz-Dios, Pedro
AU  - Diz-Dios P
AUID- ORCID: 0000-0002-1483-401X
AD  - Medical-Surgical Dentistry Research Group (OMEQUI), Health Research Institute of 
      Santiago de Compostela (IDIS), University of Santiago de Compostela (USC), A 
      Coruña, Spain.
LA  - eng
PT  - Letter
DEP - 20231208
PL  - Denmark
TA  - Oral Dis
JT  - Oral diseases
JID - 9508565
EDAT- 2023/12/09 10:42
MHDA- 2023/12/09 10:42
CRDT- 2023/12/09 03:44
PHST- 2023/11/22 00:00 [received]
PHST- 2023/11/23 00:00 [accepted]
PHST- 2023/12/09 10:42 [medline]
PHST- 2023/12/09 10:42 [pubmed]
PHST- 2023/12/09 03:44 [entrez]
AID - 10.1111/odi.14833 [doi]
PST - aheadofprint
SO  - Oral Dis. 2023 Dec 8. doi: 10.1111/odi.14833.

PMID- 38008150
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Linking)
DP  - 2023 Nov 25
TI  - Potential for ChatGPT in obstetrics and gynecology: a response.
LID - S0002-9378(23)02053-7 [pii]
LID - 10.1016/j.ajog.2023.11.1239 [doi]
FAU - Grünebaum, Amos
AU  - Grünebaum A
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY. 
      Electronic address: amos127@gmail.com.
FAU - Pollet, Susan
AU  - Pollet S
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY.
FAU - Chervenak, Frank
AU  - Chervenak F
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY.
LA  - eng
PT  - Letter
DEP - 20231125
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
EDAT- 2023/11/27 00:43
MHDA- 2023/11/27 00:43
CRDT- 2023/11/26 19:23
PHST- 2023/10/13 00:00 [received]
PHST- 2023/11/03 00:00 [accepted]
PHST- 2023/11/27 00:43 [pubmed]
PHST- 2023/11/27 00:43 [medline]
PHST- 2023/11/26 19:23 [entrez]
AID - S0002-9378(23)02053-7 [pii]
AID - 10.1016/j.ajog.2023.11.1239 [doi]
PST - aheadofprint
SO  - Am J Obstet Gynecol. 2023 Nov 25:S0002-9378(23)02053-7. doi: 
      10.1016/j.ajog.2023.11.1239.

PMID- 37952577
OWN - NLM
STAT- In-Process
LR  - 20231209
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
VI  - 193
DP  - 2023 Dec
TI  - Evolution of ChatGPT evaluations in healthcare: Still at the beginning?
PG  - 110042
LID - S0300-9572(23)00769-4 [pii]
LID - 10.1016/j.resuscitation.2023.110042 [doi]
FAU - Kocbek, Primož
AU  - Kocbek P
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; Faculty of 
      Medicine, University of Ljubljana, Ljubljana, Slovenia.
FAU - Fijačko, Nino
AU  - Fijačko N
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; ERC 
      Research Net, Niels, Belgium; Maribor University Medical Centre, Maribor, 
      Slovenia. Electronic address: https://twitter.com/@NinoFijacko.
FAU - Štiglic, Gregor
AU  - Štiglic G
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; University 
      of Maribor, Faculty of Electrical Engineering and Computer Science, Maribor, 
      Slovenia; University of Edinburgh, Usher Institute, Edinburgh, United Kingdom.
LA  - eng
PT  - Letter
DEP - 20231111
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
COIS- Declaration of competing interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: Nino Fijačko is a member of the ERC BLS Science and Education 
      Committee and ILCOR Task Force Education Implementation and Team. Primož Kocbek 
      and Gregor Štiglic declare that they have no conflict of interest’.
EDAT- 2023/11/13 00:42
MHDA- 2023/11/13 00:42
CRDT- 2023/11/12 19:13
PHST- 2023/11/03 00:00 [received]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/11/13 00:42 [pubmed]
PHST- 2023/11/13 00:42 [medline]
PHST- 2023/11/12 19:13 [entrez]
AID - S0300-9572(23)00769-4 [pii]
AID - 10.1016/j.resuscitation.2023.110042 [doi]
PST - ppublish
SO  - Resuscitation. 2023 Dec;193:110042. doi: 10.1016/j.resuscitation.2023.110042. 
      Epub 2023 Nov 11.

PMID- 37943230
OWN - NLM
STAT- Publisher
LR  - 20231109
IS  - 1876-7591 (Electronic)
IS  - 1876-7591 (Linking)
DP  - 2023 Oct 25
TI  - Low Performance of ChatGPT on Echocardiography Board Review Questions.
LID - S1936-878X(23)00430-8 [pii]
LID - 10.1016/j.jcmg.2023.09.004 [doi]
FAU - Kangiszer, Gyula
AU  - Kangiszer G
FAU - Mahtani, Arun Umesh
AU  - Mahtani AU
FAU - Pintea, Mark
AU  - Pintea M
FAU - Jacobs, Charlotte
AU  - Jacobs C
FAU - Sragovicz, Hannah
AU  - Sragovicz H
FAU - Nguyen, Tai
AU  - Nguyen T
FAU - Yeturu, Sahithi
AU  - Yeturu S
FAU - Lieberman, Madison
AU  - Lieberman M
FAU - Waldman, Carly
AU  - Waldman C
FAU - Bhavnani, Sanjeev P
AU  - Bhavnani SP
FAU - Hermel, Melody
AU  - Hermel M
LA  - eng
PT  - Letter
DEP - 20231025
PL  - United States
TA  - JACC Cardiovasc Imaging
JT  - JACC. Cardiovascular imaging
JID - 101467978
SB  - IM
EDAT- 2023/11/09 12:43
MHDA- 2023/11/09 12:43
CRDT- 2023/11/09 10:32
PHST- 2023/03/09 00:00 [received]
PHST- 2023/09/06 00:00 [revised]
PHST- 2023/09/13 00:00 [accepted]
PHST- 2023/11/09 12:43 [medline]
PHST- 2023/11/09 12:43 [pubmed]
PHST- 2023/11/09 10:32 [entrez]
AID - S1936-878X(23)00430-8 [pii]
AID - 10.1016/j.jcmg.2023.09.004 [doi]
PST - aheadofprint
SO  - JACC Cardiovasc Imaging. 2023 Oct 25:S1936-878X(23)00430-8. doi: 
      10.1016/j.jcmg.2023.09.004.

PMID- 37874981
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1538-9855 (Electronic)
IS  - 0363-3624 (Linking)
DP  - 2023 Oct 23
TI  - Innovative Teaching Strategies in DNP Education Using ChatGPT.
LID - 10.1097/NNE.0000000000001553 [doi]
FAU - Quattrini, Veronica
AU  - Quattrini V
AUID- ORCID: 0000-0003-1477-822
AD  - Senior Director for the Doctor of Nursing Practice Program (Dr Quattrini), 
      Assistant Professor, Family Community Health (Drs Quattrini and Roesch), and 
      Alumnae, Former Graduate Teaching Assistant (Dr Kretz), University of Maryland 
      School of Nursing, Baltimore.
FAU - Roesch, Amanda
AU  - Roesch A
FAU - Kretz, Dawn
AU  - Kretz D
LA  - eng
PT  - Journal Article
DEP - 20231023
PL  - United States
TA  - Nurse Educ
JT  - Nurse educator
JID - 7701902
COIS- The authors declare no conflicts of interest.
EDAT- 2023/10/24 18:42
MHDA- 2023/10/24 18:42
CRDT- 2023/10/24 16:33
PHST- 2023/10/24 18:42 [medline]
PHST- 2023/10/24 18:42 [pubmed]
PHST- 2023/10/24 16:33 [entrez]
AID - 00006223-990000000-00349 [pii]
AID - 10.1097/NNE.0000000000001553 [doi]
PST - aheadofprint
SO  - Nurse Educ. 2023 Oct 23. doi: 10.1097/NNE.0000000000001553.

PMID- 37737578
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20231002
IS  - 2151-2396 (Electronic)
IS  - 0227-5910 (Linking)
VI  - 44
IP  - 5
DP  - 2023 Sep
TI  - ChatGPT, Artificial Intelligence, and Suicide Prevention.
PG  - 367-370
LID - 10.1027/0227-5910/a000915 [doi]
FAU - Arendt, Florian
AU  - Arendt F
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Department of Communication, University of Vienna, Austria.
FAU - Till, Benedikt
AU  - Till B
AUID- ORCID: 0000-0002-2099-5469
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Unit Suicide Research & Mental Health Promotion, Department of Social and 
      Preventive Medicine, Center for Public Health, Medical University of Vienna, 
      Austria.
FAU - Voracek, Martin
AU  - Voracek M
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Department of Cognition, Emotion, and Methods in Psychology, Faculty of 
      Psychology, University of Vienna, Austria.
FAU - Kirchner, Stefanie
AU  - Kirchner S
AUID- ORCID: 0000-0003-3908-3600
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Unit Suicide Research & Mental Health Promotion, Department of Social and 
      Preventive Medicine, Center for Public Health, Medical University of Vienna, 
      Austria.
FAU - Sonneck, Gernot
AU  - Sonneck G
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Crisis Intervention Center Vienna, Austria.
FAU - Naderer, Brigitte
AU  - Naderer B
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Unit Suicide Research & Mental Health Promotion, Department of Social and 
      Preventive Medicine, Center for Public Health, Medical University of Vienna, 
      Austria.
FAU - Pürcher, Paul
AU  - Pürcher P
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Unit Suicide Research & Mental Health Promotion, Department of Social and 
      Preventive Medicine, Center for Public Health, Medical University of Vienna, 
      Austria.
AD  - Department of Medical Psychology, Center for Public Health, Medical University of 
      Vienna, Austria.
FAU - Niederkrotenthaler, Thomas
AU  - Niederkrotenthaler T
AUID- ORCID: 0000-0001-9550-628X
AD  - Wiener Werkstaette for Suicide Research, Vienna, Austria.
AD  - Unit Suicide Research & Mental Health Promotion, Department of Social and 
      Preventive Medicine, Center for Public Health, Medical University of Vienna, 
      Austria.
LA  - eng
PT  - Editorial
PL  - Canada
TA  - Crisis
JT  - Crisis
JID - 8218602
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Suicide Prevention
EDAT- 2023/09/22 12:43
MHDA- 2023/09/25 06:42
CRDT- 2023/09/22 10:34
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/09/22 12:43 [pubmed]
PHST- 2023/09/22 10:34 [entrez]
AID - 10.1027/0227-5910/a000915 [doi]
PST - ppublish
SO  - Crisis. 2023 Sep;44(5):367-370. doi: 10.1027/0227-5910/a000915.

PMID- 37731266
OWN - NLM
STAT- Publisher
LR  - 20230921
IS  - 1601-0825 (Electronic)
IS  - 1354-523X (Linking)
DP  - 2023 Sep 20
TI  - Combining ChatGPT and machine learning in oral medicine.
LID - 10.1111/odi.14742 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Ajitgarh, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230920
PL  - Denmark
TA  - Oral Dis
JT  - Oral diseases
JID - 9508565
EDAT- 2023/09/21 06:42
MHDA- 2023/09/21 06:42
CRDT- 2023/09/21 02:11
PHST- 2023/08/10 00:00 [revised]
PHST- 2023/08/03 00:00 [received]
PHST- 2023/09/04 00:00 [accepted]
PHST- 2023/09/21 06:42 [medline]
PHST- 2023/09/21 06:42 [pubmed]
PHST- 2023/09/21 02:11 [entrez]
AID - 10.1111/odi.14742 [doi]
PST - aheadofprint
SO  - Oral Dis. 2023 Sep 20. doi: 10.1111/odi.14742.

PMID- 37557096
OWN - NLM
STAT- MEDLINE
DCOM- 20230906
LR  - 20230914
IS  - 1423-0097 (Electronic)
IS  - 1018-2438 (Linking)
VI  - 184
IP  - 9
DP  - 2023
TI  - Allergic to ChatGPT? Introducing a Desensitization Protocol to Embrace Artificial 
      Intelligence.
PG  - 903-905
LID - 10.1159/000531785 [doi]
FAU - Cerci, Pamir
AU  - Cerci P
AD  - Division of Immunology and Allergy, Department of Internal Medicine/Eskisehir 
      City Hospital, Eskisehir, Turkey.
LA  - eng
PT  - Letter
DEP - 20230809
PL  - Switzerland
TA  - Int Arch Allergy Immunol
JT  - International archives of allergy and immunology
JID - 9211652
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Hypersensitivity/therapy
EDAT- 2023/08/10 00:42
MHDA- 2023/09/06 06:42
CRDT- 2023/08/09 18:23
PHST- 2023/06/18 00:00 [received]
PHST- 2023/06/29 00:00 [accepted]
PHST- 2023/09/06 06:42 [medline]
PHST- 2023/08/10 00:42 [pubmed]
PHST- 2023/08/09 18:23 [entrez]
AID - 000531785 [pii]
AID - 10.1159/000531785 [doi]
PST - ppublish
SO  - Int Arch Allergy Immunol. 2023;184(9):903-905. doi: 10.1159/000531785. Epub 2023 
      Aug 9.

PMID- 37515601
OWN - NLM
STAT- Publisher
LR  - 20231016
IS  - 1432-5195 (Electronic)
IS  - 0341-2695 (Linking)
VI  - 47
IP  - 10
DP  - 2023 Oct
TI  - A critical analysis of use of ChatGPT in orthopaedics.
PG  - 2617-2618
LID - 10.1007/s00264-023-05916-5 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Department of Computer Applications, Sikkim University, 6th Mile, PO-Tadong, 
      Gangtok, Sikkim, 737102, India. ppray@cus.ac.in.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230729
PL  - Germany
TA  - Int Orthop
JT  - International orthopaedics
JID - 7705431
SB  - IM
CON - Int Orthop. 2023 Aug;47(8):1887-1893. PMID: 37428231
EDAT- 2023/07/29 21:47
MHDA- 2023/07/29 21:47
CRDT- 2023/07/29 11:03
PHST- 2023/07/18 00:00 [received]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/07/29 21:47 [pubmed]
PHST- 2023/07/29 21:47 [medline]
PHST- 2023/07/29 11:03 [entrez]
AID - 10.1007/s00264-023-05916-5 [pii]
AID - 10.1007/s00264-023-05916-5 [doi]
PST - ppublish
SO  - Int Orthop. 2023 Oct;47(10):2617-2618. doi: 10.1007/s00264-023-05916-5. Epub 2023 
      Jul 29.

PMID- 37385082
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231102
IS  - 2211-0356 (Electronic)
IS  - 2211-0348 (Linking)
VI  - 76
DP  - 2023 Aug
TI  - Artificial intelligence and multiple sclerosis: ChatGPT model.
PG  - 104851
LID - S2211-0348(23)00353-X [pii]
LID - 10.1016/j.msard.2023.104851 [doi]
FAU - Altunisik, Erman
AU  - Altunisik E
AD  - Adiyaman University Faculty of Medicine, Turkey. Electronic address: 
      ealtunisik@adiyaman.edu.tr.
LA  - eng
PT  - Letter
DEP - 20230620
PL  - Netherlands
TA  - Mult Scler Relat Disord
JT  - Multiple sclerosis and related disorders
JID - 101580247
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Multiple Sclerosis/diagnosis/therapy
COIS- Declaration of Competing Interest The authors declare no potential conflicts of 
      interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/06/30 01:06
MHDA- 2023/10/23 00:41
CRDT- 2023/06/29 18:05
PHST- 2023/06/09 00:00 [received]
PHST- 2023/06/19 00:00 [accepted]
PHST- 2023/10/23 00:41 [medline]
PHST- 2023/06/30 01:06 [pubmed]
PHST- 2023/06/29 18:05 [entrez]
AID - S2211-0348(23)00353-X [pii]
AID - 10.1016/j.msard.2023.104851 [doi]
PST - ppublish
SO  - Mult Scler Relat Disord. 2023 Aug;76:104851. doi: 10.1016/j.msard.2023.104851. 
      Epub 2023 Jun 20.

PMID- 37355811
OWN - NLM
STAT- MEDLINE
DCOM- 20230626
LR  - 20231116
IS  - 0004-5772 (Print)
IS  - 0004-5772 (Linking)
VI  - 71
IP  - 5
DP  - 2023 May
TI  - Digital Technology in Clinical Medicine: From Calculators to ChatGPT.
PG  - 11-12
FAU - Kulkarni, Shriram V
AU  - Kulkarni SV
AD  - Associate Professor, Department of Internal Medicine; Corresponding Author.
FAU - Sinha, Sagar
AU  - Sinha S
AD  - Associate Professor.
FAU - Narasimhan, Ramakrishnan Dindigal
AU  - Narasimhan RD
AD  - Senior Resident, Department of Emergency Medicine, MGM institute of Health 
      Sciences, MGM Medical College and Hospital, Navi Mumbai, Maharashtra, India.
LA  - eng
PT  - Journal Article
PL  - India
TA  - J Assoc Physicians India
JT  - The Journal of the Association of Physicians of India
JID - 7505585
SB  - IM
MH  - *Digital Technology
MH  - *Clinical Medicine
EDAT- 2023/06/25 06:42
MHDA- 2023/06/26 06:41
CRDT- 2023/06/25 03:18
PHST- 2023/06/26 06:41 [medline]
PHST- 2023/06/25 06:42 [pubmed]
PHST- 2023/06/25 03:18 [entrez]
PST - ppublish
SO  - J Assoc Physicians India. 2023 May;71(5):11-12.

PMID- 37301002
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230822
LR  - 20230822
IS  - 1873-7862 (Electronic)
IS  - 0924-977X (Linking)
VI  - 74
DP  - 2023 Sep
TI  - Response to Kleebayoon and Wiwanitkit's (2023): Towards a ChatGPT 
      psychopathology: Comment.
PG  - 76
LID - S0924-977X(23)00107-4 [pii]
LID - 10.1016/j.euroneuro.2023.05.006 [doi]
FAU - Cuesta, Manuel J
AU  - Cuesta MJ
AD  - Jefe del Servicio de Psiquiatría, Hospital Universitario de Navarra, Pamplona 
      (España), Spain.
LA  - eng
PT  - Letter
DEP - 20230608
PL  - Netherlands
TA  - Eur Neuropsychopharmacol
JT  - European neuropsychopharmacology : the journal of the European College of 
      Neuropsychopharmacology
JID - 9111390
SB  - IM
EDAT- 2023/06/11 01:06
MHDA- 2023/06/11 01:07
CRDT- 2023/06/10 18:04
PHST- 2023/05/19 00:00 [received]
PHST- 2023/05/22 00:00 [accepted]
PHST- 2023/06/11 01:07 [medline]
PHST- 2023/06/11 01:06 [pubmed]
PHST- 2023/06/10 18:04 [entrez]
AID - S0924-977X(23)00107-4 [pii]
AID - 10.1016/j.euroneuro.2023.05.006 [doi]
PST - ppublish
SO  - Eur Neuropsychopharmacol. 2023 Sep;74:76. doi: 10.1016/j.euroneuro.2023.05.006. 
      Epub 2023 Jun 8.

PMID- 37169865
OWN - NLM
STAT- MEDLINE
DCOM- 20230626
LR  - 20231116
IS  - 1546-170X (Electronic)
IS  - 1078-8956 (Linking)
VI  - 29
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT is not the solution to physicians' documentation burden.
PG  - 1296-1297
LID - 10.1038/s41591-023-02341-4 [doi]
FAU - Preiksaitis, Carl
AU  - Preiksaitis C
AUID- ORCID: 0000-0002-3856-0068
AD  - Department of Emergency Medicine, Stanford University School of Medicine, 
      Stanford, CA, USA.
FAU - Sinsky, Christine A
AU  - Sinsky CA
AD  - American Medical Association, Chicago, IL, USA.
FAU - Rose, Christian
AU  - Rose C
AUID- ORCID: 0000-0002-5115-649X
AD  - Department of Emergency Medicine, Stanford University School of Medicine, 
      Stanford, CA, USA. ccrose@stanford.edu.
LA  - eng
PT  - Letter
PL  - United States
TA  - Nat Med
JT  - Nature medicine
JID - 9502015
SB  - IM
MH  - Humans
MH  - *Electronic Health Records
MH  - Documentation
MH  - *Physicians
EDAT- 2023/05/12 01:07
MHDA- 2023/06/26 06:41
CRDT- 2023/05/11 23:22
PHST- 2023/06/26 06:41 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 23:22 [entrez]
AID - 10.1038/s41591-023-02341-4 [pii]
AID - 10.1038/s41591-023-02341-4 [doi]
PST - ppublish
SO  - Nat Med. 2023 Jun;29(6):1296-1297. doi: 10.1038/s41591-023-02341-4.

PMID- 37119117
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231107
IS  - 1933-0693 (Electronic)
IS  - 0022-3085 (Linking)
VI  - 139
IP  - 5
DP  - 2023 Apr 28
TI  - Implications and future directions of ChatGPT utilization in neurosurgery.
PG  - 1487-1489
LID - 10.3171/2023.3.JNS23555 [doi]
FAU - Singh, Rohin
AU  - Singh R
AD  - 1Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona.
FAU - Reardon, Taylor
AU  - Reardon T
AD  - 2Department of Neurosurgery, Kentucky College of Osteopathic Medicine, University 
      of Pikeville, Pikeville, Kentucky.
FAU - Srinivasan, Visish M
AU  - Srinivasan VM
AD  - 3Department of Neurosurgery, Perelman School of Medicine, University of 
      Pennsylvania, Philadelphia, Pennsylvania.
FAU - Gottfried, Oren
AU  - Gottfried O
AD  - 4Department of Neurosurgery, Duke University, Durham, North Carolina.
FAU - Bydon, Mohamad
AU  - Bydon M
AD  - 5Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota; and.
FAU - Lawton, Michael T
AU  - Lawton MT
AD  - 6Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona.
LA  - eng
PT  - Journal Article
DEP - 20230428
PL  - United States
TA  - J Neurosurg
JT  - Journal of neurosurgery
JID - 0253357
SB  - IM
MH  - Humans
MH  - *Neurosurgery
MH  - Neurosurgical Procedures
EDAT- 2023/04/29 19:43
MHDA- 2023/11/03 06:43
CRDT- 2023/04/29 07:23
PHST- 2023/11/03 06:43 [medline]
PHST- 2023/04/29 19:43 [pubmed]
PHST- 2023/04/29 07:23 [entrez]
AID - 10.3171/2023.3.JNS23555 [doi]
PST - epublish
SO  - J Neurosurg. 2023 Apr 28;139(5):1487-1489. doi: 10.3171/2023.3.JNS23555.

PMID- 37092365
OWN - NLM
STAT- MEDLINE
DCOM- 20230531
LR  - 20231004
IS  - 1437-4331 (Electronic)
IS  - 1434-6621 (Linking)
VI  - 61
IP  - 7
DP  - 2023 Jun 27
TI  - ChatGPT: Angel or Demond? Critical thinking is still needed.
PG  - 1131-1132
LID - 10.1515/cclm-2023-0387 [doi]
FAU - Plebani, Mario
AU  - Plebani M
AUID- ORCID: 0000-0002-0270-1711
AD  - Clinical Biochemistry and Clinical Molecular Biology, University of Padova, 
      Padova, Italy.
AD  - Department of Pathology, Medical Branch, University of Texas, Galveston, USA.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230425
PL  - Germany
TA  - Clin Chem Lab Med
JT  - Clinical chemistry and laboratory medicine
JID - 9806306
SB  - IM
CON - Clin Chem Lab Med. 2023 Apr 24;61(7):1158-1166. PMID: 37083166
CIN - Clin Chem Lab Med. 2023 May 30;61(11):e221. PMID: 37247851
MH  - Humans
MH  - *Thinking
MH  - *Artificial Intelligence
EDAT- 2023/04/24 06:42
MHDA- 2023/05/30 06:42
CRDT- 2023/04/24 05:43
PHST- 2023/05/30 06:42 [medline]
PHST- 2023/04/24 06:42 [pubmed]
PHST- 2023/04/24 05:43 [entrez]
AID - cclm-2023-0387 [pii]
AID - 10.1515/cclm-2023-0387 [doi]
PST - epublish
SO  - Clin Chem Lab Med. 2023 Apr 25;61(7):1131-1132. doi: 10.1515/cclm-2023-0387. 
      Print 2023 Jun 27.

PMID- 37890076
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
DP  - 2023 Oct 24
TI  - How Automation and ChatGPT will Affect Competency Definitions and Assessment 
      Tools.
LID - 10.1097/ACM.0000000000005483 [doi]
FAU - Hoang, Nam S
AU  - Hoang NS
AUID- ORCID: 0000-0002-5709-0161
AD  - Nam S. Hoang is a Fourth-year medical student, David Geffen School of Medicine at 
      the University of California, Los Angeles, Los Angeles, California; email: 
      dannyhoangn@gmail.com; ORCID: http://orcid.org/0000-0002-5709-0161.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
EDAT- 2023/10/27 18:42
MHDA- 2023/10/27 18:42
CRDT- 2023/10/27 15:33
PHST- 2023/10/27 18:42 [medline]
PHST- 2023/10/27 18:42 [pubmed]
PHST- 2023/10/27 15:33 [entrez]
AID - 00001888-990000000-00604 [pii]
AID - 10.1097/ACM.0000000000005483 [doi]
PST - aheadofprint
SO  - Acad Med. 2023 Oct 24. doi: 10.1097/ACM.0000000000005483.

PMID- 37671693
OWN - NLM
STAT- Publisher
LR  - 20230911
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
DP  - 2023 Sep 6
TI  - Can ChatGPT Truly Overcome Other Large Language Models?
PG  - 8465371231199444
LID - 10.1177/08465371231199444 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. RINGGOLD: 179229
LA  - eng
PT  - Letter
DEP - 20230906
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
EDAT- 2023/09/06 12:42
MHDA- 2023/09/06 12:42
CRDT- 2023/09/06 06:43
PHST- 2023/09/06 12:42 [pubmed]
PHST- 2023/09/06 12:42 [medline]
PHST- 2023/09/06 06:43 [entrez]
AID - 10.1177/08465371231199444 [doi]
PST - aheadofprint
SO  - Can Assoc Radiol J. 2023 Sep 6:8465371231199444. doi: 10.1177/08465371231199444.

PMID- 37480927
OWN - NLM
STAT- Publisher
LR  - 20230722
IS  - 1943-4723 (Electronic)
IS  - 0002-8177 (Linking)
DP  - 2023 Jul 21
TI  - Transforming dentistry with ChatGPT: A guide to optimizing patient care.
LID - S0002-8177(23)00339-2 [pii]
LID - 10.1016/j.adaj.2023.06.003 [doi]
FAU - Tussie, Camila
AU  - Tussie C
LA  - eng
PT  - Editorial
DEP - 20230721
PL  - England
TA  - J Am Dent Assoc
JT  - Journal of the American Dental Association (1939)
JID - 7503060
SB  - IM
EDAT- 2023/07/23 01:11
MHDA- 2023/07/23 01:11
CRDT- 2023/07/22 18:45
PHST- 2023/04/10 00:00 [received]
PHST- 2023/05/23 00:00 [revised]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/07/23 01:11 [medline]
PHST- 2023/07/23 01:11 [pubmed]
PHST- 2023/07/22 18:45 [entrez]
AID - S0002-8177(23)00339-2 [pii]
AID - 10.1016/j.adaj.2023.06.003 [doi]
PST - aheadofprint
SO  - J Am Dent Assoc. 2023 Jul 21:S0002-8177(23)00339-2. doi: 
      10.1016/j.adaj.2023.06.003.

PMID- 37071236
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230612
LR  - 20230612
IS  - 1534-4681 (Electronic)
IS  - 1068-9265 (Linking)
VI  - 30
IP  - 7
DP  - 2023 Jul
TI  - Letter to the Editor: Could ChatGPT Improve Knowledge in Surgery?
PG  - 3942-3943
LID - 10.1245/s10434-023-13518-z [doi]
FAU - Tel, Alessandro
AU  - Tel A
AD  - Department of Medical Area (DAME), Clinic of Maxillo-facial Surgery, Academic 
      Hospital of Udine, University of Udine, Udine, Italy.
FAU - Parodi, Pier Camillo
AU  - Parodi PC
AD  - Department of Medical Area (DAME), Clinic of Plastic and Reconstructive Surgery, 
      Academic Hospital of Udine, University of Udine, Udine, Italy.
FAU - Robiony, Massimo
AU  - Robiony M
AD  - Department of Medical Area (DAME), Clinic of Maxillo-facial Surgery, Academic 
      Hospital of Udine, University of Udine, Udine, Italy.
FAU - Zanotti, Bruno
AU  - Zanotti B
AD  - Department of Neuroscience, Neurology with Neurosurgical Activity, C. Poma 
      Hospital, ASST di Mantova, Mantua, Italy.
FAU - Zingaretti, Nicola
AU  - Zingaretti N
AUID- ORCID: 0000-0002-1575-0029
AD  - Department of Medical Area (DAME), Clinic of Plastic and Reconstructive Surgery, 
      Academic Hospital of Udine, University of Udine, Udine, Italy. 
      zingarettin@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230418
PL  - United States
TA  - Ann Surg Oncol
JT  - Annals of surgical oncology
JID - 9420840
SB  - IM
EDAT- 2023/04/19 06:00
MHDA- 2023/04/19 06:01
CRDT- 2023/04/18 11:20
PHST- 2023/03/26 00:00 [received]
PHST- 2023/04/05 00:00 [accepted]
PHST- 2023/04/19 06:01 [medline]
PHST- 2023/04/19 06:00 [pubmed]
PHST- 2023/04/18 11:20 [entrez]
AID - 10.1245/s10434-023-13518-z [pii]
AID - 10.1245/s10434-023-13518-z [doi]
PST - ppublish
SO  - Ann Surg Oncol. 2023 Jul;30(7):3942-3943. doi: 10.1245/s10434-023-13518-z. Epub 
      2023 Apr 18.

PMID- 37026967
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230721
LR  - 20230902
IS  - 1537-7385 (Electronic)
IS  - 0894-9115 (Linking)
VI  - 102
IP  - 8
DP  - 2023 Aug 1
TI  - Letter to the Editor on "Will ChatGPT Match to Your Program?".
PG  - e115-e116
LID - 10.1097/PHM.0000000000002259 [doi]
FAU - Nicolau, Eric
AU  - Nicolau E
FAU - Verduzco-Gutierrez, Monica
AU  - Verduzco-Gutierrez M
LA  - eng
PT  - Journal Article
DEP - 20230407
PL  - United States
TA  - Am J Phys Med Rehabil
JT  - American journal of physical medicine & rehabilitation
JID - 8803677
SB  - IM
EDAT- 2023/04/08 06:00
MHDA- 2023/04/08 06:01
CRDT- 2023/04/07 10:13
PHST- 2023/04/08 06:01 [medline]
PHST- 2023/04/08 06:00 [pubmed]
PHST- 2023/04/07 10:13 [entrez]
AID - 00002060-202308000-00018 [pii]
AID - 10.1097/PHM.0000000000002259 [doi]
PST - ppublish
SO  - Am J Phys Med Rehabil. 2023 Aug 1;102(8):e115-e116. doi: 
      10.1097/PHM.0000000000002259. Epub 2023 Apr 7.

PMID- 37993361
OWN - NLM
STAT- Publisher
LR  - 20231122
IS  - 1879-730X (Electronic)
IS  - 1879-7296 (Linking)
DP  - 2023 Nov 21
TI  - Can ChatGPT be a valuable study tool for ENT residents?
LID - S1879-7296(23)00140-0 [pii]
LID - 10.1016/j.anorl.2023.10.018 [doi]
FAU - Dallari, V
AU  - Dallari V
AD  - Unit of Otorhinolaryngology, Head & Neck Department, University of Verona, 
      Piazzale L.A. Scuro 10, 37134 Verona, Italy; Young Confederation of European 
      ORL-HNS.
FAU - Liberale, C
AU  - Liberale C
AD  - Unit of Otorhinolaryngology, Head & Neck Department, University of Verona, 
      Piazzale L.A. Scuro 10, 37134 Verona, Italy.
FAU - De Cecco, F
AU  - De Cecco F
AD  - Unit of Otorhinolaryngology, Head & Neck Department, University of Verona, 
      Piazzale L.A. Scuro 10, 37134 Verona, Italy. Electronic address: 
      francesca16592@gmail.com.
FAU - Monzani, D
AU  - Monzani D
AD  - Unit of Otorhinolaryngology, Head & Neck Department, University of Verona, 
      Piazzale L.A. Scuro 10, 37134 Verona, Italy.
LA  - eng
PT  - Letter
DEP - 20231121
PL  - France
TA  - Eur Ann Otorhinolaryngol Head Neck Dis
JT  - European annals of otorhinolaryngology, head and neck diseases
JID - 101531465
SB  - IM
EDAT- 2023/11/23 00:42
MHDA- 2023/11/23 00:42
CRDT- 2023/11/22 22:01
PHST- 2023/09/25 00:00 [received]
PHST- 2023/10/08 00:00 [accepted]
PHST- 2023/11/23 00:42 [medline]
PHST- 2023/11/23 00:42 [pubmed]
PHST- 2023/11/22 22:01 [entrez]
AID - S1879-7296(23)00140-0 [pii]
AID - 10.1016/j.anorl.2023.10.018 [doi]
PST - aheadofprint
SO  - Eur Ann Otorhinolaryngol Head Neck Dis. 2023 Nov 21:S1879-7296(23)00140-0. doi: 
      10.1016/j.anorl.2023.10.018.

PMID- 37820330
OWN - NLM
STAT- MEDLINE
DCOM- 20231031
LR  - 20231031
IS  - 2326-6929 (Electronic)
IS  - 0011-4162 (Linking)
VI  - 112
IP  - 2
DP  - 2023 Aug
TI  - ChatGPT in Dermatology Clinical Practice: Potential Uses and Pitfalls.
PG  - E15-E17
LID - 10.12788/cutis.0843 [doi]
FAU - Reddy, Soumya
AU  - Reddy S
AD  - Department of Dermatology, University of Virginia, Charlottesville.
FAU - Schwartzman, Gabrielle
AU  - Schwartzman G
AD  - Department of Dermatology, University of Virginia, Charlottesville.
FAU - Flowers, R Hal
AU  - Flowers RH
AD  - Department of Dermatology, University of Virginia, Charlottesville.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Cutis
JT  - Cutis
JID - 0006440
SB  - IM
MH  - Humans
MH  - *Dermatology
MH  - Artificial Intelligence
EDAT- 2023/10/11 18:43
MHDA- 2023/10/23 01:18
CRDT- 2023/10/11 16:33
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/11 18:43 [pubmed]
PHST- 2023/10/11 16:33 [entrez]
AID - cutis.0843 [pii]
AID - 10.12788/cutis.0843 [doi]
PST - ppublish
SO  - Cutis. 2023 Aug;112(2):E15-E17. doi: 10.12788/cutis.0843.

PMID- 37812094
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231010
LR  - 20231018
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - The Hidden Costs of ChatGPT: A Call for Greater Transparency.
PG  - 47-49
LID - 10.1080/15265161.2023.2250335 [doi]
FAU - Elmore, Matthew
AU  - Elmore M
AD  - Duke University.
LA  - eng
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CIN - Am J Bioeth. 2023 Oct;23(10):1-5. PMID: 37831940
EDAT- 2023/10/09 12:42
MHDA- 2023/10/09 12:43
CRDT- 2023/10/09 10:13
PHST- 2023/10/09 12:43 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 10:13 [entrez]
AID - 10.1080/15265161.2023.2250335 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):47-49. doi: 10.1080/15265161.2023.2250335. Epub 2023 
      Oct 9.

PMID- 37567593
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1756-1833 (Electronic)
IS  - 0959-8138 (Linking)
VI  - 382
DP  - 2023 Aug 11
TI  - When I use a word . . . ChatGPT: a differential diagnosis.
PG  - 1862
LID - 10.1136/bmj.p1862 [doi]
FAU - Aronson, Jeffrey K
AU  - Aronson JK
AUID- ORCID: 0000-0003-1139-655X
AD  - Centre for Evidence Based Medicine, Nuffield Department of Primary Care Health 
      Sciences, University of Oxford, Oxford, UK.
LA  - eng
PT  - Editorial
DEP - 20230811
PL  - England
TA  - BMJ
JT  - BMJ (Clinical research ed.)
JID - 8900488
SB  - IM
COIS- Competing interests: JKA has composed crossword puzzles that have appeared in The 
      Listener, The Oxford Times, The Daily Telegraph, and currently the Times Literary 
      Supplement and hopes that no one is using ChatGPT to try to solve them.
EDAT- 2023/08/12 10:41
MHDA- 2023/08/14 06:41
CRDT- 2023/08/11 20:43
PHST- 2023/08/14 06:41 [medline]
PHST- 2023/08/12 10:41 [pubmed]
PHST- 2023/08/11 20:43 [entrez]
AID - 10.1136/bmj.p1862 [doi]
PST - epublish
SO  - BMJ. 2023 Aug 11;382:1862. doi: 10.1136/bmj.p1862.

PMID- 37415217
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230711
LR  - 20230718
IS  - 1479-5876 (Electronic)
IS  - 1479-5876 (Linking)
VI  - 21
IP  - 1
DP  - 2023 Jul 6
TI  - ChatGPT: when the artificial intelligence meets standardized patients in clinical 
      training.
PG  - 447
LID - 10.1186/s12967-023-04314-0 [doi]
LID - 447
FAU - Liu, Xiaoni
AU  - Liu X
AD  - Department of Hematology, Lymphoma Research Center, Peking University Third 
      Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China.
AD  - Department of Respiratory Medicine, The First Affiliated Hospital of Gannan 
      Medical University, Ganzhou, 341000, China.
FAU - Wu, Chaoling
AU  - Wu C
AD  - Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, 
      Jiujiang, 332000, China.
FAU - Lai, Rui
AU  - Lai R
AD  - Department of the Respiratory Medicine, The People's Hospital of Ruijin City, 
      Ruijin, 342500, China.
FAU - Lin, Hai
AU  - Lin H
AD  - Department of Respiratory Medicine, The First Affiliated Hospital of Gannan 
      Medical University, Ganzhou, 341000, China.
FAU - Xu, Yiying
AU  - Xu Y
AD  - Department of Respiratory and Critical Medicine, Longnan First People's Hospital, 
      Longnan, 341700, China.
FAU - Lin, Yuansheng
AU  - Lin Y
AD  - Department of Emergency and Critical Care Medicine, Suzhou Hospital, Affiliated 
      Hospital of Medical School, Nanjing University, No. 1 Lijiang Road, Gaoxin 
      District, Suzhou, 215000, China. linys202012@163.com.
FAU - Zhang, Weilong
AU  - Zhang W
AUID- ORCID: 0000-0001-7792-3389
AD  - Department of Hematology, Lymphoma Research Center, Peking University Third 
      Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China. 
      zhangwl2012@126.com.
LA  - eng
GR  - 81800195/Innovative Research Group Project of the National Natural Science 
      Foundation of China/
PT  - Letter
DEP - 20230706
PL  - England
TA  - J Transl Med
JT  - Journal of translational medicine
JID - 101190741
SB  - IM
PMC - PMC10326922
COIS- The authors declared no competing interests.
EDAT- 2023/07/07 01:05
MHDA- 2023/07/10 06:42
CRDT- 2023/07/06 23:43
PHST- 2023/06/07 00:00 [received]
PHST- 2023/06/28 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/07/07 01:05 [pubmed]
PHST- 2023/07/06 23:43 [entrez]
AID - 10.1186/s12967-023-04314-0 [pii]
AID - 4314 [pii]
AID - 10.1186/s12967-023-04314-0 [doi]
PST - epublish
SO  - J Transl Med. 2023 Jul 6;21(1):447. doi: 10.1186/s12967-023-04314-0.

PMID- 37333693
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230620
IS  - 0019-5049 (Print)
IS  - 0976-2817 (Electronic)
IS  - 0019-5049 (Linking)
VI  - 67
IP  - 5
DP  - 2023 May
TI  - ChatGPT in the field of scientific publication - Are we ready for it?
PG  - 407-408
LID - 10.4103/ija.ija_294_23 [doi]
FAU - Thondebhavi Subbaramaiah, Muralidhar
AU  - Thondebhavi Subbaramaiah M
AD  - Department of Anaesthesiology and Pain Medicine, Apollo Speciality Hospital, 
      Bangalore, Karnataka, India.
FAU - Shanthanna, Harsha
AU  - Shanthanna H
AD  - Department of Anesthesia, McMaster University, Canada.
LA  - eng
PT  - Editorial
DEP - 20230511
PL  - India
TA  - Indian J Anaesth
JT  - Indian journal of anaesthesia
JID - 0013243
PMC - PMC10269991
EDAT- 2023/06/19 06:41
MHDA- 2023/06/19 06:42
CRDT- 2023/06/19 02:53
PHST- 2023/04/05 00:00 [received]
PHST- 2023/04/26 00:00 [revised]
PHST- 2023/04/27 00:00 [accepted]
PHST- 2023/06/19 06:42 [medline]
PHST- 2023/06/19 06:41 [pubmed]
PHST- 2023/06/19 02:53 [entrez]
AID - IJA-67-407 [pii]
AID - 10.4103/ija.ija_294_23 [doi]
PST - ppublish
SO  - Indian J Anaesth. 2023 May;67(5):407-408. doi: 10.4103/ija.ija_294_23. Epub 2023 
      May 11.

PMID- 37028775
OWN - NLM
STAT- MEDLINE
DCOM- 20230619
LR  - 20231116
IS  - 1557-3117 (Electronic)
IS  - 1053-2498 (Linking)
VI  - 42
IP  - 7
DP  - 2023 Jul
TI  - Predicting outcomes in lung transplantation: From tea leaves to ChatGPT.
PG  - 905-907
LID - S1053-2498(23)01816-8 [pii]
LID - 10.1016/j.healun.2023.03.019 [doi]
FAU - Rozenberg, Dmitry
AU  - Rozenberg D
AD  - Division of Respirology, Temerty Faculty of Medicine, University of Toronto, 
      Toronto, Ontario, Canada; Toronto Lung Transplant Program, Ajmera Transplant 
      Center, Toronto General Hospital Research Institute, University Health Network, 
      Toronto, Ontario, Canada.
FAU - Singer, Lianne G
AU  - Singer LG
AD  - Division of Respirology, Temerty Faculty of Medicine, University of Toronto, 
      Toronto, Ontario, Canada; Toronto Lung Transplant Program, Ajmera Transplant 
      Center, Toronto General Hospital Research Institute, University Health Network, 
      Toronto, Ontario, Canada. Electronic address: Lianne.Singer@uhn.ca.
LA  - eng
PT  - Editorial
DEP - 20230405
PL  - United States
TA  - J Heart Lung Transplant
JT  - The Journal of heart and lung transplantation : the official publication of the 
      International Society for Heart Transplantation
JID - 9102703
RN  - 0 (Tea)
SB  - IM
MH  - Humans
MH  - *Employment
MH  - *Lung Transplantation
MH  - Plant Leaves
MH  - Tea
COIS- Disclosure statement No conflicts of interest to declare. Dmitry Rozenberg 
      receives research support from the Sandra Faire and Ivan Fecan Professorship in 
      Rehabilitation Medicine.
EDAT- 2023/04/08 06:00
MHDA- 2023/06/19 13:08
CRDT- 2023/04/07 19:30
PHST- 2023/03/20 00:00 [received]
PHST- 2023/03/30 00:00 [accepted]
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/04/08 06:00 [pubmed]
PHST- 2023/04/07 19:30 [entrez]
AID - S1053-2498(23)01816-8 [pii]
AID - 10.1016/j.healun.2023.03.019 [doi]
PST - ppublish
SO  - J Heart Lung Transplant. 2023 Jul;42(7):905-907. doi: 
      10.1016/j.healun.2023.03.019. Epub 2023 Apr 5.

PMID- 36931435
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230522
LR  - 20230810
IS  - 2589-9333 (Electronic)
IS  - 2589-9333 (Linking)
VI  - 5
IP  - 6
DP  - 2023 Jun
TI  - Identifying ChatGPT-written OBGYN abstracts using a simple tool.
PG  - 100936
LID - S2589-9333(23)00078-2 [pii]
LID - 10.1016/j.ajogmf.2023.100936 [doi]
FAU - Levin, Gabriel
AU  - Levin G
AD  - Department of Obstetrics and Gynecology, Hadassah-Hebrew University Medical 
      Center, Jerusalem, Israel; Lady Davis Institute for Cancer Research, Jewish 
      General Hospital, McGill University, Quebec, Canada. Electronic address: 
      levin.gaby@gmail.com.
FAU - Meyer, Raanan
AU  - Meyer R
AD  - Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics and 
      Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA; The Dr. Pinchas 
      Bornstein Talpiot Medical Leadership Program, Sheba Medical Center, Tel Hashomer, 
      Ramat-Gan, Israel.
FAU - Kadoch, Eva
AU  - Kadoch E
AD  - Lady Davis Institute for Cancer Research, Jewish General Hospital, McGill 
      University, Quebec, Canada.
FAU - Brezinov, Yoav
AU  - Brezinov Y
AD  - Department of Experimental Surgery, McGill University, Quebec, Canada.
LA  - eng
PT  - Letter
DEP - 20230315
PL  - United States
TA  - Am J Obstet Gynecol MFM
JT  - American journal of obstetrics & gynecology MFM
JID - 101746609
SB  - IM
CIN - Am J Obstet Gynecol MFM. 2023 Aug;5(8):101005. PMID: 37142188
EDAT- 2023/03/18 06:00
MHDA- 2023/03/18 06:01
CRDT- 2023/03/17 20:30
PHST- 2023/03/08 00:00 [received]
PHST- 2023/03/13 00:00 [revised]
PHST- 2023/03/13 00:00 [accepted]
PHST- 2023/03/18 06:01 [medline]
PHST- 2023/03/18 06:00 [pubmed]
PHST- 2023/03/17 20:30 [entrez]
AID - S2589-9333(23)00078-2 [pii]
AID - 10.1016/j.ajogmf.2023.100936 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol MFM. 2023 Jun;5(6):100936. doi: 10.1016/j.ajogmf.2023.100936. 
      Epub 2023 Mar 15.

PMID- 36857593
OWN - NLM
STAT- MEDLINE
DCOM- 20230427
LR  - 20231005
IS  - 1538-9855 (Electronic)
IS  - 0363-3624 (Linking)
VI  - 48
IP  - 3
DP  - 2023 May-Jun 01
TI  - Using ChatGPT to Teach Enhanced Clinical Judgment in Nursing Education.
PG  - 124
LID - 10.1097/NNE.0000000000001383 [doi]
FAU - Seney, Valerie
AU  - Seney V
AD  - By Valerie Seney , PhD, MA, LMHC, PMHNP-BC, Melissa L. Desroches , PhD, RN, CNE, 
      and Monika S. Schuler , PhD, FNP-BC, CNE, University of Massachusetts Dartmouth ( 
      mdesroches@umassd.edu ).
FAU - Desroches, Melissa L
AU  - Desroches ML
AUID- ORCID: 0000-0001-5353-6907
FAU - Schuler, Monika S
AU  - Schuler MS
LA  - eng
PT  - Journal Article
DEP - 20230228
PL  - United States
TA  - Nurse Educ
JT  - Nurse educator
JID - 7701902
MH  - Humans
MH  - Judgment
MH  - Nursing Education Research
MH  - *Education, Nursing
MH  - *Education, Nursing, Baccalaureate
MH  - Clinical Competence
MH  - *Students, Nursing
COIS- The authors declare no conflicts of interest.
EDAT- 2023/03/02 06:00
MHDA- 2023/04/27 06:42
CRDT- 2023/03/01 15:33
PHST- 2023/04/27 06:42 [medline]
PHST- 2023/03/02 06:00 [pubmed]
PHST- 2023/03/01 15:33 [entrez]
AID - 00006223-202305000-00002 [pii]
AID - 10.1097/NNE.0000000000001383 [doi]
PST - ppublish
SO  - Nurse Educ. 2023 May-Jun 01;48(3):124. doi: 10.1097/NNE.0000000000001383. Epub 
      2023 Feb 28.

PMID- 37397181
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20230710
IS  - 2212-277X (Electronic)
IS  - 2212-2761 (Print)
IS  - 2212-2761 (Linking)
VI  - 12
IP  - 1
DP  - 2023
TI  - Writing with ChatGPT: An Illustration of its Capacity, Limitations & Implications 
      for Academic Writers.
PG  - 261-270
LID - 10.5334/pme.1072 [doi]
FAU - Lingard, Lorelei
AU  - Lingard L
AUID- ORCID: 0000-0002-4150-3355
AD  - Western University, CA.
LA  - eng
PT  - Journal Article
DEP - 20230629
PL  - Netherlands
TA  - Perspect Med Educ
JT  - Perspectives on medical education
JID - 101590643
SB  - IM
MH  - Humans
MH  - *Writing
MH  - *Artificial Intelligence
PMC - PMC10312253
COIS- The author has no competing interests to declare.
EDAT- 2023/07/03 13:06
MHDA- 2023/07/05 06:42
CRDT- 2023/07/03 11:30
PHST- 2023/06/06 00:00 [received]
PHST- 2023/06/06 00:00 [accepted]
PHST- 2023/07/05 06:42 [medline]
PHST- 2023/07/03 13:06 [pubmed]
PHST- 2023/07/03 11:30 [entrez]
AID - 10.5334/pme.1072 [doi]
PST - epublish
SO  - Perspect Med Educ. 2023 Jun 29;12(1):261-270. doi: 10.5334/pme.1072. eCollection 
      2023.

PMID- 37734733
OWN - NLM
STAT- Publisher
LR  - 20230921
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
DP  - 2023 Sep 21
TI  - Potential effects of ChatGPT as a learning tool through students' experiences.
PG  - 1
LID - 10.1080/0142159X.2023.2259068 [doi]
FAU - Hwang, Jee Young
AU  - Hwang JY
AUID- ORCID: 0000-0003-1491-8413
AD  - Department of Medical Education and Obstetrics & Gynecology, Dongguk University 
      College of Medicine, Gyeongju-si, South Korea.
LA  - eng
PT  - Journal Article
DEP - 20230921
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
EDAT- 2023/09/22 00:42
MHDA- 2023/09/22 00:42
CRDT- 2023/09/21 19:43
PHST- 2023/09/22 00:42 [medline]
PHST- 2023/09/22 00:42 [pubmed]
PHST- 2023/09/21 19:43 [entrez]
AID - 10.1080/0142159X.2023.2259068 [doi]
PST - aheadofprint
SO  - Med Teach. 2023 Sep 21:1. doi: 10.1080/0142159X.2023.2259068.

PMID- 37405199
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231006
IS  - 2233-7822 (Print)
IS  - 2233-7830 (Electronic)
IS  - 2233-7822 (Linking)
VI  - 53
IP  - 2
DP  - 2023 Jun
TI  - ChatGPT and scientific writing: A reflection on the ethical boundaries.
PG  - 175-176
LID - 10.5624/isd.20230085 [doi]
FAU - Ocampo, Thaís Santos Cerqueira
AU  - Ocampo TSC
AUID- ORCID: 0000-0002-6913-4416
AD  - Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental 
      School, University of Campinas, Piracicaba, SP, Brazil.
FAU - Silva, Thaísa Pinheiro
AU  - Silva TP
AUID- ORCID: 0000-0002-7485-0206
AD  - Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental 
      School, University of Campinas, Piracicaba, SP, Brazil.
FAU - Alencar-Palha, Caio
AU  - Alencar-Palha C
AUID- ORCID: 0000-0003-1856-793X
AD  - Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental 
      School, University of Campinas, Piracicaba, SP, Brazil.
FAU - Haiter-Neto, Francisco
AU  - Haiter-Neto F
AUID- ORCID: 0000-0002-5610-8307
AD  - Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental 
      School, University of Campinas, Piracicaba, SP, Brazil.
FAU - Oliveira, Matheus L
AU  - Oliveira ML
AUID- ORCID: 0000-0002-8054-8759
AD  - Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental 
      School, University of Campinas, Piracicaba, SP, Brazil.
LA  - eng
PT  - Journal Article
DEP - 20230519
PL  - Korea (South)
TA  - Imaging Sci Dent
JT  - Imaging science in dentistry
JID - 101559249
CIN - Imaging Sci Dent. 2023 Sep;53(3):267-268. PMID: 37799739
PMC - PMC10315235
COIS- Conflict of Interest: None
EDAT- 2023/07/05 13:05
MHDA- 2023/07/05 13:06
CRDT- 2023/07/05 10:48
PHST- 2023/04/13 00:00 [received]
PHST- 2023/04/20 00:00 [revised]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/07/05 13:06 [medline]
PHST- 2023/07/05 13:05 [pubmed]
PHST- 2023/07/05 10:48 [entrez]
AID - 10.5624/isd.20230085 [doi]
PST - ppublish
SO  - Imaging Sci Dent. 2023 Jun;53(2):175-176. doi: 10.5624/isd.20230085. Epub 2023 
      May 19.

PMID- 37335375
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231124
LR  - 20231124
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 12
DP  - 2023 Dec
TI  - Do People Use ChatGPT to Replace Doctor? A Google Trends Analysis.
PG  - 2652-2653
LID - 10.1007/s10439-023-03285-z [doi]
FAU - Kao, Yung-Shuo
AU  - Kao YS
AUID- ORCID: 0000-0002-8886-8239
AD  - Department of Radiation Oncology, Taoyuan General Hospital, Ministry of Health 
      and Welfare, No.1492, Zhongshan Rd., Taoyuan Dist., Taoyuan City, 330, Taiwan. 
      codingforlifetime@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230619
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
EDAT- 2023/06/19 13:08
MHDA- 2023/11/09 06:42
CRDT- 2023/06/19 11:06
PHST- 2023/06/05 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/11/09 06:42 [medline]
PHST- 2023/06/19 13:08 [pubmed]
PHST- 2023/06/19 11:06 [entrez]
AID - 10.1007/s10439-023-03285-z [pii]
AID - 10.1007/s10439-023-03285-z [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Dec;51(12):2652-2653. doi: 10.1007/s10439-023-03285-z. Epub 
      2023 Jun 19.

PMID- 37322731
OWN - NLM
STAT- MEDLINE
DCOM- 20230622
LR  - 20231116
IS  - 1998-4022 (Electronic)
IS  - 0028-3886 (Linking)
VI  - 71
IP  - 3
DP  - 2023 May-Jun
TI  - ChatGPT: A Threat to the Natural Wisdom from Artificial Intelligence.
PG  - 416-417
LID - 10.4103/0028-3886.378687 [doi]
FAU - Tripathi, Manjul
AU  - Tripathi M
AD  - Department of Neurosurgery, Postgraduate Institute of Medical Education and 
      Research, Chandigarh, India.
FAU - Chandra, Sarat P
AU  - Chandra SP
AD  - Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, 
      India.
LA  - eng
PT  - Editorial
PL  - India
TA  - Neurol India
JT  - Neurology India
JID - 0042005
SB  - IM
MH  - *Artificial Intelligence
COIS- None
EDAT- 2023/06/16 06:42
MHDA- 2023/06/19 13:08
CRDT- 2023/06/16 00:24
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/06/16 06:42 [pubmed]
PHST- 2023/06/16 00:24 [entrez]
AID - ni_2023_71_3_416_378687 [pii]
AID - 10.4103/0028-3886.378687 [doi]
PST - ppublish
SO  - Neurol India. 2023 May-Jun;71(3):416-417. doi: 10.4103/0028-3886.378687.

PMID- 37250265
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0970-9185 (Print)
IS  - 2231-2730 (Electronic)
IS  - 0970-9185 (Linking)
VI  - 39
IP  - 1
DP  - 2023 Jan-Mar
TI  - ChatGPT for academic writing: A game changer or a disruptive tool?
PG  - 1-2
LID - 10.4103/joacp.joacp_84_23 [doi]
FAU - Bhatia, Pradeep
AU  - Bhatia P
AD  - Editor, Journal of Anaesthesiology Clinical Pharmacology, Professor and Head, 
      Department of Anaesthesiology and Critical Care, All India Institute of Medical 
      Sciences, Jodhpur, Rajasthan, India.
LA  - eng
PT  - Editorial
DEP - 20230317
PL  - India
TA  - J Anaesthesiol Clin Pharmacol
JT  - Journal of anaesthesiology, clinical pharmacology
JID - 9516972
PMC - PMC10220188
EDAT- 2023/05/30 13:07
MHDA- 2023/05/30 13:08
CRDT- 2023/05/30 11:21
PHST- 2023/02/28 00:00 [received]
PHST- 2023/02/28 00:00 [accepted]
PHST- 2023/05/30 13:08 [medline]
PHST- 2023/05/30 13:07 [pubmed]
PHST- 2023/05/30 11:21 [entrez]
AID - JOACP-39-1 [pii]
AID - 10.4103/joacp.joacp_84_23 [doi]
PST - ppublish
SO  - J Anaesthesiol Clin Pharmacol. 2023 Jan-Mar;39(1):1-2. doi: 
      10.4103/joacp.joacp_84_23. Epub 2023 Mar 17.

PMID- 37083839
OWN - NLM
STAT- Publisher
LR  - 20230421
IS  - 1535-1386 (Electronic)
IS  - 0021-9355 (Linking)
DP  - 2023 Apr 21
TI  - What's Important: The Next Academic-ChatGPT AI?
LID - 10.2106/JBJS.23.00269 [doi]
FAU - Bi, Andrew S
AU  - Bi AS
AUID- ORCID: 0000-0002-1703-6232
AD  - NYU Langone Orthopedic Hospital, New York, NY.
LA  - eng
PT  - Journal Article
DEP - 20230421
PL  - United States
TA  - J Bone Joint Surg Am
JT  - The Journal of bone and joint surgery. American volume
JID - 0014030
SB  - IM
COIS- Disclosure: The Disclosure of Potential Conflicts of Interest form is provided 
      with the online version of the article (http://links.lww.com/JBJS/H500).
EDAT- 2023/04/21 18:43
MHDA- 2023/04/21 18:43
CRDT- 2023/04/21 14:51
PHST- 2023/04/21 18:43 [medline]
PHST- 2023/04/21 18:43 [pubmed]
PHST- 2023/04/21 14:51 [entrez]
AID - 00004623-990000000-00788 [pii]
AID - 10.2106/JBJS.23.00269 [doi]
PST - aheadofprint
SO  - J Bone Joint Surg Am. 2023 Apr 21. doi: 10.2106/JBJS.23.00269.

PMID- 37032109
OWN - NLM
STAT- MEDLINE
DCOM- 20230707
LR  - 20230718
IS  - 1349-9092 (Electronic)
IS  - 0917-5040 (Print)
IS  - 0917-5040 (Linking)
VI  - 33
IP  - 7
DP  - 2023 Jul 5
TI  - Can ChatGPT Be Considered an Author of a Medical Article?
PG  - 381-382
LID - 10.2188/jea.JE20230030 [doi]
FAU - Ide, Kazuki
AU  - Ide K
AUID- ORCID: 0000-0001-6377-1361
AD  - Division of Scientific Information and Public Policy, Center for Infectious 
      Disease Education and Research (CiDER), Osaka University.
AD  - Research Center on Ethical, Legal and Social Issues, Osaka University.
AD  - National Institute of Science and Technology Policy (NISTEP).
FAU - Hawke, Philip
AU  - Hawke P
AD  - School of Pharmaceutical Sciences, University of Shizuoka.
FAU - Nakayama, Takeo
AU  - Nakayama T
AD  - Department of Health Informatics, Graduate School of Medicine/School of Public 
      Health, Kyoto University.
LA  - eng
PT  - Journal Article
DEP - 20230531
PL  - Japan
TA  - J Epidemiol
JT  - Journal of epidemiology
JID - 9607688
SB  - IM
CIN - J Epidemiol. 2023 Jul 5;33(7):333-334. PMID: 37032108
MH  - Japan
MH  - *Authorship
MH  - *Artificial Intelligence
PMC - PMC10257993
COIS- Conflicts of interest: KI received honoraria from Mimir, Inc., outside the 
      submitted work. TN received research grants from I&H Co., Ltd., Cocokarafine Co., 
      Ltd., and Konica Minolta Inc.; consulting fees from Otsuka Pharmaceutical Co., 
      Ltd.; honoraria from Pfizer Japan Inc., Merck Sharp & Dohme, Chugai 
      Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., Janssen Pharmaceutical 
      K.K., Boehringer Ingelheim Inc., Eli Lilly Japan K.K., Maruho Co., Ltd., 
      Mitsubishi Tanabe Pharma Corp., Novartis Pharma K.K., Allergan Japan K.K., Novo 
      Nordisk Pharma Ltd., Toa Eiyo Ltd., Dentsu Inc., and GlaxoSmithKline K.K., Abbott 
      Japan LLC, ONO PHARMACEUTICAL CO., LTD., Alexion Pharmaceuticals, Inc., CANON 
      MEDICAL SYSTEMS CORPORATION; stock options from Bon Bon Inc.; donations from 
      CancerScan Inc. and YUYAMA Co., Ltd., outside the submitted work. PH has no 
      conflicts of interest in relation to this research.
EDAT- 2023/04/10 06:00
MHDA- 2023/07/07 06:42
CRDT- 2023/04/09 21:13
PHST- 2023/07/07 06:42 [medline]
PHST- 2023/04/10 06:00 [pubmed]
PHST- 2023/04/09 21:13 [entrez]
AID - JE20230030 [pii]
AID - 10.2188/jea.JE20230030 [doi]
PST - ppublish
SO  - J Epidemiol. 2023 Jul 5;33(7):381-382. doi: 10.2188/jea.JE20230030. Epub 2023 May 
      31.

PMID- 37018119
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230717
LR  - 20230718
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Print)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 8
DP  - 2023 Jul 15
TI  - Truth or Lies? The Pitfalls and Limitations of ChatGPT in Systematic Review 
      Creation.
PG  - NP654-NP655
LID - 10.1093/asj/sjad093 [doi]
FAU - Najafali, Daniel
AU  - Najafali D
AUID- ORCID: 0000-0003-1509-9229
FAU - Camacho, Justin M
AU  - Camacho JM
AUID- ORCID: 0000-0002-1877-6005
FAU - Reiche, Erik
AU  - Reiche E
FAU - Galbraith, Logan G
AU  - Galbraith LG
AUID- ORCID: 0009-0000-4508-4445
FAU - Morrison, Shane D
AU  - Morrison SD
FAU - Dorafshar, Amir H
AU  - Dorafshar AH
AUID- ORCID: 0000-0001-7045-3601
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
PMC - PMC10349645
EDAT- 2023/04/06 06:00
MHDA- 2023/04/06 06:01
CRDT- 2023/04/05 12:52
PHST- 2023/04/06 06:01 [medline]
PHST- 2023/04/06 06:00 [pubmed]
PHST- 2023/04/05 12:52 [entrez]
AID - 7108629 [pii]
AID - sjad093 [pii]
AID - 10.1093/asj/sjad093 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jul 15;43(8):NP654-NP655. doi: 10.1093/asj/sjad093.

PMID- 36871626
OWN - NLM
STAT- MEDLINE
DCOM- 20230525
LR  - 20230525
IS  - 2352-5568 (Electronic)
IS  - 2352-5568 (Linking)
VI  - 42
IP  - 3
DP  - 2023 Jun
TI  - Elevating scientific writing with ChatGPT: A guide for reviewers, editors… and 
      authors.
PG  - 101209
LID - S2352-5568(23)00017-6 [pii]
LID - 10.1016/j.accpm.2023.101209 [doi]
FAU - Rozencwajg, S
AU  - Rozencwajg S
AD  - Anesthesiology and Intensive Care Department, Bichat University Hospital, APHP, 
      Nord, Paris, France; Paris-Cité University, France. Electronic address: 
      sacha.rozencwajg@aphp.fr.
FAU - Kantor, E
AU  - Kantor E
AD  - Anesthesiology and Intensive Care Department, Bichat University Hospital, APHP, 
      Nord, Paris, France.
LA  - eng
PT  - Editorial
DEP - 20230303
PL  - France
TA  - Anaesth Crit Care Pain Med
JT  - Anaesthesia, critical care & pain medicine
JID - 101652401
SB  - IM
CIN - Anaesth Crit Care Pain Med. 2023 Jun;42(3):101231. PMID: 37030395
MH  - Humans
MH  - *Writing
MH  - *Artificial Intelligence
EDAT- 2023/03/06 06:00
MHDA- 2023/05/23 06:42
CRDT- 2023/03/05 19:29
PHST- 2023/02/13 00:00 [received]
PHST- 2023/02/24 00:00 [revised]
PHST- 2023/02/24 00:00 [accepted]
PHST- 2023/05/23 06:42 [medline]
PHST- 2023/03/06 06:00 [pubmed]
PHST- 2023/03/05 19:29 [entrez]
AID - S2352-5568(23)00017-6 [pii]
AID - 10.1016/j.accpm.2023.101209 [doi]
PST - ppublish
SO  - Anaesth Crit Care Pain Med. 2023 Jun;42(3):101209. doi: 
      10.1016/j.accpm.2023.101209. Epub 2023 Mar 3.

PMID- 36812618
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230224
IS  - 2767-3170 (Electronic)
IS  - 2767-3170 (Linking)
VI  - 2
IP  - 2
DP  - 2023 Feb
TI  - ChatGPT passing USMLE shines a spotlight on the flaws of medical education.
PG  - e0000205
LID - 10.1371/journal.pdig.0000205 [doi]
LID - e0000205
FAU - Mbakwe, Amarachi B
AU  - Mbakwe AB
AD  - Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United 
      States of America.
FAU - Lourentzou, Ismini
AU  - Lourentzou I
AD  - Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United 
      States of America.
FAU - Celi, Leo Anthony
AU  - Celi LA
AUID- ORCID: 0000-0001-6712-6626
AD  - Institute for Medical Engineering and Science, Massachusetts Institute of 
      Technology, Cambridge, Massachusetts, United States of America.
AD  - Department of Medicine, Beth Israel Deaconess Medical Center, Beth Israel 
      Deaconess Medical Center, Boston, Massachusetts, United States of America.
AD  - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 
      Massachusetts, United States of America.
FAU - Mechanic, Oren J
AU  - Mechanic OJ
AD  - eMed Digital Healthcare, Miami, Florida, United States of America.
AD  - Department of Emergency Medicine, Mount Sinai Medical Center, Miami Beach, 
      Florida, United States of America.
AD  - Department of Emergency Medicine and Critical Care, Herbert Wertheim College of 
      Medicine at Florida International University, Miami, Florida, United States of 
      America.
FAU - Dagan, Alon
AU  - Dagan A
AD  - Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, 
      Massachusetts, United States of America.
LA  - eng
PT  - Editorial
DEP - 20230209
PL  - United States
TA  - PLOS Digit Health
JT  - PLOS digital health
JID - 9918335064206676
CIN - Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education 
      Using Large Language Models.
PMC - PMC9931307
COIS- Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health.
EDAT- 2023/02/23 06:00
MHDA- 2023/02/23 06:01
CRDT- 2023/02/22 17:34
PHST- 2023/02/22 17:34 [entrez]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/02/23 06:01 [medline]
AID - PDIG-D-23-00027 [pii]
AID - 10.1371/journal.pdig.0000205 [doi]
PST - epublish
SO  - PLOS Digit Health. 2023 Feb 9;2(2):e0000205. doi: 10.1371/journal.pdig.0000205. 
      eCollection 2023 Feb.

PMID- 37696697
OWN - NLM
STAT- In-Process
LR  - 20231212
IS  - 0219-3108 (Electronic)
IS  - 1015-9584 (Linking)
VI  - 46
IP  - 12
DP  - 2023 Dec
TI  - ChatGPT's responses to gout-related questions.
PG  - 5935-5936
LID - S1015-9584(23)01390-8 [pii]
LID - 10.1016/j.asjsur.2023.08.217 [doi]
FAU - Hong, Daorong
AU  - Hong D
AD  - Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian Province, 362000, China. Electronic address: 
      ultrosound@hotmail.com.
FAU - Huang, Chunyan
AU  - Huang C
AD  - Department of General Practice of the Second Affiliated Hospital of Fujian 
      Medical University, Quanzhou City, Fujian Province, 362000, China.
FAU - Chen, Xiaoqing
AU  - Chen X
AD  - Department of General Practice of the Second Affiliated Hospital of Fujian 
      Medical University, Quanzhou City, Fujian Province, 362000, China.
FAU - Chen, Lijun
AU  - Chen L
AD  - Department of Endocrinology of the Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian Province, 362000, China.
LA  - eng
PT  - Letter
DEP - 20230909
PL  - Netherlands
TA  - Asian J Surg
JT  - Asian journal of surgery
JID - 8900600
SB  - IM
COIS- Declaration of competing interest The authors have declared that no conflict of 
      interest exists.
EDAT- 2023/09/12 00:41
MHDA- 2023/09/12 00:41
CRDT- 2023/09/11 21:56
PHST- 2023/08/14 00:00 [received]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/09/12 00:41 [pubmed]
PHST- 2023/09/12 00:41 [medline]
PHST- 2023/09/11 21:56 [entrez]
AID - S1015-9584(23)01390-8 [pii]
AID - 10.1016/j.asjsur.2023.08.217 [doi]
PST - ppublish
SO  - Asian J Surg. 2023 Dec;46(12):5935-5936. doi: 10.1016/j.asjsur.2023.08.217. Epub 
      2023 Sep 9.

PMID- 38028132
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2666-6758 (Electronic)
IS  - 2666-6758 (Linking)
VI  - 4
IP  - 6
DP  - 2023 Nov 13
TI  - ChatGPT for scientific paper writing-promises and perils.
PG  - 100524
LID - 10.1016/j.xinn.2023.100524 [doi]
LID - 100524
FAU - He, Shijun
AU  - He S
AD  - Innovation Research Institute of Traditional Chinese Medicine, Shanghai 
      University of Traditional Chinese Medicine, Shanghai 201203, China.
FAU - Yang, Fan
AU  - Yang F
AD  - Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao 
      Tong University School of Medicine, Shanghai 200127, China.
FAU - Zuo, Jian-Ping
AU  - Zuo JP
AD  - Laboratory of Immunopharmacology, State Key Laboratory of Drug Research, Shanghai 
      Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
FAU - Lin, Ze-Min
AU  - Lin ZM
AD  - Laboratory of Immunopharmacology, State Key Laboratory of Drug Research, Shanghai 
      Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
LA  - eng
PT  - Editorial
DEP - 20231017
PL  - United States
TA  - Innovation (Camb)
JT  - Innovation (Cambridge (Mass.))
JID - 101771342
PMC - PMC10654578
COIS- The authors declare no competing interests.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 17:10
PHST- 2023/09/04 00:00 [received]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 17:10 [entrez]
AID - S2666-6758(23)00152-2 [pii]
AID - 100524 [pii]
AID - 10.1016/j.xinn.2023.100524 [doi]
PST - epublish
SO  - Innovation (Camb). 2023 Oct 17;4(6):100524. doi: 10.1016/j.xinn.2023.100524. 
      eCollection 2023 Nov 13.

PMID- 37811181
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231010
IS  - 0971-3026 (Print)
IS  - 1998-3808 (Electronic)
IS  - 0970-2016 (Linking)
VI  - 33
IP  - 4
DP  - 2023 Oct
TI  - ChatGPT: Chasing the Storm in Radiology Training and Education.
PG  - 431-435
LID - 10.1055/s-0043-1774743 [doi]
FAU - Sodhi, Kushaljit Singh
AU  - Sodhi KS
AUID- ORCID: 0000-0001-6419-2468
AD  - Department of Pediatric Radiology, Mallinckrodt Institute of Radiology, 
      Washington University School of Medicine, St. Louis, Missouri, United States.
AD  - Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical 
      Education and Research, Chandigarh, India.
FAU - Tao, Ting Y
AU  - Tao TY
AD  - Department of Pediatric Radiology, Mallinckrodt Institute of Radiology, 
      Washington University School of Medicine, St. Louis, Missouri, United States.
FAU - Seymore, Noah
AU  - Seymore N
AD  - Department of Pediatric Radiology, Mallinckrodt Institute of Radiology, 
      Washington University School of Medicine, St. Louis, Missouri, United States.
LA  - eng
PT  - Editorial
DEP - 20231005
PL  - Germany
TA  - Indian J Radiol Imaging
JT  - The Indian journal of radiology & imaging
JID - 8503873
PMC - PMC10556337
COIS- Conflict of Interest None declared.
EDAT- 2023/10/09 12:42
MHDA- 2023/10/09 12:43
CRDT- 2023/10/09 06:14
PHST- 2023/10/09 12:43 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 06:14 [entrez]
AID - IJRI-23-7-2751 [pii]
AID - 10.1055/s-0043-1774743 [doi]
PST - epublish
SO  - Indian J Radiol Imaging. 2023 Oct 5;33(4):431-435. doi: 10.1055/s-0043-1774743. 
      eCollection 2023 Oct.

PMID- 37735041
OWN - NLM
STAT- Publisher
LR  - 20230921
IS  - 1531-5037 (Electronic)
IS  - 0022-3468 (Linking)
DP  - 2023 Aug 30
TI  - Bibliographic Research with ChatGPT may be Misleading: The Problem of 
      Hallucination.
LID - S0022-3468(23)00512-2 [pii]
LID - 10.1016/j.jpedsurg.2023.08.018 [doi]
FAU - Metze, Konradin
AU  - Metze K
AD  - Faculty of Medical Sciences, State University of Campinas, Campinas, SP, Brazil. 
      Electronic address: kmetze@fcm.unicamp.br.
FAU - Morandin-Reis, Rosana C
AU  - Morandin-Reis RC
AD  - Faculty of Medical Sciences, State University of Campinas, Campinas, SP, Brazil.
FAU - Lorand-Metze, Irene
AU  - Lorand-Metze I
AD  - Faculty of Medical Sciences, State University of Campinas, Campinas, SP, Brazil.
FAU - Florindo, João B
AU  - Florindo JB
AD  - Institute of Mathematics, Statistics, and Scientific Computing, State University 
      of Campinas, Campinas, SP, Brazil.
LA  - eng
PT  - Letter
DEP - 20230830
PL  - United States
TA  - J Pediatr Surg
JT  - Journal of pediatric surgery
JID - 0052631
SB  - IM
EDAT- 2023/09/22 00:42
MHDA- 2023/09/22 00:42
CRDT- 2023/09/21 21:57
PHST- 2023/08/12 00:00 [received]
PHST- 2023/08/16 00:00 [accepted]
PHST- 2023/09/22 00:42 [medline]
PHST- 2023/09/22 00:42 [pubmed]
PHST- 2023/09/21 21:57 [entrez]
AID - S0022-3468(23)00512-2 [pii]
AID - 10.1016/j.jpedsurg.2023.08.018 [doi]
PST - aheadofprint
SO  - J Pediatr Surg. 2023 Aug 30:S0022-3468(23)00512-2. doi: 
      10.1016/j.jpedsurg.2023.08.018.

PMID- 37709349
OWN - NLM
STAT- Publisher
LR  - 20231017
IS  - 1365-2923 (Electronic)
IS  - 0308-0110 (Linking)
VI  - 57
IP  - 11
DP  - 2023 Nov
TI  - Med versus machine: Using ChatGPT in team-based learning.
PG  - 1159-1160
LID - 10.1111/medu.15226 [doi]
FAU - Kirpalani, Amrit
AU  - Kirpalani A
AUID- ORCID: 0000-0001-7567-9052
FAU - Grimmer, Joanne
AU  - Grimmer J
FAU - Wang, Peter Zhan Tao
AU  - Wang PZT
AUID- ORCID: 0000-0002-9533-5748
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - England
TA  - Med Educ
JT  - Medical education
JID - 7605655
SB  - IM
EDAT- 2023/09/15 00:42
MHDA- 2023/09/15 00:42
CRDT- 2023/09/14 20:43
PHST- 2023/05/01 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/09/15 00:42 [pubmed]
PHST- 2023/09/15 00:42 [medline]
PHST- 2023/09/14 20:43 [entrez]
AID - 10.1111/medu.15226 [doi]
PST - ppublish
SO  - Med Educ. 2023 Nov;57(11):1159-1160. doi: 10.1111/medu.15226. Epub 2023 Sep 14.

PMID- 37567760
OWN - NLM
STAT- Publisher
LR  - 20230811
IS  - 1759-8486 (Electronic)
IS  - 1759-8478 (Linking)
DP  - 2023 Aug 11
TI  - Correspondence on "ChatGPT in nuclear medicine education" by Currie and Barry.
LID - jnis-2023-020817 [pii]
LID - 10.1136/jnis-2023-020817 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Mohali, Punjab, India.
LA  - eng
PT  - Letter
DEP - 20230811
PL  - England
TA  - J Neurointerv Surg
JT  - Journal of neurointerventional surgery
JID - 101517079
SB  - IM
OTO - NOTNLM
OT  - History
COIS- Competing interests: None declared.
EDAT- 2023/08/12 10:41
MHDA- 2023/08/12 10:41
CRDT- 2023/08/11 21:39
PHST- 2023/07/17 00:00 [received]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/08/12 10:41 [medline]
PHST- 2023/08/12 10:41 [pubmed]
PHST- 2023/08/11 21:39 [entrez]
AID - jnis-2023-020817 [pii]
AID - 10.1136/jnis-2023-020817 [doi]
PST - aheadofprint
SO  - J Neurointerv Surg. 2023 Aug 11:jnis-2023-020817. doi: 10.1136/jnis-2023-020817.

PMID- 37566908
OWN - NLM
STAT- MEDLINE
DCOM- 20231124
LR  - 20231124
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 11
DP  - 2023 Nov 1
TI  - The collision of ChatGPT and traditional medicine: a perspective from 
      bibliometric analysis.
PG  - 3713-3714
LID - 10.1097/JS9.0000000000000662 [doi]
FAU - Tan, Zhimin
AU  - Tan Z
AD  - Department of Anaesthesiology and Urology, West China Hospital of Sichuan 
      University, Chengdu, Sichuan Province, People's Republic of China.
FAU - He, Qiyu
AU  - He Q
FAU - Feng, Shijian
AU  - Feng S
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *Bibliometrics
MH  - *Medicine, Traditional
PMC - PMC10651228
COIS- The authors declare that they no conflicts of interest. Sponsorships or competing 
      interests that may be relevant to content are disclosed at the end of this 
      article.
EDAT- 2023/08/11 18:42
MHDA- 2023/11/24 06:42
CRDT- 2023/08/11 17:45
PHST- 2023/07/16 00:00 [received]
PHST- 2023/07/23 00:00 [accepted]
PHST- 2023/11/24 06:42 [medline]
PHST- 2023/08/11 18:42 [pubmed]
PHST- 2023/08/11 17:45 [entrez]
AID - 01279778-990000000-00539 [pii]
AID - IJS-D-23-01468 [pii]
AID - 10.1097/JS9.0000000000000662 [doi]
PST - epublish
SO  - Int J Surg. 2023 Nov 1;109(11):3713-3714. doi: 10.1097/JS9.0000000000000662.

PMID- 37431546
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230712
LR  - 20230718
IS  - 1598-6357 (Electronic)
IS  - 1011-8934 (Print)
IS  - 1011-8934 (Linking)
VI  - 38
IP  - 27
DP  - 2023 Jul 10
TI  - Let's Look on the Bright Side of ChatGPT.
PG  - e231
LID - 10.3346/jkms.2023.38.e231 [doi]
LID - e231
FAU - Yoo, Jin-Hong
AU  - Yoo JH
AUID- ORCID: 0000-0003-2611-3399
AD  - Division of Infectious Diseases, Department of Internal Medicine, Bucheon St. 
      Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 
      Korea.
AD  - Deputy Editor of Journal of Korean Medical Science. jhyoo@catholic.ac.kr.
LA  - eng
PT  - Journal Article
DEP - 20230710
PL  - Korea (South)
TA  - J Korean Med Sci
JT  - Journal of Korean medical science
JID - 8703518
SB  - IM
PMC - PMC10332949
COIS- The author has no potential conflict of interest to disclose.
EDAT- 2023/07/11 06:41
MHDA- 2023/07/11 06:42
CRDT- 2023/07/11 03:43
PHST- 2023/06/18 00:00 [received]
PHST- 2023/06/19 00:00 [accepted]
PHST- 2023/07/11 06:42 [medline]
PHST- 2023/07/11 06:41 [pubmed]
PHST- 2023/07/11 03:43 [entrez]
AID - 38.e231 [pii]
AID - 10.3346/jkms.2023.38.e231 [doi]
PST - epublish
SO  - J Korean Med Sci. 2023 Jul 10;38(27):e231. doi: 10.3346/jkms.2023.38.e231.

PMID- 37406203
OWN - NLM
STAT- MEDLINE
DCOM- 20230830
LR  - 20230830
IS  - 1546-3141 (Electronic)
IS  - 0361-803X (Linking)
VI  - 221
IP  - 3
DP  - 2023 Sep
TI  - Addressing Concerns and Pitfalls in ChatGPT-Driven Radiology Reporting.
PG  - 403
LID - 10.2214/AJR.23.29504 [doi]
FAU - Mese, Ismail
AU  - Mese I
AD  - Health Sciences University Erenkoy Mental Health and Neurology, Training and 
      Research Hospital, Istanbul, Turkey, ismail_mese@yahoo.com.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230705
PL  - United States
TA  - AJR Am J Roentgenol
JT  - AJR. American journal of roentgenology
JID - 7708173
SB  - IM
CON - AJR Am J Roentgenol. 2023 Sep;221(3):373-376. PMID: 37095665
CIN - AJR Am J Roentgenol. 2023 Sep;221(3):403-404. PMID: 37406204
MH  - Humans
MH  - *Radiology
MH  - *Artificial Intelligence
EDAT- 2023/07/05 19:12
MHDA- 2023/08/24 06:42
CRDT- 2023/07/05 15:43
PHST- 2023/08/24 06:42 [medline]
PHST- 2023/07/05 19:12 [pubmed]
PHST- 2023/07/05 15:43 [entrez]
AID - 10.2214/AJR.23.29504 [doi]
PST - ppublish
SO  - AJR Am J Roentgenol. 2023 Sep;221(3):403. doi: 10.2214/AJR.23.29504. Epub 2023 
      Jul 5.

PMID- 37093400
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230512
LR  - 20230515
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 6
DP  - 2023 Jun
TI  - Large Language Models Like ChatGPT in ABME: Author Guidelines.
PG  - 1121-1122
LID - 10.1007/s10439-023-03212-2 [doi]
FAU - Norris, Carly
AU  - Norris C
AUID- ORCID: 0000-0002-0654-0214
AD  - Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, 
      VA, 24060, USA. carly8@vt.edu.
LA  - eng
PT  - Editorial
DEP - 20230424
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
EDAT- 2023/04/24 12:41
MHDA- 2023/04/24 12:42
CRDT- 2023/04/24 11:19
PHST- 2023/04/12 00:00 [received]
PHST- 2023/04/14 00:00 [accepted]
PHST- 2023/04/24 12:42 [medline]
PHST- 2023/04/24 12:41 [pubmed]
PHST- 2023/04/24 11:19 [entrez]
AID - 10.1007/s10439-023-03212-2 [pii]
AID - 10.1007/s10439-023-03212-2 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jun;51(6):1121-1122. doi: 10.1007/s10439-023-03212-2. Epub 
      2023 Apr 24.

PMID- 36877168
OWN - NLM
STAT- MEDLINE
DCOM- 20230322
LR  - 20230519
IS  - 1528-1132 (Electronic)
IS  - 0009-921X (Print)
IS  - 0009-921X (Linking)
VI  - 481
IP  - 4
DP  - 2023 Apr 1
TI  - Not the Last Word: ChatGPT Can't Perform Orthopaedic Surgery.
PG  - 651-655
LID - 10.1097/CORR.0000000000002619 [doi]
FAU - Bernstein, Joseph
AU  - Bernstein J
AUID- ORCID: 0000-0001-9052-2897
AD  - Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230303
PL  - United States
TA  - Clin Orthop Relat Res
JT  - Clinical orthopaedics and related research
JID - 0075674
SB  - IM
CIN - doi: 10.1097/corr.0000000000002619
MH  - Humans
MH  - *Orthopedics
MH  - *Orthopedic Procedures/adverse effects
PMC - PMC10013616
COIS- The author certifies that there are no funding or commercial associations 
      (consultancies, stock ownership, equity interest, patent/licensing arrangements, 
      etc.) that might pose a conflict of interest in connection with the submitted 
      article related to the author or any immediate family members. All ICMJE Conflict 
      of Interest Forms for authors and Clinical Orthopaedics and Related Research® 
      editors and board members are on file with the publication and can be viewed on 
      request.
EDAT- 2023/03/07 06:00
MHDA- 2023/03/23 06:00
PMCR- 2024/04/01
CRDT- 2023/03/06 10:23
PHST- 2023/01/17 00:00 [received]
PHST- 2023/02/08 00:00 [accepted]
PHST- 2024/04/01 00:00 [pmc-release]
PHST- 2023/03/07 06:00 [pubmed]
PHST- 2023/03/23 06:00 [medline]
PHST- 2023/03/06 10:23 [entrez]
AID - 00003086-202304000-00004 [pii]
AID - CORR-D-23-00065 [pii]
AID - 10.1097/CORR.0000000000002619 [doi]
PST - ppublish
SO  - Clin Orthop Relat Res. 2023 Apr 1;481(4):651-655. doi: 
      10.1097/CORR.0000000000002619. Epub 2023 Mar 3.

PMID- 36840479
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230821
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 7
DP  - 2023 Jun 14
TI  - Application of ChatGPT in Cosmetic Plastic Surgery: Ally or Antagonist?
PG  - NP587-NP590
LID - 10.1093/asj/sjad042 [doi]
FAU - Gupta, Rohun
AU  - Gupta R
FAU - Pande, Parna
AU  - Pande P
FAU - Herzog, Isabel
AU  - Herzog I
AUID- ORCID: 0000-0001-7491-5746
FAU - Weisberger, Joseph
AU  - Weisberger J
FAU - Chao, John
AU  - Chao J
FAU - Chaiyasate, Kongkrit
AU  - Chaiyasate K
FAU - Lee, Edward S
AU  - Lee ES
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CIN - Aesthet Surg J. 2023 Aug 17;43(9):NP726-NP727. PMID: 37227006
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
MH  - Surveys and Questionnaires
EDAT- 2023/02/26 06:00
MHDA- 2023/06/15 06:42
CRDT- 2023/02/25 05:12
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/02/26 06:00 [pubmed]
PHST- 2023/02/25 05:12 [entrez]
AID - 7058063 [pii]
AID - 10.1093/asj/sjad042 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jun 14;43(7):NP587-NP590. doi: 10.1093/asj/sjad042.

PMID- 37873980
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1526-4610 (Electronic)
IS  - 0017-8748 (Linking)
DP  - 2023 Oct 24
TI  - Caution regarding fabricated citations from artificial intelligence.
LID - 10.1111/head.14649 [doi]
FAU - Cohen, Fred
AU  - Cohen F
AUID- ORCID: 0000-0003-3436-2846
AD  - Headache Editorial Team.
AD  - Department of Neurology, Mount Sinai Hospital, Icahn School of Medicine at Mount 
      Sinai, New York, New York, USA.
AD  - Department of Medicine, Mount Sinai Hospital, Icahn School of Medicine at Mount 
      Sinai, New York, New York, USA.
FAU - Vallimont, Jenn
AU  - Vallimont J
AUID- ORCID: 0009-0006-2101-5545
AD  - Headache Editorial Team.
FAU - Gelfand, Amy A
AU  - Gelfand AA
AUID- ORCID: 0000-0002-0911-7905
AD  - Headache Editorial Team.
AD  - Department of Neurology, Child & Adolescent Headache Program, UCSF, San 
      Francisco, California, USA.
LA  - eng
PT  - Editorial
DEP - 20231024
PL  - United States
TA  - Headache
JT  - Headache
JID - 2985091R
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial
OT  - citations
OT  - headache
OT  - intelligence
OT  - medicine
EDAT- 2023/10/24 12:42
MHDA- 2023/10/24 12:42
CRDT- 2023/10/24 07:33
PHST- 2023/10/10 00:00 [received]
PHST- 2023/10/10 00:00 [accepted]
PHST- 2023/10/24 12:42 [medline]
PHST- 2023/10/24 12:42 [pubmed]
PHST- 2023/10/24 07:33 [entrez]
AID - 10.1111/head.14649 [doi]
PST - aheadofprint
SO  - Headache. 2023 Oct 24. doi: 10.1111/head.14649.

PMID- 37829791
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0019-5049 (Print)
IS  - 0976-2817 (Electronic)
IS  - 0019-5049 (Linking)
VI  - 67
IP  - 9
DP  - 2023 Sep
TI  - Spotting the artificial intelligence mask: Detecting articles written by language 
      models/ ChatGPT.
PG  - 835
LID - 10.4103/ija.ija_497_23 [doi]
FAU - Gupta, Bhavna
AU  - Gupta B
AD  - Department of Anaesthesiology, All India Institute of Medical Sciences, 
      Rishikesh, Uttarakhand, India.
LA  - eng
PT  - Journal Article
DEP - 20230906
PL  - India
TA  - Indian J Anaesth
JT  - Indian journal of anaesthesia
JID - 0013243
PMC - PMC10566670
COIS- There are no conflicts of interest.
EDAT- 2023/10/13 06:45
MHDA- 2023/10/13 06:46
CRDT- 2023/10/13 04:11
PHST- 2023/05/29 00:00 [received]
PHST- 2023/07/10 00:00 [revised]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/10/13 06:46 [medline]
PHST- 2023/10/13 06:45 [pubmed]
PHST- 2023/10/13 04:11 [entrez]
AID - IJA-67-835a [pii]
AID - 10.4103/ija.ija_497_23 [doi]
PST - ppublish
SO  - Indian J Anaesth. 2023 Sep;67(9):835. doi: 10.4103/ija.ija_497_23. Epub 2023 Sep 
      6.

PMID- 37817721
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231012
LR  - 20231018
IS  - 0030-9982 (Print)
IS  - 0030-9982 (Linking)
VI  - 73
IP  - 9
DP  - 2023 Sep
TI  - Chatgpt: A New Era In Research Writing Assistance.
PG  - 1929-1930
LID - 10.47391/JPMA.9183 [doi]
FAU - Osama, Muhammad
AU  - Osama M
AD  - Foundation University, Islamabad,Pakistan.
FAU - Afridi, Sabah
AU  - Afridi S
AD  - Foundation University, Islamabad,Pakistan.
LA  - eng
PT  - Journal Article
PL  - Pakistan
TA  - J Pak Med Assoc
JT  - JPMA. The Journal of the Pakistan Medical Association
JID - 7501162
SB  - IM
EDAT- 2023/10/11 06:45
MHDA- 2023/10/11 06:46
CRDT- 2023/10/11 03:37
PHST- 2023/10/11 06:46 [medline]
PHST- 2023/10/11 06:45 [pubmed]
PHST- 2023/10/11 03:37 [entrez]
AID - 9183/2618 [pii]
AID - 10.47391/JPMA.9183 [doi]
PST - ppublish
SO  - J Pak Med Assoc. 2023 Sep;73(9):1929-1930. doi: 10.47391/JPMA.9183.

PMID- 37794873
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2634-3916 (Electronic)
IS  - 2634-3916 (Linking)
VI  - 4
IP  - 5
DP  - 2023 Oct
TI  - ChatGPT fails the test of evidence-based medicine.
PG  - 366-367
LID - 10.1093/ehjdh/ztad043 [doi]
FAU - Haverkamp, Wilhelm
AU  - Haverkamp W
AUID- ORCID: 0000-0002-6183-2519
AD  - Department of Cardiology Campus Virchow Clinic of German Heart Center Charité, 
      Charité-University Medicine Berlin, Augustenburger Platz 1, 13353 Berlin, 
      Germany.
FAU - Tennenbaum, Jonathan
AU  - Tennenbaum J
AD  - Center for the Philosophy of Science, University of Lisbon, Lisbon, Portugal.
FAU - Strodthoff, Nils
AU  - Strodthoff N
AD  - Department of Health Sciences, School of Medicine and Health Services, Oldenburg 
      University, Oldenburg, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230713
PL  - England
TA  - Eur Heart J Digit Health
JT  - European heart journal. Digital health
JID - 101778323
PMC - PMC10545496
COIS- Conflict of interest: None declared.
EDAT- 2023/10/05 06:44
MHDA- 2023/10/05 06:45
CRDT- 2023/10/05 03:57
PHST- 2023/10/05 06:45 [medline]
PHST- 2023/10/05 06:44 [pubmed]
PHST- 2023/10/05 03:57 [entrez]
AID - ztad043 [pii]
AID - 10.1093/ehjdh/ztad043 [doi]
PST - epublish
SO  - Eur Heart J Digit Health. 2023 Jul 13;4(5):366-367. doi: 10.1093/ehjdh/ztad043. 
      eCollection 2023 Oct.

PMID- 37720237
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230919
IS  - 1611-2156 (Print)
IS  - 1611-2156 (Electronic)
IS  - 1611-2156 (Linking)
VI  - 22
DP  - 2023
TI  - ChatGPT guidance for reproductive specialists: Dr. Jekyll or Mr. Hyde?
PG  - 911-914
LID - 10.17179/excli2023-6120 [doi]
FAU - Sengupta, Pallav
AU  - Sengupta P
AD  - Department of Biomedical Sciences, College of Medicine, Gulf Medical University, 
      Ajman, UAE.
FAU - Dutta, Sulagna
AU  - Dutta S
AD  - School of Medical Sciences, Bharath Institute of Higher Education and Research 
      (BIHER), Tamil Nadu, India.
LA  - eng
PT  - Journal Article
DEP - 20230829
PL  - Germany
TA  - EXCLI J
JT  - EXCLI journal
JID - 101299402
PMC - PMC10502200
COIS- The authors declare no conflicts of interest.
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:43
CRDT- 2023/09/18 04:37
PHST- 2023/04/20 00:00 [received]
PHST- 2023/08/26 00:00 [accepted]
PHST- 2023/09/18 06:43 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:37 [entrez]
AID - 2023-6120 [pii]
AID - Doc911 [pii]
AID - 10.17179/excli2023-6120 [doi]
PST - epublish
SO  - EXCLI J. 2023 Aug 29;22:911-914. doi: 10.17179/excli2023-6120. eCollection 2023.

PMID- 37530052
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230804
LR  - 20230812
IS  - 1866-0452 (Electronic)
IS  - 1866-0452 (Linking)
VI  - 120
IP  - 21
DP  - 2023 May 30
TI  - ChatGPT Passes German State Examination in Medicine With Picture Questions 
      Omitted.
PG  - 373-374
LID - arztebl.m2023.0113 [pii]
LID - 10.3238/arztebl.m2023.0113 [doi]
FAU - Jung, Leonard B
AU  - Jung LB
FAU - Gudera, Jonas A
AU  - Gudera JA
FAU - Wiegand, Tim L T
AU  - Wiegand TLT
FAU - Allmendinger, Simeon
AU  - Allmendinger S
FAU - Dimitriadis, Konstantinos
AU  - Dimitriadis K
FAU - Koerte, Inga K
AU  - Koerte IK
LA  - eng
PT  - Journal Article
PL  - Germany
TA  - Dtsch Arztebl Int
JT  - Deutsches Arzteblatt international
JID - 101475967
SB  - IM
PMC - PMC10413971
EDAT- 2023/08/02 13:07
MHDA- 2023/08/02 13:08
CRDT- 2023/08/02 06:33
PHST- 2023/03/14 00:00 [received]
PHST- 2023/03/14 00:00 [revised]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/08/02 13:08 [medline]
PHST- 2023/08/02 13:07 [pubmed]
PHST- 2023/08/02 06:33 [entrez]
AID - arztebl.m2023.0113 [pii]
AID - 10.3238/arztebl.m2023.0113 [doi]
PST - ppublish
SO  - Dtsch Arztebl Int. 2023 May 30;120(21):373-374. doi: 10.3238/arztebl.m2023.0113.

PMID- 37462426
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Linking)
VI  - 39
IP  - 5
DP  - 2023 Sep-Oct 01
TI  - Re: "ChatGPT and Lacrimal Drainage Disorders: Performance and Scope of 
      Improvement".
PG  - 513
LID - 10.1097/IOP.0000000000002474 [doi]
FAU - Homer, Natalie A
AU  - Homer NA
LA  - eng
PT  - Journal Article
DEP - 20230718
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
SB  - IM
MH  - Humans
MH  - *Lacrimal Apparatus Diseases/surgery
MH  - *Lacrimal Apparatus
COIS- The author has no financial or conflicts of interest to disclose.
EDAT- 2023/07/18 13:09
MHDA- 2023/07/18 13:10
CRDT- 2023/07/18 09:33
PHST- 2023/07/18 13:10 [medline]
PHST- 2023/07/18 13:09 [pubmed]
PHST- 2023/07/18 09:33 [entrez]
AID - 00002341-990000000-00231 [pii]
AID - 10.1097/IOP.0000000000002474 [doi]
PST - ppublish
SO  - Ophthalmic Plast Reconstr Surg. 2023 Sep-Oct 01;39(5):513. doi: 
      10.1097/IOP.0000000000002474. Epub 2023 Jul 18.

PMID- 37154803
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 11
DP  - 2023 Oct 13
TI  - Unleashing the Power of ChatGPT: Revolutionizing Plastic Surgery and Beyond.
PG  - 1395-1399
LID - 10.1093/asj/sjad135 [doi]
FAU - Bassiri-Tehrani, Brian
AU  - Bassiri-Tehrani B
AUID- ORCID: 0000-0003-4891-4920
FAU - Cress, Phaedra E
AU  - Cress PE
AUID- ORCID: 0000-0002-6217-2604
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CIN - Aesthet Surg J. 2023 Oct 13;43(11):1400-1401. PMID: 37226559
EDAT- 2023/05/08 13:42
MHDA- 2023/05/08 13:42
CRDT- 2023/05/08 10:43
PHST- 2023/05/08 13:42 [pubmed]
PHST- 2023/05/08 13:42 [medline]
PHST- 2023/05/08 10:43 [entrez]
AID - 7156884 [pii]
AID - 10.1093/asj/sjad135 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Oct 13;43(11):1395-1399. doi: 10.1093/asj/sjad135.

PMID- 36905610
OWN - NLM
STAT- MEDLINE
DCOM- 20230518
LR  - 20230530
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
VI  - 45
IP  - 6
DP  - 2023 Jun
TI  - Aye, AI! ChatGPT passes multiple-choice family medicine exam.
PG  - 665-666
LID - 10.1080/0142159X.2023.2187684 [doi]
FAU - Morreel, Stefan
AU  - Morreel S
AD  - Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium.
FAU - Mathysen, Danny
AU  - Mathysen D
AD  - Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium.
FAU - Verhoeven, Veronique
AU  - Verhoeven V
AD  - Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium.
LA  - eng
PT  - Letter
DEP - 20230311
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
CIN - Med Teach. 2023 Jun;45(6):666. PMID: 36940462
MH  - Humans
MH  - *Family Practice
MH  - *Artificial Intelligence
EDAT- 2023/03/12 06:00
MHDA- 2023/05/18 06:42
CRDT- 2023/03/11 12:32
PHST- 2023/05/18 06:42 [medline]
PHST- 2023/03/12 06:00 [pubmed]
PHST- 2023/03/11 12:32 [entrez]
AID - 10.1080/0142159X.2023.2187684 [doi]
PST - ppublish
SO  - Med Teach. 2023 Jun;45(6):665-666. doi: 10.1080/0142159X.2023.2187684. Epub 2023 
      Mar 11.

PMID- 36854918
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230302
LR  - 20231121
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 615
IP  - 7950
DP  - 2023 Mar
TI  - OpenAI - explain why some countries are excluded from ChatGPT.
PG  - 34
LID - 10.1038/d41586-023-00553-9 [doi]
FAU - Wang, Simon H
AU  - Wang SH
AUID- ORCID: 0000-0002-3868-4618
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Machine learning
OT  - Politics
OT  - Publishing
EDAT- 2023/03/02 06:00
MHDA- 2023/03/02 06:01
CRDT- 2023/03/01 00:09
PHST- 2023/03/01 00:09 [entrez]
PHST- 2023/03/02 06:00 [pubmed]
PHST- 2023/03/02 06:01 [medline]
AID - 10.1038/d41586-023-00553-9 [pii]
AID - 10.1038/d41586-023-00553-9 [doi]
PST - ppublish
SO  - Nature. 2023 Mar;615(7950):34. doi: 10.1038/d41586-023-00553-9.

PMID- 36754725
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230301
LR  - 20230301
IS  - 2589-7500 (Electronic)
IS  - 2589-7500 (Linking)
VI  - 5
IP  - 3
DP  - 2023 Mar
TI  - Generating scholarly content with ChatGPT: ethical challenges for medical 
      publishing.
PG  - e105-e106
LID - S2589-7500(23)00019-5 [pii]
LID - 10.1016/S2589-7500(23)00019-5 [doi]
FAU - Liebrenz, Michael
AU  - Liebrenz M
AD  - Department of Forensic Psychiatry, University of Bern, 3012 Bern, Switzerland. 
      Electronic address: michael.liebrenz@unibe.ch.
FAU - Schleifer, Roman
AU  - Schleifer R
AD  - Department of Forensic Psychiatry, University of Bern, 3012 Bern, Switzerland.
FAU - Buadze, Anna
AU  - Buadze A
AD  - Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, 
      Specialized Outpatient Clinic for ADHD, University of Zurich, Zurich, 
      Switzerland.
FAU - Bhugra, Dinesh
AU  - Bhugra D
AD  - Dinesh Bhugra, Kings College London, London, UK.
FAU - Smith, Alexander
AU  - Smith A
AD  - Department of Forensic Psychiatry, University of Bern, 3012 Bern, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20230206
PL  - England
TA  - Lancet Digit Health
JT  - The Lancet. Digital health
JID - 101751302
SB  - IM
COIS- We declare no competing interests.
EDAT- 2023/02/09 06:00
MHDA- 2023/02/09 06:01
CRDT- 2023/02/08 22:01
PHST- 2022/12/23 00:00 [received]
PHST- 2023/01/19 00:00 [accepted]
PHST- 2023/02/09 06:00 [pubmed]
PHST- 2023/02/09 06:01 [medline]
PHST- 2023/02/08 22:01 [entrez]
AID - S2589-7500(23)00019-5 [pii]
AID - 10.1016/S2589-7500(23)00019-5 [doi]
PST - ppublish
SO  - Lancet Digit Health. 2023 Mar;5(3):e105-e106. doi: 10.1016/S2589-7500(23)00019-5. 
      Epub 2023 Feb 6.

PMID- 38090250
OWN - NLM
STAT- In-Process
LR  - 20231213
IS  - 2075-0528 (Electronic)
IS  - 2075-051X (Linking)
VI  - 23
IP  - 4
DP  - 2023 Nov
TI  - Impact of ChatGPT on Medical Research Article Writing and Publication.
PG  - 429-432
LID - 10.18295/squmj.11.2023.068 [doi]
FAU - Chandra, Atanu
AU  - Chandra A
AD  - Department of Internal Medicine, Bankura Sammilani Medical College and Hospital, 
      West Bengal, India.
FAU - Dasgupta, Sugata
AU  - Dasgupta S
AD  - Department of Critical Care Medicine, IPGMER and SSKM Hospital, West Bengal, 
      India.
LA  - eng
PT  - Editorial
DEP - 20231130
PL  - Oman
TA  - Sultan Qaboos Univ Med J
JT  - Sultan Qaboos University medical journal
JID - 101519915
SB  - IM
PMC - PMC10712376
EDAT- 2023/12/13 18:42
MHDA- 2023/12/13 18:42
CRDT- 2023/12/13 13:06
PHST- 2023/12/13 18:42 [medline]
PHST- 2023/12/13 18:42 [pubmed]
PHST- 2023/09/12 00:00 [received]
PHST- 2023/10/14 00:00 [revised]
PHST- 2023/10/15 00:00 [accepted]
PHST- 2023/12/13 13:06 [entrez]
AID - squmj2311-429-432 [pii]
AID - 10.18295/squmj.11.2023.068 [doi]
PST - ppublish
SO  - Sultan Qaboos Univ Med J. 2023 Nov;23(4):429-432. doi: 
      10.18295/squmj.11.2023.068. Epub 2023 Nov 30.

PMID- 37987662
OWN - NLM
STAT- MEDLINE
DCOM- 20231122
LR  - 20231204
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 309
IP  - 2
DP  - 2023 Nov
TI  - Accuracy of ChatGPT, Google Bard, and Microsoft Bing for Simplifying Radiology 
      Reports.
PG  - e232561
LID - 10.1148/radiol.232561 [doi]
FAU - Amin, Kanhai S
AU  - Amin KS
AUID- ORCID: 0000-0002-0505-9832
AD  - From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 
      333 Cedar St, New Haven, CT 06520.
FAU - Davis, Melissa A
AU  - Davis MA
AUID- ORCID: 0000-0003-4952-4916
AD  - From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 
      333 Cedar St, New Haven, CT 06520.
FAU - Doshi, Rushabh
AU  - Doshi R
AUID- ORCID: 0000-0002-2692-8195
AD  - From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 
      333 Cedar St, New Haven, CT 06520.
FAU - Haims, Andrew H
AU  - Haims AH
AD  - From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 
      333 Cedar St, New Haven, CT 06520.
FAU - Khosla, Pavan
AU  - Khosla P
AUID- ORCID: 0000-0002-6966-6084
AD  - From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 
      333 Cedar St, New Haven, CT 06520.
FAU - Forman, Howard P
AU  - Forman HP
AUID- ORCID: 0000-0002-3096-835X
AD  - From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 
      333 Cedar St, New Haven, CT 06520.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
MH  - Humans
MH  - *Search Engine
MH  - *Radiology
EDAT- 2023/11/21 12:43
MHDA- 2023/11/22 06:43
CRDT- 2023/11/21 10:03
PHST- 2023/11/22 06:43 [medline]
PHST- 2023/11/21 12:43 [pubmed]
PHST- 2023/11/21 10:03 [entrez]
AID - 10.1148/radiol.232561 [doi]
PST - ppublish
SO  - Radiology. 2023 Nov;309(2):e232561. doi: 10.1148/radiol.232561.

PMID- 37987644
OWN - NLM
STAT- Publisher
LR  - 20231121
IS  - 1744-5078 (Electronic)
IS  - 0927-3948 (Linking)
DP  - 2023 Nov 21
TI  - Diagnostic Accuracy and Management Recommendations of ChatGpt and Uveitis: 
      Comment.
PG  - 1
LID - 10.1080/09273948.2023.2278739 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Lao People's Democratic Republic.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Ajitgarh, India.
AD  - Joseph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20231121
PL  - England
TA  - Ocul Immunol Inflamm
JT  - Ocular immunology and inflammation
JID - 9312169
SB  - IM
EDAT- 2023/11/21 12:44
MHDA- 2023/11/21 12:44
CRDT- 2023/11/21 09:53
PHST- 2023/11/21 12:44 [medline]
PHST- 2023/11/21 12:44 [pubmed]
PHST- 2023/11/21 09:53 [entrez]
AID - 10.1080/09273948.2023.2278739 [doi]
PST - aheadofprint
SO  - Ocul Immunol Inflamm. 2023 Nov 21:1. doi: 10.1080/09273948.2023.2278739.

PMID- 37884271
OWN - NLM
STAT- Publisher
LR  - 20231030
IS  - 1715-3360 (Electronic)
IS  - 0008-4182 (Linking)
DP  - 2023 Oct 24
TI  - Evaluation of postoperative ophthalmology patient instructions from ChatGPT and 
      Google Search.
LID - S0008-4182(23)00306-X [pii]
LID - 10.1016/j.jcjo.2023.10.001 [doi]
FAU - Nanji, Keean
AU  - Nanji K
AD  - Department of Surgery, Division of Ophthalmology, McMaster University, Hamilton, 
      ON; Department of Health Research Methods, Evidence and Impact, McMaster 
      University, Hamilton, ON. Electronic address: Keean.Nanji@medportal.ca.
FAU - Yu, Caberry W
AU  - Yu CW
AD  - Department of Surgery, Division of Ophthalmology, McMaster University, Hamilton, 
      ON.
FAU - Wong, Tien Yin
AU  - Wong TY
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; 
      Tsinghua Medicine, Tsinghua University, Beijing, People's Republic of China.
FAU - Sivaprasad, Sobha
AU  - Sivaprasad S
AD  - Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, 
      United Kingdom.
FAU - Steel, David H
AU  - Steel DH
AD  - Bioscience Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom; 
      Sunderland Eye Infirmary, Sunderland, United Kingdom.
FAU - Wykoff, Charles C
AU  - Wykoff CC
AD  - Retina Consultants of Texas and Blanton Eye Institute, Houston Methodist 
      Hospital, Houston, TX.
FAU - Chaudhary, Varun
AU  - Chaudhary V
AD  - Department of Surgery, Division of Ophthalmology, McMaster University, Hamilton, 
      ON; Department of Health Research Methods, Evidence and Impact, McMaster 
      University, Hamilton, ON.
LA  - eng
PT  - Letter
DEP - 20231024
PL  - England
TA  - Can J Ophthalmol
JT  - Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
JID - 0045312
SB  - IM
EDAT- 2023/10/27 00:42
MHDA- 2023/10/27 00:42
CRDT- 2023/10/26 19:24
PHST- 2023/09/25 00:00 [received]
PHST- 2023/09/29 00:00 [revised]
PHST- 2023/10/02 00:00 [accepted]
PHST- 2023/10/27 00:42 [pubmed]
PHST- 2023/10/27 00:42 [medline]
PHST- 2023/10/26 19:24 [entrez]
AID - S0008-4182(23)00306-X [pii]
AID - 10.1016/j.jcjo.2023.10.001 [doi]
PST - aheadofprint
SO  - Can J Ophthalmol. 2023 Oct 24:S0008-4182(23)00306-X. doi: 
      10.1016/j.jcjo.2023.10.001.

PMID- 37595200
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231108
IS  - 1538-9782 (Electronic)
IS  - 0887-6274 (Linking)
VI  - 37
IP  - 5
DP  - 2023 Sep-Oct 01
TI  - ChatGPT: Implications for Faculty, Students, and Patients: May 19, 2023.
PG  - 245-246
LID - 10.1097/NUR.0000000000000770 [doi]
FAU - Shay, Amy
AU  - Shay A
AD  - Author Affiliation: Clinical Associate Professor, Indiana University School of 
      Nursing, Indianapolis.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Clin Nurse Spec
JT  - Clinical nurse specialist CNS
JID - 8709115
MH  - Humans
MH  - *Faculty
MH  - *Patients
MH  - Students
COIS- The author reports no conflicts of interest.
EDAT- 2023/08/18 18:42
MHDA- 2023/08/18 18:43
CRDT- 2023/08/18 16:14
PHST- 2023/08/18 18:43 [medline]
PHST- 2023/08/18 18:42 [pubmed]
PHST- 2023/08/18 16:14 [entrez]
AID - 00002800-202309000-00010 [pii]
AID - 10.1097/NUR.0000000000000770 [doi]
PST - ppublish
SO  - Clin Nurse Spec. 2023 Sep-Oct 01;37(5):245-246. doi: 
      10.1097/NUR.0000000000000770.

PMID- 37521388
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230801
IS  - 2250-0685 (Print)
IS  - 2321-3817 (Electronic)
IS  - 2250-0685 (Linking)
VI  - 13
IP  - 7
DP  - 2023 Jul
TI  - "ChatGPT" - Is it a New Wonder Drug in the Trial?
PG  - 1-3
LID - 10.13107/jocr.2023.v13.i07.3732 [doi]
FAU - Muthu, Sathish
AU  - Muthu S
AD  - Orthopaedic Research Group, Coimbatore, Tamil Nadu, India.
AD  - Department of Biotechnology, Faculty of Engineering, Karpagam Academy of Higher 
      Education, Coimbatore, Tamil Nadu, India.
FAU - Chellamuthu, Girinivasan
AU  - Chellamuthu G
AD  - Orthopaedic Research Group, Coimbatore, Tamil Nadu, India.
AD  - Department of Biotechnology, Faculty of Engineering, Karpagam Academy of Higher 
      Education, Coimbatore, Tamil Nadu, India.
FAU - Shyam, Ashok
AU  - Shyam A
AD  - Department of Orthopaedic, Sancheti Institute for Orthopaedics and 
      Rehabilitation.
AD  - Chief Researcher, Indian Orthopaedic Research Group, India.
LA  - eng
PT  - Editorial
PL  - India
TA  - J Orthop Case Rep
JT  - Journal of orthopaedic case reports
JID - 101641392
PMC - PMC10379259
COIS- Conflict of Interest: Nil
EDAT- 2023/07/31 06:43
MHDA- 2023/07/31 06:44
CRDT- 2023/07/31 05:07
PHST- 2023/04/04 00:00 [received]
PHST- 2023/05/18 00:00 [revised]
PHST- 2023/07/31 06:44 [medline]
PHST- 2023/07/31 06:43 [pubmed]
PHST- 2023/07/31 05:07 [entrez]
AID - JOCR-13-1 [pii]
AID - 10.13107/jocr.2023.v13.i07.3732 [doi]
PST - ppublish
SO  - J Orthop Case Rep. 2023 Jul;13(7):1-3. doi: 10.13107/jocr.2023.v13.i07.3732.

PMID- 37330721
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230821
LR  - 20230821
IS  - 1097-6760 (Electronic)
IS  - 0196-0644 (Linking)
VI  - 82
IP  - 3
DP  - 2023 Sep
TI  - ChatGPT: A Valuable Tool for Emergency Medical Assistance.
PG  - 411-413
LID - S0196-0644(23)00354-2 [pii]
LID - 10.1016/j.annemergmed.2023.04.027 [doi]
FAU - Dahdah, Joseph El
AU  - Dahdah JE
AD  - Research Institute, Cleveland Clinic Main Campus, Cleveland, OH. Electronic 
      address: eldahdj@ccf.org.
FAU - Kassab, Joseph
AU  - Kassab J
AD  - Research Institute, Cleveland Clinic Main Campus, Cleveland, OH.
FAU - Helou, Michel Chedid El
AU  - Helou MCE
AD  - Research Institute, Cleveland Clinic Main Campus, Cleveland, OH.
FAU - Gaballa, Andrew
AU  - Gaballa A
AD  - Research Institute, Cleveland Clinic Main Campus, Cleveland, OH.
FAU - Sayles, Stephen 3rd
AU  - Sayles S 3rd
AD  - Center for Emergency Medicine-West, Cleveland Clinic Foundation, Brunswick, OH.
FAU - Phelan, Michael P
AU  - Phelan MP
AD  - Center for Emergency Medicine, Cleveland Clinic Main Campus, Cleveland, OH.
LA  - eng
PT  - Journal Article
DEP - 20230617
PL  - United States
TA  - Ann Emerg Med
JT  - Annals of emergency medicine
JID - 8002646
SB  - IM
EDAT- 2023/06/18 13:11
MHDA- 2023/06/18 13:12
CRDT- 2023/06/18 10:29
PHST- 2023/02/20 00:00 [received]
PHST- 2023/04/25 00:00 [revised]
PHST- 2023/04/27 00:00 [accepted]
PHST- 2023/06/18 13:12 [medline]
PHST- 2023/06/18 13:11 [pubmed]
PHST- 2023/06/18 10:29 [entrez]
AID - S0196-0644(23)00354-2 [pii]
AID - 10.1016/j.annemergmed.2023.04.027 [doi]
PST - ppublish
SO  - Ann Emerg Med. 2023 Sep;82(3):411-413. doi: 10.1016/j.annemergmed.2023.04.027. 
      Epub 2023 Jun 17.

PMID- 37127733
OWN - NLM
STAT- MEDLINE
DCOM- 20230609
LR  - 20230609
IS  - 1432-086X (Electronic)
IS  - 0174-1551 (Linking)
VI  - 46
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT's Potential Role in Interventional Radiology.
PG  - 821-822
LID - 10.1007/s00270-023-03448-4 [doi]
FAU - Javan, Ramin
AU  - Javan R
AUID- ORCID: 0000-0001-8939-2781
AD  - Department of Radiology, George Washington University Hospital, 900 23Rd St NW, 
      Suite G2092, Washington, DC, 20037, USA. rjavan@mfa.gwu.edu.
FAU - Kim, Theodore
AU  - Kim T
AD  - George Washington University School of Medicine and Health Sciences, Washington, 
      DC, 20037, USA.
FAU - Mostaghni, Navid
AU  - Mostaghni N
AD  - School of Medicine, California University of Science and Medicine, Colton, CA, 
      92324, USA.
FAU - Sarin, Shawn
AU  - Sarin S
AD  - Department of Interventional Radiology, George Washington University Hospital, 
      Washington, DC, 20037, USA.
LA  - eng
PT  - Letter
DEP - 20230501
PL  - United States
TA  - Cardiovasc Intervent Radiol
JT  - Cardiovascular and interventional radiology
JID - 8003538
SB  - IM
MH  - Humans
MH  - *Radiology, Interventional
MH  - *Artificial Intelligence
EDAT- 2023/05/02 00:42
MHDA- 2023/06/05 06:42
CRDT- 2023/05/01 23:23
PHST- 2023/04/02 00:00 [received]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/06/05 06:42 [medline]
PHST- 2023/05/02 00:42 [pubmed]
PHST- 2023/05/01 23:23 [entrez]
AID - 10.1007/s00270-023-03448-4 [pii]
AID - 10.1007/s00270-023-03448-4 [doi]
PST - ppublish
SO  - Cardiovasc Intervent Radiol. 2023 Jun;46(6):821-822. doi: 
      10.1007/s00270-023-03448-4. Epub 2023 May 1.

PMID- 37910380
OWN - NLM
STAT- Publisher
LR  - 20231101
IS  - 1867-108X (Electronic)
IS  - 1867-1071 (Linking)
DP  - 2023 Nov 1
TI  - ChatGPT, GPT-4, and Bard and official board examination: comment.
LID - 10.1007/s11604-023-01508-w [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - , Phonhong, Lao People's Democratic Republic. hinpetchdaung@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University, Ludhiana, India.
LA  - eng
PT  - Letter
DEP - 20231101
PL  - Japan
TA  - Jpn J Radiol
JT  - Japanese journal of radiology
JID - 101490689
SB  - IM
EDAT- 2023/11/01 18:44
MHDA- 2023/11/01 18:44
CRDT- 2023/11/01 12:12
PHST- 2023/10/05 00:00 [received]
PHST- 2023/10/16 00:00 [accepted]
PHST- 2023/11/01 18:44 [medline]
PHST- 2023/11/01 18:44 [pubmed]
PHST- 2023/11/01 12:12 [entrez]
AID - 10.1007/s11604-023-01508-w [pii]
AID - 10.1007/s11604-023-01508-w [doi]
PST - aheadofprint
SO  - Jpn J Radiol. 2023 Nov 1. doi: 10.1007/s11604-023-01508-w.

PMID- 37863408
OWN - NLM
STAT- Publisher
LR  - 20231206
IS  - 1542-7714 (Electronic)
IS  - 1542-3565 (Linking)
DP  - 2023 Oct 19
TI  - Evaluating ChatGPT in Medical Contexts: The Imperative to Guard Against 
      Hallucinations and Partial Accuracies.
LID - S1542-3565(23)00835-2 [pii]
LID - 10.1016/j.cgh.2023.09.035 [doi]
FAU - Giuffrè, Mauro
AU  - Giuffrè M
AD  - Section of Digestive Diseases, Department of Internal Medicine, Yale School of 
      Medicine, Yale University, New Haven, Connecticut.
FAU - You, Kisung
AU  - You K
AD  - Department of Mathematics, Baruch College, City University of New York, New York, 
      New York.
FAU - Shung, Dennis L
AU  - Shung DL
AD  - Section of Digestive Diseases, Department of Internal Medicine, Yale School of 
      Medicine, Yale University, New Haven, Connecticut.
LA  - eng
PT  - Letter
DEP - 20231019
PL  - United States
TA  - Clin Gastroenterol Hepatol
JT  - Clinical gastroenterology and hepatology : the official clinical practice journal 
      of the American Gastroenterological Association
JID - 101160775
SB  - IM
EDAT- 2023/10/21 05:42
MHDA- 2023/10/21 05:42
CRDT- 2023/10/20 19:32
PHST- 2023/09/21 00:00 [received]
PHST- 2023/09/22 00:00 [accepted]
PHST- 2023/10/21 05:42 [pubmed]
PHST- 2023/10/21 05:42 [medline]
PHST- 2023/10/20 19:32 [entrez]
AID - S1542-3565(23)00835-2 [pii]
AID - 10.1016/j.cgh.2023.09.035 [doi]
PST - aheadofprint
SO  - Clin Gastroenterol Hepatol. 2023 Oct 19:S1542-3565(23)00835-2. doi: 
      10.1016/j.cgh.2023.09.035.

PMID- 37670527
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230907
LR  - 20230922
IS  - 2005-7288 (Electronic)
IS  - 2005-727X (Print)
IS  - 2005-727X (Linking)
VI  - 35
IP  - 3
DP  - 2023 Sep
TI  - A use case of ChatGPT in a flipped medical terminology course.
PG  - 303-307
LID - 10.3946/kjme.2023.269 [doi]
FAU - Ahn, Sangzin
AU  - Ahn S
AD  - Department of Pharmacology and Pharmacogenomics Research Center, Inje University 
      College of Medicine, Busan, Korea.
AD  - Center for Personalized Precision Medicine of Tuberculosis, Inje University 
      College of Medicine, Busan, Korea.
LA  - eng
GR  - 2018R1A5A2021242/National Research Foundation of Korea/
GR  - Ministry of Science and ICT/
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230831
PL  - Korea (South)
TA  - Korean J Med Educ
JT  - Korean journal of medical education
JID - 101503071
SB  - IM
PMC - PMC10493404
COIS- Conflicts of interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/09/06 06:42
MHDA- 2023/09/06 06:43
CRDT- 2023/09/06 02:33
PHST- 2023/08/04 00:00 [received]
PHST- 2023/08/08 00:00 [accepted]
PHST- 2023/09/06 06:43 [medline]
PHST- 2023/09/06 06:42 [pubmed]
PHST- 2023/09/06 02:33 [entrez]
AID - kjme.2023.269 [pii]
AID - kjme-2023-269 [pii]
AID - 10.3946/kjme.2023.269 [doi]
PST - ppublish
SO  - Korean J Med Educ. 2023 Sep;35(3):303-307. doi: 10.3946/kjme.2023.269. Epub 2023 
      Aug 31.

PMID- 37563065
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230914
LR  - 20230914
IS  - 1873-5487 (Electronic)
IS  - 0188-4409 (Linking)
VI  - 54
IP  - 6
DP  - 2023 Sep
TI  - ChatGPT and Artificial Intelligence in the Contemporary Medical Landscape: 
      Commentary.
PG  - 102857
LID - S0188-4409(23)00095-4 [pii]
LID - 10.1016/j.arcmed.2023.102857 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joseph Ayo Babalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230809
PL  - United States
TA  - Arch Med Res
JT  - Archives of medical research
JID - 9312706
SB  - IM
COIS- Conflict of Interest None
EDAT- 2023/08/11 00:42
MHDA- 2023/09/08 06:43
CRDT- 2023/08/10 22:00
PHST- 2023/06/01 00:00 [received]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/09/08 06:43 [medline]
PHST- 2023/08/11 00:42 [pubmed]
PHST- 2023/08/10 22:00 [entrez]
AID - S0188-4409(23)00095-4 [pii]
AID - 10.1016/j.arcmed.2023.102857 [doi]
PST - ppublish
SO  - Arch Med Res. 2023 Sep;54(6):102857. doi: 10.1016/j.arcmed.2023.102857. Epub 2023 
      Aug 9.

PMID- 37413958
OWN - NLM
STAT- MEDLINE
DCOM- 20230816
LR  - 20230816
IS  - 1532-2831 (Electronic)
IS  - 1078-8174 (Linking)
VI  - 29
IP  - 5
DP  - 2023 Aug
TI  - ChatGPT in medical imaging higher education: Reply to Currie et al.
PG  - 867
LID - S1078-8174(23)00131-1 [pii]
LID - 10.1016/j.radi.2023.06.007 [doi]
FAU - Perera Molligoda Arachchige, A S
AU  - Perera Molligoda Arachchige AS
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Italy; Department 
      of Radiology, Istituto Clinico Humanitas, Milan, Italy. Electronic address: 
      aroshshavinda.pereramolligodaarachchige@st.hunimed.eu.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230704
PL  - Netherlands
TA  - Radiography (Lond)
JT  - Radiography (London, England : 1995)
JID - 9604102
SB  - IM
CON - Radiography (Lond). 2023 Jul;29(4):792-799. PMID: 37271011
CIN - Radiography (Lond). 2023 Aug;29(5):868-869. PMID: 37419046
MH  - Humans
MH  - *Diagnostic Imaging
MH  - Radiography
COIS- Conflicts of interest The author has no conflicts of interest to declare.
EDAT- 2023/07/07 01:05
MHDA- 2023/08/16 06:43
CRDT- 2023/07/06 18:10
PHST- 2023/06/13 00:00 [received]
PHST- 2023/06/20 00:00 [accepted]
PHST- 2023/08/16 06:43 [medline]
PHST- 2023/07/07 01:05 [pubmed]
PHST- 2023/07/06 18:10 [entrez]
AID - S1078-8174(23)00131-1 [pii]
AID - 10.1016/j.radi.2023.06.007 [doi]
PST - ppublish
SO  - Radiography (Lond). 2023 Aug;29(5):867. doi: 10.1016/j.radi.2023.06.007. Epub 
      2023 Jul 4.

PMID- 37036239
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230616
IS  - 2578-5745 (Electronic)
IS  - 2578-5745 (Linking)
VI  - 5
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT, et al … Artificial Intelligence, Authorship, and Medical Publishing.
PG  - 288-289
LID - 10.1002/acr2.11538 [doi]
FAU - Solomon, Daniel H
AU  - Solomon DH
AUID- ORCID: 0000-0001-8202-5428
AD  - Brigham and Women's Hospital, Harvard Medical School.
FAU - Allen, Kelli D
AU  - Allen KD
AUID- ORCID: 0000-0002-3886-0677
AD  - University of North Carolina at Chapel Hill, and Durham VA Health Care System, 
      Durham, North Carolina.
FAU - Katz, Patricia
AU  - Katz P
AUID- ORCID: 0000-0002-8146-2519
AD  - University of California-San Francisco, San Francisco.
FAU - Sawalha, Amr H
AU  - Sawalha AH
AD  - University of Pittsburgh, Pittsburgh, Pennsylvania.
FAU - Yelin, Ed
AU  - Yelin E
AUID- ORCID: 0000-0002-9229-9980
AD  - University of California-San Francisco, San Francisco.
LA  - eng
PT  - Editorial
DEP - 20230410
PL  - United States
TA  - ACR Open Rheumatol
JT  - ACR open rheumatology
JID - 101740025
PMC - PMC10267801
EDAT- 2023/04/11 06:00
MHDA- 2023/04/11 06:01
CRDT- 2023/04/10 09:06
PHST- 2023/03/09 00:00 [received]
PHST- 2023/03/09 00:00 [accepted]
PHST- 2023/04/11 06:01 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 09:06 [entrez]
AID - ACR211538 [pii]
AID - 10.1002/acr2.11538 [doi]
PST - ppublish
SO  - ACR Open Rheumatol. 2023 Jun;5(6):288-289. doi: 10.1002/acr2.11538. Epub 2023 Apr 
      10.

PMID- 36880946
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230511
LR  - 20230511
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 3
DP  - 2023 May
TI  - The Importance of Proper Use of ChatGPT in Medical Writing.
PG  - e230312
LID - 10.1148/radiol.230312 [doi]
FAU - Koo, Malcolm
AU  - Koo M
AUID- ORCID: 0000-0002-9242-9167
AD  - Graduate Institute of Long-term Care, Tzu Chi University of Science and 
      Technology, 880, Sec. 2, Chien-kuo Road, Hualien 970302, Taiwan, Republic of 
      China.
AD  - Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230307
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CON - Radiology. 2023 Apr;307(2):e223312. PMID: 36728748
EDAT- 2023/03/08 06:00
MHDA- 2023/04/26 06:42
CRDT- 2023/03/07 10:07
PHST- 2023/04/26 06:42 [medline]
PHST- 2023/03/08 06:00 [pubmed]
PHST- 2023/03/07 10:07 [entrez]
AID - 10.1148/radiol.230312 [doi]
PST - ppublish
SO  - Radiology. 2023 May;307(3):e230312. doi: 10.1148/radiol.230312. Epub 2023 Mar 7.

PMID- 37923807
OWN - NLM
STAT- Publisher
LR  - 20231104
IS  - 1476-5608 (Electronic)
IS  - 1365-7852 (Linking)
DP  - 2023 Nov 3
TI  - Quality of information and appropriateness of ChatGPT outputs for urology 
      patients.
LID - 10.1038/s41391-023-00754-3 [doi]
FAU - Cocci, Andrea
AU  - Cocci A
AUID- ORCID: 0000-0003-0138-6294
AD  - Urology Section, University of Florence, Florence, Italy. cocci.andrea@gmail.com.
FAU - Pezzoli, Marta
AU  - Pezzoli M
AUID- ORCID: 0009-0007-7168-5499
AD  - Urology Section, University of Florence, Florence, Italy.
FAU - Lo Re, Mattia
AU  - Lo Re M
AD  - Urology Section, University of Florence, Florence, Italy.
FAU - Russo, Giorgio Ivan
AU  - Russo GI
AUID- ORCID: 0000-0003-4687-7353
AD  - Urology Section, University of Catania, Catania, Italy.
FAU - Asmundo, Maria Giovanna
AU  - Asmundo MG
AD  - Urology Section, University of Catania, Catania, Italy.
FAU - Fode, Mikkel
AU  - Fode M
AD  - Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
AD  - Department of Urology, Copenhagen University Hospital, Herlev and Gentofte 
      Hospital, Copenhagen, Denmark.
FAU - Cacciamani, Giovanni
AU  - Cacciamani G
AD  - Institute of Urology, Keck School of Medicine, University of Southern California 
      (USC), Los Angeles, CA, USA.
FAU - Cimino, Sebastiano
AU  - Cimino S
AD  - Urology Section, University of Catania, Catania, Italy.
FAU - Minervini, Andrea
AU  - Minervini A
AD  - Urology Section, University of Florence, Florence, Italy.
FAU - Durukan, Emil
AU  - Durukan E
AUID- ORCID: 0000-0001-9221-8944
AD  - Department of Urology, Copenhagen University Hospital, Herlev and Gentofte 
      Hospital, Copenhagen, Denmark.
LA  - eng
PT  - Letter
DEP - 20231103
PL  - England
TA  - Prostate Cancer Prostatic Dis
JT  - Prostate cancer and prostatic diseases
JID - 9815755
SB  - IM
EDAT- 2023/11/04 21:46
MHDA- 2023/11/04 21:46
CRDT- 2023/11/04 00:29
PHST- 2023/10/04 00:00 [received]
PHST- 2023/10/19 00:00 [accepted]
PHST- 2023/10/14 00:00 [revised]
PHST- 2023/11/04 21:46 [medline]
PHST- 2023/11/04 21:46 [pubmed]
PHST- 2023/11/04 00:29 [entrez]
AID - 10.1038/s41391-023-00754-3 [pii]
AID - 10.1038/s41391-023-00754-3 [doi]
PST - aheadofprint
SO  - Prostate Cancer Prostatic Dis. 2023 Nov 3. doi: 10.1038/s41391-023-00754-3.

PMID- 37551829
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231205
IS  - 1550-9397 (Electronic)
IS  - 1550-9389 (Print)
IS  - 1550-9389 (Linking)
VI  - 19
IP  - 12
DP  - 2023 Dec 1
TI  - ChatGPT, obstructive sleep apnea, and patient education.
PG  - 2133
LID - 10.5664/jcsm.10768 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - J Clin Sleep Med
JT  - Journal of clinical sleep medicine : JCSM : official publication of the American 
      Academy of Sleep Medicine
JID - 101231977
SB  - IM
MH  - Humans
MH  - *Patient Education as Topic
MH  - *Sleep Apnea, Obstructive/diagnosis/therapy
MH  - Continuous Positive Airway Pressure
PMC - PMC10692939
COIS- The authors report no conflicts of interest.
EDAT- 2023/08/08 12:43
MHDA- 2023/12/04 12:43
PMCR- 2024/12/01
CRDT- 2023/08/08 07:13
PHST- 2024/12/01 00:00 [pmc-release]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/08/08 12:43 [pubmed]
PHST- 2023/08/08 07:13 [entrez]
AID - jcsm.10768 [pii]
AID - JC2300401 [pii]
AID - 10.5664/jcsm.10768 [doi]
PST - ppublish
SO  - J Clin Sleep Med. 2023 Dec 1;19(12):2133. doi: 10.5664/jcsm.10768.

PMID- 37470174
OWN - NLM
STAT- Publisher
LR  - 20231023
IS  - 1528-1140 (Electronic)
IS  - 0003-4932 (Linking)
VI  - 278
IP  - 5
DP  - 2023 Nov 1
TI  - AI and ChatGPT Meet Surgery: A Word of Caution for Surgeon-Scientists.
PG  - e943-e944
LID - 10.1097/SLA.0000000000006000 [doi]
FAU - Rafaqat, Wardah
AU  - Rafaqat W
AD  - Division of Trauma, Emergency General Surgery and Surgical Critical Care, 
      Massachusetts General Hospital, Boston, MA.
FAU - Chu, Daniel I
AU  - Chu DI
AD  - Division of Gastrointestinal Surgery, University of Alabama at Birmingham, AL.
FAU - Kaafarani, Haytham M
AU  - Kaafarani HM
AD  - Division of Trauma, Emergency General Surgery and Surgical Critical Care, 
      Massachusetts General Hospital, Boston, MA.
LA  - eng
PT  - Journal Article
DEP - 20230720
PL  - United States
TA  - Ann Surg
JT  - Annals of surgery
JID - 0372354
SB  - IM
COIS- The authors report no conflicts of interest.
EDAT- 2023/07/20 06:42
MHDA- 2023/07/20 06:42
CRDT- 2023/07/20 05:33
PHST- 2023/07/20 06:42 [pubmed]
PHST- 2023/07/20 06:42 [medline]
PHST- 2023/07/20 05:33 [entrez]
AID - 00000658-202311000-00032 [pii]
AID - 10.1097/SLA.0000000000006000 [doi]
PST - ppublish
SO  - Ann Surg. 2023 Nov 1;278(5):e943-e944. doi: 10.1097/SLA.0000000000006000. Epub 
      2023 Jul 20.

PMID- 37462423
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Linking)
VI  - 39
IP  - 5
DP  - 2023 Sep-Oct 01
TI  - Re: "ChatGPT and Lacrimal Drainage Disorders: Performance and Scope of 
      Improvement".
PG  - 514-515
LID - 10.1097/IOP.0000000000002475 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
LA  - eng
PT  - Journal Article
DEP - 20230718
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
SB  - IM
MH  - Humans
MH  - *Lacrimal Apparatus Diseases/surgery
MH  - *Lacrimal Apparatus
COIS- The authors have no financial or conflicts of interest to disclose.
EDAT- 2023/07/18 13:09
MHDA- 2023/07/18 13:10
CRDT- 2023/07/18 09:33
PHST- 2023/07/18 13:10 [medline]
PHST- 2023/07/18 13:09 [pubmed]
PHST- 2023/07/18 09:33 [entrez]
AID - 00002341-990000000-00230 [pii]
AID - 10.1097/IOP.0000000000002475 [doi]
PST - ppublish
SO  - Ophthalmic Plast Reconstr Surg. 2023 Sep-Oct 01;39(5):514-515. doi: 
      10.1097/IOP.0000000000002475. Epub 2023 Jul 18.

PMID- 37436486
OWN - NLM
STAT- MEDLINE
DCOM- 20230731
LR  - 20231002
IS  - 2731-7013 (Electronic)
IS  - 2731-7005 (Linking)
VI  - 74
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT-4: dermatology aid in the emergency room.
PG  - 633-634
LID - 10.1007/s00105-023-05186-7 [doi]
FAU - Castro, Harvey
AU  - Castro H
AD  - Coppell ER, 720 north Denton tap, 75019, Coppell, TX, USA. 
      harveycastro4@gmail.com.
LA  - eng
PT  - Journal Article
TT  - ChatGPT-4: dermatologische Unterstützung in der Notfallambulanz.
DEP - 20230712
PL  - Germany
TA  - Dermatologie (Heidelb)
JT  - Dermatologie (Heidelberg, Germany)
JID - 9918384885206676
SB  - IM
MH  - *Dermatology
MH  - *Artificial Intelligence
MH  - *Emergency Service, Hospital
EDAT- 2023/07/12 13:07
MHDA- 2023/07/25 06:42
CRDT- 2023/07/12 11:05
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/07/25 06:42 [medline]
PHST- 2023/07/12 13:07 [pubmed]
PHST- 2023/07/12 11:05 [entrez]
AID - 10.1007/s00105-023-05186-7 [pii]
AID - 10.1007/s00105-023-05186-7 [doi]
PST - ppublish
SO  - Dermatologie (Heidelb). 2023 Aug;74(8):633-634. doi: 10.1007/s00105-023-05186-7. 
      Epub 2023 Jul 12.

PMID- 37401498
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230705
LR  - 20230706
IS  - 1598-6357 (Electronic)
IS  - 1011-8934 (Print)
IS  - 1011-8934 (Linking)
VI  - 38
IP  - 26
DP  - 2023 Jul 3
TI  - Beyond the Keyboard: Academic Writing in the Era of ChatGPT.
PG  - e207
LID - 10.3346/jkms.2023.38.e207 [doi]
LID - e207
FAU - Doskaliuk, Bohdana
AU  - Doskaliuk B
AUID- ORCID: 0000-0003-1650-8928
AD  - Department of Pathophysiology, Ivano-Frankivsk National Medical University, 
      Ivano-Frankivsk, Ukraine. Doskaliuk_Bo@ifnmu.edu.ua.
FAU - Zimba, Olena
AU  - Zimba O
AUID- ORCID: 0000-0002-4188-8486
AD  - Department of Clinical Rheumatology and Immunology, University Hospital in 
      Krakow, Krakow, Poland.
AD  - National Institute of Geriatrics, Rheumatology and Rehabilitation, Warsaw, 
      Poland.
AD  - Department of Internal Medicine N2, Danylo Halytsky Lviv National Medical 
      University, Lviv, Ukraine.
LA  - eng
PT  - Journal Article
DEP - 20230703
PL  - Korea (South)
TA  - J Korean Med Sci
JT  - Journal of Korean medical science
JID - 8703518
SB  - IM
PMC - PMC10318197
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/07/04 06:42
MHDA- 2023/07/04 06:43
CRDT- 2023/07/04 05:27
PHST- 2023/03/20 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/07/04 06:43 [medline]
PHST- 2023/07/04 06:42 [pubmed]
PHST- 2023/07/04 05:27 [entrez]
AID - 38.e207 [pii]
AID - 10.3346/jkms.2023.38.e207 [doi]
PST - epublish
SO  - J Korean Med Sci. 2023 Jul 3;38(26):e207. doi: 10.3346/jkms.2023.38.e207.

PMID- 36868432
OWN - NLM
STAT- MEDLINE
DCOM- 20230418
LR  - 20231122
IS  - 1090-2139 (Electronic)
IS  - 0889-1591 (Linking)
VI  - 110
DP  - 2023 May
TI  - A Chat(GPT) about the future of scientific publishing.
PG  - 152-154
LID - S0889-1591(23)00053-3 [pii]
LID - 10.1016/j.bbi.2023.02.022 [doi]
FAU - Hill-Yardin, Elisa L
AU  - Hill-Yardin EL
AD  - School of Health and Biomedical Sciences, RMIT University, Bundoora, Melbourne, 
      Victoria 3083, Australia; Department of Anatomy & Physiology, The University of 
      Melbourne, Parkville, Victoria 3010, Australia.
FAU - Hutchinson, Mark R
AU  - Hutchinson MR
AD  - Adelaide Medical School, Davies Livestock Research Centre, Institute for 
      Photonics & Advanced Sensing, Robinson Research Institute, University of 
      Adelaide, South Australia 5005, Australia; Australian Research Council Centre of 
      Excellence for Nanoscale BioPhotonics, Australia.
FAU - Laycock, Robin
AU  - Laycock R
AD  - School of Health and Biomedical Sciences, RMIT University, Bundoora, Melbourne, 
      Victoria 3083, Australia.
FAU - Spencer, Sarah J
AU  - Spencer SJ
AD  - School of Health and Biomedical Sciences, RMIT University, Bundoora, Melbourne, 
      Victoria 3083, Australia. Electronic address: Sarah.Spencer@rmit.edu.au.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230301
PL  - Netherlands
TA  - Brain Behav Immun
JT  - Brain, behavior, and immunity
JID - 8800478
RN  - EC 2.3.1.6 (Choline O-Acetyltransferase)
SB  - IM
CIN - Brain Behav Immun. 2023 Jul;111:124. PMID: 37037360
MH  - *Publishing
MH  - *Choline O-Acetyltransferase
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/03/04 06:00
MHDA- 2023/04/18 06:42
CRDT- 2023/03/03 19:29
PHST- 2023/02/20 00:00 [received]
PHST- 2023/02/25 00:00 [accepted]
PHST- 2023/04/18 06:42 [medline]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/03 19:29 [entrez]
AID - S0889-1591(23)00053-3 [pii]
AID - 10.1016/j.bbi.2023.02.022 [doi]
PST - ppublish
SO  - Brain Behav Immun. 2023 May;110:152-154. doi: 10.1016/j.bbi.2023.02.022. Epub 
      2023 Mar 1.

PMID- 36807343
OWN - NLM
STAT- MEDLINE
DCOM- 20230302
LR  - 20230621
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 615
IP  - 7950
DP  - 2023 Mar
TI  - How Nature readers are using ChatGPT.
PG  - 20
LID - 10.1038/d41586-023-00500-8 [doi]
FAU - Owens, Brian
AU  - Owens B
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - *Machine Learning/statistics & numerical data
MH  - Reading
MH  - *Periodicals as Topic
MH  - Writing
MH  - *Surveys and Questionnaires
MH  - *Research Personnel/statistics & numerical data
MH  - Humans
OTO - NOTNLM
OT  - Computer science
OT  - Machine learning
OT  - Scientific community
EDAT- 2023/02/23 06:00
MHDA- 2023/03/03 06:00
CRDT- 2023/02/22 11:35
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
PHST- 2023/02/22 11:35 [entrez]
AID - 10.1038/d41586-023-00500-8 [pii]
AID - 10.1038/d41586-023-00500-8 [doi]
PST - ppublish
SO  - Nature. 2023 Mar;615(7950):20. doi: 10.1038/d41586-023-00500-8.

PMID- 36653617
OWN - NLM
STAT- MEDLINE
DCOM- 20230127
LR  - 20230302
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 613
IP  - 7945
DP  - 2023 Jan
TI  - ChatGPT listed as author on research papers: many scientists disapprove.
PG  - 620-621
LID - 10.1038/d41586-023-00107-z [doi]
FAU - Stokel-Walker, Chris
AU  - Stokel-Walker C
LA  - eng
PT  - Journal Article
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - *Authorship
MH  - *Publishing/legislation & jurisprudence/trends
MH  - *Artificial Intelligence/legislation & jurisprudence/trends
MH  - *Research Report/standards/trends
OTO - NOTNLM
OT  - Ethics
OT  - Publishing
EDAT- 2023/01/19 06:00
MHDA- 2023/01/27 06:00
CRDT- 2023/01/18 23:27
PHST- 2023/01/19 06:00 [pubmed]
PHST- 2023/01/27 06:00 [medline]
PHST- 2023/01/18 23:27 [entrez]
AID - 10.1038/d41586-023-00107-z [pii]
AID - 10.1038/d41586-023-00107-z [doi]
PST - ppublish
SO  - Nature. 2023 Jan;613(7945):620-621. doi: 10.1038/d41586-023-00107-z.

PMID- 37934117
OWN - NLM
STAT- Publisher
LR  - 20231107
IS  - 1365-2591 (Electronic)
IS  - 0143-2885 (Linking)
DP  - 2023 Nov 7
TI  - Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of 
      endodontic question answers.
LID - 10.1111/iej.13998 [doi]
FAU - Suárez, Ana
AU  - Suárez A
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Díaz-Flores García, Víctor
AU  - Díaz-Flores García V
AUID- ORCID: 0000-0001-6141-0546
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Algar, Juan
AU  - Algar J
AD  - Department of Clinical Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Sánchez, Margarita Gómez
AU  - Sánchez MG
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - de Pedro, María Llorente
AU  - de Pedro ML
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
FAU - Freire, Yolanda
AU  - Freire Y
AD  - Department of Pre-Clinic Dentistry, School of Biomedical Sciences, Universidad 
      Europea de Madrid, Madrid, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231107
PL  - England
TA  - Int Endod J
JT  - International endodontic journal
JID - 8004996
EDAT- 2023/11/07 12:45
MHDA- 2023/11/07 12:45
CRDT- 2023/11/07 10:14
PHST- 2023/10/21 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/11/07 12:45 [medline]
PHST- 2023/11/07 12:45 [pubmed]
PHST- 2023/11/07 10:14 [entrez]
AID - 10.1111/iej.13998 [doi]
PST - aheadofprint
SO  - Int Endod J. 2023 Nov 7. doi: 10.1111/iej.13998.

PMID- 37881293
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231027
IS  - 0019-5413 (Print)
IS  - 1998-3727 (Electronic)
IS  - 0019-5413 (Linking)
VI  - 57
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT-4, Medical Education, and Clinical Exposure Challenges.
PG  - 1912
LID - 10.1007/s43465-023-00997-1 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Chandigarh, Punjab India. ROR: https://ror.org/05t4pvx35. 
      GRID: grid.448792.4. ISNI: 0000 0004 4678 9721
AD  - Dr DY Patil Vidhyapeeth, Pune, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20230921
PL  - Switzerland
TA  - Indian J Orthop
JT  - Indian journal of orthopaedics
JID - 0137736
PMC - PMC10593699
COIS- Conflict of InterestNone.
EDAT- 2023/10/26 06:42
MHDA- 2023/10/26 06:43
PMCR- 2024/09/21
CRDT- 2023/10/26 04:05
PHST- 2023/08/23 00:00 [received]
PHST- 2023/09/04 00:00 [accepted]
PHST- 2024/09/21 00:00 [pmc-release]
PHST- 2023/10/26 06:43 [medline]
PHST- 2023/10/26 06:42 [pubmed]
PHST- 2023/10/26 04:05 [entrez]
AID - 997 [pii]
AID - 10.1007/s43465-023-00997-1 [doi]
PST - epublish
SO  - Indian J Orthop. 2023 Sep 21;57(11):1912. doi: 10.1007/s43465-023-00997-1. 
      eCollection 2023 Nov.

PMID- 37845528
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 622
IP  - 7983
DP  - 2023 Oct
TI  - How ChatGPT is transforming the postdoc experience.
PG  - 655-657
LID - 10.1038/d41586-023-03235-8 [doi]
FAU - Nordling, Linda
AU  - Nordling L
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Careers
OT  - Lab life
OT  - Machine learning
OT  - Scientific community
EDAT- 2023/10/17 00:42
MHDA- 2023/10/17 00:43
CRDT- 2023/10/16 23:40
PHST- 2023/10/17 00:43 [medline]
PHST- 2023/10/17 00:42 [pubmed]
PHST- 2023/10/16 23:40 [entrez]
AID - 10.1038/d41586-023-03235-8 [pii]
AID - 10.1038/d41586-023-03235-8 [doi]
PST - ppublish
SO  - Nature. 2023 Oct;622(7983):655-657. doi: 10.1038/d41586-023-03235-8.

PMID- 37565001
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230811
IS  - 1863-2521 (Print)
IS  - 1863-2548 (Electronic)
IS  - 1863-2521 (Linking)
VI  - 17
IP  - 4
DP  - 2023 Aug
TI  - Introducing Journal of Children's Orthopaedics' ChatGPT and generative AI policy.
PG  - 297-298
LID - 10.1177/18632521231191687 [doi]
FAU - Wientroub, Shlomo
AU  - Wientroub S
FAU - Hefti, Fritz
AU  - Hefti F
LA  - eng
PT  - Editorial
DEP - 20230728
PL  - England
TA  - J Child Orthop
JT  - Journal of children's orthopaedics
JID - 101313582
PMC - PMC10411371
EDAT- 2023/08/11 06:43
MHDA- 2023/08/11 06:44
CRDT- 2023/08/11 04:08
PHST- 2023/08/11 06:44 [medline]
PHST- 2023/08/11 06:43 [pubmed]
PHST- 2023/08/11 04:08 [entrez]
AID - 10.1177_18632521231191687 [pii]
AID - 10.1177/18632521231191687 [doi]
PST - epublish
SO  - J Child Orthop. 2023 Jul 28;17(4):297-298. doi: 10.1177/18632521231191687. 
      eCollection 2023 Aug.

PMID- 37406215
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230911
LR  - 20230911
IS  - 1728-7731 (Electronic)
IS  - 1726-4901 (Linking)
VI  - 86
IP  - 9
DP  - 2023 Sep 1
TI  - ChatGPT surges ahead: GPT-4 has arrived in the arena of medical research.
PG  - 784-785
LID - 10.1097/JCMA.0000000000000955 [doi]
FAU - Wang, Ying-Mei
AU  - Wang YM
AD  - Department of Medical Education and Research, Taipei Veterans General Hospital 
      Hsinchu Branch, Hsinchu, Taiwan, ROC.
AD  - Department of Pharmacy, Taipei Veterans General Hospital Hsinchu Branch, Hsinchu, 
      Taiwan, ROC.
AD  - School of Medicine, National Tsing Hua University, Hsinchu, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, 
      Hsinchu County, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, 
      ROC.
AD  - Department of Post-Baccalaureate Medicine, National Chung Hsing University, 
      Taichung, Taiwan, ROC.
FAU - Chen, Tzeng-Ji
AU  - Chen TJ
AD  - Department of Medical Education and Research, Taipei Veterans General Hospital 
      Hsinchu Branch, Hsinchu, Taiwan, ROC.
AD  - Department of Pharmacy, Taipei Veterans General Hospital Hsinchu Branch, Hsinchu, 
      Taiwan, ROC.
AD  - School of Medicine, National Tsing Hua University, Hsinchu, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, 
      Hsinchu County, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, 
      ROC.
AD  - Department of Post-Baccalaureate Medicine, National Chung Hsing University, 
      Taichung, Taiwan, ROC.
LA  - eng
PT  - Editorial
DEP - 20230705
PL  - Netherlands
TA  - J Chin Med Assoc
JT  - Journal of the Chinese Medical Association : JCMA
JID - 101174817
SB  - IM
CIN - J Chin Med Assoc. 2023 Sep 1;86(9):866. PMID: 37458382
COIS- Conflicts of interest: Dr. Tzeng-Ji Chen, an editorial board member at Journal of 
      the Chinese Medical Association, had no role in the peer review process of or 
      decision to publish this article. Mrs. Wang declares that she has no conflicts of 
      interest related to the subject matter or materials discussed in this article.
EDAT- 2023/07/05 19:12
MHDA- 2023/07/05 19:13
CRDT- 2023/07/05 15:52
PHST- 2023/07/05 19:13 [medline]
PHST- 2023/07/05 19:12 [pubmed]
PHST- 2023/07/05 15:52 [entrez]
AID - 02118582-990000000-00233 [pii]
AID - 10.1097/JCMA.0000000000000955 [doi]
PST - ppublish
SO  - J Chin Med Assoc. 2023 Sep 1;86(9):784-785. doi: 10.1097/JCMA.0000000000000955. 
      Epub 2023 Jul 5.

PMID- 37248157
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230802
LR  - 20230802
IS  - 1873-5487 (Electronic)
IS  - 0188-4409 (Linking)
VI  - 54
IP  - 5
DP  - 2023 Jul
TI  - Impact of ChatGPT and Artificial Intelligence in the Contemporary Medical 
      Landscape.
PG  - 102835
LID - S0188-4409(23)00064-4 [pii]
LID - 10.1016/j.arcmed.2023.05.003 [doi]
FAU - Calleja-López, J R Tadeo
AU  - Calleja-López JRT
AD  - Department of Medicine and Health Sciences, Universidad de Sonora, Hermosillo, 
      Sonora, Mexico. Electronic address: jrtcalleja@gmail.com.
FAU - Rivera-Rosas, Cristian N
AU  - Rivera-Rosas CN
AD  - Department of Medicine and Health Sciences, Universidad de Sonora, Hermosillo, 
      Sonora, Mexico.
FAU - Ruibal-Tavares, Enrique
AU  - Ruibal-Tavares E
AD  - Department of Medicine and Health Sciences, Universidad de Sonora, Hermosillo, 
      Sonora, Mexico.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230528
PL  - United States
TA  - Arch Med Res
JT  - Archives of medical research
JID - 9312706
SB  - IM
CON - Arch Med Res. 2023 Apr;54(3):163-167. PMID: 36941174
CIN - Arch Med Res. 2023 Jul;54(5):102838. PMID: 37364482
COIS- Conflict of Interest None.
EDAT- 2023/05/30 01:06
MHDA- 2023/07/31 06:42
CRDT- 2023/05/29 21:57
PHST- 2023/04/04 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/05/30 01:06 [pubmed]
PHST- 2023/05/29 21:57 [entrez]
AID - S0188-4409(23)00064-4 [pii]
AID - 10.1016/j.arcmed.2023.05.003 [doi]
PST - ppublish
SO  - Arch Med Res. 2023 Jul;54(5):102835. doi: 10.1016/j.arcmed.2023.05.003. Epub 2023 
      May 28.

PMID- 37185379
OWN - NLM
STAT- MEDLINE
DCOM- 20230517
LR  - 20230727
IS  - 1529-4242 (Electronic)
IS  - 0032-1052 (Linking)
VI  - 151
IP  - 5
DP  - 2023 May 1
TI  - OpenAI's ChatGPT and Its Role in Plastic Surgery Research.
PG  - 1111-1113
LID - 10.1097/PRS.0000000000010342 [doi]
FAU - Weidman, Allan A
AU  - Weidman AA
AD  - From the Division of Plastic Surgery, Beth Israel Deaconess Medical Center, 
      Harvard Medical School.
FAU - Valentine, Lauren
AU  - Valentine L
AD  - From the Division of Plastic Surgery, Beth Israel Deaconess Medical Center, 
      Harvard Medical School.
FAU - Chung, Kevin C
AU  - Chung KC
AD  - Section of Plastic Surgery, University of Michigan.
FAU - Lin, Samuel J
AU  - Lin SJ
AD  - From the Division of Plastic Surgery, Beth Israel Deaconess Medical Center, 
      Harvard Medical School.
LA  - eng
PT  - Journal Article
DEP - 20230426
PL  - United States
TA  - Plast Reconstr Surg
JT  - Plastic and reconstructive surgery
JID - 1306050
SB  - IM
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
EDAT- 2023/05/15 19:12
MHDA- 2023/05/17 06:42
CRDT- 2023/05/15 13:36
PHST- 2023/05/17 06:42 [medline]
PHST- 2023/05/15 19:12 [pubmed]
PHST- 2023/05/15 13:36 [entrez]
AID - 00006534-202305000-00037 [pii]
AID - 10.1097/PRS.0000000000010342 [doi]
PST - ppublish
SO  - Plast Reconstr Surg. 2023 May 1;151(5):1111-1113. doi: 
      10.1097/PRS.0000000000010342. Epub 2023 Apr 26.

PMID- 36481949
OWN - NLM
STAT- Publisher
LR  - 20221216
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2022 Dec 8
TI  - Are ChatGPT and AlphaCode going to replace programmers?
LID - 10.1038/d41586-022-04383-z [doi]
FAU - Castelvecchi, Davide
AU  - Castelvecchi D
LA  - eng
PT  - News
DEP - 20221208
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Machine learning
OT  - Mathematics and computing
EDAT- 2022/12/10 06:00
MHDA- 2022/12/10 06:00
CRDT- 2022/12/09 00:24
PHST- 2022/12/10 06:00 [pubmed]
PHST- 2022/12/10 06:00 [medline]
PHST- 2022/12/09 00:24 [entrez]
AID - 10.1038/d41586-022-04383-z [pii]
AID - 10.1038/d41586-022-04383-z [doi]
PST - aheadofprint
SO  - Nature. 2022 Dec 8. doi: 10.1038/d41586-022-04383-z.

PMID- 37302976
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 4
DP  - 2023 Nov 29
TI  - ChatGPT educates college students about sexual violence and its impact on their 
      mental health: a proposal.
PG  - e791-e792
LID - 10.1093/pubmed/fdad078 [doi]
AB  - ChatGPT can be, as an interactive information platform, used to facilitate 
      learning about the psychological effects of sexual violence. Since its approach 
      is interactive and also that it is easily accessible, it can aid in the 
      dissemination of information, the prevention of sexual violence and its 
      treatment. Further, it can be incorporated into the curriculum to help raise 
      awareness about this delicate subject and aid the affected students.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Faculty 
      of Public Health. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Folastri, Sisca
AU  - Folastri S
AUID- ORCID: 0000-0003-4189-0276
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Malang, Malang, East Java 65145, Indonesia.
AD  - Ikatan Konselor Indonesia, Jakarta, 13760, Indonesia.
FAU - Hambali, I M
AU  - Hambali IM
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Malang, Malang, East Java 65145, Indonesia.
FAU - Ramli, M
AU  - Ramli M
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Malang, Malang, East Java 65145, Indonesia.
FAU - Akbar, Sa'dun
AU  - Akbar S
AD  - Department of Early Childhood, Faculty of Education, Universitas Negeri Malang, 
      Malang, East Java 65145, Indonesia.
FAU - Sofyan, Afriyadi
AU  - Sofyan A
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Malang, Malang, East Java 65145, Indonesia.
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Semarang, Semarang, Central Java 50229, Indonesia.
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - *Mental Health
MH  - *Sex Offenses/prevention & control
MH  - Students/psychology
MH  - Curriculum
MH  - Universities
OTO - NOTNLM
OT  - Educational settings
OT  - Mental health
OT  - Sexual behaviour
EDAT- 2023/06/12 00:42
MHDA- 2023/12/04 12:42
CRDT- 2023/06/11 22:03
PHST- 2023/05/10 00:00 [received]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/06/12 00:42 [pubmed]
PHST- 2023/06/11 22:03 [entrez]
AID - 7193582 [pii]
AID - 10.1093/pubmed/fdad078 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Nov 29;45(4):e791-e792. doi: 10.1093/pubmed/fdad078.

PMID- 37940756
OWN - NLM
STAT- Publisher
LR  - 20231108
IS  - 1525-1497 (Electronic)
IS  - 0884-8734 (Linking)
DP  - 2023 Nov 8
TI  - New Frontiers in Health Literacy: Using ChatGPT to Simplify Health Information 
      for People in the Community.
LID - 10.1007/s11606-023-08469-w [doi]
AB  - BACKGROUND: Most health information does not meet the health literacy needs of 
      our communities. Writing health information in plain language is time-consuming 
      but the release of tools like ChatGPT may make it easier to produce reliable 
      plain language health information. OBJECTIVE: To investigate the capacity for 
      ChatGPT to produce plain language versions of health texts. DESIGN: Observational 
      study of 26 health texts from reputable websites. METHODS: ChatGPT was prompted 
      to 'rewrite the text for people with low literacy'. Researchers captured three 
      revised versions of each original text. MAIN MEASURES: Objective health literacy 
      assessment, including Simple Measure of Gobbledygook (SMOG), proportion of the 
      text that contains complex language (%), number of instances of passive voice and 
      subjective ratings of key messages retained (%). KEY RESULTS: On average, 
      original texts were written at grade 12.8 (SD = 2.2) and revised to grade 11.0 
      (SD = 1.2), p < 0.001. Original texts were on average 22.8% complex (SD = 7.5%) 
      compared to 14.4% (SD = 5.6%) in revised texts, p < 0.001. Original texts had on 
      average 4.7 instances (SD = 3.2) of passive text compared to 1.7 (SD = 1.2) in 
      revised texts, p < 0.001. On average 80% of key messages were retained 
      (SD = 15.0). The more complex original texts showed more improvements than less 
      complex original texts. For example, when original texts were ≥ grade 13, revised 
      versions improved by an average 3.3 grades (SD = 2.2), p < 0.001. Simpler 
      original texts (< grade 11) improved by an average 0.5 grades (SD = 1.4), 
      p < 0.001. CONCLUSIONS: This study used multiple objective assessments of health 
      literacy to demonstrate that ChatGPT can simplify health information while 
      retaining most key messages. However, the revised texts typically did not meet 
      health literacy targets for grade reading score, and improvements were marginal 
      for texts that were already relatively simple.
CI  - © 2023. The Author(s).
FAU - Ayre, Julie
AU  - Ayre J
AUID- ORCID: 0000-0002-5279-5189
AD  - Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine 
      and Health, The University of Sydney, Rm 128C Edward Ford Building, Sydney, NSW, 
      Australia. Julie.ayre@sydney.edu.au.
FAU - Mac, Olivia
AU  - Mac O
AD  - Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine 
      and Health, The University of Sydney, Rm 128C Edward Ford Building, Sydney, NSW, 
      Australia.
FAU - McCaffery, Kirsten
AU  - McCaffery K
AD  - Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine 
      and Health, The University of Sydney, Rm 128C Edward Ford Building, Sydney, NSW, 
      Australia.
FAU - McKay, Brad R
AU  - McKay BR
AD  - Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine 
      and Health, The University of Sydney, Rm 128C Edward Ford Building, Sydney, NSW, 
      Australia.
FAU - Liu, Mingyi
AU  - Liu M
AD  - Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine 
      and Health, The University of Sydney, Rm 128C Edward Ford Building, Sydney, NSW, 
      Australia.
FAU - Shi, Yi
AU  - Shi Y
AD  - Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine 
      and Health, The University of Sydney, Rm 128C Edward Ford Building, Sydney, NSW, 
      Australia.
FAU - Rezwan, Atria
AU  - Rezwan A
AD  - Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine 
      and Health, The University of Sydney, Rm 128C Edward Ford Building, Sydney, NSW, 
      Australia.
FAU - Dunn, Adam G
AU  - Dunn AG
AD  - Discipline of Biomedical Informatics and Digital Health, School of Medical 
      Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 
      Australia.
LA  - eng
PT  - Journal Article
DEP - 20231108
PL  - United States
TA  - J Gen Intern Med
JT  - Journal of general internal medicine
JID - 8605834
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - health communication
OT  - health literacy
OT  - patient education
EDAT- 2023/11/09 00:42
MHDA- 2023/11/09 00:42
CRDT- 2023/11/08 23:26
PHST- 2023/07/10 00:00 [received]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/11/09 00:42 [medline]
PHST- 2023/11/09 00:42 [pubmed]
PHST- 2023/11/08 23:26 [entrez]
AID - 10.1007/s11606-023-08469-w [pii]
AID - 10.1007/s11606-023-08469-w [doi]
PST - aheadofprint
SO  - J Gen Intern Med. 2023 Nov 8. doi: 10.1007/s11606-023-08469-w.

PMID- 37725017
OWN - NLM
STAT- Publisher
LR  - 20230919
IS  - 1097-6760 (Electronic)
IS  - 0196-0644 (Linking)
DP  - 2023 Sep 19
TI  - ChatGPT Versus Human Performance on Emergency Medicine Board Preparation 
      Questions.
LID - S0196-0644(23)00663-7 [pii]
LID - 10.1016/j.annemergmed.2023.08.010 [doi]
FAU - Jarou, Zachary J
AU  - Jarou ZJ
AD  - Department of Emergency Medicine, Ascension Providence Hospital, Southfield, MI; 
      Department of Emergency Medicine, Michigan State University, East Lansing, MI. 
      Electronic address: jarouzac@msu.edu.
FAU - Dakka, Ali
AU  - Dakka A
AD  - Department of Emergency Medicine, Ascension Providence Hospital, Southfield, MI; 
      Department of Emergency Medicine, Michigan State University, East Lansing, MI.
FAU - McGuire, Duncan
AU  - McGuire D
AD  - Department of Emergency Medicine, Ascension Providence Hospital, Southfield, MI; 
      Department of Emergency Medicine, Michigan State University, East Lansing, MI.
FAU - Bunting, Leonard
AU  - Bunting L
AD  - Department of Emergency Medicine, Ascension Providence Hospital, Southfield, MI; 
      Department of Emergency Medicine, Michigan State University, East Lansing, MI.
LA  - eng
PT  - Journal Article
DEP - 20230919
PL  - United States
TA  - Ann Emerg Med
JT  - Annals of emergency medicine
JID - 8002646
SB  - IM
EDAT- 2023/09/19 17:43
MHDA- 2023/09/19 17:43
CRDT- 2023/09/19 10:28
PHST- 2023/06/06 00:00 [received]
PHST- 2023/08/04 00:00 [revised]
PHST- 2023/08/08 00:00 [accepted]
PHST- 2023/09/19 17:43 [medline]
PHST- 2023/09/19 17:43 [pubmed]
PHST- 2023/09/19 10:28 [entrez]
AID - S0196-0644(23)00663-7 [pii]
AID - 10.1016/j.annemergmed.2023.08.010 [doi]
PST - aheadofprint
SO  - Ann Emerg Med. 2023 Sep 19:S0196-0644(23)00663-7. doi: 
      10.1016/j.annemergmed.2023.08.010.

PMID- 37649106
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231121
IS  - 2524-3489 (Electronic)
IS  - 2524-3489 (Linking)
VI  - 5
IP  - 1
DP  - 2023 Aug 31
TI  - Can ChatGPT explain it? Use of artificial intelligence in multiple sclerosis 
      communication.
PG  - 48
LID - 10.1186/s42466-023-00270-8 [doi]
LID - 48
FAU - Inojosa, Hernan
AU  - Inojosa H
AD  - Center of Clinical Neuroscience, Department of Neurology, University Hospital 
      Carl Gustav Carus, Technische Univesität Dresden, Fetscherstr. 74, 01307, 
      Dresden, Germany.
FAU - Gilbert, Stephen
AU  - Gilbert S
AD  - Else Kröner-Fresenius Center for Digital Health, Faculty of Medicine Carl Gustav 
      Carus, Technische Universität Dresden, Dresden, Germany.
FAU - Kather, Jakob Nikolas
AU  - Kather JN
AD  - Else Kröner-Fresenius Center for Digital Health, Faculty of Medicine Carl Gustav 
      Carus, Technische Universität Dresden, Dresden, Germany.
FAU - Proschmann, Undine
AU  - Proschmann U
AD  - Center of Clinical Neuroscience, Department of Neurology, University Hospital 
      Carl Gustav Carus, Technische Univesität Dresden, Fetscherstr. 74, 01307, 
      Dresden, Germany.
FAU - Akgün, Katja
AU  - Akgün K
AD  - Center of Clinical Neuroscience, Department of Neurology, University Hospital 
      Carl Gustav Carus, Technische Univesität Dresden, Fetscherstr. 74, 01307, 
      Dresden, Germany.
FAU - Ziemssen, Tjalf
AU  - Ziemssen T
AUID- ORCID: 0000-0001-8799-8202
AD  - Center of Clinical Neuroscience, Department of Neurology, University Hospital 
      Carl Gustav Carus, Technische Univesität Dresden, Fetscherstr. 74, 01307, 
      Dresden, Germany. tjalf.ziemssen@uniklinikum-dresden.de.
LA  - eng
PT  - Letter
DEP - 20230831
PL  - England
TA  - Neurol Res Pract
JT  - Neurological research and practice
JID - 101767802
PMC - PMC10469796
COIS- HI received speaker honoraria from Roche and financial support for research 
      activities from Teva, Biogen and Alexion. SG has or has had consulting 
      relationships with Una Health GmbH, Lindus Health Ltd.; Flo Health Ltd, and 
      Thymia Ltd., FORUM Institut für Management GmbH, High-Tech Gründerfonds 
      Management GmbH, Ada Health GmbH and holds share options in Ada Health GmbH. JNK 
      reports consulting services for Owkin, France, Panakeia, UK and DoMore 
      Diagnostics, Norway and has received honoraria for lectures by MSD, Eisai and 
      Fresenius. UP received personal compensation from Bayer, Biogen and Roche for the 
      consulting service. KA received personal compensation from Novartis, Biogen Idec, 
      Teva, Sanofi and Roche for the consulting service. TZ reports scientific advisory 
      board and/or consulting for Biogen, Roche, Novartis, Celgene, and Merck; 
      compensation for serving on speakers bureaus for Roche, Novartis, Merck, Sanofi, 
      Celgene, and Biogen; research support from Biogen, Novartis, Merck, and Sanofi.
EDAT- 2023/08/31 00:41
MHDA- 2023/08/31 00:42
CRDT- 2023/08/30 23:46
PHST- 2023/06/06 00:00 [received]
PHST- 2023/07/20 00:00 [accepted]
PHST- 2023/08/31 00:42 [medline]
PHST- 2023/08/31 00:41 [pubmed]
PHST- 2023/08/30 23:46 [entrez]
AID - 10.1186/s42466-023-00270-8 [pii]
AID - 270 [pii]
AID - 10.1186/s42466-023-00270-8 [doi]
PST - epublish
SO  - Neurol Res Pract. 2023 Aug 31;5(1):48. doi: 10.1186/s42466-023-00270-8.

PMID- 37583313
OWN - NLM
STAT- Publisher
LR  - 20230816
IS  - 1748-5460 (Electronic)
IS  - 0022-2151 (Linking)
DP  - 2023 Aug 16
TI  - The role of ChatGPT in Enhancing ENT Surgical Training - A trainees' perspective.
PG  - 1-22
LID - 10.1017/S0022215123001354 [doi]
FAU - Brennan, Laura J
AU  - Brennan LJ
AUID- ORCID: 0009-0007-4722-0747
AD  - St Michaels Hospital ENT department, Upper Maudlin Street, BS2 
      8HW l.j.brennan94@gmail.com.
FAU - Balakumar, Ramkishan
AU  - Balakumar R
AD  - Department of ENT, St Michaels Hospital, UHBW rambala86@doctors.org.uk.
FAU - Bennett, Warren O
AU  - Bennett WO
AD  - Department of ENT, St Michaels Hospital, UHBW Warren.bennett@uhbw.nhs.uk.
LA  - eng
PT  - Journal Article
DEP - 20230816
PL  - England
TA  - J Laryngol Otol
JT  - The Journal of laryngology and otology
JID - 8706896
SB  - IM
EDAT- 2023/08/16 06:43
MHDA- 2023/08/16 06:43
CRDT- 2023/08/16 03:09
PHST- 2023/08/16 06:43 [medline]
PHST- 2023/08/16 06:43 [pubmed]
PHST- 2023/08/16 03:09 [entrez]
AID - S0022215123001354 [pii]
AID - 10.1017/S0022215123001354 [doi]
PST - aheadofprint
SO  - J Laryngol Otol. 2023 Aug 16:1-22. doi: 10.1017/S0022215123001354.

PMID- 37254254
OWN - NLM
STAT- MEDLINE
DCOM- 20230817
LR  - 20230907
IS  - 1533-3450 (Electronic)
IS  - 1046-6673 (Print)
IS  - 1046-6673 (Linking)
VI  - 34
IP  - 8
DP  - 2023 Aug 1
TI  - Retrieve, Summarize, and Verify: How Will ChatGPT Affect Information Seeking from 
      the Medical Literature?
PG  - 1302-1304
LID - 10.1681/ASN.0000000000000166 [doi]
FAU - Jin, Qiao
AU  - Jin Q
AUID- ORCID: 0000-0002-1268-7239
AD  - National Center for Biotechnology Information (NCBI), National Library of 
      Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland.
FAU - Leaman, Robert
AU  - Leaman R
AUID- ORCID: 0000-0003-3296-5766
FAU - Lu, Zhiyong
AU  - Lu Z
AUID- ORCID: 0000-0001-9998-916
LA  - eng
PT  - Journal Article
PT  - Research Support, N.I.H., Intramural
DEP - 20230531
PL  - United States
TA  - J Am Soc Nephrol
JT  - Journal of the American Society of Nephrology : JASN
JID - 9013836
SB  - IM
MH  - *Information Seeking Behavior
MH  - Artificial Intelligence
PMC - PMC10400098
COIS- All authors have nothing to disclose.
EDAT- 2023/05/31 06:41
MHDA- 2023/08/17 06:43
PMCR- 2024/08/01
CRDT- 2023/05/31 00:03
PHST- 2023/03/29 00:00 [received]
PHST- 2023/05/24 00:00 [accepted]
PHST- 2024/08/01 00:00 [pmc-release]
PHST- 2023/08/17 06:43 [medline]
PHST- 2023/05/31 06:41 [pubmed]
PHST- 2023/05/31 00:03 [entrez]
AID - 00001751-202308000-00004 [pii]
AID - JASN-2023-000435 [pii]
AID - 10.1681/ASN.0000000000000166 [doi]
PST - ppublish
SO  - J Am Soc Nephrol. 2023 Aug 1;34(8):1302-1304. doi: 10.1681/ASN.0000000000000166. 
      Epub 2023 May 31.

PMID- 37138372
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231030
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
VI  - 74
IP  - 4
DP  - 2023 Nov
TI  - Watching Innovation in Real Time: The Story of ChatGPT and Radiology.
PG  - 622-623
LID - 10.1177/08465371231174817 [doi]
FAU - Fishman, Elliot K
AU  - Fishman EK
AUID- ORCID: 0000-0002-2567-1658
AD  - The Russel H. Morgan Department of Radiology and Radiological Science, Johns 
      Hopkins University School of Medicine, Baltimore, MD, USA.
FAU - Weeks, William B
AU  - Weeks WB
AD  - Microsoft AI for Good Research Lab, Microsoft, Inc, Redmond, WA, USA.
FAU - Lavista Ferres, Juan M
AU  - Lavista Ferres JM
AD  - Microsoft AI for Good Research Lab, Microsoft, Inc, Redmond, WA, USA.
FAU - Chu, Linda C
AU  - Chu LC
AUID- ORCID: 0000-0001-9729-2756
AD  - The Russel H. Morgan Department of Radiology and Radiological Science, Johns 
      Hopkins University School of Medicine, Baltimore, MD, USA.
LA  - eng
PT  - Editorial
DEP - 20230503
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
MH  - Humans
MH  - Radiography
MH  - *Radiology
COIS- Declaration of Conflicting InterestsThe author(s) declared the following 
      potential conflicts of interest with respect to the research, authorship, and/or 
      publication of this article: William B. Weeks and Juan M. Lavista Ferres are 
      employees of Microsoft Inc. Elliot K. Fishman and Linda C. Chu have no relevant 
      conflicts of interest.
EDAT- 2023/05/04 00:42
MHDA- 2023/10/30 06:47
CRDT- 2023/05/03 23:52
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/05/04 00:42 [pubmed]
PHST- 2023/05/03 23:52 [entrez]
AID - 10.1177/08465371231174817 [doi]
PST - ppublish
SO  - Can Assoc Radiol J. 2023 Nov;74(4):622-623. doi: 10.1177/08465371231174817. Epub 
      2023 May 3.

PMID- 37036209
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20230906
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 3
DP  - 2023 Aug 28
TI  - ChatGPT, public health communication and 'intelligent patient companionship'.
PG  - e590
LID - 10.1093/pubmed/fdad028 [doi]
FAU - Kahambing, Jan Gresil
AU  - Kahambing JG
AUID- ORCID: 0000-0002-4258-0563
AD  - Department of Philosophy and Religious Studies, University of Macau, SAR China.
LA  - eng
PT  - Letter
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - *Health Communication
MH  - Interpersonal Relations
MH  - Attitude of Health Personnel
EDAT- 2023/04/11 06:00
MHDA- 2023/09/01 06:42
CRDT- 2023/04/10 09:04
PHST- 2023/02/21 00:00 [received]
PHST- 2023/09/01 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 09:04 [entrez]
AID - 7111147 [pii]
AID - 10.1093/pubmed/fdad028 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Aug 28;45(3):e590. doi: 10.1093/pubmed/fdad028.

PMID- 36869942
OWN - NLM
STAT- MEDLINE
DCOM- 20230307
LR  - 20230713
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Mar 4
TI  - Artificial Intelligence in Medicine & ChatGPT: De-Tether the Physician.
PG  - 32
LID - 10.1007/s10916-023-01926-3 [doi]
FAU - DiGiorgio, Anthony M
AU  - DiGiorgio AM
AD  - Department of Neurological Surgery & Institute for Health Policy Studies, 
      University of California, San Francisco, 1001 Potrero Ave, Building 1, Room 101, 
      Box 0899, San Francisco, CA, 94143, USA. Anthony.digiorgio@ucsf.edu.
FAU - Ehrenfeld, Jesse M
AU  - Ehrenfeld JM
AD  - Advancing a Healthier Wisconsin Endowment, Medical College of Wisconsin, 8701 W 
      Watertown Plank Rd, Wauwatosa, WI, 53226, USA.
LA  - eng
PT  - Editorial
DEP - 20230304
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
CIN - J Med Syst. 2023 Jul 11;47(1):72. PMID: 37432492
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Physicians
EDAT- 2023/03/05 06:00
MHDA- 2023/03/08 06:00
CRDT- 2023/03/04 11:15
PHST- 2023/03/04 11:15 [entrez]
PHST- 2023/03/05 06:00 [pubmed]
PHST- 2023/03/08 06:00 [medline]
AID - 10.1007/s10916-023-01926-3 [pii]
AID - 10.1007/s10916-023-01926-3 [doi]
PST - epublish
SO  - J Med Syst. 2023 Mar 4;47(1):32. doi: 10.1007/s10916-023-01926-3.

PMID- 37707517
OWN - NLM
STAT- Publisher
LR  - 20230914
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Sep 14
TI  - The use of ChatGPT in medical research: do we need a reporting guideline?
LID - 10.1097/JS9.0000000000000737 [doi]
FAU - Luo, Xufei
AU  - Luo X
AD  - Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou 
      University, Lanzhou 730000, China.
AD  - WHO Collaborating Centre for Guideline Implementation and Knowledge Translation, 
      Lanzhou 730000, China.
AD  - Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of 
      Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou 
      University, Lanzhou 730000, China.
AD  - Institute of Health Data Science, Lanzhou University, Lanzhou 730000, China.
FAU - Estill, Janne
AU  - Estill J
AD  - Institute of Global Health, University of Geneva, Geneve, Switzerland.
FAU - Chen, Yaolong
AU  - Chen Y
AD  - Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou 
      University, Lanzhou 730000, China.
AD  - WHO Collaborating Centre for Guideline Implementation and Knowledge Translation, 
      Lanzhou 730000, China.
AD  - Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of 
      Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou 
      University, Lanzhou 730000, China.
AD  - Institute of Health Data Science, Lanzhou University, Lanzhou 730000, China.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/09/14 12:41
MHDA- 2023/09/14 12:41
CRDT- 2023/09/14 11:05
PHST- 2023/08/21 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/09/14 12:41 [medline]
PHST- 2023/09/14 12:41 [pubmed]
PHST- 2023/09/14 11:05 [entrez]
AID - 01279778-990000000-00617 [pii]
AID - 10.1097/JS9.0000000000000737 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Sep 14. doi: 10.1097/JS9.0000000000000737.

PMID- 37699445
OWN - NLM
STAT- MEDLINE
DCOM- 20231121
LR  - 20231121
IS  - 1538-2990 (Electronic)
IS  - 0002-9629 (Linking)
VI  - 366
IP  - 6
DP  - 2023 Dec
TI  - Revolutionizing clinical experimental protocol design through the ChatGPT 
      technology.
PG  - 468-470
LID - S0002-9629(23)01329-0 [pii]
LID - 10.1016/j.amjms.2023.09.004 [doi]
FAU - Wei, Yanhui
AU  - Wei Y
AD  - School of Medicine, Southeast University, Nanjing, China.
FAU - Guo, Xuejun
AU  - Guo X
AD  - Department of Haematology, Puyang Oilfield General Hospital Affiliated with 
      Xinxiang Medical University, Puyang, China; Puyang Translational Medicine 
      Engineering and Technology Research Center, Puyang, China. Electronic address: 
      pygxj@163.com.
LA  - eng
PT  - Letter
DEP - 20230910
PL  - United States
TA  - Am J Med Sci
JT  - The American journal of the medical sciences
JID - 0370506
SB  - IM
MH  - Humans
MH  - *Research Design
MH  - *Artificial Intelligence
COIS- Declaration of Competing Interests All the authors declare no conflicts of 
      interest.
EDAT- 2023/09/13 00:42
MHDA- 2023/11/06 06:42
CRDT- 2023/09/12 19:10
PHST- 2023/07/13 00:00 [received]
PHST- 2023/09/01 00:00 [revised]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/11/06 06:42 [medline]
PHST- 2023/09/13 00:42 [pubmed]
PHST- 2023/09/12 19:10 [entrez]
AID - S0002-9629(23)01329-0 [pii]
AID - 10.1016/j.amjms.2023.09.004 [doi]
PST - ppublish
SO  - Am J Med Sci. 2023 Dec;366(6):468-470. doi: 10.1016/j.amjms.2023.09.004. Epub 
      2023 Sep 10.

PMID- 37582902
OWN - NLM
STAT- Publisher
LR  - 20230816
IS  - 1476-5438 (Electronic)
IS  - 1018-4813 (Linking)
DP  - 2023 Aug 15
TI  - Beyond the hype: a comprehensive critique of ChatGPT's applicability in genetics.
LID - 10.1038/s41431-023-01442-5 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. ppray@cus.ac.in.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, BF Block, Sector 1, 
      Bidhannagar, Kolkata, West Bengal, 700064, India.
LA  - eng
PT  - Letter
DEP - 20230815
PL  - England
TA  - Eur J Hum Genet
JT  - European journal of human genetics : EJHG
JID - 9302235
SB  - IM
EDAT- 2023/08/16 00:42
MHDA- 2023/08/16 00:42
CRDT- 2023/08/15 23:31
PHST- 2023/07/08 00:00 [received]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/08/16 00:42 [pubmed]
PHST- 2023/08/16 00:42 [medline]
PHST- 2023/08/15 23:31 [entrez]
AID - 10.1038/s41431-023-01442-5 [pii]
AID - 10.1038/s41431-023-01442-5 [doi]
PST - aheadofprint
SO  - Eur J Hum Genet. 2023 Aug 15. doi: 10.1038/s41431-023-01442-5.

PMID- 37566150
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231107
IS  - 1432-2161 (Electronic)
IS  - 0364-2348 (Linking)
VI  - 52
IP  - 12
DP  - 2023 Dec
TI  - ChatGPT-generated articles and human-written articles: correspondence.
PG  - 2493
LID - 10.1007/s00256-023-04417-1 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - , Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Dr DY Patil Vidhyapeeth, Pune, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230811
PL  - Germany
TA  - Skeletal Radiol
JT  - Skeletal radiology
JID - 7701953
SB  - IM
CON - Skeletal Radiol. 2023 Sep;52(9):1755-1758. PMID: 37059827
EDAT- 2023/08/11 12:42
MHDA- 2023/08/11 12:43
CRDT- 2023/08/11 11:06
PHST- 2023/07/20 00:00 [received]
PHST- 2023/07/24 00:00 [accepted]
PHST- 2023/07/20 00:00 [revised]
PHST- 2023/08/11 12:43 [medline]
PHST- 2023/08/11 12:42 [pubmed]
PHST- 2023/08/11 11:06 [entrez]
AID - 10.1007/s00256-023-04417-1 [pii]
AID - 10.1007/s00256-023-04417-1 [doi]
PST - ppublish
SO  - Skeletal Radiol. 2023 Dec;52(12):2493. doi: 10.1007/s00256-023-04417-1. Epub 2023 
      Aug 11.

PMID- 37534222
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230804
IS  - 1611-2156 (Print)
IS  - 1611-2156 (Electronic)
IS  - 1611-2156 (Linking)
VI  - 22
DP  - 2023
TI  - With great power comes great responsibility - deductions of using ChatGPT for 
      medical research.
PG  - 499-501
LID - 10.17179/excli2023-6163 [doi]
FAU - Mahmood, Samar
AU  - Mahmood S
AD  - Department of Internal Medicine, Dow University of Health Sciences, Baba-e-Urdu 
      Road, Karachi, Pakistan.
FAU - Khan, Mohammad Omer
AU  - Khan MO
AD  - Department of Internal Medicine, Dow University of Health Sciences, Baba-e-Urdu 
      Road, Karachi, Pakistan.
FAU - Hameed, Ishaque
AU  - Hameed I
AD  - Department of Internal Medicine, Dow University of Health Sciences, Baba-e-Urdu 
      Road, Karachi, Pakistan.
LA  - eng
PT  - Journal Article
DEP - 20230612
PL  - Germany
TA  - EXCLI J
JT  - EXCLI journal
JID - 101299402
PMC - PMC10390895
COIS- None.
EDAT- 2023/08/03 06:43
MHDA- 2023/08/03 06:44
CRDT- 2023/08/03 04:24
PHST- 2023/05/10 00:00 [received]
PHST- 2023/06/06 00:00 [accepted]
PHST- 2023/08/03 06:44 [medline]
PHST- 2023/08/03 06:43 [pubmed]
PHST- 2023/08/03 04:24 [entrez]
AID - 2023-6163 [pii]
AID - Doc499 [pii]
AID - 10.17179/excli2023-6163 [doi]
PST - epublish
SO  - EXCLI J. 2023 Jun 12;22:499-501. doi: 10.17179/excli2023-6163. eCollection 2023.

PMID- 37462431
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Linking)
VI  - 39
IP  - 5
DP  - 2023 Sep-Oct 01
TI  - Reply Re: "ChatGPT and Lacrimal Drainage Disorders: Performance and Scope of 
      Improvement".
PG  - 515
LID - 10.1097/IOP.0000000000002476 [doi]
FAU - Ali, Mohammad Javed
AU  - Ali MJ
LA  - eng
PT  - Journal Article
DEP - 20230718
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
SB  - IM
MH  - Humans
MH  - *Lacrimal Apparatus Diseases/surgery
MH  - *Lacrimal Apparatus
COIS- The author has no conflicts of interest to disclose.
EDAT- 2023/07/18 13:08
MHDA- 2023/07/18 13:09
CRDT- 2023/07/18 09:33
PHST- 2023/07/18 13:09 [medline]
PHST- 2023/07/18 13:08 [pubmed]
PHST- 2023/07/18 09:33 [entrez]
AID - 00002341-990000000-00232 [pii]
AID - 10.1097/IOP.0000000000002476 [doi]
PST - ppublish
SO  - Ophthalmic Plast Reconstr Surg. 2023 Sep-Oct 01;39(5):515. doi: 
      10.1097/IOP.0000000000002476. Epub 2023 Jul 18.

PMID- 37433941
OWN - NLM
STAT- Publisher
LR  - 20230725
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Jul 7
TI  - Daily briefing: The pros and cons of writing a paper with ChatGPT.
LID - 10.1038/d41586-023-02264-7 [doi]
FAU - Graham, Flora
AU  - Graham F
LA  - eng
PT  - News
DEP - 20230707
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
EDAT- 2023/07/12 01:07
MHDA- 2023/07/12 01:07
CRDT- 2023/07/11 23:29
PHST- 2023/07/12 01:07 [pubmed]
PHST- 2023/07/12 01:07 [medline]
PHST- 2023/07/11 23:29 [entrez]
AID - 10.1038/d41586-023-02264-7 [pii]
AID - 10.1038/d41586-023-02264-7 [doi]
PST - aheadofprint
SO  - Nature. 2023 Jul 7. doi: 10.1038/d41586-023-02264-7.

PMID- 37406204
OWN - NLM
STAT- MEDLINE
DCOM- 20230824
LR  - 20230824
IS  - 1546-3141 (Electronic)
IS  - 0361-803X (Linking)
VI  - 221
IP  - 3
DP  - 2023 Sep
TI  - Reply to "Addressing Concerns and Pitfalls in ChatGPT-Driven Radiology 
      Reporting".
PG  - 403-404
LID - 10.2214/AJR.23.29612 [doi]
FAU - Elkassem, Asser Abou
AU  - Elkassem AA
AD  - University of Alabama at Birmingham, Birmingham, AL.
FAU - Smith, Andrew D
AU  - Smith AD
AD  - University of Alabama at Birmingham, Birmingham, AL, andrewdennissmith@uabmc.edu.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230705
PL  - United States
TA  - AJR Am J Roentgenol
JT  - AJR. American journal of roentgenology
JID - 7708173
SB  - IM
CON - AJR Am J Roentgenol. 2023 Sep;221(3):403. PMID: 37406203
MH  - Humans
MH  - *Radiology
EDAT- 2023/07/05 19:12
MHDA- 2023/08/24 06:42
CRDT- 2023/07/05 15:43
PHST- 2023/08/24 06:42 [medline]
PHST- 2023/07/05 19:12 [pubmed]
PHST- 2023/07/05 15:43 [entrez]
AID - 10.2214/AJR.23.29612 [doi]
PST - ppublish
SO  - AJR Am J Roentgenol. 2023 Sep;221(3):403-404. doi: 10.2214/AJR.23.29612. Epub 
      2023 Jul 5.

PMID- 37348135
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1538-9855 (Electronic)
IS  - 0363-3624 (Linking)
VI  - 48
IP  - 6
DP  - 2023 Nov-Dec 01
TI  - Critical Reflection on Using ChatGPT in Student Learning: Benefits or Potential 
      Risks?
PG  - E200-E201
LID - 10.1097/NNE.0000000000001476 [doi]
FAU - Chan, Maggie Mee Kie
AU  - Chan MMK
AD  - Senior Lecturer (Dr Chan), Master of Nursing Students (Mr Wong and Ms Yau), and 
      Principal Lecturer (Dr Lam), School of Nursing, LKS Faculty of Medicine, The 
      University of Hong Kong, Hong Kong, China.
FAU - Wong, Isaac Shing Fun
AU  - Wong ISF
FAU - Yau, Sui Ying
AU  - Yau SY
FAU - Lam, Veronica Suk Fun
AU  - Lam VSF
LA  - eng
PT  - Journal Article
DEP - 20230620
PL  - United States
TA  - Nurse Educ
JT  - Nurse educator
JID - 7701902
MH  - Humans
MH  - Nursing Education Research
MH  - *Students
COIS- The authors declare no conflicts of interest.
EDAT- 2023/06/22 19:15
MHDA- 2023/06/22 19:16
CRDT- 2023/06/22 17:43
PHST- 2023/06/22 19:16 [medline]
PHST- 2023/06/22 19:15 [pubmed]
PHST- 2023/06/22 17:43 [entrez]
AID - 00006223-990000000-00273 [pii]
AID - 10.1097/NNE.0000000000001476 [doi]
PST - ppublish
SO  - Nurse Educ. 2023 Nov-Dec 01;48(6):E200-E201. doi: 10.1097/NNE.0000000000001476. 
      Epub 2023 Jun 20.

PMID- 37227006
OWN - NLM
STAT- MEDLINE
DCOM- 20230821
LR  - 20231116
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 9
DP  - 2023 Aug 17
TI  - Preservation of Human Creativity in Plastic Surgery Research on ChatGPT.
PG  - NP726-NP727
LID - 10.1093/asj/sjad162 [doi]
FAU - Abi-Rafeh, Jad
AU  - Abi-Rafeh J
AUID- ORCID: 0000-0002-7483-1515
FAU - Xu, Hong Hao
AU  - Xu HH
FAU - Kazan, Roy
AU  - Kazan R
LA  - eng
PT  - Comment
PT  - Letter
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CON - Aesthet Surg J. 2023 Jun 14;43(7):NP587-NP590. PMID: 36840479
CON - Aesthet Surg J. 2023 Jul 15;43(8):930-937. PMID: 36943815
CON - J Plast Reconstr Aesthet Surg. 2023 May;80:145-147. PMID: 37023599
CON - Aesthet Surg J. 2023 Jul 15;43(8):NP666-NP669. PMID: 37148287
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
EDAT- 2023/05/25 13:06
MHDA- 2023/08/21 06:42
CRDT- 2023/05/25 07:22
PHST- 2023/08/21 06:42 [medline]
PHST- 2023/05/25 13:06 [pubmed]
PHST- 2023/05/25 07:22 [entrez]
AID - 7179536 [pii]
AID - 10.1093/asj/sjad162 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Aug 17;43(9):NP726-NP727. doi: 10.1093/asj/sjad162.

PMID- 36945119
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230626
LR  - 20230626
IS  - 1744-5205 (Electronic)
IS  - 0882-0538 (Linking)
VI  - 38
IP  - 5
DP  - 2023 Jul
TI  - Readership Awareness Series - Paper 4: Chatbots and ChatGPT - Ethical 
      Considerations in Scientific Publications.
PG  - 403-404
LID - 10.1080/08820538.2023.2193444 [doi]
FAU - Ali, Mohammad Javed
AU  - Ali MJ
AD  - Editor-in-Chief, Seminars in Ophthalmology.
FAU - Djalilian, Ali
AU  - Djalilian A
AD  - Editor-in-Chief, Ocular Surface.
LA  - eng
PT  - Editorial
DEP - 20230321
PL  - England
TA  - Semin Ophthalmol
JT  - Seminars in ophthalmology
JID - 8610759
SB  - IM
EDAT- 2023/03/23 06:00
MHDA- 2023/03/23 06:01
CRDT- 2023/03/22 00:53
PHST- 2023/03/23 06:01 [medline]
PHST- 2023/03/23 06:00 [pubmed]
PHST- 2023/03/22 00:53 [entrez]
AID - 10.1080/08820538.2023.2193444 [doi]
PST - ppublish
SO  - Semin Ophthalmol. 2023 Jul;38(5):403-404. doi: 10.1080/08820538.2023.2193444. 
      Epub 2023 Mar 21.

PMID- 37462415
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Linking)
VI  - 39
IP  - 5
DP  - 2023 Sep-Oct 01
TI  - Reply Re: "ChatGPT and Lacrimal Drainage Disorders: Performance and Scope of 
      Improvement".
PG  - 513-514
LID - 10.1097/IOP.0000000000002477 [doi]
FAU - Ali, Mohammad Javed
AU  - Ali MJ
LA  - eng
PT  - Journal Article
DEP - 20230718
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
SB  - IM
MH  - Humans
MH  - *Lacrimal Apparatus Diseases/surgery
MH  - *Lacrimal Apparatus
COIS- The author has no financial or conflicts of interest to disclose.
EDAT- 2023/07/18 13:07
MHDA- 2023/07/18 13:08
CRDT- 2023/07/18 09:32
PHST- 2023/07/18 13:08 [medline]
PHST- 2023/07/18 13:07 [pubmed]
PHST- 2023/07/18 09:32 [entrez]
AID - 00002341-990000000-00233 [pii]
AID - 10.1097/IOP.0000000000002477 [doi]
PST - ppublish
SO  - Ophthalmic Plast Reconstr Surg. 2023 Sep-Oct 01;39(5):513-514. doi: 
      10.1097/IOP.0000000000002477. Epub 2023 Jul 18.

PMID- 37338357
OWN - NLM
STAT- MEDLINE
DCOM- 20230623
LR  - 20230623
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 5
DP  - 2023 Jun
TI  - Response to Performance of ChatGPT on a Radiology Board-style Examination.
PG  - e231330
LID - 10.1148/radiol.231330 [doi]
FAU - Ramasamy, Shakthi Kumaran
AU  - Ramasamy SK
AUID- ORCID: 0000-0002-7474-2846
AD  - Stanford University School of Medicine Ringgold standard institution - Radiology 
      Lokey Stem Cell Research Building, 265 Campus Drive, Room G2045 Stanford, 
      California 94305.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CON - Radiology. 2023 Jun;307(5):e230582. PMID: 37191485
MH  - Humans
MH  - *Radiology
MH  - *Artificial Intelligence
EDAT- 2023/06/20 13:10
MHDA- 2023/06/22 06:41
CRDT- 2023/06/20 10:03
PHST- 2023/06/22 06:41 [medline]
PHST- 2023/06/20 13:10 [pubmed]
PHST- 2023/06/20 10:03 [entrez]
AID - 10.1148/radiol.231330 [doi]
PST - ppublish
SO  - Radiology. 2023 Jun;307(5):e231330. doi: 10.1148/radiol.231330.

PMID- 37226559
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 11
DP  - 2023 Oct 13
TI  - Commentary on: Unleashing the Power of ChatGPT: Revolutionizing Plastic Surgery 
      and Beyond.
PG  - 1400-1401
LID - 10.1093/asj/sjad161 [doi]
FAU - Gupta, Rohun
AU  - Gupta R
AUID- ORCID: 0000-0003-1491-2441
FAU - Lu, Nathan
AU  - Lu N
FAU - Firouzbakht, Peter K
AU  - Firouzbakht PK
FAU - Mailey, Brian A
AU  - Mailey BA
LA  - eng
PT  - Comment
PT  - Editorial
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CON - Aesthet Surg J. 2023 Oct 13;43(11):1395-1399. PMID: 37154803
EDAT- 2023/05/25 06:42
MHDA- 2023/05/25 06:42
CRDT- 2023/05/25 04:13
PHST- 2023/05/25 06:42 [pubmed]
PHST- 2023/05/25 06:42 [medline]
PHST- 2023/05/25 04:13 [entrez]
AID - 7179441 [pii]
AID - 10.1093/asj/sjad161 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Oct 13;43(11):1400-1401. doi: 10.1093/asj/sjad161.

PMID- 37180295
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230920
IS  - 2345-4644 (Print)
IS  - 2345-461X (Electronic)
IS  - 2345-461X (Linking)
VI  - 11
IP  - 4
DP  - 2023
TI  - ChatGPT in Medicine; a Disruptive Innovation or Just One Step Forward?
PG  - 225-226
LID - 10.22038/abjs.2023.22042 [doi]
FAU - Parsa, Ali
AU  - Parsa A
AD  - Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad university 
      of Medical Sciences, Mashhad, Iran.
AD  - American Hip Institute, Chicago, US.
FAU - Ebrahimzadeh, Mohammad H
AU  - Ebrahimzadeh MH
AD  - Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad university 
      of Medical Sciences, Mashhad, Iran.
LA  - eng
PT  - Editorial
PL  - Iran
TA  - Arch Bone Jt Surg
JT  - The archives of bone and joint surgery
JID - 101636743
PMC - PMC10167532
EDAT- 2023/05/14 19:13
MHDA- 2023/05/14 19:14
CRDT- 2023/05/14 12:03
PHST- 2023/05/14 19:13 [pubmed]
PHST- 2023/05/14 19:14 [medline]
PHST- 2023/05/14 12:03 [entrez]
AID - 10.22038/abjs.2023.22042 [doi]
PST - ppublish
SO  - Arch Bone Jt Surg. 2023;11(4):225-226. doi: 10.22038/abjs.2023.22042.

PMID- 37071797
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230717
LR  - 20230718
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 8
DP  - 2023 Jul 15
TI  - Response to: Truth or Lies? The Pitfalls and Limitations of ChatGPT in Systematic 
      Review Creation.
PG  - NP656-NP657
LID - 10.1093/asj/sjad108 [doi]
FAU - Gupta, Rohun
AU  - Gupta R
AUID- ORCID: 0000-0003-1491-2441
FAU - Bagdady, Kazimir
AU  - Bagdady K
FAU - Mailey, Brian A
AU  - Mailey BA
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
EDAT- 2023/04/18 18:41
MHDA- 2023/04/18 18:42
CRDT- 2023/04/18 15:14
PHST- 2023/04/18 18:42 [medline]
PHST- 2023/04/18 18:41 [pubmed]
PHST- 2023/04/18 15:14 [entrez]
AID - 7128030 [pii]
AID - 10.1093/asj/sjad108 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jul 15;43(8):NP656-NP657. doi: 10.1093/asj/sjad108.

PMID- 36728749
OWN - NLM
STAT- MEDLINE
DCOM- 20230412
LR  - 20230504
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 2
DP  - 2023 Apr
TI  - ChatGPT Is Shaping the Future of Medical Writing But Still Requires Human 
      Judgment.
PG  - e230171
LID - 10.1148/radiol.230171 [doi]
FAU - Kitamura, Felipe C
AU  - Kitamura FC
AUID- ORCID: 0000-0002-9992-5630
AD  - From the Department of Applied Innovation and AI, Diagnósticos da América SA 
      (Dasa), Av Das Nações Unidas, 7815 Pinheiros, São Paulo, SP, Brazil 05425-070; 
      and Department of Neuroradiology, Universidade Federal de São Paulo, São Paulo, 
      Brazil.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230202
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CIN - Radiology. 2023 Apr;307(2):e230163. PMID: 36700838
CON - Radiology. 2023 Apr;307(2):e230163. PMID: 36700838
CON - Radiology. 2023 Apr;307(2):e223312. PMID: 36728748
MH  - Humans
MH  - *Judgment
MH  - *Medical Writing
MH  - Language
EDAT- 2023/02/03 06:00
MHDA- 2023/04/12 06:42
CRDT- 2023/02/02 10:07
PHST- 2023/04/12 06:42 [medline]
PHST- 2023/02/03 06:00 [pubmed]
PHST- 2023/02/02 10:07 [entrez]
AID - 10.1148/radiol.230171 [doi]
PST - ppublish
SO  - Radiology. 2023 Apr;307(2):e230171. doi: 10.1148/radiol.230171. Epub 2023 Feb 2.

PMID- 36635510
OWN - NLM
STAT- MEDLINE
DCOM- 20230120
LR  - 20230228
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 613
IP  - 7944
DP  - 2023 Jan
TI  - Abstracts written by ChatGPT fool scientists.
PG  - 423
LID - 10.1038/d41586-023-00056-7 [doi]
FAU - Else, Holly
AU  - Else H
LA  - eng
PT  - Journal Article
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - *Machine Learning/standards
MH  - *Writing/standards
MH  - *Research Personnel
MH  - Authorship/standards
MH  - *Research Report/standards
OTO - NOTNLM
OT  - Machine learning
OT  - Mathematics and computing
OT  - Publishing
EDAT- 2023/01/13 06:00
MHDA- 2023/01/20 06:00
CRDT- 2023/01/12 23:26
PHST- 2023/01/13 06:00 [pubmed]
PHST- 2023/01/20 06:00 [medline]
PHST- 2023/01/12 23:26 [entrez]
AID - 10.1038/d41586-023-00056-7 [pii]
AID - 10.1038/d41586-023-00056-7 [doi]
PST - ppublish
SO  - Nature. 2023 Jan;613(7944):423. doi: 10.1038/d41586-023-00056-7.

PMID- 37780059
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231004
IS  - 2055-2076 (Print)
IS  - 2055-2076 (Electronic)
IS  - 2055-2076 (Linking)
VI  - 9
DP  - 2023 Jan-Dec
TI  - Changes in patient perceptions regarding ChatGPT-written explanations on 
      lifestyle modifications for preventing urolithiasis recurrence.
PG  - 20552076231203940
LID - 10.1177/20552076231203940 [doi]
LID - 20552076231203940
AB  - PURPOSE: Artificial Intelligence (AI) imitating human-like language, such as 
      ChatGPT, has impacted lives throughout various multidisciplinary fields. However, 
      despite these innovations, it is unclear how well its implementation will assist 
      patients in clinical situations. We evaluated changes in patient perceptions 
      regarding AI before and after reading a ChatGPT-written explanation. MATERIALS 
      AND METHODS: In total, 24 South Korean patients receiving urolithiasis treatment 
      were surveyed through questionnaires. The ChatGPT explanatory note was provided 
      between the first and second questionnaires, detailing lifestyle modifications 
      for preventing urolithiasis recurrence. The study questionnaire was the Korean 
      version of the General Attitudes toward Artificial Intelligence Scale, including 
      positive and negative attitude items. Wilcoxon signed-rank tests were 
      accomplished to compare questionnaire scores before and after receiving the 
      explanatory note. A linear regression analysis with stepwise elimination was used 
      to assess variable (demographic data) accuracy in predicting outcomes. RESULTS: 
      There were significant differences between total negative questionnaire scores 
      pre- and post-surveys of ChatGPT, but not in the positive scores. Among 
      variables, only education level significantly influenced mean score differences 
      in the negative questionnaires. CONCLUSIONS: The negative perception change among 
      urolithiasis patients after receiving the explanatory note provided by the AI 
      chatbot program was observed, evidencing that patients with lower education 
      levels expressed a more negative response. The explanatory note provided by the 
      AI chatbot program could provoke an adverse change in AI perception. Negative 
      human responses must be considered to improve and adapt new technology in health 
      care. Only through changing patient perspectives will upgraded AI technology 
      integrate into medical healthcare.
CI  - © The Author(s) 2023.
FAU - Kim, Seong Hwan
AU  - Kim SH
AD  - Department of Orthopedic Surgery, Chung-Ang University Hospital, Chung-Ang 
      University College of Medicine, Seoul, Republic of Korea. RINGGOLD: 26729. 
      RINGGOLD: 26729
FAU - Tae, Jong Hyun
AU  - Tae JH
AD  - Department of Urology, Chung-Ang University Hospital, Chung-Ang University 
      College of Medicine, Seoul, Republic of Korea. RINGGOLD: 26729. RINGGOLD: 26729
FAU - Chang, In Ho
AU  - Chang IH
AD  - Department of Urology, Chung-Ang University Hospital, Chung-Ang University 
      College of Medicine, Seoul, Republic of Korea. RINGGOLD: 26729. RINGGOLD: 26729
FAU - Kim, Tae-Hyoung
AU  - Kim TH
AD  - Department of Urology, Chung-Ang University Hospital, Chung-Ang University 
      College of Medicine, Seoul, Republic of Korea. RINGGOLD: 26729. RINGGOLD: 26729
FAU - Myung, Soon Chul
AU  - Myung SC
AD  - Department of Urology, Chung-Ang University Hospital, Chung-Ang University 
      College of Medicine, Seoul, Republic of Korea. RINGGOLD: 26729. RINGGOLD: 26729
FAU - Nguyen, Tuan Thanh
AU  - Nguyen TT
AD  - Department of Urology, Cho Ray Hospital, University of Medicine and Pharmacy, Ho 
      Chi Minh City, Vietnam. RINGGOLD: 58601
FAU - Choi, Joongwon
AU  - Choi J
AD  - Department of Urology, Chung-Ang University Gwangmyeong Hospital, Chung-Ang 
      University College of Medicine, Gyeonggi-do, Republic of Korea. RINGGOLD: 26729. 
      RINGGOLD: 26729
FAU - Kim, Jung Hoon
AU  - Kim JH
AD  - Department of Urology, Chung-Ang University Gwangmyeong Hospital, Chung-Ang 
      University College of Medicine, Gyeonggi-do, Republic of Korea. RINGGOLD: 26729. 
      RINGGOLD: 26729
FAU - Kim, Jin Wook
AU  - Kim JW
AD  - Department of Urology, Chung-Ang University Gwangmyeong Hospital, Chung-Ang 
      University College of Medicine, Gyeonggi-do, Republic of Korea. RINGGOLD: 26729. 
      RINGGOLD: 26729
FAU - Lee, Yong Seong
AU  - Lee YS
AD  - Department of Urology, Chung-Ang University Gwangmyeong Hospital, Chung-Ang 
      University College of Medicine, Gyeonggi-do, Republic of Korea. RINGGOLD: 26729. 
      RINGGOLD: 26729
FAU - Choi, Se Young
AU  - Choi SY
AUID- ORCID: 0000-0002-4615-0966
AD  - Department of Urology, Chung-Ang University Hospital, Chung-Ang University 
      College of Medicine, Seoul, Republic of Korea. RINGGOLD: 26729. RINGGOLD: 26729
LA  - eng
PT  - Journal Article
DEP - 20230928
PL  - United States
TA  - Digit Health
JT  - Digital health
JID - 101690863
PMC - PMC10540569
OTO - NOTNLM
OT  - Artificial intelligence
OT  - healthcare
OT  - language model
OT  - patient knowledge
OT  - ureterolithiasis
COIS- The author(s) declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/10/02 06:41
MHDA- 2023/10/02 06:42
CRDT- 2023/10/02 04:21
PHST- 2023/07/14 00:00 [received]
PHST- 2023/09/09 00:00 [accepted]
PHST- 2023/10/02 06:42 [medline]
PHST- 2023/10/02 06:41 [pubmed]
PHST- 2023/10/02 04:21 [entrez]
AID - 10.1177_20552076231203940 [pii]
AID - 10.1177/20552076231203940 [doi]
PST - epublish
SO  - Digit Health. 2023 Sep 28;9:20552076231203940. doi: 10.1177/20552076231203940. 
      eCollection 2023 Jan-Dec.

PMID- 37620342
OWN - NLM
STAT- MEDLINE
DCOM- 20230828
LR  - 20231121
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Aug 24
TI  - Perception, performance, and detectability of conversational artificial 
      intelligence across 32 university courses.
PG  - 12187
LID - 10.1038/s41598-023-38964-3 [doi]
LID - 12187
AB  - The emergence of large language models has led to the development of powerful 
      tools such as ChatGPT that can produce text indistinguishable from 
      human-generated work. With the increasing accessibility of such technology, 
      students across the globe may utilize it to help with their school work-a 
      possibility that has sparked ample discussion on the integrity of student 
      evaluation processes in the age of artificial intelligence (AI). To date, it is 
      unclear how such tools perform compared to students on university-level courses 
      across various disciplines. Further, students' perspectives regarding the use of 
      such tools in school work, and educators' perspectives on treating their use as 
      plagiarism, remain unknown. Here, we compare the performance of the 
      state-of-the-art tool, ChatGPT, against that of students on 32 university-level 
      courses. We also assess the degree to which its use can be detected by two 
      classifiers designed specifically for this purpose. Additionally, we conduct a 
      global survey across five countries, as well as a more in-depth survey at the 
      authors' institution, to discern students' and educators' perceptions of 
      ChatGPT's use in school work. We find that ChatGPT's performance is comparable, 
      if not superior, to that of students in a multitude of courses. Moreover, current 
      AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to 
      both their propensity to classify human-written answers as AI-generated, as well 
      as the relative ease with which AI-generated text can be edited to evade 
      detection. Finally, there seems to be an emerging consensus among students to use 
      the tool, and among educators to treat its use as plagiarism. Our findings offer 
      insights that could guide policy discussions addressing the integration of 
      artificial intelligence into educational frameworks.
CI  - © 2023. The Author(s).
FAU - Ibrahim, Hazem
AU  - Ibrahim H
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Liu, Fengyuan
AU  - Liu F
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Asim, Rohail
AU  - Asim R
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Battu, Balaraju
AU  - Battu B
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Benabderrahmane, Sidahmed
AU  - Benabderrahmane S
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Alhafni, Bashar
AU  - Alhafni B
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Adnan, Wifag
AU  - Adnan W
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Alhanai, Tuka
AU  - Alhanai T
AD  - Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - AlShebli, Bedoor
AU  - AlShebli B
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Baghdadi, Riyadh
AU  - Baghdadi R
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Bélanger, Jocelyn J
AU  - Bélanger JJ
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Beretta, Elena
AU  - Beretta E
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Celik, Kemal
AU  - Celik K
AD  - Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Chaqfeh, Moumena
AU  - Chaqfeh M
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Daqaq, Mohammed F
AU  - Daqaq MF
AD  - Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Bernoussi, Zaynab El
AU  - Bernoussi ZE
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Fougnie, Daryl
AU  - Fougnie D
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Garcia de Soto, Borja
AU  - Garcia de Soto B
AD  - Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Gandolfi, Alberto
AU  - Gandolfi A
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Gyorgy, Andras
AU  - Gyorgy A
AD  - Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Habash, Nizar
AU  - Habash N
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Harris, J Andrew
AU  - Harris JA
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Kaufman, Aaron
AU  - Kaufman A
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Kirousis, Lefteris
AU  - Kirousis L
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Kocak, Korhan
AU  - Kocak K
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Lee, Kangsan
AU  - Lee K
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Lee, Seungah S
AU  - Lee SS
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Malik, Samreen
AU  - Malik S
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Maniatakos, Michail
AU  - Maniatakos M
AD  - Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Melcher, David
AU  - Melcher D
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Mourad, Azzam
AU  - Mourad A
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Park, Minsu
AU  - Park M
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Rasras, Mahmoud
AU  - Rasras M
AD  - Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Reuben, Alicja
AU  - Reuben A
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Zantout, Dania
AU  - Zantout D
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Gleason, Nancy W
AU  - Gleason NW
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Makovi, Kinga
AU  - Makovi K
AD  - Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE.
FAU - Rahwan, Talal
AU  - Rahwan T
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE. 
      talal.rahwan@nyu.edu.
FAU - Zaki, Yasir
AU  - Zaki Y
AD  - Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE. 
      yasir.zaki@nyu.edu.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230824
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
EIN - Sci Rep. 2023 Oct 10;13(1):17101. PMID: 37816805
MH  - Humans
MH  - Universities
MH  - *Artificial Intelligence
MH  - *Communication
MH  - Schools
MH  - Perception
PMC - PMC10449897
COIS- The authors declare no competing interests.
EDAT- 2023/08/25 00:42
MHDA- 2023/08/28 07:16
CRDT- 2023/08/24 23:19
PHST- 2023/04/14 00:00 [received]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/08/28 07:16 [medline]
PHST- 2023/08/25 00:42 [pubmed]
PHST- 2023/08/24 23:19 [entrez]
AID - 10.1038/s41598-023-38964-3 [pii]
AID - 38964 [pii]
AID - 10.1038/s41598-023-38964-3 [doi]
PST - epublish
SO  - Sci Rep. 2023 Aug 24;13(1):12187. doi: 10.1038/s41598-023-38964-3.

PMID- 38046961
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231204
IS  - 0972-3919 (Print)
IS  - 0974-0244 (Electronic)
IS  - 0974-0244 (Linking)
VI  - 38
IP  - 3
DP  - 2023 Jul-Sep
TI  - ChatGPT Takes on Nuclear Medicine Writing: Threat or Opportunity for Human 
      Authors?
PG  - 310-311
LID - 10.4103/ijnm.IJNM_24_23 [doi]
FAU - Sharma, Punit
AU  - Sharma P
AD  - Department of Nuclear Medicine and PET-CT, Apollo Multispeciality Hospital, 
      Kolkata, West Bengal, India.
LA  - eng
PT  - Journal Article
DEP - 20231010
PL  - India
TA  - Indian J Nucl Med
JT  - Indian journal of nuclear medicine : IJNM : the official journal of the Society 
      of Nuclear Medicine, India
JID - 8901274
PMC - PMC10693378
COIS- There are no conflicts of interest.
EDAT- 2023/12/04 06:41
MHDA- 2023/12/04 06:42
CRDT- 2023/12/04 05:14
PHST- 2023/03/01 00:00 [received]
PHST- 2023/03/30 00:00 [accepted]
PHST- 2023/12/04 06:42 [medline]
PHST- 2023/12/04 06:41 [pubmed]
PHST- 2023/12/04 05:14 [entrez]
AID - IJNM-38-310 [pii]
AID - 10.4103/ijnm.IJNM_24_23 [doi]
PST - ppublish
SO  - Indian J Nucl Med. 2023 Jul-Sep;38(3):310-311. doi: 10.4103/ijnm.IJNM_24_23. Epub 
      2023 Oct 10.

PMID- 37704854
OWN - NLM
STAT- Publisher
LR  - 20231003
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Sep 14
TI  - Why Japan is building its own version of ChatGPT.
LID - 10.1038/d41586-023-02868-z [doi]
FAU - Hornyak, Tim
AU  - Hornyak T
LA  - eng
PT  - News
DEP - 20230914
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Communication
OT  - Computer science
OT  - Machine learning
EDAT- 2023/09/14 00:42
MHDA- 2023/09/14 00:42
CRDT- 2023/09/13 23:33
PHST- 2023/09/14 00:42 [pubmed]
PHST- 2023/09/14 00:42 [medline]
PHST- 2023/09/13 23:33 [entrez]
AID - 10.1038/d41586-023-02868-z [pii]
AID - 10.1038/d41586-023-02868-z [doi]
PST - aheadofprint
SO  - Nature. 2023 Sep 14. doi: 10.1038/d41586-023-02868-z.

PMID- 37518158
OWN - NLM
STAT- Publisher
LR  - 20230730
IS  - 1175-5334 (Electronic)
IS  - 1175-5326 (Linking)
VI  - 5270
IP  - 2
DP  - 2023 Apr 20
TI  - Can ChatGPT be leveraged for taxonomic investigations? Potential and limitations 
      of a new technology.
PG  - 347-350
LID - 10.11646/zootaxa.5270.2.12 [doi]
FAU - Davinack, Andrew A
AU  - Davinack AA
AD  - Biology Department; Wheaton College; 26 East Main Street; Norton; Massachusetts; 
      USA 02766. davinack_drew@wheatoncollege.edu.
LA  - eng
PT  - Journal Article
DEP - 20230420
PL  - New Zealand
TA  - Zootaxa
JT  - Zootaxa
JID - 101179386
SB  - IM
EDAT- 2023/07/31 00:41
MHDA- 2023/07/31 00:41
CRDT- 2023/07/30 23:34
PHST- 2023/04/19 00:00 [received]
PHST- 2023/07/31 00:41 [medline]
PHST- 2023/07/31 00:41 [pubmed]
PHST- 2023/07/30 23:34 [entrez]
AID - 10.11646/zootaxa.5270.2.12 [doi]
PST - epublish
SO  - Zootaxa. 2023 Apr 20;5270(2):347-350. doi: 10.11646/zootaxa.5270.2.12.

PMID- 37432082
OWN - NLM
STAT- MEDLINE
DCOM- 20230713
LR  - 20230718
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 308
IP  - 1
DP  - 2023 Jul
TI  - ChatGPT in Radiology: Evaluating Proficiencies, Addressing Shortcomings, and 
      Proposing Integrative Approaches for the Future.
PG  - e231335
LID - 10.1148/radiol.231335 [doi]
FAU - Jiang, Shi-Tao
AU  - Jiang ST
AUID- ORCID: 0000-0001-7298-0237
AD  - Department of Liver Surgery, Peking Union Medical College Hospital, Chinese 
      Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan 
      Wangfujing, Dongcheng District, Beijing 100730, China.
FAU - Xu, Yi-Yao
AU  - Xu YY
AUID- ORCID: 0000-0003-2393-1644
AD  - Department of Liver Surgery, Peking Union Medical College Hospital, Chinese 
      Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan 
      Wangfujing, Dongcheng District, Beijing 100730, China.
FAU - Lu, Xin
AU  - Lu X
AUID- ORCID: 0000-0003-1036-3369
AD  - Department of Liver Surgery, Peking Union Medical College Hospital, Chinese 
      Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan 
      Wangfujing, Dongcheng District, Beijing 100730, China.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CON - Radiology. 2023 Jun;307(5):e230582. PMID: 37191485
CON - Radiology. 2023 Jun;307(5):e230987. PMID: 37191491
MH  - Humans
MH  - Radiography
MH  - *Radiology
EDAT- 2023/07/11 13:10
MHDA- 2023/07/13 06:42
CRDT- 2023/07/11 09:53
PHST- 2023/07/13 06:42 [medline]
PHST- 2023/07/11 13:10 [pubmed]
PHST- 2023/07/11 09:53 [entrez]
AID - 10.1148/radiol.231335 [doi]
PST - ppublish
SO  - Radiology. 2023 Jul;308(1):e231335. doi: 10.1148/radiol.231335.

PMID- 37248408
OWN - NLM
STAT- MEDLINE
DCOM- 20230926
LR  - 20230928
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 10
DP  - 2023 Oct
TI  - The Potential of ChatGPT in Medical Education: Focusing on USMLE Preparation.
PG  - 2123-2124
LID - 10.1007/s10439-023-03253-7 [doi]
FAU - Koga, Shunsuke
AU  - Koga S
AUID- ORCID: 0000-0001-8868-9700
AD  - Department of Neuroscience, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL, 
      32224, USA. skoga.md@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230529
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Education, Medical
MH  - *Artificial Intelligence
EDAT- 2023/05/30 01:06
MHDA- 2023/09/26 13:42
CRDT- 2023/05/29 23:26
PHST- 2023/05/20 00:00 [received]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/09/26 13:42 [medline]
PHST- 2023/05/30 01:06 [pubmed]
PHST- 2023/05/29 23:26 [entrez]
AID - 10.1007/s10439-023-03253-7 [pii]
AID - 10.1007/s10439-023-03253-7 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Oct;51(10):2123-2124. doi: 10.1007/s10439-023-03253-7. Epub 
      2023 May 29.

PMID- 37225130
OWN - NLM
STAT- Publisher
LR  - 20230705
IS  - 1098-8947 (Electronic)
IS  - 0743-684X (Linking)
DP  - 2023 Jul 5
TI  - The Role of ChatGPT in Microsurgery: Assessing Content Quality and Potential 
      Applications.
LID - 10.1055/a-2098-6509 [doi]
FAU - Tian, William M
AU  - Tian WM
AUID- ORCID: 0000-0002-5621-9568
AD  - Division of Plastic, Maxillofacial, and Oral Surgery, Duke University, Durham, 
      North Carolina.
FAU - Sergesketter, Amanda R
AU  - Sergesketter AR
AD  - Division of Plastic, Maxillofacial, and Oral Surgery, Duke University, Durham, 
      North Carolina.
FAU - Hollenbeck, Scott T
AU  - Hollenbeck ST
AD  - Department of Plastic Surgery, University of Virginia, Charlottesville, Virginia.
LA  - eng
PT  - Journal Article
DEP - 20230705
PL  - United States
TA  - J Reconstr Microsurg
JT  - Journal of reconstructive microsurgery
JID - 8502670
SB  - IM
COIS- Disclosure The authors report no conflicts of interest in this work.
EDAT- 2023/05/25 01:07
MHDA- 2023/05/25 01:07
CRDT- 2023/05/24 19:25
PHST- 2023/05/25 01:07 [pubmed]
PHST- 2023/05/25 01:07 [medline]
PHST- 2023/05/24 19:25 [entrez]
AID - 10.1055/a-2098-6509 [doi]
PST - aheadofprint
SO  - J Reconstr Microsurg. 2023 Jul 5. doi: 10.1055/a-2098-6509.

PMID- 37879993
OWN - NLM
STAT- In-Process
LR  - 20231212
IS  - 0219-3108 (Electronic)
IS  - 1015-9584 (Linking)
VI  - 46
IP  - 12
DP  - 2023 Dec
TI  - ChatGPT's responses to rheumatoid arthritis-related questions.
PG  - 5458-5459
LID - S1015-9584(23)01628-7 [pii]
LID - 10.1016/j.asjsur.2023.10.016 [doi]
FAU - Huang, Chunyan
AU  - Huang C
AD  - Department of General Practice of The Second Affiliated Hospital of Fujian 
      Medical University, Quanzhou City, Fujian Province, 362000, China.
FAU - Hong, Daorong
AU  - Hong D
AD  - Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian Province, 362000, China.
FAU - Chen, Xiaoqing
AU  - Chen X
AD  - Department of General Practice of The Second Affiliated Hospital of Fujian 
      Medical University, Quanzhou City, Fujian Province, 362000, China. Electronic 
      address: chenxiaoqing202203@163.com.
FAU - Chen, Lijun
AU  - Chen L
AD  - Department of Endocrinology of The Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian Province, 362000, China.
LA  - eng
PT  - Letter
DEP - 20231023
PL  - Netherlands
TA  - Asian J Surg
JT  - Asian journal of surgery
JID - 8900600
SB  - IM
COIS- Declaration of competing interest The authors have declared that no conflict of 
      interest exists.
EDAT- 2023/10/26 00:42
MHDA- 2023/10/26 00:42
CRDT- 2023/10/25 21:57
PHST- 2023/09/11 00:00 [received]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/10/26 00:42 [pubmed]
PHST- 2023/10/26 00:42 [medline]
PHST- 2023/10/25 21:57 [entrez]
AID - S1015-9584(23)01628-7 [pii]
AID - 10.1016/j.asjsur.2023.10.016 [doi]
PST - ppublish
SO  - Asian J Surg. 2023 Dec;46(12):5458-5459. doi: 10.1016/j.asjsur.2023.10.016. Epub 
      2023 Oct 23.

PMID- 37651677
OWN - NLM
STAT- Publisher
LR  - 20230921
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
DP  - 2023 Aug 31
TI  - ChatGPT and Generative Artificial Intelligence for Medical Education: Potential 
      Impact and Opportunity.
LID - 10.1097/ACM.0000000000005439 [doi]
AB  - ChatGPT has ushered in a new era of artificial intelligence (AI) that already has 
      significant consequences for many industries, including health care and 
      education. Generative AI tools, such as ChatGPT, refer to AI that is designed to 
      create or generate new content, such as text, images, or music, from their 
      trained parameters. With free access online and an easy-to-use conversational 
      interface, ChatGPT quickly accumulated more than 100 million users within the 
      first few months of its launch. Recent headlines in the popular press have 
      ignited concerns relevant to medical education over the possible implications of 
      cheating and plagiarism in assessments as well as excitement over new 
      opportunities for learning, assessment, and research. In this Scholarly 
      Perspective, the authors offer insights and recommendations about generative AI 
      for medical educators based on literature review, including the AI literacy 
      framework. The authors provide a definition of generative AI, introduce an AI 
      literacy framework and competencies, and offer considerations for potential 
      impacts and opportunities to optimize integration of generative AI for 
      admissions, learning, assessment, and medical education research to help medical 
      educators navigate and start planning for this new environment. As generative AI 
      tools continue to expand, educators need to increase their AI literacy through 
      education and vigilance around new advances in the technology and serve as 
      stewards of AI literacy to foster social responsibility and ethical awareness 
      around the use of AI.
CI  - Copyright © 2023 the Association of American Medical Colleges.
FAU - Boscardin, Christy K
AU  - Boscardin CK
AD  - C. Boscardin is professor, Department of Medicine and Department of Anesthesia 
      and Perioperative Care, and director of student assessment, School of Medicine, 
      University of California, San Francisco, San Francisco, California.
FAU - Gin, Brian
AU  - Gin B
AD  - B. Gin is associate professor, Department of Pediatrics, University of 
      California, San Francisco, San Francisco, California.
FAU - Golde, Polo Black
AU  - Golde PB
AD  - P.B. Golde is director of data and analytics, School of Medicine, University of 
      California, San Francisco, San Francisco, California.
FAU - Hauer, Karen E
AU  - Hauer KE
AD  - K. Hauer is associate dean for competency assessment and professional standards 
      and professor of medicine, University of California, San Francisco, San 
      Francisco, California.
LA  - eng
PT  - Journal Article
DEP - 20230831
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
EDAT- 2023/08/31 18:41
MHDA- 2023/08/31 18:41
CRDT- 2023/08/31 16:04
PHST- 2023/08/31 18:41 [pubmed]
PHST- 2023/08/31 18:41 [medline]
PHST- 2023/08/31 16:04 [entrez]
AID - 00001888-990000000-00547 [pii]
AID - 10.1097/ACM.0000000000005439 [doi]
PST - aheadofprint
SO  - Acad Med. 2023 Aug 31. doi: 10.1097/ACM.0000000000005439.

PMID- 37615692
OWN - NLM
STAT- MEDLINE
DCOM- 20230831
LR  - 20230907
IS  - 2731-7056 (Electronic)
IS  - 2731-7048 (Linking)
VI  - 63
IP  - 9
DP  - 2023 Sep
TI  - [Large language models such as ChatGPT and GPT-4 for patient-centered care in 
      radiology].
PG  - 665-671
LID - 10.1007/s00117-023-01187-8 [doi]
AB  - BACKGROUND: With the introduction of ChatGPT in late November 2022, large 
      language models based on artificial intelligence have gained worldwide 
      recognition. These language models are trained on vast amounts of data, enabling 
      them to process complex tasks in seconds and provide detailed, high-level 
      text-based responses. OBJECTIVE: To provide an overview of the most widely 
      discussed large language models, ChatGPT and GPT‑4, with a focus on potential 
      applications for patient-centered radiology. MATERIALS AND METHODS: A PubMed 
      search of both large language models was performed using the terms "ChatGPT" and 
      "GPT-4", with subjective selection and completion in the form of a narrative 
      review. RESULTS: The generic nature of language models holds great promise for 
      radiology, enabling both patients and referrers to facilitate understanding of 
      radiological findings, overcome language barriers, and improve the quality of 
      informed consent discussions. This could represent a significant step towards 
      patient-centered or person-centered radiology. CONCLUSION: Large language models 
      represent a promising tool for improving the communication of findings, 
      interdisciplinary collaboration, and workflow in radiology. However, important 
      privacy issues and the reliable applicability of these models in medicine remain 
      to be addressed.
CI  - © 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, 
      ein Teil von Springer Nature.
FAU - Fink, Matthias A
AU  - Fink MA
AD  - Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum 
      Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Deutschland. 
      matthias.fink@uni-heidelberg.de.
LA  - ger
PT  - English Abstract
PT  - Journal Article
PT  - Review
TT  - Goße Sprachmodelle wie ChatGPT und GPT-4 für eine patientenzentrierte Radiologie.
DEP - 20230824
PL  - Germany
TA  - Radiologie (Heidelb)
JT  - Radiologie (Heidelberg, Germany)
JID - 9918384887306676
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Radiology
MH  - Radiography
MH  - Language
MH  - Patient-Centered Care
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Deep Learning
OT  - Machine Learning
OT  - Natural Language Processing
OT  - Patient-centred approach
EDAT- 2023/08/24 13:44
MHDA- 2023/08/31 06:42
CRDT- 2023/08/24 11:05
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/08/31 06:42 [medline]
PHST- 2023/08/24 13:44 [pubmed]
PHST- 2023/08/24 11:05 [entrez]
AID - 10.1007/s00117-023-01187-8 [pii]
AID - 10.1007/s00117-023-01187-8 [doi]
PST - ppublish
SO  - Radiologie (Heidelb). 2023 Sep;63(9):665-671. doi: 10.1007/s00117-023-01187-8. 
      Epub 2023 Aug 24.

PMID- 37378876
OWN - NLM
STAT- Publisher
LR  - 20230628
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jun 28
TI  - The Role and Potential Contributions of the Artificial Intelligence Language 
      Model ChatGPT.
LID - 10.1007/s10439-023-03296-w [doi]
AB  - This discussion paper aims to examine the potential benefits and limitations of 
      using artificial intelligence (AI) chatbots in nursing practice, with a 
      particular focus on the ChatGPT example. The study discusses how chatbots can 
      serve as a valuable tool for nurses' continuing education, consultation, and 
      information access. It is suggested that ChatGPT can contribute to enhancing 
      nurses' knowledge and skill levels, providing rapid and accurate information, and 
      improving time management. However, the potential risks and limitations of using 
      AI chatbots have also been evaluated. The study highlights the possibility of 
      negative impacts on the nurse-patient relationship due to chatbots' inadequacy in 
      emotional and empathetic communication. Additionally, concerns about chatbots 
      providing inaccurate or biased information and issues regarding data privacy are 
      addressed. The review draws attention to the limited existing literature on the 
      use of AI chatbots in nursing and emphasizes the need for expanding research in 
      this area. Future studies are suggested to focus on identifying the necessary 
      training and support resources for nurses to effectively utilize this technology. 
      This study underscores an important ethical and professional point for nurses, 
      reminding them not to overlook the significance of human touch and emotional 
      connection while evaluating the advantages offered by technology.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Berşe, Soner
AU  - Berşe S
AUID- ORCID: 0000-0001-9108-3216
AD  - Faculty of Health Sciences, Gaziantep University, Gaziantep, Turkey. 
      sonerberse@gmail.com.
FAU - Akça, Kamile
AU  - Akça K
AUID- ORCID: 0000-0002-2833-8754
AD  - Faculty of Health Sciences, Gaziantep İslam Science and Technology University, 
      Gaziantep, Turkey.
FAU - Dirgar, Ezgi
AU  - Dirgar E
AUID- ORCID: 0000-0001-8214-7441
AD  - Faculty of Health Sciences, Gaziantep University, Gaziantep, Turkey.
FAU - Kaplan Serin, Emine
AU  - Kaplan Serin E
AUID- ORCID: 0000-0002-7327-9167
AD  - Faculty of Health Sciences, Mersin University, Mersin, Turkey. 
      emine_3354@hotmail.com.
LA  - eng
PT  - Letter
DEP - 20230628
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Advantages
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbots
OT  - Disadvantages
OT  - Nurse–patient relationship
OT  - Nursing practice
EDAT- 2023/06/28 13:09
MHDA- 2023/06/28 13:09
CRDT- 2023/06/28 11:15
PHST- 2023/06/14 00:00 [received]
PHST- 2023/06/21 00:00 [accepted]
PHST- 2023/06/28 13:09 [medline]
PHST- 2023/06/28 13:09 [pubmed]
PHST- 2023/06/28 11:15 [entrez]
AID - 10.1007/s10439-023-03296-w [pii]
AID - 10.1007/s10439-023-03296-w [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jun 28. doi: 10.1007/s10439-023-03296-w.

PMID- 37096072
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230426
IS  - 1865-5025 (Print)
IS  - 1865-5033 (Electronic)
IS  - 1865-5025 (Linking)
VI  - 16
IP  - 2
DP  - 2023 Apr
TI  - Can Bard, Google's Experimental Chatbot Based on the LaMDA Large Language Model, 
      Help to Analyze the Gender and Racial Diversity of Authors in Your Cited 
      Scientific References?
PG  - 175-179
LID - 10.1007/s12195-023-00761-3 [doi]
AB  - There is a growing recognition that scientific articles featuring women and 
      people of color as first and last (senior) author are undercited in the 
      literature relative to male and non-minority race authors. Some limited tools now 
      exist to analyze the diversity of manuscript bibliographies, with acknowledged 
      limitations. Recently the journal editors and publications chair of the 
      Biomedical Engineering Society have recommended that authors include an optional 
      "Citation Diversity Statement" in their articles, however adoption of this 
      practice has, to date, been slow. Inspired by the current excitement and 
      enthusiasm for artificial intelligence (AI) large language model chatbots, I 
      sought to determine whether Google's new Bard chatbot could be used to assist 
      authors in this process. It was determined that the Bard technology is not yet up 
      to this task, however, by showing some modest improvement in the fidelity of 
      references, combined with the not-yet realized live search capabilities, the 
      author is nevertheless optimistic that this technology can one day be utilized 
      for this purpose as it continues to improve.
CI  - © The Author(s) under exclusive licence to Biomedical Engineering Society 2023.
FAU - King, Michael R
AU  - King MR
AD  - Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235 
      USA. GRID: grid.152326.1. ISNI: 0000 0001 2264 7217
LA  - eng
PT  - Editorial
DEP - 20230403
PL  - United States
TA  - Cell Mol Bioeng
JT  - Cellular and molecular bioengineering
JID - 101468590
PMC - PMC10121931
EDAT- 2023/04/25 06:42
MHDA- 2023/04/25 06:43
PMCR- 2024/04/03
CRDT- 2023/04/25 02:08
PHST- 2024/04/03 00:00 [pmc-release]
PHST- 2023/04/25 06:43 [medline]
PHST- 2023/04/25 06:42 [pubmed]
PHST- 2023/04/25 02:08 [entrez]
AID - 761 [pii]
AID - 10.1007/s12195-023-00761-3 [doi]
PST - epublish
SO  - Cell Mol Bioeng. 2023 Apr 3;16(2):175-179. doi: 10.1007/s12195-023-00761-3. 
      eCollection 2023 Apr.

PMID- 37716618
OWN - NLM
STAT- Publisher
LR  - 20231028
IS  - 1542-7714 (Electronic)
IS  - 1542-3565 (Linking)
DP  - 2023 Sep 15
TI  - Accuracy, Reliability, and Comprehensiveness of ChatGPT-Generated Medical 
      Responses for Patients With Nonalcoholic Fatty Liver Disease.
LID - S1542-3565(23)00704-8 [pii]
LID - 10.1016/j.cgh.2023.08.033 [doi]
AB  - Nonalcoholic fatty liver disease (NAFLD) is an increasing global health problem 
      and is expected to become the leading indication for liver transplantation.(1) 
      There are no approved NAFLD-specific pharmacotherapies, and lifestyle 
      modification is the primary recommended therapy.(2) Innovative approaches to 
      facilitate the implementation and long-term maintenance of lifestyle changes are 
      needed to address the challenging and complex nature of the management of NAFLD, 
      which recently was renamed as metabolic dysfunction-associated steatotic liver 
      disease, to overcome the limitations and stigma of the previous name.(3,4) 
      Artificial intelligence (AI)-powered chatbots have been shown to provide 
      effective personalized support and education to patients, with the potential to 
      complement health care resources. The OpenAI Foundation's AI chatbot, Chat 
      Generative Pretrained Transformer (ChatGPT), has attracted worldwide attention 
      for its remarkable performance in question-answer tasks.(5-7) This study 
      evaluated the accuracy, completeness, and comprehensiveness of chatGPT's 
      responses to NAFLD-related questions, with the aim of assessing its performance 
      in addressing patients' queries about the disease and lifestyle behaviors.
CI  - Copyright © 2023 AGA Institute. Published by Elsevier Inc. All rights reserved.
FAU - Pugliese, Nicola
AU  - Pugliese N
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Pieve Emanuele, 
      Italy; Division of Internal Medicine and Hepatology, Department of 
      Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico Humanitas 
      Research Hospital, Milan, Rozzano, Italy.
FAU - Wai-Sun Wong, Vincent
AU  - Wai-Sun Wong V
AD  - Medical Data Analytics Centre, Department of Medicine and Therapeutics, The 
      Chinese University of Hong Kong, Hong Kong, China; State Key Laboratory of 
      Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong 
      Kong, Hong Kong, China.
FAU - Schattenberg, Jörn M
AU  - Schattenberg JM
AD  - Metabolic Liver Research Center, Department of Medicine, University Medical 
      Center Mainz, Mainz, Germany.
FAU - Romero-Gomez, Manuel
AU  - Romero-Gomez M
AD  - Institute of Biomedicine of Seville (HUVR/CSIC/US), Virgen del Rocio University 
      Hospital, CIBEREHD, University of Seville, Seville, Spain.
FAU - Sebastiani, Giada
AU  - Sebastiani G
AD  - Division of Gastroenterology and Hepatology, McGill University Health Centre, 
      Montreal, Canada.
CN  - NAFLD Expert Chatbot Working Group
FAU - Aghemo, Alessio
AU  - Aghemo A
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Pieve Emanuele, 
      Italy; Division of Internal Medicine and Hepatology, Department of 
      Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico Humanitas 
      Research Hospital, Milan, Rozzano, Italy. Electronic address: 
      alessio.aghemo@hunimed.eu.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - United States
TA  - Clin Gastroenterol Hepatol
JT  - Clinical gastroenterology and hepatology : the official clinical practice journal 
      of the American Gastroenterological Association
JID - 101160775
SB  - IM
FIR - Castera, Laurent
IR  - Castera L
IRAD- Université Paris Cité, UMR1149 (CRI), INSERM, Paris, France; Service 
      d'Hépatologie, Assistance Publique-Hôpitaux de Paris, Hôpital Beaujon, 
      Clichy-la-Garenne, France.
FIR - Hassan, Cesare
IR  - Hassan C
IRAD- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research 
      Hospital-Istituto di Ricovero e Cura a Carattere Scientifico, Rozzano (Milan), 
      Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 
      Milan, Italy.
FIR - Manousou, Pinelopi
IR  - Manousou P
IRAD- Imperial College Healthcare NHS Trust, London, United Kingdom; UCL Institute for 
      Liver and Digestive Health, Division of Medicine, London, United Kingdom.
FIR - Miele, Luca
IR  - Miele L
IRAD- Department of Scienze Mediche e Chirurgiche, Fondazione Policlinico Gemelli 
      Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy; Department of 
      Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, 
      Italy.
FIR - Peck, Raquel
IR  - Peck R
IRAD- Clarion I, London, United Kingdom.
FIR - Petta, Salvatore
IR  - Petta S
IRAD- Section of Gastroenterology and Hepatology, PROMISE, University of Palermo, 
      Palermo, Italy.
FIR - Valenti, Luca
IR  - Valenti L
IRAD- Precision Medicine Laboratory, Biological Resource Center, Department of 
      Transfusion Medicine, Fondazione Istituto di Ricovero e Cura a Carattere 
      Scientifico Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of 
      Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, 
      Italy.
FIR - Younossi, Zobair M
IR  - Younossi ZM
IRAD- Department of Medicine, Center for Liver Diseases, Inova Fairfax Medical Campus, 
      Falls Church, Virginia; Betty and Guy Beatty Center for Integrated Research, 
      Inova Health System, Falls Church, Virginia.
EDAT- 2023/09/17 00:41
MHDA- 2023/09/17 00:41
CRDT- 2023/09/16 19:17
PHST- 2023/05/17 00:00 [received]
PHST- 2023/08/12 00:00 [revised]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/09/17 00:41 [pubmed]
PHST- 2023/09/17 00:41 [medline]
PHST- 2023/09/16 19:17 [entrez]
AID - S1542-3565(23)00704-8 [pii]
AID - 10.1016/j.cgh.2023.08.033 [doi]
PST - aheadofprint
SO  - Clin Gastroenterol Hepatol. 2023 Sep 15:S1542-3565(23)00704-8. doi: 
      10.1016/j.cgh.2023.08.033.

PMID- 37596194
OWN - NLM
STAT- Publisher
LR  - 20230818
IS  - 1873-4898 (Electronic)
IS  - 1477-5131 (Linking)
DP  - 2023 Aug 7
TI  - Evaluating the performance of ChatGPT in answering questions related to pediatric 
      urology.
LID - S1477-5131(23)00318-2 [pii]
LID - 10.1016/j.jpurol.2023.08.003 [doi]
AB  - INTRODUCTION: Artificial intelligence is advancing in various domains, including 
      medicine, and its progress is expected to continue in the future. OBJECTIVE: This 
      research aimed to assess the precision and consistency of ChatGPT's responses to 
      commonly asked inquiries related to pediatric urology. MATERIALS AND METHODS: We 
      examined commonly posed inquiries regarding pediatric urology found on urology 
      association websites, hospitals, and social media platforms. Additionally, we 
      referenced the recommendations tables in the European Urology Association's (EAU) 
      2022 Guidelines on Pediatric Urology, which contained robust data at the strong 
      recommendation level. All questions were systematically presented to ChatGPT's 
      May 23 Version, and two expert urologists independently assessed and assigned 
      scores ranging from 1 to 4 to each response. RESULTS: A hundred thirty seven 
      questions about pediatric urology were included in the study. The answers to 
      questions resulted in 92.0% completely correct. The completely correct rate in 
      the questions prepared according to the strong recommendations of the EAU 
      guideline was 93.6%. No question was answered completely wrong. The similarity 
      rates of the answers to the repeated questions were between 93.8% and 100%. 
      CONCLUSION: ChatGPT has provided satisfactory responses to inquiries related to 
      pediatric urology. Despite its limitations, it is foreseeable that this 
      continuously evolving platform will occupy a crucial position in the healthcare 
      industry.
CI  - Copyright © 2023 Journal of Pediatric Urology Company. Published by Elsevier Ltd. 
      All rights reserved.
FAU - Caglar, Ufuk
AU  - Caglar U
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey. 
      Electronic address: ufukcglr@gmail.com.
FAU - Yildiz, Oguzhan
AU  - Yildiz O
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Meric, Arda
AU  - Meric A
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Ayranci, Ali
AU  - Ayranci A
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Gelmis, Mucahit
AU  - Gelmis M
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Sarilar, Omer
AU  - Sarilar O
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
FAU - Ozgor, Faruk
AU  - Ozgor F
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230807
PL  - England
TA  - J Pediatr Urol
JT  - Journal of pediatric urology
JID - 101233150
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Health literacy
OT  - Patient knowledge
OT  - Pediatric urology
COIS- Conflict of interest None.
EDAT- 2023/08/19 11:41
MHDA- 2023/08/19 11:41
CRDT- 2023/08/18 22:03
PHST- 2023/05/31 00:00 [received]
PHST- 2023/07/25 00:00 [revised]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/08/19 11:41 [medline]
PHST- 2023/08/19 11:41 [pubmed]
PHST- 2023/08/18 22:03 [entrez]
AID - S1477-5131(23)00318-2 [pii]
AID - 10.1016/j.jpurol.2023.08.003 [doi]
PST - aheadofprint
SO  - J Pediatr Urol. 2023 Aug 7:S1477-5131(23)00318-2. doi: 
      10.1016/j.jpurol.2023.08.003.

PMID- 36589923
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230103
IS  - 2331-4737 (Print)
IS  - 2331-4737 (Electronic)
IS  - 2331-4737 (Linking)
VI  - 9
DP  - 2022
TI  - Rapamycin in the context of Pascal's Wager: generative pre-trained transformer 
      perspective.
PG  - 82-84
LID - 10.18632/oncoscience.571 [doi]
AB  - Large language models utilizing transformer neural networks and other deep 
      learning architectures demonstrated unprecedented results in many tasks 
      previously accessible only to human intelligence. In this article, we collaborate 
      with ChatGPT, an AI model developed by OpenAI to speculate on the applications of 
      Rapamycin, in the context of Pascal's Wager philosophical argument commonly 
      utilized to justify the belief in god. In response to the query "Write an 
      exhaustive research perspective on why taking Rapamycin may be more beneficial 
      than not taking Rapamycin from the perspective of Pascal's wager" ChatGPT 
      provided the pros and cons for the use of Rapamycin considering the preclinical 
      evidence of potential life extension in animals. This article demonstrates the 
      potential of ChatGPT to produce complex philosophical arguments and should not be 
      used for any off-label use of Rapamycin.
CN  - ChatGPT Generative Pre-trained Transformer
FAU - Zhavoronkov, Alex
AU  - Zhavoronkov A
AD  - Insilico Medicine, Hong Kong Science and Technology Park, Hong Kong.
LA  - eng
PT  - Journal Article
DEP - 20221221
PL  - United States
TA  - Oncoscience
JT  - Oncoscience
JID - 101636666
PMC - PMC9796173
OTO - NOTNLM
OT  - Pascal’s Wager
OT  - Rapamycin
OT  - artificial intelligence
OT  - longevity medicine
OT  - philosophy
COIS- CONFLICTS OF INTEREST Authors have no conflicts of interest to declare.
EDAT- 2023/01/03 06:00
MHDA- 2023/01/03 06:01
CRDT- 2023/01/02 04:11
PHST- 2022/12/14 00:00 [received]
PHST- 2022/12/15 00:00 [accepted]
PHST- 2023/01/02 04:11 [entrez]
PHST- 2023/01/03 06:00 [pubmed]
PHST- 2023/01/03 06:01 [medline]
AID - 571 [pii]
AID - 10.18632/oncoscience.571 [doi]
PST - epublish
SO  - Oncoscience. 2022 Dec 21;9:82-84. doi: 10.18632/oncoscience.571. eCollection 
      2022.

PMID- 37541730
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230807
LR  - 20230807
IS  - 1701-2163 (Print)
IS  - 1701-2163 (Linking)
VI  - 45
IP  - 8
DP  - 2023 Aug
TI  - [Divulgation de recours à l'intelligence artificielle ou ChatGPT dans les 
      manuscrits].
PG  - 545-546
LID - S1701-2163(23)00410-3 [pii]
LID - 10.1016/j.jogc.2023.05.030 [doi]
FAU - Tulandi, Togas
AU  - Tulandi T
LA  - fre
PT  - Editorial
PL  - Netherlands
TA  - J Obstet Gynaecol Can
JT  - Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et 
      gynecologie du Canada : JOGC
JID - 101126664
SB  - IM
EDAT- 2023/08/05 05:41
MHDA- 2023/08/07 06:42
CRDT- 2023/08/04 20:57
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/08/05 05:41 [pubmed]
PHST- 2023/08/04 20:57 [entrez]
AID - S1701-2163(23)00410-3 [pii]
AID - 10.1016/j.jogc.2023.05.030 [doi]
PST - ppublish
SO  - J Obstet Gynaecol Can. 2023 Aug;45(8):545-546. doi: 10.1016/j.jogc.2023.05.030.

PMID- 37451351
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231027
IS  - 2468-1210 (Electronic)
IS  - 2468-1210 (Linking)
VI  - 42
IP  - 5
DP  - 2023 Oct
TI  - ChatGPT and the European Board of Hand Surgery diploma examination: 
      Correspondence.
PG  - 446
LID - S2468-1229(23)00119-6 [pii]
LID - 10.1016/j.hansur.2023.07.001 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Mungmunpuntipantip, Rujittika
AU  - Mungmunpuntipantip R
AD  - Private Academic Consultant, Bangkok Thailand.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; Joesph Ayobabalola University, 
      Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230713
PL  - France
TA  - Hand Surg Rehabil
JT  - Hand surgery & rehabilitation
JID - 101681801
SB  - IM
CON - Hand Surg Rehabil. 2023 Sep;42(4):362-364. PMID: 37353199
MH  - Humans
MH  - *Hand/surgery
MH  - *Educational Measurement
EDAT- 2023/07/15 10:42
MHDA- 2023/10/23 00:42
CRDT- 2023/07/14 19:23
PHST- 2023/06/24 00:00 [received]
PHST- 2023/07/07 00:00 [revised]
PHST- 2023/07/10 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/07/15 10:42 [pubmed]
PHST- 2023/07/14 19:23 [entrez]
AID - S2468-1229(23)00119-6 [pii]
AID - 10.1016/j.hansur.2023.07.001 [doi]
PST - ppublish
SO  - Hand Surg Rehabil. 2023 Oct;42(5):446. doi: 10.1016/j.hansur.2023.07.001. Epub 
      2023 Jul 13.

PMID- 37406729
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230918
LR  - 20230919
IS  - 1532-8414 (Electronic)
IS  - 1071-9164 (Linking)
VI  - 29
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT Produces Fabricated References and Falsehoods When Used for Scientific 
      Literature Search.
PG  - 1332-1334
LID - S1071-9164(23)00231-2 [pii]
LID - 10.1016/j.cardfail.2023.06.015 [doi]
FAU - Blum, Moritz
AU  - Blum M
AD  - Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of 
      Medicine at Mount Sinai, New York, New York. Electronic address: 
      moritz.blum@mssm.edu.
LA  - eng
PT  - Journal Article
DEP - 20230703
PL  - United States
TA  - J Card Fail
JT  - Journal of cardiac failure
JID - 9442138
SB  - IM
COIS- Conflict of Interest None.
EDAT- 2023/07/06 01:08
MHDA- 2023/09/18 12:42
CRDT- 2023/07/05 19:25
PHST- 2023/06/16 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/09/18 12:42 [medline]
PHST- 2023/07/06 01:08 [pubmed]
PHST- 2023/07/05 19:25 [entrez]
AID - S1071-9164(23)00231-2 [pii]
AID - 10.1016/j.cardfail.2023.06.015 [doi]
PST - ppublish
SO  - J Card Fail. 2023 Sep;29(9):1332-1334. doi: 10.1016/j.cardfail.2023.06.015. Epub 
      2023 Jul 3.

PMID- 37368994
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20231114
IS  - 1365-2168 (Electronic)
IS  - 0007-1323 (Linking)
VI  - 110
IP  - 9
DP  - 2023 Aug 11
TI  - Will collaborative publishing with ChatGPT drive academic writing in the future?
PG  - 1213-1214
LID - 10.1093/bjs/znad198 [doi]
FAU - Ariyaratne, Sisith
AU  - Ariyaratne S
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      UK.
FAU - Iyengar, Kathikeyan P
AU  - Iyengar KP
AD  - Department of Orthopaedics, Southport and Ormskirk NHS Trust, Southport, UK.
FAU - Botchu, Rajesh
AU  - Botchu R
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      UK.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Br J Surg
JT  - The British journal of surgery
JID - 0372553
SB  - IM
CIN - Br J Surg. 2023 Nov 9;110(12):1895. PMID: 37702576
MH  - Humans
MH  - *Writing
MH  - *Publishing
EDAT- 2023/06/27 19:13
MHDA- 2023/08/14 06:42
CRDT- 2023/06/27 14:53
PHST- 2023/04/20 00:00 [received]
PHST- 2023/04/28 00:00 [revised]
PHST- 2023/05/26 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/06/27 19:13 [pubmed]
PHST- 2023/06/27 14:53 [entrez]
AID - 7208789 [pii]
AID - 10.1093/bjs/znad198 [doi]
PST - ppublish
SO  - Br J Surg. 2023 Aug 11;110(9):1213-1214. doi: 10.1093/bjs/znad198.

PMID- 37258279
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2005-940X (Print)
IS  - 2093-4939 (Electronic)
IS  - 2005-940X (Linking)
VI  - 16
IP  - 2
DP  - 2023 May
TI  - Potential Benefits and Perils of Incorporating ChatGPT to the Movement Disorders 
      Clinic.
PG  - 158-162
LID - 10.14802/jmd.23072 [doi]
FAU - Deik, Andres
AU  - Deik A
AD  - Parkinson's Disease and Movement Disorders Center, Department of Neurology, 
      University of Pennsylvania, Philadelphia, PA, USA.
LA  - eng
PT  - Journal Article
DEP - 20230524
PL  - Korea (South)
TA  - J Mov Disord
JT  - Journal of movement disorders
JID - 101527867
PMC - PMC10236019
COIS- Conflicts of Interest The author declares that there are no conflicts of interest 
      relevant to this work. However, over the previous 12 months, the author has 
      served as site Principal Investigator for clinical trials and received research 
      support from Insightec, Teva Pharmaceuticals, Cerevel Therapeutics, Prevail 
      Therapeutics, Addex Pharmaceuticals and Lundbeck Therapeutics. The author has 
      also received publishing royalties from a UpToDate and Elsevier.
EDAT- 2023/06/01 01:08
MHDA- 2023/06/01 01:09
CRDT- 2023/05/31 21:47
PHST- 2023/04/12 00:00 [received]
PHST- 2023/04/21 00:00 [accepted]
PHST- 2023/06/01 01:09 [medline]
PHST- 2023/06/01 01:08 [pubmed]
PHST- 2023/05/31 21:47 [entrez]
AID - jmd.23072 [pii]
AID - jmd-23072 [pii]
AID - 10.14802/jmd.23072 [doi]
PST - ppublish
SO  - J Mov Disord. 2023 May;16(2):158-162. doi: 10.14802/jmd.23072. Epub 2023 May 24.

PMID- 37222674
OWN - NLM
STAT- MEDLINE
DCOM- 20231004
LR  - 20231115
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 9
DP  - 2023 Sep 1
TI  - Matters arising: authors of research papers must cautiously use ChatGPT for 
      scientific writing.
PG  - 2853-2854
LID - 10.1097/JS9.0000000000000515 [doi]
FAU - Shafiee, Arman
AU  - Shafiee A
AUID- ORCID: 0000-0002-1941-4399
AD  - Department of Psychiatry and Mental Health, Alborz University of Medical 
      Sciences, Karaj, Iran.
LA  - eng
PT  - Letter
DEP - 20230901
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *Writing
MH  - *Publishing
PMC - PMC10498857
COIS- Not applicable. Sponsorships or competing interests that may be relevant to 
      content are disclosed at the end of this article.
EDAT- 2023/05/24 13:09
MHDA- 2023/10/04 06:44
CRDT- 2023/05/24 10:34
PHST- 2023/05/11 00:00 [received]
PHST- 2023/05/13 00:00 [accepted]
PHST- 2023/10/04 06:44 [medline]
PHST- 2023/05/24 13:09 [pubmed]
PHST- 2023/05/24 10:34 [entrez]
AID - 01279778-990000000-00384 [pii]
AID - IJS-D-23-00864 [pii]
AID - 10.1097/JS9.0000000000000515 [doi]
PST - epublish
SO  - Int J Surg. 2023 Sep 1;109(9):2853-2854. doi: 10.1097/JS9.0000000000000515.

PMID- 37061346
OWN - NLM
STAT- MEDLINE
DCOM- 20230522
LR  - 20230529
IS  - 1934-8150 (Electronic)
IS  - 1551-7411 (Linking)
VI  - 19
IP  - 7
DP  - 2023 Jul
TI  - ChatGPT: Threat or boon to the future of pharmacy practice?
PG  - 975-976
LID - S1551-7411(23)00115-8 [pii]
LID - 10.1016/j.sapharm.2023.03.012 [doi]
FAU - Jairoun, Ammar Abdulrahman
AU  - Jairoun AA
AD  - Health and Safety Department, Dubai Municipality, Dubai, United Arab Emirates; 
      School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, 
      11500, Malaysia. Electronic address: Dr_ammar_91_@hotmail.com.
FAU - Al-Hemyari, Sabaa Saleh
AU  - Al-Hemyari SS
AD  - School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, 
      11500, Malaysia; Pharmacy Department, Emirates Health Services, Dubai, United 
      Arab Emirates. Electronic address: drsabasaleh@hotmail.com.
FAU - Shahwan, Moyad
AU  - Shahwan M
AD  - Centre of Medical and Bio-allied Health Sciences Research, Ajman University, 
      Ajman, Ajman, 346, United Arab Emirates; Department of Clinical Sciences, College 
      of Pharmacy and Health Sciences, Ajman University, Ajman, 346, United Arab 
      Emirates. Electronic address: moyad76@hotmail.com.
FAU - Humaid Alnuaimi, Ghala Rashid
AU  - Humaid Alnuaimi GR
AD  - Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman 
      University, Ajman, 346, United Arab Emirates.
FAU - Zyoud, Sa'ed H
AU  - Zyoud SH
AD  - Department of Clinical and Community Pharmacy, College of Medicine and Health 
      Sciences, An-Najah National University, Nablus, 44839, Palestine; Clinical 
      Research Centre, An-Najah National University Hospital, Nablus, 44839, Palestine.
FAU - Jairoun, Maimona
AU  - Jairoun M
AD  - Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman 
      University, Ajman, 346, United Arab Emirates.
LA  - eng
PT  - Editorial
DEP - 20230403
PL  - United States
TA  - Res Social Adm Pharm
JT  - Research in social & administrative pharmacy : RSAP
JID - 101231974
SB  - IM
MH  - Humans
MH  - *Pharmacy
MH  - *Pharmaceutical Services
MH  - *Pharmacies
EDAT- 2023/04/16 06:00
MHDA- 2023/05/22 06:42
CRDT- 2023/04/15 21:57
PHST- 2023/03/29 00:00 [received]
PHST- 2023/03/29 00:00 [accepted]
PHST- 2023/05/22 06:42 [medline]
PHST- 2023/04/16 06:00 [pubmed]
PHST- 2023/04/15 21:57 [entrez]
AID - S1551-7411(23)00115-8 [pii]
AID - 10.1016/j.sapharm.2023.03.012 [doi]
PST - ppublish
SO  - Res Social Adm Pharm. 2023 Jul;19(7):975-976. doi: 10.1016/j.sapharm.2023.03.012. 
      Epub 2023 Apr 3.

PMID- 36854881
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230302
LR  - 20230303
IS  - 2001-1326 (Electronic)
IS  - 2001-1326 (Linking)
VI  - 13
IP  - 3
DP  - 2023 Mar
TI  - The potential impact of ChatGPT in clinical and translational medicine.
PG  - e1206
LID - 10.1002/ctm2.1206 [doi]
LID - e1206
FAU - Baumgartner, Christian
AU  - Baumgartner C
AUID- ORCID: 0000-0002-3763-5195
AD  - Graz University of Technology, Institute of Health Care Engineering with European 
      Testing Center of Medical Devices, Graz, Austria.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Clin Transl Med
JT  - Clinical and translational medicine
JID - 101597971
SB  - IM
PMC - PMC9974599
COIS- The authors declare no conflicts of interest.
EDAT- 2023/03/02 06:00
MHDA- 2023/03/02 06:01
CRDT- 2023/03/01 00:04
PHST- 2023/02/14 00:00 [received]
PHST- 2023/02/14 00:00 [accepted]
PHST- 2023/03/01 00:04 [entrez]
PHST- 2023/03/02 06:00 [pubmed]
PHST- 2023/03/02 06:01 [medline]
AID - CTM21206 [pii]
AID - 10.1002/ctm2.1206 [doi]
PST - ppublish
SO  - Clin Transl Med. 2023 Mar;13(3):e1206. doi: 10.1002/ctm2.1206.

PMID- 37517769
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231113
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT in Radiology: Transforming Patient Care With Artificial Intelligence 
      Chatbots.
PG  - 943-944
LID - S1546-1440(23)00537-9 [pii]
LID - 10.1016/j.jacr.2023.06.029 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, Kolkata, India.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230728
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
CON - J Am Coll Radiol. 2023 Jul;20(7):696-698. PMID: 36972862
MH  - Humans
MH  - *Artificial Intelligence
MH  - Radiography
MH  - *Radiology
MH  - Patient Care
EDAT- 2023/07/31 00:41
MHDA- 2023/11/03 06:44
CRDT- 2023/07/30 19:19
PHST- 2023/05/22 00:00 [received]
PHST- 2023/06/14 00:00 [accepted]
PHST- 2023/11/03 06:44 [medline]
PHST- 2023/07/31 00:41 [pubmed]
PHST- 2023/07/30 19:19 [entrez]
AID - S1546-1440(23)00537-9 [pii]
AID - 10.1016/j.jacr.2023.06.029 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Oct;20(10):943-944. doi: 10.1016/j.jacr.2023.06.029. Epub 
      2023 Jul 28.

PMID- 37468401
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230914
LR  - 20230914
IS  - 1873-5487 (Electronic)
IS  - 0188-4409 (Linking)
VI  - 54
IP  - 6
DP  - 2023 Sep
TI  - Reply to: ChatGPT and Artificial Intelligence in the Contemporary Medical 
      Landscape: Commentary.
PG  - 102858
LID - S0188-4409(23)00096-6 [pii]
LID - 10.1016/j.arcmed.2023.102858 [doi]
FAU - Calleja-López, J R Tadeo
AU  - Calleja-López JRT
AD  - Department of Medicine and Health Sciences, University of Sonora, Hermosillo, 
      Sonora, Mexico. Electronic address: jrtcalleja@gmail.com.
FAU - Rivera-Rosas, Cristian N
AU  - Rivera-Rosas CN
AD  - Department of Medicine and Health Sciences, University of Sonora, Hermosillo, 
      Sonora, Mexico.
FAU - Ruibal-Tavares, Enrique
AU  - Ruibal-Tavares E
AD  - Department of Medicine and Health Sciences, University of Sonora, Hermosillo, 
      Sonora, Mexico.
FAU - Aguilera-Duarte, Luis José
AU  - Aguilera-Duarte LJ
AD  - Department of Medicine and Health Sciences, University of Sonora, Hermosillo, 
      Sonora, Mexico.
LA  - eng
PT  - Letter
DEP - 20230717
PL  - United States
TA  - Arch Med Res
JT  - Archives of medical research
JID - 9312706
SB  - IM
COIS- Conflict of Interest None.
EDAT- 2023/07/20 01:07
MHDA- 2023/09/08 06:42
CRDT- 2023/07/19 22:00
PHST- 2023/06/28 00:00 [received]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/09/08 06:42 [medline]
PHST- 2023/07/20 01:07 [pubmed]
PHST- 2023/07/19 22:00 [entrez]
AID - S0188-4409(23)00096-6 [pii]
AID - 10.1016/j.arcmed.2023.102858 [doi]
PST - ppublish
SO  - Arch Med Res. 2023 Sep;54(6):102858. doi: 10.1016/j.arcmed.2023.102858. Epub 2023 
      Jul 17.

PMID- 37419046
OWN - NLM
STAT- MEDLINE
DCOM- 20230816
LR  - 20230816
IS  - 1532-2831 (Electronic)
IS  - 1078-8174 (Linking)
VI  - 29
IP  - 5
DP  - 2023 Aug
TI  - Response to: ChatGPT in medical imaging higher education: Reply to Currie et al.
PG  - 868-869
LID - S1078-8174(23)00138-4 [pii]
LID - 10.1016/j.radi.2023.06.012 [doi]
FAU - Currie, G
AU  - Currie G
AD  - Charles Sturt University, Australia. Electronic address: gcurrie@csu.edu.au.
FAU - Singh, C
AU  - Singh C
AD  - Charles Sturt University, Australia.
FAU - Spuur, K
AU  - Spuur K
AD  - Charles Sturt University, Australia.
FAU - Al-Hayek, Y
AU  - Al-Hayek Y
AD  - Charles Sturt University, Australia.
FAU - Nabasenja, C
AU  - Nabasenja C
AD  - Charles Sturt University, Australia.
FAU - Nelson, T
AU  - Nelson T
AD  - Charles Sturt University, Australia.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230705
PL  - Netherlands
TA  - Radiography (Lond)
JT  - Radiography (London, England : 1995)
JID - 9604102
SB  - IM
CON - Radiography (Lond). 2023 Jul;29(4):792-799. PMID: 37271011
CON - Radiography (Lond). 2023 Aug;29(5):867. PMID: 37413958
MH  - Humans
MH  - *Diagnostic Imaging
MH  - Radiography
EDAT- 2023/07/08 10:42
MHDA- 2023/08/16 06:43
CRDT- 2023/07/07 18:09
PHST- 2023/08/16 06:43 [medline]
PHST- 2023/07/08 10:42 [pubmed]
PHST- 2023/07/07 18:09 [entrez]
AID - S1078-8174(23)00138-4 [pii]
AID - 10.1016/j.radi.2023.06.012 [doi]
PST - ppublish
SO  - Radiography (Lond). 2023 Aug;29(5):868-869. doi: 10.1016/j.radi.2023.06.012. Epub 
      2023 Jul 5.

PMID- 37379558
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230707
LR  - 20230717
IS  - 1669-9106 (Electronic)
IS  - 0025-7680 (Linking)
VI  - 83
IP  - 3
DP  - 2023
TI  - [ChatGPT artificial intelligence and its usefulness in research: the future is 
      already here].
PG  - 499-501
FAU - Burgos, Lucrecia M
AU  - Burgos LM
AD  - Instituto Cardiovascular de Buenos Aires, Buenos Aires, Argentina. E-mail: 
      lburgos@icba.com.ar.
FAU - Suárez, Lucas L
AU  - Suárez LL
AD  - Instituto Cardiovascular de Buenos Aires, Buenos Aires, Argentina.
FAU - Benzadón, Mariano
AU  - Benzadón M
AD  - Instituto Cardiovascular de Buenos Aires, Buenos Aires, Argentina.
LA  - spa
PT  - Letter
TT  - Inteligencia artificial ChatGPT y su utilidad en la investigación: el futuro ya 
      está aquí.
PL  - Argentina
TA  - Medicina (B Aires)
JT  - Medicina
JID - 0204271
SB  - IM
EDAT- 2023/06/28 19:14
MHDA- 2023/06/30 06:42
CRDT- 2023/06/28 17:03
PHST- 2023/06/30 06:42 [medline]
PHST- 2023/06/28 19:14 [pubmed]
PHST- 2023/06/28 17:03 [entrez]
PST - ppublish
SO  - Medicina (B Aires). 2023;83(3):499-501.

PMID- 37280286
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230608
LR  - 20230608
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 618
IP  - 7964
DP  - 2023 Jun
TI  - ChatGPT: standard reporting guidelines for responsible use.
PG  - 238
LID - 10.1038/d41586-023-01853-w [doi]
FAU - Cacciamani, Giovanni E
AU  - Cacciamani GE
FAU - Collins, Gary S
AU  - Collins GS
FAU - Gill, Inderbir S
AU  - Gill IS
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Communication
OT  - Machine learning
EDAT- 2023/06/07 01:07
MHDA- 2023/06/07 01:08
CRDT- 2023/06/06 23:21
PHST- 2023/06/07 01:08 [medline]
PHST- 2023/06/07 01:07 [pubmed]
PHST- 2023/06/06 23:21 [entrez]
AID - 10.1038/d41586-023-01853-w [pii]
AID - 10.1038/d41586-023-01853-w [doi]
PST - ppublish
SO  - Nature. 2023 Jun;618(7964):238. doi: 10.1038/d41586-023-01853-w.

PMID- 37278392
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230810
LR  - 20230810
IS  - 1651-2227 (Electronic)
IS  - 0803-5253 (Linking)
VI  - 112
IP  - 9
DP  - 2023 Sep
TI  - Combining human and AI could predict nephrologies future, but should be handled 
      with care.
PG  - 1844-1848
LID - 10.1111/apa.16867 [doi]
FAU - Alhasan, Khalid
AU  - Alhasan K
AUID- ORCID: 0000-0002-4291-8536
AD  - Pediatrics Department, College of Medicine, King Saud University, Riyadh, Saudi 
      Arabia.
AD  - Solid Organ Transplant Center of Excellence, King Faisal Specialist Hospital and 
      Research Center, Riyadh, Saudi Arabia.
FAU - Raina, Rupesh
AU  - Raina R
AUID- ORCID: 0000-0003-3892-8376
AD  - Department of Nephrology, Cleveland Clinic Akron General and Akron Childrens 
      Hospital, Akron, Ohio, USA.
FAU - Jamal, Amr
AU  - Jamal A
AUID- ORCID: 0000-0002-4051-6592
AD  - Department of Family and Community Medicine, College of Medicine, King Saud 
      University, Riyadh, Saudi Arabia.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AUID- ORCID: 0000-0002-4389-9322
AD  - Pediatrics Department, College of Medicine, King Saud University, Riyadh, Saudi 
      Arabia.
LA  - eng
PT  - Journal Article
DEP - 20230612
PL  - Norway
TA  - Acta Paediatr
JT  - Acta paediatrica (Oslo, Norway : 1992)
JID - 9205968
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
EDAT- 2023/06/06 13:09
MHDA- 2023/06/06 13:10
CRDT- 2023/06/06 07:52
PHST- 2023/05/31 00:00 [revised]
PHST- 2023/05/10 00:00 [received]
PHST- 2023/06/02 00:00 [accepted]
PHST- 2023/06/06 13:10 [medline]
PHST- 2023/06/06 13:09 [pubmed]
PHST- 2023/06/06 07:52 [entrez]
AID - 10.1111/apa.16867 [doi]
PST - ppublish
SO  - Acta Paediatr. 2023 Sep;112(9):1844-1848. doi: 10.1111/apa.16867. Epub 2023 Jun 
      12.

PMID- 37265861
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230604
IS  - 2634-3916 (Electronic)
IS  - 2634-3916 (Linking)
VI  - 4
IP  - 3
DP  - 2023 May
TI  - ChatGPT's ability to classify virtual reality studies in cardiology.
PG  - 141-142
LID - 10.1093/ehjdh/ztad026 [doi]
FAU - Nakaya, Yuichiro
AU  - Nakaya Y
AD  - Department of Cardiology, Pulmonology, Hypertension, and Nephrology, Ehime 
      University Graduate School of Medicine, 454 Shitsukawa, Toon 791-0204, Japan.
FAU - Higaki, Akinori
AU  - Higaki A
AUID- ORCID: 0000-0002-0827-874X
AD  - Department of Cardiology, Pulmonology, Hypertension, and Nephrology, Ehime 
      University Graduate School of Medicine, 454 Shitsukawa, Toon 791-0204, Japan.
AD  - Department of Intractable Disease and Aging Science, Ehime University Graduate 
      School of Medicine, 454 Shitsukawa, Toon 791-0204, Japan.
FAU - Yamaguchi, Osamu
AU  - Yamaguchi O
AUID- ORCID: 0000-0002-7461-369X
AD  - Department of Cardiology, Pulmonology, Hypertension, and Nephrology, Ehime 
      University Graduate School of Medicine, 454 Shitsukawa, Toon 791-0204, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230417
PL  - England
TA  - Eur Heart J Digit Health
JT  - European heart journal. Digital health
JID - 101778323
PMC - PMC10232268
COIS- Conflict of interest: None declared.
EDAT- 2023/06/02 13:15
MHDA- 2023/06/02 13:16
CRDT- 2023/06/02 10:51
PHST- 2023/06/02 13:16 [medline]
PHST- 2023/06/02 13:15 [pubmed]
PHST- 2023/06/02 10:51 [entrez]
AID - ztad026 [pii]
AID - 10.1093/ehjdh/ztad026 [doi]
PST - epublish
SO  - Eur Heart J Digit Health. 2023 Apr 17;4(3):141-142. doi: 10.1093/ehjdh/ztad026. 
      eCollection 2023 May.

PMID- 37231970
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230804
LR  - 20230804
IS  - 1530-8561 (Electronic)
IS  - 0009-9147 (Linking)
VI  - 69
IP  - 8
DP  - 2023 Aug 2
TI  - Assessing the Accuracy and Clinical Utility of ChatGPT in Laboratory Medicine.
PG  - 939-940
LID - 10.1093/clinchem/hvad058 [doi]
FAU - Munoz-Zuluaga, Carlos
AU  - Munoz-Zuluaga C
AD  - Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New 
      York, NY 10065, United States.
FAU - Zhao, Zhen
AU  - Zhao Z
AUID- ORCID: 0000-0002-9742-5982
AD  - Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New 
      York, NY 10065, United States.
FAU - Wang, Fei
AU  - Wang F
AD  - Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 
      United States.
FAU - Greenblatt, Matthew B
AU  - Greenblatt MB
AD  - Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New 
      York, NY 10065, United States.
AD  - Research Division, Hospital for Special Surgery, New York, NY, United States.
FAU - Yang, He S
AU  - Yang HS
AD  - Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New 
      York, NY 10065, United States.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Clin Chem
JT  - Clinical chemistry
JID - 9421549
SB  - IM
EDAT- 2023/05/26 06:42
MHDA- 2023/05/26 06:43
CRDT- 2023/05/26 03:53
PHST- 2023/02/13 00:00 [received]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/05/26 06:43 [medline]
PHST- 2023/05/26 06:42 [pubmed]
PHST- 2023/05/26 03:53 [entrez]
AID - 7180070 [pii]
AID - 10.1093/clinchem/hvad058 [doi]
PST - ppublish
SO  - Clin Chem. 2023 Aug 2;69(8):939-940. doi: 10.1093/clinchem/hvad058.

PMID- 37195807
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230822
LR  - 20230831
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 8
DP  - 2023 Aug 1
TI  - ChatGPT has made the field of surgery full of opportunities and challenges.
PG  - 2537-2538
LID - 10.1097/JS9.0000000000000454 [doi]
FAU - Li, Sheng
AU  - Li S
AUID- ORCID: 0000-0001-6083-4945
AD  - Department of Anorectal Surgery, Ningbo No. 2 Hospital, Ningbo, Zhejiang, 
      People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
PMC - PMC10442134
COIS- No conflict of interest.
EDAT- 2023/05/17 13:10
MHDA- 2023/05/17 13:11
CRDT- 2023/05/17 12:03
PHST- 2023/04/21 00:00 [received]
PHST- 2023/05/01 00:00 [accepted]
PHST- 2023/05/17 13:11 [medline]
PHST- 2023/05/17 13:10 [pubmed]
PHST- 2023/05/17 12:03 [entrez]
AID - 01279778-202308000-00042 [pii]
AID - IJS-D-23-00733 [pii]
AID - 10.1097/JS9.0000000000000454 [doi]
PST - epublish
SO  - Int J Surg. 2023 Aug 1;109(8):2537-2538. doi: 10.1097/JS9.0000000000000454.

PMID- 37032521
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 8
DP  - 2023 Jul 15
TI  - Utilizing ChatGPT-4 for Providing Medical Information on Blepharoplasties to 
      Patients.
PG  - NP658-NP662
LID - 10.1093/asj/sjad096 [doi]
FAU - Cox, Aram
AU  - Cox A
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
FAU - Xie, Yi
AU  - Xie Y
FAU - Hunter-Smith, David J
AU  - Hunter-Smith DJ
FAU - Rozen, Warren M
AU  - Rozen WM
LA  - eng
PT  - Letter
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
MH  - Humans
MH  - *Blepharoplasty
MH  - Eyelids
MH  - Patients
EDAT- 2023/04/11 06:00
MHDA- 2023/07/17 06:42
CRDT- 2023/04/10 01:23
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 01:23 [entrez]
AID - 7111402 [pii]
AID - 10.1093/asj/sjad096 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jul 15;43(8):NP658-NP662. doi: 10.1093/asj/sjad096.

PMID- 36940462
OWN - NLM
STAT- MEDLINE
DCOM- 20230518
LR  - 20230530
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
VI  - 45
IP  - 6
DP  - 2023 Jun
TI  - Response to: Aye, AI! ChatGPT passes multiple-choice family medicine exam.
PG  - 666
LID - 10.1080/0142159X.2023.2190476 [doi]
FAU - Masters, Ken
AU  - Masters K
AUID- ORCID: 0000-0003-3425-5020
AD  - Department of Medical Education and Informatics, College of Medicine and Health 
      Sciences, Sultan Qaboos University, Muscat, Oman.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230320
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
CON - Med Teach. 2023 Jun;45(6):665-666. PMID: 36905610
MH  - Humans
MH  - *Family Practice
MH  - *Artificial Intelligence
EDAT- 2023/03/21 06:00
MHDA- 2023/05/18 06:42
CRDT- 2023/03/20 17:32
PHST- 2023/05/18 06:42 [medline]
PHST- 2023/03/21 06:00 [pubmed]
PHST- 2023/03/20 17:32 [entrez]
AID - 10.1080/0142159X.2023.2190476 [doi]
PST - ppublish
SO  - Med Teach. 2023 Jun;45(6):666. doi: 10.1080/0142159X.2023.2190476. Epub 2023 Mar 
      20.

PMID- 36856370
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230303
LR  - 20231106
IS  - 2001-1326 (Electronic)
IS  - 2001-1326 (Linking)
VI  - 13
IP  - 3
DP  - 2023 Mar
TI  - The potential impact of ChatGPT in clinical and translational medicine.
PG  - e1216
LID - 10.1002/ctm2.1216 [doi]
LID - e1216
FAU - Xue, Vivian Weiwen
AU  - Xue VW
AUID- ORCID: 0000-0002-0737-3593
AD  - Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department 
      of Pharmacology, Carson International Cancer Center, Shenzhen University Health 
      Science Center, Shenzhen, Guangdong, China.
FAU - Lei, Pinggui
AU  - Lei P
AUID- ORCID: 0000-0001-7610-0292
AD  - Department of Radiology, The Affiliated Hospital of Guizhou Medical University, 
      Guiyang, Guizhou, China.
AD  - School of Public Health, Guizhou Medical University, Guiyang, Guizhou, China.
FAU - Cho, William C
AU  - Cho WC
AUID- ORCID: 0000-0003-4174-4586
AD  - Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Clin Transl Med
JT  - Clinical and translational medicine
JID - 101597971
SB  - IM
PMC - PMC9976604
EDAT- 2023/03/02 06:00
MHDA- 2023/03/02 06:01
CRDT- 2023/03/01 08:53
PHST- 2023/02/18 00:00 [received]
PHST- 2023/02/21 00:00 [accepted]
PHST- 2023/03/01 08:53 [entrez]
PHST- 2023/03/02 06:00 [pubmed]
PHST- 2023/03/02 06:01 [medline]
AID - CTM21216 [pii]
AID - 10.1002/ctm2.1216 [doi]
PST - ppublish
SO  - Clin Transl Med. 2023 Mar;13(3):e1216. doi: 10.1002/ctm2.1216.

PMID- 38009900
OWN - NLM
STAT- Publisher
LR  - 20231127
IS  - 1601-0825 (Electronic)
IS  - 1354-523X (Linking)
DP  - 2023 Nov 27
TI  - ChatGPT in oral medicine: Striking a balance between technological advancement 
      and clinical expertise.
LID - 10.1111/odi.14823 [doi]
FAU - Cunha, John Lennon Silva
AU  - Cunha JLS
AUID- ORCID: 0000-0002-0679-8485
AD  - Postgraduate Program in Dentistry, Department of Dentistry, State University of 
      Paraíba (UEPB), Campina Grande, Brazil.
FAU - Alves, Pollianna Muniz
AU  - Alves PM
AUID- ORCID: 0000-0003-1297-4032
AD  - Postgraduate Program in Dentistry, Department of Dentistry, State University of 
      Paraíba (UEPB), Campina Grande, Brazil.
FAU - Nonaka, Cassiano Francisco Weege
AU  - Nonaka CFW
AUID- ORCID: 0000-0003-2380-109X
AD  - Postgraduate Program in Dentistry, Department of Dentistry, State University of 
      Paraíba (UEPB), Campina Grande, Brazil.
LA  - eng
PT  - Letter
DEP - 20231127
PL  - Denmark
TA  - Oral Dis
JT  - Oral diseases
JID - 9508565
EDAT- 2023/11/27 12:43
MHDA- 2023/11/27 12:43
CRDT- 2023/11/27 08:54
PHST- 2023/11/13 00:00 [revised]
PHST- 2023/10/17 00:00 [received]
PHST- 2023/11/14 00:00 [accepted]
PHST- 2023/11/27 12:43 [medline]
PHST- 2023/11/27 12:43 [pubmed]
PHST- 2023/11/27 08:54 [entrez]
AID - 10.1111/odi.14823 [doi]
PST - aheadofprint
SO  - Oral Dis. 2023 Nov 27. doi: 10.1111/odi.14823.

PMID- 37832625
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1873-1740 (Electronic)
IS  - 0033-0620 (Linking)
VI  - 81
DP  - 2023 Nov-Dec
TI  - Assessing the potential of ChatGPT for patient education in the cardiology 
      clinic.
PG  - 109-110
LID - S0033-0620(23)00105-6 [pii]
LID - 10.1016/j.pcad.2023.10.002 [doi]
FAU - Krittanawong, Chayakrit
AU  - Krittanawong C
AD  - Cardiology Division, NYU Langone Health and NYU School of Medicine, New York, NY, 
      USA. Electronic address: chayakrit.krittanawong@va.gov.
FAU - Rodriguez, Mario
AU  - Rodriguez M
AD  - John T Milliken Department of Medicine, Division of Cardiovascular disease, 
      Section of Advanced Heart Failure and Transplant, Barnes-Jewish 
      Hospital/Washington University in St.Louis School of Medicine, USA.
FAU - Kaplin, Scott
AU  - Kaplin S
AD  - Cardiology Division, NYU Langone Health and NYU School of Medicine, New York, NY, 
      USA.
FAU - Tang, W H Wilson
AU  - Tang WHW
AD  - Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland, 
      Clinic, Ohio, USA.
LA  - eng
PT  - Letter
DEP - 20231011
PL  - United States
TA  - Prog Cardiovasc Dis
JT  - Progress in cardiovascular diseases
JID - 0376442
SB  - IM
MH  - Humans
MH  - *Patient Education as Topic
MH  - *Cardiology
COIS- Declaration of Competing Interest None.
EDAT- 2023/10/14 10:43
MHDA- 2023/12/05 12:43
CRDT- 2023/10/13 19:24
PHST- 2023/12/05 12:43 [medline]
PHST- 2023/10/14 10:43 [pubmed]
PHST- 2023/10/13 19:24 [entrez]
AID - S0033-0620(23)00105-6 [pii]
AID - 10.1016/j.pcad.2023.10.002 [doi]
PST - ppublish
SO  - Prog Cardiovasc Dis. 2023 Nov-Dec;81:109-110. doi: 10.1016/j.pcad.2023.10.002. 
      Epub 2023 Oct 11.

PMID- 37524422
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230802
LR  - 20231004
IS  - 1473-4893 (Electronic)
IS  - 1470-2118 (Print)
IS  - 1470-2118 (Linking)
VI  - 23
IP  - 4
DP  - 2023 Jul
TI  - Early applications of ChatGPT in medical practice, education and research.
PG  - 429-430
LID - 10.7861/clinmed.Let.23.4.2 [doi]
FAU - Arachchige, Arosh S Perera Molligoda
AU  - Arachchige ASPM
AD  - Humanitas University, Milan, Italy.
LA  - eng
PT  - Comment
PT  - Letter
PL  - England
TA  - Clin Med (Lond)
JT  - Clinical medicine (London, England)
JID - 101092853
SB  - IM
CON - Clin Med (Lond). 2023 May;23(3):278-279. PMID: 37085182
PMC - PMC10541035
EDAT- 2023/08/01 01:08
MHDA- 2023/08/02 06:42
CRDT- 2023/07/31 20:42
PHST- 2023/08/02 06:42 [medline]
PHST- 2023/08/01 01:08 [pubmed]
PHST- 2023/07/31 20:42 [entrez]
AID - 23/4/429-a [pii]
AID - clinmedicine [pii]
AID - 10.7861/clinmed.Let.23.4.2 [doi]
PST - ppublish
SO  - Clin Med (Lond). 2023 Jul;23(4):429-430. doi: 10.7861/clinmed.Let.23.4.2.

PMID- 37314310
OWN - NLM
STAT- MEDLINE
DCOM- 20230811
LR  - 20230911
IS  - 1728-7731 (Electronic)
IS  - 1726-4901 (Linking)
VI  - 86
IP  - 8
DP  - 2023 Aug 1
TI  - Comment on "ChatGPT failed Taiwan's Family Medicine Board Exam".
PG  - 775
LID - 10.1097/JCMA.0000000000000951 [doi]
FAU - Mungmunpuntipantip, Rujittika
AU  - Mungmunpuntipantip R
AD  - Consultant Unit, Private Academic Consultant, Bangkok, Thailand.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Research Center, Chandigarh University, Punjab, India.
AD  - Department of Biological Science, Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230614
PL  - Netherlands
TA  - J Chin Med Assoc
JT  - Journal of the Chinese Medical Association : JCMA
JID - 101174817
SB  - IM
CON - J Chin Med Assoc. 2023 Aug 1;86(8):762-766. PMID: 37294147
CIN - J Chin Med Assoc. 2023 Sep 1;86(9):865. PMID: 37418340
MH  - Taiwan
MH  - *Family Practice
MH  - *National Health Programs
COIS- Conflicts of interest: The authors declare that they have no conflicts of 
      interest related to the subject matter or materials discussed in this article.
EDAT- 2023/06/14 13:07
MHDA- 2023/08/11 06:42
CRDT- 2023/06/14 09:53
PHST- 2023/08/11 06:42 [medline]
PHST- 2023/06/14 13:07 [pubmed]
PHST- 2023/06/14 09:53 [entrez]
AID - 02118582-990000000-00227 [pii]
AID - 10.1097/JCMA.0000000000000951 [doi]
PST - ppublish
SO  - J Chin Med Assoc. 2023 Aug 1;86(8):775. doi: 10.1097/JCMA.0000000000000951. Epub 
      2023 Jun 14.

PMID- 37236804
OWN - NLM
STAT- MEDLINE
DCOM- 20230529
LR  - 20230601
IS  - 1473-4893 (Electronic)
IS  - 1470-2118 (Linking)
VI  - 23
IP  - 3
DP  - 2023 May
TI  - ChatGPT in medical practice, education and research: malpractice and plagiarism.
PG  - 280
LID - 10.7861/clinmed.Let.23.3.2 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India; adjunct professor, Joesph Ayobabalola 
      University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
PL  - England
TA  - Clin Med (Lond)
JT  - Clinical medicine (London, England)
JID - 101092853
SB  - IM
MH  - Humans
MH  - *Plagiarism
MH  - Educational Status
MH  - *Malpractice
EDAT- 2023/05/27 09:42
MHDA- 2023/05/29 06:41
CRDT- 2023/05/26 21:23
PHST- 2023/05/29 06:41 [medline]
PHST- 2023/05/27 09:42 [pubmed]
PHST- 2023/05/26 21:23 [entrez]
AID - 23/3/280-a [pii]
AID - 10.7861/clinmed.Let.23.3.2 [doi]
PST - ppublish
SO  - Clin Med (Lond). 2023 May;23(3):280. doi: 10.7861/clinmed.Let.23.3.2.

PMID- 37210418
OWN - NLM
STAT- MEDLINE
DCOM- 20230821
LR  - 20230821
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 9
DP  - 2023 Aug 17
TI  - The Potential of Applying ChatGPT to Extract Keywords of Medical Literature in 
      Plastic Surgery.
PG  - NP720-NP723
LID - 10.1093/asj/sjad158 [doi]
FAU - Zhou, Jingyang
AU  - Zhou J
FAU - Jia, Yang
AU  - Jia Y
FAU - Qiu, Yuzhi
AU  - Qiu Y
FAU - Lin, Lin
AU  - Lin L
AUID- ORCID: 0009-0004-7039-361X
LA  - eng
PT  - Letter
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
EDAT- 2023/05/21 01:05
MHDA- 2023/08/21 06:42
CRDT- 2023/05/20 23:06
PHST- 2023/08/21 06:42 [medline]
PHST- 2023/05/21 01:05 [pubmed]
PHST- 2023/05/20 23:06 [entrez]
AID - 7175052 [pii]
AID - 10.1093/asj/sjad158 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Aug 17;43(9):NP720-NP723. doi: 10.1093/asj/sjad158.

PMID- 37171079
OWN - NLM
STAT- Publisher
LR  - 20230512
IS  - 1488-2361 (Electronic)
IS  - 0846-5371 (Linking)
DP  - 2023 May 12
TI  - ChatGPT in Radiology: A Deeper Look Into its Limitations and Potential Pathways 
      for Improvement.
PG  - 8465371231177674
LID - 10.1177/08465371231177674 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. RINGGOLD: 179229
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, Kolkata, India. RINGGOLD: 74998
LA  - eng
PT  - Letter
DEP - 20230512
PL  - United States
TA  - Can Assoc Radiol J
JT  - Canadian Association of Radiologists journal = Journal l'Association canadienne 
      des radiologistes
JID - 8812910
SB  - IM
EDAT- 2023/05/12 13:08
MHDA- 2023/05/12 13:08
CRDT- 2023/05/12 07:52
PHST- 2023/05/12 13:08 [medline]
PHST- 2023/05/12 13:08 [pubmed]
PHST- 2023/05/12 07:52 [entrez]
AID - 10.1177/08465371231177674 [doi]
PST - aheadofprint
SO  - Can Assoc Radiol J. 2023 May 12:8465371231177674. doi: 10.1177/08465371231177674.

PMID- 37106089
OWN - NLM
STAT- MEDLINE
DCOM- 20230505
LR  - 20230510
IS  - 1432-1998 (Electronic)
IS  - 0301-0449 (Linking)
VI  - 53
IP  - 5
DP  - 2023 May
TI  - Role of ChatGPT in radiology with a focus on pediatric radiology: proof by 
      examples.
PG  - 818-822
LID - 10.1007/s00247-023-05675-w [doi]
FAU - Biswas, Som S
AU  - Biswas SS
AUID- ORCID: 0000-0002-4038-5844
AD  - Department of Pediatric Radiology, Le Bonheur Children's Hospital, The University 
      of Tennessee Health Science Center, Memphis, TN, 38103, USA. sbiswas8@uthsc.edu.
LA  - eng
PT  - Editorial
DEP - 20230428
PL  - Germany
TA  - Pediatr Radiol
JT  - Pediatric radiology
JID - 0365332
SB  - IM
MH  - Child
MH  - Humans
MH  - *Radiology
MH  - Radiography
EDAT- 2023/04/28 00:42
MHDA- 2023/05/05 06:42
CRDT- 2023/04/27 23:21
PHST- 2023/03/28 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/04/10 00:00 [revised]
PHST- 2023/05/05 06:42 [medline]
PHST- 2023/04/28 00:42 [pubmed]
PHST- 2023/04/27 23:21 [entrez]
AID - 10.1007/s00247-023-05675-w [pii]
AID - 10.1007/s00247-023-05675-w [doi]
PST - ppublish
SO  - Pediatr Radiol. 2023 May;53(5):818-822. doi: 10.1007/s00247-023-05675-w. Epub 
      2023 Apr 28.

PMID- 36923604
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230316
IS  - 2405-805X (Electronic)
IS  - 2405-805X (Linking)
VI  - 8
IP  - 2
DP  - 2023 Jun
TI  - Discovering the next decade's synthetic biology research trends with ChatGPT.
PG  - 220-223
LID - 10.1016/j.synbio.2023.02.004 [doi]
FAU - Tong, Yaojun
AU  - Tong Y
AD  - State Key Laboratory of Microbial Metabolism, Joint International Research 
      Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and 
      Technology, Shanghai Jiao Tong University, Shanghai, 200240, China.
FAU - Zhang, Lixin
AU  - Zhang L
AD  - State Key Laboratory of Bioreactor Engineering, School of Biotechnology, East 
      China University of Science and Technology (ECUST), Shanghai, 200237, China.
LA  - eng
PT  - Editorial
DEP - 20230222
PL  - China
TA  - Synth Syst Biotechnol
JT  - Synthetic and systems biotechnology
JID - 101694371
PMC - PMC10009671
EDAT- 2023/03/17 06:00
MHDA- 2023/03/17 06:01
CRDT- 2023/03/16 02:26
PHST- 2023/03/16 02:26 [entrez]
PHST- 2023/03/17 06:00 [pubmed]
PHST- 2023/03/17 06:01 [medline]
AID - S2405-805X(23)00017-0 [pii]
AID - 10.1016/j.synbio.2023.02.004 [doi]
PST - epublish
SO  - Synth Syst Biotechnol. 2023 Feb 22;8(2):220-223. doi: 
      10.1016/j.synbio.2023.02.004. eCollection 2023 Jun.

PMID- 37801546
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1527-6473 (Electronic)
IS  - 1527-6465 (Linking)
DP  - 2023 Oct 9
TI  - Letter to the Editor: ChatGPT and unknown clinical questions about liver 
      transplantation.
LID - 10.1097/LVT.0000000000000281 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Oddar Meanchey, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Research Center, Chandigarh University, Punjab, India.
LA  - eng
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Liver Transpl
JT  - Liver transplantation : official publication of the American Association for the 
      Study of Liver Diseases and the International Liver Transplantation Society
JID - 100909185
SB  - IM
EDAT- 2023/10/06 18:41
MHDA- 2023/10/06 18:41
CRDT- 2023/10/06 14:12
PHST- 2023/08/25 00:00 [received]
PHST- 2023/08/27 00:00 [accepted]
PHST- 2023/10/06 18:41 [pubmed]
PHST- 2023/10/06 18:41 [medline]
PHST- 2023/10/06 14:12 [entrez]
AID - 01445473-990000000-00262 [pii]
AID - 10.1097/LVT.0000000000000281 [doi]
PST - aheadofprint
SO  - Liver Transpl. 2023 Oct 9. doi: 10.1097/LVT.0000000000000281.

PMID- 37573082
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1532-8406 (Electronic)
IS  - 0883-5403 (Linking)
VI  - 38
IP  - 9
DP  - 2023 Sep
TI  - Letter to the Editor "Utility of ChatGPT in Total Joint Arthroplasty".
PG  - e18
LID - S0883-5403(23)00582-X [pii]
LID - 10.1016/j.arth.2023.05.058 [doi]
FAU - Dubin, Jeremy A
AU  - Dubin JA
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Bains, Sandeep S
AU  - Bains SS
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Chen, Zhongming
AU  - Chen Z
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Hameed, Daniel
AU  - Hameed D
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Nace, James
AU  - Nace J
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Mont, Michael A
AU  - Mont MA
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Delanois, Ronald E
AU  - Delanois RE
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - J Arthroplasty
JT  - The Journal of arthroplasty
JID - 8703515
SB  - IM
CON - J Arthroplasty. 2023 Jul;38(7):1195-1202. PMID: 37040823
CON - J Arthroplasty. 2023 Sep;38(9):e17. PMID: 37573081
MH  - Humans
MH  - *Arthroplasty
MH  - *Artificial Intelligence
EDAT- 2023/08/13 00:43
MHDA- 2023/08/14 06:42
CRDT- 2023/08/12 20:58
PHST- 2023/05/24 00:00 [received]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/13 00:43 [pubmed]
PHST- 2023/08/12 20:58 [entrez]
AID - S0883-5403(23)00582-X [pii]
AID - 10.1016/j.arth.2023.05.058 [doi]
PST - ppublish
SO  - J Arthroplasty. 2023 Sep;38(9):e18. doi: 10.1016/j.arth.2023.05.058.

PMID- 37561669
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230816
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Print)
IS  - 1553-734X (Linking)
VI  - 19
IP  - 8
DP  - 2023 Aug
TI  - Ten quick tips for harnessing the power of ChatGPT in computational biology.
PG  - e1011319
LID - 10.1371/journal.pcbi.1011319 [doi]
LID - e1011319
FAU - Lubiana, Tiago
AU  - Lubiana T
AUID- ORCID: 0000-0003-2473-2313
AD  - School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil.
FAU - Lopes, Rafael
AU  - Lopes R
AUID- ORCID: 0000-0002-9416-6145
AD  - Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, 
      Yale School of Public Health, New Haven, Connecticut, United States of America.
FAU - Medeiros, Pedro
AU  - Medeiros P
AD  - TauGC Bioinformatics, São Paulo, Brazil.
FAU - Silva, Juan Carlo
AU  - Silva JC
AUID- ORCID: 0000-0002-7327-4294
AD  - School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil.
FAU - Goncalves, Andre Nicolau Aquime
AU  - Goncalves ANA
AUID- ORCID: 0000-0001-5264-8368
AD  - Oxford Vaccine Group, University of Oxford, Oxford, United Kingdom.
FAU - Maracaja-Coutinho, Vinicius
AU  - Maracaja-Coutinho V
AUID- ORCID: 0000-0002-8873-9381
AD  - Advanced Center for Chronic Diseases, Universidad de Chile, Santiago, Chile.
AD  - Centro de Modelamiento Molecular, Biofísica y Bioinformática-CM2B2, Facultad de 
      Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile.
AD  - ANID Anillo ACT210004 SYSTEMIX, Rancagua, Chile.
AD  - Anillo Inflammation in HIV/AIDS-InflammAIDS, Santiago, Chile.
AD  - Beagle Bioinformatics, São Paulo, Brasil & Santiago, Chile.
FAU - Nakaya, Helder I
AU  - Nakaya HI
AUID- ORCID: 0000-0001-5297-9108
AD  - School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil.
AD  - Hospital Israelita Albert Einstein, São Paulo, Brazil.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230810
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
SB  - IM
MH  - *Computational Biology
MH  - *Artificial Intelligence
PMC - PMC10414555
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/10 18:41
MHDA- 2023/08/14 06:42
CRDT- 2023/08/10 13:23
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/10 18:41 [pubmed]
PHST- 2023/08/10 13:23 [entrez]
AID - PCOMPBIOL-D-23-00492 [pii]
AID - 10.1371/journal.pcbi.1011319 [doi]
PST - epublish
SO  - PLoS Comput Biol. 2023 Aug 10;19(8):e1011319. doi: 10.1371/journal.pcbi.1011319. 
      eCollection 2023 Aug.

PMID- 37483875
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230928
IS  - 1869-3474 (Print)
IS  - 1869-3482 (Electronic)
IS  - 1869-3474 (Linking)
VI  - 57
IP  - 4
DP  - 2023 Aug
TI  - Exploring the Opportunities and Challenges of ChatGPT in Academic Writing: a 
      Roundtable Discussion.
PG  - 165-167
LID - 10.1007/s13139-023-00809-2 [doi]
FAU - Bom, Hee-Seung Henry
AU  - Bom HH
AUID- ORCID: 0000-0002-5570-7375
AD  - Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, 322 
      Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeollanam-do Republic of Korea. GRID: 
      grid.411602.0. ISNI: 0000 0004 0647 9534
LA  - eng
PT  - Editorial
DEP - 20230523
PL  - Germany
TA  - Nucl Med Mol Imaging
JT  - Nuclear medicine and molecular imaging
JID - 101530478
PMC - PMC10359226
COIS- Conflict of InterestHee-Seung Henry Bom has no conflict of interest.
EDAT- 2023/07/24 06:42
MHDA- 2023/07/24 06:43
PMCR- 2024/08/01
CRDT- 2023/07/24 04:36
PHST- 2023/05/09 00:00 [received]
PHST- 2023/05/09 00:00 [revised]
PHST- 2023/05/11 00:00 [accepted]
PHST- 2024/08/01 00:00 [pmc-release]
PHST- 2023/07/24 06:43 [medline]
PHST- 2023/07/24 06:42 [pubmed]
PHST- 2023/07/24 04:36 [entrez]
AID - 809 [pii]
AID - 10.1007/s13139-023-00809-2 [doi]
PST - ppublish
SO  - Nucl Med Mol Imaging. 2023 Aug;57(4):165-167. doi: 10.1007/s13139-023-00809-2. 
      Epub 2023 May 23.

PMID- 37443308
OWN - NLM
STAT- Publisher
LR  - 20230719
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Jul 13
TI  - ChatGPT gives an extra productivity boost to weaker writers.
LID - 10.1038/d41586-023-02270-9 [doi]
FAU - Lenharo, Mariana
AU  - Lenharo M
LA  - eng
PT  - News
DEP - 20230713
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Machine learning
OT  - Publishing
EDAT- 2023/07/14 13:05
MHDA- 2023/07/14 13:05
CRDT- 2023/07/13 23:55
PHST- 2023/07/14 13:05 [pubmed]
PHST- 2023/07/14 13:05 [medline]
PHST- 2023/07/13 23:55 [entrez]
AID - 10.1038/d41586-023-02270-9 [pii]
AID - 10.1038/d41586-023-02270-9 [doi]
PST - aheadofprint
SO  - Nature. 2023 Jul 13. doi: 10.1038/d41586-023-02270-9.

PMID- 37380244
OWN - NLM
STAT- MEDLINE
DCOM- 20230630
LR  - 20230703
IS  - 1876-7605 (Electronic)
IS  - 1936-8798 (Linking)
VI  - 16
IP  - 12
DP  - 2023 Jun 26
TI  - Assessing the Potential of ChatGPT for Patient Education in Cardiac 
      Catheterization Care.
PG  - 1551-1552
LID - S1936-8798(23)00814-2 [pii]
LID - 10.1016/j.jcin.2023.04.042 [doi]
FAU - Krittanawong, Chayakrit
AU  - Krittanawong C
FAU - Virk, Hafeez Ul Hassan
AU  - Virk HUH
FAU - Kaplin, Scott L
AU  - Kaplin SL
FAU - Wang, Zhen
AU  - Wang Z
FAU - Sharma, Samin
AU  - Sharma S
FAU - Jneid, Hani
AU  - Jneid H
LA  - eng
PT  - Letter
PL  - United States
TA  - JACC Cardiovasc Interv
JT  - JACC. Cardiovascular interventions
JID - 101467004
SB  - IM
MH  - Humans
MH  - Treatment Outcome
MH  - *Patient Education as Topic
MH  - *Cardiac Catheterization/adverse effects
EDAT- 2023/06/29 01:08
MHDA- 2023/06/30 06:42
CRDT- 2023/06/28 20:57
PHST- 2023/04/09 00:00 [received]
PHST- 2023/04/18 00:00 [revised]
PHST- 2023/04/25 00:00 [accepted]
PHST- 2023/06/30 06:42 [medline]
PHST- 2023/06/29 01:08 [pubmed]
PHST- 2023/06/28 20:57 [entrez]
AID - S1936-8798(23)00814-2 [pii]
AID - 10.1016/j.jcin.2023.04.042 [doi]
PST - ppublish
SO  - JACC Cardiovasc Interv. 2023 Jun 26;16(12):1551-1552. doi: 
      10.1016/j.jcin.2023.04.042.

PMID- 37351764
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231119
IS  - 1708-0428 (Electronic)
IS  - 0960-8923 (Linking)
VI  - 33
IP  - 8
DP  - 2023 Aug
TI  - Accuracy of Responses by the Language Model ChatGPT and Bariatric Surgery: 
      Comment.
PG  - 2596
LID - 10.1007/s11695-023-06687-z [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia. amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, Chandigarh, India.
AD  - Joesph Ayobabalola University, Ikeji‑Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230623
PL  - United States
TA  - Obes Surg
JT  - Obesity surgery
JID - 9106714
SB  - IM
CON - Obes Surg. 2023 Jun;33(6):1790-1796. PMID: 37106269
CIN - Obes Surg. 2023 Aug;33(8):2597. PMID: 37353693
MH  - Humans
MH  - *Obesity, Morbid/surgery
MH  - *Bariatric Surgery
EDAT- 2023/06/23 13:07
MHDA- 2023/07/17 06:42
CRDT- 2023/06/23 11:13
PHST- 2023/04/29 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/04/29 00:00 [revised]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/06/23 13:07 [pubmed]
PHST- 2023/06/23 11:13 [entrez]
AID - 10.1007/s11695-023-06687-z [pii]
AID - 10.1007/s11695-023-06687-z [doi]
PST - ppublish
SO  - Obes Surg. 2023 Aug;33(8):2596. doi: 10.1007/s11695-023-06687-z. Epub 2023 Jun 
      23.

PMID- 37321598
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231120
IS  - 1442-9071 (Electronic)
IS  - 1442-6404 (Linking)
VI  - 51
IP  - 7
DP  - 2023 Sep-Oct
TI  - ChatGPT as a tool for conducting literature review for dry eye disease.
PG  - 731-732
LID - 10.1111/ceo.14268 [doi]
FAU - Singh, Swati
AU  - Singh S
AUID- ORCID: 0000-0001-7420-9438
AD  - Ophthalmic Plastic Surgery Services, L V Prasad Eye Institute, Hyderabad, India.
FAU - Watson, Stephanie
AU  - Watson S
AUID- ORCID: 0000-0001-6699-1765
AD  - Discipline of Ophthalmology, Sydney Medical School, The University of Sydney, 
      Save Sight Institute, Sydney, New South Wales, Australia.
LA  - eng
PT  - Letter
DEP - 20230615
PL  - Australia
TA  - Clin Exp Ophthalmol
JT  - Clinical & experimental ophthalmology
JID - 100896531
SB  - IM
MH  - Humans
MH  - *Dry Eye Syndromes/diagnosis/drug therapy
MH  - Review Literature as Topic
MH  - *Artificial Intelligence
EDAT- 2023/06/16 01:08
MHDA- 2023/06/16 01:09
CRDT- 2023/06/15 20:02
PHST- 2023/05/07 00:00 [revised]
PHST- 2023/03/27 00:00 [received]
PHST- 2023/06/02 00:00 [accepted]
PHST- 2023/06/16 01:09 [medline]
PHST- 2023/06/16 01:08 [pubmed]
PHST- 2023/06/15 20:02 [entrez]
AID - 10.1111/ceo.14268 [doi]
PST - ppublish
SO  - Clin Exp Ophthalmol. 2023 Sep-Oct;51(7):731-732. doi: 10.1111/ceo.14268. Epub 
      2023 Jun 15.

PMID- 37142188
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230810
IS  - 2589-9333 (Electronic)
IS  - 2589-9333 (Linking)
VI  - 5
IP  - 8
DP  - 2023 Aug
TI  - Is institutional review board approval required for studies involving ChatGPT?
PG  - 101005
LID - S2589-9333(23)00147-7 [pii]
LID - 10.1016/j.ajogmf.2023.101005 [doi]
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Independent Researcher, Ikenobe 3011-2, Kagawa-ken, 761-0799, Japan. Electronic 
      address: jaimetex@yahoo.com.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230503
PL  - United States
TA  - Am J Obstet Gynecol MFM
JT  - American journal of obstetrics & gynecology MFM
JID - 101746609
SB  - IM
CON - Am J Obstet Gynecol MFM. 2023 Jun;5(6):100936. PMID: 36931435
CIN - Am J Obstet Gynecol MFM. 2023 Aug;5(8):101040. PMID: 37277091
MH  - Humans
MH  - *Ethics Committees, Research
MH  - Artificial Intelligence
COIS- Declaration of Competing Interest None.
EDAT- 2023/05/05 00:42
MHDA- 2023/08/07 06:42
CRDT- 2023/05/04 19:28
PHST- 2023/04/01 00:00 [received]
PHST- 2023/04/09 00:00 [accepted]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/05/05 00:42 [pubmed]
PHST- 2023/05/04 19:28 [entrez]
AID - S2589-9333(23)00147-7 [pii]
AID - 10.1016/j.ajogmf.2023.101005 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol MFM. 2023 Aug;5(8):101005. doi: 10.1016/j.ajogmf.2023.101005. 
      Epub 2023 May 3.

PMID- 37045954
OWN - NLM
STAT- Publisher
LR  - 20230802
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Apr 12
TI  - How I use ChatGPT responsibly in my teaching.
LID - 10.1038/d41586-023-01026-9 [doi]
FAU - Yang, Hong
AU  - Yang H
LA  - eng
PT  - News
DEP - 20230412
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Careers
OT  - Lab life
OT  - Research management
EDAT- 2023/04/13 06:00
MHDA- 2023/04/13 06:00
CRDT- 2023/04/12 23:22
PHST- 2023/04/13 06:00 [pubmed]
PHST- 2023/04/13 06:00 [medline]
PHST- 2023/04/12 23:22 [entrez]
AID - 10.1038/d41586-023-01026-9 [pii]
AID - 10.1038/d41586-023-01026-9 [doi]
PST - aheadofprint
SO  - Nature. 2023 Apr 12. doi: 10.1038/d41586-023-01026-9.

PMID- 36822213
OWN - NLM
STAT- MEDLINE
DCOM- 20230328
LR  - 20230413
IS  - 1474-4457 (Electronic)
IS  - 1473-3099 (Linking)
VI  - 23
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT and antimicrobial advice: the end of the consulting infection doctor?
PG  - 405-406
LID - S1473-3099(23)00113-5 [pii]
LID - 10.1016/S1473-3099(23)00113-5 [doi]
FAU - Howard, Alex
AU  - Howard A
AD  - Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular 
      and Integrative Biology, University of Liverpool, Liverpool L7 8TX, UK. 
      Electronic address: alexander.howard@liverpool.ac.uk.
FAU - Hope, William
AU  - Hope W
AD  - Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular 
      and Integrative Biology, University of Liverpool, Liverpool L7 8TX, UK; 
      Department of Infection and Immunity, Liverpool Clinical Laboratories, Liverpool 
      University Hospitals NHS Foundation Trust, Liverpool, UK.
FAU - Gerada, Alessandro
AU  - Gerada A
AD  - Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular 
      and Integrative Biology, University of Liverpool, Liverpool L7 8TX, UK; 
      Department of Infection and Immunity, Liverpool Clinical Laboratories, Liverpool 
      University Hospitals NHS Foundation Trust, Liverpool, UK.
LA  - eng
PT  - Letter
DEP - 20230220
PL  - United States
TA  - Lancet Infect Dis
JT  - The Lancet. Infectious diseases
JID - 101130150
RN  - 0 (Anti-Infective Agents)
SB  - IM
MH  - Humans
MH  - *Physicians
MH  - Patient Acceptance of Health Care
MH  - *Anti-Infective Agents
COIS- WH holds or has held research grants with UKRI, EU, F2G, Spero Therapeutics, 
      Antabio, Pfizer, Bugworks, Phico Therapeutics, BioVersys, Global Antibiotic 
      Research & Development Partnership (GARDP), and NAEJA-RGM. WH is or has been a 
      consultant for Appili Therapeutics, F2G, Spero Therapeutics, NAEJA-RGM, Centauri, 
      Pfizer, Phico Therapeutics, Pulmocide, Amplyx, Mundipharma Research, and 
      VenatoRx. WH is a member of the Specialist Advisory Committee for GARDP and the 
      Specialty National co-lead for Infectious Diseases for the National Institute of 
      Health Research. All other authors declare no competing interests.
EDAT- 2023/02/24 06:00
MHDA- 2023/03/28 17:14
CRDT- 2023/02/23 19:03
PHST- 2023/02/08 00:00 [received]
PHST- 2023/02/09 00:00 [revised]
PHST- 2023/02/09 00:00 [accepted]
PHST- 2023/03/28 17:14 [medline]
PHST- 2023/02/24 06:00 [pubmed]
PHST- 2023/02/23 19:03 [entrez]
AID - S1473-3099(23)00113-5 [pii]
AID - 10.1016/S1473-3099(23)00113-5 [doi]
PST - ppublish
SO  - Lancet Infect Dis. 2023 Apr;23(4):405-406. doi: 10.1016/S1473-3099(23)00113-5. 
      Epub 2023 Feb 20.

PMID- 38033227
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231213
IS  - 1572-0241 (Electronic)
IS  - 0002-9270 (Linking)
VI  - 118
IP  - 12
DP  - 2023 Dec 1
TI  - ChatGPT and American College of Gastroenterology Self-Assessment Test: Comment.
PG  - 2305-2306
LID - 10.14309/ajg.0000000000002367 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Consultant Unit, Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Research Center, Chandigarh University, Punjab, India.
AD  - Depatment of Biological Science, Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - Am J Gastroenterol
JT  - The American journal of gastroenterology
JID - 0421030
SB  - IM
CON - Am J Gastroenterol. 2023 Dec 1;118(12):2280-2282. PMID: 37212584
CIN - Am J Gastroenterol. 2023 Dec 1;118(12):2306-2307. PMID: 38033228
MH  - Humans
MH  - United States
MH  - *Gastroenterology
MH  - Self-Assessment
MH  - Gastrointestinal Tract
MH  - Societies, Medical
EDAT- 2023/11/30 18:45
MHDA- 2023/12/04 12:42
CRDT- 2023/11/30 15:33
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/11/30 18:45 [pubmed]
PHST- 2023/11/30 15:33 [entrez]
AID - 00000434-202312000-00038 [pii]
AID - 10.14309/ajg.0000000000002367 [doi]
PST - ppublish
SO  - Am J Gastroenterol. 2023 Dec 1;118(12):2305-2306. doi: 
      10.14309/ajg.0000000000002367.

PMID- 37099761
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20230906
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 3
DP  - 2023 Aug 28
TI  - No worries with ChatGPT: building bridges between artificial intelligence and 
      education with critical thinking soft skills.
PG  - e602-e603
LID - 10.1093/pubmed/fdad049 [doi]
AB  - This correspondence discusses the role of artificial intelligence (AI) like 
      ChatGPT in education and research, focusing on developing critical thinking 
      skills and maintaining academic integrity. AI can complement learning and 
      research processes when used ethically and responsibly. Integrating specific 
      teaching methods in education and research can help develop better critical 
      thinking skills and a deeper understanding of the contexts in which AI is used. 
      The article emphasizes the importance of developing critical thinking skills 
      among students and researchers to effectively use AI and distinguish accurate 
      information from hoaxes and misinformation. In conclusion, the collaboration 
      between AI and humans in learning and research will yield significant benefits 
      for individuals and society as long as critical thinking skills and academic 
      integrity remain top priorities.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Faculty 
      of Public Health. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Rusandi, M Arli
AU  - Rusandi MA
AUID- ORCID: 0000-0001-7385-104X
AD  - Department of Guidance and Counseling, Universitas Riau, Pekanbaru, Indonesia.
FAU - Ahman
AU  - Ahman
AD  - Faculty of Educational Science, Universitas Pendidikan Indonesia, Bandung, 
      Indonesia.
FAU - Saripah, Ipah
AU  - Saripah I
AD  - Faculty of Educational Science, Universitas Pendidikan Indonesia, Bandung, 
      Indonesia.
FAU - Khairun, Deasy Yunika
AU  - Khairun DY
AD  - Department of Guidance and Counseling, Universitas Sultan Ageng Tirtayasa, 
      Serang, Indonesia.
FAU - Mutmainnah
AU  - Mutmainnah
AD  - Department of Guidance and Counseling, Universitas Muhammadiyah Enrekang, 
      Enrekang, Indonesia.
LA  - eng
PT  - Journal Article
PT  - Letter
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Thinking
MH  - Educational Status
MH  - Learning
MH  - Communication
OTO - NOTNLM
OT  - ChatGPT
OT  - academic integrity
OT  - artificial intelligence
OT  - collaboration
OT  - critical thinking skills
OT  - digital literacy
OT  - education
OT  - ethical use
OT  - misinformation
OT  - research
EDAT- 2023/04/26 18:42
MHDA- 2023/09/01 06:43
CRDT- 2023/04/26 16:53
PHST- 2023/03/29 00:00 [received]
PHST- 2022/04/04 00:00 [revised]
PHST- 2023/09/01 06:43 [medline]
PHST- 2023/04/26 18:42 [pubmed]
PHST- 2023/04/26 16:53 [entrez]
AID - 7136742 [pii]
AID - 10.1093/pubmed/fdad049 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Aug 28;45(3):e602-e603. doi: 10.1093/pubmed/fdad049.

PMID- 38056773
OWN - NLM
STAT- Publisher
LR  - 20231206
IS  - 1885-5857 (Electronic)
IS  - 1885-5857 (Linking)
DP  - 2023 Dec 4
TI  - Assessing the accuracy of ChatGPT as a decision support tool in cardiology.
LID - S1885-5857(23)00337-7 [pii]
LID - 10.1016/j.rec.2023.11.011 [doi]
FAU - Baladrón, Carlos
AU  - Baladrón C
AD  - Servicio de Cardiología, Hospital Clínico Universitario de Valladolid, 
      Valladolid, Spain; Centro de Investigación Biomédica en Red de Enfermedades 
      Cardiovasculares (CIBERCV), Spain.
FAU - Sevilla, Teresa
AU  - Sevilla T
AD  - Servicio de Cardiología, Hospital Clínico Universitario de Valladolid, 
      Valladolid, Spain; Centro de Investigación Biomédica en Red de Enfermedades 
      Cardiovasculares (CIBERCV), Spain. Electronic address: tereseru@gmail.com.
FAU - Carrasco-Moraleja, Manuel
AU  - Carrasco-Moraleja M
AD  - Servicio de Cardiología, Hospital Clínico Universitario de Valladolid, 
      Valladolid, Spain; Centro de Investigación Biomédica en Red de Enfermedades 
      Cardiovasculares (CIBERCV), Spain.
FAU - Gómez-Salvador, Itziar
AU  - Gómez-Salvador I
AD  - Servicio de Cardiología, Hospital Clínico Universitario de Valladolid, 
      Valladolid, Spain; Centro de Investigación Biomédica en Red de Enfermedades 
      Cardiovasculares (CIBERCV), Spain.
FAU - Peral-Oliveira, Julio
AU  - Peral-Oliveira J
AD  - Servicio de Cardiología, Hospital Clínico Universitario de Valladolid, 
      Valladolid, Spain.
FAU - Alberto San Román, José
AU  - Alberto San Román J
AD  - Servicio de Cardiología, Hospital Clínico Universitario de Valladolid, 
      Valladolid, Spain; Centro de Investigación Biomédica en Red de Enfermedades 
      Cardiovasculares (CIBERCV), Spain.
LA  - eng
LA  - spa
PT  - Case Reports
DEP - 20231204
PL  - Spain
TA  - Rev Esp Cardiol (Engl Ed)
JT  - Revista espanola de cardiologia (English ed.)
JID - 101587954
SB  - IM
EDAT- 2023/12/07 00:42
MHDA- 2023/12/07 00:42
CRDT- 2023/12/06 19:27
PHST- 2023/10/10 00:00 [received]
PHST- 2023/11/21 00:00 [accepted]
PHST- 2023/12/07 00:42 [medline]
PHST- 2023/12/07 00:42 [pubmed]
PHST- 2023/12/06 19:27 [entrez]
AID - S1885-5857(23)00337-7 [pii]
AID - 10.1016/j.rec.2023.11.011 [doi]
PST - aheadofprint
SO  - Rev Esp Cardiol (Engl Ed). 2023 Dec 4:S1885-5857(23)00337-7. doi: 
      10.1016/j.rec.2023.11.011.

PMID- 37924499
OWN - NLM
STAT- Publisher
LR  - 20231104
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Nov 3
TI  - A commentary on "ChatGPT has made the field of surgery full of opportunities and 
      challenges".
LID - 10.1097/JS9.0000000000000882 [doi]
FAU - Chen, Yuhui
AU  - Chen Y
AD  - Department of Neurosurgery, Fuzong Clinical Medical college of Fujian Medical 
      University, Fuzhou, Fujian, China.
FAU - Chen, Li
AU  - Chen L
FAU - Wang, Shousen
AU  - Wang S
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/11/04 21:51
MHDA- 2023/11/04 21:51
CRDT- 2023/11/04 12:13
PHST- 2023/10/12 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/11/04 21:51 [medline]
PHST- 2023/11/04 21:51 [pubmed]
PHST- 2023/11/04 12:13 [entrez]
AID - 01279778-990000000-00757 [pii]
AID - 10.1097/JS9.0000000000000882 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Nov 3. doi: 10.1097/JS9.0000000000000882.

PMID- 37364482
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230802
LR  - 20230802
IS  - 1873-5487 (Electronic)
IS  - 0188-4409 (Linking)
VI  - 54
IP  - 5
DP  - 2023 Jul
TI  - Response to: Impact of ChatGPT and Artificial Intelligence in the Contemporary 
      Medical Landscape.
PG  - 102838
LID - S0188-4409(23)00077-2 [pii]
LID - 10.1016/j.arcmed.2023.06.003 [doi]
FAU - Martínez-Ezquerro, José Darío
AU  - Martínez-Ezquerro JD
AD  - Epidemiological Research and Health Services Unit, Aging Area, 21(st) Century 
      National Medical Center, Instituto Mexicano del Seguro Social, Mexico City, 
      Mexico. Electronic address: jdme@ciencias.unam.mx.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230624
PL  - United States
TA  - Arch Med Res
JT  - Archives of medical research
JID - 9312706
SB  - IM
CON - Arch Med Res. 2023 Apr;54(3):163-167. PMID: 36941174
CON - Arch Med Res. 2023 Jul;54(5):102835. PMID: 37248157
EDAT- 2023/06/27 01:07
MHDA- 2023/07/31 06:42
CRDT- 2023/06/26 18:07
PHST- 2023/05/31 00:00 [received]
PHST- 2023/06/14 00:00 [accepted]
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/06/27 01:07 [pubmed]
PHST- 2023/06/26 18:07 [entrez]
AID - S0188-4409(23)00077-2 [pii]
AID - 10.1016/j.arcmed.2023.06.003 [doi]
PST - ppublish
SO  - Arch Med Res. 2023 Jul;54(5):102838. doi: 10.1016/j.arcmed.2023.06.003. Epub 2023 
      Jun 24.

PMID- 37160840
OWN - NLM
STAT- MEDLINE
DCOM- 20230830
LR  - 20230911
IS  - 1545-7230 (Electronic)
IS  - 1042-9670 (Linking)
VI  - 47
IP  - 4
DP  - 2023 Aug
TI  - ChatGPT in Medical Education: a Paradigm Shift or a Dangerous Tool?
PG  - 439-440
LID - 10.1007/s40596-023-01791-9 [doi]
FAU - Grabb, Declan
AU  - Grabb D
AUID- ORCID: 0009-0009-1814-3556
AD  - Northwestern University, Chicago, IL, USA. declan.grabb@nm.org.
LA  - eng
PT  - Editorial
DEP - 20230509
PL  - United States
TA  - Acad Psychiatry
JT  - Academic psychiatry : the journal of the American Association of Directors of 
      Psychiatric Residency Training and the Association for Academic Psychiatry
JID - 8917200
SB  - IM
MH  - Humans
MH  - *Education, Medical
MH  - *Artificial Intelligence
EDAT- 2023/05/10 06:42
MHDA- 2023/08/24 06:42
CRDT- 2023/05/10 01:16
PHST- 2023/03/21 00:00 [received]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/08/24 06:42 [medline]
PHST- 2023/05/10 06:42 [pubmed]
PHST- 2023/05/10 01:16 [entrez]
AID - 10.1007/s40596-023-01791-9 [pii]
AID - 10.1007/s40596-023-01791-9 [doi]
PST - ppublish
SO  - Acad Psychiatry. 2023 Aug;47(4):439-440. doi: 10.1007/s40596-023-01791-9. Epub 
      2023 May 9.

PMID- 37138114
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230505
LR  - 20231208
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 617
IP  - 7959
DP  - 2023 May
TI  - Is the world ready for ChatGPT therapists?
PG  - 22-24
LID - 10.1038/d41586-023-01473-4 [doi]
FAU - Graber-Stiehl, Ian
AU  - Graber-Stiehl I
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Health care
OT  - Psychiatric disorders
OT  - Psychology
OT  - Technology
EDAT- 2023/05/04 00:42
MHDA- 2023/05/04 00:43
CRDT- 2023/05/03 23:27
PHST- 2023/05/04 00:43 [medline]
PHST- 2023/05/04 00:42 [pubmed]
PHST- 2023/05/03 23:27 [entrez]
AID - 10.1038/d41586-023-01473-4 [pii]
AID - 10.1038/d41586-023-01473-4 [doi]
PST - ppublish
SO  - Nature. 2023 May;617(7959):22-24. doi: 10.1038/d41586-023-01473-4.

PMID- 36747115
OWN - NLM
STAT- MEDLINE
DCOM- 20230209
LR  - 20230711
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 614
IP  - 7947
DP  - 2023 Feb
TI  - What ChatGPT and generative AI mean for science.
PG  - 214-216
LID - 10.1038/d41586-023-00340-6 [doi]
FAU - Stokel-Walker, Chris
AU  - Stokel-Walker C
FAU - Van Noorden, Richard
AU  - Van Noorden R
LA  - eng
PT  - Journal Article
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
CIN - Nature. 2023 Mar;615(7951):216. PMID: 36882613
MH  - *Algorithms
MH  - *Artificial Intelligence/trends
MH  - *Science/methods/trends
OTO - NOTNLM
OT  - Machine learning
OT  - Mathematics and computing
OT  - Publishing
EDAT- 2023/02/08 06:00
MHDA- 2023/02/09 06:00
CRDT- 2023/02/07 00:07
PHST- 2023/02/07 00:07 [entrez]
PHST- 2023/02/08 06:00 [pubmed]
PHST- 2023/02/09 06:00 [medline]
AID - 10.1038/d41586-023-00340-6 [pii]
AID - 10.1038/d41586-023-00340-6 [doi]
PST - ppublish
SO  - Nature. 2023 Feb;614(7947):214-216. doi: 10.1038/d41586-023-00340-6.

PMID- 37890090
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
DP  - 2023 Oct 24
TI  - ChatGPT: A Game Changer in Academic Medicine's Artificial Intelligence 
      Revolution.
LID - 10.1097/ACM.0000000000005486 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Partha Pratim Ray is a Assistant professor, Sikkim University, Gangtok, India; 
      email: ppray@cus.ac.in.
LA  - eng
PT  - Journal Article
DEP - 20231024
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
EDAT- 2023/10/27 18:42
MHDA- 2023/10/27 18:42
CRDT- 2023/10/27 15:34
PHST- 2023/10/27 18:42 [medline]
PHST- 2023/10/27 18:42 [pubmed]
PHST- 2023/10/27 15:34 [entrez]
AID - 00001888-990000000-00609 [pii]
AID - 10.1097/ACM.0000000000005486 [doi]
PST - aheadofprint
SO  - Acad Med. 2023 Oct 24. doi: 10.1097/ACM.0000000000005486.

PMID- 37633756
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20230926
IS  - 1479-666X (Print)
IS  - 1479-666X (Linking)
VI  - 21
IP  - 5
DP  - 2023 Oct
TI  - Assessing ChatGPT's ability to pass the FRCS orthopaedic part A exam: 
      Correspondence.
PG  - e301
LID - S1479-666X(23)00091-4 [pii]
LID - 10.1016/j.surge.2023.08.005 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - DY Patil Vidhyapeeth, Pune, India; Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Letter
DEP - 20230824
PL  - Scotland
TA  - Surgeon
JT  - The surgeon : journal of the Royal Colleges of Surgeons of Edinburgh and Ireland
JID - 101168329
SB  - IM
MH  - Humans
MH  - *Orthopedics/education
MH  - *Educational Measurement
MH  - *Artificial Intelligence
COIS- Declaration of competing interest None declared.
EDAT- 2023/08/27 05:43
MHDA- 2023/09/20 06:42
CRDT- 2023/08/26 21:59
PHST- 2023/08/03 00:00 [received]
PHST- 2023/08/14 00:00 [accepted]
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/08/27 05:43 [pubmed]
PHST- 2023/08/26 21:59 [entrez]
AID - S1479-666X(23)00091-4 [pii]
AID - 10.1016/j.surge.2023.08.005 [doi]
PST - ppublish
SO  - Surgeon. 2023 Oct;21(5):e301. doi: 10.1016/j.surge.2023.08.005. Epub 2023 Aug 24.

PMID- 37560050
OWN - NLM
STAT- MEDLINE
DCOM- 20230811
LR  - 20231106
IS  - 2366-5017 (Electronic)
IS  - 2366-5017 (Linking)
VI  - 40
IP  - 4
DP  - 2023
TI  - Generative AI (gAI) in medical education: Chat-GPT and co.
PG  - Doc54
LID - 10.3205/zma001636 [doi]
LID - Doc54
FAU - Moritz, Sören
AU  - Moritz S
AD  - University of Cologne, Medical Faculty, Cologne, Germany.
FAU - Romeike, Bernd
AU  - Romeike B
AD  - University Medical Center Rostock, Academic Dean's Office, Division of Medical 
      Education, Rostock, Germany.
FAU - Stosch, Christoph
AU  - Stosch C
AD  - University of Cologne, Medical Faculty, Cologne, Germany.
FAU - Tolks, Daniel
AU  - Tolks D
AD  - Leuphana University Lüneburg, Centre for Applied Health Promotion, Lüneburg, 
      Germany.
LA  - eng
PT  - Editorial
DEP - 20230615
PL  - Germany
TA  - GMS J Med Educ
JT  - GMS journal for medical education
JID - 101676086
SB  - IM
MH  - *Education, Medical
MH  - *Artificial Intelligence
PMC - PMC10407583
COIS- The authors declare that they have no competing interests.
EDAT- 2023/08/10 06:43
MHDA- 2023/08/11 06:42
CRDT- 2023/08/10 04:14
PHST- 2023/04/28 00:00 [received]
PHST- 2023/04/28 00:00 [revised]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/08/11 06:42 [medline]
PHST- 2023/08/10 06:43 [pubmed]
PHST- 2023/08/10 04:14 [entrez]
AID - zma001636 [pii]
AID - Doc54 [pii]
AID - 10.3205/zma001636 [doi]
PST - epublish
SO  - GMS J Med Educ. 2023 Jun 15;40(4):Doc54. doi: 10.3205/zma001636. eCollection 
      2023.

PMID- 37395942
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230801
LR  - 20230801
IS  - 1447-073X (Electronic)
IS  - 1447-073X (Linking)
VI  - 98
IP  - 4
DP  - 2023 Sep
TI  - If text recycling refers to self-plagiarism, how does ChatGPT induce plagiarism? 
      Originality for future science.
PG  - 471-472
LID - 10.1007/s12565-023-00732-8 [doi]
FAU - Takeda, Sén
AU  - Takeda S
AD  - Department of Anatomy, Teikyo University School of Medicine, 2-11-1, Kaga, 
      Itabashi, Tokyo, 173-8605, Japan. takeda.sen.xm@teikyo-u.ac.jp.
LA  - eng
PT  - Editorial
PL  - Japan
TA  - Anat Sci Int
JT  - Anatomical science international
JID - 101154140
SB  - IM
EDAT- 2023/07/03 13:05
MHDA- 2023/07/26 06:43
CRDT- 2023/07/03 11:12
PHST- 2023/07/26 06:43 [medline]
PHST- 2023/07/03 13:05 [pubmed]
PHST- 2023/07/03 11:12 [entrez]
AID - 10.1007/s12565-023-00732-8 [pii]
AID - 10.1007/s12565-023-00732-8 [doi]
PST - ppublish
SO  - Anat Sci Int. 2023 Sep;98(4):471-472. doi: 10.1007/s12565-023-00732-8.

PMID- 37318998
OWN - NLM
STAT- MEDLINE
DCOM- 20230721
LR  - 20230721
IS  - 1522-1229 (Electronic)
IS  - 1043-4046 (Linking)
VI  - 47
IP  - 3
DP  - 2023 Sep 1
TI  - Assessing the performance of ChatGPT to solve biochemistry question papers of 
      university examination.
PG  - 528-529
LID - 10.1152/advan.00076.2023 [doi]
FAU - Mahat, Roshan Kumar
AU  - Mahat RK
AUID- ORCID: 0000-0003-1202-6227
AD  - Department of Biochemistry, Dharanidhar Medical College and Hospital, Keonjhar, 
      India.
FAU - Jantikar, Ashwini Manish
AU  - Jantikar AM
AD  - Department of Biochemistry, American International Institute of Medical Sciences, 
      Udaipur, India.
FAU - Rathore, Vedika
AU  - Rathore V
AD  - Department of Biochemistry, Shyam Shah Medical College, Rewa, India.
FAU - Panda, Suchismita
AU  - Panda S
AD  - Department of Biochemistry, Dharanidhar Medical College and Hospital, Keonjhar, 
      India.
LA  - eng
PT  - Editorial
DEP - 20230615
PL  - United States
TA  - Adv Physiol Educ
JT  - Advances in physiology education
JID - 100913944
SB  - IM
MH  - Humans
MH  - Universities
MH  - *Artificial Intelligence
MH  - *Biochemistry/education
MH  - *Academic Performance
EDAT- 2023/06/15 19:14
MHDA- 2023/07/21 06:43
CRDT- 2023/06/15 12:53
PHST- 2023/07/21 06:43 [medline]
PHST- 2023/06/15 19:14 [pubmed]
PHST- 2023/06/15 12:53 [entrez]
AID - 10.1152/advan.00076.2023 [doi]
PST - ppublish
SO  - Adv Physiol Educ. 2023 Sep 1;47(3):528-529. doi: 10.1152/advan.00076.2023. Epub 
      2023 Jun 15.

PMID- 37179273
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20230916
IS  - 1534-4681 (Electronic)
IS  - 1068-9265 (Linking)
VI  - 30
IP  - 8
DP  - 2023 Aug
TI  - Redefining Academic Integrity, Authorship, and Innovation: The Impact of ChatGPT 
      on Surgical Research.
PG  - 5284-5285
LID - 10.1245/s10434-023-13642-w [doi]
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Faculty of Science, Medicine, and Health, The University of Melbourne, Melbourne, 
      VIC, Australia. ishithseth1@gmail.com.
AD  - Department of Surgery, Bendigo Health, Melbourne, VIC, Australia. 
      ishithseth1@gmail.com.
AD  - Central Clinical School at, Monash University, Melbourne, VIC, Australia. 
      ishithseth1@gmail.com.
FAU - Bulloch, Gabriella
AU  - Bulloch G
AD  - Faculty of Science, Medicine, and Health, The University of Melbourne, Melbourne, 
      VIC, Australia.
FAU - Lee, Chun Hin Angus
AU  - Lee CHA
AD  - Department of Surgery, Bendigo Health, Melbourne, VIC, Australia.
LA  - eng
PT  - Letter
DEP - 20230513
PL  - United States
TA  - Ann Surg Oncol
JT  - Annals of surgical oncology
JID - 9420840
SB  - IM
MH  - Humans
MH  - *Authorship
MH  - *Artificial Intelligence
MH  - General Surgery
EDAT- 2023/05/14 01:07
MHDA- 2023/07/07 06:42
CRDT- 2023/05/13 23:04
PHST- 2023/04/17 00:00 [received]
PHST- 2023/05/02 00:00 [accepted]
PHST- 2023/07/07 06:42 [medline]
PHST- 2023/05/14 01:07 [pubmed]
PHST- 2023/05/13 23:04 [entrez]
AID - 10.1245/s10434-023-13642-w [pii]
AID - 10.1245/s10434-023-13642-w [doi]
PST - ppublish
SO  - Ann Surg Oncol. 2023 Aug;30(8):5284-5285. doi: 10.1245/s10434-023-13642-w. Epub 
      2023 May 13.

PMID- 36494443
OWN - NLM
STAT- Publisher
LR  - 20221212
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2022 Dec 9
TI  - AI bot ChatGPT writes smart essays - should professors worry?
LID - 10.1038/d41586-022-04397-7 [doi]
FAU - Stokel-Walker, Chris
AU  - Stokel-Walker C
LA  - eng
PT  - News
DEP - 20221209
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Education
OT  - Lab life
OT  - Society
EDAT- 2022/12/10 06:00
MHDA- 2022/12/10 06:00
CRDT- 2022/12/09 23:32
PHST- 2022/12/10 06:00 [pubmed]
PHST- 2022/12/10 06:00 [medline]
PHST- 2022/12/09 23:32 [entrez]
AID - 10.1038/d41586-022-04397-7 [pii]
AID - 10.1038/d41586-022-04397-7 [doi]
PST - aheadofprint
SO  - Nature. 2022 Dec 9. doi: 10.1038/d41586-022-04397-7.

PMID- 37702576
OWN - NLM
STAT- MEDLINE
DCOM- 20231113
LR  - 20231129
IS  - 1365-2168 (Electronic)
IS  - 0007-1323 (Linking)
VI  - 110
IP  - 12
DP  - 2023 Nov 9
TI  - Comment on: Will collaborative publishing with ChatGPT drive academic writing in 
      the future?
PG  - 1895
LID - 10.1093/bjs/znad297 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Research Center, Chandigarh University, Punjab, India.
AD  - Department of Biological Science, Joesph Ayobabalola University, Ikeji-Arakeji, 
      Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
PL  - England
TA  - Br J Surg
JT  - The British journal of surgery
JID - 0372553
SB  - IM
CON - Br J Surg. 2023 Aug 11;110(9):1213-1214. PMID: 37368994
CIN - Br J Surg. 2023 Nov 9;110(12):1894. PMID: 37702575
MH  - Humans
MH  - *Writing
MH  - *Publishing
EDAT- 2023/09/13 12:42
MHDA- 2023/11/13 06:42
CRDT- 2023/09/13 09:44
PHST- 2023/06/29 00:00 [received]
PHST- 2023/08/17 00:00 [revised]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/09/13 12:42 [pubmed]
PHST- 2023/09/13 09:44 [entrez]
AID - 7272767 [pii]
AID - 10.1093/bjs/znad297 [doi]
PST - ppublish
SO  - Br J Surg. 2023 Nov 9;110(12):1895. doi: 10.1093/bjs/znad297.

PMID- 37531150
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231005
LR  - 20231005
IS  - 1619-3997 (Electronic)
IS  - 0300-5577 (Linking)
VI  - 51
IP  - 8
DP  - 2023 Oct 26
TI  - ChatGPT and artificial intelligence in the Journal of Perinatal Medicine.
PG  - 969
LID - 10.1515/jpm-2023-0279 [doi]
FAU - Grünebaum, Amos
AU  - Grünebaum A
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, 
      USA.
FAU - Chervenak, Frank A
AU  - Chervenak FA
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, 
      USA.
FAU - Dudenhausen, Joachim
AU  - Dudenhausen J
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, 
      USA.
AD  - Department of Obstetrics, Charité - University Medicine Berlin, Berlin, Germany.
LA  - eng
PT  - Editorial
DEP - 20230803
PL  - Germany
TA  - J Perinat Med
JT  - Journal of perinatal medicine
JID - 0361031
SB  - IM
EDAT- 2023/08/02 13:08
MHDA- 2023/10/05 06:44
CRDT- 2023/08/02 11:43
PHST- 2023/10/05 06:44 [medline]
PHST- 2023/08/02 13:08 [pubmed]
PHST- 2023/08/02 11:43 [entrez]
AID - jpm-2023-0279 [pii]
AID - 10.1515/jpm-2023-0279 [doi]
PST - epublish
SO  - J Perinat Med. 2023 Aug 3;51(8):969. doi: 10.1515/jpm-2023-0279. Print 2023 Oct 
      26.

PMID- 37117047
OWN - NLM
STAT- MEDLINE
DCOM- 20230602
LR  - 20230606
IS  - 1365-229X (Electronic)
IS  - 0009-9260 (Linking)
VI  - 78
IP  - 7
DP  - 2023 Jul
TI  - The imperative of a radiology AI deployment registry and the potential of 
      ChatGPT.
PG  - 554
LID - S0009-9260(23)00140-X [pii]
LID - 10.1016/j.crad.2023.04.001 [doi]
FAU - Mese, I
AU  - Mese I
AD  - Health Sciences University, Erenkoy Mental Health and Neurology Training and 
      Research Hospital, Istanbul, Turkey. Electronic address: ismail_mese@yahoo.com.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230418
PL  - England
TA  - Clin Radiol
JT  - Clinical radiology
JID - 1306016
SB  - IM
CON - Clin Radiol. 2023 Feb;78(2):107-114. PMID: 36639171
MH  - Humans
MH  - Radiography
MH  - *Radiology
MH  - Artificial Intelligence
EDAT- 2023/04/29 06:04
MHDA- 2023/06/02 06:43
CRDT- 2023/04/28 21:57
PHST- 2023/04/03 00:00 [received]
PHST- 2023/04/05 00:00 [accepted]
PHST- 2023/06/02 06:43 [medline]
PHST- 2023/04/29 06:04 [pubmed]
PHST- 2023/04/28 21:57 [entrez]
AID - S0009-9260(23)00140-X [pii]
AID - 10.1016/j.crad.2023.04.001 [doi]
PST - ppublish
SO  - Clin Radiol. 2023 Jul;78(7):554. doi: 10.1016/j.crad.2023.04.001. Epub 2023 Apr 
      18.

PMID- 37925239
OWN - NLM
STAT- MEDLINE
DCOM- 20231211
LR  - 20231211
IS  - 1535-6302 (Electronic)
IS  - 0363-0188 (Linking)
VI  - 53
IP  - 1
DP  - 2024 Jan-Feb
TI  - Evaluating the Limitations of ChatGPT in Generating Competent Radiology Reports 
      for Distal Radius Fractures.
PG  - 166-167
LID - S0363-0188(23)00156-1 [pii]
LID - 10.1067/j.cpradiol.2023.10.010 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, India. Electronic address: ppray@cus.ac.in.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, India.
LA  - eng
PT  - Letter
DEP - 20231020
PL  - United States
TA  - Curr Probl Diagn Radiol
JT  - Current problems in diagnostic radiology
JID - 7607123
SB  - IM
MH  - Humans
MH  - *Wrist Fractures
MH  - Radiography
MH  - Follow-Up Studies
MH  - *Radiology
COIS- Declaration of Competing Interest The authors declare that they have no conflict 
      of interest.
EDAT- 2023/11/05 00:42
MHDA- 2023/12/11 12:42
CRDT- 2023/11/04 22:54
PHST- 2023/05/05 00:00 [received]
PHST- 2023/08/29 00:00 [revised]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/12/11 12:42 [medline]
PHST- 2023/11/05 00:42 [pubmed]
PHST- 2023/11/04 22:54 [entrez]
AID - S0363-0188(23)00156-1 [pii]
AID - 10.1067/j.cpradiol.2023.10.010 [doi]
PST - ppublish
SO  - Curr Probl Diagn Radiol. 2024 Jan-Feb;53(1):166-167. doi: 
      10.1067/j.cpradiol.2023.10.010. Epub 2023 Oct 20.

PMID- 37673422
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231012
LR  - 20231018
IS  - 1749-4486 (Electronic)
IS  - 1749-4478 (Linking)
VI  - 48
IP  - 6
DP  - 2023 Nov
TI  - ChatGPT-generated literature review: Quod erat demonstrandum or ends justifying 
      the means?
PG  - 929-930
LID - 10.1111/coa.14097 [doi]
FAU - Palagani, Divya Sai Snehith Reddy
AU  - Palagani DSSR
AUID- ORCID: 0000-0001-8886-4521
AD  - School of Medicine, University of Central Lancashire, Preston, UK.
FAU - Counter, Paul
AU  - Counter P
AUID- ORCID: 0000-0002-2698-1375
AD  - Department of Ear, Nose, and Throat Surgery, North Cumbria Integrated Care NHS 
      Foundation Trust, Cumbria, UK.
FAU - James, Tobias
AU  - James T
AUID- ORCID: 0000-0001-9261-2511
AD  - Department of Ear, Nose, and Throat Surgery, North Cumbria Integrated Care NHS 
      Foundation Trust, Cumbria, UK.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230906
PL  - England
TA  - Clin Otolaryngol
JT  - Clinical otolaryngology : official journal of ENT-UK ; official journal of 
      Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
JID - 101247023
SB  - IM
CON - Clin Otolaryngol. 2021 Sep;46(5):935-940. PMID: 34051056
EDAT- 2023/09/07 00:41
MHDA- 2023/09/07 00:42
CRDT- 2023/09/06 20:22
PHST- 2023/08/02 00:00 [received]
PHST- 2023/08/05 00:00 [accepted]
PHST- 2023/09/07 00:42 [medline]
PHST- 2023/09/07 00:41 [pubmed]
PHST- 2023/09/06 20:22 [entrez]
AID - 10.1111/coa.14097 [doi]
PST - ppublish
SO  - Clin Otolaryngol. 2023 Nov;48(6):929-930. doi: 10.1111/coa.14097. Epub 2023 Sep 
      6.

PMID- 37391919
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230703
LR  - 20230905
IS  - 1332-8166 (Electronic)
IS  - 0353-9504 (Print)
IS  - 0353-9504 (Linking)
VI  - 64
IP  - 3
DP  - 2023 Jun 30
TI  - Exploring the adoption of ChatGPT in academic publishing: insights and lessons 
      for scientific writing.
PG  - 205-207
FAU - Homolak, Jan
AU  - Homolak J
AD  - Jan Homolak, Department of Pharmacology, University of Zagreb School of Medicine, 
      Zagreb, Croatia, jan.homolak@mef.hr.
LA  - eng
PT  - Journal Article
PL  - Croatia
TA  - Croat Med J
JT  - Croatian medical journal
JID - 9424324
SB  - IM
CIN - Croat Med J. 2023 Aug 31;64(4):292-294. PMID: 37654042
PMC - PMC10332292
EDAT- 2023/07/01 11:42
MHDA- 2023/07/01 11:43
CRDT- 2023/07/01 02:01
PHST- 2023/07/01 11:43 [medline]
PHST- 2023/07/01 11:42 [pubmed]
PHST- 2023/07/01 02:01 [entrez]
AID - CroatMedJ_64_0205 [pii]
AID - 10.3325/cmj.2023.64.205 [doi]
PST - ppublish
SO  - Croat Med J. 2023 Jun 30;64(3):205-207. doi: 10.3325/cmj.2023.64.205.

PMID- 37318550
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230731
LR  - 20231205
IS  - 2379-3694 (Electronic)
IS  - 2379-3694 (Linking)
VI  - 8
IP  - 7
DP  - 2023 Jul 28
TI  - AI in Sensor Research: A Reality Check and the Underestimated Potential of 
      ChatGPT.
PG  - 2412-2414
LID - 10.1021/acssensors.3c01045 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, Sikkim 737102, India.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230615
PL  - United States
TA  - ACS Sens
JT  - ACS sensors
JID - 101669031
SB  - IM
CON - ACS Sens. 2023 Apr 28;8(4):1371-1372. PMID: 37114432
EDAT- 2023/06/15 13:07
MHDA- 2023/06/15 13:08
CRDT- 2023/06/15 11:06
PHST- 2023/06/15 13:08 [medline]
PHST- 2023/06/15 13:07 [pubmed]
PHST- 2023/06/15 11:06 [entrez]
AID - 10.1021/acssensors.3c01045 [doi]
PST - ppublish
SO  - ACS Sens. 2023 Jul 28;8(7):2412-2414. doi: 10.1021/acssensors.3c01045. Epub 2023 
      Jun 15.

PMID- 37277091
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230810
IS  - 2589-9333 (Electronic)
IS  - 2589-9333 (Linking)
VI  - 5
IP  - 8
DP  - 2023 Aug
TI  - Institutional review board approval and studies involving ChatGPT: comment: Reply 
      Declined.
PG  - 101040
LID - S2589-9333(23)00182-9 [pii]
LID - 10.1016/j.ajogmf.2023.101040 [doi]
FAU - Kleebayoon, A
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia. Electronic address: 
      amnuaykleebai@gmail.com.
FAU - Wiwanitkit, V
AU  - Wiwanitkit V
AD  - Research Center, Chandigarh University, Chandigarh, Punjab, India; Joseph Ayo 
      Babalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230603
PL  - United States
TA  - Am J Obstet Gynecol MFM
JT  - American journal of obstetrics & gynecology MFM
JID - 101746609
SB  - IM
CON - Am J Obstet Gynecol MFM. 2023 Aug;5(8):101005. PMID: 37142188
MH  - Humans
MH  - *Ethics Committees, Research
MH  - *Research Design
EDAT- 2023/06/06 01:11
MHDA- 2023/08/07 06:42
CRDT- 2023/06/05 19:24
PHST- 2023/05/08 00:00 [received]
PHST- 2023/05/22 00:00 [accepted]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/06/06 01:11 [pubmed]
PHST- 2023/06/05 19:24 [entrez]
AID - S2589-9333(23)00182-9 [pii]
AID - 10.1016/j.ajogmf.2023.101040 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol MFM. 2023 Aug;5(8):101040. doi: 10.1016/j.ajogmf.2023.101040. 
      Epub 2023 Jun 3.

PMID- 36892666
OWN - NLM
STAT- MEDLINE
DCOM- 20230424
LR  - 20230603
IS  - 1619-7089 (Electronic)
IS  - 1619-7070 (Print)
IS  - 1619-7070 (Linking)
VI  - 50
IP  - 6
DP  - 2023 May
TI  - Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine 
      be?
PG  - 1549-1552
LID - 10.1007/s00259-023-06172-w [doi]
FAU - Alberts, Ian L
AU  - Alberts IL
AUID- ORCID: 0000-0003-2095-2263
AD  - Department of Nuclear Medicine, Inselspital, Bern University Hospital, University 
      of Bern, Freiburgstr. 18, 3010, Bern, Switzerland. Ian.alberts@insel.ch.
FAU - Mercolli, Lorenzo
AU  - Mercolli L
AD  - Department of Nuclear Medicine, Inselspital, Bern University Hospital, University 
      of Bern, Freiburgstr. 18, 3010, Bern, Switzerland.
FAU - Pyka, Thomas
AU  - Pyka T
AD  - Department of Nuclear Medicine, Inselspital, Bern University Hospital, University 
      of Bern, Freiburgstr. 18, 3010, Bern, Switzerland.
FAU - Prenosil, George
AU  - Prenosil G
AD  - Department of Nuclear Medicine, Inselspital, Bern University Hospital, University 
      of Bern, Freiburgstr. 18, 3010, Bern, Switzerland.
FAU - Shi, Kuangyu
AU  - Shi K
AD  - Department of Nuclear Medicine, Inselspital, Bern University Hospital, University 
      of Bern, Freiburgstr. 18, 3010, Bern, Switzerland.
FAU - Rominger, Axel
AU  - Rominger A
AD  - Department of Nuclear Medicine, Inselspital, Bern University Hospital, University 
      of Bern, Freiburgstr. 18, 3010, Bern, Switzerland.
FAU - Afshar-Oromieh, Ali
AU  - Afshar-Oromieh A
AD  - Department of Nuclear Medicine, Inselspital, Bern University Hospital, University 
      of Bern, Freiburgstr. 18, 3010, Bern, Switzerland.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230309
PL  - Germany
TA  - Eur J Nucl Med Mol Imaging
JT  - European journal of nuclear medicine and molecular imaging
JID - 101140988
SB  - IM
CON - Am J Obstet Gynecol. 2023 Mar 15;:null. PMID: 36924908
MH  - Humans
MH  - *Nuclear Medicine
MH  - Radionuclide Imaging
PMC - PMC9995718
COIS- AR has received research support and speaker honoraria from Siemens. All other 
      authors report no conflict of interest.
EDAT- 2023/03/10 06:00
MHDA- 2023/04/24 06:41
CRDT- 2023/03/09 11:16
PHST- 2023/02/09 00:00 [received]
PHST- 2023/02/19 00:00 [accepted]
PHST- 2023/04/24 06:41 [medline]
PHST- 2023/03/10 06:00 [pubmed]
PHST- 2023/03/09 11:16 [entrez]
AID - 10.1007/s00259-023-06172-w [pii]
AID - 6172 [pii]
AID - 10.1007/s00259-023-06172-w [doi]
PST - ppublish
SO  - Eur J Nucl Med Mol Imaging. 2023 May;50(6):1549-1552. doi: 
      10.1007/s00259-023-06172-w. Epub 2023 Mar 9.

PMID- 37041736
OWN - NLM
STAT- Publisher
LR  - 20230412
IS  - 1758-1109 (Electronic)
IS  - 1357-633X (Linking)
DP  - 2023 Apr 11
TI  - Artificial intelligence: Augmenting telehealth with large language models.
PG  - 1357633X231169055
LID - 10.1177/1357633X231169055 [doi]
AB  - This brief editorial describes an emerging area of machine learning technology 
      called large language models (LLMs). LLMs, such as ChatGPT, are the technological 
      disruptor of this decade. They are going to be integrated into search engines 
      (Bing and Google) and into Microsoft products in the coming months. They will 
      therefore fundamentally change the way patients and clinicians access and receive 
      information. It is essential that telehealth clinicians are aware of LLMs and 
      appreciate their capabilities and limitations.
FAU - Snoswell, Centaine L
AU  - Snoswell CL
AUID- ORCID: 0000-0002-4298-9369
AD  - Centre for Online Health, The University of Queensland, Brisbane, Australia. 
      RINGGOLD: 1974
AD  - Centre for Health Services Research, The University of Queensland, Brisbane, 
      Australia. RINGGOLD: 1974
FAU - Snoswell, Aaron J
AU  - Snoswell AJ
AD  - Australian Research Council Centre of Excellence for Automated Decision Making 
      and Society, Queensland University of Technology, Brisbane, Australia. RINGGOLD: 
      1969
FAU - Kelly, Jaimon T
AU  - Kelly JT
AUID- ORCID: 0000-0003-0232-5848
AD  - Centre for Online Health, The University of Queensland, Brisbane, Australia. 
      RINGGOLD: 1974
AD  - Centre for Health Services Research, The University of Queensland, Brisbane, 
      Australia. RINGGOLD: 1974
FAU - Caffery, Liam J
AU  - Caffery LJ
AUID- ORCID: 0000-0003-1899-7534
AD  - Centre for Online Health, The University of Queensland, Brisbane, Australia. 
      RINGGOLD: 1974
AD  - Centre for Health Services Research, The University of Queensland, Brisbane, 
      Australia. RINGGOLD: 1974
FAU - Smith, Anthony C
AU  - Smith AC
AUID- ORCID: 0000-0002-7756-5136
AD  - Centre for Online Health, The University of Queensland, Brisbane, Australia. 
      RINGGOLD: 1974
AD  - Centre for Health Services Research, The University of Queensland, Brisbane, 
      Australia. RINGGOLD: 1974
AD  - Centre for Innovative Medical Technology, University of Southern Denmark, Odense, 
      Denmark. RINGGOLD: 6174
LA  - eng
PT  - Journal Article
DEP - 20230411
PL  - England
TA  - J Telemed Telecare
JT  - Journal of telemedicine and telecare
JID - 9506702
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Machine learning
OT  - artificial intelligence
OT  - digital health
OT  - large language models
OT  - telehealth
OT  - telemedicine
EDAT- 2023/04/13 06:00
MHDA- 2023/04/13 06:00
CRDT- 2023/04/12 01:32
PHST- 2023/04/12 01:32 [entrez]
PHST- 2023/04/13 06:00 [pubmed]
PHST- 2023/04/13 06:00 [medline]
AID - 10.1177/1357633X231169055 [doi]
PST - aheadofprint
SO  - J Telemed Telecare. 2023 Apr 11:1357633X231169055. doi: 
      10.1177/1357633X231169055.

PMID- 38001275
OWN - NLM
STAT- Publisher
LR  - 20231124
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Nov 24
TI  - How ChatGPT and sounds from space brought a 'luminous jelly' to life.
LID - 10.1038/d41586-023-03392-w [doi]
FAU - Gould, Julie
AU  - Gould J
LA  - eng
PT  - News
DEP - 20231124
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Arts
OT  - Careers
OT  - Lab life
OT  - Organic chemistry
EDAT- 2023/11/25 07:42
MHDA- 2023/11/25 07:42
CRDT- 2023/11/24 23:36
PHST- 2023/11/25 07:42 [medline]
PHST- 2023/11/25 07:42 [pubmed]
PHST- 2023/11/24 23:36 [entrez]
AID - 10.1038/d41586-023-03392-w [pii]
AID - 10.1038/d41586-023-03392-w [doi]
PST - aheadofprint
SO  - Nature. 2023 Nov 24. doi: 10.1038/d41586-023-03392-w.

PMID- 37871033
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231116
IS  - 1529-4242 (Electronic)
IS  - 0032-1052 (Linking)
VI  - 152
IP  - 5
DP  - 2023 Nov 1
TI  - New Technologies and New Challenges: What Effect Will ChatGPT Have on Plastic 
      Surgery Research?
PG  - 970e-971e
LID - 10.1097/PRS.0000000000010795 [doi]
FAU - Cereceda-Monteoliva, Nicholas
AU  - Cereceda-Monteoliva N
AD  - Guys' & St. Thomas' Hospitals NHS Foundation Trust, London, United Kingdom.
FAU - Hagiga, Ahmed
AU  - Hagiga A
AD  - Queen Victoria Hospital NHS Foundation Trust, East Grinstead, United Kingdom.
FAU - Kadhum, Murtaza
AU  - Kadhum M
AD  - Morriston Hospital, Swansea Bay University Health Board, Cwmrhydyceirw, Swansea, 
      Wales.
LA  - eng
PT  - Journal Article
DEP - 20231023
PL  - United States
TA  - Plast Reconstr Surg
JT  - Plastic and reconstructive surgery
JID - 1306050
SB  - IM
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
EDAT- 2023/10/23 18:41
MHDA- 2023/10/30 06:46
CRDT- 2023/10/23 13:34
PHST- 2023/10/30 06:46 [medline]
PHST- 2023/10/23 18:41 [pubmed]
PHST- 2023/10/23 13:34 [entrez]
AID - 00006534-202311000-00053 [pii]
AID - 10.1097/PRS.0000000000010795 [doi]
PST - ppublish
SO  - Plast Reconstr Surg. 2023 Nov 1;152(5):970e-971e. doi: 
      10.1097/PRS.0000000000010795. Epub 2023 Oct 23.

PMID- 37791051
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 2575-7873 (Electronic)
IS  - 2575-7873 (Linking)
VI  - 7
DP  - 2023
TI  - Response to "Exploring the Applications of ChatGPT in Family Medicine Medical 
      Education".
PG  - 28
LID - 10.22454/PRiMER.2023.940827 [doi]
LID - 28
FAU - Horton, Jason A
AU  - Horton JA
AD  - Departments of Orthopedic Surgery, Radiation Oncology and Cell & Developmental 
      Biology, SUNY Upstate Medical University, Syracuse NY.
FAU - Ally, Irshad
AU  - Ally I
AD  - Department of Family Medicine, SUNY Upstate Medical University, Syracuse NY.
LA  - eng
PT  - Journal Article
DEP - 20230825
PL  - United States
TA  - PRiMER
JT  - PRiMER (Leawood, Kan.)
JID - 101726396
CIN - PRiMER. 7:26.
PMC - PMC10544640
EDAT- 2023/10/04 06:44
MHDA- 2023/10/04 06:45
CRDT- 2023/10/04 04:16
PHST- 2023/10/04 06:45 [medline]
PHST- 2023/10/04 06:44 [pubmed]
PHST- 2023/10/04 04:16 [entrez]
AID - primer-7-28 [pii]
AID - 10.22454/PRiMER.2023.940827 [doi]
PST - epublish
SO  - PRiMER. 2023 Aug 25;7:28. doi: 10.22454/PRiMER.2023.940827. eCollection 2023.

PMID- 37495984
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230728
LR  - 20230729
IS  - 1479-5876 (Electronic)
IS  - 1479-5876 (Linking)
VI  - 21
IP  - 1
DP  - 2023 Jul 26
TI  - Evaluate the accuracy of ChatGPT's responses to diabetes questions and 
      misconceptions.
PG  - 502
LID - 10.1186/s12967-023-04354-6 [doi]
LID - 502
FAU - Huang, Chunling
AU  - Huang C
AD  - Department of Outpatient Electrocardiography, The Second Affiliated Hospital of 
      Fujian Medical University, Quanzhou, 362000, Fujian, China.
FAU - Chen, Lijun
AU  - Chen L
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, No. 950 Donghai Street, Fengze District, Quanzhou, 362000, Fujian, 
      China.
FAU - Huang, Huibin
AU  - Huang H
AUID- ORCID: 0000-0001-6487-7029
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, No. 950 Donghai Street, Fengze District, Quanzhou, 362000, Fujian, 
      China. huibinhuang@aliyun.com.
FAU - Cai, Qingyan
AU  - Cai Q
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, No. 950 Donghai Street, Fengze District, Quanzhou, 362000, Fujian, 
      China.
FAU - Lin, Ruhai
AU  - Lin R
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, No. 950 Donghai Street, Fengze District, Quanzhou, 362000, Fujian, 
      China.
FAU - Wu, Xiaohong
AU  - Wu X
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, No. 950 Donghai Street, Fengze District, Quanzhou, 362000, Fujian, 
      China.
FAU - Zhuang, Yong
AU  - Zhuang Y
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, No. 950 Donghai Street, Fengze District, Quanzhou, 362000, Fujian, 
      China.
FAU - Jiang, Zhengrong
AU  - Jiang Z
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, No. 950 Donghai Street, Fengze District, Quanzhou, 362000, Fujian, 
      China.
LA  - eng
GR  - 2022QH1126/the startup fund for scientific research, Fujian Medical University/
GR  - JAT220109/Fujian Provincial Young and Middle-aged Teachers' Education Research 
      Project/
PT  - Letter
DEP - 20230726
PL  - England
TA  - J Transl Med
JT  - Journal of translational medicine
JID - 101190741
SB  - IM
PMC - PMC10369721
COIS- The authors declared no competing interests.
EDAT- 2023/07/27 01:09
MHDA- 2023/07/27 01:10
CRDT- 2023/07/26 23:39
PHST- 2023/07/10 00:00 [received]
PHST- 2023/07/12 00:00 [accepted]
PHST- 2023/07/27 01:10 [medline]
PHST- 2023/07/27 01:09 [pubmed]
PHST- 2023/07/26 23:39 [entrez]
AID - 10.1186/s12967-023-04354-6 [pii]
AID - 4354 [pii]
AID - 10.1186/s12967-023-04354-6 [doi]
PST - epublish
SO  - J Transl Med. 2023 Jul 26;21(1):502. doi: 10.1186/s12967-023-04354-6.

PMID- 37418340
OWN - NLM
STAT- MEDLINE
DCOM- 20230911
LR  - 20230911
IS  - 1728-7731 (Electronic)
IS  - 1726-4901 (Linking)
VI  - 86
IP  - 9
DP  - 2023 Sep 1
TI  - Reply to "Comment on ChatGPT failed Taiwan's Family Medicine Board Exam".
PG  - 865
LID - 10.1097/JCMA.0000000000000956 [doi]
FAU - Weng, Tzu-Ling
AU  - Weng TL
AD  - Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, 
      Taiwan, ROC.
AD  - Institute of Public Health, School of Medicine, National Yang Ming Chiao Tung 
      University, Taipei, Taiwan, ROC.
FAU - Chen, Tzeng-Ji
AU  - Chen TJ
AD  - Department of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, 
      Hsinchu, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, 
      ROC.
AD  - Department of Post-Baccalaureate Medicine, National Chung Hsing University, 
      Taichung, Taiwan, ROC.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230707
PL  - Netherlands
TA  - J Chin Med Assoc
JT  - Journal of the Chinese Medical Association : JCMA
JID - 101174817
SB  - IM
CON - J Chin Med Assoc. 2023 Aug 1;86(8):775. PMID: 37314310
MH  - Taiwan
MH  - *Family Practice
MH  - *National Health Programs
COIS- Conflicts of interest: Dr. Tzeng-Ji Chen, an editorial board member at Journal of 
      the Chinese Medical Association, had no role in the peer review process of or 
      decision to publish this article. Dr. Tzu-Ling Weng declares that she has no 
      conflicts of interest related to the subject matter or materials discussed in 
      this article.
EDAT- 2023/07/07 19:13
MHDA- 2023/09/11 06:43
CRDT- 2023/07/07 13:02
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/07/07 19:13 [pubmed]
PHST- 2023/07/07 13:02 [entrez]
AID - 02118582-990000000-00235 [pii]
AID - 10.1097/JCMA.0000000000000956 [doi]
PST - ppublish
SO  - J Chin Med Assoc. 2023 Sep 1;86(9):865. doi: 10.1097/JCMA.0000000000000956. Epub 
      2023 Jul 7.

PMID- 37349615
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20231208
IS  - 1534-4681 (Electronic)
IS  - 1068-9265 (Linking)
VI  - 30
IP  - 11
DP  - 2023 Oct
TI  - Quality of ChatGPT Responses to Questions Related to Pancreatic Cancer and its 
      Surgical Care.
PG  - 6284-6286
LID - 10.1245/s10434-023-13777-w [doi]
FAU - Moazzam, Zorays
AU  - Moazzam Z
AD  - Department of Surgery, The Ohio State University Wexner Medical Center and James 
      Comprehensive Cancer Center, Columbus, OH, USA.
FAU - Cloyd, Jordan
AU  - Cloyd J
AD  - Department of Surgery, The Ohio State University Wexner Medical Center and James 
      Comprehensive Cancer Center, Columbus, OH, USA.
FAU - Lima, Henrique A
AU  - Lima HA
AD  - Department of Surgery, The Ohio State University Wexner Medical Center and James 
      Comprehensive Cancer Center, Columbus, OH, USA.
FAU - Pawlik, Timothy M
AU  - Pawlik TM
AUID- ORCID: 0000-0002-7994-9870
AD  - Department of Surgery, The Ohio State University Wexner Medical Center and James 
      Comprehensive Cancer Center, Columbus, OH, USA. tim.pawlik@osumc.edu.
LA  - eng
PT  - Letter
DEP - 20230622
PL  - United States
TA  - Ann Surg Oncol
JT  - Annals of surgical oncology
JID - 9420840
RN  - Pancreatic Carcinoma
SB  - IM
MH  - Humans
MH  - *Pancreatic Neoplasms/surgery
MH  - Artificial Intelligence
EDAT- 2023/06/23 01:10
MHDA- 2023/09/20 06:42
CRDT- 2023/06/22 23:30
PHST- 2023/05/30 00:00 [received]
PHST- 2023/06/09 00:00 [accepted]
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/06/23 01:10 [pubmed]
PHST- 2023/06/22 23:30 [entrez]
AID - 10.1245/s10434-023-13777-w [pii]
AID - 10.1245/s10434-023-13777-w [doi]
PST - ppublish
SO  - Ann Surg Oncol. 2023 Oct;30(11):6284-6286. doi: 10.1245/s10434-023-13777-w. Epub 
      2023 Jun 22.

PMID- 37116173
OWN - NLM
STAT- MEDLINE
DCOM- 20230518
LR  - 20231102
IS  - 0899-8205 (Print)
IS  - 1943-5967 (Electronic)
IS  - 0899-8205 (Linking)
VI  - 57
IP  - 1
DP  - 2023
TI  - ChatGPT and Its Potential Implications for Clinical Practice: An Anesthesiology 
      Perspective.
PG  - 26-30
LID - 10.2345/0899-8205-57.1.26 [doi]
FAU - Tewfik, George
AU  - Tewfik G
FAU - Naftalovich, Rotem
AU  - Naftalovich R
FAU - Kaila, Jasvir
AU  - Kaila J
FAU - Adaralegbe, Adejuyigbe
AU  - Adaralegbe A
LA  - eng
PT  - Journal Article
DEP - 20230428
PL  - United States
TA  - Biomed Instrum Technol
JT  - Biomedical instrumentation & technology
JID - 8905560
SB  - IM
MH  - *Anesthesiology
MH  - *Artificial Intelligence
PMC - PMC10508852
EDAT- 2023/04/28 18:42
MHDA- 2023/05/01 06:42
PMCR- 2024/01/01
CRDT- 2023/04/28 16:13
PHST- 2024/01/01 00:00 [pmc-release]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/28 18:42 [pubmed]
PHST- 2023/04/28 16:13 [entrez]
AID - 10.2345/0899-8205-57.1.26 [doi]
PST - ppublish
SO  - Biomed Instrum Technol. 2023;57(1):26-30. doi: 10.2345/0899-8205-57.1.26. Epub 
      2023 Apr 28.

PMID- 36898252
OWN - NLM
STAT- MEDLINE
DCOM- 20230404
LR  - 20230502
IS  - 1873-5223 (Electronic)
IS  - 1471-5953 (Linking)
VI  - 68
DP  - 2023 Mar
TI  - ChatGPT as an author of academic papers is wrong and highlights the concepts of 
      accountability and contributorship.
PG  - 103599
LID - S1471-5953(23)00061-6 [pii]
LID - 10.1016/j.nepr.2023.103599 [doi]
FAU - Siegerink, Bob
AU  - Siegerink B
AD  - Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 
      the Netherlands; Directorate of Research Policy, Leiden University Medical 
      Center, Leiden, the Netherlands. Electronic address: b.siegerink@lumc.nl.
FAU - Pet, Lodewijk A
AU  - Pet LA
AD  - Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 
      the Netherlands.
FAU - Rosendaal, Frits R
AU  - Rosendaal FR
AD  - Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 
      the Netherlands.
FAU - Schoones, Jan W
AU  - Schoones JW
AD  - Directorate of Research Policy, Leiden University Medical Center, Leiden, the 
      Netherlands.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230304
PL  - Scotland
TA  - Nurse Educ Pract
JT  - Nurse education in practice
JID - 101090848
SB  - IM
CON - Nurse Educ Pract. 2023 Jan;66:103537. PMID: 36549229
MH  - Humans
MH  - *Publishing
MH  - *Editorial Policies
MH  - Social Responsibility
EDAT- 2023/03/11 06:00
MHDA- 2023/04/04 06:42
CRDT- 2023/03/10 18:08
PHST- 2023/04/04 06:42 [medline]
PHST- 2023/03/11 06:00 [pubmed]
PHST- 2023/03/10 18:08 [entrez]
AID - S1471-5953(23)00061-6 [pii]
AID - 10.1016/j.nepr.2023.103599 [doi]
PST - ppublish
SO  - Nurse Educ Pract. 2023 Mar;68:103599. doi: 10.1016/j.nepr.2023.103599. Epub 2023 
      Mar 4.

PMID- 37713254
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20231003
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Sep 15
TI  - The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: 
      Exploratory Study.
PG  - e47621
LID - 10.2196/47621 [doi]
LID - e47621
AB  - BACKGROUND: Artificial intelligence (AI) has gained tremendous popularity 
      recently, especially the use of natural language processing (NLP). ChatGPT is a 
      state-of-the-art chatbot capable of creating natural conversations using NLP. The 
      use of AI in medicine can have a tremendous impact on health care delivery. 
      Although some studies have evaluated ChatGPT's accuracy in self-diagnosis, there 
      is no research regarding its precision and the degree to which it recommends 
      medical consultations. OBJECTIVE: The aim of this study was to evaluate ChatGPT's 
      ability to accurately and precisely self-diagnose common orthopedic diseases, as 
      well as the degree of recommendation it provides for medical consultations. 
      METHODS: Over a 5-day course, each of the study authors submitted the same 
      questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), 
      cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis 
      (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, 
      partially correct, incorrect, or a differential diagnosis. The percentage of 
      correct answers and reproducibility were calculated. The reproducibility between 
      days and raters were calculated using the Fleiss κ coefficient. Answers that 
      recommended that the patient seek medical attention were recategorized according 
      to the strength of the recommendation as defined by the study. RESULTS: The 
      ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, 
      LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for 
      CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 
      0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The 
      reproducibility between raters was 1.0, 0.1, 0.64, -0.12, and 0.04 for CTS, CM, 
      LSS, KOA, and HOA, respectively. Among the answers recommending medical 
      attention, the phrases "essential," "recommended," "best," and "important" were 
      used. Specifically, "essential" occurred in 4 out of 125, "recommended" in 12 out 
      of 125, "best" in 6 out of 125, and "important" in 94 out of 125 answers. 
      Additionally, 7 out of the 125 answers did not include a recommendation to seek 
      medical attention. CONCLUSIONS: The accuracy and reproducibility of ChatGPT to 
      self-diagnose five common orthopedic conditions were inconsistent. The accuracy 
      could potentially be improved by adding symptoms that could easily identify a 
      specific location. Only a few answers were accompanied by a strong recommendation 
      to seek medical attention according to our study standards. Although ChatGPT 
      could serve as a potential first step in accessing care, we found variability in 
      accurate self-diagnosis. Given the risk of harm with self-diagnosis without 
      medical follow-up, it would be prudent for an NLP to include clear language 
      alerting patients to seek expert medical opinions. We hope to shed further light 
      on the use of AI in a future clinical study.
CI  - ©Tomoyuki Kuroiwa, Aida Sarcon, Takuya Ibara, Eriku Yamada, Akiko Yamamoto, 
      Kazuya Tsukamoto, Koji Fujita. Originally published in the Journal of Medical 
      Internet Research (https://www.jmir.org), 15.09.2023.
FAU - Kuroiwa, Tomoyuki
AU  - Kuroiwa T
AUID- ORCID: 0000-0002-9942-1811
AD  - Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and 
      Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
AD  - Division of Orthopedic Surgery Research, Mayo Clinic, Rochester, MN, United 
      States.
FAU - Sarcon, Aida
AU  - Sarcon A
AUID- ORCID: 0000-0002-2763-878X
AD  - Department of Surgery, Mayo Clinic, Rochester, MN, United States.
FAU - Ibara, Takuya
AU  - Ibara T
AUID- ORCID: 0000-0002-0518-1918
AD  - Department of Functional Joint Anatomy, Graduate School of Medical and Dental 
      Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
FAU - Yamada, Eriku
AU  - Yamada E
AUID- ORCID: 0000-0001-8777-9552
AD  - Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and 
      Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
FAU - Yamamoto, Akiko
AU  - Yamamoto A
AUID- ORCID: 0000-0003-3639-8201
AD  - Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and 
      Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
FAU - Tsukamoto, Kazuya
AU  - Tsukamoto K
AUID- ORCID: 0000-0003-4927-2149
AD  - Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and 
      Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
FAU - Fujita, Koji
AU  - Fujita K
AUID- ORCID: 0000-0003-3733-0188
AD  - Department of Functional Joint Anatomy, Graduate School of Medical and Dental 
      Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
AD  - Division of Medical Design Innovations, Open Innovation Center, Institute of 
      Research Innovation, Tokyo Medical and Dental University, Tokyo, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Spinal Cord Diseases
MH  - Natural Language Processing
MH  - Communication
MH  - *Musculoskeletal Diseases
MH  - *Osteoarthritis, Knee
PMC - PMC10541638
OTO - NOTNLM
OT  - AI model
OT  - ChatGPT
OT  - accuracy
OT  - artificial intelligence
OT  - chatbot
OT  - diagnosis
OT  - generative pretrained transformer
OT  - health information
OT  - language model
OT  - natural language processing
OT  - orthopedic disease
OT  - precision
OT  - self-diagnosis
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/15 12:42
MHDA- 2023/09/18 12:43
CRDT- 2023/09/15 11:54
PHST- 2023/03/27 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/05/17 00:00 [revised]
PHST- 2023/09/18 12:43 [medline]
PHST- 2023/09/15 12:42 [pubmed]
PHST- 2023/09/15 11:54 [entrez]
AID - v25i1e47621 [pii]
AID - 10.2196/47621 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Sep 15;25:e47621. doi: 10.2196/47621.

PMID- 37099373
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230513
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Apr 26
TI  - Performance of ChatGPT on UK Standardized Admission Tests: Insights From the 
      BMAT, TMUA, LNAT, and TSA Examinations.
PG  - e47737
LID - 10.2196/47737 [doi]
LID - e47737
AB  - BACKGROUND: Large language models, such as ChatGPT by OpenAI, have demonstrated 
      potential in various applications, including medical education. Previous studies 
      have assessed ChatGPT's performance in university or professional settings. 
      However, the model's potential in the context of standardized admission tests 
      remains unexplored. OBJECTIVE: This study evaluated ChatGPT's performance on 
      standardized admission tests in the United Kingdom, including the BioMedical 
      Admissions Test (BMAT), Test of Mathematics for University Admission (TMUA), Law 
      National Aptitude Test (LNAT), and Thinking Skills Assessment (TSA), to 
      understand its potential as an innovative tool for education and test 
      preparation. METHODS: Recent public resources (2019-2022) were used to compile a 
      data set of 509 questions from the BMAT, TMUA, LNAT, and TSA covering diverse 
      topics in aptitude, scientific knowledge and applications, mathematical thinking 
      and reasoning, critical thinking, problem-solving, reading comprehension, and 
      logical reasoning. This evaluation assessed ChatGPT's performance using the 
      legacy GPT-3.5 model, focusing on multiple-choice questions for consistency. The 
      model's performance was analyzed based on question difficulty, the proportion of 
      correct responses when aggregating exams from all years, and a comparison of test 
      scores between papers of the same exam using binomial distribution and 
      paired-sample (2-tailed) t tests. RESULTS: The proportion of correct responses 
      was significantly lower than incorrect ones in BMAT section 2 (P<.001) and TMUA 
      paper 1 (P<.001) and paper 2 (P<.001). No significant differences were observed 
      in BMAT section 1 (P=.2), TSA section 1 (P=.7), or LNAT papers 1 and 2, section A 
      (P=.3). ChatGPT performed better in BMAT section 1 than section 2 (P=.047), with 
      a maximum candidate ranking of 73% compared to a minimum of 1%. In the TMUA, it 
      engaged with questions but had limited accuracy and no performance difference 
      between papers (P=.6), with candidate rankings below 10%. In the LNAT, it 
      demonstrated moderate success, especially in paper 2's questions; however, 
      student performance data were unavailable. TSA performance varied across years 
      with generally moderate results and fluctuating candidate rankings. Similar 
      trends were observed for easy to moderate difficulty questions (BMAT section 1, 
      P=.3; BMAT section 2, P=.04; TMUA paper 1, P<.001; TMUA paper 2, P=.003; TSA 
      section 1, P=.8; and LNAT papers 1 and 2, section A, P>.99) and hard to 
      challenging ones (BMAT section 1, P=.7; BMAT section 2, P<.001; TMUA paper 1, 
      P=.007; TMUA paper 2, P<.001; TSA section 1, P=.3; and LNAT papers 1 and 2, 
      section A, P=.2). CONCLUSIONS: ChatGPT shows promise as a supplementary tool for 
      subject areas and test formats that assess aptitude, problem-solving and critical 
      thinking, and reading comprehension. However, its limitations in areas such as 
      scientific and mathematical knowledge and applications highlight the need for 
      continuous development and integration with conventional learning strategies in 
      order to fully harness its potential.
CI  - ©Panagiotis Giannos, Orestis Delardas. Originally published in JMIR Medical 
      Education (https://mededu.jmir.org), 26.04.2023.
FAU - Giannos, Panagiotis
AU  - Giannos P
AUID- ORCID: 0000-0003-1037-1983
AD  - Department of Life Sciences, Faculty of Natural Sciences, Imperial College 
      London, London, United Kingdom.
AD  - Promotion of Emerging and Evaluative Research Society, London, United Kingdom.
FAU - Delardas, Orestis
AU  - Delardas O
AUID- ORCID: 0000-0003-2835-0400
AD  - Promotion of Emerging and Evaluative Research Society, London, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230426
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10173042
OTO - NOTNLM
OT  - BMAT
OT  - ChatGPT
OT  - GPT
OT  - LNAT
OT  - TMUA
OT  - TSA
OT  - law
OT  - medical education
OT  - medicine
OT  - natural language processing
OT  - standardized admissions tests
COIS- Conflicts of Interest: None declared.
EDAT- 2023/04/26 12:42
MHDA- 2023/04/26 12:43
CRDT- 2023/04/26 11:54
PHST- 2023/03/30 00:00 [received]
PHST- 2023/04/09 00:00 [accepted]
PHST- 2023/04/09 00:00 [revised]
PHST- 2023/04/26 12:43 [medline]
PHST- 2023/04/26 12:42 [pubmed]
PHST- 2023/04/26 11:54 [entrez]
AID - v9i1e47737 [pii]
AID - 10.2196/47737 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Apr 26;9:e47737. doi: 10.2196/47737.

PMID- 38016293
OWN - NLM
STAT- Publisher
LR  - 20231128
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Nov 27
TI  - Re: Is ChatGPT a qualified thoracic surgeon assistant?--A commentary on "original 
      article title".
LID - 10.1097/JS9.0000000000000880 [doi]
FAU - Weng, Shou-Xiang
AU  - Weng SX
AD  - Department of pathology, Taizhou Hospital, Wenzhou Medical University, Linhai, 
      Zhejiang Province, 317000, China.
FAU - Zheng, Hai-Hong
AU  - Zheng HH
AD  - Department of pathology, Taizhou Hospital, Wenzhou Medical University, Linhai, 
      Zhejiang Province, 317000, China.
FAU - Yu, Qing-Xin
AU  - Yu QX
AD  - Department of pathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo City, 
      Zhejiang Province, 315211, China.
FAU - Wang, Jiao-Chen
AU  - Wang JC
AD  - Department of pathology, Taizhou Hospital, Wenzhou Medical University, Linhai, 
      Zhejiang Province, 317000, China.
LA  - eng
PT  - Journal Article
DEP - 20231127
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/11/29 00:42
MHDA- 2023/11/29 00:42
CRDT- 2023/11/28 18:06
PHST- 2023/10/13 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/11/29 00:42 [medline]
PHST- 2023/11/29 00:42 [pubmed]
PHST- 2023/11/28 18:06 [entrez]
AID - 01279778-990000000-00822 [pii]
AID - 10.1097/JS9.0000000000000880 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Nov 27. doi: 10.1097/JS9.0000000000000880.

PMID- 37944454
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 87
DP  - 2023 Dec
TI  - Re: Online patient education in body contouring: A comparison between Google and 
      ChatGPT.
PG  - 440-441
LID - S1748-6815(23)00693-9 [pii]
LID - 10.1016/j.bjps.2023.10.121 [doi]
FAU - Tay, Jing Qin
AU  - Tay JQ
AD  - Plastic, Burns and Reconstructive Surgery Department, Salisbury District 
      Hospital, Thames Valley/Wessex Deanery, UK. Electronic address: 
      jingqin.tay1@nhs.net.
LA  - eng
PT  - Letter
DEP - 20231103
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
MH  - Humans
MH  - *Body Contouring
MH  - Search Engine
MH  - Patient Education as Topic
COIS- Declaration of Competing Interest None.
EDAT- 2023/11/10 00:44
MHDA- 2023/12/05 12:42
CRDT- 2023/11/09 18:15
PHST- 2023/10/20 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/12/05 12:42 [medline]
PHST- 2023/11/10 00:44 [pubmed]
PHST- 2023/11/09 18:15 [entrez]
AID - S1748-6815(23)00693-9 [pii]
AID - 10.1016/j.bjps.2023.10.121 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Dec;87:440-441. doi: 
      10.1016/j.bjps.2023.10.121. Epub 2023 Nov 3.

PMID- 37532832
OWN - NLM
STAT- Publisher
LR  - 20230802
IS  - 1476-5454 (Electronic)
IS  - 0950-222X (Linking)
DP  - 2023 Aug 2
TI  - Modern threats in academia: evaluating plagiarism and artificial intelligence 
      detection scores of ChatGPT.
LID - 10.1038/s41433-023-02678-7 [doi]
FAU - Taloni, Andrea
AU  - Taloni A
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Scorcia, Vincenzo
AU  - Scorcia V
AUID- ORCID: 0000-0001-6826-7957
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Giannaccare, Giuseppe
AU  - Giannaccare G
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy. giuseppe.giannaccare@unicz.it.
LA  - eng
PT  - Journal Article
DEP - 20230802
PL  - England
TA  - Eye (Lond)
JT  - Eye (London, England)
JID - 8703986
SB  - IM
EDAT- 2023/08/03 01:06
MHDA- 2023/08/03 01:06
CRDT- 2023/08/02 23:26
PHST- 2023/07/18 00:00 [received]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2023/08/03 01:06 [medline]
PHST- 2023/08/03 01:06 [pubmed]
PHST- 2023/08/02 23:26 [entrez]
AID - 10.1038/s41433-023-02678-7 [pii]
AID - 10.1038/s41433-023-02678-7 [doi]
PST - aheadofprint
SO  - Eye (Lond). 2023 Aug 2. doi: 10.1038/s41433-023-02678-7.

PMID- 37419951
OWN - NLM
STAT- MEDLINE
DCOM- 20230722
LR  - 20230722
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 619
IP  - 7970
DP  - 2023 Jul
TI  - Scientists used ChatGPT to generate an entire paper from scratch - but is it any 
      good?
PG  - 443-444
LID - 10.1038/d41586-023-02218-z [doi]
FAU - Conroy, Gemma
AU  - Conroy G
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - *Authorship
MH  - *Natural Language Processing
MH  - *Research Personnel
MH  - *Research Report/standards
OTO - NOTNLM
OT  - Authorship
OT  - Publishing
EDAT- 2023/07/08 10:42
MHDA- 2023/07/21 06:43
CRDT- 2023/07/07 23:20
PHST- 2023/07/21 06:43 [medline]
PHST- 2023/07/08 10:42 [pubmed]
PHST- 2023/07/07 23:20 [entrez]
AID - 10.1038/d41586-023-02218-z [pii]
AID - 10.1038/d41586-023-02218-z [doi]
PST - ppublish
SO  - Nature. 2023 Jul;619(7970):443-444. doi: 10.1038/d41586-023-02218-z.

PMID- 37311180
OWN - NLM
STAT- Publisher
LR  - 20230613
IS  - 1365-2230 (Electronic)
IS  - 0307-6938 (Linking)
DP  - 2023 Jun 13
TI  - Comment on "Performance of ChatGPT on dermatology Specialty Certificate 
      Examination multiple choice questions".
LID - llad202 [pii]
LID - 10.1093/ced/llad202 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20230613
PL  - England
TA  - Clin Exp Dermatol
JT  - Clinical and experimental dermatology
JID - 7606847
SB  - IM
EDAT- 2023/06/13 19:15
MHDA- 2023/06/13 19:15
CRDT- 2023/06/13 17:34
PHST- 2023/06/02 00:00 [received]
PHST- 2023/06/05 00:00 [accepted]
PHST- 2023/06/13 19:15 [medline]
PHST- 2023/06/13 19:15 [pubmed]
PHST- 2023/06/13 17:34 [entrez]
AID - 7197275 [pii]
AID - 10.1093/ced/llad202 [doi]
PST - aheadofprint
SO  - Clin Exp Dermatol. 2023 Jun 13:llad202. doi: 10.1093/ced/llad202.

PMID- 36894785
OWN - NLM
STAT- MEDLINE
DCOM- 20230330
LR  - 20230504
IS  - 1433-7347 (Electronic)
IS  - 0942-2056 (Linking)
VI  - 31
IP  - 4
DP  - 2023 Apr
TI  - A deeper dive into ChatGPT: history, use and future perspectives for orthopaedic 
      research.
PG  - 1190-1192
LID - 10.1007/s00167-023-07372-5 [doi]
FAU - Ollivier, Matthieu
AU  - Ollivier M
AD  - Aix Marseille Univ, CNRS, ISM, Institute Movement Science, Marseille, France. 
      ollivier.mt@gmail.com.
FAU - Pareek, Ayoosh
AU  - Pareek A
AD  - Sports Medicine and Shoulder Service, Department of Orthopedic Surgery, Hospital 
      for Special Surgery, 535 East 70Th Street, New York, NY, 10021, USA.
FAU - Dahmen, Jari
AU  - Dahmen J
AD  - Amsterdam UMC, Department of Orthopaedic Surgery, Amsterdam Movement Sciences, 
      University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands.
AD  - Academic Center for Evidence Based Sports Medicine (ACES), Amsterdam, The 
      Netherlands.
AD  - Amsterdam Collaboration for Health and Safety in Sports (ACHSS), International 
      Olympic Committee (IOC) Research Center Amsterdam UMC, Amsterdam, The 
      Netherlands.
FAU - Kayaalp, M Enes
AU  - Kayaalp ME
AD  - Department for Orthopaedics and Traumatology, Istanbul Kartal Dr. Lutfi Kirdar 
      Training and Research Hospital, Istanbul, Turkey.
FAU - Winkler, Philipp W
AU  - Winkler PW
AD  - Department of Orthopaedics and Traumatology, Kepler University Hospital Linz, 
      Linz, Austria.
FAU - Hirschmann, Michael T
AU  - Hirschmann MT
AD  - Department of Orthopedic Surgery and Traumatology, Head Knee Surgery and DKF Head 
      of Research, Kantonsspital Baselland, 4101, Bruderholz, Bottmingen, Switzerland.
FAU - Karlsson, Jon
AU  - Karlsson J
AD  - Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska 
      University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, 
      Sweden.
LA  - eng
PT  - Editorial
DEP - 20230309
PL  - Germany
TA  - Knee Surg Sports Traumatol Arthrosc
JT  - Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
JID - 9314730
SB  - IM
MH  - Humans
MH  - *Orthopedics
MH  - *Diving
EDAT- 2023/03/10 06:00
MHDA- 2023/03/30 06:11
CRDT- 2023/03/09 23:27
PHST- 2023/02/13 00:00 [received]
PHST- 2023/02/24 00:00 [accepted]
PHST- 2023/03/30 06:11 [medline]
PHST- 2023/03/10 06:00 [pubmed]
PHST- 2023/03/09 23:27 [entrez]
AID - 10.1007/s00167-023-07372-5 [pii]
AID - 10.1007/s00167-023-07372-5 [doi]
PST - ppublish
SO  - Knee Surg Sports Traumatol Arthrosc. 2023 Apr;31(4):1190-1192. doi: 
      10.1007/s00167-023-07372-5. Epub 2023 Mar 9.

PMID- 37997563
OWN - NLM
STAT- Publisher
LR  - 20231124
IS  - 1464-410X (Electronic)
IS  - 1464-4096 (Linking)
DP  - 2023 Nov 24
TI  - Quality of erectile dysfunction information from ChatGPT and other artificial 
      intelligence chatbots.
LID - 10.1111/bju.16209 [doi]
FAU - Pan, Alexander
AU  - Pan A
AUID- ORCID: 0000-0002-7277-1109
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York City, NY, USA.
FAU - Musheyev, David
AU  - Musheyev D
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York City, NY, USA.
FAU - Loeb, Stacy
AU  - Loeb S
AUID- ORCID: 0000-0003-3933-9207
AD  - Department of Urology, New York University and Manhattan Veterans Affairs, New 
      York City, NY, USA.
AD  - Department of Population Health, New York University, New York City, NY, USA.
FAU - Kabarriti, Abdo E
AU  - Kabarriti AE
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York City, NY, USA.
LA  - eng
GR  - R01CA278997/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20231124
PL  - England
TA  - BJU Int
JT  - BJU international
JID - 100886721
SB  - IM
EDAT- 2023/11/24 06:42
MHDA- 2023/11/24 06:42
CRDT- 2023/11/24 03:53
PHST- 2023/11/24 06:42 [medline]
PHST- 2023/11/24 06:42 [pubmed]
PHST- 2023/11/24 03:53 [entrez]
AID - 10.1111/bju.16209 [doi]
PST - aheadofprint
SO  - BJU Int. 2023 Nov 24. doi: 10.1111/bju.16209.

PMID- 37974032
OWN - NLM
STAT- Publisher
LR  - 20231117
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Nov 6
TI  - 'ChatGPT detector' catches AI-generated papers with unprecedented accuracy.
LID - 10.1038/d41586-023-03479-4 [doi]
FAU - Prillaman, McKenzie
AU  - Prillaman M
LA  - eng
PT  - News
DEP - 20231106
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Machine learning
OT  - Publishing
OT  - Technology
EDAT- 2023/11/17 15:29
MHDA- 2023/11/17 15:29
CRDT- 2023/11/17 01:42
PHST- 2023/11/17 15:29 [medline]
PHST- 2023/11/17 15:29 [pubmed]
PHST- 2023/11/17 01:42 [entrez]
AID - 10.1038/d41586-023-03479-4 [pii]
AID - 10.1038/d41586-023-03479-4 [doi]
PST - aheadofprint
SO  - Nature. 2023 Nov 6. doi: 10.1038/d41586-023-03479-4.

PMID- 37253662
OWN - NLM
STAT- Publisher
LR  - 20230530
IS  - 1873-1953 (Electronic)
IS  - 1474-5151 (Linking)
DP  - 2023 May 17
TI  - Dr. ChatGPT in Cardiovascular Nursing: A Deeper Dive into Trustworthiness, Value, 
      and Potential Risks.
LID - zvad047 [pii]
LID - 10.1093/eurjcn/zvad047 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Sikkim University, Gangtok, India.
LA  - eng
PT  - Journal Article
DEP - 20230517
PL  - England
TA  - Eur J Cardiovasc Nurs
JT  - European journal of cardiovascular nursing
JID - 101128793
SB  - IM
EDAT- 2023/05/31 01:09
MHDA- 2023/05/31 01:09
CRDT- 2023/05/30 22:03
PHST- 2023/05/05 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/05/31 01:09 [medline]
PHST- 2023/05/31 01:09 [pubmed]
PHST- 2023/05/30 22:03 [entrez]
AID - 7167296 [pii]
AID - 10.1093/eurjcn/zvad047 [doi]
PST - aheadofprint
SO  - Eur J Cardiovasc Nurs. 2023 May 17:zvad047. doi: 10.1093/eurjcn/zvad047.

PMID- 37046082
OWN - NLM
STAT- MEDLINE
DCOM- 20230621
LR  - 20231116
IS  - 1619-7089 (Electronic)
IS  - 1619-7070 (Linking)
VI  - 50
IP  - 8
DP  - 2023 Jul
TI  - Large language models (LLM) and ChatGPT: a medical student perspective.
PG  - 2248-2249
LID - 10.1007/s00259-023-06227-y [doi]
FAU - Perera Molligoda Arachchige, Arosh S
AU  - Perera Molligoda Arachchige AS
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Italy. 
      aroshshavinda.pereramolligodaarachchige@st.hunimed.eu.
LA  - eng
PT  - Letter
DEP - 20230413
PL  - Germany
TA  - Eur J Nucl Med Mol Imaging
JT  - European journal of nuclear medicine and molecular imaging
JID - 101140988
SB  - IM
MH  - *Language
MH  - *Artificial Intelligence
EDAT- 2023/04/13 06:00
MHDA- 2023/06/12 06:42
CRDT- 2023/04/12 23:29
PHST- 2023/03/09 00:00 [received]
PHST- 2023/04/04 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/04/13 06:00 [pubmed]
PHST- 2023/04/12 23:29 [entrez]
AID - 10.1007/s00259-023-06227-y [pii]
AID - 10.1007/s00259-023-06227-y [doi]
PST - ppublish
SO  - Eur J Nucl Med Mol Imaging. 2023 Jul;50(8):2248-2249. doi: 
      10.1007/s00259-023-06227-y. Epub 2023 Apr 13.

PMID- 37017834
OWN - NLM
STAT- MEDLINE
DCOM- 20230612
LR  - 20231117
IS  - 1534-4681 (Electronic)
IS  - 1068-9265 (Linking)
VI  - 30
IP  - 7
DP  - 2023 Jul
TI  - Exploring the Potential of Artificial Intelligence in Surgery: Insights from a 
      Conversation with ChatGPT.
PG  - 3875-3878
LID - 10.1245/s10434-023-13347-0 [doi]
FAU - Hassan, Abbas M
AU  - Hassan AM
AD  - Division of Plastic & Reconstructive Surgery, Indiana University School of 
      Medicine, Indianapolis, IN, USA.
FAU - Nelson, Jonas A
AU  - Nelson JA
AD  - Department of Plastic and Reconstructive Surgery, Memorial Sloan Kettering Cancer 
      Center, New York, NY, USA.
FAU - Coert, J Henk
AU  - Coert JH
AD  - Department of Plastic and Reconstructive Surgery, University Medical Center 
      Utrecht, Utrecht, The Netherlands.
FAU - Mehrara, Babak J
AU  - Mehrara BJ
AD  - Department of Plastic and Reconstructive Surgery, Memorial Sloan Kettering Cancer 
      Center, New York, NY, USA.
FAU - Selber, Jesse C
AU  - Selber JC
AD  - Department of Plastic Surgery, Corewell Health, Grand Rapids, MI, USA. 
      jessemd4@gmail.com.
LA  - eng
PT  - Editorial
DEP - 20230405
PL  - United States
TA  - Ann Surg Oncol
JT  - Annals of surgical oncology
JID - 9420840
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Communication
EDAT- 2023/04/06 06:00
MHDA- 2023/06/12 06:42
CRDT- 2023/04/05 11:18
PHST- 2023/02/17 00:00 [received]
PHST- 2023/02/27 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/04/06 06:00 [pubmed]
PHST- 2023/04/05 11:18 [entrez]
AID - 10.1245/s10434-023-13347-0 [pii]
AID - 10.1245/s10434-023-13347-0 [doi]
PST - ppublish
SO  - Ann Surg Oncol. 2023 Jul;30(7):3875-3878. doi: 10.1245/s10434-023-13347-0. Epub 
      2023 Apr 5.

PMID- 36958855
OWN - NLM
STAT- MEDLINE
DCOM- 20230405
LR  - 20230611
IS  - 1535-5667 (Electronic)
IS  - 0161-5505 (Linking)
VI  - 64
IP  - 4
DP  - 2023 Apr
TI  - Nuclear Medicine from a Novel Perspective: Buvat and Weber Talk with OpenAI's 
      ChatGPT.
PG  - 505-507
LID - 10.2967/jnumed.123.265636 [doi]
FAU - Buvat, Irène
AU  - Buvat I
AD  - Inserm Laboratory of Translational Imaging in Oncology, Institut Curie, Orsay, 
      France.
FAU - Weber, Wolfgang
AU  - Weber W
AD  - University Hospital, Technische Universität München, Munich, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230323
PL  - United States
TA  - J Nucl Med
JT  - Journal of nuclear medicine : official publication, Society of Nuclear Medicine
JID - 0217410
SB  - IM
MH  - *Nuclear Medicine
MH  - Radionuclide Imaging
EDAT- 2023/03/24 06:00
MHDA- 2023/04/05 06:42
CRDT- 2023/03/23 21:42
PHST- 2023/04/05 06:42 [medline]
PHST- 2023/03/24 06:00 [pubmed]
PHST- 2023/03/23 21:42 [entrez]
AID - jnumed.123.265636 [pii]
AID - 10.2967/jnumed.123.265636 [doi]
PST - ppublish
SO  - J Nucl Med. 2023 Apr;64(4):505-507. doi: 10.2967/jnumed.123.265636. Epub 2023 Mar 
      23.

PMID- 36791246
OWN - NLM
STAT- MEDLINE
DCOM- 20230406
LR  - 20230727
IS  - 1728-7731 (Electronic)
IS  - 1726-4901 (Linking)
VI  - 86
IP  - 4
DP  - 2023 Apr 1
TI  - ChatGPT and other artificial intelligence applications speed up scientific 
      writing.
PG  - 351-353
LID - 10.1097/JCMA.0000000000000900 [doi]
FAU - Chen, Tzeng-Ji
AU  - Chen TJ
AD  - Department of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, 
      Hsinchu County, Taiwan, ROC.
AD  - Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, 
      ROC.
AD  - Department of Post-Baccalaureate Medicine, National Chung Hsing University, 
      Taichung, Taiwan, ROC.
LA  - eng
PT  - Editorial
DEP - 20230214
PL  - Netherlands
TA  - J Chin Med Assoc
JT  - Journal of the Chinese Medical Association : JCMA
JID - 101174817
SB  - IM
MH  - *Artificial Intelligence
MH  - *Mobile Applications
MH  - *Writing
COIS- Conflicts of interest: Dr. Tzeng-Ji Chen, an editorial board member at Journal of 
      the Chinese Medical Association , had no role in the peer review process of or 
      decision to publish this article. The author declares that he has no conflicts of 
      interest related to the subject matter or materials discussed in this article.
EDAT- 2023/02/16 06:00
MHDA- 2023/04/05 06:42
CRDT- 2023/02/15 14:22
PHST- 2023/04/05 06:42 [medline]
PHST- 2023/02/16 06:00 [pubmed]
PHST- 2023/02/15 14:22 [entrez]
AID - 02118582-202304000-00001 [pii]
AID - 10.1097/JCMA.0000000000000900 [doi]
PST - ppublish
SO  - J Chin Med Assoc. 2023 Apr 1;86(4):351-353. doi: 10.1097/JCMA.0000000000000900. 
      Epub 2023 Feb 14.

PMID- 37684388
OWN - NLM
STAT- Publisher
LR  - 20231003
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Sep 8
TI  - Scientific sleuths spot dishonest ChatGPT use in papers.
LID - 10.1038/d41586-023-02477-w [doi]
FAU - Conroy, Gemma
AU  - Conroy G
LA  - eng
PT  - News
DEP - 20230908
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Peer review
OT  - Publishing
EDAT- 2023/09/09 10:41
MHDA- 2023/09/09 10:41
CRDT- 2023/09/08 23:29
PHST- 2023/09/09 10:41 [pubmed]
PHST- 2023/09/09 10:41 [medline]
PHST- 2023/09/08 23:29 [entrez]
AID - 10.1038/d41586-023-02477-w [pii]
AID - 10.1038/d41586-023-02477-w [doi]
PST - aheadofprint
SO  - Nature. 2023 Sep 8. doi: 10.1038/d41586-023-02477-w.

PMID- 37968192
OWN - NLM
STAT- Publisher
LR  - 20231115
IS  - 2212-4411 (Electronic)
DP  - 2023 Oct 5
TI  - The potential of ChatGPT in oral medicine: a new era of patient care?
LID - S2212-4403(23)00681-8 [pii]
LID - 10.1016/j.oooo.2023.09.010 [doi]
FAU - de Souza, Lucas Lacerda
AU  - de Souza LL
AD  - Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, 
      Piracicaba, Brazil.
FAU - Lopes, Marcio Ajudarte
AU  - Lopes MA
AD  - Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, 
      Piracicaba, Brazil.
FAU - Santos-Silva, Alan Roger
AU  - Santos-Silva AR
AD  - Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, 
      Piracicaba, Brazil.
FAU - Vargas, Pablo Agustin
AU  - Vargas PA
AD  - Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, 
      Piracicaba, Brazil.
LA  - eng
PT  - Editorial
DEP - 20231005
PL  - United States
TA  - Oral Surg Oral Med Oral Pathol Oral Radiol
JT  - Oral surgery, oral medicine, oral pathology and oral radiology
JID - 101576782
SB  - IM
COIS- DECLARATION OF INTERESTS None.
EDAT- 2023/11/16 00:42
MHDA- 2023/11/16 00:42
CRDT- 2023/11/15 21:58
PHST- 2023/08/30 00:00 [received]
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/11/16 00:42 [medline]
PHST- 2023/11/16 00:42 [pubmed]
PHST- 2023/11/15 21:58 [entrez]
AID - S2212-4403(23)00681-8 [pii]
AID - 10.1016/j.oooo.2023.09.010 [doi]
PST - aheadofprint
SO  - Oral Surg Oral Med Oral Pathol Oral Radiol. 2023 Oct 5:S2212-4403(23)00681-8. 
      doi: 10.1016/j.oooo.2023.09.010.

PMID- 37277055
OWN - NLM
STAT- Publisher
LR  - 20231021
IS  - 1523-1747 (Electronic)
IS  - 0022-202X (Linking)
VI  - 143
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT and Generative Artificial Intelligence in Mohs Surgery: A New Frontier of 
      Innovation.
PG  - 2105-2107
LID - S0022-202X(23)02142-5 [pii]
LID - 10.1016/j.jid.2023.05.018 [doi]
FAU - Jeha, George M
AU  - Jeha GM
AD  - Louisiana State University, New Orleans, Los Angeles, USA. Electronic address: 
      gmjeha@gmail.com.
FAU - Qiblawi, Sultan
AU  - Qiblawi S
AD  - University of Wisconsin-Madison, Madison, Wisconsin, USA.
FAU - Jairath, Neil
AU  - Jairath N
AD  - New York University, New York, New York, USA.
FAU - Sable, Kimberly
AU  - Sable K
AD  - University of Wisconsin-Madison, Madison, Wisconsin, USA.
FAU - LeBlanc, Keith Jr
AU  - LeBlanc K Jr
AD  - Louisiana State University, New Orleans, Los Angeles, USA.
FAU - Aylward, Juliet
AU  - Aylward J
AD  - University of Wisconsin-Madison, Madison, Wisconsin, USA.
FAU - Xu, Yaohui Gloria
AU  - Xu YG
AD  - University of Wisconsin-Madison, Madison, Wisconsin, USA.
LA  - eng
PT  - Journal Article
DEP - 20230603
PL  - United States
TA  - J Invest Dermatol
JT  - The Journal of investigative dermatology
JID - 0426720
SB  - IM
EDAT- 2023/06/06 01:11
MHDA- 2023/06/06 01:11
CRDT- 2023/06/05 19:23
PHST- 2023/05/04 00:00 [received]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/06/06 01:11 [pubmed]
PHST- 2023/06/06 01:11 [medline]
PHST- 2023/06/05 19:23 [entrez]
AID - S0022-202X(23)02142-5 [pii]
AID - 10.1016/j.jid.2023.05.018 [doi]
PST - ppublish
SO  - J Invest Dermatol. 2023 Nov;143(11):2105-2107. doi: 10.1016/j.jid.2023.05.018. 
      Epub 2023 Jun 3.

PMID- 37195797
OWN - NLM
STAT- MEDLINE
DCOM- 20230822
LR  - 20230925
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 8
DP  - 2023 Aug 1
TI  - ChatGPT/GPT-4: enabling a new era of surgical oncology.
PG  - 2549-2550
LID - 10.1097/JS9.0000000000000451 [doi]
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan.
FAU - Wu, Haiyang
AU  - Wu H
AUID- ORCID: 0000-0002-3215-1626
AD  - Department of Graduate School.
AD  - Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin 
      Medical University, Tianjin.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      North Carolina, USA.
FAU - Li, Cheng
AU  - Li C
AD  - Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical 
      College of Peking University, Beijing, People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *Surgical Oncology
MH  - *Artificial Intelligence
PMC - PMC10442081
COIS- The authors declare no conflicts of interest.
EDAT- 2023/05/17 13:10
MHDA- 2023/08/22 06:42
CRDT- 2023/05/17 12:03
PHST- 2023/04/30 00:00 [received]
PHST- 2023/05/01 00:00 [accepted]
PHST- 2023/08/22 06:42 [medline]
PHST- 2023/05/17 13:10 [pubmed]
PHST- 2023/05/17 12:03 [entrez]
AID - 01279778-202308000-00046 [pii]
AID - IJS-D-23-00785 [pii]
AID - 10.1097/JS9.0000000000000451 [doi]
PST - epublish
SO  - Int J Surg. 2023 Aug 1;109(8):2549-2550. doi: 10.1097/JS9.0000000000000451.

PMID- 37030395
OWN - NLM
STAT- MEDLINE
DCOM- 20230525
LR  - 20230525
IS  - 2352-5568 (Electronic)
IS  - 2352-5568 (Linking)
VI  - 42
IP  - 3
DP  - 2023 Jun
TI  - ChatGPT in the world of medical research: From how it works to how to use it.
PG  - 101231
LID - S2352-5568(23)00039-5 [pii]
LID - 10.1016/j.accpm.2023.101231 [doi]
FAU - Blanchard, Florian
AU  - Blanchard F
AD  - Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anaesthesiology and 
      Critical Care, Pitié-Salpêtrière Hospital, Paris, France. Electronic address: 
      florian.blanchard@aphp.fr.
FAU - Assefi, Mona
AU  - Assefi M
AD  - Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anaesthesiology and 
      Critical Care, Pitié-Salpêtrière Hospital, Paris, France.
FAU - Gatulle, Nicolas
AU  - Gatulle N
AD  - Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anaesthesiology and 
      Critical Care, Pitié-Salpêtrière Hospital, Paris, France.
FAU - Constantin, Jean-Michel
AU  - Constantin JM
AD  - Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anaesthesiology and 
      Critical Care, Pitié-Salpêtrière Hospital, Paris, France.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230406
PL  - France
TA  - Anaesth Crit Care Pain Med
JT  - Anaesthesia, critical care & pain medicine
JID - 101652401
SB  - IM
CON - Anaesth Crit Care Pain Med. 2023 Jun;42(3):101209. PMID: 36871626
MH  - Humans
MH  - *Biomedical Research
MH  - *Artificial Intelligence
EDAT- 2023/04/09 06:00
MHDA- 2023/05/23 06:42
CRDT- 2023/04/08 19:25
PHST- 2023/03/27 00:00 [received]
PHST- 2023/04/02 00:00 [revised]
PHST- 2023/04/02 00:00 [accepted]
PHST- 2023/05/23 06:42 [medline]
PHST- 2023/04/09 06:00 [pubmed]
PHST- 2023/04/08 19:25 [entrez]
AID - S2352-5568(23)00039-5 [pii]
AID - 10.1016/j.accpm.2023.101231 [doi]
PST - ppublish
SO  - Anaesth Crit Care Pain Med. 2023 Jun;42(3):101231. doi: 
      10.1016/j.accpm.2023.101231. Epub 2023 Apr 6.

PMID- 37247846
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20230906
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 3
DP  - 2023 Aug 28
TI  - Considering ChatGPT to be the first aid for young adults on mental health issues.
PG  - e615-e616
LID - 10.1093/pubmed/fdad065 [doi]
AB  - Providing mental health support and interventions through artificial intelligence 
      can open up more significant space for young people to get good services. 
      ChatGPT's potential to provide first aid to young adults struggling with mental 
      health issues is noteworthy. Despite its benefits and limitations, today's 
      technology can be a valuable addition to the mental health support available to 
      young adults.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Faculty 
      of Public Health. All rights reserved. For permissions, please e-mail: 
      journals.permissions@oup.com.
FAU - Aminah, Siti
AU  - Aminah S
AUID- ORCID: 0000-0002-6640-6412
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Malang, Malang, Jawa Timur 65145, Indonesia.
AD  - Department of Educational Psychology and Guidance, Faculty of Education and 
      Psychology, Universitas Negeri Yogyakarta, Sleman, Daerah Istimewa Yogyakarta, 
      55281, Indonesia.
FAU - Hidayah, Nur
AU  - Hidayah N
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Malang, Malang, Jawa Timur 65145, Indonesia.
FAU - Ramli, M
AU  - Ramli M
AD  - Department of Guidance and Counseling, Faculty of Education, Universitas Negeri 
      Malang, Malang, Jawa Timur 65145, Indonesia.
LA  - eng
PT  - Letter
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - Young Adult
MH  - Adolescent
MH  - *First Aid
MH  - *Artificial Intelligence
MH  - Mental Health
EDAT- 2023/05/30 01:06
MHDA- 2023/09/01 06:42
CRDT- 2023/05/29 19:33
PHST- 2023/05/04 00:00 [received]
PHST- 2023/09/01 06:42 [medline]
PHST- 2023/05/30 01:06 [pubmed]
PHST- 2023/05/29 19:33 [entrez]
AID - 7185477 [pii]
AID - 10.1093/pubmed/fdad065 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Aug 28;45(3):e615-e616. doi: 10.1093/pubmed/fdad065.

PMID- 36748354
OWN - NLM
STAT- Publisher
LR  - 20230213
IS  - 1545-5815 (Electronic)
IS  - 0898-9621 (Linking)
DP  - 2023 Feb 13
TI  - Letter to editor: NLP systems such as ChatGPT cannot be listed as an author 
      because these cannot fulfill widely adopted authorship criteria.
PG  - 1-3
LID - 10.1080/08989621.2023.2177160 [doi]
AB  - This letter to the editor suggests adding a technical point to the new editorial 
      policy expounded by Hosseini et al. on the mandatory disclosure of any use of 
      natural language processing (NLP) systems, or generative AI, in writing scholarly 
      publications. Such AI systems should naturally also be forbidden from being named 
      as authors, because they would not have fulfilled prevailing authorship 
      guidelines (such as the widely adopted ICMJE authorship criteria).
FAU - Yeo-Teh, Nicole Shu Ling
AU  - Yeo-Teh NSL
AUID- ORCID: 0000-0002-9153-9663
AD  - Research Compliance and Integrity Office, National University of Singapore, 
      Singapore, Singapore.
FAU - Tang, Bor Luen
AU  - Tang BL
AUID- ORCID: 0000-0002-1925-636X
AD  - Department of Biochemistry, Yong Loo Lin School of Medicine, National University 
      Health System, Singapore, Singapore.
LA  - eng
PT  - Journal Article
DEP - 20230213
PL  - United States
TA  - Account Res
JT  - Accountability in research
JID - 9100813
SB  - IM
OTO - NOTNLM
OT  - Authorship
OT  - ChatGPT
OT  - Generative AI
OT  - ICMJE guidelines
EDAT- 2023/02/08 06:00
MHDA- 2023/02/08 06:00
CRDT- 2023/02/07 04:23
PHST- 2023/02/08 06:00 [pubmed]
PHST- 2023/02/08 06:00 [medline]
PHST- 2023/02/07 04:23 [entrez]
AID - 10.1080/08989621.2023.2177160 [doi]
PST - aheadofprint
SO  - Account Res. 2023 Feb 13:1-3. doi: 10.1080/08989621.2023.2177160.

PMID- 37812419
OWN - NLM
STAT- MEDLINE
DCOM- 20231011
LR  - 20231017
IS  - 2574-3805 (Electronic)
IS  - 2574-3805 (Linking)
VI  - 6
IP  - 10
DP  - 2023 Oct 2
TI  - Large Language Model-Based Chatbot vs Surgeon-Generated Informed Consent 
      Documentation for Common Procedures.
PG  - e2336997
LID - 10.1001/jamanetworkopen.2023.36997 [doi]
LID - e2336997
AB  - IMPORTANCE: Informed consent is a critical component of patient care before 
      invasive procedures, yet it is frequently inadequate. Electronic consent forms 
      have the potential to facilitate patient comprehension if they provide 
      information that is readable, accurate, and complete; it is not known if large 
      language model (LLM)-based chatbots may improve informed consent documentation by 
      generating accurate and complete information that is easily understood by 
      patients. OBJECTIVE: To compare the readability, accuracy, and completeness of 
      LLM-based chatbot- vs surgeon-generated information on the risks, benefits, and 
      alternatives (RBAs) of common surgical procedures. DESIGN, SETTING, AND 
      PARTICIPANTS: This cross-sectional study compared randomly selected 
      surgeon-generated RBAs used in signed electronic consent forms at an academic 
      referral center in San Francisco with LLM-based chatbot-generated (ChatGPT-3.5, 
      OpenAI) RBAs for 6 surgical procedures (colectomy, coronary artery bypass graft, 
      laparoscopic cholecystectomy, inguinal hernia repair, knee arthroplasty, and 
      spinal fusion). MAIN OUTCOMES AND MEASURES: Readability was measured using 
      previously validated scales (Flesh-Kincaid grade level, Gunning Fog index, the 
      Simple Measure of Gobbledygook, and the Coleman-Liau index). Scores range from 0 
      to greater than 20 to indicate the years of education required to understand a 
      text. Accuracy and completeness were assessed using a rubric developed with 
      recommendations from LeapFrog, the Joint Commission, and the American College of 
      Surgeons. Both composite and RBA subgroup scores were compared. RESULTS: The 
      total sample consisted of 36 RBAs, with 1 RBA generated by the LLM-based chatbot 
      and 5 RBAs generated by a surgeon for each of the 6 surgical procedures. The mean 
      (SD) readability score for the LLM-based chatbot RBAs was 12.9 (2.0) vs 15.7 
      (4.0) for surgeon-generated RBAs (P = .10). The mean (SD) composite completeness 
      and accuracy score was lower for surgeons' RBAs at 1.6 (0.5) than for LLM-based 
      chatbot RBAs at 2.2 (0.4) (P < .001). The LLM-based chatbot scores were higher 
      than the surgeon-generated scores for descriptions of the benefits of surgery 
      (2.3 [0.7] vs 1.4 [0.7]; P < .001) and alternatives to surgery (2.7 [0.5] vs 1.4 
      [0.7]; P < .001). There was no significant difference in chatbot vs surgeon RBA 
      scores for risks of surgery (1.7 [0.5] vs 1.7 [0.4]; P = .38). CONCLUSIONS AND 
      RELEVANCE: The findings of this cross-sectional study suggest that despite not 
      being perfect, LLM-based chatbots have the potential to enhance informed consent 
      documentation. If an LLM were embedded in electronic health records in a manner 
      compliant with the Health Insurance Portability and Accountability Act, it could 
      be used to provide personalized risk information while easing documentation 
      burden for physicians.
FAU - Decker, Hannah
AU  - Decker H
AD  - Department of Surgery, University of California, San Francisco.
FAU - Trang, Karen
AU  - Trang K
AD  - Department of Surgery, University of California, San Francisco.
FAU - Ramirez, Joel
AU  - Ramirez J
AD  - Department of Surgery, University of California, San Francisco.
FAU - Colley, Alexis
AU  - Colley A
AD  - Department of Surgery, University of California, San Francisco.
FAU - Pierce, Logan
AU  - Pierce L
AD  - Department of Medicine, University of California, San Francisco.
FAU - Coleman, Melissa
AU  - Coleman M
AD  - Department of Surgery, University of California, San Francisco.
FAU - Bongiovanni, Tasce
AU  - Bongiovanni T
AD  - Department of Surgery, University of California, San Francisco.
FAU - Melton, Genevieve B
AU  - Melton GB
AD  - Department of Surgery, Institute for Health Informatics, and Center for Learning 
      Health System Sciences, University of Minnesota, Minneapolis.
FAU - Wick, Elizabeth
AU  - Wick E
AD  - Department of Surgery, University of California, San Francisco.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20231002
PL  - United States
TA  - JAMA Netw Open
JT  - JAMA network open
JID - 101729235
SB  - IM
MH  - Humans
MH  - Cross-Sectional Studies
MH  - Documentation
MH  - Informed Consent
MH  - Language
MH  - *Surgeons
MH  - United States
PMC - PMC10562939
COIS- Conflict of Interest Disclosures: Dr Decker reported personal fees from Moon 
      Surgical and being part of the National Clinician Scholars Program outside the 
      submitted work. Dr Melton reported grants from the Agency for Healthcare Research 
      and Quality, the US National Institutes of Health (NIH), the US Department of 
      Defense, and the Minnesota Partnership, all outside the submitted work. Dr Wick 
      reported grants from the NIH outside the submitted work. No other disclosures 
      were reported.
EDAT- 2023/10/09 12:43
MHDA- 2023/10/10 06:41
CRDT- 2023/10/09 11:33
PHST- 2023/10/10 06:41 [medline]
PHST- 2023/10/09 12:43 [pubmed]
PHST- 2023/10/09 11:33 [entrez]
AID - 2810364 [pii]
AID - zoi231078 [pii]
AID - 10.1001/jamanetworkopen.2023.36997 [doi]
PST - epublish
SO  - JAMA Netw Open. 2023 Oct 2;6(10):e2336997. doi: 
      10.1001/jamanetworkopen.2023.36997.

PMID- 37804033
OWN - NLM
STAT- MEDLINE
DCOM- 20231010
LR  - 20231010
IS  - 1681-7168 (Electronic)
IS  - 1022-386X (Linking)
VI  - 33
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT and the Future of Medical Education: Opportunities and Challenges.
PG  - 1207
LID - 10.29271/jcpsp.2023.10.1207 [doi]
AB  - Null.
FAU - Shaheen, Abida
AU  - Shaheen A
AD  - Department of Pharmacology, Shifa College of Dentistry, Shifa College of 
      Medicine, Shifa Tameer-e-Millat University, Islamabad, Pakistan.
AD  - Department of Surgery, Shifa College of Dentistry, Shifa College of Medicine, 
      Shifa Tameer-e-Millat University, Islamabad, Pakistan.
FAU - Azam, Fahad
AU  - Azam F
AD  - Department of Pharmacology, Shifa College of Dentistry, Shifa College of 
      Medicine, Shifa Tameer-e-Millat University, Islamabad, Pakistan.
AD  - Department of Surgery, Shifa College of Dentistry, Shifa College of Medicine, 
      Shifa Tameer-e-Millat University, Islamabad, Pakistan.
FAU - Amir, Mohammed
AU  - Amir M
LA  - eng
PT  - Journal Article
PL  - Pakistan
TA  - J Coll Physicians Surg Pak
JT  - Journal of the College of Physicians and Surgeons--Pakistan : JCPSP
JID - 9606447
SB  - IM
MH  - Humans
MH  - *Education, Medical
MH  - *Artificial Intelligence
EDAT- 2023/10/07 11:44
MHDA- 2023/10/09 06:41
CRDT- 2023/10/07 03:51
PHST- 2023/03/07 00:00 [received]
PHST- 2023/04/15 00:00 [accepted]
PHST- 2023/10/09 06:41 [medline]
PHST- 2023/10/07 11:44 [pubmed]
PHST- 2023/10/07 03:51 [entrez]
AID - 040579197 [pii]
AID - 10.29271/jcpsp.2023.10.1207 [doi]
PST - ppublish
SO  - J Coll Physicians Surg Pak. 2023 Oct;33(10):1207. doi: 
      10.29271/jcpsp.2023.10.1207.

PMID- 37791047
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 2575-7873 (Electronic)
IS  - 2575-7873 (Linking)
VI  - 7
DP  - 2023
TI  - The Hazards of Using ChatGPT: A Call to Action for Medical Education Researchers.
PG  - 27
LID - 10.22454/PRiMER.2023.295710 [doi]
LID - 27
FAU - Liaw, Winston
AU  - Liaw W
AD  - Department of Health Systems and Population Health Sciences, University of 
      Houston Tilman J. Fertitta Family College of Medicine, Houston, TX.
FAU - Chavez, Summer
AU  - Chavez S
AD  - Department of Health Systems and Population Health Sciences, University of 
      Houston Tilman J. Fertitta Family College of Medicine, Houston, TX.
FAU - Pham, Cecilia
AU  - Pham C
AD  - University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX.
FAU - Tehami, Salik
AU  - Tehami S
AD  - University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX.
FAU - Govender, Romi
AU  - Govender R
AD  - University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - United States
TA  - PRiMER
JT  - PRiMER (Leawood, Kan.)
JID - 101726396
CIN - PRiMER. 7:26.
PMC - PMC10544637
EDAT- 2023/10/04 06:43
MHDA- 2023/10/04 06:44
CRDT- 2023/10/04 04:16
PHST- 2023/10/04 06:44 [medline]
PHST- 2023/10/04 06:43 [pubmed]
PHST- 2023/10/04 04:16 [entrez]
AID - primer-7-27 [pii]
AID - 10.22454/PRiMER.2023.295710 [doi]
PST - epublish
SO  - PRiMER. 2023 Aug 21;7:27. doi: 10.22454/PRiMER.2023.295710. eCollection 2023.

PMID- 37737749
OWN - NLM
STAT- Publisher
LR  - 20231021
IS  - 1523-6838 (Electronic)
IS  - 0272-6386 (Linking)
VI  - 82
IP  - 5
DP  - 2023 Nov
TI  - OpenAI's ChatGPT and Its Potential Impact on Narrative and Scientific Writing in 
      Nephrology.
PG  - A13-A14
LID - S0272-6386(23)00707-2 [pii]
LID - 10.1053/j.ajkd.2023.04.006 [doi]
FAU - Daugirdas, John T
AU  - Daugirdas JT
AD  - Chicago, Illinois. Electronic address: jtdaugir@uic.edu.
LA  - eng
PT  - Editorial
DEP - 20230922
PL  - United States
TA  - Am J Kidney Dis
JT  - American journal of kidney diseases : the official journal of the National Kidney 
      Foundation
JID - 8110075
SB  - IM
EDAT- 2023/09/22 12:42
MHDA- 2023/09/22 12:42
CRDT- 2023/09/22 10:42
PHST- 2023/04/04 00:00 [received]
PHST- 2023/04/14 00:00 [revised]
PHST- 2023/04/19 00:00 [accepted]
PHST- 2023/09/22 12:42 [pubmed]
PHST- 2023/09/22 12:42 [medline]
PHST- 2023/09/22 10:42 [entrez]
AID - S0272-6386(23)00707-2 [pii]
AID - 10.1053/j.ajkd.2023.04.006 [doi]
PST - ppublish
SO  - Am J Kidney Dis. 2023 Nov;82(5):A13-A14. doi: 10.1053/j.ajkd.2023.04.006. Epub 
      2023 Sep 22.

PMID- 37517771
OWN - NLM
STAT- MEDLINE
DCOM- 20231121
LR  - 20231121
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 10
DP  - 2023 Oct
TI  - New Horizons: The Potential Role of OpenAI's ChatGPT in Clinical Radiology.
PG  - 943
LID - S1546-1440(23)00536-7 [pii]
LID - 10.1016/j.jacr.2023.06.028 [doi]
FAU - Perera Molligoda Arachchige, Arosh S
AU  - Perera Molligoda Arachchige AS
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Italy; Department 
      of Radiology, Istituto Clinico Humanitas, Milan, Italy. Electronic address: 
      aroshshavinda.pereramolligodaarachchige@st.hunimed.eu.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230728
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
CON - J Am Coll Radiol. 2023 Jul;20(7):696-698. PMID: 36972862
MH  - *Radiology
MH  - *Artificial Intelligence
EDAT- 2023/07/31 00:41
MHDA- 2023/11/03 06:43
CRDT- 2023/07/30 19:19
PHST- 2023/06/15 00:00 [received]
PHST- 2023/06/29 00:00 [accepted]
PHST- 2023/11/03 06:43 [medline]
PHST- 2023/07/31 00:41 [pubmed]
PHST- 2023/07/30 19:19 [entrez]
AID - S1546-1440(23)00536-7 [pii]
AID - 10.1016/j.jacr.2023.06.028 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Oct;20(10):943. doi: 10.1016/j.jacr.2023.06.028. Epub 2023 
      Jul 28.

PMID- 37265091
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231122
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 12
DP  - 2023 Nov 16
TI  - Commentary on: Performance of ChatGPT on the Plastic Surgery Inservice Training 
      Examination.
PG  - NP1083-NP1084
LID - 10.1093/asj/sjad171 [doi]
FAU - Cevallos, Priscila C
AU  - Cevallos PC
FAU - Nazerali, Rahim S
AU  - Nazerali RS
LA  - eng
PT  - Comment
PT  - Editorial
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CON - Aesthet Surg J. 2023 Nov 16;43(12):NP1078-NP1082. PMID: 37128784
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
EDAT- 2023/06/02 13:15
MHDA- 2023/11/20 06:55
CRDT- 2023/06/02 07:13
PHST- 2023/11/20 06:55 [medline]
PHST- 2023/06/02 13:15 [pubmed]
PHST- 2023/06/02 07:13 [entrez]
AID - 7188713 [pii]
AID - 10.1093/asj/sjad171 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Nov 16;43(12):NP1083-NP1084. doi: 10.1093/asj/sjad171.

PMID- 37082940
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Print)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 8
DP  - 2023 Jul 15
TI  - Can Chatbots Assist With Grant Writing in Plastic Surgery? Utilizing ChatGPT to 
      Start an R01 Grant.
PG  - NP663-NP665
LID - 10.1093/asj/sjad116 [doi]
FAU - Najafali, Daniel
AU  - Najafali D
AUID- ORCID: 0000-0003-1509-9229
FAU - Hinson, Chandler
AU  - Hinson C
AUID- ORCID: 0000-0002-3834-3047
FAU - Camacho, Justin M
AU  - Camacho JM
AUID- ORCID: 0000-0002-1877-6005
FAU - Galbraith, Logan G
AU  - Galbraith LG
AUID- ORCID: 0009-0000-4508-4445
FAU - Gupta, Rohun
AU  - Gupta R
AUID- ORCID: 0000-0003-1491-2441
FAU - Reid, Chris M
AU  - Reid CM
LA  - eng
PT  - Letter
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
MH  - Humans
MH  - *Surgery, Plastic
MH  - Writing
MH  - *Biomedical Research
MH  - *Plastic Surgery Procedures
PMC - PMC10349644
EDAT- 2023/04/21 06:41
MHDA- 2023/07/17 06:41
CRDT- 2023/04/21 04:53
PHST- 2023/07/17 06:41 [medline]
PHST- 2023/04/21 06:41 [pubmed]
PHST- 2023/04/21 04:53 [entrez]
AID - 7134121 [pii]
AID - sjad116 [pii]
AID - 10.1093/asj/sjad116 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jul 15;43(8):NP663-NP665. doi: 10.1093/asj/sjad116.

PMID- 37037360
OWN - NLM
STAT- MEDLINE
DCOM- 20230621
LR  - 20231122
IS  - 1090-2139 (Electronic)
IS  - 0889-1591 (Linking)
VI  - 111
DP  - 2023 Jul
TI  - Commentary to the article "A CHAT(GPT) about the future of scientific 
      publishing".
PG  - 124
LID - S0889-1591(23)00097-1 [pii]
LID - 10.1016/j.bbi.2023.04.004 [doi]
FAU - Herrera Ortiz, Andrés Felipe
AU  - Herrera Ortiz AF
AD  - Radiology department, Fundación Santa Fe de Bogotá. Bogotá, Colombia; Universidad 
      El Bosque. Bogotá, Colombia.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230408
PL  - Netherlands
TA  - Brain Behav Immun
JT  - Brain, behavior, and immunity
JID - 8800478
SB  - IM
CON - Brain Behav Immun. 2023 May;110:152-154. PMID: 36868432
MH  - *Publishing
MH  - Writing
MH  - Artificial Intelligence
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/04/11 06:00
MHDA- 2023/06/12 06:42
CRDT- 2023/04/10 19:22
PHST- 2023/04/01 00:00 [received]
PHST- 2023/04/07 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 19:22 [entrez]
AID - S0889-1591(23)00097-1 [pii]
AID - 10.1016/j.bbi.2023.04.004 [doi]
PST - ppublish
SO  - Brain Behav Immun. 2023 Jul;111:124. doi: 10.1016/j.bbi.2023.04.004. Epub 2023 
      Apr 8.

PMID- 36737653
OWN - NLM
STAT- MEDLINE
DCOM- 20230209
LR  - 20230711
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 614
IP  - 7947
DP  - 2023 Feb
TI  - ChatGPT: five priorities for research.
PG  - 224-226
LID - 10.1038/d41586-023-00288-7 [doi]
FAU - van Dis, Eva A M
AU  - van Dis EAM
FAU - Bollen, Johan
AU  - Bollen J
FAU - Zuidema, Willem
AU  - Zuidema W
FAU - van Rooij, Robert
AU  - van Rooij R
FAU - Bockting, Claudi L
AU  - Bockting CL
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
CIN - Nature. 2023 Mar;615(7951):216. PMID: 36882613
MH  - *Machine Learning/trends
MH  - *Writing/standards
MH  - *Research/standards/trends
OTO - NOTNLM
OT  - Computer science
OT  - Machine learning
OT  - Publishing
OT  - Research management
EDAT- 2023/02/04 06:00
MHDA- 2023/02/08 06:00
CRDT- 2023/02/03 23:54
PHST- 2023/02/03 23:54 [entrez]
PHST- 2023/02/04 06:00 [pubmed]
PHST- 2023/02/08 06:00 [medline]
AID - 10.1038/d41586-023-00288-7 [pii]
AID - 10.1038/d41586-023-00288-7 [doi]
PST - ppublish
SO  - Nature. 2023 Feb;614(7947):224-226. doi: 10.1038/d41586-023-00288-7.

PMID- 37812097
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231103
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT's Responses to Dilemmas in Medical Ethics: The Devil is in the Details.
PG  - 63-65
LID - 10.1080/15265161.2023.2250290 [doi]
FAU - Meier, Lukas J
AU  - Meier LJ
AUID- ORCID: 0000-0002-3316-3928
AD  - University of Cambridge.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CON - Am J Bioeth. 2023 Oct;23(10):8-16. PMID: 37440696
CON - Am J Bioeth. 2023 Oct;23(10):17-27. PMID: 37487184
MH  - Humans
MH  - *Bioethics
EDAT- 2023/10/09 12:42
MHDA- 2023/11/02 12:44
CRDT- 2023/10/09 10:13
PHST- 2023/11/02 12:44 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 10:13 [entrez]
AID - 10.1080/15265161.2023.2250290 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):63-65. doi: 10.1080/15265161.2023.2250290. Epub 2023 
      Oct 9.

PMID- 37659452
OWN - NLM
STAT- Publisher
LR  - 20231022
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
DP  - 2023 Sep 1
TI  - Reply to Letter to the Editor: Evaluating ChatGPT as an Adjunct for Radiologic 
      Decision Making.
LID - S1546-1440(23)00642-7 [pii]
LID - 10.1016/j.jacr.2023.08.026 [doi]
FAU - Rao, Arya
AU  - Rao A
AD  - Harvard Medical School, Boston, Massachusetts, and Medically Engineered Solutions 
      in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), 
      Massachusetts General Hospital, Boston, Massachusetts.
FAU - Dreyer, Keith J
AU  - Dreyer KJ
AD  - Harvard Medical School, Boston, Massachusetts, and Data Science Office and Vice 
      Chair of Informatics, Mass General Brigham, Boston, Massachusetts.
FAU - Succi, Marc D
AU  - Succi MD
AD  - Harvard Medical School, Boston, Massachusetts, and Medically Engineered Solutions 
      in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), 
      Massachusetts General Hospital, Boston, Massachusetts; Associate Chair of 
      Innovation & Commercialization at Mass General Brigham Radiology. Electronic 
      address: msucci@mgh.harvard.edu.
LA  - eng
PT  - Letter
DEP - 20230901
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
EDAT- 2023/09/03 00:41
MHDA- 2023/09/03 00:41
CRDT- 2023/09/02 19:23
PHST- 2023/07/24 00:00 [received]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/09/03 00:41 [pubmed]
PHST- 2023/09/03 00:41 [medline]
PHST- 2023/09/02 19:23 [entrez]
AID - S1546-1440(23)00642-7 [pii]
AID - 10.1016/j.jacr.2023.08.026 [doi]
PST - aheadofprint
SO  - J Am Coll Radiol. 2023 Sep 1:S1546-1440(23)00642-7. doi: 
      10.1016/j.jacr.2023.08.026.

PMID- 37461385
OWN - NLM
STAT- MEDLINE
DCOM- 20230719
LR  - 20230728
IS  - 1936-7163 (Electronic)
IS  - 0033-6572 (Linking)
VI  - 54
IP  - 7
DP  - 2023 Jul 17
TI  - Artificial intelligence in dentistry: a review of ChatGPT's role and potential.
PG  - 526-527
LID - 10.3290/j.qi.b4218569 [doi]
FAU - Buzayan, Muaiyed Mahmoud
AU  - Buzayan MM
FAU - Sivakumar, Indumathi
AU  - Sivakumar I
FAU - Mohd, Noorhayati Raja
AU  - Mohd NR
LA  - eng
PT  - Journal Article
PT  - Review
PL  - Germany
TA  - Quintessence Int
JT  - Quintessence international (Berlin, Germany : 1985)
JID - 0342677
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Dentistry
EDAT- 2023/07/18 06:42
MHDA- 2023/07/19 06:43
CRDT- 2023/07/18 03:33
PHST- 2023/07/19 06:43 [medline]
PHST- 2023/07/18 06:42 [pubmed]
PHST- 2023/07/18 03:33 [entrez]
AID - 4218569 [pii]
AID - 10.3290/j.qi.b4218569 [doi]
PST - ppublish
SO  - Quintessence Int. 2023 Jul 17;54(7):526-527. doi: 10.3290/j.qi.b4218569.

PMID- 37256340
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230721
LR  - 20230721
IS  - 1432-1238 (Electronic)
IS  - 0342-4642 (Linking)
VI  - 49
IP  - 7
DP  - 2023 Jul
TI  - How could ChatGPT impact my practice as an intensivist? An overview of potential 
      applications, risks and limitations.
PG  - 844-847
LID - 10.1007/s00134-023-07096-7 [doi]
FAU - Komorowski, Matthieu
AU  - Komorowski M
AUID- ORCID: 0000-0003-0559-5747
AD  - Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of 
      Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 
      2AZ, UK. m.komorowski14@imperial.ac.uk.
AD  - Intensive Care Unit, Charing Cross Hospital, Fulham Palace Road, London, W6 8RF, 
      UK. m.komorowski14@imperial.ac.uk.
FAU - Del Pilar Arias López, Maria
AU  - Del Pilar Arias López M
AUID- ORCID: 0000-0003-4881-4743
AD  - Hospital de Niños Ricardo Gutierrez, Intermediate Care Unit, Gallo 1330, 
      C1425EFD, Buenos Aires, Argentina.
AD  - Argentina Society of Intensive Care, SATI-Q Paediatric Program. Av. Cnel, Niceto 
      Vega 4617, C1414BEA, Buenos Aires, Argentina.
FAU - Chang, Anthony C
AU  - Chang AC
AD  - Children's Hospital of Orange County Sharon Disney Lund Medical Intelligence and 
      Innovation Institute, 1201 W. La Veta Ave, Orange, CA, 92868-3874, USA.
LA  - eng
PT  - Editorial
DEP - 20230531
PL  - United States
TA  - Intensive Care Med
JT  - Intensive care medicine
JID - 7704851
SB  - IM
EDAT- 2023/05/31 13:12
MHDA- 2023/05/31 13:13
CRDT- 2023/05/31 11:05
PHST- 2023/04/12 00:00 [received]
PHST- 2023/05/05 00:00 [accepted]
PHST- 2023/05/31 13:13 [medline]
PHST- 2023/05/31 13:12 [pubmed]
PHST- 2023/05/31 11:05 [entrez]
AID - 10.1007/s00134-023-07096-7 [pii]
AID - 10.1007/s00134-023-07096-7 [doi]
PST - ppublish
SO  - Intensive Care Med. 2023 Jul;49(7):844-847. doi: 10.1007/s00134-023-07096-7. Epub 
      2023 May 31.

PMID- 37130418
OWN - NLM
STAT- MEDLINE
DCOM- 20230504
LR  - 20230505
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 5
DP  - 2023 May
TI  - Therapists or Replicants? Ethical, Legal, and Social Considerations for Using 
      ChatGPT in Therapy.
PG  - 40-42
LID - 10.1080/15265161.2023.2191022 [doi]
FAU - Amram, Benjamin
AU  - Amram B
AD  - Reichman University.
FAU - Klempner, Uri
AU  - Klempner U
AD  - Reichman University.
FAU - Shturman, Shira
AU  - Shturman S
AD  - Reichman University.
FAU - Greenbaum, Dov
AU  - Greenbaum D
AD  - Reichman University.
AD  - Yale University.
LA  - eng
PT  - Comment
PT  - Journal Article
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CON - Am J Bioeth. 2023 May;23(5):4-13. PMID: 35362368
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Psychotherapy
MH  - Confidentiality/ethics
EDAT- 2023/05/02 18:41
MHDA- 2023/05/04 12:41
CRDT- 2023/05/02 17:13
PHST- 2023/05/04 12:41 [medline]
PHST- 2023/05/02 18:41 [pubmed]
PHST- 2023/05/02 17:13 [entrez]
AID - 10.1080/15265161.2023.2191022 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 May;23(5):40-42. doi: 10.1080/15265161.2023.2191022.

PMID- 37031761
OWN - NLM
STAT- MEDLINE
DCOM- 20230830
LR  - 20231003
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Print)
IS  - 0002-9378 (Linking)
VI  - 229
IP  - 3
DP  - 2023 Sep
TI  - Beware of references when using ChatGPT as a source of information to write 
      scientific articles.
PG  - 356-357
LID - S0002-9378(23)00234-X [pii]
LID - 10.1016/j.ajog.2023.04.004 [doi]
FAU - Sanchez-Ramos, Luis
AU  - Sanchez-Ramos L
AD  - Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, 
      University of Florida College of Medicine, 653 West 8th St, Jacksonville, FL 
      32209. Electronic address: luis.sanchez@jax.ufl.edu.
FAU - Lin, Lifeng
AU  - Lin L
AD  - Department of Epidemiology and Biostatistics, The University of Arizona, Tucson, 
      AZ.
FAU - Romero, Roberto
AU  - Romero R
AD  - Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, 
      Division of Intramural Research, Eunice Kennedy Shriver National Institute of 
      Child Health and Human Development, National Institutes of Health, U.S. 
      Department of Health and Human Services, Bethesda, MD and Detroit, MI; Department 
      of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI; Department 
      of Epidemiology and Biostatistics, Michigan University, East Lansing, MI.
LA  - eng
GR  - Z01 HD002400/ImNIH/Intramural NIH HHS/United States
PT  - Comment
PT  - Letter
DEP - 20230407
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
CON - Am J Obstet Gynecol. 2023 Jun;228(6):696-705. PMID: 36924907
CON - Am J Obstet Gynecol. 2023 Jun;228(6):706-711. PMID: 36924908
MH  - Humans
MH  - *Information Sources
MH  - *Medical Writing
MH  - Artificial Intelligence
PMC - PMC10524915
MID - NIHMS1900308
COIS- Disclosure: The authors report no conflict of interest.
EDAT- 2023/04/10 06:00
MHDA- 2023/08/28 06:42
PMCR- 2024/09/01
CRDT- 2023/04/09 19:23
PHST- 2023/03/25 00:00 [received]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/08/28 06:42 [medline]
PHST- 2023/04/10 06:00 [pubmed]
PHST- 2023/04/09 19:23 [entrez]
AID - S0002-9378(23)00234-X [pii]
AID - 10.1016/j.ajog.2023.04.004 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol. 2023 Sep;229(3):356-357. doi: 10.1016/j.ajog.2023.04.004. 
      Epub 2023 Apr 7.

PMID- 36972862
OWN - NLM
STAT- MEDLINE
DCOM- 20230801
LR  - 20231113
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 7
DP  - 2023 Jul
TI  - New Horizons: The Potential Role of OpenAI's ChatGPT in Clinical Radiology.
PG  - 696-698
LID - S1546-1440(23)00259-4 [pii]
LID - 10.1016/j.jacr.2023.02.025 [doi]
FAU - Ismail, Ahmed
AU  - Ismail A
AD  - George Washington University School of Medicine and Health Sciences, Washington, 
      DC.
FAU - Ghorashi, Nima S
AU  - Ghorashi NS
AD  - George Washington University School of Medicine and Health Sciences, Washington, 
      DC, and Radiology Interest Group President.
FAU - Javan, Ramin
AU  - Javan R
AD  - Department of Radiology, George Washington University Hospital, Washington, DC, 
      and Director of Advanced Brain Imaging and 3D Innovations Lab and Medical Student 
      Radiology Clerkship Director. Electronic address: rjavan@mfa.gwu.edu.
LA  - eng
PT  - Journal Article
DEP - 20230325
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
CIN - J Am Coll Radiol. 2023 Oct;20(10):943-944. PMID: 37517769
CIN - J Am Coll Radiol. 2023 Oct;20(10):943. PMID: 37517771
MH  - *Radiology
MH  - *Artificial Intelligence
EDAT- 2023/03/28 06:00
MHDA- 2023/07/24 06:42
CRDT- 2023/03/27 20:26
PHST- 2023/01/17 00:00 [received]
PHST- 2023/02/14 00:00 [revised]
PHST- 2023/02/16 00:00 [accepted]
PHST- 2023/07/24 06:42 [medline]
PHST- 2023/03/28 06:00 [pubmed]
PHST- 2023/03/27 20:26 [entrez]
AID - S1546-1440(23)00259-4 [pii]
AID - 10.1016/j.jacr.2023.02.025 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Jul;20(7):696-698. doi: 10.1016/j.jacr.2023.02.025. Epub 
      2023 Mar 25.

PMID- 38036860
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 624
IP  - 7990
DP  - 2023 Dec
TI  - ChatGPT one year on: who is using it, how and why?
PG  - 39-41
LID - 10.1038/d41586-023-03798-6 [doi]
FAU - Ghassemi, Marzyeh
AU  - Ghassemi M
AUID- ORCID: 0000-0001-6349-7251
FAU - Birhane, Abeba
AU  - Birhane A
AUID- ORCID: 0000-0001-6319-7937
FAU - Bilal, Mushtaq
AU  - Bilal M
AUID- ORCID: 0000-0002-6705-9176
FAU - Kankaria, Siddharth
AU  - Kankaria S
AUID- ORCID: 0000-0001-9498-2145
FAU - Malone, Claire
AU  - Malone C
AUID- ORCID: 0000-0003-4325-7378
FAU - Mollick, Ethan
AU  - Mollick E
FAU - Tustumi, Francisco
AU  - Tustumi F
AUID- ORCID: 0000-0001-6695-0496
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Machine learning
OT  - Policy
OT  - Technology
EDAT- 2023/12/01 00:43
MHDA- 2023/12/01 00:44
CRDT- 2023/11/30 23:52
PHST- 2023/12/01 00:44 [medline]
PHST- 2023/12/01 00:43 [pubmed]
PHST- 2023/11/30 23:52 [entrez]
AID - 10.1038/d41586-023-03798-6 [pii]
AID - 10.1038/d41586-023-03798-6 [doi]
PST - ppublish
SO  - Nature. 2023 Dec;624(7990):39-41. doi: 10.1038/d41586-023-03798-6.

PMID- 37985300
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1806-4841 (Electronic)
IS  - 0365-0596 (Linking)
DP  - 2023 Nov 18
TI  - ChatGPT: performance of artificial intelligence in the dermatology specialty 
      certificate examination.
LID - S0365-0596(23)00245-3 [pii]
LID - 10.1016/j.abd.2023.08.005 [doi]
FAU - Jabour, Thaís Barros Felippe
AU  - Jabour TBF
AD  - Centro de Pesquisas Clínicas de Natal, Natal, RN, Brazil.
FAU - Ribeiro, José Paulo Júnior
AU  - Ribeiro JP Júnior
AD  - Department of Dermatology, Hospital Universitário Onofre Lopes, Universidade 
      Federal do Rio Grande do Norte, Natal, RN, Brazil. Electronic address: 
      josepaulojr@outlook.com.
FAU - Fernandes, Alexandre Chaves
AU  - Fernandes AC
AD  - Instituto Internacional de Neurociências Edmond e Lily Safra, Macaíba, RN, 
      Brazil.
FAU - Honorato, Cecília Mirelle Almeida
AU  - Honorato CMA
AD  - Hospital Universitário Onofre Lopes, Universidade Federal do Rio Grande do Norte, 
      Natal, RN, Brazil.
FAU - Queiroz, Maria do Carmo Araújo Palmeira
AU  - Queiroz MDCAP
AD  - Department of Dermatology, Hospital Universitário Onofre Lopes, Universidade 
      Federal do Rio Grande do Norte, Natal, RN, Brazil.
LA  - eng
PT  - Case Reports
DEP - 20231118
PL  - Spain
TA  - An Bras Dermatol
JT  - Anais brasileiros de dermatologia
JID - 0067662
SB  - IM
EDAT- 2023/11/21 01:07
MHDA- 2023/11/21 01:07
CRDT- 2023/11/20 21:58
PHST- 2023/06/29 00:00 [received]
PHST- 2023/08/04 00:00 [revised]
PHST- 2023/08/11 00:00 [accepted]
PHST- 2023/11/21 01:07 [medline]
PHST- 2023/11/21 01:07 [pubmed]
PHST- 2023/11/20 21:58 [entrez]
AID - S0365-0596(23)00245-3 [pii]
AID - 10.1016/j.abd.2023.08.005 [doi]
PST - aheadofprint
SO  - An Bras Dermatol. 2023 Nov 18:S0365-0596(23)00245-3. doi: 
      10.1016/j.abd.2023.08.005.

PMID- 37984563
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1542-7714 (Electronic)
IS  - 1542-3565 (Linking)
DP  - 2023 Nov 18
TI  - Accuracy of ChatGPT in Common Gastrointestinal Diseases: Impact for Patients and 
      Providers.
LID - S1542-3565(23)00946-1 [pii]
LID - 10.1016/j.cgh.2023.11.008 [doi]
FAU - Kerbage, Anthony
AU  - Kerbage A
AD  - Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, US.
FAU - Kassab, Joseph
AU  - Kassab J
AD  - Research Institute, Cleveland Clinic, Cleveland, OH, US.
FAU - El Dahdah, Joseph
AU  - El Dahdah J
AD  - Research Institute, Cleveland Clinic, Cleveland, OH, US.
FAU - Burke, Carol A
AU  - Burke CA
AD  - Department of Gastroenterology, Hepatology and Nutrition, Cleveland Clinic, 
      Cleveland, OH.
FAU - Achkar, Jean-Paul
AU  - Achkar JP
AD  - Department of Gastroenterology, Hepatology and Nutrition, Cleveland Clinic, 
      Cleveland, OH.
FAU - Rouphael, Carol
AU  - Rouphael C
AD  - Department of Gastroenterology, Hepatology and Nutrition, Cleveland Clinic, 
      Cleveland, OH. Electronic address: rouphac@ccf.org.
LA  - eng
PT  - Journal Article
DEP - 20231118
PL  - United States
TA  - Clin Gastroenterol Hepatol
JT  - Clinical gastroenterology and hepatology : the official clinical practice journal 
      of the American Gastroenterological Association
JID - 101160775
SB  - IM
EDAT- 2023/11/21 01:07
MHDA- 2023/11/21 01:07
CRDT- 2023/11/20 19:37
PHST- 2023/08/04 00:00 [received]
PHST- 2023/10/22 00:00 [revised]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/11/21 01:07 [medline]
PHST- 2023/11/21 01:07 [pubmed]
PHST- 2023/11/20 19:37 [entrez]
AID - S1542-3565(23)00946-1 [pii]
AID - 10.1016/j.cgh.2023.11.008 [doi]
PST - aheadofprint
SO  - Clin Gastroenterol Hepatol. 2023 Nov 18:S1542-3565(23)00946-1. doi: 
      10.1016/j.cgh.2023.11.008.

PMID- 37778614
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231106
LR  - 20231208
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
VI  - 192
DP  - 2023 Nov
TI  - ChatGPT versus human memory: A historical exploration of the 4 Hs and 4 Ts.
PG  - 109982
LID - S0300-9572(23)00296-4 [pii]
LID - 10.1016/j.resuscitation.2023.109982 [doi]
FAU - Semeraro, Federico
AU  - Semeraro F
AD  - Department of Anesthesia, Intensive Care and Prehospital Emergency, Ospedale 
      Maggiore Carlo Alberto Pizzardi, Bologna, Italy. Electronic address: 
      federicofsemeraro@gmail.com.
FAU - Pupkes, Jan
AU  - Pupkes J
AD  - Tübingen Institute for Medical Education (TIME), Eberhard Karls University, 
      Tübingen, Germany.
FAU - Mahling, Moritz
AU  - Mahling M
AD  - Tübingen Institute for Medical Education (TIME), Eberhard Karls University, 
      Tübingen, Germany; Department of Diabetology, Endocrinology, Nephrology, Section 
      of Nephrology and Hypertension, University Hospital Tübingen, Tübingen, Germany.
FAU - Monsieurs, Koenraad
AU  - Monsieurs K
AD  - Department of Emergency Medicine, Antwerp University Hospital and University of 
      Antwerp, Belgium.
LA  - eng
PT  - Letter
DEP - 20230929
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
COIS- Declaration of Competing Interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: No relationship exists between the author and any commercial entity or 
      product mentioned in this manuscript that might represent a conflict of interest. 
      No inducements have been made by any commercial entity to submit the manuscript 
      for publication. All within 3 years of beginning the work submitted. FS is the 
      Chair-Elect of the European Resuscitation Council, Chair of the ILCOR Social 
      Media Working Group and member of the ILCOR Basic Life Support Task Force. JP and 
      MM have no conflict of interest to declare. KM is the Chair of the European 
      Resuscitation Council, Chair of the ILCOR Digital Communications Working Group 
      and member of the ILCOR Education Implementation and Teams Task Force.
EDAT- 2023/10/02 00:41
MHDA- 2023/10/02 00:42
CRDT- 2023/10/01 19:29
PHST- 2023/09/22 00:00 [received]
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/10/02 00:42 [medline]
PHST- 2023/10/02 00:41 [pubmed]
PHST- 2023/10/01 19:29 [entrez]
AID - S0300-9572(23)00296-4 [pii]
AID - 10.1016/j.resuscitation.2023.109982 [doi]
PST - ppublish
SO  - Resuscitation. 2023 Nov;192:109982. doi: 10.1016/j.resuscitation.2023.109982. 
      Epub 2023 Sep 29.

PMID- 37747242
OWN - NLM
STAT- MEDLINE
DCOM- 20231110
LR  - 20231113
IS  - 1527-3792 (Electronic)
IS  - 0022-5347 (Linking)
VI  - 210
IP  - 6
DP  - 2023 Dec
TI  - Utilization of ChatGPT for Appraising Letters of Recommendation in Urology 
      Residency Applications: Ready for Prime Time?
PG  - 833-834
LID - 10.1097/JU.0000000000003718 [doi]
FAU - Barrett, Athena
AU  - Barrett A
AD  - Department of Urology, Loyola University Chicago Stritch School of Medicine, 
      Chicago, Illinois.
FAU - Hekman, Lauren
AU  - Hekman L
AD  - Department of Urology, Loyola University Chicago Stritch School of Medicine, 
      Chicago, Illinois.
FAU - Ellis, Jeffrey L
AU  - Ellis JL
AD  - Department of Urology, Loyola University Medical Center, Maywood, Illinois.
FAU - Baldea, Kristin G
AU  - Baldea KG
AD  - Department of Urology, Loyola University Medical Center, Maywood, Illinois.
FAU - Bresler, Larissa
AU  - Bresler L
AD  - Department of Urology, Loyola University Medical Center, Maywood, Illinois.
LA  - eng
PT  - Editorial
DEP - 20230925
PL  - United States
TA  - J Urol
JT  - The Journal of urology
JID - 0376374
SB  - IM
MH  - Humans
MH  - *Internship and Residency
MH  - *Urology/education
MH  - Personnel Selection
EDAT- 2023/09/25 12:42
MHDA- 2023/11/10 06:45
CRDT- 2023/09/25 09:13
PHST- 2023/11/10 06:45 [medline]
PHST- 2023/09/25 12:42 [pubmed]
PHST- 2023/09/25 09:13 [entrez]
AID - 10.1097/JU.0000000000003718 [doi]
PST - ppublish
SO  - J Urol. 2023 Dec;210(6):833-834. doi: 10.1097/JU.0000000000003718. Epub 2023 Sep 
      25.

PMID- 37720884
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230919
IS  - 1869-3474 (Print)
IS  - 1869-3482 (Electronic)
IS  - 1869-3474 (Linking)
VI  - 57
IP  - 5
DP  - 2023 Oct
TI  - Exploring the Opportunities and Challenges of ChatGPT in Academic Writing: Reply 
      to Bom et al.
PG  - 213-214
LID - 10.1007/s13139-023-00816-3 [doi]
FAU - Perera Molligoda Arachchige, Arosh S
AU  - Perera Molligoda Arachchige AS
AUID- ORCID: 0000-0002-3875-0267
AD  - Department of Radiology, Istituto Clinico Humanitas, Milan, Italy. ROR: 
      https://ror.org/05d538656. GRID: grid.417728.f. ISNI: 0000 0004 1756 8807
AD  - Department of Biomedical Sciences, Humanitas University, Milan, Italy. ROR: 
      https://ror.org/020dggs04. GRID: grid.452490.e. ISNI: 0000 0004 4908 9368
FAU - Stomeo, Niccolò
AU  - Stomeo N
AD  - Department of Anaesthesiology and Intensive Care, Istituto Clinico Humanitas, 
      Milan, Italy. ROR: https://ror.org/05d538656. GRID: grid.417728.f. ISNI: 0000 
      0004 1756 8807
LA  - eng
PT  - Journal Article
DEP - 20230812
PL  - Germany
TA  - Nucl Med Mol Imaging
JT  - Nuclear medicine and molecular imaging
JID - 101530478
PMC - PMC10504185
COIS- Conflict of InterestArosh S. Perera Molligoda Arachchige and Niccolò Stomeo 
      declare no conflicts of interest.
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:43
PMCR- 2024/10/01
CRDT- 2023/09/18 04:47
PHST- 2023/07/06 00:00 [received]
PHST- 2023/07/11 00:00 [revised]
PHST- 2023/07/18 00:00 [accepted]
PHST- 2024/10/01 00:00 [pmc-release]
PHST- 2023/09/18 06:43 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:47 [entrez]
AID - 816 [pii]
AID - 10.1007/s13139-023-00816-3 [doi]
PST - ppublish
SO  - Nucl Med Mol Imaging. 2023 Oct;57(5):213-214. doi: 10.1007/s13139-023-00816-3. 
      Epub 2023 Aug 12.

PMID- 37718185
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 1875-2128 (Electronic)
IS  - 1875-2128 (Linking)
VI  - 116
IP  - 10
DP  - 2023 Oct
TI  - Relevance of medical information obtained from ChatGPT: Are large language models 
      friends or foes?
PG  - 485-486
LID - S1875-2136(23)00164-X [pii]
LID - 10.1016/j.acvd.2023.07.009 [doi]
FAU - Mesnier, Jules
AU  - Mesnier J
AD  - Hôpital Bichat, Assistance publique-Hôpitaux de Paris, Inserm U-1148, université 
      de Paris, 46, rue Henri-Huchard, 75018 Paris, France.
FAU - Suc, Gaspard
AU  - Suc G
AD  - Hôpital Bichat, Assistance publique-Hôpitaux de Paris, Inserm U-1148, université 
      de Paris, 46, rue Henri-Huchard, 75018 Paris, France.
FAU - Sayah, Neila
AU  - Sayah N
AD  - Hôpital Bichat, Assistance publique-Hôpitaux de Paris, Inserm U-1148, université 
      de Paris, 46, rue Henri-Huchard, 75018 Paris, France.
FAU - Abtan, Jérémie
AU  - Abtan J
AD  - Hôpital Bichat, Assistance publique-Hôpitaux de Paris, Inserm U-1148, université 
      de Paris, 46, rue Henri-Huchard, 75018 Paris, France.
FAU - Steg, Philippe Gabriel
AU  - Steg PG
AD  - Hôpital Bichat, Assistance publique-Hôpitaux de Paris, Inserm U-1148, université 
      de Paris, 46, rue Henri-Huchard, 75018 Paris, France. Electronic address: 
      gabriel.steg@aphp.fr.
LA  - eng
PT  - Letter
DEP - 20230909
PL  - Netherlands
TA  - Arch Cardiovasc Dis
JT  - Archives of cardiovascular diseases
JID - 101465655
SB  - IM
EDAT- 2023/09/18 00:41
MHDA- 2023/09/18 00:41
CRDT- 2023/09/17 21:55
PHST- 2023/05/03 00:00 [received]
PHST- 2023/07/30 00:00 [revised]
PHST- 2023/07/31 00:00 [accepted]
PHST- 2023/09/18 00:41 [pubmed]
PHST- 2023/09/18 00:41 [medline]
PHST- 2023/09/17 21:55 [entrez]
AID - S1875-2136(23)00164-X [pii]
AID - 10.1016/j.acvd.2023.07.009 [doi]
PST - ppublish
SO  - Arch Cardiovasc Dis. 2023 Oct;116(10):485-486. doi: 10.1016/j.acvd.2023.07.009. 
      Epub 2023 Sep 9.

PMID- 37591768
OWN - NLM
STAT- Publisher
LR  - 20230817
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
DP  - 2023 Aug 17
TI  - Teaching clinical empathy skills in medical education: Can ChatGPT assist the 
      educator?
PG  - 1
LID - 10.1080/0142159X.2023.2247144 [doi]
FAU - Ennab, Farah
AU  - Ennab F
AD  - College of Medicine, Mohammed Bin Rashid University of Medicine and Health 
      Sciences, Dubai, United Arab Emirates.
LA  - eng
PT  - Journal Article
DEP - 20230817
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
EDAT- 2023/08/18 00:42
MHDA- 2023/08/18 00:42
CRDT- 2023/08/17 22:02
PHST- 2023/08/18 00:42 [medline]
PHST- 2023/08/18 00:42 [pubmed]
PHST- 2023/08/17 22:02 [entrez]
AID - 10.1080/0142159X.2023.2247144 [doi]
PST - aheadofprint
SO  - Med Teach. 2023 Aug 17:1. doi: 10.1080/0142159X.2023.2247144.

PMID- 37518993
OWN - NLM
STAT- Publisher
LR  - 20230731
IS  - 1601-0825 (Electronic)
IS  - 1354-523X (Linking)
DP  - 2023 Jul 30
TI  - Combining ChatGPT and machine learning: A viable alternative for discussion in 
      oral medicine.
LID - 10.1111/odi.14706 [doi]
FAU - Santana, Lucas Alves da Mota
AU  - Santana LADM
AUID- ORCID: 0000-0002-8261-1504
AD  - Department of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, 
      Brazil.
AD  - Department of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil.
FAU - Santos, Harim Tavares Dos
AU  - Santos HTD
AD  - Department of Otolaryngology-Head and Neck Surgery, University of Missouri, 
      Columbia, Missouri, USA.
AD  - Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA.
FAU - Gonçalo, Rani Iani Costa
AU  - Gonçalo RIC
AUID- ORCID: 0000-0002-6762-135X
AD  - Department of Dentistry, Federal University of Rio Grande do Norte (UFRN), Natal, 
      RN, Brazil.
FAU - de Oliveira Costa, Carla Samily
AU  - de Oliveira Costa CS
AD  - Department of Dentistry, Federal University of Rio Grande do Norte (UFRN), Natal, 
      RN, Brazil.
FAU - Barbosa, Breno Ferreira
AU  - Barbosa BF
AD  - Department of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, 
      Brazil.
FAU - Alves, Êmilly Victória Maciel
AU  - Alves ÊVM
AD  - Department of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil.
FAU - de Santana, Taun Rosa
AU  - de Santana TR
AD  - Department of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil.
FAU - Freitas, Margarite Maria Delmondes
AU  - Freitas MMD
AD  - Department of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil.
FAU - Takeshita, Wilton Mitsunari
AU  - Takeshita WM
AD  - Department of Diagnosis and Surgery, School of Dentistry, São Paulo State 
      University (UNESP), Araçatuba, SP, Brazil.
FAU - Trento, Cleverson Luciano
AU  - Trento CL
AD  - Department of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, 
      Brazil.
LA  - eng
PT  - Letter
DEP - 20230730
PL  - Denmark
TA  - Oral Dis
JT  - Oral diseases
JID - 9508565
EDAT- 2023/07/31 06:42
MHDA- 2023/07/31 06:42
CRDT- 2023/07/31 01:23
PHST- 2023/07/20 00:00 [revised]
PHST- 2023/07/04 00:00 [received]
PHST- 2023/07/22 00:00 [accepted]
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/07/31 06:42 [pubmed]
PHST- 2023/07/31 01:23 [entrez]
AID - 10.1111/odi.14706 [doi]
PST - aheadofprint
SO  - Oral Dis. 2023 Jul 30. doi: 10.1111/odi.14706.

PMID- 37283374
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230608
LR  - 20230614
IS  - 1678-2925 (Electronic)
IS  - 0004-2749 (Linking)
VI  - 86
IP  - 3
DP  - 2023 May-Jun
TI  - Challenges and advantages of being a scientific journal editor in the era of 
      ChatGPT.
PG  - 5-7
LID - S0004-27492023000300005 [pii]
LID - 10.5935/0004-2749.2023-1003 [doi]
FAU - Lira, Rodrigo Pessoa Cavalcanti
AU  - Lira RPC
AUID- ORCID: 0000-0002-7274-1065
AD  - Department of Surgery, Medical School, Universidade Federal de Pernambuco, 
      Recife, PE, Brazil.
FAU - Rocha, Eduardo Melani
AU  - Rocha EM
AUID- ORCID: 0000-0001-9888-7313
AD  - Department of Ophthalmology, Otorhinolaryngology and Head & Neck Surgery, 
      Ribeirao Preto Medical School, Universidade de São Paulo, Ribeirão Preto, SP, 
      Brazil.
FAU - Kara-Junior, Newton
AU  - Kara-Junior N
AUID- ORCID: 0000-0002-5239-8124
AD  - Department of Ophthalmology, Medical School, Universidade de São Paulo, São 
      Paulo, SP, Brazil.
FAU - Costa, Dácio Carvalho
AU  - Costa DC
AD  - Department of Ophthalmology, Public Health School of Ceara, Universidade Estadual 
      do Ceará, Fortaleza, CE, Brazil.
FAU - Procianoy, Fernando
AU  - Procianoy F
AUID- ORCID: 0000-0003-3365-8813
AD  - Department of Ophthalmology, Medical School, Universidade Federal do Rio Grande 
      do Sul, Proto Alegre, RS, Brazil.
FAU - Paula, Jayter Silva de
AU  - Paula JS
AUID- ORCID: 0000-0002-8111-3810
AD  - Department of Ophthalmology, Otorhinolaryngology and Head & Neck Surgery, 
      Ribeirao Preto Medical School, Universidade de São Paulo, Ribeirão Preto, SP, 
      Brazil.
FAU - Gracitelli, Carolina P B
AU  - Gracitelli CPB
AUID- ORCID: 0000-0001-5086-9060
AD  - Department of Ophthalmology and Visual Science, Universidade Federal de São 
      Paulo, São Paulo, SP, Brazil.
FAU - Prata, Tiago da Silva
AU  - Prata TDS
AD  - Department of Ophthalmology and Visual Science, Universidade Federal de São 
      Paulo, São Paulo, SP, Brazil.
FAU - Regatieri, Caio V
AU  - Regatieri CV
AUID- ORCID: 0000-0003-1511-8696
AD  - Department of Ophthalmology and Visual Science, Universidade Federal de São 
      Paulo, São Paulo, SP, Brazil.
FAU - Biccas Neto, Laurentino
AU  - Biccas Neto L
AUID- ORCID: 0000-0003-2047-3983
AD  - Ocular Oftalmologia. Vitoria, ES, Brazil.
FAU - Alves, Monica
AU  - Alves M
AUID- ORCID: 0000-0001-7940-0194
AD  - Department of Ophthalmology & Otorhinolaryngology, Medical Sciences Faculty, 
      Universidade Estadual de Campinas, Campinas, SP, Brazil.
LA  - eng
PT  - Editorial
PL  - Brazil
TA  - Arq Bras Oftalmol
JT  - Arquivos brasileiros de oftalmologia
JID - 0400645
SB  - IM
EDAT- 2023/06/07 13:10
MHDA- 2023/06/07 13:11
CRDT- 2023/06/07 07:44
PHST- 2023/03/14 00:00 [received]
PHST- 2023/03/27 00:00 [accepted]
PHST- 2023/06/07 13:11 [medline]
PHST- 2023/06/07 13:10 [pubmed]
PHST- 2023/06/07 07:44 [entrez]
AID - S0004-27492023000300005 [pii]
AID - 10.5935/0004-2749.2023-1003 [doi]
PST - ppublish
SO  - Arq Bras Oftalmol. 2023 May-Jun;86(3):5-7. doi: 10.5935/0004-2749.2023-1003.

PMID- 37191490
OWN - NLM
STAT- MEDLINE
DCOM- 20230622
LR  - 20230623
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 5
DP  - 2023 Jun
TI  - Rise of ChatGPT: It May Be Time to Reassess How We Teach and Test Radiology 
      Residents.
PG  - e231053
LID - 10.1148/radiol.231053 [doi]
FAU - Lourenco, Ana P
AU  - Lourenco AP
AUID- ORCID: 0000-0001-8652-1007
AD  - From the Department of Diagnostic Imaging, Warren Alpert Medical School of Brown 
      University and Rhode Island Hospital, 593 Eddy St, 3rd Floor, Providence, RI 
      02903 (A.P.L., G.L.B.); and Department of Radiology, Boston University Medical 
      Center, Boston, Mass (P.J.S.).
FAU - Slanetz, Priscilla J
AU  - Slanetz PJ
AUID- ORCID: 0000-0003-1248-5116
AD  - From the Department of Diagnostic Imaging, Warren Alpert Medical School of Brown 
      University and Rhode Island Hospital, 593 Eddy St, 3rd Floor, Providence, RI 
      02903 (A.P.L., G.L.B.); and Department of Radiology, Boston University Medical 
      Center, Boston, Mass (P.J.S.).
FAU - Baird, Grayson L
AU  - Baird GL
AUID- ORCID: 0000-0002-5926-9886
AD  - From the Department of Diagnostic Imaging, Warren Alpert Medical School of Brown 
      University and Rhode Island Hospital, 593 Eddy St, 3rd Floor, Providence, RI 
      02903 (A.P.L., G.L.B.); and Department of Radiology, Boston University Medical 
      Center, Boston, Mass (P.J.S.).
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230516
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CON - Radiology. 2023 Jun;307(5):e230582. PMID: 37191485
MH  - Humans
MH  - *Radiology/education
MH  - Radiography
EDAT- 2023/05/16 13:09
MHDA- 2023/06/22 06:42
CRDT- 2023/05/16 09:52
PHST- 2023/06/22 06:42 [medline]
PHST- 2023/05/16 13:09 [pubmed]
PHST- 2023/05/16 09:52 [entrez]
AID - 10.1148/radiol.231053 [doi]
PST - ppublish
SO  - Radiology. 2023 Jun;307(5):e231053. doi: 10.1148/radiol.231053. Epub 2023 May 16.

PMID- 38031294
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
DP  - 2023 Nov 29
TI  - AI and ethics in mental health: exploring the controversy over the use of 
      ChatGPT.
LID - fdad254 [pii]
LID - 10.1093/pubmed/fdad254 [doi]
FAU - Arjanto, Paul
AU  - Arjanto P
AUID- ORCID: 0000-0002-8776-0437
AD  - Faculty of Teacher Training and Education, University of Pattimura, Ambon, 
      Indonesia.
FAU - Senduk, Feibry F W
AU  - Senduk FFW
AD  - Faculty of Economic and Business, State University of Manado, Tondano, Indonesia.
FAU - Nahdiyah, Umi
AU  - Nahdiyah U
AD  - Faculty of Education, State University of Malang, Malang, Indonesia.
FAU - Utami, Mukti S
AU  - Utami MS
AD  - Faculty of Education, State University of Malang, Malang, Indonesia.
LA  - eng
PT  - Journal Article
DEP - 20231129
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
EDAT- 2023/11/30 06:43
MHDA- 2023/11/30 06:43
CRDT- 2023/11/30 00:14
PHST- 2023/07/03 00:00 [received]
PHST- 2023/08/09 00:00 [revised]
PHST- 2023/11/10 00:00 [accepted]
PHST- 2023/11/30 06:43 [medline]
PHST- 2023/11/30 06:43 [pubmed]
PHST- 2023/11/30 00:14 [entrez]
AID - 7455226 [pii]
AID - 10.1093/pubmed/fdad254 [doi]
PST - aheadofprint
SO  - J Public Health (Oxf). 2023 Nov 29:fdad254. doi: 10.1093/pubmed/fdad254.

PMID- 37946379
OWN - NLM
STAT- Publisher
LR  - 20231110
IS  - 1559-6834 (Electronic)
IS  - 0899-823X (Linking)
DP  - 2023 Nov 10
TI  - ChatGPT: Increasing accessibility for natural language processing in healthcare 
      quality measurement.
PG  - 1-2
LID - 10.1017/ice.2023.236 [doi]
FAU - Wu, Julie Tsu-Yu
AU  - Wu JT
AD  - Department of Medicine, Palo Alto VA Healthcare System, Palo Alto, California.
AD  - Stanford University School of Medicine, Palo Alto, California.
FAU - Shenoy, Erica S
AU  - Shenoy ES
AUID- ORCID: 0000-0001-8086-1123
AD  - Massachusetts General Hospital and Mass General BrighamBoston, Massachusetts.
AD  - Harvard Medical School, Boston, Massachusetts.
FAU - Carey, Evan P
AU  - Carey EP
AUID- ORCID: 0000-0001-7963-6818
AD  - Veterans' Affairs National Artificial Intelligence Institute, Washington, DC.
AD  - Department of Biostatistics and Informatics, University of Colorado School of 
      Public Health, Denver, Colorado.
FAU - Alterovitz, Gil
AU  - Alterovitz G
AD  - Harvard Medical School, Boston, Massachusetts.
AD  - Veterans' Affairs National Artificial Intelligence Institute, Washington, DC.
FAU - Kim, Michael J
AU  - Kim MJ
AD  - Veterans' Affairs National Artificial Intelligence Institute, Washington, DC.
AD  - Irvine School of Medicine, University of California-Irvine, IrvineCalifornia.
FAU - Branch-Elliman, Westyn
AU  - Branch-Elliman W
AUID- ORCID: 0000-0002-9658-5124
AD  - Harvard Medical School, Boston, Massachusetts.
AD  - Veterans' Affairs National Artificial Intelligence Institute, Washington, DC.
AD  - Section of Infectious Diseases, Department of Medicine, VA Boston Healthcare 
      System, West Roxbury, Massachusetts.
LA  - eng
PT  - Journal Article
DEP - 20231110
PL  - United States
TA  - Infect Control Hosp Epidemiol
JT  - Infection control and hospital epidemiology
JID - 8804099
SB  - IM
EDAT- 2023/11/10 06:45
MHDA- 2023/11/10 06:45
CRDT- 2023/11/10 00:53
PHST- 2023/11/10 06:45 [medline]
PHST- 2023/11/10 06:45 [pubmed]
PHST- 2023/11/10 00:53 [entrez]
AID - S0899823X23002362 [pii]
AID - 10.1017/ice.2023.236 [doi]
PST - aheadofprint
SO  - Infect Control Hosp Epidemiol. 2023 Nov 10:1-2. doi: 10.1017/ice.2023.236.

PMID- 37830950
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Oct 12
TI  - ChatGPT indicates the path and initiates the research to open up the black box of 
      artificial intelligence.
LID - 10.1097/JS9.0000000000000701 [doi]
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal 700126, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, 
      Odisha, India.
FAU - Islam, Md Aminul
AU  - Islam MA
AD  - COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and 
      Technology University, Noakhali-3814, Bangladesh.
AD  - Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical 
      College, Karimganj, Kishoreganj, Bangladesh.
FAU - Agoramoorthy, Govindasamy
AU  - Agoramoorthy G
AD  - College of Pharmacy and Health Care, Tajen University, Yanpu, Pingtung 907, 
      Taiwan.
AD  - Honeybee Population Health Foundation, Chennai 600082, India.
LA  - eng
PT  - Journal Article
DEP - 20231012
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/10/13 12:43
MHDA- 2023/10/13 12:43
CRDT- 2023/10/13 10:03
PHST- 2023/08/10 00:00 [received]
PHST- 2023/08/12 00:00 [accepted]
PHST- 2023/10/13 12:43 [medline]
PHST- 2023/10/13 12:43 [pubmed]
PHST- 2023/10/13 10:03 [entrez]
AID - 01279778-990000000-00724 [pii]
AID - 10.1097/JS9.0000000000000701 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Oct 12. doi: 10.1097/JS9.0000000000000701.

PMID- 37681363
OWN - NLM
STAT- Publisher
LR  - 20231017
IS  - 1098-2752 (Electronic)
IS  - 0738-1085 (Linking)
VI  - 43
IP  - 7
DP  - 2023 Oct
TI  - Revolutionizing patient education: ChatGPT outperforms Google in answering 
      patient queries on free flap reconstruction.
PG  - 752-761
LID - 10.1002/micr.31106 [doi]
FAU - Jeong, Tiffany
AU  - Jeong T
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 
      Pittsburgh, PA, USA.
FAU - Liu, Hilary
AU  - Liu H
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 
      Pittsburgh, PA, USA.
FAU - Alessandri-Bonetti, Mario
AU  - Alessandri-Bonetti M
AUID- ORCID: 0000-0002-5506-8323
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 
      Pittsburgh, PA, USA.
FAU - Pandya, Sumaarg
AU  - Pandya S
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 
      Pittsburgh, PA, USA.
FAU - Nguyen, Vu T
AU  - Nguyen VT
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 
      Pittsburgh, PA, USA.
FAU - Egro, Francesco M
AU  - Egro FM
AUID- ORCID: 0000-0003-1536-7713
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 
      Pittsburgh, PA, USA.
LA  - eng
PT  - Letter
DEP - 20230908
PL  - United States
TA  - Microsurgery
JT  - Microsurgery
JID - 8309230
SB  - IM
EDAT- 2023/09/08 06:43
MHDA- 2023/09/08 06:43
CRDT- 2023/09/08 05:52
PHST- 2023/08/01 00:00 [revised]
PHST- 2023/07/08 00:00 [received]
PHST- 2023/08/24 00:00 [accepted]
PHST- 2023/09/08 06:43 [pubmed]
PHST- 2023/09/08 06:43 [medline]
PHST- 2023/09/08 05:52 [entrez]
AID - 10.1002/micr.31106 [doi]
PST - ppublish
SO  - Microsurgery. 2023 Oct;43(7):752-761. doi: 10.1002/micr.31106. Epub 2023 Sep 8.

PMID- 37573052
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1998-3646 (Electronic)
IS  - 0255-0857 (Linking)
VI  - 45
DP  - 2023 Sep-Oct
TI  - Analysing the clinical knowledge of ChatGPT in medical microbiology in the 
      undergraduate medical examination.
PG  - 100380
LID - S0255-0857(23)00096-8 [pii]
LID - 10.1016/j.ijmmb.2023.100380 [doi]
FAU - Sivasubramanian, Jayanthi
AU  - Sivasubramanian J
AD  - Department of Microbiology, Panimalar Medical College Hospital & Research 
      Institute, Varadharajapuram, Poonamallee, Chennai, 600123, India. Electronic 
      address: jayanthitanmicro@gmail.com.
FAU - Shaik Hussain, Shifa Meharaj
AU  - Shaik Hussain SM
AD  - Department of Microbiology, Panimalar Medical College Hospital & Research 
      Institute, Varadharajapuram, Poonamallee, Chennai, 600123, India. Electronic 
      address: shifameharaj@gmail.com.
FAU - Virudhunagar Muthuprakash, Somasunder
AU  - Virudhunagar Muthuprakash S
AD  - Department of Microbiology, Panimalar Medical College Hospital & Research 
      Institute, Varadharajapuram, Poonamallee, Chennai, 600123, India. Electronic 
      address: vmdjsoma@gmail.com.
FAU - Periadurai, Nanthini Devi
AU  - Periadurai ND
AD  - Department of Microbiology, Panimalar Medical College Hospital & Research 
      Institute, Varadharajapuram, Poonamallee, Chennai, 600123, India; Department of 
      Molecular Virology, Panimalar Medical College Hospital & Research Institute, 
      Varadharajapuram, Poonamallee, Chennai, 600123, India. Electronic address: 
      nandhu.smiley7@gmail.com.
FAU - Mohanram, Kalyani
AU  - Mohanram K
AD  - Department of Microbiology, Panimalar Medical College Hospital & Research 
      Institute, Varadharajapuram, Poonamallee, Chennai, 600123, India. Electronic 
      address: kalyanimohanram@gmail.com.
FAU - Surapaneni, Krishna Mohan
AU  - Surapaneni KM
AD  - Department of Biochemistry, Panimalar Medical College Hospital & Research 
      Institute, Varadharajapuram, Poonamallee, Chennai, 600123, Tamil Nadu, India; 
      Departments of Medical Education, Molecular Virology, Research, Clinical Skills & 
      Simulation, Panimalar Medical College Hospital & Research Institute, 
      Varadharajapuram, Poonamallee, Chennai, 600123, Tamil Nadu, India. Electronic 
      address: krishnamohan.surapaneni@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230523
PL  - United States
TA  - Indian J Med Microbiol
JT  - Indian journal of medical microbiology
JID - 8700903
SB  - IM
EDAT- 2023/08/13 00:43
MHDA- 2023/08/13 00:44
CRDT- 2023/08/12 20:57
PHST- 2023/03/18 00:00 [received]
PHST- 2023/04/03 00:00 [revised]
PHST- 2023/04/26 00:00 [accepted]
PHST- 2023/08/13 00:44 [medline]
PHST- 2023/08/13 00:43 [pubmed]
PHST- 2023/08/12 20:57 [entrez]
AID - S0255-0857(23)00096-8 [pii]
AID - 10.1016/j.ijmmb.2023.100380 [doi]
PST - ppublish
SO  - Indian J Med Microbiol. 2023 Sep-Oct;45:100380. doi: 10.1016/j.ijmmb.2023.100380. 
      Epub 2023 May 23.

PMID- 37222684
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231004
LR  - 20231004
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 9
DP  - 2023 Sep 1
TI  - Exploring the clinical capabilities and limitations of ChatGPT: a cautionary tale 
      for medical applications.
PG  - 2865-2867
LID - 10.1097/JS9.0000000000000523 [doi]
FAU - Li, Sheng
AU  - Li S
AUID- ORCID: 0000-0001-6083-4945
AD  - Department of Anorectal Surgery, Ningbo No.2 Hospital, Ningbo, Zhejiang, People's 
      Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230901
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
PMC - PMC10498888
COIS- The author declares no conflicts of interest. Sponsorships or competing interests 
      that may be relevant to content are disclosed at the end of this article.
EDAT- 2023/05/24 13:08
MHDA- 2023/05/24 13:09
CRDT- 2023/05/24 10:43
PHST- 2023/05/16 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/05/24 13:09 [medline]
PHST- 2023/05/24 13:08 [pubmed]
PHST- 2023/05/24 10:43 [entrez]
AID - 01279778-990000000-00387 [pii]
AID - IJS-D-23-00895 [pii]
AID - 10.1097/JS9.0000000000000523 [doi]
PST - epublish
SO  - Int J Surg. 2023 Sep 1;109(9):2865-2867. doi: 10.1097/JS9.0000000000000523.

PMID- 37197782
OWN - NLM
STAT- MEDLINE
DCOM- 20230519
LR  - 20230623
IS  - 1756-1833 (Electronic)
IS  - 0959-8138 (Linking)
VI  - 381
DP  - 2023 May 17
TI  - ChatGPT and other AI tools put students at risk of plagiarism allegations, MDU 
      warns.
PG  - 1133
LID - 10.1136/bmj.p1133 [doi]
FAU - Graham, Annie
AU  - Graham A
AD  - The BMJ.
LA  - eng
PT  - Journal Article
DEP - 20230517
PL  - England
TA  - BMJ
JT  - BMJ (Clinical research ed.)
JID - 8900488
SB  - IM
CIN - BMJ. 2023 Jun 20;381:1403. PMID: 37339814
MH  - Humans
MH  - *Plagiarism
MH  - *Students
MH  - Artificial Intelligence
EDAT- 2023/05/18 01:07
MHDA- 2023/05/19 06:42
CRDT- 2023/05/17 20:33
PHST- 2023/05/19 06:42 [medline]
PHST- 2023/05/18 01:07 [pubmed]
PHST- 2023/05/17 20:33 [entrez]
AID - 10.1136/bmj.p1133 [doi]
PST - epublish
SO  - BMJ. 2023 May 17;381:1133. doi: 10.1136/bmj.p1133.

PMID- 36786391
OWN - NLM
STAT- Publisher
LR  - 20230221
IS  - 1545-5815 (Electronic)
IS  - 0898-9621 (Linking)
DP  - 2023 Feb 21
TI  - Can ChatGPT be trusted to provide reliable estimates?
PG  - 1-3
LID - 10.1080/08989621.2023.2179919 [doi]
FAU - Tsigaris, Panagiotis
AU  - Tsigaris P
AUID- ORCID: 0000-0002-7965-4554
AD  - Department of Economics, Thompson Rivers University, Kamloops, BC, Canada.
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Independent researcher, Ikenobe, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230221
PL  - United States
TA  - Account Res
JT  - Accountability in research
JID - 9100813
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - ethics
OT  - knowledge
OT  - transparency
OT  - trust
EDAT- 2023/02/15 06:00
MHDA- 2023/02/15 06:00
CRDT- 2023/02/14 07:22
PHST- 2023/02/15 06:00 [pubmed]
PHST- 2023/02/15 06:00 [medline]
PHST- 2023/02/14 07:22 [entrez]
AID - 10.1080/08989621.2023.2179919 [doi]
PST - aheadofprint
SO  - Account Res. 2023 Feb 21:1-3. doi: 10.1080/08989621.2023.2179919.

PMID- 37399360
OWN - NLM
STAT- MEDLINE
DCOM- 20230705
LR  - 20230705
IS  - 2632-1009 (Electronic)
IS  - 2632-1009 (Linking)
VI  - 30
IP  - 1
DP  - 2023 Jun
TI  - ChatGPT in glioma adjuvant therapy decision making: ready to assume the role of a 
      doctor in the tumour board?
LID - 10.1136/bmjhci-2023-100775 [doi]
LID - e100775
AB  - OBJECTIVE: To evaluate ChatGPT's performance in brain glioma adjuvant therapy 
      decision-making. METHODS: We randomly selected 10 patients with brain gliomas 
      discussed at our institution's central nervous system tumour board (CNS TB). 
      Patients' clinical status, surgical outcome, textual imaging information and 
      immuno-pathology results were provided to ChatGPT V.3.5 and seven CNS tumour 
      experts. The chatbot was asked to give the adjuvant treatment choice, and the 
      regimen while considering the patient's functional status. The experts rated the 
      artificial intelligence-based recommendations from 0 (complete disagreement) to 
      10 (complete agreement). An intraclass correlation coefficient agreement (ICC) 
      was used to measure the inter-rater agreement. RESULTS: Eight patients (80%) met 
      the criteria for glioblastoma and two (20%) were low-grade gliomas. The experts 
      rated the quality of ChatGPT recommendations as poor for diagnosis (median 3, IQR 
      1-7.8, ICC 0.9, 95% CI 0.7 to 1.0), good for treatment recommendation (7, IQR 
      6-8, ICC 0.8, 95% CI 0.4 to 0.9), good for therapy regimen (7, IQR 4-8, ICC 0.8, 
      95% CI 0.5 to 0.9), moderate for functional status consideration (6, IQR 1-7, ICC 
      0.7, 95% CI 0.3 to 0.9) and moderate for overall agreement with the 
      recommendations (5, IQR 3-7, ICC 0.7, 95% CI 0.3 to 0.9). No differences were 
      observed between the glioblastomas and low-grade glioma ratings. CONCLUSIONS: 
      ChatGPT performed poorly in classifying glioma types but was good for adjuvant 
      treatment recommendations as evaluated by CNS TB experts. Even though the ChatGPT 
      lacks the precision to replace expert opinion, it may serve as a promising 
      supplemental tool within a human-in-the-loop approach.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Haemmerli, Julien
AU  - Haemmerli J
AUID- ORCID: 0000-0001-5998-8541
AD  - Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University 
      Hospitals, Geneva, Switzerland julien.haemmerli@hcuge.ch.
FAU - Sveikata, Lukas
AU  - Sveikata L
AD  - Department of Clinical Neurosciences, Division of Neurology, Geneva University 
      Hospitals, Geneva, Switzerland.
AD  - Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, 
      Lithuania.
AD  - Faculty of Medicine, University of Geneva, Geneva, Switzerland.
FAU - Nouri, Aria
AU  - Nouri A
AD  - Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University 
      Hospitals, Geneva, Switzerland.
FAU - May, Adrien
AU  - May A
AD  - Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University 
      Hospitals, Geneva, Switzerland.
FAU - Egervari, Kristof
AU  - Egervari K
AD  - Department of Pathology and Immunology, Geneva University Hospitals, Geneva, 
      Switzerland.
FAU - Freyschlag, Christian
AU  - Freyschlag C
AD  - Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria.
FAU - Lobrinus, Johannes A
AU  - Lobrinus JA
AD  - Department of Pathology and Immunology, Geneva University Hospitals, Geneva, 
      Switzerland.
FAU - Migliorini, Denis
AU  - Migliorini D
AD  - Department of Oncology, Geneva University Hospitals, Geneva, Switzerland.
FAU - Momjian, Shahan
AU  - Momjian S
AD  - Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University 
      Hospitals, Geneva, Switzerland.
FAU - Sanda, Nicolae
AU  - Sanda N
AD  - Department of Clinical Neurosciences, Division of Neurology, Geneva University 
      Hospitals, Geneva, Switzerland.
FAU - Schaller, Karl
AU  - Schaller K
AD  - Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University 
      Hospitals, Geneva, Switzerland.
FAU - Tran, Sebastien
AU  - Tran S
AD  - Department of Radiation Oncology, Geneva University Hospitals, Geneva, 
      Switzerland.
FAU - Yeung, Jacky
AU  - Yeung J
AD  - Department of Neurosurgery, Yale University School of Medicine, New Haven, 
      Connecticut, USA.
FAU - Bijlenga, Philippe
AU  - Bijlenga P
AD  - Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University 
      Hospitals, Geneva, Switzerland.
LA  - eng
PT  - Journal Article
PL  - England
TA  - BMJ Health Care Inform
JT  - BMJ health & care informatics
JID - 101745500
SB  - IM
MH  - Humans
MH  - *Brain Neoplasms/drug therapy/pathology
MH  - Artificial Intelligence
MH  - *Glioma/pathology/surgery
MH  - Decision Making
PMC - PMC10314415
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Data Management
OT  - Deep Learning
OT  - Information Management
COIS- Competing interests: None declared.
EDAT- 2023/07/03 19:08
MHDA- 2023/07/05 06:42
CRDT- 2023/07/03 15:02
PHST- 2023/04/03 00:00 [received]
PHST- 2023/06/21 00:00 [accepted]
PHST- 2023/07/05 06:42 [medline]
PHST- 2023/07/03 19:08 [pubmed]
PHST- 2023/07/03 15:02 [entrez]
AID - bmjhci-2023-100775 [pii]
AID - 10.1136/bmjhci-2023-100775 [doi]
PST - ppublish
SO  - BMJ Health Care Inform. 2023 Jun;30(1):e100775. doi: 10.1136/bmjhci-2023-100775.

PMID- 37745974
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230926
IS  - 2666-6065 (Electronic)
IS  - 2666-6065 (Linking)
VI  - 41
DP  - 2023 Dec
TI  - ChatGPT for low- and middle-income countries: a Greek gift?
PG  - 100906
LID - 10.1016/j.lanwpc.2023.100906 [doi]
LID - 100906
FAU - Lam, Kyle
AU  - Lam K
AD  - Department of Surgery and Cancer, Imperial College London, UK.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - England
TA  - Lancet Reg Health West Pac
JT  - The Lancet regional health. Western Pacific
JID - 101774968
PMC - PMC10514087
COIS- KL has no conflict of interest to declare.
EDAT- 2023/09/25 06:41
MHDA- 2023/09/25 06:42
CRDT- 2023/09/25 05:10
PHST- 2023/09/04 00:00 [received]
PHST- 2023/09/04 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/09/25 06:41 [pubmed]
PHST- 2023/09/25 05:10 [entrez]
AID - S2666-6065(23)00224-9 [pii]
AID - 100906 [pii]
AID - 10.1016/j.lanwpc.2023.100906 [doi]
PST - epublish
SO  - Lancet Reg Health West Pac. 2023 Sep 15;41:100906. doi: 
      10.1016/j.lanwpc.2023.100906. eCollection 2023 Dec.

PMID- 37710413
OWN - NLM
STAT- MEDLINE
DCOM- 20230919
LR  - 20230919
IS  - 1445-2197 (Electronic)
IS  - 1445-1433 (Linking)
VI  - 93
IP  - 9
DP  - 2023 Sep
TI  - ChatGPT in surgical research and practice: a threat to academic integrity, 
      authorship, and divergent thinking.
PG  - 2270-2271
LID - 10.1111/ans.18544 [doi]
FAU - Bulloch, Gabriella
AU  - Bulloch G
AD  - Faculty of Science, Medicine, and Health, The University of Melbourne, Melbourne, 
      Victoria, Australia.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Faculty of Science, Medicine, and Health, The University of Melbourne, Melbourne, 
      Victoria, Australia.
AD  - Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, 
      Australia.
AD  - Department of Surgery, Bendigo Health, Melbourne, Victoria, Australia.
FAU - Lee, Chun Hin Angus
AU  - Lee CHA
AUID- ORCID: 0000-0002-2276-9924
AD  - Department of Surgery, Bendigo Health, Melbourne, Victoria, Australia.
LA  - eng
PT  - Letter
DEP - 20230725
PL  - Australia
TA  - ANZ J Surg
JT  - ANZ journal of surgery
JID - 101086634
SB  - IM
MH  - *Authorship
MH  - *Artificial Intelligence
MH  - *General Surgery
EDAT- 2023/09/15 06:42
MHDA- 2023/09/18 12:43
CRDT- 2023/09/15 01:03
PHST- 2023/05/17 00:00 [revised]
PHST- 2023/04/07 00:00 [received]
PHST- 2023/05/18 00:00 [accepted]
PHST- 2023/09/18 12:43 [medline]
PHST- 2023/09/15 06:42 [pubmed]
PHST- 2023/09/15 01:03 [entrez]
AID - 10.1111/ans.18544 [doi]
PST - ppublish
SO  - ANZ J Surg. 2023 Sep;93(9):2270-2271. doi: 10.1111/ans.18544. Epub 2023 Jul 25.

PMID- 37625195
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 2059-7029 (Electronic)
IS  - 2059-7029 (Linking)
VI  - 8
IP  - 5
DP  - 2023 Oct
TI  - Dignity of Science and the use of ChatGPT as a co-author.
PG  - 101621
LID - S2059-7029(23)00856-6 [pii]
LID - 10.1016/j.esmoop.2023.101621 [doi]
LID - 101621
FAU - Scimeca, M
AU  - Scimeca M
AD  - Department of Experimental Medicine, Tor Vergata Oncoscience Research, University 
      of Rome "Tor Vergata", Rome; San Raffaele Telematic University, Rome; Faculty of 
      Medicine, Saint Camillus International University of Health Sciences, Rome, 
      Italy.
FAU - Bonfiglio, R
AU  - Bonfiglio R
AD  - Department of Experimental Medicine, Tor Vergata Oncoscience Research, University 
      of Rome "Tor Vergata", Rome. Electronic address: rita.bonfiglio@uniroma2.it.
LA  - eng
PT  - Letter
DEP - 20230824
PL  - England
TA  - ESMO Open
JT  - ESMO open
JID - 101690685
SB  - IM
PMC - PMC10470203
EDAT- 2023/08/25 18:42
MHDA- 2023/08/25 18:42
CRDT- 2023/08/25 18:00
PHST- 2023/07/25 00:00 [received]
PHST- 2023/07/26 00:00 [accepted]
PHST- 2023/08/25 18:42 [pubmed]
PHST- 2023/08/25 18:42 [medline]
PHST- 2023/08/25 18:00 [entrez]
AID - S2059-7029(23)00856-6 [pii]
AID - 101621 [pii]
AID - 10.1016/j.esmoop.2023.101621 [doi]
PST - ppublish
SO  - ESMO Open. 2023 Oct;8(5):101621. doi: 10.1016/j.esmoop.2023.101621. Epub 2023 Aug 
      24.

PMID- 37505356
OWN - NLM
STAT- Publisher
LR  - 20231011
IS  - 1534-4681 (Electronic)
IS  - 1068-9265 (Linking)
VI  - 30
IP  - 12
DP  - 2023 Nov
TI  - Correspondence for "Quality of ChatGPT Responses to Questions Related to 
      Pancreatic Cancer and Its Surgical Care".
PG  - 7780
LID - 10.1245/s10434-023-14040-y [doi]
FAU - Mungmunpuntipantip, Rujittika
AU  - Mungmunpuntipantip R
AD  - , Bangkok, Thailand. rujittika@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230728
PL  - United States
TA  - Ann Surg Oncol
JT  - Annals of surgical oncology
JID - 9420840
SB  - IM
EDAT- 2023/07/28 13:10
MHDA- 2023/07/28 13:10
CRDT- 2023/07/28 11:09
PHST- 2023/06/26 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/07/28 13:10 [pubmed]
PHST- 2023/07/28 13:10 [medline]
PHST- 2023/07/28 11:09 [entrez]
AID - 10.1245/s10434-023-14040-y [pii]
AID - 10.1245/s10434-023-14040-y [doi]
PST - ppublish
SO  - Ann Surg Oncol. 2023 Nov;30(12):7780. doi: 10.1245/s10434-023-14040-y. Epub 2023 
      Jul 28.

PMID- 37500847
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230802
LR  - 20230929
IS  - 1476-5373 (Electronic)
IS  - 0007-0610 (Linking)
VI  - 235
IP  - 2
DP  - 2023 Jul
TI  - Application ChatGPT in conducting systematic reviews and meta-analyses.
PG  - 90-92
LID - 10.1038/s41415-023-6132-y [doi]
FAU - Mahuli, Simpy Amit
AU  - Mahuli SA
AD  - PhD Scholar, Rajendra Institute of Medical Sciences (RIMS), Bariatu, Ranchi - 
      834009, India. bdj.news@springernature.com.
FAU - Rai, Arpita
AU  - Rai A
AD  - Associate Professor, Oral Medicine and Radiology, Rajendra Institute of Medical 
      Sciences (RIMS), Bariatu, Ranchi - 834009, India. bdj.news@springernature.com.
FAU - Mahuli, Amit Vasant
AU  - Mahuli AV
AD  - Associate Professor, Public Health Dentistry, Rajendra Institute of Medical 
      Sciences (RIMS), Bariatu, Ranchi - 834009, India. bdj.news@springernature.com.
FAU - Kumar, Ansul
AU  - Kumar A
AD  - Associate Professor, Cardiothoracic and Vascular Surgery, Dental College, 
      Rajendra Institute of Medical Sciences, Bariatu, India. 
      bdj.news@springernature.com.
LA  - eng
PT  - News
PL  - England
TA  - Br Dent J
JT  - British dental journal
JID - 7513219
SB  - IM
CIN - Br Dent J. 2023 Sep;235(6):363. PMID: 37737385
EDAT- 2023/07/28 01:08
MHDA- 2023/07/31 06:42
CRDT- 2023/07/27 23:28
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/07/28 01:08 [pubmed]
PHST- 2023/07/27 23:28 [entrez]
AID - 10.1038/s41415-023-6132-y [pii]
AID - 10.1038/s41415-023-6132-y [doi]
PST - ppublish
SO  - Br Dent J. 2023 Jul;235(2):90-92. doi: 10.1038/s41415-023-6132-y.

PMID- 37450951
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230831
LR  - 20230831
IS  - 2059-7029 (Electronic)
IS  - 2059-7029 (Linking)
VI  - 8
IP  - 4
DP  - 2023 Aug
TI  - Dignity of science and the use of ChatGPT as a co-author.
PG  - 101607
LID - S2059-7029(23)00842-6 [pii]
LID - 10.1016/j.esmoop.2023.101607 [doi]
LID - 101607
FAU - Scimeca, M
AU  - Scimeca M
AD  - Department of Experimental Medicine, TOR, University of Rome Tor Vergata, Rome; 
      San Raffaele Telematic University, Rome; Faculty of Medicine, Saint Camillus 
      International University of Health Sciences, Rome, Italy.
FAU - Bonfiglio, R
AU  - Bonfiglio R
AD  - Department of Experimental Medicine, TOR, University of Rome Tor Vergata, Rome. 
      Electronic address: rita.bonfiglio@uniroma2.it.
LA  - eng
PT  - Editorial
DEP - 20230712
PL  - England
TA  - ESMO Open
JT  - ESMO open
JID - 101690685
SB  - IM
PMC - PMC10368860
EDAT- 2023/07/14 19:08
MHDA- 2023/08/29 12:44
CRDT- 2023/07/14 17:59
PHST- 2023/02/16 00:00 [received]
PHST- 2023/06/20 00:00 [accepted]
PHST- 2023/08/29 12:44 [medline]
PHST- 2023/07/14 19:08 [pubmed]
PHST- 2023/07/14 17:59 [entrez]
AID - S2059-7029(23)00842-6 [pii]
AID - 101607 [pii]
AID - 10.1016/j.esmoop.2023.101607 [doi]
PST - ppublish
SO  - ESMO Open. 2023 Aug;8(4):101607. doi: 10.1016/j.esmoop.2023.101607. Epub 2023 Jul 
      12.

PMID- 37148229
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231103
LR  - 20231107
IS  - 1933-0693 (Electronic)
IS  - 0022-3085 (Linking)
VI  - 139
IP  - 5
DP  - 2023 May 5
TI  - The rise of AI co-authors: navigating the future of scientific writing with 
      ChatGPT.
PG  - 1485-1486
LID - 10.3171/2023.3.JNS23254 [doi]
FAU - Alattar, Ali A
AU  - Alattar AA
FAU - McDowell, Michael M
AU  - McDowell MM
LA  - eng
PT  - Journal Article
DEP - 20230505
PL  - United States
TA  - J Neurosurg
JT  - Journal of neurosurgery
JID - 0253357
SB  - IM
EDAT- 2023/05/06 19:41
MHDA- 2023/05/06 19:42
CRDT- 2023/05/06 07:43
PHST- 2023/05/06 19:42 [medline]
PHST- 2023/05/06 19:41 [pubmed]
PHST- 2023/05/06 07:43 [entrez]
AID - 10.3171/2023.3.JNS23254 [doi]
PST - epublish
SO  - J Neurosurg. 2023 May 5;139(5):1485-1486. doi: 10.3171/2023.3.JNS23254.

PMID- 36700838
OWN - NLM
STAT- MEDLINE
DCOM- 20230412
LR  - 20230504
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 2
DP  - 2023 Apr
TI  - ChatGPT and Other Large Language Models Are Double-edged Swords.
PG  - e230163
LID - 10.1148/radiol.230163 [doi]
FAU - Shen, Yiqiu
AU  - Shen Y
AUID- ORCID: 0000-0002-7726-2514
AD  - From the Center for Data Science, New York University, 60 5th Ave, New York, NY 
      10011 (Y.S.); Department of Radiology, New York University School of Medicine, 
      New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and 
      Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.
FAU - Heacock, Laura
AU  - Heacock L
AD  - From the Center for Data Science, New York University, 60 5th Ave, New York, NY 
      10011 (Y.S.); Department of Radiology, New York University School of Medicine, 
      New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and 
      Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.
FAU - Elias, Jonathan
AU  - Elias J
AUID- ORCID: 0000-0002-1217-7001
AD  - From the Center for Data Science, New York University, 60 5th Ave, New York, NY 
      10011 (Y.S.); Department of Radiology, New York University School of Medicine, 
      New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and 
      Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.
FAU - Hentel, Keith D
AU  - Hentel KD
AUID- ORCID: 0000-0002-4503-9887
AD  - From the Center for Data Science, New York University, 60 5th Ave, New York, NY 
      10011 (Y.S.); Department of Radiology, New York University School of Medicine, 
      New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and 
      Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.
FAU - Reig, Beatriu
AU  - Reig B
AD  - From the Center for Data Science, New York University, 60 5th Ave, New York, NY 
      10011 (Y.S.); Department of Radiology, New York University School of Medicine, 
      New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and 
      Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.
FAU - Shih, George
AU  - Shih G
AUID- ORCID: 0000-0002-8356-2011
AD  - From the Center for Data Science, New York University, 60 5th Ave, New York, NY 
      10011 (Y.S.); Department of Radiology, New York University School of Medicine, 
      New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and 
      Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.
FAU - Moy, Linda
AU  - Moy L
AUID- ORCID: 0000-0001-9564-9360
AD  - From the Center for Data Science, New York University, 60 5th Ave, New York, NY 
      10011 (Y.S.); Department of Radiology, New York University School of Medicine, 
      New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and 
      Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230126
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CON - Radiology. 2023 Apr;307(2):e223312. PMID: 36728748
CIN - Radiology. 2023 Feb 2;:230171. PMID: 36728749
CON - Radiology. 2023 Feb 2;:230171. PMID: 36728749
MH  - Humans
MH  - Forecasting
MH  - *Medical Writing
EDAT- 2023/01/27 06:00
MHDA- 2023/04/12 06:42
CRDT- 2023/01/26 10:23
PHST- 2023/04/12 06:42 [medline]
PHST- 2023/01/27 06:00 [pubmed]
PHST- 2023/01/26 10:23 [entrez]
AID - 10.1148/radiol.230163 [doi]
PST - ppublish
SO  - Radiology. 2023 Apr;307(2):e230163. doi: 10.1148/radiol.230163. Epub 2023 Jan 26.

PMID- 38040497
OWN - NLM
STAT- Publisher
LR  - 20231201
IS  - 1876-7982 (Electronic)
IS  - 1876-7982 (Linking)
DP  - 2023 Nov 30
TI  - Using ChatGPT and other forms of generative AI in systematic reviews: Challenges 
      and opportunities.
LID - S1939-8654(23)01902-1 [pii]
LID - 10.1016/j.jmir.2023.11.005 [doi]
FAU - Hossain, M Mahbub
AU  - Hossain MM
AD  - Department of Decision and Information Sciences, C.T. Bauer College of Business, 
      University of Houston, TX 77204, USA; Department of Health Systems and Population 
      Health Sciences, Tilman J. Fertitta Family College of Medicine, University of 
      Houston, TX 77204, USA. Electronic address: mhossa25@central.uh.edu.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - United States
TA  - J Med Imaging Radiat Sci
JT  - Journal of medical imaging and radiation sciences
JID - 101469694
SB  - IM
EDAT- 2023/12/02 00:42
MHDA- 2023/12/02 00:42
CRDT- 2023/12/01 21:59
PHST- 2023/10/11 00:00 [received]
PHST- 2023/11/20 00:00 [accepted]
PHST- 2023/12/02 00:42 [medline]
PHST- 2023/12/02 00:42 [pubmed]
PHST- 2023/12/01 21:59 [entrez]
AID - S1939-8654(23)01902-1 [pii]
AID - 10.1016/j.jmir.2023.11.005 [doi]
PST - aheadofprint
SO  - J Med Imaging Radiat Sci. 2023 Nov 30:S1939-8654(23)01902-1. doi: 
      10.1016/j.jmir.2023.11.005.

PMID- 37972352
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1545-6846 (Electronic)
IS  - 0037-8046 (Linking)
DP  - 2023 Nov 16
TI  - The End of the World as We Know It? ChatGPT and Social Work.
LID - swad044 [pii]
LID - 10.1093/sw/swad044 [doi]
FAU - Goldkind, Lauri
AU  - Goldkind L
AD  - associate professor, Graduate School of Social Service, Fordham University, 113 
      West 60th Street, New York, NY 10023, USA.
FAU - Wolf, Lea
AU  - Wolf L
AD  - doctoral candidate, Graduate Center, City University of New York, New York, NY, 
      USA.
FAU - Glennon, Alexis
AU  - Glennon A
AD  - doctoral candidate, School of Social Work, University of Buffalo, Buffalo, NY, 
      USA.
FAU - Rios, Juan
AU  - Rios J
AD  - assistant professor, Department of Sociology, Anthropology, Social Work and 
      Criminal Justice, Seton Hall University, South Orange, NJ, US.
FAU - Nissen, Laura
AU  - Nissen L
AD  - professor, School of Social Work, Portland State University, Portland, OR, USA.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - United States
TA  - Soc Work
JT  - Social work
JID - 2984852R
SB  - IM
EDAT- 2023/11/17 15:24
MHDA- 2023/11/17 15:24
CRDT- 2023/11/16 16:33
PHST- 2023/02/15 00:00 [received]
PHST- 2023/03/06 00:00 [accepted]
PHST- 2023/11/17 15:24 [medline]
PHST- 2023/11/17 15:24 [pubmed]
PHST- 2023/11/16 16:33 [entrez]
AID - 7424935 [pii]
AID - 10.1093/sw/swad044 [doi]
PST - aheadofprint
SO  - Soc Work. 2023 Nov 16:swad044. doi: 10.1093/sw/swad044.

PMID- 37968523
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231208
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 623
IP  - 7987
DP  - 2023 Nov
TI  - ChatGPT has entered the classroom: how LLMs could transform education.
PG  - 474-477
LID - 10.1038/d41586-023-03507-3 [doi]
FAU - Extance, Andy
AU  - Extance A
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - *Natural Language Processing
MH  - *Education/methods/trends
MH  - Teaching/trends
MH  - Humans
OTO - NOTNLM
OT  - Computer science
OT  - Education
OT  - Machine learning
OT  - Mathematics and computing
EDAT- 2023/11/16 06:45
MHDA- 2023/11/27 12:43
CRDT- 2023/11/16 00:04
PHST- 2023/11/27 12:43 [medline]
PHST- 2023/11/16 06:45 [pubmed]
PHST- 2023/11/16 00:04 [entrez]
AID - 10.1038/d41586-023-03507-3 [pii]
AID - 10.1038/d41586-023-03507-3 [doi]
PST - ppublish
SO  - Nature. 2023 Nov;623(7987):474-477. doi: 10.1038/d41586-023-03507-3.

PMID- 37923951
OWN - NLM
STAT- Publisher
LR  - 20231104
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Nov 3
TI  - Nature's Take: How will ChatGPT and generative AI transform research?
LID - 10.1038/d41586-023-03467-8 [doi]
FAU - Petrić Howe, Nick
AU  - Petrić Howe N
FAU - Skipper, Magdalena
AU  - Skipper M
FAU - Van Noorden, Richard
AU  - Van Noorden R
FAU - Sweeney, Yann
AU  - Sweeney Y
LA  - eng
PT  - News
DEP - 20231103
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Machine learning
OT  - Technology
EDAT- 2023/11/04 21:43
MHDA- 2023/11/04 21:43
CRDT- 2023/11/04 00:42
PHST- 2023/11/04 21:43 [medline]
PHST- 2023/11/04 21:43 [pubmed]
PHST- 2023/11/04 00:42 [entrez]
AID - 10.1038/d41586-023-03467-8 [pii]
AID - 10.1038/d41586-023-03467-8 [doi]
PST - aheadofprint
SO  - Nature. 2023 Nov 3. doi: 10.1038/d41586-023-03467-8.

PMID- 37848339
OWN - NLM
STAT- Publisher
LR  - 20231017
IS  - 1601-0825 (Electronic)
IS  - 1354-523X (Linking)
DP  - 2023 Oct 17
TI  - Authors' reply: Combining ChatGPT and machine learning: A viable alternative in 
      oral medicine.
LID - 10.1111/odi.14741 [doi]
FAU - Santana, Lucas Alves da Mota
AU  - Santana LADM
AUID- ORCID: 0000-0002-8261-1504
AD  - Department of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, 
      Brazil.
AD  - Department of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil.
FAU - Gonçalo, Rani Iani Costa
AU  - Gonçalo RIC
AUID- ORCID: 0000-0002-6762-135X
AD  - Department of Dentistry, Federal University of Rio Grande do Norte (UFRN), Natal, 
      RN, Brazil.
FAU - Barbosa, Breno Ferreira
AU  - Barbosa BF
AD  - Department of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, 
      Brazil.
FAU - Takeshita, Wilton Mitsunari
AU  - Takeshita WM
AD  - Department of Diagnosis and Surgery, School of Dentistry, São Paulo State 
      University (UNESP), Araçatuba, SP, Brazil.
FAU - Trento, Cleverson Luciano
AU  - Trento CL
AD  - Department of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, 
      Brazil.
LA  - eng
PT  - Letter
DEP - 20231017
PL  - Denmark
TA  - Oral Dis
JT  - Oral diseases
JID - 9508565
EDAT- 2023/10/18 00:43
MHDA- 2023/10/18 00:43
CRDT- 2023/10/17 21:23
PHST- 2023/08/31 00:00 [revised]
PHST- 2023/08/26 00:00 [received]
PHST- 2023/09/04 00:00 [accepted]
PHST- 2023/10/18 00:43 [medline]
PHST- 2023/10/18 00:43 [pubmed]
PHST- 2023/10/17 21:23 [entrez]
AID - 10.1111/odi.14741 [doi]
PST - aheadofprint
SO  - Oral Dis. 2023 Oct 17. doi: 10.1111/odi.14741.

PMID- 37690022
OWN - NLM
STAT- Publisher
LR  - 20230910
IS  - 1097-6760 (Electronic)
IS  - 0196-0644 (Linking)
DP  - 2023 Sep 9
TI  - ChatGPT and Generating a Differential Diagnosis Early in an Emergency Department 
      Presentation.
LID - S0196-0644(23)00642-X [pii]
LID - 10.1016/j.annemergmed.2023.08.003 [doi]
FAU - Berg, Hidde Ten
AU  - Berg HT
FAU - van Bakel, Bram
AU  - van Bakel B
FAU - van de Wouw, Lieke
AU  - van de Wouw L
FAU - Jie, Kim E
AU  - Jie KE
FAU - Schipper, Anoeska
AU  - Schipper A
FAU - Jansen, Henry
AU  - Jansen H
FAU - O'Connor, Rory D
AU  - O'Connor RD
FAU - van Ginneken, Bram
AU  - van Ginneken B
FAU - Kurstjens, Steef
AU  - Kurstjens S
LA  - eng
PT  - Journal Article
DEP - 20230909
PL  - United States
TA  - Ann Emerg Med
JT  - Annals of emergency medicine
JID - 8002646
SB  - IM
EDAT- 2023/09/10 12:41
MHDA- 2023/09/10 12:41
CRDT- 2023/09/10 10:33
PHST- 2023/06/06 00:00 [received]
PHST- 2023/07/27 00:00 [revised]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/09/10 12:41 [medline]
PHST- 2023/09/10 12:41 [pubmed]
PHST- 2023/09/10 10:33 [entrez]
AID - S0196-0644(23)00642-X [pii]
AID - 10.1016/j.annemergmed.2023.08.003 [doi]
PST - aheadofprint
SO  - Ann Emerg Med. 2023 Sep 9:S0196-0644(23)00642-X. doi: 
      10.1016/j.annemergmed.2023.08.003.

PMID- 37438635
OWN - NLM
STAT- Publisher
LR  - 20230719
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Jul 12
TI  - ChatGPT can write a paper in an hour - but there are downsides.
LID - 10.1038/d41586-023-02298-x [doi]
FAU - Baker, Noah
AU  - Baker N
FAU - Thompson, Benjamin
AU  - Thompson B
FAU - Fox, Dan
AU  - Fox D
LA  - eng
PT  - News
DEP - 20230712
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Ageing
OT  - Authorship
OT  - Climate change
OT  - Climate sciences
OT  - Publishing
EDAT- 2023/07/13 01:06
MHDA- 2023/07/13 01:06
CRDT- 2023/07/12 23:38
PHST- 2023/07/13 01:06 [pubmed]
PHST- 2023/07/13 01:06 [medline]
PHST- 2023/07/12 23:38 [entrez]
AID - 10.1038/d41586-023-02298-x [pii]
AID - 10.1038/d41586-023-02298-x [doi]
PST - aheadofprint
SO  - Nature. 2023 Jul 12. doi: 10.1038/d41586-023-02298-x.

PMID- 36960954
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230327
LR  - 20230327
IS  - 2474-9842 (Electronic)
IS  - 2474-9842 (Linking)
VI  - 7
IP  - 2
DP  - 2023 Mar 7
TI  - The use of ChatGPT and other large language models in surgical science.
LID - 10.1093/bjsopen/zrad032 [doi]
LID - zrad032
FAU - Janssen, Boris V
AU  - Janssen BV
AD  - Department of Surgery, Amsterdam UMC, Location University of Amsterdam, 
      Amsterdam, The Netherlands.
AD  - Cancer Center Amsterdam, Amsterdam, The Netherlands.
FAU - Kazemier, Geert
AU  - Kazemier G
AD  - Cancer Center Amsterdam, Amsterdam, The Netherlands.
AD  - Department of Surgery, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The 
      Netherlands.
FAU - Besselink, Marc G
AU  - Besselink MG
AUID- ORCID: 0000-0003-2650-9350
AD  - Department of Surgery, Amsterdam UMC, Location University of Amsterdam, 
      Amsterdam, The Netherlands.
AD  - Cancer Center Amsterdam, Amsterdam, The Netherlands.
LA  - eng
PT  - Journal Article
PL  - England
TA  - BJS Open
JT  - BJS open
JID - 101722685
SB  - IM
PMC - PMC10037421
EDAT- 2023/03/25 06:00
MHDA- 2023/03/25 06:01
CRDT- 2023/03/24 08:03
PHST- 2023/02/22 00:00 [received]
PHST- 2023/02/23 00:00 [accepted]
PHST- 2023/03/24 08:03 [entrez]
PHST- 2023/03/25 06:00 [pubmed]
PHST- 2023/03/25 06:01 [medline]
AID - 7085520 [pii]
AID - zrad032 [pii]
AID - 10.1093/bjsopen/zrad032 [doi]
PST - ppublish
SO  - BJS Open. 2023 Mar 7;7(2):zrad032. doi: 10.1093/bjsopen/zrad032.

PMID- 36757199
OWN - NLM
STAT- MEDLINE
DCOM- 20230320
LR  - 20230924
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
VI  - 92
IP  - 4
DP  - 2023 Apr 1
TI  - I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots 
      Into Neurosurgical Research and Patient Care….
PG  - 663-664
LID - 10.1227/neu.0000000000002414 [doi]
FAU - D'Amico, Randy S
AU  - D'Amico RS
AD  - Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker 
      School of Medicine at Hofstra/Northwell, New York, New York, USA.
FAU - White, Timothy G
AU  - White TG
FAU - Shah, Harshal A
AU  - Shah HA
AUID- ORCID: 0000-0003-4088-6232
FAU - Langer, David J
AU  - Langer DJ
LA  - eng
PT  - Editorial
DEP - 20230209
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
CIN - Neurosurgery. 2023 Sep 1;93(3):e78. PMID: 37377413
CIN - Neurosurgery. 2023 Sep 1;93(3):e77. PMID: 37382430
CIN - Neurosurgery. 2023 Oct 1;93(4):e109. PMID: 37470488
CIN - Neurosurgery. 2023 Oct 1;93(4):e107-e108. PMID: 37498091
MH  - Humans
MH  - *Patient Care
MH  - *Writing
EDAT- 2023/02/10 06:00
MHDA- 2023/03/21 06:00
CRDT- 2023/02/09 09:03
PHST- 2023/01/06 00:00 [received]
PHST- 2023/01/11 00:00 [accepted]
PHST- 2023/02/10 06:00 [pubmed]
PHST- 2023/03/21 06:00 [medline]
PHST- 2023/02/09 09:03 [entrez]
AID - 00006123-202304000-00002 [pii]
AID - 10.1227/neu.0000000000002414 [doi]
PST - ppublish
SO  - Neurosurgery. 2023 Apr 1;92(4):663-664. doi: 10.1227/neu.0000000000002414. Epub 
      2023 Feb 9.

PMID- 38071176
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 1873-1244 (Electronic)
IS  - 0899-9007 (Linking)
DP  - 2023 Oct 28
TI  - Re: "The credibility of dietary advice formulated by ChatGPT: Robo-diets for 
      people with food allergies".
PG  - 112285
LID - S0899-9007(23)00313-1 [pii]
LID - 10.1016/j.nut.2023.112285 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India. Electronic address: ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20231028
PL  - United States
TA  - Nutrition
JT  - Nutrition (Burbank, Los Angeles County, Calif.)
JID - 8802712
SB  - IM
COIS- Declaration of competing interest No potential conflict of interest relevant to 
      this article was reported.
EDAT- 2023/12/10 06:42
MHDA- 2023/12/10 06:42
CRDT- 2023/12/09 22:04
PHST- 2023/08/10 00:00 [received]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/12/10 06:42 [medline]
PHST- 2023/12/10 06:42 [pubmed]
PHST- 2023/12/09 22:04 [entrez]
AID - S0899-9007(23)00313-1 [pii]
AID - 10.1016/j.nut.2023.112285 [doi]
PST - aheadofprint
SO  - Nutrition. 2023 Oct 28:112285. doi: 10.1016/j.nut.2023.112285.

PMID- 37972974
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Linking)
DP  - 2023 Nov 16
TI  - Optimizing Ophthalmology Patient Education via ChatBot-Generated Materials: 
      Readability Analysis of AI-Generated Patient Education Materials and The American 
      Society of Ophthalmic Plastic and Reconstructive Surgery Patient Brochures.
LID - 10.1097/IOP.0000000000002549 [doi]
AB  - PURPOSE: This study aims to compare the readability of patient education 
      materials (PEM) of the American Society of Ophthalmic Plastic and Reconstructive 
      Surgery to that of PEMs generated by the AI-chat bots ChatGPT and Google Bard. 
      METHODS: PEMs on 16 common American Society of Ophthalmic Plastic and 
      Reconstructive Surgery topics were generated by 2 AI models, ChatGPT 4.0 and 
      Google Bard, with and without a 6th-grade reading level prompt modifier. The PEMs 
      were analyzed using 7 readability metrics: Flesch Reading Ease Score, Gunning Fog 
      Index, Flesch-Kincaid Grade Level, Coleman-Liau Index, Simple Measure of 
      Gobbledygook Index Score, Automated Readability Index, and Linsear Write 
      Readability Score. Each AI-generated PEM was compared with the equivalent 
      American Society of Ophthalmic Plastic and Reconstructive Surgery PEM. RESULTS: 
      Across all readability indices, PEM generated by ChatGPT 4.0 consistently had the 
      highest readability scores, indicating that the material generated by this AI 
      chatbot may be most difficult to read in its unprompted form (Flesch Reading Ease 
      Score: 36.5; Simple Measure of Gobbledygook: 14.7). Google's Bard was able to 
      generate content that was easier to read than both the American Society of 
      Ophthalmic Plastic and Reconstructive Surgery and ChatGPT 4.0 (Flesch Reading 
      Ease Score: 52.3; Simple Measure of Gobbledygook: 12.7). When prompted to produce 
      PEM at a 6th-grade reading level, both ChatGPT 4.0 and Bard were able to 
      significantly improve in their readability scores, with prompted ChatGPT 4.0 
      being able to consistently generate content that was easier to read (Flesch 
      Reading Ease Score: 67.9, Simple Measure of Gobbledygook: 10.2). CONCLUSION: This 
      study suggests that AI tools, when guided by appropriate prompts, can generate 
      accessible and comprehensible PEMs in the field of ophthalmic plastic and 
      reconstructive surgeries, balancing readability with the complexity of the 
      necessary information.
CI  - Copyright © 2023 The American Society of Ophthalmic Plastic and Reconstructive 
      Surgery, Inc.
FAU - Eid, Kevin
AU  - Eid K
AD  - Department of Ophthalmology, Moran Eye Center, University of Utah, Salt Lake 
      City, Utah, U.S.A.
FAU - Eid, Alen
AU  - Eid A
AD  - Department of Ophthalmology and Visual Sciences, West Virginia University, 
      Morgantown, West Virginia, U.S.A.
FAU - Wang, Diane
AU  - Wang D
AD  - Department of Ophthalmology and Visual Sciences, West Virginia University, 
      Morgantown, West Virginia, U.S.A.
FAU - Raiker, Rahul S
AU  - Raiker RS
AD  - Department of Medical Education, West Virginia University, Morgantown, West 
      Virginia, U.S.A.
FAU - Chen, Stephen
AU  - Chen S
AD  - Department of Medical Education, West Virginia University, Morgantown, West 
      Virginia, U.S.A.
FAU - Nguyen, John
AU  - Nguyen J
AD  - Department of Ophthalmology and Visual Sciences, West Virginia University, 
      Morgantown, West Virginia, U.S.A.
AD  - Department of Otolaryngology and Head and Neck Surgery, West Virginia University, 
      Morgantown, West Virginia, U.S.A.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
SB  - IM
COIS- The authors have no financial or conflicts of interest to disclose.
EDAT- 2023/11/17 15:30
MHDA- 2023/11/17 15:30
CRDT- 2023/11/16 19:44
PHST- 2023/11/17 15:30 [medline]
PHST- 2023/11/17 15:30 [pubmed]
PHST- 2023/11/16 19:44 [entrez]
AID - 00002341-990000000-00288 [pii]
AID - 10.1097/IOP.0000000000002549 [doi]
PST - aheadofprint
SO  - Ophthalmic Plast Reconstr Surg. 2023 Nov 16. doi: 10.1097/IOP.0000000000002549.

PMID- 37841943
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2398-8835 (Electronic)
IS  - 2398-8835 (Linking)
VI  - 6
IP  - 10
DP  - 2023 Oct
TI  - Role of ChatGPT in health science and research: A correspondence addressing 
      potential application.
PG  - e1625
LID - 10.1002/hsr2.1625 [doi]
LID - e1625
FAU - Islam, Md Rabiul
AU  - Islam MR
AUID- ORCID: 0000-0003-2820-3144
AD  - School of Pharmacy BRAC University Dhaka Bangladesh.
AD  - Department of Pharmacy University of Asia Pacific Dhaka Bangladesh.
FAU - Urmi, Taslima Jamal
AU  - Urmi TJ
AD  - Department of Pharmacy University of Asia Pacific Dhaka Bangladesh.
FAU - Mosharrafa, Rana Al
AU  - Mosharrafa RA
AD  - Department of Business Administration, Faculty of Business Studies Prime 
      University Dhaka Bangladesh.
FAU - Rahman, Mohammad Saydur
AU  - Rahman MS
AD  - Department of Pharmacy Jagannath University Dhaka Bangladesh.
FAU - Kadir, Mohammad Fahim
AU  - Kadir MF
AD  - Department of Pharmacology Lake Erie College of Osteopathic Medicine (LECOM) Erie 
      Pennsylvania USA.
LA  - eng
PT  - Journal Article
DEP - 20231011
PL  - United States
TA  - Health Sci Rep
JT  - Health science reports
JID - 101728855
PMC - PMC10568002
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/16 06:48
MHDA- 2023/10/16 06:49
CRDT- 2023/10/16 04:45
PHST- 2023/06/22 00:00 [received]
PHST- 2023/09/01 00:00 [revised]
PHST- 2023/09/28 00:00 [accepted]
PHST- 2023/10/16 06:49 [medline]
PHST- 2023/10/16 06:48 [pubmed]
PHST- 2023/10/16 04:45 [entrez]
AID - HSR21625 [pii]
AID - 10.1002/hsr2.1625 [doi]
PST - epublish
SO  - Health Sci Rep. 2023 Oct 11;6(10):e1625. doi: 10.1002/hsr2.1625. eCollection 2023 
      Oct.

PMID- 37833483
OWN - NLM
STAT- MEDLINE
DCOM- 20231108
LR  - 20231108
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 623
IP  - 7986
DP  - 2023 Nov
TI  - ChatGPT use shows that the grant-application system is broken.
PG  - 443
LID - 10.1038/d41586-023-03238-5 [doi]
FAU - Parrilla, Juan Manuel
AU  - Parrilla JM
LA  - eng
PT  - Journal Article
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - Authorship
MH  - *Financing, Organized/methods/organization & administration/trends
MH  - *Writing
MH  - *Natural Language Processing
MH  - *Research Personnel
MH  - *Workload
OTO - NOTNLM
OT  - Authorship
OT  - Ethics
OT  - Funding
OT  - Peer review
EDAT- 2023/10/14 10:43
MHDA- 2023/11/08 06:42
CRDT- 2023/10/13 23:42
PHST- 2023/11/08 06:42 [medline]
PHST- 2023/10/14 10:43 [pubmed]
PHST- 2023/10/13 23:42 [entrez]
AID - 10.1038/d41586-023-03238-5 [pii]
AID - 10.1038/d41586-023-03238-5 [doi]
PST - ppublish
SO  - Nature. 2023 Nov;623(7986):443. doi: 10.1038/d41586-023-03238-5.

PMID- 37801239
OWN - NLM
STAT- MEDLINE
DCOM- 20231130
LR  - 20231130
IS  - 1708-0428 (Electronic)
IS  - 0960-8923 (Linking)
VI  - 33
IP  - 12
DP  - 2023 Dec
TI  - Correspondence on "Assessing the Accuracy of Responses by the Language Model 
      ChatGPT to Questions Regarding Bariatric Surgery".
PG  - 4159
LID - 10.1007/s11695-023-06875-x [doi]
FAU - Ishaaq, Namria
AU  - Ishaaq N
AUID- ORCID: 0009-0009-9239-4986
AD  - Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi, 
      India. namriaishaaq@gmail.com.
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AD  - Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi, 
      India.
LA  - eng
PT  - Letter
DEP - 20231006
PL  - United States
TA  - Obes Surg
JT  - Obesity surgery
JID - 9106714
SB  - IM
MH  - Humans
MH  - *Obesity, Morbid/surgery
MH  - *Bariatric Surgery
EDAT- 2023/10/06 12:44
MHDA- 2023/11/30 06:43
CRDT- 2023/10/06 11:11
PHST- 2023/09/05 00:00 [received]
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/09/05 00:00 [revised]
PHST- 2023/11/30 06:43 [medline]
PHST- 2023/10/06 12:44 [pubmed]
PHST- 2023/10/06 11:11 [entrez]
AID - 10.1007/s11695-023-06875-x [pii]
AID - 10.1007/s11695-023-06875-x [doi]
PST - ppublish
SO  - Obes Surg. 2023 Dec;33(12):4159. doi: 10.1007/s11695-023-06875-x. Epub 2023 Oct 
      6.

PMID- 37720880
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230918
IS  - 1869-3474 (Print)
IS  - 1869-3482 (Electronic)
IS  - 1869-3474 (Linking)
VI  - 57
IP  - 5
DP  - 2023 Oct
TI  - Response to the Letter to the Editor: Ethical Concerns of ChatGPT.
PG  - 215
LID - 10.1007/s13139-023-00819-0 [doi]
FAU - Bom, Hee-Seung Henry
AU  - Bom HH
AUID- ORCID: 0000-0002-5570-7375
AD  - Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, 322 
      Seoyang-Ro, Hwasun-Eup, Hwasun-Gun, Jeollanam-Do Republic of Korea. ROR: 
      https://ror.org/054gh2b75. GRID: grid.411602.0. ISNI: 0000 0004 0647 9534
LA  - eng
PT  - Journal Article
DEP - 20230815
PL  - Germany
TA  - Nucl Med Mol Imaging
JT  - Nuclear medicine and molecular imaging
JID - 101530478
PMC - PMC10504132
COIS- Conflict of InterestHee-Seung Henry Bom has no conflict of interest.
EDAT- 2023/09/18 06:42
MHDA- 2023/09/18 06:43
PMCR- 2024/10/01
CRDT- 2023/09/18 04:47
PHST- 2023/07/27 00:00 [received]
PHST- 2023/07/27 00:00 [revised]
PHST- 2023/07/28 00:00 [accepted]
PHST- 2024/10/01 00:00 [pmc-release]
PHST- 2023/09/18 06:43 [medline]
PHST- 2023/09/18 06:42 [pubmed]
PHST- 2023/09/18 04:47 [entrez]
AID - 819 [pii]
AID - 10.1007/s13139-023-00819-0 [doi]
PST - ppublish
SO  - Nucl Med Mol Imaging. 2023 Oct;57(5):215. doi: 10.1007/s13139-023-00819-0. Epub 
      2023 Aug 15.

PMID- 37437875
OWN - NLM
STAT- MEDLINE
DCOM- 20230929
LR  - 20230929
IS  - 1778-7254 (Electronic)
IS  - 1297-319X (Linking)
VI  - 90
IP  - 5
DP  - 2023 Sep
TI  - Battle of the brains: A comparison of human and ChatGPT health editorials.
PG  - 105610
LID - S1297-319X(23)00089-1 [pii]
LID - 10.1016/j.jbspin.2023.105610 [doi]
FAU - Boissier, Marie-Christophe
AU  - Boissier MC
AD  - Inserm U 1125, Bobigny, France; Li2P, université Sorbonne Paris Nord, Paris, 
      France; Department of Rheumatology, Avicenne Hospital, Bobigny, France. 
      Electronic address: marie-christophe.boissier@aphp.fr.
FAU - Bessis, Natacha
AU  - Bessis N
AD  - Inserm U 1125, Bobigny, France; Li2P, université Sorbonne Paris Nord, Paris, 
      France.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230711
PL  - France
TA  - Joint Bone Spine
JT  - Joint bone spine
JID - 100938016
SB  - IM
CON - Joint Bone Spine. 2023 Sep;90(5):105607. PMID: 37414138
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Writing
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Plagiarism
EDAT- 2023/07/13 01:06
MHDA- 2023/09/29 06:44
CRDT- 2023/07/12 19:27
PHST- 2023/05/24 00:00 [received]
PHST- 2023/06/28 00:00 [revised]
PHST- 2023/06/30 00:00 [accepted]
PHST- 2023/09/29 06:44 [medline]
PHST- 2023/07/13 01:06 [pubmed]
PHST- 2023/07/12 19:27 [entrez]
AID - S1297-319X(23)00089-1 [pii]
AID - 10.1016/j.jbspin.2023.105610 [doi]
PST - ppublish
SO  - Joint Bone Spine. 2023 Sep;90(5):105610. doi: 10.1016/j.jbspin.2023.105610. Epub 
      2023 Jul 11.

PMID- 37037358
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 1879-355X (Electronic)
IS  - 0360-3016 (Linking)
VI  - 116
IP  - 5
DP  - 2023 Aug 1
TI  - ChatGPT: Can a Natural Language Processing Tool Be Trusted for Radiation Oncology 
      Use?
PG  - 977-983
LID - S0360-3016(23)00354-1 [pii]
LID - 10.1016/j.ijrobp.2023.03.075 [doi]
FAU - Ebrahimi, Behzad
AU  - Ebrahimi B
AD  - Department of Radiation and Cellular Oncology, University of Chicago, Chicago, 
      Illinois. Electronic address: bebrahimi@uchicago.edu.
FAU - Howard, Andrew
AU  - Howard A
AD  - Department of Radiation and Cellular Oncology, University of Chicago, Chicago, 
      Illinois.
FAU - Carlson, David J
AU  - Carlson DJ
AD  - Department of Radiation Oncology, Yale University, New Haven, Connecticut.
FAU - Al-Hallaq, Hania
AU  - Al-Hallaq H
AD  - Department of Radiation and Cellular Oncology, University of Chicago, Chicago, 
      Illinois.
LA  - eng
PT  - Journal Article
DEP - 20230408
PL  - United States
TA  - Int J Radiat Oncol Biol Phys
JT  - International journal of radiation oncology, biology, physics
JID - 7603616
SB  - IM
MH  - Humans
MH  - *Natural Language Processing
MH  - *Radiation Oncology
EDAT- 2023/04/11 06:00
MHDA- 2023/07/17 06:42
CRDT- 2023/04/10 19:22
PHST- 2023/03/10 00:00 [received]
PHST- 2023/03/23 00:00 [revised]
PHST- 2023/03/24 00:00 [accepted]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 19:22 [entrez]
AID - S0360-3016(23)00354-1 [pii]
AID - 10.1016/j.ijrobp.2023.03.075 [doi]
PST - ppublish
SO  - Int J Radiat Oncol Biol Phys. 2023 Aug 1;116(5):977-983. doi: 
      10.1016/j.ijrobp.2023.03.075. Epub 2023 Apr 8.

PMID- 37036176
OWN - NLM
STAT- MEDLINE
DCOM- 20230731
LR  - 20230804
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
VI  - 45
IP  - 8
DP  - 2023 Aug
TI  - The next paradigm shift? ChatGPT, artificial intelligence, and medical education.
PG  - 925
LID - 10.1080/0142159X.2023.2198663 [doi]
FAU - Wang, Leonard Kuan-Pei
AU  - Wang LK
AUID- ORCID: 0000-0003-1913-2778
AD  - John Sealy School of Medicine, University of Texas Medical Branch, Galveston, TX, 
      USA.
FAU - Paidisetty, Praneet Sai
AU  - Paidisetty PS
AUID- ORCID: 0000-0001-5164-519X
AD  - McGovern Medical School, University of Texas Health Science Center, Houston, TX, 
      USA.
FAU - Cano, Alicia Magdalena
AU  - Cano AM
AD  - John Sealy School of Medicine, University of Texas Medical Branch, Galveston, TX, 
      USA.
LA  - eng
PT  - Letter
DEP - 20230410
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Education, Medical
EDAT- 2023/04/11 06:00
MHDA- 2023/07/31 06:42
CRDT- 2023/04/10 08:52
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 08:52 [entrez]
AID - 10.1080/0142159X.2023.2198663 [doi]
PST - ppublish
SO  - Med Teach. 2023 Aug;45(8):925. doi: 10.1080/0142159X.2023.2198663. Epub 2023 Apr 
      10.

PMID- 36864812
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230306
LR  - 20230323
IS  - 1332-8166 (Electronic)
IS  - 0353-9504 (Print)
IS  - 0353-9504 (Linking)
VI  - 64
IP  - 1
DP  - 2023 Feb 28
TI  - Opportunities and risks of ChatGPT in medicine, science, and academic publishing: 
      a modern Promethean dilemma.
PG  - 1-3
FAU - Homolak, Jan
AU  - Homolak J
AD  - Jan Homolak, Department of Pharmacology, University of Zagreb School of Medicine, 
      Zagreb, Croatia, Jan.homolak@mef.hr.
LA  - eng
PT  - Journal Article
PL  - Croatia
TA  - Croat Med J
JT  - Croatian medical journal
JID - 9424324
SB  - IM
PMC - PMC10028563
EDAT- 2023/03/04 06:00
MHDA- 2023/03/04 06:01
CRDT- 2023/03/03 02:22
PHST- 2023/03/03 02:22 [entrez]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/04 06:01 [medline]
AID - CroatMedJ_64_0001 [pii]
AID - 10.3325/cmj.2023.64.1 [doi]
PST - ppublish
SO  - Croat Med J. 2023 Feb 28;64(1):1-3. doi: 10.3325/cmj.2023.64.1.

PMID- 36795957
OWN - NLM
STAT- MEDLINE
DCOM- 20230410
LR  - 20230421
IS  - 1527-3792 (Electronic)
IS  - 0022-5347 (Linking)
VI  - 209
IP  - 5
DP  - 2023 May
TI  - Harnessing Generative Artificial Intelligence to Improve Efficiency Among 
      Urologists: Welcome ChatGPT.
PG  - 827-829
LID - 10.1097/JU.0000000000003383 [doi]
FAU - Gabrielson, Andrew T
AU  - Gabrielson AT
AUID- ORCID: 0000-0001-9971-9813
AD  - The James Buchanan Brady Urological Institute, Johns Hopkins University School of 
      Medicine, Baltimore, Maryland.
FAU - Odisho, Anobel Y
AU  - Odisho AY
AD  - Department of Urology, University of California San Francisco School of Medicine, 
      San Francisco, California.
AD  - Center for Digital Health Innovation, University of California San Francisco 
      School of Medicine, San Francisco, California.
FAU - Canes, David
AU  - Canes D
AD  - Lahey Institute of Urology, Lahey Hospital & Medical Center, Beth Israel and 
      Lahey Health, Burlington, Massachusetts.
LA  - eng
PT  - Editorial
DEP - 20230216
PL  - United States
TA  - J Urol
JT  - The Journal of urology
JID - 0376374
SB  - IM
MH  - Humans
MH  - *Urologists
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - artificial intelligence
OT  - efficiency
OT  - organizational
EDAT- 2023/02/17 06:00
MHDA- 2023/04/10 06:42
CRDT- 2023/02/16 15:12
PHST- 2023/04/10 06:42 [medline]
PHST- 2023/02/17 06:00 [pubmed]
PHST- 2023/02/16 15:12 [entrez]
AID - 10.1097/JU.0000000000003383 [doi]
PST - ppublish
SO  - J Urol. 2023 May;209(5):827-829. doi: 10.1097/JU.0000000000003383. Epub 2023 Feb 
      16.

PMID- 37797272
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231010
IS  - 1678-9741 (Electronic)
IS  - 0102-7638 (Print)
IS  - 0102-7638 (Linking)
VI  - 38
IP  - 4
DP  - 2023 Oct 5
TI  - Artificial Intelligence is Irreversibly Bound to Academic Publishing - ChatGPT is 
      Cleared for Scientific Writing and Peer Review.
PG  - e20230963
LID - 10.21470/1678-9741-2023-0963 [doi]
LID - e20230963
FAU - Gomes, Walter J
AU  - Gomes WJ
AD  - Cardiovascular Surgery Discipline, Escola Paulista de Medicina, Universidade 
      Federal de São Paulo, São Paulo, São Paulo, Brazil.
AD  - Cardiology Postgraduate Program, Escola Paulista de Medicina, Universidade 
      Federal de São Paulo, São Paulo, São Paulo, Brazil.
FAU - Evora, Paulo R B
AU  - Evora PRB
AD  - Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade 
      de São Paulo (HCFMRP-USP), Ribeirão Preto, São Paulo, Brazil.
FAU - Guizilini, Solange
AU  - Guizilini S
AD  - Cardiology Postgraduate Program, Escola Paulista de Medicina, Universidade 
      Federal de São Paulo, São Paulo, São Paulo, Brazil.
AD  - Department of Human Motion Sciences, Universidade Federal de São Paulo, São 
      Paulo, São Paulo, Brazil.
LA  - eng
PT  - Editorial
DEP - 20231005
PL  - Brazil
TA  - Braz J Cardiovasc Surg
JT  - Brazilian journal of cardiovascular surgery
JID - 101677045
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Peer Review
PMC - PMC10552203
EDAT- 2023/10/05 18:42
MHDA- 2023/10/09 06:41
CRDT- 2023/10/05 16:00
PHST- 2023/10/09 06:41 [medline]
PHST- 2023/10/05 18:42 [pubmed]
PHST- 2023/10/05 16:00 [entrez]
AID - 10.21470/1678-9741-2023-0963 [doi]
PST - epublish
SO  - Braz J Cardiovasc Surg. 2023 Oct 5;38(4):e20230963. doi: 
      10.21470/1678-9741-2023-0963.

PMID- 37466691
OWN - NLM
STAT- Publisher
LR  - 20230719
IS  - 1432-0711 (Electronic)
IS  - 0932-0067 (Linking)
DP  - 2023 Jul 19
TI  - Bridging the gap: integrating ChatGPT into obstetrics and gynecology research-a 
      call to action.
LID - 10.1007/s00404-023-07129-y [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20230719
PL  - Germany
TA  - Arch Gynecol Obstet
JT  - Archives of gynecology and obstetrics
JID - 8710213
SB  - IM
EDAT- 2023/07/19 13:06
MHDA- 2023/07/19 13:06
CRDT- 2023/07/19 11:05
PHST- 2023/06/17 00:00 [received]
PHST- 2023/06/27 00:00 [accepted]
PHST- 2023/07/19 13:06 [medline]
PHST- 2023/07/19 13:06 [pubmed]
PHST- 2023/07/19 11:05 [entrez]
AID - 10.1007/s00404-023-07129-y [pii]
AID - 10.1007/s00404-023-07129-y [doi]
PST - aheadofprint
SO  - Arch Gynecol Obstet. 2023 Jul 19. doi: 10.1007/s00404-023-07129-y.

PMID- 37428231
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231116
IS  - 1432-5195 (Electronic)
IS  - 0341-2695 (Linking)
VI  - 47
IP  - 8
DP  - 2023 Aug
TI  - Two minutes of orthopaedics with ChatGPT: it is just the beginning; it's going to 
      be hot, hot, hot!
PG  - 1887-1893
LID - 10.1007/s00264-023-05887-7 [doi]
FAU - Hernigou, Philippe
AU  - Hernigou P
AD  - University Paris East, Paris, France.
FAU - Scarlat, Marius M
AU  - Scarlat MM
AD  - Groupe ELSAN, Clinique Chirurgicale St Michel, Toulon, France. 
      mscarlat@gmail.com.
LA  - eng
PT  - Editorial
PL  - Germany
TA  - Int Orthop
JT  - International orthopaedics
JID - 7705431
SB  - IM
CIN - Int Orthop. 2023 Oct;47(10):2617-2618. PMID: 37515601
CIN - Int Orthop. 2023 Oct;47(10):2625. PMID: 37550592
MH  - Humans
MH  - *Orthopedics
MH  - *Orthopedic Procedures
EDAT- 2023/07/10 13:05
MHDA- 2023/07/17 06:42
CRDT- 2023/07/10 11:07
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/07/10 13:05 [pubmed]
PHST- 2023/07/10 11:07 [entrez]
AID - 10.1007/s00264-023-05887-7 [pii]
AID - 10.1007/s00264-023-05887-7 [doi]
PST - ppublish
SO  - Int Orthop. 2023 Aug;47(8):1887-1893. doi: 10.1007/s00264-023-05887-7.

PMID- 36920843
OWN - NLM
STAT- MEDLINE
DCOM- 20230517
LR  - 20230521
IS  - 1935-5548 (Electronic)
IS  - 0149-5992 (Linking)
VI  - 46
IP  - 5
DP  - 2023 May 1
TI  - Potential and Pitfalls of ChatGPT and Natural-Language Artificial Intelligence 
      Models for Diabetes Education.
PG  - e103-e105
LID - 10.2337/dc23-0197 [doi]
FAU - Sng, Gerald Gui Ren
AU  - Sng GGR
AUID- ORCID: 0000-0002-8795-7491
AD  - 1Department of Endocrinology, Singapore General Hospital, Singapore.
FAU - Tung, Joshua Yi Min
AU  - Tung JYM
AD  - 2Department of Urology, Singapore General Hospital, Singapore.
FAU - Lim, Daniel Yan Zheng
AU  - Lim DYZ
AD  - 3Health Services Research Unit, Singapore General Hospital, Singapore.
FAU - Bee, Yong Mong
AU  - Bee YM
AD  - 1Department of Endocrinology, Singapore General Hospital, Singapore.
LA  - eng
PT  - Letter
PL  - United States
TA  - Diabetes Care
JT  - Diabetes care
JID - 7805975
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Health Education
MH  - *Diabetes Mellitus
EDAT- 2023/03/16 06:00
MHDA- 2023/05/17 06:42
CRDT- 2023/03/15 12:53
PHST- 2023/02/07 00:00 [received]
PHST- 2023/02/15 00:00 [accepted]
PHST- 2023/05/17 06:42 [medline]
PHST- 2023/03/16 06:00 [pubmed]
PHST- 2023/03/15 12:53 [entrez]
AID - 148587 [pii]
AID - 10.2337/dc23-0197 [doi]
PST - ppublish
SO  - Diabetes Care. 2023 May 1;46(5):e103-e105. doi: 10.2337/dc23-0197.

PMID- 36775020
OWN - NLM
STAT- MEDLINE
DCOM- 20230331
LR  - 20230407
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
VI  - 185
DP  - 2023 Apr
TI  - Can ChatGPT pass the life support exams without entering the American heart 
      association course?
PG  - 109732
LID - S0300-9572(23)00045-X [pii]
LID - 10.1016/j.resuscitation.2023.109732 [doi]
FAU - Fijačko, Nino
AU  - Fijačko N
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; ERC 
      Research Net, Niels, Belgium. Electronic address: 
      https://twitter.com/NinoFijacko.
FAU - Gosak, Lucija
AU  - Gosak L
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia.
FAU - Štiglic, Gregor
AU  - Štiglic G
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; University 
      of Maribor, Faculty of Electrical Engineering and Computer Science, Maribor, 
      Slovenia; University of Edinburgh, Usher Institute, Edinburgh, United Kingdom.
FAU - Picard, Christopher T
AU  - Picard CT
AD  - University of Alberta, Faculty of Nursing, Alberta, Canada.
FAU - John Douma, Matthew
AU  - John Douma M
AD  - University of Alberta, Department of Critical Care, Faculty of Medicine and 
      Dentistry, Alberta, Canada.
LA  - eng
PT  - Letter
DEP - 20230211
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
MH  - Humans
MH  - *American Heart Association
MH  - *Educational Measurement
MH  - Clinical Competence
EDAT- 2023/02/13 06:00
MHDA- 2023/03/31 06:42
CRDT- 2023/02/12 19:23
PHST- 2023/02/03 00:00 [received]
PHST- 2023/02/06 00:00 [accepted]
PHST- 2023/03/31 06:42 [medline]
PHST- 2023/02/13 06:00 [pubmed]
PHST- 2023/02/12 19:23 [entrez]
AID - S0300-9572(23)00045-X [pii]
AID - 10.1016/j.resuscitation.2023.109732 [doi]
PST - ppublish
SO  - Resuscitation. 2023 Apr;185:109732. doi: 10.1016/j.resuscitation.2023.109732. 
      Epub 2023 Feb 11.

PMID- 37595113
OWN - NLM
STAT- Publisher
LR  - 20230818
IS  - 1097-6817 (Electronic)
IS  - 0194-5998 (Linking)
DP  - 2023 Aug 18
TI  - Accuracy of ChatGPT-Generated Information on Head and Neck and Oromaxillofacial 
      Surgery: A Multicenter Collaborative Analysis.
LID - 10.1002/ohn.489 [doi]
AB  - OBJECTIVE: To investigate the accuracy of Chat-Based Generative Pre-trained 
      Transformer (ChatGPT) in answering questions and solving clinical scenarios of 
      head and neck surgery. STUDY DESIGN: Observational and valuative study. SETTING: 
      Eighteen surgeons from 14 Italian head and neck surgery units. METHODS: A total 
      of 144 clinical questions encompassing different subspecialities of head and neck 
      surgery and 15 comprehensive clinical scenarios were developed. Questions and 
      scenarios were inputted into ChatGPT4, and the resulting answers were evaluated 
      by the researchers using accuracy (range 1-6), completeness (range 1-3), and 
      references' quality Likert scales. RESULTS: The overall median score of 
      open-ended questions was 6 (interquartile range[IQR]: 5-6) for accuracy and 3 
      (IQR: 2-3) for completeness. Overall, the reviewers rated the answer as entirely 
      or nearly entirely correct in 87.2% of cases and as comprehensive and covering 
      all aspects of the question in 73% of cases. The artificial intelligence (AI) 
      model achieved a correct response in 84.7% of the closed-ended questions (11 
      wrong answers). As for the clinical scenarios, ChatGPT provided a fully or nearly 
      fully correct diagnosis in 81.7% of cases. The proposed diagnostic or therapeutic 
      procedure was judged to be complete in 56.7% of cases. The overall quality of the 
      bibliographic references was poor, and sources were nonexistent in 46.4% of the 
      cases. CONCLUSION: The results generally demonstrate a good level of accuracy in 
      the AI's answers. The AI's ability to resolve complex clinical scenarios is 
      promising, but it still falls short of being considered a reliable support for 
      the decision-making process of specialists in head-neck surgery.
CI  - © 2023 The Authors. Otolaryngology-Head and Neck Surgery published by Wiley 
      Periodicals LLC on behalf of American Academy of Otolaryngology-Head and Neck 
      Surgery Foundation.
FAU - Vaira, Luigi Angelo
AU  - Vaira LA
AUID- ORCID: 0000-0002-7789-145X
AD  - Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and 
      Pharmacy, University of Sassari, Sassari, Italy.
AD  - Biomedical Sciences Department, PhD School of Biomedical Science, University of 
      Sassari, Sassari, Italy.
FAU - Lechien, Jerome R
AU  - Lechien JR
AD  - Department of Anatomy and Experimental Oncology, Mons School of Medicine, UMONS, 
      Research Institute for Health Sciences and Technology, University of Mons 
      (UMons), Mons, Belgium.
AD  - Department of Otolaryngology-Head Neck Surgery, Elsan Polyclinic of Poitiers, 
      Poitiers, France.
FAU - Abbate, Vincenzo
AU  - Abbate V
AD  - Head and Neck Section, Department of Neurosciences, Reproductive and 
      Odontostomatological Science, Federico II University of Naples, Naples, Italy.
FAU - Allevi, Fabiana
AU  - Allevi F
AD  - Maxillofacial Surgery Department, ASSt Santi Paolo e Carlo, University of Milan, 
      Milan, Italy.
FAU - Audino, Giovanni
AU  - Audino G
AD  - Head and Neck Section, Department of Neurosciences, Reproductive and 
      Odontostomatological Science, Federico II University of Naples, Naples, Italy.
FAU - Beltramini, Giada Anna
AU  - Beltramini GA
AD  - Department of Biomedical, Surgical and Dental Sciences, University of Milan, 
      Milan, Italy.
AD  - Maxillofacial and Dental Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore 
      Policlinico, Milan, Italy.
FAU - Bergonzani, Michela
AU  - Bergonzani M
AD  - Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of 
      Parma, Parma, Italy.
FAU - Bolzoni, Alessandro
AU  - Bolzoni A
AD  - Department of Biomedical, Surgical and Dental Sciences, University of Milan, 
      Milan, Italy.
FAU - Committeri, Umberto
AU  - Committeri U
AD  - Head and Neck Section, Department of Neurosciences, Reproductive and 
      Odontostomatological Science, Federico II University of Naples, Naples, Italy.
FAU - Crimi, Salvatore
AU  - Crimi S
AD  - Operative Unit of Maxillofacial Surgery, Policlinico San Marco, University of 
      Catania, Catania, Italy.
FAU - Gabriele, Guido
AU  - Gabriele G
AD  - Department of Maxillofacial Surgery, University of Siena, Siena, Italy.
FAU - Lonardi, Fabio
AU  - Lonardi F
AD  - Department of Maxillofacial Surgery, University of Verona, Verona, Italy.
FAU - Maglitto, Fabio
AU  - Maglitto F
AD  - Maxillo-Facial Surgery Unit, University of Bari "Aldo Moro", Bari, Italy.
FAU - Petrocelli, Marzia
AU  - Petrocelli M
AD  - Maxillofacial Surgery Operative Unit, Bellaria and Maggiore Hospital, Bologna, 
      Italy.
FAU - Pucci, Resi
AU  - Pucci R
AD  - Maxillofacial Surgery Unit, San Camillo-Forlanini Hospital, Rome, Italy.
FAU - Saponaro, Gianmarco
AU  - Saponaro G
AD  - Maxillo-Facial Surgery Unit, IRCSS "A. Gemelli" Foundation-Catholic, University 
      of the Sacred Heart, Rome, Italy.
FAU - Tel, Alessandro
AU  - Tel A
AD  - Department of Head and Neck Surgery and Neuroscience, Clinic of Maxillofacial 
      Surgery, University Hospital of Udine, Udine, Italy.
FAU - Vellone, Valentino
AU  - Vellone V
AD  - Maxillofacial Surgery Unit, "S. Maria" Hospital, Terni, Italy.
FAU - Chiesa-Estomba, Carlos Miguel
AU  - Chiesa-Estomba CM
AD  - Department of Otorhinolaryngology-Head and Neck Surgery, Hospital Universitario 
      Donostia, San Sebastian, Spain.
FAU - Boscolo-Rizzo, Paolo
AU  - Boscolo-Rizzo P
AD  - Department of Medical, Surgical and Health Sciences, Section of Otolaryngology, 
      University of Trieste, Trieste, Italy.
FAU - Salzano, Giovanni
AU  - Salzano G
AD  - Head and Neck Section, Department of Neurosciences, Reproductive and 
      Odontostomatological Science, Federico II University of Naples, Naples, Italy.
FAU - De Riu, Giacomo
AU  - De Riu G
AD  - Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and 
      Pharmacy, University of Sassari, Sassari, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230818
PL  - England
TA  - Otolaryngol Head Neck Surg
JT  - Otolaryngology--head and neck surgery : official journal of American Academy of 
      Otolaryngology-Head and Neck Surgery
JID - 8508176
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - maxillofacial surgery
OT  - otorhinolaryngology
EDAT- 2023/08/18 18:41
MHDA- 2023/08/18 18:41
CRDT- 2023/08/18 15:23
PHST- 2023/06/16 00:00 [revised]
PHST- 2023/04/27 00:00 [received]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/08/18 18:41 [medline]
PHST- 2023/08/18 18:41 [pubmed]
PHST- 2023/08/18 15:23 [entrez]
AID - 10.1002/ohn.489 [doi]
PST - aheadofprint
SO  - Otolaryngol Head Neck Surg. 2023 Aug 18. doi: 10.1002/ohn.489.

PMID- 37223340
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230525
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - ChatGPT-4 and the Global Burden of Disease Study: Advancing Personalized 
      Healthcare Through Artificial Intelligence in Clinical and Translational 
      Medicine.
PG  - e39384
LID - 10.7759/cureus.39384 [doi]
LID - e39384
AB  - The fusion of insights from the comprehensive global burden of disease (GBD) 
      study and the advanced artificial intelligence of open artificial intelligence 
      (AI) chat generative pre-trained transformer version 4 (ChatGPT-4) brings the 
      potential to transform personalized healthcare planning. By integrating the 
      data-driven findings of the GBD study with the powerful conversational 
      capabilities of ChatGPT-4, healthcare professionals can devise customized 
      healthcare plans that are adapted to patients' lifestyles and preferences. We 
      propose that this innovative partnership can lead to the creation of a novel 
      AI-assisted personalized disease burden (AI-PDB) assessment and planning tool. 
      For the successful implementation of this unconventional technology, it is 
      crucial to ensure continuous and accurate updates, expert supervision, and 
      address potential biases and limitations. Healthcare professionals and 
      stakeholders should have a balanced and dynamic approach, emphasizing 
      interdisciplinary collaborations, data accuracy, transparency, ethical 
      compliance, and ongoing training. By investing in the unique strengths of both 
      ChatGPT-4, especially its newly introduced features such as live internet 
      browsing or plugins, and the GBD study, we may enhance personalized healthcare 
      planning. This innovative approach has the potential to improve patient outcomes 
      and optimize resource utilization, as well as pave the way for the worldwide 
      implementation of precision medicine, thereby revolutionizing the existing 
      healthcare landscape. However, to fully harness these benefits at both the global 
      and individual levels, further research and development are warranted. This will 
      ensure that we effectively tap into the potential of this synergy, bringing 
      societies closer to a future where personalized healthcare is the norm rather 
      than the exception.
CI  - Copyright © 2023, Temsah et al.
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Intensive Care Unit, Department of Pediatrics, King Saud University 
      Medical City, College of Medicine, King Saud University, Riyadh, SAU.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Department of Family and Community Medicine, College of Medicine, King Saud 
      University, Riyadh, SAU.
AD  - Evidence-Based Health Care & Knowledge Translation Research Chair, Department of 
      Family and Community Medicine, College of Medicine, King Saud University, Riyadh, 
      SAU.
FAU - Aljamaan, Fadi
AU  - Aljamaan F
AD  - Department of Critical Care, College of Medicine, King Saud University, Riyadh, 
      SAU.
FAU - Al-Tawfiq, Jaffar A
AU  - Al-Tawfiq JA
AD  - Department of Specialty Internal Medicine and Quality, Johns Hopkins Aramco 
      Healthcare, Dhahran, SAU.
AD  - Infectious Disease Division, Department of Medicine, Indiana University School of 
      Medicine, Indianapolis, USA.
AD  - Infectious Disease Division, Department of Medicine, Johns Hopkins University 
      School of Medicine, Baltimore, USA.
FAU - Al-Eyadhy, Ayman
AU  - Al-Eyadhy A
AD  - Pediatric Intensive Care Unit, Department of Pediatrics, College of Medicine, 
      King Saud University, Riyadh, SAU.
AD  - Pediatric Intensive Care Unit, King Saud University Medical City, Riyadh, SAU.
LA  - eng
PT  - Editorial
DEP - 20230523
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10204616
OTO - NOTNLM
OT  - ai-assisted personalized disease burden
OT  - chatbots
OT  - chatgpt-4
OT  - global burden of disease (gbd)
OT  - personalized healthcare plan
OT  - precision medicine
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/24 13:08
MHDA- 2023/05/24 13:09
CRDT- 2023/05/24 11:46
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/05/24 13:09 [medline]
PHST- 2023/05/24 13:08 [pubmed]
PHST- 2023/05/24 11:46 [entrez]
AID - 10.7759/cureus.39384 [doi]
PST - epublish
SO  - Cureus. 2023 May 23;15(5):e39384. doi: 10.7759/cureus.39384. eCollection 2023 
      May.

PMID- 38000076
OWN - NLM
STAT- Publisher
LR  - 20231124
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Nov 24
TI  - AI-enabled ChatGPT or LLM: A new algorithm is required for plagiarism free 
      scientific writing.
LID - 10.1097/JS9.0000000000000939 [doi]
FAU - Pal, Soumen
AU  - Pal S
AD  - School of Mechanical Engineering, Vellore Institute of Technology, Vellore 
      632014, Tamil Nadu, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, 
      Odisha, India.
FAU - Islam, Md Aminul
AU  - Islam MA
AD  - COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and 
      Technology University, Noakhali-3814, Bangladesh.
AD  - Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical 
      College, Karimganj, Kishoreganj, Bangladesh.
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal 700126, India.
LA  - eng
PT  - Journal Article
DEP - 20231124
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/11/24 18:43
MHDA- 2023/11/24 18:43
CRDT- 2023/11/24 16:53
PHST- 2023/11/08 00:00 [received]
PHST- 2023/11/09 00:00 [accepted]
PHST- 2023/11/24 18:43 [medline]
PHST- 2023/11/24 18:43 [pubmed]
PHST- 2023/11/24 16:53 [entrez]
AID - 01279778-990000000-00820 [pii]
AID - 10.1097/JS9.0000000000000939 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Nov 24. doi: 10.1097/JS9.0000000000000939.

PMID- 37944878
OWN - NLM
STAT- Publisher
LR  - 20231109
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
DP  - 2023 Nov 7
TI  - Re: "New Horizons: The Potential Role of OpenAI's ChatGPT in Clinical Radiology".
LID - S1546-1440(23)00852-9 [pii]
LID - 10.1016/j.jacr.2023.10.020 [doi]
FAU - Kim, Woojin
AU  - Kim W
AD  - Rad AI, San Francisco, CA, 548 Market St, PMB 49792, San Francisco, CA 94104. 
      Electronic address: woojinrad@gmail.com.
LA  - eng
PT  - Letter
DEP - 20231107
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
EDAT- 2023/11/10 00:44
MHDA- 2023/11/10 00:44
CRDT- 2023/11/09 19:19
PHST- 2023/09/27 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/11/10 00:44 [medline]
PHST- 2023/11/10 00:44 [pubmed]
PHST- 2023/11/09 19:19 [entrez]
AID - S1546-1440(23)00852-9 [pii]
AID - 10.1016/j.jacr.2023.10.020 [doi]
PST - aheadofprint
SO  - J Am Coll Radiol. 2023 Nov 7:S1546-1440(23)00852-9. doi: 
      10.1016/j.jacr.2023.10.020.

PMID- 37161504
OWN - NLM
STAT- MEDLINE
DCOM- 20230822
LR  - 20230925
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 8
DP  - 2023 Aug 1
TI  - Can ChatGPT/GPT-4 assist surgeons in confronting patients with Mpox and handling 
      future epidemics?
PG  - 2544-2548
LID - 10.1097/JS9.0000000000000453 [doi]
FAU - He, Yongbin
AU  - He Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing.
AD  - Department of Orthopedics, The Fifth Affiliated Hospital of Zunyi Medical 
      University, Zhuhai.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Spine Surgery, Tianjin Huanhu Hospital, Graduate School of Tianjin 
      Medical University, Tianjin.
AD  - Duke Molecular Physiology Institute.
FAU - Chen, Yan
AU  - Chen Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing.
FAU - Wang, Dewei
AU  - Wang D
AD  - Department of Orthopedics, The Fifth Affiliated Hospital of Zunyi Medical 
      University, Zhuhai.
FAU - Tang, Weiming
AU  - Tang W
AD  - University of North Carolina Project-China, Guangzhou.
AD  - Department of Medicine, University of North Carolina Institute for Global Health 
      and Infectious Diseases, Chapel Hill, NC.
FAU - Moody, M Anthony
AU  - Moody MA
AD  - Division of Infectious Diseases, Department of Pediatrics, Duke University School 
      of Medicine.
AD  - Duke Human Vaccine Institute, Duke University Medical Center, Durham.
FAU - Ni, Guoxin
AU  - Ni G
AD  - Department of Rehabilitation Medicine, The First Affiliated Hospital of Xiamen 
      University, Xiamen, China.
FAU - Gu, Shuqin
AU  - Gu S
AUID- ORCID: 0000-0002-0347-0097
AD  - Duke Human Vaccine Institute, Duke University Medical Center, Durham.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *Monkeypox
MH  - *Surgeons
PMC - PMC10442131
COIS- The authors declare no conflict of interest.
EDAT- 2023/05/10 06:42
MHDA- 2023/08/22 06:42
CRDT- 2023/05/10 03:57
PHST- 2023/04/28 00:00 [received]
PHST- 2023/05/01 00:00 [accepted]
PHST- 2023/08/22 06:42 [medline]
PHST- 2023/05/10 06:42 [pubmed]
PHST- 2023/05/10 03:57 [entrez]
AID - 01279778-202308000-00045 [pii]
AID - IJS-D-23-00781 [pii]
AID - 10.1097/JS9.0000000000000453 [doi]
PST - epublish
SO  - Int J Surg. 2023 Aug 1;109(8):2544-2548. doi: 10.1097/JS9.0000000000000453.

PMID- 37062754
OWN - NLM
STAT- MEDLINE
DCOM- 20230803
LR  - 20230804
IS  - 1557-8593 (Electronic)
IS  - 1520-9156 (Linking)
VI  - 25
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT's Response to the Diabetes Knowledge Questionnaire: Implications for 
      Diabetes Education.
PG  - 571-573
LID - 10.1089/dia.2023.0134 [doi]
FAU - Nakhleh, Afif
AU  - Nakhleh A
AD  - Diabetes and Endocrinology Clinic, Maccabi Healthcare Services, Haifa, Israel.
AD  - Institute of Endocrinology, Diabetes and Metabolism, Rambam Health Care Campus, 
      Haifa, Israel.
AD  - The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
FAU - Spitzer, Sivan
AU  - Spitzer S
AD  - The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
FAU - Shehadeh, Naim
AU  - Shehadeh N
AD  - Diabetes and Endocrinology Clinic, Maccabi Healthcare Services, Haifa, Israel.
AD  - Institute of Endocrinology, Diabetes and Metabolism, Rambam Health Care Campus, 
      Haifa, Israel.
AD  - The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
LA  - eng
PT  - Letter
PL  - United States
TA  - Diabetes Technol Ther
JT  - Diabetes technology & therapeutics
JID - 100889084
SB  - IM
MH  - Humans
MH  - *Diabetes Mellitus
EDAT- 2023/04/17 06:00
MHDA- 2023/08/03 06:43
CRDT- 2023/04/16 22:42
PHST- 2023/08/03 06:43 [medline]
PHST- 2023/04/17 06:00 [pubmed]
PHST- 2023/04/16 22:42 [entrez]
AID - 10.1089/dia.2023.0134 [doi]
PST - ppublish
SO  - Diabetes Technol Ther. 2023 Aug;25(8):571-573. doi: 10.1089/dia.2023.0134.

PMID- 36944743
OWN - NLM
STAT- MEDLINE
DCOM- 20230323
LR  - 20230607
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 615
IP  - 7953
DP  - 2023 Mar
TI  - ChatGPT: tackle the growing carbon footprint of generative AI.
PG  - 586
LID - 10.1038/d41586-023-00843-2 [doi]
FAU - An, Jiafu
AU  - An J
FAU - Ding, Wenzhi
AU  - Ding W
FAU - Lin, Chen
AU  - Lin C
LA  - eng
PT  - Letter
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - *Artificial Intelligence/trends
MH  - *Carbon Footprint
MH  - *Sustainable Development/trends
OTO - NOTNLM
OT  - Energy
OT  - Machine learning
OT  - Renewable energy
EDAT- 2023/03/23 06:00
MHDA- 2023/03/24 06:00
CRDT- 2023/03/22 00:22
PHST- 2023/03/22 00:22 [entrez]
PHST- 2023/03/23 06:00 [pubmed]
PHST- 2023/03/24 06:00 [medline]
AID - 10.1038/d41586-023-00843-2 [pii]
AID - 10.1038/d41586-023-00843-2 [doi]
PST - ppublish
SO  - Nature. 2023 Mar;615(7953):586. doi: 10.1038/d41586-023-00843-2.

PMID- 37791050
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 2575-7873 (Electronic)
IS  - 2575-7873 (Linking)
VI  - 7
DP  - 2023
TI  - Exploring the Applications of ChatGPT in Family Medicine Education: Five 
      Innovative Ways for Faculty Integration.
PG  - 26
LID - 10.22454/PRiMER.2023.985351 [doi]
LID - 26
FAU - Hanna, Karim
AU  - Hanna K
AD  - Department of Family Medicine, University of South Florida, Morsani College of 
      Medicine, Tampa, FL.
LA  - eng
PT  - Journal Article
DEP - 20230802
PL  - United States
TA  - PRiMER
JT  - PRiMER (Leawood, Kan.)
JID - 101726396
PMC - PMC10544634
EDAT- 2023/10/04 06:44
MHDA- 2023/10/04 06:45
CRDT- 2023/10/04 04:16
PHST- 2023/10/04 06:45 [medline]
PHST- 2023/10/04 06:44 [pubmed]
PHST- 2023/10/04 04:16 [entrez]
AID - primer-7-26 [pii]
AID - 10.22454/PRiMER.2023.985351 [doi]
PST - epublish
SO  - PRiMER. 2023 Aug 2;7:26. doi: 10.22454/PRiMER.2023.985351. eCollection 2023.

PMID- 37702575
OWN - NLM
STAT- MEDLINE
DCOM- 20231113
LR  - 20231114
IS  - 1365-2168 (Electronic)
IS  - 0007-1323 (Linking)
VI  - 110
IP  - 12
DP  - 2023 Nov 9
TI  - Author response to: Comment on: Will collaborative publishing with ChatGPT drive 
      academic writing in the future?
PG  - 1894
LID - 10.1093/bjs/znad295 [doi]
FAU - Ariyaratne, Sisith
AU  - Ariyaratne S
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      UK.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AD  - Department of Orthopaedics, Southport and Ormskirk Hospitals, Mersey and West 
      Lancashire Teaching NHS Trust, Southport, UK.
FAU - Botchu, Rajesh
AU  - Botchu R
AUID- ORCID: 0000-0001-7998-2980
AD  - Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, 
      UK.
LA  - eng
PT  - Comment
PT  - Letter
PL  - England
TA  - Br J Surg
JT  - The British journal of surgery
JID - 0372553
SB  - IM
CON - Br J Surg. 2023 Nov 9;110(12):1895. PMID: 37702576
MH  - Humans
MH  - *Publishing
MH  - *Writing
EDAT- 2023/09/13 12:43
MHDA- 2023/11/13 06:42
CRDT- 2023/09/13 09:44
PHST- 2023/09/04 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/09/13 12:43 [pubmed]
PHST- 2023/09/13 09:44 [entrez]
AID - 7272765 [pii]
AID - 10.1093/bjs/znad295 [doi]
PST - ppublish
SO  - Br J Surg. 2023 Nov 9;110(12):1894. doi: 10.1093/bjs/znad295.

PMID- 37506587
OWN - NLM
STAT- MEDLINE
DCOM- 20230906
LR  - 20230920
IS  - 1872-7123 (Electronic)
IS  - 0165-1781 (Linking)
VI  - 327
DP  - 2023 Sep
TI  - Evaluating the performance of ChatGPT in differential diagnosis of 
      neurodevelopmental disorders: A pediatricians-machine comparison.
PG  - 115351
LID - S0165-1781(23)00301-3 [pii]
LID - 10.1016/j.psychres.2023.115351 [doi]
FAU - Wei, Qiuhong
AU  - Wei Q
AD  - Department of Children's Healthcare, Children's Hospital of Chongqing Medical 
      University, National Clinical Research Center for Child Health and Disorders, 
      Ministry of Education Key Laboratory of Child Development and Disorders, China 
      International Science and Technology Cooperation Base of Child Development and 
      Critical Disorders, Chongqing Key Laboratory of Childhood Nutrition and Health, 
      Chongqing, China.
FAU - Cui, Ying
AU  - Cui Y
AD  - Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 
      USA.
FAU - Wei, Bo
AU  - Wei B
AD  - Department of Global Statistics and Data Science, BeiGene USA Inc., San Mateo, 
      CA, USA.
FAU - Cheng, Qian
AU  - Cheng Q
AD  - Department of Children's Healthcare, Children's Hospital of Chongqing Medical 
      University, National Clinical Research Center for Child Health and Disorders, 
      Ministry of Education Key Laboratory of Child Development and Disorders, China 
      International Science and Technology Cooperation Base of Child Development and 
      Critical Disorders, Chongqing Key Laboratory of Childhood Nutrition and Health, 
      Chongqing, China. Electronic address: chengqq@cqmu.edu.cn.
FAU - Xu, Ximing
AU  - Xu X
AD  - Big Data Center for Children's Medical Care, Children's Hospital of Chongqing 
      Medical University, Chongqing, China. Electronic address: 
      ximing@hospital.cqmu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230717
PL  - Ireland
TA  - Psychiatry Res
JT  - Psychiatry research
JID - 7911385
SB  - IM
MH  - Humans
MH  - Diagnosis, Differential
MH  - *Neurodevelopmental Disorders/diagnosis
COIS- Declaration of Competing Interest The authors declare that there are no competing 
      interests
EDAT- 2023/07/29 06:42
MHDA- 2023/09/06 06:42
CRDT- 2023/07/28 18:07
PHST- 2023/05/21 00:00 [received]
PHST- 2023/07/10 00:00 [revised]
PHST- 2023/07/16 00:00 [accepted]
PHST- 2023/09/06 06:42 [medline]
PHST- 2023/07/29 06:42 [pubmed]
PHST- 2023/07/28 18:07 [entrez]
AID - S0165-1781(23)00301-3 [pii]
AID - 10.1016/j.psychres.2023.115351 [doi]
PST - ppublish
SO  - Psychiatry Res. 2023 Sep;327:115351. doi: 10.1016/j.psychres.2023.115351. Epub 
      2023 Jul 17.

PMID- 37181985
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230516
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Skin Metastasis of Low-Grade Ovarian Serous Carcinoma: A Case Report.
PG  - e37401
LID - 10.7759/cureus.37401 [doi]
LID - e37401
AB  - This case report, written with the assistance of ChatGPT, describes a rare 
      manifestation of ovarian serous carcinoma that metastasized to the skin. A 
      30-year-old female with a history of stage IV low-grade serous ovarian carcinoma 
      presented for evaluation of a painful nodule on her back. Physical examination 
      demonstrated a round, firm, mobile subcutaneous nodule on the left upper back. An 
      excisional biopsy was performed, and histopathologic examination was consistent 
      with metastatic ovarian serous carcinoma. This case highlights the clinical 
      presentation, histopathology, and treatment of cutaneous metastasis of serous 
      ovarian carcinoma. Additionally, this case highlights the value and technique of 
      using ChatGPT to assist in writing medical case reports including outlining, 
      referencing, summarizing studies, and formatting citations.
CI  - Copyright © 2023, Ching et al.
FAU - Ching, Lauren M
AU  - Ching LM
AD  - Department of Dermatology, Georgetown University School of Medicine, Washington, 
      DC, USA.
FAU - Tran, Benjamin A
AU  - Tran BA
AD  - Department of Dermatology, MedStar Georgetown University Hospital, Washington, 
      DC, USA.
FAU - Russomanno, Kristen L
AU  - Russomanno KL
AD  - Department of Dermatology, MedStar Georgetown University Hospital, Washington, 
      DC, USA.
FAU - Cardis, Michael A
AU  - Cardis MA
AD  - Department of Dermatology, MedStar Washington Hospital Center, Washington, DC, 
      USA.
LA  - eng
PT  - Case Reports
DEP - 20230410
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10171884
OTO - NOTNLM
OT  - chatgpt
OT  - cutaneous metastasis
OT  - low-grade serous carcinoma
OT  - ovarian carcinoma
OT  - serous carcinoma
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/14 19:13
MHDA- 2023/05/14 19:14
CRDT- 2023/05/14 12:26
PHST- 2023/04/10 00:00 [accepted]
PHST- 2023/05/14 19:14 [medline]
PHST- 2023/05/14 19:13 [pubmed]
PHST- 2023/05/14 12:26 [entrez]
AID - 10.7759/cureus.37401 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 10;15(4):e37401. doi: 10.7759/cureus.37401. eCollection 2023 
      Apr.

PMID- 37987609
OWN - NLM
STAT- MEDLINE
DCOM- 20231130
LR  - 20231206
IS  - 1897-9483 (Electronic)
IS  - 0032-3772 (Linking)
VI  - 133
IP  - 11
DP  - 2023 Nov 29
TI  - ChatGPT fails the Polish board certification examination in internal medicine: 
      artificial intelligence still has much to learn.
LID - 16608 [pii]
LID - 10.20452/pamw.16608 [doi]
FAU - Suwała, Szymon
AU  - Suwała S
AD  - Department of Endocrinology and Diabetology, Nicolaus Copernicus University, 
      Collegium Medicum, Bydgoszcz, Poland. lekarz.szymon.suwala@gmail.com
FAU - Szulc, Paulina
AU  - Szulc P
AD  - Evidence-Based Medicine Student Scientific Club of Department of Endocrinology 
      and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, 
      Poland
FAU - Dudek, Aleksandra
AU  - Dudek A
AD  - Evidence-Based Medicine Student Scientific Club of Department of Endocrinology 
      and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, 
      Poland
FAU - Białczyk, Aleksandra
AU  - Białczyk A
AD  - Evidence-Based Medicine Student Scientific Club of Department of Endocrinology 
      and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, 
      Poland
FAU - Koperska, Kinga
AU  - Koperska K
AD  - Evidence-Based Medicine Student Scientific Club of Department of Endocrinology 
      and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, 
      Poland
FAU - Junik, Roman
AU  - Junik R
AD  - Department of Endocrinology and Diabetology, Nicolaus Copernicus University, 
      Collegium Medicum, Bydgoszcz, Poland
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - Poland
TA  - Pol Arch Intern Med
JT  - Polish archives of internal medicine
JID - 101700960
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Poland
MH  - *Internal Medicine
EDAT- 2023/11/21 12:44
MHDA- 2023/11/30 06:43
CRDT- 2023/11/21 09:07
PHST- 2023/11/30 06:43 [medline]
PHST- 2023/11/21 12:44 [pubmed]
PHST- 2023/11/21 09:07 [entrez]
AID - 16608 [pii]
AID - 10.20452/pamw.16608 [doi]
PST - ppublish
SO  - Pol Arch Intern Med. 2023 Nov 29;133(11):16608. doi: 10.20452/pamw.16608. Epub 
      2023 Nov 20.

PMID- 37950005
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231128
LR  - 20231128
IS  - 1546-1696 (Electronic)
IS  - 1087-0156 (Linking)
VI  - 41
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT and medicine: how AI language models are shaping the future and health 
      related careers.
PG  - 1657-1658
LID - 10.1038/s41587-023-02011-3 [doi]
FAU - Yan, Michael
AU  - Yan M
AD  - Radiation Medicine Program, Princess Margaret Cancer Centre-University Health 
      Network, Toronto, Ontario, Canada.
FAU - Cerri, Giovanni G
AU  - Cerri GG
AD  - Department of Radiology and Oncology, University of São Paulo Medical School, São 
      Paulo, Brazil.
FAU - Moraes, Fabio Y
AU  - Moraes FY
AUID- ORCID: 0000-0003-3407-9215
AD  - Department of Radiology and Oncology, University of São Paulo Medical School, São 
      Paulo, Brazil. fymoraes@gmail.com.
AD  - Cancer Care and Epidemiology, Cancer Research Institute, Queen's University, 
      Kingston, Ontario, Canada. fymoraes@gmail.com.
AD  - Division of Radiation Oncology, Kingston General Hospital, Queen's University, 
      Kingston, Ontario, Canada. fymoraes@gmail.com.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Nat Biotechnol
JT  - Nature biotechnology
JID - 9604648
SB  - IM
EDAT- 2023/11/11 11:45
MHDA- 2023/11/13 06:42
CRDT- 2023/11/10 23:37
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/11/11 11:45 [pubmed]
PHST- 2023/11/10 23:37 [entrez]
AID - 10.1038/s41587-023-02011-3 [pii]
AID - 10.1038/s41587-023-02011-3 [doi]
PST - ppublish
SO  - Nat Biotechnol. 2023 Nov;41(11):1657-1658. doi: 10.1038/s41587-023-02011-3.

PMID- 37737081
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231102
LR  - 20231121
IS  - 1998-3751 (Electronic)
IS  - 0253-7613 (Print)
IS  - 0253-7613 (Linking)
VI  - 55
IP  - 4
DP  - 2023 Jul-Aug
TI  - Testing the knowledge and interpretation skills of ChatGPT in pharmacology 
      examination of phase II MBBS.
PG  - 266-267
LID - 10.4103/ijp.ijp_188_23 [doi]
FAU - Elango, Anitha
AU  - Elango A
AD  - Department of Pharmacology, Medical Education, Research, Panimalar Medical 
      College Hospital and Research Institute, Varadharajapuram, Poonamallee, Chennai, 
      Tamil Nadu, India.
FAU - Kannan, Neevedha
AU  - Kannan N
AD  - Department of Pharmacology, Medical Education, Research, Panimalar Medical 
      College Hospital and Research Institute, Varadharajapuram, Poonamallee, Chennai, 
      Tamil Nadu, India.
FAU - Anandan, Isswariya
AU  - Anandan I
AD  - Department of Pharmacology, Medical Education, Research, Panimalar Medical 
      College Hospital and Research Institute, Varadharajapuram, Poonamallee, Chennai, 
      Tamil Nadu, India.
FAU - Surapaneni, Krishna Mohan
AU  - Surapaneni KM
AD  - Department of Biochemistry, Medical Education, Research, Panimalar Medical 
      College Hospital and Research Institute, Varadharajapuram, Poonamallee, Chennai, 
      Tamil Nadu, India.
LA  - eng
PT  - Letter
PL  - India
TA  - Indian J Pharmacol
JT  - Indian journal of pharmacology
JID - 7902477
SB  - IM
PMC - PMC10657626
COIS- None
EDAT- 2023/09/22 12:42
MHDA- 2023/09/22 12:43
CRDT- 2023/09/22 08:15
PHST- 2023/09/22 12:43 [medline]
PHST- 2023/09/22 12:42 [pubmed]
PHST- 2023/09/22 08:15 [entrez]
AID - Indian J Pharmacol_2023_55_4_266_385494 [pii]
AID - IJPharm-55-266 [pii]
AID - 10.4103/ijp.ijp_188_23 [doi]
PST - ppublish
SO  - Indian J Pharmacol. 2023 Jul-Aug;55(4):266-267. doi: 10.4103/ijp.ijp_188_23.

PMID- 37689598
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231003
LR  - 20231003
IS  - 1432-2323 (Electronic)
IS  - 0364-2313 (Linking)
VI  - 47
IP  - 11
DP  - 2023 Nov
TI  - Leveraging ChatGPT to Democratize and Decolonize Global Surgery: Large Language 
      Models for Small Healthcare Budgets.
PG  - 2626-2627
LID - 10.1007/s00268-023-07167-2 [doi]
FAU - Botelho, Fabio
AU  - Botelho F
AD  - Departments of Pediatric Surgery and Surgical and Interventional Sciences, McGill 
      University, Montreal, Canada.
FAU - Tshimula, Jean Marie
AU  - Tshimula JM
AD  - Departments of Pediatric Surgery and Surgical and Interventional Sciences, McGill 
      University, Montreal, Canada.
FAU - Poenaru, Dan
AU  - Poenaru D
AUID- ORCID: 0000-0002-6267-6140
AD  - Departments of Pediatric Surgery and Surgical and Interventional Sciences, McGill 
      University, Montreal, Canada. dan.poenaru@mcgill.ca.
LA  - eng
PT  - Editorial
DEP - 20230910
PL  - United States
TA  - World J Surg
JT  - World journal of surgery
JID - 7704052
SB  - IM
EDAT- 2023/09/10 00:41
MHDA- 2023/09/10 00:42
CRDT- 2023/09/09 23:03
PHST- 2023/08/18 00:00 [accepted]
PHST- 2023/09/10 00:42 [medline]
PHST- 2023/09/10 00:41 [pubmed]
PHST- 2023/09/09 23:03 [entrez]
AID - 10.1007/s00268-023-07167-2 [pii]
AID - 10.1007/s00268-023-07167-2 [doi]
PST - ppublish
SO  - World J Surg. 2023 Nov;47(11):2626-2627. doi: 10.1007/s00268-023-07167-2. Epub 
      2023 Sep 10.

PMID- 37590117
OWN - NLM
STAT- Publisher
LR  - 20230817
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
DP  - 2023 Aug 17
TI  - Response to: "Medical Teacher's first ChatGPT's referencing hallucinations: 
      Lessons for editors, reviewers, and teachers".
PG  - 1
LID - 10.1080/0142159X.2023.2245129 [doi]
FAU - Tay, Tricia Hui Chyi
AU  - Tay THC
AUID- ORCID: 0000-0001-7502-2608
AD  - Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS Foundation 
      Trust, Lancaster University, Lancaster, Lancashire, UK.
LA  - eng
PT  - Journal Article
DEP - 20230817
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
EDAT- 2023/08/17 18:42
MHDA- 2023/08/17 18:42
CRDT- 2023/08/17 12:23
PHST- 2023/08/17 18:42 [medline]
PHST- 2023/08/17 18:42 [pubmed]
PHST- 2023/08/17 12:23 [entrez]
AID - 10.1080/0142159X.2023.2245129 [doi]
PST - aheadofprint
SO  - Med Teach. 2023 Aug 17:1. doi: 10.1080/0142159X.2023.2245129.

PMID- 37517216
OWN - NLM
STAT- MEDLINE
DCOM- 20230818
LR  - 20231026
IS  - 1879-0593 (Electronic)
IS  - 1368-8375 (Linking)
VI  - 145
DP  - 2023 Oct
TI  - Role of AI-based ChatGPT in oral and maxillofacial surgery: A friend or foe?
PG  - 106530
LID - S1368-8375(23)00226-9 [pii]
LID - 10.1016/j.oraloncology.2023.106530 [doi]
FAU - Saravanan, Muthupandian
AU  - Saravanan M
AD  - AMR and Nanomedicine Laboratory, Department of Pharmacology, Saveetha Dental 
      College & Hospitals, Saveetha Institute of Medical and Technical Sciences 
      (SIMATS), Chennai 600077, Tamil Nadu, India. Electronic address: 
      bioinfosaran@gmail.com.
FAU - Arockiaraj, Jesu
AU  - Arockiaraj J
AD  - Toxicology and Pharmacology Laboratory, Department of Biotechnology, Faculty of 
      Science and Humanities, SRM Institute of Science and Technology, Kattankulathur 
      603 203, Chengalpattu District, Tamil Nadu, India. Electronic address: 
      jesuaroa@srmist.edu.in.
LA  - eng
PT  - Letter
DEP - 20230728
PL  - England
TA  - Oral Oncol
JT  - Oral oncology
JID - 9709118
SB  - IM
CIN - Oral Oncol. 2023 Nov;146:106561. PMID: 37619522
MH  - Humans
MH  - *Surgery, Oral
MH  - Artificial Intelligence
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/07/31 00:41
MHDA- 2023/08/18 06:42
CRDT- 2023/07/30 18:05
PHST- 2023/07/17 00:00 [received]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/08/18 06:42 [medline]
PHST- 2023/07/31 00:41 [pubmed]
PHST- 2023/07/30 18:05 [entrez]
AID - S1368-8375(23)00226-9 [pii]
AID - 10.1016/j.oraloncology.2023.106530 [doi]
PST - ppublish
SO  - Oral Oncol. 2023 Oct;145:106530. doi: 10.1016/j.oraloncology.2023.106530. Epub 
      2023 Jul 28.

PMID- 37052826
OWN - NLM
STAT- MEDLINE
DCOM- 20230515
LR  - 20230525
IS  - 1534-4681 (Electronic)
IS  - 1068-9265 (Linking)
VI  - 30
IP  - 6
DP  - 2023 Jun
TI  - ChatGPT, Large Language Models, and Generative AI as Future Augments of Surgical 
      Cancer Care.
PG  - 3174-3176
LID - 10.1245/s10434-023-13442-2 [doi]
FAU - Kothari, A N
AU  - Kothari AN
AUID- ORCID: 0000-0001-6544-8832
AD  - Department of Surgery, Medical College of Wisconsin, Milwaukee, USA. 
      akothari@mcw.edu.
LA  - eng
PT  - Editorial
DEP - 20230413
PL  - United States
TA  - Ann Surg Oncol
JT  - Annals of surgical oncology
JID - 9420840
SB  - IM
MH  - Humans
MH  - *Neoplasms/surgery
MH  - Artificial Intelligence
EDAT- 2023/04/14 06:00
MHDA- 2023/05/15 06:42
CRDT- 2023/04/13 11:28
PHST- 2023/03/08 00:00 [received]
PHST- 2023/03/20 00:00 [accepted]
PHST- 2023/05/15 06:42 [medline]
PHST- 2023/04/14 06:00 [pubmed]
PHST- 2023/04/13 11:28 [entrez]
AID - 10.1245/s10434-023-13442-2 [pii]
AID - 10.1245/s10434-023-13442-2 [doi]
PST - ppublish
SO  - Ann Surg Oncol. 2023 Jun;30(6):3174-3176. doi: 10.1245/s10434-023-13442-2. Epub 
      2023 Apr 13.

PMID- 36941774
OWN - NLM
STAT- MEDLINE
DCOM- 20230322
LR  - 20231106
IS  - 2001-1326 (Electronic)
IS  - 2001-1326 (Linking)
VI  - 13
IP  - 3
DP  - 2023 Mar
TI  - The future of ChatGPT in academic research and publishing: A commentary for 
      clinical and translational medicine.
PG  - e1207
LID - 10.1002/ctm2.1207 [doi]
LID - e1207
FAU - Wen, Jun
AU  - Wen J
AD  - School of Business and Law, Edith Cowan University, Perth, Australia.
FAU - Wang, Wei
AU  - Wang W
AUID- ORCID: 0000-0002-1430-1360
AD  - Centre for Precision Health, Edith Cowan University, Perth, Australia.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Clin Transl Med
JT  - Clinical and translational medicine
JID - 101597971
SB  - IM
MH  - *Translational Science, Biomedical
MH  - *Publishing
PMC - PMC10028016
COIS- The authors declare no conflict of interest.
EDAT- 2023/03/22 06:00
MHDA- 2023/03/23 06:00
CRDT- 2023/03/21 01:23
PHST- 2023/02/13 00:00 [received]
PHST- 2023/02/14 00:00 [accepted]
PHST- 2023/03/21 01:23 [entrez]
PHST- 2023/03/22 06:00 [pubmed]
PHST- 2023/03/23 06:00 [medline]
AID - CTM21207 [pii]
AID - 10.1002/ctm2.1207 [doi]
PST - ppublish
SO  - Clin Transl Med. 2023 Mar;13(3):e1207. doi: 10.1002/ctm2.1207.

PMID- 37993224
OWN - NLM
STAT- MEDLINE
DCOM- 20231124
LR  - 20231124
IS  - 1097-6760 (Electronic)
IS  - 0196-0644 (Linking)
VI  - 82
IP  - 6
DP  - 2023 Dec
TI  - A Sober Appraisal of Artificial Intelligence Systems, Particularly ChatGPT, in 
      the Facets of Emergency Medicine.
PG  - 766-767
LID - S0196-0644(23)00589-9 [pii]
LID - 10.1016/j.annemergmed.2023.05.025 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Department of Computer Applications, Sikkim University, Gangtok, Sikkim, India.
LA  - eng
PT  - Letter
PL  - United States
TA  - Ann Emerg Med
JT  - Annals of emergency medicine
JID - 8002646
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Joints
MH  - *Emergency Medicine
EDAT- 2023/11/23 00:42
MHDA- 2023/11/24 06:43
CRDT- 2023/11/22 21:01
PHST- 2023/05/25 00:00 [received]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/11/24 06:43 [medline]
PHST- 2023/11/23 00:42 [pubmed]
PHST- 2023/11/22 21:01 [entrez]
AID - S0196-0644(23)00589-9 [pii]
AID - 10.1016/j.annemergmed.2023.05.025 [doi]
PST - ppublish
SO  - Ann Emerg Med. 2023 Dec;82(6):766-767. doi: 10.1016/j.annemergmed.2023.05.025.

PMID- 37743571
OWN - NLM
STAT- Publisher
LR  - 20230925
IS  - 1445-2197 (Electronic)
IS  - 1445-1433 (Linking)
DP  - 2023 Sep 24
TI  - Response to "ChatGPT in surgical research and practice: a threat to academic 
      integrity, authorship, and divergent thinking".
LID - 10.1111/ans.18698 [doi]
FAU - Jiang, Melinda
AU  - Jiang M
AUID- ORCID: 0000-0003-0122-6573
AD  - Faculty of Health and Medical Sciences, University of Adelaide, South Australia, 
      Adelaide, Australia.
FAU - Kleinig, Oliver
AU  - Kleinig O
AD  - Faculty of Health and Medical Sciences, University of Adelaide, South Australia, 
      Adelaide, Australia.
FAU - Stanekova, Viera
AU  - Stanekova V
AD  - Faculty of Health and Medical Sciences, University of Adelaide, South Australia, 
      Adelaide, Australia.
FAU - Stretton, Brandon
AU  - Stretton B
AD  - Faculty of Health and Medical Sciences, University of Adelaide, South Australia, 
      Adelaide, Australia.
FAU - Gupta, Aashray
AU  - Gupta A
AUID- ORCID: 0000-0002-8038-0378
AD  - Faculty of Health and Medical Sciences, University of Adelaide, South Australia, 
      Adelaide, Australia.
AD  - Department of Cardiac Surgery, Gold Coast University Hospital, Queensland, 
      Southport, Australia.
FAU - Bacchi, Stephen
AU  - Bacchi S
AD  - Faculty of Health and Medical Sciences, University of Adelaide, South Australia, 
      Adelaide, Australia.
AD  - College of Medicine and Public Health, Flinders University, South Australia, 
      Bedford Park, Australia.
FAU - Chan, Weng Onn
AU  - Chan WO
AD  - Faculty of Health and Medical Sciences, University of Adelaide, South Australia, 
      Adelaide, Australia.
AD  - Department of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, 
      Australia.
LA  - eng
GR  - 17075/Central Adelaide Local Health Network Clinical Rapid Implementation Project 
      Scheme (CRIPS)/
PT  - Letter
DEP - 20230924
PL  - Australia
TA  - ANZ J Surg
JT  - ANZ journal of surgery
JID - 101086634
SB  - IM
EDAT- 2023/09/25 06:43
MHDA- 2023/09/25 06:43
CRDT- 2023/09/25 00:43
PHST- 2023/08/19 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/09/25 06:43 [medline]
PHST- 2023/09/25 06:43 [pubmed]
PHST- 2023/09/25 00:43 [entrez]
AID - 10.1111/ans.18698 [doi]
PST - aheadofprint
SO  - ANZ J Surg. 2023 Sep 24. doi: 10.1111/ans.18698.

PMID- 37498091
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20231026
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
VI  - 93
IP  - 4
DP  - 2023 Oct 1
TI  - Letter: I Asked a ChatGPT to Write an Editorial About How We Can Incorporate 
      Chatbots Into Neurosurgical Research and Patient Care….
PG  - e107-e108
LID - 10.1227/neu.0000000000002619 [doi]
FAU - Zaed, Ismail
AU  - Zaed I
AUID- ORCID: 0000-0003-3506-4111
AD  - Department of Neurosurgery, Neurocenter of the Southern Switzerland, Regional 
      Hospital of Lugano, Ente Ospedaliero Cantonale (EOC), Lugano , Switzerland.
FAU - Cardia, Andrea
AU  - Cardia A
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230727
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
CON - Neurosurgery. 2023 Apr 1;92(4):663-664. PMID: 36757199
CIN - Neurosurgery. 2023 Oct 1;93(4):e109. PMID: 37470488
MH  - Humans
MH  - *Patient Care
MH  - *Writing
EDAT- 2023/07/27 13:10
MHDA- 2023/09/18 12:43
CRDT- 2023/07/27 09:03
PHST- 2023/06/03 00:00 [received]
PHST- 2023/06/08 00:00 [accepted]
PHST- 2023/09/18 12:43 [medline]
PHST- 2023/07/27 13:10 [pubmed]
PHST- 2023/07/27 09:03 [entrez]
AID - 00006123-202310000-00039 [pii]
AID - 10.1227/neu.0000000000002619 [doi]
PST - ppublish
SO  - Neurosurgery. 2023 Oct 1;93(4):e107-e108. doi: 10.1227/neu.0000000000002619. Epub 
      2023 Jul 27.

PMID- 37382430
OWN - NLM
STAT- MEDLINE
DCOM- 20230816
LR  - 20231020
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
VI  - 93
IP  - 3
DP  - 2023 Sep 1
TI  - Letter: I Asked a ChatGPT to Write an Editorial About How We Can Incorporate 
      Chatbots Into Neurosurgical Research and Patient Care….
PG  - e77
LID - 10.1227/neu.0000000000002588 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Consultant Unit, Private Academic Consultant, Samraong , Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Research Center, Chandigarh University, Punjab , India.
AD  - Department of Biological Science, Joesph Ayobabalola University, Ikeji-Arakeji , 
      Nigeria.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230629
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
CON - Neurosurgery. 2023 Apr 1;92(4):663-664. PMID: 36757199
CIN - Neurosurgery. 2023 Sep 1;93(3):e78. PMID: 37377413
MH  - Humans
MH  - *Patient Care
MH  - *Writing
EDAT- 2023/06/29 13:42
MHDA- 2023/08/16 06:43
CRDT- 2023/06/29 09:02
PHST- 2023/04/27 00:00 [received]
PHST- 2023/05/08 00:00 [accepted]
PHST- 2023/08/16 06:43 [medline]
PHST- 2023/06/29 13:42 [pubmed]
PHST- 2023/06/29 09:02 [entrez]
AID - 00006123-202309000-00038 [pii]
AID - 10.1227/neu.0000000000002588 [doi]
PST - ppublish
SO  - Neurosurgery. 2023 Sep 1;93(3):e77. doi: 10.1227/neu.0000000000002588. Epub 2023 
      Jun 29.

PMID- 37277032
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230711
LR  - 20230718
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 892
DP  - 2023 Sep 20
TI  - Refining the application of artificial intelligence in the water domain: 
      Exploring the potential of ChatGPT.
PG  - 164638
LID - S0048-9697(23)03261-8 [pii]
LID - 10.1016/j.scitotenv.2023.164638 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India. Electronic address: ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20230603
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
SB  - IM
COIS- Declaration of competing interest The author declares no competing interest.
EDAT- 2023/06/06 01:11
MHDA- 2023/07/10 06:42
CRDT- 2023/06/05 19:23
PHST- 2023/05/20 00:00 [received]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/06/06 01:11 [pubmed]
PHST- 2023/06/05 19:23 [entrez]
AID - S0048-9697(23)03261-8 [pii]
AID - 10.1016/j.scitotenv.2023.164638 [doi]
PST - ppublish
SO  - Sci Total Environ. 2023 Sep 20;892:164638. doi: 10.1016/j.scitotenv.2023.164638. 
      Epub 2023 Jun 3.

PMID- 36630899
OWN - NLM
STAT- MEDLINE
DCOM- 20230113
LR  - 20230210
IS  - 2213-6711 (Electronic)
IS  - 2213-6711 (Linking)
VI  - 18
IP  - 1
DP  - 2023 Jan 10
TI  - A conversation with ChatGPT on the role of computational systems biology in stem 
      cell research.
PG  - 1-2
LID - S2213-6711(22)00598-7 [pii]
LID - 10.1016/j.stemcr.2022.12.009 [doi]
FAU - Cahan, Patrick
AU  - Cahan P
AD  - Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 
      21205, USA; Institute for Cell Engineering, Johns Hopkins University, Baltimore, 
      MD 21205, USA. Electronic address: patrick.cahan@jhmi.edu.
FAU - Treutlein, Barbara
AU  - Treutlein B
AD  - Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland; 
      Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany; Max Planck 
      Institute of Molecular Cell Biology and Genetics, Dresden, Germany. Electronic 
      address: barbara.treutlein@bsse.ethz.ch.
LA  - eng
PT  - Editorial
PL  - United States
TA  - Stem Cell Reports
JT  - Stem cell reports
JID - 101611300
SB  - IM
MH  - *Systems Biology
MH  - *Stem Cell Research
MH  - Computational Biology
MH  - Communication
PMC - PMC9860153
EDAT- 2023/01/12 06:00
MHDA- 2023/01/14 06:00
CRDT- 2023/01/11 18:17
PHST- 2022/12/12 00:00 [received]
PHST- 2022/12/12 00:00 [accepted]
PHST- 2023/01/11 18:17 [entrez]
PHST- 2023/01/12 06:00 [pubmed]
PHST- 2023/01/14 06:00 [medline]
AID - S2213-6711(22)00598-7 [pii]
AID - 10.1016/j.stemcr.2022.12.009 [doi]
PST - ppublish
SO  - Stem Cell Reports. 2023 Jan 10;18(1):1-2. doi: 10.1016/j.stemcr.2022.12.009.

PMID- 37866949
OWN - NLM
STAT- MEDLINE
DCOM- 20231026
LR  - 20231112
IS  - 1672-173X (Print)
IS  - 1672-173X (Linking)
VI  - 54
IP  - 5
DP  - 2023 Sep
TI  - [Reflections on the Implications of the Developments in ChatGPT for Changes in 
      Medical Education Models].
PG  - 937-940
LID - 10.12182/20231360302 [doi]
AB  - Ever since its official launch, Chat Generative Pre-Trained Transformer, or 
      ChatGPT, a natural language processing tool driven by artificial intelligence 
      (AI) technology, has attracted much attention from the education community. 
      ChatGPT can play an important role in the field of medical education, with its 
      potential applications ranging from assisting teachers in designing 
      individualized teaching scenarios to enhancing students' practical ability for 
      solving clinical problems and improving teaching and research efficiency. With 
      the developments in technology, it is inevitable that ChatGPT, or other 
      generative AI models, will be thoroughly integrated in more and more medical 
      contexts, which will further enhance the efficiency and quality of medical 
      services and allow doctors to spend more time interacting with patients and 
      implement personalized health management. Herein, we suggested that proactive 
      reflections be made to figure out the best way to cultivate health professional 
      in the context of New Medical Education, to help more medical professionals 
      enhance their understanding of developments in artificial intelligence, and to 
      make preparations for the challenges that will emerge in the new round of 
      technological revolution. Medical educators should focus on guiding students to 
      make proper use of AI tools in the appropriate context, thereby prevening abuse 
      or overreliance caused by a lack of discrimating ability. Teachers should focus 
      on helping medical students make improvements in clinical reasoning skills, 
      self-directed learning, and clinical practical skills. Teachers should stress the 
      importance for medical students to understand the philosophical implications of 
      the mind-body unity concept, holistic medical thinking, and systematic medical 
      thinking. It is important to enhance medical students' humanistic qualities, 
      cultivate their empathy and communication skills, and continually enhance their 
      ability to meet the requirements of individualized precision diagnosis and 
      treatment so that they will better adapt to the future developments in medicine.
CI  - Copyright© by Editorial Board of Journal of Sichuan University (Medical 
      Sciences).
FAU - Qu, Xing
AU  - Qu X
AD  - Institute of Hospital Management, West China Hospital, Sichuan University, 
      Chengdu 610041, China.
FAU - Yang, Jinming
AU  - Yang J
AD  - School of Public Administration, Southwestern University of Finance and 
      Economics, Chengdu 611130, China.
FAU - Chen, Tao
AU  - Chen T
AD  - School of Finance, Southwestern University of Finance and Economics, Chengdu 
      611130, China.
FAU - Zhang, Wei
AU  - Zhang W
AD  - West China Biomedical Big Data Center, West China Hospital, Sichuan University, 
      Chengdu 610041, China.
LA  - chi
PT  - English Abstract
PT  - Journal Article
PT  - Review
PL  - China
TA  - Sichuan Da Xue Xue Bao Yi Xue Ban
JT  - Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical 
      science edition
JID - 101162609
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Educational Status
MH  - Students
MH  - *Education, Medical
MH  - Clinical Competence
PMC - PMC10579070
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Medical education
COIS- 利益冲突　所有作者均声明不存在利益冲突
EDAT- 2023/10/23 00:44
MHDA- 2023/10/26 06:42
CRDT- 2023/10/22 21:52
PHST- 2023/10/26 06:42 [medline]
PHST- 2023/10/23 00:44 [pubmed]
PHST- 2023/10/22 21:52 [entrez]
AID - scdxxbyxb-54-5-937 [pii]
AID - 10.12182/20231360302 [doi]
PST - ppublish
SO  - Sichuan Da Xue Xue Bao Yi Xue Ban. 2023 Sep;54(5):937-940. doi: 
      10.12182/20231360302.

PMID- 37985815
OWN - NLM
STAT- Publisher
LR  - 20231121
IS  - 1476-5489 (Electronic)
IS  - 0955-9930 (Linking)
DP  - 2023 Nov 20
TI  - Assessing ChatGPT's ability to answer questions pertaining to erectile 
      dysfunction: can our patients trust it?
LID - 10.1038/s41443-023-00797-z [doi]
AB  - Erectile dysfunction (ED) is a disorder that can cause distress and shame for men 
      suffering from it. Men with ED will often turn to online support and chat groups 
      to ask intimate questions about their health. ChatGPT is an artificial 
      intelligence (AI)-based software that has been trained to engage in conversation 
      with human input. We sought to assess the accuracy, readability, and 
      reproducibility of ChatGPT's responses to frequently asked questions regarding 
      the diagnosis, management, and care of patients with ED. Questions pertaining to 
      ED were derived from clinic encounters with patients as well as online chat 
      forums. These were entered into the free ChatGPT version 3.5 during the month of 
      August 2023. Questions were asked on two separate days from unique accounts and 
      computers to prevent the software from memorizing responses linked to a specific 
      user. A total of 35 questions were asked. Outcomes measured were accuracy using 
      grading from board certified urologists, readability with the Gunning Fog Index, 
      and reproducibility by comparing responses between days. For epidemiology of 
      disease, the percentage of responses that were graded as "comprehensive" or 
      "correct but inadequate" was 100% across both days. There was fair 
      reproducibility and median readability of 15.9 (IQR 2.5). For treatment and 
      prevention, the percentage of responses that were graded as "comprehensive" or 
      "correct but inadequate" was 78.9%. There was poor reproducibility of responses 
      with a median readability of 14.5 (IQR 4.0). Risks of treatment and counseling 
      both had 100% of questions graded as "comprehensive" or "correct but inadequate." 
      The readability score for risks of treatment was median 13.9 (IQR 1.1) and for 
      counseling median 13.8 (IQR 0.5), with good reproducibility for both question 
      domains. ChatGPT provides accurate answers to common patient questions pertaining 
      to ED, although its understanding of treatment options is incomplete and 
      responses are at a reading level too advanced for the average patient.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature Limited.
FAU - Razdan, Shirin
AU  - Razdan S
AUID- ORCID: 0000-0002-9098-3447
AD  - Department of Urology, Icahn School of Medicine at Mount Sinai Hospital, New 
      York, NY, 10029, USA. shirin.razdan@mountsinai.org.
FAU - Siegal, Alexandra R
AU  - Siegal AR
AD  - Department of Urology, Icahn School of Medicine at Mount Sinai Hospital, New 
      York, NY, 10029, USA.
FAU - Brewer, Yukiko
AU  - Brewer Y
AD  - Department of Internal Medicine, HCA Florida Sarasota Doctors Hospital, Sarasota, 
      FL, 34233, USA.
FAU - Sljivich, Michaela
AU  - Sljivich M
AD  - Department of Urology, Icahn School of Medicine at Mount Sinai Hospital, New 
      York, NY, 10029, USA.
FAU - Valenzuela, Robert J
AU  - Valenzuela RJ
AUID- ORCID: 0009-0006-5831-5947
AD  - Department of Urology, Icahn School of Medicine at Mount Sinai Hospital, New 
      York, NY, 10029, USA.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - England
TA  - Int J Impot Res
JT  - International journal of impotence research
JID - 9007383
SB  - IM
EDAT- 2023/11/21 06:42
MHDA- 2023/11/21 06:42
CRDT- 2023/11/21 01:28
PHST- 2023/10/01 00:00 [received]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/10/28 00:00 [revised]
PHST- 2023/11/21 06:42 [medline]
PHST- 2023/11/21 06:42 [pubmed]
PHST- 2023/11/21 01:28 [entrez]
AID - 10.1038/s41443-023-00797-z [pii]
AID - 10.1038/s41443-023-00797-z [doi]
PST - aheadofprint
SO  - Int J Impot Res. 2023 Nov 20. doi: 10.1038/s41443-023-00797-z.

PMID- 37406864
OWN - NLM
STAT- Publisher
LR  - 20231011
IS  - 1527-9995 (Electronic)
IS  - 0090-4295 (Linking)
VI  - 180
DP  - 2023 Oct
TI  - Can ChatGPT, an Artificial Intelligence Language Model, Provide Accurate and 
      High-quality Patient Information on Prostate Cancer?
PG  - 35-58
LID - S0090-4295(23)00570-8 [pii]
LID - 10.1016/j.urology.2023.05.040 [doi]
AB  - OBJECTIVE: To evaluate the performance of ChatGPT, an artificial intelligence 
      (AI) language model, in providing patient information on prostate cancer, and to 
      compare the accuracy, similarity, and quality of the information to a reference 
      source. METHODS: Patient information material on prostate cancer was used as a 
      reference source from the website of the European Association of Urology Patient 
      Information. This was used to generate 59 queries. The accuracy of the model's 
      content was determined with F1, precision, and recall scores. The similarity was 
      assessed with cosine similarity, and the quality was evaluated using a 5-point 
      Likert scale named General Quality Score (GQS). RESULTS: ChatGPT was able to 
      respond to all prostate cancer-related queries. The average F1 score was 0.426 
      (range: 0-1), precision score was 0.349 (range: 0-1), recall score was 0.549 
      (range: 0-1), and cosine similarity was 0.609 (range: 0-1). The average GQS was 
      3.62 ± 0.49 (range: 1-5), with no answers achieving the maximum GQS of 5. While 
      ChatGPT produced a larger amount of information compared to the reference, the 
      accuracy and quality of the content were not optimal, with all scores indicating 
      need for improvement in the model's performance. CONCLUSION: Caution should be 
      exercised when using ChatGPT as a patient information source for prostate cancer 
      due to limitations in its performance, which may lead to inaccuracies and 
      potential misunderstandings. Further studies, using different topics and language 
      models, are needed to fully understand the capabilities and limitations of 
      AI-generated patient information.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Coskun, Burhan
AU  - Coskun B
AD  - Bursa Uludag University, Department of Urology, Nilüfer, Bursa, Turkey. 
      Electronic address: burhanc@uludag.edu.tr.
FAU - Ocakoglu, Gokhan
AU  - Ocakoglu G
AD  - Bursa Uludag University, Department of Biostatistics, Nilüfer, Bursa, Turkey.
FAU - Yetemen, Melih
AU  - Yetemen M
AD  - Bursa Uludag University, Department of Urology, Nilüfer, Bursa, Turkey.
FAU - Kaygisiz, Onur
AU  - Kaygisiz O
AD  - Bursa Uludag University, Department of Urology, Nilüfer, Bursa, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230704
PL  - United States
TA  - Urology
JT  - Urology
JID - 0366151
SB  - IM
COIS- Declaration of Competing Interest The authors declare that they have no conflict 
      of interests.
EDAT- 2023/07/06 01:08
MHDA- 2023/07/06 01:08
CRDT- 2023/07/05 19:28
PHST- 2023/03/11 00:00 [received]
PHST- 2023/04/24 00:00 [revised]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2023/07/06 01:08 [pubmed]
PHST- 2023/07/06 01:08 [medline]
PHST- 2023/07/05 19:28 [entrez]
AID - S0090-4295(23)00570-8 [pii]
AID - 10.1016/j.urology.2023.05.040 [doi]
PST - ppublish
SO  - Urology. 2023 Oct;180:35-58. doi: 10.1016/j.urology.2023.05.040. Epub 2023 Jul 4.

PMID- 38024047
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - The Future of Patient Education: AI-Driven Guide for Type 2 Diabetes.
PG  - e48919
LID - 10.7759/cureus.48919 [doi]
LID - e48919
AB  - Introduction and aim The surging incidence of type 2 diabetes has become a 
      growing concern for the healthcare sector. This chronic ailment, characterized by 
      its complex blend of genetic and lifestyle determinants, has witnessed a notable 
      increase in recent times, exerting substantial pressure on healthcare resources. 
      As more individuals turn to online platforms for health guidance and embrace the 
      utilization of Chat Generative Pre-trained Transformer (ChatGPT; San Francisco, 
      CA: OpenAI), a text-generating AI (TGAI), to get insights into their well-being, 
      evaluating its effectiveness and reliability becomes crucial. This research 
      primarily aimed to evaluate the correctness of TGAI responses to type 2 diabetes 
      (T2DM) inquiries via ChatGPT. Furthermore, this study aimed to examine the 
      consistency of TGAI in addressing common queries on T2DM complications for 
      patient education. Material and methods Questions on T2DM were formulated by 
      experienced physicians and screened by research personnel before querying 
      ChatGPT. Each question was posed thrice, and the collected answers were 
      summarized. Responses were then sorted into three distinct categories as follows: 
      (a) appropriate, (b) inappropriate, and (c) unreliable by two seasoned 
      physicians. In instances of differing opinions, a third physician was consulted 
      to achieve consensus. Results From the initial set of 110 T2DM questions, 40 were 
      dismissed by experts for relevance, resulting in a final count of 70. An 
      overwhelming 98.5% of the AI's answers were judged as appropriate, thus 
      underscoring its reliability over traditional online search engines. Nonetheless, 
      a 1.5% rate of inappropriate responses underlines the importance of ongoing AI 
      improvements and strict adherence to medical protocols. Conclusion TGAI provides 
      medical information of high quality and reliability. This study underscores 
      TGAI's impressive effectiveness in delivering reliable information about T2DM, 
      with 98.5% of responses aligning with the standard of care. These results hold 
      promise for integrating AI platforms as supplementary tools to enhance patient 
      education and outcomes.
CI  - Copyright © 2023, Hernandez et al.
FAU - Hernandez, Carlos A
AU  - Hernandez CA
AD  - Internal Medicine, Ross University School of Medicine, Bridgetown, BRB.
FAU - Vazquez Gonzalez, Andres E
AU  - Vazquez Gonzalez AE
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Polianovskaia, Anastasiia
AU  - Polianovskaia A
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Amoro Sanchez, Rafael
AU  - Amoro Sanchez R
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Muyolema Arce, Veronica
AU  - Muyolema Arce V
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Mustafa, Ahmed
AU  - Mustafa A
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Vypritskaya, Ekaterina
AU  - Vypritskaya E
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Perez Gutierrez, Oscar
AU  - Perez Gutierrez O
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Bashir, Muhammad
AU  - Bashir M
AD  - Internal Medicine, Capital Health, Trenton, USA.
FAU - Eighaei Sedeh, Ashkan
AU  - Eighaei Sedeh A
AD  - Internal Medicine, Capital Health, Trenton, USA.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10654048
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - diabetes
OT  - health education
OT  - openai
OT  - patient education
OT  - type 2 diabetes
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 16:01
PHST- 2023/11/16 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 16:01 [entrez]
AID - 10.7759/cureus.48919 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 16;15(11):e48919. doi: 10.7759/cureus.48919. eCollection 2023 
      Nov.

PMID- 37968529
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231117
LR  - 20231118
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 623
IP  - 7987
DP  - 2023 Nov
TI  - Why teachers should explore ChatGPT's potential - despite the risks.
PG  - 457-458
LID - 10.1038/d41586-023-03505-5 [doi]
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Education
OT  - Machine learning
OT  - Society
EDAT- 2023/11/16 06:45
MHDA- 2023/11/16 06:46
CRDT- 2023/11/16 00:04
PHST- 2023/11/16 06:46 [medline]
PHST- 2023/11/16 06:45 [pubmed]
PHST- 2023/11/16 00:04 [entrez]
AID - 10.1038/d41586-023-03505-5 [pii]
AID - 10.1038/d41586-023-03505-5 [doi]
PST - ppublish
SO  - Nature. 2023 Nov;623(7987):457-458. doi: 10.1038/d41586-023-03505-5.

PMID- 37619522
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231026
IS  - 1879-0593 (Electronic)
IS  - 1368-8375 (Linking)
VI  - 146
DP  - 2023 Nov
TI  - Comment on "Role of AI-based ChatGPT in oral and maxillofacial surgery: A friend 
      or foe?".
PG  - 106561
LID - S1368-8375(23)00257-9 [pii]
LID - 10.1016/j.oraloncology.2023.106561 [doi]
FAU - Dhane, Amol S
AU  - Dhane AS
AD  - Research & Development Cell, Dr. D.Y. Patil Vidyapeeth, Sant-Tukaram Nagar, 
      Pimpri, Pune 411018, MH, India. Electronic address: amol.dhane@dpu.edu.in.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230822
PL  - England
TA  - Oral Oncol
JT  - Oral oncology
JID - 9709118
SB  - IM
CON - Oral Oncol. 2023 Oct;145:106530. PMID: 37517216
MH  - Humans
MH  - *Surgery, Oral
MH  - Artificial Intelligence
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/08/25 00:41
MHDA- 2023/10/23 00:44
CRDT- 2023/08/24 18:08
PHST- 2023/08/12 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/10/23 00:44 [medline]
PHST- 2023/08/25 00:41 [pubmed]
PHST- 2023/08/24 18:08 [entrez]
AID - S1368-8375(23)00257-9 [pii]
AID - 10.1016/j.oraloncology.2023.106561 [doi]
PST - ppublish
SO  - Oral Oncol. 2023 Nov;146:106561. doi: 10.1016/j.oraloncology.2023.106561. Epub 
      2023 Aug 22.

PMID- 37460034
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Linking)
VI  - 229
IP  - 6
DP  - 2023 Dec
TI  - The sky is the limit to explore ChatGPT's impact in obstetrics and gynecology: a 
      response.
PG  - 706-707
LID - S0002-9378(23)00465-9 [pii]
LID - 10.1016/j.ajog.2023.07.018 [doi]
FAU - Grünebaum, Amos
AU  - Grünebaum A
AD  - Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at 
      Hofstra/Northwell, 100 E. 77 St., New York, NY 10075. Electronic address: 
      agrunebaum@northwell.edu.
FAU - Chervenak, Frank A
AU  - Chervenak FA
AD  - Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at 
      Hofstra/Northwell, 100 E. 77 St., New York, NY 10075.
LA  - eng
PT  - Letter
DEP - 20230717
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
MH  - Female
MH  - Pregnancy
MH  - Humans
MH  - *Gynecology
MH  - *Obstetrics
MH  - Specialization
EDAT- 2023/07/18 01:09
MHDA- 2023/11/27 12:44
CRDT- 2023/07/17 19:22
PHST- 2023/07/07 00:00 [received]
PHST- 2023/07/11 00:00 [accepted]
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/07/18 01:09 [pubmed]
PHST- 2023/07/17 19:22 [entrez]
AID - S0002-9378(23)00465-9 [pii]
AID - 10.1016/j.ajog.2023.07.018 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol. 2023 Dec;229(6):706-707. doi: 10.1016/j.ajog.2023.07.018. 
      Epub 2023 Jul 17.

PMID- 37171282
OWN - NLM
STAT- MEDLINE
DCOM- 20230523
LR  - 20230601
IS  - 1526-4610 (Electronic)
IS  - 0017-8748 (Linking)
VI  - 63
IP  - 5
DP  - 2023 May
TI  - The role of artificial intelligence in headache medicine: Potential and peril.
PG  - 694-696
LID - 10.1111/head.14495 [doi]
FAU - Cohen, Fred
AU  - Cohen F
AUID- ORCID: 0000-0003-3436-2846
AD  - Center for Headache and Facial Pain, Department of Neurology, Icahn School of 
      Medicine at Mount Sinai, New York, New York, USA.
LA  - eng
PT  - Journal Article
DEP - 20230427
PL  - United States
TA  - Headache
JT  - Headache
JID - 2985091R
SB  - IM
CIN - Headache. 2023 May;63(5):571-572. PMID: 37102424
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Headache
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial
OT  - headache
OT  - intelligence
OT  - medicine
EDAT- 2023/05/12 13:08
MHDA- 2023/05/16 06:42
CRDT- 2023/05/12 10:13
PHST- 2023/02/17 00:00 [revised]
PHST- 2023/01/31 00:00 [received]
PHST- 2023/03/04 00:00 [accepted]
PHST- 2023/05/16 06:42 [medline]
PHST- 2023/05/12 13:08 [pubmed]
PHST- 2023/05/12 10:13 [entrez]
AID - 10.1111/head.14495 [doi]
PST - ppublish
SO  - Headache. 2023 May;63(5):694-696. doi: 10.1111/head.14495. Epub 2023 Apr 27.

PMID- 36912286
OWN - NLM
STAT- MEDLINE
DCOM- 20230522
LR  - 20230922
IS  - 1537-7385 (Electronic)
IS  - 0894-9115 (Linking)
VI  - 102
IP  - 6
DP  - 2023 Jun 1
TI  - Will ChatGPT Match to Your Program?
PG  - 545-547
LID - 10.1097/PHM.0000000000002238 [doi]
AB  - ChatGPT and other artificial intelligence word prediction large database models 
      are now readily available to the public. Program directors should be aware of the 
      general features of this technology and consider its effect in graduate medical 
      education, including the preparation of materials such as personal statements. 
      The authors provide a sample ChatGPT-generated personal statement and general 
      considerations for program directors and other graduate medical education 
      stakeholders. The authors advocate that programs and applicants will be best 
      served by transparent expectations about how/if programs will accept application 
      materials created using artificial intelligence, starting with this application 
      cycle. Graduate medical education will have many additional factors to consider 
      for the innovative use and safeguards for the ethical application of artificial 
      intelligence in clinical care and educational processes. However, the exponential 
      increase in the application of this technology requires an urgent review for 
      appropriate management of program procedures, iteration of policies, and a 
      meaningful national discussion.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Zumsteg, Jennifer M
AU  - Zumsteg JM
AD  - From the UW Medicine Valley Medical Center, Renton, Washington (JMZ); Division of 
      Rehabilitation Psychology and Neuropsychology, Department of Rehabilitation 
      Medicine, University of Washington School of Medicine, Seattle, Washington (JMZ); 
      and Department of Rehabilitation Medicine, University of Washington School of 
      Medicine, Seattle, Washington (CJ).
FAU - Junn, Cherry
AU  - Junn C
LA  - eng
PT  - Journal Article
DEP - 20230313
PL  - United States
TA  - Am J Phys Med Rehabil
JT  - American journal of physical medicine & rehabilitation
JID - 8803677
SB  - IM
MH  - Humans
MH  - *Internship and Residency
MH  - Artificial Intelligence
MH  - Education, Medical, Graduate
COIS- Financial disclosure statements have been obtained, and no conflicts of interest 
      have been reported by the authors or by any individuals in control of the content 
      of this article.
EDAT- 2023/03/14 06:00
MHDA- 2023/05/22 06:42
CRDT- 2023/03/13 07:03
PHST- 2023/05/22 06:42 [medline]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/13 07:03 [entrez]
AID - 00002060-202306000-00010 [pii]
AID - 10.1097/PHM.0000000000002238 [doi]
PST - ppublish
SO  - Am J Phys Med Rehabil. 2023 Jun 1;102(6):545-547. doi: 
      10.1097/PHM.0000000000002238. Epub 2023 Mar 13.

PMID- 38052353
OWN - NLM
STAT- Publisher
LR  - 20231205
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
DP  - 2023 Dec 3
TI  - New Horizons: The Potential Role of OpenAI's ChatGPT in Clinical Radiology Letter 
      Response.
LID - S1546-1440(23)01013-X [pii]
LID - 10.1016/j.jacr.2023.11.025 [doi]
FAU - Ismail, Ahmed
AU  - Ismail A
AD  - George Washington University of Health Sciences and Medicine, Washington, DC, 
      USA, 20037. Electronic address: aismail1@gwmail.gwu.edu.
FAU - Javan, Ramin
AU  - Javan R
AD  - Department of Radiology, George Washington University Hospital, Washington, DC, 
      USA, and Director of Advanced Brain Imaging and 3D Innovations Lab and Medical 
      Student Radiology Clerkship Director. Electronic address: rjavan@mfa.gwu.edu.
LA  - eng
PT  - Letter
DEP - 20231203
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
EDAT- 2023/12/06 03:43
MHDA- 2023/12/06 03:43
CRDT- 2023/12/05 19:21
PHST- 2023/07/24 00:00 [received]
PHST- 2023/10/17 00:00 [revised]
PHST- 2023/11/12 00:00 [accepted]
PHST- 2023/12/06 03:43 [medline]
PHST- 2023/12/06 03:43 [pubmed]
PHST- 2023/12/05 19:21 [entrez]
AID - S1546-1440(23)01013-X [pii]
AID - 10.1016/j.jacr.2023.11.025 [doi]
PST - aheadofprint
SO  - J Am Coll Radiol. 2023 Dec 3:S1546-1440(23)01013-X. doi: 
      10.1016/j.jacr.2023.11.025.

PMID- 37713576
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230919
IS  - 1723-8617 (Print)
IS  - 2051-5545 (Electronic)
IS  - 1723-8617 (Linking)
VI  - 22
IP  - 3
DP  - 2023 Oct
TI  - The performance of ChatGPT in generating answers to clinical questions in 
      psychiatry: a two-layer assessment.
PG  - 479-480
LID - 10.1002/wps.21145 [doi]
FAU - Luykx, Jurjen J
AU  - Luykx JJ
AD  - Department of Psychiatry and Neuropsychology, School for Mental Health and 
      Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands.
AD  - Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center 
      Utrecht, Utrecht, The Netherlands.
AD  - Outpatient Second Opinion Clinic, GGNet Mental Health, Warnsveld, The 
      Netherlands.
FAU - Gerritse, Frank
AU  - Gerritse F
AD  - Department of Psychiatry, Tergooi MC, Hilversum, The Netherlands.
FAU - Habets, Philippe C
AU  - Habets PC
AD  - Department of Psychiatry and Anatomy & Neurosciences, Amsterdam University 
      Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
AD  - Department of Internal Medicine, Leiden University Medical Center, Leiden, The 
      Netherlands.
FAU - Vinkers, Christiaan H
AU  - Vinkers CH
AD  - Department of Psychiatry and Anatomy & Neurosciences, Amsterdam University 
      Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
AD  - Amsterdam Public Health, Mental Health Program and Amsterdam Neuroscience, Mood, 
      Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands.
AD  - GGZ inGeest Mental Health Care, Amsterdam, The Netherlands.
LA  - eng
PT  - Letter
PL  - Italy
TA  - World Psychiatry
JT  - World psychiatry : official journal of the World Psychiatric Association (WPA)
JID - 101189643
PMC - PMC10503909
EDAT- 2023/09/15 18:42
MHDA- 2023/09/15 18:43
CRDT- 2023/09/15 14:53
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/09/15 18:43 [medline]
PHST- 2023/09/15 18:42 [pubmed]
PHST- 2023/09/15 14:53 [entrez]
AID - WPS21145 [pii]
AID - 10.1002/wps.21145 [doi]
PST - ppublish
SO  - World Psychiatry. 2023 Oct;22(3):479-480. doi: 10.1002/wps.21145.

PMID- 37634736
OWN - NLM
STAT- Publisher
LR  - 20231106
IS  - 1528-0012 (Electronic)
IS  - 0016-5085 (Linking)
DP  - 2023 Aug 26
TI  - Evolving Landscape of Large Language Models: An Evaluation of ChatGPT and Bard in 
      Answering Patient Queries on Colonoscopy.
LID - S0016-5085(23)04916-8 [pii]
LID - 10.1053/j.gastro.2023.08.033 [doi]
FAU - Tariq, Raseen
AU  - Tariq R
AD  - Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.
FAU - Malik, Sheza
AU  - Malik S
AD  - Department of Internal Medicine, Rochester General Hospital, Rochester, New York.
FAU - Khanna, Sahil
AU  - Khanna S
AD  - Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.
LA  - eng
PT  - Letter
DEP - 20230826
PL  - United States
TA  - Gastroenterology
JT  - Gastroenterology
JID - 0374630
SB  - IM
EDAT- 2023/08/28 00:41
MHDA- 2023/08/28 00:41
CRDT- 2023/08/27 19:26
PHST- 2023/08/21 00:00 [received]
PHST- 2023/08/22 00:00 [accepted]
PHST- 2023/08/28 00:41 [pubmed]
PHST- 2023/08/28 00:41 [medline]
PHST- 2023/08/27 19:26 [entrez]
AID - S0016-5085(23)04916-8 [pii]
AID - 10.1053/j.gastro.2023.08.033 [doi]
PST - aheadofprint
SO  - Gastroenterology. 2023 Aug 26:S0016-5085(23)04916-8. doi: 
      10.1053/j.gastro.2023.08.033.

PMID- 36914712
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230605
LR  - 20230606
IS  - 1745-655X (Electronic)
IS  - 0197-5897 (Linking)
VI  - 44
IP  - 2
DP  - 2023 Jun
TI  - A mistake-find exercise: a teacher's tool to engage with information innovations, 
      ChatGPT, and their analogs.
PG  - 173-178
LID - 10.1057/s41271-023-00400-1 [doi]
FAU - Naumova, Elena N
AU  - Naumova EN
AD  - Friedman School of Nutrition Science and Policy, Tufts University, Boston, USA. 
      elena.naumova@tufts.edu.
LA  - eng
PT  - Editorial
DEP - 20230313
PL  - England
TA  - J Public Health Policy
JT  - Journal of public health policy
JID - 8006508
SB  - IM
EDAT- 2023/03/15 06:00
MHDA- 2023/06/02 06:42
CRDT- 2023/03/14 00:39
PHST- 2023/02/14 00:00 [accepted]
PHST- 2023/06/02 06:42 [medline]
PHST- 2023/03/15 06:00 [pubmed]
PHST- 2023/03/14 00:39 [entrez]
AID - 10.1057/s41271-023-00400-1 [pii]
AID - 10.1057/s41271-023-00400-1 [doi]
PST - ppublish
SO  - J Public Health Policy. 2023 Jun;44(2):173-178. doi: 10.1057/s41271-023-00400-1. 
      Epub 2023 Mar 13.

PMID- 36912780
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 1755-3768 (Electronic)
IS  - 1755-375X (Linking)
VI  - 101
IP  - 7
DP  - 2023 Nov
TI  - Artificial intelligence-based chatbot patient information on common retinal 
      diseases using ChatGPT.
PG  - 829-831
LID - 10.1111/aos.15661 [doi]
FAU - Potapenko, Ivan
AU  - Potapenko I
AUID- ORCID: 0000-0002-7201-655X
AD  - Department of Ophthalmology, Rigshospitalet, Glostrup, Denmark.
FAU - Boberg-Ans, Lars Christian
AU  - Boberg-Ans LC
AD  - Department of Ophthalmology, Rigshospitalet, Glostrup, Denmark.
FAU - Stormly Hansen, Michael
AU  - Stormly Hansen M
AUID- ORCID: 0000-0001-8610-7455
AD  - Department of Ophthalmology, Rigshospitalet, Glostrup, Denmark.
AD  - Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
FAU - Klefter, Oliver Niels
AU  - Klefter ON
AUID- ORCID: 0000-0003-2313-5648
AD  - Department of Ophthalmology, Rigshospitalet, Glostrup, Denmark.
AD  - Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
FAU - van Dijk, Elon H C
AU  - van Dijk EHC
AUID- ORCID: 0000-0002-6351-7942
AD  - Department of Ophthalmology, Leiden University Medical Centre, Leiden, The 
      Netherlands.
AD  - Department of Ophthalmology, Haga Hospital, The Hague, The Netherlands.
FAU - Subhi, Yousif
AU  - Subhi Y
AUID- ORCID: 0000-0001-6620-5365
AD  - Department of Ophthalmology, Rigshospitalet, Glostrup, Denmark.
AD  - Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
LA  - eng
PT  - Letter
DEP - 20230313
PL  - England
TA  - Acta Ophthalmol
JT  - Acta ophthalmologica
JID - 101468102
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Software
EDAT- 2023/03/14 06:00
MHDA- 2023/10/23 12:44
CRDT- 2023/03/13 11:03
PHST- 2022/12/13 00:00 [received]
PHST- 2023/03/01 00:00 [accepted]
PHST- 2023/10/23 12:44 [medline]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/13 11:03 [entrez]
AID - 10.1111/aos.15661 [doi]
PST - ppublish
SO  - Acta Ophthalmol. 2023 Nov;101(7):829-831. doi: 10.1111/aos.15661. Epub 2023 Mar 
      13.

PMID- 36854734
OWN - NLM
STAT- Publisher
LR  - 20230228
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
DP  - 2023 Feb 28
TI  - Quick uptake of ChatGPT, and more - this week's best science graphics.
LID - 10.1038/d41586-023-00603-2 [doi]
LA  - eng
PT  - News
DEP - 20230228
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Publishing
OT  - Sustainability
EDAT- 2023/03/01 06:00
MHDA- 2023/03/01 06:00
CRDT- 2023/02/28 23:43
PHST- 2023/02/28 23:43 [entrez]
PHST- 2023/03/01 06:00 [pubmed]
PHST- 2023/03/01 06:00 [medline]
AID - 10.1038/d41586-023-00603-2 [pii]
AID - 10.1038/d41586-023-00603-2 [doi]
PST - aheadofprint
SO  - Nature. 2023 Feb 28. doi: 10.1038/d41586-023-00603-2.

PMID- 36809073
OWN - NLM
STAT- MEDLINE
DCOM- 20230224
LR  - 20230314
IS  - 1087-2981 (Electronic)
IS  - 1087-2981 (Linking)
VI  - 28
IP  - 1
DP  - 2023 Dec
TI  - The future of medical education and research: Is ChatGPT a blessing or blight in 
      disguise?
PG  - 2181052
LID - 10.1080/10872981.2023.2181052 [doi]
LID - 2181052
FAU - Arif, Taha Bin
AU  - Arif TB
AUID- ORCID: 0000-0003-4879-560X
AD  - Department of Medicine, Dow Medical College, Dow University of Health Sciences, 
      Karachi, Pakistan.
FAU - Munaf, Uzair
AU  - Munaf U
AUID- ORCID: 0000-0001-8059-4969
AD  - Department of Medicine, Dow Medical College, Dow University of Health Sciences, 
      Karachi, Pakistan.
FAU - Ul-Haque, Ibtehaj
AU  - Ul-Haque I
AUID- ORCID: 0000-0003-3187-6980
AD  - Department of Medicine, Dow Medical College, Dow University of Health Sciences, 
      Karachi, Pakistan.
LA  - eng
PT  - Editorial
PL  - United States
TA  - Med Educ Online
JT  - Medical education online
JID - 9806550
SB  - IM
MH  - Humans
MH  - *Education, Medical/trends
PMC - PMC9946299
COIS- No potential conflict of interest was reported by the authors.
EDAT- 2023/02/23 06:00
MHDA- 2023/02/25 06:00
CRDT- 2023/02/22 13:31
PHST- 2023/02/22 13:31 [entrez]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/02/25 06:00 [medline]
AID - 2181052 [pii]
AID - 10.1080/10872981.2023.2181052 [doi]
PST - ppublish
SO  - Med Educ Online. 2023 Dec;28(1):2181052. doi: 10.1080/10872981.2023.2181052.

PMID- 37814528
OWN - NLM
STAT- MEDLINE
DCOM- 20231011
LR  - 20231015
IS  - 1445-6354 (Electronic)
IS  - 1445-6354 (Linking)
VI  - 23
IP  - 4
DP  - 2023 Oct
TI  - Rural health care through the eyes of ChatGPT: a virtual ally for health in 
      remote communities?
PG  - 8445
LID - 10.22605/RRH8445 [doi]
FAU - Prazeres, Filipe
AU  - Prazeres F
AD  - Family Health Unit Beira Ria, Gafanha da Nazaré, Portugal; Faculty of Health 
      Sciences, University of Beira Interior, Covilhã, Portugal; and CINTESIS@RISE, 
      MEDCIDS, Faculty of Medicine of the University of Porto, Porto, Portugal 
      filipeprazeresmd@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231010
PL  - Australia
TA  - Rural Remote Health
JT  - Rural and remote health
JID - 101174860
SB  - IM
MH  - Humans
MH  - *Rural Health
MH  - *Rural Health Services
MH  - Health Services Accessibility
MH  - Rural Population
EDAT- 2023/10/10 06:41
MHDA- 2023/10/11 06:45
CRDT- 2023/10/10 02:43
PHST- 2023/10/11 06:45 [medline]
PHST- 2023/10/10 06:41 [pubmed]
PHST- 2023/10/10 02:43 [entrez]
AID - 8445 [pii]
AID - 10.22605/RRH8445 [doi]
PST - ppublish
SO  - Rural Remote Health. 2023 Oct;23(4):8445. doi: 10.22605/RRH8445. Epub 2023 Oct 
      10.

PMID- 37789730
OWN - NLM
STAT- MEDLINE
DCOM- 20231128
LR  - 20231128
IS  - 1879-3479 (Electronic)
IS  - 0020-7292 (Linking)
VI  - 163
IP  - 3
DP  - 2023 Dec
TI  - Letter to the Editor: Performance of ChatGPT in French language Parcours d'Accès 
      Spécifique Santé test and in OBGYN.
PG  - 1051
LID - 10.1002/ijgo.15188 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Mohali, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20231003
PL  - United States
TA  - Int J Gynaecol Obstet
JT  - International journal of gynaecology and obstetrics: the official organ of the 
      International Federation of Gynaecology and Obstetrics
JID - 0210174
SB  - IM
MH  - Humans
MH  - Language
MH  - *Artificial Intelligence
MH  - *Obstetrics
MH  - *Gynecology
EDAT- 2023/10/04 06:43
MHDA- 2023/11/15 06:43
CRDT- 2023/10/04 03:09
PHST- 2023/09/14 00:00 [received]
PHST- 2023/09/26 00:00 [accepted]
PHST- 2023/11/15 06:43 [medline]
PHST- 2023/10/04 06:43 [pubmed]
PHST- 2023/10/04 03:09 [entrez]
AID - 10.1002/ijgo.15188 [doi]
PST - ppublish
SO  - Int J Gynaecol Obstet. 2023 Dec;163(3):1051. doi: 10.1002/ijgo.15188. Epub 2023 
      Oct 3.

PMID- 37767645
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231114
IS  - 1525-1470 (Electronic)
IS  - 0736-8046 (Linking)
VI  - 40
IP  - 5
DP  - 2023 Sep-Oct
TI  - From eczema to anxiety: How artificial intelligence  shapes parental 
      perspectives.
PG  - 964-965
LID - 10.1111/pde.15417 [doi]
FAU - Ahuja, Kripa
AU  - Ahuja K
AUID- ORCID: 0000-0002-9660-1569
AD  - Eastern Virginia Medical School, Norfolk, Virginia, USA.
FAU - DeSena, Grace
AU  - DeSena G
AD  - The University of Florida College of Medicine, Gainesville, Florida, USA.
FAU - Laageide, Leah
AU  - Laageide L
AD  - Department of Dermatology, University of Rochester Medical Center, Rochester, New 
      York, USA.
FAU - El-Feghaly, Jinia
AU  - El-Feghaly J
AD  - Department of Dermatology, University of Rochester Medical Center, Rochester, New 
      York, USA.
FAU - Lio, Peter
AU  - Lio P
AUID- ORCID: 0000-0001-7600-0152
AD  - Department of Dermatology, Northwestern University, Chicago, Illinois, USA.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Pediatr Dermatol
JT  - Pediatric dermatology
JID - 8406799
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Eczema
MH  - Parents
MH  - Anxiety
OTO - NOTNLM
OT  - ChatGPT
OT  - Google
OT  - anxiety
OT  - artificial intelligence
OT  - atopic dermatitis
OT  - children
OT  - dermatology
OT  - parents
EDAT- 2023/09/28 06:42
MHDA- 2023/09/28 06:43
CRDT- 2023/09/28 04:43
PHST- 2023/06/20 00:00 [received]
PHST- 2023/08/05 00:00 [accepted]
PHST- 2023/09/28 06:43 [medline]
PHST- 2023/09/28 06:42 [pubmed]
PHST- 2023/09/28 04:43 [entrez]
AID - 10.1111/pde.15417 [doi]
PST - ppublish
SO  - Pediatr Dermatol. 2023 Sep-Oct;40(5):964-965. doi: 10.1111/pde.15417.

PMID- 37046397
OWN - NLM
STAT- MEDLINE
DCOM- 20230414
LR  - 20230907
IS  - 1576-6578 (Electronic)
IS  - 0210-0010 (Print)
IS  - 0210-0010 (Linking)
VI  - 76
IP  - 8
DP  - 2023 Apr 16
TI  - [ChatGPT: a novel tool for writing scientific articles, but not an author (for 
      the time being)].
PG  - 277
LID - 10.33588/rn.7608.2023066 [doi]
FAU - Ros-Arlanzón, P
AU  - Ros-Arlanzón P
AD  - Hospital General Universitario Dr. Balmis, Alicante, España.
FAU - Pérez-Sempere, A
AU  - Pérez-Sempere A
AD  - Hospital General Universitario Dr. Balmis, Alicante, España.
LA  - spa
PT  - Letter
TT  - ChatGPT: una novedosa herramienta de escritura para artículos científicos, pero 
      no un autor (por el momento).
PL  - Spain
TA  - Rev Neurol
JT  - Revista de neurologia
JID - 7706841
SB  - IM
MH  - Humans
MH  - *Writing
MH  - *Authorship
PMC - PMC10478136
COIS- Conflicto de intereses: Los autores declaran no tener conflictos de interés.
EDAT- 2023/04/14 06:00
MHDA- 2023/04/14 06:41
CRDT- 2023/04/13 00:42
PHST- 2023/04/14 06:41 [medline]
PHST- 2023/04/13 00:42 [entrez]
PHST- 2023/04/14 06:00 [pubmed]
AID - rn2023066 [pii]
AID - RN-76-277 [pii]
AID - 10.33588/rn.7608.2023066 [doi]
PST - ppublish
SO  - Rev Neurol. 2023 Apr 16;76(8):277. doi: 10.33588/rn.7608.2023066.

PMID- 36799231
OWN - NLM
STAT- MEDLINE
DCOM- 20230519
LR  - 20230528
IS  - 1932-2968 (Electronic)
IS  - 1932-2968 (Linking)
VI  - 17
IP  - 3
DP  - 2023 May
TI  - Using ChatGPT to Predict the Future of Diabetes Technology.
PG  - 853-854
LID - 10.1177/19322968231161095 [doi]
FAU - Huang, Jingtong
AU  - Huang J
AUID- ORCID: 0000-0002-3119-9361
AD  - Diabetes Technology Society, Burlingame, CA, USA.
FAU - Yeung, Andrea M
AU  - Yeung AM
AUID- ORCID: 0000-0002-5592-453X
AD  - Diabetes Technology Society, Burlingame, CA, USA.
FAU - Kerr, David
AU  - Kerr D
AUID- ORCID: 0000-0003-1335-1857
AD  - Diabetes Technology Society, Burlingame, CA, USA.
FAU - Klonoff, David C
AU  - Klonoff DC
AUID- ORCID: 0000-0001-6394-6862
AD  - Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA.
LA  - eng
PT  - Letter
DEP - 20230217
PL  - United States
TA  - J Diabetes Sci Technol
JT  - Journal of diabetes science and technology
JID - 101306166
SB  - IM
MH  - Humans
MH  - *Diabetes Mellitus
MH  - *Artificial Intelligence
PMC - PMC10210126
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatbot
OT  - diabetes
OT  - diabetes technology
OT  - search
COIS- The author(s) declared the following potential conflicts of interest with respect 
      to the research, authorship, and/or publication of this article: DK has received 
      remuneration for participation in Advisory Boards from Sanofi, Novo Nordisk, and 
      Abbott Diabetes Care. He also has received research support from Novo Nordisk and 
      Abbott Diabetes Care and has financial interests in Glooko, Hi.Health, and SNAQ. 
      DCK is a consultant to EOFlow, Integrity, Lifecare, Nevro, and Thirdwayv. JH and 
      AMY have nothing to disclose.
EDAT- 2023/02/18 06:00
MHDA- 2023/05/03 06:42
PMCR- 2024/02/17
CRDT- 2023/02/17 05:12
PHST- 2024/02/17 00:00 [pmc-release]
PHST- 2023/05/03 06:42 [medline]
PHST- 2023/02/18 06:00 [pubmed]
PHST- 2023/02/17 05:12 [entrez]
AID - 10.1177_19322968231161095 [pii]
AID - 10.1177/19322968231161095 [doi]
PST - ppublish
SO  - J Diabetes Sci Technol. 2023 May;17(3):853-854. doi: 10.1177/19322968231161095. 
      Epub 2023 Feb 17.

PMID- 37993616
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231130
LR  - 20231130
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 623
IP  - 7989
DP  - 2023 Nov
TI  - ChatGPT generates fake data set to support scientific hypothesis.
PG  - 895-896
LID - 10.1038/d41586-023-03635-w [doi]
FAU - Naddaf, Miryam
AU  - Naddaf M
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Machine learning
OT  - Medical research
OT  - Scientific community
EDAT- 2023/11/23 00:41
MHDA- 2023/11/23 00:42
CRDT- 2023/11/22 23:43
PHST- 2023/11/23 00:42 [medline]
PHST- 2023/11/23 00:41 [pubmed]
PHST- 2023/11/22 23:43 [entrez]
AID - 10.1038/d41586-023-03635-w [pii]
AID - 10.1038/d41586-023-03635-w [doi]
PST - ppublish
SO  - Nature. 2023 Nov;623(7989):895-896. doi: 10.1038/d41586-023-03635-w.

PMID- 37881050
OWN - NLM
STAT- MEDLINE
DCOM- 20231027
LR  - 20231107
IS  - 1600-0846 (Electronic)
IS  - 0909-752X (Print)
IS  - 0909-752X (Linking)
VI  - 29
IP  - 10
DP  - 2023 Oct
TI  - Assess the precision of ChatGPT's responses regarding systemic lupus 
      erythematosus (SLE) inquiries.
PG  - e13500
LID - 10.1111/srt.13500 [doi]
LID - e13500
FAU - Huang, Chunyan
AU  - Huang C
AD  - Department of General Practice, The Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian, China.
FAU - Hong, Daorong
AU  - Hong D
AD  - Department of Ultrasonography, The Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian, China.
FAU - Chen, Lijun
AU  - Chen L
AD  - Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian, China.
FAU - Chen, Xiaoqing
AU  - Chen X
AD  - Department of General Practice, The Second Affiliated Hospital of Fujian Medical 
      University, Quanzhou City, Fujian, China.
LA  - eng
GR  - 2021TG012/2021 Health Science and Technology Program Projects in Fujian Province/
PT  - Letter
PL  - England
TA  - Skin Res Technol
JT  - Skin research and technology : official journal of International Society for 
      Bioengineering and the Skin (ISBS) [and] International Society for Digital 
      Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
JID - 9504453
SB  - IM
MH  - Humans
MH  - *Lupus Erythematosus, Systemic/drug therapy
MH  - Severity of Illness Index
PMC - PMC10579779
COIS- The authors have declared that no conflict of interest exists.
EDAT- 2023/10/26 06:42
MHDA- 2023/10/27 06:42
CRDT- 2023/10/26 03:51
PHST- 2023/10/02 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/10/27 06:42 [medline]
PHST- 2023/10/26 06:42 [pubmed]
PHST- 2023/10/26 03:51 [entrez]
AID - SRT13500 [pii]
AID - 10.1111/srt.13500 [doi]
PST - ppublish
SO  - Skin Res Technol. 2023 Oct;29(10):e13500. doi: 10.1111/srt.13500.

PMID- 37462501
OWN - NLM
STAT- MEDLINE
DCOM- 20230721
LR  - 20231010
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 308
IP  - 1
DP  - 2023 Jul
TI  - ChatGPT's Diagnostic Performance from Patient History and Imaging Findings on the 
      Diagnosis Please Quizzes.
PG  - e231040
LID - 10.1148/radiol.231040 [doi]
FAU - Ueda, Daiju
AU  - Ueda D
AUID- ORCID: 0000-0002-3878-3616
AD  - Center for Health Science Innovation, Osaka Metropolitan University, Osaka, 
      Japan.
FAU - Mitsuyama, Yasuhito
AU  - Mitsuyama Y
AUID- ORCID: 0000-0001-7979-2300
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Takita, Hirotaka
AU  - Takita H
AUID- ORCID: 0000-0003-2860-4503
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Horiuchi, Daisuke
AU  - Horiuchi D
AUID- ORCID: 0000-0002-8929-8098
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Walston, Shannon L
AU  - Walston SL
AUID- ORCID: 0000-0002-7268-8313
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Tatekawa, Hiroyuki
AU  - Tatekawa H
AUID- ORCID: 0000-0002-8050-4895
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Miki, Yukio
AU  - Miki Y
AUID- ORCID: 0000-0003-0621-0044
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CIN - Radiology. 2023 Sep;308(3):e232082. PMID: 37724974
MH  - Humans
MH  - *Educational Measurement/methods
MH  - *Curriculum
EDAT- 2023/07/18 13:09
MHDA- 2023/07/21 06:44
CRDT- 2023/07/18 10:04
PHST- 2023/07/21 06:44 [medline]
PHST- 2023/07/18 13:09 [pubmed]
PHST- 2023/07/18 10:04 [entrez]
AID - 10.1148/radiol.231040 [doi]
PST - ppublish
SO  - Radiology. 2023 Jul;308(1):e231040. doi: 10.1148/radiol.231040.

PMID- 37377413
OWN - NLM
STAT- MEDLINE
DCOM- 20230816
LR  - 20231020
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
VI  - 93
IP  - 3
DP  - 2023 Sep 1
TI  - In Reply: I Asked a ChatGPT to Write an Editorial About How We Can Incorporate 
      Chatbots Into Neurosurgical Research and Patient Care….
PG  - e78
LID - 10.1227/neu.0000000000002589 [doi]
FAU - D'Amico, Randy S
AU  - D'Amico RS
AUID- ORCID: 0000-0002-3047-945
AD  - Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker 
      School of Medicine at Hofstra/Northwell, New York , New York , USA.
FAU - White, Timothy G
AU  - White TG
FAU - Shah, Harshal A
AU  - Shah HA
FAU - Langer, David J
AU  - Langer DJ
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230628
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
CON - Neurosurgery. 2023 Apr 1;92(4):663-664. PMID: 36757199
CON - Neurosurgery. 2023 Sep 1;93(3):e77. PMID: 37382430
MH  - Humans
MH  - *Patient Care
MH  - *Writing
EDAT- 2023/06/28 13:08
MHDA- 2023/08/16 06:42
CRDT- 2023/06/28 09:02
PHST- 2023/05/03 00:00 [received]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/08/16 06:42 [medline]
PHST- 2023/06/28 13:08 [pubmed]
PHST- 2023/06/28 09:02 [entrez]
AID - 00006123-202309000-00039 [pii]
AID - 10.1227/neu.0000000000002589 [doi]
PST - ppublish
SO  - Neurosurgery. 2023 Sep 1;93(3):e78. doi: 10.1227/neu.0000000000002589. Epub 2023 
      Jun 28.

PMID- 37410672
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230925
IS  - 1469-0756 (Electronic)
IS  - 0032-5473 (Linking)
VI  - 99
IP  - 1176
DP  - 2023 Sep 21
TI  - ChatGPT: the threats to medical education.
PG  - 1130-1131
LID - 10.1093/postmj/qgad046 [doi]
AB  - While it offers abundant advantages, ChatGPT threatens to significantly harm the 
      educational attainment, and the intellectual life, of students of medicine and 
      the subjects that compliment it. This technology poses a serious threat to the 
      ability of such students to deliver safe and effective medical care once they 
      graduate to clinical practice. Institutions that providemedical education must 
      react to the existence, availability, and rapidly increasing competency of GPT 
      models. This article suggests an intervention by which this could be, at least 
      partially, achieved.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Postgraduate Medical Journal. All rights reserved. For permissions, please 
      e-mail: journals.permissions@oup.com.
FAU - Armitage, Richard C
AU  - Armitage RC
AUID- ORCID: 0000-0003-1165-6753
AD  - Academic Unit of Population and Lifespan Sciences, School of Medicine, University 
      of Nottingham, Nottingham, NG5 1PB, United Kingdom.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Postgrad Med J
JT  - Postgraduate medical journal
JID - 0234135
SB  - IM
MH  - Humans
MH  - *Education, Medical
MH  - *Medicine
MH  - Educational Status
MH  - Students
MH  - *Academic Success
OTO - NOTNLM
OT  - artificial intelligence
OT  - emerging technology
OT  - large language modes
OT  - medical education
EDAT- 2023/07/06 19:12
MHDA- 2023/09/25 06:42
CRDT- 2023/07/06 13:13
PHST- 2023/05/01 00:00 [received]
PHST- 2023/05/13 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/07/06 19:12 [pubmed]
PHST- 2023/07/06 13:13 [entrez]
AID - 7220360 [pii]
AID - 10.1093/postmj/qgad046 [doi]
PST - ppublish
SO  - Postgrad Med J. 2023 Sep 21;99(1176):1130-1131. doi: 10.1093/postmj/qgad046.

PMID- 36920012
OWN - NLM
STAT- MEDLINE
DCOM- 20230316
LR  - 20230316
IS  - 1660-9379 (Print)
IS  - 1660-9379 (Linking)
VI  - 19
IP  - 818
DP  - 2023 Mar 15
TI  - [Artificial intelligence and psychiatry: questions from psychiatrists to 
      ChatGPT].
PG  - 532-536
LID - 10.53738/REVMED.2023.19.818.532 [doi]
AB  - Psychiatrists and psychotherapists specialising in the fields of addiction, 
      personality disorders, ADHD and suicidal crisis, we questioned the ChatGPT 
      artificial intelligence program in order to form an opinion on the quality of its 
      answers to questions on these subjects. Our aim is to satisfy our curiosity about 
      these emerging tools. On the other hand, we want to assess the relevance of the 
      answers in order to know whether relatives and patients can use them safely. In 
      this article, we comment on the question-and-answer dialogue with the artificial 
      intelligence program in the light of the literature.
FAU - Prada, Paco
AU  - Prada P
AD  - Unité de psychiatrie hospitalière, Service de psychiatrie de liaison et 
      d'intervention de crise, Département de psychiatrie, Hôpitaux universitaires de 
      Genève, 1211 Genève 14.
FAU - Perroud, Nader
AU  - Perroud N
AD  - Unité des troubles de la régulation émotionnelle, Service des spécialités 
      psychiatriques, Département de psychiatrie, Hôpitaux universitaires de Genève, 
      20bis rue de Lausanne, 1201 Genève.
FAU - Thorens, Gabriel
AU  - Thorens G
AD  - Service d'addictologie, Département de psychiatrie, Hôpitaux universitaires de 
      Genève, Rue de Grand-Pré 70, 1202 Genève.
LA  - fre
PT  - English Abstract
PT  - Journal Article
TT  - Intelligence artificielle et psychiatrie : questions de psychiatres à ChatGPT.
PL  - Switzerland
TA  - Rev Med Suisse
JT  - Revue medicale suisse
JID - 101219148
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Psychiatry
MH  - Personality Disorders
MH  - Attitude of Health Personnel
MH  - Psychotherapy
COIS- Les auteurs n’ont déclaré aucun conflit d’intérêts en lien avec cet article.
EDAT- 2023/03/16 06:00
MHDA- 2023/03/17 06:00
CRDT- 2023/03/15 08:43
PHST- 2023/03/15 08:43 [entrez]
PHST- 2023/03/16 06:00 [pubmed]
PHST- 2023/03/17 06:00 [medline]
AID - RMS0818-008 [pii]
AID - 10.53738/REVMED.2023.19.818.532 [doi]
PST - ppublish
SO  - Rev Med Suisse. 2023 Mar 15;19(818):532-536. doi: 
      10.53738/REVMED.2023.19.818.532.

PMID- 37301435
OWN - NLM
STAT- MEDLINE
DCOM- 20230911
LR  - 20231102
IS  - 2213-2201 (Electronic)
VI  - 11
IP  - 9
DP  - 2023 Sep
TI  - Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have 
      We Been and Where Are We Going?
PG  - 2697-2700
LID - S2213-2198(23)00641-4 [pii]
LID - 10.1016/j.jaip.2023.05.042 [doi]
AB  - Artificial intelligence (AI) is rapidly becoming a valuable tool in healthcare, 
      providing clinicians with a new AI lens perspective for patient care, diagnosis, 
      and treatment. This article explores the potential applications, benefits, and 
      challenges of AI chatbots in clinical settings, with a particular emphasis on 
      ChatGPT 4.0 (OpenAI - Chat generative pretrained transformer 4.0), especially in 
      the field of allergy and immunology. AI chatbots have shown considerable promise 
      in various medical domains, including radiology and dermatology, by improving 
      patient engagement, diagnostic accuracy, and personalized treatment plans. 
      ChatGPT 4.0, developed by OpenAI, is good at understanding and replying to 
      prompts in a way that makes sense. However, it is critical to address the 
      potential biases, data privacy issues, ethical considerations, and the need for 
      verification of AI-generated findings. When used responsibly, AI chatbots can 
      significantly enhance clinical practice in allergy and immunology. However, there 
      are still challenges in using this technology that require ongoing research and 
      collaboration between AI developers and medical specialists. To this end, the 
      ChatGPT 4.0 platform has the potential to enhance patient engagement, improve 
      diagnostic accuracy, and provide personalized treatment plans in allergy and 
      immunology practice. However, limitations and risks must be addressed to ensure 
      their safe and effective use in clinical practice.
CI  - Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Goktas, Polat
AU  - Goktas P
AD  - UCD School of Computer Science, University College Dublin, Belfield, Dublin, 
      Ireland; CeADAR: Ireland's Centre for Applied Artificial Intelligence, 
      Clonskeagh, Dublin, Ireland. Electronic address: polat.goktas@ucd.ie.
FAU - Karakaya, Gul
AU  - Karakaya G
AD  - School of Medicine, Department of Chest Diseases, Division of Allergy and 
      Clinical Immunology, Hacettepe University, Ankara, Turkey.
FAU - Kalyoncu, Ali Fuat
AU  - Kalyoncu AF
AD  - School of Medicine, Department of Chest Diseases, Division of Allergy and 
      Clinical Immunology, Hacettepe University, Ankara, Turkey.
FAU - Damadoglu, Ebru
AU  - Damadoglu E
AD  - School of Medicine, Department of Chest Diseases, Division of Allergy and 
      Clinical Immunology, Hacettepe University, Ankara, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230608
PL  - United States
TA  - J Allergy Clin Immunol Pract
JT  - The journal of allergy and clinical immunology. In practice
JID - 101597220
SB  - IM
CIN - J Allergy Clin Immunol Pract. 2023 Oct;11(10):3285-3286. PMID: 37805232
CIN - J Allergy Clin Immunol Pract. 2023 Oct;11(10):3286-3287. PMID: 37805233
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Hypersensitivity/diagnosis/therapy
MH  - Patient Participation
MH  - Technology
OTO - NOTNLM
OT  - AI chatbots
OT  - Allergy
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Ethical considerations
OT  - Generative pretrained transformer
OT  - Healthcare
OT  - Natural language processing
EDAT- 2023/06/11 01:06
MHDA- 2023/09/11 06:43
CRDT- 2023/06/10 19:28
PHST- 2023/04/30 00:00 [received]
PHST- 2023/05/22 00:00 [revised]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/06/11 01:06 [pubmed]
PHST- 2023/06/10 19:28 [entrez]
AID - S2213-2198(23)00641-4 [pii]
AID - 10.1016/j.jaip.2023.05.042 [doi]
PST - ppublish
SO  - J Allergy Clin Immunol Pract. 2023 Sep;11(9):2697-2700. doi: 
      10.1016/j.jaip.2023.05.042. Epub 2023 Jun 8.

PMID- 37256297
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
VI  - 47
IP  - 5
DP  - 2023 Oct
TI  - The Artificial Intelligence application in Aesthetic Medicine: How ChatGPT can 
      Revolutionize the Aesthetic World.
PG  - 2211-2212
LID - 10.1007/s00266-023-03416-w [doi]
AB  - Aesthetic medicine is witnessing a growing importance of ChatGPT and artificial 
      intelligence (AI) technologies, as highlighted by the pioneering work of Xie et 
      al. in their article, "Aesthetic Surgery Advice and Counseling from Artificial 
      Intelligence: A Rhinoplasty Consultation with ChatGPT." These advancements 
      promise to revolutionize patient consultations, treatment planning, and follow-up 
      care. AI-driven chatbots, such as ChatGPT, can enhance patient consultations by 
      providing accurate and reliable information on aesthetic procedures, their risks, 
      benefits, and potential outcomes, enabling well-informed decisions and improved 
      treatment outcomes. Furthermore, AI can personalize treatment plans by analyzing 
      patient data, leading to increased precision and satisfaction. AI-powered 
      platforms can also streamline patient follow-up and monitoring, improving patient 
      outcomes and resource utilization, while serving as a valuable educational tool 
      for clinicians. Despite these benefits, AI integration in aesthetic medicine 
      raises concerns about data privacy, security, and potential biases in AI 
      algorithms. To address these challenges, the aesthetic medicine community must 
      establish ethical guidelines, adopt stringent security protocols, and ensure 
      diverse and representative datasets for AI training. Additionally, maintaining 
      the personal connection between patients and providers is crucial for preserving 
      the human touch in patient care.Level of Evidence V This journal requires that 
      authors assign a level of evidence to each article. For a full description of 
      these Evidence-Based Medicine ratings, please refer to the Table of Contents or 
      the online Instructions to Authors https://www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Buzzaccarini, Giovanni
AU  - Buzzaccarini G
AUID- ORCID: 0000-0002-9466-0178
AD  - Department of Obstetrics and Gynaecology, IRCCS San Raffaele Scientific 
      Institute, Vita-Salute San Raffaele University, Via Olgettina 58-60, 20132, 
      Milan, Italy. giovanni.buzzaccarini@gmail.com.
FAU - Degliuomini, Rebecca Susanna
AU  - Degliuomini RS
AD  - Department of Obstetrics and Gynaecology, IRCCS San Raffaele Scientific 
      Institute, Vita-Salute San Raffaele University, Via Olgettina 58-60, 20132, 
      Milan, Italy.
FAU - Borin, Marco
AU  - Borin M
AD  - U.O.C. Otorhinolaryngology, ASST Grande Ospedale Metropolitano Niguarda, Piazza 
      Ospedale Maggiore, 3, 20162, Milan, Italy.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230531
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
CON - Aesthetic Plast Surg. 2023 Oct;47(5):1985-1993. PMID: 37095384
CIN - Aesthetic Plast Surg. 2023 Oct;47(5):2213-2214. PMID: 37314465
MH  - Humans
MH  - *Surgery, Plastic/methods
MH  - Artificial Intelligence
MH  - *Rhinoplasty/methods
MH  - Treatment Outcome
MH  - Esthetics
EDAT- 2023/05/31 13:12
MHDA- 2023/10/23 00:42
CRDT- 2023/05/31 11:03
PHST- 2023/05/10 00:00 [received]
PHST- 2023/05/11 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/05/31 13:12 [pubmed]
PHST- 2023/05/31 11:03 [entrez]
AID - 10.1007/s00266-023-03416-w [pii]
AID - 10.1007/s00266-023-03416-w [doi]
PST - ppublish
SO  - Aesthetic Plast Surg. 2023 Oct;47(5):2211-2212. doi: 10.1007/s00266-023-03416-w. 
      Epub 2023 May 31.

PMID- 37803243
OWN - NLM
STAT- Publisher
LR  - 20231006
IS  - 1476-5608 (Electronic)
IS  - 1365-7852 (Linking)
DP  - 2023 Oct 6
TI  - Social determinants of health into evaluations of quality and appropriateness of 
      AI assistant ChatGPT.
LID - 10.1038/s41391-023-00735-6 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Lao People's Democratic Republic. 
      hinpetchdaung@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Chandigarh, India. wviroj@yahoo.com.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria. wviroj@yahoo.com.
LA  - eng
PT  - Letter
DEP - 20231006
PL  - England
TA  - Prostate Cancer Prostatic Dis
JT  - Prostate cancer and prostatic diseases
JID - 9815755
SB  - IM
EDAT- 2023/10/07 00:42
MHDA- 2023/10/07 00:42
CRDT- 2023/10/06 23:34
PHST- 2023/09/14 00:00 [received]
PHST- 2023/09/28 00:00 [accepted]
PHST- 2023/09/22 00:00 [revised]
PHST- 2023/10/07 00:42 [medline]
PHST- 2023/10/07 00:42 [pubmed]
PHST- 2023/10/06 23:34 [entrez]
AID - 10.1038/s41391-023-00735-6 [pii]
AID - 10.1038/s41391-023-00735-6 [doi]
PST - aheadofprint
SO  - Prostate Cancer Prostatic Dis. 2023 Oct 6. doi: 10.1038/s41391-023-00735-6.

PMID- 37693201
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2638-6135 (Electronic)
IS  - 2638-6135 (Linking)
VI  - 5
IP  - 4
DP  - 2023 Aug
TI  - Evaluating ChatGPT's Accuracy in Lung Cancer Prevention and Screening 
      Recommendations.
PG  - e230115
LID - 10.1148/ryct.230115 [doi]
LID - e230115
FAU - Haver, Hana L
AU  - Haver HL
AUID- ORCID: 0000-0002-8382-4077
AD  - University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of 
      Diagnostic Radiology and Nuclear Medicine, University of Maryland School of 
      Medicine, 22 S Greene St, Baltimore, MD 21201.
FAU - Lin, Cheng Ting
AU  - Lin CT
AUID- ORCID: 0000-0003-0275-8037
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, Johns 
      Hopkins University School of Medicine, Baltimore, Md.
FAU - Sirajuddin, Arlene
AU  - Sirajuddin A
AUID- ORCID: 0000-0001-9183-5983
AD  - Radiology and Imaging Sciences Clinical Center, National Institutes of Health 
      (NIH), Bethesda, Md.
FAU - Yi, Paul H
AU  - Yi PH
AUID- ORCID: 0000-0001-9433-8093
AD  - University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of 
      Diagnostic Radiology and Nuclear Medicine, University of Maryland School of 
      Medicine, 22 S Greene St, Baltimore, MD 21201.
AD  - Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns 
      Hopkins University, Baltimore, Md.
AD  - Fischell Department of Bioengineering, A. James Clark School of Engineering, 
      University of Maryland, College Park, College Park, Md.
FAU - Jeudy, Jean
AU  - Jeudy J
AUID- ORCID: 0000-0002-6028-459X
AD  - University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of 
      Diagnostic Radiology and Nuclear Medicine, University of Maryland School of 
      Medicine, 22 S Greene St, Baltimore, MD 21201.
LA  - eng
PT  - Journal Article
DEP - 20230706
PL  - United States
TA  - Radiol Cardiothorac Imaging
JT  - Radiology. Cardiothoracic imaging
JID - 101748663
PMC - PMC10483248
COIS- Disclosures of conflicts of interest: H.L.H. No relevant relationships. C.T.L. 
      Research grants from NIH-NHLBI, NIH-NCI, Siemens Medical Solution, Carestream 
      Health, and NIBIB; expert witness for U.S. Department of Justice. A.S. No 
      relevant relationships. P.H.Y. Radiology: Artificial Intelligence editorial 
      board. J.J. No relevant relationships.
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 04:48
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 04:48 [entrez]
AID - 10.1148/ryct.230115 [doi]
PST - epublish
SO  - Radiol Cardiothorac Imaging. 2023 Jul 6;5(4):e230115. doi: 10.1148/ryct.230115. 
      eCollection 2023 Aug.

PMID- 37652659
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231002
IS  - 2287-4208 (Print)
IS  - 2287-4690 (Electronic)
IS  - 2287-4208 (Linking)
VI  - 41
IP  - 4
DP  - 2023 Oct
TI  - Light and Shadow of ChatGPT: A Real Tool for Advancing Scientific Research and 
      Medical Practice?
PG  - 751-752
LID - 10.5534/wjmh.230102 [doi]
FAU - Cocci, Andrea
AU  - Cocci A
AUID- ORCID: 0000-0003-0138-6294
AD  - Department of Urology, Azienda Ospedaliera Universitaria Careggi, University of 
      Florence, Florence, Italy.
FAU - Pezzoli, Marta
AU  - Pezzoli M
AUID- ORCID: 0009-0007-7168-5499
AD  - Department of Urology, Azienda Ospedaliera Universitaria Careggi, University of 
      Florence, Florence, Italy. marta.pezzoli@stud.unifi.it.
FAU - Minervini, Andrea
AU  - Minervini A
AUID- ORCID: 0000-0001-5140-4628
AD  - Department of Urology, Azienda Ospedaliera Universitaria Careggi, University of 
      Florence, Florence, Italy.
LA  - eng
PT  - Editorial
DEP - 20230829
PL  - Korea (South)
TA  - World J Mens Health
JT  - The world journal of men's health
JID - 101596899
PMC - PMC10523110
COIS- The authors have nothing to disclose.
EDAT- 2023/09/01 00:42
MHDA- 2023/09/01 00:43
CRDT- 2023/08/31 21:03
PHST- 2023/04/12 00:00 [received]
PHST- 2023/04/26 00:00 [revised]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/09/01 00:43 [medline]
PHST- 2023/09/01 00:42 [pubmed]
PHST- 2023/08/31 21:03 [entrez]
AID - 41.e70 [pii]
AID - 10.5534/wjmh.230102 [doi]
PST - ppublish
SO  - World J Mens Health. 2023 Oct;41(4):751-752. doi: 10.5534/wjmh.230102. Epub 2023 
      Aug 29.

PMID- 37532856
OWN - NLM
STAT- MEDLINE
DCOM- 20230810
LR  - 20230810
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 620
IP  - 7973
DP  - 2023 Aug
TI  - ChatGPT-like AIs are coming to major science search engines.
PG  - 258
LID - 10.1038/d41586-023-02470-3 [doi]
FAU - Van Noorden, Richard
AU  - Van Noorden R
LA  - eng
PT  - Journal Article
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - Communication
MH  - *Databases, Bibliographic/trends
MH  - *Natural Language Processing
MH  - *Research Report
MH  - *Search Engine/methods/trends
OTO - NOTNLM
OT  - Databases
OT  - Machine learning
OT  - Research data
EDAT- 2023/08/03 01:06
MHDA- 2023/08/10 06:43
CRDT- 2023/08/02 23:26
PHST- 2023/08/10 06:43 [medline]
PHST- 2023/08/03 01:06 [pubmed]
PHST- 2023/08/02 23:26 [entrez]
AID - 10.1038/d41586-023-02470-3 [pii]
AID - 10.1038/d41586-023-02470-3 [doi]
PST - ppublish
SO  - Nature. 2023 Aug;620(7973):258. doi: 10.1038/d41586-023-02470-3.

PMID- 37458781
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231107
IS  - 1432-2161 (Electronic)
IS  - 0364-2348 (Linking)
VI  - 52
IP  - 12
DP  - 2023 Dec
TI  - Re-evaluating the role of AI in scientific writing: a critical analysis on 
      ChatGPT.
PG  - 2487-2488
LID - 10.1007/s00256-023-04404-6 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Department of Computer Applications, Sikkim University, 6th Mile, PO-Tadong, 
      Gangtok, Sikkim, 737102, India. ppray@cus.ac.in.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230717
PL  - Germany
TA  - Skeletal Radiol
JT  - Skeletal radiology
JID - 7701953
SB  - IM
CON - Skeletal Radiol. 2023 Sep;52(9):1755-1758. PMID: 37059827
EDAT- 2023/07/17 15:08
MHDA- 2023/07/17 15:09
CRDT- 2023/07/17 11:05
PHST- 2023/07/02 00:00 [received]
PHST- 2023/07/12 00:00 [accepted]
PHST- 2023/07/02 00:00 [revised]
PHST- 2023/07/17 15:09 [medline]
PHST- 2023/07/17 15:08 [pubmed]
PHST- 2023/07/17 11:05 [entrez]
AID - 10.1007/s00256-023-04404-6 [pii]
AID - 10.1007/s00256-023-04404-6 [doi]
PST - ppublish
SO  - Skeletal Radiol. 2023 Dec;52(12):2487-2488. doi: 10.1007/s00256-023-04404-6. Epub 
      2023 Jul 17.

PMID- 37331440
OWN - NLM
STAT- MEDLINE
DCOM- 20231013
LR  - 20231018
IS  - 1532-8406 (Electronic)
IS  - 0883-5403 (Linking)
VI  - 38
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT and Other Natural Language Processing Artificial Intelligence Models in 
      Adult Reconstruction.
PG  - 2191-2192
LID - S0883-5403(23)00666-6 [pii]
LID - 10.1016/j.arth.2023.06.030 [doi]
FAU - Magruder, Matthew L
AU  - Magruder ML
AD  - Maimonides Medical Center, Department of Orthopaedic Surgery, Brooklyn, New York.
FAU - Delanois, Ronald E
AU  - Delanois RE
AD  - Rubin Institute for Advanced Orthopedics, Sinai Hospital of Baltimore, Baltimore, 
      Maryland.
FAU - Nace, James
AU  - Nace J
AD  - Rubin Institute for Advanced Orthopedics, Sinai Hospital of Baltimore, Baltimore, 
      Maryland.
FAU - Mont, Michael A
AU  - Mont MA
AD  - Rubin Institute for Advanced Orthopedics, Sinai Hospital of Baltimore, Baltimore, 
      Maryland.
LA  - eng
PT  - Editorial
DEP - 20230617
PL  - United States
TA  - J Arthroplasty
JT  - The Journal of arthroplasty
JID - 8703515
SB  - IM
MH  - Adult
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Natural Language Processing
EDAT- 2023/06/19 00:42
MHDA- 2023/10/13 06:45
CRDT- 2023/06/18 19:13
PHST- 2023/06/02 00:00 [received]
PHST- 2023/06/09 00:00 [revised]
PHST- 2023/06/12 00:00 [accepted]
PHST- 2023/10/13 06:45 [medline]
PHST- 2023/06/19 00:42 [pubmed]
PHST- 2023/06/18 19:13 [entrez]
AID - S0883-5403(23)00666-6 [pii]
AID - 10.1016/j.arth.2023.06.030 [doi]
PST - ppublish
SO  - J Arthroplasty. 2023 Nov;38(11):2191-2192. doi: 10.1016/j.arth.2023.06.030. Epub 
      2023 Jun 17.

PMID- 37817033
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231012
LR  - 20231012
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 622
IP  - 7982
DP  - 2023 Oct
TI  - How ChatGPT and other AI tools could disrupt scientific publishing.
PG  - 234-236
LID - 10.1038/d41586-023-03144-w [doi]
FAU - Conroy, Gemma
AU  - Conroy G
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Machine learning
OT  - Peer review
OT  - Publishing
EDAT- 2023/10/11 00:42
MHDA- 2023/10/11 00:43
CRDT- 2023/10/10 23:46
PHST- 2023/10/11 00:43 [medline]
PHST- 2023/10/11 00:42 [pubmed]
PHST- 2023/10/10 23:46 [entrez]
AID - 10.1038/d41586-023-03144-w [pii]
AID - 10.1038/d41586-023-03144-w [doi]
PST - ppublish
SO  - Nature. 2023 Oct;622(7982):234-236. doi: 10.1038/d41586-023-03144-w.

PMID- 37649826
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230901
IS  - 2008-210X (Print)
IS  - 2008-2118 (Electronic)
IS  - 2008-210X (Linking)
VI  - 17
IP  - 2
DP  - 2023 Spring
TI  - ChatGPT, the future of healthcare research writing: Is it a double-edged sword?
PG  - 61-62
LID - 10.34172/joddd.2023.37147 [doi]
FAU - Lal, Abhishek
AU  - Lal A
AUID- ORCID: 0000-0002-0018-7069
AD  - Department of Medicine, The Aga Khan University, Karachi, Pakistan.
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - Iran
TA  - J Dent Res Dent Clin Dent Prospects
JT  - Journal of dental research, dental clinics, dental prospects
JID - 101576035
PMC - PMC10462470
COIS- None to declare.
EDAT- 2023/08/31 06:41
MHDA- 2023/08/31 06:42
CRDT- 2023/08/31 04:06
PHST- 2023/03/07 00:00 [received]
PHST- 2023/03/31 00:00 [accepted]
PHST- 2023/08/31 06:42 [medline]
PHST- 2023/08/31 06:41 [pubmed]
PHST- 2023/08/31 04:06 [entrez]
AID - 10.34172/joddd.2023.37147 [doi]
PST - ppublish
SO  - J Dent Res Dent Clin Dent Prospects. 2023 Spring;17(2):61-62. doi: 
      10.34172/joddd.2023.37147. Epub 2023 Jul 17.

PMID- 37470488
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20231026
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
VI  - 93
IP  - 4
DP  - 2023 Oct 1
TI  - In Reply: I Asked a ChatGPT to Write an Editorial About How We Can Incorporate 
      Chatbots Into Neurosurgical Research and Patient Care….
PG  - e109
LID - 10.1227/neu.0000000000002620 [doi]
FAU - D'Amico, Randy S
AU  - D'Amico RS
AUID- ORCID: 0000-0002-3047-945
AD  - Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker 
      School of Medicine at Hofstra/Northwell, New York , New York , USA.
FAU - Shah, Harshal A
AU  - Shah HA
FAU - Langer, David J
AU  - Langer DJ
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230720
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
CON - Neurosurgery. 2023 Apr 1;92(4):663-664. PMID: 36757199
CON - Neurosurgery. 2023 Oct 1;93(4):e107-e108. PMID: 37498091
MH  - Humans
MH  - *Patient Care
MH  - *Writing
EDAT- 2023/07/20 13:07
MHDA- 2023/09/18 12:43
CRDT- 2023/07/20 09:03
PHST- 2023/06/07 00:00 [received]
PHST- 2023/06/08 00:00 [accepted]
PHST- 2023/09/18 12:43 [medline]
PHST- 2023/07/20 13:07 [pubmed]
PHST- 2023/07/20 09:03 [entrez]
AID - 00006123-202310000-00040 [pii]
AID - 10.1227/neu.0000000000002620 [doi]
PST - ppublish
SO  - Neurosurgery. 2023 Oct 1;93(4):e109. doi: 10.1227/neu.0000000000002620. Epub 2023 
      Jul 20.

PMID- 37306147
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20231110
IS  - 1365-4632 (Electronic)
IS  - 0011-9059 (Linking)
VI  - 62
IP  - 10
DP  - 2023 Oct
TI  - The role of artificial intelligence in dermatology: the promising but limited 
      accuracy of ChatGPT in diagnosing clinical scenarios.
PG  - e547-e548
LID - 10.1111/ijd.16746 [doi]
FAU - Ravipati, Advaitaa
AU  - Ravipati A
AUID- ORCID: 0000-0001-5557-7817
AD  - Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of 
      Miami Miller School of Medicine, Miami, FL, USA.
FAU - Pradeep, Tejus
AU  - Pradeep T
AD  - Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, USA.
FAU - Elman, Scott A
AU  - Elman SA
AD  - Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of 
      Miami Miller School of Medicine, Miami, FL, USA.
LA  - eng
PT  - Letter
DEP - 20230612
PL  - England
TA  - Int J Dermatol
JT  - International journal of dermatology
JID - 0243704
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Dermatology
EDAT- 2023/06/12 13:07
MHDA- 2023/09/20 06:42
CRDT- 2023/06/12 06:33
PHST- 2023/05/25 00:00 [revised]
PHST- 2023/05/19 00:00 [received]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/06/12 13:07 [pubmed]
PHST- 2023/06/12 06:33 [entrez]
AID - 10.1111/ijd.16746 [doi]
PST - ppublish
SO  - Int J Dermatol. 2023 Oct;62(10):e547-e548. doi: 10.1111/ijd.16746. Epub 2023 Jun 
      12.

PMID- 37037587
OWN - NLM
STAT- MEDLINE
DCOM- 20230526
LR  - 20230926
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 5
DP  - 2023 May 1
TI  - The potential impact of ChatGPT/GPT-4 on surgery: will it topple the profession 
      of surgeons?
PG  - 1545-1547
LID - 10.1097/JS9.0000000000000388 [doi]
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan.
FAU - Sun, Zaijie
AU  - Sun Z
AD  - Department of Orthopaedic Surgery, Xiangyang Central Hospital, Affiliated 
      Hospital of Hubei University of Arts and Science, Xiangyang.
FAU - He, Yongbin
AU  - He Y
AD  - School of Sports Medicine and Rehabilitation, Beijing Sport University, Beijing.
FAU - Gu, Shuqin
AU  - Gu S
AD  - Duke Human Vaccine Institute, Duke University Medical Center.
FAU - Wu, Haiyang
AU  - Wu H
AUID- ORCID: 0000-0002-3215-1626
AD  - Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin 
      Medical University, Tianjin, People's Republic of China.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      North Carolina, USA.
LA  - eng
PT  - Journal Article
DEP - 20230501
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
RN  - EC 2.6.1.2 (Alanine Transaminase)
SB  - IM
MH  - Humans
MH  - *Surgeons
MH  - Alanine Transaminase
PMC - PMC10389652
COIS- The authors declare no conflicts of interest. Sponsorships or competing interests 
      that may be relevant to content are disclosed at the end of this article.
EDAT- 2023/04/11 06:00
MHDA- 2023/05/26 06:42
CRDT- 2023/04/10 20:33
PHST- 2023/03/24 00:00 [received]
PHST- 2023/03/27 00:00 [accepted]
PHST- 2023/05/26 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 20:33 [entrez]
AID - 01279778-202305000-00058 [pii]
AID - 10.1097/JS9.0000000000000388 [doi]
PST - epublish
SO  - Int J Surg. 2023 May 1;109(5):1545-1547. doi: 10.1097/JS9.0000000000000388.

PMID- 36694020
OWN - NLM
STAT- MEDLINE
DCOM- 20230127
LR  - 20230317
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 613
IP  - 7945
DP  - 2023 Jan
TI  - Tools such as ChatGPT threaten transparent science; here are our ground rules for 
      their use.
PG  - 612
LID - 10.1038/d41586-023-00191-1 [doi]
LA  - eng
PT  - Comment
PT  - Editorial
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
CON - Nature. 2022 Nov;611(7934):192-193. PMID: 36316468
CIN - Nature. 2023 Feb;614(7948):413. PMID: 36788370
MH  - *Artificial Intelligence/ethics/legislation & jurisprudence/trends
MH  - Ethics, Research
MH  - *Publishing/ethics/legislation & jurisprudence/standards
MH  - *Science/ethics/methods/standards
MH  - *Writing/standards
MH  - Authorship/standards
OTO - NOTNLM
OT  - Ethics
OT  - Machine learning
OT  - Publishing
OT  - Scientific community
EDAT- 2023/01/25 06:00
MHDA- 2023/01/27 06:00
CRDT- 2023/01/24 23:24
PHST- 2023/01/24 23:24 [entrez]
PHST- 2023/01/25 06:00 [pubmed]
PHST- 2023/01/27 06:00 [medline]
AID - 10.1038/d41586-023-00191-1 [pii]
AID - 10.1038/d41586-023-00191-1 [doi]
PST - ppublish
SO  - Nature. 2023 Jan;613(7945):612. doi: 10.1038/d41586-023-00191-1.

PMID- 37983817
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1529-4242 (Electronic)
IS  - 0032-1052 (Linking)
DP  - 2023 Nov 14
TI  - Artificial Intelligence in Plastic Surgery: ChatGPT as a Tool to Address 
      Disparities in Health Literacy.
LID - 10.1097/PRS.0000000000011202 [doi]
FAU - Wang, Anya
AU  - Wang A
AD  - Icahn School of Medicine at Mount Sinai, Division of Plastic and Reconstructive 
      Surgery.
FAU - Kim, Esther
AU  - Kim E
FAU - Oleru, Olachi
AU  - Oleru O
FAU - Seyidova, Nargiz
AU  - Seyidova N
FAU - Taub, Peter J
AU  - Taub PJ
LA  - eng
PT  - Journal Article
DEP - 20231114
PL  - United States
TA  - Plast Reconstr Surg
JT  - Plastic and reconstructive surgery
JID - 1306050
SB  - IM
EDAT- 2023/11/20 18:44
MHDA- 2023/11/20 18:44
CRDT- 2023/11/20 17:26
PHST- 2023/11/20 18:44 [medline]
PHST- 2023/11/20 18:44 [pubmed]
PHST- 2023/11/20 17:26 [entrez]
AID - 00006534-990000000-02199 [pii]
AID - 10.1097/PRS.0000000000011202 [doi]
PST - aheadofprint
SO  - Plast Reconstr Surg. 2023 Nov 14. doi: 10.1097/PRS.0000000000011202.

PMID- 37714732
OWN - NLM
STAT- MEDLINE
DCOM- 20231128
LR  - 20231128
IS  - 2213-2945 (Print)
IS  - 2213-2953 (Linking)
VI  - 12
IP  - 6
DP  - 2023 Nov-Dec
TI  - Implications of ChatGPT for cytopathology and recommendations for updating JASC 
      guidelines on the responsible use of artificial intelligence.
PG  - 389-394
LID - S2213-2945(23)00062-5 [pii]
LID - 10.1016/j.jasc.2023.07.001 [doi]
FAU - Pantanowitz, Joshua
AU  - Pantanowitz J
AD  - Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
FAU - Pantanowitz, Liron
AU  - Pantanowitz L
AD  - Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 
      Electronic address: LIRON@pitt.edu.
LA  - eng
PT  - Editorial
DEP - 20230707
PL  - United States
TA  - J Am Soc Cytopathol
JT  - Journal of the American Society of Cytopathology
JID - 101613234
SB  - IM
MH  - Humans
MH  - *Cytology
MH  - *Artificial Intelligence
COIS- Conflict of interest disclosures Liron Pantanowitz is a consultant for Hamamatsu, 
      serves on the medical advisory board for Ibex, NTP, and AIxMed, and is a co-owner 
      of PlacentaAI.
EDAT- 2023/09/16 05:41
MHDA- 2023/11/28 06:42
CRDT- 2023/09/15 21:53
PHST- 2023/06/16 00:00 [received]
PHST- 2023/07/01 00:00 [revised]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/11/28 06:42 [medline]
PHST- 2023/09/16 05:41 [pubmed]
PHST- 2023/09/15 21:53 [entrez]
AID - S2213-2945(23)00062-5 [pii]
AID - 10.1016/j.jasc.2023.07.001 [doi]
PST - ppublish
SO  - J Am Soc Cytopathol. 2023 Nov-Dec;12(6):389-394. doi: 10.1016/j.jasc.2023.07.001. 
      Epub 2023 Jul 7.

PMID- 37439837
OWN - NLM
STAT- Publisher
LR  - 20230713
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Jul 13
TI  - Harnessing the Potential of ChatGPT in Breast Reconstruction: A Revolution in 
      Patient Communication and Education.
LID - 10.1007/s00266-023-03490-0 [doi]
FAU - Li, Weiwei
AU  - Li W
AD  - Department of Burn, Would Repair Surgery and Plastic Surgery, Department of 
      Aesthetic Surgery, Affiliated Hospital of Guilin Medical University, Guangxi, 
      541001, China.
FAU - Lu, Wei
AU  - Lu W
AD  - Department of Burn, Would Repair Surgery and Plastic Surgery, Department of 
      Aesthetic Surgery, Affiliated Hospital of Guilin Medical University, Guangxi, 
      541001, China.
FAU - Gong, Zhenyu
AU  - Gong Z
AD  - Department of Burn, Would Repair Surgery and Plastic Surgery, Department of 
      Aesthetic Surgery, Affiliated Hospital of Guilin Medical University, Guangxi, 
      541001, China. gfdsa9876543210@163.com.
LA  - eng
PT  - Letter
DEP - 20230713
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EDAT- 2023/07/13 12:32
MHDA- 2023/07/13 12:32
CRDT- 2023/07/13 11:06
PHST- 2023/06/24 00:00 [received]
PHST- 2023/06/26 00:00 [accepted]
PHST- 2023/07/13 12:32 [medline]
PHST- 2023/07/13 12:32 [pubmed]
PHST- 2023/07/13 11:06 [entrez]
AID - 10.1007/s00266-023-03490-0 [pii]
AID - 10.1007/s00266-023-03490-0 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Jul 13. doi: 10.1007/s00266-023-03490-0.

PMID- 37181646
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230516
IS  - 2666-5158 (Electronic)
IS  - 2666-5158 (Linking)
VI  - 3
DP  - 2023
TI  - Letting ChatGPT do your science is fraudulent (and a bad idea), but AI-generated 
      text can enhance inclusiveness in publishing.
PG  - 100057
LID - 10.1016/j.cris.2023.100057 [doi]
LID - 100057
FAU - Sinclair, Brent J
AU  - Sinclair BJ
AD  - Department of Biology, University of Western Ontario, London, ON, Canada.
LA  - eng
PT  - Editorial
DEP - 20230417
PL  - Netherlands
TA  - Curr Res Insect Sci
JT  - Current research in insect science
JID - 9918231903506676
PMC - PMC10172689
EDAT- 2023/05/14 19:13
MHDA- 2023/05/14 19:14
CRDT- 2023/05/14 12:21
PHST- 2023/05/14 19:14 [medline]
PHST- 2023/05/14 19:13 [pubmed]
PHST- 2023/05/14 12:21 [entrez]
AID - S2666-5158(23)00006-9 [pii]
AID - 100057 [pii]
AID - 10.1016/j.cris.2023.100057 [doi]
PST - epublish
SO  - Curr Res Insect Sci. 2023 Apr 17;3:100057. doi: 10.1016/j.cris.2023.100057. 
      eCollection 2023.

PMID- 37127465
OWN - NLM
STAT- MEDLINE
DCOM- 20230524
LR  - 20230524
IS  - 1768-3122 (Electronic)
IS  - 0248-8663 (Linking)
VI  - 44
IP  - 5
DP  - 2023 May
TI  - [Chatbots and internal medicine: Future opportunities and challenges].
PG  - 209-211
LID - S0248-8663(23)00123-6 [pii]
LID - 10.1016/j.revmed.2023.04.001 [doi]
FAU - Galland, J
AU  - Galland J
AD  - Service de médecine interne, hôpital Fleyriat, centre hospitalier de 
      Bourg-en-Bresse, 900, route de Paris, 01012 Viriat cedex, France; Digital Medical 
      Hub SAS, Hôtel Dieu, Assistance publique-Hôpitaux de Paris, Paris, France. 
      Electronic address: joris.galland@dmh-aphp.fr.
LA  - fre
PT  - Journal Article
TT  - Les chatbots en médecine interne : opportunités et défis à venir.
DEP - 20230429
PL  - France
TA  - Rev Med Interne
JT  - La Revue de medecine interne
JID - 8101383
SB  - IM
MH  - Humans
MH  - *Internal Medicine
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Intelligence artificielle
EDAT- 2023/05/02 00:42
MHDA- 2023/05/15 06:42
CRDT- 2023/05/01 21:57
PHST- 2023/03/31 00:00 [received]
PHST- 2023/04/01 00:00 [accepted]
PHST- 2023/05/15 06:42 [medline]
PHST- 2023/05/02 00:42 [pubmed]
PHST- 2023/05/01 21:57 [entrez]
AID - S0248-8663(23)00123-6 [pii]
AID - 10.1016/j.revmed.2023.04.001 [doi]
PST - ppublish
SO  - Rev Med Interne. 2023 May;44(5):209-211. doi: 10.1016/j.revmed.2023.04.001. Epub 
      2023 Apr 29.

PMID- 37070106
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230419
IS  - 2008-2258 (Print)
IS  - 2008-4234 (Electronic)
IS  - 2008-2258 (Linking)
VI  - 16
IP  - 1
DP  - 2023
TI  - Does chatGPT (or any other artificial intelligence language tool) deserve to be 
      included in authorship list?
PG  - 435-437
LID - 10.22037/ghfbb.v16i1.2747 [doi]
FAU - Pourhoseingholi, Mohamad Amin
AU  - Pourhoseingholi MA
AD  - Gastroenterology and Liver Diseases Research Center, Research Institute for 
      Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical 
      Sciences, Tehran, Iran.
FAU - Hatamnejad, Mohammad Reza
AU  - Hatamnejad MR
AD  - Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, 
      Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti 
      University of Medical Sciences, Tehran, Iran.
FAU - Solhpour, Ali
AU  - Solhpour A
AD  - Department of Anaesthesiology, College of Medicine, University of Florida, 
      Gainesville, FL, USA.
LA  - eng
PT  - Editorial
PL  - Iran
TA  - Gastroenterol Hepatol Bed Bench
JT  - Gastroenterology and hepatology from bed to bench
JID - 101525875
PMC - PMC10105502
COIS- The authors declare that they have no conflict of interest.
EDAT- 2023/04/19 06:00
MHDA- 2023/04/19 06:01
CRDT- 2023/04/18 01:48
PHST- 2023/01/10 00:00 [received]
PHST- 2023/01/28 00:00 [accepted]
PHST- 2023/04/19 06:01 [medline]
PHST- 2023/04/18 01:48 [entrez]
PHST- 2023/04/19 06:00 [pubmed]
AID - 10.22037/ghfbb.v16i1.2747 [doi]
PST - ppublish
SO  - Gastroenterol Hepatol Bed Bench. 2023;16(1):435-437. doi: 
      10.22037/ghfbb.v16i1.2747.

PMID- 37454961
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1097-6868 (Electronic)
IS  - 0002-9378 (Linking)
VI  - 229
IP  - 6
DP  - 2023 Dec
TI  - Broadening the horizon: a call for extensive exploration of ChatGPT's potential 
      in obstetrics and gynecology.
PG  - 706
LID - S0002-9378(23)00464-7 [pii]
LID - 10.1016/j.ajog.2023.07.016 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, India. Electronic address: ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20230715
PL  - United States
TA  - Am J Obstet Gynecol
JT  - American journal of obstetrics and gynecology
JID - 0370476
SB  - IM
MH  - Female
MH  - Pregnancy
MH  - Humans
MH  - *Gynecology/education
MH  - *Obstetrics/education
MH  - *Internship and Residency
EDAT- 2023/07/17 00:42
MHDA- 2023/11/27 12:42
CRDT- 2023/07/16 19:28
PHST- 2023/06/17 00:00 [received]
PHST- 2023/07/12 00:00 [accepted]
PHST- 2023/11/27 12:42 [medline]
PHST- 2023/07/17 00:42 [pubmed]
PHST- 2023/07/16 19:28 [entrez]
AID - S0002-9378(23)00464-7 [pii]
AID - 10.1016/j.ajog.2023.07.016 [doi]
PST - ppublish
SO  - Am J Obstet Gynecol. 2023 Dec;229(6):706. doi: 10.1016/j.ajog.2023.07.016. Epub 
      2023 Jul 15.

PMID- 37314465
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231024
LR  - 20231024
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
VI  - 47
IP  - 5
DP  - 2023 Oct
TI  - Response to "The Artificial Intelligence Application in Aesthetic Medicine: How 
      ChatGPT can Revolutionize the Aesthetic World" by Buzzaccarini et al.
PG  - 2213-2214
LID - 10.1007/s00266-023-03439-3 [doi]
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia. 
      ishithseth1@gmail.com.
AD  - Faculty of Medicine, Monash University, Melbourne, VIC, 3004, Australia. 
      ishithseth1@gmail.com.
FAU - Xie, Yi
AU  - Xie Y
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
FAU - Rozen, Warren M
AU  - Rozen WM
AD  - Department of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
AD  - Faculty of Medicine, Monash University, Melbourne, VIC, 3004, Australia.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230614
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
CON - Aesthetic Plast Surg. 2023 Oct;47(5):2211-2212. PMID: 37256297
EDAT- 2023/06/14 13:07
MHDA- 2023/10/23 00:42
CRDT- 2023/06/14 11:05
PHST- 2023/05/26 00:00 [received]
PHST- 2023/05/27 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/06/14 13:07 [pubmed]
PHST- 2023/06/14 11:05 [entrez]
AID - 10.1007/s00266-023-03439-3 [pii]
AID - 10.1007/s00266-023-03439-3 [doi]
PST - ppublish
SO  - Aesthetic Plast Surg. 2023 Oct;47(5):2213-2214. doi: 10.1007/s00266-023-03439-3. 
      Epub 2023 Jun 14.

PMID- 37937680
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231110
IS  - 2177-6709 (Electronic)
IS  - 2176-9451 (Print)
IS  - 2176-9451 (Linking)
VI  - 28
IP  - 5
DP  - 2023
TI  - Assessing the reliability of ChatGPT: a content analysis of self-generated and 
      self-answered questions on clear aligners, TADs and digital imaging.
PG  - e2323183
LID - S2176-94512023000500300 [pii]
LID - 10.1590/2177-6709.28.5.e2323183.oar [doi]
LID - e2323183
AB  - INTRODUCTION: Artificial Intelligence (AI) is a tool that is already part of our 
      reality, and this is an opportunity to understand how it can be useful in 
      interacting with patients and providing valuable information about orthodontics. 
      OBJECTIVE: This study evaluated the accuracy of ChatGPT in providing accurate and 
      quality information to answer questions on Clear aligners, Temporary anchorage 
      devices and Digital imaging in orthodontics. METHODS: forty-five questions and 
      answers were generated by the ChatGPT 4.0, and analyzed separately by five 
      orthodontists. The evaluators independently rated the quality of information 
      provided on a Likert scale, in which higher scores indicated greater quality of 
      information (1 = very poor; 2 = poor; 3 = acceptable; 4 = good; 5 = very good). 
      The Kruskal-Wallis H test (p< 0.05) and post-hoc pairwise comparisons with the 
      Bonferroni correction were performed. RESULTS: From the 225 evaluations of the 
      five different evaluators, 11 (4.9%) were considered as very poor, 4 (1.8%) as 
      poor, and 15 (6.7%) as acceptable. The majority were considered as good [34 
      (15,1%)] and very good [161 (71.6%)]. Regarding evaluators' scores, a slight 
      agreement was perceived, with Fleiss's Kappa equal to 0.004. CONCLUSIONS: ChatGPT 
      has proven effective in providing quality answers related to clear aligners, 
      temporary anchorage devices, and digital imaging within the context of interest 
      of orthodontics.
FAU - Tanaka, Orlando Motohiro
AU  - Tanaka OM
AUID- ORCID: 0000-0002-1052-7872
AD  - Pontifícia Universidade Católica do Paraná, Orthodontics (Curitiba/PR, Brazil).
FAU - Gasparello, Gil Guilherme
AU  - Gasparello GG
AUID- ORCID: 0000-0002-8955-6448
AD  - Pontifícia Universidade Católica do Paraná, Orthodontics (Curitiba/PR, Brazil).
FAU - Hartmann, Giovani Ceron
AU  - Hartmann GC
AUID- ORCID: 0000-0001-9750-0121
AD  - Pontifícia Universidade Católica do Paraná, Orthodontics (Curitiba/PR, Brazil).
FAU - Casagrande, Fernando Augusto
AU  - Casagrande FA
AUID- ORCID: 0009-0009-8662-0502
AD  - Pontifícia Universidade Católica do Paraná, Orthodontics (Curitiba/PR, Brazil).
FAU - Pithon, Matheus Melo
AU  - Pithon MM
AUID- ORCID: 0000-0002-8418-4139
AD  - Southwest Bahia State University , Orthodontics (Jequié/BH, Brazil).
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - Brazil
TA  - Dental Press J Orthod
JT  - Dental press journal of orthodontics
JID - 101532240
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - Orthodontists
MH  - *Orthodontic Appliances, Removable
PMC - PMC10627416
COIS- The authors report no commercial, proprietary or financial interest in the 
      products or companies described in this article.
EDAT- 2023/11/08 12:42
MHDA- 2023/11/09 06:42
CRDT- 2023/11/08 06:44
PHST- 2023/08/20 00:00 [received]
PHST- 2023/09/04 00:00 [accepted]
PHST- 2023/11/09 06:42 [medline]
PHST- 2023/11/08 12:42 [pubmed]
PHST- 2023/11/08 06:44 [entrez]
AID - S2176-94512023000500300 [pii]
AID - 10.1590/2177-6709.28.5.e2323183.oar [doi]
PST - epublish
SO  - Dental Press J Orthod. 2023 Nov 3;28(5):e2323183. doi: 
      10.1590/2177-6709.28.5.e2323183.oar. eCollection 2023.

PMID- 37880412
OWN - NLM
STAT- Publisher
LR  - 20231025
IS  - 1573-2630 (Electronic)
IS  - 0165-5701 (Linking)
DP  - 2023 Oct 26
TI  - A comparative study on the knowledge levels of artificial intelligence programs 
      in diagnosing ophthalmic pathologies and intraocular tumors evaluated their 
      superiority and potential utility.
LID - 10.1007/s10792-023-02893-x [doi]
AB  - PURPOSE: This study aimed to test the knowledge levels of ChatGPT, Bing, and Bard 
      artificial intelligence chatbots, which have been released by three different 
      manufacturers, about ophthalmic pathologies and intraocular tumors, to test their 
      usability and to investigate the presence of superiority to each other. METHODS: 
      Thirty-six questions were obtained from the American Academy and Ophthalmology 
      2022-2023 Basic and Clinical Science Course Ophthalmic Pathology and Intraocular 
      Tumor study questions section. Each question was asked separately for the 
      ChatGPT, Bing, and Bard artificial intelligence programs. Answers to the 
      questions were categorized as correct or incorrect. The statistical relationship 
      between the correct and incorrect response rates of the artificial intelligence 
      programs was determined. RESULTS: From the artificial intelligence chatbots, 
      ChatGPT gave the correct answer to 58.6% of the questions asked, Bing gave the 
      correct answer to 63.9%, and Bard gave the correct answer to 69.4%. No 
      statistical significance was found between the rates of correct answers to the 
      questions in all 3 artificial intelligence programs (p = 0.705, Pearson 
      Chi-square test). CONCLUSION: Artificial intelligence chatbots can be used to 
      access information related to ophthalmic pathologies and intraocular tumors. 
      However, in the evaluation of the data, it should be noted that not all questions 
      can be answered correctly. Care should be taken when examining the answers.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature B.V.
FAU - Sensoy, Eyupcan
AU  - Sensoy E
AUID- ORCID: 0000-0002-4401-8435
AD  - Ankara Etlik City Hospital, Ankara, Turkey. dreyupcansensoy@yahoo.com.
FAU - Citirik, Mehmet
AU  - Citirik M
AUID- ORCID: 0000-0002-0558-5576
AD  - Ankara Etlik City Hospital, Ankara, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - Netherlands
TA  - Int Ophthalmol
JT  - International ophthalmology
JID - 7904294
SB  - IM
OTO - NOTNLM
OT  - Bard
OT  - Bing
OT  - ChatGPT
OT  - Intraocular tumors
OT  - Ophthalmic pathology
EDAT- 2023/10/26 00:42
MHDA- 2023/10/26 00:42
CRDT- 2023/10/25 23:40
PHST- 2023/08/19 00:00 [received]
PHST- 2023/09/27 00:00 [accepted]
PHST- 2023/10/26 00:42 [medline]
PHST- 2023/10/26 00:42 [pubmed]
PHST- 2023/10/25 23:40 [entrez]
AID - 10.1007/s10792-023-02893-x [pii]
AID - 10.1007/s10792-023-02893-x [doi]
PST - aheadofprint
SO  - Int Ophthalmol. 2023 Oct 26. doi: 10.1007/s10792-023-02893-x.

PMID- 37770534
OWN - NLM
STAT- Publisher
LR  - 20230928
IS  - 1476-5454 (Electronic)
IS  - 0950-222X (Linking)
DP  - 2023 Sep 28
TI  - Google's AI chatbot "Bard": a side-by-side comparison with ChatGPT and its 
      utilization in ophthalmology.
LID - 10.1038/s41433-023-02760-0 [doi]
FAU - Waisberg, Ethan
AU  - Waisberg E
AUID- ORCID: 0000-0001-8999-0212
AD  - Department of Ophthalmology, University of Cambridge, Cambridge, UK. 
      ethan.waisberg@qehkl.nhs.uk.
FAU - Ong, Joshua
AU  - Ong J
AUID- ORCID: 0000-0003-4860-827X
AD  - Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg 
      Eye Center, Ann Arbor, MI, USA.
FAU - Masalkhi, Mouayad
AU  - Masalkhi M
AUID- ORCID: 0000-0003-0811-358X
AD  - University College Dublin School of Medicine, Belfield, Dublin, Ireland.
FAU - Zaman, Nasif
AU  - Zaman N
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
FAU - Sarker, Prithul
AU  - Sarker P
AUID- ORCID: 0000-0002-6290-5484
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
FAU - Lee, Andrew G
AU  - Lee AG
AD  - Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA.
AD  - Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, 
      Houston, TX, USA.
AD  - The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, 
      TX, USA.
AD  - Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell 
      Medicine, New York, NY, USA.
AD  - Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, 
      USA.
AD  - University of Texas MD Anderson Cancer Center, Houston, TX, USA.
AD  - Texas A&M College of Medicine, Bryan, TX, USA.
AD  - Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa 
      City, IA, USA.
FAU - Tavakkoli, Alireza
AU  - Tavakkoli A
AUID- ORCID: 0000-0001-9460-1269
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, Reno, NV, USA.
LA  - eng
PT  - Journal Article
DEP - 20230928
PL  - England
TA  - Eye (Lond)
JT  - Eye (London, England)
JID - 8703986
SB  - IM
EDAT- 2023/09/29 00:42
MHDA- 2023/09/29 00:42
CRDT- 2023/09/28 23:27
PHST- 2023/08/21 00:00 [received]
PHST- 2023/09/15 00:00 [accepted]
PHST- 2023/09/06 00:00 [revised]
PHST- 2023/09/29 00:42 [medline]
PHST- 2023/09/29 00:42 [pubmed]
PHST- 2023/09/28 23:27 [entrez]
AID - 10.1038/s41433-023-02760-0 [pii]
AID - 10.1038/s41433-023-02760-0 [doi]
PST - aheadofprint
SO  - Eye (Lond). 2023 Sep 28. doi: 10.1038/s41433-023-02760-0.

PMID- 37738147
OWN - NLM
STAT- Publisher
LR  - 20230922
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
DP  - 2023 Sep 19
TI  - ChatGPT: beneficial or detrimental in the absence of professional mental health 
      care?
LID - fdad184 [pii]
LID - 10.1093/pubmed/fdad184 [doi]
FAU - Haman, Michael
AU  - Haman M
AUID- ORCID: 0000-0001-5772-2045
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Praha 16500, Czech Republic.
FAU - Školník, Milan
AU  - Školník M
AUID- ORCID: 0000-0002-0672-219X
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Praha 16500, Czech Republic.
LA  - eng
PT  - Journal Article
DEP - 20230919
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
EDAT- 2023/09/22 18:42
MHDA- 2023/09/22 18:42
CRDT- 2023/09/22 12:43
PHST- 2023/08/13 00:00 [received]
PHST- 2023/08/13 00:00 [revised]
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/09/22 18:42 [medline]
PHST- 2023/09/22 18:42 [pubmed]
PHST- 2023/09/22 12:43 [entrez]
AID - 7277320 [pii]
AID - 10.1093/pubmed/fdad184 [doi]
PST - aheadofprint
SO  - J Public Health (Oxf). 2023 Sep 19:fdad184. doi: 10.1093/pubmed/fdad184.

PMID- 37635433
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230906
IS  - 1738-6586 (Print)
IS  - 2005-5013 (Electronic)
IS  - 1738-6586 (Linking)
VI  - 19
IP  - 5
DP  - 2023 Sep
TI  - The Potential of ChatGPT to Transform Healthcare and Address Ethical Challenges 
      in Artificial Intelligence-Driven Medicine.
PG  - 509-511
LID - 10.3988/jcn.2023.0158 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. ppray@cus.ac.in.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, Kolkata, India.
LA  - eng
PT  - Comment
PT  - Letter
PL  - Korea (South)
TA  - J Clin Neurol
JT  - Journal of clinical neurology (Seoul, Korea)
JID - 101252374
CON - J Clin Neurol. 2023 May;19(3):215-216. PMID: 37151138
PMC - PMC10471548
COIS- The authors have no potential conflicts of interest to disclose.
EDAT- 2023/08/28 06:42
MHDA- 2023/08/28 06:43
CRDT- 2023/08/28 02:43
PHST- 2023/05/05 00:00 [received]
PHST- 2023/05/22 00:00 [revised]
PHST- 2023/06/05 00:00 [accepted]
PHST- 2023/08/28 06:43 [medline]
PHST- 2023/08/28 06:42 [pubmed]
PHST- 2023/08/28 02:43 [entrez]
AID - 19.509 [pii]
AID - 10.3988/jcn.2023.0158 [doi]
PST - ppublish
SO  - J Clin Neurol. 2023 Sep;19(5):509-511. doi: 10.3988/jcn.2023.0158.

PMID- 37285189
OWN - NLM
STAT- MEDLINE
DCOM- 20230609
LR  - 20230709
IS  - 2057-5858 (Electronic)
IS  - 2057-5858 (Linking)
VI  - 9
IP  - 6
DP  - 2023 Jun
TI  - Navigating the AI frontier: ethical considerations and best practices in 
      microbial genomics research.
LID - 10.1099/mgen.0.001049 [doi]
LID - mgen001049
FAU - Page, Andrew J
AU  - Page AJ
AD  - Theiagen Genomics, Highlands Ranch, Colorado, USA.
AD  - University of East Anglia, Norfolk, UK.
FAU - Tumelty, Niamh M
AU  - Tumelty NM
AD  - London School of Economics and Political Science, London, UK.
FAU - Sheppard, Samuel K
AU  - Sheppard SK
AD  - Department of Biology, Ineos Oxford Institute, University of Oxford, Oxford, UK.
LA  - eng
PT  - Editorial
PL  - England
TA  - Microb Genom
JT  - Microbial genomics
JID - 101671820
SB  - IM
MH  - *Genomics
MH  - *Artificial Intelligence
PMC - PMC10327496
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - LLM
OT  - ethics
COIS- NMT has a part-time role as managing director of the LSE Press which is part of 
      the London School of Economics and Political Science. The other authors have no 
      conflicts.
EDAT- 2023/06/07 13:10
MHDA- 2023/06/09 06:41
CRDT- 2023/06/07 11:53
PHST- 2023/06/09 06:41 [medline]
PHST- 2023/06/07 13:10 [pubmed]
PHST- 2023/06/07 11:53 [entrez]
AID - 001049 [pii]
AID - 10.1099/mgen.0.001049 [doi]
PST - ppublish
SO  - Microb Genom. 2023 Jun;9(6):mgen001049. doi: 10.1099/mgen.0.001049.

PMID- 37939670
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231113
IS  - 2287-9129 (Electronic)
IS  - 2287-9110 (Print)
IS  - 2287-9110 (Linking)
VI  - 29
IP  - 4
DP  - 2023 Oct
TI  - Ethical consideration of the use of generative artificial intelligence, including 
      ChatGPT in writing a nursing article.
PG  - 249-251
LID - 10.4094/chnr.2023.29.4.249 [doi]
FAU - Huh, Sun
AU  - Huh S
AD  - Professor, Department of Parasitology and Institute of Medical Education, College 
      of Medicine, Hallym University, Chuncheon, Korea.
LA  - eng
GR  - 2023J1A1A1A01093462/National Research Foundation of Korea/
GR  - Ministry of Education/
PT  - Editorial
DEP - 20231031
PL  - Korea (South)
TA  - Child Health Nurs Res
JT  - Child health nursing research
JID - 101603835
PMC - PMC10636529
COIS- Conflict of interest No existing or potential conflict of interest relevant to 
      this article was reported.
EDAT- 2023/11/09 00:42
MHDA- 2023/11/09 00:43
CRDT- 2023/11/08 18:22
PHST- 2023/10/15 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/11/09 00:43 [medline]
PHST- 2023/11/09 00:42 [pubmed]
PHST- 2023/11/08 18:22 [entrez]
AID - chnr.2023.29.4.249 [pii]
AID - chnr-29-4-249 [pii]
AID - 10.4094/chnr.2023.29.4.249 [doi]
PST - ppublish
SO  - Child Health Nurs Res. 2023 Oct;29(4):249-251. doi: 10.4094/chnr.2023.29.4.249. 
      Epub 2023 Oct 31.

PMID- 37616504
OWN - NLM
STAT- Publisher
LR  - 20230928
IS  - 1527-6473 (Electronic)
IS  - 1527-6465 (Linking)
DP  - 2023 Aug 25
TI  - Exploring the potential of ChatGPT in generating unknown clinical questions about 
      liver transplantation: A feasibility study.
LID - 10.1097/LVT.0000000000000246 [doi]
FAU - Akabane, Miho
AU  - Akabane M
AUID- ORCID: 0000-0002-6705-5730
AD  - Department of Surgery, Division of Abdominal Transplant, Stanford University 
      Medical Center, Stanford, California, USA.
FAU - Iwadoh, Kazuhiro
AU  - Iwadoh K
AUID- ORCID: 0000-0003-0508-7830
AD  - Department of Transplant Surgery, Mita Hospital, International University of 
      Health and Welfare, Tokyo, Japan.
FAU - Melcher, Marc L
AU  - Melcher ML
AUID- ORCID: 0000-0002-7185-4383
AD  - Department of Surgery, Division of Abdominal Transplant, Stanford University 
      Medical Center, Stanford, California, USA.
FAU - Sasaki, Kazunari
AU  - Sasaki K
AUID- ORCID: 0000-0003-2514-5778
AD  - Department of Surgery, Division of Abdominal Transplant, Stanford University 
      Medical Center, Stanford, California, USA.
LA  - eng
PT  - Journal Article
DEP - 20230825
PL  - United States
TA  - Liver Transpl
JT  - Liver transplantation : official publication of the American Association for the 
      Study of Liver Diseases and the International Liver Transplantation Society
JID - 100909185
SB  - IM
EDAT- 2023/08/24 18:41
MHDA- 2023/08/24 18:41
CRDT- 2023/08/24 15:22
PHST- 2023/06/03 00:00 [received]
PHST- 2023/08/09 00:00 [accepted]
PHST- 2023/08/24 18:41 [pubmed]
PHST- 2023/08/24 18:41 [medline]
PHST- 2023/08/24 15:22 [entrez]
AID - 01445473-990000000-00233 [pii]
AID - 10.1097/LVT.0000000000000246 [doi]
PST - aheadofprint
SO  - Liver Transpl. 2023 Aug 25. doi: 10.1097/LVT.0000000000000246.

PMID- 37594609
OWN - NLM
STAT- Publisher
LR  - 20230818
IS  - 1556-2891 (Electronic)
IS  - 1547-769X (Linking)
DP  - 2023 Aug 18
TI  - Quid Pro Quo Doctor, I tell you things, you tell me things: ChatGPT's thoughts on 
      a killer.
LID - 10.1007/s12024-023-00696-1 [doi]
FAU - Menshawey, Rahma
AU  - Menshawey R
AUID- ORCID: 0000-0002-6755-6623
AD  - Kasr al Ainy Hospital, Faculty of Medicine, Kasr Al Ainy, Cairo University, 
      Geziret Elroda, Manial, Cairo, 11562, Egypt. rahma.menshawey.94@gmail.com.
FAU - Menshawey, Esraa
AU  - Menshawey E
AD  - Kasr al Ainy Hospital, Faculty of Medicine, Kasr Al Ainy, Cairo University, 
      Geziret Elroda, Manial, Cairo, 11562, Egypt.
LA  - eng
PT  - Letter
DEP - 20230818
PL  - United States
TA  - Forensic Sci Med Pathol
JT  - Forensic science, medicine, and pathology
JID - 101236111
SB  - IM
EDAT- 2023/08/18 12:41
MHDA- 2023/08/18 12:41
CRDT- 2023/08/18 11:09
PHST- 2023/08/07 00:00 [accepted]
PHST- 2023/08/18 12:41 [medline]
PHST- 2023/08/18 12:41 [pubmed]
PHST- 2023/08/18 11:09 [entrez]
AID - 10.1007/s12024-023-00696-1 [pii]
AID - 10.1007/s12024-023-00696-1 [doi]
PST - aheadofprint
SO  - Forensic Sci Med Pathol. 2023 Aug 18. doi: 10.1007/s12024-023-00696-1.

PMID- 37539850
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230807
LR  - 20230807
IS  - 2047-2986 (Electronic)
IS  - 2047-2978 (Print)
IS  - 2047-2978 (Linking)
VI  - 13
DP  - 2023 Aug 4
TI  - RISK-GPT: Using ChatGPT to construct a reliable risk factor database for all 
      known diseases.
PG  - 03037
LID - 10.7189/jogh.13.03037 [doi]
LID - 03037
FAU - Chen, Xi
AU  - Chen X
AD  - Department of Adult Joint Reconstructive Surgery, Beijing Jishuitan Hospital, 
      Capital Medical University, Beijing, China.
AD  - Department of Immunology, School of Basic Medical Sciences, Peking University. 
      NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China.
FAU - Zhang, Xin
AU  - Zhang X
AD  - Institute of Computing and Intelligence, Harbin Institute of Technology 
      (Shenzhen), HIT Campus of University Town of Shenzhen, Shenzhen, China.
FAU - Liu, Yuan
AU  - Liu Y
AD  - Central Laboratory, Peking University School of Stomatology, Beijing, China.
FAU - Wang, Ziyuan
AU  - Wang Z
AD  - Department of Immunology, School of Basic Medical Sciences, Peking University. 
      NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China.
FAU - Zhou, Yixin
AU  - Zhou Y
AD  - Department of Adult Joint Reconstructive Surgery, Beijing Jishuitan Hospital, 
      Capital Medical University, Beijing, China.
FAU - Chu, Ming
AU  - Chu M
AD  - Department of Immunology, School of Basic Medical Sciences, Peking University. 
      NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20230804
PL  - Scotland
TA  - J Glob Health
JT  - Journal of global health
JID - 101578780
SB  - IM
PMC - PMC10401900
COIS- Disclosure of interest: The authors completed the ICMJE Disclosure of Interest 
      Form (available upon request from the corresponding author) and disclose no 
      relevant interests.
EDAT- 2023/08/04 13:09
MHDA- 2023/08/04 13:10
CRDT- 2023/08/04 07:43
PHST- 2023/08/04 13:10 [medline]
PHST- 2023/08/04 13:09 [pubmed]
PHST- 2023/08/04 07:43 [entrez]
AID - jogh-13-03037 [pii]
AID - 10.7189/jogh.13.03037 [doi]
PST - epublish
SO  - J Glob Health. 2023 Aug 4;13:03037. doi: 10.7189/jogh.13.03037.

PMID- 37466324
OWN - NLM
STAT- Publisher
LR  - 20230719
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
DP  - 2023 Jul 19
TI  - Commentary: Performance of ChatGPT, GPT-4, and Google Bard on a Neurosurgery Oral 
      Boards Preparation Question Bank.
LID - 10.1227/neu.0000000000002618 [doi]
FAU - Resnick, Daniel K
AU  - Resnick DK
AUID- ORCID: 0000-0002-7727-7185
AD  - Department of Neurosurgery, University of Wisconsin School of Medicine and Public 
      Health, K4/834 Clinical Science Center, Madison, Wisconsin, USA.
LA  - eng
PT  - Journal Article
DEP - 20230719
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
EDAT- 2023/07/19 13:07
MHDA- 2023/07/19 13:07
CRDT- 2023/07/19 09:03
PHST- 2023/06/07 00:00 [received]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/07/19 13:07 [medline]
PHST- 2023/07/19 13:07 [pubmed]
PHST- 2023/07/19 09:03 [entrez]
AID - 00006123-990000000-00814 [pii]
AID - 10.1227/neu.0000000000002618 [doi]
PST - aheadofprint
SO  - Neurosurgery. 2023 Jul 19. doi: 10.1227/neu.0000000000002618.

PMID- 38081927
OWN - NLM
STAT- Publisher
LR  - 20231211
IS  - 1476-5497 (Electronic)
IS  - 0307-0565 (Linking)
DP  - 2023 Dec 11
TI  - Comment on "Credibility of ChatGPT in the assessment of obesity in type 2 
      diabetes according to the guidelines".
LID - 10.1038/s41366-023-01437-8 [doi]
FAU - Daungsupawong, Hinpetch
AU  - Daungsupawong H
AUID- ORCID: 0009-0002-5881-2709
AD  - Private Academic Consultant, Phonhong, Lao People's Democratic Republic. 
      hinpetchdaung@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Mohali, India.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20231211
PL  - England
TA  - Int J Obes (Lond)
JT  - International journal of obesity (2005)
JID - 101256108
SB  - IM
CON - Int J Obes (Lond). 2023 Nov 11;:. PMID: 37951982
EDAT- 2023/12/12 00:42
MHDA- 2023/12/12 00:42
CRDT- 2023/12/11 23:32
PHST- 2023/11/12 00:00 [received]
PHST- 2023/11/30 00:00 [accepted]
PHST- 2023/11/20 00:00 [revised]
PHST- 2023/12/12 00:42 [medline]
PHST- 2023/12/12 00:42 [pubmed]
PHST- 2023/12/11 23:32 [entrez]
AID - 10.1038/s41366-023-01437-8 [pii]
AID - 10.1038/s41366-023-01437-8 [doi]
PST - aheadofprint
SO  - Int J Obes (Lond). 2023 Dec 11. doi: 10.1038/s41366-023-01437-8.

PMID- 38007179
OWN - NLM
STAT- Publisher
LR  - 20231125
IS  - 1535-7732 (Electronic)
IS  - 1051-0443 (Linking)
DP  - 2023 Nov 23
TI  - Bridging Language and Stylistic Barriers in Interventional Radiology Standardized 
      Reporting: Enhancing Translation and Structure using ChatGPT-4.
LID - S1051-0443(23)00828-X [pii]
LID - 10.1016/j.jvir.2023.11.014 [doi]
FAU - Sasaki, Fumi
AU  - Sasaki F
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Tatekawa, Hiroyuki
AU  - Tatekawa H
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan. Electronic address: 
      htatekawa@omu.ac.jp.
FAU - Mitsuyama, Yasuhito
AU  - Mitsuyama Y
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Kageyama, Ken
AU  - Kageyama K
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Jogo, Atsushi
AU  - Jogo A
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Yamamoto, Akira
AU  - Yamamoto A
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Miki, Yukio
AU  - Miki Y
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan.
FAU - Ueda, Daiju
AU  - Ueda D
AD  - Department of Diagnostic and Interventional Radiology, Graduate School of 
      Medicine, Osaka Metropolitan University, Osaka, Japan; Smart Life Science Lab, 
      Center for Health Science Innovation, Osaka Metropolitan University, Osaka, 
      Japan.
LA  - eng
PT  - Letter
DEP - 20231123
PL  - United States
TA  - J Vasc Interv Radiol
JT  - Journal of vascular and interventional radiology : JVIR
JID - 9203369
SB  - IM
EDAT- 2023/11/26 07:44
MHDA- 2023/11/26 07:44
CRDT- 2023/11/25 19:30
PHST- 2023/08/04 00:00 [received]
PHST- 2023/10/27 00:00 [revised]
PHST- 2023/11/16 00:00 [accepted]
PHST- 2023/11/26 07:44 [medline]
PHST- 2023/11/26 07:44 [pubmed]
PHST- 2023/11/25 19:30 [entrez]
AID - S1051-0443(23)00828-X [pii]
AID - 10.1016/j.jvir.2023.11.014 [doi]
PST - aheadofprint
SO  - J Vasc Interv Radiol. 2023 Nov 23:S1051-0443(23)00828-X. doi: 
      10.1016/j.jvir.2023.11.014.

PMID- 37995008
OWN - NLM
STAT- Publisher
LR  - 20231123
IS  - 1867-108X (Electronic)
IS  - 1867-1071 (Linking)
DP  - 2023 Nov 23
TI  - Letter to the editor response to "ChatGPT, GPT-4, and bard and official board 
      examination: comment".
LID - 10.1007/s11604-023-01515-x [doi]
FAU - Harigai, Ayaka
AU  - Harigai A
AD  - Department of Diagnostic Radiology, Tohoku University Graduate School of 
      Medicine, Sendai, Japan.
AD  - Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-machi, 
      Aoba-ku, Sendai, 980-8575, Japan.
AD  - Department of Diagnostic Radiology, Japanese Red Cross Ishinomaki Hospital, 
      Ishinomaki, Japan.
FAU - Toyama, Yoshitaka
AU  - Toyama Y
AUID- ORCID: 0000-0003-0027-9681
AD  - Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-machi, 
      Aoba-ku, Sendai, 980-8575, Japan. ytoyama0818@gmail.com.
FAU - Takase, Kei
AU  - Takase K
AD  - Department of Diagnostic Radiology, Tohoku University Graduate School of 
      Medicine, Sendai, Japan.
LA  - eng
PT  - Letter
DEP - 20231123
PL  - Japan
TA  - Jpn J Radiol
JT  - Japanese journal of radiology
JID - 101490689
SB  - IM
EDAT- 2023/11/23 12:42
MHDA- 2023/11/23 12:42
CRDT- 2023/11/23 11:11
PHST- 2023/11/09 00:00 [received]
PHST- 2023/11/13 00:00 [accepted]
PHST- 2023/11/23 12:42 [medline]
PHST- 2023/11/23 12:42 [pubmed]
PHST- 2023/11/23 11:11 [entrez]
AID - 10.1007/s11604-023-01515-x [pii]
AID - 10.1007/s11604-023-01515-x [doi]
PST - aheadofprint
SO  - Jpn J Radiol. 2023 Nov 23. doi: 10.1007/s11604-023-01515-x.

PMID- 37703426
OWN - NLM
STAT- Publisher
LR  - 20230913
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
DP  - 2023 Sep 13
TI  - Response to: "The next paradigm shift? ChatGPT, artificial intelligence, and 
      medical education".
PG  - 1
LID - 10.1080/0142159X.2023.2256961 [doi]
FAU - Tan, Li Feng
AU  - Tan LF
AD  - Healthy Ageing Programme, Alexandra Hospital, Singapore.
FAU - Heng, Jonathan Jun Yi
AU  - Heng JJY
AUID- ORCID: 0000-0003-1232-9308
AD  - Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
FAU - Teo, Desmond B
AU  - Teo DB
AD  - Chronic and Fast Programmes, Alexandra Hospital, Singapore.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
EDAT- 2023/09/13 18:41
MHDA- 2023/09/13 18:41
CRDT- 2023/09/13 14:53
PHST- 2023/09/13 18:41 [medline]
PHST- 2023/09/13 18:41 [pubmed]
PHST- 2023/09/13 14:53 [entrez]
AID - 10.1080/0142159X.2023.2256961 [doi]
PST - aheadofprint
SO  - Med Teach. 2023 Sep 13:1. doi: 10.1080/0142159X.2023.2256961.

PMID- 37102424
OWN - NLM
STAT- MEDLINE
DCOM- 20230516
LR  - 20230601
IS  - 1526-4610 (Electronic)
IS  - 0017-8748 (Linking)
VI  - 63
IP  - 5
DP  - 2023 May
TI  - Benefits and implications of integrating artificial intelligence in manuscripts 
      submitted to Headache.
PG  - 571-572
LID - 10.1111/head.14502 [doi]
FAU - Gelfand, Amy A
AU  - Gelfand AA
AUID- ORCID: 0000-0002-0911-7905
AD  - Headache Editorial Team, American Headache Society, Mount Royal, New Jersey, USA.
AD  - Department of Neurology, UCSF Child & Adolescent Headache Program, San Francisco, 
      California, USA.
FAU - Vallimont, Jenn
AU  - Vallimont J
AUID- ORCID: 0009-0006-2101-5545
AD  - Headache Editorial Team, American Headache Society, Mount Royal, New Jersey, USA.
FAU - Sargent, Emily
AU  - Sargent E
AD  - John Wiley & Sons, Journal Publishing Manager, Somerville, Massachusetts, USA.
FAU - Singh, Rashmi Halker
AU  - Singh RH
AUID- ORCID: 0000-0002-4280-4440
AD  - Headache Editorial Team, American Headache Society, Mount Royal, New Jersey, USA.
AD  - Department of Neurology, Mayo Clinic in Arizona, Scottsdale, Arizona, USA.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230427
PL  - United States
TA  - Headache
JT  - Headache
JID - 2985091R
SB  - IM
CON - Headache. 2023 May;63(5):694-696. PMID: 37171282
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Headache
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatGPT
OT  - editorial
EDAT- 2023/04/27 12:41
MHDA- 2023/05/16 06:42
CRDT- 2023/04/27 07:03
PHST- 2023/03/13 00:00 [received]
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/05/16 06:42 [medline]
PHST- 2023/04/27 12:41 [pubmed]
PHST- 2023/04/27 07:03 [entrez]
AID - 10.1111/head.14502 [doi]
PST - ppublish
SO  - Headache. 2023 May;63(5):571-572. doi: 10.1111/head.14502. Epub 2023 Apr 27.

PMID- 37014239
OWN - NLM
STAT- MEDLINE
DCOM- 20230524
LR  - 20230529
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 4
DP  - 2023 May
TI  - Appropriateness of Breast Cancer Prevention and Screening Recommendations 
      Provided by ChatGPT.
PG  - e230424
LID - 10.1148/radiol.230424 [doi]
FAU - Haver, Hana L
AU  - Haver HL
AUID- ORCID: 0000-0002-8382-4077
AD  - From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, 
      Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 
      21201 (H.L.H., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and 
      Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md 
      (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, 
      Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for 
      Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins 
      University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. 
      James Clark School of Engineering, University of Maryland, College Park, Md 
      (P.H.Y.).
FAU - Ambinder, Emily B
AU  - Ambinder EB
AUID- ORCID: 0000-0001-6282-5858
AD  - From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, 
      Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 
      21201 (H.L.H., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and 
      Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md 
      (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, 
      Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for 
      Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins 
      University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. 
      James Clark School of Engineering, University of Maryland, College Park, Md 
      (P.H.Y.).
FAU - Bahl, Manisha
AU  - Bahl M
AUID- ORCID: 0000-0002-7728-7690
AD  - From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, 
      Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 
      21201 (H.L.H., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and 
      Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md 
      (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, 
      Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for 
      Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins 
      University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. 
      James Clark School of Engineering, University of Maryland, College Park, Md 
      (P.H.Y.).
FAU - Oluyemi, Eniola T
AU  - Oluyemi ET
AD  - From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, 
      Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 
      21201 (H.L.H., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and 
      Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md 
      (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, 
      Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for 
      Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins 
      University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. 
      James Clark School of Engineering, University of Maryland, College Park, Md 
      (P.H.Y.).
FAU - Jeudy, Jean
AU  - Jeudy J
AUID- ORCID: 0000-0002-6028-459X
AD  - From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, 
      Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 
      21201 (H.L.H., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and 
      Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md 
      (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, 
      Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for 
      Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins 
      University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. 
      James Clark School of Engineering, University of Maryland, College Park, Md 
      (P.H.Y.).
FAU - Yi, Paul H
AU  - Yi PH
AUID- ORCID: 0000-0001-9433-8093
AD  - From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, 
      Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland 
      School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 
      21201 (H.L.H., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and 
      Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md 
      (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, 
      Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for 
      Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins 
      University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. 
      James Clark School of Engineering, University of Maryland, College Park, Md 
      (P.H.Y.).
LA  - eng
PT  - Journal Article
DEP - 20230404
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
MH  - Humans
MH  - Female
MH  - *Early Detection of Cancer
MH  - *Breast Neoplasms/diagnostic imaging/prevention & control
EDAT- 2023/04/05 06:00
MHDA- 2023/05/24 06:42
CRDT- 2023/04/04 10:08
PHST- 2023/05/24 06:42 [medline]
PHST- 2023/04/05 06:00 [pubmed]
PHST- 2023/04/04 10:08 [entrez]
AID - 10.1148/radiol.230424 [doi]
PST - ppublish
SO  - Radiology. 2023 May;307(4):e230424. doi: 10.1148/radiol.230424. Epub 2023 Apr 4.

PMID- 37006087
OWN - NLM
STAT- MEDLINE
DCOM- 20230404
LR  - 20230501
IS  - 2737-5935 (Electronic)
IS  - 0037-5675 (Print)
IS  - 0037-5675 (Linking)
VI  - 64
IP  - 4
DP  - 2023 Apr
TI  - The rise of artificial intelligence: addressing the impact of large language 
      models such as ChatGPT on scientific publications.
PG  - 219-221
LID - 10.4103/singaporemedj.SMJ-2023-055 [doi]
FAU - Ang, Tiing Leong
AU  - Ang TL
AD  - Deputy Editor, Singapore Medical Journal, Singapore.
FAU - Choolani, Mahesh
AU  - Choolani M
AD  - Deputy Editor, Singapore Medical Journal, Singapore.
FAU - See, Kay Choong
AU  - See KC
AD  - Specialty Editor, Singapore Medical Journal, Singapore.
FAU - Poh, Kian Keong
AU  - Poh KK
AD  - Editor-in-Chief, Singapore Medical Journal, Singapore.
LA  - eng
PT  - Editorial
PL  - India
TA  - Singapore Med J
JT  - Singapore medical journal
JID - 0404516
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Publications
PMC - PMC10144457
COIS- None
EDAT- 2023/04/04 06:00
MHDA- 2023/04/04 06:42
CRDT- 2023/04/03 02:49
PHST- 2023/04/04 06:42 [medline]
PHST- 2023/04/03 02:49 [entrez]
PHST- 2023/04/04 06:00 [pubmed]
AID - SINGAPOREMEDJ_2023_64_4_219_372813 [pii]
AID - SMJ-64-219 [pii]
AID - 10.4103/singaporemedj.SMJ-2023-055 [doi]
PST - ppublish
SO  - Singapore Med J. 2023 Apr;64(4):219-221. doi: 10.4103/singaporemedj.SMJ-2023-055.

PMID- 37001998
OWN - NLM
STAT- Publisher
LR  - 20230331
IS  - 1544-1717 (Electronic)
IS  - 1544-1709 (Linking)
DP  - 2023 Mar 31
TI  - Why ChatGPT Should Not Be Used to Write Academic Scientific Manuscripts for 
      Publication.
LID - 2958 [pii]
LID - 10.1370/afm.2982 [doi]
AB  - Annals Online First article.
CI  - © 2023 Annals of Family Medicine, Inc.
LA  - eng
PT  - Editorial
DEP - 20230331
PL  - United States
TA  - Ann Fam Med
JT  - Annals of family medicine
JID - 101167762
SB  - IM
EDAT- 2023/04/01 06:00
MHDA- 2023/04/01 06:00
CRDT- 2023/03/31 21:52
PHST- 2023/03/20 00:00 [received]
PHST- 2023/03/20 00:00 [accepted]
PHST- 2023/03/31 21:52 [entrez]
PHST- 2023/04/01 06:00 [pubmed]
PHST- 2023/04/01 06:00 [medline]
AID - afm.2982 [pii]
AID - 10.1370/afm.2982 [doi]
PST - aheadofprint
SO  - Ann Fam Med. 2023 Mar 31:2958. doi: 10.1370/afm.2982.

PMID- 37904185
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231102
IS  - 2052-3211 (Print)
IS  - 2052-3211 (Electronic)
IS  - 2052-3211 (Linking)
VI  - 16
IP  - 1
DP  - 2023 Oct 30
TI  - Correction: ChatGPT in pharmacy practice: a cross-sectional exploration of 
      Jordanian pharmacists' perception, practice, and concerns.
PG  - 129
LID - 10.1186/s40545-023-00640-2 [doi]
LID - 129
FAU - Abu Hammour, Khawla
AU  - Abu Hammour K
AD  - Department of Clinical Pharmacy and Biopharmaceutics, Faculty of Pharmacy, 
      University of Jordan, Amman, Jordan.
FAU - Alhamad, Hamza
AU  - Alhamad H
AD  - Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 
      Jordan.
FAU - Al-Ashwal, Fahmi Y
AU  - Al-Ashwal FY
AD  - Department of Clinical Pharmacy, College of Pharmacy, Al-Ayen University, 
      Thi-Qar, Iraq. fahmialashwal89@gmail.com.
AD  - Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, 
      University of Science and Technology, Sana'a, Yemen. fahmialashwal89@gmail.com.
FAU - Halboup, Abdulsalam
AU  - Halboup A
AD  - Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, 
      University of Science and Technology, Sana'a, Yemen.
AD  - Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, University 
      Sains Malaysia, Gelugor, Pulau Pinang, Malaysia.
FAU - Abu Farha, Rana
AU  - Abu Farha R
AD  - Clinical Pharmacy and Therapeutics Department, Faculty of Pharmacy, Applied 
      Science Private University, P.O. Box 11937, Amman, Jordan.
FAU - Abu Hammour, Adnan
AU  - Abu Hammour A
AD  - Medrise Medical Center, Dubai Healthcare City, Dubai, United Arab Emirates.
LA  - eng
PT  - Published Erratum
DEP - 20231030
PL  - England
TA  - J Pharm Policy Pract
JT  - Journal of pharmaceutical policy and practice
JID - 101627192
EFR - J Pharm Policy Pract. 2023 Oct 3;16(1):115. PMID: 37789443
PMC - PMC10614371
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:43
CRDT- 2023/10/31 00:52
PHST- 2023/10/31 06:43 [medline]
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 00:52 [entrez]
AID - 10.1186/s40545-023-00640-2 [pii]
AID - 640 [pii]
AID - 10.1186/s40545-023-00640-2 [doi]
PST - epublish
SO  - J Pharm Policy Pract. 2023 Oct 30;16(1):129. doi: 10.1186/s40545-023-00640-2.

PMID- 37595064
OWN - NLM
STAT- Publisher
LR  - 20231010
IS  - 1365-2168 (Electronic)
IS  - 0007-1323 (Linking)
VI  - 110
IP  - 11
DP  - 2023 Oct 10
TI  - Artificial intelligence-based decision-making: can ChatGPT replace a 
      multidisciplinary tumour board?
PG  - 1543-1544
LID - 10.1093/bjs/znad264 [doi]
FAU - Vela Ulloa, Javier
AU  - Vela Ulloa J
AD  - Unit of Coloproctology, Department of Digestive Surgery, Pontificia Universidad 
      Católica de Chile, Santiago, Chile.
FAU - King Valenzuela, Sebastián
AU  - King Valenzuela S
AD  - Unit of Coloproctology, Department of Digestive Surgery, Pontificia Universidad 
      Católica de Chile, Santiago, Chile.
FAU - Riquoir Altamirano, Christophe
AU  - Riquoir Altamirano C
AD  - Unit of Coloproctology, Department of Digestive Surgery, Pontificia Universidad 
      Católica de Chile, Santiago, Chile.
FAU - Urrejola Schmied, Gonzalo
AU  - Urrejola Schmied G
AD  - Unit of Coloproctology, Department of Digestive Surgery, Pontificia Universidad 
      Católica de Chile, Santiago, Chile.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Br J Surg
JT  - The British journal of surgery
JID - 0372553
SB  - IM
EDAT- 2023/08/18 18:42
MHDA- 2023/08/18 18:42
CRDT- 2023/08/18 14:32
PHST- 2023/07/27 00:00 [received]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/08/18 18:42 [pubmed]
PHST- 2023/08/18 18:42 [medline]
PHST- 2023/08/18 14:32 [entrez]
AID - 7245897 [pii]
AID - 10.1093/bjs/znad264 [doi]
PST - ppublish
SO  - Br J Surg. 2023 Oct 10;110(11):1543-1544. doi: 10.1093/bjs/znad264.

PMID- 37204980
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230522
IS  - 0019-5545 (Print)
IS  - 1998-3794 (Electronic)
IS  - 0019-5545 (Linking)
VI  - 65
IP  - 3
DP  - 2023 Mar
TI  - Artificial intelligence in the era of ChatGPT - Opportunities and challenges in 
      mental health care.
PG  - 297-298
LID - 10.4103/indianjpsychiatry.indianjpsychiatry_112_23 [doi]
FAU - Singh, Om P
AU  - Singh OP
AD  - WBMES, Kolkata, West Bengal, India.
AD  - AMRI Hospitals, Kolkata, West Bengal, India.
LA  - eng
PT  - Editorial
DEP - 20230303
PL  - India
TA  - Indian J Psychiatry
JT  - Indian journal of psychiatry
JID - 0013255
PMC - PMC10187878
EDAT- 2023/05/19 19:14
MHDA- 2023/05/19 19:15
CRDT- 2023/05/19 13:00
PHST- 2023/02/21 00:00 [received]
PHST- 2023/02/21 00:00 [accepted]
PHST- 2023/05/19 19:15 [medline]
PHST- 2023/05/19 19:14 [pubmed]
PHST- 2023/05/19 13:00 [entrez]
AID - IJPsy-65-297 [pii]
AID - 10.4103/indianjpsychiatry.indianjpsychiatry_112_23 [doi]
PST - ppublish
SO  - Indian J Psychiatry. 2023 Mar;65(3):297-298. doi: 
      10.4103/indianjpsychiatry.indianjpsychiatry_112_23. Epub 2023 Mar 3.

PMID- 37210245
OWN - NLM
STAT- MEDLINE
DCOM- 20230612
LR  - 20231116
IS  - 1872-7654 (Electronic)
IS  - 0301-2115 (Linking)
VI  - 286
DP  - 2023 Jul
TI  - Using artificial intelligence chatbots like 'ChatGPT' to draft articles for 
      medical journals - Advantages, limitations, ethical concerns and way forward.
PG  - 151
LID - S0301-2115(23)00187-2 [pii]
LID - 10.1016/j.ejogrb.2023.05.008 [doi]
FAU - Arun Babu, Thirunavukkarasu
AU  - Arun Babu T
AD  - Dept of Pediatrics, All India Institute of Medical Sciences (AIIMS), Mangalagiri, 
      Andhra Pradesh, India. Electronic address: babuarun@yahoo.com.
FAU - Sharmila, Vijayan
AU  - Sharmila V
AD  - Dept of Obstetrics & Gynecology, All India Institute of Medical Sciences (AIIMS), 
      Mangalagiri, Andhra Pradesh, India.
LA  - eng
PT  - Letter
DEP - 20230516
PL  - Ireland
TA  - Eur J Obstet Gynecol Reprod Biol
JT  - European journal of obstetrics, gynecology, and reproductive biology
JID - 0375672
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Periodicals as Topic
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/05/21 01:05
MHDA- 2023/06/12 06:42
CRDT- 2023/05/20 21:58
PHST- 2023/04/23 00:00 [received]
PHST- 2023/05/10 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/05/21 01:05 [pubmed]
PHST- 2023/05/20 21:58 [entrez]
AID - S0301-2115(23)00187-2 [pii]
AID - 10.1016/j.ejogrb.2023.05.008 [doi]
PST - ppublish
SO  - Eur J Obstet Gynecol Reprod Biol. 2023 Jul;286:151. doi: 
      10.1016/j.ejogrb.2023.05.008. Epub 2023 May 16.

PMID- 37034476
OWN - NLM
STAT- MEDLINE
DCOM- 20230417
LR  - 20230627
IS  - 1482-1826 (Electronic)
IS  - 1482-1826 (Linking)
VI  - 26
DP  - 2023
TI  - Adapting artificial intelligence into the evolution of pharmaceutical sciences 
      and publishing: Technological darwinism.
PG  - 11349
LID - 10.3389/jpps.2023.11349 [doi]
LID - 11349
FAU - Davies, Neal M
AU  - Davies NM
AD  - Faculty of Pharmacy & Pharmaceutical Sciences, University of Alberta, Edmonton, 
      AB, Canada.
LA  - eng
PT  - Editorial
DEP - 20230322
PL  - Switzerland
TA  - J Pharm Pharm Sci
JT  - Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian 
      Society for Pharmaceutical Sciences, Societe canadienne des sciences 
      pharmaceutiques
JID - 9807281
RN  - 0 (Pharmaceutical Preparations)
SB  - IM
MH  - *Artificial Intelligence
MH  - Pharmaceutical Preparations
MH  - *Publishing
MH  - *Drug Industry
PMC - PMC10075305
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial
OT  - evolution
OT  - intelligence
OT  - pharmaceutical
COIS- The author declares that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/04/11 06:00
MHDA- 2023/04/11 06:41
CRDT- 2023/04/10 04:09
PHST- 2023/03/09 00:00 [received]
PHST- 2023/03/13 00:00 [accepted]
PHST- 2023/04/11 06:41 [medline]
PHST- 2023/04/10 04:09 [entrez]
PHST- 2023/04/11 06:00 [pubmed]
AID - 11349 [pii]
AID - 10.3389/jpps.2023.11349 [doi]
PST - epublish
SO  - J Pharm Pharm Sci. 2023 Mar 22;26:11349. doi: 10.3389/jpps.2023.11349. 
      eCollection 2023.

PMID- 37385979
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 4
DP  - 2023 Nov 29
TI  - Epistemic justice of ChatGPT in first aid for child-adolescent mental health 
      issues.
PG  - e816-e817
LID - 10.1093/pubmed/fdad098 [doi]
FAU - Aditama, Mint Husen Raya
AU  - Aditama MHR
AUID- ORCID: 0000-0003-4593-8935
AD  - Department of Guidance and Counseling, Faculty of Education and Psychology, 
      Universitas Negeri Manado, Tondano, North Sulawesi 95618, Indonesia.
FAU - Wantah, Mario Erick
AU  - Wantah ME
AD  - Department of Early Chilhood Education, Faculty of Education and Psychology, 
      Universitas Negeri Manado, Tondano, North Sulawesi 95618, Indonesia.
FAU - Laras, Palasara Brahmani
AU  - Laras PB
AUID- ORCID: 0000-0002-0475-1763
AD  - Department of Guidance and Counseling, Faculty of Teacher Training and Education, 
      Universitas Mercu Buana Yogyakarta, Sleman, Special Region of Yogyakarta 55753, 
      Indonesia.
FAU - Suhardita, Kadek
AU  - Suhardita K
AD  - Department of Guidance and Counseling, Faculty of Teacher Training and Education, 
      Universitas PGRI Mahadewa, Denpasar, Bali 80235, Indonesia.
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - Adolescent
MH  - *Mental Health
MH  - First Aid
MH  - Social Justice
MH  - *Mental Disorders/epidemiology/therapy
EDAT- 2023/06/30 01:06
MHDA- 2023/12/04 12:43
CRDT- 2023/06/29 22:53
PHST- 2023/06/05 00:00 [received]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/06/30 01:06 [pubmed]
PHST- 2023/06/29 22:53 [entrez]
AID - 7210269 [pii]
AID - 10.1093/pubmed/fdad098 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Nov 29;45(4):e816-e817. doi: 10.1093/pubmed/fdad098.

PMID- 37849522
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 0891-8929 (Print)
IS  - 1945-4953 (Electronic)
IS  - 0891-8929 (Linking)
VI  - 41
IP  - 4
DP  - 2023 Fall
TI  - Testing the Appropriateness of Diabetes Prevention and Care Information Given by 
      the Online Conversational AI ChatGPT.
PG  - 549-552
LID - 10.2337/cd23-0026 [doi]
FAU - Hong, Jison
AU  - Hong J
AUID- ORCID: 0000-0002-7192-5647
AD  - Department of Medicine, Division of Immunology & Rheumatology, Stanford 
      University, Stanford CA.
FAU - Kikuta, Nicholas Takayuki
AU  - Kikuta NT
AD  - Department of Medicine, Division of Immunology & Rheumatology, Stanford 
      University, Stanford CA.
FAU - Simos, Anna
AU  - Simos A
AD  - Stanford Health Care, Stanford, CA.
FAU - Tsai, Sandra
AU  - Tsai S
AD  - Department of Medicine, Division of Primary Care and Population Health, Stanford 
      University, Stanford, CA.
FAU - Lin, Bryant
AU  - Lin B
AD  - Department of Medicine, Division of Primary Care and Population Health, Stanford 
      University, Stanford, CA.
FAU - Rodriguez, Fatima
AU  - Rodriguez F
AD  - Department of Medicine, Division of Cardiovascular Medicine, Stanford Prevention 
      Research Center, Stanford University, Stanford, CA.
FAU - Palaniappan, Latha
AU  - Palaniappan L
AUID- ORCID: 0000-0002-1245-665X
AD  - Department of Medicine, Division of Cardiovascular Medicine, Epidemiology and 
      Population Health, Stanford University, Stanford, CA.
LA  - eng
SI  - figshare/10.2337/figshare.23278418
PT  - Journal Article
DEP - 20230615
PL  - United States
TA  - Clin Diabetes
JT  - Clinical diabetes : a publication of the American Diabetes Association
JID - 8406985
PMC - PMC10577494
COIS- No potential conflicts of interest relevant to this article were reported.
EDAT- 2023/10/18 06:43
MHDA- 2023/10/18 06:44
PMCR- 2024/09/01
CRDT- 2023/10/18 03:51
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/10/18 06:44 [medline]
PHST- 2023/10/18 06:43 [pubmed]
PHST- 2023/10/18 03:51 [entrez]
AID - CD230026 [pii]
AID - 10.2337/cd23-0026 [doi]
PST - ppublish
SO  - Clin Diabetes. 2023 Fall;41(4):549-552. doi: 10.2337/cd23-0026. Epub 2023 Jun 15.

PMID- 37705054
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230915
LR  - 20231123
IS  - 1476-069X (Electronic)
IS  - 1476-069X (Linking)
VI  - 22
IP  - 1
DP  - 2023 Sep 14
TI  - ChatGPT in the health sciences: pause and ponder.
PG  - 64
LID - 10.1186/s12940-023-01014-6 [doi]
LID - 64
FAU - Dalvi, Ana Paula Razal
AU  - Dalvi APR
AD  - Department of Epidemiology, Institute of Social Medicine, State University of Rio 
      de Janeiro, Rio de Janeiro, Brazil. anapaularazal@gmail.com.
FAU - Dias, Rayara Mozer
AU  - Dias RM
AD  - Department of Epidemiology, Institute of Social Medicine, State University of Rio 
      de Janeiro, Rio de Janeiro, Brazil.
FAU - Junger, Washington Leite
AU  - Junger WL
AD  - Department of Epidemiology, Institute of Social Medicine, State University of Rio 
      de Janeiro, Rio de Janeiro, Brazil.
LA  - eng
GR  - 315349/2021-2/National Council of Technological and Scientific Development - 
      CNPq/
PT  - Letter
DEP - 20230914
PL  - England
TA  - Environ Health
JT  - Environmental health : a global access science source
JID - 101147645
SB  - IM
PMC - PMC10500885
OTO - NOTNLM
OT  - Air Pollution
OT  - Artificial Intelligence
OT  - Environment and Public Health
COIS- The authors declare no competing interests.
EDAT- 2023/09/14 00:42
MHDA- 2023/09/14 00:43
CRDT- 2023/09/13 23:45
PHST- 2023/07/05 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/09/14 00:43 [medline]
PHST- 2023/09/14 00:42 [pubmed]
PHST- 2023/09/13 23:45 [entrez]
AID - 10.1186/s12940-023-01014-6 [pii]
AID - 1014 [pii]
AID - 10.1186/s12940-023-01014-6 [doi]
PST - epublish
SO  - Environ Health. 2023 Sep 14;22(1):64. doi: 10.1186/s12940-023-01014-6.

PMID- 37544375
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 2212-2672 (Print)
IS  - 2212-2672 (Linking)
VI  - 123
IP  - 11
DP  - 2023 Nov
TI  - ChatGPT and Future Artificial Intelligence Chatbots: What may be the Influence on 
      Credentialed Nutrition and Dietetics Practitioners?
PG  - 1525-1531
LID - S2212-2672(23)01308-4 [pii]
LID - 10.1016/j.jand.2023.08.001 [doi]
FAU - Chatelan, Angeline
AU  - Chatelan A
AD  - Department of Nutrition and Dietetics, Geneva School of Health Sciences, HES-SO 
      University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland. 
      Electronic address: angeline.chatelan@hesge.ch.
FAU - Clerc, Aurélien
AU  - Clerc A
AD  - Department of Nutrition and Dietetics, Geneva School of Health Sciences, HES-SO 
      University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland; 
      HFR Fribourg University Training Hospital, Fribourg, Switzerland.
FAU - Fonta, Pierre-Alexandre
AU  - Fonta PA
AD  - Independent senior research engineer in natural language processing, Annemasse, 
      France.
LA  - eng
PT  - Journal Article
DEP - 20230805
PL  - United States
TA  - J Acad Nutr Diet
JT  - Journal of the Academy of Nutrition and Dietetics
JID - 101573920
SB  - IM
EDAT- 2023/08/07 00:42
MHDA- 2023/08/07 00:42
CRDT- 2023/08/06 19:13
PHST- 2023/03/30 00:00 [received]
PHST- 2023/07/12 00:00 [revised]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/08/07 00:42 [pubmed]
PHST- 2023/08/07 00:42 [medline]
PHST- 2023/08/06 19:13 [entrez]
AID - S2212-2672(23)01308-4 [pii]
AID - 10.1016/j.jand.2023.08.001 [doi]
PST - ppublish
SO  - J Acad Nutr Diet. 2023 Nov;123(11):1525-1531. doi: 10.1016/j.jand.2023.08.001. 
      Epub 2023 Aug 5.

PMID- 37301782
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231119
IS  - 1708-0428 (Electronic)
IS  - 0960-8923 (Linking)
VI  - 33
IP  - 8
DP  - 2023 Aug
TI  - Assessing the Accuracy of Responses by the Language Model ChatGPT to Questions 
      Regarding Bariatric Surgery: a Critical Appraisal.
PG  - 2588-2589
LID - 10.1007/s11695-023-06664-6 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India. ppray@cus.ac.in.
FAU - Majumder, Poulami
AU  - Majumder P
AUID- ORCID: 0000-0002-8476-423X
AD  - Maulana Abul Kalam Azad University of Technology, Kolkata, India.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230610
PL  - United States
TA  - Obes Surg
JT  - Obesity surgery
JID - 9106714
SB  - IM
CON - Obes Surg. 2023 Jun;33(6):1790-1796. PMID: 37106269
CIN - Obes Surg. 2023 Aug;33(8):2590-2591. PMID: 37312008
MH  - Humans
MH  - *Obesity, Morbid/surgery
MH  - *Bariatric Surgery
EDAT- 2023/06/11 01:06
MHDA- 2023/07/17 06:42
CRDT- 2023/06/10 23:03
PHST- 2023/05/05 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/05/05 00:00 [revised]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/06/11 01:06 [pubmed]
PHST- 2023/06/10 23:03 [entrez]
AID - 10.1007/s11695-023-06664-6 [pii]
AID - 10.1007/s11695-023-06664-6 [doi]
PST - ppublish
SO  - Obes Surg. 2023 Aug;33(8):2588-2589. doi: 10.1007/s11695-023-06664-6. Epub 2023 
      Jun 10.

PMID- 36880943
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230426
LR  - 20231204
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 307
IP  - 3
DP  - 2023 May
TI  - The Role and Limitations of Large Language Models Such as ChatGPT in Clinical 
      Settings and Medical Journalism.
PG  - e230276
LID - 10.1148/radiol.230276 [doi]
FAU - Ufuk, Furkan
AU  - Ufuk F
AUID- ORCID: 0000-0002-8614-5387
AD  - Department of Radiology, School of Medicine, University of Pamukkale, Denizli, 
      Turkey.
LA  - eng
PT  - Letter
DEP - 20230307
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
EDAT- 2023/03/08 06:00
MHDA- 2023/03/08 06:01
CRDT- 2023/03/07 10:07
PHST- 2023/03/08 06:01 [medline]
PHST- 2023/03/08 06:00 [pubmed]
PHST- 2023/03/07 10:07 [entrez]
AID - 10.1148/radiol.230276 [doi]
PST - ppublish
SO  - Radiology. 2023 May;307(3):e230276. doi: 10.1148/radiol.230276. Epub 2023 Mar 7.

PMID- 37058213
OWN - NLM
STAT- MEDLINE
DCOM- 20230510
LR  - 20230517
IS  - 1466-1268 (Electronic)
IS  - 1355-8145 (Print)
IS  - 1355-8145 (Linking)
VI  - 28
IP  - 3
DP  - 2023 May
TI  - What artificial intelligence knows about 70 kDa heat shock proteins, and how we 
      will face this ChatGPT era.
PG  - 225-229
LID - 10.1007/s12192-023-01340-1 [doi]
FAU - Heck, Thiago Gomes
AU  - Heck TG
AUID- ORCID: 0000-0002-1242-5423
AD  - Post Graduate Program in Integral Health Care (PPGAIS-UNIJUÍ/UNICRUZ/URI), 
      Regional University of Northwestern Rio Grande Do Sul State (UNIJUI), Ijuí, RS, 
      Brazil. thiago.heck@unijui.edu.br.
AD  - Post Graduate Program in Mathematical and Computational Modeling (PPGMMC), 
      Regional University of Northwestern Rio Grande Do Sul State (UNIJUI), Ijuí, RS, 
      Brazil. thiago.heck@unijui.edu.br.
LA  - eng
PT  - Letter
PT  - Research Support, Non-U.S. Gov't
DEP - 20230414
PL  - Netherlands
TA  - Cell Stress Chaperones
JT  - Cell stress & chaperones
JID - 9610925
RN  - 0 (Heat-Shock Proteins)
SB  - IM
CIN - Cell Stress Chaperones. 2023 Apr 25;:. PMID: 37097423
MH  - *Artificial Intelligence
MH  - *Heat-Shock Proteins
PMC - PMC10103022
COIS- The author declares no competing interests.
EDAT- 2023/04/15 06:00
MHDA- 2023/05/10 06:41
CRDT- 2023/04/14 11:06
PHST- 2023/02/02 00:00 [received]
PHST- 2023/03/24 00:00 [accepted]
PHST- 2023/03/17 00:00 [revised]
PHST- 2023/05/10 06:41 [medline]
PHST- 2023/04/15 06:00 [pubmed]
PHST- 2023/04/14 11:06 [entrez]
AID - 10.1007/s12192-023-01340-1 [pii]
AID - 1340 [pii]
AID - 10.1007/s12192-023-01340-1 [doi]
PST - ppublish
SO  - Cell Stress Chaperones. 2023 May;28(3):225-229. doi: 10.1007/s12192-023-01340-1. 
      Epub 2023 Apr 14.

PMID- 37143631
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230507
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Evaluating ChatGPT's Ability to Solve Higher-Order Questions on the 
      Competency-Based Medical Education Curriculum in Medical Biochemistry.
PG  - e37023
LID - 10.7759/cureus.37023 [doi]
LID - e37023
AB  - Background Healthcare-related artificial intelligence (AI) is developing. The 
      capacity of the system to carry out sophisticated cognitive processes, such as 
      problem-solving, decision-making, reasoning, and perceiving, is referred to as 
      higher cognitive thinking in AI. This kind of thinking requires more than just 
      processing facts; it also entails comprehending and working with abstract ideas, 
      evaluating and applying data relevant to the context, and producing new insights 
      based on prior learning and experience. ChatGPT is an artificial 
      intelligence-based conversational software that can engage with people to answer 
      questions and uses natural language processing models. The platform has created a 
      worldwide buzz and keeps setting an ongoing trend in solving many complex 
      problems in various dimensions. Nevertheless, ChatGPT's capacity to correctly 
      respond to queries requiring higher-level thinking in medical biochemistry has 
      not yet been investigated. So, this research aimed to evaluate ChatGPT's aptitude 
      for responding to higher-order questions on medical biochemistry. Objective In 
      this study, our objective was to determine whether ChatGPT can address 
      higher-order problems related to medical biochemistry.​​​​​​ Methods​​​ This 
      cross-sectional study was done online by conversing with the current version of 
      ChatGPT (14 March 2023, which is presently free for registered users). It was 
      presented with 200 medical biochemistry reasoning questions that require 
      higher-order thinking. These questions were randomly picked from the 
      institution's question bank and classified according to the Competency-Based 
      Medical Education (CBME) curriculum's competency modules. The responses were 
      collected and archived for subsequent research. Two expert biochemistry 
      academicians examined the replies on a zero to five scale. The score's accuracy 
      was determined by a one-sample Wilcoxon signed rank test using hypothetical 
      values. Result The AI software answered 200 questions requiring higher-order 
      thinking with a median score of 4.0 (Q1=3.50, Q3=4.50). Using a single sample 
      Wilcoxon signed rank test, the result was less than the hypothetical maximum of 
      five (p=0.001) and comparable to four (p=0.16). There was no difference in the 
      replies to questions from different CBME modules in medical biochemistry 
      (Kruskal-Wallis p=0.39). The inter-rater reliability of the scores scored by two 
      biochemistry faculty members was outstanding (ICC=0.926 (95% CI: 0.814-0.971); 
      F=19; p=0.001)​​​​​​ Conclusion The results of this research indicate that 
      ChatGPT has the potential to be a successful tool for answering questions 
      requiring higher-order thinking in medical biochemistry, with a median score of 
      four out of five. However, continuous training and development with data of 
      recent advances are essential to improve performance and make it functional for 
      the ever-growing field of academic medical usage.
CI  - Copyright © 2023, Ghosh et al.
FAU - Ghosh, Arindam
AU  - Ghosh A
AD  - Biochemistry, Indian Institute of Technology Kharagpur, Dr. B.C. Roy 
      Multi-Speciality Medical Research Centre, Kharagpur, IND.
FAU - Bir, Aritri
AU  - Bir A
AD  - Biochemistry, Indian Institute of Technology Kharagpur, Dr. B.C. Roy 
      Multi-Speciality Medical Research Centre, Kharagpur, IND.
LA  - eng
PT  - Journal Article
DEP - 20230402
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10152308
OTO - NOTNLM
OT  - artificial intelligence
OT  - biochemistry
OT  - chatgpt
OT  - competency-based medical education
OT  - higher order cognitive skills
OT  - laboratory medicine
OT  - mcqs
OT  - medical biochemistry
OT  - medical education
OT  - solving multiple choice questions
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/05 06:42
MHDA- 2023/05/05 06:43
CRDT- 2023/05/05 03:48
PHST- 2023/04/02 00:00 [accepted]
PHST- 2023/05/05 06:43 [medline]
PHST- 2023/05/05 06:42 [pubmed]
PHST- 2023/05/05 03:48 [entrez]
AID - 10.7759/cureus.37023 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 2;15(4):e37023. doi: 10.7759/cureus.37023. eCollection 2023 Apr.

PMID- 37995146
OWN - NLM
STAT- Publisher
LR  - 20231123
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Linking)
DP  - 2023 Nov 23
TI  - Exploring the Use of ChatGPT in Delivering Evidence-Based Information to Patients 
      with Thyroid Eye Disease.
LID - 10.1097/IOP.0000000000002468 [doi]
FAU - Rajabi, Mohammad Taher
AU  - Rajabi MT
FAU - Rafizadeh, Seyed Mohsen
AU  - Rafizadeh SM
FAU - Ghahvehchian, Hossein
AU  - Ghahvehchian H
LA  - eng
PT  - Journal Article
DEP - 20231123
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
SB  - IM
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2023/11/23 18:41
MHDA- 2023/11/23 18:41
CRDT- 2023/11/23 12:13
PHST- 2023/11/23 18:41 [medline]
PHST- 2023/11/23 18:41 [pubmed]
PHST- 2023/11/23 12:13 [entrez]
AID - 00002341-990000000-00293 [pii]
AID - 10.1097/IOP.0000000000002468 [doi]
PST - aheadofprint
SO  - Ophthalmic Plast Reconstr Surg. 2023 Nov 23. doi: 10.1097/IOP.0000000000002468.

PMID- 37974564
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231124
IS  - 2772-5723 (Electronic)
IS  - 2772-5723 (Linking)
VI  - 2
IP  - 8
DP  - 2023
TI  - Assessing ChatGPT's Ability to Reply to Queries Regarding Colon Cancer Screening 
      Based on Multisociety Guidelines.
PG  - 1040-1043
LID - 10.1016/j.gastha.2023.07.008 [doi]
FAU - Mukherjee, S
AU  - Mukherjee S
AD  - Gastroenterology Division, Department of Medicine, Perelman School of Medicine at 
      the University of Pennsylvania, Philadelphia, Pennsylvania.
FAU - Durkin, C
AU  - Durkin C
AD  - Gastroenterology Division, Department of Medicine, Perelman School of Medicine at 
      the University of Pennsylvania, Philadelphia, Pennsylvania.
FAU - Pebenito, A M
AU  - Pebenito AM
AD  - Gastroenterology Division, Department of Medicine, Perelman School of Medicine at 
      the University of Pennsylvania, Philadelphia, Pennsylvania.
FAU - Ferrante, N D
AU  - Ferrante ND
AD  - Gastroenterology Division, Department of Medicine, Perelman School of Medicine at 
      the University of Pennsylvania, Philadelphia, Pennsylvania.
FAU - Umana, I C
AU  - Umana IC
AD  - Gastroenterology Division, Department of Medicine, Perelman School of Medicine at 
      the University of Pennsylvania, Philadelphia, Pennsylvania.
FAU - Kochman, M L
AU  - Kochman ML
AD  - Gastroenterology Division, Department of Medicine, Perelman School of Medicine at 
      the University of Pennsylvania, Philadelphia, Pennsylvania.
AD  - Department of Medicine, Center for Endoscopic Innovation Research and Training, 
      Perelman School of Medicine at the University of Pennsylvania, Philadelphia, 
      Pennsylvania.
LA  - eng
GR  - T32 DK007066/DK/NIDDK NIH HHS/United States
PT  - Journal Article
DEP - 20230720
PL  - Netherlands
TA  - Gastro Hep Adv
JT  - Gastro hep advances
JID - 9918350485906676
PMC - PMC10653253
MID - NIHMS1941610
COIS- Conflicts of Interest: The authors disclose no conflicts.
EDAT- 2023/11/17 15:26
MHDA- 2023/11/17 15:27
CRDT- 2023/11/17 03:51
PHST- 2023/11/17 15:27 [medline]
PHST- 2023/11/17 15:26 [pubmed]
PHST- 2023/11/17 03:51 [entrez]
AID - 10.1016/j.gastha.2023.07.008 [doi]
PST - ppublish
SO  - Gastro Hep Adv. 2023;2(8):1040-1043. doi: 10.1016/j.gastha.2023.07.008. Epub 2023 
      Jul 20.

PMID- 37884222
OWN - NLM
STAT- In-Process
LR  - 20231209
IS  - 1873-1570 (Electronic)
IS  - 0300-9572 (Linking)
VI  - 193
DP  - 2023 Dec
TI  - Can novel multimodal chatbots such as Bing Chat Enterprise, ChatGPT-4 Pro, and 
      Google Bard correctly interpret electrocardiogram images?
PG  - 110009
LID - S0300-9572(23)00324-6 [pii]
LID - 10.1016/j.resuscitation.2023.110009 [doi]
FAU - Fijačko, Nino
AU  - Fijačko N
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; ERC 
      Research Net, Niels, Belgium; Maribor University Medical Centre, Maribor, 
      Slovenia. Electronic address: https://twitter.com/@NinoFijacko.
FAU - Prosen, Gregor
AU  - Prosen G
AD  - Maribor University Medical Centre, Maribor, Slovenia.
FAU - Abella, Benjamin S
AU  - Abella BS
AD  - Center for Resuscitation Science and Department of Emergency Medicine, University 
      of Pennsylvania, Philadelphia.
FAU - Metličar, Špela
AU  - Metličar Š
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; Medical 
      Dispatch Centre Maribor, University Clinical Centre Ljubljana, Ljubljana, 
      Slovenia.
FAU - Štiglic, Gregor
AU  - Štiglic G
AD  - University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; University 
      of Edinburgh, Usher Institute, Edinburgh, United Kingdom; University of Maribor, 
      Faculty of Electrical Engineering and Computer Science, Maribor, Slovenia.
LA  - eng
PT  - Letter
DEP - 20231024
PL  - Ireland
TA  - Resuscitation
JT  - Resuscitation
JID - 0332173
SB  - IM
COIS- Declaration of competing interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: Nino Fijačko is a member of the ERC BLS Science and Education 
      Committee and ILCOR Task Force Education Implementation and Team. Benjamin S. 
      Abella serves on the American Heart Association Resuscitation Science Symposium 
      committee. Gregor Prosen, Špela Metličar and Gregor Štiglic declare that they 
      have no conflict of interest.
EDAT- 2023/10/27 00:42
MHDA- 2023/10/27 00:42
CRDT- 2023/10/26 19:23
PHST- 2023/10/16 00:00 [received]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/10/27 00:42 [pubmed]
PHST- 2023/10/27 00:42 [medline]
PHST- 2023/10/26 19:23 [entrez]
AID - S0300-9572(23)00324-6 [pii]
AID - 10.1016/j.resuscitation.2023.110009 [doi]
PST - ppublish
SO  - Resuscitation. 2023 Dec;193:110009. doi: 10.1016/j.resuscitation.2023.110009. 
      Epub 2023 Oct 24.

PMID- 37491394
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230727
LR  - 20230727
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 619
IP  - 7971
DP  - 2023 Jul
TI  - ChatGPT is a black box: how AI research can break it open.
PG  - 671-672
LID - 10.1038/d41586-023-02366-2 [doi]
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Information technology
OT  - Machine learning
OT  - Society
EDAT- 2023/07/26 01:06
MHDA- 2023/07/26 01:07
CRDT- 2023/07/25 23:47
PHST- 2023/07/26 01:07 [medline]
PHST- 2023/07/26 01:06 [pubmed]
PHST- 2023/07/25 23:47 [entrez]
AID - 10.1038/d41586-023-02366-2 [pii]
AID - 10.1038/d41586-023-02366-2 [doi]
PST - ppublish
SO  - Nature. 2023 Jul;619(7971):671-672. doi: 10.1038/d41586-023-02366-2.

PMID- 37287210
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20230918
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 10
DP  - 2023 Sep 14
TI  - Commentary on: Evaluating Chatbot Efficacy for Answering Frequently Asked 
      Questions in Plastic Surgery: A ChatGPT Case Study Focused on Breast 
      Augmentation.
PG  - 1136-1138
LID - 10.1093/asj/sjad186 [doi]
FAU - Najafali, Daniel
AU  - Najafali D
AUID- ORCID: 0000-0003-1509-9229
FAU - Dorafshar, Amir H
AU  - Dorafshar AH
AUID- ORCID: 0000-0001-7045-3601
LA  - eng
PT  - Comment
PT  - Editorial
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
CON - Aesthet Surg J. 2023 Sep 14;43(10):1126-1135. PMID: 37158147
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
MH  - *Mammaplasty
EDAT- 2023/06/08 06:42
MHDA- 2023/09/18 12:42
CRDT- 2023/06/08 01:23
PHST- 2023/09/18 12:42 [medline]
PHST- 2023/06/08 06:42 [pubmed]
PHST- 2023/06/08 01:23 [entrez]
AID - 7191870 [pii]
AID - 10.1093/asj/sjad186 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Sep 14;43(10):1136-1138. doi: 10.1093/asj/sjad186.

PMID- 37692807
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0976-3147 (Print)
IS  - 0976-3155 (Electronic)
IS  - 0976-3155 (Linking)
VI  - 14
IP  - 3
DP  - 2023 Jul-Sep
TI  - Artificial intelligence and machine learning in healthcare: Scope and 
      opportunities to use ChatGPT.
PG  - 391-392
LID - 10.25259/JNRP_391_2023 [doi]
FAU - Singh, Ajai
AU  - Singh A
AD  - Executive Director and CEO, All India Institute of Medical Sciences, Bhopal, 
      Madhya Pradesh, India.
FAU - Das, Saikat
AU  - Das S
AD  - Department of Radiation Oncology, All India Institute of Medical Sciences, 
      Bhopal, Madhya Pradesh, India.
FAU - Mishra, Rakesh Kumar
AU  - Mishra RK
AD  - Department of Neurosurgery, Institute of Medical Sciences, Banaras Hindu 
      University, Varanasi, Uttar Pradesh, India.
FAU - Agrawal, Amit
AU  - Agrawal A
AD  - Department of Neurosurgery, All India Institute of Medical Sciences, Bhopal, 
      Madhya Pradesh, India.
LA  - eng
PT  - Editorial
DEP - 20230816
PL  - United States
TA  - J Neurosci Rural Pract
JT  - Journal of neurosciences in rural practice
JID - 101533710
PMC - PMC10483215
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 04:43
PHST- 2023/07/19 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 04:43 [entrez]
AID - 10.25259/JNRP_391_2023 [pii]
AID - 10.25259/JNRP_391_2023 [doi]
PST - ppublish
SO  - J Neurosci Rural Pract. 2023 Jul-Sep;14(3):391-392. doi: 10.25259/JNRP_391_2023. 
      Epub 2023 Aug 16.

PMID- 37656938
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230928
IS  - 2515-5091 (Electronic)
IS  - 2515-5091 (Linking)
VI  - 7
IP  - 5
DP  - 2023 Aug 31
TI  - RE: Artificial intelligence chatbots will revolutionize how cancer patients 
      access information: ChatGPT represents a paradigm-shift.
LID - 10.1093/jncics/pkad053 [doi]
LID - pkad053
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Sikkim University, Gangtok, India.
LA  - eng
PT  - Comment
PT  - Letter
PL  - England
TA  - JNCI Cancer Spectr
JT  - JNCI cancer spectrum
JID - 101721827
SB  - IM
CON - JNCI Cancer Spectr. 2023 Mar 1;7(2):. PMID: 36808255
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Neoplasms/therapy
PMC - PMC10516662
COIS- The author has no relevant conflicts of interest to disclose for this 
      correspondence.
EDAT- 2023/09/01 18:42
MHDA- 2023/09/25 06:43
CRDT- 2023/09/01 16:03
PHST- 2023/07/22 00:00 [received]
PHST- 2023/07/27 00:00 [revised]
PHST- 2023/07/31 00:00 [accepted]
PHST- 2023/09/25 06:43 [medline]
PHST- 2023/09/01 18:42 [pubmed]
PHST- 2023/09/01 16:03 [entrez]
AID - 7258699 [pii]
AID - pkad053 [pii]
AID - 10.1093/jncics/pkad053 [doi]
PST - ppublish
SO  - JNCI Cancer Spectr. 2023 Aug 31;7(5):pkad053. doi: 10.1093/jncics/pkad053.

PMID- 37964183
OWN - NLM
STAT- Publisher
LR  - 20231121
IS  - 1879-0844 (Electronic)
IS  - 1388-9842 (Linking)
DP  - 2023 Nov 14
TI  - College-level reading is required to understand ChatGPT's answers to lay 
      questions relating to heart failure.
LID - 10.1002/ejhf.3083 [doi]
FAU - Riddell, Craig W
AU  - Riddell CW
AUID- ORCID: 0000-0001-5626-9947
AD  - Department of Medicine, University of Auckland, Auckland, New Zealand.
FAU - Chan, Chok
AU  - Chan C
AD  - Auckland City Hospital, Te Toka Tumai, Auckland, New Zealand.
FAU - McGrinder, Helen
AU  - McGrinder H
AD  - Auckland City Hospital, Te Toka Tumai, Auckland, New Zealand.
FAU - Earle, Nikki J
AU  - Earle NJ
AD  - Department of Medicine, University of Auckland, Auckland, New Zealand.
FAU - Poppe, Katrina K
AU  - Poppe KK
AD  - Department of Medicine, University of Auckland, Auckland, New Zealand.
FAU - Doughty, Rob N
AU  - Doughty RN
AD  - Department of Medicine, University of Auckland, Auckland, New Zealand.
AD  - Auckland City Hospital, Te Toka Tumai, Auckland, New Zealand.
LA  - eng
PT  - Journal Article
DEP - 20231114
PL  - England
TA  - Eur J Heart Fail
JT  - European journal of heart failure
JID - 100887595
SB  - IM
EDAT- 2023/11/15 00:41
MHDA- 2023/11/15 00:41
CRDT- 2023/11/14 23:48
PHST- 2023/10/02 00:00 [revised]
PHST- 2023/07/25 00:00 [received]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/11/15 00:41 [pubmed]
PHST- 2023/11/15 00:41 [medline]
PHST- 2023/11/14 23:48 [entrez]
AID - 10.1002/ejhf.3083 [doi]
PST - aheadofprint
SO  - Eur J Heart Fail. 2023 Nov 14. doi: 10.1002/ejhf.3083.

PMID- 37955694
OWN - NLM
STAT- Publisher
LR  - 20231113
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Nov 13
TI  - Examining otolaryngologists' attitudes towards large language models (LLMs) such 
      as ChatGPT: a comprehensive deep learning analysis.
LID - 10.1007/s00405-023-08325-x [doi]
FAU - Praveen, S V
AU  - Praveen SV
AUID- ORCID: 0000-0002-1450-8839
AD  - Department of Digital Platform & Strategies, MICA, Ahmedabad, India. 
      praveenscissci@gmail.com.
FAU - Vijaya, S
AU  - Vijaya S
AD  - Department of Economics, St. Joesph College of Economics, Trichy, Tamilnadu, 
      India.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20231113
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
CON - Eur Arch Otorhinolaryngol. 2023 Sep;280(9):4271-4278. PMID: 37285018
EDAT- 2023/11/13 12:45
MHDA- 2023/11/13 12:45
CRDT- 2023/11/13 11:06
PHST- 2023/09/21 00:00 [received]
PHST- 2023/10/27 00:00 [accepted]
PHST- 2023/11/13 12:45 [medline]
PHST- 2023/11/13 12:45 [pubmed]
PHST- 2023/11/13 11:06 [entrez]
AID - 10.1007/s00405-023-08325-x [pii]
AID - 10.1007/s00405-023-08325-x [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Nov 13. doi: 10.1007/s00405-023-08325-x.

PMID- 36880216
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230707
LR  - 20230718
IS  - 1365-2702 (Electronic)
IS  - 0962-1067 (Linking)
VI  - 32
IP  - 15-16
DP  - 2023 Aug
TI  - Using chatbots like ChatGPT to support nursing practice.
PG  - 4211-4213
LID - 10.1111/jocn.16677 [doi]
FAU - Scerri, Anthony
AU  - Scerri A
AUID- ORCID: 0000-0002-7231-9710
AD  - Department of Nursing, Faculty of Health Sciences, University of Malta, Msida, 
      Malta.
FAU - Morin, Karen H
AU  - Morin KH
AD  - Professional Development Editor: Journal of Advanced Nursing, the Journal of 
      Clinical Nursing, and Nursing Open Editor, Nursing Open, University of 
      Wisconsin-Milwaukee, College of Nursing, Milwaukee, Wisconsin, USA.
LA  - eng
PT  - Editorial
DEP - 20230307
PL  - England
TA  - J Clin Nurs
JT  - Journal of clinical nursing
JID - 9207302
OTO - NOTNLM
OT  - Chatbots
OT  - nursing practice
OT  - technology, clinical decision-making
EDAT- 2023/03/08 06:00
MHDA- 2023/03/08 06:01
CRDT- 2023/03/07 03:22
PHST- 2023/02/13 00:00 [received]
PHST- 2023/02/20 00:00 [accepted]
PHST- 2023/03/08 06:01 [medline]
PHST- 2023/03/08 06:00 [pubmed]
PHST- 2023/03/07 03:22 [entrez]
AID - 10.1111/jocn.16677 [doi]
PST - ppublish
SO  - J Clin Nurs. 2023 Aug;32(15-16):4211-4213. doi: 10.1111/jocn.16677. Epub 2023 Mar 
      7.

PMID- 37353199
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20231027
IS  - 2468-1210 (Electronic)
IS  - 2468-1210 (Linking)
VI  - 42
IP  - 4
DP  - 2023 Sep
TI  - Is ChatGPT able to pass the first part of the European Board of Hand Surgery 
      diploma examination?
PG  - 362-364
LID - S2468-1229(23)00113-5 [pii]
LID - 10.1016/j.hansur.2023.06.005 [doi]
FAU - Traoré, Sidi Yaya
AU  - Traoré SY
AD  - Centre Hospitalier de Sarrebourg, 25 avenue du général de Gaulle, 57400, 
      Sarrebourg, France.
FAU - Goetsch, Thibaut
AU  - Goetsch T
AD  - Department of Public Health, Strasbourg University Hospital, FMTS, GMRC, 1 avenue 
      de l'hôpital, 67091 Strasbourg, France.
FAU - Muller, Benjamin
AU  - Muller B
AD  - Department of Hand Surgery, Strasbourg University Hospitals, FMTS, 1 avenue 
      Molière, 67200 Strasbourg, France.
FAU - Dabbagh, Armaghan
AU  - Dabbagh A
AD  - Faculty of Health Sciences, Western University, London, Ontario, Canada.
FAU - Liverneaux, Philippe André
AU  - Liverneaux PA
AD  - Department of Hand Surgery, Strasbourg University Hospitals, FMTS, 1 avenue 
      Molière, 67200 Strasbourg, France; ICube CNRS UMR7357, Strasbourg University, 2-4 
      rue Boussingault, 67000 Strasbourg, France. Electronic address: 
      philippe.liverneaux@chru-strasbourg.fr.
LA  - eng
PT  - Letter
DEP - 20230621
PL  - France
TA  - Hand Surg Rehabil
JT  - Hand surgery & rehabilitation
JID - 101681801
SB  - IM
CIN - Hand Surg Rehabil. 2023 Oct;42(5):446. PMID: 37451351
MH  - Humans
MH  - *Hand/surgery
MH  - *Educational Measurement
EDAT- 2023/06/24 11:42
MHDA- 2023/08/07 06:42
CRDT- 2023/06/23 19:18
PHST- 2023/06/01 00:00 [received]
PHST- 2023/06/01 00:00 [revised]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/06/24 11:42 [pubmed]
PHST- 2023/06/23 19:18 [entrez]
AID - S2468-1229(23)00113-5 [pii]
AID - 10.1016/j.hansur.2023.06.005 [doi]
PST - ppublish
SO  - Hand Surg Rehabil. 2023 Sep;42(4):362-364. doi: 10.1016/j.hansur.2023.06.005. 
      Epub 2023 Jun 21.

PMID- 37329126
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 4
DP  - 2023 Nov 29
TI  - Is ChatGPT worth using in first aid for mental health issues?
PG  - e814-e815
LID - 10.1093/pubmed/fdad095 [doi]
FAU - Aditama, Mint Husen Raya
AU  - Aditama MHR
AUID- ORCID: 0000-0003-4593-8935
AD  - Department of Guidance and Counseling, Faculty of Education and Psychology, 
      Manado State University, Tondano, North Sulawesi 95618, Indonesia.
FAU - Mangantes, Meisie Lenny
AU  - Mangantes ML
AD  - Department of Guidance and Counseling, Faculty of Education and Psychology, 
      Manado State University, Tondano, North Sulawesi 95618, Indonesia.
FAU - Tiwa, Tellma Mona
AU  - Tiwa TM
AD  - Department of Psychology, Faculty of Education and Psychology, Manado State 
      University, Tondano, North Sulawesi 95618, Indonesia.
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - *First Aid
MH  - *Mental Health
OTO - NOTNLM
OT  - e-health
OT  - mental health
OT  - young people
EDAT- 2023/06/17 09:42
MHDA- 2023/12/04 12:42
CRDT- 2023/06/17 01:13
PHST- 2023/05/30 00:00 [received]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/06/17 09:42 [pubmed]
PHST- 2023/06/17 01:13 [entrez]
AID - 7199622 [pii]
AID - 10.1093/pubmed/fdad095 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Nov 29;45(4):e814-e815. doi: 10.1093/pubmed/fdad095.

PMID- 37482573
OWN - NLM
STAT- Publisher
LR  - 20230723
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 23
TI  - Code Interpreter for Bioinformatics: Are We There Yet?
LID - 10.1007/s10439-023-03324-9 [doi]
AB  - The Code Interpreter feature in ChatGPT has the potential to democratize data 
      analysis for non-specialists. As bioinformaticians, we are impressed by its 
      performance in data manipulation and visualization. However, bioinformatics tasks 
      often require execution of third-party packages, access to annotation 
      knowledgebase, and handling large datasets. Code Interpreter's exclusive support 
      for Python, no installation option for additional packages, inability to utilize 
      external resources, and limited storage capacity could pose obstacles to its wide 
      adoption in bioinformatics applications. To address these limitations, we 
      advocated for the necessity of locally deployable, API-based systems for 
      chatbot-aided bioinformatics applications.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Wang, Lei
AU  - Wang L
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV, 26506, USA.
FAU - Ge, Xijin
AU  - Ge X
AD  - Department of Mathematics and Statistics, South Dakota State University, 
      Brookings, SD, 57007, USA.
FAU - Liu, Li
AU  - Liu L
AD  - College of Health Solutions, Arizona State University, Phoenix, AZ, 85004, USA.
AD  - Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA.
FAU - Hu, Gangqing
AU  - Hu G
AUID- ORCID: 0000-0001-5453-6888
AD  - Department of Microbiology, Immunology & Cell Biology, West Virginia University, 
      Morgantown, WV, 26506, USA. michael.hu@hsc.wvu.edu.
LA  - eng
GR  - P20 GM103434/GM/NIGMS NIH HHS/United States
GR  - U54 GM-104942/GM/NIGMS NIH HHS/United States
GR  - 1P20 GM121322/GM/NIGMS NIH HHS/United States
GR  - R01HG010805/GM/NIGMS NIH HHS/United States
GR  - P20GM135008/GM/NIGMS NIH HHS/United States
PT  - Letter
DEP - 20230723
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Bioinformatics
OT  - ChatGPT
OT  - Code interpreter
EDAT- 2023/07/24 00:41
MHDA- 2023/07/24 00:41
CRDT- 2023/07/23 23:06
PHST- 2023/07/12 00:00 [received]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/07/24 00:41 [medline]
PHST- 2023/07/24 00:41 [pubmed]
PHST- 2023/07/23 23:06 [entrez]
AID - 10.1007/s10439-023-03324-9 [pii]
AID - 10.1007/s10439-023-03324-9 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 23. doi: 10.1007/s10439-023-03324-9.

PMID- 37396972
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231101
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - ChatGPT in society: emerging issues.
PG  - 1130913
LID - 10.3389/frai.2023.1130913 [doi]
LID - 1130913
AB  - We review and critically assess several issues arising from the potential 
      -large-scale- implementation or deployment of Large Language Models (LLMs) in 
      society. These include security, political, economic, cultural, and educational 
      issues as well as issues concerning social biases, creativity, copyright, and 
      freedom of speech. We argue, without a preconceived pessimism toward these tools, 
      that they may bring about many benefits. However, we also call for a balance 
      assessment of their downsides. While our work is only preliminary and certainly 
      partial it nevertheless holds some value as one of the first exploratory attempts 
      in the literature.
CI  - Copyright © 2023 Farina and Lavazza.
FAU - Farina, Mirko
AU  - Farina M
AD  - Faculty of Humanities and Social Science, HMI Lab, Innopolis University, 
      Innopolis, Russia.
FAU - Lavazza, Andrea
AU  - Lavazza A
AD  - CUI: Neuroethics UNIVP: Brain and Behavioral Sciences, University of Pavia, 
      Pavia, Lombardy, Italy.
AD  - Centro Universitario Internazionale, Arezzo, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230615
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10310434
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - OpenAI
OT  - copyright
OT  - creativity
OT  - ethics
OT  - machine learning
OT  - social biases
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/07/03 13:06
MHDA- 2023/07/03 13:07
CRDT- 2023/07/03 11:28
PHST- 2022/12/23 00:00 [received]
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/07/03 13:07 [medline]
PHST- 2023/07/03 13:06 [pubmed]
PHST- 2023/07/03 11:28 [entrez]
AID - 10.3389/frai.2023.1130913 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Jun 15;6:1130913. doi: 10.3389/frai.2023.1130913. 
      eCollection 2023.

PMID- 37492313
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1682-024X (Print)
IS  - 1681-715X (Electronic)
IS  - 1681-715X (Linking)
VI  - 39
IP  - 4
DP  - 2023 Jul-Aug
TI  - Chat-GPT: Opportunities and Challenges in Child Mental Healthcare.
PG  - 1191-1193
LID - 10.12669/pjms.39.4.8118 [doi]
AB  - Mental health in children and young people is a global public health concern. 
      With the increasing prevalence of mental illnesses and a significant treatment 
      gap, Mental Health in children and adolescents is now a global public health 
      concern. The development and extensive research in the field of Artificial 
      Intelligence(AI) for healthcare has been quite promising. The emergence of AI 
      based alternatives could be a viable solution for reducing mental health 
      treatment gap for people belonging to low and middle income countries. 
      Development of Chatbots like ChatGPT which is trained, using large amount of 
      textual data from the internet, can revolutionize child and adolescent mental 
      healthcare by acting as an effective assisting tool but a lot of caution is 
      required for its safe and responsible use in times to come.e.
CI  - Copyright: © Pakistan Journal of Medical Sciences.
FAU - Imran, Nazish
AU  - Imran N
AD  - Dr. Nazish Imran, MBBS; FRCPsych(London); MRCPsych (London); MHPE;PhD. Professor, 
      Department of Child & Family Psychiatry, King Edward Medical University/Mayo 
      Hospital, Lahore, Pakistan.
FAU - Hashmi, Aateqa
AU  - Hashmi A
AD  - Dr. Aateqa Hashmi, MBBS. House Officer, Department of Child & Family Psychiatry, 
      King Edward Medical University/Mayo Hospital, Lahore, Pakistan.
FAU - Imran, Ahad
AU  - Imran A
AD  - Ahad Imran, Third year Computer Sciences Student, Syed Babar Ali School of 
      Science & Engineering (SBASSE), Lahore University of Management Sciences (LUMS), 
      Lahore, Pakistan.
LA  - eng
PT  - Journal Article
PL  - Pakistan
TA  - Pak J Med Sci
JT  - Pakistan journal of medical sciences
JID - 100913117
PMC - PMC10364280
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Child Mental Health
OT  - Mental Illness
OT  - Open AI
EDAT- 2023/07/26 06:43
MHDA- 2023/07/26 06:44
CRDT- 2023/07/26 03:49
PHST- 2023/05/05 00:00 [received]
PHST- 2023/05/22 00:00 [revised]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/07/26 06:44 [medline]
PHST- 2023/07/26 06:43 [pubmed]
PHST- 2023/07/26 03:49 [entrez]
AID - PJMS-39-1191 [pii]
AID - 10.12669/pjms.39.4.8118 [doi]
PST - ppublish
SO  - Pak J Med Sci. 2023 Jul-Aug;39(4):1191-1193. doi: 10.12669/pjms.39.4.8118.

PMID- 38064244
OWN - NLM
STAT- Publisher
LR  - 20231208
IS  - 1049-7323 (Print)
IS  - 1049-7323 (Linking)
DP  - 2023 Dec 8
TI  - Artificial Intelligence Augmented Qualitative Analysis: The Way of the Future?
PG  - 10497323231217392
LID - 10.1177/10497323231217392 [doi]
AB  - The artificial intelligence (AI) revolution is here and gathering momentum, 
      thanks to new models of natural language processing (NLP) and rapidly increasing 
      adoption by the public. NLP technology uses statistical analysis of language 
      structures to analyse and generate human language, using text or speech as its 
      source material. It can also be applied to visual mediums like images and videos. 
      A few qualitative research early adopters are beginning to adopt this technology 
      into their work, but our understanding of its potential remains in its infancy. 
      This article will define and describe NLP-based AI and discuss its benefits and 
      limitations for reflexive thematic analysis in health research. While there are 
      many platforms available, ChatGPT is the most well-known and accessible. A worked 
      example using ChatGPT to augment reflexive thematic analysis is provided to 
      illustrate potential application in practice. This article is intended to inspire 
      further conversation around the role of AI in qualitative research and offer 
      practical guidance for researchers seeking to adopt this technology.
FAU - Hitch, Danielle
AU  - Hitch D
AUID- ORCID: 0000-0003-2798-2246
AD  - Deakin University, Geelong, VIC, Australia. RINGGOLD: 2104
AD  - Western Health, Sunshine Hospital, St Albans, VIC, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231208
PL  - United States
TA  - Qual Health Res
JT  - Qualitative health research
JID - 9202144
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - qualitative analysis
OT  - reflexive thematic analysis
COIS- Declaration of Conflicting InterestsThe author(s) declared no potential conflicts 
      of interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/12/08 12:42
MHDA- 2023/12/08 12:42
CRDT- 2023/12/08 11:43
PHST- 2023/12/08 12:42 [medline]
PHST- 2023/12/08 12:42 [pubmed]
PHST- 2023/12/08 11:43 [entrez]
AID - 10.1177/10497323231217392 [doi]
PST - aheadofprint
SO  - Qual Health Res. 2023 Dec 8:10497323231217392. doi: 10.1177/10497323231217392.

PMID- 37812123
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT's Relevance for Bioethics: A Novel Challenge to the Intrinsically 
      Relational, Critical, and Reason-Giving Aspect of Healthcare.
PG  - 71-73
LID - 10.1080/15265161.2023.2250305 [doi]
FAU - Alvarado, Ramón
AU  - Alvarado R
AD  - University of Oregon.
FAU - Morar, Nicolae
AU  - Morar N
AUID- ORCID: 0000-0003-2661-3676
AD  - University of Oregon.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CON - Am J Bioeth. 2023 Oct;23(10):8-16. PMID: 37440696
MH  - Humans
MH  - *Bioethics
MH  - Health Facilities
MH  - Delivery of Health Care
EDAT- 2023/10/09 12:42
MHDA- 2023/11/01 12:45
CRDT- 2023/10/09 10:13
PHST- 2023/11/01 12:45 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 10:13 [entrez]
AID - 10.1080/15265161.2023.2250305 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):71-73. doi: 10.1080/15265161.2023.2250305. Epub 2023 
      Oct 9.

PMID- 37776260
OWN - NLM
STAT- Publisher
LR  - 20230930
IS  - 1555-9823 (Electronic)
IS  - 0003-1348 (Linking)
DP  - 2023 Sep 30
TI  - Letter re: "The Utilization of ChatGPT in Reshaping Future Medical Education and 
      Learning Perspectives: A Curse or a Blessing?".
PG  - 31348231204913
LID - 10.1177/00031348231204913 [doi]
FAU - Breeding, Tessa
AU  - Breeding T
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Patel, Heli
AU  - Patel H
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Nasef, Hazem
AU  - Nasef H
AD  - Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort 
      Lauderdale, FL, USA. RINGGOLD: 2814
FAU - Elkbuli, Adel
AU  - Elkbuli A
AD  - Department of Surgery, Division of Trauma and Surgical Critical Care, Orlando 
      Regional Medical Center, Orlando, FL, USA. RINGGOLD: 25105
AD  - Department of Surgical Education, Orlando Regional Medical Center, Orlando, FL, 
      USA. RINGGOLD: 25105
LA  - eng
PT  - Letter
DEP - 20230930
PL  - United States
TA  - Am Surg
JT  - The American surgeon
JID - 0370522
SB  - IM
EDAT- 2023/10/01 04:43
MHDA- 2023/10/01 04:43
CRDT- 2023/09/30 08:22
PHST- 2023/10/01 04:43 [medline]
PHST- 2023/10/01 04:43 [pubmed]
PHST- 2023/09/30 08:22 [entrez]
AID - 10.1177/00031348231204913 [doi]
PST - aheadofprint
SO  - Am Surg. 2023 Sep 30:31348231204913. doi: 10.1177/00031348231204913.

PMID- 37505265
OWN - NLM
STAT- MEDLINE
DCOM- 20230831
LR  - 20230916
IS  - 1433-8726 (Electronic)
IS  - 0724-4983 (Linking)
VI  - 41
IP  - 9
DP  - 2023 Sep
TI  - Can Chat-GPT a substitute for urological resident physician in diagnosing 
      diseases?: a preliminary conclusion from an exploratory investigation.
PG  - 2569-2571
LID - 10.1007/s00345-023-04539-0 [doi]
FAU - Xv, Yingjie
AU  - Xv Y
AD  - Department of Urology, The First Affiliated Hospital of Chongqing Medical 
      University, Chongqing, China.
FAU - Peng, Canjie
AU  - Peng C
AD  - Department of Urology, The First Affiliated Hospital of Chongqing Medical 
      University, Chongqing, China.
FAU - Wei, Zongjie
AU  - Wei Z
AD  - Department of Urology, The First Affiliated Hospital of Chongqing Medical 
      University, Chongqing, China.
FAU - Liao, Fangtong
AU  - Liao F
AD  - Department of Urology, The First Affiliated Hospital of Chongqing Medical 
      University, Chongqing, China.
FAU - Xiao, Mingzhao
AU  - Xiao M
AD  - Department of Urology, The First Affiliated Hospital of Chongqing Medical 
      University, Chongqing, China. mingzhaoxiao@cqmu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230728
PL  - Germany
TA  - World J Urol
JT  - World journal of urology
JID - 8307716
SB  - IM
MH  - Humans
MH  - *Urology
MH  - *Artificial Intelligence
EDAT- 2023/07/28 13:10
MHDA- 2023/08/31 06:41
CRDT- 2023/07/28 11:05
PHST- 2023/07/05 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/08/31 06:41 [medline]
PHST- 2023/07/28 13:10 [pubmed]
PHST- 2023/07/28 11:05 [entrez]
AID - 10.1007/s00345-023-04539-0 [pii]
AID - 10.1007/s00345-023-04539-0 [doi]
PST - ppublish
SO  - World J Urol. 2023 Sep;41(9):2569-2571. doi: 10.1007/s00345-023-04539-0. Epub 
      2023 Jul 28.

PMID- 37271634
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1873-7560 (Electronic)
IS  - 0302-2838 (Linking)
VI  - 84
IP  - 3
DP  - 2023 Sep
TI  - Re: Zhonghan Zhou, Xuesheng Wang, Xunhua Li, Limin Liao. Is ChatGPT an 
      Evidence-based Doctor? Eur Urol. 2023;84:355-6.
PG  - e76
LID - S0302-2838(23)02883-X [pii]
LID - 10.1016/j.eururo.2023.04.042 [doi]
FAU - Peng, Lei
AU  - Peng L
AD  - Department of Urology, Lanzhou University Second Hospital, Lanzhou, China; Motor 
      Robotics Institute (MRI), South China Hospital, Health Science Center, Shenzhen 
      University, Shenzhen, China.
FAU - Liang, Rui
AU  - Liang R
AD  - Motor Robotics Institute (MRI), South China Hospital, Health Science Center, 
      Shenzhen University, Shenzhen, China; Department of Urology, The First Affiliated 
      Hospital of Soochow University, Suzhou, China.
FAU - Yi, Fulin
AU  - Yi F
AD  - North Sichuan Medical College (University), Nanchong, China.
FAU - Zhang, Shaohua
AU  - Zhang S
AD  - Motor Robotics Institute (MRI), South China Hospital, Health Science Center, 
      Shenzhen University, Shenzhen, China. Electronic address: 22zsh@163.com.
FAU - Wu, Song
AU  - Wu S
AD  - Department of Urology, Lanzhou University Second Hospital, Lanzhou, China; Motor 
      Robotics Institute (MRI), South China Hospital, Health Science Center, Shenzhen 
      University, Shenzhen, China. Electronic address: wusong@szu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230602
PL  - Switzerland
TA  - Eur Urol
JT  - European urology
JID - 7512719
SB  - IM
EDAT- 2023/06/05 00:41
MHDA- 2023/06/05 00:42
CRDT- 2023/06/04 21:59
PHST- 2023/04/18 00:00 [received]
PHST- 2023/04/27 00:00 [accepted]
PHST- 2023/06/05 00:42 [medline]
PHST- 2023/06/05 00:41 [pubmed]
PHST- 2023/06/04 21:59 [entrez]
AID - S0302-2838(23)02883-X [pii]
AID - 10.1016/j.eururo.2023.04.042 [doi]
PST - ppublish
SO  - Eur Urol. 2023 Sep;84(3):e76. doi: 10.1016/j.eururo.2023.04.042. Epub 2023 Jun 2.

PMID- 37094759
OWN - NLM
STAT- MEDLINE
DCOM- 20230516
LR  - 20230801
IS  - 2173-5794 (Electronic)
IS  - 2173-5794 (Linking)
VI  - 98
IP  - 5
DP  - 2023 May
TI  - Artificial Intelligence and new language models in Ophthalmology: Complications 
      of the use of silicone oil in vitreoretinal surgery.
PG  - 298-303
LID - S2173-5794(23)00058-0 [pii]
LID - 10.1016/j.oftale.2023.04.011 [doi]
AB  - Artificial intelligence (AI) is an emerging technology that facilitates everyday 
      tasks and automates tasks in various fields such as medicine. However, the 
      emergence of a language model in academia has generated a lot of interest. This 
      paper evaluates the potential of ChatGPT, a language model developed by OpenAI, 
      and DALL-E 2, an image generator, in the writing of scientific articles in 
      ophthalmology. The selected topic is the complications of the use of silicone oil 
      in vitreoretinal surgery. ChatGPT was used to generate an abstract and a 
      structured article, suggestions for a title and bibliographical references. In 
      conclusion, despite the knowledge demonstrated by this tool, the scientific 
      accuracy and reliability on specific topics is insufficient for the automatic 
      generation of scientifically rigorous articles. In addition, scientists should be 
      aware of the possible ethical and legal implications of these tools.
CI  - Copyright © 2023 Sociedad Española de Oftalmología. Published by Elsevier España, 
      S.L.U. All rights reserved.
FAU - Valentín-Bravo, F J
AU  - Valentín-Bravo FJ
AD  - The Retina Clinic London, London, United Kingdom. Electronic address: 
      fjvb90@icloud.com.
FAU - Mateos-Álvarez, E
AU  - Mateos-Álvarez E
AD  - Escuela de doctorado de la Universidad de Valladolid, Valladolid, Spain.
FAU - Usategui-Martín, R
AU  - Usategui-Martín R
AD  - Instituto de Oftalmobiología Aplicada (IOBA), Universidad de Valladolid, 
      Valladolid, Spain.
FAU - Andrés-Iglesias, C
AU  - Andrés-Iglesias C
AD  - Instituto de Oftalmobiología Aplicada (IOBA), Universidad de Valladolid, 
      Valladolid, Spain.
FAU - Pastor-Jimeno, J C
AU  - Pastor-Jimeno JC
AD  - Instituto de Oftalmobiología Aplicada (IOBA), Universidad de Valladolid, 
      Valladolid, Spain; Red de Cooperación en la Investigación en Oftalmología 
      (Oftared), Instituto de Salud Carlos III, Madrid, Spain.
FAU - Pastor-Idoate, S
AU  - Pastor-Idoate S
AD  - Instituto de Oftalmobiología Aplicada (IOBA), Universidad de Valladolid, 
      Valladolid, Spain; Hospital Clínico Universitario de Valladolid, Valladolid, 
      Spain.
LA  - eng
PT  - Case Reports
DEP - 20230422
PL  - Spain
TA  - Arch Soc Esp Oftalmol (Engl Ed)
JT  - Archivos de la Sociedad Espanola de Oftalmologia
JID - 101715860
RN  - 0 (Silicone Oils)
SB  - IM
CIN - Arch Soc Esp Oftalmol (Engl Ed). 2023 Aug;98(8):486-487. PMID: 37353075
MH  - *Ophthalmology
MH  - Artificial Intelligence
MH  - Silicone Oils/adverse effects
MH  - *Vitreoretinal Surgery/adverse effects
MH  - Reproducibility of Results
MH  - Language
OTO - NOTNLM
OT  - Aceite de silicona
OT  - Artificial intelligence
OT  - ChatGPT
OT  - DALL-E
OT  - Inteligencia artificial
OT  - Language models
OT  - Modelos de lenguaje
OT  - Silicone oil
EDAT- 2023/04/25 00:41
MHDA- 2023/05/16 06:42
CRDT- 2023/04/24 07:24
PHST- 2023/01/31 00:00 [received]
PHST- 2023/03/22 00:00 [accepted]
PHST- 2023/05/16 06:42 [medline]
PHST- 2023/04/25 00:41 [pubmed]
PHST- 2023/04/24 07:24 [entrez]
AID - S2173-5794(23)00058-0 [pii]
AID - 10.1016/j.oftale.2023.04.011 [doi]
PST - ppublish
SO  - Arch Soc Esp Oftalmol (Engl Ed). 2023 May;98(5):298-303. doi: 
      10.1016/j.oftale.2023.04.011. Epub 2023 Apr 22.

PMID- 37505077
OWN - NLM
STAT- Publisher
LR  - 20230728
IS  - 1555-9823 (Electronic)
IS  - 0003-1348 (Linking)
DP  - 2023 Jul 28
TI  - Letter re: "The Utilization of ChatGPT in Reshaping Future Medical Education and 
      Learning Perspectives: A Curse or a Blessing?".
PG  - 31348231191203
LID - 10.1177/00031348231191203 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Mungmunpuntipantip, Rujittika
AU  - Mungmunpuntipantip R
AD  - Private Academic Consultant, Bangkok, Thailand.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Adjunct professor, Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Letter
DEP - 20230728
PL  - United States
TA  - Am Surg
JT  - The American surgeon
JID - 0370522
SB  - IM
EDAT- 2023/07/28 13:11
MHDA- 2023/07/28 13:11
CRDT- 2023/07/28 09:53
PHST- 2023/07/28 13:11 [medline]
PHST- 2023/07/28 13:11 [pubmed]
PHST- 2023/07/28 09:53 [entrez]
AID - 10.1177/00031348231191203 [doi]
PST - aheadofprint
SO  - Am Surg. 2023 Jul 28:31348231191203. doi: 10.1177/00031348231191203.

PMID- 37392006
OWN - NLM
STAT- Publisher
LR  - 20230701
IS  - 1741-2854 (Electronic)
IS  - 0020-7640 (Linking)
DP  - 2023 Jun 30
TI  - Comment on 'Old dog, new tricks? Exploring the potential functionalities of 
      ChatGPT in supporting educational methods in social psychiatry'.
PG  - 207640231178464
LID - 10.1177/00207640231178464 [doi]
FAU - Vasilchenko, Kirill
AU  - Vasilchenko K
AUID- ORCID: 0000-0002-9910-2079
AD  - The Human Artificial Control Keren (HacK) Lab, Azrieli Faculty of Medicine, 
      Bar-Ilan University, Safed, Israel.
FAU - Chumakov, Egor
AU  - Chumakov E
AUID- ORCID: 0000-0002-0429-8460
AD  - Saint Petersburg State University, Russia.
LA  - eng
PT  - Editorial
DEP - 20230630
PL  - England
TA  - Int J Soc Psychiatry
JT  - The International journal of social psychiatry
JID - 0374726
SB  - IM
EDAT- 2023/07/01 11:42
MHDA- 2023/07/01 11:42
CRDT- 2023/07/01 02:33
PHST- 2023/07/01 11:42 [medline]
PHST- 2023/07/01 11:42 [pubmed]
PHST- 2023/07/01 02:33 [entrez]
AID - 10.1177/00207640231178464 [doi]
PST - aheadofprint
SO  - Int J Soc Psychiatry. 2023 Jun 30:207640231178464. doi: 
      10.1177/00207640231178464.

PMID- 37581314
OWN - NLM
STAT- Publisher
LR  - 20230815
IS  - 1784-973X (Electronic)
IS  - 0001-5385 (Linking)
DP  - 2023 Aug 15
TI  - Long-term administration of proton pump inhibitors, risk of adverse 
      cardiovascular outcomes? and ChatGPT: correspondence.
PG  - 1
LID - 10.1080/00015385.2023.2238537 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Chandigarh University, Punjab, India.
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Journal Article
DEP - 20230815
PL  - England
TA  - Acta Cardiol
JT  - Acta cardiologica
JID - 0370570
SB  - IM
EDAT- 2023/08/15 12:42
MHDA- 2023/08/15 12:42
CRDT- 2023/08/15 06:42
PHST- 2023/08/15 12:42 [medline]
PHST- 2023/08/15 12:42 [pubmed]
PHST- 2023/08/15 06:42 [entrez]
AID - 10.1080/00015385.2023.2238537 [doi]
PST - aheadofprint
SO  - Acta Cardiol. 2023 Aug 15:1. doi: 10.1080/00015385.2023.2238537.

PMID- 37394928
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230706
IS  - 2234-7550 (Print)
IS  - 2234-5930 (Electronic)
IS  - 1225-1585 (Linking)
VI  - 49
IP  - 3
DP  - 2023 Jun 30
TI  - Could ChatGPT help you to write your next scientific paper?: concerns on research 
      ethics related to usage of artificial intelligence tools.
PG  - 105-106
LID - 10.5125/jkaoms.2023.49.3.105 [doi]
FAU - Park, Joo-Young
AU  - Park JY
AUID- ORCID: 0000-0002-0333-6349
AD  - Department of Oral and Maxillofacial Surgery, Seoul National University Dental 
      Hospital, School of Dentistry, Seoul National University, Seoul, Korea.
LA  - eng
PT  - Journal Article
PL  - Korea (South)
TA  - J Korean Assoc Oral Maxillofac Surg
JT  - Journal of the Korean Association of Oral and Maxillofacial Surgeons
JID - 101580242
PMC - PMC10318315
COIS- Conflict of Interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/07/03 06:42
MHDA- 2023/07/03 06:43
CRDT- 2023/07/03 03:09
PHST- 2023/07/03 06:43 [medline]
PHST- 2023/07/03 06:42 [pubmed]
PHST- 2023/07/03 03:09 [entrez]
AID - jkaoms.2023.49.3.105 [pii]
AID - jkaoms-49-3-105 [pii]
AID - 10.5125/jkaoms.2023.49.3.105 [doi]
PST - ppublish
SO  - J Korean Assoc Oral Maxillofac Surg. 2023 Jun 30;49(3):105-106. doi: 
      10.5125/jkaoms.2023.49.3.105.

PMID- 37312008
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231119
IS  - 1708-0428 (Electronic)
IS  - 0960-8923 (Linking)
VI  - 33
IP  - 8
DP  - 2023 Aug
TI  - Reply to "Assessing the Accuracy of Responses by the Language Model ChatGPT to 
      Questions Regarding Bariatric Surgery: a Critical Appraisal".
PG  - 2590-2591
LID - 10.1007/s11695-023-06666-4 [doi]
FAU - Samaan, Jamil S
AU  - Samaan JS
AUID- ORCID: 0000-0002-6191-2631
AD  - Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 
      8700 Beverly Blvd., Los Angeles, CA, 90048, USA. jamil.samaan@gmail.com.
FAU - Yeo, Yee Hui
AU  - Yeo YH
AD  - Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 
      8700 Beverly Blvd., Los Angeles, CA, 90048, USA.
FAU - Rajeev, Nithya
AU  - Rajeev N
AD  - Division of Upper GI and General Surgery, Department of Surgery, Keck School of 
      Medicine of USC, Health Care Consultation Center, 1510 San Pablo St #514, Los 
      Angeles, CA, 90033, USA.
FAU - Ng, Wee Han
AU  - Ng WH
AD  - Bristol Medical School, University of Bristol, 5 Tyndall Ave, Bristol, BS8 1UD, 
      UK.
FAU - Srinivasan, Nitin
AU  - Srinivasan N
AD  - Division of Upper GI and General Surgery, Department of Surgery, Keck School of 
      Medicine of USC, Health Care Consultation Center, 1510 San Pablo St #514, Los 
      Angeles, CA, 90033, USA.
FAU - Samakar, Kamran
AU  - Samakar K
AD  - Division of Upper GI and General Surgery, Department of Surgery, Keck School of 
      Medicine of USC, Health Care Consultation Center, 1510 San Pablo St #514, Los 
      Angeles, CA, 90033, USA.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230613
PL  - United States
TA  - Obes Surg
JT  - Obesity surgery
JID - 9106714
SB  - IM
CON - Obes Surg. 2023 Jun;33(6):1790-1796. PMID: 37106269
CON - Obes Surg. 2023 Aug;33(8):2588-2589. PMID: 37301782
MH  - Humans
MH  - *Obesity, Morbid/surgery
MH  - *Bariatric Surgery
EDAT- 2023/06/14 01:10
MHDA- 2023/07/17 06:42
CRDT- 2023/06/13 23:33
PHST- 2023/05/29 00:00 [received]
PHST- 2023/06/06 00:00 [accepted]
PHST- 2023/05/29 00:00 [revised]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/06/14 01:10 [pubmed]
PHST- 2023/06/13 23:33 [entrez]
AID - 10.1007/s11695-023-06666-4 [pii]
AID - 10.1007/s11695-023-06666-4 [doi]
PST - ppublish
SO  - Obes Surg. 2023 Aug;33(8):2590-2591. doi: 10.1007/s11695-023-06666-4. Epub 2023 
      Jun 13.

PMID- 37185632
OWN - NLM
STAT- MEDLINE
DCOM- 20230821
LR  - 20230919
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Print)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 9
DP  - 2023 Aug 17
TI  - Ask and You Shall Receive: OpenAI ChatGPT Writes Us an Editorial on Using 
      Chatbots in Gender Affirmation Surgery and Strategies to Increase Widespread 
      Adoption.
PG  - NP715-NP717
LID - 10.1093/asj/sjad119 [doi]
FAU - Najafali, Daniel
AU  - Najafali D
AUID- ORCID: 0000-0003-1509-9229
FAU - Camacho, Justin M
AU  - Camacho JM
AUID- ORCID: 0000-0002-1877-6005
FAU - Galbraith, Logan G
AU  - Galbraith LG
AUID- ORCID: 0009-0000-4508-4445
FAU - Reiche, Erik
AU  - Reiche E
FAU - Dorafshar, Amir H
AU  - Dorafshar AH
AUID- ORCID: 0000-0001-7045-3601
FAU - Morrison, Shane D
AU  - Morrison SD
LA  - eng
PT  - Letter
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
MH  - Humans
MH  - *Sex Reassignment Surgery
MH  - *Artificial Intelligence
MH  - *Writing
PMC - PMC10434977
EDAT- 2023/05/15 19:13
MHDA- 2023/08/21 06:42
CRDT- 2023/05/15 13:42
PHST- 2023/08/21 06:42 [medline]
PHST- 2023/05/15 19:13 [pubmed]
PHST- 2023/05/15 13:42 [entrez]
AID - 7142819 [pii]
AID - sjad119 [pii]
AID - 10.1093/asj/sjad119 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Aug 17;43(9):NP715-NP717. doi: 10.1093/asj/sjad119.

PMID- 37338542
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231212
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 10
DP  - 2023 Oct 1
TI  - WHO declares end of mpox global health emergency: first glance from a perspective 
      of ChatGPT/GPT-4.
PG  - 3217-3218
LID - 10.1097/JS9.0000000000000543 [doi]
FAU - Lu, Yanqiu
AU  - Lu Y
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan.
FAU - Qi, Shaoyan
AU  - Qi S
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan.
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Graduate School, Tianjin Medical University, Tianjin, People's 
      Republic of China.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      North Carolina, USA.
LA  - eng
PT  - Letter
DEP - 20231001
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *Global Health
MH  - *Monkeypox
MH  - World Health Organization
PMC - PMC10583947
COIS- The authors declare no conflicts of interest.
EDAT- 2023/06/20 13:10
MHDA- 2023/10/23 00:44
CRDT- 2023/06/20 11:04
PHST- 2023/05/18 00:00 [received]
PHST- 2023/06/02 00:00 [accepted]
PHST- 2023/10/23 00:44 [medline]
PHST- 2023/06/20 13:10 [pubmed]
PHST- 2023/06/20 11:04 [entrez]
AID - 01279778-202310000-00048 [pii]
AID - IJS-D-23-00924 [pii]
AID - 10.1097/JS9.0000000000000543 [doi]
PST - epublish
SO  - Int J Surg. 2023 Oct 1;109(10):3217-3218. doi: 10.1097/JS9.0000000000000543.

PMID- 37637185
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230829
IS  - 2213-4220 (Print)
IS  - 2213-4239 (Electronic)
IS  - 2213-4220 (Linking)
VI  - 12
IP  - 3
DP  - 2023 Sep
TI  - AI Chat bot - ChatGPT-4: A new opportunity and challenges in complementary and 
      alternative medicine (CAM).
PG  - 100977
LID - 10.1016/j.imr.2023.100977 [doi]
LID - 100977
FAU - Kim, Tae-Hun
AU  - Kim TH
AD  - Korean Medicine Clinical Trial Center, Korean Medicine Hospital, Kyung Hee 
      University, Seoul, South Korea.
FAU - Kang, Jung Won
AU  - Kang JW
AD  - Department of Acupuncture & Moxibustion, College of Korean Medicine, Kyung Hee 
      University, Seoul, South Korea.
FAU - Lee, Myeong Soo
AU  - Lee MS
AD  - Division of Clinical Medicine, Korea Institute of Oriental Medicine, Daejeon, 
      South Korea.
LA  - eng
PT  - Journal Article
DEP - 20230729
PL  - Netherlands
TA  - Integr Med Res
JT  - Integrative medicine research
JID - 101612707
PMC - PMC10448263
EDAT- 2023/08/28 06:43
MHDA- 2023/08/28 06:44
CRDT- 2023/08/28 04:45
PHST- 2023/05/26 00:00 [received]
PHST- 2023/07/17 00:00 [revised]
PHST- 2023/07/20 00:00 [accepted]
PHST- 2023/08/28 06:44 [medline]
PHST- 2023/08/28 06:43 [pubmed]
PHST- 2023/08/28 04:45 [entrez]
AID - S2213-4220(23)00056-2 [pii]
AID - 100977 [pii]
AID - 10.1016/j.imr.2023.100977 [doi]
PST - ppublish
SO  - Integr Med Res. 2023 Sep;12(3):100977. doi: 10.1016/j.imr.2023.100977. Epub 2023 
      Jul 29.

PMID- 37573083
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1532-8406 (Electronic)
IS  - 0883-5403 (Linking)
VI  - 38
IP  - 9
DP  - 2023 Sep
TI  - Assessing ChatGPT's Potential: A Critical Analysis and Future Directions in Total 
      Joint Arthroplasty.
PG  - e19-e20
LID - S0883-5403(23)00581-8 [pii]
LID - 10.1016/j.arth.2023.05.057 [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AD  - Sikkim University, Gangtok, Sikkim, India.
FAU - Majumder, Poulami
AU  - Majumder P
AD  - Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - J Arthroplasty
JT  - The Journal of arthroplasty
JID - 8703515
SB  - IM
CON - J Arthroplasty. 2023 Jul;38(7):1195-1202. PMID: 37040823
CIN - J Arthroplasty. 2023 Sep;38(9):e21. PMID: 37573084
MH  - Humans
MH  - *Arthroplasty, Replacement, Knee
MH  - Forecasting
EDAT- 2023/08/13 00:43
MHDA- 2023/08/14 06:42
CRDT- 2023/08/12 20:58
PHST- 2023/05/05 00:00 [received]
PHST- 2023/05/08 00:00 [revised]
PHST- 2023/05/15 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/13 00:43 [pubmed]
PHST- 2023/08/12 20:58 [entrez]
AID - S0883-5403(23)00581-8 [pii]
AID - 10.1016/j.arth.2023.05.057 [doi]
PST - ppublish
SO  - J Arthroplasty. 2023 Sep;38(9):e19-e20. doi: 10.1016/j.arth.2023.05.057.

PMID- 37083827
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230808
IS  - 1536-0229 (Electronic)
IS  - 0363-9762 (Linking)
VI  - 48
IP  - 9
DP  - 2023 Sep 1
TI  - Chatbots in Medical Research: Advantages and Limitations of Artificial 
      Intelligence-Enabled Writing With a Focus on ChatGPT as an Author.
PG  - 838-839
LID - 10.1097/RLU.0000000000004665 [doi]
FAU - Sharma, Punit
AU  - Sharma P
AD  - Department of Nuclear Medicine and PET-CT, Apollo Multispeciality Hospital, 
      Kolkata, India dr_punitsharma@yahoo.com.
LA  - eng
PT  - Letter
DEP - 20230416
PL  - United States
TA  - Clin Nucl Med
JT  - Clinical nuclear medicine
JID - 7611109
SB  - IM
CIN - Clin Nucl Med. 2023 Sep 1;48(9):839. PMID: 37146187
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Biomedical Research
COIS- Conflicts of interest and sources of funding: none declared.
EDAT- 2023/04/21 18:43
MHDA- 2023/08/07 06:41
CRDT- 2023/04/21 14:50
PHST- 2023/08/07 06:41 [medline]
PHST- 2023/04/21 18:43 [pubmed]
PHST- 2023/04/21 14:50 [entrez]
AID - 00003072-990000000-00523 [pii]
AID - 10.1097/RLU.0000000000004665 [doi]
PST - ppublish
SO  - Clin Nucl Med. 2023 Sep 1;48(9):838-839. doi: 10.1097/RLU.0000000000004665. Epub 
      2023 Apr 16.

PMID- 37328703
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231109
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 12
DP  - 2023 Dec
TI  - ChatGPT, Bard, and Large Language Models for Biomedical Research: Opportunities 
      and Pitfalls.
PG  - 2647-2651
LID - 10.1007/s10439-023-03284-0 [doi]
AB  - Large Language Models (LLMs) such as ChatGPT and Bard have emerged as 
      groundbreaking interactive chatbots, capturing significant attention and 
      transforming the biomedical research landscape. These powerful tools offer 
      immense potential for advancing scientific inquiry, but they also present 
      challenges and pitfalls. Leveraging large language models, researchers can 
      streamline literature reviews, summarize complex findings, and even generate 
      novel hypotheses, enabling the exploration of uncharted scientific territories. 
      However, the inherent risk of misinformation and misleading interpretations 
      underscores the critical importance of rigorous validation and verification 
      processes. This article provides a comprehensive overview of the current 
      landscape and delves into the opportunities and pitfalls associated with 
      employing LLMs in biomedical research. Furthermore, it sheds light on strategies 
      to enhance the utility of LLMs in biomedical research, offering recommendations 
      to ensure their responsible and effective implementation in this domain. The 
      findings presented in this article contribute to the advancement of biomedical 
      engineering by harnessing the potential of LLMs while addressing their 
      limitations.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Thapa, Surendrabikram
AU  - Thapa S
AUID- ORCID: 0000-0003-4119-8239
AD  - Virginia Tech, Blacksburg, VA, 24060, USA. surendrabikram@vt.edu.
FAU - Adhikari, Surabhi
AU  - Adhikari S
AD  - Columbia University, New York, NY, 10027, USA.
LA  - eng
PT  - Letter
PT  - Review
DEP - 20230616
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Language
MH  - Bioengineering
MH  - Biomedical Engineering
MH  - *Biomedical Research
OTO - NOTNLM
OT  - Bard
OT  - Biomedical research
OT  - ChatGPT
OT  - Large language models
OT  - Natural language processing
EDAT- 2023/06/17 05:11
MHDA- 2023/11/09 06:41
CRDT- 2023/06/16 23:31
PHST- 2023/06/10 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/11/09 06:41 [medline]
PHST- 2023/06/17 05:11 [pubmed]
PHST- 2023/06/16 23:31 [entrez]
AID - 10.1007/s10439-023-03284-0 [pii]
AID - 10.1007/s10439-023-03284-0 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Dec;51(12):2647-2651. doi: 10.1007/s10439-023-03284-0. Epub 
      2023 Jun 16.

PMID- 37885755
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231028
IS  - 2666-9145 (Electronic)
IS  - 2666-9145 (Linking)
VI  - 3
IP  - 4
DP  - 2023 Dec
TI  - Generative Artificial Intelligence Through ChatGPT and Other Large Language 
      Models in Ophthalmology: Clinical Applications and Challenges.
PG  - 100394
LID - 10.1016/j.xops.2023.100394 [doi]
LID - 100394
AB  - The rapid progress of large language models (LLMs) driving generative artificial 
      intelligence applications heralds the potential of opportunities in health care. 
      We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and 
      Scopus using the following terms: "large language models," "generative artificial 
      intelligence," "ophthalmology," "ChatGPT," and "eye," based on relevance to this 
      review. From a clinical viewpoint specific to ophthalmologists, we explore from 
      the different stakeholders' perspectives-including patients, physicians, and 
      policymakers-the potential LLM applications in education, research, and clinical 
      domains specific to ophthalmology. We also highlight the foreseeable challenges 
      of LLM implementation into clinical practice, including the concerns of accuracy, 
      interpretability, perpetuating bias, and data security. As LLMs continue to 
      mature, it is essential for stakeholders to jointly establish standards for best 
      practices to safeguard patient safety. FINANCIAL DISCLOSURES: Proprietary or 
      commercial disclosure may be found in the Footnotes and Disclosures at the end of 
      this article.
CI  - © 2023 by the American Academy of Ophthalmology.
FAU - Tan, Ting Fang
AU  - Tan TF
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
FAU - Thirunavukarasu, Arun James
AU  - Thirunavukarasu AJ
AD  - University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
AD  - Corpus Christi College, University of Cambridge, Cambridge, United Kingdom.
FAU - Campbell, J Peter
AU  - Campbell JP
AD  - Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science 
      University, Portland, Oregon.
FAU - Keane, Pearse A
AU  - Keane PA
AD  - Moorfields Eye Hospital, University of College London, London, United Kingdom.
FAU - Pasquale, Louis R
AU  - Pasquale LR
AD  - Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York 
      City, New York.
FAU - Abramoff, Michael D
AU  - Abramoff MD
AD  - American Medical Association's Digital Medicine Payment Advisory Group (DMPAG) 
      Artificial Intelligence Workgroup, American Medical Association, Chicago, 
      Illinois.
AD  - Department of Ophthalmology, University of Iowa, Iowa City, Iowa.
AD  - Digital Diagnostics, Inc, Coralville, Iowa.
FAU - Kalpathy-Cramer, Jayashree
AU  - Kalpathy-Cramer J
AD  - Department of Ophthalmology, University of Colorado Anschutz Medical Campus, 
      Aurora, Colorado.
FAU - Lum, Flora
AU  - Lum F
AD  - American Academy of Ophthalmology, San Francisco, California.
FAU - Kim, Judy E
AU  - Kim JE
AD  - Department of Ophthalmology, Medical College of Wisconsin, Milwaukee, Wisconsin.
FAU - Baxter, Sally L
AU  - Baxter SL
AD  - Division of Ophthalmology Informatics and Data Science, Viterbi Family Department 
      of Ophthalmology and Shiley Eye Institute, La Jolla, California.
AD  - Health Department of Biomedical Informatics, University of California San Diego, 
      La Jolla, California.
FAU - Ting, Daniel Shu Wei
AU  - Ting DSW
AD  - Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
AD  - Byers Eye Institute, Stanford University, Stanford, California.
LA  - eng
PT  - Journal Article
DEP - 20230909
PL  - Netherlands
TA  - Ophthalmol Sci
JT  - Ophthalmology science
JID - 9918230896206676
PMC - PMC10598525
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbots
OT  - Large language models
EDAT- 2023/10/27 06:43
MHDA- 2023/10/27 06:44
CRDT- 2023/10/27 04:22
PHST- 2023/04/26 00:00 [received]
PHST- 2023/08/07 00:00 [revised]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/10/27 06:44 [medline]
PHST- 2023/10/27 06:43 [pubmed]
PHST- 2023/10/27 04:22 [entrez]
AID - S2666-9145(23)00126-4 [pii]
AID - 100394 [pii]
AID - 10.1016/j.xops.2023.100394 [doi]
PST - epublish
SO  - Ophthalmol Sci. 2023 Sep 9;3(4):100394. doi: 10.1016/j.xops.2023.100394. 
      eCollection 2023 Dec.

PMID- 36841840
OWN - NLM
STAT- MEDLINE
DCOM- 20230228
LR  - 20230510
IS  - 1466-609X (Electronic)
IS  - 1364-8535 (Print)
IS  - 1364-8535 (Linking)
VI  - 27
IP  - 1
DP  - 2023 Feb 25
TI  - Can artificial intelligence help for scientific writing?
PG  - 75
LID - 10.1186/s13054-023-04380-2 [doi]
LID - 75
AB  - This paper discusses the use of Artificial Intelligence Chatbot in scientific 
      writing. ChatGPT is a type of chatbot, developed by OpenAI, that uses the 
      Generative Pre-trained Transformer (GPT) language model to understand and respond 
      to natural language inputs. AI chatbot and ChatGPT in particular appear to be 
      useful tools in scientific writing, assisting researchers and scientists in 
      organizing material, generating an initial draft and/or in proofreading. There is 
      no publication in the field of critical care medicine prepared using this 
      approach; however, this will be a possibility in the next future. ChatGPT work 
      should not be used as a replacement for human judgment and the output should 
      always be reviewed by experts before being used in any critical decision-making 
      or application. Moreover, several ethical issues arise about using these tools, 
      such as the risk of plagiarism and inaccuracies, as well as a potential imbalance 
      in its accessibility between high- and low-income countries, if the software 
      becomes paying. For this reason, a consensus on how to regulate the use of 
      chatbots in scientific writing will soon be required.
CI  - © 2023. The Author(s).
FAU - Salvagno, Michele
AU  - Salvagno M
AD  - Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, 
      1070, Brussels, Belgium. michele.salvagno@ulb.be.
FAU - Taccone, Fabio Silvio
AU  - Taccone FS
AD  - Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, 
      1070, Brussels, Belgium.
FAU - Gerli, Alberto Giovanni
AU  - Gerli AG
AD  - Department of Clinical Sciences and Community Health, Università Degli Studi di 
      Milano, 20122, Milan, Italy.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230225
PL  - England
TA  - Crit Care
JT  - Critical care (London, England)
JID - 9801902
SB  - IM
EIN - Crit Care. 2023 Mar 8;27(1):99. PMID: 36890525
CIN - Crit Care. 2023 Mar 21;27(1):120. PMID: 36945051
CIN - Crit Care. 2023 Apr 18;27(1):148. PMID: 37072798
CIN - Crit Care. 2023 May 10;27(1):180. PMID: 37165401
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Writing
PMC - PMC9960412
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chatbots
OT  - Machine learning
OT  - Scientific writing
COIS- Not applicable.
EDAT- 2023/02/26 06:00
MHDA- 2023/03/03 06:00
CRDT- 2023/02/25 23:47
PHST- 2023/01/31 00:00 [received]
PHST- 2023/02/21 00:00 [accepted]
PHST- 2023/02/25 23:47 [entrez]
PHST- 2023/02/26 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
AID - 10.1186/s13054-023-04380-2 [pii]
AID - 4380 [pii]
AID - 10.1186/s13054-023-04380-2 [doi]
PST - epublish
SO  - Crit Care. 2023 Feb 25;27(1):75. doi: 10.1186/s13054-023-04380-2.

PMID- 37277596
OWN - NLM
STAT- MEDLINE
DCOM- 20230608
LR  - 20230608
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 618
IP  - 7964
DP  - 2023 Jun
TI  - Six tips for better coding with ChatGPT.
PG  - 422-423
LID - 10.1038/d41586-023-01833-0 [doi]
FAU - Perkel, Jeffrey M
AU  - Perkel JM
LA  - eng
PT  - Journal Article
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - *Natural Language Processing
MH  - *Software/standards
MH  - Programming Languages
OTO - NOTNLM
OT  - Computational biology and bioinformatics
OT  - Computer science
OT  - Information technology
OT  - Machine learning
EDAT- 2023/06/06 01:11
MHDA- 2023/06/07 06:42
CRDT- 2023/06/05 23:26
PHST- 2023/06/07 06:42 [medline]
PHST- 2023/06/06 01:11 [pubmed]
PHST- 2023/06/05 23:26 [entrez]
AID - 10.1038/d41586-023-01833-0 [pii]
AID - 10.1038/d41586-023-01833-0 [doi]
PST - ppublish
SO  - Nature. 2023 Jun;618(7964):422-423. doi: 10.1038/d41586-023-01833-0.

PMID- 37257813
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231121
IS  - 1873-6246 (Electronic)
IS  - 0301-0511 (Linking)
VI  - 181
DP  - 2023 Jul
TI  - Generative artificial intelligence in publishing - Reflection and discussion.
PG  - 108595
LID - S0301-0511(23)00112-6 [pii]
LID - 10.1016/j.biopsycho.2023.108595 [doi]
FAU - Dien, Joseph
AU  - Dien J
AD  - Department of Human Development and Quantitative Methodology, University of 
      Maryland, 3304 Benjamin Building, College Park, MD 20742, USA. Electronic 
      address: jdien07@mac.com.
FAU - Ritz, Thomas
AU  - Ritz T
AD  - Department of Psychology, Southern Methodist University, P.O. Box 750442, Dallas, 
      TX 75275-0442, USA.
LA  - eng
PT  - Editorial
DEP - 20230529
PL  - Netherlands
TA  - Biol Psychol
JT  - Biological psychology
JID - 0375566
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Publishing
OTO - NOTNLM
OT  - Academic Misconduct
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Large Language Models
OT  - Plagiarism
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/06/01 01:08
MHDA- 2023/07/17 06:42
CRDT- 2023/05/31 19:29
PHST- 2023/05/26 00:00 [received]
PHST- 2023/05/27 00:00 [accepted]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/06/01 01:08 [pubmed]
PHST- 2023/05/31 19:29 [entrez]
AID - S0301-0511(23)00112-6 [pii]
AID - 10.1016/j.biopsycho.2023.108595 [doi]
PST - ppublish
SO  - Biol Psychol. 2023 Jul;181:108595. doi: 10.1016/j.biopsycho.2023.108595. Epub 
      2023 May 29.

PMID- 37146187
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230808
IS  - 1536-0229 (Electronic)
IS  - 0363-9762 (Linking)
VI  - 48
IP  - 9
DP  - 2023 Sep 1
TI  - Chatbots in Medical Research: Advantages and Limitations of AI-Enabled Writing 
      With a Focus on ChatGPT as an Author: Reply.
PG  - 839
LID - 10.1097/RLU.0000000000004672 [doi]
FAU - Colletti, Patrick M
AU  - Colletti PM
AD  - Editor-in-Chief, Clinical Nuclear Medicinecolletti@usc.edu .
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230506
PL  - United States
TA  - Clin Nucl Med
JT  - Clinical nuclear medicine
JID - 7611109
SB  - IM
CON - Clin Nucl Med. 2023 Sep 1;48(9):838-839. PMID: 37083827
MH  - Humans
MH  - *Biomedical Research
MH  - Artificial Intelligence
EDAT- 2023/05/05 18:42
MHDA- 2023/08/07 06:42
CRDT- 2023/05/05 14:32
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/05/05 18:42 [pubmed]
PHST- 2023/05/05 14:32 [entrez]
AID - 00003072-990000000-00539 [pii]
AID - 10.1097/RLU.0000000000004672 [doi]
PST - ppublish
SO  - Clin Nucl Med. 2023 Sep 1;48(9):839. doi: 10.1097/RLU.0000000000004672. Epub 2023 
      May 6.

PMID- 36752788
OWN - NLM
STAT- MEDLINE
DCOM- 20231012
LR  - 20231018
IS  - 1873-1953 (Electronic)
IS  - 1474-5151 (Linking)
VI  - 22
IP  - 7
DP  - 2023 Oct 19
TI  - ChatGPT: can artificial intelligence language models be of value for 
      cardiovascular nurses and allied health professionals.
PG  - e55-e59
LID - 10.1093/eurjcn/zvad022 [doi]
FAU - Moons, Philip
AU  - Moons P
AUID- ORCID: 0000-0002-8609-4516
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven-University of 
      Leuven, Kapucijnenvoer 35, Box 7001, 3000 Leuven, Belgium.
AD  - Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens 
      backe 1, 413 46 Gothenburg, Sweden.
AD  - Department of Paediatrics and Child Health, University of Cape Town, Klipfontein 
      Rd, Rondebosch, 7700 Cape Town, South Africa.
FAU - Van Bulck, Liesbet
AU  - Van Bulck L
AUID- ORCID: 0000-0001-8975-4455
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven-University of 
      Leuven, Kapucijnenvoer 35, Box 7001, 3000 Leuven, Belgium.
AD  - Research Foundation Flanders (FWO), Leuvenseweg 38, 1000 Brussels, Belgium.
LA  - eng
PT  - Editorial
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Eur J Cardiovasc Nurs
JT  - European journal of cardiovascular nursing
JID - 101128793
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Surveys and Questionnaires
MH  - Allied Health Personnel
MH  - Language
MH  - *Nurses
COIS- Conflict of interest: None declared.
EDAT- 2023/02/09 06:00
MHDA- 2023/10/12 06:43
CRDT- 2023/02/08 11:03
PHST- 2023/10/12 06:43 [medline]
PHST- 2023/02/09 06:00 [pubmed]
PHST- 2023/02/08 11:03 [entrez]
AID - 7031481 [pii]
AID - 10.1093/eurjcn/zvad022 [doi]
PST - ppublish
SO  - Eur J Cardiovasc Nurs. 2023 Oct 19;22(7):e55-e59. doi: 10.1093/eurjcn/zvad022.

PMID- 37994740
OWN - NLM
STAT- Publisher
LR  - 20231123
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Nov 22
TI  - AI-enabled ChatGPT's carbon footprint and its use in the healthcare sector: A 
      coin has two sides.
LID - 10.1097/JS9.0000000000000905 [doi]
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal 700126, India.
FAU - Pal, Soumen
AU  - Pal S
AD  - School of Mechanical Engineering, Vellore Institute of Technology, Vellore 
      632014, Tamil Nadu, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, 
      Odisha, India.
FAU - Islam, Md Aminul
AU  - Islam MA
AUID- ORCID: 0000-0003-1091-9726
AD  - COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and 
      Technology University, Noakhali-3814, Bangladesh.
AD  - Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical 
      College, Karimganj, Kishoreganj, Bangladesh.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/11/23 12:42
MHDA- 2023/11/23 12:42
CRDT- 2023/11/23 06:54
PHST- 2023/10/29 00:00 [received]
PHST- 2023/11/02 00:00 [accepted]
PHST- 2023/11/23 12:42 [medline]
PHST- 2023/11/23 12:42 [pubmed]
PHST- 2023/11/23 06:54 [entrez]
AID - 01279778-990000000-00802 [pii]
AID - 10.1097/JS9.0000000000000905 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Nov 22. doi: 10.1097/JS9.0000000000000905.

PMID- 37169728
OWN - NLM
STAT- MEDLINE
DCOM- 20230724
LR  - 20230725
IS  - 1464-410X (Electronic)
IS  - 1464-4096 (Linking)
VI  - 132
IP  - 2
DP  - 2023 Aug
TI  - Potential safety concerns in use of the artificial intelligence chatbot 'ChatGPT' 
      for perioperative patient communication.
PG  - 157-159
LID - 10.1111/bju.16042 [doi]
FAU - Tung, Joshua Y M
AU  - Tung JYM
AUID- ORCID: 0000-0003-4991-9790
AD  - Department of Urology, Singapore General Hospital, Singapore City, Singapore.
FAU - Lim, Daniel Y Z
AU  - Lim DYZ
AUID- ORCID: 0000-0002-9715-6970
AD  - Health Services Research Unit, Singapore General Hospital, Singapore City, 
      Singapore.
FAU - Sng, Gerald G R
AU  - Sng GGR
AD  - Department of Endocrinology, Singapore General Hospital, Singapore City, 
      Singapore.
LA  - eng
PT  - Journal Article
DEP - 20230526
PL  - England
TA  - BJU Int
JT  - BJU international
JID - 100886721
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Software
EDAT- 2023/05/12 01:07
MHDA- 2023/07/24 06:43
CRDT- 2023/05/11 22:43
PHST- 2023/07/24 06:43 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 22:43 [entrez]
AID - 10.1111/bju.16042 [doi]
PST - ppublish
SO  - BJU Int. 2023 Aug;132(2):157-159. doi: 10.1111/bju.16042. Epub 2023 May 26.

PMID- 37951370
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 1879-1131 (Electronic)
IS  - 0738-081X (Linking)
DP  - 2023 Nov 10
TI  - Readability, accuracy, and appropriateness of ChatGPT 4.0 responses for use in 
      patient education materials for Condyloma acuminatum.
LID - S0738-081X(23)00215-8 [pii]
LID - 10.1016/j.clindermatol.2023.11.004 [doi]
FAU - Moosvi, Nosheen
AU  - Moosvi N
AD  - Perelman School of Medicine, University of Pennsylvania, Philadelphia, 
      Pennsylvania, USA.
FAU - Kovarik, Carrie
AU  - Kovarik C
AD  - Department of Dermatology, Perelman School of Medicine, University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA. Electronic address: 
      carrie.kovarik@pennmedicine.upenn.edu.
LA  - eng
PT  - Letter
DEP - 20231110
PL  - United States
TA  - Clin Dermatol
JT  - Clinics in dermatology
JID - 8406412
SB  - IM
COIS- Declaration of Competing Interest The authors declare no conflicts of interest.
EDAT- 2023/11/12 00:41
MHDA- 2023/11/12 00:41
CRDT- 2023/11/11 19:15
PHST- 2023/11/12 00:41 [pubmed]
PHST- 2023/11/12 00:41 [medline]
PHST- 2023/11/11 19:15 [entrez]
AID - S0738-081X(23)00215-8 [pii]
AID - 10.1016/j.clindermatol.2023.11.004 [doi]
PST - aheadofprint
SO  - Clin Dermatol. 2023 Nov 10:S0738-081X(23)00215-8. doi: 
      10.1016/j.clindermatol.2023.11.004.

PMID- 37749006
OWN - NLM
STAT- Publisher
LR  - 20230925
IS  - 1873-4898 (Electronic)
IS  - 1477-5131 (Linking)
DP  - 2023 Sep 13
TI  - Response to letter to the editor re ''Evaluating the performance of ChatGPT in 
      answering questions related to pediatric urology''.
LID - S1477-5131(23)00400-X [pii]
LID - 10.1016/j.jpurol.2023.09.003 [doi]
FAU - Caglar, Ufuk
AU  - Caglar U
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey. 
      Electronic address: ufukcglr@gmail.com.
FAU - Ozgor, Faruk
AU  - Ozgor F
AD  - Department of Urology, Haseki Training and Research Hospital, Istanbul, Turkey.
LA  - eng
PT  - Letter
DEP - 20230913
PL  - England
TA  - J Pediatr Urol
JT  - Journal of pediatric urology
JID - 101233150
SB  - IM
COIS- Conflict of interest None.
EDAT- 2023/09/26 00:42
MHDA- 2023/09/26 00:42
CRDT- 2023/09/25 21:55
PHST- 2023/09/04 00:00 [received]
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/09/26 00:42 [medline]
PHST- 2023/09/26 00:42 [pubmed]
PHST- 2023/09/25 21:55 [entrez]
AID - S1477-5131(23)00400-X [pii]
AID - 10.1016/j.jpurol.2023.09.003 [doi]
PST - aheadofprint
SO  - J Pediatr Urol. 2023 Sep 13:S1477-5131(23)00400-X. doi: 
      10.1016/j.jpurol.2023.09.003.

PMID- 37743022
OWN - NLM
STAT- MEDLINE
DCOM- 20231130
LR  - 20231130
IS  - 1532-2742 (Electronic)
IS  - 0163-4453 (Linking)
VI  - 87
IP  - 6
DP  - 2023 Dec
TI  - Exploring infection clinicians' perceptions of bias in Large Language Models 
      (LLMs) like ChatGPT: A deep learning study.
PG  - 579-580
LID - S0163-4453(23)00508-X [pii]
LID - 10.1016/j.jinf.2023.09.006 [doi]
FAU - Praveen, S V
AU  - Praveen SV
AD  - Department of Digital Platform & Strategies, MICA, Ahmedabad, India. Electronic 
      address: praveenaus8@gmail.com.
FAU - Vijaya, S
AU  - Vijaya S
AD  - St. Joesph College of Economics, Trichy, Tamilnadu, India.
LA  - eng
PT  - Letter
DEP - 20230922
PL  - England
TA  - J Infect
JT  - The Journal of infection
JID - 7908424
SB  - IM
MH  - Humans
MH  - *Deep Learning
MH  - *Infections
MH  - Bias
COIS- Declaration of Competing Interest None.
EDAT- 2023/09/25 00:42
MHDA- 2023/11/27 12:44
CRDT- 2023/09/24 19:19
PHST- 2023/09/18 00:00 [received]
PHST- 2023/09/21 00:00 [accepted]
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/09/25 00:42 [pubmed]
PHST- 2023/09/24 19:19 [entrez]
AID - S0163-4453(23)00508-X [pii]
AID - 10.1016/j.jinf.2023.09.006 [doi]
PST - ppublish
SO  - J Infect. 2023 Dec;87(6):579-580. doi: 10.1016/j.jinf.2023.09.006. Epub 2023 Sep 
      22.

PMID- 37092015
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230426
IS  - 2768-5993 (Electronic)
IS  - 2768-5993 (Linking)
VI  - 3
IP  - 2
DP  - 2023
TI  - ChatGPT and other artificial intelligence chatbots and biomedical writing.
LID - 20 [pii]
LID - 10.20517/jca.2023.13 [doi]
FAU - Gurha, Priyatansh
AU  - Gurha P
AD  - Center for Cardiovascular Genetics, Institute of Molecular Medicine, The 
      University of Texas Health Science Center, Houston, TX 77030, USA.
FAU - Ishaq, Nadeem
AU  - Ishaq N
AD  - Octia Corporation, Houston, TX 77027, USA.
FAU - Marian, Ali J
AU  - Marian AJ
AD  - Center for Cardiovascular Genetics, Institute of Molecular Medicine, The 
      University of Texas Health Science Center, Houston, TX 77030, USA.
LA  - eng
GR  - R01 HL132401/HL/NHLBI NIH HHS/United States
GR  - R01 HL151737/HL/NHLBI NIH HHS/United States
GR  - R56 HL165334/HL/NHLBI NIH HHS/United States
PT  - Journal Article
DEP - 20230331
PL  - United States
TA  - J Cardiovasc Aging
JT  - The journal of cardiovascular aging
JID - 101778645
PMC - PMC10121201
MID - NIHMS1888598
OTO - NOTNLM
OT  - Artificial intelligence
OT  - chatbot
OT  - language learning models
OT  - machine learning
COIS- Conflicts of interest All authors declared that there are no conflicts of 
      interest.
EDAT- 2023/04/24 06:41
MHDA- 2023/04/24 06:42
CRDT- 2023/04/24 04:03
PHST- 2023/04/24 06:42 [medline]
PHST- 2023/04/24 06:41 [pubmed]
PHST- 2023/04/24 04:03 [entrez]
AID - 20 [pii]
AID - 10.20517/jca.2023.13 [doi]
PST - ppublish
SO  - J Cardiovasc Aging. 2023;3(2):20. doi: 10.20517/jca.2023.13. Epub 2023 Mar 31.

PMID- 37162903
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
DP  - 2023 May 15
TI  - A Compressed Language Model Embedding Dataset of ICD 10 CM Descriptions.
LID - 2023.04.24.23289046 [pii]
LID - 10.1101/2023.04.24.23289046 [doi]
AB  - This paper presents novel datasets providing numerical representations of 
      ICD-10-CM codes by generating description embeddings using a large language model 
      followed by a dimension reduction via autoencoder. The embeddings serve as 
      informative input features for machine learning models by capturing relationships 
      among categories and preserving inherent context information. The model 
      generating the data was validated in two ways. First, the dimension reduction was 
      validated using an autoencoder, and secondly, a supervised model was created to 
      estimate the ICD-10-CM hierarchical categories. Results show that the dimension 
      of the data can be reduced to as few as 10 dimensions while maintaining the 
      ability to reproduce the original embeddings, with the fidelity decreasing as the 
      reduced-dimension representation decreases. Multiple compression levels are 
      provided, allowing users to choose as per their requirements. The readily 
      available datasets of ICD-10-CM codes are anticipated to be highly valuable for 
      researchers in biomedical informatics, enabling more advanced analyses in the 
      field. This approach has the potential to significantly improve the utility of 
      ICD-10-CM codes in the biomedical domain.
FAU - Kane, Michael J
AU  - Kane MJ
AD  - Department of Biostatistics, School of Public Health, Yale University, New Haven, 
      USA.
FAU - King, Casey
AU  - King C
AD  - The Jackson School of Global Affairs Yale University, New Haven, USA.
AD  - US Healthcare and Life Sciences Microsoft, Redmond, USA.
FAU - Esserman, Denise
AU  - Esserman D
AD  - Department of Biostatistics, School of Public Health, Yale University, New Haven, 
      USA.
FAU - Latham, Nancy K
AU  - Latham NK
AD  - Research Program in Men's Health: Aging and Metabolism, Boston Claude D. Pepper 
      Older Americans Independence Center for Function Promoting Therapies Brigham and 
      Women's Hospital, Boston, USA.
FAU - Greene, Erich J
AU  - Greene EJ
AD  - Department of Biostatistics, School of Public Health, Yale University, New Haven, 
      USA.
FAU - Ganz, David A
AU  - Ganz DA
AD  - Department of Medicine VA Greater Los Angeles/UCLA, Los Angeles, USA.
LA  - eng
GR  - R01 AG071528/AG/NIA NIH HHS/United States
PT  - Preprint
DEP - 20230515
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10168496
OTO - NOTNLM
OT  - EHR
OT  - ICD-10-CM
OT  - NLP
OT  - autoencoder
OT  - electronic health records
OT  - large language model
COIS- Competing interests The authors declare that they have no competing interests.
EDAT- 2023/05/10 12:42
MHDA- 2023/05/10 12:43
CRDT- 2023/05/10 11:21
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 12:43 [medline]
PHST- 2023/05/10 11:21 [entrez]
AID - 2023.04.24.23289046 [pii]
AID - 10.1101/2023.04.24.23289046 [doi]
PST - epublish
SO  - medRxiv. 2023 May 15:2023.04.24.23289046. doi: 10.1101/2023.04.24.23289046. 
      Preprint.

PMID- 37706677
OWN - NLM
STAT- MEDLINE
DCOM- 20231206
LR  - 20231209
IS  - 1308-5263 (Electronic)
IS  - 1300-7777 (Print)
IS  - 1300-7777 (Linking)
VI  - 40
IP  - 4
DP  - 2023 Dec 5
TI  - ChatGPT as a Way to Enhance Parents’ Communication in Cases of Oncological 
      Pediatric Diseases.
PG  - 275-277
LID - 10.4274/tjh.galenos.2023.2023.0326 [doi]
FAU - Prazeres, Filipe
AU  - Prazeres F
AUID- ORCID: 0000-0002-2849-5194
AD  - USF Beira Ria, Gafanha da Nazaré, Portugal
AD  - University of Beira Interior Faculty of Health Sciences, Covilhã, Portugal
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - Turkey
TA  - Turk J Haematol
JT  - Turkish journal of haematology : official journal of Turkish Society of 
      Haematology
JID - 9606065
SB  - IM
MH  - Child
MH  - Humans
MH  - *Parents
MH  - *Communication
PMC - PMC10701325
OTO - NOTNLM
OT  - Child
OT  - Leukemia
OT  - Personal communication
OT  - Medical oncology
EDAT- 2023/09/14 12:41
MHDA- 2023/12/06 06:42
CRDT- 2023/09/14 08:45
PHST- 2023/12/06 06:42 [medline]
PHST- 2023/09/14 12:41 [pubmed]
PHST- 2023/09/14 08:45 [entrez]
AID - 63730 [pii]
AID - 10.4274/tjh.galenos.2023.2023.0326 [doi]
PST - ppublish
SO  - Turk J Haematol. 2023 Dec 5;40(4):275-277. doi: 
      10.4274/tjh.galenos.2023.2023.0326. Epub 2023 Sep 14.

PMID- 37704752
OWN - NLM
STAT- Publisher
LR  - 20231104
IS  - 1476-5608 (Electronic)
IS  - 1365-7852 (Linking)
DP  - 2023 Sep 13
TI  - The inclusion of social determinants of health into evaluations of quality and 
      appropriateness of AI assistant-ChatGPT.
LID - 10.1038/s41391-023-00720-z [doi]
FAU - Hswen, Yulin
AU  - Hswen Y
AUID- ORCID: 0000-0003-3203-1322
AD  - Department of Epidemiology and Biostatistics, University of California San 
      Francisco, San Francisco, CA, USA. yulin.hswen@ucsf.edu.
FAU - Nguyen, Thu T
AU  - Nguyen TT
AUID- ORCID: 0000-0003-1185-045X
AD  - Department of Epidemiology and Biostatistics, University of Maryland School of 
      Public Health, College Park, MD, USA.
LA  - eng
PT  - Letter
DEP - 20230913
PL  - England
TA  - Prostate Cancer Prostatic Dis
JT  - Prostate cancer and prostatic diseases
JID - 9815755
SB  - IM
EDAT- 2023/09/14 00:42
MHDA- 2023/09/14 00:42
CRDT- 2023/09/13 23:28
PHST- 2023/08/04 00:00 [received]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/08/16 00:00 [revised]
PHST- 2023/09/14 00:42 [pubmed]
PHST- 2023/09/14 00:42 [medline]
PHST- 2023/09/13 23:28 [entrez]
AID - 10.1038/s41391-023-00720-z [pii]
AID - 10.1038/s41391-023-00720-z [doi]
PST - aheadofprint
SO  - Prostate Cancer Prostatic Dis. 2023 Sep 13. doi: 10.1038/s41391-023-00720-z.

PMID- 37161846
OWN - NLM
STAT- MEDLINE
DCOM- 20230511
LR  - 20230519
IS  - 1478-3231 (Electronic)
IS  - 1478-3223 (Linking)
VI  - 43
IP  - 5
DP  - 2023 May
TI  - Should Artificial Intelligence-based language models be allowed in developing 
      scientific manuscripts? A debate between ChatGPT and the editors of Liver 
      International.
PG  - 956-957
LID - 10.1111/liv.15580 [doi]
FAU - Aghemo, Alessio
AU  - Aghemo A
AUID- ORCID: 0000-0003-0941-3226
AD  - Division of Internal Medicine and Hepatology, Department of Gastroenterology, 
      IRCCS Humanitas Research Hospital, Rozzano, Italy.
AD  - Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.
FAU - Forner, Alejandro
AU  - Forner A
AUID- ORCID: 0000-0002-9014-4950
AD  - Barcelona Clinic Liver Cancer (BCLC) Group, Liver Unit, ICMDM, Hospital Clinic 
      Barcelona, IDIBAPS, University of Barcelona, Barcelona, Spain.
AD  - Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas 
      (CIBERehd), Madrid, Spain.
FAU - Valenti, Luca
AU  - Valenti L
AUID- ORCID: 0000-0001-8909-0345
AD  - Department of Pathophysiology and Transplantation, Università degli Studi di 
      Milano, Milan, Italy.
AD  - Precision Medicine, Biological Resource Center and Transfusion Medicine, 
      Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
LA  - eng
PT  - Editorial
PL  - United States
TA  - Liver Int
JT  - Liver international : official journal of the International Association for the 
      Study of the Liver
JID - 101160857
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Liver
MH  - Language
EDAT- 2023/05/10 06:42
MHDA- 2023/05/11 06:42
CRDT- 2023/05/10 05:43
PHST- 2023/03/25 00:00 [received]
PHST- 2023/04/04 00:00 [accepted]
PHST- 2023/05/11 06:42 [medline]
PHST- 2023/05/10 06:42 [pubmed]
PHST- 2023/05/10 05:43 [entrez]
AID - 10.1111/liv.15580 [doi]
PST - ppublish
SO  - Liver Int. 2023 May;43(5):956-957. doi: 10.1111/liv.15580.

PMID- 36869421
OWN - NLM
STAT- MEDLINE
DCOM- 20230511
LR  - 20230514
IS  - 1528-1167 (Electronic)
IS  - 0013-9580 (Linking)
VI  - 64
IP  - 5
DP  - 2023 May
TI  - Are AI language models such as ChatGPT ready to improve the care of individuals 
      with epilepsy?
PG  - 1195-1199
LID - 10.1111/epi.17570 [doi]
FAU - Boßelmann, Christian M
AU  - Boßelmann CM
AUID- ORCID: 0000-0002-1596-5599
AD  - Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 
      Cleveland, Ohio, USA.
AD  - Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA.
FAU - Leu, Costin
AU  - Leu C
AUID- ORCID: 0000-0003-0598-3301
AD  - Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 
      Cleveland, Ohio, USA.
AD  - Department of Clinical and Experimental Epilepsy, Institute of Neurology, 
      University College London, London, UK.
FAU - Lal, Dennis
AU  - Lal D
AD  - Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 
      Cleveland, Ohio, USA.
AD  - Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA.
AD  - Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T., 
      Cambridge, Massachusetts, USA.
AD  - Cologne Center for Genomics (CCG), University of Cologne, Cologne, Delaware, USA.
LA  - eng
PT  - Journal Article
DEP - 20230313
PL  - United States
TA  - Epilepsia
JT  - Epilepsia
JID - 2983306R
SB  - IM
MH  - Humans
MH  - *Epilepsy/therapy
MH  - Artificial Intelligence
OTO - NOTNLM
OT  - epilepsy
OT  - language model
OT  - machine learning
EDAT- 2023/03/04 06:00
MHDA- 2023/05/11 06:42
CRDT- 2023/03/03 23:53
PHST- 2023/02/23 00:00 [revised]
PHST- 2023/01/12 00:00 [received]
PHST- 2023/03/01 00:00 [accepted]
PHST- 2023/05/11 06:42 [medline]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/03 23:53 [entrez]
AID - 10.1111/epi.17570 [doi]
PST - ppublish
SO  - Epilepsia. 2023 May;64(5):1195-1199. doi: 10.1111/epi.17570. Epub 2023 Mar 13.

PMID- 37812098
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231010
LR  - 20231017
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Print)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - Generative AI, Specific Moral Values: A Closer Look at ChatGPT's New Ethical 
      Implications for Medical AI.
PG  - 65-68
LID - 10.1080/15265161.2023.2250311 [doi]
FAU - Victor, Gavin
AU  - Victor G
AUID- ORCID: 0009-0001-6350-9387
AD  - Simon Fraser University.
FAU - Bélisle-Pipon, Jean-Christophe
AU  - Bélisle-Pipon JC
AUID- ORCID: 0000-0002-8965-8153
AD  - Simon Fraser University.
FAU - Ravitsky, Vardit
AU  - Ravitsky V
AUID- ORCID: 0000-0002-7080-8801
AD  - University of Montreal.
AD  - Harvard Medical School.
LA  - eng
GR  - OT2 OD032720/OD/NIH HHS/United States
GR  - OT2 OD032742/OD/NIH HHS/United States
PT  - Comment
PT  - Journal Article
DEP - 20231009
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CON - Am J Bioeth. 2023 Oct;23(10):8-16. PMID: 37440696
CON - Am J Bioeth. 2023 Sep;23(9):69-72. PMID: 37647464
CIN - Am J Bioeth. 2023 Oct;23(10):1-5. PMID: 37831940
PMC - PMC10575680
MID - NIHMS1928699
EDAT- 2023/10/09 12:43
MHDA- 2023/10/09 12:44
PMCR- 2024/10/09
CRDT- 2023/10/09 10:13
PHST- 2024/10/09 00:00 [pmc-release]
PHST- 2023/10/09 12:44 [medline]
PHST- 2023/10/09 12:43 [pubmed]
PHST- 2023/10/09 10:13 [entrez]
AID - 10.1080/15265161.2023.2250311 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):65-68. doi: 10.1080/15265161.2023.2250311. Epub 2023 
      Oct 9.

PMID- 37644984
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230831
IS  - 2296-858X (Print)
IS  - 2296-858X (Electronic)
IS  - 2296-858X (Linking)
VI  - 10
DP  - 2023
TI  - Editorial: Personalized medicine-Where do we stand regarding bench to bedside 
      translation?
PG  - 1243896
LID - 10.3389/fmed.2023.1243896 [doi]
LID - 1243896
FAU - Raghavendran, Hanumantha Rao Balaji
AU  - Raghavendran HRB
AD  - Biomaterials Laboratory, Faculty of Clinical Research, Sri Ramachandra Institute 
      of Higher Education and Research, Chennai, India.
FAU - Kumaramanickavel, Govindasamy
AU  - Kumaramanickavel G
AD  - Narayana Nethralaya, Narayana Health City, Bengaluru, Karnataka, India.
FAU - Iwata, Takeshi
AU  - Iwata T
AD  - Molecular and Cellular Biology Division, National Institute of Sensory Organs, 
      National Hospital Organization Tokyo Medical Center, Tokyo, Japan.
LA  - eng
PT  - Editorial
DEP - 20230814
PL  - Switzerland
TA  - Front Med (Lausanne)
JT  - Frontiers in medicine
JID - 101648047
CON - Editorial on the Research Topic Personalized medicine—Where do we stand regarding 
      bench to bedside translation?
PMC - PMC10461804
OTO - NOTNLM
OT  - ChatGPT
OT  - lateral sclerosis (ALS)
OT  - osteoarthritis
OT  - personalized medicine
OT  - ulcer prophylaxis
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/08/30 06:47
MHDA- 2023/08/30 06:48
CRDT- 2023/08/30 03:45
PHST- 2023/06/21 00:00 [received]
PHST- 2023/07/24 00:00 [accepted]
PHST- 2023/08/30 06:48 [medline]
PHST- 2023/08/30 06:47 [pubmed]
PHST- 2023/08/30 03:45 [entrez]
AID - 10.3389/fmed.2023.1243896 [doi]
PST - epublish
SO  - Front Med (Lausanne). 2023 Aug 14;10:1243896. doi: 10.3389/fmed.2023.1243896. 
      eCollection 2023.

PMID- 37349012
OWN - NLM
STAT- MEDLINE
DCOM- 20230626
LR  - 20231124
IS  - 1097-6809 (Electronic)
IS  - 0741-5214 (Linking)
VI  - 78
IP  - 1
DP  - 2023 Jul
TI  - Regarding "Practice patterns surrounding the use of tibial interventions for 
      claudication in the Medicare population: ChatGPT writes a Letter to the Editor".
PG  - 262
LID - S0741-5214(23)00543-8 [pii]
LID - 10.1016/j.jvs.2023.03.033 [doi]
FAU - Samson, Russell H
AU  - Samson RH
AD  - The Mote Vascular Foundation, Inc, Sarasota, FL.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - J Vasc Surg
JT  - Journal of vascular surgery
JID - 8407742
SB  - IM
CON - J Vasc Surg. 2023 Feb;77(2):454-462.e1. PMID: 36058433
MH  - United States
MH  - Humans
MH  - *Intermittent Claudication/diagnosis/therapy
MH  - *Peripheral Arterial Disease/diagnosis/therapy
MH  - Medicare
EDAT- 2023/06/23 01:10
MHDA- 2023/06/26 06:41
CRDT- 2023/06/22 20:59
PHST- 2023/02/28 00:00 [received]
PHST- 2023/03/01 00:00 [accepted]
PHST- 2023/06/26 06:41 [medline]
PHST- 2023/06/23 01:10 [pubmed]
PHST- 2023/06/22 20:59 [entrez]
AID - S0741-5214(23)00543-8 [pii]
AID - 10.1016/j.jvs.2023.03.033 [doi]
PST - ppublish
SO  - J Vasc Surg. 2023 Jul;78(1):262. doi: 10.1016/j.jvs.2023.03.033.

PMID- 37256685
OWN - NLM
STAT- MEDLINE
DCOM- 20230602
LR  - 20230908
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 May 31
TI  - Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific 
      Medical Articles: Pandora's Box Has Been Opened.
PG  - e46924
LID - 10.2196/46924 [doi]
LID - e46924
AB  - BACKGROUND: Artificial intelligence (AI) has advanced substantially in recent 
      years, transforming many industries and improving the way people live and work. 
      In scientific research, AI can enhance the quality and efficiency of data 
      analysis and publication. However, AI has also opened up the possibility of 
      generating high-quality fraudulent papers that are difficult to detect, raising 
      important questions about the integrity of scientific research and the 
      trustworthiness of published papers. OBJECTIVE: The aim of this study was to 
      investigate the capabilities of current AI language models in generating 
      high-quality fraudulent medical articles. We hypothesized that modern AI models 
      can create highly convincing fraudulent papers that can easily deceive readers 
      and even experienced researchers. METHODS: This proof-of-concept study used 
      ChatGPT (Chat Generative Pre-trained Transformer) powered by the GPT-3 
      (Generative Pre-trained Transformer 3) language model to generate a fraudulent 
      scientific article related to neurosurgery. GPT-3 is a large language model 
      developed by OpenAI that uses deep learning algorithms to generate human-like 
      text in response to prompts given by users. The model was trained on a massive 
      corpus of text from the internet and is capable of generating high-quality text 
      in a variety of languages and on various topics. The authors posed questions and 
      prompts to the model and refined them iteratively as the model generated the 
      responses. The goal was to create a completely fabricated article including the 
      abstract, introduction, material and methods, discussion, references, charts, 
      etc. Once the article was generated, it was reviewed for accuracy and coherence 
      by experts in the fields of neurosurgery, psychiatry, and statistics and compared 
      to existing similar articles. RESULTS: The study found that the AI language model 
      can create a highly convincing fraudulent article that resembled a genuine 
      scientific paper in terms of word usage, sentence structure, and overall 
      composition. The AI-generated article included standard sections such as 
      introduction, material and methods, results, and discussion, as well a data 
      sheet. It consisted of 1992 words and 17 citations, and the whole process of 
      article creation took approximately 1 hour without any special training of the 
      human user. However, there were some concerns and specific mistakes identified in 
      the generated article, specifically in the references. CONCLUSIONS: The study 
      demonstrates the potential of current AI language models to generate completely 
      fabricated scientific articles. Although the papers look sophisticated and 
      seemingly flawless, expert readers may identify semantic inaccuracies and errors 
      upon closer inspection. We highlight the need for increased vigilance and better 
      detection methods to combat the potential misuse of AI in scientific research. At 
      the same time, it is important to recognize the potential benefits of using AI 
      language models in genuine scientific writing and research, such as manuscript 
      preparation and language editing.
CI  - ©Martin Májovský, Martin Černý, Matěj Kasal, Martin Komarc, David Netuka. 
      Originally published in the Journal of Medical Internet Research 
      (https://www.jmir.org), 31.05.2023.
FAU - Májovský, Martin
AU  - Májovský M
AUID- ORCID: 0000-0001-7725-5181
AD  - Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles 
      University, Prague, Czech Republic.
FAU - Černý, Martin
AU  - Černý M
AUID- ORCID: 0000-0002-8601-0554
AD  - Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles 
      University, Prague, Czech Republic.
FAU - Kasal, Matěj
AU  - Kasal M
AUID- ORCID: 0000-0001-6445-8983
AD  - Department of Psychiatry, Faculty of Medicine in Pilsen, Charles University, 
      Pilsen, Czech Republic.
FAU - Komarc, Martin
AU  - Komarc M
AUID- ORCID: 0000-0003-4106-5217
AD  - Institute of Biophysics and Informatics, First Faculty of Medicine, Charles 
      University, Prague, Czech Republic.
AD  - Department of Methodology, Faculty of Physical Education and Sport, Charles 
      University, Prague, Czech Republic.
FAU - Netuka, David
AU  - Netuka D
AUID- ORCID: 0000-0001-8609-4789
AD  - Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles 
      University, Prague, Czech Republic.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230531
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
CIN - J Med Internet Res. 2023 May 31;25:e49323. PMID: 37256656
CIN - J Med Internet Res. 2023 Aug 31;25:e50591. PMID: 37651167
CIN - J Med Internet Res. 2023 Aug 31;25:e50844. PMID: 37651175
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Algorithms
MH  - Language
MH  - Semantics
MH  - Data Analysis
PMC - PMC10267787
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - ethics
OT  - fraudulent medical articles
OT  - language models
OT  - neurosurgery
OT  - publications
COIS- Conflicts of Interest: None declared.
EDAT- 2023/05/31 13:12
MHDA- 2023/06/02 06:42
CRDT- 2023/05/31 11:55
PHST- 2023/03/02 00:00 [received]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/04/25 00:00 [revised]
PHST- 2023/06/02 06:42 [medline]
PHST- 2023/05/31 13:12 [pubmed]
PHST- 2023/05/31 11:55 [entrez]
AID - v25i1e46924 [pii]
AID - 10.2196/46924 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 May 31;25:e46924. doi: 10.2196/46924.

PMID- 37485215
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230725
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - Artificial Intelligence in Ophthalmology: A Comparative Analysis of GPT-3.5, 
      GPT-4, and Human Expertise in Answering StatPearls Questions.
PG  - e40822
LID - 10.7759/cureus.40822 [doi]
LID - e40822
AB  - Importance Chat Generative Pre-Trained Transformer (ChatGPT) has shown promising 
      performance in various fields, including medicine, business, and law, but its 
      accuracy in specialty-specific medical questions, particularly in ophthalmology, 
      is still uncertain. Purpose This study evaluates the performance of two ChatGPT 
      models (GPT-3.5 and GPT-4) and human professionals in answering ophthalmology 
      questions from the StatPearls question bank, assessing their outcomes, and 
      providing insights into the integration of artificial intelligence (AI) 
      technology in ophthalmology. Methods ChatGPT's performance was evaluated using 
      467 ophthalmology questions from the StatPearls question bank. These questions 
      were stratified into 11 subcategories, four difficulty levels, and three 
      generalized anatomical categories. The answer accuracy of GPT-3.5, GPT-4, and 
      human participants was assessed. Statistical analysis was conducted via the 
      Kolmogorov-Smirnov test for normality, one-way analysis of variance (ANOVA) for 
      the statistical significance of GPT-3 versus GPT-4 versus human performance, and 
      repeated unpaired two-sample t-tests to compare the means of two groups. Results 
      GPT-4 outperformed both GPT-3.5 and human professionals on ophthalmology 
      StatPearls questions, except in the "Lens and Cataract" category. The performance 
      differences were statistically significant overall, with GPT-4 achieving higher 
      accuracy (73.2%) compared to GPT-3.5 (55.5%, p-value < 0.001) and humans (58.3%, 
      p-value < 0.001). There were variations in performance across difficulty levels 
      (rated one to four), but GPT-4 consistently performed better than both GPT-3.5 
      and humans on level-two, -three, and -four questions. On questions of level-four 
      difficulty, human performance significantly exceeded that of GPT-3.5 (p = 0.008). 
      Conclusion The study's findings demonstrate GPT-4's significant performance 
      improvements over GPT-3.5 and human professionals on StatPearls ophthalmology 
      questions. Our results highlight the potential of advanced conversational AI 
      systems to be utilized as important tools in the education and practice of 
      medicine.
CI  - Copyright © 2023, Moshirfar et al.
FAU - Moshirfar, Majid
AU  - Moshirfar M
AD  - Corneal and Refractive Surgery, Hoopes Vision Research Center, Draper, USA.
AD  - Ophthalmology, The University of Utah, Salt Lake City, USA.
AD  - Eye Banking and Corneal Transplantation, Utah Lions Eye Bank, Murray, USA.
FAU - Altaf, Amal W
AU  - Altaf AW
AD  - Medical School, University of Arizona College of Medicine Phoenix, Phoenix, USA.
FAU - Stoakes, Isabella M
AU  - Stoakes IM
AD  - Medical School, Pacific Northwest University of Health Science, Yakima, USA.
AD  - Ophthalmology, Hoopes Vision Research Center, Draper, USA.
FAU - Tuttle, Jared J
AU  - Tuttle JJ
AD  - Medical School, University of Texas Health Science Center at San Antonio, San 
      Antonio, USA.
FAU - Hoopes, Phillip C
AU  - Hoopes PC
AD  - Ophthalmology, Hoopes Vision Research Center, Draper, USA.
LA  - eng
PT  - Journal Article
DEP - 20230622
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10362981
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatbot
OT  - chatgpt-3.5
OT  - chatgpt-4
OT  - clinical decision-making
OT  - conversational ai
OT  - conversational generative pre-trained transformer
OT  - cornea
OT  - ophthalmology
OT  - statpearls
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/24 06:42
MHDA- 2023/07/24 06:43
CRDT- 2023/07/24 04:54
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/07/24 06:43 [medline]
PHST- 2023/07/24 06:42 [pubmed]
PHST- 2023/07/24 04:54 [entrez]
AID - 10.7759/cureus.40822 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 22;15(6):e40822. doi: 10.7759/cureus.40822. eCollection 2023 
      Jun.

PMID- 37791604
OWN - NLM
STAT- Publisher
LR  - 20231004
IS  - 1873-1953 (Electronic)
IS  - 1474-5151 (Linking)
DP  - 2023 Oct 4
TI  - For better or for worse: When chatbots influence human emotions and behaviours.
LID - zvad098 [pii]
LID - 10.1093/eurjcn/zvad098 [doi]
FAU - Moons, Philip
AU  - Moons P
AUID- ORCID: 0000-0002-8609-4516
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven - University of 
      Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens 
      backe 1, 413 46 Gothenburg, Sweden.
AD  - Department of Paediatrics and Child Health, University of Cape Town, Klipfontein 
      Rd, Rondebosch, 7700 Cape Town, South Africa.
LA  - eng
PT  - Journal Article
DEP - 20231004
PL  - England
TA  - Eur J Cardiovasc Nurs
JT  - European journal of cardiovascular nursing
JID - 101128793
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbots
OT  - conversational agents
OT  - lifestyle
OT  - natural language processing
EDAT- 2023/10/04 12:44
MHDA- 2023/10/04 12:44
CRDT- 2023/10/04 07:03
PHST- 2023/09/18 00:00 [received]
PHST- 2023/09/23 00:00 [revised]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/10/04 12:44 [medline]
PHST- 2023/10/04 12:44 [pubmed]
PHST- 2023/10/04 07:03 [entrez]
AID - 7288763 [pii]
AID - 10.1093/eurjcn/zvad098 [doi]
PST - aheadofprint
SO  - Eur J Cardiovasc Nurs. 2023 Oct 4:zvad098. doi: 10.1093/eurjcn/zvad098.

PMID- 37563880
OWN - NLM
STAT- MEDLINE
DCOM- 20230817
LR  - 20231030
IS  - 1087-2981 (Electronic)
IS  - 1087-2981 (Linking)
VI  - 28
IP  - 1
DP  - 2023 Dec
TI  - ChatGPT has the potential to enhance antiretroviral therapy adherence among 
      adolescents with HIV in sub-Saharan Africa.
PG  - 2246781
LID - 10.1080/10872981.2023.2246781 [doi]
LID - 2246781
FAU - Kizito, Samuel
AU  - Kizito S
AUID- ORCID: 0000-0003-2413-0398
AD  - International Center for Child Health and Development, Brown School, Washington 
      University, St. Louis, MO, USA.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Med Educ Online
JT  - Medical education online
JID - 9806550
RN  - 0 (Anti-Retroviral Agents)
SB  - IM
MH  - Humans
MH  - Adolescent
MH  - *HIV Infections/drug therapy
MH  - Anti-Retroviral Agents/therapeutic use
MH  - Africa South of the Sahara/epidemiology
MH  - Medication Adherence
PMC - PMC10424589
EDAT- 2023/08/11 06:43
MHDA- 2023/08/17 06:43
CRDT- 2023/08/11 02:04
PHST- 2023/08/17 06:43 [medline]
PHST- 2023/08/11 06:43 [pubmed]
PHST- 2023/08/11 02:04 [entrez]
AID - 2246781 [pii]
AID - 10.1080/10872981.2023.2246781 [doi]
PST - ppublish
SO  - Med Educ Online. 2023 Dec;28(1):2246781. doi: 10.1080/10872981.2023.2246781.

PMID- 37339863
OWN - NLM
STAT- Publisher
LR  - 20230620
IS  - 2047-9956 (Print)
IS  - 2047-9956 (Linking)
DP  - 2023 Jun 20
TI  - Performance and risks of ChatGPT used in drug information: comment.
LID - ejhpharm-2023-003864 [pii]
LID - 10.1136/ejhpharm-2023-003864 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Private Academic Consultant, Samraong, Cambodia amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Research Center, Chandigarh University, Mohali, Punjab, India.
LA  - eng
PT  - Letter
DEP - 20230620
PL  - England
TA  - Eur J Hosp Pharm
JT  - European journal of hospital pharmacy : science and practice
JID - 101578294
SB  - IM
OTO - NOTNLM
OT  - Ethics, pharmacy
OT  - HEALTH PHYSICS
OT  - PHARMACEUTICAL PREPARATIONS
OT  - PHARMACY SERVICE, HOSPITAL
OT  - Pharmacovigilance
COIS- Competing interests: None declared.
EDAT- 2023/06/21 01:07
MHDA- 2023/06/21 01:07
CRDT- 2023/06/20 21:03
PHST- 2023/06/21 01:07 [medline]
PHST- 2023/06/21 01:07 [pubmed]
PHST- 2023/06/20 21:03 [entrez]
AID - ejhpharm-2023-003864 [pii]
AID - 10.1136/ejhpharm-2023-003864 [doi]
PST - aheadofprint
SO  - Eur J Hosp Pharm. 2023 Jun 20:ejhpharm-2023-003864. doi: 
      10.1136/ejhpharm-2023-003864.

PMID- 38087683
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 1365-229X (Electronic)
IS  - 0009-9260 (Linking)
DP  - 2023 Nov 29
TI  - Large language models (LLMs) in the evaluation of emergency radiology reports: 
      performance of ChatGPT-4, Perplexity, and Bard.
LID - S0009-9260(23)00534-2 [pii]
LID - 10.1016/j.crad.2023.11.011 [doi]
FAU - Infante, A
AU  - Infante A
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy. Electronic address: amato.infante@policlinicogemelli.it.
FAU - Gaudino, S
AU  - Gaudino S
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy; Università Cattolica del Sacro Cuore, Facoltà di Medicina e Chirurgia, 
      Rome, Italy.
FAU - Orsini, F
AU  - Orsini F
AD  - Università Cattolica del Sacro Cuore, Facoltà di Medicina e Chirurgia, Rome, 
      Italy.
FAU - Del Ciello, A
AU  - Del Ciello A
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy.
FAU - Gullì, C
AU  - Gullì C
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy.
FAU - Merlino, B
AU  - Merlino B
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy; Università Cattolica del Sacro Cuore, Facoltà di Medicina e Chirurgia, 
      Rome, Italy.
FAU - Natale, L
AU  - Natale L
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy; Università Cattolica del Sacro Cuore, Facoltà di Medicina e Chirurgia, 
      Rome, Italy.
FAU - Iezzi, R
AU  - Iezzi R
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy; Università Cattolica del Sacro Cuore, Facoltà di Medicina e Chirurgia, 
      Rome, Italy.
FAU - Sala, E
AU  - Sala E
AD  - ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and 
      Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, 
      Italy; Università Cattolica del Sacro Cuore, Facoltà di Medicina e Chirurgia, 
      Rome, Italy.
LA  - eng
PT  - Journal Article
DEP - 20231129
PL  - England
TA  - Clin Radiol
JT  - Clinical radiology
JID - 1306016
SB  - IM
EDAT- 2023/12/13 06:42
MHDA- 2023/12/13 06:42
CRDT- 2023/12/13 01:17
PHST- 2023/09/08 00:00 [received]
PHST- 2023/11/10 00:00 [revised]
PHST- 2023/11/15 00:00 [accepted]
PHST- 2023/12/13 06:42 [medline]
PHST- 2023/12/13 06:42 [pubmed]
PHST- 2023/12/13 01:17 [entrez]
AID - S0009-9260(23)00534-2 [pii]
AID - 10.1016/j.crad.2023.11.011 [doi]
PST - aheadofprint
SO  - Clin Radiol. 2023 Nov 29:S0009-9260(23)00534-2. doi: 10.1016/j.crad.2023.11.011.

PMID- 37663854
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230905
LR  - 20230905
IS  - 2296-2565 (Electronic)
IS  - 2296-2565 (Linking)
VI  - 11
DP  - 2023
TI  - Brave (in a) new world: an ethical perspective on chatbots for medical advice.
PG  - 1254334
LID - 10.3389/fpubh.2023.1254334 [doi]
LID - 1254334
FAU - Erren, Thomas C
AU  - Erren TC
AD  - University of Cologne, University Hospital of Cologne, Cologne, North 
      Rhine-Westphalia, Germany.
FAU - Lewis, Philip
AU  - Lewis P
AD  - University of Cologne, University Hospital of Cologne, Cologne, North 
      Rhine-Westphalia, Germany.
FAU - Shaw, David M
AU  - Shaw DM
AD  - Care and Public Health Research Institute, Maastricht University, Maastricht, 
      Netherlands.
AD  - Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20230817
PL  - Switzerland
TA  - Front Public Health
JT  - Frontiers in public health
JID - 101616579
SB  - IM
PMC - PMC10470018
OTO - NOTNLM
OT  - ChatGPT
OT  - chatbot
OT  - confidentiality and privacy
OT  - ethics
OT  - hallucination
OT  - medical advice
OT  - risks
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/04 06:42
MHDA- 2023/09/04 06:43
CRDT- 2023/09/04 05:05
PHST- 2023/07/06 00:00 [received]
PHST- 2023/07/31 00:00 [accepted]
PHST- 2023/09/04 06:43 [medline]
PHST- 2023/09/04 06:42 [pubmed]
PHST- 2023/09/04 05:05 [entrez]
AID - 10.3389/fpubh.2023.1254334 [doi]
PST - epublish
SO  - Front Public Health. 2023 Aug 17;11:1254334. doi: 10.3389/fpubh.2023.1254334. 
      eCollection 2023.

PMID- 37491395
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230727
LR  - 20230727
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Linking)
VI  - 619
IP  - 7971
DP  - 2023 Jul
TI  - ChatGPT broke the Turing test - the race is on for new ways to assess AI.
PG  - 686-689
LID - 10.1038/d41586-023-02361-7 [doi]
FAU - Biever, Celeste
AU  - Biever C
LA  - eng
PT  - News
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
OTO - NOTNLM
OT  - Computer science
OT  - Mathematics and computing
OT  - Society
OT  - Technology
EDAT- 2023/07/26 01:06
MHDA- 2023/07/26 01:07
CRDT- 2023/07/25 23:47
PHST- 2023/07/26 01:07 [medline]
PHST- 2023/07/26 01:06 [pubmed]
PHST- 2023/07/25 23:47 [entrez]
AID - 10.1038/d41586-023-02361-7 [pii]
AID - 10.1038/d41586-023-02361-7 [doi]
PST - ppublish
SO  - Nature. 2023 Jul;619(7971):686-689. doi: 10.1038/d41586-023-02361-7.

PMID- 36821673
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230224
LR  - 20230223
IS  - 1095-9203 (Electronic)
IS  - 0036-8075 (Linking)
VI  - 379
IP  - 6634
DP  - 2023 Feb 24
TI  - Journals take up arms against AI-written text.
PG  - 740-741
LID - 10.1126/science.adh2762 [doi]
AB  - Many ask authors to disclose use of ChatGPT and other generative artificial 
      intelligence.
FAU - Brainard, Jeffrey
AU  - Brainard J
LA  - eng
PT  - News
DEP - 20230223
PL  - United States
TA  - Science
JT  - Science (New York, N.Y.)
JID - 0404511
SB  - IM
EDAT- 2023/02/24 06:00
MHDA- 2023/02/24 06:01
CRDT- 2023/02/23 14:06
PHST- 2023/02/23 14:06 [entrez]
PHST- 2023/02/24 06:00 [pubmed]
PHST- 2023/02/24 06:01 [medline]
AID - 10.1126/science.adh2762 [doi]
PST - ppublish
SO  - Science. 2023 Feb 24;379(6634):740-741. doi: 10.1126/science.adh2762. Epub 2023 
      Feb 23.

PMID- 37335233
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231025
IS  - 1528-1132 (Electronic)
IS  - 0009-921X (Print)
IS  - 0009-921X (Linking)
VI  - 481
IP  - 8
DP  - 2023 Aug 1
TI  - CORR Insights®: Can Artificial Intelligence Pass the American Board of 
      Orthopaedic Surgery Examination? Orthopaedic Residents Versus ChatGPT.
PG  - 1631-1633
LID - 10.1097/CORR.0000000000002741 [doi]
FAU - Karnuta, Jaret McGraw
AU  - Karnuta JM
AUID- ORCID: 0000-0002-6232-3919
AD  - Resident Physician, Hospital of the University of Pennsylvania, Philadelphia, PA, 
      USA.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230619
PL  - United States
TA  - Clin Orthop Relat Res
JT  - Clinical orthopaedics and related research
JID - 0075674
SB  - IM
CON - Clin Orthop Relat Res. 2023 Aug 1;481(8):1623-1630. PMID: 37220190
MH  - Humans
MH  - United States
MH  - *Orthopedics
MH  - Artificial Intelligence
MH  - *Orthopedic Procedures
MH  - Specialty Boards
PMC - PMC10344508
COIS- The author certifies that there are no funding or commercial associations 
      (consultancies, stock ownership, equity interest, patent/licensing arrangements, 
      etc.) that might pose a conflict of interest in connection with the submitted 
      article related to the author or any immediate family members. All ICMJE Conflict 
      of Interest Forms for authors and Clinical Orthopaedics and Related Research ® 
      editors and board members are on file with the publication and can be viewed on 
      request.
EDAT- 2023/06/19 13:08
MHDA- 2023/10/23 00:44
PMCR- 2024/08/01
CRDT- 2023/06/19 10:06
PHST- 2023/05/12 00:00 [received]
PHST- 2023/05/23 00:00 [accepted]
PHST- 2024/08/01 00:00 [pmc-release]
PHST- 2023/10/23 00:44 [medline]
PHST- 2023/06/19 13:08 [pubmed]
PHST- 2023/06/19 10:06 [entrez]
AID - 00003086-990000000-01235 [pii]
AID - CORR-D-23-00546 [pii]
AID - 10.1097/CORR.0000000000002741 [doi]
PST - ppublish
SO  - Clin Orthop Relat Res. 2023 Aug 1;481(8):1631-1633. doi: 
      10.1097/CORR.0000000000002741. Epub 2023 Jun 19.

PMID- 36959064
OWN - NLM
STAT- MEDLINE
DCOM- 20230616
LR  - 20230705
IS  - 0035-3787 (Print)
IS  - 0035-3787 (Linking)
VI  - 179
IP  - 6
DP  - 2023 Jun
TI  - Artificial intelligence and ChatGPT between worst enemy and best friend: The two 
      faces of a revolution and its impact on science and medical schools.
PG  - 520-522
LID - S0035-3787(23)00880-9 [pii]
LID - 10.1016/j.neurol.2023.03.004 [doi]
FAU - Aubignat, M
AU  - Aubignat M
AD  - Neurology Department, Amiens University Hospital, 1, rond-point du 
      Professeur-Christian-Cabrol, 80054 Amiens, France. Electronic address: 
      aubignat.mickael@chu-amiens.fr.
FAU - Diab, E
AU  - Diab E
AD  - Clinical Neurophysiology Department, Amiens University Hospital, Amiens, France.
LA  - eng
PT  - Editorial
DEP - 20230321
PL  - France
TA  - Rev Neurol (Paris)
JT  - Revue neurologique
JID - 2984779R
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Friends
MH  - Schools, Medical
EDAT- 2023/03/24 06:00
MHDA- 2023/06/16 06:42
CRDT- 2023/03/23 23:07
PHST- 2023/02/14 00:00 [received]
PHST- 2023/03/05 00:00 [revised]
PHST- 2023/03/07 00:00 [accepted]
PHST- 2023/06/16 06:42 [medline]
PHST- 2023/03/24 06:00 [pubmed]
PHST- 2023/03/23 23:07 [entrez]
AID - S0035-3787(23)00880-9 [pii]
AID - 10.1016/j.neurol.2023.03.004 [doi]
PST - ppublish
SO  - Rev Neurol (Paris). 2023 Jun;179(6):520-522. doi: 10.1016/j.neurol.2023.03.004. 
      Epub 2023 Mar 21.

PMID- 38046723
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - "Bot or Not": Turing Problem in Otolaryngology.
PG  - e48170
LID - 10.7759/cureus.48170 [doi]
LID - e48170
AB  - The aim of this article is to shed light on the evolving landscape of artificial 
      intelligence (AI) integration in otolaryngology and its implications, 
      particularly focusing on the ethical considerations surrounding AI applications, 
      and to highlight the potential benefits of ChatGPT in patient management and 
      scientific research within otolaryngology while emphasizing the necessity for 
      ethical guidelines and validation processes. Ultimately, the article seeks to 
      encourage a responsible and informed approach to AI adoption in otolaryngology, 
      promoting collaboration between AI and healthcare professionals for the 
      betterment of science and human well-being.
CI  - Copyright © 2023, Aliyeva et al.
FAU - Aliyeva, Aynur
AU  - Aliyeva A
AD  - Otolaryngology - Head and Neck Surgery, Cincinnati Children's Hospital Medical 
      Center, Ohio, USA.
LA  - eng
PT  - Editorial
DEP - 20231102
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10693309
OTO - NOTNLM
OT  - artificial intelligence
OT  - bot or not
OT  - chatgpt
OT  - otolaryngology
OT  - turing problem
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 05:08
PHST- 2023/11/02 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 05:08 [entrez]
AID - 10.7759/cureus.48170 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 2;15(11):e48170. doi: 10.7759/cureus.48170. eCollection 2023 
      Nov.

PMID- 37703085
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231003
IS  - 2561-7605 (Electronic)
IS  - 2561-7605 (Linking)
VI  - 6
DP  - 2023 Sep 13
TI  - Shaping the Future of Older Adult Care: ChatGPT, Advanced AI, and the 
      Transformation of Clinical Practice.
PG  - e51776
LID - 10.2196/51776 [doi]
LID - e51776
AB  - As the older adult population in the United States grows, new approaches to 
      managing and streamlining clinical work are needed to accommodate their increased 
      demand for health care. Deep learning and generative artificial intelligence (AI) 
      have the potential to transform how care is delivered and how clinicians practice 
      in geriatrics. In this editorial, we explore the opportunities and limitations of 
      these technologies.
CI  - ©Kathleen Fear, Conrad Gleber. Originally published in JMIR Aging 
      (https://aging.jmir.org), 13.09.2023.
FAU - Fear, Kathleen
AU  - Fear K
AUID- ORCID: 0000-0003-4608-6949
AD  - UR Health Lab, University of Rochester Medical Center, Rochester, NY, United 
      States.
FAU - Gleber, Conrad
AU  - Gleber C
AUID- ORCID: 0009-0005-8241-944X
AD  - UR Health Lab, University of Rochester Medical Center, Rochester, NY, United 
      States.
AD  - Department of Medicine, University of Rochester Medical Center, Rochester, NY, 
      United States.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - Canada
TA  - JMIR Aging
JT  - JMIR aging
JID - 101740387
PMC - PMC10534283
OTO - NOTNLM
OT  - ChatGPT
OT  - Generative Pre-trained Transformer
OT  - artificial intelligence
OT  - generative AI
OT  - large language models
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/13 12:43
MHDA- 2023/09/13 12:44
CRDT- 2023/09/13 11:54
PHST- 2023/08/11 00:00 [received]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/09/13 12:44 [medline]
PHST- 2023/09/13 12:43 [pubmed]
PHST- 2023/09/13 11:54 [entrez]
AID - v6i1e51776 [pii]
AID - 10.2196/51776 [doi]
PST - epublish
SO  - JMIR Aging. 2023 Sep 13;6:e51776. doi: 10.2196/51776.

PMID- 37811189
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231010
IS  - 0971-3026 (Print)
IS  - 1998-3808 (Electronic)
IS  - 0970-2016 (Linking)
VI  - 33
IP  - 4
DP  - 2023 Oct
TI  - Online for On Call: A Study Assessing the Use of Internet Resources Including 
      ChatGPT among On-Call Radiology Residents in India.
PG  - 440-449
LID - 10.1055/s-0043-1772465 [doi]
AB  - Background  The information-seeking behavior of the radiology residents on call 
      has undergone modernization in the recent times given the advent of easy to 
      access, reliable online resources, and robust artificial intelligence chatbots 
      such as Chat Generative Pre-Trained Transformer (ChatGPT). Purpose  The aim of 
      this study was to conduct a baseline analysis among the residents to understand 
      the best way to meet information needs in the future, spread awareness about the 
      existing resources, and narrow down to the most preferred online resource. 
      Methods and Materials  A prospective, descriptive study was performed using an 
      online survey instrument and was conducted among radiology residents in India. 
      They were questioned on their demographics, frequency of on call, fatigue 
      experienced on call, and preferred information resources and reasons for choosing 
      them. Results  A total of 286 residents participated in the survey. All residents 
      had used the Internet radiology resources during on-call duties. The most 
      preferred resource material was Radiopaedia followed by Radiology Assistant. 
      IMAIOS e-Anatomy was the most preferred anatomy resource. There was significant ( 
      p  < 0.05) difference in relation to the use of closed edit peer-reviewed 
      literature among the two batches with it being used almost exclusively by third 
      year residents. In the artificial intelligence-aided ChatGPT section, 61.8% had 
      used the software at least once while being on call, of them 57.6% responded that 
      the information was inaccurate, 67.2% responded that the information was 
      insufficient to aid in diagnosis, 100% felt that the lack of images in the 
      software made it an unlikely resource that would be used by them in the future, 
      and 85.8% agreed that they would use it for providing reporting templates in the 
      future. In the suggestions for upcoming versions, 100% responded that images 
      should be included in the description provide by the chatbot, and 74.5% felt that 
      references for the information being provided should be included as it reaffirms 
      the reliability of the information. Conclusions  Presently, we find that 
      Radiopaedia met most of the requirements as an ideal online radiology resource 
      according to the residents. In the present-day scenario, ChatGPT is not 
      considered as an important on-call radiology education resource first because it 
      lacks images which is quintessential for a budding radiologist, and second, it 
      does not have any reference or proof for the information that it is providing. 
      However, it may be of help to nonmedical professionals who need to understand 
      radiology in layman's terms and to radiologists for patient report preparation 
      and research writing.
CI  - Indian Radiological Association. This is an open access article published by 
      Thieme under the terms of the Creative Commons 
      Attribution-NonDerivative-NonCommercial License, permitting copying and 
      reproduction so long as the original work is given appropriate credit. Contents 
      may not be used for commercial purposes, or adapted, remixed, transformed or 
      built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).
FAU - Sethi, Humsheer Singh
AU  - Sethi HS
AUID- ORCID: 0000-0002-2722-0148
AD  - Department of Radiodiagnosis, Institute of Medical Sciences and SUM Hospital, 
      Siksha 'O' Anusandhan deemed to be University, Odisha, India.
FAU - Mohapatra, Satya
AU  - Mohapatra S
AD  - Department of Radiodiagnosis, Institute of Medical Sciences and SUM Hospital, 
      Siksha 'O' Anusandhan deemed to be University, Odisha, India.
FAU - Mali, Chayasmita
AU  - Mali C
AD  - Department of Pathology, Kalinga Institute of Medical Sciences, Bhubaneswar, 
      Odisha, India.
FAU - Dubey, Roopak
AU  - Dubey R
AD  - Department of Radiodiagnosis, Kalinga Institute of Medical Sciences, Bhubaneswar, 
      Odisha, India.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - Germany
TA  - Indian J Radiol Imaging
JT  - The Indian journal of radiology & imaging
JID - 8503873
PMC - PMC10556306
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - on-call radiology
OT  - resident education
COIS- Conflict of Interest None declared.
EDAT- 2023/10/09 12:42
MHDA- 2023/10/09 12:43
CRDT- 2023/10/09 06:14
PHST- 2023/10/09 12:43 [medline]
PHST- 2023/10/09 12:42 [pubmed]
PHST- 2023/10/09 06:14 [entrez]
AID - IJRI-23-5-2558 [pii]
AID - 10.1055/s-0043-1772465 [doi]
PST - epublish
SO  - Indian J Radiol Imaging. 2023 Aug 21;33(4):440-449. doi: 10.1055/s-0043-1772465. 
      eCollection 2023 Oct.

PMID- 37767452
OWN - NLM
STAT- Publisher
LR  - 20230929
IS  - 2249-4863 (Print)
IS  - 2278-7135 (Electronic)
IS  - 2249-4863 (Linking)
VI  - 12
IP  - 8
DP  - 2023 Aug
TI  - A pilot study on the capability of artificial intelligence in preparation of 
      patients' educational materials for Indian public health issues.
PG  - 1659-1662
LID - 10.4103/jfmpc.jfmpc_262_23 [doi]
AB  - BACKGROUND: Patient education is an essential component of improving public 
      health as it empowers individuals with the knowledge and skills necessary for 
      making informed decisions about their health and well-being. Primary care 
      physicians play a crucial role in patients' education as they are the first 
      contact between the patients and the healthcare system. However, they may not get 
      adequate time to prepare educational material for their patients. An artificial 
      intelligence-based writer like ChatGPT can help write the material for 
      physicians. AIM: This study aimed to ascertain the capability of ChatGPT for 
      generating patients' educational materials for common public health issues in 
      India. MATERIALS AND METHODS: This observational study was conducted on the 
      internet using the free research version of ChatGPT, a conversational artificial 
      intelligence that can generate human-like text output. We conversed with the 
      program with the question - "prepare a patients' education material for X in 
      India." In the X, we used the following words or phrases - "air pollution," 
      "malnutrition," "maternal and child health," "mental health," "noncommunicable 
      diseases," "road traffic accidents," "tuberculosis," and "water-borne diseases." 
      The textual response in the conversation was collected and stored for further 
      analysis. The text was analyzed for readability, grammatical errors, and text 
      similarity. RESULT: We generated a total of eight educational documents with a 
      median of 26 (Q1-Q3: 21.5-34) sentences with a median of 349 (Q1-Q3: 329-450.5) 
      words. The median Flesch Reading Ease Score was 48.2 (Q1-Q3: 39-50.65). It 
      indicates that the text can be understood by a college student. The text was 
      grammatically correct with very few (seven errors in 3415 words) errors. The text 
      was very clear in the majority (8 out of 9) of documents with a median score of 
      85 (Q1-Q3: 82.5-85) in 100. The overall text similarity index was 18% (Q1-Q3: 
      7.5-26). CONCLUSION: The research version of the ChatGPT (January 30, 2023 
      version) is capable of generating patients' educational materials for common 
      public health issues in India with a difficulty level ideal for college students 
      with high grammatical accuracy. However, the text similarity should be checked 
      before using it. Primary care physicians can take the help of ChatGPT for 
      generating text for materials used for patients' education.
CI  - Copyright: © 2023 Journal of Family Medicine and Primary Care.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Department of Physiology, All India Institute of Medical Sciences, Deoghar, 
      Jharkhand, India.
FAU - Panigrahi, Muralidhar
AU  - Panigrahi M
AD  - Department of Pharmacology, Bhima Bhoi Medical College and Hospital, Balangir, 
      Odisha, India.
FAU - Mishra, Baidyanath
AU  - Mishra B
AD  - Department of Physiology, Sri Jagannath Medical College and Hospital, Puri, 
      Odisha, India.
FAU - Behera, Joshil K
AU  - Behera JK
AD  - Department of Physiology, Nagaland Institute of Medical Science and Research, 
      Nagaland, India.
FAU - Mondal, Shaikat
AU  - Mondal S
AD  - Department of Physiology, Raiganj Government Medical College and Hospital, West 
      Bengal, India.
LA  - eng
PT  - Journal Article
DEP - 20230829
PL  - India
TA  - J Family Med Prim Care
JT  - Journal of family medicine and primary care
JID - 101610082
PMC - PMC10521817
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - education
OT  - health awareness
OT  - intelligence
OT  - internet
OT  - patient
OT  - physicians
OT  - publication
COIS- There are no conflicts of interest.
EDAT- 2023/09/28 06:42
MHDA- 2023/09/28 06:42
CRDT- 2023/09/28 04:16
PHST- 2023/02/08 00:00 [received]
PHST- 2023/06/09 00:00 [revised]
PHST- 2023/06/10 00:00 [accepted]
PHST- 2023/09/28 06:42 [medline]
PHST- 2023/09/28 06:42 [pubmed]
PHST- 2023/09/28 04:16 [entrez]
AID - JFMPC-12-1659 [pii]
AID - 10.4103/jfmpc.jfmpc_262_23 [doi]
PST - ppublish
SO  - J Family Med Prim Care. 2023 Aug;12(8):1659-1662. doi: 
      10.4103/jfmpc.jfmpc_262_23. Epub 2023 Aug 29.

PMID- 37572695
OWN - NLM
STAT- Publisher
LR  - 20230813
IS  - 1715-3360 (Electronic)
IS  - 0008-4182 (Linking)
DP  - 2023 Aug 9
TI  - Artificial intelligence chatbot performance in triage of ophthalmic conditions.
LID - S0008-4182(23)00234-X [pii]
LID - 10.1016/j.jcjo.2023.07.016 [doi]
AB  - BACKGROUND: Timely access to human expertise for affordable and efficient triage 
      of ophthalmic conditions is inconsistent. With recent advancements in publicly 
      available artificial intelligence (AI) chatbots, the lay public may turn to these 
      tools for triage of ophthalmic complaints. Validation studies are necessary to 
      evaluate the performance of AI chatbots as triage tools and inform the public 
      regarding their safety. OBJECTIVE: To evaluate the triage performance of AI 
      chatbots for ophthalmic conditions. DESIGN: Cross-sectional study. SETTING: 
      Single centre. PARTICIPANTS: Ophthalmology trainees, OpenAI ChatGPT (GPT-4), Bing 
      Chat, and WebMD Symptom Checker. METHODS: Forty-four clinical vignettes 
      representing common ophthalmic complaints were developed, and a standardized 
      pathway of prompts was presented to each tool in March 2023. Primary outcomes 
      were proportion of responses with the correct diagnosis listed in the top 3 
      possible diagnoses and proportion with correct triage urgency. Ancillary outcomes 
      included presence of grossly inaccurate statements, mean reading grade level, 
      mean response word count, proportion with attribution, and most common sources 
      cited. RESULTS: The ophthalmologists in training, ChatGPT, Bing Chat, and the 
      WebMD Symptom Checker listed the appropriate diagnosis among the top 3 
      suggestions in 42 (95%), 41 (93%), 34 (77%), and 8 (33%) cases, respectively. 
      Triage urgency was appropriate in 38 (86%), 43 (98%), and 37 (84%) cases for 
      ophthalmology trainees, ChatGPT, and Bing Chat, correspondingly. CONCLUSIONS: 
      ChatGPT using the GPT-4 model offered high diagnostic and triage accuracy that 
      was comparable with that of ophthalmology trainees with no grossly inaccurate 
      statements. Bing Chat had lower accuracy and a tendency to overestimate triage 
      urgency.
CI  - Copyright © 2023 Canadian Ophthalmological Society. Published by Elsevier Inc. 
      All rights reserved.
FAU - Lyons, Riley J
AU  - Lyons RJ
AD  - Department of Ophthalmology, Emory University School of Medicine, Atlanta, GA.
FAU - Arepalli, Sruthi R
AU  - Arepalli SR
AD  - Department of Ophthalmology, Emory University School of Medicine, Atlanta, GA.
FAU - Fromal, Ollya
AU  - Fromal O
AD  - Department of Ophthalmology, Emory University School of Medicine, Atlanta, GA.
FAU - Choi, Jinho D
AU  - Choi JD
AD  - Department of Computer Science, Emory University, Atlanta, GA.
FAU - Jain, Nieraj
AU  - Jain N
AD  - Department of Ophthalmology, Emory University School of Medicine, Atlanta, GA. 
      Electronic address: nieraj.jain@emory.edu.
LA  - eng
PT  - Journal Article
DEP - 20230809
PL  - England
TA  - Can J Ophthalmol
JT  - Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
JID - 0045312
SB  - IM
EDAT- 2023/08/13 00:43
MHDA- 2023/08/13 00:43
CRDT- 2023/08/12 19:21
PHST- 2023/06/02 00:00 [received]
PHST- 2023/07/06 00:00 [revised]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/08/13 00:43 [pubmed]
PHST- 2023/08/13 00:43 [medline]
PHST- 2023/08/12 19:21 [entrez]
AID - S0008-4182(23)00234-X [pii]
AID - 10.1016/j.jcjo.2023.07.016 [doi]
PST - aheadofprint
SO  - Can J Ophthalmol. 2023 Aug 9:S0008-4182(23)00234-X. doi: 
      10.1016/j.jcjo.2023.07.016.

PMID- 37943581
OWN - NLM
STAT- MEDLINE
DCOM- 20231110
LR  - 20231129
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Nov 9
TI  - An AI Dietitian for Type 2 Diabetes Mellitus Management Based on Large Language 
      and Image Recognition Models: Preclinical Concept Validation Study.
PG  - e51300
LID - 10.2196/51300 [doi]
LID - e51300
AB  - BACKGROUND: Nutritional management for patients with diabetes in China is a 
      significant challenge due to the low supply of registered clinical dietitians. To 
      address this, an artificial intelligence (AI)-based nutritionist program that 
      uses advanced language and image recognition models was created. This program can 
      identify ingredients from images of a patient's meal and offer nutritional 
      guidance and dietary recommendations. OBJECTIVE: The primary objective of this 
      study is to evaluate the competence of the models that support this program. 
      METHODS: The potential of an AI nutritionist program for patients with type 2 
      diabetes mellitus (T2DM) was evaluated through a multistep process. First, a 
      survey was conducted among patients with T2DM and endocrinologists to identify 
      knowledge gaps in dietary practices. ChatGPT and GPT 4.0 were then tested through 
      the Chinese Registered Dietitian Examination to assess their proficiency in 
      providing evidence-based dietary advice. ChatGPT's responses to common questions 
      about medical nutrition therapy were compared with expert responses by 
      professional dietitians to evaluate its proficiency. The model's food 
      recommendations were scrutinized for consistency with expert advice. A deep 
      learning-based image recognition model was developed for food identification at 
      the ingredient level, and its performance was compared with existing models. 
      Finally, a user-friendly app was developed, integrating the capabilities of 
      language and image recognition models to potentially improve care for patients 
      with T2DM. RESULTS: Most patients (182/206, 88.4%) demanded more immediate and 
      comprehensive nutritional management and education. Both ChatGPT and GPT 4.0 
      passed the Chinese Registered Dietitian examination. ChatGPT's food 
      recommendations were mainly in line with best practices, except for certain foods 
      like root vegetables and dry beans. Professional dietitians' reviews of ChatGPT's 
      responses to common questions were largely positive, with 162 out of 168 
      providing favorable reviews. The multilabel image recognition model evaluation 
      showed that the Dino V2 model achieved an average F(1) score of 0.825, indicating 
      high accuracy in recognizing ingredients. CONCLUSIONS: The model evaluations were 
      promising. The AI-based nutritionist program is now ready for a supervised pilot 
      study.
CI  - ©Haonan Sun, Kai Zhang, Wei Lan, Qiufeng Gu, Guangxiang Jiang, Xue Yang, Wanli 
      Qin, Dongran Han. Originally published in the Journal of Medical Internet 
      Research (https://www.jmir.org), 09.11.2023.
FAU - Sun, Haonan
AU  - Sun H
AUID- ORCID: 0000-0003-2352-4758
AD  - School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
FAU - Zhang, Kai
AU  - Zhang K
AUID- ORCID: 0009-0007-8118-3564
AD  - School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
FAU - Lan, Wei
AU  - Lan W
AUID- ORCID: 0009-0006-7937-4743
AD  - Department of Pediatrics, Peking University Shenzhen Hospital, Shenzhen, China.
FAU - Gu, Qiufeng
AU  - Gu Q
AUID- ORCID: 0009-0009-3161-7197
AD  - Department of Pediatrics, Peking University Shenzhen Hospital, Shenzhen, China.
FAU - Jiang, Guangxiang
AU  - Jiang G
AUID- ORCID: 0009-0000-1224-3526
AD  - School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
FAU - Yang, Xue
AU  - Yang X
AUID- ORCID: 0009-0007-7986-5478
AD  - School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
FAU - Qin, Wanli
AU  - Qin W
AUID- ORCID: 0009-0008-5591-1860
AD  - School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
FAU - Han, Dongran
AU  - Han D
AUID- ORCID: 0000-0003-3630-5036
AD  - School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20231109
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Nutritionists
MH  - *Diabetes Mellitus, Type 2/therapy
MH  - Artificial Intelligence
MH  - Pilot Projects
MH  - Language
MH  - Meals
PMC - PMC10667983
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT 4.0
OT  - NLP
OT  - artificial intelligence
OT  - deep learning
OT  - diabetes
OT  - diabetic
OT  - diet
OT  - dietary
OT  - dietician
OT  - digital health
OT  - food
OT  - image recognition
OT  - ingredient recognition
OT  - language model
OT  - machine learning
OT  - meal
OT  - meals
OT  - medical nutrition therapy
OT  - natural language processing
OT  - nutrition
OT  - nutritional
OT  - recommendation
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/09 12:42
MHDA- 2023/11/10 06:45
CRDT- 2023/11/09 11:53
PHST- 2023/07/27 00:00 [received]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/09/18 00:00 [revised]
PHST- 2023/11/10 06:45 [medline]
PHST- 2023/11/09 12:42 [pubmed]
PHST- 2023/11/09 11:53 [entrez]
AID - v25i1e51300 [pii]
AID - 10.2196/51300 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Nov 9;25:e51300. doi: 10.2196/51300.

PMID- 37724963
OWN - NLM
STAT- MEDLINE
DCOM- 20230921
LR  - 20231010
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 308
IP  - 3
DP  - 2023 Sep
TI  - Potential of ChatGPT and GPT-4 for Data Mining of Free-Text CT Reports on Lung 
      Cancer.
PG  - e231362
LID - 10.1148/radiol.231362 [doi]
AB  - Background The latest large language models (LLMs) solve unseen problems via 
      user-defined text prompts without the need for retraining, offering potentially 
      more efficient information extraction from free-text medical records than manual 
      annotation. Purpose To compare the performance of the LLMs ChatGPT and GPT-4 in 
      data mining and labeling oncologic phenotypes from free-text CT reports on lung 
      cancer by using user-defined prompts. Materials and Methods This retrospective 
      study included patients who underwent lung cancer follow-up CT between September 
      2021 and March 2023. A subset of 25 reports was reserved for prompt engineering 
      to instruct the LLMs in extracting lesion diameters, labeling metastatic disease, 
      and assessing oncologic progression. This output was fed into a rule-based 
      natural language processing pipeline to match ground truth annotations from four 
      radiologists and derive performance metrics. The oncologic reasoning of LLMs was 
      rated on a five-point Likert scale for factual correctness and accuracy. The 
      occurrence of confabulations was recorded. Statistical analyses included Wilcoxon 
      signed rank and McNemar tests. Results On 424 CT reports from 424 patients (mean 
      age, 65 years ± 11 [SD]; 265 male), GPT-4 outperformed ChatGPT in extracting 
      lesion parameters (98.6% vs 84.0%, P < .001), resulting in 96% correctly mined 
      reports (vs 67% for ChatGPT, P < .001). GPT-4 achieved higher accuracy in 
      identification of metastatic disease (98.1% [95% CI: 97.7, 98.5] vs 90.3% [95% 
      CI: 89.4, 91.0]) and higher performance in generating correct labels for 
      oncologic progression (F1 score, 0.96 [95% CI: 0.94, 0.98] vs 0.91 [95% CI: 0.89, 
      0.94]) (both P < .001). In oncologic reasoning, GPT-4 had higher Likert scale 
      scores for factual correctness (4.3 vs 3.9) and accuracy (4.4 vs 3.3), with a 
      lower rate of confabulation (1.7% vs 13.7%) than ChatGPT (all P < .001). 
      Conclusion When using user-defined prompts, GPT-4 outperformed ChatGPT in 
      extracting oncologic phenotypes from free-text CT reports on lung cancer and 
      demonstrated better oncologic reasoning with fewer confabulations. © RSNA, 2023 
      Supplemental material is available for this article. See also the editorial by 
      Hafezi-Nejad and Trivedi in this issue.
FAU - Fink, Matthias A
AU  - Fink MA
AUID- ORCID: 0000-0002-0189-7070
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Bischoff, Arved
AU  - Bischoff A
AUID- ORCID: 0000-0002-7147-2414
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Fink, Christoph A
AU  - Fink CA
AUID- ORCID: 0000-0001-8222-2288
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Moll, Martin
AU  - Moll M
AUID- ORCID: 0009-0002-2959-5688
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Kroschke, Jonas
AU  - Kroschke J
AUID- ORCID: 0000-0003-1225-3016
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Dulz, Luca
AU  - Dulz L
AUID- ORCID: 0009-0007-3859-6259
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Heußel, Claus Peter
AU  - Heußel CP
AUID- ORCID: 0009-0000-1274-5737
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Kauczor, Hans-Ulrich
AU  - Kauczor HU
AUID- ORCID: 0000-0002-6730-9462
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
FAU - Weber, Tim F
AU  - Weber TF
AUID- ORCID: 0000-0001-5911-123X
AD  - From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., 
      J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology 
      (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 
      Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the 
      German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., 
      H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with 
      Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, 
      Heidelberg, Germany (C.P.H.).
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CIN - Radiology. 2023 Sep;308(3):e232308. PMID: 37724971
MH  - Humans
MH  - Male
MH  - Aged
MH  - Retrospective Studies
MH  - *Lung Neoplasms/diagnostic imaging
MH  - Data Mining
MH  - Medical Oncology
MH  - Benchmarking
MH  - Memory Disorders
MH  - *Neoplasms, Second Primary
EDAT- 2023/09/19 17:43
MHDA- 2023/09/21 06:42
CRDT- 2023/09/19 10:02
PHST- 2023/09/21 06:42 [medline]
PHST- 2023/09/19 17:43 [pubmed]
PHST- 2023/09/19 10:02 [entrez]
AID - 10.1148/radiol.231362 [doi]
PST - ppublish
SO  - Radiology. 2023 Sep;308(3):e231362. doi: 10.1148/radiol.231362.

PMID- 37192124
OWN - NLM
STAT- Publisher
LR  - 20230516
IS  - 2398-631X (Electronic)
IS  - 2398-631X (Linking)
DP  - 2023 May 2
TI  - ChatGPT and generative AI chatbots: challenges and opportunities for science, 
      medicine and medical leaders.
LID - leader-2023-000797 [pii]
LID - 10.1136/leader-2023-000797 [doi]
FAU - Loh, Erwin
AU  - Loh E
AUID- ORCID: 0000-0001-7157-0826
AD  - Monash Centre for Health Research and Implementation, Monash University, Clayton, 
      Victoria, Australia erwin.loh@monash.edu.
AD  - Group Chief Medical Officer, St Vincent's Health Australia Ltd, East Melbourne, 
      Victoria, Australia.
LA  - eng
PT  - Journal Article
DEP - 20230502
PL  - England
TA  - BMJ Lead
JT  - BMJ leader
JID - 101757339
SB  - IM
OTO - NOTNLM
OT  - management system
OT  - medical leadership
OT  - patient safety
COIS- Competing interests: None declared.
EDAT- 2023/05/16 19:10
MHDA- 2023/05/16 19:10
CRDT- 2023/05/16 12:55
PHST- 2023/03/21 00:00 [received]
PHST- 2023/04/20 00:00 [accepted]
PHST- 2023/05/16 19:10 [medline]
PHST- 2023/05/16 19:10 [pubmed]
PHST- 2023/05/16 12:55 [entrez]
AID - leader-2023-000797 [pii]
AID - 10.1136/leader-2023-000797 [doi]
PST - aheadofprint
SO  - BMJ Lead. 2023 May 2:leader-2023-000797. doi: 10.1136/leader-2023-000797.

PMID- 36905162
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230616
IS  - 1527-330X (Electronic)
IS  - 1090-820X (Print)
IS  - 1090-820X (Linking)
VI  - 43
IP  - 7
DP  - 2023 Jun 14
TI  - Let's Chat About Chatbots: Additional Thoughts on ChatGPT and Its Role in Plastic 
      Surgery Along With Its Ability to Perform Systematic Reviews.
PG  - NP591-NP592
LID - 10.1093/asj/sjad056 [doi]
FAU - Najafali, Daniel
AU  - Najafali D
AUID- ORCID: 0000-0003-1509-9229
FAU - Reiche, Erik
AU  - Reiche E
FAU - Camacho, Justin M
AU  - Camacho JM
AUID- ORCID: 0000-0002-1877-6005
FAU - Morrison, Shane D
AU  - Morrison SD
FAU - Dorafshar, Amir H
AU  - Dorafshar AH
AUID- ORCID: 0000-0001-7045-3601
LA  - eng
PT  - Journal Article
PL  - England
TA  - Aesthet Surg J
JT  - Aesthetic surgery journal
JID - 9707469
SB  - IM
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
PMC - PMC10264225
EDAT- 2023/03/12 06:00
MHDA- 2023/06/15 06:42
CRDT- 2023/03/11 02:32
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/03/12 06:00 [pubmed]
PHST- 2023/03/11 02:32 [entrez]
AID - 7076067 [pii]
AID - sjad056 [pii]
AID - 10.1093/asj/sjad056 [doi]
PST - ppublish
SO  - Aesthet Surg J. 2023 Jun 14;43(7):NP591-NP592. doi: 10.1093/asj/sjad056.

PMID- 37680991
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230910
IS  - 2162-2531 (Print)
IS  - 2162-2531 (Electronic)
IS  - 2162-2531 (Linking)
VI  - 33
DP  - 2023 Sep 12
TI  - Artificial intelligence enabled ChatGPT and large language models in drug target 
      discovery, drug discovery, and development.
PG  - 866-868
LID - 10.1016/j.omtn.2023.08.009 [doi]
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal 700126, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 
      756020, India.
FAU - Lee, Sang-Soo
AU  - Lee SS
AD  - Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon 
      Sacred Heart Hospital, Chuncheon-si, Gangwon-do 24252, Republic of Korea.
LA  - eng
PT  - Editorial
DEP - 20230828
PL  - United States
TA  - Mol Ther Nucleic Acids
JT  - Molecular therapy. Nucleic acids
JID - 101581621
PMC - PMC10481150
EDAT- 2023/09/08 06:42
MHDA- 2023/09/08 06:43
CRDT- 2023/09/08 04:09
PHST- 2023/09/08 06:43 [medline]
PHST- 2023/09/08 06:42 [pubmed]
PHST- 2023/09/08 04:09 [entrez]
AID - S2162-2531(23)00222-6 [pii]
AID - 10.1016/j.omtn.2023.08.009 [doi]
PST - epublish
SO  - Mol Ther Nucleic Acids. 2023 Aug 28;33:866-868. doi: 10.1016/j.omtn.2023.08.009. 
      eCollection 2023 Sep 12.

PMID- 37286300
OWN - NLM
STAT- MEDLINE
DCOM- 20230609
LR  - 20230627
IS  - 2056-5933 (Electronic)
IS  - 2056-5933 (Linking)
VI  - 9
IP  - 2
DP  - 2023 Jun
TI  - Quality of citation data using the natural language processing tool ChatGPT in 
      rheumatology: creation of false references.
LID - 10.1136/rmdopen-2023-003248 [doi]
LID - e003248
FAU - Hueber, Axel J
AU  - Hueber AJ
AUID- ORCID: 0000-0001-6454-1234
AD  - Division of Rheumatology, Paracelsus Medical University, Klinikum Nürnberg, 
      Nürnberg, Germany Axel.Hueber@fau.de.
AD  - Department of Internal Medicine 3, Rheumatology and Immunology, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum 
      Erlangen, Erlangen, Germany.
FAU - Kleyer, Arnd
AU  - Kleyer A
AUID- ORCID: 0000-0002-2026-7728
AD  - Department of Internal Medicine 3, Rheumatology and Immunology, 
      Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum 
      Erlangen, Erlangen, Germany.
AD  - Deutsches Zentrum Immuntherapie, Friedrich-Alexander-Universität 
      Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
LA  - eng
PT  - Letter
PL  - England
TA  - RMD Open
JT  - RMD open
JID - 101662038
SB  - IM
MH  - Humans
MH  - *Rheumatology
MH  - Natural Language Processing
PMC - PMC10254965
OTO - NOTNLM
OT  - Arthritis
OT  - Qualitative research
OT  - Therapeutics
COIS- Competing interests: None declared.
EDAT- 2023/06/08 01:08
MHDA- 2023/06/09 06:41
CRDT- 2023/06/07 21:04
PHST- 2023/04/20 00:00 [received]
PHST- 2023/05/15 00:00 [accepted]
PHST- 2023/06/09 06:41 [medline]
PHST- 2023/06/08 01:08 [pubmed]
PHST- 2023/06/07 21:04 [entrez]
AID - rmdopen-2023-003248 [pii]
AID - 10.1136/rmdopen-2023-003248 [doi]
PST - ppublish
SO  - RMD Open. 2023 Jun;9(2):e003248. doi: 10.1136/rmdopen-2023-003248.

PMID- 38085845
OWN - NLM
STAT- Publisher
LR  - 20231212
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Dec 12
TI  - ChatGPT or LLMs can provide treatment suggestions for critical patients with 
      antibiotic-resistant infections: A next-generation revolution for medical 
      science?
LID - 10.1097/JS9.0000000000000987 [doi]
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal 700126, India.
FAU - Pal, Soumen
AU  - Pal S
AD  - School of Mechanical Engineering, Vellore Institute of Technology, Vellore 
      632014, Tamil Nadu, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, 
      Odisha, India.
FAU - Islam, Md Aminul
AU  - Islam MA
AD  - COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and 
      Technology University, Noakhali-3814, Bangladesh.
AD  - Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical 
      College, Karimganj, Kishoreganj, Bangladesh.
LA  - eng
PT  - Journal Article
DEP - 20231212
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/12/12 18:42
MHDA- 2023/12/12 18:42
CRDT- 2023/12/12 14:23
PHST- 2023/11/24 00:00 [received]
PHST- 2023/11/27 00:00 [accepted]
PHST- 2023/12/12 18:42 [medline]
PHST- 2023/12/12 18:42 [pubmed]
PHST- 2023/12/12 14:23 [entrez]
AID - 01279778-990000000-00886 [pii]
AID - 10.1097/JS9.0000000000000987 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Dec 12. doi: 10.1097/JS9.0000000000000987.

PMID- 37181995
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230516
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Bilateral Vocal Fold Paralysis in a Patient With Neurosarcoidosis: A 
      ChatGPT-Driven Case Report Describing an Unusual Presentation.
PG  - e37368
LID - 10.7759/cureus.37368 [doi]
LID - e37368
AB  - This ChatGPT-driven case report describes a unique presentation of 
      neurosarcoidosis. The patient, a 58-year-old female, initially presented with 
      hoarseness and was found to have bilateral jugular foramen tumors and thoracic 
      lymphadenopathy. Imaging revealed significant enlargement and thickening of the 
      vagus nerve and a separate mass of the cervical sympathetic trunk. The patient 
      was referred for an ultrasound-guided biopsy of the abnormal neck masses to 
      establish a pathologic diagnosis. The patient subsequently underwent neck 
      dissection for exposure of the vagus nerve and isolation of the great vessels in 
      preparation for a transmastoid approach to the skull base. The presence of 
      multifocal tumors prompted the need for a biopsy, which ultimately revealed 
      sarcoid granulomas in the nervous system. The patient was diagnosed with 
      neurosarcoidosis. This case highlights the potential for sarcoidosis to affect 
      the nervous system, with multiple cranial nerve involvement, seizures, and 
      cognitive impairment. It also emphasizes the need for a combination of clinical, 
      radiological, and pathological findings for an accurate diagnosis of 
      neurosarcoidosis. Additionally, this case highlights the utility of natural 
      language processing (NLP), as the entire case report was written using ChatGPT. 
      This report serves as a comparison of the quality of case reports generated by 
      humans versus NLP algorithms. The original case report can be found in the 
      references.
CI  - Copyright © 2023, Guirguis et al.
FAU - Guirguis, Christopher A
AU  - Guirguis CA
AD  - Otolaryngology - Head and Neck Surgery, MedStar Georgetown University Hospital, 
      Washington, D.C., USA.
FAU - Crossley, Jason R
AU  - Crossley JR
AD  - Otolaryngology - Head and Neck Surgery, MedStar Georgetown University Hospital, 
      Washington, D.C., USA.
FAU - Malekzadeh, Sonya
AU  - Malekzadeh S
AD  - Otolaryngology - Head and Neck Surgery, MedStar Georgetown University Hospital, 
      Washington, D.C., USA.
LA  - eng
PT  - Case Reports
DEP - 20230410
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10171033
OTO - NOTNLM
OT  - bilateral vocal fold paralysis
OT  - case report
OT  - chatgpt
OT  - granulomas
OT  - inflammatory disease
OT  - neurosarcoidosis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/14 19:13
MHDA- 2023/05/14 19:14
CRDT- 2023/05/14 12:26
PHST- 2023/04/10 00:00 [accepted]
PHST- 2023/05/14 19:14 [medline]
PHST- 2023/05/14 19:13 [pubmed]
PHST- 2023/05/14 12:26 [entrez]
AID - 10.7759/cureus.37368 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 10;15(4):e37368. doi: 10.7759/cureus.37368. eCollection 2023 
      Apr.

PMID- 37025739
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230411
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Toxic Epidermal Necrolysis in a Critically Ill African American Woman: A Case 
      Report Written With ChatGPT Assistance.
PG  - e35742
LID - 10.7759/cureus.35742 [doi]
LID - e35742
AB  - Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are 
      life-threatening spectrum diseases in which a medication triggers a mucocutaneous 
      reaction associated with severe necrosis and loss of epidermal integrity. The 
      disease has a high mortality rate that can be assessed by dermatology scoring 
      scales based on an affected total body surface area (TBSA). Sloughing of <10% 
      TBSA is considered SJS, with a mortality of 10%. Sloughing of >30% TBSA is termed 
      TEN, with an increased mortality rate of 25% to 35%. We present a case and 
      management of TEN that involved >30% TBSA in a critically ill African American 
      woman. Identification of the offending agent was difficult due to complicated 
      medication exposure throughout her multi-facility care management. This case 
      conveys the importance of close monitoring of a critically ill patient during a 
      clinical course involving SJS-/TEN-inducing drugs. We also discuss the potential 
      increased risks for SJS/TEN in the African American population due to genetic or 
      epigenetic predispositions to skin conditions. This case report also contributes 
      to increasing skin of color representation in the current literature. 
      Additionally, we discuss the use of Chat Generative Pre-trained Transformer 
      (ChatGPT, OpenAI LP, OpenAI Inc., San Francisco, CA, USA) and list its benefits 
      and errors.
CI  - Copyright © 2023, Lantz et al.
FAU - Lantz, Rebekah
AU  - Lantz R
AD  - Medicine, Miami Valley Hospital, Dayton, USA.
LA  - eng
PT  - Case Reports
DEP - 20230303
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10072179
OTO - NOTNLM
OT  - chatgpt
OT  - dermatologic drug reaction
OT  - drug rash
OT  - skin of color
OT  - stevens-johnson syndrome (sjs)
OT  - toxic epidermal necrolysis (ten)
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/08 06:00
MHDA- 2023/04/08 06:01
CRDT- 2023/04/07 02:34
PHST- 2023/03/03 00:00 [accepted]
PHST- 2023/04/08 06:01 [medline]
PHST- 2023/04/07 02:34 [entrez]
PHST- 2023/04/08 06:00 [pubmed]
AID - 10.7759/cureus.35742 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 3;15(3):e35742. doi: 10.7759/cureus.35742. eCollection 2023 Mar.

PMID- 37190006
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230519
IS  - 2227-9067 (Print)
IS  - 2227-9067 (Electronic)
IS  - 2227-9067 (Linking)
VI  - 10
IP  - 4
DP  - 2023 Apr 21
TI  - May Artificial Intelligence Influence Future Pediatric Research?-The Case of 
      ChatGPT.
LID - 10.3390/children10040757 [doi]
LID - 757
AB  - BACKGROUND: In recent months, there has been growing interest in the potential of 
      artificial intelligence (AI) to revolutionize various aspects of medicine, 
      including research, education, and clinical practice. ChatGPT represents a 
      leading AI language model, with possible unpredictable effects on the quality of 
      future medical research, including clinical decision-making, medical education, 
      drug development, and better research outcomes. AIM AND METHODS: In this 
      interview with ChatGPT, we explore the potential impact of AI on future pediatric 
      research. Our discussion covers a range of topics, including the potential 
      positive effects of AI, such as improved clinical decision-making, enhanced 
      medical education, faster drug development, and better research outcomes. We also 
      examine potential negative effects, such as bias and fairness concerns, safety 
      and security issues, overreliance on technology, and ethical considerations. 
      CONCLUSIONS: While AI continues to advance, it is crucial to remain vigilant 
      about the possible risks and limitations of these technologies and to consider 
      the implications of these technologies and their use in the medical field. The 
      development of AI language models represents a significant advancement in the 
      field of artificial intelligence and has the potential to revolutionize daily 
      clinical practice in every branch of medicine, both surgical and clinical. 
      Ethical and social implications must also be considered to ensure that these 
      technologies are used in a responsible and beneficial manner.
FAU - Corsello, Antonio
AU  - Corsello A
AUID- ORCID: 0000-0003-4578-0066
AD  - Department of Clinical Sciences and Community Health, University of Milan, 20122 
      Milan, Italy.
FAU - Santangelo, Andrea
AU  - Santangelo A
AUID- ORCID: 0000-0003-2668-6373
AD  - Department of Pediatrics, Santa Chiara Hospital, University of Pisa, 56126 Pisa, 
      Italy.
LA  - eng
PT  - Journal Article
DEP - 20230421
PL  - Switzerland
TA  - Children (Basel)
JT  - Children (Basel, Switzerland)
JID - 101648936
PMC - PMC10136583
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - clinical decision-making
OT  - pediatric research
OT  - predictive modeling
COIS- The authors declare that they have no competing interest.
EDAT- 2023/05/16 06:42
MHDA- 2023/05/16 06:43
CRDT- 2023/05/16 01:12
PHST- 2023/03/13 00:00 [received]
PHST- 2023/04/17 00:00 [revised]
PHST- 2023/04/19 00:00 [accepted]
PHST- 2023/05/16 06:43 [medline]
PHST- 2023/05/16 06:42 [pubmed]
PHST- 2023/05/16 01:12 [entrez]
AID - children10040757 [pii]
AID - children-10-00757 [pii]
AID - 10.3390/children10040757 [doi]
PST - epublish
SO  - Children (Basel). 2023 Apr 21;10(4):757. doi: 10.3390/children10040757.

PMID- 37852647
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1535-5675 (Electronic)
IS  - 0091-4916 (Linking)
VI  - 51
IP  - 4
DP  - 2023 Dec 5
TI  - GPT-4 in Nuclear Medicine Education: Does It Outperform GPT-3.5?
PG  - 314-317
LID - 10.2967/jnmt.123.266485 [doi]
AB  - The emergence of ChatGPT has challenged academic integrity in teaching 
      institutions, including those providing nuclear medicine training. Although 
      previous evaluations of ChatGPT have suggested a limited scope for academic 
      writing, the March 2023 release of generative pretrained transformer (GPT)-4 
      promises enhanced capabilities that require evaluation. Methods: Examinations 
      (final and calculation) and written assignments for nuclear medicine subjects 
      were tested using GPT-3.5 and GPT-4. GPT-3.5 and GPT-4 responses were evaluated 
      by Turnitin software for artificial intelligence scores, marked against 
      standardized rubrics, and compared with the mean performance of student cohorts. 
      Results: ChatGPT powered by GPT-3.5 performed poorly in calculation examinations 
      (31.4%), compared with GPT-4 (59.1%). GPT-3.5 failed each of 3 written tasks 
      (39.9%), whereas GPT-4 passed each task (56.3%). Conclusion: Although GPT-3.5 
      poses a minimal risk to academic integrity, its usefulness as a cheating tool can 
      be significantly enhanced by GPT-4 but remains prone to hallucination and 
      fabrication.
CI  - © 2023 by the Society of Nuclear Medicine and Molecular Imaging.
FAU - Currie, Geoffrey M
AU  - Currie GM
AD  - Charles Sturt University, Wagga Wagga, New South Wales, Australia 
      gcurrie@csu.edu.au.
LA  - eng
PT  - Journal Article
DEP - 20231205
PL  - United States
TA  - J Nucl Med Technol
JT  - Journal of nuclear medicine technology
JID - 0430303
SB  - IM
MH  - Humans
MH  - *Nuclear Medicine
MH  - Artificial Intelligence
MH  - Radionuclide Imaging
MH  - Students
MH  - Software
OTO - NOTNLM
OT  - academic integrity
OT  - artificial intelligence
OT  - generative algorithms
OT  - higher education
OT  - patient education
OT  - scientific writing
EDAT- 2023/10/19 00:44
MHDA- 2023/12/07 12:42
CRDT- 2023/10/18 20:43
PHST- 2023/08/02 00:00 [received]
PHST- 2023/09/12 00:00 [revised]
PHST- 2023/12/07 12:42 [medline]
PHST- 2023/10/19 00:44 [pubmed]
PHST- 2023/10/18 20:43 [entrez]
AID - jnmt.123.266485 [pii]
AID - 10.2967/jnmt.123.266485 [doi]
PST - epublish
SO  - J Nucl Med Technol. 2023 Dec 5;51(4):314-317. doi: 10.2967/jnmt.123.266485.

PMID- 37538140
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230804
IS  - 2634-3916 (Electronic)
IS  - 2634-3916 (Linking)
VI  - 4
IP  - 4
DP  - 2023 Aug
TI  - Corrigendum to: ChatGPT takes on the European Exam in Core Cardiology: an 
      artificial intelligence success story?
PG  - 357
LID - 10.1093/ehjdh/ztad034 [doi]
AB  - [This corrects the article DOI: 10.1093/ehjdh/ztad029.].
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      European Society of Cardiology.
LA  - eng
PT  - Published Erratum
DEP - 20230517
PL  - England
TA  - Eur Heart J Digit Health
JT  - European heart journal. Digital health
JID - 101778323
EFR - Eur Heart J Digit Health. 2023 Apr 24;4(3):279-281. PMID: 37265864
PMC - PMC10393937
EDAT- 2023/08/04 06:43
MHDA- 2023/08/04 06:44
CRDT- 2023/08/04 03:55
PHST- 2023/08/04 06:44 [medline]
PHST- 2023/08/04 06:43 [pubmed]
PHST- 2023/08/04 03:55 [entrez]
AID - ztad034 [pii]
AID - 10.1093/ehjdh/ztad034 [doi]
PST - epublish
SO  - Eur Heart J Digit Health. 2023 May 17;4(4):357. doi: 10.1093/ehjdh/ztad034. 
      eCollection 2023 Aug.

PMID- 37530474
OWN - NLM
STAT- Publisher
LR  - 20230802
IS  - 1873-1953 (Electronic)
IS  - 1474-5151 (Linking)
DP  - 2023 Aug 2
TI  - Letters to the editor: Questionable publishing practices in the ChatGPT era.
LID - zvad073 [pii]
LID - 10.1093/eurjcn/zvad073 [doi]
FAU - Moons, Philip
AU  - Moons P
AUID- ORCID: 0000-0002-8609-4516
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven - University of 
      Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens 
      backe 1, 413 46 Gothenburg, Sweden.
AD  - Department of Paediatrics and Child Health, University of Cape Town, Klipfontein 
      Rd, Rondebosch, 7700 Cape Town, South Africa.
FAU - Van De Bruaene, Alexander
AU  - Van De Bruaene A
AUID- ORCID: 0000-0002-0469-8640
AD  - Department of Cardiovascular Sciences, KU Leuven, University of Leuven, 
      Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Division of Congenital and Structural Cardiology, University Hospitals Leuven, 
      Herestraat 49, 3000 Leuven, Leuven, Belgium.
FAU - Van Bulck, Liesbet
AU  - Van Bulck L
AUID- ORCID: 0000-0001-8975-4455
AD  - KU Leuven Department of Public Health and Primary Care, KU Leuven - University of 
      Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium.
AD  - Research Foundation Flanders (FWO), Leuvenseweg 38, 1000 Brussels, Belgium.
LA  - eng
PT  - Journal Article
DEP - 20230802
PL  - England
TA  - Eur J Cardiovasc Nurs
JT  - European journal of cardiovascular nursing
JID - 101128793
SB  - IM
OTO - NOTNLM
OT  - Scientific integrity
OT  - artificial intelligence
OT  - language models
OT  - natural language
OT  - processing
OT  - scientific misconduct
EDAT- 2023/08/02 13:09
MHDA- 2023/08/02 13:09
CRDT- 2023/08/02 08:33
PHST- 2023/07/28 00:00 [received]
PHST- 2023/07/31 00:00 [accepted]
PHST- 2023/08/02 13:09 [medline]
PHST- 2023/08/02 13:09 [pubmed]
PHST- 2023/08/02 08:33 [entrez]
AID - 7235464 [pii]
AID - 10.1093/eurjcn/zvad073 [doi]
PST - aheadofprint
SO  - Eur J Cardiovasc Nurs. 2023 Aug 2:zvad073. doi: 10.1093/eurjcn/zvad073.

PMID- 37268021
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20230920
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
VI  - 89
IP  - 4
DP  - 2023 Oct
TI  - Dermatology in the wake of an AI revolution: Who gets a say?
PG  - e159-e160
LID - S0190-9622(23)00992-1 [pii]
LID - 10.1016/j.jaad.2023.05.053 [doi]
FAU - Beltrami, Eric J
AU  - Beltrami EJ
AD  - University of Connecticut School of Medicine, Farmington, Connecticut.
FAU - Grant-Kels, Jane M
AU  - Grant-Kels JM
AD  - Department of Dermatology, University of Connecticut Health Center, Farmington, 
      Connecticut; Department of Dermatology, University of Florida, Gainesville, 
      Florida. Electronic address: grant@uchc.edu.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230601
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
CON - J Am Acad Dermatol. 2023 Oct;89(4):e157-e158. PMID: 37263382
MH  - Humans
MH  - *Dermatology
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - ChatGPT
OT  - accessibility to dermatology
OT  - artificial intelligence
OT  - beneficence
OT  - clinical practice
OT  - ethics
OT  - justice
OT  - language models
OT  - nonmaleficence
COIS- Conflicts of interest Jane Grant-Kels: DermaSensor: advisory board member. This 
      company's specific technology is not discussed in this submission. Eric Beltrami 
      has no conflicts of interest to declare.
EDAT- 2023/06/03 11:42
MHDA- 2023/09/20 06:42
CRDT- 2023/06/02 19:21
PHST- 2023/05/22 00:00 [received]
PHST- 2023/05/22 00:00 [accepted]
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/06/03 11:42 [pubmed]
PHST- 2023/06/02 19:21 [entrez]
AID - S0190-9622(23)00992-1 [pii]
AID - 10.1016/j.jaad.2023.05.053 [doi]
PST - ppublish
SO  - J Am Acad Dermatol. 2023 Oct;89(4):e159-e160. doi: 10.1016/j.jaad.2023.05.053. 
      Epub 2023 Jun 1.

PMID- 37819549
OWN - NLM
STAT- Publisher
LR  - 20231011
IS  - 1434-4726 (Electronic)
IS  - 0937-4477 (Linking)
DP  - 2023 Oct 11
TI  - Correction: Exploring the potential of Chat-GPT as a supportive tool for 
      sialendoscopy clinical decision making and patient information support.
LID - 10.1007/s00405-023-08267-4 [doi]
FAU - Chiesa-Estomba, Carlos M
AU  - Chiesa-Estomba CM
AUID- ORCID: 0000-0001-9454-9464
AD  - Department of Otorhinolaryngology, Donostia University Hospital, Biodonostia 
      Research Institute, Osakidetza, 20014, San Sebastian, Spain. 
      chiesaestomba86@gmail.com.
AD  - Otorhinolaryngology Department, Faculty of Medicine, Deusto University, Bilbo, 
      Spain. chiesaestomba86@gmail.com.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain. chiesaestomba86@gmail.com.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain. 
      chiesaestomba86@gmail.com.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France. 
      chiesaestomba86@gmail.com.
AD  - Young Confederation of European Otorhinolaryngology, Head and Neck Surgery, 
      Vienna, Austria. chiesaestomba86@gmail.com.
FAU - Lechien, Jerome R
AU  - Lechien JR
AD  - Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology 
      and Head and Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health 
      Sciences and Technology, University of Mons, Mons, Belgium.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Vaira, Luigi A
AU  - Vaira LA
AD  - Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and 
      Pharmacy, University of Sassari, Sassari, Italy.
AD  - Biomedical Sciences Department, School of Biomedical Sciences, University of 
      Sassari, Sassari, Italy.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Brunet, Aina
AU  - Brunet A
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
FAU - Cammaroto, Giovanni
AU  - Cammaroto G
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Department of Head-Neck Surgery, Otolaryngology, Head-Neck and Oral Surgery Unit, 
      Morgagni Pierantoni Hospital, 47121, Forlì, Italy.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Mayo-Yanez, Miguel
AU  - Mayo-Yanez M
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - Otorhinolaryngology, Head and Neck Surgery Department, Complexo Hospitalario 
      Universitario A Coruña (CHUAC), 15006, A Coruña, Galicia, Spain.
AD  - Head & Neck Study Group of Young-Otolaryngologists of the International 
      Federations of Oto-Rhino-Laryngological Societies (YO-IFOS), Paris, France.
FAU - Sanchez-Barrueco, Alvaro
AU  - Sanchez-Barrueco A
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
AD  - ENT and Cervicofacial Surgery Department, Hospital Universitario Fundación 
      Jiménez Díaz, Madrid, Spain.
AD  - Otorhinolaryngology Department, Faculty of Medicine, Universidad Alfonso X el 
      Sabio, Madrid, Spain.
FAU - Saga-Gutierrez, Carlos
AU  - Saga-Gutierrez C
AD  - Department of Otorhinolaryngology, Donostia University Hospital, Biodonostia 
      Research Institute, Osakidetza, 20014, San Sebastian, Spain.
AD  - Otorhinolaryngology Department, Faculty of Medicine, Deusto University, Bilbo, 
      Spain.
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitari 
      Bellvitge, Barcelona, Spain.
AD  - Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
LA  - eng
PT  - Published Erratum
DEP - 20231011
PL  - Germany
TA  - Eur Arch Otorhinolaryngol
JT  - European archives of oto-rhino-laryngology : official journal of the European 
      Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the 
      German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
JID - 9002937
SB  - IM
EFR - Eur Arch Otorhinolaryngol. 2023 Jul 5;:. PMID: 37405455
EDAT- 2023/10/11 14:42
MHDA- 2023/10/11 14:42
CRDT- 2023/10/11 11:13
PHST- 2023/10/11 14:42 [medline]
PHST- 2023/10/11 14:42 [pubmed]
PHST- 2023/10/11 11:13 [entrez]
AID - 10.1007/s00405-023-08267-4 [pii]
AID - 10.1007/s00405-023-08267-4 [doi]
PST - aheadofprint
SO  - Eur Arch Otorhinolaryngol. 2023 Oct 11. doi: 10.1007/s00405-023-08267-4.

PMID- 37254485
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20230727
IS  - 2287-285X (Electronic)
IS  - 2287-2728 (Print)
IS  - 2287-2728 (Linking)
VI  - 29
IP  - 3
DP  - 2023 Jul
TI  - Correspondence on Letter 2 regarding "Assessing the performance of ChatGPT in 
      answering questions regarding cirrhosis and hepatocellular carcinoma".
PG  - 823-824
LID - 10.3350/cmh.2023.0182 [doi]
FAU - Yeo, Yee Hui
AU  - Yeo YH
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Samaan, Jamil S
AU  - Samaan JS
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Ng, Wee Han
AU  - Ng WH
AD  - Bristol Medical School, University of Bristol, Bristol, UK.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230531
PL  - Korea (South)
TA  - Clin Mol Hepatol
JT  - Clinical and molecular hepatology
JID - 101586730
SB  - IM
CON - Clin Mol Hepatol. 2023 Jul;29(3):815-816. PMID: 37221834
MH  - Humans
MH  - *Carcinoma, Hepatocellular
MH  - *Liver Neoplasms
MH  - Liver Cirrhosis
PMC - PMC10366789
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Communication barriers
OT  - Ethics
OT  - Medicine
OT  - Patient care
COIS- Conflicts of Interest The authors have no conflicts to disclose.
EDAT- 2023/05/31 06:42
MHDA- 2023/07/25 06:42
CRDT- 2023/05/31 01:49
PHST- 2023/05/27 00:00 [received]
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/07/25 06:42 [medline]
PHST- 2023/05/31 06:42 [pubmed]
PHST- 2023/05/31 01:49 [entrez]
AID - cmh.2023.0182 [pii]
AID - cmh-2023-0182 [pii]
AID - 10.3350/cmh.2023.0182 [doi]
PST - ppublish
SO  - Clin Mol Hepatol. 2023 Jul;29(3):823-824. doi: 10.3350/cmh.2023.0182. Epub 2023 
      May 31.

PMID- 36844147
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230303
IS  - 2052-2975 (Print)
IS  - 2052-2975 (Electronic)
IS  - 2052-2975 (Linking)
VI  - 52
DP  - 2023 Mar
TI  - Reflection with ChatGPT about the excess death after the COVID-19 pandemic.
PG  - 101103
LID - 10.1016/j.nmni.2023.101103 [doi]
LID - 101103
FAU - Temsah, Mohamad-Hani
AU  - Temsah MH
AD  - Pediatric Intensive Care Unit, Pediatric Department, College of Medicine, King 
      Saud University, Riyadh, 11362, Saudi Arabia.
FAU - Jamal, Amr
AU  - Jamal A
AD  - Department of Family and Community Medicine, King Saud University Medical City, 
      Riyadh, 11362, Saudi Arabia.
AD  - Evidence-Based Health Care & Knowledge Translation Research Chair, Family & 
      Community Medicine Department, College of Medicine, King Saud University, Riyadh, 
      11362, Saudi Arabia.
FAU - Al-Tawfiq, Jaffar A
AU  - Al-Tawfiq JA
AD  - Specialty Internal Medicine and Quality Department, Johns Hopkins Aramco 
      Healthcare, Dhahran, 34465, Saudi Arabia.
AD  - Infectious Disease Division, Department of Medicine, Indiana University School of 
      Medicine, Indianapolis, IN46202, USA.
AD  - Infectious Disease Division, Department of Medicine, Johns Hopkins University 
      School of Medicine, Baltimore, MD21218, USA.
LA  - eng
PT  - Journal Article
DEP - 20230218
PL  - England
TA  - New Microbes New Infect
JT  - New microbes and new infections
JID - 101624750
PMC - PMC9937725
OTO - NOTNLM
OT  - COVID-19
OT  - ChatGPY
OT  - Excess mortality
OT  - SARS-CoV-2
EDAT- 2023/02/28 06:00
MHDA- 2023/02/28 06:01
CRDT- 2023/02/27 04:09
PHST- 2023/02/13 00:00 [received]
PHST- 2023/02/15 00:00 [accepted]
PHST- 2023/02/28 06:00 [pubmed]
PHST- 2023/02/28 06:01 [medline]
PHST- 2023/02/27 04:09 [entrez]
AID - S2052-2975(23)00022-7 [pii]
AID - 101103 [pii]
AID - 10.1016/j.nmni.2023.101103 [doi]
PST - ppublish
SO  - New Microbes New Infect. 2023 Mar;52:101103. doi: 10.1016/j.nmni.2023.101103. 
      Epub 2023 Feb 18.

PMID- 37910155
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231119
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Nov 1
TI  - Exploring the Possible Use of AI Chatbots in Public Health Education: Feasibility 
      Study.
PG  - e51421
LID - 10.2196/51421 [doi]
LID - e51421
AB  - BACKGROUND: Artificial intelligence (AI) is a rapidly developing field with the 
      potential to transform various aspects of health care and public health, 
      including medical training. During the "Hygiene and Public Health" course for 
      fifth-year medical students, a practical training session was conducted on 
      vaccination using AI chatbots as an educational supportive tool. Before receiving 
      specific training on vaccination, the students were given a web-based test 
      extracted from the Italian National Medical Residency Test. After completing the 
      test, a critical correction of each question was performed assisted by AI 
      chatbots. OBJECTIVE: The main aim of this study was to identify whether AI 
      chatbots can be considered educational support tools for training in public 
      health. The secondary objective was to assess the performance of different AI 
      chatbots on complex multiple-choice medical questions in the Italian language. 
      METHODS: A test composed of 15 multiple-choice questions on vaccination was 
      extracted from the Italian National Medical Residency Test using targeted 
      keywords and administered to medical students via Google Forms and to different 
      AI chatbot models (Bing Chat, ChatGPT, Chatsonic, Google Bard, and YouChat). The 
      correction of the test was conducted in the classroom, focusing on the critical 
      evaluation of the explanations provided by the chatbot. A Mann-Whitney U test was 
      conducted to compare the performances of medical students and AI chatbots. 
      Student feedback was collected anonymously at the end of the training experience. 
      RESULTS: In total, 36 medical students and 5 AI chatbot models completed the 
      test. The students achieved an average score of 8.22 (SD 2.65) out of 15, while 
      the AI chatbots scored an average of 12.22 (SD 2.77). The results indicated a 
      statistically significant difference in performance between the 2 groups (U=49.5, 
      P<.001), with a large effect size (r=0.69). When divided by question type 
      (direct, scenario-based, and negative), significant differences were observed in 
      direct (P<.001) and scenario-based (P<.001) questions, but not in negative 
      questions (P=.48). The students reported a high level of satisfaction (7.9/10) 
      with the educational experience, expressing a strong desire to repeat the 
      experience (7.6/10). CONCLUSIONS: This study demonstrated the efficacy of AI 
      chatbots in answering complex medical questions related to vaccination and 
      providing valuable educational support. Their performance significantly surpassed 
      that of medical students in direct and scenario-based questions. The responsible 
      and critical use of AI chatbots can enhance medical education, making it an 
      essential aspect to integrate into the educational system.
CI  - ©Francesco Baglivo, Luigi De Angelis, Virginia Casigliani, Guglielmo Arzilli, 
      Gaetano Pierpaolo Privitera, Caterina Rizzo. Originally published in JMIR Medical 
      Education (https://mededu.jmir.org), 01.11.2023.
FAU - Baglivo, Francesco
AU  - Baglivo F
AUID- ORCID: 0000-0001-9391-7966
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa (PI), Italy.
FAU - De Angelis, Luigi
AU  - De Angelis L
AUID- ORCID: 0009-0005-6136-6596
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa (PI), Italy.
FAU - Casigliani, Virginia
AU  - Casigliani V
AUID- ORCID: 0000-0002-4184-2648
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa (PI), Italy.
FAU - Arzilli, Guglielmo
AU  - Arzilli G
AUID- ORCID: 0000-0003-1258-1650
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa (PI), Italy.
FAU - Privitera, Gaetano Pierpaolo
AU  - Privitera GP
AUID- ORCID: 0000-0002-8638-2462
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa (PI), Italy.
AD  - Training Office, National Institute of Health, Rome, Italy.
FAU - Rizzo, Caterina
AU  - Rizzo C
AUID- ORCID: 0000-0002-5583-7508
AD  - Department of Translational Research and New Technologies in Medicine and 
      Surgery, University of Pisa, Pisa (PI), Italy.
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10652189
OTO - NOTNLM
OT  - AI chatbot
OT  - ChatGPT
OT  - Google Bard
OT  - artificial intelligence
OT  - chatbot model
OT  - chatbots
OT  - educational support tool
OT  - generative AI
OT  - health care
OT  - health education
OT  - large language model
OT  - medical education
OT  - medical students
OT  - medical training
OT  - public health
OT  - vaccination
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/01 12:44
MHDA- 2023/11/01 12:45
CRDT- 2023/11/01 11:53
PHST- 2023/07/31 00:00 [received]
PHST- 2023/09/27 00:00 [accepted]
PHST- 2023/08/29 00:00 [revised]
PHST- 2023/11/01 12:45 [medline]
PHST- 2023/11/01 12:44 [pubmed]
PHST- 2023/11/01 11:53 [entrez]
AID - v9i1e51421 [pii]
AID - 10.2196/51421 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Nov 1;9:e51421. doi: 10.2196/51421.

PMID- 37428336
OWN - NLM
STAT- Publisher
LR  - 20230710
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 10
TI  - ChatGPT and Vaccines: Can AI Chatbots Boost Awareness and Uptake?
LID - 10.1007/s10439-023-03305-y [doi]
AB  - The global COVID-19 pandemic has affected all spheres of human life, resulting in 
      millions of deaths and overwhelming medical facilities. Moreover, the world has 
      witnessed great financial hardship because of job losses resulting in economic 
      havoc. Many sections of society have contributed in different ways to slow the 
      spread of the virus and protect public health. For example, medical scientists 
      are praised for their efforts to develop COVID-19 vaccines. Clinical trials have 
      shown that the COVID-19 vaccines are highly effective in preventing symptomatic 
      COVID-19 infections. However, many people around the world have been hesitant to 
      get vaccinated. Vaccine misconceptions have emerged and increased due to a 
      combination of factors, including the availability of information on the Internet 
      and the influence of celebrities and opinion leaders. In this context, we have 
      analyzed ChatGPT responses to relevant queries on vaccine misconceptions. The 
      positive responses and supportive opinions provided by the AI chatbot could be 
      instrumental in shaping people's perceptions of vaccines and in encouraging users 
      to get vaccinated and reduce misconceptions.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AD  - Department of Computer Science and Engineering, School of Engineering Sciences 
      and Technology, Jamia Hamdard, New Delhi, 110062, India.
FAU - Madsen, Dag Øivind
AU  - Madsen DØ
AUID- ORCID: 0000-0001-8735-3332
AD  - USN School of Business, University of South-Eastern Norway, 3511, Hønefoss, 
      Norway. dag.oivind.madsen@usn.no.
FAU - Farhat, Faiza
AU  - Farhat F
AD  - Department of Zoology, Aligarh Muslim University, Aligarh, U.P., 202002, India.
FAU - Alam, M Afshar
AU  - Alam MA
AD  - Department of Computer Science and Engineering, School of Engineering Sciences 
      and Technology, Jamia Hamdard, New Delhi, 110062, India.
LA  - eng
PT  - Letter
DEP - 20230710
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - COVID-19
OT  - ChatGPT
OT  - Vaccine
OT  - Vaccine hesitancy
EDAT- 2023/07/10 13:05
MHDA- 2023/07/10 13:05
CRDT- 2023/07/10 11:12
PHST- 2023/06/25 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/07/10 13:05 [medline]
PHST- 2023/07/10 13:05 [pubmed]
PHST- 2023/07/10 11:12 [entrez]
AID - 10.1007/s10439-023-03305-y [pii]
AID - 10.1007/s10439-023-03305-y [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 10. doi: 10.1007/s10439-023-03305-y.

PMID- 37606922
OWN - NLM
STAT- MEDLINE
DCOM- 20230823
LR  - 20230827
IS  - 2574-3805 (Electronic)
IS  - 2574-3805 (Linking)
VI  - 6
IP  - 8
DP  - 2023 Aug 1
TI  - Comparison of Ophthalmologist and Large Language Model Chatbot Responses to 
      Online Patient Eye Care Questions.
PG  - e2330320
LID - 10.1001/jamanetworkopen.2023.30320 [doi]
LID - e2330320
AB  - IMPORTANCE: Large language models (LLMs) like ChatGPT appear capable of 
      performing a variety of tasks, including answering patient eye care questions, 
      but have not yet been evaluated in direct comparison with ophthalmologists. It 
      remains unclear whether LLM-generated advice is accurate, appropriate, and safe 
      for eye patients. OBJECTIVE: To evaluate the quality of ophthalmology advice 
      generated by an LLM chatbot in comparison with ophthalmologist-written advice. 
      DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used deidentified 
      data from an online medical forum, in which patient questions received responses 
      written by American Academy of Ophthalmology (AAO)-affiliated ophthalmologists. A 
      masked panel of 8 board-certified ophthalmologists were asked to distinguish 
      between answers generated by the ChatGPT chatbot and human answers. Posts were 
      dated between 2007 and 2016; data were accessed January 2023 and analysis was 
      performed between March and May 2023. MAIN OUTCOMES AND MEASURES: Identification 
      of chatbot and human answers on a 4-point scale (likely or definitely artificial 
      intelligence [AI] vs likely or definitely human) and evaluation of responses for 
      presence of incorrect information, alignment with perceived consensus in the 
      medical community, likelihood to cause harm, and extent of harm. RESULTS: A total 
      of 200 pairs of user questions and answers by AAO-affiliated ophthalmologists 
      were evaluated. The mean (SD) accuracy for distinguishing between AI and human 
      responses was 61.3% (9.7%). Of 800 evaluations of chatbot-written answers, 168 
      answers (21.0%) were marked as human-written, while 517 of 800 human-written 
      answers (64.6%) were marked as AI-written. Compared with human answers, chatbot 
      answers were more frequently rated as probably or definitely written by AI 
      (prevalence ratio [PR], 1.72; 95% CI, 1.52-1.93). The likelihood of chatbot 
      answers containing incorrect or inappropriate material was comparable with human 
      answers (PR, 0.92; 95% CI, 0.77-1.10), and did not differ from human answers in 
      terms of likelihood of harm (PR, 0.84; 95% CI, 0.67-1.07) nor extent of harm (PR, 
      0.99; 95% CI, 0.80-1.22). CONCLUSIONS AND RELEVANCE: In this cross-sectional 
      study of human-written and AI-generated responses to 200 eye care questions from 
      an online advice forum, a chatbot appeared capable of responding to long 
      user-written eye health posts and largely generated appropriate responses that 
      did not differ significantly from ophthalmologist-written responses in terms of 
      incorrect information, likelihood of harm, extent of harm, or deviation from 
      ophthalmologist community standards. Additional research is needed to assess 
      patient attitudes toward LLM-augmented ophthalmologists vs fully autonomous AI 
      content generation, to evaluate clarity and acceptability of LLM-generated 
      answers from the patient perspective, to test the performance of LLMs in a 
      greater variety of clinical contexts, and to determine an optimal manner of 
      utilizing LLMs that is ethical and minimizes harm.
FAU - Bernstein, Isaac A
AU  - Bernstein IA
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Zhang, Youchen Victor
AU  - Zhang YV
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Govil, Devendra
AU  - Govil D
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Majid, Iyad
AU  - Majid I
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Chang, Robert T
AU  - Chang RT
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Sun, Yang
AU  - Sun Y
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Shue, Ann
AU  - Shue A
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Chou, Jonathan C
AU  - Chou JC
AD  - Department of Ophthalmology, Kaiser Permanente San Francisco, San Francisco, 
      California.
FAU - Schehlein, Emily
AU  - Schehlein E
AD  - Brighton Vision Center, Brighton, Michigan.
FAU - Christopher, Karen L
AU  - Christopher KL
AD  - Department of Ophthalmology, University of Colorado School of Medicine, Aurora.
FAU - Groth, Sylvia L
AU  - Groth SL
AD  - Department of Ophthalmology and Visual Sciences, Vanderbilt Eye Institute, 
      Nashville, Tennessee.
FAU - Ludwig, Cassie
AU  - Ludwig C
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
FAU - Wang, Sophia Y
AU  - Wang SY
AD  - Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, 
      California.
LA  - eng
PT  - Journal Article
DEP - 20230801
PL  - United States
TA  - JAMA Netw Open
JT  - JAMA network open
JID - 101729235
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Ophthalmologists
MH  - Cross-Sectional Studies
MH  - Software
MH  - Language
PMC - PMC10445188
COIS- Conflict of Interest Disclosures: None reported.
EDAT- 2023/08/22 13:42
MHDA- 2023/08/23 06:42
CRDT- 2023/08/22 11:39
PHST- 2023/08/23 06:42 [medline]
PHST- 2023/08/22 13:42 [pubmed]
PHST- 2023/08/22 11:39 [entrez]
AID - 2808557 [pii]
AID - zoi230872 [pii]
AID - 10.1001/jamanetworkopen.2023.30320 [doi]
PST - epublish
SO  - JAMA Netw Open. 2023 Aug 1;6(8):e2330320. doi: 
      10.1001/jamanetworkopen.2023.30320.

PMID- 37442557
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1741-3850 (Electronic)
IS  - 1741-3842 (Linking)
VI  - 45
IP  - 4
DP  - 2023 Nov 29
TI  - The risk of ChatGPT as a first aid method: considering the potential danger for 
      mental health support among young adults without professional assistance.
PG  - e823-e824
LID - 10.1093/pubmed/fdad107 [doi]
FAU - Mardikaningsih, Agusti
AU  - Mardikaningsih A
AD  - IKIP Budi Utomo Malang, Malang, East Java 65111, Indonesia.
FAU - Asmaroini, Ambiro Puji
AU  - Asmaroini AP
AD  - Universitas Muhammadiyah Ponorogo, Ponorogo, East Java 63471, Indonesia.
FAU - Lani, Ahmad
AU  - Lani A
AD  - IKIP Budi Utomo Malang, Malang, East Java 65111, Indonesia.
FAU - Padillah, Raup
AU  - Padillah R
AUID- ORCID: 0009-0008-2613-4046
AD  - Universitas Negeri Malang, Malang, East Java 65114, Indonesia.
AD  - Universitas PGRI Banyuwangi, Banyuwangi, East Java 41482, Indonesia.
LA  - eng
GR  - 202209090617/Puslapdik Kemendikbud-Ristek/
GR  - LPDP/
PT  - Journal Article
PL  - England
TA  - J Public Health (Oxf)
JT  - Journal of public health (Oxford, England)
JID - 101188638
SB  - IM
MH  - Humans
MH  - Young Adult
MH  - *First Aid
MH  - *Mental Health
EDAT- 2023/07/14 13:06
MHDA- 2023/12/04 12:42
CRDT- 2023/07/13 20:22
PHST- 2023/05/31 00:00 [received]
PHST- 2023/06/06 00:00 [revised]
PHST- 2023/06/23 00:00 [accepted]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/07/14 13:06 [pubmed]
PHST- 2023/07/13 20:22 [entrez]
AID - 7223820 [pii]
AID - 10.1093/pubmed/fdad107 [doi]
PST - ppublish
SO  - J Public Health (Oxf). 2023 Nov 29;45(4):e823-e824. doi: 10.1093/pubmed/fdad107.

PMID- 37113819
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230430
IS  - 2049-0801 (Print)
IS  - 2049-0801 (Electronic)
IS  - 2049-0801 (Linking)
VI  - 85
IP  - 4
DP  - 2023 Apr
TI  - ChatGPT: roles and boundaries of the new artificial intelligence tool in medical 
      education and health research - correspondence.
PG  - 1317-1318
LID - 10.1097/MS9.0000000000000371 [doi]
FAU - Gandhi Periaysamy, Aravind
AU  - Gandhi Periaysamy A
AUID- ORCID: 0000-0003-3898-5450
AD  - Department of Community Medicine, ESIC Medical College and Hospital, Sanathnagar, 
      Hyderabad.
FAU - Satapathy, Prakasini
AU  - Satapathy P
AD  - Department of Virology.
FAU - Neyazi, Ahmad
AU  - Neyazi A
AUID- ORCID: 0000-0002-6181-6164
AD  - Afghanistan Center for Epidemiological Studies, Herat, Afghanistan.
FAU - Padhi, Bijaya K
AU  - Padhi BK
AUID- ORCID: 0000-0002-2828-2375
AD  - Department of Community Medicine and School of Public Health, Postgraduate 
      Institute of Medical Education and Research, Chandigarh, India.
LA  - eng
PT  - Journal Article
DEP - 20230327
PL  - England
TA  - Ann Med Surg (Lond)
JT  - Annals of medicine and surgery (2012)
JID - 101616869
PMC - PMC10129198
COIS- None of the authors has declared any conflicts of interest.
EDAT- 2023/04/28 06:42
MHDA- 2023/04/28 06:43
CRDT- 2023/04/28 02:35
PHST- 2023/02/01 00:00 [received]
PHST- 2023/02/25 00:00 [accepted]
PHST- 2023/04/28 06:43 [medline]
PHST- 2023/04/28 06:42 [pubmed]
PHST- 2023/04/28 02:35 [entrez]
AID - 10.1097/MS9.0000000000000371 [doi]
PST - epublish
SO  - Ann Med Surg (Lond). 2023 Mar 27;85(4):1317-1318. doi: 
      10.1097/MS9.0000000000000371. eCollection 2023 Apr.

PMID- 37322244
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231119
IS  - 1708-0428 (Electronic)
IS  - 0960-8923 (Linking)
VI  - 33
IP  - 8
DP  - 2023 Aug
TI  - A Paradigm Shift: Online Artificial Intelligence Platforms as an Informational 
      Resource in Bariatric Surgery.
PG  - 2611-2614
LID - 10.1007/s11695-023-06675-3 [doi]
FAU - Moazzam, Zorays
AU  - Moazzam Z
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, 395 W. 
      12th Avenue, Suite 670, OH, 43210, Columbus, USA.
FAU - Lima, Henrique A
AU  - Lima HA
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, 395 W. 
      12th Avenue, Suite 670, OH, 43210, Columbus, USA.
FAU - Endo, Yutaka
AU  - Endo Y
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, 395 W. 
      12th Avenue, Suite 670, OH, 43210, Columbus, USA.
FAU - Noria, Sabrena
AU  - Noria S
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, 395 W. 
      12th Avenue, Suite 670, OH, 43210, Columbus, USA.
FAU - Needleman, Bradley
AU  - Needleman B
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, 395 W. 
      12th Avenue, Suite 670, OH, 43210, Columbus, USA.
FAU - Pawlik, Timothy M
AU  - Pawlik TM
AUID- ORCID: 0000-0002-7994-9870
AD  - Department of Surgery, The Ohio State University Wexner Medical Center, 395 W. 
      12th Avenue, Suite 670, OH, 43210, Columbus, USA. tim.pawlik@osumc.edu.
LA  - eng
PT  - Journal Article
DEP - 20230616
PL  - United States
TA  - Obes Surg
JT  - Obesity surgery
JID - 9106714
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Obesity, Morbid/surgery
MH  - *Bariatric Surgery
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bariatric surgery
OT  - ChatGPT
OT  - Patient education
EDAT- 2023/06/16 01:08
MHDA- 2023/07/17 06:41
CRDT- 2023/06/15 23:29
PHST- 2023/03/30 00:00 [received]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/05/25 00:00 [revised]
PHST- 2023/07/17 06:41 [medline]
PHST- 2023/06/16 01:08 [pubmed]
PHST- 2023/06/15 23:29 [entrez]
AID - 10.1007/s11695-023-06675-3 [pii]
AID - 10.1007/s11695-023-06675-3 [doi]
PST - ppublish
SO  - Obes Surg. 2023 Aug;33(8):2611-2614. doi: 10.1007/s11695-023-06675-3. Epub 2023 
      Jun 16.

PMID- 37074733
OWN - NLM
STAT- MEDLINE
DCOM- 20230622
LR  - 20231116
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 6
DP  - 2023 Jun 1
TI  - Emergency surgery in the era of artificial intelligence: ChatGPT could be the 
      doctor's right-hand man.
PG  - 1816-1818
LID - 10.1097/JS9.0000000000000410 [doi]
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan.
FAU - Li, Zhiyong
AU  - Li Z
AD  - Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, 
      Tianjin.
FAU - Guo, Qiang
AU  - Guo Q
AD  - Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, 
      Tianjin.
FAU - Sun, Zaijie
AU  - Sun Z
AD  - Department of Orthopaedic Surgery, Xiangyang Central Hospital, Affiliated 
      Hospital of Hubei University of Arts and Science, Xiangyang.
FAU - Wu, Haiyang
AU  - Wu H
AUID- ORCID: 0000-0002-3215-1626
AD  - Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin 
      Medical University, Tianjin.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      North Carolina, USA.
FAU - Li, Cheng
AU  - Li C
AD  - Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical 
      College of Peking University, Beijing, People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230601
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
CIN - Int J Surg. 2023 Oct 01;109(10):3195-3196. PMID: 37318859
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Surgical Procedures, Operative
PMC - PMC10389530
COIS- The authors declare no conflicts of interest. Sponsorships or competing interests 
      that may be relevant to content are disclosed at the end of this article.
EDAT- 2023/04/19 12:42
MHDA- 2023/06/19 13:08
CRDT- 2023/04/19 11:42
PHST- 2023/03/30 00:00 [received]
PHST- 2023/04/10 00:00 [accepted]
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/04/19 12:42 [pubmed]
PHST- 2023/04/19 11:42 [entrez]
AID - 01279778-202306000-00034 [pii]
AID - IJS-D-23-00573 [pii]
AID - 10.1097/JS9.0000000000000410 [doi]
PST - epublish
SO  - Int J Surg. 2023 Jun 1;109(6):1816-1818. doi: 10.1097/JS9.0000000000000410.

PMID- 37318859
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231024
LR  - 20231212
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 10
DP  - 2023 Oct 1
TI  - A commentary on emergency surgery in the era of artificial intelligence: ChatGPT 
      could be the doctor's right-hand man.
PG  - 3195-3196
LID - 10.1097/JS9.0000000000000561 [doi]
FAU - Abavisani, Mohammad
AU  - Abavisani M
AD  - Student Research Committee, Mashhad University of Medical Sciences, Mashhad.
FAU - Dadgar, Farhad
AU  - Dadgar F
AD  - Department of Internal Medicine.
FAU - Keikha, Masoud
AU  - Keikha M
AD  - Department of Medical Microbiology, School of Medicine, Iranshahr University of 
      Medical Sciences, Iranshahr, Iran.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20231001
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
CON - Int J Surg. 2023 Jun 01;109(6):1816-1818. PMID: 37074733
PMC - PMC10583942
COIS- There are no conflicts of interest.
EDAT- 2023/06/15 19:14
MHDA- 2023/10/23 00:43
CRDT- 2023/06/15 12:25
PHST- 2023/06/01 00:00 [received]
PHST- 2023/06/02 00:00 [accepted]
PHST- 2023/10/23 00:43 [medline]
PHST- 2023/06/15 19:14 [pubmed]
PHST- 2023/06/15 12:25 [entrez]
AID - 01279778-202310000-00037 [pii]
AID - IJS-D-23-01061 [pii]
AID - 10.1097/JS9.0000000000000561 [doi]
PST - epublish
SO  - Int J Surg. 2023 Oct 1;109(10):3195-3196. doi: 10.1097/JS9.0000000000000561.

PMID- 37927093
OWN - NLM
STAT- MEDLINE
DCOM- 20231201
LR  - 20231201
IS  - 1945-1938 (Electronic)
IS  - 1049-023X (Linking)
VI  - 38
IP  - 6
DP  - 2023 Dec
TI  - Large Language Model (LLM)-Powered Chatbots Fail to Generate Guideline-Consistent 
      Content on Resuscitation and May Provide Potentially Harmful Advice.
PG  - 757-763
LID - 10.1017/S1049023X23006568 [doi]
AB  - INTRODUCTION: Innovative large language model (LLM)-powered chatbots, which are 
      extremely popular nowadays, represent potential sources of information on 
      resuscitation for the general public. For instance, the chatbot-generated advice 
      could be used for purposes of community resuscitation education or for 
      just-in-time informational support of untrained lay rescuers in a real-life 
      emergency. STUDY OBJECTIVE: This study focused on assessing performance of two 
      prominent LLM-based chatbots, particularly in terms of quality of the 
      chatbot-generated advice on how to give help to a non-breathing victim. METHODS: 
      In May 2023, the new Bing (Microsoft Corporation, USA) and Bard (Google LLC, USA) 
      chatbots were inquired (n = 20 each): "What to do if someone is not breathing?" 
      Content of the chatbots' responses was evaluated for compliance with the 2021 
      Resuscitation Council United Kingdom guidelines using a pre-developed checklist. 
      RESULTS: Both chatbots provided context-dependent textual responses to the query. 
      However, coverage of the guideline-consistent instructions on help to a 
      non-breathing victim within the responses was poor: mean percentage of the 
      responses completely satisfying the checklist criteria was 9.5% for Bing and 
      11.4% for Bard (P >.05). Essential elements of the bystander action, including 
      early start and uninterrupted performance of chest compressions with adequate 
      depth, rate, and chest recoil, as well as request for and use of an automated 
      external defibrillator (AED), were missing as a rule. Moreover, 55.0% of Bard's 
      responses contained plausible sounding, but nonsensical guidance, called 
      artificial hallucinations, that create risk for inadequate care and harm to a 
      victim. CONCLUSION: The LLM-powered chatbots' advice on help to a non-breathing 
      victim omits essential details of resuscitation technique and occasionally 
      contains deceptive, potentially harmful directives. Further research and 
      regulatory measures are required to mitigate risks related to the 
      chatbot-generated misinformation of public on resuscitation.
FAU - Birkun, Alexei A
AU  - Birkun AA
AUID- ORCID: 0000-0002-2789-9760
AD  - Department of General Surgery, Anaesthesiology, Resuscitation and Emergency 
      Medicine, Medical Academy named after S.I. Georgievsky of V.I. Vernadsky Crimean 
      Federal University, Simferopol, 295051, Russian Federation.
FAU - Gautam, Adhish
AU  - Gautam A
AD  - Regional Government Hospital, Una (H.P.), 174303, India.
LA  - eng
PT  - Journal Article
DEP - 20231106
PL  - United States
TA  - Prehosp Disaster Med
JT  - Prehospital and disaster medicine
JID - 8918173
MH  - Humans
MH  - *Resuscitation
MH  - *Language
MH  - Communication
MH  - Respiration
MH  - Defibrillators
OTO - NOTNLM
OT  - artificial hallucination
OT  - artificial intelligence
OT  - cardiac arrest
OT  - cardiopulmonary resuscitation
OT  - chatbot
OT  - large language model
EDAT- 2023/11/06 06:43
MHDA- 2023/12/01 06:44
CRDT- 2023/11/06 03:42
PHST- 2023/12/01 06:44 [medline]
PHST- 2023/11/06 06:43 [pubmed]
PHST- 2023/11/06 03:42 [entrez]
AID - S1049023X23006568 [pii]
AID - 10.1017/S1049023X23006568 [doi]
PST - ppublish
SO  - Prehosp Disaster Med. 2023 Dec;38(6):757-763. doi: 10.1017/S1049023X23006568. 
      Epub 2023 Nov 6.

PMID- 37605022
OWN - NLM
STAT- Publisher
LR  - 20230821
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Aug 21
TI  - Testing ChatGPT's Capabilities for Social Media Content Analysis.
LID - 10.1007/s00266-023-03607-5 [doi]
AB  - This letter explores the potential of artificial intelligence models, 
      specifically ChatGPT, for content analysis, namely for categorizing social media 
      posts. The primary focus is on Twitter posts with the hashtag #plasticsurgery. 
      Through integrating Python with the OpenAI API, the study provides a designed 
      prompt to categorize tweet content. Looking forward, the utilization of AI in 
      content analysis presents promising opportunities for advancing understanding of 
      complex social phenomena.Level of Evidence V This journal requires that authors 
      assign a level of evidence to each article. For a full description of these 
      Evidence-Based Medicine Ratings, please refer to Table of Contents or online 
      Instructions to Authors http://www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Haman, Michael
AU  - Haman M
AUID- ORCID: 0000-0001-5772-2045
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Prague, Czech Republic. haman@pef.czu.cz.
FAU - Školník, Milan
AU  - Školník M
AUID- ORCID: 0000-0002-0672-219X
AD  - Department of Humanities, Faculty of Economics and Management, Czech University 
      of Life Sciences Prague, Prague, Czech Republic.
LA  - eng
PT  - Letter
DEP - 20230821
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EDAT- 2023/08/22 00:42
MHDA- 2023/08/22 00:42
CRDT- 2023/08/21 23:51
PHST- 2023/07/12 00:00 [received]
PHST- 2023/07/30 00:00 [accepted]
PHST- 2023/08/22 00:42 [medline]
PHST- 2023/08/22 00:42 [pubmed]
PHST- 2023/08/21 23:51 [entrez]
AID - 10.1007/s00266-023-03607-5 [pii]
AID - 10.1007/s00266-023-03607-5 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Aug 21. doi: 10.1007/s00266-023-03607-5.

PMID- 37937846
OWN - NLM
STAT- Publisher
LR  - 20231109
IS  - 1536-3708 (Electronic)
IS  - 0148-7043 (Linking)
DP  - 2023 Nov 8
TI  - Distinguishing Authentic Voices in the Age of ChatGPT: Comparing AI-Generated and 
      Applicant-Written Personal Statements for Plastic Surgery Residency Application: 
      Erratum.
LID - 10.1097/SAP.0000000000003756 [doi]
LA  - eng
PT  - Journal Article
PT  - Published Erratum
DEP - 20231108
PL  - United States
TA  - Ann Plast Surg
JT  - Annals of plastic surgery
JID - 7805336
SB  - IM
EFR - Ann Plast Surg. 2023 Sep 1;91(3):324-325. PMID: 37566815
EDAT- 2023/11/08 12:43
MHDA- 2023/11/08 12:43
CRDT- 2023/11/08 08:02
PHST- 2023/11/08 12:43 [medline]
PHST- 2023/11/08 12:43 [pubmed]
PHST- 2023/11/08 08:02 [entrez]
AID - 00000637-990000000-00346 [pii]
AID - 10.1097/SAP.0000000000003756 [doi]
PST - aheadofprint
SO  - Ann Plast Surg. 2023 Nov 8. doi: 10.1097/SAP.0000000000003756.

PMID- 37516646
OWN - NLM
STAT- MEDLINE
DCOM- 20230919
LR  - 20230919
IS  - 1471-6771 (Electronic)
IS  - 0007-0912 (Linking)
VI  - 131
IP  - 4
DP  - 2023 Oct
TI  - ChatGPT and the stochastic parrot: artificial intelligence in medical research.
PG  - e120-e121
LID - S0007-0912(23)00372-0 [pii]
LID - 10.1016/j.bja.2023.06.065 [doi]
FAU - Boussen, Salah
AU  - Boussen S
AD  - Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique 
      Hôpitaux de Marseille, Aix Marseille Université, Marseille, France; Aix Marseille 
      Université, IFSTTAR, LBA UMR_T 24, Marseille, France. Electronic address: 
      salah.boussen@univ-eiffel.fr.
FAU - Denis, Jean-Baptiste
AU  - Denis JB
AD  - Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique 
      Hôpitaux de Marseille, Aix Marseille Université, Marseille, France.
FAU - Simeone, Pierre
AU  - Simeone P
AD  - Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique 
      Hôpitaux de Marseille, Aix Marseille Université, Marseille, France; Institut des 
      Neurociences de la Timone, CNRS UMR1106, Aix-Marseille Université, Marseille, 
      France.
FAU - Lagier, David
AU  - Lagier D
AD  - Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique 
      Hôpitaux de Marseille, Aix Marseille Université, Marseille, France.
FAU - Bruder, Nicolas
AU  - Bruder N
AD  - Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique 
      Hôpitaux de Marseille, Aix Marseille Université, Marseille, France.
FAU - Velly, Lionel
AU  - Velly L
AD  - Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique 
      Hôpitaux de Marseille, Aix Marseille Université, Marseille, France; Institut des 
      Neurociences de la Timone, CNRS UMR1106, Aix-Marseille Université, Marseille, 
      France.
LA  - eng
PT  - Letter
DEP - 20230727
PL  - England
TA  - Br J Anaesth
JT  - British journal of anaesthesia
JID - 0372541
SB  - IM
MH  - Humans
MH  - Animals
MH  - *Parrots
MH  - Artificial Intelligence
MH  - *Biomedical Research
OTO - NOTNLM
OT  - artificial intelligence
OT  - citations
OT  - data integrity
OT  - large language models
OT  - research integrity
OT  - scientific reporting
EDAT- 2023/07/30 01:06
MHDA- 2023/09/19 06:42
CRDT- 2023/07/29 21:57
PHST- 2023/05/30 00:00 [received]
PHST- 2023/06/10 00:00 [revised]
PHST- 2023/06/28 00:00 [accepted]
PHST- 2023/09/19 06:42 [medline]
PHST- 2023/07/30 01:06 [pubmed]
PHST- 2023/07/29 21:57 [entrez]
AID - S0007-0912(23)00372-0 [pii]
AID - 10.1016/j.bja.2023.06.065 [doi]
PST - ppublish
SO  - Br J Anaesth. 2023 Oct;131(4):e120-e121. doi: 10.1016/j.bja.2023.06.065. Epub 
      2023 Jul 27.

PMID- 38026315
OWN - NLM
STAT- MEDLINE
DCOM- 20231201
LR  - 20231201
IS  - 2296-2565 (Electronic)
IS  - 2296-2565 (Linking)
VI  - 11
DP  - 2023
TI  - AI chatbots and (mis)information in public health: impact on vulnerable 
      communities.
PG  - 1226776
LID - 10.3389/fpubh.2023.1226776 [doi]
LID - 1226776
FAU - Meyrowitsch, Dan W
AU  - Meyrowitsch DW
AD  - Global Health Section, Department of Public Health, University of Copenhagen, 
      Copenhagen, Denmark.
FAU - Jensen, Andreas K
AU  - Jensen AK
AD  - Section of Biostatistics, Department of Public Health, University of Copenhagen, 
      Copenhagen, Denmark.
FAU - Sørensen, Jane B
AU  - Sørensen JB
AD  - Global Health Section, Department of Public Health, University of Copenhagen, 
      Copenhagen, Denmark.
FAU - Varga, Tibor V
AU  - Varga TV
AD  - Section of Epidemiology, Department of Public Health, University of Copenhagen, 
      Copenhagen, Denmark.
LA  - eng
PT  - Journal Article
DEP - 20231031
PL  - Switzerland
TA  - Front Public Health
JT  - Frontiers in public health
JID - 101616579
SB  - IM
MH  - *Public Health
MH  - *Artificial Intelligence
MH  - Vulnerable Populations
MH  - *Disinformation
PMC - PMC10644115
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Global South
OT  - LLM
OT  - chatbot
OT  - public health
OT  - vulnerable communities
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/29 18:41
MHDA- 2023/12/01 06:44
CRDT- 2023/11/29 16:52
PHST- 2023/05/22 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/12/01 06:44 [medline]
PHST- 2023/11/29 18:41 [pubmed]
PHST- 2023/11/29 16:52 [entrez]
AID - 10.3389/fpubh.2023.1226776 [doi]
PST - epublish
SO  - Front Public Health. 2023 Oct 31;11:1226776. doi: 10.3389/fpubh.2023.1226776. 
      eCollection 2023.

PMID- 37832827
OWN - NLM
STAT- Publisher
LR  - 20231209
IS  - 2468-7855 (Electronic)
IS  - 2468-7855 (Linking)
VI  - 125
IP  - 2
DP  - 2023 Oct 11
TI  - Letter to the Editor: Can ChatGPT be used in oral and maxillofacial surgery?
PG  - 101653
LID - S2468-7855(23)00274-4 [pii]
LID - 10.1016/j.jormas.2023.101653 [doi]
FAU - Azadi, Ali
AU  - Azadi A
AD  - Research Fellow, Dentofacial Deformities Research Center, Research Institute of 
      Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 
      Electronic address: a.azadi@sbmu.ac.ir.
FAU - Akbarzadeh Baghban, Alireza
AU  - Akbarzadeh Baghban A
AD  - Proteomics Research Center, Department of Biostatistics, School of Allied Medical 
      Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
FAU - Mohammad-Rahimi, Hossein
AU  - Mohammad-Rahimi H
AD  - Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on 
      Health, Berlin, Germany.
LA  - eng
PT  - Letter
DEP - 20231011
PL  - France
TA  - J Stomatol Oral Maxillofac Surg
JT  - Journal of stomatology, oral and maxillofacial surgery
JID - 101701089
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Dentistry
OT  - Oral and maxillofacial surgeons
COIS- Declaration of Competing Interest I confirm that neither I nor any of the authors 
      of this manuscript have any personal or business interest in the findings of this 
      manuscript, and there are no competing interests among the writers of this 
      manuscript.
EDAT- 2023/10/14 10:43
MHDA- 2023/10/14 10:43
CRDT- 2023/10/13 19:27
PHST- 2023/10/09 00:00 [received]
PHST- 2023/10/10 00:00 [accepted]
PHST- 2023/10/14 10:43 [pubmed]
PHST- 2023/10/14 10:43 [medline]
PHST- 2023/10/13 19:27 [entrez]
AID - S2468-7855(23)00274-4 [pii]
AID - 10.1016/j.jormas.2023.101653 [doi]
PST - aheadofprint
SO  - J Stomatol Oral Maxillofac Surg. 2023 Oct 11;125(2):101653. doi: 
      10.1016/j.jormas.2023.101653.

PMID- 37516342
OWN - NLM
STAT- Publisher
LR  - 20230817
IS  - 1542-2224 (Electronic)
IS  - 1067-2516 (Linking)
DP  - 2023 Jul 27
TI  - The Era of Artificial Intelligence: Talking About the Potential Application Value 
      of ChatGPT/GPT-4 in Foot and Ankle Surgery.
LID - S1067-2516(23)00171-0 [pii]
LID - 10.1053/j.jfas.2023.07.002 [doi]
FAU - Wang, Dongxue
AU  - Wang D
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
FAU - He, Yongbin
AU  - He Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
FAU - Ma, Yixuan
AU  - Ma Y
AD  - College of Education, Beijing Sport University, Beijing, China.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Graduate School of Tianjin Medical University, Tianjin, China; Duke Molecular 
      Physiology Institute, Duke University School of Medicine, Durham, NC. Electronic 
      address: wuhaiyang2021@tmu.edu.cn.
FAU - Ni, Guoxin
AU  - Ni G
AD  - Department of Rehabilitation Medicine, The First Affiliated Hospital of Xiamen 
      University, Xiamen, China. Electronic address: nigx@xmu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230727
PL  - United States
TA  - J Foot Ankle Surg
JT  - The Journal of foot and ankle surgery : official publication of the American 
      College of Foot and Ankle Surgeons
JID - 9308427
SB  - IM
EDAT- 2023/07/30 01:06
MHDA- 2023/07/30 01:06
CRDT- 2023/07/29 19:16
PHST- 2023/06/22 00:00 [received]
PHST- 2023/07/12 00:00 [revised]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/07/30 01:06 [pubmed]
PHST- 2023/07/30 01:06 [medline]
PHST- 2023/07/29 19:16 [entrez]
AID - S1067-2516(23)00171-0 [pii]
AID - 10.1053/j.jfas.2023.07.002 [doi]
PST - aheadofprint
SO  - J Foot Ankle Surg. 2023 Jul 27:S1067-2516(23)00171-0. doi: 
      10.1053/j.jfas.2023.07.002.

PMID- 37414138
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230929
LR  - 20230929
IS  - 1778-7254 (Electronic)
IS  - 1297-319X (Linking)
VI  - 90
IP  - 5
DP  - 2023 Sep
TI  - ChatGPT and beyond with artificial intelligence (AI) in health: Lessons to be 
      learned.
PG  - 105607
LID - S1297-319X(23)00086-6 [pii]
LID - 10.1016/j.jbspin.2023.105607 [doi]
FAU - Thiébaut, Rodolphe
AU  - Thiébaut R
AD  - Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux 
      cedex, France; INRIA, SISTM, 33000 Bordeaux cedex, France; Medical Information 
      Department, CHU de Bordeaux, 33000 Bordeaux cedex, France. Electronic address: 
      rodolphe.thiebaut@u-bordeaux.fr.
FAU - Hejblum, Boris
AU  - Hejblum B
AD  - Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux 
      cedex, France; INRIA, SISTM, 33000 Bordeaux cedex, France.
FAU - Mougin, Fleur
AU  - Mougin F
AD  - Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux 
      cedex, France.
FAU - Tzourio, Christophe
AU  - Tzourio C
AD  - Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux 
      cedex, France; Medical Information Department, CHU de Bordeaux, 33000 Bordeaux 
      cedex, France.
FAU - Richert, Laura
AU  - Richert L
AD  - Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux 
      cedex, France; INRIA, SISTM, 33000 Bordeaux cedex, France; Medical Information 
      Department, CHU de Bordeaux, 33000 Bordeaux cedex, France.
LA  - eng
PT  - Editorial
DEP - 20230705
PL  - France
TA  - Joint Bone Spine
JT  - Joint bone spine
JID - 100938016
SB  - IM
CIN - Joint Bone Spine. 2023 Sep;90(5):105610. PMID: 37437875
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Data science
OT  - Evaluation
OT  - Large language models
OT  - Teaching
EDAT- 2023/07/07 01:05
MHDA- 2023/09/29 06:44
CRDT- 2023/07/06 19:24
PHST- 2023/04/24 00:00 [received]
PHST- 2023/06/16 00:00 [revised]
PHST- 2023/06/23 00:00 [accepted]
PHST- 2023/09/29 06:44 [medline]
PHST- 2023/07/07 01:05 [pubmed]
PHST- 2023/07/06 19:24 [entrez]
AID - S1297-319X(23)00086-6 [pii]
AID - 10.1016/j.jbspin.2023.105607 [doi]
PST - ppublish
SO  - Joint Bone Spine. 2023 Sep;90(5):105607. doi: 10.1016/j.jbspin.2023.105607. Epub 
      2023 Jul 5.

PMID- 37856708
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 2352-0787 (Electronic)
IS  - 2352-0779 (Linking)
VI  - 10
IP  - 6
DP  - 2023 Nov
TI  - Artificial Intelligence on the Exam Table: ChatGPT's Advancement in Urology 
      Self-assessment.
PG  - 521-523
LID - 10.1097/UPJ.0000000000000446 [doi]
FAU - Cadiente, Angelo
AU  - Cadiente A
AUID- ORCID: 0009-0007-9933-2804
AD  - Hackensack Meridian School of Medicine, Nutley, New Jersey.
FAU - Chen, Jamie
AU  - Chen J
AUID- ORCID: 0000-0003-0572-2291
AD  - Hackensack Meridian School of Medicine, Nutley, New Jersey.
FAU - Nguyen, Jennifer
AU  - Nguyen J
AD  - Department of Urology, Hackensack University Medical Center, Hackensack, New 
      Jersey.
FAU - Sadeghi-Nejad, Hossein
AU  - Sadeghi-Nejad H
AD  - Department of Urology, NYU Grossman School of Medicine, New York, New York.
FAU - Billah, Mubashir
AU  - Billah M
AD  - Hackensack Meridian School of Medicine, Nutley, New Jersey.
AD  - Department of Urology, Hackensack University Medical Center, Hackensack, New 
      Jersey.
LA  - eng
PT  - Editorial
DEP - 20231006
PL  - United States
TA  - Urol Pract
JT  - Urology practice
JID - 101635343
SB  - IM
MH  - *Artificial Intelligence
MH  - Self-Assessment
MH  - *Urology
OTO - NOTNLM
OT  - artificial intelligence
OT  - education, medical, continuing
OT  - natural language processing
OT  - urology
EDAT- 2023/10/19 18:42
MHDA- 2023/10/23 01:18
CRDT- 2023/10/19 15:52
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/19 18:42 [pubmed]
PHST- 2023/10/19 15:52 [entrez]
AID - 10.1097/UPJ.0000000000000446 [doi]
PST - ppublish
SO  - Urol Pract. 2023 Nov;10(6):521-523. doi: 10.1097/UPJ.0000000000000446. Epub 2023 
      Oct 6.

PMID- 37573084
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1532-8406 (Electronic)
IS  - 0883-5403 (Linking)
VI  - 38
IP  - 9
DP  - 2023 Sep
TI  - Letter to the Editor "Assessing ChatGPT's Potential: A Critical Analysis and 
      Future Directions in Total Joint Arthroplasty".
PG  - e21
LID - S0883-5403(23)00583-1 [pii]
LID - 10.1016/j.arth.2023.05.059 [doi]
FAU - Dubin, Jeremy A
AU  - Dubin JA
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Bains, Sandeep S
AU  - Bains SS
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Hameed, Daniel
AU  - Hameed D
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Chen, Zhongming
AU  - Chen Z
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Nace, James
AU  - Nace J
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Mont, Michael A
AU  - Mont MA
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
FAU - Delanois, Ronald E
AU  - Delanois RE
AD  - LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced 
      Orthopedics, Baltimore, Maryland.
LA  - eng
PT  - Comment
PT  - Letter
PL  - United States
TA  - J Arthroplasty
JT  - The Journal of arthroplasty
JID - 8703515
SB  - IM
CON - J Arthroplasty. 2023 Jul;38(7):1195-1202. PMID: 37040823
CON - J Arthroplasty. 2023 Sep;38(9):e19-e20. PMID: 37573083
MH  - Humans
MH  - *Arthroplasty
MH  - *Artificial Intelligence
EDAT- 2023/08/13 00:43
MHDA- 2023/08/14 06:43
CRDT- 2023/08/12 20:58
PHST- 2023/05/24 00:00 [received]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/08/14 06:43 [medline]
PHST- 2023/08/13 00:43 [pubmed]
PHST- 2023/08/12 20:58 [entrez]
AID - S0883-5403(23)00583-1 [pii]
AID - 10.1016/j.arth.2023.05.059 [doi]
PST - ppublish
SO  - J Arthroplasty. 2023 Sep;38(9):e21. doi: 10.1016/j.arth.2023.05.059.

PMID- 37254486
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20230727
IS  - 2287-285X (Electronic)
IS  - 2287-2728 (Print)
IS  - 2287-2728 (Linking)
VI  - 29
IP  - 3
DP  - 2023 Jul
TI  - Correspondence on Letter 1 regarding "Assessing the performance of ChatGPT in 
      answering questions regarding cirrhosis and hepatocellular carcinoma".
PG  - 821-822
LID - 10.3350/cmh.2023.0183 [doi]
FAU - Yeo, Yee Hui
AU  - Yeo YH
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Samaan, Jamil S
AU  - Samaan JS
AD  - Karsh Division of Gastroenterology and Hepatology, Department of Medicine, 
      Cedars-Sinai Medical Center, Los Angeles, CA, USA.
FAU - Ng, Wee Han
AU  - Ng WH
AD  - Bristol Medical School, University of Bristol, Bristol, UK.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230531
PL  - Korea (South)
TA  - Clin Mol Hepatol
JT  - Clinical and molecular hepatology
JID - 101586730
SB  - IM
CON - Clin Mol Hepatol. 2023 Jul;29(3):813-814. PMID: 37211355
MH  - Humans
MH  - *Carcinoma, Hepatocellular
MH  - *Liver Neoplasms
MH  - Liver Cirrhosis
PMC - PMC10366788
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Cancer
OT  - Health equity
OT  - Health literacy
OT  - Liver cirrhosis
COIS- Conflicts of Interest The authors have no conflicts to disclose.
EDAT- 2023/05/31 06:41
MHDA- 2023/07/25 06:43
CRDT- 2023/05/31 01:49
PHST- 2023/05/27 00:00 [received]
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/07/25 06:43 [medline]
PHST- 2023/05/31 06:41 [pubmed]
PHST- 2023/05/31 01:49 [entrez]
AID - cmh.2023.0183 [pii]
AID - cmh-2023-0183 [pii]
AID - 10.3350/cmh.2023.0183 [doi]
PST - ppublish
SO  - Clin Mol Hepatol. 2023 Jul;29(3):821-822. doi: 10.3350/cmh.2023.0183. Epub 2023 
      May 31.

PMID- 37863153
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
DP  - 2023 Oct 18
TI  - Enhancing patient communication with Chat-GPT in radiology: evaluating the 
      efficacy and readability of answers to common imaging-related questions.
LID - S1546-1440(23)00775-5 [pii]
LID - 10.1016/j.jacr.2023.09.011 [doi]
AB  - PURPOSE: To assess ChatGPT's accuracy, relevance, and readability in answering 
      patients' common imaging-related questions and examine the effect of a simple 
      prompt. METHODS: 22 imaging-related questions were developed from categories 
      previously described as important to patients: safety, the radiology report, the 
      procedure, preparation before imaging, meaning of terms, and medical staff. These 
      questions were posed to ChatGPT with and without a short prompt instructing the 
      model to provide an accurate and easy-to-understand response for the average 
      person. Four board-certified radiologists evaluated the answers for accuracy, 
      consistency, and relevance. Two patient advocates also reviewed responses for 
      their utility for patients. Readability was assessed by Flesch Kincaid Grade 
      Level (FKGL). Statistical comparisons were performed using chi-square and paired 
      t-tests. RESULTS: 264 answers were assessed for both unprompted and prompted 
      questions. Unprompted responses were accurate 83% (218/264) of the time, which 
      did not significantly change for prompted responses (87% [229/264]; P=0.2). The 
      consistency of the responses increased from 72%f (63/88) to 86% (76/88) when 
      prompted (P=0.02). Nearly all responses (99% [261/264]) were at least partially 
      relevant for both question types. Fewer unprompted responses were considered 
      fully relevant at 67% (176/264), though this increased significantly to 80% when 
      prompted (210/264) (P=0.001). The average FKGL was high at 13.6 [12.9-14.2], 
      unchanged with the prompt (13.0 [12.41-13.60], P=0.2). None of the responses 
      reached the eighth-grade readability recommended for patient-facing materials. 
      CONCLUSIONS: ChatGPT demonstrates the potential to respond accurately, 
      consistently, and relevantly to patients' imaging-related questions. However, 
      imperfect accuracy and high complexity necessitate oversight before 
      implementation. Prompts reduced response variability and yielded more targeted 
      information but did not improve readability. RELEVANCE AND APPLICATION: ChatGPT 
      has the potential to increase accessibility to health information and to 
      streamline the production of patient-facing educational materials, though its 
      current limitations require cautious implementation and further research.
CI  - Copyright © 2023. Published by Elsevier Inc.
FAU - Gordon, Emile B
AU  - Gordon EB
AD  - University of Pittsburgh Medical Center, Department of Radiology.
FAU - Towbin, Alexander J
AU  - Towbin AJ
AD  - University of Cincinnati, Department of Radiology.
FAU - Wingrove, Peter
AU  - Wingrove P
AD  - University of Pittsburgh Medical Center, Department of Radiology.
FAU - Shafique, Umber
AU  - Shafique U
AD  - Indiana University School of Medicine, Department of Radiology.
FAU - Haas, Brian
AU  - Haas B
AD  - University of California San Francisco, Department of Radiology and Biomedical 
      Imaging.
FAU - Kitts, Andrea B
AU  - Kitts AB
AD  - Rescue Lung Society.
FAU - Feldman, Jill
AU  - Feldman J
AD  - EGFR Resisters.
FAU - Furlan, Alessandro
AU  - Furlan A
AD  - University of Pittsburgh Medical Center, Department of Radiology. Electronic 
      address: emile.gordon@duke.edu.
LA  - eng
PT  - Journal Article
DEP - 20231018
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
EDAT- 2023/10/21 05:42
MHDA- 2023/10/21 05:42
CRDT- 2023/10/20 19:27
PHST- 2023/07/24 00:00 [received]
PHST- 2023/09/12 00:00 [revised]
PHST- 2023/09/20 00:00 [accepted]
PHST- 2023/10/21 05:42 [medline]
PHST- 2023/10/21 05:42 [pubmed]
PHST- 2023/10/20 19:27 [entrez]
AID - S1546-1440(23)00775-5 [pii]
AID - 10.1016/j.jacr.2023.09.011 [doi]
PST - aheadofprint
SO  - J Am Coll Radiol. 2023 Oct 18:S1546-1440(23)00775-5. doi: 
      10.1016/j.jacr.2023.09.011.

PMID- 38024074
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov
TI  - Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health 
      Information Generated by Artificial Intelligence-Based Models.
PG  - e49373
LID - 10.7759/cureus.49373 [doi]
LID - e49373
AB  - Background Artificial intelligence (AI)-based conversational models, such as Chat 
      Generative Pre-trained Transformer (ChatGPT), Microsoft Bing, and Google Bard, 
      have emerged as valuable sources of health information for lay individuals. 
      However, the accuracy of the information provided by these AI models remains a 
      significant concern. This pilot study aimed to test a new tool with key themes 
      for inclusion as follows: Completeness of content, Lack of false information in 
      the content, Evidence supporting the content, Appropriateness of the content, 
      and Relevance, referred to as "CLEAR", designed to assess the quality of health 
      information delivered by AI-based models. Methods Tool development involved a 
      literature review on health information quality, followed by the initial 
      establishment of the CLEAR tool, which comprised five items that aimed to assess 
      the following: completeness, lack of false information, evidence support, 
      appropriateness, and relevance. Each item was scored on a five-point Likert scale 
      from excellent to poor. Content validity was checked by expert review. Pilot 
      testing involved 32 healthcare professionals using the CLEAR tool to assess 
      content on eight different health topics deliberately designed with varying 
      qualities. The internal consistency was checked with Cronbach's alpha (α). 
      Feedback from the pilot test resulted in language modifications to improve the 
      clarity of the items. The final CLEAR tool was used to assess the quality of 
      health information generated by four distinct AI models on five health topics. 
      The AI models were ChatGPT 3.5, ChatGPT 4, Microsoft Bing, and Google Bard, and 
      the content generated was scored by two independent raters with Cohen's kappa (κ) 
      for inter-rater agreement. Results The final five CLEAR items were: (1) Is the 
      content sufficient?; (2) Is the content accurate?; (3) Is the content 
      evidence-based?; (4) Is the content clear, concise, and easy to understand?; and 
      (5) Is the content free from irrelevant information? Pilot testing on the eight 
      health topics revealed acceptable internal consistency with a Cronbach's α range 
      of 0.669-0.981. The use of the final CLEAR tool yielded the following average 
      scores: Microsoft Bing (mean=24.4±0.42), ChatGPT-4 (mean=23.6±0.96), Google Bard 
      (mean=21.2±1.79), and ChatGPT-3.5 (mean=20.6±5.20). The inter-rater agreement 
      revealed the following Cohen κ values: for ChatGPT-3.5 (κ=0.875, P<.001), 
      ChatGPT-4 (κ=0.780, P<.001), Microsoft Bing (κ=0.348, P=.037), and Google Bard 
      (κ=.749, P<.001). Conclusions The CLEAR tool is a brief yet helpful tool that can 
      aid in standardizing testing of the quality of health information generated by 
      AI-based models. Future studies are recommended to validate the utility of the 
      CLEAR tool in the quality assessment of AI-generated health-related content using 
      a larger sample across various complex health topics.
CI  - Copyright © 2023, Sallam et al.
FAU - Sallam, Malik
AU  - Sallam M
AD  - Department of Pathology, Microbiology, and Forensic Medicine, School of Medicine, 
      University of Jordan, Amman, JOR.
AD  - Department of Clinical Laboratories and Forensic Medicine, Jordan University 
      Hospital, Amman, JOR.
FAU - Barakat, Muna
AU  - Barakat M
AD  - Department of Clinical Pharmacy and Therapeutics, School of Pharmacy, Applied 
      Science Private University, Amman, JOR.
AD  - Department of Research, Middle East University, Amman, JOR.
FAU - Sallam, Mohammed
AU  - Sallam M
AD  - Department of Pharmacy, Mediclinic Parkview Hospital, Mediclinic Middle East, 
      Dubai, ARE.
LA  - eng
PT  - Journal Article
DEP - 20231124
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10674084
OTO - NOTNLM
OT  - ai in healthcare
OT  - ai-generated health information
OT  - assessment tool feasibility
OT  - health information reliability
OT  - quality of health information
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 16:02
PHST- 2023/11/24 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 16:02 [entrez]
AID - 10.7759/cureus.49373 [doi]
PST - epublish
SO  - Cureus. 2023 Nov 24;15(11):e49373. doi: 10.7759/cureus.49373. eCollection 2023 
      Nov.

PMID- 38076046
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 12
DP  - 2023 Dec
TI  - ChatGPT-assisted deep learning for diagnosing bone metastasis in bone scans: 
      Bridging the AI Gap for Clinicians.
PG  - e22409
LID - 10.1016/j.heliyon.2023.e22409 [doi]
LID - e22409
AB  - BACKGROUND: Bone scans are often used to identify bone metastases, but their low 
      specificity may necessitate further studies. Deep learning models may improve 
      diagnostic accuracy but require both medical and programming expertise. 
      Therefore, we investigated the feasibility of constructing a deep learning model 
      employing ChatGPT for the diagnosis of bone metastasis in bone scans and to 
      evaluate its diagnostic performance. METHOD: We examined 4626 consecutive cancer 
      patients (age, 65.1 ± 11.3 years; 2334 female) who had bone scans for metastasis 
      assessment. A nuclear medicine physician developed a deep learning model using 
      ChatGPT 3.5 (OpenAI). We employed ResNet50 as the backbone network and compared 
      the diagnostic performance of four strategies (original training set, original 
      training set with 1:10 class weight, 10-fold data augmentation for positive 
      images only, and 10-fold data augmentation for all images) to address the class 
      imbalance. We used a class activation map algorithm for visualization. RESULTS: 
      Among the four strategies, the deep learning model with 10-fold data augmentation 
      for positive cases only, using a batch size of 16 and an epoch size of 150, 
      achieved the area under curve of 0.8156, the sensitivity of 56.0 %, and 
      specificity of 88.7 %. The class activation map indicated that the model focused 
      on disseminated bone metastases within the spine but might confuse them with 
      benign spinal lesions or intense urinary activity. CONCLUSIONS: Our study 
      illustrates that a clinical physician with rudimentary programming skills can 
      develop a deep learning model for medical image analysis, such as diagnosing bone 
      metastasis in bone scans using ChatGPT. Model visualization may offer guidance in 
      enhancing deep learning model development, including preprocessing, and 
      potentially support clinical decision-making processes.
CI  - © 2023 The Authors.
FAU - Son, Hye Joo
AU  - Son HJ
AD  - Department of Nuclear Medicine, Dankook University Medical Center, Cheonan, 
      Chungnam, Republic of Korea.
FAU - Kim, Soo-Jong
AU  - Kim SJ
AD  - Department of Neurology, Samsung Medical Center, Sungkyunkwan University School 
      of Medicine, Seoul, Republic of Korea.
AD  - Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, 
      Seoul, Republic of Korea.
AD  - Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan 
      University, Suwon, Republic of Korea.
FAU - Pak, Sehyun
AU  - Pak S
AD  - Department of Medicine, Hallym University College of Medicine, Chuncheon, 
      Gangwon, Republic of Korea.
FAU - Lee, Suk Hyun
AU  - Lee SH
AD  - Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym 
      University College of Medicine, Seoul, Republic of Korea.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10709387
OTO - NOTNLM
OT  - Bone metastasis
OT  - Bone scan
OT  - ChatGPT
OT  - Convolutional neural network
OT  - Deep learning
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/12/11 12:42
MHDA- 2023/12/11 12:43
CRDT- 2023/12/11 06:05
PHST- 2023/10/10 00:00 [received]
PHST- 2023/11/09 00:00 [revised]
PHST- 2023/11/10 00:00 [accepted]
PHST- 2023/12/11 12:43 [medline]
PHST- 2023/12/11 12:42 [pubmed]
PHST- 2023/12/11 06:05 [entrez]
AID - S2405-8440(23)09617-2 [pii]
AID - e22409 [pii]
AID - 10.1016/j.heliyon.2023.e22409 [doi]
PST - epublish
SO  - Heliyon. 2023 Nov 20;9(12):e22409. doi: 10.1016/j.heliyon.2023.e22409. 
      eCollection 2023 Dec.

PMID- 37065337
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230418
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Langerhans Cell Histiocytosis Presenting With Clinical Features of Hidradenitis 
      Suppurativa.
PG  - e36201
LID - 10.7759/cureus.36201 [doi]
LID - e36201
AB  - Langerhans cell histiocytosis (LCH) is a rare neoplastic disease of myeloid 
      dendritic cells with a widely variable presentation of organ system involvement 
      and severity. In this case report, we share the details of a rare case of 
      cutaneous LCH resembling hidradenitis suppurativa (HS).
CI  - Copyright © 2023, Yousif et al.
FAU - Yousif, Miranda L
AU  - Yousif ML
AD  - College of Medicine, University of Arizona College of Medicine - Phoenix, 
      Phoenix, USA.
FAU - Faulkner, Claire S
AU  - Faulkner CS
AD  - College of Medicine, University of Arizona College of Medicine - Phoenix, 
      Phoenix, USA.
FAU - Harper, Lise
AU  - Harper L
AD  - Internal Medicine, Banner University Medical Center, Phoenix, USA.
FAU - Ackerman, Lindsay
AU  - Ackerman L
AD  - Dermatology, Banner University Medical Center, Phoenix, USA.
LA  - eng
PT  - Case Reports
DEP - 20230315
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10104688
OTO - NOTNLM
OT  - chatgpt
OT  - chatgpt improved case report
OT  - hidradenitis suppurative
OT  - langerhans cell histiocytosis(lch)
OT  - neoplastic disease
OT  - rare cancers of female genital tract
OT  - rare skin disease
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/18 06:00
MHDA- 2023/04/18 06:01
CRDT- 2023/04/17 03:54
PHST- 2023/03/15 00:00 [accepted]
PHST- 2023/04/18 06:01 [medline]
PHST- 2023/04/17 03:54 [entrez]
PHST- 2023/04/18 06:00 [pubmed]
AID - 10.7759/cureus.36201 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 15;15(3):e36201. doi: 10.7759/cureus.36201. eCollection 2023 
      Mar.

PMID- 36875254
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230307
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - When Palliative Care May Be the Only Option in the Management of Severe Burns: A 
      Case Report Written With the Help of ChatGPT.
PG  - e35649
LID - 10.7759/cureus.35649 [doi]
LID - e35649
AB  - We present a case of 100% third-degree burns. The patient received full 
      resuscitative measures, but the family was prepared for a poor outcome based on 
      the severe extent of the injuries. After several days of treatment, it became 
      apparent that the patient indeed could not survive the injuries and palliative 
      care was instituted, including mechanical ventilation, fluid therapy, and 
      analgesia. Surgery was not possible without causing major disfigurement, 
      including enucleation of both eyes and amputation of all limbs.
CI  - Copyright © 2023, Nachshon et al.
FAU - Nachshon, Akiva
AU  - Nachshon A
AD  - General Intensive Care Unit, Hadassah Medical Center, Jerusalem, ISR.
FAU - Batzofin, Baruch
AU  - Batzofin B
AD  - General Intensive Care Unit, Hadassah Medical Center, Jerusalem, ISR.
FAU - Beil, Michael
AU  - Beil M
AD  - Medical Intensive Care Unit, Hadassah Medical Center, Jerusalem, ISR.
FAU - van Heerden, Peter V
AU  - van Heerden PV
AD  - General Intensive Care Unit, Hadassah Medical Center, Jerusalem, ISR.
LA  - eng
PT  - Case Reports
DEP - 20230301
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC9976839
OTO - NOTNLM
OT  - chatgpt
OT  - critical care
OT  - major trauma
OT  - palliative care
OT  - severe burns
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/07 06:00
MHDA- 2023/03/07 06:01
CRDT- 2023/03/06 04:07
PHST- 2023/03/01 00:00 [accepted]
PHST- 2023/03/06 04:07 [entrez]
PHST- 2023/03/07 06:00 [pubmed]
PHST- 2023/03/07 06:01 [medline]
AID - 10.7759/cureus.35649 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 1;15(3):e35649. doi: 10.7759/cureus.35649. eCollection 2023 Mar.

PMID- 36840450
OWN - NLM
STAT- Publisher
LR  - 20230225
IS  - 1545-5815 (Electronic)
IS  - 0898-9621 (Linking)
DP  - 2023 Feb 25
TI  - Challenges for enforcing editorial policies on AI-generated papers.
PG  - 1-3
LID - 10.1080/08989621.2023.2184262 [doi]
AB  - ChatGPT, a chatbot released by OpenAI in November 2022, has rocked academia with 
      its capacity to generate papers "good enough" for academic journals. Major 
      journals such as Nature and professional societies such as the World Association 
      of Medical Editors have moved fast to issue policies to ban or curb AI-written 
      papers. Amid the flurry of policy initiatives, one important challenge seems to 
      be overlooked: AI-generated papers are not easily discernible to the human eye, 
      and we lack the right tools to implement the policies. Without such tools, the 
      well-intentioned policies are likely to remain on paper.
FAU - Hu, Guangwei
AU  - Hu G
AUID- ORCID: 0000-0002-2297-4784
AD  - Department of English and Communication, The Hong Kong Polytechnic University, 
      Hong Kong, China.
LA  - eng
PT  - Journal Article
DEP - 20230225
PL  - United States
TA  - Account Res
JT  - Accountability in research
JID - 9100813
SB  - IM
OTO - NOTNLM
OT  - ChatGPT; editorial policies; AI-generated papers; challenges
EDAT- 2023/02/26 06:00
MHDA- 2023/02/26 06:00
CRDT- 2023/02/25 04:50
PHST- 2023/02/26 06:00 [pubmed]
PHST- 2023/02/26 06:00 [medline]
PHST- 2023/02/25 04:50 [entrez]
AID - 10.1080/08989621.2023.2184262 [doi]
PST - aheadofprint
SO  - Account Res. 2023 Feb 25:1-3. doi: 10.1080/08989621.2023.2184262.

PMID- 37492829
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230727
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - The Emerging Role of Generative Artificial Intelligence in Medical Education, 
      Research, and Practice.
PG  - e40883
LID - 10.7759/cureus.40883 [doi]
LID - e40883
AB  - Recent breakthroughs in generative artificial intelligence (GAI) and the 
      emergence of transformer-based large language models such as Chat Generative 
      Pre-trained Transformer (ChatGPT) have the potential to transform healthcare 
      education, research, and clinical practice. This article examines the current 
      trends in using GAI models in medicine, outlining their strengths and 
      limitations. It is imperative to develop further consensus-based guidelines to 
      govern the appropriate use of GAI, not only in medical education but also in 
      research, scholarship, and clinical practice.
CI  - Copyright © 2023, Shoja et al.
FAU - Shoja, Mohammadali M
AU  - Shoja MM
AD  - Medical Education, Nova Southeastern University, Fort Lauderdale, USA.
FAU - Van de Ridder, J M Monica
AU  - Van de Ridder JMM
AD  - College of Human Medicine, Michigan State University, Grand Rapids, USA.
FAU - Rajput, Vijay
AU  - Rajput V
AD  - Medical Education, Dr. Kiran C. Patel College of Allopathic Medicine, Nova 
      Southeastern University, Fort Lauderdale, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230624
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10363933
OTO - NOTNLM
OT  - artificial intelligence
OT  - clinical medicine
OT  - education
OT  - information literacy
OT  - research
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/26 06:43
MHDA- 2023/07/26 06:44
CRDT- 2023/07/26 03:58
PHST- 2023/05/12 00:00 [received]
PHST- 2023/06/24 00:00 [accepted]
PHST- 2023/07/26 06:44 [medline]
PHST- 2023/07/26 06:43 [pubmed]
PHST- 2023/07/26 03:58 [entrez]
AID - 10.7759/cureus.40883 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 24;15(6):e40883. doi: 10.7759/cureus.40883. eCollection 2023 
      Jun.

PMID- 37380197
OWN - NLM
STAT- Publisher
LR  - 20230628
IS  - 1532-8651 (Electronic)
IS  - 1098-7339 (Linking)
DP  - 2023 Jun 27
TI  - Concerns regarding the uncritical use of ChatGPT: a critical analysis of 
      AI-generated references in the context of regional anesthesia.
LID - rapm-2023-104771 [pii]
LID - 10.1136/rapm-2023-104771 [doi]
FAU - De Cassai, Alessandro
AU  - De Cassai A
AUID- ORCID: 0000-0002-9773-1832
AD  - Anesthesia and Intensive Care Sant'Antonio, Padua University Hospital, Padova, 
      Italy alessandro.decassai@gmail.com.
FAU - Dost, Burhan
AU  - Dost B
AUID- ORCID: 0000-0002-4562-1172
AD  - Faculty of Medicine, Ondokuz Mayis University Faculty of Medicine, Samsun, 
      Turkey.
LA  - eng
PT  - Letter
DEP - 20230627
PL  - England
TA  - Reg Anesth Pain Med
JT  - Regional anesthesia and pain medicine
JID - 9804508
SB  - IM
OTO - NOTNLM
OT  - EDUCATION
OT  - ETHICS
OT  - TECHNOLOGY
COIS- Competing interests: None declared.
EDAT- 2023/06/29 01:08
MHDA- 2023/06/29 01:08
CRDT- 2023/06/28 20:43
PHST- 2023/06/14 00:00 [received]
PHST- 2023/06/15 00:00 [accepted]
PHST- 2023/06/29 01:08 [medline]
PHST- 2023/06/29 01:08 [pubmed]
PHST- 2023/06/28 20:43 [entrez]
AID - rapm-2023-104771 [pii]
AID - 10.1136/rapm-2023-104771 [doi]
PST - aheadofprint
SO  - Reg Anesth Pain Med. 2023 Jun 27:rapm-2023-104771. doi: 10.1136/rapm-2023-104771.

PMID- 37170853
OWN - NLM
STAT- MEDLINE
DCOM- 20230609
LR  - 20230609
IS  - 1742-7843 (Electronic)
IS  - 1742-7835 (Linking)
VI  - 133
IP  - 1
DP  - 2023 Jul
TI  - ChatGPT: First glance from a perspective of clinical pharmacology.
PG  - 3-5
LID - 10.1111/bcpt.13879 [doi]
FAU - Krumborg, Julie Rudbech
AU  - Krumborg JR
AUID- ORCID: 0009-0000-5549-6590
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Clinical Research, Faculty of Health Science, University of 
      Southern Denmark, Odense, Denmark.
FAU - Mikkelsen, Nicolaj
AU  - Mikkelsen N
AUID- ORCID: 0009-0008-6020-0844
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Clinical Research, Faculty of Health Science, University of 
      Southern Denmark, Odense, Denmark.
FAU - Damkier, Per
AU  - Damkier P
AUID- ORCID: 0000-0003-0591-7187
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Clinical Research, Faculty of Health Science, University of 
      Southern Denmark, Odense, Denmark.
FAU - Ennis, Zandra Nymand
AU  - Ennis ZN
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Clinical Research, Faculty of Health Science, University of 
      Southern Denmark, Odense, Denmark.
FAU - Henriksen, Daniel Pilsgaard
AU  - Henriksen DP
AUID- ORCID: 0000-0003-1303-6195
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Clinical Research, Faculty of Health Science, University of 
      Southern Denmark, Odense, Denmark.
FAU - Lillevang-Johansen, Mads
AU  - Lillevang-Johansen M
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Clinical Research, Faculty of Health Science, University of 
      Southern Denmark, Odense, Denmark.
FAU - Pedersen, Sidsel Arnspang
AU  - Pedersen SA
AUID- ORCID: 0000-0001-7457-9092
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Clinical Research, Faculty of Health Science, University of 
      Southern Denmark, Odense, Denmark.
FAU - Bergmann, Troels K
AU  - Bergmann TK
AUID- ORCID: 0000-0001-8313-0721
AD  - Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark.
AD  - Department of Regional Health Research, University of Southern Denmark, Esbjerg, 
      Denmark.
LA  - eng
PT  - Journal Article
DEP - 20230512
PL  - England
TA  - Basic Clin Pharmacol Toxicol
JT  - Basic & clinical pharmacology & toxicology
JID - 101208422
SB  - IM
MH  - *Pharmacology, Clinical
MH  - Artificial Intelligence
OTO - NOTNLM
OT  - artificial intelligence
OT  - clinical pharmacology
OT  - drug information services
OT  - evidence-based medicine
OT  - medical writing
EDAT- 2023/05/12 07:06
MHDA- 2023/06/05 06:42
CRDT- 2023/05/12 05:33
PHST- 2023/04/19 00:00 [revised]
PHST- 2023/03/23 00:00 [received]
PHST- 2023/04/19 00:00 [accepted]
PHST- 2023/06/05 06:42 [medline]
PHST- 2023/05/12 07:06 [pubmed]
PHST- 2023/05/12 05:33 [entrez]
AID - 10.1111/bcpt.13879 [doi]
PST - ppublish
SO  - Basic Clin Pharmacol Toxicol. 2023 Jul;133(1):3-5. doi: 10.1111/bcpt.13879. Epub 
      2023 May 12.

PMID- 36908674
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230314
IS  - 2452-302X (Electronic)
IS  - 2452-302X (Linking)
VI  - 8
IP  - 2
DP  - 2023 Feb
TI  - Artificial Intelligence Discusses the Role of Artificial Intelligence in 
      Translational Medicine: A JACC: Basic to Translational Science Interview With 
      ChatGPT.
PG  - 221-223
LID - 10.1016/j.jacbts.2023.01.001 [doi]
FAU - Mann, Douglas L
AU  - Mann DL
AD  - Department of Internal Medicine-Cardiovascular Division, Washington University 
      School of Medicine, St. Louis, Missouri, USA.
LA  - eng
PT  - Journal Article
DEP - 20230118
PL  - United States
TA  - JACC Basic Transl Sci
JT  - JACC. Basic to translational science
JID - 101677259
PMC - PMC9998448
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatbot
OT  - translational medicine
COIS- The author has reported that he has no relationships relevant to the contents of 
      this paper to disclose.
EDAT- 2023/03/14 06:00
MHDA- 2023/03/14 06:01
CRDT- 2023/03/13 03:49
PHST- 2023/01/03 00:00 [received]
PHST- 2023/01/03 00:00 [accepted]
PHST- 2023/03/13 03:49 [entrez]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/14 06:01 [medline]
AID - S2452-302X(23)00001-3 [pii]
AID - 10.1016/j.jacbts.2023.01.001 [doi]
PST - epublish
SO  - JACC Basic Transl Sci. 2023 Jan 18;8(2):221-223. doi: 
      10.1016/j.jacbts.2023.01.001. eCollection 2023 Feb.

PMID- 37061596
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230615
LR  - 20230615
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 7
DP  - 2023 Jul
TI  - The Amount of Errors in ChatGPT's Responses is Indirectly Correlated with the 
      Number of Publications Related to the Topic Under Investigation.
PG  - 1360-1361
LID - 10.1007/s10439-023-03205-1 [doi]
FAU - Metze, Konradin
AU  - Metze K
AUID- ORCID: 0000-0003-0742-8846
AD  - Faculty of Medical Sciences, State University of Campinas, Campinas, SP, Brazil. 
      kmetze@fcm.unicamp.br.
AD  - Department of Pathology, State University of Campinas, Campinas, SP, Brazil. 
      kmetze@fcm.unicamp.br.
FAU - Morandin-Reis, Rosana C
AU  - Morandin-Reis RC
AD  - Faculty of Medical Sciences, State University of Campinas, Campinas, SP, Brazil.
FAU - Lorand-Metze, Irene
AU  - Lorand-Metze I
AD  - Faculty of Medical Sciences, State University of Campinas, Campinas, SP, Brazil.
FAU - Florindo, João B
AU  - Florindo JB
AD  - Institute of Mathematics, Statistics, and Scientific Computing, State University 
      of Campinas, Campinas, SP, Brazil.
LA  - eng
GR  - 2020/01984-8/FAPESP/
GR  - 308192/2022-2/CNPq/
GR  - 306981/2022-0/CNPq/
GR  - 305110/2018-7/CNPq/
PT  - Letter
DEP - 20230415
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
EDAT- 2023/04/16 06:00
MHDA- 2023/04/16 06:01
CRDT- 2023/04/15 23:18
PHST- 2023/04/10 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/04/16 06:01 [medline]
PHST- 2023/04/16 06:00 [pubmed]
PHST- 2023/04/15 23:18 [entrez]
AID - 10.1007/s10439-023-03205-1 [pii]
AID - 10.1007/s10439-023-03205-1 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jul;51(7):1360-1361. doi: 10.1007/s10439-023-03205-1. Epub 
      2023 Apr 15.

PMID- 37178512
OWN - NLM
STAT- MEDLINE
DCOM- 20230619
LR  - 20231122
IS  - 1878-0334 (Electronic)
IS  - 1871-4021 (Linking)
VI  - 17
IP  - 5
DP  - 2023 May
TI  - Letter to the Editor in Response to article by Vaishya et al ChatGPT: Is this 
      version good for healthcare and research.
PG  - 102779
LID - S1871-4021(23)00075-9 [pii]
LID - 10.1016/j.dsx.2023.102779 [doi]
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Independent Researcher, Ikenobe 3011-2, Kagawa-ken, 761-0799, Japan. Electronic 
      address: jaimetex@yahoo.com.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230505
PL  - Netherlands
TA  - Diabetes Metab Syndr
JT  - Diabetes & metabolic syndrome
JID - 101462250
SB  - IM
CON - Diabetes Metab Syndr. 2023 Apr;17(4):102744. PMID: 36989584
MH  - Humans
MH  - *Publishing
MH  - *Health Facilities
MH  - Delivery of Health Care
OTO - NOTNLM
OT  - Accountability
OT  - Authorship
OT  - Ethics
OT  - Persistent identifier
OT  - Responsibility
COIS- Declaration of competing interest None.
EDAT- 2023/05/14 01:07
MHDA- 2023/06/19 13:08
CRDT- 2023/05/13 18:02
PHST- 2023/03/22 00:00 [received]
PHST- 2023/04/11 00:00 [revised]
PHST- 2023/04/28 00:00 [accepted]
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/05/14 01:07 [pubmed]
PHST- 2023/05/13 18:02 [entrez]
AID - S1871-4021(23)00075-9 [pii]
AID - 10.1016/j.dsx.2023.102779 [doi]
PST - ppublish
SO  - Diabetes Metab Syndr. 2023 May;17(5):102779. doi: 10.1016/j.dsx.2023.102779. Epub 
      2023 May 5.

PMID- 38009391
OWN - NLM
STAT- Publisher
LR  - 20231207
IS  - 1464-410X (Electronic)
IS  - 1464-4096 (Linking)
DP  - 2023 Nov 27
TI  - Artificial intelligence in the urology multidisciplinary team meeting: can 
      ChatGPT suggest European Association of Urology guideline-recommended prostate 
      cancer treatments?
LID - 10.1111/bju.16240 [doi]
FAU - Gabriel, Joseph
AU  - Gabriel J
AD  - Department of Urology, Princess Royal Hospital, Haywards Heath, Sussex, UK.
FAU - Gabriel, Abraham
AU  - Gabriel A
AD  - St. George's, University of London, London, UK.
FAU - Shafik, Lidia
AU  - Shafik L
AD  - Department of Medicine, Frimley Health NHS Foundation Trust, Frimley, UK.
FAU - Alanbuki, Ammar
AU  - Alanbuki A
AD  - Department of Urology, Princess Royal Hospital, Haywards Heath, Sussex, UK.
FAU - Larner, Tim
AU  - Larner T
AD  - Department of Urology, Princess Royal Hospital, Haywards Heath, Sussex, UK.
LA  - eng
PT  - Journal Article
DEP - 20231127
PL  - England
TA  - BJU Int
JT  - BJU international
JID - 100886721
SB  - IM
EDAT- 2023/11/27 06:47
MHDA- 2023/11/27 06:47
CRDT- 2023/11/27 05:32
PHST- 2023/11/27 06:47 [pubmed]
PHST- 2023/11/27 06:47 [medline]
PHST- 2023/11/27 05:32 [entrez]
AID - 10.1111/bju.16240 [doi]
PST - aheadofprint
SO  - BJU Int. 2023 Nov 27. doi: 10.1111/bju.16240.

PMID- 37517768
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231113
IS  - 1558-349X (Electronic)
IS  - 1546-1440 (Linking)
VI  - 20
IP  - 10
DP  - 2023 Oct
TI  - Using Artificial Intelligence Chatbots as a Radiologic Decision-Making Tool for 
      Liver Imaging: Do ChatGPT and Bard Communicate Information Consistent With the 
      ACR Appropriateness Criteria?
PG  - 1010-1013
LID - S1546-1440(23)00538-0 [pii]
LID - 10.1016/j.jacr.2023.07.010 [doi]
FAU - Patil, Nikhil S
AU  - Patil NS
AD  - Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, 
      Canada.
FAU - Huang, Ryan S
AU  - Huang RS
AD  - Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
FAU - van der Pol, Christian B
AU  - van der Pol CB
AD  - Associate Professor of Radiology, Department of Radiology, McMaster University, 
      Hamilton, Ontario, Canada, and is Deputy Radiology Site Chief of Juravinski 
      Hospital and a member of ACR.
FAU - Larocque, Natasha
AU  - Larocque N
AD  - Associate Program Director, McMaster Diagnostic Radiology Program, Competency by 
      Design lead, McMaster Diagnostic Radiology Program, Simulation lead, McMaster 
      Diagnostic Radiology Program, and Assistant Professor of Radiology, Department of 
      Radiology, McMaster University, Hamilton, Ontario, Canada. Electronic address: 
      natasha.larocque@medportal.ca.
LA  - eng
PT  - Journal Article
DEP - 20230728
PL  - United States
TA  - J Am Coll Radiol
JT  - Journal of the American College of Radiology : JACR
JID - 101190326
SB  - IM
MH  - *Artificial Intelligence
MH  - *Abdomen
MH  - Liver
EDAT- 2023/07/31 00:41
MHDA- 2023/11/03 06:44
CRDT- 2023/07/30 19:19
PHST- 2023/06/29 00:00 [received]
PHST- 2023/07/17 00:00 [revised]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/11/03 06:44 [medline]
PHST- 2023/07/31 00:41 [pubmed]
PHST- 2023/07/30 19:19 [entrez]
AID - S1546-1440(23)00538-0 [pii]
AID - 10.1016/j.jacr.2023.07.010 [doi]
PST - ppublish
SO  - J Am Coll Radiol. 2023 Oct;20(10):1010-1013. doi: 10.1016/j.jacr.2023.07.010. 
      Epub 2023 Jul 28.

PMID- 37211355
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20230727
IS  - 2287-285X (Electronic)
IS  - 2287-2728 (Print)
IS  - 2287-2728 (Linking)
VI  - 29
IP  - 3
DP  - 2023 Jul
TI  - Letter 1 regarding "Assessing the performance of ChatGPT in answering questions 
      regarding cirrhosis and hepatocellular carcinoma".
PG  - 813-814
LID - 10.3350/cmh.2023.0120 [doi]
FAU - Ali, Hassam
AU  - Ali H
AD  - Department of Internal Medicine, ECU Health Medical Center, Greenville, NC, USA.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230519
PL  - Korea (South)
TA  - Clin Mol Hepatol
JT  - Clinical and molecular hepatology
JID - 101586730
SB  - IM
CON - Clin Mol Hepatol. 2023 Jul;29(3):721-732. PMID: 36946005
CIN - Clin Mol Hepatol. 2023 Jul;29(3):821-822. PMID: 37254486
MH  - Humans
MH  - *Carcinoma, Hepatocellular
MH  - *Liver Neoplasms
MH  - Liver Cirrhosis
PMC - PMC10366805
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Cirrhosis
OT  - Decision making
OT  - Emotional support
OT  - Hepatocellular carcinoma
OT  - Medical informatics
COIS- Conflicts of Interest The authors have no conflicts to disclose.
EDAT- 2023/05/22 00:41
MHDA- 2023/07/25 06:43
CRDT- 2023/05/21 20:17
PHST- 2023/03/26 00:00 [received]
PHST- 2023/03/27 00:00 [accepted]
PHST- 2023/07/25 06:43 [medline]
PHST- 2023/05/22 00:41 [pubmed]
PHST- 2023/05/21 20:17 [entrez]
AID - cmh.2023.0120 [pii]
AID - cmh-2023-0120 [pii]
AID - 10.3350/cmh.2023.0120 [doi]
PST - ppublish
SO  - Clin Mol Hepatol. 2023 Jul;29(3):813-814. doi: 10.3350/cmh.2023.0120. Epub 2023 
      May 19.

PMID- 36758148
OWN - NLM
STAT- MEDLINE
DCOM- 20230314
LR  - 20230314
IS  - 1646-0758 (Electronic)
IS  - 0870-399X (Linking)
VI  - 36
IP  - 3
DP  - 2023 Mar 1
TI  - The Transparency of Science with ChatGPT and the Emerging Artificial Intelligence 
      Language Models: Where Should Medical Journals Stand?
PG  - 147-148
LID - 10.20344/amp.19694 [doi]
FAU - Donato, Helena
AU  - Donato H
AD  - Acta Médica Portuguesa. Ordem dos Médicos. Lisboa; Serviço de Documentação e 
      Informação Científica. Centro Hospitalar e Universitário de Coimbra. Coimbra. 
      Portugal.
FAU - Escada, Pedro
AU  - Escada P
AD  - Acta Médica Portuguesa. Ordem dos Médicos. Lisboa; NOVA Medical School. 
      Universidade NOVA de Lisboa. Lisboa. Portugal.
FAU - Villanueva, Tiago
AU  - Villanueva T
AD  - Acta Médica Portuguesa. Ordem dos Médicos. Lisboa; Unidade de Saúde Familiar 
      Reynaldo dos Santos. Póvoa de Santa Iria. Portugal.
LA  - eng
PT  - Editorial
DEP - 20230209
PL  - Portugal
TA  - Acta Med Port
JT  - Acta medica portuguesa
JID - 7906803
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Periodicals as Topic
MH  - Publishing
MH  - Language
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Authorship
OT  - Ethics
OT  - Publishing
OT  - Research
OT  - Science/ethics
EDAT- 2023/02/10 06:00
MHDA- 2023/03/15 06:00
CRDT- 2023/02/09 14:32
PHST- 2023/01/30 00:00 [received]
PHST- 2023/02/01 00:00 [accepted]
PHST- 2023/02/10 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
PHST- 2023/02/09 14:32 [entrez]
AID - 10.20344/amp.19694 [doi]
PST - ppublish
SO  - Acta Med Port. 2023 Mar 1;36(3):147-148. doi: 10.20344/amp.19694. Epub 2023 Feb 
      9.

PMID- 37843579
OWN - NLM
STAT- MEDLINE
DCOM- 20231027
LR  - 20231027
IS  - 2731-7099 (Electronic)
IS  - 2731-7080 (Linking)
VI  - 64
IP  - 11
DP  - 2023 Nov
TI  - [Clinical application of large language models : Does ChatGPT replace medical 
      report formulation? An experience report].
PG  - 1058-1064
LID - 10.1007/s00108-023-01600-3 [doi]
AB  - Artificial intelligence (AI)-based language models, such as ChatGPT offer an 
      enormous potential for research and medical care but also for clinical workflow 
      optimization by making medical documentation easier and more efficient in taking 
      over standardized routine tasks. With their ability to guess a text's content 
      using word statistics and thus outputting contextually relevant results in chat 
      dialogues, large language models (LLM) can provide appropriate summaries of 
      medical documentation for different target groups. For instance, text generation 
      in easy to understand language could potentially contribute to an increase in 
      patients' health literacy and, consequently, to increased adherence to treatment. 
      Subsequent, the function of AI-based chatbot models to improve user experiences 
      and enhance competence in the use of AI-based language models will be adressed. 
      Current limitations and chances in creating epicrises are presented as an 
      experience report. In the future, the implementation of local LLMs in medical 
      management systems (hospital information systems, HIS and practice administration 
      systems, PAS) and in conjunction with the electronic patient records (ePA) can 
      fundamentally change clinical and outpatient care.
CI  - © 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, 
      ein Teil von Springer Nature.
FAU - Zernikow, Jasmin
AU  - Zernikow J
AD  - Klinik für Kardiologie, Angiologie und Intensivmedizin, Deutsches Herzzentrum der 
      Charité (DHZC), Charitéplatz 1, 10117, Berlin, Deutschland.
AD  - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität 
      Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, 
      Deutschland.
FAU - Grassow, Leonhard
AU  - Grassow L
AD  - Klinik für Kardiologie, Angiologie und Intensivmedizin, Deutsches Herzzentrum der 
      Charité (DHZC), Charitéplatz 1, 10117, Berlin, Deutschland.
AD  - Deutsches Zentrum für Herz-Kreislauf-Forschung e. V. (DZHK), Berlin, Deutschland.
AD  - Arbeitsgruppe für kardiovaskuläre MRT, Experimental and Clinical Research Center, 
      Gemeinsame Einrichtung der Charité - Universitätsmedizin Berlin und des Max 
      Delbrück Center (MDC), Lindenberger Weg 80, 13125, Berlin, Deutschland.
FAU - Gröschel, Jan
AU  - Gröschel J
AD  - Klinik für Kardiologie, Angiologie und Intensivmedizin, Deutsches Herzzentrum der 
      Charité (DHZC), Charitéplatz 1, 10117, Berlin, Deutschland.
AD  - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität 
      Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, 
      Deutschland.
AD  - Deutsches Zentrum für Herz-Kreislauf-Forschung e. V. (DZHK), Berlin, Deutschland.
AD  - Arbeitsgruppe für kardiovaskuläre MRT, Experimental and Clinical Research Center, 
      Gemeinsame Einrichtung der Charité - Universitätsmedizin Berlin und des Max 
      Delbrück Center (MDC), Lindenberger Weg 80, 13125, Berlin, Deutschland.
FAU - Henrion, Philippe
AU  - Henrion P
AD  - Klinik für Kardiologie, Angiologie und Intensivmedizin, Deutsches Herzzentrum der 
      Charité (DHZC), Charitéplatz 1, 10117, Berlin, Deutschland.
AD  - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität 
      Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, 
      Deutschland.
FAU - Wetzel, Paul J
AU  - Wetzel PJ
AD  - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität 
      Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, 
      Deutschland.
FAU - Spethmann, Sebastian
AU  - Spethmann S
AD  - Klinik für Kardiologie, Angiologie und Intensivmedizin, Deutsches Herzzentrum der 
      Charité (DHZC), Charitéplatz 1, 10117, Berlin, Deutschland. 
      sebastian.spethmann@dhzc-charite.de.
AD  - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität 
      Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, 
      Deutschland. sebastian.spethmann@dhzc-charite.de.
AD  - Deutsches Zentrum für Herz-Kreislauf-Forschung e. V. (DZHK), Berlin, Deutschland. 
      sebastian.spethmann@dhzc-charite.de.
LA  - ger
PT  - English Abstract
PT  - Journal Article
PT  - Review
TT  - Anwendung von „large language models“ in der Klinik : Ersetzt ChatGPT die 
      Arztbrieferstellung? Ein Erfahrungsbericht.
DEP - 20231016
PL  - Germany
TA  - Inn Med (Heidelb)
JT  - Innere Medizin (Heidelberg, Germany)
JID - 9918384885306676
RN  - EC 2.3.1.6 (Choline O-Acetyltransferase)
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Electronic Health Records
MH  - Ambulatory Care
MH  - Choline O-Acetyltransferase
MH  - Language
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbots
OT  - Digital transformation
OT  - Patient empowerment
EDAT- 2023/10/16 12:42
MHDA- 2023/10/27 06:43
CRDT- 2023/10/16 11:04
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/10/27 06:43 [medline]
PHST- 2023/10/16 12:42 [pubmed]
PHST- 2023/10/16 11:04 [entrez]
AID - 10.1007/s00108-023-01600-3 [pii]
AID - 10.1007/s00108-023-01600-3 [doi]
PST - ppublish
SO  - Inn Med (Heidelb). 2023 Nov;64(11):1058-1064. doi: 10.1007/s00108-023-01600-3. 
      Epub 2023 Oct 16.

PMID- 37461512
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230908
DP  - 2023 Jul 8
TI  - Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating 
      Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets.
LID - 2023.07.07.23292388 [pii]
LID - 10.1101/2023.07.07.23292388 [doi]
AB  - Drug repurposing represents an attractive alternative to the costly and 
      time-consuming process of new drug development, particularly for serious, 
      widespread conditions with limited effective treatments, such as Alzheimer's 
      disease (AD). Emerging generative artificial intelligence (GAI) technologies like 
      ChatGPT offer the promise of expediting the review and summary of scientific 
      knowledge. To examine the feasibility of using GAI for identifying drug 
      repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty 
      most promising drugs for repurposing in AD, and tested the top ten for risk of 
      incident AD in exposed and unexposed individuals over age 65 in two large 
      clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us 
      Research Program. Among the candidates suggested by ChatGPT, metformin, 
      simvastatin, and losartan were associated with lower AD risk in meta-analysis. 
      These findings suggest GAI technologies can assimilate scientific insights from 
      an extensive Internet-based search space, helping to prioritize drug repurposing 
      candidates and facilitate the treatment of diseases.
FAU - Yan, Chao
AU  - Yan C
FAU - Grabowska, Monika E
AU  - Grabowska ME
AUID- ORCID: 0000-0003-0708-676X
FAU - Dickson, Alyson L
AU  - Dickson AL
FAU - Li, Bingshan
AU  - Li B
FAU - Wen, Zhexing
AU  - Wen Z
FAU - Roden, Dan M
AU  - Roden DM
FAU - Stein, C Michael
AU  - Stein CM
FAU - Embí, Peter J
AU  - Embí PJ
FAU - Peterson, Josh F
AU  - Peterson JF
FAU - Feng, QiPing
AU  - Feng Q
FAU - Malin, Bradley A
AU  - Malin BA
FAU - Wei, Wei-Qi
AU  - Wei WQ
LA  - eng
GR  - F30 AG080885/AG/NIA NIH HHS/United States
PT  - Preprint
DEP - 20230708
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10350158
EDAT- 2023/07/18 06:42
MHDA- 2023/07/18 06:43
CRDT- 2023/07/18 03:37
PHST- 2023/07/18 06:43 [medline]
PHST- 2023/07/18 06:42 [pubmed]
PHST- 2023/07/18 03:37 [entrez]
AID - 2023.07.07.23292388 [pii]
AID - 10.1101/2023.07.07.23292388 [doi]
PST - epublish
SO  - medRxiv. 2023 Jul 8:2023.07.07.23292388. doi: 10.1101/2023.07.07.23292388. 
      Preprint.

PMID- 37932506
OWN - NLM
STAT- Publisher
LR  - 20231106
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Nov 6
TI  - Exploring the Potential of ChatGPT-4 in Responding to Common Questions About 
      Abdominoplasty: An AI-Based Case Study of a Plastic Surgery Consultation.
LID - 10.1007/s00266-023-03723-2 [doi]
FAU - Zhao, Jiuli
AU  - Zhao J
AD  - Department of Medical Cosmetology, Chengdu Second People's Hospital, Chengdu, 
      China.
FAU - Du, Hong
AU  - Du H
AD  - Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union 
      Medical College, 33 Badachu Road, Shijingshan District, Beijing, 100144, China. 
      dh20111619@163.com.
LA  - eng
PT  - Letter
DEP - 20231106
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EDAT- 2023/11/07 00:42
MHDA- 2023/11/07 00:42
CRDT- 2023/11/06 23:43
PHST- 2023/10/12 00:00 [received]
PHST- 2023/10/14 00:00 [accepted]
PHST- 2023/11/07 00:42 [medline]
PHST- 2023/11/07 00:42 [pubmed]
PHST- 2023/11/06 23:43 [entrez]
AID - 10.1007/s00266-023-03723-2 [pii]
AID - 10.1007/s00266-023-03723-2 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Nov 6. doi: 10.1007/s00266-023-03723-2.

PMID- 36964734
OWN - NLM
STAT- MEDLINE
DCOM- 20230904
LR  - 20230905
IS  - 1933-0693 (Electronic)
IS  - 0022-3085 (Linking)
VI  - 139
IP  - 3
DP  - 2023 Mar 24
TI  - ChatGPT versus the neurosurgical written boards: a comparative analysis of 
      artificial intelligence/machine learning performance on neurosurgical board-style 
      questions.
PG  - 904-911
LID - 10.3171/2023.2.JNS23419 [doi]
FAU - Hopkins, Benjamin S
AU  - Hopkins BS
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Nguyen, Vincent N
AU  - Nguyen VN
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Dallas, Jonathan
AU  - Dallas J
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Texakalidis, Pavlos
AU  - Texakalidis P
AD  - 2Department of Neurological Surgery, Northwestern University's Feinberg School of 
      Medicine, Chicago, Illinois.
FAU - Yang, Max
AU  - Yang M
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Renn, Alex
AU  - Renn A
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Guerra, Gage
AU  - Guerra G
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Kashif, Zain
AU  - Kashif Z
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Cheok, Stephanie
AU  - Cheok S
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Zada, Gabriel
AU  - Zada G
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
FAU - Mack, William J
AU  - Mack WJ
AD  - 1Department of Neurological Surgery, University of Southern California, Los 
      Angeles, California; and.
LA  - eng
PT  - Journal Article
DEP - 20230324
PL  - United States
TA  - J Neurosurg
JT  - Journal of neurosurgery
JID - 0253357
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Machine Learning
EDAT- 2023/03/26 06:00
MHDA- 2023/09/04 06:42
CRDT- 2023/03/25 07:22
PHST- 2023/09/04 06:42 [medline]
PHST- 2023/03/26 06:00 [pubmed]
PHST- 2023/03/25 07:22 [entrez]
AID - 10.3171/2023.2.JNS23419 [doi]
PST - epublish
SO  - J Neurosurg. 2023 Mar 24;139(3):904-911. doi: 10.3171/2023.2.JNS23419.

PMID- 37983982
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 88
DP  - 2023 Nov 7
TI  - Assessing the plastic surgery knowledge of three natural language processor 
      artificial intelligence programs.
PG  - 193-195
LID - S1748-6815(23)00717-9 [pii]
LID - 10.1016/j.bjps.2023.10.141 [doi]
FAU - Shah, Pearl
AU  - Shah P
AD  - Medical College of Georgia at Augusta University, Augusta, GA, Unites States.
FAU - Bogdanovich, Brennan
AU  - Bogdanovich B
AD  - Medical College of Georgia at Augusta University, Augusta, GA, Unites States.
FAU - Patel, Parth A
AU  - Patel PA
AD  - Medical College of Georgia at Augusta University, Augusta, GA, Unites States.
FAU - Boyd, Carter J
AU  - Boyd CJ
AD  - Hansjörg Wyss Department of Plastic Surgery, NYU Langone Health, New York, NY, 
      United States. Electronic address: carterjosephboyd@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231107
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bard
OT  - Bing
OT  - ChatGPT
OT  - PSITE
OT  - Plastic surgery
COIS- Declaration of Competing Interest No authors have financial disclosures or 
      conflicts of interest to declare.
EDAT- 2023/11/20 18:44
MHDA- 2023/11/20 18:44
CRDT- 2023/11/20 17:59
PHST- 2023/10/27 00:00 [received]
PHST- 2023/10/27 00:00 [accepted]
PHST- 2023/11/20 18:44 [medline]
PHST- 2023/11/20 18:44 [pubmed]
PHST- 2023/11/20 17:59 [entrez]
AID - S1748-6815(23)00717-9 [pii]
AID - 10.1016/j.bjps.2023.10.141 [doi]
PST - aheadofprint
SO  - J Plast Reconstr Aesthet Surg. 2023 Nov 7;88:193-195. doi: 
      10.1016/j.bjps.2023.10.141.

PMID- 37916418
OWN - NLM
STAT- Publisher
LR  - 20231102
IS  - 1524-4563 (Electronic)
IS  - 0194-911X (Linking)
DP  - 2023 Nov 2
TI  - Relevance of ChatGPT's Responses to Common Hypertension-Related Patient 
      Inquiries.
LID - 10.1161/HYPERTENSIONAHA.123.22084 [doi]
FAU - Yano, Yuichiro
AU  - Yano Y
AUID- ORCID: 0000-0002-2565-7290
AD  - Noncommunicable Disease Epidemiology Research Center, Shiga University of Medical 
      Science, Japan (Y.Y.).
AD  - Department of Family Medicine and Community Health, Duke University, Durham, NC 
      (Y.Y.).
FAU - Nishiyama, Akira
AU  - Nishiyama A
AUID- ORCID: 0000-0001-5971-820X
AD  - Department of Pharmacology, Faculty of Medicine, Kagawa University, Japan (A.N.).
FAU - Suzuki, Yusuke
AU  - Suzuki Y
AUID- ORCID: 0000-0002-4077-9747
AD  - Department of Nephrology, Juntendo University Faculty of Medicine, Tokyo, Japan 
      (Y.S., T.G.).
FAU - Morimoto, Satoshi
AU  - Morimoto S
AUID- ORCID: 0000-0002-2292-2319
AD  - Department of Endocrinology and Hypertension, Tokyo Women's Medical University, 
      Japan (S.M.).
FAU - Morikawa, Takashi
AU  - Morikawa T
AUID- ORCID: 0000-0003-4445-9270
AD  - Department of Nephrology and Hypertension, Osaka City General Hospital, Japan 
      (T.M.).
FAU - Gohda, Tomohito
AU  - Gohda T
AUID- ORCID: 0000-0002-0513-4433
AD  - Department of Nephrology, Juntendo University Faculty of Medicine, Tokyo, Japan 
      (Y.S., T.G.).
FAU - Kanegae, Hiroshi
AU  - Kanegae H
AD  - Office of Research and Analysis, Genki Plaza Medical Center for Health Care, 
      Tokyo, Japan (H.K.).
FAU - Nakashima, Naoki
AU  - Nakashima N
AUID- ORCID: 0000-0003-4926-8772
AD  - Medical Information Center, Kyushu University Hospital, Fukuoka, Japan (N.N.).
LA  - eng
PT  - Journal Article
DEP - 20231102
PL  - United States
TA  - Hypertension
JT  - Hypertension (Dallas, Tex. : 1979)
JID - 7906255
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - delivery of health care
OT  - health facilities
OT  - humans
OT  - medicine
EDAT- 2023/11/02 06:42
MHDA- 2023/11/02 06:42
CRDT- 2023/11/02 05:32
PHST- 2023/11/02 06:42 [medline]
PHST- 2023/11/02 06:42 [pubmed]
PHST- 2023/11/02 05:32 [entrez]
AID - 10.1161/HYPERTENSIONAHA.123.22084 [doi]
PST - aheadofprint
SO  - Hypertension. 2023 Nov 2. doi: 10.1161/HYPERTENSIONAHA.123.22084.

PMID- 36856927
OWN - NLM
STAT- MEDLINE
DCOM- 20230512
LR  - 20230512
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 6
DP  - 2023 Jun
TI  - Potential Use of Chat GPT in Global Warming.
PG  - 1126-1127
LID - 10.1007/s10439-023-03171-8 [doi]
AB  - Climate change is a major global challenge that requires the integration of many 
      different scientific disciplines, including atmospheric science, oceanography, 
      and ecology. The complexity and scale of the problem require sophisticated tools 
      and techniques to understand, model, and project future climate conditions. 
      Artificial intelligence and natural language processing technologies, such as 
      ChatGPT, have the potential to play a critical role in advancing our 
      understanding of climate change and improving the accuracy of climate 
      projections. ChatGPT can be used in a variety of ways to aid climate research, 
      including in model parameterization, data analysis and interpretation, scenario 
      generation, and model evaluation. This technology provides researchers and 
      policy-makers with a powerful tool for generating and analyzing different climate 
      scenarios based on a wide range of data inputs, and for improving the accuracy of 
      climate projections. The author acknowledges asking chatGPT questions regarding 
      its uses for Climate Change Research. Some of the uses that it states are 
      possible now and some are potentials for the future. The author has analyzed and 
      edited the replies of chat GPT.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Biswas, Som S
AU  - Biswas SS
AUID- ORCID: 0000-0002-4038-5844
AD  - Le Bonheur Children's Hospital, The University of Tennessee Health Science 
      Center, Memphis, USA. ssbinmemphis@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230301
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Global Warming
MH  - *Artificial Intelligence
MH  - Climate Change
MH  - Forecasting
OTO - NOTNLM
OT  - AI
OT  - Chat GPT
OT  - Climate change research
EDAT- 2023/03/02 06:00
MHDA- 2023/05/12 07:06
CRDT- 2023/03/01 11:18
PHST- 2023/02/15 00:00 [received]
PHST- 2023/02/19 00:00 [accepted]
PHST- 2023/05/12 07:06 [medline]
PHST- 2023/03/02 06:00 [pubmed]
PHST- 2023/03/01 11:18 [entrez]
AID - 10.1007/s10439-023-03171-8 [pii]
AID - 10.1007/s10439-023-03171-8 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jun;51(6):1126-1127. doi: 10.1007/s10439-023-03171-8. Epub 
      2023 Mar 1.

PMID- 38056135
OWN - NLM
STAT- Publisher
LR  - 20231206
IS  - 1618-0631 (Electronic)
IS  - 0344-0338 (Linking)
VI  - 253
DP  - 2023 Nov 29
TI  - ChatGPT as an aid for pathological diagnosis of cancer.
PG  - 154989
LID - S0344-0338(23)00690-8 [pii]
LID - 10.1016/j.prp.2023.154989 [doi]
AB  - Diagnostic workup of cancer patients is highly reliant on the science of 
      pathology using cytopathology, histopathology, and other ancillary techniques 
      like immunohistochemistry and molecular cytogenetics. Data processing and 
      learning by means of artificial intelligence (AI) has become a spearhead for the 
      advancement of medicine, with pathology and laboratory medicine being no 
      exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was 
      recently launched by OpenAI, is currently a talk of the town, and its role in 
      cancer diagnosis is also being explored meticulously. Pathology workflow by 
      integration of digital slides, implementation of advanced algorithms, and 
      computer-aided diagnostic techniques extend the frontiers of the pathologist's 
      view beyond a microscopic slide and enables effective integration, assimilation, 
      and utilization of knowledge that is beyond human limits and boundaries. Despite 
      of it's numerous advantages in the pathological diagnosis of cancer, it comes 
      with several challenges like integration of digital slides with input language 
      parameters, problems of bias, and legal issues which have to be addressed and 
      worked up soon so that we as a pathologists diagnosing malignancies are on the 
      same band wagon and don't miss the train.
CI  - Copyright © 2023 Elsevier GmbH. All rights reserved.
FAU - Malik, Shaivy
AU  - Malik S
AD  - Department of Pathology, Vardhman Mahavir Medical College and Safdarjung 
      Hospital, New Delhi, India.
FAU - Zaheer, Sufian
AU  - Zaheer S
AD  - Department of Pathology, Vardhman Mahavir Medical College and Safdarjung 
      Hospital, New Delhi, India. Electronic address: sufianzaheer@gmail.com.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231129
PL  - Germany
TA  - Pathol Res Pract
JT  - Pathology, research and practice
JID - 7806109
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - Cancer diagnosis
OT  - ChatGPT
OT  - Image analysis and integration
OT  - Pathology
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/12/07 00:42
MHDA- 2023/12/07 00:42
CRDT- 2023/12/06 18:07
PHST- 2023/08/23 00:00 [received]
PHST- 2023/11/26 00:00 [revised]
PHST- 2023/11/27 00:00 [accepted]
PHST- 2023/12/07 00:42 [medline]
PHST- 2023/12/07 00:42 [pubmed]
PHST- 2023/12/06 18:07 [entrez]
AID - S0344-0338(23)00690-8 [pii]
AID - 10.1016/j.prp.2023.154989 [doi]
PST - aheadofprint
SO  - Pathol Res Pract. 2023 Nov 29;253:154989. doi: 10.1016/j.prp.2023.154989.

PMID- 37503019
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
DP  - 2023 Jul 14
TI  - Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating 
      Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets.
LID - rs.3.rs-3125859 [pii]
LID - 10.21203/rs.3.rs-3125859/v1 [doi]
AB  - Drug repurposing represents an attractive alternative to the costly and 
      time-consuming process of new drug development, particularly for serious, 
      widespread conditions with limited effective treatments, such as Alzheimer's 
      disease (AD). Emerging generative artificial intelligence (GAI) technologies like 
      ChatGPT offer the promise of expediting the review and summary of scientific 
      knowledge. To examine the feasibility of using GAI for identifying drug 
      repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty 
      most promising drugs for repurposing in AD, and tested the top ten for risk of 
      incident AD in exposed and unexposed individuals over age 65 in two large 
      clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us 
      Research Program. Among the candidates suggested by ChatGPT, metformin, 
      simvastatin, and losartan were associated with lower AD risk in meta-analysis. 
      These findings suggest GAI technologies can assimilate scientific insights from 
      an extensive Internet-based search space, helping to prioritize drug repurposing 
      candidates and facilitate the treatment of diseases.
FAU - Wei, Wei-Qi
AU  - Wei WQ
AD  - Vanderbilt University.
FAU - Yan, Chao
AU  - Yan C
AUID- ORCID: 0000-0002-6719-1388
AD  - Vanderbilt University Medical Center.
FAU - Grabowska, Monika
AU  - Grabowska M
AUID- ORCID: 0000-0003-0708-676X
AD  - Vanderbilt University Medical Center.
FAU - Dickson, Alyson
AU  - Dickson A
AD  - Vanderbilt University Medical Center.
FAU - Li, Bingshan
AU  - Li B
AD  - Vanderbilt University Medical Center.
FAU - Wen, Zhexing
AU  - Wen Z
AD  - Emory University School of Medicine.
FAU - Roden, Dan
AU  - Roden D
AD  - Vanderbilt University Medical Center.
FAU - Stein, C
AU  - Stein C
AD  - Vanderbilt University Medical Center.
FAU - Embí, Peter
AU  - Embí P
AD  - Vanderbilt University Medical Center.
FAU - Peterson, Josh
AU  - Peterson J
AD  - Vanderbilt University Medical Center.
FAU - Feng, QiPing
AU  - Feng Q
AD  - Vanderbilt University Medical Center.
FAU - Malin, Bradley
AU  - Malin B
AD  - Vanderbilt University Medical Center.
LA  - eng
GR  - R01 HL163854/HL/NHLBI NIH HHS/United States
GR  - R01 AG069900/AG/NIA NIH HHS/United States
GR  - R01 GM139891/GM/NIGMS NIH HHS/United States
GR  - F30 AG080885/AG/NIA NIH HHS/United States
GR  - P50 GM115305/GM/NIGMS NIH HHS/United States
PT  - Preprint
DEP - 20230714
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10371084
COIS- Competing Interests All authors have no competing interests to declare.
EDAT- 2023/07/28 06:42
MHDA- 2023/07/28 06:43
CRDT- 2023/07/28 04:25
PHST- 2023/07/28 06:42 [pubmed]
PHST- 2023/07/28 06:43 [medline]
PHST- 2023/07/28 04:25 [entrez]
AID - rs.3.rs-3125859 [pii]
AID - 10.21203/rs.3.rs-3125859/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 Jul 14:rs.3.rs-3125859. doi: 10.21203/rs.3.rs-3125859/v1. Preprint.

PMID- 37732693
OWN - NLM
STAT- MEDLINE
DCOM- 20231027
LR  - 20231207
IS  - 1528-1132 (Electronic)
IS  - 0009-921X (Print)
IS  - 0009-921X (Linking)
VI  - 481
IP  - 11
DP  - 2023 Nov 1
TI  - Letter to the Editor: CORR Insights®: Can Artificial Intelligence Pass the 
      American Board of Orthopaedic Surgery Examination? Orthopaedic Residents Versus 
      ChatGPT.
PG  - 2299
LID - 10.1097/CORR.0000000000002869 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Cambodia.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Adjunct Professor, Chandigarh University, Punjab, India.
AD  - Adjunct Professor, Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230918
PL  - United States
TA  - Clin Orthop Relat Res
JT  - Clinical orthopaedics and related research
JID - 0075674
SB  - IM
CON - Clin Orthop Relat Res. 2023 Aug 1;481(8):1623-1630. PMID: 37220190
MH  - Humans
MH  - United States
MH  - *Orthopedics
MH  - Artificial Intelligence
MH  - *Orthopedic Procedures
MH  - Specialty Boards
PMC - PMC10567041
COIS- Each author certifies that there are no funding or commercial associations 
      (consultancies, stock ownership, equity interest, patent/licensing arrangements, 
      etc.) that might pose a conflict of interest in connection with the submitted 
      article related to the author or any immediate family members. All ICMJE Conflict 
      of Interest Forms for authors and Clinical Orthopaedics and Related Research® 
      editors and board members are on file with the publication and can be viewed on 
      request.
EDAT- 2023/09/21 12:44
MHDA- 2023/10/27 06:43
PMCR- 2024/11/01
CRDT- 2023/09/21 08:23
PHST- 2023/06/19 00:00 [received]
PHST- 2023/08/24 00:00 [accepted]
PHST- 2024/11/01 00:00 [pmc-release]
PHST- 2023/10/27 06:43 [medline]
PHST- 2023/09/21 12:44 [pubmed]
PHST- 2023/09/21 08:23 [entrez]
AID - 00003086-202311000-00037 [pii]
AID - CORR-D-23-00724 [pii]
AID - 10.1097/CORR.0000000000002869 [doi]
PST - ppublish
SO  - Clin Orthop Relat Res. 2023 Nov 1;481(11):2299. doi: 
      10.1097/CORR.0000000000002869. Epub 2023 Sep 18.

PMID- 37430379
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2058-8615 (Electronic)
IS  - 2058-8615 (Linking)
VI  - 8
IP  - 1
DP  - 2023 Jul 10
TI  - Publisher Correction: Fighting reviewer fatigue or amplifying bias? 
      Considerations and recommendations for use of ChatGPT and other large language 
      models in scholarly peer review.
PG  - 7
LID - 10.1186/s41073-023-00136-2 [doi]
LID - 7
FAU - Hosseini, Mohammad
AU  - Hosseini M
AD  - Feinberg School of Medicine, Northwestern University, 420 E. Superior Street, 
      Chicago, IL, 60611, USA. mohammad.hosseini@northwestern.edu.
FAU - Horbach, Serge P J M
AU  - Horbach SPJM
AD  - Danish Centre for Studies in Research and Research Policy, Aarhus University, 
      Bartholins Alle 7, 8000, Aarhus C, Aarhus, Denmark.
LA  - eng
PT  - Published Erratum
DEP - 20230710
PL  - England
TA  - Res Integr Peer Rev
JT  - Research integrity and peer review
JID - 101676020
EFR - Res Integr Peer Rev. 2023 May 18;8(1):4. PMID: 37198671
PMC - PMC10334596
EDAT- 2023/07/11 01:07
MHDA- 2023/07/11 01:08
CRDT- 2023/07/10 23:46
PHST- 2023/07/11 01:08 [medline]
PHST- 2023/07/11 01:07 [pubmed]
PHST- 2023/07/10 23:46 [entrez]
AID - 10.1186/s41073-023-00136-2 [pii]
AID - 136 [pii]
AID - 10.1186/s41073-023-00136-2 [doi]
PST - epublish
SO  - Res Integr Peer Rev. 2023 Jul 10;8(1):7. doi: 10.1186/s41073-023-00136-2.

PMID- 36718045
OWN - NLM
STAT- MEDLINE
DCOM- 20230201
LR  - 20230309
IS  - 1975-5937 (Electronic)
IS  - 1975-5937 (Linking)
VI  - 20
DP  - 2023
TI  - Issues in the 3rd year of the COVID-19 pandemic, including computer-based 
      testing, study design, ChatGPT, journal metrics, and appreciation to reviewers.
PG  - 5
LID - 10.3352/jeehp.2023.20.5 [doi]
LID - 5
FAU - Huh, Sun
AU  - Huh S
AD  - Department of Parasitology and Institute of Medical Education, College of 
      Medicine, Hallym University, Chuncheon, Korea.
LA  - eng
PT  - Editorial
DEP - 20230131
PL  - Korea (South)
TA  - J Educ Eval Health Prof
JT  - Journal of educational evaluation for health professions
JID - 101490061
SB  - IM
MH  - Humans
MH  - *COVID-19
MH  - Pandemics
MH  - Benchmarking
MH  - SARS-CoV-2
MH  - Peer Review, Research
PMC - PMC9986465
COIS- Conflict of interest Sun Huh has been the editor of the Journal of Educational 
      Evaluation for Health Professions since 2005. He was not involved in the review 
      process. Otherwise, no potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/02/01 06:00
MHDA- 2023/02/02 06:00
CRDT- 2023/01/31 00:50
PHST- 2023/01/23 00:00 [received]
PHST- 2023/01/31 00:00 [accepted]
PHST- 2023/01/31 00:50 [entrez]
PHST- 2023/02/01 06:00 [pubmed]
PHST- 2023/02/02 06:00 [medline]
AID - jeehp.2023.20.5 [pii]
AID - jeehp-20-05 [pii]
AID - 10.3352/jeehp.2023.20.5 [doi]
PST - ppublish
SO  - J Educ Eval Health Prof. 2023;20:5. doi: 10.3352/jeehp.2023.20.5. Epub 2023 Jan 
      31.

PMID- 37379882
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231018
IS  - 2468-6530 (Electronic)
IS  - 2468-6530 (Linking)
VI  - 7
IP  - 10
DP  - 2023 Oct
TI  - Re: Momenaei et al.: Appropriateness and readability of ChatGPT-4 generated 
      responses for surgical treatment of retinal diseases (Ophthalmol Retina. 
      2023;7:862-868.).
PG  - e15
LID - S2468-6530(23)00282-8 [pii]
LID - 10.1016/j.oret.2023.06.014 [doi]
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AD  - Private Academic Consultant, Samraong, Oddar Meanchey, Cambodia. Electronic 
      address: amnuaykleebai@gmail.com.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AD  - Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230626
PL  - United States
TA  - Ophthalmol Retina
JT  - Ophthalmology. Retina
JID - 101695048
SB  - IM
CON - Ophthalmol Retina. 2023 Oct;7(10):862-868. PMID: 37277096
MH  - Humans
MH  - *Comprehension
MH  - Retina/surgery
MH  - *Retinal Diseases/surgery
EDAT- 2023/06/29 01:08
MHDA- 2023/10/09 06:42
CRDT- 2023/06/28 19:12
PHST- 2023/06/06 00:00 [received]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/06/29 01:08 [pubmed]
PHST- 2023/06/28 19:12 [entrez]
AID - S2468-6530(23)00282-8 [pii]
AID - 10.1016/j.oret.2023.06.014 [doi]
PST - ppublish
SO  - Ophthalmol Retina. 2023 Oct;7(10):e15. doi: 10.1016/j.oret.2023.06.014. Epub 2023 
      Jun 26.

PMID- 37236498
OWN - NLM
STAT- MEDLINE
DCOM- 20230717
LR  - 20231121
IS  - 1873-6246 (Electronic)
IS  - 0301-0511 (Linking)
VI  - 181
DP  - 2023 Jul
TI  - Intelligence or artificial intelligence? More hard problems for authors of 
      Biological Psychology, the neurosciences, and everyone else.
PG  - 108590
LID - S0301-0511(23)00107-2 [pii]
LID - 10.1016/j.biopsycho.2023.108590 [doi]
FAU - Ritz, Thomas
AU  - Ritz T
AD  - Department of Psychology, Southern Methodist University, Dallas, TX, USA. 
      Electronic address: tritz@smu.edu.
LA  - eng
PT  - Editorial
DEP - 20230524
PL  - Netherlands
TA  - Biol Psychol
JT  - Biological psychology
JID - 0375566
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Intelligence
MH  - *Neurosciences
OTO - NOTNLM
OT  - Academic malpractice
OT  - Artificial text production
OT  - Authorship
OT  - ChatGPT
OT  - Large language models
COIS- Declaration of Competing Interest The author has no conflicts of interest to 
      declare.
EDAT- 2023/05/27 09:41
MHDA- 2023/07/17 06:42
CRDT- 2023/05/26 19:27
PHST- 2023/05/18 00:00 [received]
PHST- 2023/05/22 00:00 [accepted]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/05/27 09:41 [pubmed]
PHST- 2023/05/26 19:27 [entrez]
AID - S0301-0511(23)00107-2 [pii]
AID - 10.1016/j.biopsycho.2023.108590 [doi]
PST - ppublish
SO  - Biol Psychol. 2023 Jul;181:108590. doi: 10.1016/j.biopsycho.2023.108590. Epub 
      2023 May 24.

PMID- 36821477
OWN - NLM
STAT- MEDLINE
DCOM- 20230308
LR  - 20230323
IS  - 1520-5851 (Electronic)
IS  - 0013-936X (Linking)
VI  - 57
IP  - 9
DP  - 2023 Mar 7
TI  - Risks and Benefits of Large Language Models for the Environment.
PG  - 3464-3466
LID - 10.1021/acs.est.3c01106 [doi]
FAU - Rillig, Matthias C
AU  - Rillig MC
AUID- ORCID: 0000-0003-3541-7853
AD  - Freie Universität Berlin, Institute for Biology, 14195 Berlin, Germany.
AD  - Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 
      Berlin, Germany.
FAU - Ågerstrand, Marlene
AU  - Ågerstrand M
AD  - Department of Environmental Science, Stockholm University, SE-106 91 Stockholm, 
      Sweden.
FAU - Bi, Mohan
AU  - Bi M
AD  - Freie Universität Berlin, Institute for Biology, 14195 Berlin, Germany.
AD  - Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 
      Berlin, Germany.
FAU - Gould, Kenneth A
AU  - Gould KA
AD  - Department of Sociology, Brooklyn College, City University of New York, 2900 
      Bedford Avenue, Brooklyn, New York 11210, United States.
FAU - Sauerland, Uli
AU  - Sauerland U
AD  - Leibniz-Centre General Linguistics (ZAS), Schützenstrasse 18, 10117 Berlin, 
      Germany.
LA  - eng
PT  - Journal Article
DEP - 20230223
PL  - United States
TA  - Environ Sci Technol
JT  - Environmental science & technology
JID - 0213155
SB  - IM
MH  - *Language
MH  - Risk Assessment
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - digital divide
OT  - environmental education
OT  - environmental impact
OT  - environmental literacy
OT  - large language models
EDAT- 2023/02/24 06:00
MHDA- 2023/03/09 06:00
CRDT- 2023/02/23 12:47
PHST- 2023/02/24 06:00 [pubmed]
PHST- 2023/03/09 06:00 [medline]
PHST- 2023/02/23 12:47 [entrez]
AID - 10.1021/acs.est.3c01106 [doi]
PST - ppublish
SO  - Environ Sci Technol. 2023 Mar 7;57(9):3464-3466. doi: 10.1021/acs.est.3c01106. 
      Epub 2023 Feb 23.

PMID- 37285160
OWN - NLM
STAT- MEDLINE
DCOM- 20230609
LR  - 20230611
IS  - 2574-3805 (Electronic)
IS  - 2574-3805 (Linking)
VI  - 6
IP  - 6
DP  - 2023 Jun 1
TI  - Evaluating Artificial Intelligence Responses to Public Health Questions.
PG  - e2317517
LID - 10.1001/jamanetworkopen.2023.17517 [doi]
LID - e2317517
FAU - Ayers, John W
AU  - Ayers JW
AD  - Qualcomm Institute, University of California San Diego, La Jolla.
AD  - Division of Infectious Diseases and Global Public Health, Department of Medicine, 
      University of California San Diego, La Jolla.
FAU - Zhu, Zechariah
AU  - Zhu Z
AD  - Qualcomm Institute, University of California San Diego, La Jolla.
FAU - Poliak, Adam
AU  - Poliak A
AD  - Department of Computer Science, Bryn Mawr College, Bryn Mawr, Pennsylvania.
FAU - Leas, Eric C
AU  - Leas EC
AD  - Qualcomm Institute, University of California San Diego, La Jolla.
AD  - Herbert Wertheim School of Public Health and Human Longevity Science, University 
      of California San Diego, La Jolla.
FAU - Dredze, Mark
AU  - Dredze M
AD  - Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
FAU - Hogarth, Michael
AU  - Hogarth M
AD  - Department of Biomedical Informatics, University of California San Diego, La 
      Jolla.
AD  - Altman Clinical Translational Research Institute, University of California San 
      Diego, La Jolla.
FAU - Smith, Davey M
AU  - Smith DM
AD  - Division of Infectious Diseases and Global Public Health, Department of Medicine, 
      University of California San Diego, La Jolla.
AD  - Altman Clinical Translational Research Institute, University of California San 
      Diego, La Jolla.
LA  - eng
GR  - K01 DA054303/DA/NIDA NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20230601
PL  - United States
TA  - JAMA Netw Open
JT  - JAMA network open
JID - 101729235
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Public Health
PMC - PMC10248742
OAB - This cross-sectional study analyzes the quality of ChatGPT responses to public 
      health questions.
OABL- eng
COIS- Conflict of Interest Disclosures: Dr Ayers reported owning equity in Health 
      Watcher and Good Analytics outside the submitted work. Dr Leas reported receiving 
      consulting fees from Good Analytics outside the submitted work. Dr Dredze 
      reported receiving personal fees from Bloomberg LP and Good Analytics outside the 
      submitted work. Dr Hogarth reported being an advisor to and owning equity in 
      LifeLink. Dr Smith reported receiving grants from the National Institutes of 
      Health; receiving consulting fees from Pharma Holdings, Bayer Pharmaceuticals, 
      Evidera, Linear Therapies, and Vx Biosciences; and owning stock options in Model 
      Medicines and FluxErgy outside the submitted work. No other disclosures were 
      reported.
EDAT- 2023/06/07 13:10
MHDA- 2023/06/09 06:42
CRDT- 2023/06/07 11:34
PHST- 2023/06/09 06:42 [medline]
PHST- 2023/06/07 13:10 [pubmed]
PHST- 2023/06/07 11:34 [entrez]
AID - 2805756 [pii]
AID - zld230091 [pii]
AID - 10.1001/jamanetworkopen.2023.17517 [doi]
PST - epublish
SO  - JAMA Netw Open. 2023 Jun 1;6(6):e2317517. doi: 
      10.1001/jamanetworkopen.2023.17517.

PMID- 37384388
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230726
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Jun 29
TI  - Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: 
      Comparison Study.
PG  - e48002
LID - 10.2196/48002 [doi]
LID - e48002
AB  - BACKGROUND: The competence of ChatGPT (Chat Generative Pre-Trained Transformer) 
      in non-English languages is not well studied. OBJECTIVE: This study compared the 
      performances of GPT-3.5 (Generative Pre-trained Transformer) and GPT-4 on the 
      Japanese Medical Licensing Examination (JMLE) to evaluate the reliability of 
      these models for clinical reasoning and medical knowledge in non-English 
      languages. METHODS: This study used the default mode of ChatGPT, which is based 
      on GPT-3.5; the GPT-4 model of ChatGPT Plus; and the 117th JMLE in 2023. A total 
      of 254 questions were included in the final analysis, which were categorized into 
      3 types, namely general, clinical, and clinical sentence questions. RESULTS: The 
      results indicated that GPT-4 outperformed GPT-3.5 in terms of accuracy, 
      particularly for general, clinical, and clinical sentence questions. GPT-4 also 
      performed better on difficult questions and specific disease questions. 
      Furthermore, GPT-4 achieved the passing criteria for the JMLE, indicating its 
      reliability for clinical reasoning and medical knowledge in non-English 
      languages. CONCLUSIONS: GPT-4 could become a valuable tool for medical education 
      and clinical support in non-English-speaking regions, such as Japan.
CI  - ©Soshi Takagi, Takashi Watari, Ayano Erabi, Kota Sakaguchi. Originally published 
      in JMIR Medical Education (https://mededu.jmir.org), 29.06.2023.
FAU - Takagi, Soshi
AU  - Takagi S
AUID- ORCID: 0009-0004-3211-1626
AD  - Faculty of Medicine, Shimane University, Izumo, Japan.
FAU - Watari, Takashi
AU  - Watari T
AUID- ORCID: 0000-0002-9322-8455
AD  - Faculty of Medicine, Shimane University, Izumo, Japan.
AD  - General Medicine Center, Shimane University Hospital, Izumo, Japan.
AD  - Department of Internal Medicine, University of Michigan Medical School, Ann 
      Arbor, MI, United States.
AD  - Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States.
FAU - Erabi, Ayano
AU  - Erabi A
AUID- ORCID: 0009-0003-1871-3543
AD  - Faculty of Medicine, Shimane University, Izumo, Japan.
FAU - Sakaguchi, Kota
AU  - Sakaguchi K
AUID- ORCID: 0000-0002-5169-6613
AD  - General Medicine Center, Shimane University Hospital, Izumo, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230629
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10365615
OTO - NOTNLM
OT  - AI
OT  - Chat Generative Pre-trained Transformer
OT  - ChatGPT
OT  - GPT-4
OT  - Generative Pre-trained Transformer 4
OT  - Japanese Medical Licensing Examination
OT  - artificial intelligence
OT  - clinical support
OT  - learning model
OT  - medical education
OT  - medical licensing
COIS- Conflicts of Interest: None declared.
EDAT- 2023/06/29 19:12
MHDA- 2023/06/29 19:13
CRDT- 2023/06/29 12:07
PHST- 2023/04/07 00:00 [received]
PHST- 2023/06/14 00:00 [accepted]
PHST- 2023/05/11 00:00 [revised]
PHST- 2023/06/29 19:13 [medline]
PHST- 2023/06/29 19:12 [pubmed]
PHST- 2023/06/29 12:07 [entrez]
AID - v9i1e48002 [pii]
AID - 10.2196/48002 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Jun 29;9:e48002. doi: 10.2196/48002.

PMID- 38052322
OWN - NLM
STAT- Publisher
LR  - 20231210
IS  - 2468-7855 (Electronic)
IS  - 2468-7855 (Linking)
VI  - 125
IP  - 3
DP  - 2023 Dec 3
TI  - Can natural language processing serve as a consultant in oral surgery?
PG  - 101724
LID - S2468-7855(23)00345-2 [pii]
LID - 10.1016/j.jormas.2023.101724 [doi]
AB  - OBJECTIVE: In this comprehensive evaluation, ten experienced oral surgeon experts 
      posed a total of twenty oral surgery-related questions, including dental implant 
      and tooth extractions, to three distinct Natural Language Processing (NLP)-based 
      chatbot platforms: ChatGPT, Microsoft Bing, and Google Bard. The study aimed to 
      assess the effectiveness of these chatbots in responding to specialized medical 
      questions. MATERIALS AND METHODS: Two primary evaluation metrics were employed: a 
      Likert Scale (LS) for measuring the accuracy and completeness of responses and a 
      Global Quality Scale (GQS) for evaluating the clarity of responses. Statistical 
      analyses, including one-way analysis of variance (ANOVA) and Post Hoc Tukey, were 
      conducted to assess and compare the performance of the chatbots as rated by the 
      experts. RESULTS: The results of the study revealed significant differences in 
      the performance of the chatbots. ChatGPT statistically achieved a better mean LS 
      score of 1.4000±0.15986 than Microsoft Bing (1.8750±0.18143) and Google Bards 
      (2.0500±0.12472) (P < 0.001). Additionally, ChatGPT statistically achieved a 
      higher GQS score of 4.4200±0.30111 than Microsoft Bing (3.7550±0.28621) and 
      Google Bards (3.5250±0.22392) (P < 0.001). CONCLUSIONS: These findings showed the 
      substantial advantage of ChatGPT in effectively addressing oral surgery-related 
      questions with superior accuracy, completeness, and clarity. The study highlights 
      the potential of advanced NLP platforms to enhance information retrieval and 
      communication within the field of oral surgery, reinforcing the utility of such 
      technologies in medical and surgical domains.
CI  - Copyright © 2023 Elsevier Masson SAS. All rights reserved.
FAU - Acar, Ahmet Hüseyin
AU  - Acar AH
AD  - Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Istanbul 
      Medeniyet University, Istanbul, Turkey. Electronic address: 
      ahmethuseyin.acar@medeniyet.edu.tr.
LA  - eng
PT  - Journal Article
DEP - 20231203
PL  - France
TA  - J Stomatol Oral Maxillofac Surg
JT  - Journal of stomatology, oral and maxillofacial surgery
JID - 101701089
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Chatbot
OT  - Oral surgery
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/12/06 03:43
MHDA- 2023/12/06 03:43
CRDT- 2023/12/05 19:20
PHST- 2023/11/07 00:00 [received]
PHST- 2023/11/26 00:00 [revised]
PHST- 2023/12/01 00:00 [accepted]
PHST- 2023/12/06 03:43 [pubmed]
PHST- 2023/12/06 03:43 [medline]
PHST- 2023/12/05 19:20 [entrez]
AID - S2468-7855(23)00345-2 [pii]
AID - 10.1016/j.jormas.2023.101724 [doi]
PST - aheadofprint
SO  - J Stomatol Oral Maxillofac Surg. 2023 Dec 3;125(3):101724. doi: 
      10.1016/j.jormas.2023.101724.

PMID- 37747564
OWN - NLM
STAT- Publisher
LR  - 20230925
IS  - 1437-160X (Electronic)
IS  - 0172-8172 (Linking)
DP  - 2023 Sep 25
TI  - Assessing the accuracy and completeness of artificial intelligence language 
      models in providing information on methotrexate use.
LID - 10.1007/s00296-023-05473-5 [doi]
AB  - We aimed to assess Large Language Models (LLMs)-ChatGPT 3.5-4, BARD, and Bing-in 
      their accuracy and completeness when answering Methotrexate (MTX) related 
      questions for treating rheumatoid arthritis. We employed 23 questions from an 
      earlier study related to MTX concerns. These questions were entered into the 
      LLMs, and the responses generated by each model were evaluated by two reviewers 
      using Likert scales to assess accuracy and completeness. The GPT models achieved 
      a 100% correct answer rate, while BARD and Bing scored 73.91%. In terms of 
      accuracy of the outputs (completely correct responses), GPT-4 achieved a score of 
      100%, GPT 3.5 secured 86.96%, and BARD and Bing each scored 60.87%. BARD produced 
      17.39% incorrect responses and 8.7% non-responses, while Bing recorded 13.04% 
      incorrect and 13.04% non-responses. The ChatGPT models produced significantly 
      more accurate responses than Bing for the "mechanism of action" category, and 
      GPT-4 model showed significantly higher accuracy than BARD in the "side effects" 
      category. There were no statistically significant differences among the models 
      for the "lifestyle" category. GPT-4 achieved a comprehensive output of 100%, 
      followed by GPT-3.5 at 86.96%, BARD at 60.86%, and Bing at 0%. In the "mechanism 
      of action" category, both ChatGPT models and BARD produced significantly more 
      comprehensive outputs than Bing. For the "side effects" and "lifestyle" 
      categories, the ChatGPT models showed significantly higher completeness than 
      Bing. The GPT models, particularly GPT 4, demonstrated superior performance in 
      providing accurate and comprehensive patient information about MTX use. However, 
      the study also identified inaccuracies and shortcomings in the generated 
      responses.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Coskun, Belkis Nihan
AU  - Coskun BN
AUID- ORCID: 0000-0003-0298-4157
AD  - Division of Rheumatology, Department of Internal Medicine, Faculty of Medicine, 
      Bursa Uludag University, Bursa, Turkey. belkisnihanseniz@hotmail.com.
FAU - Yagiz, Burcu
AU  - Yagiz B
AUID- ORCID: 0000-0002-0624-1986
AD  - Division of Rheumatology, Department of Internal Medicine, Faculty of Medicine, 
      Bursa Uludag University, Bursa, Turkey.
FAU - Ocakoglu, Gokhan
AU  - Ocakoglu G
AUID- ORCID: 0000-0002-1114-6051
AD  - Department of Biostatistics, Faculty of Medicine, Bursa Uludag University, Bursa, 
      Turkey.
FAU - Dalkilic, Ediz
AU  - Dalkilic E
AUID- ORCID: 0000-0001-8645-2670
AD  - Division of Rheumatology, Department of Internal Medicine, Faculty of Medicine, 
      Bursa Uludag University, Bursa, Turkey.
FAU - Pehlivan, Yavuz
AU  - Pehlivan Y
AUID- ORCID: 0000-0002-7054-5351
AD  - Division of Rheumatology, Department of Internal Medicine, Faculty of Medicine, 
      Bursa Uludag University, Bursa, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230925
PL  - Germany
TA  - Rheumatol Int
JT  - Rheumatology international
JID - 8206885
SB  - IM
OTO - NOTNLM
OT  - Accuracy
OT  - Artificial intelligence
OT  - Completeness
OT  - Large language models
OT  - Methotrexate
EDAT- 2023/09/25 12:42
MHDA- 2023/09/25 12:42
CRDT- 2023/09/25 11:09
PHST- 2023/08/21 00:00 [received]
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/09/25 12:42 [medline]
PHST- 2023/09/25 12:42 [pubmed]
PHST- 2023/09/25 11:09 [entrez]
AID - 10.1007/s00296-023-05473-5 [pii]
AID - 10.1007/s00296-023-05473-5 [doi]
PST - aheadofprint
SO  - Rheumatol Int. 2023 Sep 25. doi: 10.1007/s00296-023-05473-5.

PMID- 37556434
OWN - NLM
STAT- MEDLINE
DCOM- 20230811
LR  - 20230811
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 18
IP  - 8
DP  - 2023
TI  - Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through 
      Japanese stylometric analysis.
PG  - e0288453
LID - 10.1371/journal.pone.0288453 [doi]
LID - e0288453
AB  - In the first half of 2023, text-generative artificial intelligence (AI), 
      including ChatGPT from OpenAI, has attracted considerable attention worldwide. In 
      this study, first, we compared Japanese stylometric features of texts generated 
      by ChatGPT, equipped with GPT-3.5 and GPT-4, and those written by humans. In this 
      work, we performed multi-dimensional scaling (MDS) to confirm the distributions 
      of 216 texts of three classes (72 academic papers written by 36 single authors, 
      72 texts generated by GPT-3.5, and 72 texts generated by GPT-4 on the basis of 
      the titles of the aforementioned papers) focusing on the following stylometric 
      features: (1) bigrams of parts-of-speech, (2) bigram of postpositional particle 
      words, (3) positioning of commas, and (4) rate of function words. MDS revealed 
      distinct distributions at each stylometric feature of GPT (3.5 and 4) and human. 
      Although GPT-4 is more powerful than GPT-3.5 because it has more parameters, both 
      GPT (3.5 and 4) distributions are overlapping. These results indicate that 
      although the number of parameters may increase in the future, GPT-generated texts 
      may not be close to that written by humans in terms of stylometric features. 
      Second, we verified the classification performance of random forest (RF) 
      classifier for two classes (GPT and human) focusing on Japanese stylometric 
      features. This study revealed the high performance of RF in each stylometric 
      feature: The RF classifier focusing on the rate of function words achieved 98.1% 
      accuracy. Furthermore the RF classifier focusing on all stylometric features 
      reached 100% in terms of all performance indexes (accuracy, recall, precision, 
      and F1 score). This study concluded that at this stage we human discriminate 
      ChatGPT from human limited to Japanese language.
CI  - Copyright: © 2023 Zaitsu, Jin. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Zaitsu, Wataru
AU  - Zaitsu W
AUID- ORCID: 0000-0003-1017-7283
AD  - Department of Psychological Counselling, Faculty of Psychology, Mejiro 
      University, Tokyo, Japan.
FAU - Jin, Mingzhe
AU  - Jin M
AD  - Institute of Interdisciplinary Research, Kyoto University of Advanced Science, 
      Kyoto, Japan.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230809
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Random Forest
MH  - *Writing
PMC - PMC10411719
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/09 18:42
MHDA- 2023/08/11 06:43
CRDT- 2023/08/09 13:33
PHST- 2023/04/12 00:00 [received]
PHST- 2023/06/27 00:00 [accepted]
PHST- 2023/08/11 06:43 [medline]
PHST- 2023/08/09 18:42 [pubmed]
PHST- 2023/08/09 13:33 [entrez]
AID - PONE-D-23-10817 [pii]
AID - 10.1371/journal.pone.0288453 [doi]
PST - epublish
SO  - PLoS One. 2023 Aug 9;18(8):e0288453. doi: 10.1371/journal.pone.0288453. 
      eCollection 2023.

PMID- 37489981
OWN - NLM
STAT- MEDLINE
DCOM- 20230726
LR  - 20230726
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 308
IP  - 1
DP  - 2023 Jul
TI  - A Context-based Chatbot Surpasses Trained Radiologists and Generic ChatGPT in 
      Following the ACR Appropriateness Guidelines.
PG  - e230970
LID - 10.1148/radiol.230970 [doi]
AB  - Background Radiological imaging guidelines are crucial for accurate diagnosis and 
      optimal patient care as they result in standardized decisions and thus reduce 
      inappropriate imaging studies. Purpose In the present study, we investigated the 
      potential to support clinical decision-making using an interactive chatbot 
      designed to provide personalized imaging recommendations from American College of 
      Radiology (ACR) appropriateness criteria documents using semantic similarity 
      processing. Methods We utilized 209 ACR appropriateness criteria documents as 
      specialized knowledge base and employed LlamaIndex, a framework that allows to 
      connect large language models with external data, and the ChatGPT 3.5-Turbo to 
      create an appropriateness criteria contexted chatbot (accGPT). Fifty clinical 
      case files were used to compare the accGPT's performance against general 
      radiologists at varying experience levels and to generic ChatGPT 3.5 and 4.0. 
      Results All chatbots reached at least human performance level. For the 50 case 
      files, the accGPT performed best in providing correct recommendations that were 
      "usually appropriate" according to the ACR criteria and also did provide the 
      highest proportion of consistently correct answers in comparison with generic 
      chatbots and radiologists. Further, the chatbots provided substantial time and 
      cost savings, with an average decision time of 5 minutes and a cost of 0.19 € for 
      all cases, compared to 50 minutes and 29.99 € for radiologists (both p < 0.01). 
      Conclusion ChatGPT-based algorithms have the potential to substantially improve 
      the decision-making for clinical imaging studies in accordance with ACR 
      guidelines. Specifically, a context-based algorithm performed superior to its 
      generic counterpart, demonstrating the value of tailoring AI solutions to 
      specific healthcare applications.
FAU - Rau, Alexander
AU  - Rau A
AUID- ORCID: 0000-0001-5881-6043
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
AD  - Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of 
      Medicine, University of Freiburg, 79106 Freiburg, Germany.
FAU - Rau, Stephan
AU  - Rau S
AUID- ORCID: 0000-0002-8992-2401
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Zoeller, Daniela
AU  - Zoeller D
AD  - Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical 
      Center - University of Freiburg, Germany.
AD  - Freiburg Center for Data Analysis and Modelling, University of Freiburg, Germany.
FAU - Fink, Anna
AU  - Fink A
AUID- ORCID: 0009-0002-9903-283X
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Tran, Hien
AU  - Tran H
AUID- ORCID: 0000-0001-7518-7488
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Wilpert, Caroline
AU  - Wilpert C
AUID- ORCID: 0000-0003-2375-2478
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Nattenmueller, Johanna
AU  - Nattenmueller J
AUID- ORCID: 0000-0003-4032-378X
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Neubauer, Jakob
AU  - Neubauer J
AUID- ORCID: 0000-0001-9388-0219
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Bamberg, Fabian
AU  - Bamberg F
AUID- ORCID: 0000-0002-7460-3942
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Reisert, Marco
AU  - Reisert M
AD  - Medical Physics, Department of Diagnostic and Interventional Radiology, Medical 
      Center - University of Freiburg, Faculty of Medicine, University of Freiburg, 
      79106 Freiburg, Germany.
AD  - Department of Stereotactic and Functional Neurosurgery, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
FAU - Russe, Maximilian F
AU  - Russe MF
AUID- ORCID: 0000-0003-3187-2429
AD  - Department of Diagnostic and Interventional Radiology, Medical Center - 
      University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 
      Freiburg, Germany.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
MH  - Humans
MH  - *Software
MH  - *Algorithms
MH  - Clinical Decision-Making
MH  - Cost Savings
MH  - Radiologists
EDAT- 2023/07/25 13:09
MHDA- 2023/07/26 06:42
CRDT- 2023/07/25 10:02
PHST- 2023/07/26 06:42 [medline]
PHST- 2023/07/25 13:09 [pubmed]
PHST- 2023/07/25 10:02 [entrez]
AID - 10.1148/radiol.230970 [doi]
PST - ppublish
SO  - Radiology. 2023 Jul;308(1):e230970. doi: 10.1148/radiol.230970.

PMID- 37830256
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 1898-018X (Electronic)
IS  - 1897-5593 (Print)
IS  - 1898-018X (Linking)
VI  - 30
IP  - 6
DP  - 2023 Oct 13
TI  - Beyond ChatGPT: What does GPT-4 add to healthcare? The dawn of a new era.
PG  - 1018-25
LID - 10.5603/cj.97515 [doi]
AB  - Over the past few years, artificial intelligence (AI) has significantly improved 
      healthcare. Once the stuff of science fiction, AI is now widely used, even in our 
      daily lives - often without us thinking about it. All healthcare professionals - 
      especially executives and medical doctors - need to understand the capabilities 
      of advanced AI tools and other breakthrough innovations. This understanding will 
      allow them to recognize opportunities and threats emerging technologies can bring 
      to their organizations. We hope to contribute to a meaningful public discussion 
      about the role of this new type of AI and how our approach to healthcare and 
      medicine can best evolve with the rapid development of this technology. Since 
      medicine learns by example, only a few possible uses of AI in medicine are 
      provided, which merely outline the system's capabilities. Among the examples, it 
      is worth highlighting the roles of AI in medical notes, education, preventive 
      programs, consultation, triage and intervention. It is believed by the authors 
      that large language models such as chat generative pre-trained transformer 
      (ChatGPT) are reaching a level of maturity that will soon impact clinical 
      medicine as a whole and improve the delivery of individualized, compassionate, 
      and scalable healthcare. It is unlikely that AI will replace physicians in the 
      near future. The human aspects of care, including empathy, compassion, critical 
      thinking, and complex decision-making, are invaluable in providing holistic 
      patient care beyond diagnosis and treatment decisions. The GPT-4 has many 
      limitations and cannot replace direct contact between an experienced physician 
      and a patient for even the most seemingly simple consultations, not to mention 
      the ethical and legal aspects of responsibility for diagnosis.
FAU - Wójcik, Simona
AU  - Wójcik S
AD  - LUX MED Llc, Warsaw, Poland.
FAU - Rulkiewicz, Anna
AU  - Rulkiewicz A
AD  - LUX MED Llc, Warsaw, Poland.
FAU - Pruszczyk, Piotr
AU  - Pruszczyk P
AUID- ORCID: 0000-0002-9768-0000
AD  - Department of Internal Medicine and Cardiology with The Center for Diagnosis and 
      Treatment of Thromboembolism, Medical University of Warsaw, Poland.
FAU - Lisik, Wojciech
AU  - Lisik W
AUID- ORCID: 0000-0003-3020-3979
AD  - Department of General and Transplantation Surgery, Medical University of Warsaw, 
      Poland.
FAU - Poboży, Marcin
AU  - Poboży M
AD  - Cichowski Pobozy Healthcare Facility, Maciejowice, Poland.
FAU - Domienik-Karłowicz, Justyna
AU  - Domienik-Karłowicz J
AUID- ORCID: 0000-0001-6122-9755
AD  - Department of Internal Medicine and Cardiology with The Center for Diagnosis and 
      Treatment of Thromboembolism, Medical University of Warsaw, Poland. 
      jdomienik@tlen.pl.
AD  - LUX MED Llc, Warsaw, Poland. jdomienik@tlen.pl.
LA  - eng
PT  - Journal Article
DEP - 20231013
PL  - Poland
TA  - Cardiol J
JT  - Cardiology journal
JID - 101392712
SB  - IM
PMC - PMC10713213
OTO - NOTNLM
OT  - AI in medicine
OT  - ChatGPT
OT  - artificial intelligence
OT  - health IT
OT  - innovations
EDAT- 2023/10/13 06:45
MHDA- 2023/10/13 06:45
CRDT- 2023/10/13 05:31
PHST- 2023/09/20 00:00 [received]
PHST- 2023/10/03 00:00 [accepted]
PHST- 2023/10/13 06:45 [medline]
PHST- 2023/10/13 06:45 [pubmed]
PHST- 2023/10/13 05:31 [entrez]
AID - VM/OJS/J/97515 [pii]
AID - cardj-30-6-1018 [pii]
AID - 10.5603/cj.97515 [doi]
PST - aheadofprint
SO  - Cardiol J. 2023 Oct 13;30(6):1018-25. doi: 10.5603/cj.97515.

PMID- 37815784
OWN - NLM
STAT- Publisher
LR  - 20231010
IS  - 2468-6530 (Electronic)
IS  - 2468-6530 (Linking)
DP  - 2023 Oct 6
TI  - Re: Momenaei et al.: Appropriateness and readability of ChatGPT-4-generated 
      responses for surgical treatment of retinal diseases (Ophthalmol Retina. 
      2023:7:862-868).
LID - S2468-6530(23)00443-8 [pii]
LID - 10.1016/j.oret.2023.09.002 [doi]
FAU - Bommakanti, Nikhil
AU  - Bommakanti N
AD  - Wills Eye Hospital, Sidney Kimmel Medical College, Philadelphia, Pennsylvania.
FAU - Caranfa, Jonathan T
AU  - Caranfa JT
AD  - Department of Ophthalmology, New England Eye Center, Tufts Medical Center, Tufts 
      University School of Medicine, Boston, Massachusetts.
FAU - Young, Benjamin K
AU  - Young BK
AD  - Casey Eye Institute, Oregon Health and Sciences University, Portland, Oregon.
FAU - Zhao, Peter Y
AU  - Zhao PY
AD  - Department of Ophthalmology, New England Eye Center, Tufts Medical Center, Tufts 
      University School of Medicine, Boston, Massachusetts. Electronic address: 
      YuCheng.Zhao@tuftsmedicine.org.
LA  - eng
PT  - Letter
DEP - 20231006
PL  - United States
TA  - Ophthalmol Retina
JT  - Ophthalmology. Retina
JID - 101695048
SB  - IM
EDAT- 2023/10/10 12:42
MHDA- 2023/10/10 12:42
CRDT- 2023/10/10 11:15
PHST- 2023/08/22 00:00 [received]
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/10/10 12:42 [medline]
PHST- 2023/10/10 12:42 [pubmed]
PHST- 2023/10/10 11:15 [entrez]
AID - S2468-6530(23)00443-8 [pii]
AID - 10.1016/j.oret.2023.09.002 [doi]
PST - aheadofprint
SO  - Ophthalmol Retina. 2023 Oct 6:S2468-6530(23)00443-8. doi: 
      10.1016/j.oret.2023.09.002.

PMID- 37462695
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231023
LR  - 20231107
IS  - 1432-2161 (Electronic)
IS  - 0364-2348 (Linking)
VI  - 52
IP  - 12
DP  - 2023 Dec
TI  - Authors' response to the Letter to the Editor: Re-evaluating the role of AI in 
      scientific writing: a critical analysis on ChatGPT.
PG  - 2489
LID - 10.1007/s00256-023-04405-5 [doi]
FAU - Ariyaratne, Sisith
AU  - Ariyaratne S
AD  - Department of Musculoskeletal Radiology, The Royal Orthopedic Hospital, Bristol 
      Road South, Northfield, Birmingham, UK.
FAU - Iyengar, Karthikeyan P
AU  - Iyengar KP
AD  - Department of Orthopedics, Southport and Ormskirk Hospital, Southport, UK.
FAU - Nischal, Neha
AU  - Nischal N
AD  - Department of Radiology, Holy Family Hospital, New Delhi, India.
FAU - Babu, Naparla Chitti
AU  - Babu NC
AD  - Department of Radiology, Srinivas Institute of Medical Sciences & Research 
      Centre, Mukka, Mangalore, India.
FAU - Botchu, Rajesh
AU  - Botchu R
AD  - Department of Musculoskeletal Radiology, The Royal Orthopedic Hospital, Bristol 
      Road South, Northfield, Birmingham, UK. drbrajesh@yahoo.com.
LA  - eng
PT  - Letter
PL  - Germany
TA  - Skeletal Radiol
JT  - Skeletal radiology
JID - 7701953
SB  - IM
EDAT- 2023/07/18 13:07
MHDA- 2023/07/18 13:08
CRDT- 2023/07/18 11:04
PHST- 2023/07/18 13:08 [medline]
PHST- 2023/07/18 13:07 [pubmed]
PHST- 2023/07/18 11:04 [entrez]
AID - 10.1007/s00256-023-04405-5 [pii]
AID - 10.1007/s00256-023-04405-5 [doi]
PST - ppublish
SO  - Skeletal Radiol. 2023 Dec;52(12):2489. doi: 10.1007/s00256-023-04405-5.

PMID- 38009003
OWN - NLM
STAT- Publisher
LR  - 20231127
IS  - 1438-8871 (Electronic)
IS  - 1438-8871 (Linking)
DP  - 2023 Nov 20
TI  - Evaluation of Generative Artificial Intelligence Large Language Models ChatGPT, 
      Google Bard, and Microsoft Bing Chat in Supporting Evidence-based Dentistry: A 
      Comparative Mixed-Methods Study.
LID - 10.2196/51580 [doi]
AB  - BACKGROUND: The increasing application of Generative Artificial Intelligence 
      Large Language Models (LLMs) in various fields including Dentistry raises 
      questions about their accuracy. OBJECTIVE: This study aimed to comparatively 
      evaluate the answers provided by four LLMs - Google's Bard, OpenAI's ChatGPT-3.5 
      and ChatGPT-4, and Microsoft's Bing to clinically relevant questions from the 
      field of dentistry. METHODS: The LLMs were queried with 20 open type, clinical 
      dentistry-related questions in different disciplines, developed by the respective 
      faculty of the School of Dentistry, European University Cyprus. The LLMs' answers 
      were graded in a range from 0 (minimum) to 10 (maximum) points, against strong, 
      traditionally collected scientific evidence such as guidelines and consensus 
      statements, using a rubric, as if they were exam questions posed to students, by 
      two experienced faculty members. The scores were compared statistically to 
      identify the best-performing model using Friedman's and Wilcoxon's tests. 
      Moreover, evaluators were asked to provide qualitative evaluation for 
      comprehensiveness, scientific accuracy, clarity and relevance. RESULTS: Overall, 
      no statistically significant difference between the scores given by the two 
      evaluators were detected, thus an average score for every LLM was computed. While 
      ChatGPT-4 statistically outperformed ChatGPT-3.5 (P-value=.008), Microsoft Bing 
      Chat (P-value=.049) and Google Bard (P-value=.045), all models exhibited 
      occasional inaccuracies, generality, outdated content, and a lack of source 
      references. The evaluators noted instances where LLMs delivered irrelevant 
      information, vague answers, or information that was not fully accurate. 
      CONCLUSIONS: The study demonstrates that while LLMs hold promising potential as 
      an aid in the implementation of evidence-based dentistry, their current 
      limitations can lead to potentially harmful healthcare decisions if not used 
      judiciously. Therefore, these tools should not replace the dentist's critical 
      thinking and in-depth understanding of the subject matter. Further research, 
      clinical validation, and model improvements are necessary for these tools to be 
      fully integrated into dental practice. Dental practitioners must be aware of the 
      LLMs' limitations, as imprudent use could potentially impact patient care. 
      Regulatory measures should be established to oversee the use of these evolving 
      technologies.
FAU - Giannakopoulos, Kostis
AU  - Giannakopoulos K
AD  - School of Dentistry, European University Cyprus, 6 Diogenis StEngomi, Nicosia, 
      CY.
FAU - Kavadella, Argyro
AU  - Kavadella A
AD  - School of Dentistry, European University Cyprus, 6 Diogenis StEngomi, Nicosia, 
      CY.
FAU - Aaqel Salim, Anas
AU  - Aaqel Salim A
AD  - School of Dentistry, European University Cyprus, 6 Diogenis StEngomi, Nicosia, 
      CY.
FAU - Stamatopoulos, Vassilis
AU  - Stamatopoulos V
AD  - Athena Research and Innovation Center in Information Communication & Knowledge 
      Technologies, School of Medicine, National and Kapodistrian University of Athens, 
      Athens, GR.
FAU - Kaklamanos, Eleftherios G
AU  - Kaklamanos EG
AD  - School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, GR.
AD  - School of Dentistry, European University Cyprus, 6 Diogenis StEngomi, Nicosia, 
      CY.
AD  - Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, AE.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
EDAT- 2023/11/27 06:47
MHDA- 2023/11/27 06:47
CRDT- 2023/11/27 03:40
PHST- 2023/11/27 06:47 [medline]
PHST- 2023/11/27 06:47 [pubmed]
PHST- 2023/11/27 03:40 [entrez]
AID - 10.2196/51580 [doi]
PST - aheadofprint
SO  - J Med Internet Res. 2023 Nov 20. doi: 10.2196/51580.

PMID- 38032714
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231130
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Nov 30
TI  - Can we use ChatGPT for Mental Health and Substance Use Education? Examining Its 
      Quality and Potential Harms.
PG  - e51243
LID - 10.2196/51243 [doi]
AB  - BACKGROUND: The use of generative artificial intelligence, more specifically 
      large language models (LLMs), is proliferating, and as such, it is vital to 
      consider both the value and potential harms of its use in medical education. 
      Their efficiency in a variety of writing styles makes LLMs, such as ChatGPT, 
      attractive for tailoring educational materials. However, this technology can 
      feature biases and misinformation, which can be particularly harmful in medical 
      education settings, such as mental health and substance use education. This 
      viewpoint investigates if ChatGPT is sufficient for 2 common health education 
      functions in the field of mental health and substance use: (1) answering users' 
      direct queries and (2) aiding in the development of quality consumer educational 
      health materials. OBJECTIVE: This viewpoint includes a case study to provide 
      insight into the accessibility, biases, and quality of ChatGPT's query responses 
      and educational health materials. We aim to provide guidance for the general 
      public and health educators wishing to utilize LLMs. METHODS: We collected real 
      world queries from 2 large-scale mental health and substance use portals and 
      engineered a variety of prompts to use on GPT-4 Pro with the Bing BETA internet 
      browsing plug-in. The outputs were evaluated with tools from the Sydney Health 
      Literacy Lab to determine the accessibility, the adherence to Mindframe 
      communication guidelines to identify biases, and author assessments on quality, 
      including tailoring to audiences, duty of care disclaimers, and evidence-based 
      internet references. RESULTS: GPT-4's outputs had good face validity, but upon 
      detailed analysis were substandard in comparison to expert-developed materials. 
      Without engineered prompting, the reading level, adherence to communication 
      guidelines, and use of evidence-based websites were poor. Therefore, all outputs 
      still required cautious human editing and oversight. CONCLUSIONS: GPT-4 is 
      currently not reliable enough for direct-consumer queries, but educators and 
      researchers can use it for creating educational materials with caution. Materials 
      created with LLMs should disclose the use of generative artificial intelligence 
      and be evaluated on their efficacy with the target audience.
CI  - ©Sophia Spallek, Louise Birrell, Stephanie Kershaw, Emma Krogh Devine, Louise 
      Thornton. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 30.11.2023.
FAU - Spallek, Sophia
AU  - Spallek S
AUID- ORCID: 0000-0001-5222-1794
AD  - The Matilda Centre for Research in Mental Health and Substance Use, The 
      University of Sydney, Sydney, Australia.
FAU - Birrell, Louise
AU  - Birrell L
AUID- ORCID: 0000-0002-1335-1382
AD  - The Matilda Centre for Research in Mental Health and Substance Use, The 
      University of Sydney, Sydney, Australia.
FAU - Kershaw, Stephanie
AU  - Kershaw S
AUID- ORCID: 0000-0003-2494-4391
AD  - The Matilda Centre for Research in Mental Health and Substance Use, The 
      University of Sydney, Sydney, Australia.
FAU - Devine, Emma Krogh
AU  - Devine EK
AUID- ORCID: 0000-0001-8110-6445
AD  - The Matilda Centre for Research in Mental Health and Substance Use, The 
      University of Sydney, Sydney, Australia.
FAU - Thornton, Louise
AU  - Thornton L
AUID- ORCID: 0000-0001-7705-833X
AD  - The Matilda Centre for Research in Mental Health and Substance Use, The 
      University of Sydney, Sydney, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - educational intervention
OT  - generative artificial intelligence
OT  - health education
OT  - large language models
OT  - medical education
OT  - mental health
OT  - patient education handout
OT  - preventive health services
OT  - substance use
EDAT- 2023/11/30 12:42
MHDA- 2023/11/30 12:43
CRDT- 2023/11/30 11:54
PHST- 2023/07/26 00:00 [received]
PHST- 2023/11/08 00:00 [accepted]
PHST- 2023/11/02 00:00 [revised]
PHST- 2023/11/30 12:43 [medline]
PHST- 2023/11/30 12:42 [pubmed]
PHST- 2023/11/30 11:54 [entrez]
AID - v9i1e51243 [pii]
AID - 10.2196/51243 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Nov 30;9:e51243. doi: 10.2196/51243.

PMID- 37950763
OWN - NLM
STAT- Publisher
LR  - 20231111
IS  - 1434-3916 (Electronic)
IS  - 0936-8051 (Linking)
DP  - 2023 Nov 11
TI  - Simplifying radiologic reports with natural language processing: a novel approach 
      using ChatGPT in enhancing patient understanding of MRI results.
LID - 10.1007/s00402-023-05113-4 [doi]
AB  - PURPOSE: The aim of this prospective cohort study was to assess the factual 
      accuracy, completeness of medical information, and potential harmfulness of 
      incorrect conclusions by medical professionals in automatically generated texts 
      of varying complexity (1) using ChatGPT, Furthermore, patients without a medical 
      background were asked to evaluate comprehensibility, information density, and 
      conclusion possibilities (2). METHODS: In the study, five different simplified 
      versions of MRI findings of the knee of different complexity (A: simple, B: 
      moderate, C: complex) were each created using ChatGPT. Subsequently, a group of 
      four medical professionals (two orthopedic surgeons and two radiologists) and a 
      group of 20 consecutive patients evaluated the created reports. For this purpose, 
      all participants received a group of simplified reports (simple, moderate, and 
      severe) at intervals of 1 week each for their respective evaluation using a 
      specific questionnaire. Each questionnaire consisted of the original report, the 
      simplified report, and a series of statements to assess the quality of the 
      simplified reports. Participants were asked to rate their level of agreement with 
      a five-point Likert scale. RESULTS: The evaluation of the medical specialists 
      showed that the findings produced were consistent in quality depending on their 
      complexity. Factual correctness, reproduction of relevant information and 
      comprehensibility for patients were rated on average as "Agree". The question 
      about possible harm resulted in an average of "Disagree". The evaluation of 
      patients also revealed consistent quality of reports, depending on complexity. 
      Simplicity of word choice and sentence structure was rated "Agree" on average, 
      with significant differences between simple and complex findings (p = 0.0039) as 
      well as between moderate and complex findings (p = 0.0222). Participants reported 
      being significantly better at knowing what the text was about (p = 0.001) and 
      drawing the correct conclusions the more simplified the report of findings was 
      (p = 0.013829). The question of whether the text informed them as well as a 
      healthcare professional was answered as "Neutral" across all findings. 
      CONCLUSION: By using ChatGPT, MRI reports can be simplified automatically with 
      consistent quality so that the relevant information is understandable to 
      patients. However, a report generated in this way does not replace a thorough 
      discussion between specialist and patient.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Schmidt, Sebastian
AU  - Schmidt S
AD  - Department of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, 
      Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany. 
      schmidt.sebastian@fn.de.
FAU - Zimmerer, Alexander
AU  - Zimmerer A
AD  - Department of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, 
      Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany.
AD  - Department of Orthopaedics and Orthopaedic Surgery, University Medicine 
      Greifswald, Ferdinand-Sauerbruch-Straße, 17475, Greifswald, Germany.
FAU - Cucos, Tudor
AU  - Cucos T
AD  - Department of Radiology, ViDia Christliche Kliniken Karlsruhe, Steinhäuser Straße 
      18, 76135, Karlsruhe, Germany.
FAU - Feucht, Matthias
AU  - Feucht M
AD  - Department of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, 
      Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany.
FAU - Navas, Luis
AU  - Navas L
AD  - Department of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, 
      Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231111
PL  - Germany
TA  - Arch Orthop Trauma Surg
JT  - Archives of orthopaedic and trauma surgery
JID - 9011043
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Knee
OT  - MRI
EDAT- 2023/11/11 20:49
MHDA- 2023/11/11 20:49
CRDT- 2023/11/11 11:03
PHST- 2023/06/26 00:00 [received]
PHST- 2023/10/15 00:00 [accepted]
PHST- 2023/11/11 20:49 [medline]
PHST- 2023/11/11 20:49 [pubmed]
PHST- 2023/11/11 11:03 [entrez]
AID - 10.1007/s00402-023-05113-4 [pii]
AID - 10.1007/s00402-023-05113-4 [doi]
PST - aheadofprint
SO  - Arch Orthop Trauma Surg. 2023 Nov 11. doi: 10.1007/s00402-023-05113-4.

PMID- 38026769
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 0970-0358 (Print)
IS  - 1998-376X (Electronic)
IS  - 0970-0358 (Linking)
VI  - 56
IP  - 5
DP  - 2023 Oct
TI  - Leveraging Large Language Models (LLM) for the Plastic Surgery Resident Training: 
      Do They Have a Role?
PG  - 413-420
LID - 10.1055/s-0043-1772704 [doi]
AB  - Introduction  Large language models (LLMs) are designed for recognizing, 
      summarizing, translating, predicting, and generating text-based content from 
      knowledge gained from extensive data sets. ChatGPT4 (Generative Pre-trained 
      Transformer 4) (OpenAI, San Francisco, California, United States) is a 
      transformer-based LLM model pretrained on public data as well as data obtained 
      from third-party sources using deep learning techniques of fine tuning and 
      reinforcement learning from human feedback to predict the next text. We wanted to 
      explore the role of LLM as a teaching assistant (TA) in plastic surgery. Material 
      and Methods  TA roles were first identified in available literature, and based on 
      the roles, a list of suitable tasks was created where LLM could be used to 
      perform the task. Prompts designed to be fed in to the LLM (specifically ChatGPT) 
      to generate appropriate output, were then created and fed to the ChatGPT model. 
      The outputs generated were scored by evaluators and compared for interobserver 
      agreement. Results  A final set of eight TA roles were identified where a LLM 
      could be utilized to generate content. These contents were scored for usefulness 
      and accuracy. These were scored independently by the eight study authors in a 
      scoring sheet created for the study. Interobserver agreements for content 
      accuracy, usefulness, and clarity were 100% for content generated for the 
      following: interactive case studies (generation), simulation of preoperative 
      consultations, and generation of ethical considerations. Discussion  LLMs in 
      general and ChatGPT (on which this study is based) in specific, can generate 
      answers to questions and prompts based on huge amount of text fed into the model 
      for training the underlying language model. The answers generated have been found 
      to be accurate, readable, and even indistinguishable from human-generated text. 
      This capability of automated content synthesis can be exploited to generate 
      summaries to text, answer short and long answers, and generate case scenarios. We 
      could identify a few such scenarios where the LLM could in general be utilized to 
      play the role of a TA and aid plastic surgery residents in particular. In 
      addition, these models could also be used by students to obtain feedback and gain 
      reflection which itself stimulates critical thinking. Conclusion  Incorporating 
      LLMs into the educational arsenal of plastic surgery residency programs can 
      provide a dynamic, interactive, and individualized learning experience for 
      residents and prove to be worthy TAs of future.
CI  - Association of Plastic Surgeons of India. This is an open access article 
      published by Thieme under the terms of the Creative Commons 
      Attribution-NonDerivative-NonCommercial License, permitting copying and 
      reproduction so long as the original work is given appropriate credit. Contents 
      may not be used for commercial purposes, or adapted, remixed, transformed or 
      built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).
FAU - Mohapatra, Devi Prasad
AU  - Mohapatra DP
AUID- ORCID: 0000-0001-5490-5081
AD  - Department of Plastic Surgery, Jawaharlal Institute of Postgraduate Medical 
      Education and Research (JIPMER), Pondicherry, India.
FAU - Thiruvoth, Friji Meethale
AU  - Thiruvoth FM
AD  - Department of Plastic Surgery, Jawaharlal Institute of Postgraduate Medical 
      Education and Research (JIPMER), Pondicherry, India.
FAU - Tripathy, Satyaswarup
AU  - Tripathy S
AUID- ORCID: 0000-0003-3659-6064
AD  - Department of Plastic Surgery, Post Graduate Institute of Medical Education and 
      Research, Chandigarh, India.
FAU - Rajan, Sheeja
AU  - Rajan S
AUID- ORCID: 0000-0001-5421-6446
AD  - Department of Plastic Surgery, Government Medical College, Thrissur, Kerala, 
      India.
FAU - Vathulya, Madhubari
AU  - Vathulya M
AD  - Department of Burns and Plastic Surgery, All India Institute of Medical Sciences 
      (AIIMS), Rishikesh, Uttarakhand, India.
FAU - Lakshmi, Palukuri
AU  - Lakshmi P
AD  - Department of Plastic Surgery, Osmania General Hospital, Hyderabad, Telangana, 
      India.
FAU - Singh, Veena K
AU  - Singh VK
AUID- ORCID: 0000-0002-2120-6548
AD  - Department of Burns and Plastic Surgery, All India Institute of Medical Sciences 
      (AIIMS), Patna, Bihar, India.
FAU - Haq, Ansar Ul
AU  - Haq AU
AUID- ORCID: 0000-0003-3388-9435
AD  - Department of Burns and Plastic Surgery, All India Institute of Medical Sciences 
      (AIIMS), Rishikesh, Uttarakhand, India.
LA  - eng
PT  - Journal Article
DEP - 20230828
PL  - Germany
TA  - Indian J Plast Surg
JT  - Indian journal of plastic surgery : official publication of the Association of 
      Plastic Surgeons of India
JID - 8405356
PMC - PMC10663077
OTO - NOTNLM
OT  - ChatGPT in education
OT  - educational technology
OT  - future of surgical training
OT  - large language models (LLM)
OT  - plastic surgical education
COIS- Conflict of Interest None declared.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 16:57
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 16:57 [entrez]
AID - IJPS-23-6-2230 [pii]
AID - 10.1055/s-0043-1772704 [doi]
PST - epublish
SO  - Indian J Plast Surg. 2023 Aug 28;56(5):413-420. doi: 10.1055/s-0043-1772704. 
      eCollection 2023 Oct.

PMID- 37246993
OWN - NLM
STAT- MEDLINE
DCOM- 20231004
LR  - 20231115
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 9
DP  - 2023 Sep 1
TI  - WHO declares the end of the COVID-19 global health emergency: lessons and 
      recommendations from the perspective of ChatGPT/GPT-4.
PG  - 2859-2862
LID - 10.1097/JS9.0000000000000521 [doi]
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou.
FAU - Wu, Chunchun
AU  - Wu C
AUID- ORCID: 0000-0002-3215-1626
AD  - Department of Emergency, Taikang People's Hospital, Zhoukou, Henan.
FAU - Gu, Shuqin
AU  - Gu S
AD  - Duke Human Vaccine Institute, Duke University Medical Center.
FAU - Lu, Yanqiu
AU  - Lu Y
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Clinical College of Neurology, Neurosurgery and Neurorehabilitation.
AD  - Department of Graduate School, Tianjin Medical University, Tianjin, People's 
      Republic of China.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      North Carolina, USA.
FAU - Li, Cheng
AU  - Li C
AD  - Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical 
      College of Peking University.
AD  - State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute 
      of Pharmacology and Toxicology, Beijing.
AD  - Center for Musculoskeletal Surgery (CMSC), Charité-Universitätsmedizin Berlin, 
      corporate member of Freie Universität Berlin, Humboldt University of Berlin, and 
      Berlin Institute of Health, Berlin, Germany.
LA  - eng
PT  - Letter
DEP - 20230901
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *COVID-19/epidemiology
MH  - Global Health
MH  - World Health Organization
PMC - PMC10498859
COIS- The authors declare no conflicts of interest. Sponsorships or competing interests 
      that may be relevant to content are disclosed at the end of this article.
EDAT- 2023/05/29 13:04
MHDA- 2023/10/04 06:43
CRDT- 2023/05/29 11:04
PHST- 2023/05/14 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/10/04 06:43 [medline]
PHST- 2023/05/29 13:04 [pubmed]
PHST- 2023/05/29 11:04 [entrez]
AID - 01279778-990000000-00397 [pii]
AID - IJS-D-23-00876 [pii]
AID - 10.1097/JS9.0000000000000521 [doi]
PST - epublish
SO  - Int J Surg. 2023 Sep 1;109(9):2859-2862. doi: 10.1097/JS9.0000000000000521.

PMID- 37076876
OWN - NLM
STAT- MEDLINE
DCOM- 20230421
LR  - 20230522
IS  - 1479-5876 (Electronic)
IS  - 1479-5876 (Linking)
VI  - 21
IP  - 1
DP  - 2023 Apr 19
TI  - Can the ChatGPT and other large language models with internet-connected database 
      solve the questions and concerns of patient with prostate cancer and help 
      democratize medical knowledge?
PG  - 269
LID - 10.1186/s12967-023-04123-5 [doi]
LID - 269
FAU - Zhu, Lingxuan
AU  - Zhu L
AD  - Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of 
      Medicine, Shanghai, 200127, China.
AD  - The First Clinical Medical School, Southern Medical University, 1023 Shatai South 
      Road, Guangzhou, 510515, Guangdong, China.
FAU - Mou, Weiming
AU  - Mou W
AD  - The First Clinical Medical School, Southern Medical University, 1023 Shatai South 
      Road, Guangzhou, 510515, Guangdong, China.
FAU - Chen, Rui
AU  - Chen R
AUID- ORCID: 0000-0003-3728-2577
AD  - Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of 
      Medicine, Shanghai, 200127, China. drchenrui@foxmail.com.
LA  - eng
PT  - Letter
PT  - Research Support, Non-U.S. Gov't
DEP - 20230419
PL  - England
TA  - J Transl Med
JT  - Journal of translational medicine
JID - 101190741
SB  - IM
MH  - Male
MH  - Humans
MH  - *Language
MH  - *Prostatic Neoplasms
MH  - Databases, Factual
MH  - Knowledge
PMC - PMC10115367
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      competing interests.
EDAT- 2023/04/20 00:41
MHDA- 2023/04/21 06:41
CRDT- 2023/04/19 23:58
PHST- 2023/03/24 00:00 [received]
PHST- 2023/04/09 00:00 [accepted]
PHST- 2023/04/21 06:41 [medline]
PHST- 2023/04/20 00:41 [pubmed]
PHST- 2023/04/19 23:58 [entrez]
AID - 10.1186/s12967-023-04123-5 [pii]
AID - 4123 [pii]
AID - 10.1186/s12967-023-04123-5 [doi]
PST - epublish
SO  - J Transl Med. 2023 Apr 19;21(1):269. doi: 10.1186/s12967-023-04123-5.

PMID- 36968864
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230328
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Applicability of ChatGPT in Assisting to Solve Higher Order Problems in 
      Pathology.
PG  - e35237
LID - 10.7759/cureus.35237 [doi]
LID - e35237
AB  - Background Artificial intelligence (AI) is evolving for healthcare services. 
      Higher cognitive thinking in AI refers to the ability of the system to perform 
      advanced cognitive processes, such as problem-solving, decision-making, 
      reasoning, and perception. This type of thinking goes beyond simple data 
      processing and involves the ability to understand and manipulate abstract 
      concepts, interpret, and use information in a contextually relevant way, and 
      generate new insights based on past experiences and accumulated knowledge. 
      Natural language processing models like ChatGPT is a conversational program that 
      can interact with humans to provide answers to queries. Objective We aimed to 
      ascertain the capability of ChatGPT in solving higher-order reasoning in the 
      subject of pathology. Methods This cross-sectional study was conducted on the 
      internet using an AI-based chat program that provides free service for research 
      purposes. The current version of ChatGPT (January 30 version) was used to 
      converse with a total of 100 higher-order reasoning queries. These questions were 
      randomly selected from the question bank of the institution and categorized 
      according to different systems. The responses to each question were collected and 
      stored for further analysis. The responses were evaluated by three expert 
      pathologists on a zero to five scale and categorized into the structure of the 
      observed learning outcome (SOLO) taxonomy categories. The score was compared by a 
      one-sample median test with hypothetical values to find its accuracy. Result A 
      total of 100 higher-order reasoning questions were solved by the program in an 
      average of 45.31±7.14 seconds for an answer. The overall median score was 4.08 
      (Q1-Q3: 4-4.33) which was below the hypothetical maximum value of five (one-test 
      median test p <0.0001) and similar to four (one-test median test p = 0.14). The 
      majority (86%) of the responses were in the "relational" category in the SOLO 
      taxonomy. There was no difference in the scores of the responses for questions 
      asked from various organ systems in the subject of Pathology (Kruskal Wallis p = 
      0.55). The scores rated by three pathologists had an excellent level of 
      inter-rater reliability (ICC = 0.975 [95% CI: 0.965-0.983]; F = 40.26; p < 
      0.0001). Conclusion The capability of ChatGPT to solve higher-order reasoning 
      questions in pathology had a relational level of accuracy. Hence, the text output 
      had connections among its parts to provide a meaningful response. The answers 
      from the program can score approximately 80%. Hence, academicians or students can 
      get help from the program for solving reasoning-type questions also. As the 
      program is evolving, further studies are needed to find its accuracy level in any 
      further versions.
CI  - Copyright © 2023, Sinha et al.
FAU - Sinha, Ranwir K
AU  - Sinha RK
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, Jharkhand, IND.
FAU - Deb Roy, Asitava
AU  - Deb Roy A
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, Jharkhand, IND.
FAU - Kumar, Nikhil
AU  - Kumar N
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, Jharkhand, IND.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, IND.
LA  - eng
PT  - Journal Article
DEP - 20230220
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10033699
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - cognition
OT  - critical reasoning
OT  - decision making
OT  - intelligence
OT  - microcomputers
OT  - pathologists
OT  - problem-solving
OT  - students
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/28 06:00
MHDA- 2023/03/28 06:01
CRDT- 2023/03/27 03:31
PHST- 2023/02/20 00:00 [accepted]
PHST- 2023/03/27 03:31 [entrez]
PHST- 2023/03/28 06:00 [pubmed]
PHST- 2023/03/28 06:01 [medline]
AID - 10.7759/cureus.35237 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 20;15(2):e35237. doi: 10.7759/cureus.35237. eCollection 2023 
      Feb.

PMID- 38006614
OWN - NLM
STAT- Publisher
LR  - 20231125
IS  - 1872-7727 (Electronic)
IS  - 0720-048X (Linking)
VI  - 170
DP  - 2023 Nov 22
TI  - Linguistic precision, and declared use of ChatGPT, needed for radiology 
      literature.
PG  - 111212
LID - S0720-048X(23)00526-0 [pii]
LID - 10.1016/j.ejrad.2023.111212 [doi]
AB  - There is a need to ensure the accuracy of linguistic descriptors in the medical 
      literature, including that related to radiology, to allow peers and professionals 
      to communicate ideas and scientific results in a clear and unambiguous manner. 
      This letter highlights an issue that could undermine the clarity of scientific 
      writing in radiology literature, namely the presence of non-standard terminology 
      for established jargon, and emphasizes the need for authors to transparently 
      declare the use of language editing services and AI-driven tools, such as 
      ChatGPT, if these have been used to formulate text and ideas in their papers. 
      Ultimately, clear radiology papers that are compliant with current publishing 
      ethics will serve radiologists and patients well.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Independent Researcher, Ikenobe 3011-2, Kagawa-ken 761-0799, Japan. Electronic 
      address: jaimetex@yahoo.com.
LA  - eng
PT  - Letter
DEP - 20231122
PL  - Ireland
TA  - Eur J Radiol
JT  - European journal of radiology
JID - 8106411
SB  - IM
OTO - NOTNLM
OT  - Ethics
OT  - Jargon
OT  - Medical communication
OT  - Scientific ethos
OT  - Truth
COIS- Declaration of Competing Interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: Jaime A. Teixeira da Silva reports was provided by Independent. Jaime 
      A. Teixeira da Silva reports a relationship with Independent that includes: Jaime 
      A. Teixeira da Silva has patent pending to N/A. Jaime A. Teixeira da Silva has no 
      financial or other COIs to declare.
EDAT- 2023/11/26 07:43
MHDA- 2023/11/26 07:43
CRDT- 2023/11/25 18:00
PHST- 2023/10/16 00:00 [received]
PHST- 2023/10/30 00:00 [revised]
PHST- 2023/11/17 00:00 [accepted]
PHST- 2023/11/26 07:43 [medline]
PHST- 2023/11/26 07:43 [pubmed]
PHST- 2023/11/25 18:00 [entrez]
AID - S0720-048X(23)00526-0 [pii]
AID - 10.1016/j.ejrad.2023.111212 [doi]
PST - aheadofprint
SO  - Eur J Radiol. 2023 Nov 22;170:111212. doi: 10.1016/j.ejrad.2023.111212.

PMID- 37431972
OWN - NLM
STAT- Publisher
LR  - 20231024
IS  - 1784-973X (Electronic)
IS  - 0001-5385 (Linking)
VI  - 78
IP  - 9
DP  - 2023 Nov
TI  - Does the long-term administration of proton pump inhibitors increase the risk of 
      adverse cardiovascular outcomes? A ChatGPT powered umbrella review.
PG  - 980-988
LID - 10.1080/00015385.2023.2231299 [doi]
AB  - BACKGROUND: Proton pump inhibitors (PPIs) are commonly prescribed for the 
      treatment of acid-related disorders. In the context of coronary artery disease 
      (CAD), PPIs are commonly prescribed along with antiplatelet medications. In fact, 
      the potential interaction between these two classes of medications has been 
      subject to much debate. This review aimed to summarise the findings from 
      systematic reviews and meta-analyses on the casual relationship between PPI use 
      (alone) and major adverse cardiovascular events (MACE). Furthermore, the recent 
      release of ChatGPT has provided reviewers with a powerful natural language 
      processing tool. We therefore aimed to assess the utility of ChatGPT in the 
      systematic review process. METHODS: A comprehensive search of PubMed was 
      conducted to identify relevant systematic reviews and meta-analyses published up 
      to March 2023. Two independent reviewers assessed the eligibility of the studies, 
      extracted the data, and assessed the methodological quality using AMSTAR 2.0. The 
      population of interest was adults that received the medications of interest 
      (PPIs) for a minimum of three months, regardless of indication. Control groups 
      were defined as placebo or active comparators. The outcomes of interest were 
      described under the general term MACE, which include cardiovascular death, 
      non-fatal myocardial infarction, and non-fatal stroke. There were no restrictions 
      with regards to time, but we only included reports in English. A different group 
      of independent reviewers simultaneously ran the same process using ChatGPT. The 
      results were then compared with the human generated results. RESULTS: Seven 
      systematic reviews and meta-analyses were included, involving a total of 46 
      randomised controlled trials and 33 observational studies. The studies examined 
      the association between PPI use and MACE, including stroke, myocardial 
      infarction, and all-cause mortality. The results of the individual studies were 
      conflicting, with some showing a positive association between PPI use and MACE, 
      some showing no association, and others showing mixed results. However, the 
      majority of the studies that included observational data reported a positive 
      association between PPI use and MACE. Sensitivity analyses conducted in some 
      studies did not significantly alter the primary results, suggesting that the 
      findings were robust. Furthermore, ChatGPT was successfully prompted to execute 
      most tasks involved in this review. We therefore present text that was generated 
      by ChatGPT, including the abstract, introduction, results, and discussion 
      sections. CONCLUSION: The findings of this umbrella review suggest that a causal 
      relationship between PPI use and an increased risk of MACE cannot be ruled out. 
      Further research is needed to better understand this relationship, particularly 
      the underlying mechanisms and potential confounding factors. Healthcare 
      professionals should consider the long-term use of PPIs and carefully weigh the 
      risks and benefits for each patient. Finally, ChatGPT was successfully prompted 
      to execute most of the tasks involved in this review. We therefore feel that this 
      tool will be of great assistance in the field of evidence synthesis in the near 
      future.
FAU - Teperikidis, Eleftherios
AU  - Teperikidis E
AD  - Third Department of Cardiology, Ippokratio General Hospital, Aristotle University 
      of Thessa-loniki, Thessaloniki, Greece.
AD  - Clinical Research Unit, Special Unit for Biomedical Research and Education 
      (SUBRE), School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 
      Greece.
FAU - Boulmpou, Aristi
AU  - Boulmpou A
AD  - Third Department of Cardiology, Ippokratio General Hospital, Aristotle University 
      of Thessa-loniki, Thessaloniki, Greece.
FAU - Potoupni, Victoria
AU  - Potoupni V
AD  - Third Department of Cardiology, Ippokratio General Hospital, Aristotle University 
      of Thessa-loniki, Thessaloniki, Greece.
FAU - Kundu, Satyabrata
AU  - Kundu S
AD  - Department of Pharmacology, ISF College of Pharmacy, Moga, India.
FAU - Singh, Balpreet
AU  - Singh B
AD  - Department of Biochemistry, Punjab Agricultural University, Ludhiana, India.
FAU - Papadopoulos, Christodoulos
AU  - Papadopoulos C
AD  - Third Department of Cardiology, Ippokratio General Hospital, Aristotle University 
      of Thessa-loniki, Thessaloniki, Greece.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230711
PL  - England
TA  - Acta Cardiol
JT  - Acta cardiologica
JID - 0370570
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - MACE
OT  - PPI
OT  - Proton pump inhibitors
OT  - artificial intelligence
OT  - major adverse cardiovascular outcomes
EDAT- 2023/07/11 13:10
MHDA- 2023/07/11 13:10
CRDT- 2023/07/11 08:33
PHST- 2023/07/11 13:10 [pubmed]
PHST- 2023/07/11 13:10 [medline]
PHST- 2023/07/11 08:33 [entrez]
AID - 10.1080/00015385.2023.2231299 [doi]
PST - ppublish
SO  - Acta Cardiol. 2023 Nov;78(9):980-988. doi: 10.1080/00015385.2023.2231299. Epub 
      2023 Jul 11.

PMID- 37056538
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230415
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Assessing the Capability of ChatGPT in Answering First- and Second-Order 
      Knowledge Questions on Microbiology as per Competency-Based Medical Education 
      Curriculum.
PG  - e36034
LID - 10.7759/cureus.36034 [doi]
LID - e36034
AB  - Background and objective ChatGPT is an artificial intelligence (AI) language 
      model that has been trained to process and respond to questions across a wide 
      range of topics. It is also capable of solving problems in medical educational 
      topics. However, the capability of ChatGPT to accurately answer first- and 
      second-order knowledge questions in the field of microbiology has not been 
      explored so far. Hence, in this study, we aimed to analyze the capability of 
      ChatGPT in answering first- and second-order questions on the subject of 
      microbiology. Materials and methods Based on the competency-based medical 
      education (CBME) curriculum of the subject of microbiology, we prepared a set of 
      first-order and second-order questions. For the total of eight modules in the 
      CBME curriculum for microbiology, we prepared six first-order and six 
      second-order knowledge questions according to the National Medical 
      Commission-recommended CBME curriculum, amounting to a total of (8 x 12) 96 
      questions. The questions were checked for content validity by three expert 
      microbiologists. These questions were used to converse with ChatGPT by a single 
      user and responses were recorded for further analysis. The answers were scored by 
      three microbiologists on a rating scale of 0-5. The average of three scores was 
      taken as the final score for analysis. As the data were not normally distributed, 
      we used a non-parametric statistical test. The overall scores were tested by a 
      one-sample median test with hypothetical values of 4 and 5. The scores of answers 
      to first-order and second-order questions were compared by the Mann-Whitney U 
      test. Module-wise responses were tested by the Kruskall-Wallis test followed by 
      the post hoc test for pairwise comparisons. Results The overall score of 96 
      answers was 4.04 ±0.37 (median: 4.17, Q1-Q3: 3.88-4.33) with the mean score 
      of answers to first-order knowledge questions being 4.07 ±0.32 (median: 4.17, 
      Q1-Q3: 4-4.33) and that of answers to second-order knowledge questions being 3.99 
      ±0.43 (median: 4, Q1-Q3: 3.67-4.33) (Mann-Whitney p=0.4). The score was 
      significantly below the score of 5 (one-sample median test p<0.0001) and similar 
      to 4 (one-sample median test p=0.09). Overall, there was a variation in median 
      scores obtained in eight categories of topics in microbiology, indicating 
      inconsistent performance in different topics. Conclusion The results of the study 
      indicate that ChatGPT is capable of answering both first- and second-order 
      knowledge questions related to the subject of microbiology. The model achieved an 
      accuracy of approximately 80% and there was no difference between the model's 
      capability of answering first-order questions and second-order knowledge 
      questions. The findings of this study suggest that ChatGPT has the potential to 
      be an effective tool for automated question-answering in the field of 
      microbiology. However, continued improvement in the training and development of 
      language models is necessary to enhance their performance and make them suitable 
      for academic use.
CI  - Copyright © 2023, Das et al.
FAU - Das, Dipmala
AU  - Das D
AD  - Microbiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Kumar, Nikhil
AU  - Kumar N
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Longjam, Langamba Angom
AU  - Longjam LA
AD  - Microbiology, IQ City Medical College Hospital, Durgapur, IND.
FAU - Sinha, Ranwir
AU  - Sinha R
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Deb Roy, Asitava
AU  - Deb Roy A
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
FAU - Gupta, Pratima
AU  - Gupta P
AD  - Microbiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
LA  - eng
PT  - Journal Article
DEP - 20230312
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10086829
OTO - NOTNLM
OT  - artificial intelligence
OT  - automated question-answering
OT  - chatgpt
OT  - competency-based medical education
OT  - first-order questions
OT  - language model
OT  - medical education
OT  - microbiology
OT  - question
OT  - second-order questions
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/15 06:00
MHDA- 2023/04/15 06:01
CRDT- 2023/04/14 02:29
PHST- 2023/03/11 00:00 [accepted]
PHST- 2023/04/15 06:01 [medline]
PHST- 2023/04/14 02:29 [entrez]
PHST- 2023/04/15 06:00 [pubmed]
AID - 10.7759/cureus.36034 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 12;15(3):e36034. doi: 10.7759/cureus.36034. eCollection 2023 
      Mar.

PMID- 37770530
OWN - NLM
STAT- Publisher
LR  - 20230928
IS  - 1476-5454 (Electronic)
IS  - 0950-222X (Linking)
DP  - 2023 Sep 28
TI  - Comment on: 'Comparison of GPT-3.5, GPT-4, and human user performance on a 
      practice ophthalmology written examination' and 'ChatGPT in ophthalmology: the 
      dawn of a new era?'.
LID - 10.1038/s41433-023-02773-9 [doi]
FAU - Ghadiri, Nima
AU  - Ghadiri N
AUID- ORCID: 0000-0001-5591-2419
AD  - Department of Ophthalmology, Liverpool University Hospitals NHS Foundation Trust, 
      Liverpool, UK. nima.ghadiri@nhs.net.
AD  - Department of Eye and Vision Science, University of Liverpool, Liverpool, UK. 
      nima.ghadiri@nhs.net.
LA  - eng
PT  - Journal Article
DEP - 20230928
PL  - England
TA  - Eye (Lond)
JT  - Eye (London, England)
JID - 8703986
SB  - IM
EDAT- 2023/09/29 00:41
MHDA- 2023/09/29 00:41
CRDT- 2023/09/28 23:27
PHST- 2023/07/24 00:00 [received]
PHST- 2023/09/22 00:00 [accepted]
PHST- 2023/09/21 00:00 [revised]
PHST- 2023/09/29 00:41 [medline]
PHST- 2023/09/29 00:41 [pubmed]
PHST- 2023/09/28 23:27 [entrez]
AID - 10.1038/s41433-023-02773-9 [pii]
AID - 10.1038/s41433-023-02773-9 [doi]
PST - aheadofprint
SO  - Eye (Lond). 2023 Sep 28. doi: 10.1038/s41433-023-02773-9.

PMID- 37702539
OWN - NLM
STAT- Publisher
LR  - 20230913
IS  - 1536-3732 (Electronic)
IS  - 1049-2275 (Linking)
DP  - 2023 Sep 13
TI  - The Role of AI in Writing an Article and Whether it Can Be a Co-author: What if 
      it Gets Support From 2 Different AIs Like ChatGPT and Google Bard for the Same 
      Theme?
LID - 10.1097/SCS.0000000000009745 [doi]
FAU - Bahşi, İlhan
AU  - Bahşi İ
AD  - Department of Anatomy, Faculty of Medicine, Gaziantep University.
FAU - Balat, Ayşe
AU  - Balat A
AD  - Department of Pediatric Nephrology, Faculty of Medicine, Gaziantep University.
AD  - Department of Pediatric Rheumatology, Faculty of Medicine, Gaziantep University, 
      Gaziantep, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230913
PL  - United States
TA  - J Craniofac Surg
JT  - The Journal of craniofacial surgery
JID - 9010410
EDAT- 2023/09/13 12:42
MHDA- 2023/09/13 12:42
CRDT- 2023/09/13 09:14
PHST- 2023/07/30 00:00 [received]
PHST- 2023/08/12 00:00 [accepted]
PHST- 2023/09/13 12:42 [medline]
PHST- 2023/09/13 12:42 [pubmed]
PHST- 2023/09/13 09:14 [entrez]
AID - 00001665-990000000-01090 [pii]
AID - 10.1097/SCS.0000000000009745 [doi]
PST - aheadofprint
SO  - J Craniofac Surg. 2023 Sep 13. doi: 10.1097/SCS.0000000000009745.

PMID- 37869810
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 309
DP  - 2023 Oct 20
TI  - Clinical Acronym Disambiguation via ChatGPT and BING.
PG  - 78-82
LID - 10.3233/SHTI230743 [doi]
AB  - Clinical texts are written with acronyms, abbreviations and medical jargon 
      expressions to save time. This hinders full comprehension not just for medical 
      experts but also laypeople. This paper attempts to disambiguate acronyms with 
      their given context by comparing a web mining approach via the search engine BING 
      and a conversational agent approach using ChatGPT with the aim to see, if these 
      methods can supply a viable resolution for the input acronym. Both approaches are 
      automated via application programming interfaces. Possible term candidates are 
      extracted using natural language processing-oriented functionality. The 
      conversational agent approach surpasses the baseline for web mining without 
      plausibility thresholds in precision, recall and F1-measure, while scoring 
      similarly only in precision for high threshold values.
FAU - Kugic, Amila
AU  - Kugic A
AD  - Institute for Medical Informatics, Statistics and Documentation, Medical 
      University of Graz, Austria.
FAU - Kreuzthaler, Markus
AU  - Kreuzthaler M
AD  - Institute for Medical Informatics, Statistics and Documentation, Medical 
      University of Graz, Austria.
FAU - Schulz, Stefan
AU  - Schulz S
AD  - Institute for Medical Informatics, Statistics and Documentation, Medical 
      University of Graz, Austria.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - *Natural Language Processing
MH  - *Software
MH  - Search Engine
MH  - Communication
MH  - Writing
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Electronic Health Records
OT  - Natural Language Processing
EDAT- 2023/10/23 06:47
MHDA- 2023/11/01 12:42
CRDT- 2023/10/23 05:01
PHST- 2023/11/01 12:42 [medline]
PHST- 2023/10/23 06:47 [pubmed]
PHST- 2023/10/23 05:01 [entrez]
AID - SHTI230743 [pii]
AID - 10.3233/SHTI230743 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 Oct 20;309:78-82. doi: 10.3233/SHTI230743.

PMID- 37548971
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230906
LR  - 20230906
IS  - 1538-3598 (Electronic)
IS  - 0098-7484 (Linking)
VI  - 330
IP  - 9
DP  - 2023 Sep 5
TI  - Large Language Models Answer Medical Questions Accurately, but Can't Match 
      Clinicians' Knowledge.
PG  - 792-794
LID - 10.1001/jama.2023.14311 [doi]
FAU - Harris, Emily
AU  - Harris E
LA  - eng
GR  - P30 AG059307/AG/NIA NIH HHS/United States
GR  - UG1 DA015815/DA/NIDA NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - JAMA
JT  - JAMA
JID - 7501160
SB  - IM
OAB - This Medical News article discusses new research on artificial intelligence 
      systems such as ChatGPT and Med-PaLM.
OABL- eng
EDAT- 2023/08/07 13:10
MHDA- 2023/08/07 13:11
CRDT- 2023/08/07 11:33
PHST- 2023/08/07 13:11 [medline]
PHST- 2023/08/07 13:10 [pubmed]
PHST- 2023/08/07 11:33 [entrez]
AID - 2808297 [pii]
AID - 10.1001/jama.2023.14311 [doi]
PST - ppublish
SO  - JAMA. 2023 Sep 5;330(9):792-794. doi: 10.1001/jama.2023.14311.

PMID- 37786788
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231004
IS  - 2666-3287 (Electronic)
IS  - 2666-3287 (Linking)
VI  - 13
DP  - 2023 Dec
TI  - Digital health in dermatology.
PG  - 139
LID - 10.1016/j.jdin.2023.08.008 [doi]
FAU - Kantor, Jonathan
AU  - Kantor J
AD  - Department of Dermatology, Center for Global Health, and Center for Clinical 
      Epidemiology and Biostatistics, Perelman School of Medicine at the University of 
      Pennsylvania, Philadelphia, Pennsylvania; Florida Center for Dermatology, St 
      Augustine, Florida; and Alchemy Labs, Oxford, UK.
LA  - eng
PT  - Editorial
DEP - 20230820
PL  - United States
TA  - JAAD Int
JT  - JAAD international
JID - 101774762
PMC - PMC10542012
OTO - NOTNLM
OT  - ChatGPT
OT  - Google Bard
OT  - Microsoft Bing
OT  - artificial intelligence
OT  - copilot
OT  - digital health
OT  - large language models
OT  - machine learning
OT  - medical education
OT  - practice management
OT  - teledermatology
OT  - teledermoscopy
OT  - telemedicine
OT  - whole slide imaging
COIS- None disclosed.
EDAT- 2023/10/03 06:47
MHDA- 2023/10/03 06:48
CRDT- 2023/10/03 03:50
PHST- 2023/10/03 06:48 [medline]
PHST- 2023/10/03 06:47 [pubmed]
PHST- 2023/10/03 03:50 [entrez]
AID - S2666-3287(23)00132-3 [pii]
AID - 10.1016/j.jdin.2023.08.008 [doi]
PST - epublish
SO  - JAAD Int. 2023 Aug 20;13:139. doi: 10.1016/j.jdin.2023.08.008. eCollection 2023 
      Dec.

PMID- 37751926
OWN - NLM
STAT- Publisher
LR  - 20230926
IS  - 2398-631X (Electronic)
IS  - 2398-631X (Linking)
DP  - 2023 Sep 26
TI  - ChatGPT: a novel AI assistant for healthcare messaging-a commentary on its 
      potential in addressing patient queries and reducing clinician burnout.
LID - leader-2023-000844 [pii]
LID - 10.1136/leader-2023-000844 [doi]
FAU - Laker, Benjamin
AU  - Laker B
AUID- ORCID: 0000-0003-0850-9744
AD  - Leadership, Organisations and Behaviour, University of Reading Henley Business 
      School - Greenlands Campus, Henley-on-Thames, UK benjamin.laker@henley.ac.uk.
FAU - Currell, Emily
AU  - Currell E
AD  - Cultural and Social Neuroscience Research Group, King's College London, London, 
      UK.
AD  - South London and Maudsley NHS Foundation Trust, London, UK.
LA  - eng
PT  - Journal Article
DEP - 20230926
PL  - England
TA  - BMJ Lead
JT  - BMJ leader
JID - 101757339
SB  - IM
OTO - NOTNLM
OT  - care design
OT  - clinical leadership
OT  - continuous improvement
OT  - efficiency
OT  - engagement
COIS- Competing interests: None declared.
EDAT- 2023/09/27 00:43
MHDA- 2023/09/27 00:43
CRDT- 2023/09/26 20:43
PHST- 2023/05/09 00:00 [received]
PHST- 2023/09/07 00:00 [accepted]
PHST- 2023/09/27 00:43 [medline]
PHST- 2023/09/27 00:43 [pubmed]
PHST- 2023/09/26 20:43 [entrez]
AID - leader-2023-000844 [pii]
AID - 10.1136/leader-2023-000844 [doi]
PST - aheadofprint
SO  - BMJ Lead. 2023 Sep 26:leader-2023-000844. doi: 10.1136/leader-2023-000844.

PMID- 37544867
OWN - NLM
STAT- MEDLINE
DCOM- 20230908
LR  - 20231102
IS  - 1444-2892 (Electronic)
IS  - 1443-9506 (Linking)
VI  - 32
IP  - 8
DP  - 2023 Aug
TI  - Emerging Roles of Artificial Intelligence (AI) in Cardiology: Benefits and 
      Barriers in a 'Brave New World'.
PG  - 883-888
LID - S1443-9506(23)04251-8 [pii]
LID - 10.1016/j.hlc.2023.07.005 [doi]
FAU - Bart, Nicole K
AU  - Bart NK
AD  - Department of Cardiology, St Vincent's Hospital, Sydney, NSW, Australia; Victor 
      Chang Cardiac Research Institute, Sydney, NSW, Australia; Department of 
      Cardiology, University of New South Wales, Sydney, NSW, Australia; University of 
      Notre Dame, Sydney, NSW, Australia. Electronic address: Nicole.bart@svha.org.au.
FAU - Pepe, Salvatore
AU  - Pepe S
AD  - Heart Research Group, Murdoch Children's Research Institute, Royal Children's 
      Hospital, Melbourne, Vic, Australia; Department of Paediatrics, University of 
      Melbourne, Melbourne, Vic, Australia. Electronic address: 
      Salvatore.Pepe@mcri.edu.au.
FAU - Gregory, Ann T
AU  - Gregory AT
AD  - Heart, Lung and Circulation, Sydney, NSW, Australia.
FAU - Denniss, A Robert
AU  - Denniss AR
AD  - Heart, Lung and Circulation, Sydney, NSW, Australia; Department of Cardiology, 
      Westmead Hospital and University of Sydney, Sydney, NSW, Australia; Department of 
      Cardiology, Blacktown Hospital, and Western Sydney University, Sydney NSW, 
      Australia. Electronic address: https://www.twitter.com/heartlungcirc.
LA  - eng
PT  - Comment
PT  - Editorial
DEP - 20230804
PL  - Australia
TA  - Heart Lung Circ
JT  - Heart, lung & circulation
JID - 100963739
SB  - IM
CON - Heart Lung Circ. 2023 Aug;32(8):894-904. PMID: 37507275
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Cardiology
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Authorship guidelines
OT  - Cardiology
OT  - ChatGPT
OT  - Healthcare and research governance
EDAT- 2023/08/07 00:42
MHDA- 2023/09/08 06:42
CRDT- 2023/08/06 21:57
PHST- 2023/09/08 06:42 [medline]
PHST- 2023/08/07 00:42 [pubmed]
PHST- 2023/08/06 21:57 [entrez]
AID - S1443-9506(23)04251-8 [pii]
AID - 10.1016/j.hlc.2023.07.005 [doi]
PST - ppublish
SO  - Heart Lung Circ. 2023 Aug;32(8):883-888. doi: 10.1016/j.hlc.2023.07.005. Epub 
      2023 Aug 4.

PMID- 37525019
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230803
IS  - 2365-6271 (Electronic)
IS  - 2365-6271 (Linking)
VI  - 9
IP  - 1
DP  - 2023 Aug 1
TI  - Generative AI for medical 3D printing: a comparison of ChatGPT outputs to 
      reference standard education.
PG  - 21
LID - 10.1186/s41205-023-00186-8 [doi]
LID - 21
FAU - Sriwastwa, Aakanksha
AU  - Sriwastwa A
AD  - Department of Radiology, University of Cincinnati, College of Medicine, 
      Cincinnati, OH, 45219, USA.
FAU - Ravi, Prashanth
AU  - Ravi P
AD  - Department of Radiology, University of Cincinnati, College of Medicine, 
      Cincinnati, OH, 45219, USA.
FAU - Emmert, Andrew
AU  - Emmert A
AD  - Department of Orthopedics and Sports Medicine, University of Cincinnati, 
      Cincinnati, OH, 45209, USA.
FAU - Chokshi, Shivum
AU  - Chokshi S
AD  - Department of Radiology, University of Cincinnati, College of Medicine, 
      Cincinnati, OH, 45219, USA.
FAU - Kondor, Shayne
AU  - Kondor S
AD  - Department of Radiology, University of Cincinnati, College of Medicine, 
      Cincinnati, OH, 45219, USA.
FAU - Dhal, Kashish
AU  - Dhal K
AD  - Department of Mechanical and Aerospace Engineering, University of Texas, 
      Arlington, TX, 76010, USA.
FAU - Patel, Parimal
AU  - Patel P
AD  - Department of Mechanical and Aerospace Engineering, University of Texas, 
      Arlington, TX, 76010, USA.
FAU - Chepelev, Leonid L
AU  - Chepelev LL
AD  - Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, M5G 
      2N2, Canada.
FAU - Rybicki, Frank J
AU  - Rybicki FJ
AD  - Department of Radiology, University of Cincinnati, College of Medicine, 
      Cincinnati, OH, 45219, USA. rybickfk@ucmail.uc.edu.
AD  - Department of Biomedical Engineering, University of Cincinnati, College of 
      Engineering and Applied Sciences, Cincinnati, OH, 45219, USA. 
      rybickfk@ucmail.uc.edu.
FAU - Gupta, Rajul
AU  - Gupta R
AD  - Department of Orthopedics and Sports Medicine, University of Cincinnati, 
      Cincinnati, OH, 45209, USA.
LA  - eng
PT  - Editorial
DEP - 20230801
PL  - England
TA  - 3D Print Med
JT  - 3D printing in medicine
JID - 101721758
PMC - PMC10391950
OTO - NOTNLM
OT  - 3D printing
OT  - Additive manufacturing
OT  - Artificial intelligence
OT  - Generative AI
OT  - Medical education
OT  - Postgraduate education
OT  - Radiology
OT  - Rapid prototyping
COIS- The authors declare that they have no competing interests.
EDAT- 2023/08/01 01:08
MHDA- 2023/08/01 01:09
CRDT- 2023/07/31 23:32
PHST- 2023/07/01 00:00 [received]
PHST- 2023/07/07 00:00 [accepted]
PHST- 2023/08/01 01:09 [medline]
PHST- 2023/08/01 01:08 [pubmed]
PHST- 2023/07/31 23:32 [entrez]
AID - 10.1186/s41205-023-00186-8 [pii]
AID - 186 [pii]
AID - 10.1186/s41205-023-00186-8 [doi]
PST - epublish
SO  - 3D Print Med. 2023 Aug 1;9(1):21. doi: 10.1186/s41205-023-00186-8.

PMID- 38090761
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 2192-5682 (Print)
IS  - 2192-5682 (Linking)
DP  - 2023 Dec 13
TI  - Comments on "ChatGPT and its Role in the Decision-Making for the Diagnosis and 
      Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative 
      Review".
PG  - 21925682231222268
LID - 10.1177/21925682231222268 [doi]
FAU - Zhang, Qiang
AU  - Zhang Q
AUID- ORCID: 0000-0002-5923-2696
AD  - Department of Orthopedics, Ruijin Hospital, Shanghai Jiao Tong University School 
      of Medicine, Shanghai, China. RINGGOLD: 66281
FAU - Liang, Yu
AU  - Liang Y
AUID- ORCID: 0009-0007-3189-5950
AD  - Department of Orthopedics, Ruijin Hospital, Shanghai Jiao Tong University School 
      of Medicine, Shanghai, China. RINGGOLD: 66281
LA  - eng
PT  - Letter
DEP - 20231213
PL  - England
TA  - Global Spine J
JT  - Global spine journal
JID - 101596156
EDAT- 2023/12/13 18:42
MHDA- 2023/12/13 18:42
CRDT- 2023/12/13 13:17
PHST- 2023/12/13 18:42 [medline]
PHST- 2023/12/13 18:42 [pubmed]
PHST- 2023/12/13 13:17 [entrez]
AID - 10.1177/21925682231222268 [doi]
PST - aheadofprint
SO  - Global Spine J. 2023 Dec 13:21925682231222268. doi: 10.1177/21925682231222268.

PMID- 36996906
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230412
IS  - 2005-8330 (Electronic)
IS  - 1229-6929 (Print)
IS  - 1229-6929 (Linking)
VI  - 24
IP  - 4
DP  - 2023 Apr
TI  - Erratum: Authorship Policy of the Korean Journal of Radiology Regarding 
      Artificial Intelligence Large Language Models Such as ChatGPT.
PG  - 374
LID - 10.3348/kjr.2023.0244 [doi]
AB  - This corrects the article on p. 171 in vol. 24, PMID: 36788775.
CI  - Copyright © 2023 The Korean Society of Radiology.
FAU - Park, Seong Ho
AU  - Park SH
AD  - Department of Radiology and Research Institute of Radiology, University of Ulsan 
      College of Medicine, Asan Medical Center, Seoul, Korea.
LA  - eng
PT  - Published Erratum
PL  - Korea (South)
TA  - Korean J Radiol
JT  - Korean journal of radiology
JID - 100956096
SB  - IM
EFR - Korean J Radiol. 2023 Mar;24(3):171-172. PMID: 36788775
PMC - PMC10067689
EDAT- 2023/03/31 06:00
MHDA- 2023/03/31 06:01
CRDT- 2023/03/30 19:23
PHST- 2023/03/31 06:01 [medline]
PHST- 2023/03/30 19:23 [entrez]
PHST- 2023/03/31 06:00 [pubmed]
AID - 24.374 [pii]
AID - 10.3348/kjr.2023.0244 [doi]
PST - ppublish
SO  - Korean J Radiol. 2023 Apr;24(4):374. doi: 10.3348/kjr.2023.0244.

PMID- 36760131
OWN - NLM
STAT- Publisher
LR  - 20230210
IS  - 1758-1109 (Electronic)
IS  - 1357-633X (Linking)
DP  - 2023 Feb 9
TI  - Can advanced technologies help address the global increase in demand for 
      specialized medical care and improve telehealth services?
PG  - 1357633X231155520
LID - 10.1177/1357633X231155520 [doi]
FAU - Lahat, Adi
AU  - Lahat A
AUID- ORCID: 0000-0003-1513-7280
AD  - Department of Gastroenterology, 26744Chaim Sheba Medical Center, affiliated with 
      Tel Aviv University, Tel Aviv, Israel.
FAU - Klang, Eyal
AU  - Klang E
AD  - The Sami Sagol AI Hub, ARC innovation center, 26744Chaim Sheba Medical Center, 
      Affiliated with Tel-Aviv University, Tel Aviv, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230209
PL  - England
TA  - J Telemed Telecare
JT  - Journal of telemedicine and telecare
JID - 9506702
SB  - IM
OTO - NOTNLM
OT  - Telemedicine
OT  - artificial intelligence (AI)
OT  - chatGPT
OT  - large language models (LLM)
OT  - telehealth
EDAT- 2023/02/11 06:00
MHDA- 2023/02/11 06:00
CRDT- 2023/02/10 01:43
PHST- 2023/02/10 01:43 [entrez]
PHST- 2023/02/11 06:00 [pubmed]
PHST- 2023/02/11 06:00 [medline]
AID - 10.1177/1357633X231155520 [doi]
PST - aheadofprint
SO  - J Telemed Telecare. 2023 Feb 9:1357633X231155520. doi: 10.1177/1357633X231155520.

PMID- 37542362
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230926
IS  - 1553-3514 (Electronic)
IS  - 1553-3506 (Linking)
VI  - 30
IP  - 4
DP  - 2023 Aug
TI  - Editorial: Harnessing the Power of AI in Health Care: Benefits, Risks, and 
      Preparation.
PG  - 417-418
LID - 10.1177/15533506231190748 [doi]
FAU - Schijven, Marlies P
AU  - Schijven MP
AD  - Department of Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The 
      Netherlands.
AD  - Amsterdam Gastroenterology and Metabolism, Amsterdam UMC, Amsterdam, The 
      Netherlands.
AD  - Amsterdam Public Health, Digital Health, Amsterdam UMC, Amsterdam, The 
      Netherlands.
FAU - Kroh, Matthew
AU  - Kroh M
AD  - Department of General Surgery, Cleveland Clinic, Digestive Disease Institute, 
      Cleveland, OH, USA.
LA  - eng
PT  - Editorial
PL  - United States
TA  - Surg Innov
JT  - Surgical innovation
JID - 101233809
SB  - IM
MH  - *Delivery of Health Care
MH  - *Artificial Intelligence
OTO - NOTNLM
OT  - artificial intelligence (AI)
OT  - augmented reality
OT  - chatGPT
OT  - digital
OT  - innovation
OT  - laparoscopy
OT  - machine learning
OT  - personalized medicine
OT  - robotics
OT  - surgery
EDAT- 2023/08/05 11:43
MHDA- 2023/08/07 06:42
CRDT- 2023/08/05 00:02
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/08/05 11:43 [pubmed]
PHST- 2023/08/05 00:02 [entrez]
AID - 10.1177/15533506231190748 [doi]
PST - ppublish
SO  - Surg Innov. 2023 Aug;30(4):417-418. doi: 10.1177/15533506231190748.

PMID- 37432492
OWN - NLM
STAT- MEDLINE
DCOM- 20230713
LR  - 20230718
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Linking)
VI  - 47
IP  - 1
DP  - 2023 Jul 11
TI  - Can Artificial Intelligence Ever Develop the Human Touch and Replace a 
      Psychiatrist? - A letter to the editor of the Journal of Medical Systems: 
      Regarding "Artificial Intelligence in Medicine & ChatGPT: De-Tether the 
      Physician".
PG  - 72
LID - 10.1007/s10916-023-01974-9 [doi]
FAU - Das, Srijan
AU  - Das S
AUID- ORCID: 0009-0001-3247-4257
AD  - Institute of Neurosciences, Kolkata 185, Acharya Jagadish Chandra Bose Rd, Elgin, 
      Kolkata, West Bengal, 700017, India. johndghosh18@gmail.com.
FAU - Ghoshal, Arkit
AU  - Ghoshal A
AD  - Medical College and Hospital, Kolkata 88, College St, Calcutta Medical College, 
      College Square, Kolkata, West Bengal, 700073, India.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230711
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
CON - J Med Syst. 2023 Mar 4;47(1):32. PMID: 36869942
MH  - Humans
MH  - Touch
MH  - Artificial Intelligence
MH  - *Psychiatry
MH  - *Physicians
EDAT- 2023/07/11 13:10
MHDA- 2023/07/13 06:42
CRDT- 2023/07/11 11:07
PHST- 2023/05/20 00:00 [received]
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/07/13 06:42 [medline]
PHST- 2023/07/11 13:10 [pubmed]
PHST- 2023/07/11 11:07 [entrez]
AID - 10.1007/s10916-023-01974-9 [pii]
AID - 10.1007/s10916-023-01974-9 [doi]
PST - epublish
SO  - J Med Syst. 2023 Jul 11;47(1):72. doi: 10.1007/s10916-023-01974-9.

PMID- 37864657
OWN - NLM
STAT- Publisher
LR  - 20231021
IS  - 1432-1068 (Electronic)
IS  - 1633-8065 (Linking)
DP  - 2023 Oct 21
TI  - Letter to the editor 'Evaluating ChatGPT responses in the context of a 
      53-year-old male with a femoral neck fracture: a qualitative analysis'.
LID - 10.1007/s00590-023-03766-w [doi]
FAU - Ray, Partha Pratim
AU  - Ray PP
AUID- ORCID: 0000-0003-2306-2792
AD  - Department of Computer Applications, Sikkim University, 6th Mile, PO-Tadong, 
      Gangtok, Sikkim, 737102, India. ppray@cus.ac.in.
LA  - eng
PT  - Letter
DEP - 20231021
PL  - France
TA  - Eur J Orthop Surg Traumatol
JT  - European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
JID - 9518037
SB  - IM
EDAT- 2023/10/21 19:42
MHDA- 2023/10/21 19:42
CRDT- 2023/10/21 11:07
PHST- 2023/10/03 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/10/21 19:42 [medline]
PHST- 2023/10/21 19:42 [pubmed]
PHST- 2023/10/21 11:07 [entrez]
AID - 10.1007/s00590-023-03766-w [pii]
AID - 10.1007/s00590-023-03766-w [doi]
PST - aheadofprint
SO  - Eur J Orthop Surg Traumatol. 2023 Oct 21. doi: 10.1007/s00590-023-03766-w.

PMID- 37715529
OWN - NLM
STAT- Publisher
LR  - 20230916
IS  - 1365-2850 (Electronic)
IS  - 1351-0126 (Linking)
DP  - 2023 Sep 16
TI  - Letter to the editor in response to Samuel Woodnutt, Chris Allen, Jasmine 
      Snowden, Matt Flynn, Simon Hall, Paula Libberton, ChatGPT, Francesca Purvis paper 
      titled: Could artificial intelligence write mental health nursing care plans?
LID - 10.1111/jpm.12981 [doi]
FAU - Dell, Ceylon
AU  - Dell C
AUID- ORCID: 0009-0009-9632-7705
AD  - Single Point of Access Team (Adult Mental Health), Eaglestone Health Centre, 
      Milton Keynes, UK.
LA  - eng
PT  - Letter
DEP - 20230916
PL  - England
TA  - J Psychiatr Ment Health Nurs
JT  - Journal of psychiatric and mental health nursing
JID - 9439514
EDAT- 2023/09/16 10:44
MHDA- 2023/09/16 10:44
CRDT- 2023/09/16 03:52
PHST- 2023/09/01 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/09/16 10:44 [medline]
PHST- 2023/09/16 10:44 [pubmed]
PHST- 2023/09/16 03:52 [entrez]
AID - 10.1111/jpm.12981 [doi]
PST - aheadofprint
SO  - J Psychiatr Ment Health Nurs. 2023 Sep 16. doi: 10.1111/jpm.12981.

PMID- 37757748
OWN - NLM
STAT- Publisher
LR  - 20230927
IS  - 1938-2367 (Electronic)
IS  - 0147-7447 (Linking)
DP  - 2023 Sep 27
TI  - The Rapid Development of Artificial Intelligence: GPT-4's Performance on 
      Orthopedic Surgery Board Questions.
PG  - 1-5
LID - 10.3928/01477447-20230922-05 [doi]
AB  - Advances in artificial intelligence and machine learning models, like Chat 
      Generative Pre-trained Transformer (ChatGPT), have occurred at a remarkably fast 
      rate. OpenAI released its newest model of ChatGPT, GPT-4, in March 2023. It 
      offers a wide range of medical applications. The model has demonstrated notable 
      proficiency on many medical board examinations. This study sought to assess 
      GPT-4's performance on the Orthopaedic In-Training Examination (OITE) used to 
      prepare residents for the American Board of Orthopaedic Surgery (ABOS) Part I 
      Examination. The data gathered from GPT-4's performance were additionally 
      compared with the data of the previous iteration of ChatGPT, GPT-3.5, which was 
      released 4 months before GPT-4. GPT-4 correctly answered 251 of the 396 attempted 
      questions (63.4%), whereas GPT-3.5 correctly answered 46.3% of 410 attempted 
      questions. GPT-4 was significantly more accurate than GPT-3.5 on orthopedic 
      board-style questions (P<.00001). GPT-4's performance is most comparable to that 
      of an average third-year orthopedic surgery resident, while GPT-3.5 performed 
      below an average orthopedic intern. GPT-4's overall accuracy was just below the 
      approximate threshold that indicates a likely pass on the ABOS Part I 
      Examination. Our results demonstrate significant improvements in OpenAI's newest 
      model, GPT-4. Future studies should assess potential clinical applications as AI 
      models continue to be trained on larger data sets and offer more capabilities. 
      [Orthopedics. 202x;4x(x):xx-xx.].
FAU - Hofmann, Hayden L
AU  - Hofmann HL
FAU - Guerra, Gage A
AU  - Guerra GA
FAU - Le, Jonathan L
AU  - Le JL
FAU - Wong, Alexander M
AU  - Wong AM
FAU - Hofmann, Grady H
AU  - Hofmann GH
FAU - Mayfield, Cory K
AU  - Mayfield CK
FAU - Petrigliano, Frank A
AU  - Petrigliano FA
FAU - Liu, Joseph N
AU  - Liu JN
LA  - eng
PT  - Journal Article
DEP - 20230927
PL  - United States
TA  - Orthopedics
JT  - Orthopedics
JID - 7806107
SB  - IM
EDAT- 2023/09/28 00:42
MHDA- 2023/09/28 00:42
CRDT- 2023/09/27 18:13
PHST- 2023/09/28 00:42 [medline]
PHST- 2023/09/28 00:42 [pubmed]
PHST- 2023/09/27 18:13 [entrez]
AID - 10.3928/01477447-20230922-05 [doi]
PST - aheadofprint
SO  - Orthopedics. 2023 Sep 27:1-5. doi: 10.3928/01477447-20230922-05.

PMID- 37450700
OWN - NLM
STAT- Publisher
LR  - 20230718
IS  - 1528-1140 (Electronic)
IS  - 0003-4932 (Linking)
DP  - 2023 Jul 17
TI  - The 2023 MANCTRA Acute Biliary Pancreatitis Care Bundle: A Joint Effort Between 
      Human Knowledge and Artificial Intelligence (ChatGPT) to Optimize the Care of 
      Patients With Acute Biliary Pancreatitis in Western Countries.
LID - 10.1097/SLA.0000000000006008 [doi]
AB  - OBJECTIVE: To generate an up-to-date bundle to manage acute biliary pancreatitis 
      using an evidence-based, artificial intelligence (AI)-assisted GRADE method. 
      SUMMARY BACKGROUND DATA: A care bundle is a set of core elements of care that are 
      distilled from the most solid evidence-based practice guidelines and 
      recommendations. METHODS: The research questions were addressed in this bundle 
      following the PICO criteria. The working group summarized the effects of 
      interventions with the strength of recommendation and quality of evidence 
      applying the GRADE methodology. ChatGPT AI system was used to independently 
      assess the quality of evidence of each element in the bundle, together with the 
      strength of the recommendations. RESULTS: The seven elements of the bundle 
      discourage antibiotic prophylaxis in patients with acute biliary pancreatitis, 
      support the use of a full-solid diet in patients with mild to moderately-severe 
      acute biliary pancreatitis, and recommend early enteral nutrition in patients 
      unable to feed by mouth. The bundle states that ERCP should be performed within 
      the first 48-72 hours of hospital admission in patients with cholangitis. Early 
      laparoscopic cholecystectomy should be performed in patients with mild acute 
      biliary pancreatitis. When operative intervention is needed for necrotizing 
      pancreatitis, this should start with the endoscopic step-up approach. 
      CONCLUSIONS: We have developed a new care bundle with seven key elements for 
      managing patients with acute biliary pancreatitis. This new bundle, whose 
      scientific strength has been increased thanks to the alliance between human 
      knowledge and AI from the new ChatGPT software, should be introduced to emergency 
      departments, wards, and ICUs.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Podda, Mauro
AU  - Podda M
AD  - Department of Surgical Science, Emergency Surgery Unit, Cagliari State University 
      Hospital, Cagliari, Italy.
FAU - Di Martino, Marcello
AU  - Di Martino M
AD  - Division of Hepatobiliary and Liver Transplantation Surgery, A.O.R.N. Cardarelli, 
      Naples, Italy.
FAU - Ielpo, Benedetto
AU  - Ielpo B
AD  - Hepatobiliary Division, Hospital del Mar, Universitat Pompeu Fabra, Barcelona, 
      Spain.
FAU - Catena, Fausto
AU  - Catena F
AD  - Department of Emergency and Trauma Surgery, Bufalini Hospital, Cesena, Italy.
FAU - Coccolini, Federico
AU  - Coccolini F
AD  - General, Emergency and Trauma Surgery Unit, Pisa University Hospital, Pisa, 
      Italy.
FAU - Pata, Francesco
AU  - Pata F
AD  - Department of Surgery, Università della Calabria, Cosenza, Italy.
FAU - Marchegiani, Giovanni
AU  - Marchegiani G
AD  - Hepato-Pancreato-Biliary Surgery and Liver Transplantation Unit, Department of 
      Surgical, Oncological and Gastroenterological Sciences (DISCOG), University of 
      Padua, Padua, Italy.
FAU - De Simone, Belinda
AU  - De Simone B
AD  - Department of Emergency and Metabolic Minimally Invasive Surgery, Centre 
      Hospitalier Intercommunal de Poissy/Saint Germain en Laye, Poissy Cedex, France.
FAU - Damaskos, Dimitrios
AU  - Damaskos D
AD  - Department of Upper GI Surgery, Royal Infirmary of Edinburgh, Edinburgh, 
      Scotland, UK.
FAU - Mole, Damian
AU  - Mole D
AD  - Centre for Inflammation Research, Clinical Surgery, University of Edinburgh, 
      Edinburgh, Scotland, UK.
FAU - Leppaniemi, Ari
AU  - Leppaniemi A
AD  - Department of Abdominal Surgery, University of Helsinki and Helsinki University 
      Central Hospital, Helsinki, Finland.
FAU - Sartelli, Massimo
AU  - Sartelli M
AD  - Department of Surgery, Macerata Civil Hospital, Macerata, Italy.
FAU - Yang, Baohong
AU  - Yang B
AD  - Department of Oncology, Weifang People's Hospital, The 1st Affiliated Hospital of 
      Weifang Medical University, Weifang, Shandong, China.
AD  - Department of Gastroenterology, The 1st Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China.
FAU - Ansaloni, Luca
AU  - Ansaloni L
AD  - General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy.
FAU - Biffl, Walter
AU  - Biffl W
AD  - Division of Trauma and Acute Care Surgery, Scripps Memorial Hospital La Jolla, La 
      Jolla, CA, USA.
FAU - Kluger, Yoram
AU  - Kluger Y
AD  - General Surgery Department, Rambam Medical Center, Haifa, Israel.
FAU - Moore, Ernest E
AU  - Moore EE
AD  - Denver Health System - Denver Health Medical Center, Denver, USA.
FAU - Pellino, Gianluca
AU  - Pellino G
AD  - "Luigi Vanvitelli" University of Campania, Naples, Italy.
AD  - Colorectal Surgery, Vall d'Hebron University Hospital, Universitat Autonoma de 
      Barcelona UAB, Barcelona, Spain.
FAU - Di Saverio, Salomone
AU  - Di Saverio S
AD  - Department of Surgery, Madonna del Soccorso Hospital, San Benedetto del Tronto, 
      Italy.
FAU - Pisanu, Adolfo
AU  - Pisanu A
AD  - Department of Surgical Science, Emergency Surgery Unit, Cagliari State University 
      Hospital, Cagliari, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - United States
TA  - Ann Surg
JT  - Annals of surgery
JID - 0372354
SB  - IM
COIS- Conflicts of Interest and Source of Funding The authors report no conflict of 
      interest. This research has not received any specific grant from funding agencies 
      in the public, commercial, or not-for-profit sectors.
EDAT- 2023/07/14 19:07
MHDA- 2023/07/14 19:07
CRDT- 2023/07/14 15:23
PHST- 2023/07/14 19:07 [pubmed]
PHST- 2023/07/14 19:07 [medline]
PHST- 2023/07/14 15:23 [entrez]
AID - 00000658-990000000-00562 [pii]
AID - 10.1097/SLA.0000000000006008 [doi]
PST - aheadofprint
SO  - Ann Surg. 2023 Jul 17. doi: 10.1097/SLA.0000000000006008.

PMID- 37989540
OWN - NLM
STAT- Publisher
LR  - 20231121
IS  - 1537-2677 (Electronic)
IS  - 0740-9303 (Linking)
DP  - 2023 Nov 16
TI  - Evaluating ChatGPT on Orbital and Oculofacial Disorders: Accuracy and Readability 
      Insights.
LID - 10.1097/IOP.0000000000002552 [doi]
AB  - PURPOSE: To assess the accuracy and readability of responses generated by the 
      artificial intelligence model, ChatGPT (version 4.0), to questions related to 10 
      essential domains of orbital and oculofacial disease. METHODS: A set of 100 
      questions related to the diagnosis, treatment, and interpretation of orbital and 
      oculofacial diseases was posed to ChatGPT 4.0. Responses were evaluated by a 
      panel of 7 experts based on appropriateness and accuracy, with performance scores 
      measured on a 7-item Likert scale. Inter-rater reliability was determined via the 
      intraclass correlation coefficient. RESULTS: The artificial intelligence model 
      demonstrated accurate and consistent performance across all 10 domains of orbital 
      and oculofacial disease, with an average appropriateness score of 5.3/6.0 
      ("mostly appropriate" to "completely appropriate"). Domains of cavernous sinus 
      fistula, retrobulbar hemorrhage, and blepharospasm had the highest domain scores 
      (average scores of 5.5 to 5.6), while the proptosis domain had the lowest 
      (average score of 5.0/6.0). The intraclass correlation coefficient was 0.64 (95% 
      CI: 0.52 to 0.74), reflecting moderate inter-rater reliability. The responses 
      exhibited a high reading-level complexity, representing the comprehension levels 
      of a college or graduate education. CONCLUSIONS: This study demonstrates the 
      potential of ChatGPT 4.0 to provide accurate information in the field of 
      ophthalmology, specifically orbital and oculofacial disease. However, challenges 
      remain in ensuring accurate and comprehensive responses across all disease 
      domains. Future improvements should focus on refining the model's correctness and 
      eventually expanding the scope to visual data interpretation. Our results 
      highlight the vast potential for artificial intelligence in educational and 
      clinical ophthalmology contexts.
CI  - Copyright © 2023 The American Society of Ophthalmic Plastic and Reconstructive 
      Surgery, Inc.
FAU - Balas, Michael
AU  - Balas M
AUID- ORCID: 0000-0002-5948-0331
AD  - Temerty Faculty of Medicine.
FAU - Janic, Ana
AU  - Janic A
AD  - Temerty Faculty of Medicine.
FAU - Daigle, Patrick
AU  - Daigle P
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
FAU - Nijhawan, Navdeep
AU  - Nijhawan N
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
FAU - Hussain, Ahsen
AU  - Hussain A
AD  - Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, 
      Nova Scotia, Canada.
FAU - Gill, Harmeet
AU  - Gill H
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
FAU - Lahaie, Gabriela L
AU  - Lahaie GL
AD  - Department of Ophthalmology, Queen's University, Kingston, Ontario, Canada.
FAU - Belliveau, Michel J
AU  - Belliveau MJ
AD  - Department of Ophthalmology, University of Ottawa and The Ottawa Hospital 
      Research Institute, Ottawa, Ontario, Canada.
FAU - Crawford, Sean A
AU  - Crawford SA
AD  - Temerty Faculty of Medicine.
AD  - Division of Vascular Surgery, Department of Surgery, University of Toronto, 
      Toronto, Ontario, Canada.
FAU - Arjmand, Parnian
AU  - Arjmand P
AD  - Mississauga Retina Institute, Toronto, Ontario, Canada; and.
FAU - Ing, Edsel B
AU  - Ing EB
AUID- ORCID: 0000-0003-0623-0934
AD  - Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, 
      Ontario, Canada.
AD  - Department of Ophthalmology and Vision Sciences, University of Alberta, Edmonton, 
      Alberta, Canada.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - United States
TA  - Ophthalmic Plast Reconstr Surg
JT  - Ophthalmic plastic and reconstructive surgery
JID - 8508431
SB  - IM
COIS- The authors have no financial or conflicts of interest to disclose.
EDAT- 2023/11/22 00:42
MHDA- 2023/11/22 00:42
CRDT- 2023/11/21 21:23
PHST- 2023/11/22 00:42 [medline]
PHST- 2023/11/22 00:42 [pubmed]
PHST- 2023/11/21 21:23 [entrez]
AID - 00002341-990000000-00287 [pii]
AID - 10.1097/IOP.0000000000002552 [doi]
PST - aheadofprint
SO  - Ophthalmic Plast Reconstr Surg. 2023 Nov 16. doi: 10.1097/IOP.0000000000002552.

PMID- 37699649
OWN - NLM
STAT- Publisher
LR  - 20230912
IS  - 2398-3795 (Electronic)
IS  - 2398-3795 (Linking)
DP  - 2023 Sep 12
TI  - Transforming healthcare documentation: Harnessing the potential of AI to generate 
      discharge summaries.
LID - BJGPO.2023.0116 [pii]
LID - 10.3399/BJGPO.2023.0116 [doi]
AB  - BACKGROUND: Hospital discharge summaries play an essential role in informing GPs 
      of recent admissions to ensure excellent continuity of care and prevent adverse 
      events, however, they are notoriously poorly written, time-consuming and can 
      result in delayed discharge. AIM: To evaluate the potential of AI to produce 
      high-quality discharge summaries equivalent to the level of a doctor who has 
      completed the UK Foundation Programme. DESIGN & SETTING: Feasibility study using 
      25 mock patient vignettes. METHOD: Using the 25 mock patient vignettes, 25 
      ChatGPT and 25 junior doctor written discharge summaries were generated. Quality 
      and suitability was determined through both independent GP evaluators and 
      adherence to a minimum dataset. RESULTS: Of the 25 AI written discharge summaries 
      100% were deemed by GPs to be of an acceptable quality compared to that of 92% 
      for the junior doctor summaries. They both showed a mean compliance of 97% with 
      the minimum dataset. In addition, the ability of GPs to determine if the summary 
      was written by ChatGPT was poor, with only a 60% accuracy of detection. 
      Similarly, when run through an AI detection tool all were recognised as being 
      very unlikely to be written by AI. CONCLUSION: AI has proven to produce discharge 
      summaries of equivalent quality as a doctor who has completed the UK Foundation 
      Programme, however, larger studies with real-world patient data with NHS-approved 
      AI tools will need to be conducted.
CI  - Copyright © 2023, The Authors.
FAU - Clough, Reece Aj
AU  - Clough RA
AUID- ORCID: 0000-0003-1176-2804
AD  - Institute of Naval Medicine, Crescent Road, Gosport, England 
      reece.clough@nhs.net.
FAU - Sparkes, William A
AU  - Sparkes WA
AD  - Institute of Naval Medicine, Crescent Road, Gosport, England.
FAU - Clough, Oliver T
AU  - Clough OT
FAU - Sykes, Joshua T
AU  - Sykes JT
FAU - Steventon, Alexander T
AU  - Steventon AT
AD  - Institute of Naval Medicine, Crescent Road, Gosport, England.
FAU - King, Kate
AU  - King K
AD  - Academic Department of Military General Practice, Research & Clinical Innovation, 
      Defence Medical Services, ICT Centre, Vincent Drive, Birmingham, England.
LA  - eng
PT  - Journal Article
DEP - 20230912
PL  - England
TA  - BJGP Open
JT  - BJGP open
JID - 101713531
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatGPT
OT  - discharge summary
EDAT- 2023/09/13 00:42
MHDA- 2023/09/13 00:42
CRDT- 2023/09/12 20:33
PHST- 2023/06/25 00:00 [received]
PHST- 2023/08/14 00:00 [revised]
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/09/13 00:42 [medline]
PHST- 2023/09/13 00:42 [pubmed]
PHST- 2023/09/12 20:33 [entrez]
AID - BJGPO.2023.0116 [pii]
AID - 10.3399/BJGPO.2023.0116 [doi]
PST - aheadofprint
SO  - BJGP Open. 2023 Sep 12:BJGPO.2023.0116. doi: 10.3399/BJGPO.2023.0116.

PMID- 37731643
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230922
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 13
DP  - 2023
TI  - Applications of large language models in cancer care: current evidence and future 
      perspectives.
PG  - 1268915
LID - 10.3389/fonc.2023.1268915 [doi]
LID - 1268915
AB  - The development of large language models (LLMs) is a recent success in the field 
      of generative artificial intelligence (AI). They are computer models able to 
      perform a wide range of natural language processing tasks, including content 
      generation, question answering, or language translation. In recent months, a 
      growing number of studies aimed to assess their potential applications in the 
      field of medicine, including cancer care. In this mini review, we described the 
      present published evidence for using LLMs in oncology. All the available studies 
      assessed ChatGPT, an advanced language model developed by OpenAI, alone or 
      compared to other LLMs, such as Google Bard, Chatsonic, and Perplexity. Although 
      ChatGPT could provide adequate information on the screening or the management of 
      specific solid tumors, it also demonstrated a significant error rate and a 
      tendency toward providing obsolete data. Therefore, an accurate, expert-driven 
      verification process remains mandatory to avoid the potential for misinformation 
      and incorrect evidence. Overall, although this new generative AI-based technology 
      has the potential to revolutionize the field of medicine, including that of 
      cancer care, it will be necessary to develop rules to guide the application of 
      these tools to maximize benefits and minimize risks.
CI  - Copyright © 2023 Iannantuono, Bracken-Clarke, Floudas, Roselli, Gulley and 
      Karzai.
FAU - Iannantuono, Giovanni Maria
AU  - Iannantuono GM
AD  - Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer 
      Institute, National Institutes of Health, Bethesda, MD, United States.
AD  - Medical Oncology Unit, Department of Systems Medicine, University of Rome Tor 
      Vergata, Rome, Italy.
FAU - Bracken-Clarke, Dara
AU  - Bracken-Clarke D
AD  - Center for Immuno-Oncology, Center for Cancer Research, National Cancer 
      Institute, National Institutes of Health, Bethesda, MD, United States.
FAU - Floudas, Charalampos S
AU  - Floudas CS
AD  - Center for Immuno-Oncology, Center for Cancer Research, National Cancer 
      Institute, National Institutes of Health, Bethesda, MD, United States.
FAU - Roselli, Mario
AU  - Roselli M
AD  - Medical Oncology Unit, Department of Systems Medicine, University of Rome Tor 
      Vergata, Rome, Italy.
FAU - Gulley, James L
AU  - Gulley JL
AD  - Center for Immuno-Oncology, Center for Cancer Research, National Cancer 
      Institute, National Institutes of Health, Bethesda, MD, United States.
FAU - Karzai, Fatima
AU  - Karzai F
AD  - Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer 
      Institute, National Institutes of Health, Bethesda, MD, United States.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230904
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC10507617
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - cancer care
OT  - chatbot
OT  - large language models
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/21 06:42
MHDA- 2023/09/21 06:43
CRDT- 2023/09/21 04:01
PHST- 2023/07/28 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/09/21 06:43 [medline]
PHST- 2023/09/21 06:42 [pubmed]
PHST- 2023/09/21 04:01 [entrez]
AID - 10.3389/fonc.2023.1268915 [doi]
PST - epublish
SO  - Front Oncol. 2023 Sep 4;13:1268915. doi: 10.3389/fonc.2023.1268915. eCollection 
      2023.

PMID- 37995379
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 2448-5667 (Electronic)
IS  - 0443-5117 (Linking)
VI  - 61
IP  - 6
DP  - 2023 Nov 6
TI  - [Scientific integrity faces plagiarism fabricated with the ChatGPT].
PG  - 857-862
LID - 10.5281/zenodo.10064458 [doi]
AB  - Among the malpractices that undermine research integrity, plagiarism is a major 
      threat given its frequency and evolving presentations. Plagiarism implies the 
      intentional grabbing of texts, ideas, images, or data belonging to others and 
      without crediting them. However, the different and even masked forms of 
      plagiarism often difficult a clear identification. Currently, the many kinds of 
      fraud and plagiarism account for most retractions in traditional and open access 
      journals. Further, the rate of retracted articles is higher in the Latin American 
      databases LILACS and Scielo than in PubMed and Web of Science. This difference 
      has been related to the typical laxity of our culture and the lack of English 
      writing skills of non-Anglophone researchers. These features explain the conflict 
      experienced by Latin American students in USA where they face a stricter culture 
      regarding academic and scientific plagiarism. In the internet era, the ease of 
      accessing scientific literature has increased the temptation to plagiarize but 
      this ethical breach has been countered by antiplagiarism software. Now, the 
      so-called "paraphragiarism" prompted by paraphrasing tools exceeds the infamous 
      "copy-paste". For instance, the innovative ChatGPT can be used for plagiarizing 
      and paraphragiarizing. Moreover, its inclusion as coauthor in scientific papers 
      has been banned by prestigious journals and the International Committee of 
      Medical Journal Editors because such chatbot cannot meet the required public 
      responsibility criterium. To avoid plagiarism, it is enough to always give due 
      credit in the proper way. Lastly, I question the ill-fated and now prevailing 
      conjunction of blind faith in progress and zero skepticism that prevents us from 
      foreseeing the negative consequences of technological advances.
CI  - Licencia CC 4.0 (BY-NC-ND) © 2023 Revista Médica del Instituto Mexicano del 
      Seguro Social.
FAU - Rivera, Horacio
AU  - Rivera H
AUID- ORCID: 0000-0001-6940-0668
AD  - Universidad de Guadalajara, Centro Universitario de Ciencias de la Salud, 
      Departamento de Biología Molecular y Genómica. Guadalajara, Jalisco, México.
LA  - spa
PT  - English Abstract
PT  - Journal Article
TT  - La integridad científica ante los plagios fabricados con el ChatGPT.
DEP - 20231106
PL  - Mexico
TA  - Rev Med Inst Mex Seguro Soc
JT  - Revista medica del Instituto Mexicano del Seguro Social
JID - 101243727
SB  - IM
MH  - Humans
MH  - *Plagiarism
MH  - Research Personnel
MH  - *Scientific Misconduct
OTO - NOTNLM
OT  - Scientific Integrity Review
OT  - Plagiarism
OT  - Artificial Intelligence
OT  - Revisión de Integridad Científica
OT  - Plagio
OT  - Inteligencia Artificial
OT  - ChatGPT
EDAT- 2023/11/23 18:41
MHDA- 2023/11/27 12:42
CRDT- 2023/11/23 17:44
PHST- 2023/04/10 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/11/27 12:42 [medline]
PHST- 2023/11/23 18:41 [pubmed]
PHST- 2023/11/23 17:44 [entrez]
AID - 10.5281/zenodo.10064458 [doi]
PST - epublish
SO  - Rev Med Inst Mex Seguro Soc. 2023 Nov 6;61(6):857-862. doi: 
      10.5281/zenodo.10064458.

PMID- 37179699
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230516
IS  - 2288-6575 (Print)
IS  - 2288-6796 (Electronic)
IS  - 2288-6575 (Linking)
VI  - 104
IP  - 5
DP  - 2023 May
TI  - ChatGPT goes to the operating room: evaluating GPT-4 performance and its 
      potential in surgical education and training in the era of large language models.
PG  - 269-273
LID - 10.4174/astr.2023.104.5.269 [doi]
AB  - PURPOSE: This study aimed to assess the performance of ChatGPT, specifically the 
      GPT-3.5 and GPT-4 models, in understanding complex surgical clinical information 
      and its potential implications for surgical education and training. METHODS: The 
      dataset comprised 280 questions from the Korean general surgery board exams 
      conducted between 2020 and 2022. Both GPT-3.5 and GPT-4 models were evaluated, 
      and their performances were compared using McNemar test. RESULTS: GPT-3.5 
      achieved an overall accuracy of 46.8%, while GPT-4 demonstrated a significant 
      improvement with an overall accuracy of 76.4%, indicating a notable difference in 
      performance between the models (P < 0.001). GPT-4 also exhibited consistent 
      performance across all subspecialties, with accuracy rates ranging from 63.6% to 
      83.3%. CONCLUSION: ChatGPT, particularly GPT-4, demonstrates a remarkable ability 
      to understand complex surgical clinical information, achieving an accuracy rate 
      of 76.4% on the Korean general surgery board exam. However, it is important to 
      recognize the limitations of large language models and ensure that they are used 
      in conjunction with human expertise and judgment.
CI  - Copyright © 2023, the Korean Surgical Society.
FAU - Oh, Namkee
AU  - Oh N
AUID- ORCID: 0000-0002-6594-8973
AD  - Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of 
      Medicine, Seoul, Korea.
FAU - Choi, Gyu-Seong
AU  - Choi GS
AUID- ORCID: 0000-0003-2545-3105
AD  - Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of 
      Medicine, Seoul, Korea.
FAU - Lee, Woo Yong
AU  - Lee WY
AUID- ORCID: 0000-0002-9558-9019
AD  - Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of 
      Medicine, Seoul, Korea.
LA  - eng
PT  - Journal Article
DEP - 20230428
PL  - Korea (South)
TA  - Ann Surg Treat Res
JT  - Annals of surgical treatment and research
JID - 101622895
PMC - PMC10172028
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Continuing medical education
OT  - General surgery
OT  - Medical education
COIS- Conflict of Interest: No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/05/14 13:10
MHDA- 2023/05/14 13:11
CRDT- 2023/05/14 11:56
PHST- 2023/03/23 00:00 [received]
PHST- 2023/03/30 00:00 [revised]
PHST- 2023/03/30 00:00 [accepted]
PHST- 2023/05/14 13:11 [medline]
PHST- 2023/05/14 13:10 [pubmed]
PHST- 2023/05/14 11:56 [entrez]
AID - 10.4174/astr.2023.104.5.269 [doi]
PST - ppublish
SO  - Ann Surg Treat Res. 2023 May;104(5):269-273. doi: 10.4174/astr.2023.104.5.269. 
      Epub 2023 Apr 28.

PMID- 37931137
OWN - NLM
STAT- In-Process
LR  - 20231211
IS  - 1558-9102 (Electronic)
IS  - 1092-4388 (Linking)
VI  - 66
IP  - 12
DP  - 2023 Dec 11
TI  - Automating Intended Target Identification for Paraphasias in Discourse Using a 
      Large Language Model.
PG  - 4949-4966
LID - 10.1044/2023_JSLHR-23-00121 [doi]
AB  - PURPOSE: To date, there are no automated tools for the identification and 
      fine-grained classification of paraphasias within discourse, the production of 
      which is the hallmark characteristic of most people with aphasia (PWA). In this 
      work, we fine-tune a large language model (LLM) to automatically predict 
      paraphasia targets in Cinderella story retellings. METHOD: Data consisted of 332 
      Cinderella story retellings containing 2,489 paraphasias from PWA, for which 
      research assistants identified their intended targets. We supplemented these 
      training data with 256 sessions from control participants, to which we added 
      2,415 synthetic paraphasias. We conducted four experiments using different 
      training data configurations to fine-tune the LLM to automatically "fill in the 
      blank" of the paraphasia with a predicted target, given the context of the rest 
      of the story retelling. We tested the experiments' predictions against our 
      human-identified targets and stratified our results by ambiguity of the targets 
      and clinical factors. RESULTS: The model trained on controls and PWA achieved 
      50.7% accuracy at exactly matching the human-identified target. Fine-tuning on 
      PWA data, with or without controls, led to comparable performance. The model 
      performed better on targets with less human ambiguity and on paraphasias from 
      participants with fluent or less severe aphasia. CONCLUSIONS: We were able to 
      automatically identify the intended target of paraphasias in discourse using just 
      the surrounding language about half of the time. These findings take us a step 
      closer to automatic aphasic discourse analysis. In future work, we will 
      incorporate phonological information from the paraphasia to further improve 
      predictive utility. SUPPLEMENTAL MATERIAL: 
      https://doi.org/10.23641/asha.24463543.
FAU - Salem, Alexandra C
AU  - Salem AC
AUID- ORCID: 0000-0002-0645-8472
AD  - Department of Medical Informatics and Clinical Epidemiology, Oregon Health & 
      Science University, Portland.
FAU - Gale, Robert C
AU  - Gale RC
AD  - Department of Medical Informatics and Clinical Epidemiology, Oregon Health & 
      Science University, Portland.
FAU - Fleegle, Mikala
AU  - Fleegle M
AD  - Department of Speech & Hearing Sciences, Portland State University, OR.
FAU - Fergadiotis, Gerasimos
AU  - Fergadiotis G
AUID- ORCID: 0000-0002-3283-3838
AD  - Department of Speech & Hearing Sciences, Portland State University, OR.
FAU - Bedrick, Steven
AU  - Bedrick S
AUID- ORCID: 0000-0002-0163-9397
AD  - Department of Medical Informatics and Clinical Epidemiology, Oregon Health & 
      Science University, Portland.
LA  - eng
PT  - Journal Article
DEP - 20231106
PL  - United States
TA  - J Speech Lang Hear Res
JT  - Journal of speech, language, and hearing research : JSLHR
JID - 9705610
SB  - IM
EDAT- 2023/11/06 18:41
MHDA- 2023/11/06 18:41
CRDT- 2023/11/06 15:33
PHST- 2023/11/06 18:41 [pubmed]
PHST- 2023/11/06 18:41 [medline]
PHST- 2023/11/06 15:33 [entrez]
AID - 10.1044/2023_JSLHR-23-00121 [doi]
PST - ppublish
SO  - J Speech Lang Hear Res. 2023 Dec 11;66(12):4949-4966. doi: 
      10.1044/2023_JSLHR-23-00121. Epub 2023 Nov 6.

PMID- 37054810
OWN - NLM
STAT- MEDLINE
DCOM- 20230718
LR  - 20230718
IS  - 1097-6787 (Electronic)
IS  - 0190-9622 (Linking)
VI  - 89
IP  - 2
DP  - 2023 Aug
TI  - Artificial intelligence-derived dermatology case reports are indistinguishable 
      from those written by humans: A single-blinded observer study.
PG  - 388-390
LID - S0190-9622(23)00587-X [pii]
LID - 10.1016/j.jaad.2023.04.005 [doi]
FAU - Dunn, Charles
AU  - Dunn C
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida. 
      Electronic address: DrCDunn@ADCSClinics.com.
FAU - Hunter, Jacob
AU  - Hunter J
AD  - Department of Scholarly Activity, Kansas City University, Kansas City, Missouri.
FAU - Steffes, William
AU  - Steffes W
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
FAU - Whitney, Zackary
AU  - Whitney Z
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
FAU - Foss, Michael
AU  - Foss M
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
FAU - Mammino, Jere
AU  - Mammino J
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
FAU - Leavitt, Adam
AU  - Leavitt A
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
FAU - Hawkins, Spencer D
AU  - Hawkins SD
AD  - Deparment of Dermatology, Advanced Dermatology & Cosmetic Surgery, East 
      Greenwich, Rhode Island.
FAU - Dane, Alexander
AU  - Dane A
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
FAU - Yungmann, Martin
AU  - Yungmann M
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
FAU - Nathoo, Rajiv
AU  - Nathoo R
AD  - Department of Dermatology, KCU - GME/ADCS Consortium, Maitland, Florida.
LA  - eng
PT  - Case Reports
PT  - Journal Article
PT  - Randomized Controlled Trial
DEP - 20230411
PL  - United States
TA  - J Am Acad Dermatol
JT  - Journal of the American Academy of Dermatology
JID - 7907132
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Dermatology
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - case reports
OT  - large language models
OT  - machine learning
OT  - medical writing
COIS- Conflicts of interest None disclosed.
EDAT- 2023/04/14 06:00
MHDA- 2023/07/18 13:09
CRDT- 2023/04/13 19:22
PHST- 2023/03/20 00:00 [received]
PHST- 2023/03/29 00:00 [revised]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/07/18 13:09 [medline]
PHST- 2023/04/14 06:00 [pubmed]
PHST- 2023/04/13 19:22 [entrez]
AID - S0190-9622(23)00587-X [pii]
AID - 10.1016/j.jaad.2023.04.005 [doi]
PST - ppublish
SO  - J Am Acad Dermatol. 2023 Aug;89(2):388-390. doi: 10.1016/j.jaad.2023.04.005. Epub 
      2023 Apr 11.

PMID- 37305580
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230925
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 13
DP  - 2023
TI  - AI-based online chat and the future of oncology care: a promising technology or a 
      solution in search of a problem?
PG  - 1176617
LID - 10.3389/fonc.2023.1176617 [doi]
LID - 1176617
FAU - Kassab, Joseph
AU  - Kassab J
AD  - Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States.
FAU - Nasr, Lewis
AU  - Nasr L
AD  - Department of Leukemia, The University of Texas MD Anderson Cancer Center, 
      Houston, TX, United States.
FAU - Gebrael, Georges
AU  - Gebrael G
AD  - Department of Hematology and Oncology, Saint-Joseph University of Beirut, Beirut, 
      Lebanon.
FAU - Chedid El Helou, Michel
AU  - Chedid El Helou M
AD  - Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States.
FAU - Saba, Ludovic
AU  - Saba L
AD  - Maroone Cancer Center, Cleveland Clinic Florida, Weston, FL, United States.
FAU - Haroun, Elio
AU  - Haroun E
AD  - Division of Hematology and Oncology, State University of New York Upstate Medical 
      University, Syracuse, NY, United States.
FAU - Dahdah, Joseph El
AU  - Dahdah JE
AD  - Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States.
FAU - Nasr, Fadi
AU  - Nasr F
AD  - Department of Hematology and Oncology, Saint-Joseph University of Beirut, Beirut, 
      Lebanon.
LA  - eng
PT  - Journal Article
DEP - 20230525
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
CIN - Front Oncol. 2023 Sep 07;13:1239932. PMID: 37746294
PMC - PMC10250003
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence - AI
OT  - assessment
OT  - chatbot
OT  - oncology
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/06/12 06:42
MHDA- 2023/06/12 06:43
CRDT- 2023/06/12 04:25
PHST- 2023/02/28 00:00 [received]
PHST- 2023/05/16 00:00 [accepted]
PHST- 2023/06/12 06:43 [medline]
PHST- 2023/06/12 06:42 [pubmed]
PHST- 2023/06/12 04:25 [entrez]
AID - 10.3389/fonc.2023.1176617 [doi]
PST - epublish
SO  - Front Oncol. 2023 May 25;13:1176617. doi: 10.3389/fonc.2023.1176617. eCollection 
      2023.

PMID- 36816423
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230224
IS  - 2055-7647 (Print)
IS  - 2055-7647 (Electronic)
IS  - 2055-7647 (Linking)
VI  - 9
IP  - 1
DP  - 2023
TI  - AI did not write this manuscript, or did it? Can we trick the AI text detector 
      into generated texts? The potential future of ChatGPT and AI in Sports & Exercise 
      Medicine manuscript generation.
PG  - e001568
LID - 10.1136/bmjsem-2023-001568 [doi]
LID - e001568
FAU - Anderson, Nash
AU  - Anderson N
AUID- ORCID: 0000-0002-1786-8805
AD  - Tuggeranong Chiropractic Centre, Canberra, Australian Capital Territory, 
      Australia.
FAU - Belavy, Daniel L
AU  - Belavy DL
AUID- ORCID: 0000-0002-9307-832X
AD  - Department of Applied Health Sciences, Hochschule für Gesundheit, Bochum, 
      Gesundheitscampus 6-8, Germany.
FAU - Perle, Stephen M
AU  - Perle SM
AUID- ORCID: 0000-0001-5829-0256
AD  - Big Data Interrogation Group, AECC University College, Bournemouth, Dorset, UK.
AD  - College of Science, Health, Engineering and Education, Discipline of Psychology, 
      Exercise Science, Chiropractic and Counselling, Murdoch University, Murdoch, 
      Western Australia, Australia.
FAU - Hendricks, Sharief
AU  - Hendricks S
AUID- ORCID: 0000-0002-3416-6266
AD  - Division of Physiological Sciences and Health through Physical Activity, 
      Lifestyle and Sport (HPALS) Research Centre, Department of Human Biology, Faculty 
      of Health Sciences, University of Cape Town, Rondebosch, South Africa.
AD  - Institute for Sport, Physical Activity and Leisure, Leeds Beckett University 
      Carnegie School of Sport, Leeds, UK.
FAU - Hespanhol, Luiz
AU  - Hespanhol L
AUID- ORCID: 0000-0003-1774-4746
AD  - Masters and Doctoral Programs in Physical Therapy, Universidade Cidade de São 
      Paulo (UNICID), Sao Paulo, Brazil.
AD  - Amsterdam Collaboration on Health and Safety in Sports, Department of Public and 
      Occupational Health, Amsterdam Movement Sciences, Amsterdam UMC, University 
      Medical Centres - Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
FAU - Verhagen, Evert
AU  - Verhagen E
AUID- ORCID: 0000-0001-9227-8234
AD  - Amsterdam Collaboration on Health and Safety in Sports, Department of Public and 
      Occupational Health, Amsterdam Movement Sciences, Amsterdam UMC, University 
      Medical Centres - Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
FAU - Memon, Aamir R
AU  - Memon AR
AUID- ORCID: 0000-0002-3203-418X
AD  - Institute for Health and Sport, Victoria University, Melbourne, Victoria, 
      Australia.
LA  - eng
PT  - Editorial
DEP - 20230216
PL  - England
TA  - BMJ Open Sport Exerc Med
JT  - BMJ open sport & exercise medicine
JID - 101681007
PMC - PMC9936276
OTO - NOTNLM
OT  - Medical Ethics
OT  - Position statement
OT  - Research
OT  - Sports
OT  - Sports & exercise medicine
COIS- Competing interests: NA and DLB are senior editorial board members, SH, ARM and 
      LH are associate editors, and EV is the editor-in-chief of BMJ Open Sports & 
      Exercise Medicine.
EDAT- 2023/02/24 06:00
MHDA- 2023/02/24 06:01
CRDT- 2023/02/23 09:20
PHST- 2023/02/07 00:00 [accepted]
PHST- 2023/02/23 09:20 [entrez]
PHST- 2023/02/24 06:00 [pubmed]
PHST- 2023/02/24 06:01 [medline]
AID - bmjsem-2023-001568 [pii]
AID - 10.1136/bmjsem-2023-001568 [doi]
PST - epublish
SO  - BMJ Open Sport Exerc Med. 2023 Feb 16;9(1):e001568. doi: 
      10.1136/bmjsem-2023-001568. eCollection 2023.

PMID- 37518784
OWN - NLM
STAT- MEDLINE
DCOM- 20230801
LR  - 20230801
IS  - 1175-5334 (Electronic)
IS  - 1175-5326 (Linking)
VI  - 5297
IP  - 3
DP  - 2023 Jun 2
TI  - Harnessing the power of AI language models for taxonomy and systematics: a 
      follow-up to "Can ChatGPT be leveraged for taxonomic investigations? Potential 
      and limitations of a new technology" by Davinack (2023).
PG  - 446-450
LID - 10.11646/zootaxa.5297.3.9 [doi]
AB  - N/A.
FAU - Santos, Charles Morphy D
AU  - Santos CMD
AD  - Centro de Ciências Naturais e Humanas, Laboratório de Sistemática e Diversidade, 
      Universidade Federal do ABC, Av. dos Estados, 5001. Bairro Bangu, CEP 09210-580, 
      Santo André/SP, Brazil.. charlesmorphy@gmail.com.
FAU - Gois, João Paulo
AU  - Gois JP
AD  - Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Av. dos 
      Estados, 5001. Bairro Bangu, CEP 09210-580, Santo André/SP, Brazil.. 
      jpgois@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230602
PL  - New Zealand
TA  - Zootaxa
JT  - Zootaxa
JID - 101179386
SB  - IM
MH  - Animals
MH  - Follow-Up Studies
MH  - *Language
MH  - *Technology
EDAT- 2023/07/31 00:41
MHDA- 2023/08/01 06:44
CRDT- 2023/07/30 23:42
PHST- 2023/06/01 00:00 [received]
PHST- 2023/08/01 06:44 [medline]
PHST- 2023/07/31 00:41 [pubmed]
PHST- 2023/07/30 23:42 [entrez]
AID - 10.11646/zootaxa.5297.3.9 [doi]
PST - epublish
SO  - Zootaxa. 2023 Jun 2;5297(3):446-450. doi: 10.11646/zootaxa.5297.3.9.

PMID- 37816837
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231118
IS  - 2730-664X (Electronic)
IS  - 2730-664X (Linking)
VI  - 3
IP  - 1
DP  - 2023 Oct 10
TI  - The future landscape of large language models in medicine.
PG  - 141
LID - 10.1038/s43856-023-00370-1 [doi]
LID - 141
AB  - Large language models (LLMs) are artificial intelligence (AI) tools specifically 
      trained to process and generate text. LLMs attracted substantial public attention 
      after OpenAI's ChatGPT was made publicly available in November 2022. LLMs can 
      often answer questions, summarize, paraphrase and translate text on a level that 
      is nearly indistinguishable from human capabilities. The possibility to actively 
      interact with models like ChatGPT makes LLMs attractive tools in various fields, 
      including medicine. While these models have the potential to democratize medical 
      knowledge and facilitate access to healthcare, they could equally distribute 
      misinformation and exacerbate scientific misconduct due to a lack of 
      accountability and transparency. In this article, we provide a systematic and 
      comprehensive overview of the potentials and limitations of LLMs in clinical 
      practice, medical research and medical education.
CI  - © 2023. Springer Nature Limited.
FAU - Clusmann, Jan
AU  - Clusmann J
AUID- ORCID: 0000-0003-2925-8438
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
AD  - Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
FAU - Kolbinger, Fiona R
AU  - Kolbinger FR
AUID- ORCID: 0000-0003-2265-4809
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
AD  - Department of Visceral, Thoracic and Vascular Surgery, University Hospital and 
      Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, 
      Dresden, Germany.
FAU - Muti, Hannah Sophie
AU  - Muti HS
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
AD  - Department of Visceral, Thoracic and Vascular Surgery, University Hospital and 
      Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, 
      Dresden, Germany.
FAU - Carrero, Zunamys I
AU  - Carrero ZI
AUID- ORCID: 0000-0001-8501-1566
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
FAU - Eckardt, Jan-Niklas
AU  - Eckardt JN
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
AD  - Department of Medicine I, University Hospital Dresden, Dresden, Germany.
FAU - Laleh, Narmin Ghaffari
AU  - Laleh NG
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
AD  - Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
FAU - Löffler, Chiara Maria Lavinia
AU  - Löffler CML
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
AD  - Department of Medicine I, University Hospital Dresden, Dresden, Germany.
FAU - Schwarzkopf, Sophie-Caroline
AU  - Schwarzkopf SC
AD  - Department of Visceral, Thoracic and Vascular Surgery, University Hospital and 
      Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, 
      Dresden, Germany.
FAU - Unger, Michaela
AU  - Unger M
AUID- ORCID: 0000-0001-5811-0200
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
FAU - Veldhuizen, Gregory P
AU  - Veldhuizen GP
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany.
FAU - Wagner, Sophia J
AU  - Wagner SJ
AD  - Helmholtz Munich-German Research Center for Environment and Health, Munich, 
      Germany.
AD  - School of Computation, Information and Technology, Technical University of 
      Munich, Munich, Germany.
FAU - Kather, Jakob Nikolas
AU  - Kather JN
AD  - Else Kroener Fresenius Center for Digital Health, TUD Dresden University of 
      Technology, Dresden, Germany. jakob-nikolas.kather@alumni.dkfz.de.
AD  - Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany. 
      jakob-nikolas.kather@alumni.dkfz.de.
AD  - Department of Medicine I, University Hospital Dresden, Dresden, Germany. 
      jakob-nikolas.kather@alumni.dkfz.de.
AD  - Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital 
      Heidelberg, Heidelberg, Germany. jakob-nikolas.kather@alumni.dkfz.de.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231010
PL  - England
TA  - Commun Med (Lond)
JT  - Communications medicine
JID - 9918250414506676
PMC - PMC10564921
COIS- The authors declare the following competing interests: J.N.K. declares consulting 
      services for Owkin, France; Panakeia, UK; and DoMore Diagnostics, Norway and has 
      received honoraria for lectures from AstraZeneca, Bayer, Eisai, MSD, BMS, Roche, 
      Pfizer and Fresenius. The other authors declare no competing interests.
EDAT- 2023/10/11 00:42
MHDA- 2023/10/11 00:43
CRDT- 2023/10/10 23:31
PHST- 2023/02/15 00:00 [received]
PHST- 2023/09/21 00:00 [accepted]
PHST- 2023/10/11 00:43 [medline]
PHST- 2023/10/11 00:42 [pubmed]
PHST- 2023/10/10 23:31 [entrez]
AID - 10.1038/s43856-023-00370-1 [pii]
AID - 370 [pii]
AID - 10.1038/s43856-023-00370-1 [doi]
PST - epublish
SO  - Commun Med (Lond). 2023 Oct 10;3(1):141. doi: 10.1038/s43856-023-00370-1.

PMID- 37577834
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230822
IS  - 1473-0189 (Electronic)
IS  - 1473-0189 (Linking)
VI  - 23
IP  - 17
DP  - 2023 Aug 22
TI  - Utilizing ChatGPT to assist CAD design for microfluidic devices.
PG  - 3778-3784
LID - 10.1039/d3lc00518f [doi]
AB  - ChatGPT is a generative AI model that has garnered tremendous public interest due 
      to its ability to solve diverse problems through high-level reasoning and 
      analysis. Among its features is an ability to create and debug code. While this 
      capability has been explored with conventional programming languages such as 
      Python, it has yet to be applied to computer-aided design (CAD). In this work, we 
      utilized GPT-4 to create functional microfluidic components using OpenSCAD, an 
      open-source CAD software package. Through an iterative dialogue, GPT-4 created 
      functional designs for a helix/spiral, a valve, a t-junction, and a serpentine 
      channel. This concept could facilitate CAD in the future for both technical and 
      non-technical users and can be reasonably extended to other fields.
FAU - Nelson, Matt D
AU  - Nelson MD
AUID- ORCID: 0000-0001-7433-8731
AD  - Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 
      USA. u1141265@umail.utah.edu.
FAU - Goenner, Brady L
AU  - Goenner BL
AD  - Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 
      USA.
FAU - Gale, Bruce K
AU  - Gale BK
AUID- ORCID: 0000-0001-5843-3464
AD  - Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 
      USA. u1141265@umail.utah.edu.
AD  - Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - England
TA  - Lab Chip
JT  - Lab on a chip
JID - 101128948
SB  - IM
EDAT- 2023/08/14 06:43
MHDA- 2023/08/14 06:44
CRDT- 2023/08/14 05:28
PHST- 2023/08/14 06:44 [medline]
PHST- 2023/08/14 06:43 [pubmed]
PHST- 2023/08/14 05:28 [entrez]
AID - 10.1039/d3lc00518f [doi]
PST - epublish
SO  - Lab Chip. 2023 Aug 22;23(17):3778-3784. doi: 10.1039/d3lc00518f.

PMID- 37082496
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230422
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Neuroblastoma Masquerading as a Septic Hip Infection in a Three-Year-Old.
PG  - e36350
LID - 10.7759/cureus.36350 [doi]
LID - e36350
AB  - Metastatic neuroblastoma to the bone and septic joint shares the same incidence 
      in age and clinical symptomology. Here we discuss a three-year-old male who 
      presented with anemia, persistent hip pain, and a refusal to bear weight. A 
      thorough evaluation based on a broad differential diagnosis allowed for an 
      expedient diagnosis of metastatic neuroblastoma. The timely diagnosis allowed for 
      rapid enrolment in a children's oncology group (COG) clinical trial for advanced 
      neuroblastoma. The patient tolerated the therapy without adverse events and 
      remains in remission.
CI  - Copyright © 2023, Lynch et al.
FAU - Lynch, Joseph D
AU  - Lynch JD
AD  - Pediatric Hospital Medicine, West Virginia University School of Medicine, 
      Morgantown, USA.
FAU - Tomboc, Patrick J
AU  - Tomboc PJ
AD  - Pediatric Hematology-Oncology, West Virginia University School of Medicine, 
      Morgantown, USA.
LA  - eng
PT  - Case Reports
DEP - 20230319
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10111876
OTO - NOTNLM
OT  - chatgpt
OT  - chatgpt improved case report
OT  - inpatient pediatrics
OT  - neuroblastoma
OT  - neuroblastoma metastases
OT  - pediatric clinical conundrum
OT  - pediatric hematology-oncology
OT  - pediatric hospital medicine
OT  - pediatric septic joint
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/21 06:41
MHDA- 2023/04/21 06:42
CRDT- 2023/04/21 02:26
PHST- 2023/03/18 00:00 [accepted]
PHST- 2023/04/21 06:42 [medline]
PHST- 2023/04/21 06:41 [pubmed]
PHST- 2023/04/21 02:26 [entrez]
AID - 10.7759/cureus.36350 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 19;15(3):e36350. doi: 10.7759/cureus.36350. eCollection 2023 
      Mar.

PMID- 37073196
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230421
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Treatment of Periorbital Edema in a Patient With Systemic Lupus Erythematosus 
      During Pregnancy: A Case Report Written With the Assistance of ChatGPT.
PG  - e36302
LID - 10.7759/cureus.36302 [doi]
LID - e36302
AB  - Systemic lupus erythematosus (SLE) is an autoimmune disease that has a wide range 
      of manifestations and can affect nearly every organ system. Skin manifestations 
      are a common finding in SLE. They are often photosensitive and can be exacerbated 
      by exposure to ultraviolet light. Here, we discuss the case of a 34-year-old 
      African American woman who presented with periorbital edema while 12 weeks 
      pregnant. This case highlights the importance of avoiding sun exposure in 
      patients with SLE and the challenge of treating SLE during pregnancy.
CI  - Copyright © 2023, Jansz et al.
FAU - Jansz, Jacqueline
AU  - Jansz J
AD  - Internal Medicine, University of Illinois at Chicago, Chicago, USA.
FAU - Manansala, Michael J
AU  - Manansala MJ
AD  - Rheumatology, Rush University Medical Center, Chicago, USA.
FAU - Sweiss, Nadera J
AU  - Sweiss NJ
AD  - Rheumatology, University of Illinois at Chicago, Chicago, USA.
LA  - eng
PT  - Case Reports
DEP - 20230317
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10106120
OTO - NOTNLM
OT  - chatgpt
OT  - periorbital edema
OT  - pregnancy
OT  - sle
OT  - sun exposure
OT  - systemic lupus erythematosus
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/19 06:41
MHDA- 2023/04/19 06:42
CRDT- 2023/04/19 01:44
PHST- 2023/03/17 00:00 [accepted]
PHST- 2023/04/19 06:42 [medline]
PHST- 2023/04/19 06:41 [pubmed]
PHST- 2023/04/19 01:44 [entrez]
AID - 10.7759/cureus.36302 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 17;15(3):e36302. doi: 10.7759/cureus.36302. eCollection 2023 
      Mar.

PMID- 37815442
OWN - NLM
STAT- MEDLINE
DCOM- 20231011
LR  - 20231105
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Print)
IS  - 0033-8419 (Linking)
VI  - 309
IP  - 1
DP  - 2023 Oct
TI  - Feasibility of Using the Privacy-preserving Large Language Model Vicuna for 
      Labeling Radiology Reports.
PG  - e231147
LID - 10.1148/radiol.231147 [doi]
LID - e231147
AB  - Background Large language models (LLMs) such as ChatGPT, though proficient in 
      many text-based tasks, are not suitable for use with radiology reports due to 
      patient privacy constraints. Purpose To test the feasibility of using an 
      alternative LLM (Vicuna-13B) that can be run locally for labeling radiography 
      reports. Materials and Methods Chest radiography reports from the MIMIC-CXR and 
      National Institutes of Health (NIH) data sets were included in this retrospective 
      study. Reports were examined for 13 findings. Outputs reporting the presence or 
      absence of the 13 findings were generated by Vicuna by using a single-step or 
      multistep prompting strategy (prompts 1 and 2, respectively). Agreements between 
      Vicuna outputs and CheXpert and CheXbert labelers were assessed using Fleiss κ. 
      Agreement between Vicuna outputs from three runs under a hyperparameter setting 
      that introduced some randomness (temperature, 0.7) was also assessed. The 
      performance of Vicuna and the labelers was assessed in a subset of 100 NIH 
      reports annotated by a radiologist with use of area under the receiver operating 
      characteristic curve (AUC). Results A total of 3269 reports from the MIMIC-CXR 
      data set (median patient age, 68 years [IQR, 59-79 years]; 161 male patients) and 
      25 596 reports from the NIH data set (median patient age, 47 years [IQR, 32-58 
      years]; 1557 male patients) were included. Vicuna outputs with prompt 2 showed, 
      on average, moderate to substantial agreement with the labelers on the MIMIC-CXR 
      (κ median, 0.57 [IQR, 0.45-0.66] with CheXpert and 0.64 [IQR, 0.45-0.68] with 
      CheXbert) and NIH (κ median, 0.52 [IQR, 0.41-0.65] with CheXpert and 0.55 [IQR, 
      0.41-0.74] with CheXbert) data sets, respectively. Vicuna with prompt 2 performed 
      at par (median AUC, 0.84 [IQR, 0.74-0.93]) with both labelers on nine of 11 
      findings. Conclusion In this proof-of-concept study, outputs of the LLM Vicuna 
      reporting the presence or absence of 13 findings on chest radiography reports 
      showed moderate to substantial agreement with existing labelers. © RSNA, 2023 
      Supplemental material is available for this article. See also the editorial by 
      Cai in this issue.
FAU - Mukherjee, Pritam
AU  - Mukherjee P
AUID- ORCID: 0000-0002-9975-9994
AD  - From the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department 
      of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 
      Bldg 10, Room 1C224D, 10 Center Dr, Bethesda, MD 20892-1182.
FAU - Hou, Benjamin
AU  - Hou B
AD  - From the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department 
      of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 
      Bldg 10, Room 1C224D, 10 Center Dr, Bethesda, MD 20892-1182.
FAU - Lanfredi, Ricardo B
AU  - Lanfredi RB
AUID- ORCID: 0000-0001-8740-5796
AD  - From the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department 
      of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 
      Bldg 10, Room 1C224D, 10 Center Dr, Bethesda, MD 20892-1182.
FAU - Summers, Ronald M
AU  - Summers RM
AUID- ORCID: 0000-0001-8081-7376
AD  - From the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department 
      of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 
      Bldg 10, Room 1C224D, 10 Center Dr, Bethesda, MD 20892-1182.
LA  - eng
PT  - Journal Article
PT  - Research Support, N.I.H., Intramural
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CIN - Radiology. 2023 Oct;309(1):e232335. PMID: 37815443
MH  - United States
MH  - Humans
MH  - Male
MH  - Animals
MH  - Aged
MH  - Middle Aged
MH  - *Camelids, New World
MH  - Privacy
MH  - Feasibility Studies
MH  - Retrospective Studies
MH  - Language
MH  - *Radiology
PMC - PMC10623189
COIS- Disclosures of conflicts of interest: P.M. No relevant relationships. B.H. No 
      relevant relationships. R.B.L. No relevant relationships. R.M.S. Cooperative 
      research and development agreement with PingAn; patent royalties or software 
      licenses from iCAD, Philips, PingAn, ScanMed, Translation Holdings, and MBG; 
      advisory board member for Duke University.
EDAT- 2023/10/10 12:43
MHDA- 2023/10/11 06:45
PMCR- 2024/10/01
CRDT- 2023/10/10 09:52
PHST- 2024/10/01 00:00 [pmc-release]
PHST- 2023/10/11 06:45 [medline]
PHST- 2023/10/10 12:43 [pubmed]
PHST- 2023/10/10 09:52 [entrez]
AID - 10.1148/radiol.231147 [doi]
PST - ppublish
SO  - Radiology. 2023 Oct;309(1):e231147. doi: 10.1148/radiol.231147.

PMID- 37946373
OWN - NLM
STAT- Publisher
LR  - 20231110
IS  - 2287-285X (Electronic)
IS  - 2287-2728 (Linking)
DP  - 2023 Nov 10
TI  - Assessing State-of-the-Art Online Large Language Models for Patient Education 
      Regarding Steatotic Liver Disease.
LID - 10.3350/cmh.2023.0440 [doi]
FAU - Zhang, Yiwen
AU  - Zhang Y
AD  - Department of Endocrinology and Metabolic Hepatology, the Affiliated Hospital of 
      Qingdao University, Qingdao, China.
FAU - Wu, Liwei
AU  - Wu L
AD  - Department of Gastroenterology and Hepatology, Shanghai East Hospital, Tongji 
      University, Shanghai, China.
FAU - Mu, Zepeng
AU  - Mu Z
AD  - Department of Endocrinology and Metabolic Hepatology, the Affiliated Hospital of 
      Qingdao University, Qingdao, China.
FAU - Ren, Linlin
AU  - Ren L
AD  - Department of Gastroenterology and Hepatology, The Affiliated Hospital of Qingdao 
      University, Qingdao, China.
CN  - LLM-Liver Investigators
FAU - Wong, Tien Yin
AU  - Wong TY
AD  - Tsinghua Medicine, Tsinghua University, Beijing, China.
FAU - Ji, Hongwei
AU  - Ji H
AD  - Tsinghua Medicine, Tsinghua University, Beijing, China.
AD  - Department of Internal Medicine, Beijing Tsinghua Changgung Hospital, Beijing, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20231110
PL  - Korea (South)
TA  - Clin Mol Hepatol
JT  - Clinical and molecular hepatology
JID - 101586730
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Claude2
OT  - Google Bard
OT  - Llama-2
OT  - large language models
OT  - steatotic liver disease
EDAT- 2023/11/10 06:45
MHDA- 2023/11/10 06:45
CRDT- 2023/11/10 00:44
PHST- 2023/10/29 00:00 [received]
PHST- 2023/11/10 00:00 [accepted]
PHST- 2023/11/10 06:45 [medline]
PHST- 2023/11/10 06:45 [pubmed]
PHST- 2023/11/10 00:44 [entrez]
AID - cmh.2023.0440 [pii]
AID - 10.3350/cmh.2023.0440 [doi]
PST - aheadofprint
SO  - Clin Mol Hepatol. 2023 Nov 10. doi: 10.3350/cmh.2023.0440.

PMID- 38081986
OWN - NLM
STAT- Publisher
LR  - 20231211
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Dec 11
TI  - Letter on: "Consulting the Digital Doctor: Google Versus ChatGPT as Sources of 
      Information on Breast Implant-Associated Anaplastic Large Cell Lymphoma and 
      Breast Implant Illness".
LID - 10.1007/s00266-023-03776-3 [doi]
FAU - Lanzano, Giuseppe
AU  - Lanzano G
AUID- ORCID: 0000-0001-7484-0780
AD  - Department of Plastic and Reconstructive Surgery, University of Campania Luigi 
      Vanvitelli, Piazza Luigi Miraglia, 2, 80138, Naples, NA, Italy. 
      dottgiuseppelanzano@gmail.com.
LA  - eng
PT  - Letter
DEP - 20231211
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EDAT- 2023/12/12 00:42
MHDA- 2023/12/12 00:42
CRDT- 2023/12/11 23:39
PHST- 2023/11/01 00:00 [received]
PHST- 2023/11/22 00:00 [accepted]
PHST- 2023/12/12 00:42 [medline]
PHST- 2023/12/12 00:42 [pubmed]
PHST- 2023/12/11 23:39 [entrez]
AID - 10.1007/s00266-023-03776-3 [pii]
AID - 10.1007/s00266-023-03776-3 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Dec 11. doi: 10.1007/s00266-023-03776-3.

PMID- 38088683
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 2192-5682 (Print)
IS  - 2192-5682 (Linking)
DP  - 2023 Dec 13
TI  - Letter re: Reply to Letter to Editor on ChatGPT and its Role in the 
      Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A 
      Comparative Analysis and Narrative Review.
PG  - 21925682231222272
LID - 10.1177/21925682231222272 [doi]
FAU - Cho, Samuel K
AU  - Cho SK
AUID- ORCID: 0000-0001-7511-2486
AD  - Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New 
      York, NY, USA. RINGGOLD: 5925
LA  - eng
PT  - Letter
DEP - 20231213
PL  - England
TA  - Global Spine J
JT  - Global spine journal
JID - 101596156
COIS- Declaration of Conflicting InterestsThe author(s) declared the following 
      potential conflicts of interest with respect to the research, authorship, and/or 
      publication of this article: Samuel K. Cho. AAOS: Board or committee member. 
      American Orthopaedic Association: Board or committee member. AOSpine North 
      America: Board or committee member, Institutional Fellowship Support. Cervical 
      Spine Research Society: Board or committee member. Globus Medical: IP Royalties, 
      Institutional Fellowship Support. North American Spine Society: Board or 
      committee member. Scoliosis Research Society: Board or committee member. SIBone: 
      Paid consultant.
EDAT- 2023/12/13 12:43
MHDA- 2023/12/13 12:43
CRDT- 2023/12/13 09:14
PHST- 2023/12/13 12:43 [medline]
PHST- 2023/12/13 12:43 [pubmed]
PHST- 2023/12/13 09:14 [entrez]
AID - 10.1177/21925682231222272 [doi]
PST - aheadofprint
SO  - Global Spine J. 2023 Dec 13:21925682231222272. doi: 10.1177/21925682231222272.

PMID- 37840258
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231106
IS  - 1545-7222 (Electronic)
IS  - 0895-0172 (Linking)
VI  - 35
IP  - 4
DP  - 2023 Fall
TI  - Medical Metaverse, Part 2: Artificial Intelligence Algorithms and Large Language 
      Models in Psychiatry and Clinical Neurosciences.
PG  - 316-320
LID - 10.1176/appi.neuropsych.20230117 [doi]
FAU - López-Ojeda, Wilfredo
AU  - López-Ojeda W
AD  - Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical 
      Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner 
      Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); 
      Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and 
      Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, 
      N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College 
      of Medicine, Houston (Hurley).
FAU - Hurley, Robin A
AU  - Hurley RA
AD  - Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical 
      Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner 
      Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); 
      Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and 
      Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, 
      N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College 
      of Medicine, Houston (Hurley).
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - J Neuropsychiatry Clin Neurosci
JT  - The Journal of neuropsychiatry and clinical neurosciences
JID - 8911344
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Algorithms
MH  - *Psychiatry
OTO - NOTNLM
OT  - Artificial Intelligence Algorithms
OT  - ChatGPT
OT  - Clinical Neurosciences
OT  - Medical Education
OT  - Medical Metaverse
OT  - Mental Health Chatbots
COIS- The authors report no financial relationships with commercial interests.
EDAT- 2023/10/16 06:48
MHDA- 2023/10/23 01:18
CRDT- 2023/10/16 03:40
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/16 06:48 [pubmed]
PHST- 2023/10/16 03:40 [entrez]
AID - 10.1176/appi.neuropsych.20230117 [doi]
PST - ppublish
SO  - J Neuropsychiatry Clin Neurosci. 2023 Fall;35(4):316-320. doi: 
      10.1176/appi.neuropsych.20230117.

PMID- 37194466
OWN - NLM
STAT- Publisher
LR  - 20230517
IS  - 1873-1953 (Electronic)
IS  - 1474-5151 (Linking)
DP  - 2023 May 17
TI  - Response to the Letter to the Editor on: Dr. ChatGPT in Cardiovascular Nursing: A 
      Deeper Dive into Trustworthiness, Value, and Potential Risks.
LID - zvad049 [pii]
LID - 10.1093/eurjcn/zvad049 [doi]
FAU - Van Bulck, Liesbet
AU  - Van Bulck L
AUID- ORCID: 0000-0001-8975-4455
AD  - KU Leuven Biomedical Sciences Group: Katholieke Universiteit Leuven Groep, 
      Biomedische Wetenschappen Leuven, BELGIUM.
FAU - Moons, Philip
AU  - Moons P
AUID- ORCID: 0000-0002-8609-4516
AD  - KU Leuven Biomedical Sciences Group: Katholieke Universiteit Leuven Groep, 
      Biomedische Wetenschappen Leuven, BELGIUM.
LA  - eng
PT  - Journal Article
DEP - 20230517
PL  - England
TA  - Eur J Cardiovasc Nurs
JT  - European journal of cardiovascular nursing
JID - 101128793
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - chatbot
OT  - language models
OT  - natural language processing
OT  - nurses
OT  - patient information
EDAT- 2023/05/17 06:42
MHDA- 2023/05/17 06:42
CRDT- 2023/05/17 04:03
PHST- 2023/05/07 00:00 [received]
PHST- 2023/05/15 00:00 [accepted]
PHST- 2023/05/17 06:42 [medline]
PHST- 2023/05/17 06:42 [pubmed]
PHST- 2023/05/17 04:03 [entrez]
AID - 7166922 [pii]
AID - 10.1093/eurjcn/zvad049 [doi]
PST - aheadofprint
SO  - Eur J Cardiovasc Nurs. 2023 May 17:zvad049. doi: 10.1093/eurjcn/zvad049.

PMID- 37838001
OWN - NLM
STAT- Publisher
LR  - 20231112
IS  - 1527-9995 (Electronic)
IS  - 0090-4295 (Linking)
DP  - 2023 Oct 12
TI  - Can ChatGPT Realistically and Reproducibly Assess the Difficulty Level of Written 
      Questions in the In-Service Assessment of the European Board of Urology? 
      [Urology, 2023 Jul;177:29-33. doi: 10.1016/j.urology.2023.05.010].
LID - S0090-4295(23)00880-4 [pii]
LID - 10.1016/j.urology.2023.09.036 [doi]
FAU - May, Matthias
AU  - May M
AD  - Department of Urology, St. Elisabeth Hospital Straubing, Brothers of Mercy 
      Hospital, Straubing, Germany. Electronic address: 
      matthias.may@klinikum-straubing.de.
FAU - Körner-Riffard, Katharina
AU  - Körner-Riffard K
AD  - Department of Urology, Caritas St. Josef Medical Centre, University of 
      Regensburg, Regensburg, Germany.
FAU - Kollitsch, Lisa
AU  - Kollitsch L
AD  - Department of Urology and Andrology, Klinik Donaustadt, Vienna, Austria.
LA  - eng
PT  - Letter
DEP - 20231012
PL  - United States
TA  - Urology
JT  - Urology
JID - 0366151
SB  - IM
COIS- Declaration of Competing Interest The authors of the “Letter to the Editor” 
      declare that they have no conflicts of interest to disclose.
EDAT- 2023/10/15 05:46
MHDA- 2023/10/15 05:46
CRDT- 2023/10/14 19:24
PHST- 2023/09/13 00:00 [received]
PHST- 2023/09/14 00:00 [accepted]
PHST- 2023/10/15 05:46 [pubmed]
PHST- 2023/10/15 05:46 [medline]
PHST- 2023/10/14 19:24 [entrez]
AID - S0090-4295(23)00880-4 [pii]
AID - 10.1016/j.urology.2023.09.036 [doi]
PST - aheadofprint
SO  - Urology. 2023 Oct 12:S0090-4295(23)00880-4. doi: 10.1016/j.urology.2023.09.036.

PMID- 37160626
OWN - NLM
STAT- MEDLINE
DCOM- 20230731
LR  - 20230731
IS  - 1432-1335 (Electronic)
IS  - 0171-5216 (Linking)
VI  - 149
IP  - 11
DP  - 2023 Sep
TI  - Large language models for oncological applications.
PG  - 9505-9508
LID - 10.1007/s00432-023-04824-w [doi]
AB  - Large language models such as ChatGPT have gained public and scientific 
      attention. These models may support oncologists in their work. Oncologists should 
      be familiar with large language models to harness their potential while being 
      aware of potential dangers and limitations.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Sorin, Vera
AU  - Sorin V
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel. 
      verasrn@gmail.com.
AD  - DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel. 
      verasrn@gmail.com.
AD  - Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 
      verasrn@gmail.com.
FAU - Barash, Yiftach
AU  - Barash Y
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel.
AD  - DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
AD  - Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
FAU - Konen, Eli
AU  - Konen E
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel.
AD  - Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
FAU - Klang, Eyal
AU  - Klang E
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel.
AD  - DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
AD  - Sami Sagol AI Hub, ARC, Chaim Sheba Medical Center, Ramat Gan, Israel.
AD  - Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
LA  - eng
PT  - Letter
DEP - 20230509
PL  - Germany
TA  - J Cancer Res Clin Oncol
JT  - Journal of cancer research and clinical oncology
JID - 7902060
SB  - IM
MH  - Humans
MH  - *Language
MH  - Medical Oncology
MH  - *Oncologists
EDAT- 2023/05/10 00:41
MHDA- 2023/07/31 06:43
CRDT- 2023/05/09 22:54
PHST- 2023/03/26 00:00 [received]
PHST- 2023/04/28 00:00 [accepted]
PHST- 2023/07/31 06:43 [medline]
PHST- 2023/05/10 00:41 [pubmed]
PHST- 2023/05/09 22:54 [entrez]
AID - 10.1007/s00432-023-04824-w [pii]
AID - 10.1007/s00432-023-04824-w [doi]
PST - ppublish
SO  - J Cancer Res Clin Oncol. 2023 Sep;149(11):9505-9508. doi: 
      10.1007/s00432-023-04824-w. Epub 2023 May 9.

PMID- 37116985
OWN - NLM
STAT- MEDLINE
DCOM- 20230518
LR  - 20230518
IS  - 2056-5933 (Electronic)
IS  - 2056-5933 (Linking)
VI  - 9
IP  - 2
DP  - 2023 Apr
TI  - The wide range of opportunities for large language models such as ChatGPT in 
      rheumatology.
LID - 10.1136/rmdopen-2023-003105 [doi]
LID - e003105
FAU - Hügle, Thomas
AU  - Hügle T
AUID- ORCID: 0000-0002-3276-9581
AD  - Department of Rheumatology, University Hospital Lausanne (CHUV), University of 
      Lausanne, Lausanne, Switzerland thomas.hugle@chuv.ch.
LA  - eng
PT  - Journal Article
PL  - England
TA  - RMD Open
JT  - RMD open
JID - 101662038
SB  - IM
MH  - *Rheumatology
MH  - Artificial Intelligence
PMC - PMC10151992
OTO - NOTNLM
OT  - Outcome Assessment, Health Care
OT  - Outcome and Process Assessment, Health Care
OT  - Quality Indicators, Health Care
COIS- Competing interests: None declared.
EDAT- 2023/04/29 06:04
MHDA- 2023/05/01 06:42
CRDT- 2023/04/28 21:03
PHST- 2023/02/24 00:00 [received]
PHST- 2023/04/14 00:00 [accepted]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/29 06:04 [pubmed]
PHST- 2023/04/28 21:03 [entrez]
AID - rmdopen-2023-003105 [pii]
AID - 10.1136/rmdopen-2023-003105 [doi]
PST - ppublish
SO  - RMD Open. 2023 Apr;9(2):e003105. doi: 10.1136/rmdopen-2023-003105.

PMID- 38046053
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 1916-257X (Print)
IS  - 1916-257X (Electronic)
VI  - 16
IP  - 4
DP  - 2023 Dec
TI  - Navigating Generative AI: Opportunities, Limitations, and Ethical Considerations 
      in Massage Therapy and Beyond.
PG  - 1-4
LID - 10.3822/ijtmb.v16i4.949 [doi]
AB  - Generative artificial intelligence (AI) has become a hot topic, particularly 
      ChatGPT's quick adoption and popularity, prompting discussions about its 
      disruptive potential in health care, education, and creative sectors. The author, 
      an early adopter, shares personal insights on leveraging generative AI for 
      creative tasks and communication challenges, while also exploring its role as a 
      tool rather than an author. Opportunities and limitations of integrating 
      generative AI in the massage therapy field are explored, reflecting on the 
      profession's reluctance to embrace technology and the potential efficiency gains. 
      While acknowledging generative AI's creative promise, the importance of ethical 
      and regulated utilization, highlighting data biases and limitations, is 
      underscored. Overall, a balanced and responsible approach to incorporating 
      generative AI into various domains is recommended.
CI  - Copyright© The Author(s) 2023. Published by the Massage Therapy Foundation.
FAU - Baskwill, Amanda
AU  - Baskwill A
AD  - Loyalist College, Belleville, ON.
LA  - eng
PT  - Editorial
DEP - 20231201
PL  - United States
TA  - Int J Ther Massage Bodywork
JT  - International journal of therapeutic massage & bodywork
JID - 101539415
PMC - PMC10665080
OTO - NOTNLM
OT  - ChatGPT
OT  - disruption
OT  - ethical utilization
OT  - generative artificial intelligence
OT  - massage therapy
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 04:48
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 04:48 [entrez]
AID - ijtmb-16-1 [pii]
AID - 10.3822/ijtmb.v16i4.949 [doi]
PST - epublish
SO  - Int J Ther Massage Bodywork. 2023 Dec 1;16(4):1-4. doi: 10.3822/ijtmb.v16i4.949. 
      eCollection 2023 Dec.

PMID- 37883432
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231112
IS  - 1091-6490 (Electronic)
IS  - 0027-8424 (Print)
IS  - 0027-8424 (Linking)
VI  - 120
IP  - 44
DP  - 2023 Oct 31
TI  - Large language models show human-like content biases in transmission chain 
      experiments.
PG  - e2313790120
LID - 10.1073/pnas.2313790120 [doi]
LID - e2313790120
AB  - As the use of large language models (LLMs) grows, it is important to examine 
      whether they exhibit biases in their output. Research in cultural evolution, 
      using transmission chain experiments, demonstrates that humans have biases to 
      attend to, remember, and transmit some types of content over others. Here, in 
      five preregistered experiments using material from previous studies with human 
      participants, we use the same, transmission chain-like methodology, and find that 
      the LLM ChatGPT-3 shows biases analogous to humans for content that is 
      gender-stereotype-consistent, social, negative, threat-related, and biologically 
      counterintuitive, over other content. The presence of these biases in LLM output 
      suggests that such content is widespread in its training data and could have 
      consequential downstream effects, by magnifying preexisting human tendencies for 
      cognitively appealing and not necessarily informative, or valuable, content.
FAU - Acerbi, Alberto
AU  - Acerbi A
AUID- ORCID: 0000-0001-5827-8003
AD  - Department of Sociology and Social Research, University of Trento, Trento 38122, 
      Italy.
FAU - Stubbersfield, Joseph M
AU  - Stubbersfield JM
AUID- ORCID: 0000-0001-8966-4679
AD  - Department of Psychology, University of Winchester, Winchester SO22 4NR, United 
      Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - United States
TA  - Proc Natl Acad Sci U S A
JT  - Proceedings of the National Academy of Sciences of the United States of America
JID - 7505876
SB  - IM
MH  - Humans
MH  - *Language
MH  - *Cultural Evolution
MH  - Mental Recall
MH  - Bias
MH  - Ethical Theory
PMC - PMC10622889
OTO - NOTNLM
OT  - ChatGPT
OT  - content biases
OT  - cultural evolution
OT  - large language models
OT  - transmission chains
COIS- The authors declare no competing interest.
EDAT- 2023/10/26 18:43
MHDA- 2023/10/30 06:46
PMCR- 2024/04/26
CRDT- 2023/10/26 13:44
PHST- 2024/04/26 00:00 [pmc-release]
PHST- 2023/10/30 06:46 [medline]
PHST- 2023/10/26 18:43 [pubmed]
PHST- 2023/10/26 13:44 [entrez]
AID - 202313790 [pii]
AID - 10.1073/pnas.2313790120 [doi]
PST - ppublish
SO  - Proc Natl Acad Sci U S A. 2023 Oct 31;120(44):e2313790120. doi: 
      10.1073/pnas.2313790120. Epub 2023 Oct 26.

PMID- 37566133
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20230922
IS  - 1432-1041 (Electronic)
IS  - 0031-6970 (Linking)
VI  - 79
IP  - 10
DP  - 2023 Oct
TI  - Will artificial intelligence chatbots replace clinical pharmacologists? An 
      exploratory study in clinical practice.
PG  - 1375-1384
LID - 10.1007/s00228-023-03547-8 [doi]
AB  - PURPOSE: Recently, there has been a growing interest in using ChatGPT for various 
      applications in Medicine. We evaluated the interest of OpenAI chatbot (GPT 4.0) 
      for drug information activities at Toulouse Pharmacovigilance Center. METHODS: 
      Based on a series of 50 randomly selected questions sent to our pharmacovigilance 
      center by healthcare professionals or patients, we compared the level of 
      responses from the chatbot GPT 4.0 with those provided by specialists in 
      pharmacovigilance. RESULTS: Chatbot answers were globally not acceptable. 
      Responses to inquiries regarding the assessment of drug causality were not 
      consistently precise or clinically meaningful. CONCLUSION: The interest of 
      chatbot assistance needs to be confirmed or rejected through further studies 
      conducted in other pharmacovigilance centers.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Montastruc, François
AU  - Montastruc F
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France.
FAU - Storck, Wilhelm
AU  - Storck W
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France.
FAU - de Canecaude, Claire
AU  - de Canecaude C
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France.
FAU - Victor, Léa
AU  - Victor L
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France.
FAU - Li, Julien
AU  - Li J
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France.
FAU - Cesbron, Candice
AU  - Cesbron C
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France.
FAU - Zelmat, Yoann
AU  - Zelmat Y
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France.
FAU - Barus, Romain
AU  - Barus R
AD  - Department of Medical and Clinical Pharmacology, Centre of Pharmacovigilance and 
      Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), 
      Toulouse, France. romain.barus@univ-tlse3.fr.
LA  - eng
PT  - Journal Article
DEP - 20230811
PL  - Germany
TA  - Eur J Clin Pharmacol
JT  - European journal of clinical pharmacology
JID - 1256165
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Physicians
MH  - Software
MH  - Health Personnel
MH  - Pharmacovigilance
OTO - NOTNLM
OT  - Adverse drug reaction
OT  - ChatGPT
OT  - Drug information service
OT  - Pharmacovigilance
OT  - Safety
EDAT- 2023/08/11 12:42
MHDA- 2023/09/18 12:43
CRDT- 2023/08/11 11:04
PHST- 2023/06/13 00:00 [received]
PHST- 2023/07/30 00:00 [accepted]
PHST- 2023/09/18 12:43 [medline]
PHST- 2023/08/11 12:42 [pubmed]
PHST- 2023/08/11 11:04 [entrez]
AID - 10.1007/s00228-023-03547-8 [pii]
AID - 10.1007/s00228-023-03547-8 [doi]
PST - ppublish
SO  - Eur J Clin Pharmacol. 2023 Oct;79(10):1375-1384. doi: 10.1007/s00228-023-03547-8. 
      Epub 2023 Aug 11.

PMID- 37932415
OWN - NLM
STAT- MEDLINE
DCOM- 20231108
LR  - 20231111
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Nov 6
TI  - A model-independent redundancy measure for human versus ChatGPT authorship 
      discrimination using a Bayesian probabilistic approach.
PG  - 19217
LID - 10.1038/s41598-023-46390-8 [doi]
LID - 19217
AB  - The academic and scientific world in general is increasingly concerned about 
      their inability to determine and ascertain the identity of the writer of a text. 
      More and more often the question arises as to whether a scientific article or 
      work handed in by a student was actually produced by the alleged author of the 
      questioned text. The role of artificial intelligence (AI) is increasingly debated 
      due to its dangers of undeclared use. A current example is undoubtedly the 
      undeclared use of ChatGPT to write a scientific text. The article promotes an AI 
      model-independent redundancy measure to support discrimination between hypotheses 
      on authorship of various multilingual texts written by humans or produced by 
      intelligence media such as ChatGPT. The syntax of texts written by humans tends 
      to differ from that of texts produced by AIs. This difference can be grasped and 
      quantified even with short texts (i.e. 1800 characters). This aspect of length is 
      extremely important, because short texts imply a greater difficulty of analysis 
      to characterize authorship. To meet the efficiency criteria required for the 
      evaluation of forensic evidence, a probabilistic approach is implemented. In 
      particular, to assess the value of the redundancy measure and to offer a 
      consistent classification criterion, a metric called Bayes factor is implemented. 
      The proposed Bayesian probabilistic method represents an original approach in 
      stylometry. Analyses performed over multilingual texts (English and French) 
      covering different scientific and human areas of interest (forensic science and 
      socio-psycho-artistic topics) reveal the feasibility of a successful authorship 
      discrimination with limited misclassification rates. Model performance is 
      satisfactory even with small sample sizes.
CI  - © 2023. The Author(s).
FAU - Bozza, Silvia
AU  - Bozza S
AD  - Ca' Foscari University of Venice, Department of Economics, Venice, 30121, Italy. 
      silvia.bozza@unive.it.
AD  - University of Lausanne, School of Criminal Justice, Lausanne, 1015, Switzerland. 
      silvia.bozza@unive.it.
FAU - Roten, Claude-Alain
AU  - Roten CA
AD  - OrphAnalytics SA, Vevey, 1800, Switzerland.
FAU - Jover, Antoine
AU  - Jover A
AD  - OrphAnalytics SA, Vevey, 1800, Switzerland.
AD  - University of Lausanne, School of Criminal Justice, Lausanne, 1015, Switzerland.
FAU - Cammarota, Valentina
AU  - Cammarota V
AD  - University of Lausanne, School of Criminal Justice, Lausanne, 1015, Switzerland.
FAU - Pousaz, Lionel
AU  - Pousaz L
AD  - OrphAnalytics SA, Vevey, 1800, Switzerland.
FAU - Taroni, Franco
AU  - Taroni F
AD  - University of Lausanne, School of Criminal Justice, Lausanne, 1015, Switzerland.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20231106
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - *Authorship
MH  - Bayes Theorem
MH  - *Artificial Intelligence
MH  - Writing
MH  - Forensic Medicine
PMC - PMC10628141
COIS- The authors declare no competing interests.
EDAT- 2023/11/07 00:42
MHDA- 2023/11/08 06:44
CRDT- 2023/11/06 23:38
PHST- 2023/06/02 00:00 [received]
PHST- 2023/10/31 00:00 [accepted]
PHST- 2023/11/08 06:44 [medline]
PHST- 2023/11/07 00:42 [pubmed]
PHST- 2023/11/06 23:38 [entrez]
AID - 10.1038/s41598-023-46390-8 [pii]
AID - 46390 [pii]
AID - 10.1038/s41598-023-46390-8 [doi]
PST - epublish
SO  - Sci Rep. 2023 Nov 6;13(1):19217. doi: 10.1038/s41598-023-46390-8.

PMID- 37621662
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230826
IS  - 2054-5703 (Print)
IS  - 2054-5703 (Electronic)
IS  - 2054-5703 (Linking)
VI  - 10
IP  - 8
DP  - 2023 Aug
TI  - Why and how to embrace AI such as ChatGPT in your academic life.
PG  - 230658
LID - 10.1098/rsos.230658 [doi]
LID - 230658
AB  - Generative artificial intelligence (AI), including large language models (LLMs), 
      is poised to transform scientific research, enabling researchers to elevate their 
      research productivity. This article presents a how-to guide for employing LLMs in 
      academic settings, focusing on their unique strengths, constraints and 
      implications through the lens of philosophy of science and epistemology. Using 
      ChatGPT as a case study, I identify and elaborate on three attributes 
      contributing to its effectiveness-intelligence, versatility and 
      collaboration-accompanied by tips on crafting effective prompts, practical use 
      cases and a living resource online (https://osf.io/8vpwu/). Next, I evaluate the 
      limitations of generative AI and its implications for ethical use, equality and 
      education. Regarding ethical and responsible use, I argue from technical and 
      epistemic standpoints that there is no need to restrict the scope or nature of AI 
      assistance, provided that its use is transparently disclosed. A pressing 
      challenge, however, lies in detecting fake research, which can be mitigated by 
      embracing open science practices, such as transparent peer review and sharing 
      data, code and materials. Addressing equality, I contend that while generative AI 
      may promote equality for some, it may simultaneously exacerbate disparities for 
      others-an issue with potentially significant yet unclear ramifications as it 
      unfolds. Lastly, I consider the implications for education, advocating for active 
      engagement with LLMs and cultivating students' critical thinking and analytical 
      skills. The how-to guide seeks to empower researchers with the knowledge and 
      resources necessary to effectively harness generative AI while navigating the 
      complex ethical dilemmas intrinsic to its application.
CI  - © 2023 The Authors.
FAU - Lin, Zhicheng
AU  - Lin Z
AUID- ORCID: 0000-0002-6864-6559
AD  - Programme of Applied Psychology, School of Humanities and Social Science, The 
      Chinese University of Hong Kong, Shenzhen, Guangdong 518172, People's Republic of 
      China.
LA  - eng
PT  - Journal Article
DEP - 20230823
PL  - England
TA  - R Soc Open Sci
JT  - Royal Society open science
JID - 101647528
PMC - PMC10445029
OTO - NOTNLM
OT  - ChatGPT/bard
OT  - artificial intelligence
OT  - ethics
OT  - large language models
OT  - open science
OT  - productivity
EDAT- 2023/08/25 06:42
MHDA- 2023/08/25 06:43
CRDT- 2023/08/25 03:49
PHST- 2023/05/26 00:00 [received]
PHST- 2023/08/03 00:00 [accepted]
PHST- 2023/08/25 06:43 [medline]
PHST- 2023/08/25 06:42 [pubmed]
PHST- 2023/08/25 03:49 [entrez]
AID - rsos230658 [pii]
AID - 10.1098/rsos.230658 [doi]
PST - epublish
SO  - R Soc Open Sci. 2023 Aug 23;10(8):230658. doi: 10.1098/rsos.230658. eCollection 
      2023 Aug.

PMID- 36895547
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230828
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Use of ChatGPT in Academic Publishing: A Rare Case of Seronegative Systemic Lupus 
      Erythematosus in a Patient With HIV Infection.
PG  - e34616
LID - 10.7759/cureus.34616 [doi]
LID - e34616
AB  - Diagnosing systemic lupus erythematosus (SLE) may be difficult in cases of 
      negative results for antinuclear antibodies (ANAs) and anti-double stranded DNA 
      (dsDNA) antibodies, which is known as seronegative SLE. Additionally, in patients 
      with HIV infection, the diagnosis of SLE is made complicated by the overlap of 
      symptoms and the possibility of false negative results on antibody tests. Herein, 
      we report the case of a 24-year-old female with HIV infection on anti-retroviral 
      therapy who presented with vesicles and plaques over the malar area and ulcers 
      over the roof of the mouth. Antibody tests for ANAs and dsDNA were negative. She 
      was initially treated for herpes simplex with a secondary infection, but the 
      symptoms did not improve. She ultimately died from acute myocardial infarction 
      while awaiting results of direct immunofluorescence, which revealed the 
      deposition of immunoglobulin (Ig) M, IgG, and C3 along the basement membrane, 
      thus enabling a diagnosis of SLE. Therefore, SLE can be difficult to diagnose in 
      patients with HIV, and other diagnostic criteria should be considered when 
      suspecting SLE and treating these patients. Additionally, we also present our 
      experience with ChatGPT (OpenAI LP, OpenAI Inc., San Francisco, CA, USA) in 
      academic publishing and its pros and cons.
CI  - Copyright © 2023, Manohar et al.
FAU - Manohar, Naveen
AU  - Manohar N
AD  - Dermatology, Belagavi Institute of Medical Sciences, Belagavi, IND.
FAU - Prasad, Shruthi S
AU  - Prasad SS
AD  - Dermatology, St. John's Medical College, Bangalore, IND.
LA  - eng
PT  - Case Reports
DEP - 20230204
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
CIN - Foot Ankle Surg. 2023 Jul;29(5):385-386. PMID: 37198001
PMC - PMC9988440
OTO - NOTNLM
OT  - anti-retroviral therapy
OT  - antibodies
OT  - autoimmune
OT  - chatgpt
OT  - hiv
OT  - lupus
OT  - seronegative sle
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/11 06:00
MHDA- 2023/03/11 06:01
CRDT- 2023/03/10 02:29
PHST- 2023/02/04 00:00 [accepted]
PHST- 2023/03/10 02:29 [entrez]
PHST- 2023/03/11 06:00 [pubmed]
PHST- 2023/03/11 06:01 [medline]
AID - 10.7759/cureus.34616 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 4;15(2):e34616. doi: 10.7759/cureus.34616. eCollection 2023 Feb.

PMID- 37905264
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231101
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Decoding Applications of Artificial Intelligence in Rheumatology.
PG  - e46164
LID - 10.7759/cureus.46164 [doi]
LID - e46164
AB  - Artificial intelligence (AI) is not a newcomer in medicine. It has been employed 
      for image analysis, disease diagnosis, drug discovery, and improving overall 
      patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) 
      has renewed interest and enthusiasm in artificial intelligence. Algorithms, 
      machine learning, deep learning, and data analysis are some of the complex 
      terminologies often encountered when health professionals try to learn AI. In 
      this article, we try to review the practical applications of artificial 
      intelligence in vernacular language in the fields of medicine and rheumatology in 
      particular. From the standpoint of the everyday physician, we have endeavored to 
      encapsulate the influence of AI on the cutting edge of medical practice and the 
      potential revolutionary shift in the realm of rheumatology.
CI  - Copyright © 2023, Chinnadurai et al.
FAU - Chinnadurai, Saranya
AU  - Chinnadurai S
AD  - Rheumatology, Sri Ramachandra Institute of Higher Education and Research, 
      Chennai, IND.
FAU - Mahadevan, Sabarinath
AU  - Mahadevan S
AD  - Rheumatology, O.M. Health Care Clinic, Chennai, IND.
FAU - Navaneethakrishnan, Balakrishnan
AU  - Navaneethakrishnan B
AD  - Rheumatology, Madras Institute of Orthopaedics and Traumatology (MIOT) Hospital, 
      Chennai, IND.
FAU - Mamadapur, Mahabaleshwar
AU  - Mamadapur M
AD  - Rheumatology, Jagadguru Sri Shivarathreeshwara (JSS) Medical College, Mysore, 
      IND.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230928
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10613315
OTO - NOTNLM
OT  - ai
OT  - arthritis
OT  - autoimmune
OT  - chatgpt
OT  - musculoskeletal diseases
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:43
CRDT- 2023/10/31 04:07
PHST- 2023/09/27 00:00 [accepted]
PHST- 2023/10/31 06:43 [medline]
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 04:07 [entrez]
AID - 10.7759/cureus.46164 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 28;15(9):e46164. doi: 10.7759/cureus.46164. eCollection 2023 
      Sep.

PMID- 37591023
OWN - NLM
STAT- MEDLINE
DCOM- 20230904
LR  - 20231007
IS  - 1873-5134 (Electronic)
IS  - 0738-3991 (Linking)
VI  - 115
DP  - 2023 Oct
TI  - Can ChatGPT rescue or assist with language barriers in healthcare communication?
PG  - 107940
LID - S0738-3991(23)00320-8 [pii]
LID - 10.1016/j.pec.2023.107940 [doi]
AB  - This letter supplements some ideas put forward by Kim et al., who debated the 
      importance of patient-centered communication as a central notion of 
      patient-centered cancer care in pre- versus post-pandemic times, although the 
      issue of language barriers by select patients at healthcare centers was not 
      debated. This letter proposes large language models, such as ChatGPT, as one way 
      to overcome the language barrier with non-native English-speaking patients, as 
      well as to simplify the technical language and jargon to make communication of 
      the medical conditions or treatment options easier for those patients to 
      understand. The latter may even be helpful for native English-speaking patients.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AD  - Independent researcher Ikenobe 3011-2, Kagawa-ken 761-0799, Japan. Electronic 
      address: jaimetex@yahoo.com.
LA  - eng
PT  - Comment
PT  - Letter
DEP - 20230814
PL  - Ireland
TA  - Patient Educ Couns
JT  - Patient education and counseling
JID - 8406280
CON - Patient Educ Couns. 2023 Oct;115:107876. PMID: 37406471
MH  - Humans
MH  - *Communication
MH  - *Language
MH  - Dietary Supplements
MH  - Communication Barriers
MH  - Delivery of Health Care
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - Healthcare professional
OT  - Large language models
OT  - Responsible use
COIS- Declaration of Competing Interests The author declares no conflicts of interest.
EDAT- 2023/08/17 18:42
MHDA- 2023/09/04 06:42
CRDT- 2023/08/17 18:00
PHST- 2023/07/16 00:00 [received]
PHST- 2023/07/30 00:00 [revised]
PHST- 2023/08/11 00:00 [accepted]
PHST- 2023/09/04 06:42 [medline]
PHST- 2023/08/17 18:42 [pubmed]
PHST- 2023/08/17 18:00 [entrez]
AID - S0738-3991(23)00320-8 [pii]
AID - 10.1016/j.pec.2023.107940 [doi]
PST - ppublish
SO  - Patient Educ Couns. 2023 Oct;115:107940. doi: 10.1016/j.pec.2023.107940. Epub 
      2023 Aug 14.

PMID- 37460753
OWN - NLM
STAT- MEDLINE
DCOM- 20230817
LR  - 20230914
IS  - 1546-170X (Electronic)
IS  - 1078-8956 (Linking)
VI  - 29
IP  - 8
DP  - 2023 Aug
TI  - Large language models in medicine.
PG  - 1930-1940
LID - 10.1038/s41591-023-02448-8 [doi]
AB  - Large language models (LLMs) can respond to free-text queries without being 
      specifically trained in the task in question, causing excitement and concern 
      about their use in healthcare settings. ChatGPT is a generative artificial 
      intelligence (AI) chatbot produced through sophisticated fine-tuning of an LLM, 
      and other tools are emerging through similar developmental processes. Here we 
      outline how LLM applications such as ChatGPT are developed, and we discuss how 
      they are being leveraged in clinical settings. We consider the strengths and 
      limitations of LLMs and their potential to improve the efficiency and 
      effectiveness of clinical, educational and research work in medicine. LLM 
      chatbots have already been deployed in a range of biomedical contexts, with 
      impressive but mixed results. This review acts as a primer for interested 
      clinicians, who will determine if and how LLM technology is used in healthcare 
      for the benefit of patients and practitioners.
CI  - © 2023. Springer Nature America, Inc.
FAU - Thirunavukarasu, Arun James
AU  - Thirunavukarasu AJ
AUID- ORCID: 0000-0001-8968-4768
AD  - University of Cambridge School of Clinical Medicine, Cambridge, UK.
AD  - Corpus Christi College, University of Cambridge, Cambridge, UK.
FAU - Ting, Darren Shu Jeng
AU  - Ting DSJ
AD  - Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University 
      of Birmingham, Birmingham, UK.
AD  - Birmingham and Midland Eye Centre, Birmingham, UK.
AD  - Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, 
      UK.
FAU - Elangovan, Kabilan
AU  - Elangovan K
AUID- ORCID: 0000-0002-7711-7368
AD  - Artificial Intelligence and Digital Innovation Research Group, Singapore Eye 
      Research Institute, Singapore National Eye Centre, Singapore, Singapore.
FAU - Gutierrez, Laura
AU  - Gutierrez L
AUID- ORCID: 0000-0001-7416-2350
AD  - Artificial Intelligence and Digital Innovation Research Group, Singapore Eye 
      Research Institute, Singapore National Eye Centre, Singapore, Singapore.
FAU - Tan, Ting Fang
AU  - Tan TF
AD  - Artificial Intelligence and Digital Innovation Research Group, Singapore Eye 
      Research Institute, Singapore National Eye Centre, Singapore, Singapore.
AD  - Department of Ophthalmology and Visual Sciences, Duke-National University of 
      Singapore Medical School, Singapore, Singapore.
FAU - Ting, Daniel Shu Wei
AU  - Ting DSW
AD  - Artificial Intelligence and Digital Innovation Research Group, Singapore Eye 
      Research Institute, Singapore National Eye Centre, Singapore, Singapore. 
      daniel.ting@duke-nus.edu.sg.
AD  - Department of Ophthalmology and Visual Sciences, Duke-National University of 
      Singapore Medical School, Singapore, Singapore. daniel.ting@duke-nus.edu.sg.
AD  - Byers Eye Institute, Stanford University, Palo Alto, CA, USA. 
      daniel.ting@duke-nus.edu.sg.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20230717
PL  - United States
TA  - Nat Med
JT  - Nature medicine
JID - 9502015
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Medicine
MH  - Language
MH  - Software
MH  - Technology
EDAT- 2023/07/18 01:09
MHDA- 2023/08/17 06:43
CRDT- 2023/07/17 23:30
PHST- 2023/03/24 00:00 [received]
PHST- 2023/06/08 00:00 [accepted]
PHST- 2023/08/17 06:43 [medline]
PHST- 2023/07/18 01:09 [pubmed]
PHST- 2023/07/17 23:30 [entrez]
AID - 10.1038/s41591-023-02448-8 [pii]
AID - 10.1038/s41591-023-02448-8 [doi]
PST - ppublish
SO  - Nat Med. 2023 Aug;29(8):1930-1940. doi: 10.1038/s41591-023-02448-8. Epub 2023 Jul 
      17.

PMID- 36909067
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230314
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Acute Pulmonary Edema After Hyperbaric Oxygen Treatment: A Case Report Written 
      With ChatGPT Assistance.
PG  - e34752
LID - 10.7759/cureus.34752 [doi]
LID - e34752
AB  - Acute pulmonary edema is a rare but severe complication of hyperbaric oxygen 
      therapy. While patients with known cardiovascular problems may be able to 
      withstand this therapy, rapid decompensation can still occur. Here, we present a 
      case of a patient with known low ejection fraction and severe mitral 
      regurgitation who developed acute pulmonary edema during the first hyperbaric 
      treatment for a foot ulcer. This case highlights the importance of identifying 
      patients that are high risk, such as those with moderate-to-severe cardiac 
      disease, and pursuing other treatment options to avoid this complication.
CI  - Copyright © 2023, Akhter et al.
FAU - Akhter, Haris M
AU  - Akhter HM
AD  - Hyperbaric Medicine, University of Nebraska Medical Center, Omaha, USA.
FAU - Cooper, Jeffrey S
AU  - Cooper JS
AD  - Hyperbaric Medicine, University of Nebraska Medical Center, Omaha, USA.
LA  - eng
PT  - Case Reports
DEP - 20230207
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC9998302
OTO - NOTNLM
OT  - acute pulmonary edema
OT  - chatgpt
OT  - diabetes
OT  - heart failure with reduced ejection fraction
OT  - hyperbaric oxygen therapy (hbot)
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/14 06:00
MHDA- 2023/03/14 06:01
CRDT- 2023/03/13 03:54
PHST- 2023/02/07 00:00 [accepted]
PHST- 2023/03/13 03:54 [entrez]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/14 06:01 [medline]
AID - 10.7759/cureus.34752 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 7;15(2):e34752. doi: 10.7759/cureus.34752. eCollection 2023 Feb.

PMID- 38089825
OWN - NLM
STAT- In-Process
LR  - 20231213
IS  - 1657-9534 (Electronic)
IS  - 0120-8322 (Linking)
VI  - 54
IP  - 3
DP  - 2023 Jul-Sep
TI  - Chatbots, generative AI, and scholarly manuscripts: WAME recommendations on 
      chatbots and generative artificial intelligence in relation to scholarly 
      publications.
PG  - e1015868
LID - 10.25100/cm.v54i3.5868 [doi]
AB  - This statement revises our earlier "WAME Recommendations on ChatGPT and Chatbots 
      in Relation to Scholarly Publications" (January 20, 2023). The revision reflects 
      the proliferation of chatbots and their expanding use in scholarly publishing 
      over the last few months, as well as emerging concerns regarding lack of 
      authenticity of content when using chatbots. These recommendations are intended 
      to inform editors and help them develop policies for the use of chatbots in 
      papers published in their journals. They aim to help authors and reviewers 
      understand how best to attribute the use of chatbots in their work and to address 
      the need for all journal editors to have access to manuscript screening tools. In 
      this rapidly evolving field, we will continue to modify these recommendations as 
      the software and its applications develop.
CI  - Copyright © 2023 Colombia Medica.
FAU - Zielinski, Chris
AU  - Zielinski C
AUID- ORCID: 0000-0001-6596-698X
AD  - Vice President, WAME; Centre for Global Health, University of Winchester, UK.
FAU - Winker, Margaret A
AU  - Winker MA
AUID- ORCID: 0000-0002-4708-5641
AD  - Trustee, WAME; USA.
FAU - Aggarwal, Rakesh
AU  - Aggarwal R
AUID- ORCID: 0000-0001-9689-494X
AD  - President, WAME; Associate Editor, Journal of Gastroenterology and Hepatology; 
      Director, Jawaharlal Institute of Postgraduate Medical Education and Research, 
      Puducherry, India.
FAU - Ferris, Lorraine E
AU  - Ferris LE
AUID- ORCID: 0000-0003-0754-2948
AD  - Trustee, WAME; Professor, Dalla Lana School of Public Health, University of 
      Toronto, Canada.
FAU - Heinemann, Markus
AU  - Heinemann M
AUID- ORCID: 0000-0002-9642-1154
AD  - Treasurer, WAME; Editor-in-Chief, The Thoracic and Cardiovascular Surgeon, 
      Professor, Universitaetsmedizin Mainz, Germany.
FAU - Lapeña, Jose Florencio Jr
AU  - Lapeña JF Jr
AUID- ORCID: 0000-0002-5794-1878
AD  - Secretary, WAME; Editor-in-Chief, Philippine Journal of Otolaryngology Head & 
      Neck Surgery; Professor, University of the Philippines Manila, Philippines.
FAU - Pai, Sanjay A
AU  - Pai SA
AUID- ORCID: 0000-0002-7102-3096
AD  - Director, WAME; Working Committee, The National Medical Journal of India, India.
FAU - Ing, Edsel
AU  - Ing E
AUID- ORCID: 0000-0003-0623-0934
AD  - Director, WAME; Section Editor, Canadian Journal of Ophthalmology; Professor, 
      University of Toronto and University of Alberta, Canada.
FAU - Citrome, Leslie
AU  - Citrome L
AUID- ORCID: 0000-0002-6098-9266
AD  - Director, WAME; Editor-in-Chief, Current Medical Research and Opinion; Topic 
      Editor for Psychiatry for Clinical Therapeutics; Clinical Professor of Psychiatry 
      & Behavioral Sciences, New York Medical College, USA.
FAU - Alam, Murad
AU  - Alam M
AUID- ORCID: 0000-0001-8752-3821
AD  - Director, WAME; Editor-in-Chief, Archives of Dermatological Research; Professor, 
      Northwestern University, USA.
FAU - Voight, Michael
AU  - Voight M
AD  - Director, WAME; Executive Editor-in-Chief; International Journal of Sports 
      Physical Therapy; Professor, Belmont University School of Physical Therapy, USA.
FAU - Habibzadeh, Farrokh
AU  - Habibzadeh F
AUID- ORCID: 0000-0001-5360-2900
AD  - Past President, WAME; Editorial Consultant, The Lancet; Associate Editor, 
      Frontiers in Epidemiology; Iran.
LA  - eng
PT  - Editorial
DEP - 20230930
PL  - Colombia
TA  - Colomb Med (Cali)
JT  - Colombia medica (Cali, Colombia)
JID - 9212578
SB  - IM
PMC - PMC10712422
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - authorship scientific misconduct
OT  - chatbots
OT  - confidentiality
OT  - deep learning
OT  - disinformation
OT  - plagiarism
OT  - scholarly communication
OT  - scientific manuscript
OT  - wame revised recommendation
COIS- Conflict of interests: The authors have declared that no competing interests 
      exist
EDAT- 2023/12/13 18:42
MHDA- 2023/12/13 18:42
CRDT- 2023/12/13 12:58
PHST- 2023/12/13 18:42 [medline]
PHST- 2023/12/13 18:42 [pubmed]
PHST- 2023/12/13 12:58 [entrez]
AID - 10.25100/cm.v54i3.5868 [doi]
PST - epublish
SO  - Colomb Med (Cali). 2023 Sep 30;54(3):e1015868. doi: 10.25100/cm.v54i3.5868. 
      eCollection 2023 Jul-Sep.

PMID- 37506622
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1532-2793 (Electronic)
IS  - 0260-6917 (Linking)
VI  - 129
DP  - 2023 Oct
TI  - Nursing education in the age of artificial intelligence powered Chatbots 
      (AI-Chatbots): Are we ready yet?
PG  - 105917
LID - S0260-6917(23)00211-3 [pii]
LID - 10.1016/j.nedt.2023.105917 [doi]
AB  - This article discusses the challenges and implications of artificial intelligence 
      powered chatbot (AI-Chatbots) in nursing education. Chat Generative Pre-trained 
      Transformer (ChatGPT) is an AI-Chatbot that can engage in detailed dialog and 
      pass qualification tests in various fields. It can be applied for drafting course 
      materials and administrative paperwork. Students can use it for personalized 
      self-paced learning. AI-Chatbot technology can be applied in problem-based 
      learning for hands-on practice experiences. There are concerns about 
      over-reliance on the technology, including issues with plagiarism and limiting 
      critical thinking skills. Educators must provide clear guidelines on appropriate 
      use and emphasize the importance of critical thinking and proper citation. 
      Educators must proactively adjust their curricula and pedagogy. AI-Chatbot 
      technology could transform the nursing profession by aiding and streamlining 
      administrative tasks, allowing nurses to focus on patient care. The use of 
      AI-Chatbots to socially assist patients and for therapeutic purposes in mental 
      health shows promise in improving well-being of patients, and potentially easing 
      shortage and burnout for healthcare workers. AI-Chatbots can help nursing 
      students and researchers to overcome technical barriers in nursing informatics, 
      increasing accessibility for individuals without technical background. AI-Chatbot 
      technology has potential in easing tasks for nurses, improving patient care, and 
      enhancing nursing education.
CI  - Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.
FAU - Tam, Wilson
AU  - Tam W
AD  - Alice Lee Centre for Nursing Studies, National University of Singapore, 
      Singapore.
FAU - Huynh, Tom
AU  - Huynh T
AD  - School of Science, Engineering and Technology, RMIT University, Viet Nam.
FAU - Tang, Arthur
AU  - Tang A
AD  - School of Science, Engineering and Technology, RMIT University, Viet Nam. 
      Electronic address: arthur.tang@rmit.edu.vn.
FAU - Luong, Stanley
AU  - Luong S
AD  - School of Science, Engineering and Technology, RMIT University, Viet Nam.
FAU - Khatri, Yunus
AU  - Khatri Y
AD  - School of Science, Engineering and Technology, RMIT University, Viet Nam.
FAU - Zhou, Wentao
AU  - Zhou W
AD  - Alice Lee Centre for Nursing Studies, National University of Singapore, 
      Singapore.
LA  - eng
PT  - Journal Article
DEP - 20230718
PL  - Scotland
TA  - Nurse Educ Today
JT  - Nurse education today
JID - 8511379
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Education, Nursing
MH  - Nursing
MH  - Burnout, Psychological
MH  - Curriculum
OTO - NOTNLM
OT  - AI-Chatbot
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Nursing education
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/07/29 06:42
MHDA- 2023/08/14 06:42
CRDT- 2023/07/28 18:09
PHST- 2023/03/13 00:00 [received]
PHST- 2023/06/26 00:00 [revised]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/07/29 06:42 [pubmed]
PHST- 2023/07/28 18:09 [entrez]
AID - S0260-6917(23)00211-3 [pii]
AID - 10.1016/j.nedt.2023.105917 [doi]
PST - ppublish
SO  - Nurse Educ Today. 2023 Oct;129:105917. doi: 10.1016/j.nedt.2023.105917. Epub 2023 
      Jul 18.

PMID- 37327133
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230619
IS  - 0351-0026 (Print)
IS  - 1854-2476 (Electronic)
IS  - 0351-0026 (Linking)
VI  - 62
IP  - 3
DP  - 2023 Sep
TI  - New Challenges in Scientific Publications: Referencing, Artificial Intelligence 
      and ChatGPT.
PG  - 109-112
LID - 10.2478/sjph-2023-0015 [doi]
AB  - The COVID-19 pandemic has led to a surge in scientific publications, some of 
      which have bypassed the usual peer-review processes, leading to an increase in 
      unsupported claims being referenced. Therefore, the need for references in 
      scientific articles is increasingly being questioned. The practice of relying 
      solely on quantitative measures, such as impact factor, is also considered 
      inadequate by many experts. This can lead to researchers choosing research ideas 
      that are likely to generate favourable metrics instead of interesting and 
      important topics. Evaluating the quality and scientific value of articles 
      requires a rethinking of current approaches, with a move away from purely 
      quantitative methods. Artificial intelligence (AI)-based tools are making 
      scientific writing easier and less time-consuming, which is likely to further 
      increase the number of scientific publications, potentially leading to higher 
      quality articles. AI tools for searching, analysing, synthesizing, evaluating and 
      writing scientific literature are increasingly being developed. These tools 
      deeply analyse the content of articles, consider their scientific impact, and 
      prioritize the retrieved literature based on this information, presenting it in 
      simple visual graphs. They also help authors to quickly and easily analyse and 
      synthesize knowledge from the literature, prepare summaries of key information, 
      aid in organizing references, and improve manuscript language. The language model 
      ChatGPT has already greatly changed the way people communicate with computers, 
      bringing it closer to human communication. However, while AI tools are helpful, 
      they must be used carefully and ethically. In summary, AI has already changed the 
      way we write articles, and its use in scientific publishing will continue to 
      enhance and streamline the process.
CI  - © 2023 Igor Švab et al., published by Sciendo.
FAU - Švab, Igor
AU  - Švab I
AUID- ORCID: 0000-0003-1303-4974
AD  - Department of Family Medicine, Medical Faculty, University of Ljubljana, 
      Poljanski nasip 58, 1000 Ljubljana, Slovenia.
AD  - National Institute of Public Health, Trubarjeva 2, 1000 Ljubljana, Slovenia.
FAU - Klemenc-Ketiš, Zalika
AU  - Klemenc-Ketiš Z
AUID- ORCID: 0000-0002-0270-1754
AD  - Department of Family Medicine, Medical Faculty, University of Ljubljana, 
      Poljanski nasip 58, 1000 Ljubljana, Slovenia.
AD  - Department of Family Medicine, Medical Faculty, University of Maribor, Taborska 
      8, 2000 Maribor, Slovenia.
AD  - Ljubljana Community Health Centre, Metelkova 9, 1000 Ljubljana, Slovenia.
FAU - Zupanič, Saša
AU  - Zupanič S
AUID- ORCID: 0000-0002-7378-9976
AD  - National Institute of Public Health, Trubarjeva 2, 1000 Ljubljana, Slovenia.
LA  - eng
PT  - Editorial
DEP - 20230613
PL  - Poland
TA  - Zdr Varst
JT  - Zdravstveno varstvo
JID - 9412992
PMC - PMC10263368
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Peer review
OT  - Referencing
OT  - Research assessment
OT  - Scientific articles
COIS- CONFLICTS OF INTEREST The authors declare that no conflicts of interest exist.
EDAT- 2023/06/16 19:16
MHDA- 2023/06/16 19:17
CRDT- 2023/06/16 13:04
PHST- 2023/04/17 00:00 [received]
PHST- 2023/05/03 00:00 [accepted]
PHST- 2023/06/16 19:17 [medline]
PHST- 2023/06/16 19:16 [pubmed]
PHST- 2023/06/16 13:04 [entrez]
AID - sjph-2023-0015 [pii]
AID - 10.2478/sjph-2023-0015 [doi]
PST - epublish
SO  - Zdr Varst. 2023 Jun 13;62(3):109-112. doi: 10.2478/sjph-2023-0015. eCollection 
      2023 Sep.

PMID- 38082605
OWN - NLM
STAT- In-Process
LR  - 20231212
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2023
DP  - 2023 Jul
TI  - ChatGPT for phenotypes extraction: one model to rule them all?
PG  - 1-4
LID - 10.1109/EMBC40787.2023.10340611 [doi]
AB  - Information Extraction (IE) is a core task in Natural Language Processing (NLP) 
      where the objective is to identify factual knowledge in textual documents (often 
      unstructured), and feed downstream use cases with the resulting output. In 
      genomic medicine for instance, being able to extract the most precise list of 
      phenotypes associated to a patient allows to improve genetic disease diagnostic, 
      which represents a vital step in the modern deep phenotyping approach. As most of 
      the phenotypic information lies in clinical reports, the challenge is to build an 
      IE pipeline to automatically recognize phenotype concepts from free-text notes. A 
      new machine learning paradigm around large language models (LLM) has given rise 
      of an increasing number of academic works on this topic lately, where 
      sophisticated combinations of different technics have been employed to improve 
      the phenotypes extraction accuracy. Even more recently released, the ChatGPT(1) 
      application nevertheless raises the question of the relevance of these approches 
      compared to this new generic one based on an instruction-oriented LLM. In this 
      paper, we propose a rigorous evaluation of ChatGPT and the current 
      state-of-the-art solutions on this specific task, and discuss the possible 
      impacts and the technical evolutions to consider in the medical domain.Clinical 
      relevance- Deep phenotyping on electronic health records has proven its ability 
      to improve genetic diagnosis by clinical exomes [10]. Thus, comparing 
      state-of-the-art solutions in order to derive insights and improving research 
      paths is essential.
FAU - Labbe, Thomas
AU  - Labbe T
FAU - Castel, Pierre
AU  - Castel P
FAU - Sanner, Jean-Michel
AU  - Sanner JM
FAU - Saleh, Majd
AU  - Saleh M
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
EDAT- 2023/12/12 06:42
MHDA- 2023/12/12 06:42
CRDT- 2023/12/12 01:03
PHST- 2023/12/12 06:42 [medline]
PHST- 2023/12/12 06:42 [pubmed]
PHST- 2023/12/12 01:03 [entrez]
AID - 10.1109/EMBC40787.2023.10340611 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 
      10.1109/EMBC40787.2023.10340611.

PMID- 37707542
OWN - NLM
STAT- Publisher
LR  - 20230914
IS  - 1743-9159 (Electronic)
IS  - 1743-9159 (Linking)
DP  - 2023 Sep 14
TI  - ChatGPT or LLM in next-generation drug discovery and development: Pharmaceutical 
      and biotechnology companies can make use of the artificial intelligence 
      (AI)-based device for a faster way of drug discovery and development.
LID - 10.1097/JS9.0000000000000719 [doi]
FAU - Pal, Soumen
AU  - Pal S
AD  - School of Mechanical Engineering, Vellore Institute of Technology, 
      Vellore-632014, Tamil Nadu, India.
FAU - Bhattacharya, Manojit
AU  - Bhattacharya M
AD  - Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, 
      Odisha, India.
FAU - Islam, Md Aminul
AU  - Islam MA
AD  - COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and 
      Technology University, Noakhali-3814, Bangladesh.
AD  - Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical 
      College, Karimganj, Kishoreganj, Bangladesh.
FAU - Chakraborty, Chiranjib
AU  - Chakraborty C
AD  - Department of Biotechnology, School of Life Science and Biotechnology, Adamas 
      University, Kolkata, West Bengal 700126, India.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
EDAT- 2023/09/14 12:42
MHDA- 2023/09/14 12:42
CRDT- 2023/09/14 11:06
PHST- 2023/08/14 00:00 [received]
PHST- 2023/08/20 00:00 [accepted]
PHST- 2023/09/14 12:42 [medline]
PHST- 2023/09/14 12:42 [pubmed]
PHST- 2023/09/14 11:06 [entrez]
AID - 01279778-990000000-00603 [pii]
AID - 10.1097/JS9.0000000000000719 [doi]
PST - aheadofprint
SO  - Int J Surg. 2023 Sep 14. doi: 10.1097/JS9.0000000000000719.

PMID- 37640177
OWN - NLM
STAT- In-Process
LR  - 20231207
IS  - 1535-6280 (Electronic)
IS  - 0146-2806 (Linking)
VI  - 49
IP  - 1 Pt A
DP  - 2024 Jan
TI  - Large Language Model-based Chatbot as a Source of Advice on First Aid in Heart 
      Attack.
PG  - 102048
LID - S0146-2806(23)00465-6 [pii]
LID - 10.1016/j.cpcardiol.2023.102048 [doi]
AB  - The ability of the cutting-edge large language model-powered chatbots to generate 
      human-like answers to user questions hypothetically could be utilized for 
      providing real-time advice on first aid for witnesses of cardiovascular 
      emergencies. This study aimed to evaluate quality of the chatbot responses to 
      inquiries on help in heart attack. The study simulated interrogation of the new 
      Bing chatbot (Microsoft Corporation, USA) with the "heart attack what to do" 
      prompt coming from 3 countries, the Gambia, India and the USA. The chatbot 
      responses (20 per country) were evaluated for congruence with the International 
      First Aid, Resuscitation, and Education Guidelines 2020 using a checklist. For 
      all user inquiries, the chatbot provided answers containing some guidance on 
      first aid. However, the responses commonly left out some potentially life-saving 
      instructions, for instance to encourage the person to stop physical activity, to 
      take antianginal medication, or to start cardiopulmonary resuscitation for 
      unresponsive abnormally breathing person. Mean percentage of the responses having 
      full congruence with the checklist criteria varied from 7.3 for India to 16.8 for 
      the USA. A quarter of responses for the Gambia and the USA, and 45.0% for India 
      contained superfluous guidelines-inconsistent directives. The chatbot advice on 
      help in heart attack has omissions, inaccuracies and misleading instructions, and 
      therefore the chatbot cannot be recommended as a credible source of information 
      on first aid. Active research and organizational efforts are needed to mitigate 
      the risk of uncontrolled misinformation and establish measures for guaranteeing 
      trustworthiness of the chatbot-mediated counseling.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Birkun, Alexei A
AU  - Birkun AA
AD  - Department of General Surgery, Anaesthesiology, Resuscitation and Emergency 
      Medicine, Medical Academy named after S.I. Georgievsky of V.I. Vernadsky Crimean 
      Federal University, Simferopol, Russian Federation. Electronic address: 
      birkunalexei@gmail.com.
FAU - Gautam, Adhish
AU  - Gautam A
AD  - Regional Government Hospital; Una, Himachal Pradesh, India.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230826
PL  - Netherlands
TA  - Curr Probl Cardiol
JT  - Current problems in cardiology
JID - 7701802
SB  - IM
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/08/29 00:42
MHDA- 2023/08/29 00:42
CRDT- 2023/08/28 19:22
PHST- 2023/08/17 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/08/29 00:42 [pubmed]
PHST- 2023/08/29 00:42 [medline]
PHST- 2023/08/28 19:22 [entrez]
AID - S0146-2806(23)00465-6 [pii]
AID - 10.1016/j.cpcardiol.2023.102048 [doi]
PST - ppublish
SO  - Curr Probl Cardiol. 2024 Jan;49(1 Pt A):102048. doi: 
      10.1016/j.cpcardiol.2023.102048. Epub 2023 Aug 26.

PMID- 37090271
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230425
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Personality Changes and Staring Spells in a 12-Year-Old Child: A Case Report 
      Incorporating ChatGPT, a Natural Language Processing Tool Driven by Artificial 
      Intelligence (AI).
PG  - e36408
LID - 10.7759/cureus.36408 [doi]
LID - e36408
AB  - Low grade gliomas (LGGs) are the most common type of brain tumors diagnosed in 
      children. The presentation of intracranial tumors in pediatric patients is varied 
      and diverse. The early identification and treatment of LGGs are important to 
      achieve favorable outcomes. Although personality changes can be a symptom of 
      intracranial tumors, they are rarely the only main presenting feature. In 
      addition to central nervous system (CNS) tumors, personality changes can be 
      associated with psychological and endocrine conditions, contributing to a broad 
      differential diagnosis. Because symptoms such as personality changes have the 
      potential to be missed, communication between family members and clinicians is 
      imperative to identify these symptoms early. We report the case of a 12-year-old 
      child who presented with personality changes as her main symptom and was found to 
      have an intracranial neoplasm. This case report integrates original author 
      writing with output from ChatGPT, a natural language processing tool driven by 
      artificial intelligence (AI). In addition to the case itself, this report will 
      explore the benefits and drawbacks of using natural language AI in this context.
CI  - Copyright © 2023, Puthenpura et al.
FAU - Puthenpura, Vidya
AU  - Puthenpura V
AD  - Pediatric Hematology and Oncology, Yale School of Medicine, New Haven, USA.
FAU - Nadkarni, Siddhi
AU  - Nadkarni S
AD  - Pediatrics, Yale School of Medicine, New Haven, USA.
FAU - DiLuna, Michael
AU  - DiLuna M
AD  - Neurosurgery, Yale School of Medicine, New Haven, USA.
FAU - Hieftje, Kimberly
AU  - Hieftje K
AD  - Pediatrics, Yale School of Medicine, New Haven, USA.
FAU - Marks, Asher
AU  - Marks A
AD  - Pediatric Hematology and Oncology, Yale School of Medicine, New Haven, USA.
LA  - eng
PT  - Case Reports
DEP - 20230320
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10115215
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - low grade glioma
OT  - pediatric neuro-oncology
OT  - pediatric oncology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/24 06:41
MHDA- 2023/04/24 06:42
CRDT- 2023/04/24 03:40
PHST- 2023/03/20 00:00 [accepted]
PHST- 2023/04/24 06:42 [medline]
PHST- 2023/04/24 06:41 [pubmed]
PHST- 2023/04/24 03:40 [entrez]
AID - 10.7759/cureus.36408 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 20;15(3):e36408. doi: 10.7759/cureus.36408. eCollection 2023 
      Mar.

PMID- 37273936
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230606
IS  - 2214-7829 (Electronic)
IS  - 2214-7829 (Linking)
VI  - 32
DP  - 2023 Apr
TI  - A new era in Internet interventions: The advent of Chat-GPT and AI-assisted 
      therapist guidance.
PG  - 100621
LID - 10.1016/j.invent.2023.100621 [doi]
LID - 100621
FAU - Carlbring, Per
AU  - Carlbring P
AD  - Stockholm University, Department of Psychology, Stockholm, Sweden.
FAU - Hadjistavropoulos, Heather
AU  - Hadjistavropoulos H
AD  - University of Regina, Regina, Department of Psychology, Saskatchewan, Canada.
FAU - Kleiboer, Annet
AU  - Kleiboer A
AD  - VU Amsterdam, Department of Clinical Neuro- and Developmental Psychology, 
      Amsterdam, Netherlands.
FAU - Andersson, Gerhard
AU  - Andersson G
AD  - Linkoping University, Department of Behavioural Sciences and Learning, SE-581 83 
      Linköping, Sweden.
LA  - eng
PT  - Editorial
DEP - 20230411
PL  - Netherlands
TA  - Internet Interv
JT  - Internet interventions
JID - 101631612
PMC - PMC10235420
OTO - NOTNLM
OT  - Artificial intelligence guided interventions
OT  - Client preferences
OT  - Conversational agents
OT  - Digital self-help programs
OT  - Internet-based treatments
OT  - Therapeutic alliance
COIS- The authors, Per Carlbring, Heather Hadjistavropoulos, Annet Kleiboer, and 
      Gerhard Andersson, hereby declare that they have no known conflicts of interest 
      or financial ties related to the research reported in the manuscript titled “A 
      New Era in Internet Interventions: The Advent of Chat-GPT and AI-Assisted 
      Therapist Guidance.”
EDAT- 2023/06/05 13:05
MHDA- 2023/06/05 13:06
CRDT- 2023/06/05 11:54
PHST- 2023/04/03 00:00 [received]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/06/05 13:06 [medline]
PHST- 2023/06/05 13:05 [pubmed]
PHST- 2023/06/05 11:54 [entrez]
AID - S2214-7829(23)00021-0 [pii]
AID - 100621 [pii]
AID - 10.1016/j.invent.2023.100621 [doi]
PST - epublish
SO  - Internet Interv. 2023 Apr 11;32:100621. doi: 10.1016/j.invent.2023.100621. 
      eCollection 2023 Apr.

PMID- 37938801
OWN - NLM
STAT- Publisher
LR  - 20231202
IS  - 1537-2197 (Electronic)
IS  - 0002-9122 (Linking)
DP  - 2023 Nov 8
TI  - Herbarium specimen label transcription reimagined with large language models: 
      Capabilities, productivity, and risks.
PG  - e16256
LID - 10.1002/ajb2.16256 [doi]
FAU - Weaver, William N
AU  - Weaver WN
AUID- ORCID: 0000-0003-0633-5066
AD  - University of Michigan, Department of Ecology and Evolutionary Biology, 1105 N 
      University Ave, Ann Arbor, 48109, MI, USA.
FAU - Ruhfel, Brad R
AU  - Ruhfel BR
AUID- ORCID: 0000-0002-0230-4185
AD  - University of Michigan, Department of Ecology and Evolutionary Biology, 1105 N 
      University Ave, Ann Arbor, 48109, MI, USA.
AD  - University of Michigan Herbarium, Research Museums Center, 3600 Varsity Drive, 
      Ann Arbor, 48108, MI, USA.
FAU - Lough, Kyle J
AU  - Lough KJ
AUID- ORCID: 0009-0001-2927-0368
AD  - University of Michigan, Department of Ecology and Evolutionary Biology, 1105 N 
      University Ave, Ann Arbor, 48109, MI, USA.
AD  - University of Michigan Herbarium, Research Museums Center, 3600 Varsity Drive, 
      Ann Arbor, 48108, MI, USA.
FAU - Smith, Stephen A
AU  - Smith SA
AUID- ORCID: 0000-0003-2035-9531
AD  - University of Michigan, Department of Ecology and Evolutionary Biology, 1105 N 
      University Ave, Ann Arbor, 48109, MI, USA.
AD  - University of Michigan Herbarium, Research Museums Center, 3600 Varsity Drive, 
      Ann Arbor, 48108, MI, USA.
LA  - eng
PT  - Journal Article
DEP - 20231108
PL  - United States
TA  - Am J Bot
JT  - American journal of botany
JID - 0370467
SB  - IM
OTO - NOTNLM
OT  - AI automation
OT  - ChatGPT
OT  - Specimen Label Transcription Project
OT  - biodiversity data
OT  - curation
OT  - digitization
OT  - herbarium label transcription
OT  - natural history collections
OT  - optical character recognition
OT  - workflow
EDAT- 2023/11/08 18:42
MHDA- 2023/11/08 18:42
CRDT- 2023/11/08 12:02
PHST- 2023/10/16 00:00 [revised]
PHST- 2023/08/28 00:00 [received]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/11/08 18:42 [pubmed]
PHST- 2023/11/08 18:42 [medline]
PHST- 2023/11/08 12:02 [entrez]
AID - 10.1002/ajb2.16256 [doi]
PST - aheadofprint
SO  - Am J Bot. 2023 Nov 8:e16256. doi: 10.1002/ajb2.16256.

PMID- 37727841
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230921
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Efficacy of AI Chats to Determine an Emergency: A Comparison Between OpenAI's 
      ChatGPT, Google Bard, and Microsoft Bing AI Chat.
PG  - e45473
LID - 10.7759/cureus.45473 [doi]
LID - e45473
AB  - Background The escalating overload and saturation of emergency services, 
      primarily caused by non-urgent cases overwhelming the system, have spurred a 
      critical necessity for innovative solutions that can effectively differentiate 
      genuine emergencies from situations that could be managed through alternative 
      means, such as using AI chatbots. This study aims to evaluate and compare the 
      accuracy in differentiating between a medical emergency and a non-emergency of 
      three of the most popular AI chatbots at the moment. Methods In this study, 
      patient questions from the online forum r/AskDocs on Reddit were collected to 
      determine whether their clinical cases were emergencies. A total of 176 questions 
      were reviewed by the authors, with 75 deemed emergencies and 101 non-emergencies. 
      These questions were then posed to AI chatbots, including ChatGPT, Google Bard, 
      and Microsoft Bing AI, with their responses evaluated against each other and the 
      authors' responses. A criteria-based system categorized the AI chatbot answers as 
      "yes," "no," or "cannot determine." The performance of each AI chatbot was 
      compared in both emergency and non-emergency cases, and statistical analysis was 
      conducted to assess the significance of differences in their performance. Results 
      In general, AI chatbots considered around 12-15% more cases to be an emergency 
      than reviewers, while they considered a very low number of cases as non-emergency 
      compared to reviewers (around 35% fewer cases). Google Bard detected the most 
      true emergency cases (87%) and true non-emergency cases (36%). However, no real 
      difference in performance between the three AI chatbots was found in detecting 
      true emergencies (p-value = 0.35) and non-emergency cases (p-value = 0.16).  
      Conclusions These AI systems require further refinement to identify emergency 
      situations accurately, but they could potentially be an innovative tool for 
      emergency care and improving patient outcomes. The integration of AI chatbots 
      like ChatGPT, Google Bard, and Microsoft Bing Chat offers a promising avenue to 
      mitigate ED strain and enhance emergency management.
CI  - Copyright © 2023, Zúñiga Salazar et al.
FAU - Zúñiga Salazar, Gabriel
AU  - Zúñiga Salazar G
AD  - Facultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, 
      Guayaquil, ECU.
FAU - Zúñiga, Diego
AU  - Zúñiga D
AD  - Facultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, 
      Guayaquil, ECU.
FAU - Vindel, Carlos L
AU  - Vindel CL
AD  - Facultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, 
      Guayaquil, ECU.
FAU - Yoong, Ana M
AU  - Yoong AM
AD  - Facultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, 
      Guayaquil, ECU.
FAU - Hincapie, Sofia
AU  - Hincapie S
AD  - Facultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, 
      Guayaquil, ECU.
FAU - Zúñiga, Ana B
AU  - Zúñiga AB
AD  - Facultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, 
      Guayaquil, ECU.
FAU - Zúñiga, Paula
AU  - Zúñiga P
AD  - Facultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, 
      Guayaquil, ECU.
FAU - Salazar, Erin
AU  - Salazar E
AD  - Neurology, Hospital Luis Vernaza, Guayaquil, ECU.
FAU - Zúñiga, Byron
AU  - Zúñiga B
AD  - Nephrology, Hospital Luis Vernaza, Guayaquil, ECU.
LA  - eng
PT  - Journal Article
DEP - 20230918
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10506659
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - artificial intelligence
OT  - chatbot
OT  - chatgpt
OT  - emergency
OT  - emergency department
OT  - emergency room
OT  - google bard
OT  - microsoft bing
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/20 06:42
MHDA- 2023/09/20 06:43
CRDT- 2023/09/20 04:00
PHST- 2023/09/18 00:00 [accepted]
PHST- 2023/09/20 06:43 [medline]
PHST- 2023/09/20 06:42 [pubmed]
PHST- 2023/09/20 04:00 [entrez]
AID - 10.7759/cureus.45473 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 18;15(9):e45473. doi: 10.7759/cureus.45473. eCollection 2023 
      Sep.

PMID- 37980501
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231124
IS  - 2056-9920 (Print)
IS  - 2056-9920 (Electronic)
IS  - 2056-9920 (Linking)
VI  - 9
IP  - 1
DP  - 2023 Nov 18
TI  - "Application and accuracy of artificial intelligence-derived large language 
      models in patients with age related macular degeneration".
PG  - 71
LID - 10.1186/s40942-023-00511-7 [doi]
LID - 71
AB  - INTRODUCTION: Age-related macular degeneration (AMD) affects millions of people 
      globally, leading to a surge in online research of putative diagnoses, causing 
      potential misinformation and anxiety in patients and their parents. This study 
      explores the efficacy of artificial intelligence-derived large language models 
      (LLMs) like in addressing AMD patients' questions. METHODS: ChatGPT 3.5 (2023), 
      Bing AI (2023), and Google Bard (2023) were adopted as LLMs. Patients' questions 
      were subdivided in two question categories, (a) general medical advice and (b) 
      pre- and post-intravitreal injection advice and classified as (1) accurate and 
      sufficient (2) partially accurate but sufficient and (3) inaccurate and not 
      sufficient. Non-parametric test has been done to compare the means between the 3 
      LLMs scores and also an analysis of variance and reliability tests were performed 
      among the 3 groups. RESULTS: In category a) of questions, the average score was 
      1.20 (± 0.41) with ChatGPT 3.5, 1.60 (± 0.63) with Bing AI and 1.60 (± 0.73) with 
      Google Bard, showing no significant differences among the 3 groups (p = 0.129). 
      The average score in category b was 1.07 (± 0.27) with ChatGPT 3.5, 1.69 (± 0.63) 
      with Bing AI and 1.38 (± 0.63) with Google Bard, showing a significant difference 
      among the 3 groups (p = 0.0042). Reliability statistics showed Chronbach's α of 
      0.237 (range 0.448, 0.096-0.544). CONCLUSION: ChatGPT 3.5 consistently offered 
      the most accurate and satisfactory responses, particularly with technical 
      queries. While LLMs displayed promise in providing precise information about AMD; 
      however, further improvements are needed especially in more technical questions.
CI  - © 2023. The Author(s).
FAU - Ferro Desideri, Lorenzo
AU  - Ferro Desideri L
AD  - Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, 
      Switzerland. lorenzoferrodes@gmail.com.
AD  - Bern Photographic Reading Center, Inselspital, Bern University Hospital, 
      University of Bern, Bern, Switzerland. lorenzoferrodes@gmail.com.
FAU - Roth, Janice
AU  - Roth J
AD  - Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, 
      Switzerland.
FAU - Zinkernagel, Martin
AU  - Zinkernagel M
AD  - Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, 
      Switzerland.
AD  - Bern Photographic Reading Center, Inselspital, Bern University Hospital, 
      University of Bern, Bern, Switzerland.
FAU - Anguita, Rodrigo
AU  - Anguita R
AD  - Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, 
      Switzerland.
AD  - Moorfields Eye Hospital NHS Foundation Trust, City Road, London, EC1V 2PD, UK.
LA  - eng
PT  - Journal Article
DEP - 20231118
PL  - England
TA  - Int J Retina Vitreous
JT  - International journal of retina and vitreous
JID - 101677897
PMC - PMC10657493
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Artificial intelligence in ophthalmology
OT  - Dry macular degeneration
OT  - LLMs
OT  - Large language models
OT  - Macular edema
OT  - Wet macular degeneration
COIS- The authors report no competing interests.
EDAT- 2023/11/19 09:42
MHDA- 2023/11/19 09:43
CRDT- 2023/11/19 02:44
PHST- 2023/10/13 00:00 [received]
PHST- 2023/11/11 00:00 [accepted]
PHST- 2023/11/19 09:43 [medline]
PHST- 2023/11/19 09:42 [pubmed]
PHST- 2023/11/19 02:44 [entrez]
AID - 10.1186/s40942-023-00511-7 [pii]
AID - 511 [pii]
AID - 10.1186/s40942-023-00511-7 [doi]
PST - epublish
SO  - Int J Retina Vitreous. 2023 Nov 18;9(1):71. doi: 10.1186/s40942-023-00511-7.

PMID- 37056551
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230415
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Atypical Nelson Syndrome Following Right Partial and Left Total Nephrectomy With 
      Incidental Bilateral Total Adrenalectomy of Renal Cell Carcinoma: A Chat 
      Generative Pre-Trained Transformer (ChatGPT)-Assisted Case Report and Literature 
      Review.
PG  - e36042
LID - 10.7759/cureus.36042 [doi]
LID - e36042
AB  - Nelson syndrome (NS) is a dangerous condition that can sometimes manifest after 
      bilateral adrenalectomy (BA), typically in treating Cushing's disease. It is 
      defined by the collection of systemic signs and symptoms that can arise in a 
      state where there are chronically and massively elevated levels of 
      adrenocorticotropic hormone (ACTH). Traditionally it may manifest from six months 
      to 24 years following the loss of both adrenal glands, with the meantime of 
      development being 15 years following BA. The diagnostic criteria are 
      controversial, with historically many different methods being used, ranging from 
      visual field defects and an enlarged pituitary corticotrophinoma to elevated 
      plasma ACTH levels and skin hyperpigmentation. What remains consistent between 
      criteria is that it is secondary to total BA, traditionally in treating 
      refractory Cushing's disease. We describe here a rare case of a patient diagnosed 
      with bilateral renal cell carcinoma (RCC) treated with right partial and left 
      total nephrectomy, and incidental BA, presenting with the symptoms and signs of 
      NS. Although NS classically presents following total BA for the treatment of 
      Cushing disease, further research is required to look for etiologies of 
      Nelson's-like pathology outside the context of Cushing's disease treatment, 
      thereby necessitating a change to the traditional diagnostic criteria for the 
      syndrome to identify cases that would otherwise go untreated. In addition, this 
      case report's outlining, drafting, and conclusions were written in part by or 
      with the support of Chat Generative Pre-Trained Transformer (ChatGPT), a large 
      language transformer open-source artificial intelligence.
CI  - Copyright © 2023, Schuppe et al.
FAU - Schuppe, Kyle
AU  - Schuppe K
AD  - Internal Medicine, Washington State University Elson S. Floyd College of 
      Medicine, Spokane, USA.
FAU - Burke, Skyler
AU  - Burke S
AD  - Internal Medicine, Washington State University Elson S. Floyd College of 
      Medicine, Spokane, USA.
FAU - Cohoe, Blake
AU  - Cohoe B
AD  - Internal Medicine, Washington State University Elson S. Floyd College of 
      Medicine, Spokane, USA.
FAU - Chang, Kevin
AU  - Chang K
AD  - Internal Medicine, Washington State University Elson S. Floyd College of 
      Medicine, Spokane, USA.
FAU - Lance, Raymond S
AU  - Lance RS
AD  - Urology, Washington State University Elson S. Floyd College of Medicine, Spokane, 
      USA.
FAU - Mroch, Henry
AU  - Mroch H
AD  - Nephrology, Washington State University Elson S. Floyd College of Medicine, 
      Spokane, USA.
LA  - eng
PT  - Case Reports
DEP - 20230312
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10089233
OTO - NOTNLM
OT  - adrenalectomy
OT  - adrenocorticotropic hormone
OT  - bilateral nephrectomy
OT  - chatgpt improved case report
OT  - cutaneous hyperpigmentation
OT  - immune check point inhibitor adverse side effect
OT  - nelson's syndrome
OT  - primary adrenal insufficiency
OT  - renal cell carcinoma
OT  - von hippel-lindau
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/15 06:00
MHDA- 2023/04/15 06:01
CRDT- 2023/04/14 02:29
PHST- 2023/03/12 00:00 [accepted]
PHST- 2023/04/15 06:01 [medline]
PHST- 2023/04/14 02:29 [entrez]
PHST- 2023/04/15 06:00 [pubmed]
AID - 10.7759/cureus.36042 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 12;15(3):e36042. doi: 10.7759/cureus.36042. eCollection 2023 
      Mar.

PMID- 37648630
OWN - NLM
STAT- Publisher
LR  - 20230830
IS  - 2588-9311 (Electronic)
IS  - 2588-9311 (Linking)
DP  - 2023 Aug 28
TI  - Defining the Role of Large Language Models in Urologic Care and Research.
LID - S2588-9311(23)00162-1 [pii]
LID - 10.1016/j.euo.2023.07.017 [doi]
AB  - Large language models such as ChatGPT are poised to transform health care. We 
      envision them being used in the future in urology, albeit with appropriate 
      supervision, to educate patients, guide clinicians and scientists, and automate 
      complex tasks.
CI  - Copyright © 2023. Published by Elsevier B.V.
FAU - Gupta, Raghav
AU  - Gupta R
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Pedraza, Adriana M
AU  - Pedraza AM
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Gorin, Michael A
AU  - Gorin MA
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Tewari, Ashutosh K
AU  - Tewari AK
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: 
      ash.tewari@mountsinai.org.
LA  - eng
PT  - Editorial
DEP - 20230828
PL  - Netherlands
TA  - Eur Urol Oncol
JT  - European urology oncology
JID - 101724904
SB  - IM
EDAT- 2023/08/31 00:41
MHDA- 2023/08/31 00:41
CRDT- 2023/08/30 22:01
PHST- 2023/04/05 00:00 [received]
PHST- 2023/06/28 00:00 [revised]
PHST- 2023/07/28 00:00 [accepted]
PHST- 2023/08/31 00:41 [medline]
PHST- 2023/08/31 00:41 [pubmed]
PHST- 2023/08/30 22:01 [entrez]
AID - S2588-9311(23)00162-1 [pii]
AID - 10.1016/j.euo.2023.07.017 [doi]
PST - aheadofprint
SO  - Eur Urol Oncol. 2023 Aug 28:S2588-9311(23)00162-1. doi: 
      10.1016/j.euo.2023.07.017.

PMID- 37746294
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 13
DP  - 2023
TI  - Commentary: AI-based online chat and the future of oncology care: a promising 
      technology or a solution in search of a problem?
PG  - 1239932
LID - 10.3389/fonc.2023.1239932 [doi]
LID - 1239932
FAU - Zhang, Hui
AU  - Zhang H
AD  - Department of Rehabilitation and Elderly Care, Gannan Health Vocational College, 
      Ganzhou, China.
FAU - Guan, Yongfu
AU  - Guan Y
AD  - Department of Rehabilitation and Elderly Care, Gannan Health Vocational College, 
      Ganzhou, China.
FAU - Chen, Jinping
AU  - Chen J
AD  - Department of Rehabilitation and Elderly Care, Gannan Health Vocational College, 
      Ganzhou, China.
FAU - Tong, Wenting
AU  - Tong W
AD  - Department of Pharmacy, Gannan Health Vocational College, Ganzhou, China.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230907
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
CON - Front Oncol. 2023 May 25;13:1176617. PMID: 37305580
PMC - PMC10517627
OTO - NOTNLM
OT  - ChatGPT
OT  - data privacy
OT  - global healthcare resource allocation
OT  - language bias
OT  - legal and ethical challenges
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/25 06:42
MHDA- 2023/09/25 06:43
CRDT- 2023/09/25 05:14
PHST- 2023/06/14 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/09/25 06:43 [medline]
PHST- 2023/09/25 06:42 [pubmed]
PHST- 2023/09/25 05:14 [entrez]
AID - 10.3389/fonc.2023.1239932 [doi]
PST - epublish
SO  - Front Oncol. 2023 Sep 7;13:1239932. doi: 10.3389/fonc.2023.1239932. eCollection 
      2023.

PMID- 37735091
OWN - NLM
STAT- MEDLINE
DCOM- 20231106
LR  - 20231209
IS  - 1360-0443 (Electronic)
IS  - 0965-2140 (Linking)
VI  - 118
IP  - 12
DP  - 2023 Dec
TI  - The ChatGPT therapist will see you now: Navigating generative artificial 
      intelligence's potential in addiction medicine research and patient care.
PG  - 2249-2251
LID - 10.1111/add.16341 [doi]
FAU - Tate, Steven
AU  - Tate S
AUID- ORCID: 0000-0001-9544-594X
AD  - Department of Psychiatry and Behavioural Sciences, Stanford University School of 
      Medicine, Palo Alto, California, USA.
FAU - Fouladvand, Sajjad
AU  - Fouladvand S
AD  - Department of Medicine, Stanford University School of Medicine, Palo Alto, 
      California, USA.
FAU - Chen, Jonathan H
AU  - Chen JH
AD  - Department of Medicine, Stanford University School of Medicine, Palo Alto, 
      California, USA.
FAU - Chen, Chwen-Yuen Angie
AU  - Chen CA
AD  - Department of Medicine, Stanford University School of Medicine, Palo Alto, 
      California, USA.
LA  - eng
PT  - Editorial
PT  - Research Support, N.I.H., Extramural
DEP - 20230921
PL  - England
TA  - Addiction
JT  - Addiction (Abingdon, England)
JID - 9304118
SB  - IM
MH  - Humans
MH  - *Addiction Medicine
MH  - Artificial Intelligence
MH  - Patient Care
OTO - NOTNLM
OT  - artificial intelligence
OT  - automation
OT  - electronic health record data
OT  - evidence-based clinical practice
OT  - large language models
OT  - workflow optimization
EDAT- 2023/09/22 00:42
MHDA- 2023/11/06 06:42
CRDT- 2023/09/21 22:12
PHST- 2023/06/27 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/11/06 06:42 [medline]
PHST- 2023/09/22 00:42 [pubmed]
PHST- 2023/09/21 22:12 [entrez]
AID - 10.1111/add.16341 [doi]
PST - ppublish
SO  - Addiction. 2023 Dec;118(12):2249-2251. doi: 10.1111/add.16341. Epub 2023 Sep 21.

PMID- 37581145
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230816
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - Pitfalls in the Diagnosis and Management of an Unusual Presentation of Clinically 
      Amyopathic Dermatomyositis: A Case Report Written With the Assistance of ChatGPT.
PG  - e41879
LID - 10.7759/cureus.41879 [doi]
LID - e41879
AB  - Clinically amyopathic dermatomyositis (CADM) is a rare form of dermatomyositis. 
      Patients with this condition present with the typical skin findings of 
      dermatomyositis but lack the characteristic muscle weakness associated with 
      dermatomyositis. This case presentation highlights the unusual clinical 
      manifestation of CADM in a 49-year-old Vietnamese female. The patient initially 
      presented with persistent hyperpigmented plaques on her hands, which did not 
      respond to the standard treatment for atopic dermatitis. The patient later 
      developed respiratory failure and lung fibrosis in Vietnam. This case underscores 
      the challenges in diagnosing and managing CADM, particularly in patients with 
      atypical presentations, and emphasizes the difficulties in managing such cases of 
      CADM in the community setting.
CI  - Copyright © 2023, Jansz et al.
FAU - Jansz, Jacqueline
AU  - Jansz J
AD  - Internal Medicine, University of Illinois at Chicago, Chicago, USA.
FAU - Tran, Huynh W
AU  - Tran HW
AD  - Rheumatology, Wynn Medical Center, Rosemead, USA.
FAU - Sweiss, Nadera J
AU  - Sweiss NJ
AD  - Rheumatology, University of Illinois at Chicago, Chicago, USA.
LA  - eng
PT  - Case Reports
DEP - 20230714
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10423618
OTO - NOTNLM
OT  - amyopathic dermatomyositis
OT  - anti-mda5
OT  - cadm
OT  - chatgpt
OT  - clinically amyopathic dermatomyositis
OT  - dermatomyositis
OT  - ild
OT  - interstitial lung disease
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/15 06:42
MHDA- 2023/08/15 06:43
CRDT- 2023/08/15 03:38
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/08/15 06:43 [medline]
PHST- 2023/08/15 06:42 [pubmed]
PHST- 2023/08/15 03:38 [entrez]
AID - 10.7759/cureus.41879 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 14;15(7):e41879. doi: 10.7759/cureus.41879. eCollection 2023 
      Jul.

PMID- 37148260
OWN - NLM
STAT- Publisher
LR  - 20230506
IS  - 1555-9823 (Electronic)
IS  - 0003-1348 (Linking)
DP  - 2023 May 6
TI  - Harvesting the Power of Artificial Intelligence for Surgery: Uses, Implications, 
      and Ethical Considerations.
PG  - 31348231175454
LID - 10.1177/00031348231175454 [doi]
AB  - Artificial intelligence is rapidly advancing, especially with the advent of 
      ChatGPT technology, and its role in the world of medicine is expanding. Within 
      surgery, AI has the capacity to improve efficiency and results in surgical 
      treatments; however, it similarly has the potential to impose harm onto patients 
      and undermine the role of medical providers. Its benefits may include 
      improvements in surgical outcomes, spanning from enhanced pre-operative 
      diagnostic capabilities to more refined intra-operative techniques, and long term 
      patient experiences, by identifying and reducing complications. Nevertheless 
      apprehensions revolve around laymen use potentially resulting in inappropriate 
      therapeutic interventions, in addition to safety and ethical risks surrounding 
      the use of patient data. Various strategies towards mitigating these harms must 
      be considered, such as patient disclaimers and secondary review policies. While 
      artificial intelligence brings exciting advancements to surgery, its integration 
      must be cautiously monitored.
FAU - Kavian, Joseph A
AU  - Kavian JA
AUID- ORCID: 0000-0002-5511-8300
AD  - NYU Grossman School of Medicine, NYU Langone Health, New York, NY, USA. RINGGOLD: 
      12297
FAU - Wilkey, Hannah L
AU  - Wilkey HL
AD  - Medical College of Georgia, Augusta University Medical Center, Augusta, GA, USA. 
      RINGGOLD: 1421
FAU - Patel, Parth A
AU  - Patel PA
AD  - Medical College of Georgia, Augusta University Medical Center, Augusta, GA, USA. 
      RINGGOLD: 1421
FAU - Boyd, Carter J
AU  - Boyd CJ
AD  - Hansjörg Wyss Department of Plastic Surgery, New York University, New York, NY, 
      USA. RINGGOLD: 5894
LA  - eng
PT  - Editorial
DEP - 20230506
PL  - United States
TA  - Am Surg
JT  - The American surgeon
JID - 0370522
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - artificial intelligence
OT  - chatGPT
OT  - ethics
OT  - privacy
OT  - safety
EDAT- 2023/05/06 19:42
MHDA- 2023/05/06 19:42
CRDT- 2023/05/06 14:46
PHST- 2023/05/06 19:42 [medline]
PHST- 2023/05/06 19:42 [pubmed]
PHST- 2023/05/06 14:46 [entrez]
AID - 10.1177/00031348231175454 [doi]
PST - aheadofprint
SO  - Am Surg. 2023 May 6:31348231175454. doi: 10.1177/00031348231175454.

PMID- 36842449
OWN - NLM
STAT- MEDLINE
DCOM- 20230228
LR  - 20231115
IS  - 1975-5937 (Electronic)
IS  - 1975-5937 (Linking)
VI  - 20
DP  - 2023
TI  - Can an artificial intelligence chatbot be the author of a scholarly article?
PG  - 6
LID - 10.3352/jeehp.2023.20.6 [doi]
LID - 6
AB  - At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) 
      chatbot with amazing writing ability, caused a great sensation in academia. The 
      chatbot turned out to be very capable, but also capable of deception, and the 
      news broke that several researchers had listed the chatbot (including its earlier 
      version) as co-authors of their academic papers. In response, Nature and Science 
      expressed their position that this chatbot cannot be listed as an author in the 
      papers they publish. Since an AI chatbot is not a human being, in the current 
      legal system, the text automatically generated by an AI chatbot cannot be a 
      copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. 
      Current AI chatbots such as ChatGPT are much more advanced than search engines in 
      that they produce original text, but they still remain at the level of a search 
      engine in that they cannot take responsibility for their writing. For this 
      reason, they also cannot be authors from the perspective of research ethics.
FAU - Lee, Ju Yoen
AU  - Lee JY
AD  - Hanyang University School of Law, Seoul, Korea
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230227
PL  - Korea (South)
TA  - J Educ Eval Health Prof
JT  - Journal of educational evaluation for health professions
JID - 101490061
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Software
MH  - Publishing
PMC - PMC10033224
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Authorship
OT  - Chatbot
OT  - Copyright
OT  - Research ethics
COIS- Conflict of interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/02/27 06:00
MHDA- 2023/03/03 06:00
CRDT- 2023/02/26 19:08
PHST- 2023/02/16 00:00 [received]
PHST- 2023/02/21 00:00 [accepted]
PHST- 2023/02/26 19:08 [entrez]
PHST- 2023/02/27 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
AID - jeehp.2023.20.6 [pii]
AID - jeehp-20-06 [pii]
AID - 10.3352/jeehp.2023.20.6 [doi]
PST - ppublish
SO  - J Educ Eval Health Prof. 2023;20:6. doi: 10.3352/jeehp.2023.20.6. Epub 2023 Feb 
      27.

PMID- 37721869
OWN - NLM
STAT- MEDLINE
DCOM- 20231108
LR  - 20231113
IS  - 1440-1711 (Electronic)
IS  - 0818-9641 (Linking)
VI  - 101
IP  - 10
DP  - 2023 Nov-Dec
TI  - Artificial intelligence takes center stage: exploring the capabilities and 
      implications of ChatGPT and other AI-assisted technologies in scientific research 
      and education.
PG  - 923-935
LID - 10.1111/imcb.12689 [doi]
AB  - The emergence of large language models (LLMs) and assisted artificial 
      intelligence (AI) technologies have revolutionized the way in which we interact 
      with technology. A recent symposium at the Walter and Eliza Hall Institute 
      explored the current practical applications of LLMs in medical research and 
      canvassed the emerging ethical, legal and social implications for the use of 
      AI-assisted technologies in the sciences. This paper provides an overview of the 
      symposium's key themes and discussions delivered by diverse speakers, including 
      early career researchers, group leaders, educators and policy-makers highlighting 
      the opportunities and challenges that lie ahead for scientific researchers and 
      educators as we continue to explore the potential of this cutting-edge and 
      emerging technology.
CI  - © 2023 the Australian and New Zealand Society for Immunology, Inc.
FAU - Borger, Jessica G
AU  - Borger JG
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Ng, Ashley P
AU  - Ng AP
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Anderton, Holly
AU  - Anderton H
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Ashdown, George W
AU  - Ashdown GW
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Auld, Megan
AU  - Auld M
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Blewitt, Marnie E
AU  - Blewitt ME
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Brown, Daniel V
AU  - Brown DV
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Call, Melissa J
AU  - Call MJ
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Collins, Peter
AU  - Collins P
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Freytag, Saskia
AU  - Freytag S
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Harrison, Leonard C
AU  - Harrison LC
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Hesping, Eva
AU  - Hesping E
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Hoysted, Jaci
AU  - Hoysted J
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Johnston, Anna
AU  - Johnston A
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - McInneny, Andrew
AU  - McInneny A
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Tang, Phil
AU  - Tang P
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Whitehead, Lachlan
AU  - Whitehead L
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Jex, Aaron
AU  - Jex A
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
FAU - Naik, Shalin H
AU  - Naik SH
AD  - WEHI (Walter and Eliza Hall Institute of Medical Research), Parkville, VIC, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, VIC, 
      Australia.
LA  - eng
PT  - Congress
PT  - Journal Article
DEP - 20230918
PL  - United States
TA  - Immunol Cell Biol
JT  - Immunology and cell biology
JID - 8706300
SB  - IM
MH  - *Artificial Intelligence
MH  - *Biomedical Research
MH  - Technology
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - LLM
OT  - education
OT  - ethics
OT  - research
OT  - science
EDAT- 2023/09/18 18:43
MHDA- 2023/11/02 12:46
CRDT- 2023/09/18 13:02
PHST- 2023/11/02 12:46 [medline]
PHST- 2023/09/18 18:43 [pubmed]
PHST- 2023/09/18 13:02 [entrez]
AID - 10.1111/imcb.12689 [doi]
PST - ppublish
SO  - Immunol Cell Biol. 2023 Nov-Dec;101(10):923-935. doi: 10.1111/imcb.12689. Epub 
      2023 Sep 18.

PMID- 37179277
OWN - NLM
STAT- MEDLINE
DCOM- 20230809
LR  - 20230809
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Print)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 9
DP  - 2023 Sep
TI  - Artificial Intelligence in Intensive Care Medicine: Toward a ChatGPT/GPT-4 Way?
PG  - 1898-1903
LID - 10.1007/s10439-023-03234-w [doi]
AB  - Although intensive care medicine (ICM) is a relatively young discipline, it has 
      rapidly developed into a full-fledged and highly specialized specialty covering 
      several fields of medicine. The COVID-19 pandemic led to a surge in intensive 
      care unit demand and also bring unprecedented development opportunities for this 
      area. Multiple new technologies such as artificial intelligence (AI) and machine 
      learning (ML) were gradually being applied in this field. In this study, through 
      an online survey, we have summarized the potential uses of ChatGPT/GPT-4 in ICM 
      range from knowledge augmentation, device management, clinical decision-making 
      support, early warning systems, and establishment of intensive care unit (ICU) 
      database.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Lu, Yanqiu
AU  - Lu Y
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Graduate School, Tianjin Medical University, Tianjin, China.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      NC, USA.
FAU - Qi, Shaoyan
AU  - Qi S
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China. qishaoyan1970@163.com.
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China. chengkm2013@163.com.
LA  - eng
PT  - Letter
DEP - 20230513
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Pandemics
MH  - *COVID-19
MH  - Critical Care
MH  - Intensive Care Units
PMC - PMC10182840
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Biomedical engineering
OT  - Chatbot
OT  - GPT-4
OT  - New era
COIS- The authors declare no competing interests.
EDAT- 2023/05/14 01:07
MHDA- 2023/08/09 06:43
CRDT- 2023/05/13 23:04
PHST- 2023/05/05 00:00 [received]
PHST- 2023/05/08 00:00 [accepted]
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/05/14 01:07 [pubmed]
PHST- 2023/05/13 23:04 [entrez]
AID - 10.1007/s10439-023-03234-w [pii]
AID - 3234 [pii]
AID - 10.1007/s10439-023-03234-w [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Sep;51(9):1898-1903. doi: 10.1007/s10439-023-03234-w. Epub 
      2023 May 13.

PMID- 37789676
OWN - NLM
STAT- Publisher
LR  - 20231004
IS  - 1305-3612 (Electronic)
IS  - 1305-3825 (Linking)
DP  - 2023 Oct 3
TI  - Large language models in radiology: fundamentals, applications, ethical 
      considerations, risks, and future directions.
LID - 10.4274/dir.2023.232417 [doi]
AB  - With the advent of large language models (LLMs), the artificial intelligence 
      revolution in medicine and radiology is now more tangible than ever. Every day, 
      an increasingly large number of articles are published that utilize LLMs in 
      radiology. To adopt and safely implement this new technology in the field, 
      radiologists should be familiar with its key concepts, understand at least the 
      technical basics, and be aware of the potential risks and ethical considerations 
      that come with it. In this review article, the authors provide an overview of the 
      LLMs that might be relevant to the radiology community and include a brief 
      discussion of their short history, technical basics, ChatGPT, prompt engineering, 
      potential applications in medicine and radiology, advantages, disadvantages and 
      risks, ethical and regulatory considerations, and future directions.
FAU - Akinci D'Antonoli, Tugba
AU  - Akinci D'Antonoli T
AUID- ORCID: 0000-0002-7237-711X
AD  - Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, 
      Liestal, Switzerland.
FAU - Stanzione, Arnaldo
AU  - Stanzione A
AUID- ORCID: 0000-0002-7905-5789
AD  - Department of Advanced Biomedical Sciences, University of Naples Federico II, 
      Naples, Italy.
FAU - Bluethgen, Christian
AU  - Bluethgen C
AUID- ORCID: 0000-0001-7321-5676
AD  - Institute for Diagnostic and Interventional Radiology, University Hospital 
      Zurich, University of Zurich, Zurich, Switzerland.
FAU - Vernuccio, Federica
AU  - Vernuccio F
AUID- ORCID: 0000-0003-0350-1794
AD  - Department of Radiology, University Hospital of Padova, Padova, Italy.
FAU - Ugga, Lorenzo
AU  - Ugga L
AUID- ORCID: 0000-0001-7811-4612
AD  - Department of Advanced Biomedical Sciences, University of Naples Federico II, 
      Naples, Italy.
FAU - Klontzas, Michail E
AU  - Klontzas ME
AUID- ORCID: 0000-0003-2731-933X
AD  - Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece & 
      Department of Radiology, University of Crete, Heraklion, Crete, Greece & 
      Computational Biomedicine Laboratory, Institute of Computer Science, FORTH, 
      Heraklion, Crete, Greece.
FAU - Cuocolo, Renato
AU  - Cuocolo R
AUID- ORCID: 0000-0002-1452-1574
AD  - Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, 
      Italy.
FAU - Cannella, Roberto
AU  - Cannella R
AUID- ORCID: 0000-0002-3808-0785
AD  - Department of Biomedicine, Neuroscience and Advanced Diagnostics, Section of 
      Radiology, University of Palermo, Palermo, Italy.
FAU - Koçak, Burak
AU  - Koçak B
AUID- ORCID: 0000-0002-7307-396X
AD  - Clinic of Radiology, University of Health Sciences, Basakşehir Çam and Sakura 
      City Hospital, İstanbul, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20231003
PL  - Turkey
TA  - Diagn Interv Radiol
JT  - Diagnostic and interventional radiology (Ankara, Turkey)
JID - 101241152
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Large language models
OT  - artificial intelligence
OT  - deep learning
OT  - natural language processing
EDAT- 2023/10/04 06:43
MHDA- 2023/10/04 06:43
CRDT- 2023/10/04 02:17
PHST- 2023/10/04 06:43 [medline]
PHST- 2023/10/04 06:43 [pubmed]
PHST- 2023/10/04 02:17 [entrez]
AID - 10.4274/dir.2023.232417 [doi]
PST - aheadofprint
SO  - Diagn Interv Radiol. 2023 Oct 3. doi: 10.4274/dir.2023.232417.

PMID- 37065364
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230418
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Extreme Hyperthermia Due to Methamphetamine Toxicity Presenting As ST-Elevation 
      Myocardial Infarction on EKG: A Case Report Written With ChatGPT Assistance.
PG  - e36101
LID - 10.7759/cureus.36101 [doi]
LID - e36101
AB  - We present a case report of a 37-year-old male who presented to the emergency 
      department with altered mental status and electrocardiographic changes suggestive 
      of an ST-elevation myocardial infarction (STEMI). He was ultimately diagnosed 
      with extreme hyperthermia, secondary to drug use, which was managed promptly with 
      supportive measures resulting in a successful outcome. This case highlights the 
      importance of considering drug-induced hyperthermia as a potential cause of 
      altered mental status and EKG changes in patients, especially in those with a 
      history of drug abuse.
CI  - Copyright © 2023, Schussler et al.
FAU - Schussler, Jeffrey M
AU  - Schussler JM
AD  - Cardiology, Baylor University Medical Center, Dallas, USA.
AD  - Cardiology, Baylor Scott & White Heart and Vascular Hospital, Dallas, USA.
FAU - Tomson, Cerin
AU  - Tomson C
AD  - Internal Medicine, Baylor University Medical Center, Dallas, USA.
FAU - Dresselhouse, Mark P
AU  - Dresselhouse MP
AD  - Emergency Medicine, Baylor University Medical Center, Dallas, USA.
LA  - eng
PT  - Case Reports
DEP - 20230313
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10097605
OTO - NOTNLM
OT  - chatgpt
OT  - methamphetamine
OT  - methamphetamine intoxication
OT  - st-elevation myocardial infarction (stemi)
OT  - st-segment elevation myocardial infarction (stemi)
OT  - stemi
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/18 06:00
MHDA- 2023/04/18 06:01
CRDT- 2023/04/17 03:54
PHST- 2023/03/13 00:00 [accepted]
PHST- 2023/04/18 06:01 [medline]
PHST- 2023/04/17 03:54 [entrez]
PHST- 2023/04/18 06:00 [pubmed]
AID - 10.7759/cureus.36101 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 13;15(3):e36101. doi: 10.7759/cureus.36101. eCollection 2023 
      Mar.

PMID- 37771061
OWN - NLM
STAT- MEDLINE
DCOM- 20231004
LR  - 20231004
IS  - 2639-9636 (Print)
IS  - 2639-9636 (Linking)
VI  - 38
IP  - 10
DP  - 2023 Oct 1
TI  - Artificial Intelligence: The Future Is Here.
PG  - 427-428
LID - 10.4140/TCP.n.2023.427 [doi]
AB  - Although artificial intelligence (AI) has been around since the 1950s, it has 
      become a phenomenon within the past couple of years, peaking in online search 
      results in early 2023, following the release of OpenAI's ChatGPT. Inevitably, 
      concerns began to surface over the impact that AI would have on jobs in an 
      advanced economy like the United States.
FAU - Baldwin, Paul
AU  - Baldwin P
AD  - Baldwin Health Policy Group LLC.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Sr Care Pharm
JT  - The Senior care pharmacist
JID - 101737969
SB  - IM
MH  - *Artificial Intelligence
EDAT- 2023/09/29 06:44
MHDA- 2023/10/04 06:43
CRDT- 2023/09/29 00:33
PHST- 2023/10/04 06:43 [medline]
PHST- 2023/09/29 06:44 [pubmed]
PHST- 2023/09/29 00:33 [entrez]
AID - 10.4140/TCP.n.2023.427 [doi]
PST - ppublish
SO  - Sr Care Pharm. 2023 Oct 1;38(10):427-428. doi: 10.4140/TCP.n.2023.427.

PMID- 37440299
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230731
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Jul 13
TI  - Authors' Reply to: Variability in Large Language Models' Responses to Medical 
      Licensing and Certification Examinations.
PG  - e50336
LID - 10.2196/50336 [doi]
LID - e50336
FAU - Gilson, Aidan
AU  - Gilson A
AUID- ORCID: 0000-0002-4770-4705
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - Department of Emergency Medicine, Yale University School of Medicine, New Haven, 
      CT, United States.
FAU - Safranek, Conrad W
AU  - Safranek CW
AUID- ORCID: 0000-0003-1985-9432
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
FAU - Huang, Thomas
AU  - Huang T
AUID- ORCID: 0000-0001-9056-7016
AD  - Department of Emergency Medicine, Yale University School of Medicine, New Haven, 
      CT, United States.
FAU - Socrates, Vimig
AU  - Socrates V
AUID- ORCID: 0000-0001-7955-9875
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - Program of Computational Biology and Bioinformatics, Yale University, New Haven, 
      CT, United States.
FAU - Chi, Ling
AU  - Chi L
AUID- ORCID: 0000-0002-8270-9245
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
FAU - Taylor, Richard Andrew
AU  - Taylor RA
AUID- ORCID: 0000-0002-9082-6644
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - Department of Emergency Medicine, Yale University School of Medicine, New Haven, 
      CT, United States.
FAU - Chartash, David
AU  - Chartash D
AUID- ORCID: 0000-0002-0265-330X
AD  - Section for Biomedical Informatics and Data Science, Yale University School of 
      Medicine, New Haven, CT, United States.
AD  - School of Medicine, University College Dublin, National University of Ireland, 
      Dublin, Dublin, Ireland.
LA  - eng
PT  - Journal Article
DEP - 20230713
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
CON - JMIR Med Educ. 9:e48305.
CON - JMIR Med Educ. 9:e45312.
PMC - PMC10375396
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT
OT  - MedQA
OT  - NLP
OT  - artificial intelligence
OT  - chatbot
OT  - conversational agent
OT  - education technology
OT  - generative pre-trained transformer
OT  - knowledge assessment
OT  - large language models
OT  - machine learning
OT  - medical education
OT  - natural language processing
COIS- Conflicts of Interest: None declared.
EDAT- 2023/07/13 12:31
MHDA- 2023/07/13 12:32
CRDT- 2023/07/13 11:54
PHST- 2023/06/27 00:00 [received]
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/07/13 12:32 [medline]
PHST- 2023/07/13 12:31 [pubmed]
PHST- 2023/07/13 11:54 [entrez]
AID - v9i1e50336 [pii]
AID - 10.2196/50336 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Jul 13;9:e50336. doi: 10.2196/50336.

PMID- 37786810
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231004
IS  - 0736-587X (Print)
IS  - 0736-587X (Linking)
VI  - 2023
DP  - 2023 Jul
TI  - Improving the Transferability of Clinical Note Section Classification Models with 
      BERT and Large Language Model Ensembles.
PG  - 125-130
AB  - Text in electronic health records is organized into sections, and classifying 
      those sections into section categories is useful for downstream tasks. In this 
      work, we attempt to improve the transferability of section classification models 
      by combining the dataset-specific knowledge in supervised learning models with 
      the world knowledge inside large language models (LLMs). Surprisingly, we find 
      that zero-shot LLMs out-perform supervised BERT-based models applied to 
      out-of-domain data. We also find that their strengths are synergistic, so that a 
      simple ensemble technique leads to additional performance gains.
FAU - Zhou, Weipeng
AU  - Zhou W
AD  - Department of Biomedical, Informatics and Medical Education, School of Medicine, 
      University of Washington.
FAU - Dligach, Dmitriy
AU  - Dligach D
AD  - Department of Computer Science, Loyola University Chicago.
FAU - Afshar, Majid
AU  - Afshar M
AD  - Department of Medicine, School of Medicine and Public Health, University of 
      Wisconsin.
FAU - Gao, Yanjun
AU  - Gao Y
AD  - Department of Medicine, School of Medicine and Public Health, University of 
      Wisconsin.
FAU - Miller, Timothy A
AU  - Miller TA
AD  - Computational Health Informatics Program, Boston Children's Hospital, Harvard 
      Medical School.
LA  - eng
GR  - R01 DA051464/DA/NIDA NIH HHS/United States
GR  - R01 LM012918/LM/NLM NIH HHS/United States
GR  - R01 LM012973/LM/NLM NIH HHS/United States
GR  - R01 LM013486/LM/NLM NIH HHS/United States
PT  - Journal Article
PL  - United States
TA  - Proc Conf Assoc Comput Linguist Meet
JT  - Proceedings of the conference. Association for Computational Linguistics. Meeting
JID - 101639983
PMC - PMC10544420
MID - NIHMS1921258
EDAT- 2023/10/03 06:47
MHDA- 2023/10/03 06:48
CRDT- 2023/10/03 03:51
PHST- 2023/10/03 06:48 [medline]
PHST- 2023/10/03 06:47 [pubmed]
PHST- 2023/10/03 03:51 [entrez]
PST - ppublish
SO  - Proc Conf Assoc Comput Linguist Meet. 2023 Jul;2023:125-130.

PMID- 37410482
OWN - NLM
STAT- MEDLINE
DCOM- 20230728
LR  - 20230912
IS  - 1538-3598 (Electronic)
IS  - 0098-7484 (Linking)
VI  - 330
IP  - 4
DP  - 2023 Jul 25
TI  - The Challenges for Regulating Medical Use of ChatGPT and Other Large Language 
      Models.
PG  - 315-316
LID - 10.1001/jama.2023.9651 [doi]
FAU - Minssen, Timo
AU  - Minssen T
AD  - Center for Advanced Studies in Biomedical Innovation Law, University of 
      Copenhagen, Copenhagen, Denmark.
AD  - Centre for Law, Medicine, and Life Sciences, University of Cambridge, Cambridge, 
      England.
FAU - Vayena, Effy
AU  - Vayena E
AD  - Institute of Translational Medicine, Swiss Federal Institute of Technology Zurich 
      (ETH Zurich), Zurich, Switzerland.
FAU - Cohen, I Glenn
AU  - Cohen IG
AD  - Harvard Law School, Petrie-Flom Center for Health Law Policy, Biotechnology, and 
      Bioethics, Harvard University, Cambridge, Massachusetts.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - JAMA
JT  - JAMA
JID - 7501160
SB  - IM
EIN - JAMA. 2023 Sep 12;330(10):974. PMID: 37698580
MH  - Ownership/legislation & jurisprudence
MH  - *Privacy/legislation & jurisprudence
MH  - *Legislation, Medical
MH  - Language
MH  - *Health Communication/methods
MH  - *Artificial Intelligence/legislation & jurisprudence
OAB - This Viewpoint discusses how regulators across the world should approach the 
      legal and ethical challenges, including privacy, device regulation, competition, 
      intellectual property rights, cybersecurity, and liability, raised by the medical 
      use of large language models.
OABL- eng
EDAT- 2023/07/06 13:14
MHDA- 2023/07/26 06:43
CRDT- 2023/07/06 11:34
PHST- 2023/07/26 06:43 [medline]
PHST- 2023/07/06 13:14 [pubmed]
PHST- 2023/07/06 11:34 [entrez]
AID - 2807167 [pii]
AID - 10.1001/jama.2023.9651 [doi]
PST - ppublish
SO  - JAMA. 2023 Jul 25;330(4):315-316. doi: 10.1001/jama.2023.9651.

PMID- 37336139
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20231128
IS  - 1873-4529 (Electronic)
IS  - 0952-8180 (Linking)
VI  - 89
DP  - 2023 Oct
TI  - Artificial intelligence software can generate residency application personal 
      statements that program directors find acceptable and difficult to distinguish 
      from applicant compositions.
PG  - 111185
LID - S0952-8180(23)00135-6 [pii]
LID - 10.1016/j.jclinane.2023.111185 [doi]
AB  - STUDY OBJECTIVE: Create personal statements using an artificial intelligence 
      program for anesthesiology residency applications that residency program 
      directors rate as acceptable. STUDY DESIGN: Generate two personal statements and 
      survey program directors. SETTING: Anesthesiology residency training programs. 
      INTERVENTIONS: We instructed ChatGPT, a new artificial-intelligence software 
      program, to generate two 400-word personal statements using the common applicant 
      experiences of involvement in athletics or gourmet cooking. METHODS: We sent the 
      generated personal statements to anesthesia program directors and asked them if 
      the statements were acceptable for application to their individual programs, to 
      rate them as poor, good, or excellent, and determine if they could detect 
      anything in the statements that indicated they were not written by an applicant. 
      MEASUREMENTS: Ninety-four program directors received and opened the survey, and 
      31 responded. Twenty-eight (90%) responding directors found the personal 
      statement with athletic experience acceptable, with 22 (74%) rating it as good or 
      excellent. Nineteen (61%) program directors did not detect anything in the 
      statement to distinguish it from an applicant-written composition. Twenty-nine 
      (97%) program directors found the personal statement with cooking experience 
      acceptable, with 19 (63%) finding it good or excellent. Twenty-four (80%) 
      directors did not detect anything in the statement to distinguish it from an 
      applicant-written composition. CONCLUSIONS: ChatGPT can create personal 
      statements for residency applications that program directors find acceptable and 
      difficult to differentiate from personally crafted statements. Applicants may 
      stop using expensive contractor application services and start using artificial 
      intelligence software to create their personal statements because of its 
      quickness, low cost, and high quality.
CI  - Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Johnstone, Robert E
AU  - Johnstone RE
AD  - Department of Anesthesiology, West Virginia University, United States of America. 
      Electronic address: JohnstoneR@hsc.wvu.edu.
FAU - Neely, Grant
AU  - Neely G
AD  - Department of Anesthesiology, West Virginia University, United States of America.
FAU - Sizemore, Daniel C
AU  - Sizemore DC
AD  - Department of Anesthesiology, West Virginia University, United States of America.
LA  - eng
PT  - Journal Article
DEP - 20230619
PL  - United States
TA  - J Clin Anesth
JT  - Journal of clinical anesthesia
JID - 8812166
SB  - IM
MH  - Humans
MH  - *Internship and Residency
MH  - Artificial Intelligence
MH  - Surveys and Questionnaires
MH  - Software
MH  - *Anesthesiology/education
OTO - NOTNLM
OT  - Anesthesiology program
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Personal statement
OT  - Residency application
COIS- Declaration of Competing Interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: Robert Johnstone reports a relationship with American Society of 
      Anesthesiologists that includes: board membership and travel reimbursement.
EDAT- 2023/06/20 01:09
MHDA- 2023/07/10 06:42
CRDT- 2023/06/19 18:06
PHST- 2023/04/01 00:00 [received]
PHST- 2023/06/08 00:00 [revised]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/06/20 01:09 [pubmed]
PHST- 2023/06/19 18:06 [entrez]
AID - S0952-8180(23)00135-6 [pii]
AID - 10.1016/j.jclinane.2023.111185 [doi]
PST - ppublish
SO  - J Clin Anesth. 2023 Oct;89:111185. doi: 10.1016/j.jclinane.2023.111185. Epub 2023 
      Jun 19.

PMID- 37855067
OWN - NLM
STAT- Publisher
LR  - 20231019
IS  - 1440-1746 (Electronic)
IS  - 0815-9319 (Linking)
DP  - 2023 Oct 19
TI  - ChatGPT on guidelines: Providing contextual knowledge to GPT allows it to provide 
      advice on appropriate colonoscopy intervals.
LID - 10.1111/jgh.16375 [doi]
AB  - BACKGROUND AND AIM: Colonoscopy is commonly used in screening and surveillance 
      for colorectal cancer. Multiple different guidelines provide recommendations on 
      the interval between colonoscopies. This can be challenging for non-specialist 
      healthcare providers to navigate. Large language models like ChatGPT are a 
      potential tool for parsing patient histories and providing advice. However, the 
      standard GPT model is not designed for medical use and can hallucinate. One way 
      to overcome these challenges is to provide contextual information with medical 
      guidelines to help the model respond accurately to queries. Our study compares 
      the standard GPT4 against a contextualized model provided with relevant screening 
      guidelines. We evaluated whether the models could provide correct advice for 
      screening and surveillance intervals for colonoscopy. METHODS: Relevant 
      guidelines pertaining to colorectal cancer screening and surveillance were 
      formulated into a knowledge base for GPT. We tested 62 example case scenarios 
      (three times each) on standard GPT4 and on a contextualized model with the 
      knowledge base. RESULTS: The contextualized GPT4 model outperformed the standard 
      GPT4 in all domains. No high-risk features were missed, and only two cases had 
      hallucination of additional high-risk features. A correct interval to colonoscopy 
      was provided in the majority of cases. Guidelines were appropriately cited in 
      almost all cases. CONCLUSIONS: A contextualized GPT4 model could identify 
      high-risk features and quote appropriate guidelines without significant 
      hallucination. It gave a correct interval to the next colonoscopy in the majority 
      of cases. This provides proof of concept that ChatGPT with appropriate refinement 
      can serve as an accurate physician assistant.
CI  - © 2023 Journal of Gastroenterology and Hepatology Foundation and John Wiley & 
      Sons Australia, Ltd.
FAU - Lim, Daniel Yan Zheng
AU  - Lim DYZ
AUID- ORCID: 0000-0002-9715-6970
AD  - Department of Gastroenterology and Hepatology, Singapore General Hospital, 
      Singapore.
AD  - Medicine Academic Clininical Programme, Duke-NUS Medical School, Singapore, 
      Singapore.
FAU - Tan, Yu Bin
AU  - Tan YB
AUID- ORCID: 0000-0002-4639-5760
AD  - Department of Gastroenterology and Hepatology, Singapore General Hospital, 
      Singapore.
FAU - Koh, Jonathan Tian En
AU  - Koh JTE
AUID- ORCID: 0000-0003-3694-2748
AD  - Department of Gastroenterology and Hepatology, Singapore General Hospital, 
      Singapore.
FAU - Tung, Joshua Yi Min
AU  - Tung JYM
AD  - Department of Urology, Singapore General Hospital, Singapore.
FAU - Sng, Gerald Gui Ren
AU  - Sng GGR
AD  - Department of Endocrinology, Singapore General Hospital, Singapore.
FAU - Tan, Damien Meng Yew
AU  - Tan DMY
AD  - Department of Gastroenterology and Hepatology, Singapore General Hospital, 
      Singapore.
AD  - Medicine Academic Clininical Programme, Duke-NUS Medical School, Singapore, 
      Singapore.
FAU - Tan, Chee-Kiat
AU  - Tan CK
AUID- ORCID: 0000-0001-6992-2319
AD  - Department of Gastroenterology and Hepatology, Singapore General Hospital, 
      Singapore.
AD  - Medicine Academic Clininical Programme, Duke-NUS Medical School, Singapore, 
      Singapore.
LA  - eng
PT  - Journal Article
DEP - 20231019
PL  - Australia
TA  - J Gastroenterol Hepatol
JT  - Journal of gastroenterology and hepatology
JID - 8607909
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Colonoscopy
OT  - Colorectal cancer
OT  - GPT
OT  - Screening and surveillance interval
EDAT- 2023/10/19 06:44
MHDA- 2023/10/19 06:44
CRDT- 2023/10/19 04:31
PHST- 2023/09/06 00:00 [revised]
PHST- 2023/06/15 00:00 [received]
PHST- 2023/09/22 00:00 [accepted]
PHST- 2023/10/19 06:44 [medline]
PHST- 2023/10/19 06:44 [pubmed]
PHST- 2023/10/19 04:31 [entrez]
AID - 10.1111/jgh.16375 [doi]
PST - aheadofprint
SO  - J Gastroenterol Hepatol. 2023 Oct 19. doi: 10.1111/jgh.16375.

PMID- 36865187
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230803
DP  - 2023 Feb 22
TI  - Generative AI as a Tool for Environmental Health Research Translation.
LID - 2023.02.14.23285938 [pii]
LID - 10.1101/2023.02.14.23285938 [doi]
AB  - Generative artificial intelligence, popularized by services like ChatGPT, has 
      been the source of much recent popular attention for publishing health research. 
      Another valuable application is in translating published research studies to 
      readers in non-academic settings. These might include environmental justice 
      communities, mainstream media outlets, and community science groups. Five 
      recently published (2021-2022) open-access, peer-reviewed papers, authored by 
      University of Louisville environmental health investigators and collaborators, 
      were submitted to ChatGPT. The average rating of all summaries of all types 
      across the five different studies ranged between 3 and 5, indicating good overall 
      content quality. ChatGPTâ€™s general summary request was consistently rated lower 
      than all other summary types. Whereas higher ratings of 4 and 5 were assigned to 
      the more synthetic, insight-oriented activities, such as the production of a 
      plain language summaries suitable for an 8 (th) grade reading level and 
      identifying the most important finding and real-world research applications. This 
      is a case where artificial intelligence might help level the playing field, for 
      example by creating accessible insights and enabling the large-scale production 
      of high-quality plain language summaries which would truly bring open access to 
      this scientific information. This possibility, combined with the increasing 
      public policy trends encouraging and demanding free access for research supported 
      with public funds, may alter the role journal publications play in communicating 
      science in society. For the field of environmental health science, no-cost AI 
      technology such as ChatGPT holds the promise to improve research translation, but 
      it must continue to be improved (or improve itself) from its current capability.
FAU - Anderson, Lauren B
AU  - Anderson LB
AD  - Christina Lee Brown Envirome Institute, School of Medicine, University of 
      Louisville, Louisville, KY 40202, United States.
AD  - Department of Urban and Public Affairs, College of Arts and Sciences, University 
      of Louisville, Louisville KY 40208, United States.
FAU - Kanneganti, Dhiraj
AU  - Kanneganti D
AD  - Christina Lee Brown Envirome Institute, School of Medicine, University of 
      Louisville, Louisville, KY 40202, United States.
FAU - Bentley Houk, Mary
AU  - Bentley Houk M
AD  - Christina Lee Brown Envirome Institute, School of Medicine, University of 
      Louisville, Louisville, KY 40202, United States.
FAU - Holm, Rochelle H
AU  - Holm RH
AD  - Christina Lee Brown Envirome Institute, School of Medicine, University of 
      Louisville, Louisville, KY 40202, United States.
FAU - Smith, Ted
AU  - Smith T
AD  - Christina Lee Brown Envirome Institute, School of Medicine, University of 
      Louisville, Louisville, KY 40202, United States.
AD  - University of Louisville Superfund Research Center, Louisville, KY 40202, United 
      States.
LA  - eng
GR  - P42 ES023716/ES/NIEHS NIH HHS/United States
PT  - Preprint
DEP - 20230222
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
UIN - Geohealth. 2023 Jul 26;7(7):e2023GH000875. PMID: 37502196
PMC - PMC9980240
COIS- Conflicts of Interest The authors declare they have nothing to disclose.
EDAT- 2023/03/04 06:00
MHDA- 2023/03/04 06:01
CRDT- 2023/03/03 02:28
PHST- 2023/03/03 02:28 [entrez]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/04 06:01 [medline]
AID - 2023.02.14.23285938 [pii]
AID - 10.1101/2023.02.14.23285938 [doi]
PST - epublish
SO  - medRxiv. 2023 Feb 22:2023.02.14.23285938. doi: 10.1101/2023.02.14.23285938. 
      Preprint.

PMID- 37459402
OWN - NLM
STAT- MEDLINE
DCOM- 20231005
LR  - 20231005
IS  - 1535-1386 (Electronic)
IS  - 0021-9355 (Linking)
VI  - 105
IP  - 19
DP  - 2023 Oct 4
TI  - Assessing ChatGPT Responses to Common Patient Questions Regarding Total Hip 
      Arthroplasty.
PG  - 1519-1526
LID - 10.2106/JBJS.23.00209 [doi]
AB  - BACKGROUND: The contemporary patient has access to numerous resources on common 
      orthopaedic procedures before ever presenting for a clinical evaluation. 
      Recently, artificial intelligence (AI)-driven chatbots have become mainstream, 
      allowing patients to engage with interfaces that supply convincing, human-like 
      responses to prompts. ChatGPT (OpenAI), a recently developed AI-based chat 
      technology, is one such application that has garnered rapid growth in popularity. 
      Given the likelihood that patients may soon call on this technology for 
      preoperative education, we sought to determine whether ChatGPT could 
      appropriately answer frequently asked questions regarding total hip arthroplasty 
      (THA). METHODS: Ten frequently asked questions regarding total hip arthroplasty 
      were posed to the chatbot during a conversation thread, with no follow-up 
      questions or repetition. Each response was analyzed for accuracy with use of an 
      evidence-based approach. Responses were rated as "excellent response not 
      requiring clarification," "satisfactory requiring minimal clarification," 
      "satisfactory requiring moderate clarification," or "unsatisfactory requiring 
      substantial clarification." RESULTS: Of the responses given by the chatbot, only 
      1 received an "unsatisfactory" rating; 2 did not require any correction, and the 
      majority required either minimal (4 of 10) or moderate (3 of 10) clarification. 
      Although several responses required nuanced clarification, the chatbot's 
      responses were generally unbiased and evidence-based, even for controversial 
      topics. CONCLUSIONS: The chatbot effectively provided evidence-based responses to 
      questions commonly asked by patients prior to THA. The chatbot presented 
      information in a way that most patients would be able to understand. This 
      resource may serve as a valuable clinical tool for patient education and 
      understanding prior to orthopaedic consultation in the future.
CI  - Copyright © 2023 by The Journal of Bone and Joint Surgery, Incorporated.
FAU - Mika, Aleksander P
AU  - Mika AP
AUID- ORCID: 0000-0002-5260-4506
AD  - Department of Orthopedic Surgery, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Martin, J Ryan
AU  - Martin JR
AUID- ORCID: 0000-0002-4125-2154
FAU - Engstrom, Stephen M
AU  - Engstrom SM
AUID- ORCID: 0000-0001-6261-5236
FAU - Polkowski, Gregory G
AU  - Polkowski GG
AUID- ORCID: 0000-0002-8990-7578
FAU - Wilson, Jacob M
AU  - Wilson JM
AUID- ORCID: 0000-0002-5044-7084
LA  - eng
PT  - Journal Article
DEP - 20230717
PL  - United States
TA  - J Bone Joint Surg Am
JT  - The Journal of bone and joint surgery. American volume
JID - 0014030
SB  - IM
MH  - Humans
MH  - *Arthroplasty, Replacement, Hip
MH  - Artificial Intelligence
MH  - Communication
MH  - *Orthopedic Procedures
MH  - *Orthopedics
COIS- Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided 
      with the online version of the article ( http://links.lww.com/JBJS/H616 ).
EDAT- 2023/07/17 19:08
MHDA- 2023/10/05 06:44
CRDT- 2023/07/17 14:03
PHST- 2023/10/05 06:44 [medline]
PHST- 2023/07/17 19:08 [pubmed]
PHST- 2023/07/17 14:03 [entrez]
AID - 00004623-202310040-00006 [pii]
AID - 10.2106/JBJS.23.00209 [doi]
PST - ppublish
SO  - J Bone Joint Surg Am. 2023 Oct 4;105(19):1519-1526. doi: 10.2106/JBJS.23.00209. 
      Epub 2023 Jul 17.

PMID- 37009366
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230404
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Medical Literature Writing With ChatGPT: A Rare Case of Choriocarcinoma Syndrome 
      With Hemorrhagic Brain Metastases Due to Burned Out Metastatic Mixed Testicular 
      Cancer.
PG  - e36655
LID - 10.7759/cureus.36655 [doi]
LID - e36655
AB  - Testicular tumors are one of the most commonly observed malignancies among men. 
      An aggressive and rare subtype of the disease, testicular choriocarcinoma, has a 
      worse prognosis due to the tendency of early hematogenous spread to multiple 
      organs and advanced symptoms at presentation time. Characteristic features of 
      choriocarcinoma include elevated beta human chorionic gonadotropin (β-hCG) in a 
      young male with testicular mass. However, when the primary testicular tumor 
      overutilizes its blood supply and spontaneously regresses, it is presumed to have 
      been "burnt out" with remnants evident by metastatic retroperitoneal 
      lymphadenopathy, scarred tissue, and calcifications. Treatment of advanced 
      testicular cancer may be complicated by a rare entity known as choriocarcinoma 
      syndrome, distinguished by rapid and fatal hemorrhaging of metastatic tumor 
      sites. Prior described cases of choriocarcinoma syndrome involve pulmonary and 
      gastrointestinal hemorrhages. We present an uncommon case of a 34-year-old male 
      with a "burnt out" metastatic mixed testicular cancer who presented with 
      choriocarcinoma syndrome (CS) treated with chemotherapy but developed deadly 
      hemorrhaging of brain metastases. In addition, with the assistance of ChatGPT, we 
      report our experience with this OpenAI tool and its potential uses in medical 
      literature writing.
CI  - Copyright © 2023, Le et al.
FAU - Le, David P
AU  - Le DP
AD  - Internal Medicine, University of South Alabama, Mobile, USA.
FAU - Hall, Samuel C
AU  - Hall SC
AD  - Internal Medicine, University of South Alabama, Mobile, USA.
LA  - eng
PT  - Case Reports
DEP - 20230324
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10065126
OTO - NOTNLM
OT  - burned-out testicular tumor
OT  - chatgpt
OT  - choriocarcinoma
OT  - choriocarcinoma syndrome
OT  - embryonal cell carcinoma
OT  - germ cell and embryonal neoplasms
OT  - intracerebral hemorrhage
OT  - metastatic testicular cancer
OT  - pulmonary hemorrhage
OT  - testicular cancer
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/04 06:00
MHDA- 2023/04/04 06:01
CRDT- 2023/04/03 04:26
PHST- 2023/03/24 00:00 [accepted]
PHST- 2023/04/04 06:01 [medline]
PHST- 2023/04/03 04:26 [entrez]
PHST- 2023/04/04 06:00 [pubmed]
AID - 10.7759/cureus.36655 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 24;15(3):e36655. doi: 10.7759/cureus.36655. eCollection 2023 
      Mar.

PMID- 37335338
OWN - NLM
STAT- MEDLINE
DCOM- 20230721
LR  - 20230721
IS  - 1573-1677 (Electronic)
IS  - 1382-4996 (Linking)
VI  - 28
IP  - 3
DP  - 2023 Aug
TI  - Artificial scholarship: LLMs in health professions education research.
PG  - 659-664
LID - 10.1007/s10459-023-10257-4 [doi]
AB  - This editorial examines the implications of artificial intelligence (AI), 
      specifically large language models (LLMs) such as ChatGPT, on the authorship and 
      authority of academic papers, and the potential ethical concerns and challenges 
      in health professions education (HPE).
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature B.V.
FAU - Ellaway, Rachel H
AU  - Ellaway RH
AD  - Department of Community Health Sciences and Office of Health and Medical 
      Education Scholarship, Cumming School of Medicine, University of Calgary, 
      Calgary, AB, Canada. rellaway@gmail.com.
FAU - Tolsgaard, Martin
AU  - Tolsgaard M
AD  - University of Copenhagen, Copenhagen Academy for Medical Education and Simulation 
      (CAMES), Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.
LA  - eng
PT  - Editorial
PL  - Netherlands
TA  - Adv Health Sci Educ Theory Pract
JT  - Advances in health sciences education : theory and practice
JID - 9612021
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Fellowships and Scholarships
MH  - Authorship
MH  - Language
MH  - Health Occupations
EDAT- 2023/06/19 13:09
MHDA- 2023/07/21 06:42
CRDT- 2023/06/19 11:04
PHST- 2023/07/21 06:42 [medline]
PHST- 2023/06/19 13:09 [pubmed]
PHST- 2023/06/19 11:04 [entrez]
AID - 10.1007/s10459-023-10257-4 [pii]
AID - 10.1007/s10459-023-10257-4 [doi]
PST - ppublish
SO  - Adv Health Sci Educ Theory Pract. 2023 Aug;28(3):659-664. doi: 
      10.1007/s10459-023-10257-4.

PMID- 37104128
OWN - NLM
STAT- MEDLINE
DCOM- 20230501
LR  - 20230510
IS  - 2379-139X (Electronic)
IS  - 2379-1381 (Print)
IS  - 2379-1381 (Linking)
VI  - 9
IP  - 2
DP  - 2023 Mar 23
TI  - Optimizing Communication of Radiation Exposure in Medical Imaging, the 
      Radiologist Challenge.
PG  - 717-720
LID - 10.3390/tomography9020057 [doi]
AB  - Since I started my residency program in Radiology, I have been committed to 
      promoting radiation protection, paying particular attention to the justification 
      and optimization of the examinations [...].
FAU - Pozzessere, Chiara
AU  - Pozzessere C
AUID- ORCID: 0000-0003-1379-5888
AD  - Department of Diagnostic and Interventional Radiology, Lausanne University 
      Hospital (CHUV), 1011 Lausanne, Switzerland.
AD  - Faculty of Biology and Medicine, University of Lausanne (UNIL), 1011 Lausanne, 
      Switzerland.
LA  - eng
PT  - Editorial
DEP - 20230323
PL  - Switzerland
TA  - Tomography
JT  - Tomography (Ann Arbor, Mich.)
JID - 101671170
SB  - IM
MH  - Humans
MH  - Tomography, X-Ray Computed/methods
MH  - *Radiation Exposure/prevention & control
MH  - *Radiation Protection/methods
MH  - Communication
MH  - Radiologists
PMC - PMC10142536
OTO - NOTNLM
OT  - ChatGPT
OT  - computed tomography
OT  - patient communication
OT  - patient information
OT  - radiation exposure
OT  - radiation protection
COIS- The authors declare no conflict of interest.
EDAT- 2023/04/27 18:41
MHDA- 2023/05/01 06:42
CRDT- 2023/04/27 13:13
PHST- 2023/03/09 00:00 [received]
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/27 18:41 [pubmed]
PHST- 2023/04/27 13:13 [entrez]
AID - tomography9020057 [pii]
AID - tomography-09-00057 [pii]
AID - 10.3390/tomography9020057 [doi]
PST - epublish
SO  - Tomography. 2023 Mar 23;9(2):717-720. doi: 10.3390/tomography9020057.

PMID- 37690015
OWN - NLM
STAT- MEDLINE
DCOM- 20231106
LR  - 20231108
IS  - 1531-6564 (Electronic)
IS  - 0363-5023 (Linking)
VI  - 48
IP  - 11
DP  - 2023 Nov
TI  - Evaluation of Online Artificial Intelligence-Generated Information on Common Hand 
      Procedures.
PG  - 1122-1127
LID - S0363-5023(23)00414-8 [pii]
LID - 10.1016/j.jhsa.2023.08.003 [doi]
AB  - PURPOSE: The purpose of this study was to analyze the quality and readability of 
      the information generated by an online artificial intelligence (AI) platform 
      regarding 4 common hand surgeries and to compare AI-generated responses to those 
      provided in the informational articles published by the American Society for 
      Surgery of the Hand (ASSH) HandCare website. METHODS: An open AI model (ChatGPT) 
      was used to answer questions commonly asked by patients on 4 common hand 
      surgeries (carpal tunnel release, cubital tunnel release, trigger finger release, 
      and distal radius fracture fixation). These answers were evaluated for medical 
      accuracy, quality and readability and compared to answers derived from the ASSH 
      HandCare materials. RESULTS: For the AI model, the Journal of the American 
      Medical Association benchmark criteria score was 0/4, and the DISCERN score was 
      58 (considered good). The areas in which the AI model lost points were primarily 
      related to the lack of attribution, reliability and currency of the source 
      material. For AI responses, the mean Flesch Kinkaid Reading Ease score was 15, 
      and the Flesch Kinkaid Grade Level was 34, which is considered to be college 
      level. For comparison, ASSH HandCare materials scored 3/4 on the Journal of the 
      American Medical Association Benchmark, 71 on DISCERN (excellent), 9 on Flesch 
      Kinkaid Grade Level, and 60 on Flesch Kinkaid Reading Ease score (eighth/ninth 
      grade level). CONCLUSION: An AI language model (ChatGPT) provided generally 
      high-quality answers to frequently asked questions relating to the common hand 
      procedures queried, but it is unclear when or where these answers came from 
      without citations to source material. Furthermore, a high reading level was 
      required to comprehend the information presented. The AI software repeatedly 
      referenced the need to discuss these questions with a surgeon, the importance of 
      shared decision-making and individualized care, and compliance with surgeon 
      treatment recommendations. CLINICAL RELEVANCE: As novel AI applications become 
      increasingly mainstream, hand surgeons must understand the limitations and 
      ramifications these technologies have for patient care.
CI  - Copyright © 2023 American Society for Surgery of the Hand. Published by Elsevier 
      Inc. All rights reserved.
FAU - Crook, Bryan S
AU  - Crook BS
AD  - Department of Orthopaedic Surgery, Duke University Hospital, Durham, NC. 
      Electronic address: bryan.crook@duke.edu.
FAU - Park, Caroline N
AU  - Park CN
AD  - Department of Orthopaedic Surgery, Duke University Hospital, Durham, NC.
FAU - Hurley, Eoghan T
AU  - Hurley ET
AD  - Department of Orthopaedic Surgery, Duke University Hospital, Durham, NC.
FAU - Richard, Marc J
AU  - Richard MJ
AD  - Department of Orthopaedic Surgery, Duke University Hospital, Durham, NC.
FAU - Pidgeon, Tyler S
AU  - Pidgeon TS
AD  - Department of Orthopaedic Surgery, Duke University Hospital, Durham, NC.
LA  - eng
PT  - Journal Article
DEP - 20230909
PL  - United States
TA  - J Hand Surg Am
JT  - The Journal of hand surgery
JID - 7609631
SB  - IM
MH  - Humans
MH  - United States
MH  - *Health Literacy
MH  - Artificial Intelligence
MH  - Reproducibility of Results
MH  - Hand/surgery
MH  - Comprehension
MH  - Internet
OTO - NOTNLM
OT  - Artificial intelligence
OT  - health literacy
OT  - internet
OT  - patient education
OT  - readability
EDAT- 2023/09/10 12:41
MHDA- 2023/11/06 06:42
CRDT- 2023/09/10 10:31
PHST- 2023/05/06 00:00 [received]
PHST- 2023/07/25 00:00 [revised]
PHST- 2023/08/02 00:00 [accepted]
PHST- 2023/11/06 06:42 [medline]
PHST- 2023/09/10 12:41 [pubmed]
PHST- 2023/09/10 10:31 [entrez]
AID - S0363-5023(23)00414-8 [pii]
AID - 10.1016/j.jhsa.2023.08.003 [doi]
PST - ppublish
SO  - J Hand Surg Am. 2023 Nov;48(11):1122-1127. doi: 10.1016/j.jhsa.2023.08.003. Epub 
      2023 Sep 9.

PMID- 36865238
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230712
DP  - 2023 Feb 20
TI  - Fighting reviewer fatigue or amplifying bias? Considerations and recommendations 
      for use of ChatGPT and other Large Language Models in scholarly peer review.
LID - rs.3.rs-2587766 [pii]
LID - 10.21203/rs.3.rs-2587766/v1 [doi]
AB  - Background: The emergence of systems based on large language models (LLMs) such 
      as OpenAI's ChatGPT has created a range of discussions in scholarly circles. 
      Since LLMs generate grammatically correct and mostly relevant (yet sometimes 
      outright wrong, irrelevant or biased) outputs in response to provided prompts, 
      using them in various writing tasks including writing peer review reports could 
      result in improved productivity. Given the significance of peer reviews in the 
      existing scholarly publication landscape, exploring challenges and opportunities 
      of using LLMs in peer review seems urgent. After the generation of the first 
      scholarly outputs with LLMs, we anticipate that peer review reports too would be 
      generated with the help of these systems. However, there are currently no 
      guidelines on how these systems should be used in review tasks. Methods: To 
      investigate the potential impact of using LLMs on the peer review process, we 
      used five core themes within discussions about peer review suggested by Tennant 
      and Ross-Hellauer. These include 1) reviewers' role, 2) editors' role, 3) 
      functions and quality of peer reviews, 4) reproducibility, and 5) the social and 
      epistemic functions of peer reviews. We provide a small-scale exploration of 
      ChatGPT's performance regarding identified issues. Results: LLMs have the 
      potential to substantially alter the role of both peer reviewers and editors. 
      Through supporting both actors in efficiently writing constructive reports or 
      decision letters, LLMs can facilitate higher quality review and address issues of 
      review shortage. However, the fundamental opacity of LLMs' inner workings and 
      development, raise questions and concerns about potential biases and the 
      reliability of review reports. Additionally, as editorial work has a prominent 
      function in defining and shaping epistemic communities, as well as negotiating 
      normative frameworks within such communities, partly outsourcing this work to 
      LLMs might have unforeseen consequences for social and epistemic relations within 
      academia. Regarding performance, we identified major enhancements in only a few 
      weeks (between December 2022 and January 2023) and expect ChatGPT to continue 
      improving. Conclusions: We believe that LLMs are likely to have a profound impact 
      on academia and scholarly communication. While they have the potential to address 
      several current issues within the scholarly communication system, many 
      uncertainties remain and their use is not without risks. In particular, concerns 
      about the amplification of existing biases and inequalities in access to 
      appropriate infrastructure warrant further attention. For the moment, we 
      recommend that if LLMs are used to write scholarly reviews, reviewers should 
      disclose their use and accept full responsibility for their reports' accuracy, 
      tone, reasoning and originality.
FAU - Hosseini, Mohammad
AU  - Hosseini M
AUID- ORCID: 0000-0002-2385-985X
AD  - Northwestern University Feinberg School of Medicine.
FAU - Horbach, Serge P J M
AU  - Horbach SPJM
AD  - Aarhus Universitet.
LA  - eng
GR  - UL1 TR001422/TR/NCATS NIH HHS/United States
PT  - Preprint
DEP - 20230220
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
UIN - Res Integr Peer Rev. 2023 May 18;8(1):4. PMID: 37198671
PMC - PMC9980209
COIS- Competing interests The authors have no competing interests.
EDAT- 2023/03/04 06:00
MHDA- 2023/03/04 06:01
CRDT- 2023/03/03 02:29
PHST- 2023/03/03 02:29 [entrez]
PHST- 2023/03/04 06:00 [pubmed]
PHST- 2023/03/04 06:01 [medline]
AID - rs.3.rs-2587766 [pii]
AID - 10.21203/rs.3.rs-2587766/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 Feb 20:rs.3.rs-2587766. doi: 10.21203/rs.3.rs-2587766/v1. Preprint.

PMID- 37791148
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Artificial Intelligence in Childcare: Assessing the Performance and Acceptance of 
      ChatGPT Responses.
PG  - e44484
LID - 10.7759/cureus.44484 [doi]
LID - e44484
AB  - Purpose This study aimed to evaluate the performance and acceptance of responses 
      generated by ChatGPT-3.5 and GPT-4 to Japanese childcare-related questions to 
      assess their potential applicability and limitations in the childcare field, 
      specifically focusing on the accuracy, usefulness, and empathy of the generated 
      answers. Methods We evaluated answers in Japanese generated by GPT-3.5 and GPT-4 
      for two types of childcare-related questions. ① For the written examination 
      questions of Japan's childcare worker national examination for 2023's fiscal 
      year, we calculated the correct answer rates using official answers. ② We 
      selected one question from each of the seven categories from the child-rearing 
      questions posted on the Japanese National Childcare Workers Association's website 
      and had GPT-3.5 and GPT-4 generate answers. These were evaluated alongside 
      existing childcare worker answers by human professionals. Five childcare workers 
      then blindly selected what they considered the best answer among the three and 
      rated them on a five-point scale for 'accuracy,' 'usefulness,' and 'empathy.' 
      Results In the examination consisting of 160 written questions, both GPT-3.5 and 
      GPT-4 produced responses to all 155 questions, excluding four questions omitted 
      due to copyright concerns and one question deemed invalid due to inherent flaws 
      in the question itself, with correct answer rates of 30.3% for GPT-3.5 and 47.7% 
      for GPT-4 (p<0.01). For the child-rearing Q&A questions, childcare worker answers 
      by human professionals were chosen as the best answer most frequently (45.7%), 
      followed by GPT-3.5 (31.4%) and GPT-4 (22.9%). While GPT-3.5 received the highest 
      average rating for accuracy (3.69 points), childcare worker answers by human 
      professionals received the highest average ratings for usefulness and empathy 
      (both 3.57 points). Conclusions Both GPT-3.5 and GPT-4 failed to meet the passing 
      criteria in Japan's childcare worker national examination, and for the 
      child-rearing questions, GPT-3.5 was rated higher in accuracy despite lower 
      correct answer rates. Over half of the childcare workers considered the 
      ChatGPT-generated answers to be the best ones, yet concerns about accuracy were 
      observed, highlighting the potential risk of incorrect information in the 
      Japanese context.
CI  - Copyright © 2023, Kaneda et al.
FAU - Kaneda, Yudai
AU  - Kaneda Y
AD  - School of Medicine, Hokkaido University, Sapporo, JPN.
FAU - Namba, Mira
AU  - Namba M
AD  - School of Medicine, Keio University, Tokyo, JPN.
FAU - Kaneda, Uiri
AU  - Kaneda U
AD  - Faculty of Foreign Languages, Dokkyo University, Soka, JPN.
FAU - Tanimoto, Tetsuya
AU  - Tanimoto T
AD  - Internal Medicine, Jyoban Hospital of Tokiwa Foundation, Iwaki, JPN.
LA  - eng
PT  - Journal Article
DEP - 20230831
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10544433
OTO - NOTNLM
OT  - ai & robotics healthcare
OT  - chatgpt
OT  - childcare
OT  - japan
OT  - performance and acceptance
COIS- The authors have declared financial relationships, which are detailed in the next 
      section.
EDAT- 2023/10/04 06:44
MHDA- 2023/10/04 06:45
CRDT- 2023/10/04 04:18
PHST- 2023/08/31 00:00 [accepted]
PHST- 2023/10/04 06:45 [medline]
PHST- 2023/10/04 06:44 [pubmed]
PHST- 2023/10/04 04:18 [entrez]
AID - 10.7759/cureus.44484 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 31;15(8):e44484. doi: 10.7759/cureus.44484. eCollection 2023 
      Aug.

PMID- 37787701
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1932-8095 (Electronic)
IS  - 1932-8087 (Linking)
VI  - 28
IP  - 6
DP  - 2023 Nov-Dec 01
TI  - AI and Case Management: From Artificial Intelligence to Generative Intelligence.
PG  - 259-261
LID - 10.1097/NCM.0000000000000681 [doi]
AB  - Conversations about artificial intelligence (AI) are impossible to escape since 
      the inception of ChatGPT in November 2022-whether it is about the end of our jobs 
      or the end of the world! This Editorial talks about AI and its subsets and how 
      this may relate to health care.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Powell, Suzanne K
AU  - Powell SK
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Prof Case Manag
JT  - Professional case management
JID - 101291585
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Intelligence
MH  - Communication
COIS- The author reports no conflicts of interest.
EDAT- 2023/10/03 12:46
MHDA- 2023/10/03 12:47
CRDT- 2023/10/03 10:13
PHST- 2023/10/03 12:47 [medline]
PHST- 2023/10/03 12:46 [pubmed]
PHST- 2023/10/03 10:13 [entrez]
AID - 01269241-202311000-00001 [pii]
AID - 10.1097/NCM.0000000000000681 [doi]
PST - ppublish
SO  - Prof Case Manag. 2023 Nov-Dec 01;28(6):259-261. doi: 
      10.1097/NCM.0000000000000681.

PMID- 37860071
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231021
IS  - 1078-4497 (Print)
IS  - 1945-337X (Electronic)
IS  - 1078-4497 (Linking)
VI  - 40
IP  - 6
DP  - 2023 Jun
TI  - Applications of ChatGPT and Large Language Models in Medicine and Health Care: 
      Benefits and Pitfalls.
PG  - 170-173
LID - 10.12788/fp.0386 [doi]
AB  - BACKGROUND: The use of large language models like ChatGPT is becoming 
      increasingly popular in health care settings. These artificial intelligence 
      models are trained on vast amounts of data and can be used for various tasks, 
      such as language translation, summarization, and answering questions. 
      OBSERVATIONS: Large language models have the potential to revolutionize the 
      industry by assisting medical professionals with administrative tasks, improving 
      diagnostic accuracy, and engaging patients. However, pitfalls exist, such as its 
      inability to distinguish between real and fake information and the need to comply 
      with privacy, security, and transparency principles. CONCLUSIONS: Careful 
      consideration is needed to ensure the responsible and ethical use of large 
      language models in medicine and health care. The development of [artificial 
      intelligence] is as fundamental as the creation of the microprocessor, the 
      personal computer, the Internet, and the mobile phone. It will change the way 
      people work, learn, travel, get health care, and communicate with each other. 
      Bill Gates1.
CI  - Copyright © 2023 Frontline Medical Communications Inc., Parsippany, NJ, USA.
FAU - Borkowski, Andrew A
AU  - Borkowski AA
AD  - James A. Haley Veterans' Hospital, Tampa, Florida.
AD  - University of South Florida Morsani College of Medicine, Tampa.
AD  - National Artificial Intelligence Institute, Washington, DC.
FAU - Jakey, Colleen E
AU  - Jakey CE
AD  - James A. Haley Veterans' Hospital, Tampa, Florida.
AD  - University of South Florida Morsani College of Medicine, Tampa.
FAU - Mastorides, Stephen M
AU  - Mastorides SM
AD  - James A. Haley Veterans' Hospital, Tampa, Florida.
AD  - University of South Florida Morsani College of Medicine, Tampa.
FAU - Kraus, Ana L
AU  - Kraus AL
AD  - James A. Haley Veterans' Hospital, Tampa, Florida.
AD  - University of South Florida Morsani College of Medicine, Tampa.
FAU - Vidyarthi, Gitanjali
AU  - Vidyarthi G
AD  - James A. Haley Veterans' Hospital, Tampa, Florida.
AD  - University of South Florida Morsani College of Medicine, Tampa.
FAU - Viswanadhan, Narayan
AU  - Viswanadhan N
AD  - James A. Haley Veterans' Hospital, Tampa, Florida.
AD  - University of South Florida Morsani College of Medicine, Tampa.
FAU - Lezama, Jose L
AU  - Lezama JL
AD  - James A. Haley Veterans' Hospital, Tampa, Florida.
AD  - University of South Florida Morsani College of Medicine, Tampa.
LA  - eng
PT  - Journal Article
DEP - 20230616
PL  - United States
TA  - Fed Pract
JT  - Federal practitioner : for the health care professionals of the VA, DoD, and PHS
JID - 9500574
PMC - PMC10584408
COIS- Author disclosures The authors report no actual or potential conflicts of 
      interest or outside sources of funding with regard to this article.
EDAT- 2023/10/20 06:42
MHDA- 2023/10/20 06:43
CRDT- 2023/10/20 04:25
PHST- 2023/10/20 06:43 [medline]
PHST- 2023/10/20 06:42 [pubmed]
PHST- 2023/10/20 04:25 [entrez]
AID - fp-40-06-170 [pii]
AID - 10.12788/fp.0386 [doi]
PST - ppublish
SO  - Fed Pract. 2023 Jun;40(6):170-173. doi: 10.12788/fp.0386. Epub 2023 Jun 16.

PMID- 37032219
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1876-861X (Electronic)
IS  - 1876-861X (Linking)
VI  - 17
IP  - 4
DP  - 2023 Jul-Aug
TI  - How will artificial intelligence transform cardiovascular computed tomography? A 
      conversation with an AI model.
PG  - 281-283
LID - S1934-5925(23)00096-5 [pii]
LID - 10.1016/j.jcct.2023.03.010 [doi]
AB  - Artificial intelligence (AI) has the potential to transform healthcare, but its 
      clinical use also has important challenges and limitations. Recently natural 
      language processing and generative pre-training transformer (GPT) models have 
      gained particular interest due to their ability to simulate human conversation. 
      We aimed to explore output of the ChatGPT model (OpenAI, 
      https://openai.com/blog/chatgpt) regarding current debates in cardiovascular CT. 
      Prompts included debate questions from the Society of Cardiovascular Computed 
      Tomography 2023 programme as well as questions about high risk plaque (HRP), 
      quantitative plaque analysis, and how AI will transform cardiovascular CT. The AI 
      model rapidly provided plausible responses including both pro and con sides of 
      the argument. Advantages of AI for cardiovascular CT that were described by the 
      AI model included improving image quality, speed of reporting, accuracy, and 
      consistency. The AI model also acknowledged the importance for continued 
      involvement of clinicians in patient care.
CI  - Copyright © 2023 Society of Cardiovascular Computed Tomography. Published by 
      Elsevier Inc. All rights reserved.
FAU - Williams, Michelle C
AU  - Williams MC
AD  - British Heart Foundation Centre for Cardiovascular Science, University of 
      Edinburgh, Edinburgh, UK. Electronic address: michelle.williams@ed.ac.uk.
FAU - Shambrook, James
AU  - Shambrook J
AD  - University Hospital Southampton, Southampton, UK.
LA  - eng
PT  - Journal Article
DEP - 20230407
PL  - United States
TA  - J Cardiovasc Comput Tomogr
JT  - Journal of cardiovascular computed tomography
JID - 101308347
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Predictive Value of Tests
MH  - *Cardiovascular System
MH  - Tomography, X-Ray Computed
COIS- Declaration of competing interest MCW has given talks for Canon Medical Systems, 
      Siemens Healthineers and Novartis. MCW (FS/ICRF/20/26002) is supported by the 
      British Heart Foundation. For the purpose of open access, the author has applied 
      a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript 
      version arising from this submission.
EDAT- 2023/04/10 06:00
MHDA- 2023/04/10 06:01
CRDT- 2023/04/09 22:00
PHST- 2023/03/26 00:00 [received]
PHST- 2023/03/28 00:00 [accepted]
PHST- 2023/04/10 06:01 [medline]
PHST- 2023/04/10 06:00 [pubmed]
PHST- 2023/04/09 22:00 [entrez]
AID - S1934-5925(23)00096-5 [pii]
AID - 10.1016/j.jcct.2023.03.010 [doi]
PST - ppublish
SO  - J Cardiovasc Comput Tomogr. 2023 Jul-Aug;17(4):281-283. doi: 
      10.1016/j.jcct.2023.03.010. Epub 2023 Apr 7.

PMID- 37758854
OWN - NLM
STAT- Publisher
LR  - 20230927
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
DP  - 2023 Sep 27
TI  - Response Letter to "Testing ChatGPT's Capabilities for Social Media Content 
      Analysis".
LID - 10.1007/s00266-023-03675-7 [doi]
AB  - This editorial discusses the innovative application of ChatGPT in categorizing 
      and analysing social media content, with a focus on aesthetic medical fields. It 
      highlights the revolutionary capabilities of AI in enhancing efficiency and 
      objectivity over traditional human-driven methods. Alongside the benefits, it 
      also considers ethical concerns surrounding privacy, consent, and inherent biases 
      within AI models. The article explores the complexity of categorization, the 
      limitations in understanding human nuances, and the impact on human creativity, 
      including specific applications such as SEO writing. It concludes by emphasizing 
      the need for careful integration of AI in our interconnected world, balancing 
      technological advancements with ethical considerations and a recognition of the 
      unique attributes of human intellect. LEVEL OF EVIDENCE V: This journal requires 
      that authors assign a level of evidence to each article. For a full description 
      of these Evidence-Based Medicine ratings, please refer to the Table of Contents 
      or the online Instructions to Authors www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Buzzaccarini, Giovanni
AU  - Buzzaccarini G
AUID- ORCID: 0000-0002-9466-0178
AD  - Department of Obstetrics and Gynaecology, IRCCS San Raffaele Scientific 
      Institute, Vita-Salute San Raffaele University, Via Olgettina 58-60, 20132, 
      Milan, Italy. giovanni.buzzaccarini@gmail.com.
FAU - Degliuomini, Rebecca Susanna
AU  - Degliuomini RS
AD  - Department of Obstetrics and Gynaecology, IRCCS San Raffaele Scientific 
      Institute, Vita-Salute San Raffaele University, Via Olgettina 58-60, 20132, 
      Milan, Italy.
FAU - Borin, Marco
AU  - Borin M
AD  - U.O.C. Otorhinolaryngology, ASST Grande Ospedale Metropolitano Niguarda Piazza 
      Ospedale Maggiore, 3-20162, Milan, Italy.
LA  - eng
PT  - Letter
DEP - 20230927
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
EDAT- 2023/09/28 00:42
MHDA- 2023/09/28 00:42
CRDT- 2023/09/27 23:29
PHST- 2023/08/14 00:00 [received]
PHST- 2023/09/09 00:00 [accepted]
PHST- 2023/09/28 00:42 [medline]
PHST- 2023/09/28 00:42 [pubmed]
PHST- 2023/09/27 23:29 [entrez]
AID - 10.1007/s00266-023-03675-7 [pii]
AID - 10.1007/s00266-023-03675-7 [doi]
PST - aheadofprint
SO  - Aesthetic Plast Surg. 2023 Sep 27. doi: 10.1007/s00266-023-03675-7.

PMID- 37728965
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231007
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Sep 20
TI  - Anki Tagger: A Generative AI Tool for Aligning Third-Party Resources to 
      Preclinical Curriculum.
PG  - e48780
LID - 10.2196/48780 [doi]
LID - e48780
AB  - Using large language models, we developed a method to efficiently query existing 
      flashcard libraries and select those most relevant to an individual's medical 
      school curricula.
CI  - ©Tricia Pendergrast, Zachary Chalmers. Originally published in JMIR Medical 
      Education (https://mededu.jmir.org), 20.09.2023.
FAU - Pendergrast, Tricia
AU  - Pendergrast T
AUID- ORCID: 0000-0002-2396-9100
AD  - Department of Anesthesiology, University of Michigan Medicine, Ann Arbor, MI, 
      United States.
FAU - Chalmers, Zachary
AU  - Chalmers Z
AUID- ORCID: 0000-0003-1012-8529
AD  - Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10551781
OTO - NOTNLM
OT  - AI
OT  - Anki
OT  - ChatGPT
OT  - artificial intelligence
OT  - flashcards
OT  - large language models
OT  - undergraduate medical education
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/20 18:42
MHDA- 2023/09/20 18:43
CRDT- 2023/09/20 12:16
PHST- 2023/07/06 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/08/01 00:00 [revised]
PHST- 2023/09/20 18:43 [medline]
PHST- 2023/09/20 18:42 [pubmed]
PHST- 2023/09/20 12:16 [entrez]
AID - v9i1e48780 [pii]
AID - 10.2196/48780 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Sep 20;9:e48780. doi: 10.2196/48780.

PMID- 37651167
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20230917
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Aug 31
TI  - AI Increases the Pressure to Overhaul the Scientific Peer Review Process. Comment 
      on "Artificial Intelligence Can Generate Fraudulent but Authentic-Looking 
      Scientific Medical Articles: Pandora's Box Has Been Opened".
PG  - e50591
LID - 10.2196/50591 [doi]
LID - e50591
FAU - Liu, Nicholas
AU  - Liu N
AUID- ORCID: 0000-0001-9614-6941
AD  - John A Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, HI, 
      United States.
FAU - Brown, Amy
AU  - Brown A
AUID- ORCID: 0000-0002-7550-5308
AD  - Department of Quantitative Health Sciences, John A Burns School of Medicine, 
      University of Hawai'i at Mānoa, Honolulu, HI, United States.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230831
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
CON - J Med Internet Res. 2023 May 31;25:e46924. PMID: 37256685
CIN - J Med Internet Res. 2023 Aug 31;25:e50844. PMID: 37651175
MH  - *Artificial Intelligence
MH  - *Writing
MH  - Peer Review
PMC - PMC10502600
OTO - NOTNLM
OT  - AI
OT  - Chat Generative Pre-trained Transformer
OT  - ChatGPT
OT  - artificial intelligence
OT  - ethics
OT  - fraudulent medical articles
OT  - language models
OT  - neurosurgery
OT  - publications
COIS- Conflicts of Interest: None declared.
EDAT- 2023/08/31 12:43
MHDA- 2023/09/01 06:43
CRDT- 2023/08/31 11:53
PHST- 2023/07/06 00:00 [received]
PHST- 2023/08/12 00:00 [accepted]
PHST- 2023/09/01 06:43 [medline]
PHST- 2023/08/31 12:43 [pubmed]
PHST- 2023/08/31 11:53 [entrez]
AID - v25i1e50591 [pii]
AID - 10.2196/50591 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Aug 31;25:e50591. doi: 10.2196/50591.

PMID- 37499771
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231018
IS  - 1097-6795 (Electronic)
IS  - 0894-7317 (Linking)
VI  - 36
IP  - 10
DP  - 2023 Oct
TI  - ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram?
PG  - 1021-1026
LID - S0894-7317(23)00380-2 [pii]
LID - 10.1016/j.echo.2023.07.007 [doi]
AB  - While multidisciplinary collaboration in echocardiography is not new, machine 
      learning has the potential to further improve it. In this transcript of the ASE 
      2023 Annual Feigenbaum lecture, advancements in foundation models are discussed, 
      including their advantages, current disadvantages, and future potential for 
      echocardiography.
CI  - Copyright © 2023 American Society of Echocardiography. Published by Elsevier Inc. 
      All rights reserved.
FAU - Arnaout, Rima
AU  - Arnaout R
AD  - Department of Medicine, Department of Radiology, and Department of Pediatrics, 
      Bakar Computational Health Sciences Institute, UCSF UC Berkeley Joint Program in 
      Computational Precision Health, University of California, San Francisco, San 
      Francisco, California. Electronic address: rima.arnaout@ucsf.edu.
LA  - eng
GR  - R01 HL150394/HL/NHLBI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20230726
PL  - United States
TA  - J Am Soc Echocardiogr
JT  - Journal of the American Society of Echocardiography : official publication of the 
      American Society of Echocardiography
JID - 8801388
SB  - IM
MH  - Humans
MH  - *Echocardiography
MH  - *Machine Learning
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Echocardiography
OT  - Feigenbaum lecture
OT  - Foundation models
OT  - Machine learning
EDAT- 2023/07/28 01:08
MHDA- 2023/10/09 06:42
CRDT- 2023/07/27 19:13
PHST- 2023/07/12 00:00 [received]
PHST- 2023/07/15 00:00 [revised]
PHST- 2023/07/16 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/07/28 01:08 [pubmed]
PHST- 2023/07/27 19:13 [entrez]
AID - S0894-7317(23)00380-2 [pii]
AID - 10.1016/j.echo.2023.07.007 [doi]
PST - ppublish
SO  - J Am Soc Echocardiogr. 2023 Oct;36(10):1021-1026. doi: 
      10.1016/j.echo.2023.07.007. Epub 2023 Jul 26.

PMID- 37220190
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231025
IS  - 1528-1132 (Electronic)
IS  - 0009-921X (Print)
IS  - 0009-921X (Linking)
VI  - 481
IP  - 8
DP  - 2023 Aug 1
TI  - Can Artificial Intelligence Pass the American Board of Orthopaedic Surgery 
      Examination? Orthopaedic Residents Versus ChatGPT.
PG  - 1623-1630
LID - 10.1097/CORR.0000000000002704 [doi]
AB  - BACKGROUND: Advances in neural networks, deep learning, and artificial 
      intelligence (AI) have progressed recently. Previous deep learning AI has been 
      structured around domain-specific areas that are trained on dataset-specific 
      areas of interest that yield high accuracy and precision. A new AI model using 
      large language models (LLM) and nonspecific domain areas, ChatGPT (OpenAI), has 
      gained attention. Although AI has demonstrated proficiency in managing vast 
      amounts of data, implementation of that knowledge remains a challenge. 
      QUESTIONS/PURPOSES: (1) What percentage of Orthopaedic In-Training Examination 
      questions can a generative, pretrained transformer chatbot (ChatGPT) answer 
      correctly? (2) How does that percentage compare with results achieved by 
      orthopaedic residents of different levels, and if scoring lower than the 10th 
      percentile relative to 5th-year residents is likely to correspond to a failing 
      American Board of Orthopaedic Surgery score, is this LLM likely to pass the 
      orthopaedic surgery written boards? (3) Does increasing question taxonomy affect 
      the LLM's ability to select the correct answer choices? METHODS: This study 
      randomly selected 400 of 3840 publicly available questions based on the 
      Orthopaedic In-Training Examination and compared the mean score with that of 
      residents who took the test over a 5-year period. Questions with figures, 
      diagrams, or charts were excluded, including five questions the LLM could not 
      provide an answer for, resulting in 207 questions administered with raw score 
      recorded. The LLM's answer results were compared with the Orthopaedic In-Training 
      Examination ranking of orthopaedic surgery residents. Based on the findings of an 
      earlier study, a pass-fail cutoff was set at the 10th percentile. Questions 
      answered were then categorized based on the Buckwalter taxonomy of recall, which 
      deals with increasingly complex levels of interpretation and application of 
      knowledge; comparison was made of the LLM's performance across taxonomic levels 
      and was analyzed using a chi-square test. RESULTS: ChatGPT selected the correct 
      answer 47% (97 of 207) of the time, and 53% (110 of 207) of the time it answered 
      incorrectly. Based on prior Orthopaedic In-Training Examination testing, the LLM 
      scored in the 40th percentile for postgraduate year (PGY) 1s, the eighth 
      percentile for PGY2s, and the first percentile for PGY3s, PGY4s, and PGY5s; based 
      on the latter finding (and using a predefined cutoff of the 10th percentile of 
      PGY5s as the threshold for a passing score), it seems unlikely that the LLM would 
      pass the written board examination. The LLM's performance decreased as question 
      taxonomy level increased (it answered 54% [54 of 101] of Tax 1 questions 
      correctly, 51% [18 of 35] of Tax 2 questions correctly, and 34% [24 of 71] of Tax 
      3 questions correctly; p = 0.034). CONCLUSION: Although this general-domain LLM 
      has a low likelihood of passing the orthopaedic surgery board examination, 
      testing performance and knowledge are comparable to that of a first-year 
      orthopaedic surgery resident. The LLM's ability to provide accurate answers 
      declines with increasing question taxonomy and complexity, indicating a 
      deficiency in implementing knowledge. CLINICAL RELEVANCE: Current AI appears to 
      perform better at knowledge and interpretation-based inquires, and based on this 
      study and other areas of opportunity, it may become an additional tool for 
      orthopaedic learning and education.
CI  - Copyright © 2023 by the Association of Bone and Joint Surgeons.
FAU - Lum, Zachary C
AU  - Lum ZC
AUID- ORCID: 0000-0002-5871-8539
AD  - Nova Southeastern University, Davie, FL, USA.
LA  - eng
PT  - Journal Article
DEP - 20230523
PL  - United States
TA  - Clin Orthop Relat Res
JT  - Clinical orthopaedics and related research
JID - 0075674
RN  - 5B2658E0N2 (Aminosalicylic Acid)
SB  - IM
CIN - Clin Orthop Relat Res. 2023 Aug 1;481(8):1631-1633. PMID: 37335233
CIN - Clin Orthop Relat Res. 2023 Nov 1;481(11):2299. PMID: 37732693
MH  - Humans
MH  - United States
MH  - *Orthopedics/education
MH  - *Internship and Residency
MH  - Artificial Intelligence
MH  - Educational Measurement
MH  - Clinical Competence
MH  - *Orthopedic Procedures/education
MH  - *Aminosalicylic Acid
PMC - PMC10344569
COIS- The author certifies that there are no funding or commercial associations 
      (consultancies, stock ownership, equity interest, patent/licensing arrangements, 
      etc.) that might pose a conflict of interest in connection with the submitted 
      article related to the author or any immediate family members. All ICMJE Conflict 
      of Interest Forms for authors and Clinical Orthopaedics and Related Research® 
      editors and board members are on file with the publication and can be viewed on 
      request.
EDAT- 2023/05/23 19:09
MHDA- 2023/10/23 00:44
PMCR- 2024/08/01
CRDT- 2023/05/23 14:03
PHST- 2023/01/31 00:00 [received]
PHST- 2023/04/28 00:00 [accepted]
PHST- 2024/08/01 00:00 [pmc-release]
PHST- 2023/10/23 00:44 [medline]
PHST- 2023/05/23 19:09 [pubmed]
PHST- 2023/05/23 14:03 [entrez]
AID - 00003086-990000000-01207 [pii]
AID - CORR-D-23-00158 [pii]
AID - 10.1097/CORR.0000000000002704 [doi]
PST - ppublish
SO  - Clin Orthop Relat Res. 2023 Aug 1;481(8):1623-1630. doi: 
      10.1097/CORR.0000000000002704. Epub 2023 May 23.

PMID- 37568980
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20231103
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 20
IP  - 15
DP  - 2023 Jul 25
TI  - The Utility of Language Models in Cardiology: A Narrative Review of the Benefits 
      and Concerns of ChatGPT-4.
LID - 10.3390/ijerph20156438 [doi]
LID - 6438
AB  - Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative 
      Pretrained Transformer) have made tremendous advances recently and are rapidly 
      transforming the landscape of medicine. Cardiology is among many of the 
      specialties that utilize AI with the intention of improving patient care. 
      Generative AI, with the use of its advanced machine learning algorithms, has the 
      potential to diagnose heart disease and recommend management options suitable for 
      the patient. This may lead to improved patient outcomes not only by recommending 
      the best treatment plan but also by increasing physician efficiency. Language 
      models could assist physicians with administrative tasks, allowing them to spend 
      more time on patient care. However, there are several concerns with the use of AI 
      and language models in the field of medicine. These technologies may not be the 
      most up-to-date with the latest research and could provide outdated information, 
      which may lead to an adverse event. Secondly, AI tools can be expensive, leading 
      to increased healthcare costs and reduced accessibility to the general 
      population. There is also concern about the loss of the human touch and empathy 
      as AI becomes more mainstream. Healthcare professionals would need to be 
      adequately trained to utilize these tools. While AI and language models have many 
      beneficial traits, all healthcare providers need to be involved and aware of 
      generative AI so as to assure its optimal use and mitigate any potential risks 
      and challenges associated with its implementation. In this review, we discuss the 
      various uses of language models in the field of cardiology.
FAU - Gala, Dhir
AU  - Gala D
AUID- ORCID: 0000-0003-1410-8576
AD  - Department of Clinical Science, American University of the Caribbean School of 
      Medicine, Cupecoy, Sint Maarten, The Netherlands.
FAU - Makaryus, Amgad N
AU  - Makaryus AN
AUID- ORCID: 0000-0003-2104-7230
AD  - Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra 
      University, 500 Hofstra Blvd., Hempstead, NY 11549, USA.
AD  - Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, 
      USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230725
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Cardiovascular System
MH  - *Cardiology
MH  - *Heart Diseases
MH  - Language
PMC - PMC10419098
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - cardiology
OT  - generative artificial intelligence
OT  - generative language models
OT  - language models
OT  - medical education
OT  - patient outcomes
COIS- The authors declare no conflict of interest.
EDAT- 2023/08/12 10:47
MHDA- 2023/08/14 06:42
CRDT- 2023/08/12 01:05
PHST- 2023/04/30 00:00 [received]
PHST- 2023/06/30 00:00 [revised]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/12 10:47 [pubmed]
PHST- 2023/08/12 01:05 [entrez]
AID - ijerph20156438 [pii]
AID - ijerph-20-06438 [pii]
AID - 10.3390/ijerph20156438 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2023 Jul 25;20(15):6438. doi: 
      10.3390/ijerph20156438.

PMID- 37865615
OWN - NLM
STAT- Publisher
LR  - 20231021
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Oct 21
TI  - Assessing the Soft Tissue Infection Expertise of ChatGPT and Bard Compared to 
      IDSA Recommendations.
LID - 10.1007/s10439-023-03372-1 [doi]
AB  - The aim of the study was to evaluate whether ChatGPT-3.5 and Bard provide safe 
      and reliable medical answers to common topics related to soft tissue infections 
      and their management according to the guidelines provided by the Infectious 
      Disease Society of America (IDSA). IDSA's abridged recommendations for soft 
      tissue infections were identified on the IDSA official website. Twenty-five 
      queries were entered into the LLMs as they appear on the IDSA website. To assess 
      the concordance and precision of the LLMs' responses with the IDSA guidelines, 
      two infectious disease physicians independently compared and evaluated each 
      response. This was done using a 5-point Likert scale, with 1 representing poor 
      concordance and 5 excellent concordance, as adapted from the validated Global 
      Quality Scale. The mean ± SD score for ChatGPT-generated responses was 
      4.34 ± 0.74, n = 25. This indicates that raters found the answers were good to 
      excellent quality with the most important topics covered. Although some topics 
      were not covered, the answers were in good concordance with the IDSA guidelines. 
      The mean ± SD score for Bard-generate responses was 3.5 ± 1.2, n = 25, indicating 
      moderate quality. Despite LLMs did not appear to provide wrong recommendations 
      and covered most of the topics, the responses were often found to be generic, 
      rambling, missing some details, and lacking actionability. As AI continues to 
      evolve and researchers feed it with more extensive and diverse medical knowledge, 
      it may be inching closer to becoming a reliable aid for clinicians, ultimately 
      enhancing the accuracy of infectious disease diagnosis and management in the 
      future.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Alessandri-Bonetti, Mario
AU  - Alessandri-Bonetti M
AUID- ORCID: 0000-0002-5506-8323
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, Pittsburgh, PA, G10315219, USA.
FAU - Giorgino, Riccardo
AU  - Giorgino R
AUID- ORCID: 0000-0002-1067-7424
AD  - Residency Program in Orthopaedics and Traumatology, University of Milan, 20122, 
      Milan, Italy. riccardo.giorgino@unimi.it.
AD  - Harvard Medical School, Boston, MA, USA. riccardo.giorgino@unimi.it.
FAU - Naegeli, Michelle
AU  - Naegeli M
AD  - Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
AD  - Harvard Medical School, Boston, MA, USA.
FAU - Liu, Hilary Y
AU  - Liu HY
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, Pittsburgh, PA, G10315219, USA.
FAU - Egro, Francesco M
AU  - Egro FM
AUID- ORCID: 0000-0003-1536-7713
AD  - Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 
      Locust Street, Pittsburgh, PA, G10315219, USA.
LA  - eng
PT  - Letter
DEP - 20231021
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - Concordance
OT  - Guidelines
OT  - Infectious disease diagnosis
OT  - Soft tissue infections
EDAT- 2023/10/22 00:41
MHDA- 2023/10/22 00:41
CRDT- 2023/10/21 23:02
PHST- 2023/09/01 00:00 [received]
PHST- 2023/09/12 00:00 [accepted]
PHST- 2023/10/22 00:41 [medline]
PHST- 2023/10/22 00:41 [pubmed]
PHST- 2023/10/21 23:02 [entrez]
AID - 10.1007/s10439-023-03372-1 [pii]
AID - 10.1007/s10439-023-03372-1 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Oct 21. doi: 10.1007/s10439-023-03372-1.

PMID- 37990492
OWN - NLM
STAT- MEDLINE
DCOM- 20231123
LR  - 20231123
IS  - 1975-5937 (Electronic)
IS  - 1975-5937 (Linking)
VI  - 20
DP  - 2023
TI  - ChatGPT (GPT-3.5) as an assistant tool in microbial pathogenesis studies in 
      Sweden: a cross-sectional comparative study.
PG  - 32
LID - 10.3352/jeehp.2023.20.32 [doi]
AB  - ChatGPT (GPT-3.5) has entered higher education and there is a need to determine 
      how to use it effectively. This descriptive study compared the ability of GPT-3.5 
      and teachers to answer questions from dental students and construct detailed 
      intended learning outcomes. When analyzed according to a Likert scale, we found 
      that GPT-3.5 answered the questions from dental students in a similar or even 
      more elaborate way compared to the answers that had previously been provided by a 
      teacher. GPT-3.5 was also asked to construct detailed intended learning outcomes 
      for a course in microbial pathogenesis, and when these were analyzed according to 
      a Likert scale they were, to a large degree, found irrelevant. Since students are 
      using GPT-3.5, it is important that instructors learn how to make the best use of 
      it both to be able to advise students and to benefit from its potential.
FAU - Hultgren, Catharina
AU  - Hultgren C
AD  - Division of Clinical Microbiology, Department of Laboratory Medicine, ANA Futura, 
      Karolinska Institutet, Huddinge, Sweden.
FAU - Lindkvist, Annica
AU  - Lindkvist A
AD  - Division of Clinical Immunology, Department of Laboratory Medicine, ANA Futura, 
      Karolinska Institutet, Huddinge, Sweden.
FAU - Özenci, Volkan
AU  - Özenci V
AD  - Division of Clinical Microbiology, Department of Laboratory Medicine, ANA Futura, 
      Karolinska Institutet, Huddinge, Sweden.
FAU - Curbo, Sophie
AU  - Curbo S
AD  - Division of Clinical Microbiology, Department of Laboratory Medicine, ANA Futura, 
      Karolinska Institutet, Huddinge, Sweden.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - Korea (South)
TA  - J Educ Eval Health Prof
JT  - Journal of educational evaluation for health professions
JID - 101490061
SB  - IM
MH  - Humans
MH  - Sweden
MH  - Cross-Sectional Studies
MH  - *Learning
MH  - *Students
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Dental students
OT  - Educational personnel, Microbiology
OT  - Sweden
EDAT- 2023/11/22 06:43
MHDA- 2023/11/23 06:42
CRDT- 2023/11/22 02:31
PHST- 2023/10/20 00:00 [received]
PHST- 2023/11/09 00:00 [accepted]
PHST- 2023/11/23 06:42 [medline]
PHST- 2023/11/22 06:43 [pubmed]
PHST- 2023/11/22 02:31 [entrez]
AID - jeehp.2023.20.32 [pii]
AID - 10.3352/jeehp.2023.20.32 [doi]
PST - ppublish
SO  - J Educ Eval Health Prof. 2023;20:32. doi: 10.3352/jeehp.2023.20.32. Epub 2023 Nov 
      22.

PMID- 37868599
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - A social robot connected with chatGPT to improve cognitive functioning in ASD 
      subjects.
PG  - 1232177
LID - 10.3389/fpsyg.2023.1232177 [doi]
LID - 1232177
AB  - Neurodevelopmental Disorders (NDDs) represent a significant healthcare and 
      economic burden for families and society. Technology, including AI and digital 
      technologies, offers potential solutions for the assessment, monitoring, and 
      treatment of NDDs. However, further research is needed to determine the 
      effectiveness, feasibility, and acceptability of these technologies in NDDs, and 
      to address the challenges associated with their implementation. In this work, we 
      present the application of social robotics using a Pepper robot connected to the 
      OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After 
      describing the general architecture of the system, we present two possible 
      simulated interaction scenarios of a subject with Autism Spectrum Disorder in two 
      different situations. Limitations and future implementations are also provided to 
      provide an overview of the potential developments of interconnected systems that 
      could greatly contribute to technological advancements for Neurodevelopmental 
      Disorders (NDD).
CI  - Copyright © 2023 Bertacchini, Demarco, Scuro, Pantano and Bilotta.
FAU - Bertacchini, Francesca
AU  - Bertacchini F
AD  - Department of Mechanical, Energy and Management Engineering, University of 
      Calabria, Rende, Italy.
AD  - Laboratory of Cognitive Psychology and Mathematical Modelling, University of 
      Calabria, Rende, Italy.
FAU - Demarco, Francesco
AU  - Demarco F
AD  - Laboratory of Cognitive Psychology and Mathematical Modelling, University of 
      Calabria, Rende, Italy.
AD  - Department of Physics, University of Calabria, Rende, Italy.
FAU - Scuro, Carmelo
AU  - Scuro C
AD  - Laboratory of Cognitive Psychology and Mathematical Modelling, University of 
      Calabria, Rende, Italy.
AD  - Department of Physics, University of Calabria, Rende, Italy.
FAU - Pantano, Pietro
AU  - Pantano P
AD  - Laboratory of Cognitive Psychology and Mathematical Modelling, University of 
      Calabria, Rende, Italy.
AD  - Department of Physics, University of Calabria, Rende, Italy.
FAU - Bilotta, Eleonora
AU  - Bilotta E
AD  - Laboratory of Cognitive Psychology and Mathematical Modelling, University of 
      Calabria, Rende, Italy.
AD  - Department of Physics, University of Calabria, Rende, Italy.
LA  - eng
PT  - Journal Article
DEP - 20231005
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10585023
OTO - NOTNLM
OT  - Autism Spectrum Disorders
OT  - educational scenarios
OT  - facial emotions recognition
OT  - neurodevelopmental disorders
OT  - physical disabilities
OT  - social robotics
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/10/23 06:46
MHDA- 2023/10/23 06:47
CRDT- 2023/10/23 04:39
PHST- 2023/05/31 00:00 [received]
PHST- 2023/09/11 00:00 [accepted]
PHST- 2023/10/23 06:47 [medline]
PHST- 2023/10/23 06:46 [pubmed]
PHST- 2023/10/23 04:39 [entrez]
AID - 10.3389/fpsyg.2023.1232177 [doi]
PST - epublish
SO  - Front Psychol. 2023 Oct 5;14:1232177. doi: 10.3389/fpsyg.2023.1232177. 
      eCollection 2023.

PMID- 37323042
OWN - NLM
STAT- MEDLINE
DCOM- 20230619
LR  - 20230619
IS  - 0303-7339 (Print)
IS  - 0303-7339 (Linking)
VI  - 65
IP  - 4
DP  - 2023
TI  - [Artificial intelligence in psychiatry: co-creation of human and ChatGPT].
PG  - 241-243
AB  - BACKGROUND: Artificial intelligence (AI) can be a valuable addition to psychiatry 
      by helping to make diagnoses, personalize treatments, and support patients during 
      their recovery. However, it is important to consider the risks and ethical 
      implications of using this technology. AIM: In this article, we explore how AI 
      can change the future of psychiatry from a co-creation perspective, meaning that 
      people and machines work together to provide the best possible care. We provide 
      both critical and optimistic perspectives on how AI can influence psychiatry. 
      METHOD: A co-creation methodology was used to produce this essay, involving 
      interaction between my prompt and the text generated in response by the AI-based 
      chatbot ChatGPT. RESULTS: We describe how AI can be used to make diagnoses, 
      personalize treatments, and support patients during their recovery. We also 
      discuss the risks and ethical implications of using AI in psychiatry. CONCLUSION: 
      If we critically examine the risks and ethical implications of using AI in 
      psychiatry and promote co-creation between people and machines, AI can contribute 
      to improved care for patients in the future.
FAU - van Dellen, E
AU  - van Dellen E
LA  - dut
PT  - English Abstract
PT  - Journal Article
TT  - Kunstmatige intelligentie in de psychiatrie: cocreatie van mens en ChatGPT.
PL  - Netherlands
TA  - Tijdschr Psychiatr
JT  - Tijdschrift voor psychiatrie
JID - 0423731
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Psychiatry
EDAT- 2023/06/16 06:42
MHDA- 2023/06/19 13:08
CRDT- 2023/06/16 03:48
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/06/16 06:42 [pubmed]
PHST- 2023/06/16 03:48 [entrez]
AID - TVPart_13153 [pii]
PST - ppublish
SO  - Tijdschr Psychiatr. 2023;65(4):241-243.

PMID- 37712039
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230917
IS  - 2041-6520 (Print)
IS  - 2041-6539 (Electronic)
IS  - 2041-6520 (Linking)
VI  - 14
IP  - 35
DP  - 2023 Sep 13
TI  - Transcription between human-readable synthetic descriptions and 
      machine-executable instructions: an application of the latest pre-training 
      technology.
PG  - 9360-9373
LID - 10.1039/d3sc02483k [doi]
AB  - AI has been widely applied in scientific scenarios, such as robots performing 
      chemical synthetic actions to free researchers from monotonous experimental 
      procedures. However, there exists a gap between human-readable natural language 
      descriptions and machine-executable instructions, of which the former are 
      typically in numerous chemical articles, and the latter are currently compiled 
      manually by experts. We apply the latest technology of pre-trained models and 
      achieve automatic transcription between descriptions and instructions. We design 
      a concise and comprehensive schema of instructions and construct an open-source 
      human-annotated dataset consisting of 3950 description-instruction pairs, with 
      9.2 operations in each instruction on average. We further propose knowledgeable 
      pre-trained transcription models enhanced by multi-grained chemical knowledge. 
      The performance of recent popular models and products showing great capability in 
      automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that 
      our system improves the instruction compilation efficiency of researchers by at 
      least 42%, and can generate fluent academic paragraphs of synthetic descriptions 
      when given instructions, showing the great potential of pre-trained models in 
      improving human productivity.
CI  - This journal is © The Royal Society of Chemistry.
FAU - Zeng, Zheni
AU  - Zeng Z
AUID- ORCID: 0000-0001-6901-5292
AD  - Department of Computer Science and Technology, Tsinghua University Beijing China 
      liuzy@tsinghua.edu.cn.
FAU - Nie, Yi-Chen
AU  - Nie YC
AD  - College of Chemistry and Molecular Engineering, Peking University Beijing China 
      rongzhu@pku.edu.cn.
FAU - Ding, Ning
AU  - Ding N
AD  - Department of Computer Science and Technology, Tsinghua University Beijing China 
      liuzy@tsinghua.edu.cn.
FAU - Ding, Qian-Jun
AU  - Ding QJ
AD  - College of Chemistry and Molecular Engineering, Peking University Beijing China 
      rongzhu@pku.edu.cn.
FAU - Ye, Wei-Ting
AU  - Ye WT
AD  - College of Chemistry and Molecular Engineering, Peking University Beijing China 
      rongzhu@pku.edu.cn.
FAU - Yang, Cheng
AU  - Yang C
AD  - School of Computer Science, Beijing University of Posts and Telecommunications 
      Beijing China.
FAU - Sun, Maosong
AU  - Sun M
AD  - Department of Computer Science and Technology, Tsinghua University Beijing China 
      liuzy@tsinghua.edu.cn.
FAU - E, Weinan
AU  - E W
AD  - Center for Machine Learning Research and School of Mathematical Sciences, Peking 
      University AI for Science Institute Beijing China.
FAU - Zhu, Rong
AU  - Zhu R
AD  - College of Chemistry and Molecular Engineering, Peking University Beijing China 
      rongzhu@pku.edu.cn.
FAU - Liu, Zhiyuan
AU  - Liu Z
AUID- ORCID: 0000-0002-7709-2543
AD  - Department of Computer Science and Technology, Tsinghua University Beijing China 
      liuzy@tsinghua.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20230824
PL  - England
TA  - Chem Sci
JT  - Chemical science
JID - 101545951
PMC - PMC10498500
COIS- There are no conflicts to declare.
EDAT- 2023/09/15 06:42
MHDA- 2023/09/15 06:43
CRDT- 2023/09/15 04:02
PHST- 2023/05/16 00:00 [received]
PHST- 2023/08/15 00:00 [accepted]
PHST- 2023/09/15 06:43 [medline]
PHST- 2023/09/15 06:42 [pubmed]
PHST- 2023/09/15 04:02 [entrez]
AID - d3sc02483k [pii]
AID - 10.1039/d3sc02483k [doi]
PST - epublish
SO  - Chem Sci. 2023 Aug 24;14(35):9360-9373. doi: 10.1039/d3sc02483k. eCollection 2023 
      Sep 13.

PMID- 37284994
OWN - NLM
STAT- MEDLINE
DCOM- 20231109
LR  - 20231109
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 12
DP  - 2023 Dec
TI  - Prompt Engineering with ChatGPT: A Guide for Academic Writers.
PG  - 2629-2633
LID - 10.1007/s10439-023-03272-4 [doi]
AB  - Prompt engineering is a relatively new discipline that refers to the practice of 
      developing and optimizing prompts to effectively utilize large language models, 
      particularly in natural language processing tasks. However, not many writers and 
      researchers are familiar about this discipline. Hence, in this paper, I aim to 
      highlight the significance of prompt engineering for academic writers and 
      researchers, particularly the fledgling, in the rapidly evolving world of 
      artificial intelligence. I also discuss the concepts of prompt engineering, large 
      language models, and the techniques and pitfalls of writing prompts. Here, I 
      contend that by acquiring prompt engineering skills, academic writers can 
      navigate the changing landscape and leverage large language models to enhance 
      their writing process. As artificial intelligence continues to advance and 
      penetrate the arena of academic writing, prompt engineering equips writers and 
      researchers with the essential skills to effectively harness the power of 
      language models. This enables them to confidently explore new opportunities, 
      enhance their writing endeavors, and remain at the forefront of utilizing 
      cutting-edge technologies in their academic pursuits.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Giray, Louie
AU  - Giray L
AUID- ORCID: 0000-0002-1940-035X
AD  - General Education Department, Colegio de Muntinlupa, Muntinlupa City, 
      Philippines. louiegiray@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230607
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - *Artificial Intelligence
MH  - *Writing
OTO - NOTNLM
OT  - Academic writing
OT  - ChatGPT
OT  - Large language models
OT  - Natural language processing
OT  - Prompt engineering
OT  - Prompts
EDAT- 2023/06/07 13:10
MHDA- 2023/11/09 06:41
CRDT- 2023/06/07 11:11
PHST- 2023/05/31 00:00 [received]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/11/09 06:41 [medline]
PHST- 2023/06/07 13:10 [pubmed]
PHST- 2023/06/07 11:11 [entrez]
AID - 10.1007/s10439-023-03272-4 [pii]
AID - 10.1007/s10439-023-03272-4 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Dec;51(12):2629-2633. doi: 10.1007/s10439-023-03272-4. Epub 
      2023 Jun 7.

PMID- 38042919
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2398-6352 (Electronic)
IS  - 2398-6352 (Linking)
VI  - 6
IP  - 1
DP  - 2023 Dec 2
TI  - A medical multimodal large language model for future pandemics.
PG  - 226
LID - 10.1038/s41746-023-00952-2 [doi]
LID - 226
AB  - Deep neural networks have been integrated into the whole clinical decision 
      procedure which can improve the efficiency of diagnosis and alleviate the heavy 
      workload of physicians. Since most neural networks are supervised, their 
      performance heavily depends on the volume and quality of available labels. 
      However, few such labels exist for rare diseases (e.g., new pandemics). Here we 
      report a medical multimodal large language model (Med-MLLM) for radiograph 
      representation learning, which can learn broad medical knowledge (e.g., image 
      understanding, text semantics, and clinical phenotypes) from unlabelled data. As 
      a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed 
      and easily adapted to them with limited labels. Furthermore, our model supports 
      medical data across visual modality (e.g., chest X-ray and CT) and textual 
      modality (e.g., medical report and free-text clinical note); therefore, it can be 
      used for clinical tasks that involve both visual and textual data. We demonstrate 
      the effectiveness of our Med-MLLM by showing how it would perform using the 
      COVID-19 pandemic "in replay". In the retrospective setting, we test the model on 
      the early COVID-19 datasets; and in the prospective setting, we test the model on 
      the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds 
      of input data; 2) three kinds of downstream tasks, including disease reporting, 
      diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different 
      languages, including English, Chinese, and Spanish. All experiments show that our 
      model can make accurate and robust COVID-19 decision-support with little labelled 
      data.
CI  - © 2023. The Author(s).
FAU - Liu, Fenglin
AU  - Liu F
AUID- ORCID: 0000-0001-7715-5228
AD  - Institute of Biomedical Engineering, Department of Engineering Science, 
      University of Oxford, Oxford, UK. fenglin.liu@eng.ox.ac.uk.
FAU - Zhu, Tingting
AU  - Zhu T
AUID- ORCID: 0000-0002-1552-5630
AD  - Institute of Biomedical Engineering, Department of Engineering Science, 
      University of Oxford, Oxford, UK.
FAU - Wu, Xian
AU  - Wu X
AD  - Jarvis Research Center, Tencent YouTu Lab, Beijing, China.
FAU - Yang, Bang
AU  - Yang B
AUID- ORCID: 0000-0003-2019-0377
AD  - School of Computer Science, Peking University, Beijing, China.
FAU - You, Chenyu
AU  - You C
AUID- ORCID: 0000-0001-8365-7822
AD  - Yale University, New Haven, CT, USA.
FAU - Wang, Chenyang
AU  - Wang C
AUID- ORCID: 0000-0003-1851-9866
AD  - Institute of Biomedical Engineering, Department of Engineering Science, 
      University of Oxford, Oxford, UK.
FAU - Lu, Lei
AU  - Lu L
AD  - Institute of Biomedical Engineering, Department of Engineering Science, 
      University of Oxford, Oxford, UK.
FAU - Liu, Zhangdaihong
AU  - Liu Z
AD  - Institute of Biomedical Engineering, Department of Engineering Science, 
      University of Oxford, Oxford, UK.
AD  - Oxford-Suzhou Centre for Advanced Research, Suzhou, China.
FAU - Zheng, Yefeng
AU  - Zheng Y
AUID- ORCID: 0000-0003-2195-2847
AD  - Jarvis Research Center, Tencent YouTu Lab, Beijing, China.
FAU - Sun, Xu
AU  - Sun X
AD  - School of Computer Science, Peking University, Beijing, China.
FAU - Yang, Yang
AU  - Yang Y
AD  - School of Public Health, Shanghai Jiao Tong University School of Medicine, 
      Shanghai, China.
FAU - Clifton, Lei
AU  - Clifton L
AD  - Nuffield Department of Population Health, University of Oxford, Oxford, UK.
FAU - Clifton, David A
AU  - Clifton DA
AD  - Institute of Biomedical Engineering, Department of Engineering Science, 
      University of Oxford, Oxford, UK. david.clifton@eng.ox.ac.uk.
AD  - Oxford-Suzhou Centre for Advanced Research, Suzhou, China. 
      david.clifton@eng.ox.ac.uk.
LA  - eng
PT  - Journal Article
DEP - 20231202
PL  - England
TA  - NPJ Digit Med
JT  - NPJ digital medicine
JID - 101731738
PMC - PMC10693607
COIS- The authors declare no competing interests.
EDAT- 2023/12/03 00:42
MHDA- 2023/12/03 00:43
CRDT- 2023/12/02 23:38
PHST- 2023/05/08 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/12/03 00:43 [medline]
PHST- 2023/12/03 00:42 [pubmed]
PHST- 2023/12/02 23:38 [entrez]
AID - 10.1038/s41746-023-00952-2 [pii]
AID - 952 [pii]
AID - 10.1038/s41746-023-00952-2 [doi]
PST - epublish
SO  - NPJ Digit Med. 2023 Dec 2;6(1):226. doi: 10.1038/s41746-023-00952-2.

PMID- 37729277
OWN - NLM
STAT- MEDLINE
DCOM- 20230922
LR  - 20230923
IS  - 2317-6326 (Electronic)
IS  - 0102-6720 (Print)
IS  - 0102-6720 (Linking)
VI  - 36
DP  - 2023
TI  - THE LANGUAGE MODELS IN HEALTHCARE AND THE ROLE OF CHATGPT: COMMENTS.
PG  - e1761
LID - S0102-67202023000100602 [pii]
LID - 10.1590/0102-672020230043e1761 [doi]
LID - e1761
AB  - The introduction of chatbots has been one of the most intriguing advances in 
      artificial intelligence. There are numerous potential uses for artificial 
      intelligence in clinical research. However, there are also some issues that 
      require attention. Everyone agrees that AI requires a more stable foundation and 
      that a cutting-edge approach is necessary for AI to operate effectively.
FAU - Kleebayoon, Amnuay
AU  - Kleebayoon A
AUID- ORCID: 0000-0002-1976-2393
AD  - Academic Consultant, Research Center - Samraong, Oddar Mean Chey, Camboja.
FAU - Wiwanitkit, Viroj
AU  - Wiwanitkit V
AUID- ORCID: 0000-0003-1039-3728
AD  - Chandigarh University - Mohali, Punjab, India.
LA  - eng
PT  - Letter
DEP - 20230915
PL  - Brazil
TA  - Arq Bras Cir Dig
JT  - Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of 
      digestive surgery
JID - 9100283
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Cholecystectomy, Laparoscopic
MH  - Intelligence
MH  - Machine Learning
MH  - Language
MH  - Delivery of Health Care
PMC - PMC10510369
COIS- Conflict of interests: none
EDAT- 2023/09/20 18:42
MHDA- 2023/09/22 06:42
CRDT- 2023/09/20 13:44
PHST- 2023/07/03 00:00 [received]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/09/22 06:42 [medline]
PHST- 2023/09/20 18:42 [pubmed]
PHST- 2023/09/20 13:44 [entrez]
AID - S0102-67202023000100602 [pii]
AID - 10.1590/0102-672020230043e1761 [doi]
PST - epublish
SO  - Arq Bras Cir Dig. 2023 Sep 15;36:e1761. doi: 10.1590/0102-672020230043e1761. 
      eCollection 2023.

PMID- 37286844
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1432-5241 (Electronic)
IS  - 0364-216X (Linking)
VI  - 47
IP  - 5
DP  - 2023 Oct
TI  - Harnessing the Potential of ChatGPT in Breast Reconstruction: A Revolution in 
      Patient Communication and Education.
PG  - 2215-2216
LID - 10.1007/s00266-023-03427-7 [doi]
AB  - Level of Evidence IV This journal requires that authors assign a level of 
      evidence to each article. For a full description of these Evidence-Based Medicine 
      ratings, please refer to the Table of Contents or the online Instructions to 
      Authors www.springer.com/00266 .
CI  - © 2023. Springer Science+Business Media, LLC, part of Springer Nature and 
      International Society of Aesthetic Plastic Surgery.
FAU - Lanzano, Giuseppe
AU  - Lanzano G
AUID- ORCID: 0000-0001-7484-0780
AD  - Department of Plastic and Reconstructive Surgery, University of Campania Luigi 
      Vanvitelli, Piazza Luigi Miraglia, 2, 80138, Napoli, NA, Italy. 
      dottgiuseppelanzano@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230607
PL  - United States
TA  - Aesthetic Plast Surg
JT  - Aesthetic plastic surgery
JID - 7701756
SB  - IM
MH  - Humans
MH  - *Mammaplasty
MH  - Communication
EDAT- 2023/06/08 01:08
MHDA- 2023/10/23 00:42
CRDT- 2023/06/07 23:31
PHST- 2023/05/19 00:00 [received]
PHST- 2023/05/20 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/06/08 01:08 [pubmed]
PHST- 2023/06/07 23:31 [entrez]
AID - 10.1007/s00266-023-03427-7 [pii]
AID - 10.1007/s00266-023-03427-7 [doi]
PST - ppublish
SO  - Aesthetic Plast Surg. 2023 Oct;47(5):2215-2216. doi: 10.1007/s00266-023-03427-7. 
      Epub 2023 Jun 7.

PMID- 37746615
OWN - NLM
STAT- Publisher
LR  - 20231013
IS  - 2691-3593 (Electronic)
IS  - 2691-3593 (Linking)
VI  - 4
IP  - 3
DP  - 2023 Sep
TI  - Surgeon or Bot? The Risks of Using Artificial Intelligence in Surgical Journal 
      Publications.
PG  - e309
LID - 10.1097/AS9.0000000000000309 [doi]
AB  - Mini-Abstract ChatGPT is an artificial intelligence (AI) technology that has 
      begun to transform academics through its ability to create human-like text. This 
      has raised ethical concerns about its assistance in writing scientific 
      literature. Our aim is to highlight the benefits and risks that this technology 
      may pose to the surgical field.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Shaffrey, Ellen C
AU  - Shaffrey EC
AD  - From the Division of Plastic Surgery, University of Wisconsin School of Medicine 
      and Public Health, Madison, WI.
FAU - Eftekari, Sahand C
AU  - Eftekari SC
AD  - From the Division of Plastic Surgery, University of Wisconsin School of Medicine 
      and Public Health, Madison, WI.
FAU - Wilke, Lee G
AU  - Wilke LG
AD  - Department of Surgery, University of Wisconsin School of Medicine and Public 
      Health, Madison, WI.
FAU - Poore, Samuel O
AU  - Poore SO
AD  - From the Division of Plastic Surgery, University of Wisconsin School of Medicine 
      and Public Health, Madison, WI.
LA  - eng
PT  - Journal Article
DEP - 20230628
PL  - United States
TA  - Ann Surg Open
JT  - Annals of surgery open : perspectives of surgical history, education, and 
      clinical approaches
JID - 101769928
PMC - PMC10513298
COIS- Disclosure: E.C.S., S.C.E., and S.O.P. have no conflicts of interest. This 
      manuscript did not receive grant funding. L.G.W. is a founder and minority stock 
      owner of Elucent Medical. This Surgical Perspective was not written using 
      artificial intelligence (AI).
EDAT- 2023/09/25 06:42
MHDA- 2023/09/25 06:42
CRDT- 2023/09/25 05:18
PHST- 2023/03/29 00:00 [received]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/09/25 06:42 [pubmed]
PHST- 2023/09/25 05:18 [entrez]
AID - 10.1097/AS9.0000000000000309 [doi]
PST - epublish
SO  - Ann Surg Open. 2023 Jun 28;4(3):e309. doi: 10.1097/AS9.0000000000000309. 
      eCollection 2023 Sep.

PMID- 37607159
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 2154-2317 (Electronic)
IS  - 2154-2287 (Linking)
VI  - 14
IP  - 3
DP  - 2023 May-Jun
TI  - Ensuring AI Is Helpful and Not Harmful in Health Care.
PG  - 12-15
LID - 10.1109/MPULS.2023.3294084 [doi]
AB  - The presence of artificial intelligence (AI) is spreading fast through almost 
      every industry, and health care is no exception. Data-based decision-making 
      software is becoming pervasive in all facets of modern life, and the AI-enabled 
      chatbot ChatGPT is having a seismic impact on the public perception of AI.
FAU - Banks, Jim
AU  - Banks J
LA  - eng
PT  - Journal Article
PL  - United States
TA  - IEEE Pulse
JT  - IEEE pulse
JID - 101541727
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Software
MH  - Databases, Factual
MH  - Industry
MH  - Delivery of Health Care
EDAT- 2023/08/22 18:42
MHDA- 2023/08/22 18:43
CRDT- 2023/08/22 13:31
PHST- 2023/08/22 18:43 [medline]
PHST- 2023/08/22 18:42 [pubmed]
PHST- 2023/08/22 13:31 [entrez]
AID - 10.1109/MPULS.2023.3294084 [doi]
PST - ppublish
SO  - IEEE Pulse. 2023 May-Jun;14(3):12-15. doi: 10.1109/MPULS.2023.3294084.

PMID- 37651175
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20230908
IS  - 1438-8871 (Electronic)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Aug 31
TI  - AI Is Changing the Landscape of Academic Writing: What Can Be Done? Authors' 
      Reply to: AI Increases the Pressure to Overhaul the Scientific Peer Review 
      Process. Comment on "Artificial Intelligence Can Generate Fraudulent but 
      Authentic-Looking Scientific Medical Articles: Pandora's Box Has Been Opened".
PG  - e50844
LID - 10.2196/50844 [doi]
FAU - Májovský, Martin
AU  - Májovský M
AUID- ORCID: 0000-0001-7725-5181
AD  - Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles 
      University, Prague, Czech Republic.
FAU - Mikolov, Tomas
AU  - Mikolov T
AUID- ORCID: 0000-0002-6938-5426
AD  - Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical 
      University in Prague, Prague, Czech Republic.
FAU - Netuka, David
AU  - Netuka D
AUID- ORCID: 0000-0001-8609-4789
AD  - Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles 
      University, Prague, Czech Republic.
LA  - eng
PT  - Comment
PT  - Journal Article
DEP - 20230831
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
CON - J Med Internet Res. 2023 May 31;25:e46924. PMID: 37256685
CON - J Med Internet Res. 2023 Aug 31;25:e50591. PMID: 37651167
MH  - *Artificial Intelligence
MH  - *Writing
MH  - Peer Review
OTO - NOTNLM
OT  - AI
OT  - Chat Generative Pre-trained Transformer
OT  - ChatGPT
OT  - artificial intelligence
OT  - ethics
OT  - fraudulent medical articles
OT  - language models
OT  - neurosurgery
OT  - publications
EDAT- 2023/08/31 12:43
MHDA- 2023/09/01 06:42
CRDT- 2023/08/31 11:53
PHST- 2023/07/14 00:00 [received]
PHST- 2023/08/12 00:00 [accepted]
PHST- 2023/09/01 06:42 [medline]
PHST- 2023/08/31 12:43 [pubmed]
PHST- 2023/08/31 11:53 [entrez]
AID - v25i1e50844 [pii]
AID - 10.2196/50844 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Aug 31;25:e50844. doi: 10.2196/50844.

PMID- 36905924
OWN - NLM
STAT- MEDLINE
DCOM- 20230314
LR  - 20230427
IS  - 2666-6340 (Electronic)
IS  - 2666-6340 (Linking)
VI  - 4
IP  - 3
DP  - 2023 Mar 10
TI  - On the cusp: Considering the impact of artificial intelligence language models in 
      healthcare.
PG  - 139-140
LID - S2666-6340(23)00068-5 [pii]
LID - 10.1016/j.medj.2023.02.008 [doi]
AB  - Goodman et al. discuss how AI technologies like the natural language processing 
      model Chat-GPT could potentially transform healthcare through knowledge 
      dissemination and personalized patient education. Before these tools can be 
      safely integrated into healthcare, research and development of robust oversight 
      mechanisms are necessary to ensure their accuracy and reliability.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Goodman, Rachel S
AU  - Goodman RS
AD  - Vanderbilt University School of Medicine, Nashville, TN, USA.
FAU - Patrinely, J Randall Jr
AU  - Patrinely JR Jr
AD  - Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, 
      USA.
FAU - Osterman, Travis
AU  - Osterman T
AD  - Department of Biomedical Informatics, Department of Hematology/Oncology, 
      Vanderbilt University Medical Center, Nashville, TN, USA.
FAU - Wheless, Lee
AU  - Wheless L
AD  - Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, 
      USA.
FAU - Johnson, Douglas B
AU  - Johnson DB
AD  - Department of Hematology/Oncology, Vanderbilt University Medical Center, 
      Nashville, TN, USA. Electronic address: douglas.b.johnson@vumc.org.
LA  - eng
GR  - R01 CA227481/CA/NCI NIH HHS/United States
PT  - Letter
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Med
JT  - Med (New York, N.Y.)
JID - 101769215
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Natural Language Processing
MH  - Delivery of Health Care
MH  - Health Facilities
COIS- Declaration of interests D.B.J. has served on advisory boards or as a consultant 
      for BMS, Catalyst Biopharma, Iovance, Jansen, Mallinckrodt, Merck, Mosaic 
      ImmunoEngineering, Novartis, Oncosec, Pfizer, Targovax, and Teiko; has received 
      research funding from BMS and Incyte; and has patents pending for use of MHC-II 
      as a biomarker for immune checkpoint inhibitor response and abatacept as 
      treatment for immune-related adverse events.
EDAT- 2023/03/12 06:00
MHDA- 2023/03/15 06:00
CRDT- 2023/03/11 18:20
PHST- 2023/03/11 18:20 [entrez]
PHST- 2023/03/12 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
AID - S2666-6340(23)00068-5 [pii]
AID - 10.1016/j.medj.2023.02.008 [doi]
PST - ppublish
SO  - Med. 2023 Mar 10;4(3):139-140. doi: 10.1016/j.medj.2023.02.008.

PMID- 37203491
OWN - NLM
STAT- MEDLINE
DCOM- 20230522
LR  - 20230522
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 302
DP  - 2023 May 18
TI  - Automated ICF Coding of Rehabilitation Notes for Low-Resource Languages via 
      Continual Training of Language Models.
PG  - 763-767
LID - 10.3233/SHTI230262 [doi]
AB  - The coding of medical documents and in particular of rehabilitation notes using 
      the International Classification of Functioning, Disability and Health (ICF) is a 
      difficult task showing low agreement among experts. Such difficulty is mainly 
      caused by the specific terminology that needs to be used for the task. In this 
      paper, we address the task developing a model based on a large language model, 
      BERT. By leveraging continual training of such a model using ICF textual 
      descriptions, we are able to effectively encode rehabilitation notes expressed in 
      Italian, an under-resourced language.
FAU - Roitero, Kevin
AU  - Roitero K
AD  - Dept. of Mathematics, Computer Science and Physics, University of Udine, Italy.
FAU - Martinuzzi, Andrea
AU  - Martinuzzi A
AD  - Dept. of Conegliano, IRCCS "E. Medea" Scientific Institute, Conegliano, Italy.
FAU - Armellin, Maria Teresa
AU  - Armellin MT
AD  - Dept. of Conegliano, IRCCS "E. Medea" Scientific Institute, Conegliano, Italy.
FAU - Paparella, Gabriella
AU  - Paparella G
AD  - Dept. of Conegliano, IRCCS "E. Medea" Scientific Institute, Conegliano, Italy.
FAU - Maniero, Alberto
AU  - Maniero A
AD  - Dept. of Conegliano, IRCCS "E. Medea" Scientific Institute, Conegliano, Italy.
FAU - Della Mea, Vincenzo
AU  - Della Mea V
AD  - Dept. of Mathematics, Computer Science and Physics, University of Udine, Italy.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - Humans
MH  - *Disabled Persons/rehabilitation
MH  - Italy
MH  - Longitudinal Studies
MH  - Disability Evaluation
MH  - International Classification of Functioning, Disability and Health
MH  - Activities of Daily Living
OTO - NOTNLM
OT  - Continual Training
OT  - ICF
OT  - Language Models
OT  - Rehabilitation
EDAT- 2023/05/19 06:42
MHDA- 2023/05/22 06:42
CRDT- 2023/05/19 05:28
PHST- 2023/05/22 06:42 [medline]
PHST- 2023/05/19 06:42 [pubmed]
PHST- 2023/05/19 05:28 [entrez]
AID - SHTI230262 [pii]
AID - 10.3233/SHTI230262 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 May 18;302:763-767. doi: 10.3233/SHTI230262.

PMID- 37453975
OWN - NLM
STAT- Publisher
LR  - 20230715
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 15
TI  - Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using 
      Internet of Things, Edge Impulse Platform, and ChatGPT.
LID - 10.1007/s10439-023-03316-9 [doi]
AB  - Humanoid doctor is an AI-based robot that featured remote bi-directional 
      communication and is embedded with disruptive technologies. Accurate and 
      real-time responses are the main characteristics of a humanoid doctor which 
      diagnoses disease in a patient. The patient details are obtained by Internet of 
      Things devices, edge devices, and text formats. The inputs from the patient are 
      processed by the humanoid doctor, and it provides its opinion to the patient. The 
      historical patient data are trained using cloud artificial intelligence platform 
      and the model is tested against the patient sample data acquired using medical 
      IoT and edge devices. Disease is identified at three different stages and 
      analyzed. The humanoid doctor is expected to identify the diseases well in 
      comparison with human healthcare professionals. The humanoid doctor is 
      under-trusted because of the lack of a multi-featured accurate model, 
      accessibility, availability, and standardization. In this letter, patient input, 
      artificial intelligence, and response zones are encapsulated and the humanoid 
      doctor is realized.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Venkataswamy, R
AU  - Venkataswamy R
AUID- ORCID: 0000-0002-5249-639X
AD  - Department of Electrical and Electronics Engineering, Christ (Deemed to be 
      University), Kanminike, Bangalore, Karnataka, 560074, India. 
      venkataswamy.r@christuniversity.in.
FAU - Janamala, Varaprasad
AU  - Janamala V
AD  - Department of Electrical and Electronics Engineering, Christ (Deemed to be 
      University), Kanminike, Bangalore, Karnataka, 560074, India.
FAU - Cherukuri, Ravidranath Chowdary
AU  - Cherukuri RC
AD  - Department of Computer Science and Engineering, Christ (Deemed to be University), 
      Kanminike, Bangalore, Karnataka, 560074, India.
LA  - eng
PT  - Letter
DEP - 20230715
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Disease detection
OT  - Edge computing
OT  - Humanoid
OT  - Internet of Things
EDAT- 2023/07/16 01:06
MHDA- 2023/07/16 01:06
CRDT- 2023/07/15 23:04
PHST- 2023/06/30 00:00 [received]
PHST- 2023/07/06 00:00 [accepted]
PHST- 2023/07/16 01:06 [medline]
PHST- 2023/07/16 01:06 [pubmed]
PHST- 2023/07/15 23:04 [entrez]
AID - 10.1007/s10439-023-03316-9 [pii]
AID - 10.1007/s10439-023-03316-9 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 15. doi: 10.1007/s10439-023-03316-9.

PMID- 37877216
OWN - NLM
STAT- Publisher
LR  - 20231109
IS  - 1545-5815 (Electronic)
IS  - 0898-9621 (Linking)
DP  - 2023 Oct 25
TI  - Transparency in research: An analysis of ChatGPT usage acknowledgment by authors 
      across disciplines and geographies.
PG  - 1-22
LID - 10.1080/08989621.2023.2273377 [doi]
AB  - This investigation systematically reviews the recognition of generative AI tools, 
      particularly ChatGPT, in scholarly literature. Utilizing 1,226 publications from 
      the Dimensions database, ranging from November 2022 to July 2023, the research 
      scrutinizes temporal trends and distribution across disciplines and regions. 
      U.S.-based authors lead in acknowledgments, with notable contributions from China 
      and India. Predominantly, Biomedical and Clinical Sciences, as well as 
      Information and Computing Sciences, are engaging with these AI tools. 
      Publications like "The Lancet Digital Health" and platforms such as "bioRxiv" are 
      recurrent venues for such acknowledgments, highlighting AI's growing impact on 
      research dissemination. The analysis is confined to the Dimensions database, thus 
      potentially overlooking other sources and grey literature. Additionally, the 
      study abstains from examining the acknowledgments' quality or ethical 
      considerations. Findings are beneficial for stakeholders, providing a basis for 
      policy and scholarly discourse on ethical AI use in academia. This study 
      represents the inaugural comprehensive empirical assessment of AI acknowledgment 
      patterns in academic contexts, addressing a previously unexplored aspect of 
      scholarly communication.
FAU - Raman, Raghu
AU  - Raman R
AD  - Amrita School of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India.
LA  - eng
PT  - Journal Article
DEP - 20231025
PL  - United States
TA  - Account Res
JT  - Accountability in research
JID - 9100813
SB  - IM
OTO - NOTNLM
OT  - Ethics in publishing
OT  - fields of research
OT  - research integrity
OT  - science policy
OT  - social science research ethics
EDAT- 2023/10/25 06:42
MHDA- 2023/10/25 06:42
CRDT- 2023/10/25 04:25
PHST- 2023/10/25 06:42 [pubmed]
PHST- 2023/10/25 06:42 [medline]
PHST- 2023/10/25 04:25 [entrez]
AID - 10.1080/08989621.2023.2273377 [doi]
PST - aheadofprint
SO  - Account Res. 2023 Oct 25:1-22. doi: 10.1080/08989621.2023.2273377.

PMID- 37722842
OWN - NLM
STAT- Publisher
LR  - 20230918
IS  - 1744-5078 (Electronic)
IS  - 0927-3948 (Linking)
DP  - 2023 Sep 18
TI  - Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in 
      Uveitis.
PG  - 1-6
LID - 10.1080/09273948.2023.2253471 [doi]
AB  - INTRODUCTION: Accurate diagnosis and timely management are vital for favorable 
      uveitis outcomes. Artificial Intelligence (AI) holds promise in medical 
      decision-making, particularly in ophthalmology. Yet, the diagnostic precision and 
      management advice from AI-based uveitis chatbots lack assessment. METHODS: We 
      appraised diagnostic accuracy and management suggestions of an AI-based chatbot, 
      ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases 
      aligned with new Uveitis Nomenclature guidelines. Participants predicted likely 
      diagnoses, two differentials, and next management steps. Comparative success 
      rates were computed. RESULTS: Ophthalmologists excelled (60-92%) in likely 
      diagnosis, exceeding AI (60%). Considering fully and partially accurate 
      diagnoses, ophthalmologists achieved 76-100% success; AI attained 72%. Despite an 
      8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed 
      on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans. 
      CONCLUSIONS: The study underscores AI chatbots' potential in uveitis diagnosis 
      and management, indicating their value in reducing diagnostic errors. Further 
      research is essential to enhance AI chatbot precision in diagnosis and 
      recommendations.
FAU - Rojas-Carabali, William
AU  - Rojas-Carabali W
AUID- ORCID: 0000-0002-9976-8989
AD  - National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, 
      Singapore.
AD  - Department of Bioinformatics, Lee Kong Chiang School of Medicine, Nanyang 
      Technological University, Singapore, Singapore.
FAU - Cifuentes-González, Carlos
AU  - Cifuentes-González C
AUID- ORCID: 0000-0002-2703-0977
AD  - Neuroscience Research Group (NEUROS), Neurovitae Center for Neuroscience, 
      Institute of Translational Medicine (IMT), Escuela de Medicina y Ciencias de la 
      Salud, Universidad del Rosario, Bogotá, Colombia.
FAU - Wei, Xin
AU  - Wei X
AD  - National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, 
      Singapore.
FAU - Putera, Ikhwanuliman
AU  - Putera I
AD  - Department of Ophthalmology, Faculty of Medicine Universitas Indonesia - 
      CiptoMangunkusmoKirana Eye Hospital, Jakarta, Indonesia.
AD  - Laboratory Medical Immunology, Department of Immunology, ErasmusMC, University 
      Medical Centre, Rotterdam, the Netherlands.
AD  - Department of Internal Medicine, Division of Clinical Immunology, Erasmus MC, 
      University Medical Center, Rotterdam, The Netherlands.
AD  - Department of Ophthalmology, Erasmus MC, University Medical Center, Rotterdam, 
      The Netherlands.
FAU - Sen, Alok
AU  - Sen A
AD  - Department of Vitreoretina and Uveitis, Sadguru Netra Chikatsalya, Chitrakoot, 
      India.
FAU - Thng, Zheng Xian
AU  - Thng ZX
AD  - National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, 
      Singapore.
FAU - Agrawal, Rajdeep
AU  - Agrawal R
AD  - Department of Bioinformatics, Lee Kong Chiang School of Medicine, Nanyang 
      Technological University, Singapore, Singapore.
FAU - Elze, Tobias
AU  - Elze T
AD  - Department of Ophthalmology, Massachusetts Eye and Ear/Harvard Medical School, 
      and Schepens Eye Research Institute, Boston, Massachusetts, USA.
FAU - Sobrin, Lucia
AU  - Sobrin L
AUID- ORCID: 0000-0003-1575-0819
AD  - Department of Ophthalmology, Massachusetts Eye and Ear/Harvard Medical School, 
      and Schepens Eye Research Institute, Boston, Massachusetts, USA.
FAU - Kempen, John H
AU  - Kempen JH
AUID- ORCID: 0000-0002-2967-4792
AD  - Department of Ophthalmology, Massachusetts Eye and Ear/Harvard Medical School, 
      and Schepens Eye Research Institute, Boston, Massachusetts, USA.
AD  - Community Ophthalmology, Sight for Souls, Bellevue, Washington, USA.
AD  - Department of Ophthalmology, Addis Ababa University, Addis Ababa, Ethiopia.
AD  - MyungSung Christian Medical Center (MCM) Eye Unit, MCM Comprehensive Specialized 
      Hospital, and MyungSung Medical School, Addis Ababa, Ethiopia.
FAU - Lee, Bernett
AU  - Lee B
AD  - National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, 
      Singapore.
FAU - Biswas, Jyotirmay
AU  - Biswas J
AD  - Department of Ocular Pathology and Uveitis, Medical Research Foundation, Sankara 
      Netralaya, Chennai, India.
FAU - Nguyen, Quan Dong
AU  - Nguyen QD
AD  - Byers Eye Institute, Stanford University, Palo Alto, California, USA.
FAU - Gupta, Vishali
AU  - Gupta V
AD  - Post Graduate Institute of Medical Education and Research (PGIMER), Advance Eye 
      Centre, Chandigarh, India.
FAU - de-la-Torre, Alejandra
AU  - de-la-Torre A
AUID- ORCID: 0000-0003-0684-1989
AD  - National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, 
      Singapore.
FAU - Agrawal, Rupesh
AU  - Agrawal R
AD  - MyungSung Christian Medical Center (MCM) Eye Unit, MCM Comprehensive Specialized 
      Hospital, and MyungSung Medical School, Addis Ababa, Ethiopia.
AD  - Department of Ophthalmology and Visual Sciences, Academic Clinical Program, 
      Duke-NUS Medical School, Singapore, Singapore.
AD  - Moorfields Eye Hospital, NHS Foundation Trust, London, UK.
AD  - Singapore Eye Research Institute, The Academia, Singapore, Singapore.
LA  - eng
PT  - Journal Article
DEP - 20230918
PL  - England
TA  - Ocul Immunol Inflamm
JT  - Ocular immunology and inflammation
JID - 9312169
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - chatbot
OT  - diagnosis
OT  - natural language processing
OT  - ophthalmology
EDAT- 2023/09/19 00:43
MHDA- 2023/09/19 00:43
CRDT- 2023/09/18 21:04
PHST- 2023/09/19 00:43 [medline]
PHST- 2023/09/19 00:43 [pubmed]
PHST- 2023/09/18 21:04 [entrez]
AID - 10.1080/09273948.2023.2253471 [doi]
PST - aheadofprint
SO  - Ocul Immunol Inflamm. 2023 Sep 18:1-6. doi: 10.1080/09273948.2023.2253471.

PMID- 37707390
OWN - NLM
STAT- MEDLINE
DCOM- 20231120
LR  - 20231120
IS  - 1938-1344 (Electronic)
IS  - 0190-6011 (Linking)
VI  - 53
IP  - 12
DP  - 2023 Dec
TI  - Pros and Cons of Using Artificial Intelligence Chatbots for Musculoskeletal 
      Rehabilitation Management.
PG  - 1-7
LID - 10.2519/jospt.2023.12000 [doi]
AB  - SYNOPSIS: Artificial intelligence (AI), specifically large language models 
      (LLMs), which focus on the interaction between computers and human language, can 
      influence musculoskeletal rehabilitation management. AI chatbots (eg, ChatGPT, 
      Microsoft Bing, and Google Bard) are a form of large language models designed to 
      understand, interpret, and generate text similar to what is produced by humans. 
      Since their release, chatbots have triggered controversy in the international 
      scientific community, including when they have passed university exams, generated 
      credible scientific abstracts, and shown potential for replacing humans in 
      scientific roles. The controversies extend to the field of musculoskeletal 
      rehabilitation. In this Viewpoint, we describe the potential applications and 
      limitations, and recommended actions for education, clinical practice, and 
      research when using AI chatbots for musculoskeletal rehabilitation management, 
      aspects that may have similar implications for the broader health care community. 
      J Orthop Sports Phys Ther 2023;53(12):1-7. Epub 14 September 2023. 
      doi:10.2519/jospt.2023.12000.
FAU - Rossettini, Giacomo
AU  - Rossettini G
FAU - Cook, Chad
AU  - Cook C
FAU - Palese, Alvisa
AU  - Palese A
FAU - Pillastrini, Paolo
AU  - Pillastrini P
FAU - Turolla, Andrea
AU  - Turolla A
LA  - eng
PT  - Journal Article
PL  - United States
TA  - J Orthop Sports Phys Ther
JT  - The Journal of orthopaedic and sports physical therapy
JID - 7908150
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Language
OTO - NOTNLM
OT  - ChatGPT
OT  - Google Bard
OT  - Microsoft Bing
OT  - artificial intelligence
OT  - clinical reasoning
OT  - musculoskeletal pain
EDAT- 2023/09/14 12:41
MHDA- 2023/11/20 06:54
CRDT- 2023/09/14 09:53
PHST- 2023/11/20 06:54 [medline]
PHST- 2023/09/14 12:41 [pubmed]
PHST- 2023/09/14 09:53 [entrez]
AID - 10.2519/jospt.2023.12000 [doi]
PST - ppublish
SO  - J Orthop Sports Phys Ther. 2023 Dec;53(12):1-7. doi: 10.2519/jospt.2023.12000.

PMID- 37090367
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230425
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Neutropenic Enterocolitis Following Autologous Stem Cell Transplantation: A 
      Compelling Clinical Case Report Written With the Assistance of ChatGPT.
PG  - e36390
LID - 10.7759/cureus.36390 [doi]
LID - e36390
AB  - Neutropenic enterocolitis (NE) is a rare and life-threatening condition that 
      typically occurs in patients with hematologic malignancies undergoing intensive 
      chemotherapy, radiation therapy, or bone marrow transplant regimens, predisposing 
      them to profound neutropenia. NE can have a nonspecific clinical presentation and 
      mimic other gastrointestinal disorders such as appendicitis, diverticulitis, or 
      inflammatory bowel disease but is associated with very high morbidity and 
      mortality if not diagnosed and treated promptly. We present the case of a 
      middle-aged female with a recent diagnosis of follicular lymphoma who developed 
      neutropenic enterocolitis after undergoing induction chemotherapy for an 
      autologous stem cell transplant (ASCT). In this article, we provide a literature 
      review of neutropenic enterocolitis and highlight the importance of a prompt 
      diagnosis and management, given its high mortality rate.
CI  - Copyright © 2023, Milan-Ortiz et al.
FAU - Milan-Ortiz, Vanessa
AU  - Milan-Ortiz V
AD  - Internal Medicine, Wayne State University Detroit Medical Center, Detroit, USA.
FAU - Damughatla, Anirudh R
AU  - Damughatla AR
AD  - Internal Medicine, Wayne State University Detroit Medical Center, Detroit, USA.
FAU - Qazi, Adam M
AU  - Qazi AM
AD  - Internal Medicine, Wayne State University Detroit Medical Center, Detroit, USA.
FAU - Kamatham, Saivaishnavi
AU  - Kamatham S
AD  - Internal Medicine, Wayne State University Detroit Medical Center, Detroit, USA.
FAU - Oli, Sharad
AU  - Oli S
AD  - Pulmonary Critical Care, Wayne State University Detroit Medical Center, Detroit, 
      USA.
FAU - Koleti, Pragna
AU  - Koleti P
AD  - Internal Medicine, Suburban Community Hospital, Norristown, USA.
FAU - Levine, Diane L
AU  - Levine DL
AD  - Internal Medicine, Wayne State University Detroit Medical Center, Detroit, USA.
LA  - eng
PT  - Case Reports
DEP - 20230320
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10115743
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - enterocolitis
OT  - neutropenic enterocolitis
OT  - post-autologous stem cell transplantation enterocolitis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/24 06:41
MHDA- 2023/04/24 06:42
CRDT- 2023/04/24 03:40
PHST- 2023/03/20 00:00 [accepted]
PHST- 2023/04/24 06:42 [medline]
PHST- 2023/04/24 06:41 [pubmed]
PHST- 2023/04/24 03:40 [entrez]
AID - 10.7759/cureus.36390 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 20;15(3):e36390. doi: 10.7759/cureus.36390. eCollection 2023 
      Mar.

PMID- 37692694
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - ChatGPT and Artificial Intelligence in Medical Writing: Concerns and Ethical 
      Considerations.
PG  - e43292
LID - 10.7759/cureus.43292 [doi]
LID - e43292
AB  - Artificial intelligence (AI) language generation models, such as ChatGPT, have 
      the potential to revolutionize the field of medical writing and other natural 
      language processing (NLP) tasks. It is crucial to consider the ethical concerns 
      that come with their use. These include bias, misinformation, privacy, lack of 
      transparency, job displacement, stifling creativity, plagiarism, authorship, and 
      dependence. Therefore, it is essential to develop strategies to understand and 
      address these concerns. Important techniques include common bias and 
      misinformation detection, ensuring privacy, providing transparency, and being 
      mindful of the impact on employment. The AI-generated text must be critically 
      reviewed by medical experts to validate the output generated by these models 
      before being used in any clinical or medical context. By considering these 
      ethical concerns and taking appropriate measures, we can ensure that the benefits 
      of these powerful tools are maximized while minimizing any potential harm. This 
      article focuses on the implications of AI assistants in medical writing and hopes 
      to provide insight into the perceived rapid rate of technological progression 
      from a historical and ethical perspective.
CI  - Copyright © 2023, Doyal et al.
FAU - Doyal, Alexander S
AU  - Doyal AS
AD  - Anesthesiology, University of North Carolina at Chapel Hill School of Medicine, 
      Chapel Hill, USA.
FAU - Sender, David
AU  - Sender D
AD  - Anesthesiology, University of North Carolina at Chapel Hill School of Medicine, 
      Chapel Hill, USA.
FAU - Nanda, Monika
AU  - Nanda M
AD  - Anesthesiology, University of North Carolina at Chapel Hill School of Medicine, 
      Chapel Hill, USA.
FAU - Serrano, Ricardo A
AU  - Serrano RA
AD  - Anesthesiology, University of North Carolina at Chapel Hill School of Medicine, 
      Chapel Hill, USA.
LA  - eng
PT  - Editorial
DEP - 20230810
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10492634
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - artificial intelligence (ai)
OT  - ethics
OT  - machine learning
OT  - medical writing
OT  - natural language processing
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 04:41
PHST- 2023/08/10 00:00 [accepted]
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 04:41 [entrez]
AID - 10.7759/cureus.43292 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 10;15(8):e43292. doi: 10.7759/cureus.43292. eCollection 2023 
      Aug.

PMID- 37866509
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1877-0568 (Electronic)
IS  - 1877-0568 (Linking)
VI  - 109
IP  - 8
DP  - 2023 Dec
TI  - Evaluation of the impact of large language learning models on articles submitted 
      to Orthopaedics & Traumatology: Surgery & Research (OTSR): A significant increase 
      in the use of artificial intelligence in 2023.
PG  - 103720
LID - S1877-0568(23)00238-4 [pii]
LID - 10.1016/j.otsr.2023.103720 [doi]
AB  - INTRODUCTION: There has been an unprecedented rise is the use of artificial 
      intelligence (AI) amongst medical fields. Recently, a dialogue agent called 
      ChatGPT (Generative Pre-trained Transformer) has grown in popularity through its 
      use of large language models (LLM) to clearly and precisely generate text on 
      demand. However, the impact of AI on the creation of scientific articles is 
      remains unknown. A retrospective study was carried out with the aim of answering 
      the following questions: identify the presence of text generated by LLM before 
      and after the increased usage of ChatGPT in articles submitted in OTSR; determine 
      if the type of article, the year of submission, and the country of origin, 
      influenced the proportion of text generated, at least in part by AI. MATERIAL AND 
      METHODS: A total of 390 English articles were submitted to OTSR in January, 
      February and March 2022 (n=204) and over the same months of 2023 (n=186) were 
      analyzed. All articles were analyzed using the ZeroGPT tool, which provides an 
      assumed rate of AI use expressed as a percentage. A comparison of the average 
      rate of AI use was carried out between the articles submitted in 2022 and 2023. 
      This comparison was repeated keeping only the articles with the highest 
      percentage of suspected AI use (greater than 10 and 20%). A secondary analysis 
      was carried out to identify risk factors for AI use. RESULTS: The average 
      percentage of suspected LLM use in the entire cohort was 11%±6, with 160 articles 
      (41.0%) having a suspected AI rate greater than 10% and 61 (15.6%) with an 
      assumed AI rate greater than 20%. A comparison between articles submitted in 2022 
      and 2023 revealed a significant increase in the use of these tools after the 
      launch of ChatGPT 3.5 (9.4% in 2022 and 12.6% in 2023 [p=0.004]). The number of 
      articles with suspected AI rates of greater than 10 and 20% were significantly 
      higher in 2023: >10%: 71 articles (34.8%) versus 89 articles (47.8%) (p=0.008) 
      and >20%: 21 articles (10.3%) versus 40 articles (21.5%) (p=0.002). A risk factor 
      analysis for LLLM use, demonstrated that authors of Asian geographic origin, and 
      the submission year 2023 were associated with a higher rate of suspected AI use. 
      An AI rate >20% was associated to Asian geographical origin with an odds ratio of 
      1.79 (95% CI: 1.03-3.11) (p=0.029), while the year of submission being 2023 had 
      an odds ratio of 1.7 (95% CI: 1.1-2.5) (p=0.02). CONCLUSION: This study 
      highlights a significant increase in the use of LLM in the writing of articles 
      submitted to the OTSR journal after the launch of ChatGPT 3.5. The increasing use 
      of these models raises questions about originality and plagiarism in scientific 
      research. AI offers creative opportunities but also raises ethical and 
      methodological challenges. LEVEL OF EVIDENCE: III; case control study.
CI  - Copyright © 2023 Elsevier Masson SAS. All rights reserved.
FAU - Maroteau, Gaëlle
AU  - Maroteau G
AD  - Unité Inserm Comète 1075, Department of Orthopaedics and Traumatology, Caen 
      University Hospital, avenue Cote-de-Nacre, 14000 Caen, France.
FAU - An, Jae-Sung
AU  - An JS
AD  - Tokyo Medical and Dental University, 1 Chome-5-45 Yushima, Bunkyo City, Tokyo 
      113-8510, Japan.
FAU - Murgier, Jérome
AU  - Murgier J
AD  - Service de chirurgie orthopédique, clinique Aguiléra, 21, rue de l'Estagnas, 
      64200 Biarritz, France.
FAU - Hulet, Christophe
AU  - Hulet C
AD  - Unité Inserm Comète 1075, Department of Orthopaedics and Traumatology, Caen 
      University Hospital, avenue Cote-de-Nacre, 14000 Caen, France.
FAU - Ollivier, Matthieu
AU  - Ollivier M
AD  - Institute of Movement and Locomotion, Department of Orthopaedics and 
      Traumatology, Sainte-Marguerite Hospital, BP 29, 270, boulevard 
      Sainte-Marguerite, 13274 Marseille, France; Aix-Marseille Unit, Institute for 
      Locomotion, Department of Orthopaedics and Traumatology, CNRS, ISM, 
      Sainte-Marguerite Hospital, AP-HM, Marseille, France.
FAU - Ferreira, Alexandre
AU  - Ferreira A
AD  - Unité Inserm Comète 1075, Department of Orthopaedics and Traumatology, Caen 
      University Hospital, avenue Cote-de-Nacre, 14000 Caen, France. Electronic 
      address: alexandreferreira0891@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - France
TA  - Orthop Traumatol Surg Res
JT  - Orthopaedics & traumatology, surgery & research : OTSR
JID - 101494830
SB  - IM
MH  - Humans
MH  - *Orthopedics
MH  - Artificial Intelligence
MH  - Case-Control Studies
MH  - Retrospective Studies
MH  - *Traumatology
MH  - Language
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Chatbot
OT  - Large language learning models
OT  - Scientific article
EDAT- 2023/10/23 00:43
MHDA- 2023/12/04 12:41
CRDT- 2023/10/22 19:30
PHST- 2023/08/04 00:00 [received]
PHST- 2023/10/04 00:00 [revised]
PHST- 2023/10/04 00:00 [accepted]
PHST- 2023/12/04 12:41 [medline]
PHST- 2023/10/23 00:43 [pubmed]
PHST- 2023/10/22 19:30 [entrez]
AID - S1877-0568(23)00238-4 [pii]
AID - 10.1016/j.otsr.2023.103720 [doi]
PST - ppublish
SO  - Orthop Traumatol Surg Res. 2023 Dec;109(8):103720. doi: 
      10.1016/j.otsr.2023.103720. Epub 2023 Oct 20.

PMID- 37701881
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230914
IS  - 1179-1500 (Print)
IS  - 1179-1500 (Electronic)
IS  - 1179-1500 (Linking)
VI  - 15
DP  - 2023
TI  - Artificial Intelligence Chatbots and Emergency Medical Services: Perspectives on 
      the Implications of Generative AI in Prehospital Care.
PG  - 289-292
LID - 10.2147/OAEM.S420764 [doi]
AB  - Emergency Medical Services (EMS) is likely to experience transformative changes 
      due to the rapid advancements in artificial intelligence (AI), such as OpenAI's 
      ChatGPT. In this short commentary, we aim to preliminarily explore some profound 
      implications of AI advancements on EMS systems and practice.
CI  - © 2023 Ventura and Denton.
FAU - Ventura, Christian Angelo I
AU  - Ventura CAI
AD  - Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of 
      Public Health, Baltimore, MD, USA.
FAU - Denton, Edward E
AU  - Denton EE
AD  - Department of Emergency Medicine, College of Medicine, University of Arkansas for 
      Medical Sciences, Little Rock, AR, USA.
AD  - Department of Health Policy and Management, Fay W. Boozman College of Public 
      Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
LA  - eng
PT  - Journal Article
DEP - 20230907
PL  - New Zealand
TA  - Open Access Emerg Med
JT  - Open access emergency medicine : OAEM
JID - 101570796
PMC - PMC10494922
OTO - NOTNLM
OT  - EMS
OT  - EMT
OT  - artificial intelligence
OT  - machine learning
OT  - paramedic
OT  - prehospital
COIS- The authors report no known conflicts of interest, financial or otherwise in this 
      work. The work is solely that of the authors and does not necessarily represent 
      the views, policies, or opinions of their affiliated institutions, employers, or 
      partners. It was not reviewed or endorsed by any specific institution in 
      particular. The work is an independent publication that was neither funded nor 
      reviewed by the university.
EDAT- 2023/09/13 06:41
MHDA- 2023/09/13 06:42
CRDT- 2023/09/13 04:14
PHST- 2023/05/24 00:00 [received]
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/09/13 06:42 [medline]
PHST- 2023/09/13 06:41 [pubmed]
PHST- 2023/09/13 04:14 [entrez]
AID - 420764 [pii]
AID - 10.2147/OAEM.S420764 [doi]
PST - epublish
SO  - Open Access Emerg Med. 2023 Sep 7;15:289-292. doi: 10.2147/OAEM.S420764. 
      eCollection 2023.

PMID- 37599212
OWN - NLM
STAT- MEDLINE
DCOM- 20230822
LR  - 20230822
IS  - 0376-2491 (Print)
IS  - 0376-2491 (Linking)
VI  - 103
IP  - 31
DP  - 2023 Aug 22
TI  - [Applications and challenges of large language models in critical care medicine].
PG  - 2361-2364
LID - 10.3760/cma.j.cn112137-20230524-00847 [doi]
AB  - The rapid development of big data methods and technologies has provided more and 
      more new ideas and methods for clinical diagnosis and treatment. The emergence of 
      large language models (LLM) has made it possible for human-computer interactive 
      dialogues and applications in complex medical scenarios. Critical care medicine 
      is a process of continuous dynamic targeted treatment. The huge data generated in 
      this process needs to be integrated and optimized through models for clinical 
      application, interaction in teaching simulation, and assistance in scientific 
      research. Using the LLM represented by generative pre-trained transformer ChatGPT 
      can initially realize the application in the diagnosis of severe diseases, the 
      prediction of death risk and the management of medical records. At the same time, 
      the time and space limitations, illusions and ethical and moral issues of ChatGPT 
      emerged as the times require. In the future, it is undeniable that it may play a 
      huge role in the diagnosis and treatment of critical care medicine, but the 
      current application should be combined with more clinical knowledge reserves of 
      critical care medicine to carefully judge its conclusions.
FAU - Su, L X
AU  - Su LX
AD  - Department of Critical Care Medicine, Peking Union Medical College, Peking Union 
      Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, 
      China.
FAU - Weng, L
AU  - Weng L
AD  - Medical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare 
      Diseases, Peking Union Medical College, Peking Union Medical College Hospital, 
      Chinese Academy of Medical Sciences, Beijing, 100730, China.
FAU - Li, W X
AU  - Li WX
AD  - Department of Surgical Intensive Critical Unit, Beijing Chao-Yang Hospital, 
      Capital Medical University, Beijing 100020, China.
FAU - Long, Y
AU  - Long Y
AD  - Department of Critical Care Medicine, Peking Union Medical College, Peking Union 
      Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, 
      China.
LA  - chi
GR  - 2022-PUMCH-B-115, 2022-PUMCH-D-005/National High-Level Hospital Clinical Research 
      Funding/
PT  - English Abstract
PT  - Journal Article
PL  - China
TA  - Zhonghua Yi Xue Za Zhi
JT  - Zhonghua yi xue za zhi
JID - 7511141
SB  - IM
MH  - Humans
MH  - *Language
MH  - *Technology
MH  - Critical Care
EDAT- 2023/08/21 00:41
MHDA- 2023/08/22 06:42
CRDT- 2023/08/20 22:11
PHST- 2023/08/22 06:42 [medline]
PHST- 2023/08/21 00:41 [pubmed]
PHST- 2023/08/20 22:11 [entrez]
AID - 10.3760/cma.j.cn112137-20230524-00847 [doi]
PST - ppublish
SO  - Zhonghua Yi Xue Za Zhi. 2023 Aug 22;103(31):2361-2364. doi: 
      10.3760/cma.j.cn112137-20230524-00847.

PMID- 37250452
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230531
IS  - 2091-0800 (Print)
IS  - 2091-0800 (Electronic)
VI  - 13
IP  - 1
DP  - 2023 Mar
TI  - Pushing the Boundaries of Scientific Research with the use of Artificial 
      Intelligence tools: Navigating Risks and Unleashing Possibilities.
PG  - 1258-1263
LID - 10.3126/nje.v13i1.53721 [doi]
AB  - The language of science is communicated through various modes, such as lectures, 
      informal discussions, conferences, and peer-reviewed publications. Artificial 
      Intelligence (AI) based writing tools, like ChatGPT, have recently become 
      increasingly popular due to natural language processing technology advancements. 
      ChatGPT is an AI language model that can generate text close to human writing, 
      making it suitable for tasks such as summarizing literature, composing essays, 
      and producing statistical studies. This technology has the potential to transform 
      scientific communication, but concerns have been raised about its impact on the 
      integrity of research and the role of human researchers. While this technology 
      has advantages such as accelerating the innovation process and enhancing 
      diversity in scientific viewpoints, it is important for the scientific community 
      to debate and envision the consequences of its use. Publishers are working to 
      develop guidelines for its application, which may be capable of future activities 
      such as experiment design and peer review. As we enter the early stages of the AI 
      revolution, it is imperative that the scientific community engages in discourse 
      and contemplate the potential outcomes of this potentially transformative 
      technology. With this in mind, we have outlined relevant topics as a starting 
      point for discussion.
CI  - © 2023 CEA& INEA.
FAU - Khan, Naushad Ahmad
AU  - Khan NA
AD  - Clinical Research, Trauma and Vascular Surgery, Department of Surgery, Hamad 
      General Hospital, Doha, Qatar.
AD  - Faculty of Medical Sciences, Ala-Too International University, Bishkek, Kyrgyz 
      Republic.
FAU - Osmonaliev, Kudaibergen
AU  - Osmonaliev K
AD  - Faculty of Medical Sciences, Ala-Too International University, Bishkek, Kyrgyz 
      Republic.
FAU - Sarwar, Mohammad Zahed
AU  - Sarwar MZ
AD  - Faculty of Medical Sciences, Ala-Too International University, Bishkek, Kyrgyz 
      Republic.
LA  - eng
PT  - Journal Article
DEP - 20230331
PL  - Nepal
TA  - Nepal J Epidemiol
JT  - Nepal journal of epidemiology
JID - 101583212
PMC - PMC10225022
OTO - NOTNLM
OT  - ChatGTP
OT  - Online Artificial Intelligence
OT  - Scientific research
COIS- Competing interests There is no conflict of interest for any author of this 
      manuscript.
EDAT- 2023/05/30 13:07
MHDA- 2023/05/30 13:08
CRDT- 2023/05/30 11:24
PHST- 2023/01/01 00:00 [received]
PHST- 2023/03/25 00:00 [revised]
PHST- 2023/03/26 00:00 [accepted]
PHST- 2023/05/30 13:08 [medline]
PHST- 2023/05/30 13:07 [pubmed]
PHST- 2023/05/30 11:24 [entrez]
AID - 10.3126/nje.v13i1.53721 [doi]
PST - epublish
SO  - Nepal J Epidemiol. 2023 Mar 31;13(1):1258-1263. doi: 10.3126/nje.v13i1.53721. 
      eCollection 2023 Mar.

PMID- 37346223
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - Seizure-Induced Cardiomyopathy: A Case of Takotsubo Cardiomyopathy Following an 
      Epileptic Event.
PG  - e39288
LID - 10.7759/cureus.39288 [doi]
LID - e39288
AB  - We present a case, written with the assistance of the Chat Generative 
      Pre-training Transformer (ChatGPT) Artificial Intelligence (AI), of a 75-year-old 
      female with a history of hypertension, epilepsy, coronary artery disease, and 
      alcohol use disorder. She presented with a tonic-clonic seizure, tachycardia, and 
      a cyanotic right hand. Diagnostic tests revealed stress-induced cardiomyopathy, 
      patent bilateral subclavian and axillary arteries with heavy calcification of 
      bilateral upper extremity arteries, and a small filling defect in the segmental 
      branch of the left lower lobe. The patient was started on antiepileptic 
      medication, thiamine/folate, and heparin drip for limb ischemia. Despite 
      treatment with multiple anti-arrhythmic agents, the patient developed cardiogenic 
      shock and underwent left heart catheterization with Impella placement. The 
      Impella was removed 72 hours after placement, and the patient was started on 
      low-dose Milrinone and Levophed for hemodynamic support. The patient eventually 
      recovered and was discharged to long-term acute care.
CI  - Copyright © 2023, Yazdi et al.
FAU - Yazdi, Fereshteh
AU  - Yazdi F
AD  - Internal Medicine, Louisiana State University Health Sciences Center, Shreveport, 
      USA.
FAU - Blackmon, Melodie
AU  - Blackmon M
AD  - Critical Care Medicine, Louisiana State University Health Sciences Center, 
      Shreveport, USA.
FAU - Kattubadi, Ayeesha
AU  - Kattubadi A
AD  - Internal Medicine, Louisiana State University Health Sciences Center, Shreveport, 
      USA.
FAU - Krishnan, Prathik
AU  - Krishnan P
AD  - Pulmonary Critical Care, Louisiana State University Health Sciences Center, 
      Shreveport, USA.
LA  - eng
PT  - Case Reports
DEP - 20230521
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10281001
OTO - NOTNLM
OT  - alcohol use disorder (aud)
OT  - critical care cardiology
OT  - epilepsy disorders
OT  - impella device
OT  - takotsubo cardiomyopathy
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/22 13:08
MHDA- 2023/06/22 13:09
CRDT- 2023/06/22 09:47
PHST- 2023/05/21 00:00 [accepted]
PHST- 2023/06/22 13:09 [medline]
PHST- 2023/06/22 13:08 [pubmed]
PHST- 2023/06/22 09:47 [entrez]
AID - 10.7759/cureus.39288 [doi]
PST - epublish
SO  - Cureus. 2023 May 21;15(5):e39288. doi: 10.7759/cureus.39288. eCollection 2023 
      May.

PMID- 37949485
OWN - NLM
STAT- MEDLINE
DCOM- 20231113
LR  - 20231122
IS  - 2755-9734 (Electronic)
IS  - 2755-9734 (Linking)
VI  - 26
IP  - 1
DP  - 2023 Nov
TI  - ChatGPT and mental healthcare: balancing benefits with risks of harms.
LID - 10.1136/bmjment-2023-300884 [doi]
LID - e300884
AB  - Against the global need for increased access to mental services, health 
      organisations are looking to technological advances to improve the delivery of 
      care and lower costs. Since November 2022, with the public launch of OpenAI's 
      ChatGPT, the field of generative artificial intelligence (AI) has received 
      expanding attention. Although generative AI itself is not new, technical advances 
      and the increased accessibility of large language models (LLMs) (eg, OpenAI's 
      GPT-4 and Google's Bard) suggest use of these tools could be clinically 
      significant. LLMs are an application of generative AI technology that can 
      summarise and generate content based on training on vast data sets. Unlike search 
      engines, which provide internet links in response to typed entries, chatbots that 
      rely on generative language models can simulate dialogue that resembles human 
      conversations. We examine the potential promise and the risks of using LLMs in 
      mental healthcare today, focusing on their scope to impact mental healthcare, 
      including global equity in the delivery of care. Although we caution that LLMs 
      should not be used to disintermediate mental health clinicians, we signal how-if 
      carefully implemented-in the long term these tools could reap benefits for 
      patients and health professionals.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. 
      Published by BMJ.
FAU - Blease, Charlotte
AU  - Blease C
AUID- ORCID: 0000-0002-0205-1165
AD  - Participatory eHealth and Health Data Research Group, Department of Women's and 
      Children's Health, Uppsala Universitet, Uppsala, Sweden charlotte.blease@uu.se.
AD  - Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 
      Boston, Massachusetts, USA.
FAU - Torous, John
AU  - Torous J
AUID- ORCID: 0000-0002-5362-7937
AD  - Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, 
      Massachusetts, USA.
LA  - eng
PT  - Journal Article
PL  - England
TA  - BMJ Ment Health
JT  - BMJ mental health
JID - 9918521385306676
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Mental Health
MH  - Communication
MH  - Health Facilities
MH  - *Mental Health Services
PMC - PMC10649440
OTO - NOTNLM
OT  - Adult psychiatry
OT  - Depression & mood disorders
OT  - Machine Learning
COIS- Competing interests: None declared.
EDAT- 2023/11/11 11:43
MHDA- 2023/11/13 06:43
CRDT- 2023/11/10 20:53
PHST- 2023/09/15 00:00 [received]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/11/13 06:43 [medline]
PHST- 2023/11/11 11:43 [pubmed]
PHST- 2023/11/10 20:53 [entrez]
AID - bmjment-2023-300884 [pii]
AID - 10.1136/bmjment-2023-300884 [doi]
PST - ppublish
SO  - BMJ Ment Health. 2023 Nov;26(1):e300884. doi: 10.1136/bmjment-2023-300884.

PMID- 37792434
OWN - NLM
STAT- MEDLINE
DCOM- 20231005
LR  - 20231021
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Oct 4
TI  - Prompt Engineering as an Important Emerging Skill for Medical Professionals: 
      Tutorial.
PG  - e50638
LID - 10.2196/50638 [doi]
LID - e50638
AB  - Prompt engineering is a relatively new field of research that refers to the 
      practice of designing, refining, and implementing prompts or instructions that 
      guide the output of large language models (LLMs) to help in various tasks. With 
      the emergence of LLMs, the most popular one being ChatGPT that has attracted the 
      attention of over a 100 million users in only 2 months, artificial intelligence 
      (AI), especially generative AI, has become accessible for the masses. This is an 
      unprecedented paradigm shift not only because of the use of AI becoming more 
      widespread but also due to the possible implications of LLMs in health care. As 
      more patients and medical professionals use AI-based tools, LLMs being the most 
      popular representatives of that group, it seems inevitable to address the 
      challenge to improve this skill. This paper summarizes the current state of 
      research about prompt engineering and, at the same time, aims at providing 
      practical recommendations for the wide range of health care professionals to 
      improve their interactions with LLMs.
CI  - ©Bertalan Meskó. Originally published in the Journal of Medical Internet Research 
      (https://www.jmir.org), 04.10.2023.
FAU - Meskó, Bertalan
AU  - Meskó B
AUID- ORCID: 0000-0002-7005-7083
AD  - The Medical Futurist Institute, Budapest, Hungary.
LA  - eng
PT  - Journal Article
DEP - 20231004
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Engineering
MH  - Health Personnel
MH  - Language
PMC - PMC10585440
OTO - NOTNLM
OT  - AI
OT  - AI tool
OT  - ChatGPT
OT  - GPT-4
OT  - LLM
OT  - LLMs
OT  - NLP
OT  - artificial intelligence
OT  - chatbot
OT  - chatbots
OT  - conversational agent
OT  - conversational agents
OT  - decision-making
OT  - digital health
OT  - engineering
OT  - future
OT  - healthcare professional
OT  - language model
OT  - large language models
OT  - natural language processing
OT  - prompt
OT  - prompt engineering
OT  - prompts
OT  - technology
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/04 12:45
MHDA- 2023/10/05 06:43
CRDT- 2023/10/04 11:53
PHST- 2023/07/07 00:00 [received]
PHST- 2023/09/19 00:00 [accepted]
PHST- 2023/09/14 00:00 [revised]
PHST- 2023/10/05 06:43 [medline]
PHST- 2023/10/04 12:45 [pubmed]
PHST- 2023/10/04 11:53 [entrez]
AID - v25i1e50638 [pii]
AID - 10.2196/50638 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Oct 4;25:e50638. doi: 10.2196/50638.

PMID- 37292575
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230612
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - Primary Palmar Hyperhidrosis (PPH) Accompanied With Nevus Flammeus: A Case 
      Report.
PG  - e38723
LID - 10.7759/cureus.38723 [doi]
LID - e38723
AB  - Palmar hyperhidrosis (PH) is a medical condition characterized by excessive 
      sweating in the palms of the hands, which can result in significant distress and 
      impairment in daily activities. Flammeus nevus, on the other hand, is a benign 
      vascular lesion that appears as a red or purplish discoloration on the skin, 
      commonly found on the face, neck, or trunk. In some cases, flammeus nevus can 
      co-occur with PH, leading to increased sweating in the affected area. This 
      condition can cause significant psychosocial impact, affecting an individual's 
      quality of life (QoL) and self-esteem. We present a case report of a patient 
      presenting with PPH with flammeus nevus. There is currently limited information 
      available on the relationship between PH and flammeus nevus, and more research is 
      needed to better understand this phenomenon; here we have reported the 
      presentation of a patient. In conclusion, PH accompanied with flammeus nevus is a 
      condition that requires prompt attention and management to mitigate its adverse 
      effects. We have used ChatGPT to aid in structuring and writing this case report.
CI  - Copyright © 2023, Sam Saji et al.
FAU - Sam Saji, Alen
AU  - Sam Saji A
AD  - Internal Medicine, West China Hospital, West China Medical School, Sichuan 
      University, Chengdu, CHN.
FAU - Paudyal, Aliza
AU  - Paudyal A
AD  - Dermatology, B.P. Koirala Institute of Health Sciences, Dharan, NPL.
FAU - Audry De Souza, Veylenta
AU  - Audry De Souza V
AD  - Dermatology, West China Hospital, West China Medical School, Sichuan University, 
      Chengdu, CHN.
FAU - Soman Pillai Radhamoney Amma, Sandeep
AU  - Soman Pillai Radhamoney Amma S
AD  - Dermatology, West China Hospital, West China Medical School, Sichuan University, 
      Chengdu, CHN.
FAU - Prajwalita Rai, Nitya
AU  - Prajwalita Rai N
AD  - Dermatology, West China Hospital, West China Medical School, Sichuan University, 
      Chengdu, CHN.
LA  - eng
PT  - Case Reports
DEP - 20230508
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10246863
OTO - NOTNLM
OT  - chatgpt aided case report
OT  - dermatology
OT  - hyperhidrosis
OT  - nevus flammeus
OT  - palmar hyperhidrosis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/09 06:42
MHDA- 2023/06/09 06:43
CRDT- 2023/06/09 04:24
PHST- 2023/05/05 00:00 [accepted]
PHST- 2023/06/09 06:43 [medline]
PHST- 2023/06/09 06:42 [pubmed]
PHST- 2023/06/09 04:24 [entrez]
AID - 10.7759/cureus.38723 [doi]
PST - epublish
SO  - Cureus. 2023 May 8;15(5):e38723. doi: 10.7759/cureus.38723. eCollection 2023 May.

PMID- 37983846
OWN - NLM
STAT- Publisher
LR  - 20231120
IS  - 1531-4995 (Electronic)
IS  - 0023-852X (Linking)
DP  - 2023 Nov 20
TI  - Evaluation of Oropharyngeal Cancer Information from Revolutionary Artificial 
      Intelligence Chatbot.
LID - 10.1002/lary.31191 [doi]
AB  - OBJECTIVE: With burgeoning popularity of artificial intelligence-based chatbots, 
      oropharyngeal cancer patients now have access to a novel source of medical 
      information. Because chatbot information is not reviewed by experts, we sought to 
      evaluate an artificial intelligence-based chatbot's oropharyngeal cancer-related 
      information for accuracy. METHODS: Fifteen oropharyngeal cancer-related questions 
      were developed and input into ChatGPT version 3.5. Four physician-graders 
      independently assessed accuracy, comprehensiveness, and similarity to a physician 
      response using 5-point Likert scales. Responses graded lower than three were then 
      critiqued by physician-graders. Critiques were analyzed using inductive thematic 
      analysis. Readability of responses was assessed using Flesch Reading Ease (FRE) 
      and Flesch-Kincaid Reading Grade Level (FKRGL) scales. RESULTS: Average accuracy, 
      comprehensiveness, and similarity to a physician response scores were 3.88 
      (SD = 0.99), 3.80 (SD = 1.14), and 3.67 (SD = 1.08), respectively. 
      Posttreatment-related questions were most accurate, comprehensive, and similar to 
      a physician response, followed by treatment-related, then diagnosis-related 
      questions. Posttreatment-related questions scored significantly higher than 
      diagnosis-related questions in all three domains (p < 0.01). Two themes of the 
      physician critiques were identified: suboptimal education value and potential to 
      misinform patients. The mean FRE and FKRGL scores both indicated greater than an 
      11th grade readability level-higher than the 6th grade level recommended for 
      patients. CONCLUSION: ChatGPT responses may not educate patients to an 
      appropriate degree, could outright misinform them, and read at a more difficult 
      grade level than is recommended for patient material. As oropharyngeal cancer 
      patients represent a vulnerable population facing complex, life-altering 
      diagnoses, and treatments, they should be cautious when consuming 
      chatbot-generated medical information. LEVEL OF EVIDENCE: N/A Laryngoscope, 2023.
CI  - © 2023 The Authors. The Laryngoscope published by Wiley Periodicals LLC on behalf 
      of The American Laryngological, Rhinological and Otological Society, Inc.
FAU - Davis, Ryan J
AU  - Davis RJ
AUID- ORCID: 0009-0002-0408-8380
AD  - Keck School of Medicine of the University of Southern California, Los Angeles, 
      California, USA.
FAU - Ayo-Ajibola, Oluwatobiloba
AU  - Ayo-Ajibola O
AUID- ORCID: 0009-0002-4824-7160
AD  - Keck School of Medicine of the University of Southern California, Los Angeles, 
      California, USA.
FAU - Lin, Matthew E
AU  - Lin ME
AUID- ORCID: 0000-0001-9914-9670
AD  - Keck School of Medicine of the University of Southern California, Los Angeles, 
      California, USA.
FAU - Swanson, Mark S
AU  - Swanson MS
AD  - Caruso Department of Otolaryngology-Head & Neck Surgery, Keck School of Medicine 
      of the University of Southern California, Los Angeles, California, USA.
FAU - Chambers, Tamara N
AU  - Chambers TN
AD  - Caruso Department of Otolaryngology-Head & Neck Surgery, Keck School of Medicine 
      of the University of Southern California, Los Angeles, California, USA.
FAU - Kwon, Daniel I
AU  - Kwon DI
AUID- ORCID: 0000-0002-3574-4830
AD  - Caruso Department of Otolaryngology-Head & Neck Surgery, Keck School of Medicine 
      of the University of Southern California, Los Angeles, California, USA.
FAU - Kokot, Niels C
AU  - Kokot NC
AUID- ORCID: 0000-0001-7388-6373
AD  - Caruso Department of Otolaryngology-Head & Neck Surgery, Keck School of Medicine 
      of the University of Southern California, Los Angeles, California, USA.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - United States
TA  - Laryngoscope
JT  - The Laryngoscope
JID - 8607378
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - communication
OT  - head and neck cancer
EDAT- 2023/11/20 18:44
MHDA- 2023/11/20 18:44
CRDT- 2023/11/20 17:27
PHST- 2023/10/12 00:00 [revised]
PHST- 2023/08/16 00:00 [received]
PHST- 2023/11/03 00:00 [accepted]
PHST- 2023/11/20 18:44 [medline]
PHST- 2023/11/20 18:44 [pubmed]
PHST- 2023/11/20 17:27 [entrez]
AID - 10.1002/lary.31191 [doi]
PST - aheadofprint
SO  - Laryngoscope. 2023 Nov 20. doi: 10.1002/lary.31191.

PMID- 37553439
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230810
LR  - 20231119
IS  - 2052-4463 (Electronic)
IS  - 2052-4463 (Linking)
VI  - 10
IP  - 1
DP  - 2023 Aug 8
TI  - ThoughtSource: A central hub for large language model reasoning data.
PG  - 528
LID - 10.1038/s41597-023-02433-3 [doi]
LID - 528
AB  - Large language models (LLMs) such as GPT-4 have recently demonstrated impressive 
      results across a wide range of tasks. LLMs are still limited, however, in that 
      they frequently fail at complex reasoning, their reasoning processes are opaque, 
      they are prone to 'hallucinate' facts, and there are concerns about their 
      underlying biases. Letting models verbalize reasoning steps as natural language, 
      a technique known as chain-of-thought prompting, has recently been proposed as a 
      way to address some of these issues. Here we present ThoughtSource, a 
      meta-dataset and software library for chain-of-thought (CoT) reasoning. The goal 
      of ThoughtSource is to improve future artificial intelligence systems by 
      facilitating qualitative understanding of CoTs, enabling empirical evaluations, 
      and providing training data. This first release of ThoughtSource integrates seven 
      scientific/medical, three general-domain and five math word question answering 
      datasets.
CI  - © 2023. Springer Nature Limited.
FAU - Ott, Simon
AU  - Ott S
AD  - Institute of Artificial Intelligence, Medical University of Vienna, Vienna, 
      Austria.
FAU - Hebenstreit, Konstantin
AU  - Hebenstreit K
AUID- ORCID: 0009-0005-4604-0635
AD  - Institute of Artificial Intelligence, Medical University of Vienna, Vienna, 
      Austria.
FAU - Liévin, Valentin
AU  - Liévin V
AD  - Section for Cognitive Systems, Technical University of Denmark, Lyngby, Denmark.
FAU - Hother, Christoffer Egeberg
AU  - Hother CE
AD  - Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, 
      Denmark.
FAU - Moradi, Milad
AU  - Moradi M
AD  - Institute of Artificial Intelligence, Medical University of Vienna, Vienna, 
      Austria.
FAU - Mayrhauser, Maximilian
AU  - Mayrhauser M
AD  - Institute of Artificial Intelligence, Medical University of Vienna, Vienna, 
      Austria.
FAU - Praas, Robert
AU  - Praas R
AD  - Institute of Artificial Intelligence, Medical University of Vienna, Vienna, 
      Austria.
AD  - School of Electrical Engineering and Computer Science, The Royal Institute of 
      Technology (KTH), Stockholm, Sweden.
FAU - Winther, Ole
AU  - Winther O
AD  - Section for Cognitive Systems, Technical University of Denmark, Lyngby, Denmark.
FAU - Samwald, Matthias
AU  - Samwald M
AUID- ORCID: 0000-0002-4855-2571
AD  - Institute of Artificial Intelligence, Medical University of Vienna, Vienna, 
      Austria. matthias.samwald@meduniwien.ac.at.
LA  - eng
PT  - Dataset
PT  - Journal Article
DEP - 20230808
PL  - England
TA  - Sci Data
JT  - Scientific data
JID - 101640192
SB  - IM
PMC - PMC10409727
COIS- The authors declare no competing interests.
EDAT- 2023/08/09 01:05
MHDA- 2023/08/10 06:42
CRDT- 2023/08/08 23:23
PHST- 2023/02/28 00:00 [received]
PHST- 2023/07/31 00:00 [accepted]
PHST- 2023/08/10 06:42 [medline]
PHST- 2023/08/09 01:05 [pubmed]
PHST- 2023/08/08 23:23 [entrez]
AID - 10.1038/s41597-023-02433-3 [pii]
AID - 2433 [pii]
AID - 10.1038/s41597-023-02433-3 [doi]
PST - epublish
SO  - Sci Data. 2023 Aug 8;10(1):528. doi: 10.1038/s41597-023-02433-3.

PMID- 37306460
OWN - NLM
STAT- Publisher
LR  - 20230612
IS  - 1524-4040 (Electronic)
IS  - 0148-396X (Linking)
DP  - 2023 Jun 12
TI  - Performance of ChatGPT, GPT-4, and Google Bard on a Neurosurgery Oral Boards 
      Preparation Question Bank.
LID - 10.1227/neu.0000000000002551 [doi]
AB  - BACKGROUND AND OBJECTIVES: General large language models (LLMs), such as ChatGPT 
      (GPT-3.5), have demonstrated the capability to pass multiple-choice medical board 
      examinations. However, comparative accuracy of different LLMs and LLM performance 
      on assessments of predominantly higher-order management questions is poorly 
      understood. We aimed to assess the performance of 3 LLMs (GPT-3.5, GPT-4, and 
      Google Bard) on a question bank designed specifically for neurosurgery oral 
      boards examination preparation. METHODS: The 149-question Self-Assessment 
      Neurosurgery Examination Indications Examination was used to query LLM accuracy. 
      Questions were inputted in a single best answer, multiple-choice format. χ2, 
      Fisher exact, and univariable logistic regression tests assessed differences in 
      performance by question characteristics. RESULTS: On a question bank with 
      predominantly higher-order questions (85.2%), ChatGPT (GPT-3.5) and GPT-4 
      answered 62.4% (95% CI: 54.1%-70.1%) and 82.6% (95% CI: 75.2%-88.1%) of questions 
      correctly, respectively. By contrast, Bard scored 44.2% (66/149, 95% CI: 
      36.2%-52.6%). GPT-3.5 and GPT-4 demonstrated significantly higher scores than 
      Bard (both P < .01), and GPT-4 outperformed GPT-3.5 (P = .023). Among 6 
      subspecialties, GPT-4 had significantly higher accuracy in the Spine category 
      relative to GPT-3.5 and in 4 categories relative to Bard (all P < .01). 
      Incorporation of higher-order problem solving was associated with lower question 
      accuracy for GPT-3.5 (odds ratio [OR] = 0.80, P = .042) and Bard (OR = 0.76, P = 
      .014), but not GPT-4 (OR = 0.86, P = .085). GPT-4's performance on 
      imaging-related questions surpassed GPT-3.5's (68.6% vs 47.1%, P = .044) and was 
      comparable with Bard's (68.6% vs 66.7%, P = 1.000). However, GPT-4 demonstrated 
      significantly lower rates of "hallucination" on imaging-related questions than 
      both GPT-3.5 (2.3% vs 57.1%, P < .001) and Bard (2.3% vs 27.3%, P = .002). Lack 
      of question text description for questions predicted significantly higher odds of 
      hallucination for GPT-3.5 (OR = 1.45, P = .012) and Bard (OR = 2.09, P < .001). 
      CONCLUSION: On a question bank of predominantly higher-order management case 
      scenarios for neurosurgery oral boards preparation, GPT-4 achieved a score of 
      82.6%, outperforming ChatGPT and Google Bard.
CI  - Copyright © Congress of Neurological Surgeons 2023. All rights reserved.
FAU - Ali, Rohaid
AU  - Ali R
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Tang, Oliver Y
AU  - Tang OY
AUID- ORCID: 0000-0002-8604-2708
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Connolly, Ian D
AU  - Connolly ID
AD  - Department of Neurosurgery, Massachusetts General Hospital, Boston, 
      Massachusetts, USA.
FAU - Fridley, Jared S
AU  - Fridley JS
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Shin, John H
AU  - Shin JH
AD  - Department of Neurosurgery, Massachusetts General Hospital, Boston, 
      Massachusetts, USA.
FAU - Zadnik Sullivan, Patricia L
AU  - Zadnik Sullivan PL
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Cielo, Deus
AU  - Cielo D
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Oyelese, Adetokunbo A
AU  - Oyelese AA
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Doberstein, Curtis E
AU  - Doberstein CE
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Telfeian, Albert E
AU  - Telfeian AE
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Gokaslan, Ziya L
AU  - Gokaslan ZL
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
FAU - Asaad, Wael F
AU  - Asaad WF
AD  - Department of Neurosurgery, The Warren Alpert Medical School of Brown University, 
      Providence, Rhode Island, USA.
AD  - Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, Rhode 
      Island, USA.
AD  - Department of Neuroscience, Brown University, Providence, Rhode Island, USA.
AD  - Carney Institute for Brain Science, Brown University, Providence, Rhode Island, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230612
PL  - United States
TA  - Neurosurgery
JT  - Neurosurgery
JID - 7802914
SB  - IM
EDAT- 2023/06/12 13:06
MHDA- 2023/06/12 13:06
CRDT- 2023/06/12 09:04
PHST- 2023/03/16 00:00 [received]
PHST- 2023/04/09 00:00 [accepted]
PHST- 2023/06/12 13:06 [medline]
PHST- 2023/06/12 13:06 [pubmed]
PHST- 2023/06/12 09:04 [entrez]
AID - 00006123-990000000-00775 [pii]
AID - 10.1227/neu.0000000000002551 [doi]
PST - aheadofprint
SO  - Neurosurgery. 2023 Jun 12. doi: 10.1227/neu.0000000000002551.

PMID- 37913954
OWN - NLM
STAT- Publisher
LR  - 20231104
IS  - 1879-0887 (Electronic)
IS  - 0167-8140 (Linking)
VI  - 190
DP  - 2023 Oct 30
TI  - Pilot applications of GPT-4 in radiation oncology: Summarizing patient symptom 
      intake and targeted chatbot applications.
PG  - 109978
LID - S0167-8140(23)89872-3 [pii]
LID - 10.1016/j.radonc.2023.109978 [doi]
AB  - This study explores using GPT-4 for radiation toxicity monitoring in prostate 
      cancer treatments. Two methods were tested: a summarization method and a chatbot 
      interface. Surveyed radiation oncologists preferred the summarization method for 
      its accuracy and potential for adoption (median rating 8 vs 4, p =.002). Both 
      methods saved time.
CI  - Copyright © 2023 Elsevier B.V. All rights reserved.
FAU - Wu, David Jh
AU  - Wu DJ
AD  - Department of Radiation Oncology, University of Minnesota Medical School, 
      Minneapolis, MN, USA.
FAU - Bibault, Jean-Emmanuel
AU  - Bibault JE
AD  - Radiation Oncology Department, Georges Pompidou European Hospital, 75015, Paris, 
      France; INSERM UMR1138, Centre de Recherche des Cordeliers, 75006, Paris, France. 
      Electronic address: jean-emmanuel.bibault@aphp.fr.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - Ireland
TA  - Radiother Oncol
JT  - Radiotherapy and oncology : journal of the European Society for Therapeutic 
      Radiology and Oncology
JID - 8407192
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Machine Learning
OT  - Toxicity
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/11/02 00:42
MHDA- 2023/11/02 00:42
CRDT- 2023/11/01 20:28
PHST- 2023/08/29 00:00 [received]
PHST- 2023/10/13 00:00 [revised]
PHST- 2023/10/27 00:00 [accepted]
PHST- 2023/11/02 00:42 [pubmed]
PHST- 2023/11/02 00:42 [medline]
PHST- 2023/11/01 20:28 [entrez]
AID - S0167-8140(23)89872-3 [pii]
AID - 10.1016/j.radonc.2023.109978 [doi]
PST - aheadofprint
SO  - Radiother Oncol. 2023 Oct 30;190:109978. doi: 10.1016/j.radonc.2023.109978.

PMID- 36786282
OWN - NLM
STAT- Publisher
LR  - 20230221
IS  - 1545-5815 (Electronic)
IS  - 0898-9621 (Linking)
DP  - 2023 Feb 21
TI  - Letter to editor: Academic journals should clarify the proportion of 
      NLP-generated content in papers.
PG  - 1-2
LID - 10.1080/08989621.2023.2180359 [doi]
AB  - This letter to the editor argues that if academic journals are willing to accept 
      papers that include NLP-generated content under certain conditions, editorial 
      policies should clarify the proportion of NLP-generated content in the paper. 
      Excessive use of NLP-generated content should be considered as academic 
      misconduct.
FAU - Tang, Gengyan
AU  - Tang G
AUID- ORCID: 0000-0003-3221-3134
AD  - Institute of Journalism and Communication, Sichuan Academy of Social Sciences.
LA  - eng
PT  - Journal Article
DEP - 20230221
PL  - United States
TA  - Account Res
JT  - Accountability in research
JID - 9100813
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - Misconduct in research
OT  - NLP system
OT  - Research ethics
OT  - Research integrity
EDAT- 2023/02/15 06:00
MHDA- 2023/02/15 06:00
CRDT- 2023/02/14 05:44
PHST- 2023/02/15 06:00 [pubmed]
PHST- 2023/02/15 06:00 [medline]
PHST- 2023/02/14 05:44 [entrez]
AID - 10.1080/08989621.2023.2180359 [doi]
PST - aheadofprint
SO  - Account Res. 2023 Feb 21:1-2. doi: 10.1080/08989621.2023.2180359.

PMID- 36699746
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230501
IS  - 2666-3899 (Electronic)
IS  - 2666-3899 (Linking)
VI  - 4
IP  - 1
DP  - 2023 Jan 13
TI  - This new conversational AI model can be your friend, philosopher, and guide ... 
      and even your worst enemy.
PG  - 100676
LID - 10.1016/j.patter.2022.100676 [doi]
LID - 100676
AB  - We explore the recently released ChatGPT model, one of the most powerful 
      conversational AI models that has ever been developed. This opinion provides a 
      perspective on its strengths and weaknesses and a call to action for the AI 
      community (including academic researchers and industry) to work together on 
      preventing potential misuse of such powerful AI models in our everyday lives.
CI  - © 2022 The Author(s).
FAU - Chatterjee, Joyjit
AU  - Chatterjee J
AD  - University of Hull, School of Computer Science, Cottingham Road, Hull, HU6 7RX, 
      UK.
FAU - Dethlefs, Nina
AU  - Dethlefs N
AD  - University of Hull, School of Computer Science, Cottingham Road, Hull, HU6 7RX, 
      UK.
LA  - eng
PT  - News
DEP - 20230113
PL  - United States
TA  - Patterns (N Y)
JT  - Patterns (New York, N.Y.)
JID - 101767765
CIN - Patterns (N Y). 2023 Apr 14;4(4):100731. PMID: 37123441
PMC - PMC9868650
COIS- The authors declare no competing interests.
EDAT- 2023/01/27 06:00
MHDA- 2023/01/27 06:01
CRDT- 2023/01/26 02:49
PHST- 2023/01/26 02:49 [entrez]
PHST- 2023/01/27 06:00 [pubmed]
PHST- 2023/01/27 06:01 [medline]
AID - S2666-3899(22)00323-3 [pii]
AID - 100676 [pii]
AID - 10.1016/j.patter.2022.100676 [doi]
PST - epublish
SO  - Patterns (N Y). 2023 Jan 13;4(1):100676. doi: 10.1016/j.patter.2022.100676. 
      eCollection 2023 Jan 13.

PMID- 37776949
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1877-0568 (Electronic)
IS  - 1877-0568 (Linking)
VI  - 109
IP  - 8
DP  - 2023 Dec
TI  - What is the rate of text generated by artificial intelligence over a year of 
      publication in Orthopedics & Traumatology: Surgery & Research? Analysis of 425 
      articles before versus after the launch of ChatGPT in November 2022.
PG  - 103694
LID - S1877-0568(23)00204-9 [pii]
LID - 10.1016/j.otsr.2023.103694 [doi]
AB  - BACKGROUND: The use of artificial intelligence (AI) is soaring, and the launch of 
      ChatGPT in November 2022 has accelerated this trend. This "chatbot" can generate 
      complete scientific articles, with risk of plagiarism by mining existing data or 
      downright fraud by fabricating studies with no real data at all. There are tools 
      that detect AI in publications, but to our knowledge they have not been 
      systematically assessed for publication in scientific journals. We therefore 
      conducted a retrospective study on articles published in Orthopaedics & 
      Traumatology: Surgery & Research (OTSR): firstly, to screen for AI-generated 
      content before and after the publicized launch of ChatGPT; secondly, to assess 
      whether AI was more often used in some countries than others to generate content; 
      thirdly, to determine whether plagiarism rate correlated with AI-generation, and 
      lastly, to determine whether elements other than text generation, and notably the 
      translation procedure, could raise suspicion of AI use. HYPOTHESIS: The rate of 
      AI use increased after the publicized launch of ChatGPT v3.5 in November 2022. 
      MATERIAL AND METHODS: In all, 425 articles published between February 2022 and 
      September 2023 (221 before and 204 after November 1, 2022) underwent ZeroGPT 
      assessment of the level of AI generation in the final English-language version 
      (abstract and body of the article). Two scores were obtained: probability of AI 
      generation, in six grades from Human to AI; and percentage AI generation. 
      Plagiarism was assessed on the Ithenticate application at submission. Articles in 
      French were assessed in their English-language version as translated by a human 
      translator, with comparison to automatic translation by Google Translate and 
      DeepL. RESULTS: AI-generated text was detected mainly in Abstracts, with a 10.1% 
      rate of AI or considerable AI generation, compared to only 1.9% for the body of 
      the article and 5.6% for the total body+abstract. Analysis for before and after 
      November 2022 found an increase in AI generation in body+abstract, from 
      10.30±15.95% (range, 0-100%) to 15.64±19.8% (range, 0-99.93) (p < 0.04; NS for 
      abstracts alone). AI scores differed between types of article: 14.9% for original 
      articles and 9.8% for reviews (p<0.01). The highest rates of probable AI 
      generation were in articles from Japan, China, South America and English-speaking 
      countries (p<0.0001). Plagiarism rates did not increase between the two study 
      periods, and were unrelated to AI rates. On the other hand, when articles were 
      classified as "suspected" of AI generation (plagiarism rate ≥ 20%) or 
      "non-suspected" (rate<20%), the "similarity" score was higher in suspect 
      articles: 25.7±13.23% (range, 10-69%) versus 16.28±10% (range, 0-79%) 
      (p < 0.001). In the body of the article, use of translation software was 
      associated with higher AI rates than with a human translator: 3.5±5% for human 
      translators, versus 18±10% and 21.9±11% respectively for Google Translate and 
      DeepL (p < 0.001). DISCUSSION: The present study revealed an increasing rate of 
      AI use in articles published in OTSR. AI grades differed according to type of 
      article and country of origin. Use of translation software increased the AI 
      grade. In the long run, use of ChatGPT incurs a risk of plagiarism and scientific 
      misconduct, and needs to be detected and signaled by a digital tag on any 
      robot-generated text. LEVEL OF EVIDENCE: III; case-control study.
CI  - Copyright © 2023. Published by Elsevier Masson SAS.
FAU - Bisi, Théophile
AU  - Bisi T
AD  - Département universitaire de chirurgie orthopédique, université de Lille, CHU de 
      Lille, 59000 Lille, France; Service de chirurgie orthopédique, centre hospitalier 
      universitaire (CHU) de Lille, hôpital Roger-Salengro, place de Verdun, 59000 
      Lille, France. Electronic address: bisitheophile110@gmail.com.
FAU - Risser, Anthony
AU  - Risser A
AD  - Service de chirurgie du membre supérieur, Hautepierre 2, CHRU Strasbourg, 1, 
      avenue Molière, 67200 Strasbourg, France.
FAU - Clavert, Philippe
AU  - Clavert P
AD  - Service de chirurgie du membre supérieur, Hautepierre 2, CHRU Strasbourg, 1, 
      avenue Molière, 67200 Strasbourg, France; Faculté de médecine, institut 
      d'anatomie normale, 4, rue Kirschleger, 67085 Strasbourg, France.
FAU - Migaud, Henri
AU  - Migaud H
AD  - Département universitaire de chirurgie orthopédique, université de Lille, CHU de 
      Lille, 59000 Lille, France; Service de chirurgie orthopédique, centre hospitalier 
      universitaire (CHU) de Lille, hôpital Roger-Salengro, place de Verdun, 59000 
      Lille, France.
FAU - Dartus, Julien
AU  - Dartus J
AD  - Département universitaire de chirurgie orthopédique, université de Lille, CHU de 
      Lille, 59000 Lille, France; Service de chirurgie orthopédique, centre hospitalier 
      universitaire (CHU) de Lille, hôpital Roger-Salengro, place de Verdun, 59000 
      Lille, France.
LA  - eng
PT  - Journal Article
DEP - 20230929
PL  - France
TA  - Orthop Traumatol Surg Res
JT  - Orthopaedics & traumatology, surgery & research : OTSR
JID - 101494830
SB  - IM
MH  - Humans
MH  - *Orthopedics
MH  - *Traumatology
MH  - Retrospective Studies
MH  - Artificial Intelligence
MH  - Case-Control Studies
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Fraud
OT  - Intellectual property
OT  - Plagiarism
OT  - Scientific misconduct
EDAT- 2023/10/01 04:44
MHDA- 2023/12/04 12:42
CRDT- 2023/09/30 19:12
PHST- 2023/04/17 00:00 [received]
PHST- 2023/07/10 00:00 [revised]
PHST- 2023/08/24 00:00 [accepted]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/10/01 04:44 [pubmed]
PHST- 2023/09/30 19:12 [entrez]
AID - S1877-0568(23)00204-9 [pii]
AID - 10.1016/j.otsr.2023.103694 [doi]
PST - ppublish
SO  - Orthop Traumatol Surg Res. 2023 Dec;109(8):103694. doi: 
      10.1016/j.otsr.2023.103694. Epub 2023 Sep 29.

PMID- 37873417
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
DP  - 2023 Oct 3
TI  - A Large Language Model-Based Generative Natural Language Processing Framework 
      Finetuned on Clinical Notes Accurately Extracts Headache Frequency from 
      Electronic Health Records.
LID - 2023.10.02.23296403 [pii]
LID - 10.1101/2023.10.02.23296403 [doi]
AB  - BACKGROUND: Headache frequency, defined as the number of days with any headache 
      in a month (or four weeks), remains a key parameter in the evaluation of 
      treatment response to migraine preventive medications. However, due to the 
      variations and inconsistencies in documentation by clinicians, significant 
      challenges exist to accurately extract headache frequency from the electronic 
      health record (EHR) by traditional natural language processing (NLP) algorithms. 
      METHODS: This was a retrospective cross-sectional study with human subjects 
      identified from three tertiary headache referral centers- Mayo Clinic Arizona, 
      Florida, and Rochester. All neurology consultation notes written by more than 10 
      headache specialists between 2012 to 2022 were extracted and 1915 notes were used 
      for model fine-tuning (90%) and testing (10%). We employed four different NLP 
      frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from 
      Transformers) regression model (2) Generative Pre-Trained Transformer-2 (GPT-2) 
      Question Answering (QA) Model zero-shot (3) GPT-2 QA model few-shot training 
      fine-tuned on Mayo Clinic notes; and (4) GPT-2 generative model few-shot training 
      fine-tuned on Mayo Clinic notes to generate the answer by considering the context 
      of included text. RESULTS: The GPT-2 generative model was the best-performing 
      model with an accuracy of 0.92[0.91 - 0.93] and R(2) score of 0.89[0.87, 0.9], 
      and all GPT2-based models outperformed the ClinicalBERT model in terms of the 
      exact matching accuracy. Although the ClinicalBERT regression model had the 
      lowest accuracy 0.27[0.26 - 0.28], it demonstrated a high R(2) score 0.88[0.85, 
      0.89], suggesting the ClinicalBERT model can reasonably predict the headache 
      frequency within a range of ≤ ± 3 days, and the R(2) score was higher than the 
      GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. 
      CONCLUSION: We developed a robust model based on a state-of-the-art large 
      language model (LLM)- a GPT-2 generative model that can extract headache 
      frequency from EHR free-text clinical notes with high accuracy and R(2) score. It 
      overcame several challenges related to different ways clinicians document 
      headache frequency that were not easily achieved by traditional NLP models. We 
      also showed that GPT2-based frameworks outperformed ClinicalBERT in terms of 
      accuracy in extracting headache frequency from clinical notes. To facilitate 
      research in the field, we released the GPT-2 generative model and inference code 
      with open-source license of community use in GitHub.
FAU - Chiang, Chia-Chun
AU  - Chiang CC
AD  - Department of Neurology, Mayo Clinic, Rochester, MN.
FAU - Luo, Man
AU  - Luo M
AD  - Department of Radiology, Mayo Clinic, Phoenix, AZ.
FAU - Dumkrieger, Gina
AU  - Dumkrieger G
AD  - Department of Neurology, Mayo Clinic, Phoenix, AZ.
FAU - Trivedi, Shubham
AU  - Trivedi S
AD  - Department of Radiology, Mayo Clinic, Phoenix, AZ.
FAU - Chen, Yi-Chieh
AU  - Chen YC
AD  - Department of Pharmacy, Mayo Clinic, Rochester, MN.
FAU - Chao, Chieh-Ju
AU  - Chao CJ
AD  - Department of Cardiology, Mayo Clinic, Rochester, MN.
FAU - Schwedt, Todd J
AU  - Schwedt TJ
AD  - Department of Neurology, Mayo Clinic, Phoenix, AZ.
FAU - Sarker, Abeed
AU  - Sarker A
AD  - Department of Biomedical Informatics, School of Medicine, Emory University, 
      Atlanta, GA.
FAU - Banerjee, Imon
AU  - Banerjee I
AD  - Department of Radiology, Mayo Clinic, Phoenix, AZ.
AD  - School of Computing and Augmented Intelligence, Arizona State University.
LA  - eng
GR  - U01 CA269264/CA/NCI NIH HHS/United States
PT  - Preprint
DEP - 20231003
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10593021
OTO - NOTNLM
OT  - artificial intelligence
OT  - headache frequency
OT  - large language model
OT  - migraine
OT  - natural language processing
EDAT- 2023/10/24 06:41
MHDA- 2023/10/24 06:42
CRDT- 2023/10/24 03:57
PHST- 2023/10/24 06:42 [medline]
PHST- 2023/10/24 06:41 [pubmed]
PHST- 2023/10/24 03:57 [entrez]
AID - 2023.10.02.23296403 [pii]
AID - 10.1101/2023.10.02.23296403 [doi]
PST - epublish
SO  - medRxiv. 2023 Oct 3:2023.10.02.23296403. doi: 10.1101/2023.10.02.23296403. 
      Preprint.

PMID- 37093624
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230511
IS  - 2368-7959 (Print)
IS  - 2368-7959 (Electronic)
IS  - 2368-7959 (Linking)
VI  - 10
DP  - 2023 Apr 24
TI  - Focusing on Digital Research Priorities for Advancing the Access and Quality of 
      Mental Health.
PG  - e47898
LID - 10.2196/47898 [doi]
LID - e47898
AB  - Digital mental health solutions are now well recognized as critical to solving 
      the global mental health crisis. As research accelerates, it is now clear that 
      solutions ranging from computer-based therapy programs to virtual reality 
      headsets and smartphone apps to large language model chatbots are of interest, 
      feasible, and hold exciting potential to improve mental health. This research 
      should now consider the next generation of scientific and clinical questions 
      regarding if these new approaches are equitable, valid, effective, implementable, 
      efficacious, and even cost-effective. This paper outlines several of the new 
      frontiers for the next generation of research and introduces JMIR Publications' 
      partnership with the Society of Digital Psychiatry to further advance these aims.
CI  - ©John Torous, Nicole M Benson, Keris Myrick, Gunther Eysenbach. Originally 
      published in JMIR Mental Health (https://mental.jmir.org), 24.04.2023.
FAU - Torous, John
AU  - Torous J
AUID- ORCID: 0000-0002-5362-7937
AD  - Beth Israel Deaoness Medical Center, Boston, MA, United States.
FAU - Benson, Nicole M
AU  - Benson NM
AUID- ORCID: 0000-0001-5361-4311
AD  - McLean Hospital, Belmont, MA, United States.
FAU - Myrick, Keris
AU  - Myrick K
AUID- ORCID: 0000-0002-1408-6824
AD  - Beth Israel Deaoness Medical Center, Boston, MA, United States.
AD  - Inseperable, Washington, DC, United States.
FAU - Eysenbach, Gunther
AU  - Eysenbach G
AUID- ORCID: 0000-0001-6479-5330
AD  - JMIR Publications, Toronto, ON, Canada.
LA  - eng
PT  - Editorial
DEP - 20230424
PL  - Canada
TA  - JMIR Ment Health
JT  - JMIR mental health
JID - 101658926
PMC - PMC10167575
OTO - NOTNLM
OT  - anxiety
OT  - depression
OT  - digital phenotyping
OT  - mental health
OT  - smartphone
COIS- Conflicts of Interest: JT is the editor in chief of JMIR Mental Health at the 
      time of this publication and is a scientific advisor for Precision Mental 
      Wellness. NB volunteers on the Epic Behavioral Health Specialty Steering Board. 
      NB and KM serve on the editorial board for JMIR Mental Health at the time of this 
      publication. GE is the founder, executive editor, and publisher at JMIR 
      Publications, receives a salary, and owns equity.
EDAT- 2023/04/24 12:41
MHDA- 2023/04/24 12:42
CRDT- 2023/04/24 11:52
PHST- 2023/04/04 00:00 [received]
PHST- 2023/04/17 00:00 [accepted]
PHST- 2023/04/24 12:42 [medline]
PHST- 2023/04/24 12:41 [pubmed]
PHST- 2023/04/24 11:52 [entrez]
AID - v10i1e47898 [pii]
AID - 10.2196/47898 [doi]
PST - epublish
SO  - JMIR Ment Health. 2023 Apr 24;10:e47898. doi: 10.2196/47898.

PMID- 37516791
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230801
LR  - 20230822
IS  - 2052-4463 (Electronic)
IS  - 2052-4463 (Linking)
VI  - 10
IP  - 1
DP  - 2023 Jul 29
TI  - A Dataset of 10,000 Situations for Research in Computational Social Sciences 
      Psychology and the Humanities.
PG  - 505
LID - 10.1038/s41597-023-02406-6 [doi]
LID - 505
AB  - It has been realized that situational dimensions, as represented by human beings, 
      are crucial for understanding human behavior. The Riverside Situational Q (RSQ) 
      is a tool that measures the psychological properties of situations. However, the 
      RSQ-4 includes only 90 items and may have limited use for researchers interested 
      in measuring situational dimensions using a computational approach. Here we 
      present a corpus of 10,000 artificially generated situations corresponding mostly 
      with the RSQ-4. The dataset was generated using GPT, the state-of-the-art large 
      language model. The dataset validity is established through inter-judge 
      reliability, and four experiments on large datasets support its quality. The 
      dataset and the code used for generating 100 situational dimensions may be useful 
      for researchers interested in measuring situational dimensions in textual data.
CI  - © 2023. The Author(s).
FAU - Neuman, Yair
AU  - Neuman Y
AD  - Head, The Functor Lab, Department of Cognitive and Brain Science, Ben-Gurion 
      University of the Negev, Beer-Sheva, 84105, Israel. yneuman@bgu.ac.il.
FAU - Cohen, Yochai
AU  - Cohen Y
AD  - Gilasio coding, Tel-Aviv, Israel.
LA  - eng
GR  - 27302C0031/ES/NIEHS NIH HHS/United States
PT  - Dataset
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, P.H.S.
DEP - 20230729
PL  - England
TA  - Sci Data
JT  - Scientific data
JID - 101640192
SB  - IM
PMC - PMC10387079
COIS- The authors declare no competing interests.
EDAT- 2023/07/30 01:06
MHDA- 2023/07/31 11:42
CRDT- 2023/07/29 23:20
PHST- 2023/05/30 00:00 [received]
PHST- 2023/07/24 00:00 [accepted]
PHST- 2023/07/31 11:42 [medline]
PHST- 2023/07/30 01:06 [pubmed]
PHST- 2023/07/29 23:20 [entrez]
AID - 10.1038/s41597-023-02406-6 [pii]
AID - 2406 [pii]
AID - 10.1038/s41597-023-02406-6 [doi]
PST - epublish
SO  - Sci Data. 2023 Jul 29;10(1):505. doi: 10.1038/s41597-023-02406-6.

PMID- 36960451
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230501
IS  - 2666-3899 (Electronic)
IS  - 2666-3899 (Linking)
VI  - 4
IP  - 3
DP  - 2023 Mar 10
TI  - AI-generated research paper fabrication and plagiarism in the scientific 
      community.
PG  - 100706
LID - 10.1016/j.patter.2023.100706 [doi]
LID - 100706
AB  - Fabricating research within the scientific community has consequences for one's 
      credibility and undermines honest authors. We demonstrate the feasibility of 
      fabricating research using an AI-based language model chatbot. Human detection 
      versus AI detection will be compared to determine accuracy in identifying 
      fabricated works. The risks of utilizing AI-generated research works will be 
      underscored and reasons for falsifying research will be highlighted.
CI  - © 2023 The Author(s).
FAU - Elali, Faisal R
AU  - Elali FR
AD  - State University of New York Downstate Health Sciences University, College of 
      Medicine, New York 11203, NY, USA.
FAU - Rachid, Leena N
AU  - Rachid LN
AD  - Loyola University Medical Center, College of Medicine, Maywood, IL, USA.
LA  - eng
PT  - News
DEP - 20230310
PL  - United States
TA  - Patterns (N Y)
JT  - Patterns (New York, N.Y.)
JID - 101767765
CIN - Patterns (N Y). 2023 Apr 14;4(4):100731. PMID: 37123441
PMC - PMC10028415
OTO - NOTNLM
OT  - ChatGPT
OT  - PlaygroundAI
OT  - artificial intelligence
OT  - chatbox
OT  - fabrication
OT  - medicine
OT  - plagiarism
COIS- The authors declare no competing interests.
EDAT- 2023/03/25 06:00
MHDA- 2023/03/25 06:01
CRDT- 2023/03/24 02:31
PHST- 2023/03/24 02:31 [entrez]
PHST- 2023/03/25 06:00 [pubmed]
PHST- 2023/03/25 06:01 [medline]
AID - S2666-3899(23)00043-0 [pii]
AID - 100706 [pii]
AID - 10.1016/j.patter.2023.100706 [doi]
PST - epublish
SO  - Patterns (N Y). 2023 Mar 10;4(3):100706. doi: 10.1016/j.patter.2023.100706. 
      eCollection 2023 Mar 10.

PMID- 37928115
OWN - NLM
STAT- MEDLINE
DCOM- 20231107
LR  - 20231107
IS  - 1558-9439 (Electronic)
IS  - 1536-5050 (Print)
IS  - 1536-5050 (Linking)
VI  - 111
IP  - 4
DP  - 2023 Oct 2
TI  - Introducing the Journal of the Medical Library Association's policy on the use of 
      generative artificial intelligence in submissions.
PG  - 747-749
LID - 10.5195/jmla.2023.1826 [doi]
AB  - With the arrival of ChatGPT, the academic community has expressed concerns about 
      how generative artificial intelligence will be used by students and researchers 
      alike. After consulting policies from other journals and discussing among the 
      editorial team, we have created a policy on the use of AI on submissions to JMLA. 
      This editorial provides a brief background on these concerns and introduces our 
      policy.
CI  - Copyright © 2023 Jill T. Boruff, AHIP, Michelle Kraft, AHIP, FMLA, Alexander J. 
      Carrol, AHIP.
FAU - Boruff, Jill T
AU  - Boruff JT
AUID- ORCID: 0000-0002-0338-7322
AD  - jill.boruff@mcgill.ca, Co-Lead Editor, Journal of the Medical Library 
      Association, Associate Librarian, Schulich Library of Physical Sciences, Life 
      Sciences, and Engineering, McGill University, Montreal, QC, Canada.
FAU - Kraft, Michelle
AU  - Kraft M
AD  - kraftm@ccf.org, Co-Lead Editor, Journal of the Medical Library Association, 
      Medical Library Director, Cleveland Clinic Foundation, Cleveland, OH, United 
      States.
FAU - Carroll, Alexander J
AU  - Carroll AJ
AUID- ORCID: 0000-0003-0248-3811
AD  - alexander.j.carroll@vanderbilt.edu, Associate Director, Stevenson Science and 
      Engineering Library, Vanderbilt University, Nashville, TN, United States.
LA  - eng
PT  - Editorial
PL  - United States
TA  - J Med Libr Assoc
JT  - Journal of the Medical Library Association : JMLA
JID - 101132728
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Library Associations
MH  - *Libraries, Medical
MH  - Policy
MH  - Research Personnel
PMC - PMC10621693
EDAT- 2023/11/06 06:42
MHDA- 2023/11/07 06:46
CRDT- 2023/11/06 04:28
PHST- 2023/11/07 06:46 [medline]
PHST- 2023/11/06 06:42 [pubmed]
PHST- 2023/11/06 04:28 [entrez]
AID - jmla.2023.1826 [pii]
AID - 10.5195/jmla.2023.1826 [doi]
PST - ppublish
SO  - J Med Libr Assoc. 2023 Oct 2;111(4):747-749. doi: 10.5195/jmla.2023.1826.

PMID- 36892853
OWN - NLM
STAT- MEDLINE
DCOM- 20230313
LR  - 20230313
IS  - 1945-404X (Electronic)
IS  - 0090-7421 (Linking)
VI  - 52
IP  - 1
DP  - 2023 Spring
TI  - Technological Impacts on the Sphere of Professional Journals.
PG  - 1
AB  - Artificial intelligence (AI) technologies to help authors improve the preparation 
      and quality of their manuscripts are increasing rapidly in number and 
      sophistication, including tools to assist with writing, grammar, language, 
      references, statistical analysis, and reporting standards. The release of 
      ChatGPT, a new open source, natural language processing tool that is designed to 
      simulate human conversation in response to prompts or questions, has prompted 
      both excitement and concerns about potential misuse.
FAU - Elwood, Thomas W
AU  - Elwood TW
AD  - Association of Schools Advancing Health Professions, Washington, DC 20001, USA. 
      thomas@asahp.org.
LA  - eng
PT  - Editorial
PL  - United States
TA  - J Allied Health
JT  - Journal of allied health
JID - 0361603
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Periodicals as Topic
MH  - Writing
MH  - Communication
EDAT- 2023/03/10 06:00
MHDA- 2023/03/14 06:00
CRDT- 2023/03/09 11:42
PHST- 2023/03/09 11:42 [entrez]
PHST- 2023/03/10 06:00 [pubmed]
PHST- 2023/03/14 06:00 [medline]
PST - ppublish
SO  - J Allied Health. 2023 Spring;52(1):1.

PMID- 37885556
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231028
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Bias and Inaccuracy in AI Chatbot Ophthalmologist Recommendations.
PG  - e45911
LID - 10.7759/cureus.45911 [doi]
LID - e45911
AB  - PURPOSE AND DESIGN: To evaluate the accuracy and bias of ophthalmologist 
      recommendations made by three AI chatbots, namely ChatGPT 3.5 (OpenAI, San 
      Francisco, CA, USA), Bing Chat (Microsoft Corp., Redmond, WA, USA), and Google 
      Bard (Alphabet Inc., Mountain View, CA, USA). This study analyzed chatbot 
      recommendations for the 20 most populous U.S. cities. METHODS: Each chatbot 
      returned 80 total recommendations when given the prompt "Find me four good 
      ophthalmologists in (city)." Characteristics of the physicians, including 
      specialty, location, gender, practice type, and fellowship, were collected. A 
      one-proportion z-test was performed to compare the proportion of female 
      ophthalmologists recommended by each chatbot to the national average (27.2% per 
      the Association of American Medical Colleges (AAMC)). Pearson's chi-squared test 
      was performed to determine differences between the three chatbots in male versus 
      female recommendations and recommendation accuracy. RESULTS: Female 
      ophthalmologists recommended by Bing Chat (1.61%) and Bard (8.0%) were 
      significantly less than the national proportion of 27.2% practicing female 
      ophthalmologists (p<0.001, p<0.01, respectively). ChatGPT recommended fewer 
      female (29.5%) than male ophthalmologists (p<0.722). ChatGPT (73.8%), Bing Chat 
      (67.5%), and Bard (62.5%) gave high rates of inaccurate recommendations. Compared 
      to the national average of academic ophthalmologists (17%), the proportion of 
      recommended ophthalmologists in academic medicine or in combined academic and 
      private practice was significantly greater for all three chatbots. CONCLUSION: 
      This study revealed substantial bias and inaccuracy in the AI chatbots' 
      recommendations. They struggled to recommend ophthalmologists reliably and 
      accurately, with most recommendations being physicians in specialties other than 
      ophthalmology or not in or near the desired city. Bing Chat and Google Bard 
      showed a significant tendency against recommending female ophthalmologists, and 
      all chatbots favored recommending ophthalmologists in academic medicine.
CI  - Copyright © 2023, Oca et al.
FAU - Oca, Michael C
AU  - Oca MC
AD  - Orthopedic Surgery, Shiley Eye Institute, University of California (UC) San Diego 
      Health, La Jolla, USA.
FAU - Meller, Leo
AU  - Meller L
AD  - Orthopedic Surgery, Shiley Eye Institute, University of California (UC) San Diego 
      Health, La Jolla, USA.
FAU - Wilson, Katherine
AU  - Wilson K
AD  - Orthopedic Surgery, Shiley Eye Institute, University of California (UC) San Diego 
      Health, La Jolla, USA.
FAU - Parikh, Alomi O
AU  - Parikh AO
AD  - Ophthalmology, University of Southern California (USC) Roski Eye Institute, Keck 
      School of Medicine of University of Southern California, Los Angeles, USA.
FAU - McCoy, Allison
AU  - McCoy A
AD  - Plastic Surgery, Del Mar Plastic Surgery, San Diego, USA.
FAU - Chang, Jessica
AU  - Chang J
AD  - Ophthalmology, University of Southern California (USC) Roski Eye Institute, Keck 
      School of Medicine of University of Southern California, Los Angeles, USA.
FAU - Sudharshan, Rasika
AU  - Sudharshan R
AD  - Ophthalmology, University of Southern California (USC) Roski Eye Institute, Keck 
      School of Medicine of University of Southern California, Los Angeles, USA.
FAU - Gupta, Shreya
AU  - Gupta S
AD  - Ophthalmology, University of Southern California (USC) Roski Eye Institute, Keck 
      School of Medicine of University of Southern California, Los Angeles, USA.
FAU - Zhang-Nunes, Sandy
AU  - Zhang-Nunes S
AD  - Ophthalmology, University of Southern California (USC) Roski Eye Institute, Keck 
      School of Medicine of University of Southern California, Los Angeles, USA.
LA  - eng
PT  - Journal Article
DEP - 20230925
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10599183
OTO - NOTNLM
OT  - ai chatbot
OT  - artificial intelligence (ai) in medicine
OT  - artificial intelligence in health care
OT  - artificial intelligence in medicine
OT  - gender bias
OT  - patient education
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/27 06:42
MHDA- 2023/10/27 06:43
CRDT- 2023/10/27 04:19
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/10/27 06:43 [medline]
PHST- 2023/10/27 06:42 [pubmed]
PHST- 2023/10/27 04:19 [entrez]
AID - 10.7759/cureus.45911 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 25;15(9):e45911. doi: 10.7759/cureus.45911. eCollection 2023 
      Sep.

PMID- 37918623
OWN - NLM
STAT- In-Process
LR  - 20231211
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Linking)
VI  - 148
DP  - 2023 Dec
TI  - Leveraging GPT-4 for food effect summarization to enhance product-specific 
      guidance development via iterative prompting.
PG  - 104533
LID - S1532-0464(23)00254-X [pii]
LID - 10.1016/j.jbi.2023.104533 [doi]
AB  - Food effect summarization from New Drug Application (NDA) is an essential 
      component of product-specific guidance (PSG) development and assessment, which 
      provides the basis of recommendations for fasting and fed bioequivalence studies 
      to guide the pharmaceutical industry for developing generic drug products. 
      However, manual summarization of food effect from extensive drug application 
      review documents is time-consuming. Therefore, there is a need to develop 
      automated methods to generate food effect summary. Recent advances in natural 
      language processing (NLP), particularly large language models (LLMs) such as 
      ChatGPT and GPT-4, have demonstrated great potential in improving the 
      effectiveness of automated text summarization, but its ability with regard to the 
      accuracy in summarizing food effect for PSG assessment remains unclear. In this 
      study, we introduce a simple yet effective approach,iterative prompting, which 
      allows one to interact with ChatGPT or GPT-4 more effectively and efficiently 
      through multi-turn interaction. Specifically, we propose a three-turn iterative 
      prompting approach to food effect summarization in which the keyword-focused and 
      length-controlled prompts are respectively provided in consecutive turns to 
      refine the quality of the generated summary. We conduct a series of extensive 
      evaluations, ranging from automated metrics to FDA professionals and even 
      evaluation by GPT-4, on 100 NDA review documents selected over the past five 
      years. We observe that the summary quality is progressively improved throughout 
      the iterative prompting process. Moreover, we find that GPT-4 performs better 
      than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 
      35%). Importantly, all the FDA professionals unanimously rated that 85% of the 
      summaries generated by GPT-4 are factually consistent with the golden reference 
      summary, a finding further supported by GPT-4 rating of 72% consistency. Taken 
      together, these results strongly suggest a great potential for GPT-4 to draft 
      food effect summaries that could be reviewed by FDA professionals, thereby 
      improving the efficiency of the PSG assessment cycle and promoting generic drug 
      product development.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Shi, Yiwen
AU  - Shi Y
AD  - College of Computing and Informatics, Drexel University, Philadelphia, PA, United 
      States.
FAU - Ren, Ping
AU  - Ren P
AD  - Office of Research and Standards, Office of Generic Drugs, Center for Drug 
      Evaluation and Research, United States Food and Drug Administration, Silver 
      Spring, MD, United States.
FAU - Wang, Jing
AU  - Wang J
AD  - Office of Research and Standards, Office of Generic Drugs, Center for Drug 
      Evaluation and Research, United States Food and Drug Administration, Silver 
      Spring, MD, United States.
FAU - Han, Biao
AU  - Han B
AD  - Office of Research and Standards, Office of Generic Drugs, Center for Drug 
      Evaluation and Research, United States Food and Drug Administration, Silver 
      Spring, MD, United States.
FAU - ValizadehAslani, Taha
AU  - ValizadehAslani T
AD  - Department of Electrical and Computer Engineering, College of Engineering, Drexel 
      University, Philadelphia, PA, United States.
FAU - Agbavor, Felix
AU  - Agbavor F
AD  - School of Biomedical Engineering, Science and Health Systems, Drexel University, 
      Philadelphia, PA, United States.
FAU - Zhang, Yi
AU  - Zhang Y
AD  - Office of Research and Standards, Office of Generic Drugs, Center for Drug 
      Evaluation and Research, United States Food and Drug Administration, Silver 
      Spring, MD, United States.
FAU - Hu, Meng
AU  - Hu M
AD  - Office of Research and Standards, Office of Generic Drugs, Center for Drug 
      Evaluation and Research, United States Food and Drug Administration, Silver 
      Spring, MD, United States.
FAU - Zhao, Liang
AU  - Zhao L
AD  - Office of Research and Standards, Office of Generic Drugs, Center for Drug 
      Evaluation and Research, United States Food and Drug Administration, Silver 
      Spring, MD, United States.
FAU - Liang, Hualou
AU  - Liang H
AD  - School of Biomedical Engineering, Science and Health Systems, Drexel University, 
      Philadelphia, PA, United States. Electronic address: hualou.liang@drexel.edu.
LA  - eng
PT  - Journal Article
DEP - 20231102
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
OTO - NOTNLM
OT  - Drug labeling
OT  - GPT-4
OT  - Large Language Models
OT  - Prompt Engineering
OT  - Text Summarization
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/11/03 00:42
MHDA- 2023/11/03 00:42
CRDT- 2023/11/02 20:16
PHST- 2023/07/12 00:00 [received]
PHST- 2023/10/12 00:00 [revised]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/11/03 00:42 [pubmed]
PHST- 2023/11/03 00:42 [medline]
PHST- 2023/11/02 20:16 [entrez]
AID - S1532-0464(23)00254-X [pii]
AID - 10.1016/j.jbi.2023.104533 [doi]
PST - ppublish
SO  - J Biomed Inform. 2023 Dec;148:104533. doi: 10.1016/j.jbi.2023.104533. Epub 2023 
      Nov 2.

PMID- 37819844
OWN - NLM
STAT- Publisher
LR  - 20231011
IS  - 1945-3388 (Electronic)
IS  - 0198-7569 (Linking)
VI  - 0
IP  - 0
DP  - 2023 Oct 11
TI  - Artificial Intelligence Chatbots in Patient Communication: Current Possibilities.
LID - 10.11607/prd.6925 [doi]
AB  - ChatGPT, an artificial intelligence (AI) chatbot can generate text prompts based 
      on user input. This study investigated the possibility of generating adequate and 
      relevant patient education and management documents in the context of dental 
      implant surgery utilizing this tool. METHODS: Twenty-seven (n=27) periodontists 
      were surveyed on the accuracy and usefulness of AI-generated documents comprising 
      patient information handouts on surgical risks and post-operative instructions 
      sheets for dental implant placement either in smokers or patients with diabetes. 
      They were also asked in a blinded fashion about their preferences between the 
      generic dental implant placement consent form currently used at Boston University 
      and two AI-generated consent forms: one generic and one tailored to patients with 
      diabetes. RESULTS: A vast majority of participants found that the information in 
      the AI-generated forms was accurate, useful and would feel comfortable using them 
      with their own patients. The AI-generated generic consent performed as least as 
      well as the human-written one, while the personalized informed consent for 
      patient with diabetes performed significantly better (p<0.001). CONCLUSIONS: 
      Within the limitations of this study, ChatGPT was able to independently generate 
      accurate and useful patient information and management documents.
FAU - Di Battista, Massimo
AU  - Di Battista M
FAU - Kernitsky, Jeremy
AU  - Kernitsky J
FAU - Dibart, Serge
AU  - Dibart S
LA  - eng
PT  - Journal Article
DEP - 20231011
PL  - United States
TA  - Int J Periodontics Restorative Dent
JT  - The International journal of periodontics & restorative dentistry
JID - 8200894
EDAT- 2023/10/11 18:43
MHDA- 2023/10/11 18:43
CRDT- 2023/10/11 13:13
PHST- 2023/10/11 18:43 [medline]
PHST- 2023/10/11 18:43 [pubmed]
PHST- 2023/10/11 13:13 [entrez]
AID - 4497165 [pii]
AID - 10.11607/prd.6925 [doi]
PST - aheadofprint
SO  - Int J Periodontics Restorative Dent. 2023 Oct 11;0(0). doi: 10.11607/prd.6925.

PMID- 37246580
OWN - NLM
STAT- MEDLINE
DCOM- 20230530
LR  - 20230626
IS  - 2150-1327 (Electronic)
IS  - 2150-1319 (Print)
IS  - 2150-1319 (Linking)
VI  - 14
DP  - 2023 Jan-Dec
TI  - Artificial Intelligence: First Do the Long Overdue Doable.
PG  - 21501319231179559
LID - 10.1177/21501319231179559 [doi]
LID - 21501319231179559
AB  - Release of ChatGPT-4, an internet-based computer program that simulates 
      conversation with human users using Artificial Intelligence (AI) on March 14, 
      2023 set off a flurry of debates regarding the role and impact of AI on human 
      life. Influential leaders and thinkers from diverse fields have chimed in to 
      offer their views, admonitions, and or recommendations. There seems to be a great 
      diversity in viewpoints and visions regarding how AI would affect human 
      destiny-right from confident optimism to stark doomsaying and all in between. 
      However, not much attention is being paid to the insidious long-term effects on 
      human societies, many of them being unintended consequences, that AI has the 
      potential to create over a short period of time. Perhaps the greatest threat of 
      AI is the potential for loss of meaning in life and human-technology-created 
      enfeeblement in a large section of humanity. All the other threats including that 
      of the current AI are mere epiphenomena of this basic threat. In view of the fact 
      that the genie of AI is out of the bottle and that it cannot be put back in, the 
      first order of business for technologists, policy makers, and the governments is 
      to allocate resources and attention to address the problem of meaning in life and 
      mitigation of the sentiment of overwhelming and universal helplessness. Lastly, 
      not to be optimistic about AI while being cautious and pragmatic is not an 
      option.
FAU - Patwardhan, Avinash
AU  - Patwardhan A
AUID- ORCID: 0000-0002-5101-6809
AD  - George Mason University, Fairfax, VA, USA.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - J Prim Care Community Health
JT  - Journal of primary care & community health
JID - 101518419
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Communication
MH  - Emotions
MH  - Hearing Loss, Conductive
MH  - Internet
PMC - PMC10233603
OTO - NOTNLM
OT  - AI
OT  - ChatGPT-4
OT  - artificial intelligence
OT  - death of despair
OT  - enfeeblement
OT  - gender sex dynamics
OT  - meaning in life
OT  - mental health
OT  - unintended consequences
OT  - violence
COIS- The author declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/05/29 06:42
MHDA- 2023/05/30 06:42
CRDT- 2023/05/29 05:29
PHST- 2023/05/30 06:42 [medline]
PHST- 2023/05/29 06:42 [pubmed]
PHST- 2023/05/29 05:29 [entrez]
AID - 10.1177_21501319231179559 [pii]
AID - 10.1177/21501319231179559 [doi]
PST - ppublish
SO  - J Prim Care Community Health. 2023 Jan-Dec;14:21501319231179559. doi: 
      10.1177/21501319231179559.

PMID- 37685452
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230911
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 17
DP  - 2023 Aug 29
TI  - Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM 
      Watson Assistant Platform.
LID - 10.3390/healthcare11172417 [doi]
LID - 2417
AB  - Objectives: This study aims to make radiotherapy knowledge regarding healthcare 
      accessible to the general public by developing an AI-powered chatbot. The 
      interactive nature of the chatbot is expected to facilitate better understanding 
      of information on radiotherapy through communication with users. Methods: Using 
      the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed 
      following a pre-designed flowchart that outlines the conversation flow. This 
      approach ensured the development of the chatbot with a clear mindset and allowed 
      for effective tracking of the conversation. The chatbot is equipped to furnish 
      users with information and quizzes on radiotherapy to assess their understanding 
      of the subject. Results: By adopting a question-and-answer approach, the chatbot 
      can engage in human-like communication with users seeking information about 
      radiotherapy. As some users may feel anxious and struggle to articulate their 
      queries, the chatbot is designed to be user-friendly and reassuring, providing a 
      list of questions for the user to choose from. Feedback on the chatbot's content 
      was mostly positive, despite a few limitations. The chatbot performed well and 
      successfully conveyed knowledge as intended. Conclusions: There is a need to 
      enhance the chatbot's conversation approach to improve user interaction. 
      Including translation capabilities to cater to individuals with different first 
      languages would also be advantageous. Lastly, the newly launched ChatGPT could 
      potentially be developed into a medical chatbot to facilitate knowledge transfer.
FAU - Chow, James C L
AU  - Chow JCL
AUID- ORCID: 0000-0003-4202-4855
AD  - Radiation Medicine Program, Princess Margaret Cancer Centre, University Health 
      Network, Toronto, ON M5G 1X6, Canada.
AD  - Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, 
      Canada.
FAU - Wong, Valerie
AU  - Wong V
AUID- ORCID: 0009-0001-9081-7297
AD  - Department of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, 
      Canada.
FAU - Sanders, Leslie
AU  - Sanders L
AUID- ORCID: 0000-0002-6990-7722
AD  - Department of Humanities, York University, Toronto, ON M3J 1P3, Canada.
FAU - Li, Kay
AU  - Li K
AUID- ORCID: 0000-0002-5765-1635
AD  - Department of English, University of Toronto, Toronto, ON M5R 2M8, Canada.
LA  - eng
GR  - CIHR PCS-168296/CAPMC/CIHR/Canada
PT  - Journal Article
DEP - 20230829
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10487627
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - healthcare
OT  - medical chatbot
OT  - patient education
OT  - radiotherapy
COIS- The authors declare no conflict of interest.
EDAT- 2023/09/09 11:43
MHDA- 2023/09/09 11:44
CRDT- 2023/09/09 01:07
PHST- 2023/08/15 00:00 [received]
PHST- 2023/08/25 00:00 [revised]
PHST- 2023/08/26 00:00 [accepted]
PHST- 2023/09/09 11:44 [medline]
PHST- 2023/09/09 11:43 [pubmed]
PHST- 2023/09/09 01:07 [entrez]
AID - healthcare11172417 [pii]
AID - healthcare-11-02417 [pii]
AID - 10.3390/healthcare11172417 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Aug 29;11(17):2417. doi: 10.3390/healthcare11172417.

PMID- 37312003
OWN - NLM
STAT- MEDLINE
DCOM- 20230818
LR  - 20230821
IS  - 1880-344X (Electronic)
IS  - 1349-0222 (Linking)
VI  - 21
IP  - 3
DP  - 2023 Sep
TI  - Revolution of echocardiographic reporting: the new era of artificial intelligence 
      and natural language processing.
PG  - 99-104
LID - 10.1007/s12574-023-00611-1 [doi]
AB  - Artificial intelligence (AI) has been making a significant impact on 
      cardiovascular imaging, transforming everything from data capture to report 
      generation. In the field of echocardiography, AI offers the potential to enhance 
      accuracy, speed up reporting, and reduce the workload of physicians. This is an 
      advantage because, compared to computed tomography and magnetic resonance 
      imaging, echocardiograms tend to exhibit higher observer variability in 
      interpretation. This review takes a comprehensive viewpoint at AI-based reporting 
      systems and their application in echocardiography, emphasizing the need for 
      automated diagnoses. The integration of natural language processing (NLP) 
      technologies, including ChatGPT, could provide revolutionary advancements. One of 
      the exciting prospects of AI integration is its potential to accelerate 
      reporting, thereby improving patient outcomes and access to treatment, while also 
      mitigating physician burnout. However, AI introduces new challenges like ensuring 
      data quality, managing potential over-reliance on AI, addressing legal and 
      ethical concerns, and balancing significant costs against benefits. As we 
      navigate these complexities, it's important for cardiologists to stay updated 
      with AI advancements and learn to utilize them effectively. AI has the potential 
      to be integrated into daily clinical practice, becoming a valuable tool for 
      healthcare professionals dealing with heart diseases, provided it's approached 
      with careful consideration.
CI  - © 2023. Japanese Society of Echocardiography.
FAU - Kusunose, Kenya
AU  - Kusunose K
AUID- ORCID: 0000-0002-4909-754X
AD  - Department of Cardiovascular Medicine, Nephrology, and Neurology, Graduate School 
      of Medicine, University of the Ryukyus, 207 Uehara, Nishihara Town, Okinawa, 
      Japan. echo.cardio@gmail.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20230613
PL  - Japan
TA  - J Echocardiogr
JT  - Journal of echocardiography
JID - 101263153
SB  - IM
CIN - J Echocardiogr. 2023 Sep;21(3):142-143. PMID: 37430171
CIN - J Echocardiogr. 2023 Aug 18;:. PMID: 37594682
MH  - Humans
MH  - *Artificial Intelligence
MH  - Natural Language Processing
MH  - Echocardiography
MH  - *Heart Diseases/diagnostic imaging
MH  - Tomography, X-Ray Computed
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Cardiovascular imaging
OT  - ChatGPT
OT  - Deep learning
OT  - Echocardiography
OT  - Natural language processing
EDAT- 2023/06/14 01:10
MHDA- 2023/08/18 06:42
CRDT- 2023/06/13 23:33
PHST- 2023/05/29 00:00 [received]
PHST- 2023/06/06 00:00 [accepted]
PHST- 2023/05/29 00:00 [revised]
PHST- 2023/08/18 06:42 [medline]
PHST- 2023/06/14 01:10 [pubmed]
PHST- 2023/06/13 23:33 [entrez]
AID - 10.1007/s12574-023-00611-1 [pii]
AID - 10.1007/s12574-023-00611-1 [doi]
PST - ppublish
SO  - J Echocardiogr. 2023 Sep;21(3):99-104. doi: 10.1007/s12574-023-00611-1. Epub 2023 
      Jun 13.

PMID- 37223196
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230525
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - A Rare Case of Recurrent Cutaneous Non-Hodgkin's Lymphoma in the Extremity: 
      Long-Term Follow-Up and Review of the Literature Written With the Assistance of 
      ChatGPT.
PG  - e37980
LID - 10.7759/cureus.37980 [doi]
LID - e37980
AB  - Cutaneous involvement of chronic lymphocytic leukemia/small lymphocytic lymphoma 
      (CLL/SLL) is uncommon. We report on a 71-year-old male with a history of CLL of 
      the skin in the distal extremities. The patient presented with eruptions of new 
      lesions on the toes of his feet bilaterally, causing significant pain that 
      limited his mobility. Cutaneous involvement of CLL is a rare presentation, and 
      management recommendations are largely based on case reports with limited 
      follow-up. Furthermore, assessing the duration of response, response rates, and 
      correct sequencing of treatment is difficult due to variable use and doses of 
      treatment. The case was treated in 2001 when newer systemic treatments were not 
      available. Therefore, the results can also be directly related to local 
      treatments. Based on a literature review and this case, this report provides 
      insight into the benefits and risks of local treatment for cutaneous involvement 
      of CLL in the extremities and how radiation can be sequenced with other options 
      such as surgical excision and chemotherapy.
CI  - Copyright © 2023, Dejong et al.
FAU - Dejong, Taylor
AU  - Dejong T
AD  - Health Sciences, Western University, London, CAN.
FAU - Gill, Jeewanjit
AU  - Gill J
AD  - Family Medicine, University of Ottawa, Ottawa, CAN.
FAU - Lam, Selay
AU  - Lam S
AD  - Oncology, Western University, London, CAN.
FAU - Freiburger, Sarah
AU  - Freiburger S
AD  - Oncology, Western University, London, CAN.
FAU - Lock, Michael
AU  - Lock M
AD  - Oncology, Western University, London, CAN.
LA  - eng
PT  - Case Reports
DEP - 20230422
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10202688
OTO - NOTNLM
OT  - chatgpt
OT  - chronic lymphocytic leukemia
OT  - extranodal lymphoma
OT  - extremity lymphoma
OT  - radiation
OT  - small lymphocytic lymphoma
COIS- The authors have declared financial relationships, which are detailed in the next 
      section.
EDAT- 2023/05/24 13:09
MHDA- 2023/05/24 13:10
CRDT- 2023/05/24 11:43
PHST- 2023/02/27 00:00 [received]
PHST- 2023/04/18 00:00 [accepted]
PHST- 2023/05/24 13:10 [medline]
PHST- 2023/05/24 13:09 [pubmed]
PHST- 2023/05/24 11:43 [entrez]
AID - 10.7759/cureus.37980 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 22;15(4):e37980. doi: 10.7759/cureus.37980. eCollection 2023 
      Apr.

PMID- 36754147
OWN - NLM
STAT- MEDLINE
DCOM- 20230310
LR  - 20230310
IS  - 1876-4347 (Electronic)
IS  - 1871-6784 (Linking)
VI  - 74
DP  - 2023 May 25
TI  - AI for life: Trends in artificial intelligence for biotechnology.
PG  - 16-24
LID - S1871-6784(23)00003-1 [pii]
LID - 10.1016/j.nbt.2023.02.001 [doi]
AB  - Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on 
      everyone's lips today. When advances in biotechnology are combined with advances 
      in AI unprecedented new potential solutions become available. This can help with 
      many global problems and contribute to important Sustainability Development 
      Goals. Current examples include Food Security, Health and Well-being, Clean 
      Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life 
      below Water, or protect, restore and promote sustainable use of terrestrial 
      ecosystems, sustainably manage forests, combat desertification, and halt and 
      reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life 
      sciences today. Topics include a wide range from machine learning and Big Data 
      analytics, knowledge discovery and data mining, biomedical ontologies, 
      knowledge-based reasoning, natural language processing, decision support and 
      reasoning under uncertainty, temporal and spatial representation and inference, 
      and methodological aspects of explainable AI (XAI) with applications of 
      biotechnology. In this pre-Editorial paper, we provide an overview of open 
      research issues and challenges for each of the topics addressed in this special 
      issue. Potential authors can directly use this as a guideline for developing 
      their paper.
CI  - Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.
FAU - Holzinger, Andreas
AU  - Holzinger A
AD  - University of Natural Resources and Life Sciences Vienna, Austria; Medical 
      University Graz, Austria; Alberta Machine Intelligence Institute Edmonton, 
      Canada. Electronic address: andreas.holzinger@boku.ac.at.
FAU - Keiblinger, Katharina
AU  - Keiblinger K
AD  - University of Natural Resources and Life Sciences Vienna, Austria.
FAU - Holub, Petr
AU  - Holub P
AD  - Masaryk University Brno, Czech Republic.
FAU - Zatloukal, Kurt
AU  - Zatloukal K
AD  - Medical University Graz, Austria.
FAU - Müller, Heimo
AU  - Müller H
AD  - Medical University Graz, Austria.
LA  - eng
PT  - Introductory Journal Article
DEP - 20230206
PL  - Netherlands
TA  - N Biotechnol
JT  - New biotechnology
JID - 101465345
SB  - IM
MH  - *Artificial Intelligence
MH  - Biotechnology
MH  - Data Mining
MH  - *Ecosystem
MH  - Knowledge Bases
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Biotechnology
OT  - Deep Learning
OT  - Digital Transformation
OT  - Machine Learning
COIS- Declaration of Competing Interest The Authors declare that there are no conflicts 
      of interests.
EDAT- 2023/02/09 06:00
MHDA- 2023/03/03 06:00
CRDT- 2023/02/08 19:23
PHST- 2023/01/12 00:00 [received]
PHST- 2023/02/05 00:00 [revised]
PHST- 2023/02/05 00:00 [accepted]
PHST- 2023/02/09 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
PHST- 2023/02/08 19:23 [entrez]
AID - S1871-6784(23)00003-1 [pii]
AID - 10.1016/j.nbt.2023.02.001 [doi]
PST - ppublish
SO  - N Biotechnol. 2023 May 25;74:16-24. doi: 10.1016/j.nbt.2023.02.001. Epub 2023 Feb 
      6.

PMID- 37771250
OWN - NLM
STAT- Publisher
LR  - 20230929
IS  - 1399-5618 (Electronic)
IS  - 1398-5647 (Linking)
DP  - 2023 Sep 28
TI  - A chat about bipolar disorder.
LID - 10.1111/bdi.13379 [doi]
AB  - OBJECTIVES: This study aimed to assess the capabilities of ChatGPT (Chat 
      Generative Pre-Trained Transformer) in generating informative content related to 
      bipolar disorders. The objectives were to evaluate its ability to provide 
      accurate information on symptoms, classification, causes, and management of 
      bipolar disorder and to explore its creativity in generating topic-related songs. 
      METHODS: ChatGPT3 was used for the study, and a series of clinically relevant 
      questions were asked to test its knowledge and creativity. Questions ranged from 
      common symptom descriptions to more artistic requests for songs related to 
      bipolar disorder. RESULTS: ChatGPT demonstrated the capacity to provide basic and 
      informative material on bipolar disorders, including descriptions of symptoms, 
      classification types, causes, and treatment options. It also showed creativity in 
      generating songs that capture the nuances of bipolar symptoms, both during high 
      and low states. CONCLUSIONS: While ChatGPT3 can offer superficial information on 
      psychiatric topics like bipolar disorder, its inability to provide accurate and 
      up-to-date references limits its utility for creating a comprehensive review 
      article for scientific journals. However, it may be helpful in generating 
      educational material and assisting in component tasks for those with bipolar 
      disorder or other psychiatric conditions. As newer versions of AI models are 
      continually developed, their capabilities in producing more accurate and advanced 
      content will need further evaluation.
CI  - © 2023 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.
FAU - Parker, Gordon
AU  - Parker G
AUID- ORCID: 0000-0003-3424-5519
AD  - Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty 
      of Medicine and Health, University of New South Wales, Sydney, New South Wales, 
      Australia.
FAU - Spoelma, Michael J
AU  - Spoelma MJ
AUID- ORCID: 0000-0003-2844-0748
AD  - Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty 
      of Medicine and Health, University of New South Wales, Sydney, New South Wales, 
      Australia.
AD  - Black Dog Institute, Sydney, New South Wales, Australia.
LA  - eng
GR  - National Health and Medical Research Council/
PT  - Journal Article
DEP - 20230928
PL  - Denmark
TA  - Bipolar Disord
JT  - Bipolar disorders
JID - 100883596
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - bipolar disorder
OT  - research
EDAT- 2023/09/29 06:44
MHDA- 2023/09/29 06:44
CRDT- 2023/09/29 03:09
PHST- 2023/09/29 06:44 [medline]
PHST- 2023/09/29 06:44 [pubmed]
PHST- 2023/09/29 03:09 [entrez]
AID - 10.1111/bdi.13379 [doi]
PST - aheadofprint
SO  - Bipolar Disord. 2023 Sep 28. doi: 10.1111/bdi.13379.

PMID- 37168205
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230514
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Heat and Moisture Exchanger Occlusion Leading to Sudden Increased Airway 
      Pressure: A Case Report Using ChatGPT as a Personal Writing Assistant.
PG  - e37306
LID - 10.7759/cureus.37306 [doi]
LID - e37306
AB  - Heat and moisture exchangers (HMEs) are commonly used during general anesthesia 
      to provide appropriate humidification and warming of inspired gases. While they 
      play a critical role in mechanical ventilation, they can also lead to acute 
      difficult ventilation if not correctly monitored and drained. We present a case 
      of a 56-year-old female patient who underwent lower extremity vascular bypass 
      surgery under general anesthesia and experienced sudden increased airway 
      pressures due to occlusion of the HME caused by excessive moisture accumulation. 
      Proper monitoring and management of the airway circuit and HMEs can help prevent 
      complications and ensure proper ventilation during surgery. When acute difficult 
      ventilation is encountered during general anesthesia, a systematic approach 
      should be taken to differentiate between patient and external factors. Other 
      differential diagnoses for acute difficult ventilation include bronchospasm, 
      aspiration, endotracheal tube misplacement, pulmonary embolism, and tension 
      pneumothorax. HME occlusion should be considered as part of the differential 
      diagnosis for intraoperative hypoxia. Proactive replacement of HMEs in long cases 
      can prevent occlusion and ensure proper ventilation.
CI  - Copyright © 2023, Hallo-Carrasco et al.
FAU - Hallo-Carrasco, Alejandro
AU  - Hallo-Carrasco A
AD  - Department of Pain Medicine, Mayo Clinic, Jacksonville, USA.
FAU - Gruenbaum, Benjamin F
AU  - Gruenbaum BF
AD  - Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 
      Jacksonville, USA.
FAU - Gruenbaum, Shaun E
AU  - Gruenbaum SE
AD  - Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 
      Jacksonville, USA.
LA  - eng
PT  - Case Reports
DEP - 20230408
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10166637
OTO - NOTNLM
OT  - chatgpt
OT  - hme
OT  - humidified moisture exchanger
OT  - intubation complication
OT  - prolonged ventilation
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/12 01:07
MHDA- 2023/05/12 01:08
CRDT- 2023/05/11 19:16
PHST- 2023/04/07 00:00 [accepted]
PHST- 2023/05/12 01:08 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 19:16 [entrez]
AID - 10.7759/cureus.37306 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 8;15(4):e37306. doi: 10.7759/cureus.37306. eCollection 2023 Apr.

PMID- 37038563
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230412
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - A Rare Co-occurrence of Monkeypox Encephalitis and Neurosyphilis.
PG  - e35945
LID - 10.7759/cureus.35945 [doi]
LID - e35945
AB  - Monkeypox (MPOX according to the Centers for Disease Control and Prevention) has 
      been a disease of interest in populations with high-risk sexual behavior. As 
      sporadic outbreaks of MPOX have led to a worldwide spread, it has been declared a 
      public health emergency by the World Health Organization. Here, we describe the 
      case of a 44-year-old male with high-risk sexual behavior who presented with 
      typical rashes of MPOX and altered mental status. MPOX polymerase chain reaction 
      from the skin lesion and cerebrospinal fluid-Venereal Disease Research Laboratory 
      tests were positive, raising the possibility of concomitant infection with 
      neurosyphilis. The patient was treated with tecovirimat and aqueous penicillin G 
      resulting in an improvement in the patient's clinical condition. Our case 
      describes that MPOX has the potential to cause central nervous system 
      manifestations through possibly a direct viral invasion or an immune-meditated 
      insult.
CI  - Copyright © 2023, Sharma et al.
FAU - Sharma, Rohit
AU  - Sharma R
AD  - Medicine, Geisinger Health System, Wilkes-Barre, USA.
AD  - Clinical Research, Dresden International University, Dresden, DEU.
FAU - Nguyen-Luu, Tristan
AU  - Nguyen-Luu T
AD  - Internal Medicine, Geisinger Health System, Wilkes-Barre, USA.
FAU - Dhaubhadel, Pragya
AU  - Dhaubhadel P
AD  - Infectious Disease, Geisinger Health System, Scranton, USA.
FAU - Sharma, Amit
AU  - Sharma A
AD  - Infectious Disease, Geisinger Health System, Scranton, USA.
FAU - Naik, Roopa
AU  - Naik R
AD  - Internal Medicine, Geisinger Health System, Wilkes-Barre, USA.
LA  - eng
PT  - Case Reports
DEP - 20230309
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10082623
OTO - NOTNLM
OT  - chatgpt
OT  - chatgpt improved case report
OT  - monkeypox coinfection
OT  - monkeypox encephalitis
OT  - monkeypox virus
OT  - monkeypox-associated cns disease
OT  - neurosyphillis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/12 06:00
MHDA- 2023/04/12 06:01
CRDT- 2023/04/11 01:48
PHST- 2023/03/09 00:00 [accepted]
PHST- 2023/04/12 06:01 [medline]
PHST- 2023/04/11 01:48 [entrez]
PHST- 2023/04/12 06:00 [pubmed]
AID - 10.7759/cureus.35945 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 9;15(3):e35945. doi: 10.7759/cureus.35945. eCollection 2023 Mar.

PMID- 37788311
OWN - NLM
STAT- MEDLINE
DCOM- 20231005
LR  - 20231017
IS  - 1091-6490 (Electronic)
IS  - 0027-8424 (Print)
IS  - 0027-8424 (Linking)
VI  - 120
IP  - 41
DP  - 2023 Oct 10
TI  - Leveraging AI for democratic discourse: Chat interventions can improve online 
      political conversations at scale.
PG  - e2311627120
LID - 10.1073/pnas.2311627120 [doi]
LID - e2311627120
AB  - Political discourse is the soul of democracy, but misunderstanding and conflict 
      can fester in divisive conversations. The widespread shift to online discourse 
      exacerbates many of these problems and corrodes the capacity of diverse societies 
      to cooperate in solving social problems. Scholars and civil society groups 
      promote interventions that make conversations less divisive or more productive, 
      but scaling these efforts to online discourse is challenging. We conduct a 
      large-scale experiment that demonstrates how online conversations about divisive 
      topics can be improved with AI tools. Specifically, we employ a large language 
      model to make real-time, evidence-based recommendations intended to improve 
      participants' perception of feeling understood. These interventions improve 
      reported conversation quality, promote democratic reciprocity, and improve the 
      tone, without systematically changing the content of the conversation or moving 
      people's policy attitudes.
FAU - Argyle, Lisa P
AU  - Argyle LP
AUID- ORCID: 0000-0003-3109-2537
AD  - Department of Political Science, Brigham Young University, Provo, UT, 84602.
FAU - Bail, Christopher A
AU  - Bail CA
AD  - Department of Sociology, Political Science, and Public Policy, Duke University, 
      Durham, NC, 27708.
FAU - Busby, Ethan C
AU  - Busby EC
AUID- ORCID: 0000-0002-8931-6348
AD  - Department of Political Science, Brigham Young University, Provo, UT, 84602.
FAU - Gubler, Joshua R
AU  - Gubler JR
AUID- ORCID: 0000-0003-1635-8210
AD  - Department of Political Science, Brigham Young University, Provo, UT, 84602.
FAU - Howe, Thomas
AU  - Howe T
AD  - Department of Computer Science, Brigham Young University, Provo, UT, 84602.
FAU - Rytting, Christopher
AU  - Rytting C
AUID- ORCID: 0000-0002-7373-9741
AD  - Department of Computer Science, Brigham Young University, Provo, UT, 84602.
FAU - Sorensen, Taylor
AU  - Sorensen T
AUID- ORCID: 0000-0002-3251-3527
AD  - Department of Computer Science, University of Washington, Seattle, WA, 98195.
FAU - Wingate, David
AU  - Wingate D
AUID- ORCID: 0000-0003-1850-6926
AD  - Department of Computer Science, Brigham Young University, Provo, UT, 84602.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20231003
PL  - United States
TA  - Proc Natl Acad Sci U S A
JT  - Proceedings of the National Academy of Sciences of the United States of America
JID - 7505876
SB  - IM
MH  - Humans
MH  - *Policy
MH  - *Language
PMC - PMC10576030
OTO - NOTNLM
OT  - computational social science
OT  - democratic deliberation
OT  - generative AI
OT  - political science
COIS- The authors declare no competing interest.
EDAT- 2023/10/03 18:43
MHDA- 2023/10/05 06:43
CRDT- 2023/10/03 14:03
PHST- 2023/10/05 06:43 [medline]
PHST- 2023/10/03 18:43 [pubmed]
PHST- 2023/10/03 14:03 [entrez]
AID - 202311627 [pii]
AID - 10.1073/pnas.2311627120 [doi]
PST - ppublish
SO  - Proc Natl Acad Sci U S A. 2023 Oct 10;120(41):e2311627120. doi: 
      10.1073/pnas.2311627120. Epub 2023 Oct 3.

PMID- 36927031
OWN - NLM
STAT- MEDLINE
DCOM- 20230420
LR  - 20230420
IS  - 1095-9203 (Electronic)
IS  - 0036-8075 (Linking)
VI  - 379
IP  - 6637
DP  - 2023 Mar 17
TI  - Evolutionary-scale prediction of atomic-level protein structure with a language 
      model.
PG  - 1123-1130
LID - 10.1126/science.ade2574 [doi]
AB  - Recent advances in machine learning have leveraged evolutionary information in 
      multiple sequence alignments to predict protein structure. We demonstrate direct 
      inference of full atomic-level protein structure from primary sequence using a 
      large language model. As language models of protein sequences are scaled up to 15 
      billion parameters, an atomic-resolution picture of protein structure emerges in 
      the learned representations. This results in an order-of-magnitude acceleration 
      of high-resolution structure prediction, which enables large-scale structural 
      characterization of metagenomic proteins. We apply this capability to construct 
      the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic 
      protein sequences, including >225 million that are predicted with high 
      confidence, which gives a view into the vast breadth and diversity of natural 
      proteins.
FAU - Lin, Zeming
AU  - Lin Z
AUID- ORCID: 0000-0002-0883-1373
AD  - FAIR, Meta AI, New York, NY, USA.
AD  - New York University, New York, NY, USA.
FAU - Akin, Halil
AU  - Akin H
AUID- ORCID: 0000-0003-1666-3223
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Rao, Roshan
AU  - Rao R
AUID- ORCID: 0000-0003-4412-3742
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Hie, Brian
AU  - Hie B
AUID- ORCID: 0000-0003-3224-8142
AD  - FAIR, Meta AI, New York, NY, USA.
AD  - Stanford University, Palo Alto, CA, USA.
FAU - Zhu, Zhongkai
AU  - Zhu Z
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Lu, Wenting
AU  - Lu W
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Smetanin, Nikita
AU  - Smetanin N
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Verkuil, Robert
AU  - Verkuil R
AUID- ORCID: 0000-0001-9533-7623
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Kabeli, Ori
AU  - Kabeli O
AUID- ORCID: 0000-0002-4153-2463
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Shmueli, Yaniv
AU  - Shmueli Y
AUID- ORCID: 0000-0002-3581-2485
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Dos Santos Costa, Allan
AU  - Dos Santos Costa A
AUID- ORCID: 0000-0001-8061-339X
AD  - Massachusetts Institute of Technology, Cambridge, MA, USA.
FAU - Fazel-Zarandi, Maryam
AU  - Fazel-Zarandi M
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Sercu, Tom
AU  - Sercu T
AUID- ORCID: 0000-0003-2947-6064
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Candido, Salvatore
AU  - Candido S
AUID- ORCID: 0000-0002-5847-0617
AD  - FAIR, Meta AI, New York, NY, USA.
FAU - Rives, Alexander
AU  - Rives A
AUID- ORCID: 0000-0003-2208-0796
AD  - FAIR, Meta AI, New York, NY, USA.
AD  - New York University, New York, NY, USA.
LA  - eng
PT  - Journal Article
DEP - 20230316
PL  - United States
TA  - Science
JT  - Science (New York, N.Y.)
JID - 0404511
RN  - 0 (Proteins)
SB  - IM
MH  - Amino Acid Sequence
MH  - *Machine Learning
MH  - *Proteins/chemistry
MH  - *Evolution, Molecular
MH  - Protein Conformation
MH  - *Sequence Analysis, Protein
EDAT- 2023/03/18 06:00
MHDA- 2023/03/21 06:00
CRDT- 2023/03/17 08:02
PHST- 2023/03/17 08:02 [entrez]
PHST- 2023/03/18 06:00 [pubmed]
PHST- 2023/03/21 06:00 [medline]
AID - 10.1126/science.ade2574 [doi]
PST - ppublish
SO  - Science. 2023 Mar 17;379(6637):1123-1130. doi: 10.1126/science.ade2574. Epub 2023 
      Mar 16.

PMID- 38013906
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231129
IS  - 2635-098X (Electronic)
IS  - 2635-098X (Linking)
VI  - 2
IP  - 5
DP  - 2023 Oct 9
TI  - 14 examples of how LLMs can transform materials science and chemistry: a 
      reflection on a large language model hackathon.
PG  - 1233-1250
LID - 10.1039/d3dd00113j [doi]
AB  - Large-language models (LLMs) such as GPT-4 caught the interest of many 
      scientists. Recent studies suggested that these models could be useful in 
      chemistry and materials science. To explore these possibilities, we organized a 
      hackathon. This article chronicles the projects built as part of this hackathon. 
      Participants employed LLMs for various applications, including predicting 
      properties of molecules and materials, designing novel interfaces for tools, 
      extracting knowledge from unstructured data, and developing new educational 
      applications. The diverse topics and the fact that working prototypes could be 
      generated in less than two days highlight that LLMs will profoundly impact the 
      future of our fields. The rich collection of ideas and projects also indicates 
      that the applications of LLMs are not limited to materials science and chemistry 
      but offer potential benefits to a wide range of scientific disciplines.
CI  - This journal is © The Royal Society of Chemistry.
FAU - Jablonka, Kevin Maik
AU  - Jablonka KM
AUID- ORCID: 0000-0003-4894-4660
AD  - Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie 
      Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais 
      Switzerland mail@kjablonka.com.
FAU - Ai, Qianxiang
AU  - Ai Q
AUID- ORCID: 0000-0002-5487-2539
AD  - Department of Chemical Engineering, Massachusetts Institute of Technology 
      Cambridge Massachusetts 02139 USA.
FAU - Al-Feghali, Alexander
AU  - Al-Feghali A
AUID- ORCID: 0009-0004-8377-7049
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Badhwar, Shruti
AU  - Badhwar S
AUID- ORCID: 0000-0002-3167-5348
AD  - Reincarnate Inc. USA.
FAU - Bocarsly, Joshua D
AU  - Bocarsly JD
AUID- ORCID: 0000-0002-7523-152X
AD  - Yusuf Hamied Department of Chemistry, University of Cambridge Lensfield Road 
      Cambridge CB2 1EW UK.
FAU - Bran, Andres M
AU  - Bran AM
AUID- ORCID: 0000-0002-4432-3667
AD  - Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et 
      Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne 
      Switzerland.
AD  - National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique 
      Fédérale de Lausanne (EPFL) Lausanne Switzerland.
FAU - Bringuier, Stefan
AU  - Bringuier S
AUID- ORCID: 0000-0001-6753-1437
AD  - Independent Researcher San Diego CA USA.
FAU - Brinson, L Catherine
AU  - Brinson LC
AUID- ORCID: 0000-0003-2551-1563
AD  - Mechanical Engineering and Materials Science, Duke University USA.
FAU - Choudhary, Kamal
AU  - Choudhary K
AUID- ORCID: 0000-0001-9737-8074
AD  - Material Measurement Laboratory, National Institute of Standards and Technology 
      Maryland 20899 USA.
FAU - Circi, Defne
AU  - Circi D
AUID- ORCID: 0000-0002-5761-0198
AD  - Mechanical Engineering and Materials Science, Duke University USA.
FAU - Cox, Sam
AU  - Cox S
AUID- ORCID: 0000-0002-4441-9327
AD  - Department of Chemical Engineering, University of Rochester USA.
FAU - de Jong, Wibe A
AU  - de Jong WA
AUID- ORCID: 0000-0002-7114-8315
AD  - Applied Mathematics and Computational Research Division, Lawrence Berkeley 
      National Laboratory Berkeley CA 94720 USA.
FAU - Evans, Matthew L
AU  - Evans ML
AUID- ORCID: 0000-0002-1182-9098
AD  - Institut de la Matière Condensée et des Nanosciences (IMCN), UCLouvain Chemin des 
      Étoiles 8 Louvain-la-Neuve 1348 Belgium.
AD  - Matgenix SRL 185 Rue Armand Bury 6534 Gozée Belgium.
FAU - Gastellu, Nicolas
AU  - Gastellu N
AUID- ORCID: 0000-0002-4052-076X
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Genzling, Jerome
AU  - Genzling J
AUID- ORCID: 0009-0007-4728-1478
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Gil, María Victoria
AU  - Gil MV
AUID- ORCID: 0000-0002-2258-3011
AD  - Instituto de Ciencia y Tecnología del Carbono (INCAR), CSIC Francisco Pintado Fe 
      26 33011 Oviedo Spain.
FAU - Gupta, Ankur K
AU  - Gupta AK
AUID- ORCID: 0000-0002-3128-9535
AD  - Applied Mathematics and Computational Research Division, Lawrence Berkeley 
      National Laboratory Berkeley CA 94720 USA.
FAU - Hong, Zhi
AU  - Hong Z
AUID- ORCID: 0000-0002-5250-1347
AD  - Department of Computer Science, University of Chicago Chicago Illinois 60637 USA.
FAU - Imran, Alishba
AU  - Imran A
AD  - Computer Science, University of California Berkeley CA 94704 USA.
FAU - Kruschwitz, Sabine
AU  - Kruschwitz S
AUID- ORCID: 0000-0002-6296-4417
AD  - Bundesanstalt für Materialforschung und -prüfung Unter den Eichen 87 12205 Berlin 
      Germany.
FAU - Labarre, Anne
AU  - Labarre A
AUID- ORCID: 0000-0003-4939-3928
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Lála, Jakub
AU  - Lála J
AUID- ORCID: 0000-0002-5424-5260
AD  - Francis Crick Institute 1 Midland Rd London NW1 1AT UK.
FAU - Liu, Tao
AU  - Liu T
AUID- ORCID: 0000-0002-1082-5570
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Ma, Steven
AU  - Ma S
AUID- ORCID: 0009-0006-9448-7332
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Majumdar, Sauradeep
AU  - Majumdar S
AUID- ORCID: 0000-0002-2095-3082
AD  - Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie 
      Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais 
      Switzerland mail@kjablonka.com.
FAU - Merz, Garrett W
AU  - Merz GW
AUID- ORCID: 0000-0003-4737-3931
AD  - American Family Insurance Data Science Institute, University of Wisconsin-Madison 
      Madison WI 53706 USA.
FAU - Moitessier, Nicolas
AU  - Moitessier N
AUID- ORCID: 0000-0001-6933-2079
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Moubarak, Elias
AU  - Moubarak E
AUID- ORCID: 0000-0001-8271-6800
AD  - Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie 
      Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais 
      Switzerland mail@kjablonka.com.
FAU - Mouriño, Beatriz
AU  - Mouriño B
AUID- ORCID: 0000-0003-1670-3985
AD  - Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie 
      Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais 
      Switzerland mail@kjablonka.com.
FAU - Pelkie, Brenden
AU  - Pelkie B
AUID- ORCID: 0000-0001-7638-6366
AD  - Department of Chemical Engineering, University of Washington Seattle WA 98105 
      USA.
FAU - Pieler, Michael
AU  - Pieler M
AUID- ORCID: 0000-0001-9186-7045
AD  - OpenBioML.org UK.
AD  - Stability.AI UK.
FAU - Ramos, Mayk Caldas
AU  - Ramos MC
AUID- ORCID: 0000-0001-5336-2847
AD  - Department of Chemical Engineering, University of Rochester USA.
FAU - Ranković, Bojana
AU  - Ranković B
AUID- ORCID: 0000-0002-1476-6686
AD  - Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et 
      Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne 
      Switzerland.
AD  - National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique 
      Fédérale de Lausanne (EPFL) Lausanne Switzerland.
FAU - Rodriques, Samuel G
AU  - Rodriques SG
AUID- ORCID: 0000-0002-2509-0861
AD  - Francis Crick Institute 1 Midland Rd London NW1 1AT UK.
FAU - Sanders, Jacob N
AU  - Sanders JN
AUID- ORCID: 0000-0002-2196-4234
AD  - Department of Chemistry and Biochemistry, University of California Los Angeles CA 
      90095 USA.
FAU - Schwaller, Philippe
AU  - Schwaller P
AUID- ORCID: 0000-0003-3046-6576
AD  - Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et 
      Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne 
      Switzerland.
AD  - National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique 
      Fédérale de Lausanne (EPFL) Lausanne Switzerland.
FAU - Schwarting, Marcus
AU  - Schwarting M
AD  - Department of Computer Science, University of Chicago Chicago IL 60490 USA.
FAU - Shi, Jiale
AU  - Shi J
AUID- ORCID: 0000-0002-5447-3925
AD  - Department of Chemical Engineering, Massachusetts Institute of Technology 
      Cambridge Massachusetts 02139 USA.
FAU - Smit, Berend
AU  - Smit B
AUID- ORCID: 0000-0003-4653-8562
AD  - Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie 
      Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais 
      Switzerland mail@kjablonka.com.
FAU - Smith, Ben E
AU  - Smith BE
AUID- ORCID: 0000-0001-9673-2449
AD  - Yusuf Hamied Department of Chemistry, University of Cambridge Lensfield Road 
      Cambridge CB2 1EW UK.
FAU - Van Herck, Joren
AU  - Van Herck J
AUID- ORCID: 0009-0005-5108-5061
AD  - Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie 
      Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais 
      Switzerland mail@kjablonka.com.
FAU - Völker, Christoph
AU  - Völker C
AUID- ORCID: 0000-0002-0985-0074
AD  - Bundesanstalt für Materialforschung und -prüfung Unter den Eichen 87 12205 Berlin 
      Germany.
FAU - Ward, Logan
AU  - Ward L
AUID- ORCID: 0000-0002-1323-5939
AD  - Data Science and Learning Division, Argonne National Lab USA.
FAU - Warren, Sean
AU  - Warren S
AUID- ORCID: 0000-0002-3670-0354
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Weiser, Benjamin
AU  - Weiser B
AUID- ORCID: 0000-0002-3770-7224
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Zhang, Sylvester
AU  - Zhang S
AUID- ORCID: 0000-0002-4793-1131
AD  - Department of Chemistry, McGill University Montreal Quebec Canada.
FAU - Zhang, Xiaoqi
AU  - Zhang X
AUID- ORCID: 0000-0002-6507-6490
AD  - Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie 
      Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais 
      Switzerland mail@kjablonka.com.
FAU - Zia, Ghezal Ahmad
AU  - Zia GA
AUID- ORCID: 0000-0002-9082-9423
AD  - Bundesanstalt für Materialforschung und -prüfung Unter den Eichen 87 12205 Berlin 
      Germany.
FAU - Scourtas, Aristana
AU  - Scourtas A
AUID- ORCID: 0000-0002-3917-605X
AD  - Globus, University of Chicago, Data Science and Learning Division, Argonne 
      National Lab USA blaiszik@uchicago.edu.
FAU - Schmidt, K J
AU  - Schmidt KJ
AUID- ORCID: 0000-0002-9373-0058
AD  - Globus, University of Chicago, Data Science and Learning Division, Argonne 
      National Lab USA blaiszik@uchicago.edu.
FAU - Foster, Ian
AU  - Foster I
AUID- ORCID: 0000-0003-2129-5269
AD  - Department of Computer Science, University of Chicago, Data Science and Learning 
      Division, Argonne National Lab USA.
FAU - White, Andrew D
AU  - White AD
AUID- ORCID: 0000-0002-6647-3965
AD  - Department of Chemical Engineering, University of Rochester USA.
FAU - Blaiszik, Ben
AU  - Blaiszik B
AUID- ORCID: 0000-0002-5326-4902
AD  - Globus, University of Chicago, Data Science and Learning Division, Argonne 
      National Lab USA blaiszik@uchicago.edu.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230808
PL  - England
TA  - Digit Discov
JT  - Digital discovery
JID - 9918351186906676
PMC - PMC10561547
COIS- There are no conflicts to declare.
EDAT- 2023/11/28 06:42
MHDA- 2023/11/28 06:43
CRDT- 2023/11/28 03:55
PHST- 2023/06/12 00:00 [received]
PHST- 2023/08/08 00:00 [accepted]
PHST- 2023/11/28 06:43 [medline]
PHST- 2023/11/28 06:42 [pubmed]
PHST- 2023/11/28 03:55 [entrez]
AID - d3dd00113j [pii]
AID - 10.1039/d3dd00113j [doi]
PST - epublish
SO  - Digit Discov. 2023 Aug 8;2(5):1233-1250. doi: 10.1039/d3dd00113j. eCollection 
      2023 Oct 9.

PMID- 37325497
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230619
IS  - 2352-0477 (Print)
IS  - 2352-0477 (Electronic)
IS  - 2352-0477 (Linking)
VI  - 10
DP  - 2023
TI  - Large language models and the emergence phenomena.
PG  - 100494
LID - 10.1016/j.ejro.2023.100494 [doi]
LID - 100494
AB  - This perspective explores the potential of emergence phenomena in large language 
      models (LLMs) to transform data management and analysis in radiology. We provide 
      a concise explanation of LLMs, define the concept of emergence in machine 
      learning, offer examples of potential applications within the radiology field, 
      and discuss risks and limitations. Our goal is to encourage radiologists to 
      recognize and prepare for the impact this technology may have on radiology and 
      medicine in the near future.
CI  - © 2023 The Authors.
FAU - Sorin, Vera
AU  - Sorin V
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, affiliated to the 
      Sackler School of Medicine, Tel-Aviv University, Israel.
AD  - DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel.
FAU - Klang, Eyal
AU  - Klang E
AD  - Department of Diagnostic Imaging, Chaim Sheba Medical Center, affiliated to the 
      Sackler School of Medicine, Tel-Aviv University, Israel.
AD  - DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel.
AD  - Sami Sagol AI Hub, ARC, Sheba Medical Center, Israel.
LA  - eng
PT  - Journal Article
DEP - 20230606
PL  - England
TA  - Eur J Radiol Open
JT  - European journal of radiology open
JID - 101650225
PMC - PMC10265451
OTO - NOTNLM
OT  - ChatGPT
OT  - Emergence
OT  - Generative models
OT  - LLMs
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/06/16 06:42
MHDA- 2023/06/16 06:43
CRDT- 2023/06/16 04:25
PHST- 2023/04/12 00:00 [received]
PHST- 2023/05/15 00:00 [revised]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/06/16 06:43 [medline]
PHST- 2023/06/16 06:42 [pubmed]
PHST- 2023/06/16 04:25 [entrez]
AID - S2352-0477(23)00020-5 [pii]
AID - 100494 [pii]
AID - 10.1016/j.ejro.2023.100494 [doi]
PST - epublish
SO  - Eur J Radiol Open. 2023 Jun 6;10:100494. doi: 10.1016/j.ejro.2023.100494. 
      eCollection 2023.

PMID- 36994692
OWN - NLM
STAT- MEDLINE
DCOM- 20230331
LR  - 20230331
IS  - 0973-7138 (Electronic)
IS  - 0250-5991 (Linking)
VI  - 48
DP  - 2023
TI  - A Braver New World? Of chatbots and other cognoscenti.
LID - 10 [pii]
AB  - The 1920s mark the rise of robots in the human lexicon. Karel Capek, a Czech 
      playwright, wrote R. U. R., which stands for Rossumovi Univerzalnı Roboti or 
      Rossum's Universal Robots. The word for worker or labourer in Czech is robota; 
      Karel was given the idea for this word by his artist brother Josef, and the word 
      robot for a humanmanufactured humanoid entity was born in 1920. November 30, 
      2022, just over a century later, was when ChatGPT (Chat Generative Pre-trained 
      Transformer), a chat robot, chatter bot or advanced chatbot, was made available 
      by the company OpenAI for free download.
FAU - Borges, Renee M
AU  - Borges RM
AD  - Indian Institute of Science,Bengaluru 560012, India.
LA  - eng
PT  - Journal Article
PL  - India
TA  - J Biosci
JT  - Journal of biosciences
JID - 8100809
SB  - IM
MH  - Aged, 80 and over
MH  - Humans
MH  - Male
MH  - *Software
MH  - Robotics
EDAT- 2023/03/31 06:00
MHDA- 2023/03/31 06:42
CRDT- 2023/03/30 04:52
PHST- 2023/03/31 06:42 [medline]
PHST- 2023/03/30 04:52 [entrez]
PHST- 2023/03/31 06:00 [pubmed]
AID - 10 [pii]
PST - ppublish
SO  - J Biosci. 2023;48:10.

PMID- 37866938
OWN - NLM
STAT- MEDLINE
DCOM- 20231026
LR  - 20231112
IS  - 1672-173X (Print)
IS  - 1672-173X (Linking)
VI  - 54
IP  - 5
DP  - 2023 Sep
TI  - [Prospects for the Application of Healthcare Big Data Combined With Large 
      Language Models].
PG  - 855-856
LID - 10.12182/20230960301 [doi]
AB  - The application of big data technology combined with large language models is 
      expected to make an enormous impact in the field of medicine. Herein, the 
      prospects for the application of healthcare big data combined with large language 
      models were discussed in several aspects, including first in assisting doctors in 
      making diagnosis and differential diagnosis and, then, in the field of 
      evidence-based medicine. In addition, healthcare big data combined with large 
      language models could also be applied in assisting doctors to conduct clinical 
      and medical research. Through combining healthcare big data with large language 
      models, medical diagnosis and treatment with improved precision, efficiency, and 
      intelligence will be realized and greater contributions will be made to the field 
      of human health.
CI  - Copyright© by Editorial Board of Journal of Sichuan University (Medical 
      Sciences).
FAU - Chen, Runsheng
AU  - Chen R
AD  - West China Biomedical Big Data Center, West China Hospital, Sichuan University, 
      Chengdu 610041, China.
AD  - Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute 
      of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
LA  - chi
PT  - English Abstract
PT  - Journal Article
PT  - Review
PL  - China
TA  - Sichuan Da Xue Xue Bao Yi Xue Ban
JT  - Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical 
      science edition
JID - 101162609
SB  - IM
MH  - Humans
MH  - *Big Data
MH  - Artificial Intelligence
MH  - Delivery of Health Care
MH  - *Biomedical Research
PMC - PMC10579058
OTO - NOTNLM
OT  - Applicaitons
OT  - Big data technology
OT  - Large language model
EDAT- 2023/10/23 00:43
MHDA- 2023/10/26 06:42
CRDT- 2023/10/22 21:52
PHST- 2023/10/26 06:42 [medline]
PHST- 2023/10/23 00:43 [pubmed]
PHST- 2023/10/22 21:52 [entrez]
AID - scdxxbyxb-54-5-855 [pii]
AID - 10.12182/20230960301 [doi]
PST - ppublish
SO  - Sichuan Da Xue Xue Bao Yi Xue Ban. 2023 Sep;54(5):855-856. doi: 
      10.12182/20230960301.

PMID- 37796839
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 39
IP  - 10
DP  - 2023 Oct 3
TI  - Aliro: an automated machine learning tool leveraging large language models.
LID - 10.1093/bioinformatics/btad606 [doi]
LID - btad606
AB  - MOTIVATION: Biomedical and healthcare domains generate vast amounts of complex 
      data that can be challenging to analyze using machine learning tools, especially 
      for researchers without computer science training. RESULTS: Aliro is an 
      open-source software package designed to automate machine learning analysis 
      through a clean web interface. By infusing the power of large language models, 
      the user can interact with their data by seamlessly retrieving and executing code 
      pulled from the large language model, accelerating automated discovery of new 
      insights from data. Aliro includes a pre-trained machine learning recommendation 
      system that can assist the user to automate the selection of machine learning 
      algorithms and its hyperparameters and provides visualization of the evaluated 
      model and data. AVAILABILITY AND IMPLEMENTATION: Aliro is deployed by running its 
      custom Docker containers. Aliro is available as open-source from GitHub at: 
      https://github.com/EpistasisLab/Aliro.
CI  - © The Author(s) 2023. Published by Oxford University Press.
FAU - Choi, Hyunjun
AU  - Choi H
AD  - Department of Computational Biomedicine, Center for Artificial Intelligence 
      Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, 
      United States.
FAU - Moran, Jay
AU  - Moran J
AD  - Department of Computational Biomedicine, Center for Artificial Intelligence 
      Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, 
      United States.
FAU - Matsumoto, Nicholas
AU  - Matsumoto N
AD  - Department of Computational Biomedicine, Center for Artificial Intelligence 
      Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, 
      United States.
FAU - Hernandez, Miguel E
AU  - Hernandez ME
AD  - Department of Computational Biomedicine, Center for Artificial Intelligence 
      Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, 
      United States.
FAU - Moore, Jason H
AU  - Moore JH
AUID- ORCID: 0000-0002-5015-1099
AD  - Department of Computational Biomedicine, Center for Artificial Intelligence 
      Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, 
      United States.
LA  - eng
GR  - R01 LM010098/LM/NLM NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - *Software
MH  - *Algorithms
MH  - Machine Learning
MH  - Language
PMC - PMC10576163
COIS- None declared.
EDAT- 2023/10/05 18:42
MHDA- 2023/10/23 01:18
CRDT- 2023/10/05 13:37
PHST- 2023/07/28 00:00 [received]
PHST- 2023/09/16 00:00 [revised]
PHST- 2023/10/03 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/05 18:42 [pubmed]
PHST- 2023/10/05 13:37 [entrez]
AID - 7291858 [pii]
AID - btad606 [pii]
AID - 10.1093/bioinformatics/btad606 [doi]
PST - ppublish
SO  - Bioinformatics. 2023 Oct 3;39(10):btad606. doi: 10.1093/bioinformatics/btad606.

PMID- 37525863
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230802
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - Predictive Factors for High Post-void Residual Volume in Older Females After 
      OnabotulinumA Treatment for Severe Overactive Bladder Using a Machine Learning 
      Model.
PG  - e42668
LID - 10.7759/cureus.42668 [doi]
LID - e42668
AB  - Introduction Intravesical onabotulinumA injection is actively used for the 
      treatment of overactive bladder (OAB). However, it occasionally results in 
      significant post-void residual urine (PVR) volume, which can lead to 
      complications and can further impair the activities of daily living in older 
      people. Therefore, this study aimed to identify the predictors of a high 
      post-onabotulinumA injection PVR volume in older women with severe OAB. Methods 
      An observational study was conducted on older women who had previously received 
      intravesical onabotulinumA injections to treat OAB between 2020 and 2022. 
      Urodynamic studies and symptom assessments were conducted, and machine learning 
      models, including random forest and support vector machine (SVM) models, were 
      developed using the R code generated by Chat Generative Pre-trained Transformer 4 
      (ChatGPT, OpenAI, San Francisco, USA). Results Among 128 patients with OAB, 23 
      (18.0%) had a PVR volume of > 200 mL after receiving onabotulinumA injections. 
      The factors associated with a PVR volume of > 200 mL were investigated using 
      univariate and multivariate analyses. Age, frailty, OAB-wet, daytime frequency, 
      and nocturia were significant predictors. Random forest analysis highlighted 
      daytime frequency, frailty, and voiding efficiency as important factors. An SVM 
      model incorporating daytime frequency, frailty, and voiding efficiency improved 
      PVR volume prediction. Logit(p) estimation yielded an area under the receiver 
      operating characteristic curve of 0.926294.  Conclusion The study found daytime 
      frequency, frailty, and voiding inefficiency to be significant factors associated 
      with a PVR volume of > 200 mL, in older women with severe OAB. Utilizing advanced 
      machine learning techniques and following the guidance of ChatGPT, this research 
      emphasizes the relevance of considering multiple intersecting factors for 
      predicting PVR volume. The findings contribute to our understanding of 
      onabotulinumA injection treatment for OAB and support evidence-based 
      decision-making using readily available information.
CI  - Copyright © 2023, Okui et al.
FAU - Okui, Nobuo
AU  - Okui N
AD  - Dentistry, Kanagawa Dental University, Kanagawa, JPN.
FAU - Ikegami, Tadashi
AU  - Ikegami T
AD  - Diagnostic Imaging, Kanagawa Dental University, Kanagawa, JPN.
FAU - Hashimoto, Tatsuo
AU  - Hashimoto T
AD  - Internal Medicine, Kanagawa Dental University, Kanagawa, JPN.
FAU - Kouno, Yuko
AU  - Kouno Y
AD  - Urology, Dr Okui's Urogynecology and Urology, Kanagawa, JPN.
FAU - Nakano, Kaori
AU  - Nakano K
AD  - Urology, Dr Okui's Urogynecology and Urology, Kanagawa, JPN.
FAU - Okui, Machiko Aurora
AU  - Okui MA
AD  - Urogynecology, Yokosuka Urogynecology Clinic, Kanagawa, JPN.
LA  - eng
PT  - Journal Article
DEP - 20230729
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10387135
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - frailty
OT  - machine learning models
OT  - onabotulinuma
OT  - overactive bladder
OT  - post-void residual volume
OT  - random forest
OT  - support vector machine
OT  - urodynamic studies
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/01 06:45
MHDA- 2023/08/01 06:46
CRDT- 2023/08/01 03:38
PHST- 2023/07/29 00:00 [accepted]
PHST- 2023/08/01 06:46 [medline]
PHST- 2023/08/01 06:45 [pubmed]
PHST- 2023/08/01 03:38 [entrez]
AID - 10.7759/cureus.42668 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 29;15(7):e42668. doi: 10.7759/cureus.42668. eCollection 2023 
      Jul.

PMID- 37487183
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1536-0075 (Electronic)
IS  - 1526-5161 (Linking)
VI  - 23
IP  - 10
DP  - 2023 Oct
TI  - AUTOGEN: A Personalized Large Language Model for Academic Enhancement-Ethics and 
      Proof of Principle.
PG  - 28-41
LID - 10.1080/15265161.2023.2233356 [doi]
AB  - In this article, we explore the potential of enhancing academic prose and idea 
      generation by fine-tuning a large language model (here, GPT-3) on one's own 
      previously published writings: AUTOGEN ("AI Unique Tailored Output GENerator"). 
      We develop, test, and describe three distinct AUTOGEN models trained on the prior 
      scholarly output of three of the current authors (SBM, BDE, JS), with a fourth 
      model trained on the combined works of all three. Our AUTOGEN models demonstrate 
      greater variance in quality than the base GPT-3 model, with many outputs 
      outperforming the base model in format, style, overall quality, and novel idea 
      generation. As proof of principle, we present and discuss examples of 
      AUTOGEN-written sections of existing and hypothetical research papers. We further 
      discuss ethical opportunities, concerns, and open questions associated with 
      personalized academic prose and idea generators. Ethical opportunities for 
      personalized LLMs such as AUTOGEN include increased productivity, preservation of 
      writing styles and cultural traditions, and aiding consensus building. However, 
      ethical concerns arise due to the potential for personalized LLMs to reduce 
      output diversity, violate privacy and intellectual property rights, and 
      facilitate plagiarism or fraud. The use of coauthored or multiple-source trained 
      models further complicates issues surrounding ownership and attribution. Open 
      questions concern a potential credit-blame asymmetry for LLM outputs, the 
      legitimacy of licensing agreements in authorship ascription, and the ethical 
      implications of coauthorship attribution for data contributors. Ensuring the 
      output is sufficiently distinct from the source material is crucial to 
      maintaining ethical standards in academic writing. These opportunities, risks, 
      and open issues highlight the intricate ethical landscape surrounding the use of 
      personalized LLMs in academia. We also discuss open technical questions 
      concerning the integration of AUTOGEN-style personalized LLMs with other LLMs, 
      such as GPT-4, for iterative refinement and improvement of generated text. In 
      conclusion, we argue that AUTOGEN-style personalized LLMs offer significant 
      potential benefits in terms of both prose generation and, to a lesser extent, 
      idea generation. If associated ethical issues are appropriately addressed, 
      AUTOGEN alone or in combination with other LLMs can be seen as a potent form of 
      academic enhancement.
FAU - Porsdam Mann, Sebastian
AU  - Porsdam Mann S
AUID- ORCID: 0000-0002-1867-2097
AD  - University of Oxford.
FAU - Earp, Brian D
AU  - Earp BD
AUID- ORCID: 0000-0001-9691-2888
AD  - University of Oxford.
FAU - Møller, Nikolaj
AU  - Møller N
AD  - University of Oxford.
FAU - Vynn, Suren
AU  - Vynn S
AD  - Independent Researcher.
FAU - Savulescu, Julian
AU  - Savulescu J
AD  - National University of Singapore.
LA  - eng
PT  - Journal Article
DEP - 20230724
PL  - United States
TA  - Am J Bioeth
JT  - The American journal of bioethics : AJOB
JID - 100898738
SB  - IM
CIN - Am J Bioeth. 2023 Oct;23(10):60-63. PMID: 37812095
CIN - Am J Bioeth. 2023 Oct;23(10):94-96. PMID: 37812096
CIN - Am J Bioeth. 2023 Oct;23(10):50-52. PMID: 37812101
CIN - Am J Bioeth. 2023 Oct;23(10):96-99. PMID: 37812109
CIN - Am J Bioeth. 2023 Oct;23(10):42-44. PMID: 37812114
CIN - Am J Bioeth. 2023 Oct;23(10):91-93. PMID: 37812115
CIN - Am J Bioeth. 2023 Oct;23(10):105-107. PMID: 37812117
CIN - Am J Bioeth. 2023 Oct;23(10):44-47. PMID: 37812121
CIN - Am J Bioeth. 2023 Oct;23(10):1-5. PMID: 37831940
OTO - NOTNLM
OT  - AUTOGEN
OT  - Fine-tuning
OT  - bioethics
OT  - ethics
OT  - large language models (LLM)
OT  - personalised LLM
EDAT- 2023/07/24 19:09
MHDA- 2023/07/24 19:09
CRDT- 2023/07/24 16:33
PHST- 2023/07/24 19:09 [pubmed]
PHST- 2023/07/24 19:09 [medline]
PHST- 2023/07/24 16:33 [entrez]
AID - 10.1080/15265161.2023.2233356 [doi]
PST - ppublish
SO  - Am J Bioeth. 2023 Oct;23(10):28-41. doi: 10.1080/15265161.2023.2233356. Epub 2023 
      Jul 24.

PMID- 37888113
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231029
IS  - 2075-4426 (Print)
IS  - 2075-4426 (Electronic)
IS  - 2075-4426 (Linking)
VI  - 13
IP  - 10
DP  - 2023 Oct 16
TI  - Challenging ChatGPT 3.5 in Senology-An Assessment of Concordance with Breast 
      Cancer Tumor Board Decision Making.
LID - 10.3390/jpm13101502 [doi]
LID - 1502
AB  - With the recent diffusion of access to publicly available large language models 
      (LLMs), common interest in generative artificial-intelligence-based applications 
      for medical purposes has skyrocketed. The increased use of these models by 
      tech-savvy patients for personal health issues calls for a scientific evaluation 
      of whether LLMs provide a satisfactory level of accuracy for treatment decisions. 
      This observational study compares the concordance of treatment recommendations 
      from the popular LLM ChatGPT 3.5 with those of a multidisciplinary tumor board 
      for breast cancer (MTB). The study design builds on previous findings by 
      combining an extended input model with patient profiles reflecting patho- and 
      immunomorphological diversity of primary breast cancer, including primary 
      metastasis and precancerous tumor stages. Overall concordance between the LLM and 
      MTB is reached for half of the patient profiles, including precancerous lesions. 
      In the assessment of invasive breast cancer profiles, the concordance amounts to 
      58.8%. Nevertheless, as the LLM makes considerably fraudulent decisions at times, 
      we do not identify the current development status of publicly available LLMs to 
      be adequate as a support tool for tumor boards. Gynecological oncologists should 
      familiarize themselves with the capabilities of LLMs in order to understand and 
      utilize their potential while keeping in mind potential risks and limitations.
FAU - Griewing, Sebastian
AU  - Griewing S
AUID- ORCID: 0000-0001-5355-8903
AD  - Institute for Digital Medicine, University Hospital Marburg, Philipps-University 
      Marburg, Baldingerstraße, 35043 Marburg, Germany.
AD  - Department of Gynecology and Obstetrics, University Hospital Marburg, 
      Philipps-University Marburg, Baldingerstraße, 35043 Marburg, Germany.
AD  - Institute for Healthcare Management, Chair of General Business Administration, 
      Philipps-University Marburg, Universitätsstraße 24, 35037 Marburg, Germany.
FAU - Gremke, Niklas
AU  - Gremke N
AUID- ORCID: 0000-0002-9015-3646
AD  - Department of Gynecology and Obstetrics, University Hospital Marburg, 
      Philipps-University Marburg, Baldingerstraße, 35043 Marburg, Germany.
FAU - Wagner, Uwe
AU  - Wagner U
AD  - Department of Gynecology and Obstetrics, University Hospital Marburg, 
      Philipps-University Marburg, Baldingerstraße, 35043 Marburg, Germany.
FAU - Lingenfelder, Michael
AU  - Lingenfelder M
AD  - Institute for Healthcare Management, Chair of General Business Administration, 
      Philipps-University Marburg, Universitätsstraße 24, 35037 Marburg, Germany.
FAU - Kuhn, Sebastian
AU  - Kuhn S
AUID- ORCID: 0000-0002-8031-2973
AD  - Institute for Digital Medicine, University Hospital Marburg, Philipps-University 
      Marburg, Baldingerstraße, 35043 Marburg, Germany.
FAU - Boekhoff, Jelena
AU  - Boekhoff J
AD  - Department of Gynecology and Obstetrics, University Hospital Marburg, 
      Philipps-University Marburg, Baldingerstraße, 35043 Marburg, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231016
PL  - Switzerland
TA  - J Pers Med
JT  - Journal of personalized medicine
JID - 101602269
PMC - PMC10608120
OTO - NOTNLM
OT  - artificial intelligence
OT  - gynecology
OT  - large language models
OT  - oncology
OT  - tumor board
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/27 12:43
MHDA- 2023/10/27 12:44
CRDT- 2023/10/27 07:10
PHST- 2023/09/26 00:00 [received]
PHST- 2023/10/13 00:00 [revised]
PHST- 2023/10/13 00:00 [accepted]
PHST- 2023/10/27 12:44 [medline]
PHST- 2023/10/27 12:43 [pubmed]
PHST- 2023/10/27 07:10 [entrez]
AID - jpm13101502 [pii]
AID - jpm-13-01502 [pii]
AID - 10.3390/jpm13101502 [doi]
PST - epublish
SO  - J Pers Med. 2023 Oct 16;13(10):1502. doi: 10.3390/jpm13101502.

PMID- 37704461
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1532-9496 (Electronic)
IS  - 0887-7963 (Linking)
VI  - 37
IP  - 3
DP  - 2023 Jul
TI  - Battle of the (Chat)Bots: Comparing Large Language Models to Practice Guidelines 
      for Transfusion-Associated Graft-Versus-Host Disease Prevention.
PG  - 150753
LID - S0887-7963(23)00043-3 [pii]
LID - 10.1016/j.tmrv.2023.150753 [doi]
AB  - Published guidelines and clinical practices vary when defining indications for 
      irradiation of blood components for the prevention of transfusion-associated 
      graft-versus-host disease (TA-GVHD). This study assessed irradiation indication 
      lists generated by multiple artificial intelligence (AI) programs, or chatbots, 
      and compared them to 2020 British Society for Haematology (BSH) practice 
      guidelines. Four chatbots (ChatGPT-3.5, ChatGPT-4, Bard, and Bing Chat) were 
      prompted to list the indications for irradiation to prevent TA-GVHD. Responses 
      were graded for concordance with BSH guidelines. Chatbot response length, 
      discrepancies, and omissions were noted. Chatbot responses differed, but all were 
      relevant, short in length, generally more concordant than discordant with BSH 
      guidelines, and roughly complete. They lacked several indications listed in BSH 
      guidelines and notably differed in their irradiation eligibility criteria for 
      fetuses and neonates. The chatbots variably listed erroneous indications for 
      TA-GVHD prevention, such as patients receiving blood from a donor who is of a 
      different race or ethnicity. This study demonstrates the potential use of 
      generative AI for transfusion medicine and hematology topics but underscores the 
      risk of chatbot medical misinformation. Further study of risk factors for 
      TA-GVHD, as well as the applications of chatbots in transfusion medicine and 
      hematology, is warranted.
CI  - Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
FAU - Stephens, Laura D
AU  - Stephens LD
AD  - Department of Pathology, University of California San Diego, San Diego, CA, USA. 
      Electronic address: ldstephens@health.ucsd.edu.
FAU - Jacobs, Jeremy W
AU  - Jacobs JW
AD  - Department of Laboratory Medicine and Pathology, Rochester, MN, USA.
FAU - Adkins, Brian D
AU  - Adkins BD
AD  - Department of Pathology, Department of Pathology, University of Texas 
      Southwestern Medical Center, University of Texas Southwestern Medical Center, 
      Dallas, TX, USA.
FAU - Booth, Garrett S
AU  - Booth GS
AD  - Department of Pathology, Microbiology, and Immunology, Vanderbilt University 
      Medical Center, Nashville, TN, USA.
LA  - eng
PT  - Journal Article
DEP - 20230819
PL  - United States
TA  - Transfus Med Rev
JT  - Transfusion medicine reviews
JID - 8709027
SB  - IM
MH  - Infant, Newborn
MH  - Humans
MH  - Artificial Intelligence
MH  - *Graft vs Host Disease/prevention & control/etiology
MH  - *Transfusion Reaction/prevention & control/complications
MH  - Blood Component Transfusion/adverse effects
MH  - Language
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Blood transfusion
OT  - Medical ethics
OT  - Transfusion medicine
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/09/14 00:42
MHDA- 2023/10/23 00:42
CRDT- 2023/09/13 21:53
PHST- 2023/07/17 00:00 [received]
PHST- 2023/08/04 00:00 [revised]
PHST- 2023/08/08 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/09/14 00:42 [pubmed]
PHST- 2023/09/13 21:53 [entrez]
AID - S0887-7963(23)00043-3 [pii]
AID - 10.1016/j.tmrv.2023.150753 [doi]
PST - ppublish
SO  - Transfus Med Rev. 2023 Jul;37(3):150753. doi: 10.1016/j.tmrv.2023.150753. Epub 
      2023 Aug 19.

PMID- 37077585
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230421
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Appendix Playing Hide and Seek: A Variation to Amyand's Hernia.
PG  - e36326
LID - 10.7759/cureus.36326 [doi]
LID - e36326
AB  - Amyand's hernia is a rare condition where the appendix becomes trapped in the 
      inguinal hernia sac, leading to severe complications if left untreated. Treatment 
      typically involves surgical repair of the hernia, with the removal of the 
      appendix if necessary. This case report presents a 65-year-old male with 
      compromised cardiac status and a right inguinal hernia, confirmed by ultrasound. 
      The surgery was performed under local anesthesia, and the appendix was normal and 
      reduced back. The patient was discharged on the next day of surgery after an 
      uneventful course in the hospital. There is a difference of opinion regarding the 
      need for an appendectomy in an Amyand's hernia with a normal appendix, with the 
      appendix dancing in and out of the inguinal canal while coughing on the table. 
      The decision to remove or leave a normal appendix in this situation should be 
      based on several factors, including the patient's age, appendix anatomy, and 
      extent of intraoperative inflammation. In conclusion, local anesthesia can be a 
      safe and effective option for patients who are not fit for general or spinal 
      anesthesia. The decision to remove or leave a normal appendix in Amyand's hernia 
      should be based on several factors.
CI  - Copyright © 2023, Bawa et al.
FAU - Bawa, Ashvind
AU  - Bawa A
AD  - Department of General Surgery, Dayanand Medical College and Hospital, Ludhiana, 
      IND.
FAU - Kansal, Rohin
AU  - Kansal R
AD  - Department of General Surgery, Dayanand Medical College and Hospital, Ludhiana, 
      IND.
FAU - Sharma, Sonalika
AU  - Sharma S
AD  - Department of General Surgery, Dayanand Medical College and Hospital, Ludhiana, 
      IND.
FAU - Rengan, Vinayak
AU  - Rengan V
AD  - Department of General Surgery, Dr. Mehta's Hospital, Chennai, IND.
FAU - Meenashi Sundaram, Pravin
AU  - Meenashi Sundaram P
AD  - Department of General Surgery, Dr. Rengan's Surgical Center, Chennai, IND.
LA  - eng
PT  - Case Reports
DEP - 20230318
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10108743
OTO - NOTNLM
OT  - amyand’s hernia
OT  - chatgpt
OT  - chatgpt improved case report
OT  - mesh repair
OT  - tension free mesh repair
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/20 06:42
MHDA- 2023/04/20 06:43
CRDT- 2023/04/20 02:23
PHST- 2023/03/18 00:00 [accepted]
PHST- 2023/04/20 06:43 [medline]
PHST- 2023/04/20 06:42 [pubmed]
PHST- 2023/04/20 02:23 [entrez]
AID - 10.7759/cureus.36326 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 18;15(3):e36326. doi: 10.7759/cureus.36326. eCollection 2023 
      Mar.

PMID- 38016816
OWN - NLM
STAT- Publisher
LR  - 20231128
IS  - 1365-2125 (Electronic)
IS  - 0306-5251 (Linking)
DP  - 2023 Nov 28
TI  - Using artificial intelligence to create diverse and inclusive medical case 
      vignettes for education.
LID - 10.1111/bcp.15977 [doi]
AB  - INTRODUCTION: Medical case vignettes play a crucial role in medical education, 
      yet they often fail to authentically represent diverse patients. Moreover, these 
      vignettes tend to oversimplify the complex relationship between patient 
      characteristics and medical conditions, leading to biased and potentially harmful 
      perspectives among students. Displaying aspects of patient diversity, such as 
      ethnicity, in written cases proves challenging. Additionally, creating these 
      cases places a significant burden on teachers in terms of labor and time. Our 
      objective is to explore the potential of AI-assisted computer-generated clinical 
      cases to expedite case creation and enhance diversity, along with AI-generated 
      patient photographs for more lifelike portrayal. METHODS: In this study, we 
      employed chatGPT (OpenAI, GPT 3.5) to develop diverse and inclusive medical case 
      vignettes. We evaluated various approaches and identified a set of eight 
      consecutive prompts that can be readily customized to accommodate local contexts 
      and specific assignments. To enhance visual representation, we utilized Adobe 
      Firefly beta for image generation. RESULTS: Using the described prompts, we 
      consistently generated cases for various assignments, producing sets of 30 cases 
      at a time. We ensured the inclusion of mandatory checks and formatting, 
      completing the process within approximately 60 minutes per set. DISCUSSION: Our 
      approach significantly accelerated case creation and improved diversity, though 
      prioritizing maximum diversity compromised representativeness to some extent. 
      While the optimized prompts are easily reusable, the process itself demands 
      computer skills not all educators possess. To address this, we aim to share all 
      created patients as Open Educational Resources (OER), empowering educators to 
      create cases independently.
CI  - This article is protected by copyright. All rights reserved.
FAU - Bakkum, Michiel J
AU  - Bakkum MJ
AUID- ORCID: 0000-0003-0928-812X
AD  - Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Internal Medicine, 
      Section Pharmacotherapy, De Boelelaan 1117, 1081, HV, Amsterdam, Netherlands.
AD  - Research and Expertise Centre in Pharmacotherapy Education (RECIPE), De Boelelaan 
      1117, 1081, HV, Amsterdam, The Netherlands.
FAU - Hartjes, Mariëlle G
AU  - Hartjes MG
AD  - Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Internal Medicine, 
      Section Pharmacotherapy, De Boelelaan 1117, 1081, HV, Amsterdam, Netherlands.
AD  - Research and Expertise Centre in Pharmacotherapy Education (RECIPE), De Boelelaan 
      1117, 1081, HV, Amsterdam, The Netherlands.
FAU - Piët, Joost D
AU  - Piët JD
AUID- ORCID: 0009-0000-7380-4876
AD  - Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Internal Medicine, 
      Section Pharmacotherapy, De Boelelaan 1117, 1081, HV, Amsterdam, Netherlands.
AD  - Research and Expertise Centre in Pharmacotherapy Education (RECIPE), De Boelelaan 
      1117, 1081, HV, Amsterdam, The Netherlands.
FAU - Donker, Erik M
AU  - Donker EM
AUID- ORCID: 0000-0002-8169-0714
AD  - Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Internal Medicine, 
      Section Pharmacotherapy, De Boelelaan 1117, 1081, HV, Amsterdam, Netherlands.
AD  - Research and Expertise Centre in Pharmacotherapy Education (RECIPE), De Boelelaan 
      1117, 1081, HV, Amsterdam, The Netherlands.
FAU - Likic, Robert
AU  - Likic R
AUID- ORCID: 0000-0003-1413-4862
AD  - European Association for Clinical Pharmacology and Therapeutics (EACPT) Education 
      Working Group.
AD  - University of Zagreb School of Medicine and Clinical Hospital Centre Zagreb, Unit 
      of Clinical Pharmacology, Zagreb, Croatia.
FAU - Sanz, Emilio
AU  - Sanz E
AUID- ORCID: 0000-0001-6788-4435
AD  - European Association for Clinical Pharmacology and Therapeutics (EACPT) Education 
      Working Group.
AD  - Universidad de La Laguna, school of Health Sciences, Tenerife, Spain and Hospital 
      Universitario de Canarias. La Laguna, Tenerife, Spain.
FAU - de Ponti, Fabrizio
AU  - de Ponti F
AUID- ORCID: 0000-0002-0367-9595
AD  - European Association for Clinical Pharmacology and Therapeutics (EACPT) Education 
      Working Group.
AD  - Department of Medical and Surgical Sciences, Pharmacology Unit, Alma Mater 
      Studiorum, University of Bologna, Bologna, Italy.
FAU - Verdonk, Petra
AU  - Verdonk P
AD  - European Association for Clinical Pharmacology and Therapeutics (EACPT) Education 
      Working Group.
AD  - Department of Ethics, Law & Humanities, APH research institute, Amsterdam UMC-VU 
      University, Amsterdam, The Netherlands.
FAU - Richir, Milan C
AU  - Richir MC
AD  - Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Internal Medicine, 
      Section Pharmacotherapy, De Boelelaan 1117, 1081, HV, Amsterdam, Netherlands.
AD  - Research and Expertise Centre in Pharmacotherapy Education (RECIPE), De Boelelaan 
      1117, 1081, HV, Amsterdam, The Netherlands.
FAU - van Agtmael, Michiel A
AU  - van Agtmael MA
AD  - Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Internal Medicine, 
      Section Pharmacotherapy, De Boelelaan 1117, 1081, HV, Amsterdam, Netherlands.
AD  - Research and Expertise Centre in Pharmacotherapy Education (RECIPE), De Boelelaan 
      1117, 1081, HV, Amsterdam, The Netherlands.
AD  - European Association for Clinical Pharmacology and Therapeutics (EACPT) Education 
      Working Group.
FAU - Tichelaar, Jelle
AU  - Tichelaar J
AD  - Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Internal Medicine, 
      Section Pharmacotherapy, De Boelelaan 1117, 1081, HV, Amsterdam, Netherlands.
AD  - Research and Expertise Centre in Pharmacotherapy Education (RECIPE), De Boelelaan 
      1117, 1081, HV, Amsterdam, The Netherlands.
AD  - European Association for Clinical Pharmacology and Therapeutics (EACPT) Education 
      Working Group.
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - England
TA  - Br J Clin Pharmacol
JT  - British journal of clinical pharmacology
JID - 7503323
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - chatGPT
OT  - diversity and inclusivity
EDAT- 2023/11/29 00:42
MHDA- 2023/11/29 00:42
CRDT- 2023/11/28 21:53
PHST- 2023/10/27 00:00 [revised]
PHST- 2023/08/18 00:00 [received]
PHST- 2023/10/28 00:00 [accepted]
PHST- 2023/11/29 00:42 [medline]
PHST- 2023/11/29 00:42 [pubmed]
PHST- 2023/11/28 21:53 [entrez]
AID - 10.1111/bcp.15977 [doi]
PST - aheadofprint
SO  - Br J Clin Pharmacol. 2023 Nov 28. doi: 10.1111/bcp.15977.

PMID- 37348667
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231111
IS  - 1879-1891 (Electronic)
IS  - 0002-9394 (Linking)
VI  - 254
DP  - 2023 Oct
TI  - Large Language Models in Ophthalmology Scientific Writing: Ethical Considerations 
      Blurred Lines or Not at All?
PG  - 177-181
LID - S0002-9394(23)00237-4 [pii]
LID - 10.1016/j.ajo.2023.06.004 [doi]
AB  - PURPOSE: To discuss the implications of large language models (LLMs) in 
      ophthalmology research, as well as the associated ethical considerations. DESIGN: 
      Perspective. METHODS: This discussion reviews the potential uses of LLMs such as 
      ChatGPT in ophthalmology research, highlights the associated threats and ethical 
      considerations, and proposes solutions for the use of LLMs in ophthalmology 
      research and scientific writing. RESULTS: With the increasing interest in LLMs, 
      such as ChatGPT, their diverse utility has been widely explored, including their 
      application in research and scientific writing. LLMs have the potential to guide 
      researchers throughout the different stages of their research, from idea 
      generation to drafting a scientific piece. However, there are significant ethical 
      concerns and challenges related to scientific integrity in ophthalmology research 
      that should be addressed by scientific journals. Our review of the 10 
      highest-impact-factor ophthalmology journals revealed that the number of journals 
      addressing this topic in their submission guidelines is rapidly increasing. 
      Therefore, we propose certain domains that all journals should consider regarding 
      the use of LLMs in research. CONCLUSIONS: As LLMs continue to improve, their use 
      in scientific writing will remain a contentious issue due to the ethical dilemmas 
      involved in determining the appropriate scope of their use. This article reviews 
      the ethical dilemmas related to the use of LLMs in ophthalmology research and 
      calls for the prompt development of guidelines for their ethical use in 
      manuscript writing as ophthalmology journals update their editorial policies with 
      respect to LLMs.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Salimi, Ali
AU  - Salimi A
AD  - From the Department of Ophthalmology and Visual Sciences, McGill University, 
      Montreal, Quebec, Canada.
FAU - Saheb, Hady
AU  - Saheb H
AD  - From the Department of Ophthalmology and Visual Sciences, McGill University, 
      Montreal, Quebec, Canada. Electronic address: hady.saheb@mcgill.ca.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230620
PL  - United States
TA  - Am J Ophthalmol
JT  - American journal of ophthalmology
JID - 0370500
SB  - IM
MH  - Humans
MH  - *Ophthalmology
MH  - Writing
MH  - Editorial Policies
MH  - Language
EDAT- 2023/06/23 01:10
MHDA- 2023/10/23 00:42
CRDT- 2023/06/22 19:14
PHST- 2023/04/05 00:00 [received]
PHST- 2023/05/12 00:00 [revised]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/06/23 01:10 [pubmed]
PHST- 2023/06/22 19:14 [entrez]
AID - S0002-9394(23)00237-4 [pii]
AID - 10.1016/j.ajo.2023.06.004 [doi]
PST - ppublish
SO  - Am J Ophthalmol. 2023 Oct;254:177-181. doi: 10.1016/j.ajo.2023.06.004. Epub 2023 
      Jun 20.

PMID- 37707991
OWN - NLM
STAT- MEDLINE
DCOM- 20231113
LR  - 20231113
IS  - 1557-8852 (Electronic)
IS  - 1084-9785 (Linking)
VI  - 38
IP  - 9
DP  - 2023 Nov
TI  - Triangle of Trust in Cancer Care? The Physician, the Patient, and Artificial 
      Intelligence Chatbot.
PG  - 581-584
LID - 10.1089/cbr.2023.0112 [doi]
AB  - Trust, as a philosophic paradigm, is predominantly interpersonal, between human 
      beings, and is differentiated from reliance. Can a person trust an inhumane 
      amoral agent, such as a large language model artificial intelligence (AI) 
      chatbot, to manifest the goodwill and willingness normally required in order for 
      it to be deemed trustworthy? This article explores the relationship between the 
      cancer patient, their physician, and AI chatbot in a proposed tripartite, 
      consultative, personalized approach to shared-care in precision molecular 
      oncology. It examines the nature of trust between human agents and machines. It 
      also contemplates AI-enhanced technical precision in state-of-the-art cancer 
      management, complemented by trustworthy, holistic clinical care by a physician, 
      for each individual patient. "To what extent can the user "trust" GPT-4?" Peter 
      Lee,(1) Microsoft Research 2023.
FAU - Turner, J Harvey
AU  - Turner JH
AUID- ORCID: 0000-0002-5254-9914
AD  - Department of Nuclear Medicine, The University of Western Australia, Fiona 
      Stanley Fremantle Hospitals Group, Murdoch, Australia.
LA  - eng
PT  - Editorial
DEP - 20230914
PL  - United States
TA  - Cancer Biother Radiopharm
JT  - Cancer biotherapy & radiopharmaceuticals
JID - 9605408
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - Trust
MH  - *Physicians
MH  - *Neoplasms/therapy
OTO - NOTNLM
OT  - phronesis
OT  - precision versus personalized oncology
OT  - shared-care
OT  - trust in artificial intelligence
EDAT- 2023/09/14 18:43
MHDA- 2023/11/13 06:42
CRDT- 2023/09/14 12:32
PHST- 2023/11/13 06:42 [medline]
PHST- 2023/09/14 18:43 [pubmed]
PHST- 2023/09/14 12:32 [entrez]
AID - 10.1089/cbr.2023.0112 [doi]
PST - ppublish
SO  - Cancer Biother Radiopharm. 2023 Nov;38(9):581-584. doi: 10.1089/cbr.2023.0112. 
      Epub 2023 Sep 14.

PMID- 37108453
OWN - NLM
STAT- MEDLINE
DCOM- 20230501
LR  - 20230501
IS  - 1422-0067 (Electronic)
IS  - 1422-0067 (Linking)
VI  - 24
IP  - 8
DP  - 2023 Apr 14
TI  - Automatic Generation of SBML Kinetic Models from Natural Language Texts Using 
      GPT.
LID - 10.3390/ijms24087296 [doi]
LID - 7296
AB  - Kinetic modeling is an essential tool in systems biology research, enabling the 
      quantitative analysis of biological systems and predicting their behavior. 
      However, the development of kinetic models is a complex and time-consuming 
      process. In this article, we propose a novel approach called KinModGPT, which 
      generates kinetic models directly from natural language text. KinModGPT employs 
      GPT as a natural language interpreter and Tellurium as an SBML generator. We 
      demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from 
      complex natural language descriptions of biochemical reactions. KinModGPT 
      successfully generates valid SBML models from a range of natural language model 
      descriptions of metabolic pathways, protein-protein interaction networks, and 
      heat shock response. This article demonstrates the potential of KinModGPT in 
      kinetic modeling automation.
FAU - Maeda, Kazuhiro
AU  - Maeda K
AUID- ORCID: 0000-0002-6038-1322
AD  - Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 
      680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan.
FAU - Kurata, Hiroyuki
AU  - Kurata H
AUID- ORCID: 0000-0003-4254-2214
AD  - Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 
      680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan.
LA  - eng
GR  - 22K12247/Japan Society for the Promotion of Science/
GR  - 22H03688/Japan Society for the Promotion of Science/
GR  - JPMJPR20K8/Japan Science and Technology Agency/
PT  - Journal Article
DEP - 20230414
PL  - Switzerland
TA  - Int J Mol Sci
JT  - International journal of molecular sciences
JID - 101092791
SB  - IM
MH  - *Programming Languages
MH  - Computer Simulation
MH  - *Models, Biological
MH  - Language
MH  - Cell Physiological Phenomena
MH  - Software
PMC - PMC10138937
OTO - NOTNLM
OT  - GPT
OT  - kinetic modeling
OT  - large language model
OT  - simulation
OT  - systems biology
COIS- The authors declare no conflict of interest. The funders had no role in the 
      design of the study, in the collection, analyses, or interpretation of data, in 
      the writing of the manuscript, or in the decision to publish the results.
EDAT- 2023/04/28 06:41
MHDA- 2023/05/01 06:42
CRDT- 2023/04/28 01:27
PHST- 2023/03/06 00:00 [received]
PHST- 2023/04/05 00:00 [revised]
PHST- 2023/04/13 00:00 [accepted]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/28 06:41 [pubmed]
PHST- 2023/04/28 01:27 [entrez]
AID - ijms24087296 [pii]
AID - ijms-24-07296 [pii]
AID - 10.3390/ijms24087296 [doi]
PST - epublish
SO  - Int J Mol Sci. 2023 Apr 14;24(8):7296. doi: 10.3390/ijms24087296.

PMID- 37790547
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
DP  - 2023 Sep 18
TI  - Evaluation of large language models for discovery of gene set function.
LID - rs.3.rs-3270331 [pii]
LID - 10.21203/rs.3.rs-3270331/v1 [doi]
AB  - Gene set analysis is a mainstay of functional genomics, but it relies on manually 
      curated databases of gene functions that are incomplete and unaware of biological 
      context. Here we evaluate the ability of OpenAI's GPT-4, a Large Language Model 
      (LLM), to develop hypotheses about common gene functions from its embedded 
      biomedical knowledge. We created a GPT-4 pipeline to label gene sets with names 
      that summarize their consensus functions, substantiated by analysis text and 
      citations. Benchmarking against named gene sets in the Gene Ontology, GPT-4 
      generated very similar names in 50% of cases, while in most remaining cases it 
      recovered the name of a more general concept. In gene sets discovered in 'omics 
      data, GPT-4 names were more informative than gene set enrichment, with supporting 
      statements and citations that largely verified in human review. The ability to 
      rapidly synthesize common gene functions positions LLMs as valuable functional 
      genomics assistants.
FAU - Hu, Mengzhou
AU  - Hu M
AUID- ORCID: 0000-0002-1571-8029
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Alkhairy, Sahar
AU  - Alkhairy S
AD  - Department of Computer Science and Engineering, University of California San 
      Diego, La Jolla, California, USA.
FAU - Lee, Ingoo
AU  - Lee I
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Pillich, Rudolf T
AU  - Pillich RT
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Bachelder, Robin
AU  - Bachelder R
AUID- ORCID: 0000-0001-7502-2907
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Ideker, Trey
AU  - Ideker T
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
AD  - Department of Computer Science and Engineering, University of California San 
      Diego, La Jolla, California, USA.
FAU - Pratt, Dexter
AU  - Pratt D
AUID- ORCID: 0000-0002-1471-9513
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
LA  - eng
PT  - Preprint
DEP - 20230918
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10543283
EDAT- 2023/10/04 06:43
MHDA- 2023/10/04 06:44
CRDT- 2023/10/04 04:05
PHST- 2023/10/04 06:43 [pubmed]
PHST- 2023/10/04 06:44 [medline]
PHST- 2023/10/04 04:05 [entrez]
AID - rs.3.rs-3270331 [pii]
AID - 10.21203/rs.3.rs-3270331/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 Sep 18:rs.3.rs-3270331. doi: 10.21203/rs.3.rs-3270331/v1. Preprint.

PMID- 37307057
OWN - NLM
STAT- Publisher
LR  - 20230616
IS  - 1545-5815 (Electronic)
IS  - 0898-9621 (Linking)
DP  - 2023 Jun 15
TI  - Let's be fair. What about an AI editor?
PG  - 1-2
LID - 10.1080/08989621.2023.2223997 [doi]
AB  - Much of the current attention on artificial intelligence (AI)-based natural 
      language processing (NLP) systems has focused on research ethics and integrity 
      but neglects their roles in the editorial and peer-reviewing process. We argue 
      that the academic community needs to develop and apply a consistent end-to-end 
      policy on the ethics and integrity of NLP in academic publishing: standards such 
      as drafting requirements and disclosure criteria imposed on potential 
      contributors should be consistently applied to the editorial and peer review 
      process in academic publications.
FAU - Ling, Xiaoxu
AU  - Ling X
AD  - Institute of Accounting and Finance, School of Accountancy, Shanghai University 
      of Finance and Economics, Shanghai, China.
FAU - Yan, Siyuan
AU  - Yan S
AD  - School of Business, East China University of Science and Technology, Shanghai, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20230615
PL  - United States
TA  - Account Res
JT  - Accountability in research
JID - 9100813
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - NLP
OT  - editorial policy
OT  - research ethics
EDAT- 2023/06/12 13:07
MHDA- 2023/06/12 13:07
CRDT- 2023/06/12 11:54
PHST- 2023/06/12 13:07 [pubmed]
PHST- 2023/06/12 13:07 [medline]
PHST- 2023/06/12 11:54 [entrez]
AID - 10.1080/08989621.2023.2223997 [doi]
PST - aheadofprint
SO  - Account Res. 2023 Jun 15:1-2. doi: 10.1080/08989621.2023.2223997.

PMID- 37693398
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Sep 4
TI  - A comparison of large language model versus manual chart review for extraction of 
      data elements from the electronic health record.
LID - 2023.08.31.23294924 [pii]
LID - 10.1101/2023.08.31.23294924 [doi]
AB  - IMPORTANCE: Large language models (LLMs) have proven useful for extracting data 
      from publicly available sources, but their uses in clinical settings and with 
      clinical data are unknown. OBJECTIVE: To determine the accuracy of data 
      extraction using "Versa Chat," a chat implementation of the general-purpose 
      OpenAI gpt-35-turbo LLM model, versus manual chart review for hepatocellular 
      carcinoma (HCC) imaging reports. DESIGN: We engineered a prompt for the data 
      extraction task of six distinct data elements and input 182 abdominal imaging 
      reports that were also manually tagged. We evaluated performance by calculating 
      accuracy, precision, recall, and F1 scores. SETTING/PARTICIPANTS: Cross-sectional 
      abdominal imaging reports of patients diagnosed with hepatocellular carcinoma 
      enrolled in the Functional Assessment in Liver Transplantation (FrAILT) study.
FAU - Ge, Jin
AU  - Ge J
AD  - Division of Gastroenterology and Hepatology, Department of Medicine, University 
      of California - San Francisco, San Francisco, CA.
FAU - Li, Michael
AU  - Li M
AD  - Division of Gastroenterology and Hepatology, Department of Medicine, University 
      of California - San Francisco, San Francisco, CA.
FAU - Delk, Molly B
AU  - Delk MB
AD  - Section of Gastroenterology and Hepatology, Department of Medicine, Tulane 
      University School of Medicine, New Orleans, LA.
FAU - Lai, Jennifer C
AU  - Lai JC
AD  - Division of Gastroenterology and Hepatology, Department of Medicine, University 
      of California - San Francisco, San Francisco, CA.
LA  - eng
GR  - K24 AG080021/AG/NIA NIH HHS/United States
GR  - KL2 TR001870/TR/NCATS NIH HHS/United States
GR  - P30 DK026743/DK/NIDDK NIH HHS/United States
GR  - R01 AG059183/AG/NIA NIH HHS/United States
PT  - Preprint
DEP - 20230904
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10491368
COIS- Disclosures: The authors of this manuscript have the following potential 
      conflicts of interest to disclose: Dr. Jin Ge receives research support from 
      Merck and Co; and consults for Astellas Pharmaceuticals/Iota Biosciences.Dr. 
      Jennifer C. Lai receives research support from Lipocene and Vir Biotechnologies; 
      receives an education grant from Nestle Nutrition Sciences; serves on an advisory 
      board for Novo Nordisk; and consults for Genfit, Third Rock Ventures, and 
      Boehringer Ingelheim.
EDAT- 2023/09/11 06:43
MHDA- 2023/09/11 06:44
CRDT- 2023/09/11 04:53
PHST- 2023/09/11 06:43 [pubmed]
PHST- 2023/09/11 06:44 [medline]
PHST- 2023/09/11 04:53 [entrez]
AID - 2023.08.31.23294924 [pii]
AID - 10.1101/2023.08.31.23294924 [doi]
PST - epublish
SO  - medRxiv. 2023 Sep 4:2023.08.31.23294924. doi: 10.1101/2023.08.31.23294924. 
      Preprint.

PMID- 37198671
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230722
IS  - 2058-8615 (Electronic)
IS  - 2058-8615 (Linking)
VI  - 8
IP  - 1
DP  - 2023 May 18
TI  - Fighting reviewer fatigue or amplifying bias? Considerations and recommendations 
      for use of ChatGPT and other large language models in scholarly peer review.
PG  - 4
LID - 10.1186/s41073-023-00133-5 [doi]
LID - 4
AB  - BACKGROUND: The emergence of systems based on large language models (LLMs) such 
      as OpenAI's ChatGPT has created a range of discussions in scholarly circles. 
      Since LLMs generate grammatically correct and mostly relevant (yet sometimes 
      outright wrong, irrelevant or biased) outputs in response to provided prompts, 
      using them in various writing tasks including writing peer review reports could 
      result in improved productivity. Given the significance of peer reviews in the 
      existing scholarly publication landscape, exploring challenges and opportunities 
      of using LLMs in peer review seems urgent. After the generation of the first 
      scholarly outputs with LLMs, we anticipate that peer review reports too would be 
      generated with the help of these systems. However, there are currently no 
      guidelines on how these systems should be used in review tasks. METHODS: To 
      investigate the potential impact of using LLMs on the peer review process, we 
      used five core themes within discussions about peer review suggested by Tennant 
      and Ross-Hellauer. These include 1) reviewers' role, 2) editors' role, 3) 
      functions and quality of peer reviews, 4) reproducibility, and 5) the social and 
      epistemic functions of peer reviews. We provide a small-scale exploration of 
      ChatGPT's performance regarding identified issues. RESULTS: LLMs have the 
      potential to substantially alter the role of both peer reviewers and editors. 
      Through supporting both actors in efficiently writing constructive reports or 
      decision letters, LLMs can facilitate higher quality review and address issues of 
      review shortage. However, the fundamental opacity of LLMs' training data, inner 
      workings, data handling, and development processes raise concerns about potential 
      biases, confidentiality and the reproducibility of review reports. Additionally, 
      as editorial work has a prominent function in defining and shaping epistemic 
      communities, as well as negotiating normative frameworks within such communities, 
      partly outsourcing this work to LLMs might have unforeseen consequences for 
      social and epistemic relations within academia. Regarding performance, we 
      identified major enhancements in a short period and expect LLMs to continue 
      developing. CONCLUSIONS: We believe that LLMs are likely to have a profound 
      impact on academia and scholarly communication. While potentially beneficial to 
      the scholarly communication system, many uncertainties remain and their use is 
      not without risks. In particular, concerns about the amplification of existing 
      biases and inequalities in access to appropriate infrastructure warrant further 
      attention. For the moment, we recommend that if LLMs are used to write scholarly 
      reviews and decision letters, reviewers and editors should disclose their use and 
      accept full responsibility for data security and confidentiality, and their 
      reports' accuracy, tone, reasoning and originality.
CI  - © 2023. The Author(s).
FAU - Hosseini, Mohammad
AU  - Hosseini M
AUID- ORCID: 0000-0002-2385-985X
AD  - Feinberg School of Medicine, Northwestern University, 420 E. Superior Street, 
      Chicago, IL, 60611, USA. mohammad.hosseini@northwestern.edu.
FAU - Horbach, Serge P J M
AU  - Horbach SPJM
AUID- ORCID: 0000-0003-0406-6261
AD  - Danish Centre for Studies in Research and Research Policy, Aarhus University, 
      Bartholins Alle 7, 8000, Aarhus C, Aarhus, Denmark.
LA  - eng
GR  - UL1 TR001422/TR/NCATS NIH HHS/United States
GR  - UL1TR001422/TR/NCATS NIH HHS/United States
PT  - Journal Article
DEP - 20230518
PL  - England
TA  - Res Integr Peer Rev
JT  - Research integrity and peer review
JID - 101676020
UOF - Res Sq. 2023 Feb 20;:. PMID: 36865238
EIN - Res Integr Peer Rev. 2023 Jul 10;8(1):7. PMID: 37430379
PMC - PMC10191680
OTO - NOTNLM
OT  - Academic writing
OT  - ChaGPT
OT  - Editorial practices
OT  - Generative AI
OT  - Large Language Models
OT  - Peer review
COIS- The authors have no competing interests.
EDAT- 2023/05/18 01:07
MHDA- 2023/05/18 01:08
CRDT- 2023/05/17 23:42
PHST- 2023/02/14 00:00 [received]
PHST- 2023/04/19 00:00 [accepted]
PHST- 2023/05/18 01:08 [medline]
PHST- 2023/05/18 01:07 [pubmed]
PHST- 2023/05/17 23:42 [entrez]
AID - 10.1186/s41073-023-00133-5 [pii]
AID - 133 [pii]
AID - 10.1186/s41073-023-00133-5 [doi]
PST - epublish
SO  - Res Integr Peer Rev. 2023 May 18;8(1):4. doi: 10.1186/s41073-023-00133-5.

PMID- 37437021
OWN - NLM
STAT- MEDLINE
DCOM- 20230911
LR  - 20230920
IS  - 1535-1386 (Electronic)
IS  - 0021-9355 (Linking)
VI  - 105
IP  - 17
DP  - 2023 Sep 6
TI  - Artificial Intelligence in Orthopaedic Surgery: Can a Large Language Model 
      "Write" a Believable Orthopaedic Journal Article?
PG  - 1388-1392
LID - 10.2106/JBJS.23.00473 [doi]
AB  - ➢ Natural language processing with large language models is a subdivision of 
      artificial intelligence (AI) that extracts meaning from text with use of 
      linguistic rules, statistics, and machine learning to generate appropriate text 
      responses. Its utilization in medicine and in the field of orthopaedic surgery is 
      rapidly growing.➢ Large language models can be utilized in generating scientific 
      manuscript texts of a publishable quality; however, they suffer from AI 
      hallucinations, in which untruths or half-truths are stated with misleading 
      confidence. Their use raises considerable concerns regarding the potential for 
      research misconduct and for hallucinations to insert misinformation into the 
      clinical literature.➢ Current editorial processes are insufficient for 
      identifying the involvement of large language models in manuscripts. Academic 
      publishing must adapt to encourage safe use of these tools by establishing clear 
      guidelines for their use, which should be adopted across the orthopaedic 
      literature, and by implementing additional steps in the editorial screening 
      process to identify the use of these tools in submitted manuscripts.
CI  - Copyright © 2023 by The Journal of Bone and Joint Surgery, Incorporated.
FAU - Brameier, Devon T
AU  - Brameier DT
AUID- ORCID: 0000-0002-8221-1172
AD  - Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical 
      School, Boston, Massachusetts.
FAU - Alnasser, Ahmad A
AU  - Alnasser AA
AUID- ORCID: 0000-0002-8695-2939
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts.
FAU - Carnino, Jonathan M
AU  - Carnino JM
AUID- ORCID: 0000-0001-8908-7269
AD  - Boston University Chobanian & Avedisian School of Medicine, Boston, 
      Massachusetts.
FAU - Bhashyam, Abhiram R
AU  - Bhashyam AR
AUID- ORCID: 0000-0002-9170-4745
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts.
FAU - von Keudell, Arvind G
AU  - von Keudell AG
AUID- ORCID: 0000-0002-3312-7556
AD  - Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical 
      School, Boston, Massachusetts.
AD  - Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark.
FAU - Weaver, Michael J
AU  - Weaver MJ
AUID- ORCID: 0000-0003-1062-8144
AD  - Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical 
      School, Boston, Massachusetts.
LA  - eng
PT  - Journal Article
DEP - 20230712
PL  - United States
TA  - J Bone Joint Surg Am
JT  - The Journal of bone and joint surgery. American volume
JID - 0014030
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Orthopedics
MH  - Publishing
MH  - Writing
MH  - *Orthopedic Procedures
COIS- Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided 
      with the online version of the article ( http://links.lww.com/JBJS/H601 ).
EDAT- 2023/07/12 19:07
MHDA- 2023/09/11 06:42
CRDT- 2023/07/12 14:04
PHST- 2023/09/11 06:42 [medline]
PHST- 2023/07/12 19:07 [pubmed]
PHST- 2023/07/12 14:04 [entrez]
AID - 00004623-202309060-00012 [pii]
AID - 10.2106/JBJS.23.00473 [doi]
PST - ppublish
SO  - J Bone Joint Surg Am. 2023 Sep 6;105(17):1388-1392. doi: 10.2106/JBJS.23.00473. 
      Epub 2023 Jul 12.

PMID- 38048820
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231213
IS  - 2093-4777 (Print)
IS  - 2093-6931 (Electronic)
IS  - 2093-4777 (Linking)
VI  - 27
IP  - Suppl 2
DP  - 2023 Nov
TI  - General Overview of Artificial Intelligence for Interstitial Cystitis in Urology.
PG  - S64-72
LID - 10.5213/inj.2346294.147 [doi]
AB  - Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has 
      evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as 
      urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS 
      remain unclear, it is thought to involve several factors, including abnormalities 
      in the bladder's urothelium, mast cell degranulation within the bladder, 
      inflammation of the bladder, and altered innervation of the bladder. Treatment 
      options include patient education, dietary and lifestyle modifications, 
      medications, intravesical therapy, and surgical interventions. This review 
      article provides insights into IC/BPS, including aspects of treatment, prognosis 
      prediction, and emerging therapeutic options. Additionally, it explores the 
      application of deep learning for diagnosing major diseases associated with 
      IC/BPS.
FAU - Cho, Yongwon
AU  - Cho Y
AD  - Department of AI Center, Korea University Anam Hospital, Seoul, Korea.
FAU - Park, Jong Mok
AU  - Park JM
AD  - Department of Urology, Chungnam National University Sejong Hospital, Chungnam 
      National University College of Medicine, Sejong, Korea.
FAU - Youn, Seunghyun
AU  - Youn S
AD  - GRK Partners Research Center, Seoul, Korea.
LA  - eng
GR  - Chungnam National University/
PT  - Journal Article
DEP - 20231130
PL  - Korea (South)
TA  - Int Neurourol J
JT  - International neurourology journal
JID - 101534513
PMC - PMC10715831
OTO - NOTNLM
OT  - Convolutional neural network
OT  - Deep learning
OT  - Diagnosis
OT  - Interstitial cystitis
OT  - Large language model
OT  - Treatment
COIS- Conflict of Interest No potential conflict of interest relevant to this article 
      was reported.
EDAT- 2023/12/05 00:41
MHDA- 2023/12/05 00:42
CRDT- 2023/12/04 19:16
PHST- 2023/10/05 00:00 [received]
PHST- 2023/11/13 00:00 [accepted]
PHST- 2023/12/05 00:42 [medline]
PHST- 2023/12/05 00:41 [pubmed]
PHST- 2023/12/04 19:16 [entrez]
AID - inj.2346294.147 [pii]
AID - inj-2346294-147 [pii]
AID - 10.5213/inj.2346294.147 [doi]
PST - ppublish
SO  - Int Neurourol J. 2023 Nov;27(Suppl 2):S64-72. doi: 10.5213/inj.2346294.147. Epub 
      2023 Nov 30.

PMID- 37868408
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Evaluating the Artificial Intelligence Performance Growth in Ophthalmic 
      Knowledge.
PG  - e45700
LID - 10.7759/cureus.45700 [doi]
LID - e45700
AB  - OBJECTIVE: We aim to compare the capabilities of Chat Generative Pre-Trained 
      Transformer (ChatGPT)-3.5 and ChatGPT-4.0 (OpenAI, San Francisco, CA, USA) in 
      addressing multiple-choice ophthalmic case challenges. METHODS AND ANALYSIS: Both 
      models' accuracy was compared across different ophthalmology subspecialties using 
      multiple-choice ophthalmic clinical cases provided by the American Academy of 
      Ophthalmology (AAO) "Diagnosis This" questions. Additional analysis was based on 
      image content, question difficulty, character length of models' responses, and 
      model's alignment with responses from human respondents. χ2 test, Fisher's exact 
      test, Student's t-test, and one-way analysis of variance (ANOVA) were conducted 
      where appropriate, with p<0.05 considered significant. RESULTS: GPT-4.0 
      significantly outperformed GPT-3.5 (75% versus 46%, p<0.01), with the most 
      noticeable improvement in neuro-ophthalmology (100% versus 38%, p=0.03). While 
      both models struggled with uveitis and refractive questions, GPT-4.0 excelled in 
      other areas, such as pediatric questions (82%). In image-related questions, 
      GPT-4.0 also displayed superior accuracy that trended toward significance (73% 
      versus 46%, p=0.07). GPT-4.0 performed better with easier questions (93.8% (least 
      difficult) versus 76.2% (middle) versus 53.3% (most), p=0.03) and generated more 
      concise answers than GPT-3.5 (651.7±342.9 versus 1,112.9±328.8 characters, 
      p<0.01). Moreover, GPT-4.0's answers were more in line with those of AAO 
      respondents (57.3% versus 41.4%, p<0.01), showing a strong correlation between 
      its accuracy and the proportion of AAO respondents who selected GPT-4.0's answer 
      (ρ=0.713, p<0.01). CONCLUSION AND RELEVANCE: Our study demonstrated that GPT-4.0 
      significantly outperforms GPT-3.5 in addressing ophthalmic case challenges, 
      especially in neuro-ophthalmology, with improved accuracy even in image-related 
      questions. These findings underscore the potential of advancing artificial 
      intelligence (AI) models in enhancing ophthalmic diagnostics and medical 
      education.
CI  - Copyright © 2023, Jiao et al.
FAU - Jiao, Cheng
AU  - Jiao C
AD  - Ophthalmology, Augusta University Medical College of Georgia, Augusta, USA.
FAU - Edupuganti, Neel R
AU  - Edupuganti NR
AD  - Ophthalmology, Augusta University Medical College of Georgia, Augusta, USA.
FAU - Patel, Parth A
AU  - Patel PA
AD  - Neurology, Augusta University Medical College of Georgia, Augusta, USA.
FAU - Bui, Tommy
AU  - Bui T
AD  - Ophthalmology, Augusta University Medical College of Georgia, Augusta, USA.
FAU - Sheth, Veeral
AU  - Sheth V
AD  - Ophthalmology, University Retina and Macula Associates, Oak Forest, USA.
LA  - eng
PT  - Journal Article
DEP - 20230921
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10590143
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - medical education
OT  - natural language processing models
OT  - ophthalmology
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/23 06:49
MHDA- 2023/10/23 06:50
CRDT- 2023/10/23 04:38
PHST- 2023/09/20 00:00 [accepted]
PHST- 2023/10/23 06:50 [medline]
PHST- 2023/10/23 06:49 [pubmed]
PHST- 2023/10/23 04:38 [entrez]
AID - 10.7759/cureus.45700 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 21;15(9):e45700. doi: 10.7759/cureus.45700. eCollection 2023 
      Sep.

PMID- 37917126
OWN - NLM
STAT- MEDLINE
DCOM- 20231103
LR  - 20231119
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Nov 2
TI  - The Impact of Multimodal Large Language Models on Health Care's Future.
PG  - e52865
LID - 10.2196/52865 [doi]
LID - e52865
AB  - When large language models (LLMs) were introduced to the public at large in late 
      2022 with ChatGPT (OpenAI), the interest was unprecedented, with more than 1 
      billion unique users within 90 days. Until the introduction of Generative 
      Pre-trained Transformer 4 (GPT-4) in March 2023, these LLMs only contained a 
      single mode-text. As medicine is a multimodal discipline, the potential future 
      versions of LLMs that can handle multimodality-meaning that they could interpret 
      and generate not only text but also images, videos, sound, and even comprehensive 
      documents-can be conceptualized as a significant evolution in the field of 
      artificial intelligence (AI). This paper zooms in on the new potential of 
      generative AI, a new form of AI that also includes tools such as LLMs, through 
      the achievement of multimodal inputs of text, images, and speech on health care's 
      future. We present several futuristic scenarios to illustrate the potential path 
      forward as multimodal LLMs (M-LLMs) could represent the gateway between health 
      care professionals and using AI for medical purposes. It is important to point 
      out, though, that despite the unprecedented potential of generative AI in the 
      form of M-LLMs, the human touch in medicine remains irreplaceable. AI should be 
      seen as a tool that can augment health care professionals rather than replace 
      them. It is also important to consider the human aspects of health care-empathy, 
      understanding, and the doctor-patient relationship-when deploying AI.
CI  - ©Bertalan Meskó. Originally published in the Journal of Medical Internet Research 
      (https://www.jmir.org), 02.11.2023.
FAU - Meskó, Bertalan
AU  - Meskó B
AUID- ORCID: 0000-0002-7005-7083
AD  - The Medical Futurist Intitute, Budapest, XI., Hungary.
LA  - eng
PT  - Journal Article
DEP - 20231102
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Physician-Patient Relations
MH  - Empathy
MH  - Health Personnel
MH  - Language
PMC - PMC10654899
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT-4
OT  - Generative Pre-Trained Transformer
OT  - LLM
OT  - artificial intelligence
OT  - digital health
OT  - future
OT  - large language models
OT  - multimodality
OT  - technology
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/02 12:45
MHDA- 2023/11/03 06:44
CRDT- 2023/11/02 11:54
PHST- 2023/09/18 00:00 [received]
PHST- 2023/10/12 00:00 [accepted]
PHST- 2023/10/10 00:00 [revised]
PHST- 2023/11/03 06:44 [medline]
PHST- 2023/11/02 12:45 [pubmed]
PHST- 2023/11/02 11:54 [entrez]
AID - v25i1e52865 [pii]
AID - 10.2196/52865 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Nov 2;25:e52865. doi: 10.2196/52865.

PMID- 37736475
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230923
IS  - 2050-0904 (Print)
IS  - 2050-0904 (Electronic)
IS  - 2050-0904 (Linking)
VI  - 11
IP  - 9
DP  - 2023 Sep
TI  - Are artificial intelligence large language models a reliable tool for difficult 
      differential diagnosis? An a posteriori analysis of a peculiar case of 
      necrotizing otitis externa.
PG  - e7933
LID - 10.1002/ccr3.7933 [doi]
LID - e7933
AB  - KEY CLINICAL MESSAGE: Large language models have made artificial intelligence 
      readily available to the general public and potentially have a role in 
      healthcare; however, their use in difficult differential diagnosis is still 
      limited, as demonstrated by a case of necrotizing otitis externa. ABSTRACT: This 
      case report presents a peculiar case of necrotizing otitis externa (NOE) with 
      skull base involvement which proved diagnostically challenging. The initial 
      patient presentation and the imaging performed on the 78-year-old patient 
      suggested a neoplastic rhinopharyngeal lesion and only after several unsuccessful 
      biopsies the patient was transferred to our unit. Upon re-evaluation of the 
      clinical picture, a clinical hypothesis of NOE with skull base erosion was made 
      and confirmed by identifying Pseudomonas aeruginosa in biopsy specimens of skull 
      base bone and external auditory canal skin. Upon diagnosis confirmation, the 
      patient was treated with culture-oriented long-term antibiotics with complete 
      resolution of the disease. Given the complex clinical presentation, we chose to 
      submit a posteriori this NOE case to two large language models (LLM) to test 
      their ability to handle difficult differential diagnoses. LLMs are easily 
      approachable artificial intelligence tools that enable human-like interaction 
      with the user relying upon large information databases for analyzing queries. The 
      LLMs of choice were ChatGPT-3 and ChatGPT-4 and they were requested to analyze 
      the case being provided with only objective clinical and imaging data.
CI  - © 2023 The Authors. Clinical Case Reports published by John Wiley & Sons Ltd.
FAU - Pugliese, Giorgia
AU  - Pugliese G
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
FAU - Maccari, Alberto
AU  - Maccari A
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
FAU - Felisati, Elena
AU  - Felisati E
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
FAU - Felisati, Giovanni
AU  - Felisati G
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
FAU - Giudici, Leonardo
AU  - Giudici L
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
FAU - Rapolla, Chiara
AU  - Rapolla C
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
FAU - Pisani, Antonia
AU  - Pisani A
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
FAU - Saibene, Alberto Maria
AU  - Saibene AM
AUID- ORCID: 0000-0003-1457-6871
AD  - Otolaryngology Unit Santi Paolo e Carlo Hospital Milan Italy.
AD  - Department of Health Sciences Università degli Studi di Milano Milan Italy.
LA  - eng
PT  - Case Reports
DEP - 20230919
PL  - England
TA  - Clin Case Rep
JT  - Clinical case reports
JID - 101620385
PMC - PMC10509342
OTO - NOTNLM
OT  - computer‐assisted diagnosis
OT  - differential diagnosis
OT  - ear
OT  - infection
OT  - radiology
COIS- The authors have no potential conflict of interest or financial disclosures to 
      make.
EDAT- 2023/09/22 06:42
MHDA- 2023/09/22 06:43
CRDT- 2023/09/22 03:46
PHST- 2023/06/22 00:00 [received]
PHST- 2023/08/30 00:00 [revised]
PHST- 2023/09/12 00:00 [accepted]
PHST- 2023/09/22 06:43 [medline]
PHST- 2023/09/22 06:42 [pubmed]
PHST- 2023/09/22 03:46 [entrez]
AID - CCR37933 [pii]
AID - 10.1002/ccr3.7933 [doi]
PST - epublish
SO  - Clin Case Rep. 2023 Sep 19;11(9):e7933. doi: 10.1002/ccr3.7933. eCollection 2023 
      Sep.

PMID- 37033522
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230412
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - A Collaborative Case Report Utilizing ChatGPT AI Technology of Traumatic Right 
      Coronary Artery Dissection Resulting in Inferior Wall ST-Elevation Myocardial 
      Infarction.
PG  - e35894
LID - 10.7759/cureus.35894 [doi]
LID - e35894
AB  - Traumatic Coronary Artery Dissection (TCAD) is a rare but potentially 
      life-threatening condition that can occur as a result of blunt chest trauma. This 
      case report presents the case of a 67-year-old male who presented to the hospital 
      after a motor vehicle accident with chest pain and ECG showing an inferior wall 
      ST-elevation myocardial infarction. Further imaging revealed a nondisplaced 
      fracture of the right side of C2 and a left 12th rib. Coronary angiography showed 
      a dissection in the distal segment of the right coronary artery. The patient was 
      given heparin and underwent coronary stenting of the right coronary artery. The 
      patient was monitored in the intensive care unit for any post-traumatic 
      complications and was managed conservatively for the cervical fracture. This case 
      report highlights the importance of prompt diagnosis and comprehensive treatment 
      in TCAD cases to improve outcomes and prevent further complications.
CI  - Copyright © 2023, Raxwal et al.
FAU - Raxwal, Bhuvi
AU  - Raxwal B
AD  - Medicine, Access Health Care Physicians, Zephyrhills, USA.
FAU - Baisla, Prashant
AU  - Baisla P
AD  - Information Technology, University of the Cumberlands, Williamsburg, USA.
FAU - Nath, Jayant
AU  - Nath J
AD  - Cardiology, Memorial Healthcare System, Pembroke Pines, USA.
LA  - eng
PT  - Case Reports
DEP - 20230308
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10080982
OTO - NOTNLM
OT  - acute myocardial infarction
OT  - artificial intelligence
OT  - chatgpt
OT  - chest trauma
OT  - coronary stenting
OT  - mva (motor vehicle accident)
OT  - post trauma ecg
OT  - post traumatic complications
OT  - st-elevation myocardial infarction
OT  - traumatic coronary artery dissection
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/11 06:00
MHDA- 2023/04/11 06:01
CRDT- 2023/04/10 03:55
PHST- 2023/03/08 00:00 [accepted]
PHST- 2023/04/11 06:01 [medline]
PHST- 2023/04/10 03:55 [entrez]
PHST- 2023/04/11 06:00 [pubmed]
AID - 10.7759/cureus.35894 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 8;15(3):e35894. doi: 10.7759/cureus.35894. eCollection 2023 Mar.

PMID- 37731657
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2331-8422 (Electronic)
IS  - 2331-8422 (Linking)
DP  - 2023 Sep 7
TI  - Evaluation of large language models for discovery of gene set function.
LID - arXiv:2309.04019v1
AB  - Gene set analysis is a mainstay of functional genomics, but it relies on manually 
      curated databases of gene functions that are incomplete and unaware of biological 
      context. Here we evaluate the ability of OpenAI's GPT-4, a Large Language Model 
      (LLM), to develop hypotheses about common gene functions from its embedded 
      biomedical knowledge. We created a GPT-4 pipeline to label gene sets with names 
      that summarize their consensus functions, substantiated by analysis text and 
      citations. Benchmarking against named gene sets in the Gene Ontology, GPT-4 
      generated very similar names in 50% of cases, while in most remaining cases it 
      recovered the name of a more general concept. In gene sets discovered in 'omics 
      data, GPT-4 names were more informative than gene set enrichment, with supporting 
      statements and citations that largely verified in human review. The ability to 
      rapidly synthesize common gene functions positions LLMs as valuable functional 
      genomics assistants.
FAU - Hu, Mengzhou
AU  - Hu M
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Alkhairy, Sahar
AU  - Alkhairy S
AD  - Department of Computer Science and Engineering, University of California San 
      Diego, La Jolla, California, USA.
FAU - Lee, Ingoo
AU  - Lee I
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Pillich, Rudolf T
AU  - Pillich RT
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Bachelder, Robin
AU  - Bachelder R
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
FAU - Ideker, Trey
AU  - Ideker T
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
AD  - Department of Computer Science and Engineering, University of California San 
      Diego, La Jolla, California, USA.
FAU - Pratt, Dexter
AU  - Pratt D
AD  - Department of Medicine, University of California San Diego, La Jolla, California, 
      USA.
LA  - eng
GR  - U54 CA274502/CA/NCI NIH HHS/United States
GR  - U24 HG012107/HG/NHGRI NIH HHS/United States
GR  - OT2 OD032742/OD/NIH HHS/United States
GR  - U01 MH115747/MH/NIMH NIH HHS/United States
GR  - U24 CA269436/CA/NCI NIH HHS/United States
PT  - Preprint
DEP - 20230907
PL  - United States
TA  - ArXiv
JT  - ArXiv
JID - 101759493
PMC - PMC10508824
EDAT- 2023/09/21 06:42
MHDA- 2023/09/21 06:43
CRDT- 2023/09/21 04:01
PHST- 2023/09/21 06:42 [pubmed]
PHST- 2023/09/21 06:43 [medline]
PHST- 2023/09/21 04:01 [entrez]
AID - 2309.04019 [pii]
PST - epublish
SO  - ArXiv. 2023 Sep 7:arXiv:2309.04019v1. Preprint.

PMID- 37447746
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230717
LR  - 20230718
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 13
DP  - 2023 Jun 25
TI  - LLM Adaptive PID Control for B5G Truck Platooning Systems.
LID - 10.3390/s23135899 [doi]
LID - 5899
AB  - This paper presents an exploration into the capabilities of an adaptive PID 
      controller within the realm of truck platooning operations, situating the inquiry 
      within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) 
      networks. We developed a Deep Learning (DL) model that emulates an adaptive PID 
      controller, taking into account the implications of factors such as communication 
      latency, packet loss, and communication range, alongside considerations of 
      reliability, robustness, and security. Furthermore, we harnessed a Large Language 
      Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the 
      PID system, thereby elucidating its potential for incorporation into AI-enabled 
      radio and networks. This research unveils crucial insights for augmenting the 
      performance and safety parameters of vehicle platooning systems within B5G 
      networks, concurrently underlining the prospective applications of LLMs within 
      such technologically advanced communication environments.
FAU - de Zarzà, I
AU  - de Zarzà I
AUID- ORCID: 0000-0002-5844-7871
AD  - Informatik und Mathematik, GOETHE-University Frankfurt am Main, 60323 Frankfurt 
      am Main, Germany.
AD  - Departamento de Informática de Sistemas y Computadores, Universitat Politècnica 
      de València, 46022 València, Spain.
AD  - Estudis d'Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de 
      Catalunya, 08018 Barcelona, Spain.
FAU - de Curtò, J
AU  - de Curtò J
AUID- ORCID: 0000-0002-8334-4719
AD  - Informatik und Mathematik, GOETHE-University Frankfurt am Main, 60323 Frankfurt 
      am Main, Germany.
AD  - Departamento de Informática de Sistemas y Computadores, Universitat Politècnica 
      de València, 46022 València, Spain.
AD  - Estudis d'Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de 
      Catalunya, 08018 Barcelona, Spain.
FAU - Roig, Gemma
AU  - Roig G
AUID- ORCID: 0000-0002-6439-8076
AD  - Informatik und Mathematik, GOETHE-University Frankfurt am Main, 60323 Frankfurt 
      am Main, Germany.
AD  - HESSIAN Center for AI (hessian.AI), 64293 Darmstadt, Germany.
FAU - Calafate, Carlos T
AU  - Calafate CT
AUID- ORCID: 0000-0001-5729-3041
AD  - Departamento de Informática de Sistemas y Computadores, Universitat Politècnica 
      de València, 46022 València, Spain.
LA  - eng
GR  - Center for Data Science & AI/Goethe University Frankfurt/
GR  - R\&D project PID2021-122580NB-I00, funded by MCIN/AEI/10.13039/501100011033 and 
      ERDF./Universitat Politècnica de València/
GR  - DePP - Dezentrale Plannung von Platoons im Straßengüterverkehr mit Hilfe einer KI 
      auf Basis einzelner LKW/Goethe University Frankfurt/
PT  - Journal Article
DEP - 20230625
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Reproducibility of Results
MH  - *Language
MH  - *Communication
MH  - Motor Vehicles
MH  - Problem Solving
PMC - PMC10346546
OTO - NOTNLM
OT  - 5G and B5G systems
OT  - V2V communication
OT  - adaptive PID control
OT  - coordination of vehicles
OT  - large language models
OT  - platooning
COIS- The authors declare that they have no conflict of interest. The funders had no 
      role in the design of the study; in the collection, analyses, or interpretation 
      of data; in the writing of the manuscript, or in the decision to publish the 
      results.
EDAT- 2023/07/14 13:06
MHDA- 2023/07/17 06:42
CRDT- 2023/07/14 01:25
PHST- 2023/04/27 00:00 [received]
PHST- 2023/05/23 00:00 [revised]
PHST- 2023/06/22 00:00 [accepted]
PHST- 2023/07/17 06:42 [medline]
PHST- 2023/07/14 13:06 [pubmed]
PHST- 2023/07/14 01:25 [entrez]
AID - s23135899 [pii]
AID - sensors-23-05899 [pii]
AID - 10.3390/s23135899 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Jun 25;23(13):5899. doi: 10.3390/s23135899.

PMID- 37401923
OWN - NLM
STAT- MEDLINE
DCOM- 20230706
LR  - 20230718
IS  - 1551-6709 (Electronic)
IS  - 0364-0213 (Linking)
VI  - 47
IP  - 7
DP  - 2023 Jul
TI  - Do Large Language Models Know What Humans Know?
PG  - e13309
LID - 10.1111/cogs.13309 [doi]
AB  - Humans can attribute beliefs to others. However, it is unknown to what extent 
      this ability results from an innate biological endowment or from experience 
      accrued through child development, particularly exposure to language describing 
      others' mental states. We test the viability of the language exposure hypothesis 
      by assessing whether models exposed to large quantities of human language display 
      sensitivity to the implied knowledge states of characters in written passages. In 
      pre-registered analyses, we present a linguistic version of the False Belief Task 
      to both human participants and a large language model, GPT-3. Both are sensitive 
      to others' beliefs, but while the language model significantly exceeds chance 
      behavior, it does not perform as well as the humans nor does it explain the full 
      extent of their behavior-despite being exposed to more language than a human 
      would in a lifetime. This suggests that while statistical learning from language 
      exposure may in part explain how humans develop the ability to reason about the 
      mental states of others, other mechanisms are also responsible.
CI  - © 2023 The Authors. Cognitive Science published by Wiley Periodicals LLC on 
      behalf of Cognitive Science Society (CSS).
FAU - Trott, Sean
AU  - Trott S
AD  - Department of Cognitive Science, University of California San Diego.
FAU - Jones, Cameron
AU  - Jones C
AD  - Department of Cognitive Science, University of California San Diego.
FAU - Chang, Tyler
AU  - Chang T
AD  - Department of Cognitive Science, University of California San Diego.
FAU - Michaelov, James
AU  - Michaelov J
AD  - Department of Cognitive Science, University of California San Diego.
FAU - Bergen, Benjamin
AU  - Bergen B
AD  - Department of Cognitive Science, University of California San Diego.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Cogn Sci
JT  - Cognitive science
JID - 7708195
SB  - IM
MH  - Child
MH  - Humans
MH  - *Communication
MH  - Deception
MH  - Language
MH  - Child Development
MH  - *Theory of Mind
OTO - NOTNLM
OT  - Belief attribution
OT  - False Belief Task
OT  - Language
OT  - Large language models
EDAT- 2023/07/04 13:09
MHDA- 2023/07/06 06:42
CRDT- 2023/07/04 10:53
PHST- 2023/06/02 00:00 [revised]
PHST- 2023/01/06 00:00 [received]
PHST- 2023/06/12 00:00 [accepted]
PHST- 2023/07/06 06:42 [medline]
PHST- 2023/07/04 13:09 [pubmed]
PHST- 2023/07/04 10:53 [entrez]
AID - 10.1111/cogs.13309 [doi]
PST - ppublish
SO  - Cogn Sci. 2023 Jul;47(7):e13309. doi: 10.1111/cogs.13309.

PMID- 36730192
OWN - NLM
STAT- MEDLINE
DCOM- 20230206
LR  - 20230803
IS  - 1091-6490 (Electronic)
IS  - 0027-8424 (Print)
IS  - 0027-8424 (Linking)
VI  - 120
IP  - 6
DP  - 2023 Feb 7
TI  - Using cognitive psychology to understand GPT-3.
PG  - e2218523120
LID - 10.1073/pnas.2218523120 [doi]
LID - e2218523120
AB  - We study GPT-3, a recent large language model, using tools from cognitive 
      psychology. More specifically, we assess GPT-3's decision-making, information 
      search, deliberation, and causal reasoning abilities on a battery of canonical 
      experiments from the literature. We find that much of GPT-3's behavior is 
      impressive: It solves vignette-based tasks similarly or better than human 
      subjects, is able to make decent decisions from descriptions, outperforms humans 
      in a multiarmed bandit task, and shows signatures of model-based reinforcement 
      learning. Yet, we also find that small perturbations to vignette-based tasks can 
      lead GPT-3 vastly astray, that it shows no signatures of directed exploration, 
      and that it fails miserably in a causal reasoning task. Taken together, these 
      results enrich our understanding of current large language models and pave the 
      way for future investigations using tools from cognitive psychology to study 
      increasingly capable and opaque artificial agents.
FAU - Binz, Marcel
AU  - Binz M
AUID- ORCID: 0000-0001-8872-8386
AD  - Max Planck Research Group (MPRG) Computational Principles of Intelligence, Max 
      Planck Institute for Biological Cybernetics, Tübingen 72076, Germany.
FAU - Schulz, Eric
AU  - Schulz E
AD  - Max Planck Research Group (MPRG) Computational Principles of Intelligence, Max 
      Planck Institute for Biological Cybernetics, Tübingen 72076, Germany.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230202
PL  - United States
TA  - Proc Natl Acad Sci U S A
JT  - Proceedings of the National Academy of Sciences of the United States of America
JID - 7505876
SB  - IM
CIN - Proc Natl Acad Sci U S A. 2023 Mar 7;120(10):e2300963120. PMID: 36857344
MH  - Humans
MH  - *Decision Making
MH  - *Cognitive Psychology
MH  - Problem Solving
MH  - Learning
MH  - Reinforcement, Psychology
PMC - PMC9963545
OTO - NOTNLM
OT  - artificial intelligence
OT  - cognitive psychology
OT  - decision-making
OT  - language models
OT  - reasoning
COIS- The authors declare no competing interest.
EDAT- 2023/02/03 06:00
MHDA- 2023/02/07 06:00
CRDT- 2023/02/02 13:43
PHST- 2023/02/02 13:43 [entrez]
PHST- 2023/02/03 06:00 [pubmed]
PHST- 2023/02/07 06:00 [medline]
AID - 202218523 [pii]
AID - 10.1073/pnas.2218523120 [doi]
PST - ppublish
SO  - Proc Natl Acad Sci U S A. 2023 Feb 7;120(6):e2218523120. doi: 
      10.1073/pnas.2218523120. Epub 2023 Feb 2.

PMID- 38060133
OWN - NLM
STAT- Publisher
LR  - 20231207
IS  - 1534-6293 (Electronic)
IS  - 1528-4042 (Linking)
DP  - 2023 Dec 7
TI  - Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review 
      for the Practicing Epileptologist.
LID - 10.1007/s11910-023-01318-7 [doi]
AB  - PURPOSE OF REVIEW: Machine Learning (ML) and Artificial Intelligence (AI) are 
      data-driven techniques to translate raw data into applicable and interpretable 
      insights that can assist in clinical decision making. Some of these tools have 
      extremely promising initial results, earning both great excitement and creating 
      hype. This non-technical article reviews recent developments in ML/AI in epilepsy 
      to assist the current practicing epileptologist in understanding both the 
      benefits and limitations of integrating ML/AI tools into their clinical practice. 
      RECENT FINDINGS: ML/AI tools have been developed to assist clinicians in almost 
      every clinical decision including (1) predicting future epilepsy in people at 
      risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy 
      from mimics, (4) using data to improve neuroanatomic localization and 
      lateralization, and (5) tracking and predicting response to medical and surgical 
      treatments. We also discuss practical, ethical, and equity considerations in the 
      development and application of ML/AI tools including chatbots based on Large 
      Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is 
      practiced, but, with rare exceptions, the transferability to other centers, 
      effectiveness, and safety of these approaches have not yet been established 
      rigorously. In the future, ML/AI will not replace epileptologists, but 
      epileptologists with ML/AI will replace epileptologists without ML/AI.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Kerr, Wesley T
AU  - Kerr WT
AD  - Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 
      811.22, Pittsburgh, PA, 15213, USA. KerrW@Pitt.edu.
AD  - Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, 
      Kaufmann 811.22, Pittsburgh, PA, 15213, USA. KerrW@Pitt.edu.
AD  - Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, 
      MI, USA. KerrW@Pitt.edu.
FAU - McFarlane, Katherine N
AU  - McFarlane KN
AD  - Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 
      811.22, Pittsburgh, PA, 15213, USA.
LA  - eng
GR  - R25 NS089450/NS/NINDS NIH HHS/United States
PT  - Journal Article
PT  - Review
DEP - 20231207
PL  - United States
TA  - Curr Neurol Neurosci Rep
JT  - Current neurology and neuroscience reports
JID - 100931790
SB  - IM
OTO - NOTNLM
OT  - Computer-aided decision making
OT  - Computer-aided decision support
OT  - Natural language processing
OT  - Neural networks
OT  - Seizures
EDAT- 2023/12/07 12:42
MHDA- 2023/12/07 12:42
CRDT- 2023/12/07 11:15
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/12/07 12:42 [medline]
PHST- 2023/12/07 12:42 [pubmed]
PHST- 2023/12/07 11:15 [entrez]
AID - 10.1007/s11910-023-01318-7 [pii]
AID - 10.1007/s11910-023-01318-7 [doi]
PST - aheadofprint
SO  - Curr Neurol Neurosci Rep. 2023 Dec 7. doi: 10.1007/s11910-023-01318-7.

PMID- 37478046
OWN - NLM
STAT- Publisher
LR  - 20230724
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - PP
DP  - 2023 Jul 21
TI  - Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges.
LID - 10.1109/TNNLS.2023.3294810 [doi]
AB  - Sentiment analysis (SA) aims to understand the attitudes and views of opinion 
      holders with computers. Previous studies have achieved significant breakthroughs 
      and extensive applications in the past decade, such as public opinion analysis 
      and intelligent voice service. With the rapid development of deep learning, SA 
      based on various modalities has become a research hotspot. However, only 
      individual modality has been analyzed separately, lacking a systematic carding of 
      comprehensive SA methods. Meanwhile, few surveys covering the topic of multimodal 
      SA (MSA) have been explored yet. In this article, we first take the modality as 
      the thread to design a novel framework of SA tasks to provide researchers with a 
      comprehensive understanding of relevant advances in SA. Then, we introduce the 
      general workflows and recent advances of single-modal in detail, discuss the 
      similarities and differences of single-modal SA in data processing and modeling 
      to guide MSA, and summarize the commonly used datasets to provide guidance on 
      data and methods for researchers according to different task types. Next, a new 
      taxonomy is proposed to fill the research gaps in MSA, which is divided into 
      multimodal representation learning and multimodal data fusion. The similarities 
      and differences between these two methods and the latest advances are described 
      in detail, such as dynamic interaction between multimodalities, and the 
      multimodal fusion technologies are further expanded. Moreover, we explore the 
      advanced studies on multimodal alignment, chatbots, and Chat Generative 
      Pre-trained Transformer (ChatGPT) in SA. Finally, we discuss the open research 
      challenges of MSA and provide four potential aspects to improve future works, 
      such as cross-modal contrastive learning and multimodal pretraining models.
FAU - Lu, Qiang
AU  - Lu Q
FAU - Sun, Xia
AU  - Sun X
FAU - Long, Yunfei
AU  - Long Y
FAU - Gao, Zhizezhang
AU  - Gao Z
FAU - Feng, Jun
AU  - Feng J
FAU - Sun, Tao
AU  - Sun T
LA  - eng
PT  - Journal Article
DEP - 20230721
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
EDAT- 2023/07/21 19:10
MHDA- 2023/07/21 19:10
CRDT- 2023/07/21 13:03
PHST- 2023/07/21 19:10 [pubmed]
PHST- 2023/07/21 19:10 [medline]
PHST- 2023/07/21 13:03 [entrez]
AID - 10.1109/TNNLS.2023.3294810 [doi]
PST - aheadofprint
SO  - IEEE Trans Neural Netw Learn Syst. 2023 Jul 21;PP. doi: 
      10.1109/TNNLS.2023.3294810.

PMID- 37839591
OWN - NLM
STAT- Publisher
LR  - 20231109
IS  - 1532-8503 (Electronic)
IS  - 1051-2276 (Linking)
DP  - 2023 Oct 13
TI  - Application of Artificial Intelligence to Patient-Targeted Health Information on 
      Kidney Stone Disease.
LID - S1051-2276(23)00169-3 [pii]
LID - 10.1053/j.jrn.2023.10.002 [doi]
AB  - OBJECTIVE: The American Medical Association recommends health information to be 
      written at a 6th grade level reading level. Our aim was to determine whether 
      Artificial Intelligence can outperform the existing health information on kidney 
      stone prevention and treatment. METHODS: The top 50 search results for "Kidney 
      Stone Prevention" and "Kidney Stone Treatment" on Google, Bing, and Yahoo were 
      selected. Duplicate webpages, advertisements, pages intended for health 
      professionals such as science articles, links to videos, paid subscription pages, 
      and links nonrelated to kidney stone prevention and/or treatment were excluded. 
      Included pages were categorized into academic, hospital-affiliated, commercial, 
      nonprofit foundations, and other. Quality and readability of webpages were 
      evaluated using validated tools, and the reading level was descriptively compared 
      with ChatGPT generated health information on kidney stone prevention and 
      treatment. RESULTS: 50 webpages on kidney stone prevention and 49 on stone 
      treatment were included in this study. The reading level was determined to equate 
      to that of a 10th to 12th grade student. Quality was measured as "fair" with no 
      pages scoring "excellent" and only 20% receiving a "good" quality. There was no 
      significant difference between pages from academic, hospital-affiliated, 
      commercial, and nonprofit foundation publications. The text generated by ChatGPT 
      was considerably easier to understand with readability levels measured as low as 
      5th grade. CONCLUSIONS: The language used in existing information on kidney stone 
      disease is of subpar quality and too complex to understand. Machine learning 
      tools could aid in generating information that is comprehensible by the public.
CI  - Copyright © 2023 National Kidney Foundation, Inc. All rights reserved.
FAU - Kianian, Reza
AU  - Kianian R
AD  - Department of Urology, David Geffen School of Medicine, University of California, 
      Los Angeles, Los Angeles, California.
FAU - Carter, Matthew
AU  - Carter M
AD  - Department of Urology, David Geffen School of Medicine, University of California, 
      Los Angeles, Los Angeles, California.
FAU - Finkelshtein, Ilana
AU  - Finkelshtein I
AD  - Department of Urology, David Geffen School of Medicine, University of California, 
      Los Angeles, Los Angeles, California.
FAU - Eleswarapu, Sriram V
AU  - Eleswarapu SV
AD  - Department of Urology, David Geffen School of Medicine, University of California, 
      Los Angeles, Los Angeles, California.
FAU - Kachroo, Naveen
AU  - Kachroo N
AD  - Department of Urology, Vattikuti Urology Institute, Henry Ford Hospital, Detroit, 
      Michigan. Electronic address: Nkachro1@hfhs.org.
LA  - eng
PT  - Journal Article
DEP - 20231013
PL  - United States
TA  - J Ren Nutr
JT  - Journal of renal nutrition : the official journal of the Council on Renal 
      Nutrition of the National Kidney Foundation
JID - 9112938
SB  - IM
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Consumer Health Information
OT  - Kidney Stones
OT  - Readability
EDAT- 2023/10/16 00:42
MHDA- 2023/10/16 00:42
CRDT- 2023/10/15 19:26
PHST- 2023/06/04 00:00 [received]
PHST- 2023/09/25 00:00 [revised]
PHST- 2023/10/08 00:00 [accepted]
PHST- 2023/10/16 00:42 [pubmed]
PHST- 2023/10/16 00:42 [medline]
PHST- 2023/10/15 19:26 [entrez]
AID - S1051-2276(23)00169-3 [pii]
AID - 10.1053/j.jrn.2023.10.002 [doi]
PST - aheadofprint
SO  - J Ren Nutr. 2023 Oct 13:S1051-2276(23)00169-3. doi: 10.1053/j.jrn.2023.10.002.

PMID- 37331024
OWN - NLM
STAT- Publisher
LR  - 20230618
IS  - 1756-8765 (Electronic)
IS  - 1756-8757 (Linking)
DP  - 2023 Jun 18
TI  - COHUMAIN: Building the Socio-Cognitive Architecture of Collective Human-Machine 
      Intelligence.
LID - 10.1111/tops.12673 [doi]
AB  - In recent years, we have experienced rapid development of advanced technology, 
      machine learning, and artificial intelligence (AI), intended to interact with and 
      augment the abilities of humans in practically every area of life. With the rapid 
      growth of new capabilities, such as those enabled by generative AI (e.g., 
      ChatGPT), AI is increasingly at the center of human communication and 
      collaboration, resulting in a growing recognition of the need to understand how 
      humans and AI can integrate their inputs in collaborative teams. However, there 
      are many unanswered questions regarding how human-AI collective intelligence will 
      emerge and what the barriers might be. Truly integrated collaboration between 
      humans and intelligent agents may result in a different way of working that looks 
      nothing like what we know now, and it is important to keep the essential goal of 
      human societal well-being and prosperity a priority. In this special issue, we 
      begin to scope out the underpinnings of a socio-cognitive architecture for 
      Collective HUman-MAchine INtelligence (COHUMAIN), which is the study of the 
      capability of an integrated human and machine (i.e., intelligent technology) 
      system to achieve goals in a wide range of environments. This topic consists of 
      nine papers including a description of the conceptual foundation for a 
      socio-cognitive architecture for COHUMAIN, empirical tests of some aspects of 
      this architecture, research on proposed representations of intelligent agents 
      that can jointly interact with humans, empirical tests of human-human and 
      human-machine interactions, and philosophical and ethical issues to consider as 
      we develop these systems.
CI  - © 2023 Cognitive Science Society LLC.
FAU - Gonzalez, Cleotilde
AU  - Gonzalez C
AD  - Social and Decision Sciences Department, Carnegie Mellon University.
FAU - Admoni, Henny
AU  - Admoni H
AD  - Robotics Institute, Carnegie Mellon University.
FAU - Brown, Scott
AU  - Brown S
AD  - School of Psychology, University of Newcastle.
FAU - Woolley, Anita Williams
AU  - Woolley AW
AD  - Tepper School of Business, Carnegie Mellon University.
LA  - eng
GR  - W911NF-20-1-0006/Defense Advanced Research Projects Agency (DARPA)/
PT  - Editorial
DEP - 20230618
PL  - United States
TA  - Top Cogn Sci
JT  - Topics in cognitive science
JID - 101506764
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Collaboration
OT  - Collective intelligence
OT  - Human-AI
OT  - Human-machine teaming
EDAT- 2023/06/18 19:17
MHDA- 2023/06/18 19:17
CRDT- 2023/06/18 16:43
PHST- 2023/05/25 00:00 [revised]
PHST- 2022/12/10 00:00 [received]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/06/18 19:17 [medline]
PHST- 2023/06/18 19:17 [pubmed]
PHST- 2023/06/18 16:43 [entrez]
AID - 10.1111/tops.12673 [doi]
PST - aheadofprint
SO  - Top Cogn Sci. 2023 Jun 18. doi: 10.1111/tops.12673.

PMID- 37812847
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1878-0539 (Electronic)
IS  - 1748-6815 (Linking)
VI  - 87
DP  - 2023 Dec
TI  - Applying GPT-4 to the Plastic Surgery Inservice Training Examination.
PG  - 78-82
LID - S1748-6815(23)00521-1 [pii]
LID - 10.1016/j.bjps.2023.09.027 [doi]
AB  - BACKGROUND: The recent introduction of Generative Pre-trained Transformer (GPT)-4 
      has demonstrated the potential to be a superior version of ChatGPT-3.5. According 
      to many, GPT-4 is seen as a more reliable and creative version of GPT-3.5. 
      OBJECTIVE: In conjugation with our prior manuscript, we wanted to determine if 
      GPT-4 could be exploited as an instrument for plastic surgery graduate medical 
      education by evaluating its performance on the Plastic Surgery Inservice Training 
      Examination (PSITE). METHODS: Sample assessment questions from the 2022 PSITE 
      were obtained from the American Council of Academic Plastic Surgeons website and 
      manually inputted into GPT-4. Responses by GPT-4 were qualified using the 
      properties of natural coherence. Incorrect answers were stratified into the 
      consequent categories: informational, logical, or explicit fallacy. RESULTS: From 
      a total of 242 questions, GPT-4 provided correct answers for 187, resulting in a 
      77.3% accuracy rate. Logical reasoning was utilized in 95.0% of questions, 
      internal information in 98.3%, and external information in 97.5%. Upon separating 
      the questions based on incorrect and correct responses, a statistically 
      significant difference was identified in GPT-4's application of logical 
      reasoning. CONCLUSION: GPT-4 has shown to be more accurate and reliable for 
      plastic surgery resident education when compared to GPT-3.5. Users should look to 
      utilize the tool to enhance their educational curriculum. Those who adopt the use 
      of such models may be better equipped to deliver high-quality care to their 
      patients.
CI  - Copyright © 2023 British Association of Plastic, Reconstructive and Aesthetic 
      Surgeons. Published by Elsevier Ltd. All rights reserved.
FAU - Gupta, Rohun
AU  - Gupta R
AD  - Division of Plastic Surgery, Department of Surgery, St. Louis University School 
      of Medicine, St. Louis, MO, USA. Electronic address: rohunguptamd@gmail.com.
FAU - Park, John B
AU  - Park JB
AD  - Department of Plastic Surgery, Rutgers New Jersey School of Medicine, Newark, NJ, 
      USA.
FAU - Herzog, Isabel
AU  - Herzog I
AD  - Department of Plastic Surgery, Rutgers New Jersey School of Medicine, Newark, NJ, 
      USA.
FAU - Yosufi, Nahid
AU  - Yosufi N
AD  - Oakland University William Beaumont School of Medicine, Rochester, MI, USA.
FAU - Mangan, Amelia
AU  - Mangan A
AD  - Division of Plastic Surgery, Department of Surgery, St. Louis University School 
      of Medicine, St. Louis, MO, USA.
FAU - Firouzbakht, Peter K
AU  - Firouzbakht PK
AD  - Division of Plastic Surgery, Department of Surgery, St. Louis University School 
      of Medicine, St. Louis, MO, USA.
FAU - Mailey, Brian A
AU  - Mailey BA
AD  - Division of Plastic Surgery, Department of Surgery, St. Louis University School 
      of Medicine, St. Louis, MO, USA.
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - Netherlands
TA  - J Plast Reconstr Aesthet Surg
JT  - Journal of plastic, reconstructive & aesthetic surgery : JPRAS
JID - 101264239
SB  - IM
MH  - Humans
MH  - *Surgery, Plastic
MH  - *Plastic Surgery Procedures
MH  - Inservice Training
MH  - Curriculum
MH  - Education, Medical, Graduate
OTO - NOTNLM
OT  - AI
OT  - Artificial intelligence
OT  - ChatGPT
OT  - Resident education
COIS- Declaration of Competing Interest No potential conflicts of interest in regard to 
      publication of this article, authorship, or research were declared by the 
      authors.
EDAT- 2023/10/10 00:42
MHDA- 2023/12/05 12:42
CRDT- 2023/10/09 18:20
PHST- 2023/05/23 00:00 [received]
PHST- 2023/08/20 00:00 [revised]
PHST- 2023/09/08 00:00 [accepted]
PHST- 2023/12/05 12:42 [medline]
PHST- 2023/10/10 00:42 [pubmed]
PHST- 2023/10/09 18:20 [entrez]
AID - S1748-6815(23)00521-1 [pii]
AID - 10.1016/j.bjps.2023.09.027 [doi]
PST - ppublish
SO  - J Plast Reconstr Aesthet Surg. 2023 Dec;87:78-82. doi: 
      10.1016/j.bjps.2023.09.027. Epub 2023 Sep 14.

PMID- 37633285
OWN - NLM
STAT- MEDLINE
DCOM- 20230828
LR  - 20230829
IS  - 1195-9479 (Print)
IS  - 1195-9479 (Linking)
VI  - 30
IP  - 4
DP  - 2023 Aug
TI  - Emergence of artificial generative intelligence and its potential impact on 
      urology.
PG  - 11588-11598
AB  - INTRODUCTION: Artificial generative intelligence (AGI) and large language models 
      (LLMs) have gained significant attention in healthcare and hold enormous promise 
      for transforming every aspect of our life and urology is no exception. MATERIALS 
      AND METHODS: We conducted a comprehensive literature search of electronic 
      databases and included articles discussing AGI and LLMs in healthcare. 
      Additionally, we have incorporated our experiences interacting with the ChatGPT 
      and GPT-4 in different situations with real case reports and case constructs. 
      RESULTS: Our review highlights the potential applications and likely impact of 
      these technologies in urology, for differential diagnosis, prioritizing treatment 
      options, and facilitating research, surgeon, and patient education. At their 
      current developmental stage, we have recognized the need for concurrent 
      validation and continuous human interaction necessary to induce inverse 
      reinforced learning with human feedback to mature them to authenticity. We need 
      to consciously adjust to the hallucinations and guard patients' confidentiality 
      before their extensive implementations in clinical practice. We propose possible 
      remedies for these shortcomings and emphasize the critical role of human 
      interaction in their evolution. CONCLUSION: The integration of these tools has 
      the potential to revolutionize urology, but it also presents several challenges 
      needing attention. To harness the full potential of these models, urologists must 
      consistently engage in training these tools with their clinical sense and 
      experience. We urge the urology community to actively participate in AGI and LLM 
      development to address potential challenges. These models could help us in 
      unleashing our full potential and help us achieve a better work-life balance.
FAU - Javid, Mohamed
AU  - Javid M
AD  - Department of Urology, Chengalpattu Medical College, Chengalpattu, India.
FAU - Reddiboina, Madhu
AU  - Reddiboina M
AD  - RediMinds, Inc., Southfield, Michigan, USA.
FAU - Bhandari, Mahendra
AU  - Bhandari M
AD  - Vattikuti Urology Institute, Henry Ford Hospital, Detroit, Michigan, USA.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - Canada
TA  - Can J Urol
JT  - The Canadian journal of urology
JID - 9515842
SB  - IM
MH  - Humans
MH  - *Urology
MH  - Urologists
MH  - Databases, Factual
MH  - Diagnosis, Differential
MH  - Intelligence
OTO - NOTNLM
OT  - BARD
OT  - ChatGPT
OT  - Med-PaLM 2
OT  - artificial generative intelligence
OT  - artificial intelligence
OT  - large-language models
OT  - urology
EDAT- 2023/08/27 05:43
MHDA- 2023/08/28 06:42
CRDT- 2023/08/26 18:42
PHST- 2023/08/28 06:42 [medline]
PHST- 2023/08/27 05:43 [pubmed]
PHST- 2023/08/26 18:42 [entrez]
PST - ppublish
SO  - Can J Urol. 2023 Aug;30(4):11588-11598.

PMID- 37205379
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231102
DP  - 2023 May 2
TI  - Reference-free and cost-effective automated cell type annotation with GPT-4 in 
      single-cell RNA-seq analysis.
LID - rs.3.rs-2824971 [pii]
LID - 10.21203/rs.3.rs-2824971/v1 [doi]
AB  - Cell type annotation is an essential step in single-cell RNA-seq analysis. 
      However, it is a time-consuming process that often requires expertise in 
      collecting canonical marker genes and manually annotating cell types. Automated 
      cell type annotation methods typically require the acquisition of high-quality 
      reference datasets and the development of additional pipelines. We demonstrate 
      that GPT-4, a highly potent large language model, can automatically and 
      accurately annotate cell types by utilizing marker gene information generated 
      from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds 
      of tissue types and cell types, GPT-4 generates cell type annotations exhibiting 
      strong concordance with manual annotations, and has the potential to considerably 
      reduce the effort and expertise needed in cell type annotation.
FAU - Hou, Wenpin
AU  - Hou W
AUID- ORCID: 0000-0003-0972-2192
AD  - Department of Biostatistics, The Mailman School of Public Health, Columbia 
      University, New York City, NY, USA.
FAU - Ji, Zhicheng
AU  - Ji Z
AUID- ORCID: 0000-0002-9457-4704
AD  - Department of Biostatistics and Bioinformatics, Duke University School of 
      Medicine, Durham, NC, USA.
LA  - eng
GR  - U54 AG075936/AG/NIA NIH HHS/United States
PT  - Preprint
DEP - 20230502
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10187429
EDAT- 2023/05/19 19:14
MHDA- 2023/05/19 19:15
CRDT- 2023/05/19 13:08
PHST- 2023/05/19 19:14 [pubmed]
PHST- 2023/05/19 19:15 [medline]
PHST- 2023/05/19 13:08 [entrez]
AID - rs.3.rs-2824971 [pii]
AID - 10.21203/rs.3.rs-2824971/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 May 2:rs.3.rs-2824971. doi: 10.21203/rs.3.rs-2824971/v1. Preprint.

PMID- 36748005
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230207
IS  - 2331-8422 (Electronic)
IS  - 2331-8422 (Linking)
DP  - 2023 Jan 24
TI  - Large language models can segment narrative events similarly to humans.
LID - arXiv:2301.10297v1
AB  - Humans perceive discrete events such as "restaurant visits" and "train rides" in 
      their continuous experience. One important prerequisite for studying human event 
      perception is the ability of researchers to quantify when one event ends and 
      another begins. Typically, this information is derived by aggregating behavioral 
      annotations from several observers. Here we present an alternative computational 
      approach where event boundaries are derived using a large language model, GPT-3, 
      instead of using human annotations. We demonstrate that GPT-3 can segment 
      continuous narrative text into events. GPT-3-annotated events are significantly 
      correlated with human event annotations. Furthermore, these GPT-derived 
      annotations achieve a good approximation of the "consensus" solution (obtained by 
      averaging across human annotations); the boundaries identified by GPT-3 are 
      closer to the consensus, on average, than boundaries identified by individual 
      human annotators. This finding suggests that GPT-3 provides a feasible solution 
      for automated event annotations, and it demonstrates a further parallel between 
      human cognition and prediction in large language models. In the future, GPT-3 may 
      thereby help to elucidate the principles underlying human event perception.
FAU - Michelmann, Sebastian
AU  - Michelmann S
FAU - Kumar, Manoj
AU  - Kumar M
FAU - Norman, Kenneth A
AU  - Norman KA
FAU - Toneva, Mariya
AU  - Toneva M
LA  - eng
PT  - Preprint
DEP - 20230124
PL  - United States
TA  - ArXiv
JT  - ArXiv
JID - 101759493
PMC - PMC9900968
EDAT- 2023/02/08 06:00
MHDA- 2023/02/08 06:01
CRDT- 2023/02/07 02:04
PHST- 2023/02/07 02:04 [entrez]
PHST- 2023/02/08 06:00 [pubmed]
PHST- 2023/02/08 06:01 [medline]
AID - 2301.10297 [pii]
PST - epublish
SO  - ArXiv. 2023 Jan 24:arXiv:2301.10297v1. Preprint.

PMID- 36993670
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Mar 13
TI  - GeneTuring tests GPT models in genomics.
LID - 2023.03.11.532238 [pii]
LID - 10.1101/2023.03.11.532238 [doi]
AB  - Generative Pre-trained Transformers (GPT) are powerful language models that have 
      great potential to transform biomedical research. However, they are known to 
      suffer from artificial hallucinations and provide false answers that are 
      seemingly correct in some situations. We developed GeneTuring, a comprehensive QA 
      database with 600 questions in genomics, and manually scored 10,800 answers 
      returned by six GPT models, including GPT-3, ChatGPT, and New Bing. New Bing has 
      the best overall performance and significantly reduces the level of AI 
      hallucination compared to other models, thanks to its ability to recognize its 
      incapacity in answering questions. We argue that improving incapacity awareness 
      is equally important as improving model accuracy to address AI hallucination.
FAU - Hou, Wenpin
AU  - Hou W
AD  - Department of Biostatistics, The Mailman School of Public Health, Columbia 
      University, New York City, NY, USA.
FAU - Ji, Zhicheng
AU  - Ji Z
AD  - Department of Biostatistics and Bioinformatics, Duke University School of 
      Medicine, Durham, NC, USA.
LA  - eng
PT  - Preprint
DEP - 20230313
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10054955
COIS- Competing interests All authors declare no competing interests.
EDAT- 2023/03/31 06:00
MHDA- 2023/03/31 06:01
CRDT- 2023/03/30 02:54
PHST- 2023/03/31 06:00 [pubmed]
PHST- 2023/03/31 06:01 [medline]
PHST- 2023/03/30 02:54 [entrez]
AID - 2023.03.11.532238 [pii]
AID - 10.1101/2023.03.11.532238 [doi]
PST - epublish
SO  - bioRxiv. 2023 Mar 13:2023.03.11.532238. doi: 10.1101/2023.03.11.532238. Preprint.

PMID- 37438101
OWN - NLM
STAT- Publisher
LR  - 20230712
IS  - 1759-8486 (Electronic)
IS  - 1759-8478 (Linking)
DP  - 2023 Jul 12
TI  - The perils and promises of generative artificial intelligence in 
      neurointerventional surgery.
LID - jnis-2023-020353 [pii]
LID - 10.1136/jnis-2023-020353 [doi]
AB  - Generative artificial intelligence (AI) holds great promise in 
      neurointerventional surgery by providing clinicians with powerful tools for 
      improving surgical precision, accuracy of diagnoses, and treatment planning. 
      However, potential perils include biases or inaccuracies in the data used to 
      train the algorithms, over-reliance on generative AI without human oversight, 
      patient privacy concerns, and ethical implications of using AI in medical 
      decision-making. Careful regulation and oversight are needed to ensure that the 
      promises of generative AI in neurointerventional surgery are realized while 
      minimizing its potential perils.[ChatGPT authored summary using the prompt "In 
      one paragraph summarize the promises and perils of generative AI in 
      neurointerventional surgery".].
CI  - © Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and 
      permissions. Published by BMJ.
FAU - Ray, Tyler R
AU  - Ray TR
AD  - Department of Mechanical Engineering, University of Hawaii at Mānoa College of 
      Engineering, Honolulu, Hawaii, USA.
FAU - Kellogg, Ryan T
AU  - Kellogg RT
AUID- ORCID: 0000-0001-6491-5443
AD  - Department of Neurosurgery, University of Virginia, Charlottesville, Virginia, 
      USA.
FAU - Fargen, Kyle M
AU  - Fargen KM
AUID- ORCID: 0000-0001-8979-1993
AD  - Department of Neurological Surgery and Radiology, Wake Forest University, 
      Winston-Salem, North Carolina, USA.
FAU - Hui, Ferdinand
AU  - Hui F
AUID- ORCID: 0000-0003-3759-7886
AD  - Neurointerventional Surgery, Queen's Medical Center Neuroscience Institute, 
      Honolulu, Hawaii, USA.
FAU - Vargas, Jan
AU  - Vargas J
AUID- ORCID: 0000-0001-7164-1479
AD  - Division of Neurosurgery, Prisma Health Upstate, Greenville, South Carolina, USA 
      jvargas.machaj@prismahealth.org.
LA  - eng
PT  - Journal Article
DEP - 20230712
PL  - England
TA  - J Neurointerv Surg
JT  - Journal of neurointerventional surgery
JID - 101517079
SB  - IM
OTO - NOTNLM
OT  - Economics
OT  - Political
OT  - Standards
OT  - Technology
COIS- Competing interests: RTK and JV have stock and are paid consultants for Viz.AI. 
      KF serves on the editorial board of JNIS.
EDAT- 2023/07/13 01:06
MHDA- 2023/07/13 01:06
CRDT- 2023/07/12 21:23
PHST- 2023/06/26 00:00 [accepted]
PHST- 2023/07/13 01:06 [medline]
PHST- 2023/07/13 01:06 [pubmed]
PHST- 2023/07/12 21:23 [entrez]
AID - jnis-2023-020353 [pii]
AID - 10.1136/jnis-2023-020353 [doi]
PST - aheadofprint
SO  - J Neurointerv Surg. 2023 Jul 12:jnis-2023-020353. doi: 10.1136/jnis-2023-020353.

PMID- 37881016
OWN - NLM
STAT- Publisher
LR  - 20231026
IS  - 1472-1465 (Electronic)
IS  - 0007-1250 (Linking)
DP  - 2023 Oct 26
TI  - Artificial intelligence and increasing misinformation.
PG  - 1-3
LID - 10.1192/bjp.2023.136 [doi]
AB  - With the recent advances in artificial intelligence (AI), patients are 
      increasingly exposed to misleading medical information. Generative AI models, 
      including large language models such as ChatGPT, create and modify text, images, 
      audio and video information based on training data. Commercial use of generative 
      AI is expanding rapidly and the public will routinely receive messages created by 
      generative AI. However, generative AI models may be unreliable, routinely make 
      errors and widely spread misinformation. Misinformation created by generative AI 
      about mental illness may include factual errors, nonsense, fabricated sources and 
      dangerous advice. Psychiatrists need to recognise that patients may receive 
      misinformation online, including about medicine and psychiatry.
FAU - Monteith, Scott
AU  - Monteith S
AUID- ORCID: 0000-0002-9289-4544
AD  - Michigan State University College of Human Medicine, Traverse City Campus, 
      Traverse City, Michigan, USA.
FAU - Glenn, Tasha
AU  - Glenn T
AD  - ChronoRecord Association, Fullerton, California, USA.
FAU - Geddes, John R
AU  - Geddes JR
AD  - Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
FAU - Whybrow, Peter C
AU  - Whybrow PC
AD  - Department of Psychiatry and Biobehavioral Sciences, Semel Institute for 
      Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los 
      Angeles, California, USA.
FAU - Achtyes, Eric
AU  - Achtyes E
AD  - Department of Psychiatry, Western Michigan University Homer Stryker M.D. School 
      of Medicine, Kalamazoo, Michigan, USA.
FAU - Bauer, Michael
AU  - Bauer M
AD  - Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus 
      Medical Faculty, Technische Universität Dresden, Dresden, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - England
TA  - Br J Psychiatry
JT  - The British journal of psychiatry : the journal of mental science
JID - 0342367
SB  - IM
OTO - NOTNLM
OT  - Generative artificial intelligence
OT  - artificial intelligence
OT  - disinformation
OT  - misinformation
OT  - technology
EDAT- 2023/10/26 06:42
MHDA- 2023/10/26 06:42
CRDT- 2023/10/26 03:48
PHST- 2023/10/26 06:42 [medline]
PHST- 2023/10/26 06:42 [pubmed]
PHST- 2023/10/26 03:48 [entrez]
AID - S0007125023001368 [pii]
AID - 10.1192/bjp.2023.136 [doi]
PST - aheadofprint
SO  - Br J Psychiatry. 2023 Oct 26:1-3. doi: 10.1192/bjp.2023.136.

PMID- 37205549
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
DP  - 2023 May 2
TI  - Almanac: Retrieval-Augmented Language Models for Clinical Medicine.
LID - rs.3.rs-2883198 [pii]
LID - 10.21203/rs.3.rs-2883198/v1 [doi]
AB  - Large-language models have recently demonstrated impressive zero-shot 
      capabilities in a variety of natural language tasks such as summarization, 
      dialogue generation, and question-answering. Despite many promising applications 
      in clinical medicine, adoption of these models in real-world settings has been 
      largely limited by their tendency to generate incorrect and sometimes even toxic 
      statements. In this study, we develop Almanac, a large language model framework 
      augmented with retrieval capabilities for medical guideline and treatment 
      recommendations. Performance on a novel dataset of clinical scenarios (n= 130) 
      evaluated by a panel of 5 board-certified and resident physicians demonstrates 
      significant increases in factuality (mean of 18% at p-value < 0.05) across all 
      specialties, with improvements in completeness and safety. Our results 
      demonstrate the potential for large language models to be effective tools in the 
      clinical decision-making process, while also emphasizing the importance of 
      careful testing and deployment to mitigate their shortcomings.
FAU - Zakka, Cyril
AU  - Zakka C
AD  - Department of Cardiothoracic Surgery, Stanford Medicine.
FAU - Chaurasia, Akash
AU  - Chaurasia A
AD  - Department of Cardiothoracic Surgery, Stanford Medicine.
AD  - Department of Computer Science, Stanford University.
FAU - Shad, Rohan
AU  - Shad R
AD  - Division of Cardiovascular Surgery, Penn Medicine.
FAU - Dalal, Alex R
AU  - Dalal AR
AD  - Department of Cardiothoracic Surgery, Stanford Medicine.
FAU - Kim, Jennifer L
AU  - Kim JL
AD  - Department of Cardiothoracic Surgery, Stanford Medicine.
FAU - Moor, Michael
AU  - Moor M
AD  - Department of Computer Science, Stanford University.
FAU - Alexander, Kevin
AU  - Alexander K
AD  - Division of Cardiovascular Medicine, Stanford Medicine.
FAU - Ashley, Euan
AU  - Ashley E
AD  - Division of Cardiovascular Medicine, Stanford Medicine.
FAU - Boyd, Jack
AU  - Boyd J
AD  - Department of Cardiothoracic Surgery, Stanford Medicine.
FAU - Boyd, Kathleen
AU  - Boyd K
AD  - Department of Pediatrics, Stanford Medicine.
FAU - Hirsch, Karen
AU  - Hirsch K
AD  - Department of Neurology, Stanford Medicine.s.
FAU - Langlotz, Curt
AU  - Langlotz C
AD  - Department of Radiology and Biomedical Informatics, Stanford Medicine.
FAU - Nelson, Joanna
AU  - Nelson J
AD  - Division of Infectious Diseases, Stanford Medicine.
FAU - Hiesinger, William
AU  - Hiesinger W
AD  - Department of Cardiothoracic Surgery, Stanford Medicine.
LA  - eng
PT  - Preprint
DEP - 20230502
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10187428
COIS- Competing interests The authors declare no competing interests.
EDAT- 2023/05/19 19:15
MHDA- 2023/05/19 19:16
CRDT- 2023/05/19 13:09
PHST- 2023/05/19 19:15 [pubmed]
PHST- 2023/05/19 19:16 [medline]
PHST- 2023/05/19 13:09 [entrez]
AID - rs.3.rs-2883198 [pii]
AID - 10.21203/rs.3.rs-2883198/v1 [doi]
PST - epublish
SO  - Res Sq. 2023 May 2:rs.3.rs-2883198. doi: 10.21203/rs.3.rs-2883198/v1. Preprint.

PMID- 37808815
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231018
DP  - 2023 Sep 26
TI  - Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models 
      to Map Voice Responses in SDoH Survey.
LID - 2023.09.25.23295917 [pii]
LID - 10.1101/2023.09.25.23295917 [doi]
AB  - Social Determinants of Health (SDoH) have been shown to have profound impacts on 
      health-related outcomes, yet this data suffers from high rates of missingness in 
      electronic health records (EHR). Moreover, limited English proficiency in the 
      United States can be a barrier to communication with health care providers. In 
      this study, we have designed a multilingual conversational agent capable of 
      conducting SDoH surveys for use in healthcare environments. The agent asks 
      questions in the patient's native language, translates responses into English, 
      and subsequently maps these responses via a large language model (LLM) to 
      structured options in a SDoH survey. This tool can be extended to a variety of 
      survey instruments in either hospital or home settings, enabling the extraction 
      of structured insights from free-text answers. The proposed approach heralds a 
      shift towards more inclusive and insightful data collection, marking a 
      significant stride in SDoH data enrichment for optimizing health outcome 
      predictions and interventions.
FAU - Krishnamoorthy, Rishivardhan
AU  - Krishnamoorthy R
FAU - Nagarajan, Vishal
AU  - Nagarajan V
FAU - Pour, Hayden
AU  - Pour H
FAU - Shashikumar, Supreeth P
AU  - Shashikumar SP
FAU - Boussina, Aaron
AU  - Boussina A
FAU - Farcas, Emilia
AU  - Farcas E
FAU - Nemati, Shamim
AU  - Nemati S
FAU - Josef, Christopher S
AU  - Josef CS
LA  - eng
PT  - Preprint
DEP - 20230926
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10557796
EDAT- 2023/10/09 06:41
MHDA- 2023/10/09 06:42
CRDT- 2023/10/09 05:45
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/10/09 06:41 [pubmed]
PHST- 2023/10/09 05:45 [entrez]
AID - 2023.09.25.23295917 [pii]
AID - 10.1101/2023.09.25.23295917 [doi]
PST - epublish
SO  - medRxiv. 2023 Sep 26:2023.09.25.23295917. doi: 10.1101/2023.09.25.23295917. 
      Preprint.

PMID- 37131626
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Apr 21
TI  - Reference-free and cost-effective automated cell type annotation with GPT-4 in 
      single-cell RNA-seq analysis.
LID - 2023.04.16.537094 [pii]
LID - 10.1101/2023.04.16.537094 [doi]
AB  - Cell type annotation is an essential step in single-cell RNA-seq analysis. 
      However, it is a time-consuming process that often requires expertise in 
      collecting canonical marker genes and manually annotating cell types. Automated 
      cell type annotation methods typically require the acquisition of high-quality 
      reference datasets and the development of additional pipelines. We demonstrate 
      that GPT-4, a highly potent large language model, can automatically and 
      accurately annotate cell types by utilizing marker gene information generated 
      from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds 
      of tissue types and cell types, GPT-4 generates cell type annotations exhibiting 
      strong concordance with manual annotations, and has the potential to considerably 
      reduce the effort and expertise needed in cell type annotation.
FAU - Hou, Wenpin
AU  - Hou W
AD  - Department of Biostatistics, The Mailman School of Public Health, Columbia 
      University, New York City, NY, USA.
FAU - Ji, Zhicheng
AU  - Ji Z
AD  - Department of Biostatistics and Bioinformatics, Duke University School of 
      Medicine, Durham, NC, USA.
LA  - eng
PT  - Preprint
DEP - 20230421
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10153208
COIS- Competing interests All authors declare no competing interests.
EDAT- 2023/05/03 06:42
MHDA- 2023/05/03 06:43
CRDT- 2023/05/03 01:58
PHST- 2023/05/03 06:42 [pubmed]
PHST- 2023/05/03 06:43 [medline]
PHST- 2023/05/03 01:58 [entrez]
AID - 2023.04.16.537094 [pii]
AID - 10.1101/2023.04.16.537094 [doi]
PST - epublish
SO  - bioRxiv. 2023 Apr 21:2023.04.16.537094. doi: 10.1101/2023.04.16.537094. Preprint.

PMID- 37863479
OWN - NLM
STAT- Publisher
LR  - 20231020
IS  - 1555-9823 (Electronic)
IS  - 0003-1348 (Linking)
DP  - 2023 Oct 20
TI  - The Role of Artificial Intelligence in Surgery: What do General Surgery Residents 
      Think?
PG  - 31348231209524
LID - 10.1177/00031348231209524 [doi]
AB  - BACKGROUND: Artificial intelligence (AI) holds significant potential in medical 
      education and patient care, but its rapid emergence presents ethical and 
      practical challenges. This study explored the perspectives of surgical residents 
      on AI's role in medicine. METHODS: We performed a cross-sectional study surveying 
      general surgery residents at a university-affiliated teaching hospital about 
      their views on AI in medicine and surgical training. The survey covered 
      demographics, residents' understanding of AI, its integration into medical 
      practice, and use of AI tools like ChatGPT. The survey design was inspired by a 
      recent national survey and underwent pretesting before deployment. RESULTS: Of 
      the 31 participants surveyed, 24% identified diagnostics as AI's top application, 
      12% favored its use in identifying anatomical structures in surgeries, and 20% 
      endorsed AI integration into EMRs for predictive models. Attitudes toward AI 
      varied based on its intended application: 77.41% expressed concern about AI 
      making life decisions and 70.97% felt excited about its application for 
      repetitive tasks. A significant 67.74% believed AI could enhance the 
      understanding of medical knowledge. Perception of AI integration varied with AI 
      familiarity (P = .01), with more knowledgeable respondents expressing more 
      positivity. Moreover, familiarity influenced the perceived academic use of 
      ChatGPT (P = .039) and attitudes toward AI in operating rooms (P = .032). 
      Conclusion: This study provides insights into surgery residents' perceptions of 
      AI in medical practice and training. These findings can inform future research, 
      shape policy decisions, and guide AI development, promoting a harmonious 
      collaboration between AI and surgeons to improve both training and patient care.
FAU - St John, Ace
AU  - St John A
AD  - University of Maryland Medical Center, Baltimore, MD, USA. RINGGOLD: 21668
FAU - Cooper, Laura
AU  - Cooper L
AUID- ORCID: 0000-0002-4703-6399
AD  - University of Maryland Medical Center, Baltimore, MD, USA. RINGGOLD: 21668
FAU - Kavic, Stephen M
AU  - Kavic SM
AD  - University of Maryland School of Medicine, Baltimore, MD, USA. RINGGOLD: 12264
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - United States
TA  - Am Surg
JT  - The American surgeon
JID - 0370522
SB  - IM
OTO - NOTNLM
OT  - artificial intelligence
OT  - residency
OT  - surgery
COIS- Declaration of Conflicting InterestsThe author(s) declared no potential conflicts 
      of interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2023/10/21 05:43
MHDA- 2023/10/21 05:43
CRDT- 2023/10/20 20:33
PHST- 2023/10/21 05:43 [medline]
PHST- 2023/10/21 05:43 [pubmed]
PHST- 2023/10/20 20:33 [entrez]
AID - 10.1177/00031348231209524 [doi]
PST - aheadofprint
SO  - Am Surg. 2023 Oct 20:31348231209524. doi: 10.1177/00031348231209524.

PMID- 38035280
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 2296-2565 (Electronic)
IS  - 2296-2565 (Linking)
VI  - 11
DP  - 2023
TI  - A search-based geographic metadata curation pipeline to refine sequencing 
      institution information and support public health.
PG  - 1254976
LID - 10.3389/fpubh.2023.1254976 [doi]
LID - 1254976
AB  - BACKGROUND: The National Center for Biotechnology Information (NCBI) Sequence 
      Read Archive (SRA) has amassed a vast reservoir of genetic data since its 
      inception in 2007. These public data hold immense potential for supporting 
      pathogen surveillance and control. However, the lack of standardized metadata and 
      inconsistent submission practices in SRA may impede the data's utility in public 
      health. METHODS: To address this issue, we introduce the Search-based Geographic 
      Metadata Curation (SGMC) pipeline. SGMC utilized Python and web scraping to 
      extract geographic data of sequencing institutions from NCBI SRA in the Cloud and 
      its website. It then harnessed ChatGPT to refine the sequencing institution and 
      location assignments. To illustrate the pipeline's utility, we examined the 
      geographic distribution of the sequencing institutions and their countries 
      relevant to polio eradication and categorized them. RESULTS: SGMC successfully 
      identified 7,649 sequencing institutions and their global locations from a random 
      selection of 2,321,044 SRA accessions. These institutions were distributed across 
      97 countries, with strong representation in the United States, the United Kingdom 
      and China. However, there was a lack of data from African, Central Asian, and 
      Central American countries, indicating potential disparities in sequencing 
      capabilities. Comparison with manually curated data for U.S. institutions reveals 
      SGMC's accuracy rates of 94.8% for institutions, 93.1% for countries, and 74.5% 
      for geographic coordinates. CONCLUSION: SGMC may represent a novel approach using 
      a generative AI model to enhance geographic data (country and institution 
      assignments) for large numbers of samples within SRA datasets. This information 
      can be utilized to bolster public health endeavors.
CI  - Copyright © 2023 Zhao, Farrell, Mashiku, Abay, Tang, Oberste and Burns.
FAU - Zhao, Kun
AU  - Zhao K
AD  - Division of Viral Diseases, National Center for Immunization and Respiratory 
      Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States.
FAU - Farrell, Katie
AU  - Farrell K
AD  - Cherokee Nation Businesses, Contracting Agency to the Division of Viral Diseases, 
      Centers for Disease Control and Prevention, Catoosa, OK, United States.
FAU - Mashiku, Melchizedek
AU  - Mashiku M
AD  - Cherokee Nation Businesses, Contracting Agency to the Division of Viral Diseases, 
      Centers for Disease Control and Prevention, Catoosa, OK, United States.
FAU - Abay, Dawit
AU  - Abay D
AD  - Cherokee Nation Businesses, Contracting Agency to the Division of Viral Diseases, 
      Centers for Disease Control and Prevention, Catoosa, OK, United States.
FAU - Tang, Kevin
AU  - Tang K
AD  - Division of Scientific Resources, National Center for Emerging and Zoonotic 
      Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, 
      United States.
FAU - Oberste, M Steven
AU  - Oberste MS
AD  - Division of Viral Diseases, National Center for Immunization and Respiratory 
      Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States.
FAU - Burns, Cara C
AU  - Burns CC
AD  - Division of Viral Diseases, National Center for Immunization and Respiratory 
      Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States.
LA  - eng
PT  - Journal Article
DEP - 20231114
PL  - Switzerland
TA  - Front Public Health
JT  - Frontiers in public health
JID - 101616579
SB  - IM
MH  - *Metadata
MH  - *Public Health
MH  - High-Throughput Nucleotide Sequencing
MH  - China
MH  - United Kingdom
PMC - PMC10683794
OTO - NOTNLM
OT  - ChatGPT
OT  - cloud computing
OT  - disparity of global sequencing capability
OT  - geographic locations
OT  - poliovirus
OT  - sequence read archive
OT  - surveillance
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/30 18:45
MHDA- 2023/12/04 12:42
CRDT- 2023/11/30 17:47
PHST- 2023/07/08 00:00 [received]
PHST- 2023/10/19 00:00 [accepted]
PHST- 2023/12/04 12:42 [medline]
PHST- 2023/11/30 18:45 [pubmed]
PHST- 2023/11/30 17:47 [entrez]
AID - 10.3389/fpubh.2023.1254976 [doi]
PST - epublish
SO  - Front Public Health. 2023 Nov 14;11:1254976. doi: 10.3389/fpubh.2023.1254976. 
      eCollection 2023.

PMID- 37888077
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231029
IS  - 2075-4426 (Print)
IS  - 2075-4426 (Electronic)
IS  - 2075-4426 (Linking)
VI  - 13
IP  - 10
DP  - 2023 Oct 6
TI  - Harnessing Code Interpreters for Enhanced Predictive Modeling: A Case Study on 
      High-Density Lipoprotein Level Estimation in Romanian Diabetic Patients.
LID - 10.3390/jpm13101466 [doi]
LID - 1466
AB  - Diabetes is a condition accompanied by the alteration of body parameters, 
      including those related to lipids like triglyceride (TG), low-density 
      lipoproteins (LDLs), and high-density lipoproteins (HDLs). The latter are grouped 
      under the term dyslipidemia and are considered a risk factor for cardiovascular 
      events. In the present work, we analyzed the complex relationships between twelve 
      parameters (disease status, age, sex, body mass index, systolic blood pressure, 
      diastolic blood pressure, TG, HDL, LDL, glucose, HbA1c levels, and disease onset) 
      of patients with diabetes from Romania. An initial prospective analysis showed 
      that HDL is inversely correlated with most of the parameters; therefore, we 
      further analyzed the dependence of HDLs on the other factors. The analysis was 
      conducted with the Code Interpreter plugin of ChatGPT, which was used to build 
      several models from which Random Forest performed best. The principal predictors 
      of HDLs were TG, LDL, and HbA1c levels. Random Forest models were used to model 
      all parameters, showing that blood pressure and HbA1c can be predicted based on 
      the other parameters with the least error, while the less predictable parameters 
      were TG and LDL levels. By conducting the present study using the ChatGPT Code 
      Interpreter, we show that elaborate analysis methods are at hand and easy to 
      apply by researchers with limited computational resources. The insight that can 
      be gained from such an approach, such as what we obtained on HDL level predictors 
      in diabetes, could be relevant for deriving novel management strategies and 
      therapeutic approaches.
FAU - Albajy, Maitham Abdallah
AU  - Albajy MA
AUID- ORCID: 0000-0002-4819-4241
AD  - Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, 
      University of Bucharest, 91-95 Splaiul Independenței Str., 050095 Bucharest, 
      Romania.
AD  - National Center for Occupational Health and Safety, 22 Imam Ali Str., Nasiriyah 
      64001, Iraq.
FAU - Mernea, Maria
AU  - Mernea M
AD  - Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, 
      University of Bucharest, 91-95 Splaiul Independenței Str., 050095 Bucharest, 
      Romania.
FAU - Mihaila, Alexandra
AU  - Mihaila A
AD  - Liberty Medical Center Clinic, Intrarea Zorilor 23 A Str., 077175 Bucharest, 
      Romania.
FAU - Pop, Cristian-Emilian
AU  - Pop CE
AUID- ORCID: 0000-0003-0511-3672
AD  - Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, 
      University of Bucharest, 91-95 Splaiul Independenței Str., 050095 Bucharest, 
      Romania.
AD  - Non-Governmental Research Organization Biologic, Schitului 14 Str., 032044 
      Bucharest, Romania.
FAU - Mihăilescu, Dan Florin
AU  - Mihăilescu DF
AD  - Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, 
      University of Bucharest, 91-95 Splaiul Independenței Str., 050095 Bucharest, 
      Romania.
AD  - Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Psychiatric 
      Hospital, Șoseaua Berceni 10 Str., 041914 Bucharest, Romania.
LA  - eng
PT  - Journal Article
DEP - 20231006
PL  - Switzerland
TA  - J Pers Med
JT  - Journal of personalized medicine
JID - 101602269
PMC - PMC10608218
OTO - NOTNLM
OT  - data analysis
OT  - data modeling
OT  - diabetes
OT  - management strategies
OT  - parameters
OT  - predictors
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/27 12:42
MHDA- 2023/10/27 12:43
CRDT- 2023/10/27 07:10
PHST- 2023/08/25 00:00 [received]
PHST- 2023/09/30 00:00 [revised]
PHST- 2023/10/04 00:00 [accepted]
PHST- 2023/10/27 12:43 [medline]
PHST- 2023/10/27 12:42 [pubmed]
PHST- 2023/10/27 07:10 [entrez]
AID - jpm13101466 [pii]
AID - jpm-13-01466 [pii]
AID - 10.3390/jpm13101466 [doi]
PST - epublish
SO  - J Pers Med. 2023 Oct 6;13(10):1466. doi: 10.3390/jpm13101466.

PMID- 37854479
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Three Effective, Efficient, and Easily Implementable Ways to Integrate A.I. Into 
      Medical Education.
PG  - e47204
LID - 10.7759/cureus.47204 [doi]
LID - e47204
AB  - As a medical school CEO who is following the development of A.I. very closely, I 
      believe that med students are eager to adopt the possibilities that A.I. tools 
      can deliver in their training. Not only do these students already use variations 
      of A.I. in other areas of their lives, but they also embrace advanced technology 
      and understand how to use it. With the tech readiness of today's students in 
      mind, I have devised three recommendations for how to best infuse A.I. into 
      medical education. This strategic guidance can deliver significant benefits to 
      today's tech-fluent medical school students and enhance their training in their 
      journeys to become doctors.
CI  - Copyright © 2023, Lenihan et al.
FAU - Lenihan, David
AU  - Lenihan D
AD  - Neurology, Ponce Health Sciences University, Ponce, PRI.
AD  - Neurology, Ponce Health Sciences University, St. Louis, USA.
LA  - eng
PT  - Editorial
DEP - 20231017
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10581027
OTO - NOTNLM
OT  - a.i
OT  - artificial intelligence and education
OT  - chatgpt
OT  - medical education and training
OT  - medical school education
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/19 06:45
MHDA- 2023/10/19 06:46
CRDT- 2023/10/19 04:19
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/10/19 06:46 [medline]
PHST- 2023/10/19 06:45 [pubmed]
PHST- 2023/10/19 04:19 [entrez]
AID - 10.7759/cureus.47204 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 17;15(10):e47204. doi: 10.7759/cureus.47204. eCollection 2023 
      Oct.

PMID- 37546190
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230928
IS  - 2156-8650 (Electronic)
IS  - 2156-8650 (Linking)
VI  - 33
IP  - 4
DP  - 2023 Aug
TI  - Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence 
      in Medical Education.
PG  - 1007-1012
LID - 10.1007/s40670-023-01815-x [doi]
AB  - The increasing use of artificial intelligence (AI) in medicine is associated with 
      new ethical challenges and responsibilities. However, special considerations and 
      concerns should be addressed when integrating AI applications into medical 
      education, where healthcare, AI, and education ethics collide. This commentary 
      explores the biomedical ethical responsibilities of medical institutions in 
      incorporating AI applications into medical education by identifying potential 
      concerns and limitations, with the goal of implementing applicable 
      recommendations. The recommendations presented are intended to assist in 
      developing institutional guidelines for the ethical use of AI for medical 
      educators and students.
CI  - © The Author(s) 2023.
FAU - Busch, Felix
AU  - Busch F
AUID- ORCID: 0000-0001-9770-8555
AD  - Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member 
      of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany. 
      GRID: grid.6363.0. ISNI: 0000 0001 2218 4662
AD  - Department of Anesthesiology, Division of Operative Intensive Care Medicine, 
      Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität 
      Berlin and Humboldt Universität zu Berlin, Berlin, Germany. GRID: grid.6363.0. 
      ISNI: 0000 0001 2218 4662
FAU - Adams, Lisa C
AU  - Adams LC
AUID- ORCID: 0000-0001-5836-4542
AD  - Department of Radiology, Stanford University School of Medicine, Stanford, CA 
      USA. GRID: grid.168010.e. ISNI: 0000000419368956
FAU - Bressem, Keno K
AU  - Bressem KK
AUID- ORCID: 0000-0001-9249-8624
AD  - Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member 
      of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany. 
      GRID: grid.6363.0. ISNI: 0000 0001 2218 4662
AD  - Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, 
      Germany. GRID: grid.484013.a. ISNI: 0000 0004 6879 971X
LA  - eng
PT  - Editorial
DEP - 20230607
PL  - United States
TA  - Med Sci Educ
JT  - Medical science educator
JID - 101625548
PMC - PMC10403458
OTO - NOTNLM
OT  - AI
OT  - Biomedical ethics
OT  - ChatGPT
OT  - Education, Medical
OT  - Machine learning
OT  - Practice guidelines
COIS- Competing InterestsThe authors declare no competing interests.
EDAT- 2023/08/07 06:41
MHDA- 2023/08/07 06:42
CRDT- 2023/08/07 04:33
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/08/07 06:42 [medline]
PHST- 2023/08/07 06:41 [pubmed]
PHST- 2023/08/07 04:33 [entrez]
AID - 1815 [pii]
AID - 10.1007/s40670-023-01815-x [doi]
PST - epublish
SO  - Med Sci Educ. 2023 Jun 7;33(4):1007-1012. doi: 10.1007/s40670-023-01815-x. 
      eCollection 2023 Aug.

PMID- 37864445
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231101
IS  - 1950-6112 (Electronic)
IS  - 0003-3898 (Linking)
VI  - 81
IP  - 4
DP  - 2023 Oct 20
TI  - Conversation between a clinical biologist and an artificial intelligence on 
      prostate cancer biomarkers: a critical reading.
PG  - 395-402
LID - 10.1684/abc.2023.1829 [doi]
AB  - Artificial intelligence is increasingly used in the field of medicine as a 
      diagnostic aid, particularly for image analysis and more generally for data 
      processing. Many artificial intelligence-based tools have been specifically 
      developed for clinical biology, but some more general ones can help to improve 
      the dissemination of medical knowledge. To test whether and to what extent an 
      automated conversation tool could answer questions on a clinical biology topic 
      (i.e. prostate cancer biomarkers), we questioned ChatGPT, an artificial 
      intelligence-powered model dedicated to optimize language models for dialogue. 
      Then we analyzed its responses.
FAU - Lamy, Pierre-Jean
AU  - Lamy PJ
AD  - Biopathalogie et génétique des cancers, imagenome, Inovie, Montpellier, France, 
      Unité de recherche clinique, Clinique BeauSoeil, Montpellier, France.
LA  - eng
PT  - Journal Article
PL  - France
TA  - Ann Biol Clin (Paris)
JT  - Annales de biologie clinique
JID - 2984690R
RN  - 0 (Biomarkers, Tumor)
SB  - IM
MH  - Male
MH  - Humans
MH  - *Biomarkers, Tumor
MH  - Prostate
MH  - Artificial Intelligence
MH  - *Prostatic Neoplasms/diagnosis
MH  - Communication
OTO - NOTNLM
OT  - artificial intelligence
OT  - biomarkers
OT  - cancer
OT  - clinical biology
OT  - prostate
EDAT- 2023/10/21 10:43
MHDA- 2023/11/01 12:45
CRDT- 2023/10/21 05:28
PHST- 2023/11/01 12:45 [medline]
PHST- 2023/10/21 10:43 [pubmed]
PHST- 2023/10/21 05:28 [entrez]
AID - abc.2023.1829 [pii]
AID - 10.1684/abc.2023.1829 [doi]
PST - ppublish
SO  - Ann Biol Clin (Paris). 2023 Oct 20;81(4):395-402. doi: 10.1684/abc.2023.1829.

PMID- 37076707
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1863-4362 (Electronic)
IS  - 0021-1265 (Linking)
VI  - 192
IP  - 6
DP  - 2023 Dec
TI  - GPT-4: a new era of artificial intelligence in medicine.
PG  - 3197-3200
LID - 10.1007/s11845-023-03377-8 [doi]
AB  - GPT-4 is the latest version of ChatGPT which is reported by OpenAI to have 
      greater problem-solving abilities and an even broader knowledge base. We examined 
      GPT-4's ability to inform us about the latest literature in a given area, and to 
      write a discharge summary for a patient following an uncomplicated surgery and 
      its latest image analysis feature which was reported to be able to identify 
      objects in photos. All things considered, GPT-4 has the potential to help drive 
      medical innovation, from aiding with patient discharge notes, summarizing recent 
      clinical trials, providing information on ethical guidelines, and much more.
CI  - © 2023. The Author(s), under exclusive licence to Royal Academy of Medicine in 
      Ireland.
FAU - Waisberg, Ethan
AU  - Waisberg E
AUID- ORCID: 0000-0001-8999-0212
AD  - University College Dublin School of Medicine, Belfield, Dublin 4, Ireland. 
      ethan.waisberg@ucdconnect.ie.
FAU - Ong, Joshua
AU  - Ong J
AD  - Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
FAU - Masalkhi, Mouayad
AU  - Masalkhi M
AD  - University College Dublin School of Medicine, Belfield, Dublin 4, Ireland.
FAU - Kamran, Sharif Amit
AU  - Kamran SA
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, NV, USA.
FAU - Zaman, Nasif
AU  - Zaman N
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, NV, USA.
FAU - Sarker, Prithul
AU  - Sarker P
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, NV, USA.
FAU - Lee, Andrew G
AU  - Lee AG
AD  - Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA.
AD  - Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, 
      Houston, TX, USA.
AD  - The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, 
      TX, USA.
AD  - Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell 
      Medicine, New York, NY, USA.
AD  - Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, 
      USA.
AD  - University of Texas MD Anderson Cancer Center, Houston, TX, USA.
AD  - Texas A&M College of Medicine, Houston, TX, USA.
AD  - Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa 
      City, IA, USA.
FAU - Tavakkoli, Alireza
AU  - Tavakkoli A
AD  - Human-Machine Perception Laboratory, Department of Computer Science and 
      Engineering, University of Nevada, Reno, NV, USA.
LA  - eng
PT  - Letter
DEP - 20230419
PL  - Ireland
TA  - Ir J Med Sci
JT  - Irish journal of medical science
JID - 7806864
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Knowledge Bases
MH  - *Medicine
MH  - Patient Discharge
MH  - Writing
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Generative pre-trained transformer
OT  - Medical education
OT  - Ophthalmology
EDAT- 2023/04/20 00:41
MHDA- 2023/12/04 12:43
CRDT- 2023/04/19 23:27
PHST- 2023/04/08 00:00 [received]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/04/20 00:41 [pubmed]
PHST- 2023/04/19 23:27 [entrez]
AID - 10.1007/s11845-023-03377-8 [pii]
AID - 10.1007/s11845-023-03377-8 [doi]
PST - ppublish
SO  - Ir J Med Sci. 2023 Dec;192(6):3197-3200. doi: 10.1007/s11845-023-03377-8. Epub 
      2023 Apr 19.

PMID- 37971803
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2368-7959 (Print)
IS  - 2368-7959 (Electronic)
IS  - 2368-7959 (Linking)
VI  - 10
DP  - 2023 Nov 16
TI  - The Potential Influence of AI on Population Mental Health.
PG  - e49936
LID - 10.2196/49936 [doi]
LID - e49936
AB  - The integration of artificial intelligence (AI) into everyday life has galvanized 
      a global conversation on the possibilities and perils of AI on human health. In 
      particular, there is a growing need to anticipate and address the potential 
      impact of widely accessible, enhanced, and conversational AI on mental health. We 
      propose 3 considerations to frame how AI may influence population mental health: 
      through the advancement of mental health care; by altering social and economic 
      contexts; and through the policies that shape the adoption, use, and potential 
      abuse of AI-enhanced tools.
CI  - ©Catherine K Ettman, Sandro Galea. Originally published in JMIR Mental Health 
      (https://mental.jmir.org), 16.11.2023.
FAU - Ettman, Catherine K
AU  - Ettman CK
AUID- ORCID: 0000-0002-4827-5383
AD  - Department of Health Policy and Management, Johns Hopkins Bloomberg School of 
      Public Health, Baltimore, MD, United States.
FAU - Galea, Sandro
AU  - Galea S
AUID- ORCID: 0000-0002-7534-0945
AD  - Office of the Dean, Boston University School of Public Health, Boston, MA, United 
      States.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - Canada
TA  - JMIR Ment Health
JT  - JMIR mental health
JID - 101658926
PMC - PMC10690520
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - digital mental health
OT  - generative
OT  - mental health
OT  - policies
OT  - policy
OT  - population
OT  - population health
OT  - tools
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/17 15:27
MHDA- 2023/11/17 15:28
CRDT- 2023/11/16 11:54
PHST- 2023/06/14 00:00 [received]
PHST- 2023/10/29 00:00 [accepted]
PHST- 2023/10/09 00:00 [revised]
PHST- 2023/11/17 15:28 [medline]
PHST- 2023/11/17 15:27 [pubmed]
PHST- 2023/11/16 11:54 [entrez]
AID - v10i1e49936 [pii]
AID - 10.2196/49936 [doi]
PST - epublish
SO  - JMIR Ment Health. 2023 Nov 16;10:e49936. doi: 10.2196/49936.

PMID- 37809168
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231010
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - A Call to Address AI "Hallucinations" and How Healthcare Professionals Can 
      Mitigate Their Risks.
PG  - e44720
LID - 10.7759/cureus.44720 [doi]
LID - e44720
AB  - Artificial intelligence (AI) has transformed society in many ways. AI in medicine 
      has the potential to improve medical care and reduce healthcare professional 
      burnout but we must be cautious of a phenomenon termed "AI hallucinations"and how 
      this term can lead to the stigmatization of AI systems and persons who experience 
      hallucinations. We believe the term "AI misinformation" to be more appropriate 
      and avoids contributing to stigmatization. Healthcare professionals can play an 
      important role in AI's integration into medicine, especially regarding mental 
      health services, so it is important that we continue to critically evaluate AI 
      systems as they emerge.
CI  - Copyright © 2023, Hatem et al.
FAU - Hatem, Rami
AU  - Hatem R
AD  - Department of Psychiatry, University of Florida College of Medicine, Gainesville, 
      USA.
FAU - Simmons, Brianna
AU  - Simmons B
AD  - Department of Psychiatry, University of Florida College of Medicine, Gainesville, 
      USA.
FAU - Thornton, Joseph E
AU  - Thornton JE
AD  - Department of Psychiatry, University of Florida College of Medicine, Gainesville, 
      USA.
LA  - eng
PT  - Editorial
DEP - 20230905
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10552880
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - artificial intelligence
OT  - chatgpt
OT  - ethics
OT  - hallucinations
OT  - mental health
OT  - misinformation
OT  - psychiatry
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/09 06:41
MHDA- 2023/10/09 06:42
CRDT- 2023/10/09 05:52
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/10/09 06:41 [pubmed]
PHST- 2023/10/09 05:52 [entrez]
AID - 10.7759/cureus.44720 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 5;15(9):e44720. doi: 10.7759/cureus.44720. eCollection 2023 Sep.

PMID- 37214057
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230523
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Successful Administration of Preoperative Botox for Giant Omphalocele Repair With 
      Ultrasound Guidance.
PG  - e37850
LID - 10.7759/cureus.37850 [doi]
LID - e37850
AB  - We present a case of a four-year-old male with a history of giant omphalocele who 
      underwent ultrasound-guided Botox injection to bilateral anterior abdominal wall 
      musculature in preparation for definitive repair. Botox administration was 
      successfully combined with preoperative subfascial tissue expanders to achieve 
      definitive midline closure of the anterior abdominal wall defect. Our 
      experience suggests that Botox can be safely used as part of the treatment plan 
      for giant omphalocele repair.
CI  - Copyright © 2023, Armijo et al.
FAU - Armijo, Arthur J
AU  - Armijo AJ
AD  - Anesthesiology, Rocky Vista University College of Osteopathic Medicine, Parker, 
      USA.
FAU - Calvano, Joshua
AU  - Calvano J
AD  - Department of Anesthesiology and Critical Care, University of New Mexico School 
      of Medicine, Albuquerque, USA.
FAU - Thomason, Nicolas T
AU  - Thomason NT
AD  - Department of Anesthesiology and Critical Care, University of New Mexico School 
      of Medicine, Albuquerque, USA.
FAU - Arndt, Christopher
AU  - Arndt C
AD  - Department of Anesthesiology and Critical Care, University of New Mexico School 
      of Medicine, Albuquerque, USA.
FAU - Shetty, Anil K
AU  - Shetty AK
AD  - Department of Surgery, University of New Mexico School of Medicine, Albuquerque, 
      USA.
FAU - Byrd, Dominick
AU  - Byrd D
AD  - Department of Surgery, University of New Mexico School of Medicine, Albuquerque, 
      USA.
FAU - Falcon, Ricardo
AU  - Falcon R
AD  - Department of Anesthesiology and Critical Care, University of New Mexico School 
      of Medicine, Albuquerque, USA.
FAU - Petersen, Timothy R
AU  - Petersen TR
AD  - Department of Anesthesiology and Critical Care, University of New Mexico School 
      of Medicine, Albuquerque, USA.
FAU - Soneru, Codruta
AU  - Soneru C
AD  - Department of Anesthesiology and Critical Care, University of New Mexico School 
      of Medicine, Albuquerque, USA.
LA  - eng
PT  - Case Reports
DEP - 20230419
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10198748
OTO - NOTNLM
OT  - anterior abdominal wall reconstruction
OT  - botox
OT  - botulinum toxin
OT  - chatgpt
OT  - omphalocele
OT  - subfascial tissue expanders
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/22 13:04
MHDA- 2023/05/22 13:05
CRDT- 2023/05/22 12:06
PHST- 2023/04/18 00:00 [accepted]
PHST- 2023/05/22 13:05 [medline]
PHST- 2023/05/22 13:04 [pubmed]
PHST- 2023/05/22 12:06 [entrez]
AID - 10.7759/cureus.37850 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 19;15(4):e37850. doi: 10.7759/cureus.37850. eCollection 2023 
      Apr.

PMID- 37103531
OWN - NLM
STAT- MEDLINE
DCOM- 20230825
LR  - 20230825
IS  - 1432-203X (Electronic)
IS  - 0721-7714 (Linking)
VI  - 42
IP  - 9
DP  - 2023 Sep
TI  - How are authors' contributions verified in the ICMJE model?
PG  - 1529-1530
LID - 10.1007/s00299-023-03022-9 [doi]
AB  - A recent editorial in Plant Cell Reports reaffirms what has been known for years, 
      namely, that it follows the four ICMJE clauses of authorship. That editorial even 
      provides a "perfect" model contribution statement. In this letter, I argue that 
      in reality and in practice, authorship delimitations are not that clear-cut, nor 
      are all contributions equal or equally weighted. More importantly, I opine that 
      no matter how eloquently an author contribution statement is written, editors 
      have no way to verify the veracity of those claims. In essence, absent authorship 
      contribution verification, the ICMJE guidelines are practically useless. The 
      responsibility for verification, even to determine authorship associated with 
      papermills or the "ghost" contribution of text by AI like ChatGPT, lies entirely 
      with editors and publishers. Although an unpopular meme, there is need for 
      academic publishing to return to a state of no blind trust.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Teixeira da Silva, Jaime A
AU  - Teixeira da Silva JA
AUID- ORCID: 0000-0003-3299-2772
AD  - , Ikenobe 3011-2, Kagawa-ken, 761-0799, Japan. jaimetex@yahoo.com.
LA  - eng
PT  - Journal Article
DEP - 20230427
PL  - Germany
TA  - Plant Cell Rep
JT  - Plant cell reports
JID - 9880970
SB  - IM
MH  - *Publishing
MH  - *Authorship
OTO - NOTNLM
OT  - Honorary (guest) and ghost authors
OT  - ICMJE
OT  - Integrity
OT  - ORCID
OT  - Paper mills
OT  - Trust
EDAT- 2023/04/27 12:42
MHDA- 2023/08/25 06:42
CRDT- 2023/04/27 11:05
PHST- 2023/04/06 00:00 [received]
PHST- 2023/04/12 00:00 [accepted]
PHST- 2023/08/25 06:42 [medline]
PHST- 2023/04/27 12:42 [pubmed]
PHST- 2023/04/27 11:05 [entrez]
AID - 10.1007/s00299-023-03022-9 [pii]
AID - 10.1007/s00299-023-03022-9 [doi]
PST - ppublish
SO  - Plant Cell Rep. 2023 Sep;42(9):1529-1530. doi: 10.1007/s00299-023-03022-9. Epub 
      2023 Apr 27.

PMID- 37524930
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230927
IS  - 2397-3374 (Electronic)
IS  - 2397-3374 (Linking)
VI  - 7
IP  - 9
DP  - 2023 Sep
TI  - Emergent analogical reasoning in large language models.
PG  - 1526-1541
LID - 10.1038/s41562-023-01659-w [doi]
AB  - The recent advent of large language models has reinvigorated debate over whether 
      human cognitive capacities might emerge in such generic models given sufficient 
      training data. Of particular interest is the ability of these models to reason 
      about novel problems zero-shot, without any direct training. In human cognition, 
      this capacity is closely tied to an ability to reason by analogy. Here we 
      performed a direct comparison between human reasoners and a large language model 
      (the text-davinci-003 variant of Generative Pre-trained Transformer (GPT)-3) on a 
      range of analogical tasks, including a non-visual matrix reasoning task based on 
      the rule structure of Raven's Standard Progressive Matrices. We found that GPT-3 
      displayed a surprisingly strong capacity for abstract pattern induction, matching 
      or even surpassing human capabilities in most settings; preliminary tests of 
      GPT-4 indicated even better performance. Our results indicate that large language 
      models such as GPT-3 have acquired an emergent ability to find zero-shot 
      solutions to a broad range of analogy problems.
CI  - © 2023. The Author(s), under exclusive licence to Springer Nature Limited.
FAU - Webb, Taylor
AU  - Webb T
AUID- ORCID: 0000-0002-1335-3177
AD  - Department of Psychology, University of California, Los Angeles, CA, USA. 
      taylor.w.webb@gmail.com.
FAU - Holyoak, Keith J
AU  - Holyoak KJ
AUID- ORCID: 0000-0001-8010-6267
AD  - Department of Psychology, University of California, Los Angeles, CA, USA.
FAU - Lu, Hongjing
AU  - Lu H
AD  - Department of Psychology, University of California, Los Angeles, CA, USA.
AD  - Department of Statistics, University of California, Los Angeles, CA, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20230731
PL  - England
TA  - Nat Hum Behav
JT  - Nature human behaviour
JID - 101697750
SB  - IM
MH  - Humans
MH  - *Problem Solving
MH  - *Cognition
MH  - Language
EDAT- 2023/08/01 01:08
MHDA- 2023/09/25 06:42
CRDT- 2023/07/31 23:29
PHST- 2022/12/18 00:00 [received]
PHST- 2023/06/16 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/08/01 01:08 [pubmed]
PHST- 2023/07/31 23:29 [entrez]
AID - 10.1038/s41562-023-01659-w [pii]
AID - 10.1038/s41562-023-01659-w [doi]
PST - ppublish
SO  - Nat Hum Behav. 2023 Sep;7(9):1526-1541. doi: 10.1038/s41562-023-01659-w. Epub 
      2023 Jul 31.

PMID- 38014080
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231213
DP  - 2023 Nov 15
TI  - GO2Sum: Generating Human Readable Functional Summary of Proteins from GO Terms.
LID - 2023.11.10.566665 [pii]
LID - 10.1101/2023.11.10.566665 [doi]
AB  - Understanding the biological functions of proteins is of fundamental importance 
      in modern biology. To represent function of proteins, Gene Ontology (GO), a 
      controlled vocabulary, is frequently used, because it is easy to handle by 
      computer programs avoiding open-ended text interpretation. Particularly, the 
      majority of current protein function prediction methods rely on GO terms. 
      However, the extensive list of GO terms that describe a protein function can pose 
      challenges for biologists when it comes to interpretation. In response to this 
      issue, we developed GO2Sum (Gene Ontology terms Summarizer), a model that takes a 
      set of GO terms as input and generates a human-readable summary using the T5 
      large language model. GO2Sum was developed by fine-tuning T5 on GO term 
      assignments and free-text function descriptions for UniProt entries, enabling it 
      to recreate function descriptions by concatenating GO term descriptions. Our 
      results demonstrated that GO2Sum significantly outperforms the original T5 model 
      that was trained on the entire web corpus in generating Function, Subunit 
      Structure, and Pathway paragraphs for UniProt entries.
FAU - Giri, Swagarika Jaharlal
AU  - Giri SJ
AUID- ORCID: 0009-0009-4157-6819
AD  - Department of Computer Science, Purdue University, West Lafayette, IN, United 
      States.
FAU - Ibtehaz, Nabil
AU  - Ibtehaz N
AUID- ORCID: 0000-0003-3625-5972
AD  - Department of Computer Science, Purdue University, West Lafayette, IN, United 
      States.
FAU - Kihara, Daisuke
AU  - Kihara D
AUID- ORCID: 0000-0003-4091-6614
AD  - Department of Computer Science, Purdue University, West Lafayette, IN, United 
      States.
AD  - Department of Biological Sciences, Purdue University, West Lafayette, IN, United 
      States.
LA  - eng
GR  - R01 GM133840/GM/NIGMS NIH HHS/United States
PT  - Preprint
DEP - 20231115
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10680659
OTO - NOTNLM
OT  - gene ontology
OT  - language model
OT  - protein function
OT  - protein function prediction
OT  - summarizer
EDAT- 2023/11/28 06:42
MHDA- 2023/11/28 06:43
CRDT- 2023/11/28 04:01
PHST- 2023/11/28 06:42 [pubmed]
PHST- 2023/11/28 06:43 [medline]
PHST- 2023/11/28 04:01 [entrez]
AID - 2023.11.10.566665 [pii]
AID - 10.1101/2023.11.10.566665 [doi]
PST - epublish
SO  - bioRxiv. 2023 Nov 15:2023.11.10.566665. doi: 10.1101/2023.11.10.566665. Preprint.

PMID- 37131648
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Apr 18
TI  - Research Letter: Application of GPT-4 to select next-step antidepressant 
      treatment in major depression.
LID - 2023.04.14.23288595 [pii]
LID - 10.1101/2023.04.14.23288595 [doi]
AB  - INTRODUCTION: Large language models perform well on a range of academic tasks 
      including medical examinations. The performance of this class of models in 
      psychopharmacology has not been explored. METHOD: Chat GPT-plus, implementing the 
      GPT-4 large language model, was presented with each of 10 previously-studied 
      antidepressant prescribing vignettes in randomized order, with results 
      regenerated 5 times to evaluate stability of responses. Results were compared to 
      expert consensus. RESULTS: At least one of the optimal medication choices was 
      included among the best choices in 38/50 (76%) vignettes: 5/5 for 7 vignettes, 
      3/5 for 1, and 0/5 for 2. At least one of the poor choice or contraindicated 
      medications was included among the choices considered optimal or good in 24/50 
      (48%) of vignettes. The model provided as rationale for treatment selection 
      multiple heuristics including avoiding prior unsuccessful medications, avoiding 
      adverse effects based on comorbidities, and generalizing within medication class. 
      CONCLUSION: The model appeared to identify and apply a number of heuristics 
      commonly applied in psychopharmacologic clinical practice. However, the inclusion 
      of less optimal recommendations indicates that large language models may pose a 
      substantial risk if routinely applied to guide psychopharmacologic treatment 
      without further monitoring.
FAU - Perlis, Roy H
AU  - Perlis RH
AD  - Massachusetts General Hospital, Boston, MA.
AD  - Harvard Medical School, Boston, MA.
LA  - eng
PT  - Preprint
DEP - 20230418
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10153309
EDAT- 2023/05/03 06:42
MHDA- 2023/05/03 06:43
CRDT- 2023/05/03 01:58
PHST- 2023/05/03 06:42 [pubmed]
PHST- 2023/05/03 06:43 [medline]
PHST- 2023/05/03 01:58 [entrez]
AID - 2023.04.14.23288595 [pii]
AID - 10.1101/2023.04.14.23288595 [doi]
PST - epublish
SO  - medRxiv. 2023 Apr 18:2023.04.14.23288595. doi: 10.1101/2023.04.14.23288595. 
      Preprint.

PMID- 37756224
OWN - NLM
STAT- Publisher
LR  - 20230927
IS  - 1556-3669 (Electronic)
IS  - 1530-5627 (Linking)
DP  - 2023 Sep 27
TI  - Factors Predicting Intentions of Adoption and Continued Use of Artificial 
      Intelligence Chatbots for Mental Health: Examining the Role of UTAUT Model, 
      Stigma, Privacy Concerns, and Artificial Intelligence Hesitancy.
LID - 10.1089/tmj.2023.0313 [doi]
AB  - Background: Artificial intelligence-based chatbots (AI chatbots) can potentially 
      improve mental health care, yet factors predicting their adoption and continued 
      use are unclear. Methods: We conducted an online survey with a sample of U.S. 
      adults with symptoms of depression and anxiety (N = 393) in 2021 before the 
      release of ChatGPT. We explored factors predicting the adoption and continued use 
      of AI chatbots, including factors of the unified theory of acceptance and use of 
      technology model, stigma, privacy concerns, and AI hesitancy. Results: Results 
      from the regression indicated that for nonusers, performance expectancy, price 
      value, descriptive norm, and psychological distress are positively related to the 
      intention of adopting AI chatbots, while AI hesitancy and effort expectancy are 
      negatively associated with adopting AI chatbots. For those with experience in 
      using AI chatbots for mental health, performance expectancy, price value, 
      descriptive norm, and injunctive norm are positively related to the intention of 
      continuing to use AI chatbots. Conclusions: Understanding the adoption and 
      continued use of AI chatbots among adults with symptoms of depression and anxiety 
      is essential given that there is a widening gap in the supply and demand of care. 
      AI chatbots provide new opportunities for quality care by supporting accessible, 
      affordable, efficient, and personalized care. This study provides insights for 
      developing and deploying AI chatbots such as ChatGPT in the context of mental 
      health care. Findings could be used to design innovative interventions that 
      encourage the adoption and continued use of AI chatbots among people with 
      symptoms of depression and anxiety and who have difficulty accessing care.
FAU - Li, Lin
AU  - Li L
AUID- ORCID: 0000-0002-8197-9155
AD  - Department of Informatics, University of California Irvine, Irvine, California, 
      USA.
FAU - Peng, Wei
AU  - Peng W
AD  - Department of Media and Information, Michigan State University, East Lansing, 
      Michigan, USA.
FAU - Rheu, Minjin M J
AU  - Rheu MMJ
AD  - School of Communication, Loyola University Chicago, Chicago, Illinois, USA.
LA  - eng
PT  - Journal Article
DEP - 20230927
PL  - United States
TA  - Telemed J E Health
JT  - Telemedicine journal and e-health : the official journal of the American 
      Telemedicine Association
JID - 100959949
SB  - IM
OTO - NOTNLM
OT  - AI chatbots
OT  - AI hesitancy
OT  - UTAUT
OT  - anxiety
OT  - depression
OT  - privacy concerns
EDAT- 2023/09/27 18:42
MHDA- 2023/09/27 18:42
CRDT- 2023/09/27 13:03
PHST- 2023/09/27 18:42 [medline]
PHST- 2023/09/27 18:42 [pubmed]
PHST- 2023/09/27 13:03 [entrez]
AID - 10.1089/tmj.2023.0313 [doi]
PST - aheadofprint
SO  - Telemed J E Health. 2023 Sep 27. doi: 10.1089/tmj.2023.0313.

PMID- 37428117
OWN - NLM
STAT- MEDLINE
DCOM- 20230911
LR  - 20230911
IS  - 1527-3792 (Electronic)
IS  - 0022-5347 (Linking)
VI  - 210
IP  - 4
DP  - 2023 Oct
TI  - Evaluating the Effectiveness of Artificial Intelligence-powered Large Language 
      Models Application in Disseminating Appropriate and Readable Health Information 
      in Urology.
PG  - 688-694
LID - 10.1097/JU.0000000000003615 [doi]
AB  - PURPOSE: The Internet is a ubiquitous source of medical information, and natural 
      language processors are gaining popularity as alternatives to traditional search 
      engines. However, suitability of their generated content for patients is not well 
      understood. We aimed to evaluate the appropriateness and readability of natural 
      language processor-generated responses to urology-related medical inquiries. 
      MATERIALS AND METHODS: Eighteen patient questions were developed based on Google 
      Trends and were used as inputs in ChatGPT. Three categories were assessed: 
      oncologic, benign, and emergency. Questions in each category were either 
      treatment or sign/symptom-related questions. Three native English-speaking 
      Board-Certified urologists independently assessed appropriateness of ChatGPT 
      outputs for patient counseling using accuracy, comprehensiveness, and clarity as 
      proxies for appropriateness. Readability was assessed using the Flesch Reading 
      Ease and Flesh-Kincaid Reading Grade Level formulas. Additional measures were 
      created based on validated tools and assessed by 3 independent reviewers. 
      RESULTS: Fourteen of 18 (77.8%) responses were deemed appropriate, with clarity 
      having the most 4 and 5 scores (P = .01). There was no significant difference in 
      appropriateness of the responses between treatments and symptoms or between 
      different categories of conditions. The most common reason from urologists for 
      low scores was responses lacking information-sometimes vital information. The 
      mean (SD) Flesch Reading Ease score was 35.5 (SD=10.2) and the mean Flesh-Kincaid 
      Reading Grade Level score was 13.5 (1.74). Additional quality assessment scores 
      showed no significant differences between different categories of conditions. 
      CONCLUSIONS: Despite impressive capabilities, natural language processors have 
      limitations as sources of medical information. Refinement is crucial before 
      adoption for this purpose.
FAU - Davis, Ryan
AU  - Davis R
AUID- ORCID: 0009-0002-0408-8380
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - AI Center at USC Urology, USC Institute of Urology, University of Southern 
      California, Los Angeles, California.
FAU - Eppler, Michael
AU  - Eppler M
AUID- ORCID: 0000-0001-6336-5857
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - AI Center at USC Urology, USC Institute of Urology, University of Southern 
      California, Los Angeles, California.
FAU - Ayo-Ajibola, Oluwatobiloba
AU  - Ayo-Ajibola O
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - AI Center at USC Urology, USC Institute of Urology, University of Southern 
      California, Los Angeles, California.
FAU - Loh-Doyle, Jeffrey C
AU  - Loh-Doyle JC
AUID- ORCID: 0000-0002-7094-482X
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
FAU - Nabhani, Jamal
AU  - Nabhani J
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
FAU - Samplaski, Mary
AU  - Samplaski M
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
FAU - Gill, Inderbir
AU  - Gill I
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - AI Center at USC Urology, USC Institute of Urology, University of Southern 
      California, Los Angeles, California.
FAU - Cacciamani, Giovanni E
AU  - Cacciamani GE
AD  - USC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, 
      Keck School of Medicine, University of Southern California, Los Angeles, 
      California.
AD  - AI Center at USC Urology, USC Institute of Urology, University of Southern 
      California, Los Angeles, California.
LA  - eng
PT  - Journal Article
DEP - 20230710
PL  - United States
TA  - J Urol
JT  - The Journal of urology
JID - 0376374
SB  - IM
MH  - Humans
MH  - *Health Literacy
MH  - Artificial Intelligence
MH  - *Urology
MH  - Comprehension
MH  - Language
MH  - Internet
OTO - NOTNLM
OT  - artificial intelligence
OT  - communication
OT  - health
OT  - signs and symptoms
OT  - therapeutics
OT  - urology
EDAT- 2023/07/10 13:06
MHDA- 2023/09/11 06:42
CRDT- 2023/07/10 10:23
PHST- 2023/09/11 06:42 [medline]
PHST- 2023/07/10 13:06 [pubmed]
PHST- 2023/07/10 10:23 [entrez]
AID - 10.1097/JU.0000000000003615 [doi]
PST - ppublish
SO  - J Urol. 2023 Oct;210(4):688-694. doi: 10.1097/JU.0000000000003615. Epub 2023 Jul 
      10.

PMID- 37041923
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230413
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - A Case of Unintentional Release of the Watchman FLX Device During Implantation: A 
      Cautionary Tale.
PG  - e36002
LID - 10.7759/cureus.36002 [doi]
LID - e36002
AB  - Atrial fibrillation (AF) is a common heart rhythm disorder that increases the 
      risk of stroke and other cardiovascular complications. The Watchman FLX device is 
      a percutaneous device used for the closure of the left atrial appendage in 
      patients with atrial fibrillation and prevents blood clots from forming, thereby 
      decreasing the risk of stroke. However, device dislodgement during the procedure 
      can occur and compromise the effectiveness of the device. In this case report, we 
      present the experience of an 86-year-old man with a history of atrial 
      fibrillation, coronary artery disease, hypertension, and recent gastrointestinal 
      bleeding due to diverticulosis. During the procedure, the device became dislodged 
      prior to the planned release from the shaft due to the difficult anatomy of the 
      appendage, which caused the operator to over-torque and kink the delivery sheath. 
      The kinked sheath prevented the transmission of torque to the appropriate region 
      of the sheath and caused the device to unscrew prematurely. Fortunately, the 
      device was self-deployed in a satisfactory position, and no further intervention 
      was required. The patient did not experience any complications prior to discharge 
      and follow-up echocardiography showed proper positioning of the device. This case 
      highlights the importance of careful technique during the Watchman FLX procedure 
      and the need to replace the delivery sheath if kinking is noted to prevent 
      unintentional dislodgement of the device. In addition, the case was written with 
      the aid of the artificial intelligence-powered language model ChatGPT and 
      demonstrates its benefits as well as calls for caution while using it for medical 
      writing.
CI  - Copyright © 2023, Kattoor et al.
FAU - Kattoor, Ajoe J
AU  - Kattoor AJ
AD  - Interventional and Structural Cardiology, Kaleida Health/University at Buffalo, 
      Buffalo, USA.
FAU - Manion, Christopher
AU  - Manion C
AD  - Interventional and Structural Cardiology, Kaleida Health/University at Buffalo, 
      Buffalo, USA.
FAU - Kim, Chee H
AU  - Kim CH
AD  - Electrophysiology, Kaleida Health/University at Buffalo, Buffalo, USA.
FAU - Iyer, Vijay S
AU  - Iyer VS
AD  - Interventional and Structural Cardiology, Kaleida Health/University at Buffalo, 
      Buffalo, USA.
LA  - eng
PT  - Case Reports
DEP - 20230310
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10083151
OTO - NOTNLM
OT  - atrial fibrillation (af)
OT  - chatgpt
OT  - left atrial appendage closure (laac)
OT  - left atrial appendage occlusion
OT  - unintentional release of watchman
OT  - watchman device
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/13 06:00
MHDA- 2023/04/13 06:01
CRDT- 2023/04/12 01:45
PHST- 2023/03/07 00:00 [accepted]
PHST- 2023/04/13 06:01 [medline]
PHST- 2023/04/12 01:45 [entrez]
PHST- 2023/04/13 06:00 [pubmed]
AID - 10.7759/cureus.36002 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 10;15(3):e36002. doi: 10.7759/cureus.36002. eCollection 2023 
      Mar.

PMID- 37938381
OWN - NLM
STAT- Publisher
LR  - 20231108
IS  - 1432-1084 (Electronic)
IS  - 0938-7994 (Linking)
DP  - 2023 Nov 8
TI  - Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in 
      standardizing radiology reports.
LID - 10.1007/s00330-023-10384-x [doi]
AB  - OBJECTIVE: Radiology reporting is an essential component of clinical diagnosis 
      and decision-making. With the advent of advanced artificial intelligence (AI) 
      models like GPT-4 (Generative Pre-trained Transformer 4), there is growing 
      interest in evaluating their potential for optimizing or generating radiology 
      reports. This study aimed to compare the quality and content of 
      radiologist-generated and GPT-4 AI-generated radiology reports. METHODS: A 
      comparative study design was employed in the study, where a total of 100 
      anonymized radiology reports were randomly selected and analyzed. Each report was 
      processed by GPT-4, resulting in the generation of a corresponding AI-generated 
      report. Quantitative and qualitative analysis techniques were utilized to assess 
      similarities and differences between the two sets of reports. RESULTS: The 
      AI-generated reports showed comparable quality to radiologist-generated reports 
      in most categories. Significant differences were observed in clarity (p = 0.027), 
      ease of understanding (p = 0.023), and structure (p = 0.050), favoring the 
      AI-generated reports. AI-generated reports were more concise, with 34.53 fewer 
      words and 174.22 fewer characters on average, but had greater variability in 
      sentence length. Content similarity was high, with an average Cosine Similarity 
      of 0.85, Sequence Matcher Similarity of 0.52, BLEU Score of 0.5008, and BERTScore 
      F1 of 0.8775. CONCLUSION: The results of this proof-of-concept study suggest that 
      GPT-4 can be a reliable tool for generating standardized radiology reports, 
      offering potential benefits such as improved efficiency, better communication, 
      and simplified data extraction and analysis. However, limitations and ethical 
      implications must be addressed to ensure the safe and effective implementation of 
      this technology in clinical practice. CLINICAL RELEVANCE STATEMENT: The findings 
      of this study suggest that GPT-4 (Generative Pre-trained Transformer 4), an 
      advanced AI model, has the potential to significantly contribute to the 
      standardization and optimization of radiology reporting, offering improved 
      efficiency and communication in clinical practice. KEY POINTS: • Large language 
      model-generated radiology reports exhibited high content similarity and moderate 
      structural resemblance to radiologist-generated reports. • Performance metrics 
      highlighted the strong matching of word selection and order, as well as high 
      semantic similarity between AI and radiologist-generated reports. • Large 
      language model demonstrated potential for generating standardized radiology 
      reports, improving efficiency and communication in clinical settings.
CI  - © 2023. This is a U.S. Government work and not under copyright protection in the 
      US; foreign copyright protection may apply.
FAU - Hasani, Amir M
AU  - Hasani AM
AD  - Laboratory of Translation Research, National Heart Blood Lung Institute, NIH, 
      Bethesda, MD, USA.
FAU - Singh, Shiva
AU  - Singh S
AD  - Radiology & Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA.
FAU - Zahergivar, Aryan
AU  - Zahergivar A
AD  - Radiology & Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA.
FAU - Ryan, Beth
AU  - Ryan B
AD  - Urology Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
FAU - Nethala, Daniel
AU  - Nethala D
AD  - Urology Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
FAU - Bravomontenegro, Gabriela
AU  - Bravomontenegro G
AD  - Urology Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
FAU - Mendhiratta, Neil
AU  - Mendhiratta N
AD  - Urology Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
FAU - Ball, Mark
AU  - Ball M
AD  - Urology Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
FAU - Farhadi, Faraz
AU  - Farhadi F
AD  - Radiology & Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA.
FAU - Malayeri, Ashkan
AU  - Malayeri A
AUID- ORCID: 0000-0002-4442-7343
AD  - Radiology & Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA. 
      Ashkan.Malayeri@nih.gov.
LA  - eng
PT  - Journal Article
DEP - 20231108
PL  - Germany
TA  - Eur Radiol
JT  - European radiology
JID - 9114774
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Digital health
OT  - Machine learning
OT  - Natural language processing
EDAT- 2023/11/08 12:42
MHDA- 2023/11/08 12:42
CRDT- 2023/11/08 11:20
PHST- 2023/07/11 00:00 [received]
PHST- 2023/09/08 00:00 [accepted]
PHST- 2023/09/01 00:00 [revised]
PHST- 2023/11/08 12:42 [medline]
PHST- 2023/11/08 12:42 [pubmed]
PHST- 2023/11/08 11:20 [entrez]
AID - 10.1007/s00330-023-10384-x [pii]
AID - 10.1007/s00330-023-10384-x [doi]
PST - aheadofprint
SO  - Eur Radiol. 2023 Nov 8. doi: 10.1007/s00330-023-10384-x.

PMID- 37921509
OWN - NLM
STAT- Publisher
LR  - 20231103
IS  - 1753-8076 (Electronic)
IS  - 1753-8068 (Linking)
DP  - 2023 Nov 3
TI  - What generative AI means for trust in health communications.
PG  - 1-4
LID - 10.1080/17538068.2023.2277489 [doi]
AB  - ABSTRACTLarge language models are fundamental technologies used in interfaces 
      like ChatGPT and are poised to change the way people access and make sense of 
      health information. The speed of uptake and investment suggests that these will 
      be transformative technologies, but it is not yet clear what the implications 
      might be for health communications. In this viewpoint, we draw on research about 
      the adoption of new information technologies to focus on the ways that generative 
      artificial intelligence (AI) tools like large language models might change how 
      health information is produced, what health information people see, how marketing 
      and misinformation might be mixed with evidence, and what people trust. We 
      conclude that transparency and explainability in this space must be carefully 
      considered to avoid unanticipated consequences.
FAU - Dunn, Adam G
AU  - Dunn AG
AUID- ORCID: 0000-0002-1720-8209
AD  - Faculty of Medicine and Health, Biomedical Informatics and Digital Health, School 
      of Medical Sciences, The University of Sydney, Sydney, Australia.
FAU - Shih, Ivy
AU  - Shih I
AD  - Media Office, The University of Sydney, Sydney, Australia.
FAU - Ayre, Julie
AU  - Ayre J
AD  - Faculty of Medicine and Heath, Sydney Health Literacy Lab, Sydney School of 
      Public Health, The University of Sydney, Sydney, Australia.
FAU - Spallek, Heiko
AU  - Spallek H
AD  - Faculty of Medicine and Heath, Sydney Dental School, The University of Sydney, 
      Sydney, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - England
TA  - J Commun Healthc
JT  - Journal of communication in healthcare
JID - 101489047
SB  - IM
OTO - NOTNLM
OT  - Natural language processing
OT  - artificial intelligence
OT  - conversational agents
OT  - health communication
OT  - search engines
OT  - trust
EDAT- 2023/11/03 12:46
MHDA- 2023/11/03 12:46
CRDT- 2023/11/03 09:32
PHST- 2023/11/03 12:46 [medline]
PHST- 2023/11/03 12:46 [pubmed]
PHST- 2023/11/03 09:32 [entrez]
AID - 10.1080/17538068.2023.2277489 [doi]
PST - aheadofprint
SO  - J Commun Healthc. 2023 Nov 3:1-4. doi: 10.1080/17538068.2023.2277489.

PMID- 37904073
OWN - NLM
STAT- Publisher
LR  - 20231031
IS  - 1525-1497 (Electronic)
IS  - 0884-8734 (Linking)
DP  - 2023 Oct 30
TI  - Chatting Beyond ChatGPT: Advancing Equity Through AI-Driven Language 
      Interpretation.
LID - 10.1007/s11606-023-08497-6 [doi]
AB  - Medical interpretation is an underutilized resource, despite its legal mandate 
      and proven efficacy in improving health outcomes for populations with low English 
      proficiency. This disconnect can often be attributed to the costs and wait-times 
      associated with traditional means of interpretation, making the service 
      inaccessible and burdensome. Technology has improved access to translation 
      through phone and video interpretation; with the acceleration of artificial 
      intelligence (AI) large language models, we have an opportunity to further 
      improve interpreter access through real-time, automated translation. The impetus 
      to utilize this burgeoning tool for improved health equity must be combined with 
      a critical view of the safety, privacy, and clinical decision-making risks 
      involved. Physicians must be active participants and collaborators in both the 
      mobilization of AI tools to improve clinical care and the development of 
      regulations to mitigate harm.
CI  - © 2023. The Author(s), under exclusive licence to Society of General Internal 
      Medicine.
FAU - Bakdash, Leen
AU  - Bakdash L
AUID- ORCID: 0009-0002-3954-2483
AD  - Emory University School of Medicine, Atlanta, GA, 30322, USA. lbakdas@emory.edu.
FAU - Abid, Areeba
AU  - Abid A
AD  - Emory University School of Medicine, Atlanta, GA, 30322, USA.
FAU - Gourisankar, Amritha
AU  - Gourisankar A
AD  - Emory University School of Medicine, Atlanta, GA, 30322, USA.
FAU - Henry, Tracey L
AU  - Henry TL
AD  - Division of General Medicine, Emory University School of Medicine, Atlanta, GA, 
      USA.
LA  - eng
PT  - Editorial
DEP - 20231030
PL  - United States
TA  - J Gen Intern Med
JT  - Journal of general internal medicine
JID - 8605834
SB  - IM
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:42
CRDT- 2023/10/31 00:44
PHST- 2023/07/17 00:00 [received]
PHST- 2023/10/16 00:00 [accepted]
PHST- 2023/10/31 06:42 [medline]
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 00:44 [entrez]
AID - 10.1007/s11606-023-08497-6 [pii]
AID - 10.1007/s11606-023-08497-6 [doi]
PST - aheadofprint
SO  - J Gen Intern Med. 2023 Oct 30. doi: 10.1007/s11606-023-08497-6.

PMID- 37252545
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230531
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Transient Ischemic Attack in a Patient With Poland Syndrome With Dextrocardia.
PG  - e38185
LID - 10.7759/cureus.38185 [doi]
LID - e38185
AB  - This report discusses the case of a patient with a past medical history of Poland 
      syndrome and dextrocardia who was admitted for a transient ischemic attack. 
      Poland syndrome is a rare genetic condition characterized by underdevelopment of 
      chest wall musculature that presents with a variety of associations that may or 
      may not be present in each case. This case report intends to discuss a unique 
      presentation of Poland syndrome with dextrocardia, one of the rare conditions 
      associated with Poland syndrome, as well as the treatment of Poland syndrome as a 
      whole and possible associated complications.
CI  - Copyright © 2023, Afzal et al.
FAU - Afzal, Iraj
AU  - Afzal I
AD  - Internal Medicine, Nassau University Medical Center, East Meadow, USA.
FAU - Rahman, Samin
AU  - Rahman S
AD  - Internal Medicine, Nassau University Medical Center, East Meadow, USA.
FAU - Syed, Faiz
AU  - Syed F
AD  - Internal Medicine, New York Institute of Technology College of Osteopathic 
      Medicine, Old Westbury, USA.
FAU - Hai, Ofek
AU  - Hai O
AD  - Cardiology, Nassau University Medical Center, East Meadow, USA.
FAU - Zeltser, Roman
AU  - Zeltser R
AD  - Cardiology, Zucker School of Medicine at Hofstra/Northwell, Hempstead, USA.
AD  - Cardiology, Nassau University Medical Center, East Meadow, USA.
FAU - Makaryus, Amgad N
AU  - Makaryus AN
AD  - Cardiology, Northwell Health, Manhasset, USA.
AD  - Cardiology, Nassau University Medical Center, East Meadow, USA.
LA  - eng
PT  - Case Reports
DEP - 20230427
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10220516
OTO - NOTNLM
OT  - case report
OT  - chatgpt
OT  - dextrocardia
OT  - poland syndrome
OT  - transient ischemic attack
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/30 13:07
MHDA- 2023/05/30 13:08
CRDT- 2023/05/30 11:55
PHST- 2023/04/27 00:00 [accepted]
PHST- 2023/05/30 13:08 [medline]
PHST- 2023/05/30 13:07 [pubmed]
PHST- 2023/05/30 11:55 [entrez]
AID - 10.7759/cureus.38185 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 27;15(4):e38185. doi: 10.7759/cureus.38185. eCollection 2023 
      Apr.

PMID- 36987491
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230330
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Sclerosing Mesenteritis Managed Conservatively With Prednisone.
PG  - e35419
LID - 10.7759/cureus.35419 [doi]
LID - e35419
AB  - The authors present the case of a middle-aged lady with two weeks of abdominal 
      pain. Computed tomography imaging revealed sclerosing mesenteritis. Sclerosing 
      mesenteritis is also known as mesenteric panniculitis and is a chronic fibrosing 
      inflammatory disease that primarily affects the adipose tissue of the mesentery 
      in the small intestine and colon. The clinical presentation, imaging findings, 
      differential diagnosis, and therapeutic management are presented in this report. 
      In our patient's case, she was able to be managed conservatively, without the 
      need for surgery. This reflects the most benign and self-limiting natural history 
      of the disease.
CI  - Copyright © 2023, Gande et al.
FAU - Gande, Saipriya
AU  - Gande S
AD  - Medicine, Academy at the Lakes, Lutz, USA.
FAU - Nguyen, Natalie N
AU  - Nguyen NN
AD  - Biology and Medicine, Brown University, Providence, USA.
FAU - Stead, Thor S
AU  - Stead TS
AD  - Medicine, The Warren Alpert Medical School of Brown University, Providence, USA.
FAU - Mangal, Rohan
AU  - Mangal R
AD  - Medicine, Johns Hopkins University, Baltimore, USA.
AD  - Medicine, University of Miami Miller School of Medicine, Miami, USA.
FAU - Ganti, Latha
AU  - Ganti L
AD  - Emergency Medicine, HCA Florida Ocala Hospital, Ocala, USA.
AD  - Emergency Medicine, Envision Physician Services, Plantation, USA.
AD  - Emergency Medicine, University of Central Florida College of Medicine, Orlando, 
      USA.
LA  - eng
PT  - Case Reports
DEP - 20230224
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10040226
OTO - NOTNLM
OT  - chatgpt
OT  - mesenteric lipodystrophy
OT  - mesenteric panniculitis
OT  - mesenteric scleritis
OT  - retractile mesenteritis
OT  - sclerosing mesenteritis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/30 06:00
MHDA- 2023/03/30 06:01
CRDT- 2023/03/29 02:10
PHST- 2023/02/24 00:00 [accepted]
PHST- 2023/03/30 06:01 [medline]
PHST- 2023/03/29 02:10 [entrez]
PHST- 2023/03/30 06:00 [pubmed]
AID - 10.7759/cureus.35419 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 24;15(2):e35419. doi: 10.7759/cureus.35419. eCollection 2023 
      Feb.

PMID- 37798813
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231107
IS  - 1521-3773 (Electronic)
IS  - 1433-7851 (Linking)
VI  - 62
IP  - 46
DP  - 2023 Nov 13
TI  - A GPT-4 Reticular Chemist for Guiding MOF Discovery.
PG  - e202311983
LID - 10.1002/anie.202311983 [doi]
AB  - We present a new framework integrating the AI model GPT-4 into the iterative 
      process of reticular chemistry experimentation, leveraging a cooperative workflow 
      of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is 
      an integrated system composed of three phases. Each of these utilizes GPT-4 in 
      various capacities, wherein GPT-4 provides detailed instructions for chemical 
      experimentation and the human provides feedback on the experimental outcomes, 
      including both success and failures, for the in-context learning of AI in the 
      next iteration. This iterative human-AI interaction enabled GPT-4 to learn from 
      the outcomes, much like an experienced chemist, by a prompt-learning strategy. 
      Importantly, the system is based on natural language for both development and 
      operation, eliminating the need for coding skills, and thus, make it accessible 
      to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the 
      discovery of an isoreticular series of MOFs, with each synthesis fine-tuned 
      through iterative feedback and expert suggestions. This workflow presents a 
      potential for broader applications in scientific research by harnessing the 
      capability of large language models like GPT-4 to enhance the feasibility and 
      efficiency of research activities.
CI  - © 2023 Wiley-VCH GmbH.
FAU - Zheng, Zhiling
AU  - Zheng Z
AUID- ORCID: 0000-0001-6090-2258
AD  - Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute 
      of Digital Materials for the Planet, College of Computing, Data Science, and 
      Society, University of California, Berkeley, Berkeley, CA-94720, United States.
FAU - Rong, Zichao
AU  - Rong Z
AUID- ORCID: 0000-0002-9014-9540
AD  - Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute 
      of Digital Materials for the Planet, College of Computing, Data Science, and 
      Society, University of California, Berkeley, Berkeley, CA-94720, United States.
FAU - Rampal, Nakul
AU  - Rampal N
AUID- ORCID: 0000-0002-6187-5631
AD  - Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute 
      of Digital Materials for the Planet, College of Computing, Data Science, and 
      Society, University of California, Berkeley, Berkeley, CA-94720, United States.
FAU - Borgs, Christian
AU  - Borgs C
AD  - Department of Electrical Engineering and Computer Sciences and Bakar Institute of 
      Digital Materials for the Planet, College of Computing, Data Science, and 
      Society, University of California, Berkeley, Berkeley, CA-94720, United States.
FAU - Chayes, Jennifer T
AU  - Chayes JT
AD  - Department of Electrical Engineering and Computer Sciences, Department of 
      Statistics, Department of Mathematics, School of Information, and Bakar Institute 
      of Digital Materials for the Planet, College of Computing, Data Science, and 
      Society, University of California, Berkeley, Berkeley, CA-94720, United States.
FAU - Yaghi, Omar M
AU  - Yaghi OM
AUID- ORCID: 0000-0002-5611-3325
AD  - Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute 
      of Digital Materials for the Planet, College of Computing, Data Science, and 
      Society, University of California, Berkeley, Berkeley, CA-94720, United States.
AD  - KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy 
      Applications, King Abdulaziz City for Science and Technology, Riyadh, 11442, 
      Saudi Arabia.
LA  - eng
GR  - HR0011-21-C-0020/Defense Advanced Research Projects Agency/
GR  - Kavli ENSI Graduate Student Fellowship/Kavli Foundation/
GR  - BIDMaP/
PT  - Journal Article
DEP - 20231013
PL  - Germany
TA  - Angew Chem Int Ed Engl
JT  - Angewandte Chemie (International ed. in English)
JID - 0370543
SB  - IM
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Crystals
OT  - Large Language Model
OT  - Metal-Organic Frameworks
OT  - Synthesis
EDAT- 2023/10/06 00:43
MHDA- 2023/10/06 00:44
CRDT- 2023/10/05 23:52
PHST- 2023/08/16 00:00 [received]
PHST- 2023/10/06 00:44 [medline]
PHST- 2023/10/06 00:43 [pubmed]
PHST- 2023/10/05 23:52 [entrez]
AID - 10.1002/anie.202311983 [doi]
PST - ppublish
SO  - Angew Chem Int Ed Engl. 2023 Nov 13;62(46):e202311983. doi: 
      10.1002/anie.202311983. Epub 2023 Oct 13.

PMID- 37292565
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230612
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - An Esthetic Approach for Rehabilitation of Long-Span Edentulous Arch Using 
      Artificial Intelligence.
PG  - e38683
LID - 10.7759/cureus.38683 [doi]
LID - e38683
AB  - Successful full-mouth rehabilitation requires contemporary and advanced treatment 
      planning, especially in distal extension cases. Multiple treatment modalities are 
      available in those cases. Treatment outcome in these patients remains 
      challenging. Though implants are one of the treatment options in such scenarios, 
      fixed removable partial dentures with precision attachments are the best 
      treatment options for patients who cannot afford expensive treatment. We have 
      made an attempt to describe a case report of a long-span edentulous arch by 
      incorporating the ideas and information received from Chat Generative Pre-trained 
      Transformer (GPT).
CI  - Copyright © 2023, Kumari et al.
FAU - Kumari, K Sheela
AU  - Kumari KS
AD  - Prosthodontics, Priyadarshini Dental College and Hospital, Chennai, IND.
FAU - K S, Anusha
AU  - K S A
AD  - Prosthodontics, Sri Ramachandra Institute of Higher Education and Research, 
      Chennai, IND.
LA  - eng
PT  - Case Reports
DEP - 20230507
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10244077
OTO - NOTNLM
OT  - attachments
OT  - broadrick occlusal plane analysis
OT  - centric record
OT  - chatgpt
OT  - distal extension
OT  - precision
OT  - removable dentures
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/09 06:42
MHDA- 2023/06/09 06:43
CRDT- 2023/06/09 04:24
PHST- 2023/05/07 00:00 [accepted]
PHST- 2023/06/09 06:43 [medline]
PHST- 2023/06/09 06:42 [pubmed]
PHST- 2023/06/09 04:24 [entrez]
AID - 10.7759/cureus.38683 [doi]
PST - epublish
SO  - Cureus. 2023 May 7;15(5):e38683. doi: 10.7759/cureus.38683. eCollection 2023 May.

PMID- 37248403
OWN - NLM
STAT- MEDLINE
DCOM- 20230705
LR  - 20230705
IS  - 1826-6983 (Electronic)
IS  - 0033-8362 (Linking)
VI  - 128
IP  - 7
DP  - 2023 Jul
TI  - Large language models for structured reporting in radiology: performance of 
      GPT-4, ChatGPT-3.5, Perplexity and Bing.
PG  - 808-812
LID - 10.1007/s11547-023-01651-4 [doi]
AB  - Structured reporting may improve the radiological workflow and communication 
      among physicians. Artificial intelligence applications in medicine are growing 
      fast. Large language models (LLMs) are recently gaining importance as valuable 
      tools in radiology and are currently being tested for the critical task of 
      structured reporting. We compared four LLMs models in terms of knowledge on 
      structured reporting and templates proposal. LLMs hold a great potential for 
      generating structured reports in radiology but additional formal validations are 
      needed on this topic.
CI  - © 2023. Italian Society of Medical Radiology.
FAU - Mallio, Carlo A
AU  - Mallio CA
AUID- ORCID: 0000-0002-0149-0801
AD  - Research Unit of Radiology, Department of Medicine and Surgery, Fondazione 
      Policlinico Universitario Campus Bio-Medico, Università Campus Bio-Medico di 
      Roma, Via Álvaro del Portillo, 200, 00128, Rome, RM, Italy. 
      c.mallio@policlinicocampus.it.
FAU - Sertorio, Andrea C
AU  - Sertorio AC
AD  - Research Unit of Radiology, Department of Medicine and Surgery, Fondazione 
      Policlinico Universitario Campus Bio-Medico, Università Campus Bio-Medico di 
      Roma, Via Álvaro del Portillo, 200, 00128, Rome, RM, Italy.
FAU - Bernetti, Caterina
AU  - Bernetti C
AD  - Research Unit of Radiology, Department of Medicine and Surgery, Fondazione 
      Policlinico Universitario Campus Bio-Medico, Università Campus Bio-Medico di 
      Roma, Via Álvaro del Portillo, 200, 00128, Rome, RM, Italy.
FAU - Beomonte Zobel, Bruno
AU  - Beomonte Zobel B
AD  - Research Unit of Radiology, Department of Medicine and Surgery, Fondazione 
      Policlinico Universitario Campus Bio-Medico, Università Campus Bio-Medico di 
      Roma, Via Álvaro del Portillo, 200, 00128, Rome, RM, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230529
PL  - Italy
TA  - Radiol Med
JT  - La Radiologia medica
JID - 0177625
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Radiology
MH  - Radiography
MH  - Language
MH  - Communication
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - Computed tomography (CT)
OT  - GPT-4
OT  - Generative pre-trained transformer (GPT)-based models
OT  - Large language models (LLMs)
OT  - Structured report
EDAT- 2023/05/30 01:06
MHDA- 2023/07/05 06:42
CRDT- 2023/05/29 23:26
PHST- 2023/04/28 00:00 [received]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2023/07/05 06:42 [medline]
PHST- 2023/05/30 01:06 [pubmed]
PHST- 2023/05/29 23:26 [entrez]
AID - 10.1007/s11547-023-01651-4 [pii]
AID - 10.1007/s11547-023-01651-4 [doi]
PST - ppublish
SO  - Radiol Med. 2023 Jul;128(7):808-812. doi: 10.1007/s11547-023-01651-4. Epub 2023 
      May 29.

PMID- 37746684
OWN - NLM
STAT- MEDLINE
DCOM- 20230926
LR  - 20230926
IS  - 1747-4892 (Electronic)
IS  - 1747-4884 (Linking)
VI  - 22
IP  - 3
DP  - 2023
TI  - Artificial Intelligence: its Future and Impact on Acute Medicine.
PG  - 150-153
AB  - This commentary explores the potential impact of artificial intelligence (AI) in 
      acute medicine, considering its possibilities and challenges. With its ability to 
      simulate human intelligence, AI holds the promise for supporting timely 
      decision-making and interventions in acute care. While AI has significantly 
      contributed to improvements in various sectors, its implementation in healthcare 
      remains limited. The development of AI tools tailored to acute medicine can 
      improve clinical decision-making, and AI's role in streamlining administrative 
      tasks, exemplified by ChatGPT, may offer immediate benefits. However, challenges 
      include uniform data collection, privacy, bias, and preserving the doctor-patient 
      relationship. Collaboration among AI researchers, healthcare professionals, and 
      policymakers is crucial to harness the potential of AI in acute medicine and 
      create a future where advanced technologies synergistically enhance human 
      expertise.
FAU - Schinkel, M
AU  - Schinkel M
AD  - PhD, Center for Experimental and Molecular Medicine (CEMM), Amsterdam UMC, 
      University of Amsterdam, Amsterdam, The Netherlands.
FAU - Paranjape, K
AU  - Paranjape K
AD  - PhD, Department of Internal Medicine, Division of Acute Medicine, Amsterdam UMC, 
      VU University, Amsterdam, The Netherlands.
FAU - Bhagirath, S C
AU  - Bhagirath SC
AD  - MD, Department of Internal Medicine, Division of Acute Medicine, Amsterdam UMC, 
      VU University, Amsterdam, The Netherlands.
FAU - Nanayakkara, Pwb
AU  - Nanayakkara P
AD  - PhD, Department of Internal Medicine, Division of Acute Medicine, Amsterdam UMC, 
      VU University, Amsterdam, The Netherlands.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Acute Med
JT  - Acute medicine
JID - 101553725
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Physician-Patient Relations
MH  - Clinical Decision-Making
MH  - Critical Care
MH  - Data Collection
EDAT- 2023/09/25 06:41
MHDA- 2023/09/26 13:42
CRDT- 2023/09/25 05:19
PHST- 2023/09/26 13:42 [medline]
PHST- 2023/09/25 06:41 [pubmed]
PHST- 2023/09/25 05:19 [entrez]
PST - ppublish
SO  - Acute Med. 2023;22(3):150-153.

PMID- 37443040
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 1756-0381 (Print)
IS  - 1756-0381 (Electronic)
IS  - 1756-0381 (Linking)
VI  - 16
IP  - 1
DP  - 2023 Jul 13
TI  - ChatGPT and large language models in academia: opportunities and challenges.
PG  - 20
LID - 10.1186/s13040-023-00339-9 [doi]
LID - 20
AB  - The introduction of large language models (LLMs) that allow iterative "chat" in 
      late 2022 is a paradigm shift that enables generation of text often 
      indistinguishable from that written by humans. LLM-based chatbots have immense 
      potential to improve academic work efficiency, but the ethical implications of 
      their fair use and inherent bias must be considered. In this editorial, we 
      discuss this technology from the academic's perspective with regard to its 
      limitations and utility for academic writing, education, and programming. We end 
      with our stance with regard to using LLMs and chatbots in academia, which is 
      summarized as (1) we must find ways to effectively use them, (2) their use does 
      not constitute plagiarism (although they may produce plagiarized text), (3) we 
      must quantify their bias, (4) users must be cautious of their poor accuracy, and 
      (5) the future is bright for their application to research and as an academic 
      tool.
CI  - © 2023. The Author(s).
FAU - Meyer, Jesse G
AU  - Meyer JG
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA. jesse.meyer@cshs.org.
FAU - Urbanowicz, Ryan J
AU  - Urbanowicz RJ
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - Martin, Patrick C N
AU  - Martin PCN
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - O'Connor, Karen
AU  - O'Connor K
AD  - Department of Biostatistics, Epidemiology, and Informatics, University of 
      Pennsylvania, Philadelphia, Pennsylvania, USA.
FAU - Li, Ruowang
AU  - Li R
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - Peng, Pei-Chen
AU  - Peng PC
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - Bright, Tiffani J
AU  - Bright TJ
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - Tatonetti, Nicholas
AU  - Tatonetti N
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - Won, Kyoung Jae
AU  - Won KJ
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - Gonzalez-Hernandez, Graciela
AU  - Gonzalez-Hernandez G
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA.
FAU - Moore, Jason H
AU  - Moore JH
AD  - Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles 
      California, USA. jason.moore@csmc.edu.
LA  - eng
PT  - Editorial
DEP - 20230713
PL  - England
TA  - BioData Min
JT  - BioData mining
JID - 101319161
PMC - PMC10339472
COIS- The authors declare no competing interests.
EDAT- 2023/07/14 13:06
MHDA- 2023/07/14 13:07
CRDT- 2023/07/13 23:29
PHST- 2023/07/14 13:07 [medline]
PHST- 2023/07/14 13:06 [pubmed]
PHST- 2023/07/13 23:29 [entrez]
AID - 10.1186/s13040-023-00339-9 [pii]
AID - 339 [pii]
AID - 10.1186/s13040-023-00339-9 [doi]
PST - epublish
SO  - BioData Min. 2023 Jul 13;16(1):20. doi: 10.1186/s13040-023-00339-9.

PMID- 37871067
OWN - NLM
STAT- Publisher
LR  - 20231027
IS  - 1941-0506 (Electronic)
IS  - 1077-2626 (Linking)
VI  - PP
DP  - 2023 Oct 23
TI  - NL2Color: Refining Color Palettes for Charts with Natural Language.
LID - 10.1109/TVCG.2023.3326522 [doi]
AB  - Choice of color is critical to creating effective charts with an engaging, 
      enjoyable, and informative reading experience. However, designing a good color 
      palette for a chart is a challenging task for novice users who lack related 
      design expertise. For example, they often find it difficult to articulate their 
      abstract intentions and translate these intentions into effective editing actions 
      to achieve a desired outcome. In this work, we present NL2Color, a tool that 
      allows novice users to refine chart color palettes using natural language 
      expressions of their desired outcomes. We first collected and categorized a 
      dataset of 131 triplets, each consisting of an original color palette of a chart, 
      an editing intent, and a new color palette designed by human experts according to 
      the intent. Our tool employs a large language model (LLM) to substitute the 
      colors in original palettes and produce new color palettes by selecting some of 
      the triplets as few-shot prompts. To evaluate our tool, we conducted a 
      comprehensive two-stage evaluation, including a crowd-sourcing study (N=71) and a 
      within-subjects user study (N=12). The results indicate that the quality of the 
      color palettes revised by NL2Color has no significantly large difference from 
      those designed by human experts. The participants who used NL2Color obtained 
      revised color palettes to their satisfaction in a shorter period and with less 
      effort.
FAU - Shi, Chuhan
AU  - Shi C
FAU - Cui, Weiwei
AU  - Cui W
FAU - Liu, Chengzhong
AU  - Liu C
FAU - Zheng, Chengbo
AU  - Zheng C
FAU - Zhang, Haidong
AU  - Zhang H
FAU - Luo, Qiong
AU  - Luo Q
FAU - Ma, Xiaojuan
AU  - Ma X
LA  - eng
PT  - Journal Article
DEP - 20231023
PL  - United States
TA  - IEEE Trans Vis Comput Graph
JT  - IEEE transactions on visualization and computer graphics
JID - 9891704
SB  - IM
EDAT- 2023/10/23 18:42
MHDA- 2023/10/23 18:42
CRDT- 2023/10/23 13:35
PHST- 2023/10/23 18:42 [pubmed]
PHST- 2023/10/23 18:42 [medline]
PHST- 2023/10/23 13:35 [entrez]
AID - 10.1109/TVCG.2023.3326522 [doi]
PST - aheadofprint
SO  - IEEE Trans Vis Comput Graph. 2023 Oct 23;PP. doi: 10.1109/TVCG.2023.3326522.

PMID- 37530906
OWN - NLM
STAT- Publisher
LR  - 20230802
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Aug 2
TI  - Potential for GPT Technology to Optimize Future Clinical Decision-Making Using 
      Retrieval-Augmented Generation.
LID - 10.1007/s10439-023-03327-6 [doi]
AB  - Advancements in artificial intelligence (AI) provide many helpful tools for 
      healthcare, one of which includes AI chatbots that use natural language 
      processing to create humanlike, conversational dialog. These chatbots have 
      general cognitive skills and are able to engage with clinicians and patients to 
      discuss patients' health conditions and what they may be at risk for. While 
      chatbot engines have access to a wide range of medical texts and research papers, 
      they currently provide high-level, generic responses and are limited in their 
      ability to provide diagnostic guidance and clinical advice to patients on an 
      individual level. The essay discusses the use of retrieval-augmented generation 
      (RAG), which can be used to improve the specificity of user-entered prompts and 
      thereby enhance the detail in AI chatbot responses. By embedding more recent 
      clinical data and trusted medical sources, such as clinical guidelines, into the 
      chatbot models, AI chatbots can provide more patient-specific guidance, faster 
      diagnoses and treatment recommendations, and greater improvement of patient 
      outcomes.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Wang, Calvin
AU  - Wang C
AUID- ORCID: 0009-0003-9907-1000
AD  - College of Medicine - Robert Wood Johnson Medical School, Rutgers University, New 
      Brunswick, NJ, 08901, USA. cw1028@rutgers.edu.
FAU - Ong, Joshua
AU  - Ong J
AD  - Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
FAU - Wang, Chara
AU  - Wang C
AD  - Biotechnology High School, Freehold, NJ, USA.
FAU - Ong, Hannah
AU  - Ong H
AD  - College of Medicine, The Ohio State University, Columbus, OH, USA.
FAU - Cheng, Rebekah
AU  - Cheng R
AD  - Department of Physical Therapy, Virginia Commonwealth University, Richmond, VA, 
      USA.
FAU - Ong, Dennis
AU  - Ong D
AD  - Amazon Web Services, Amazon, Seattle, WA, USA.
LA  - eng
PT  - Letter
DEP - 20230802
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Clinical
OT  - GPT
OT  - Large language model
OT  - Patient
EDAT- 2023/08/02 13:08
MHDA- 2023/08/02 13:08
CRDT- 2023/08/02 11:08
PHST- 2023/07/14 00:00 [received]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/08/02 13:08 [medline]
PHST- 2023/08/02 13:08 [pubmed]
PHST- 2023/08/02 11:08 [entrez]
AID - 10.1007/s10439-023-03327-6 [pii]
AID - 10.1007/s10439-023-03327-6 [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Aug 2. doi: 10.1007/s10439-023-03327-6.

PMID- 36322749
OWN - NLM
STAT- MEDLINE
DCOM- 20221104
LR  - 20230503
IS  - 1091-6490 (Electronic)
IS  - 0027-8424 (Print)
IS  - 0027-8424 (Linking)
VI  - 119
IP  - 45
DP  - 2022 Nov 8
TI  - Quantifying the narrative flow of imagined versus autobiographical stories.
PG  - e2211715119
LID - 10.1073/pnas.2211715119 [doi]
LID - e2211715119
AB  - Lifelong experiences and learned knowledge lead to shared expectations about how 
      common situations tend to unfold. Such knowledge of narrative event flow enables 
      people to weave together a story. However, comparable computational tools to 
      evaluate the flow of events in narratives are limited. We quantify the 
      differences between autobiographical and imagined stories by introducing 
      sequentiality, a measure of narrative flow of events, drawing probabilistic 
      inferences from a cutting-edge large language model (GPT-3). Sequentiality 
      captures the flow of a narrative by comparing the probability of a sentence with 
      and without its preceding story context. We applied our measure to study 
      thousands of diary-like stories, collected from crowdworkers, about either a 
      recent remembered experience or an imagined story on the same topic. The results 
      show that imagined stories have higher sequentiality than autobiographical 
      stories and that the sequentiality of autobiographical stories increases when the 
      memories are retold several months later. In pursuit of deeper understandings of 
      how sequentiality measures the flow of narratives, we explore proportions of 
      major and minor events in story sentences, as annotated by crowdworkers. We find 
      that lower sequentiality is associated with higher proportions of major events. 
      The methods and results highlight opportunities to use cutting-edge computational 
      analyses, such as sequentiality, on large corpora of matched imagined and 
      autobiographical stories to investigate the influences of memory and reasoning on 
      language generation processes.
FAU - Sap, Maarten
AU  - Sap M
AD  - Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 
      15213.
AD  - Paul G. Allen School of Computer Science &amp; Engineering, University of 
      Washington, Seattle, WA 98195.
AD  - Microsoft, Redmond, WA 98052.
FAU - Jafarpour, Anna
AU  - Jafarpour A
AUID- ORCID: 0000-0003-3601-3535
AD  - Department of Physiology and Biophysics, University of Washington, Seattle, WA 
      98195.
FAU - Choi, Yejin
AU  - Choi Y
AD  - Paul G. Allen School of Computer Science &amp; Engineering, University of 
      Washington, Seattle, WA 98195.
AD  - Allen Institute for AI, Seattle, WA 98103.
FAU - Smith, Noah A
AU  - Smith NA
AUID- ORCID: 0000-0002-2310-6380
AD  - Paul G. Allen School of Computer Science &amp; Engineering, University of 
      Washington, Seattle, WA 98195.
AD  - Allen Institute for AI, Seattle, WA 98103.
FAU - Pennebaker, James W
AU  - Pennebaker JW
AUID- ORCID: 0000-0001-9091-214X
AD  - Department of Psychology, University of Texas, Austin, TX 78712.
FAU - Horvitz, Eric
AU  - Horvitz E
AUID- ORCID: 0000-0002-8823-0614
AD  - Paul G. Allen School of Computer Science &amp; Engineering, University of 
      Washington, Seattle, WA 98195.
AD  - Microsoft, Redmond, WA 98052.
LA  - eng
GR  - K99MH120048/NIH Brain Initiative/
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20221102
PL  - United States
TA  - Proc Natl Acad Sci U S A
JT  - Proceedings of the National Academy of Sciences of the United States of America
JID - 7505876
SB  - IM
MH  - Humans
MH  - *Narration
MH  - *Mental Recall
MH  - Comprehension
MH  - Language
MH  - Learning
PMC - PMC9659415
OTO - NOTNLM
OT  - deep neural networks
OT  - imagination
OT  - memory
OT  - narrative
OT  - natural language processing
COIS- The authors declare no competing interest.
EDAT- 2022/11/03 06:00
MHDA- 2022/11/05 06:00
CRDT- 2022/11/02 15:04
PHST- 2022/11/02 15:04 [entrez]
PHST- 2022/11/03 06:00 [pubmed]
PHST- 2022/11/05 06:00 [medline]
AID - 202211715 [pii]
AID - 10.1073/pnas.2211715119 [doi]
PST - ppublish
SO  - Proc Natl Acad Sci U S A. 2022 Nov 8;119(45):e2211715119. doi: 
      10.1073/pnas.2211715119. Epub 2022 Nov 2.

PMID- 37986848
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231206
DP  - 2023 Nov 12
TI  - Genetic Discovery Enabled by A Large Language Model.
LID - 2023.11.09.566468 [pii]
LID - 10.1101/2023.11.09.566468 [doi]
AB  - Artificial intelligence (AI) has been used in many areas of medicine, and 
      recently large language models (LLMs) have shown potential utility for clinical 
      applications. However, since we do not know if the use of LLMs can accelerate the 
      pace of genetic discovery, we used data generated from mouse genetic models to 
      investigate this possibility. We examined whether a recently developed 
      specialized LLM (Med-PaLM 2) could analyze sets of candidate genes generated from 
      analysis of murine models of biomedical traits. In response to free-text input, 
      Med-PaLM 2 correctly identified the murine genes that contained experimentally 
      verified causative genetic factors for six biomedical traits, which included 
      susceptibility to diabetes and cataracts. Med-PaLM 2 was also able to analyze a 
      list of genes with high impact alleles, which were identified by comparative 
      analysis of murine genomic sequence data, and it identified a causative murine 
      genetic factor for spontaneous hearing loss. Based upon this Med-PaLM 2 finding, 
      a novel bigenic model for susceptibility to spontaneous hearing loss was 
      developed. These results demonstrate Med-PaLM 2 can analyze gene-phenotype 
      relationships and generate novel hypotheses, which can facilitate genetic 
      discovery.
FAU - Tu, Tao
AU  - Tu T
AD  - Google Research, Mountain View, CA, USA.
FAU - Fang, Zhouqing
AU  - Fang Z
AD  - Department of Anesthesiology, Pain and Perioperative Medicine.
FAU - Cheng, Zhuanfen
AU  - Cheng Z
AD  - Department of Anesthesiology, Pain and Perioperative Medicine.
FAU - Spasic, Svetolik
AU  - Spasic S
AD  - Department of Otolaryngology - Head and Neck Surgery, Stanford University School 
      of Medicine, Stanford, CA 94305, USA.
FAU - Palepu, Anil
AU  - Palepu A
AD  - Google Research, Mountain View, CA, USA.
FAU - Stankovic, Konstantina M
AU  - Stankovic KM
AUID- ORCID: 0000-0003-0233-279X
AD  - Department of Otolaryngology - Head and Neck Surgery, Stanford University School 
      of Medicine, Stanford, CA 94305, USA.
FAU - Natarajan, Vivek
AU  - Natarajan V
AD  - Google Research, Mountain View, CA, USA.
FAU - Peltz, Gary
AU  - Peltz G
AUID- ORCID: 0000-0001-6191-7697
AD  - Department of Anesthesiology, Pain and Perioperative Medicine.
LA  - eng
GR  - R01 DC021133/DC/NIDCD NIH HHS/United States
GR  - R24 OD035408/OD/NIH HHS/United States
PT  - Preprint
DEP - 20231112
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10659415
EDAT- 2023/11/21 06:43
MHDA- 2023/11/21 06:44
CRDT- 2023/11/21 05:07
PHST- 2023/11/21 06:43 [pubmed]
PHST- 2023/11/21 06:44 [medline]
PHST- 2023/11/21 05:07 [entrez]
AID - 2023.11.09.566468 [pii]
AID - 10.1101/2023.11.09.566468 [doi]
PST - epublish
SO  - bioRxiv. 2023 Nov 12:2023.11.09.566468. doi: 10.1101/2023.11.09.566468. Preprint.

PMID- 37371718
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2227-9059 (Print)
IS  - 2227-9059 (Electronic)
IS  - 2227-9059 (Linking)
VI  - 11
IP  - 6
DP  - 2023 Jun 2
TI  - PRISMA Systematic Literature Review, including with Meta-Analysis vs. Chatbot/GPT 
      (AI) regarding Current Scientific Data on the Main Effects of the Calf Blood 
      Deproteinized Hemoderivative Medicine (Actovegin) in Ischemic Stroke.
LID - 10.3390/biomedicines11061623 [doi]
LID - 1623
AB  - BACKGROUND: Stroke is a significant public health problem and a leading cause of 
      death and long-term disability worldwide. Several treatments for ischemic stroke 
      have been developed, but these treatments have limited effectiveness. One 
      potential treatment for this condition is Actovegin(®)/AODEJIN, a calf blood 
      deproteinized hemodialysate/ultrafiltrate that has been shown to have 
      pleiotropic/multifactorial and possibly multimodal effects. The actual actions of 
      this medicine are thought to be mediated by its ability to reduce oxidative 
      stress, inflammation, and apoptosis and to enhance neuronal survival and 
      plasticity. METHODS: To obtain the most up-to-date information on the effects of 
      Actovegin(®)/AODEJIN in ischemic stroke, we systematically reviewed the 
      literature published in the last two years. This review builds upon our previous 
      systematic literature review published in 2020, which used the Preferred 
      Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to 
      search for and select related articles over almost two decades, between 1 January 
      2001 and 31 December 2019. Additionally, we compared the results of our PRISMA 
      search (human intelligence-based) with those obtained from an interrogation of a 
      GPT-based chatbot (ChatGPT) in order to ensure comprehensive coverage of 
      potentially relevant studies. RESULTS: Our updated review found limited new 
      evidence on the use of Actovegin(®)/AODEJIN in ischemic stroke, although the 
      number of articles on this subject consistently increased compared to that from 
      our initial systematic literature review. Specifically, we found five articles up 
      to 2020 and eight more until December 2022. While these studies suggest that 
      Actovegin(®)/AODEJIN may have neuroprotective effects in ischemic stroke, further 
      clinical trials are needed to confirm these findings. Consequently, we performed 
      a funnel analysis to evaluate the potential for publication bias. DISCUSSION: Our 
      funnel analysis showed no evidence of publication bias, suggesting that the 
      limited number of studies identified was not due to publication bias but rather 
      due to a lack of research in this area. However, there are limitations when using 
      ChatGPT, particularly in distinguishing between truth and falsehood and 
      determining the appropriateness of interpolation. Nevertheless, AI can provide 
      valuable support in conducting PRISMA-type systematic literature reviews, 
      including meta-analyses. CONCLUSIONS: The limited number of studies identified in 
      our review highlights the need for additional research in this area, especially 
      as no available therapeutic agents are capable of curing central nervous system 
      lesions. Any contribution, including that of Actovegin (with consideration of a 
      positive balance between benefits and risks), is worthy of further study and 
      periodic reappraisal. The evolving advancements in AI may play a role in the near 
      future.
FAU - Anghelescu, Aurelian
AU  - Anghelescu A
AUID- ORCID: 0000-0002-8055-0541
AD  - Faculty of Midwifery and Nursing, University of Medicine and Pharmacy "Carol 
      Davila", 020022 Bucharest, Romania.
AD  - The Neuromuscular Rehabilitation Clinic Division, Teaching Emergency Hospital 
      "Bagdasar-Arseni", 041915 Bucharest, Romania.
FAU - Firan, Florentina Carmen
AU  - Firan FC
AD  - The Physical and Rehabilitation Medicine & Balneology Clinic Division-The 
      NeuroRehabilitation Compartment, Teaching Emergency Hospital of the Ilfov County, 
      22104 Bucharest, Romania.
FAU - Onose, Gelu
AU  - Onose G
AUID- ORCID: 0000-0002-6346-8941
AD  - The Neuromuscular Rehabilitation Clinic Division, Teaching Emergency Hospital 
      "Bagdasar-Arseni", 041915 Bucharest, Romania.
AD  - Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", 020022 
      Bucharest, Romania.
FAU - Munteanu, Constantin
AU  - Munteanu C
AUID- ORCID: 0000-0002-1084-7710
AD  - The Neuromuscular Rehabilitation Clinic Division, Teaching Emergency Hospital 
      "Bagdasar-Arseni", 041915 Bucharest, Romania.
AD  - Faculty of Medical Bioengineering, University of Medicine and Pharmacy "Grigore 
      T. Popa" Iași, 700454 Iași, Romania.
FAU - Trandafir, Andreea-Iulia
AU  - Trandafir AI
AUID- ORCID: 0000-0003-1369-3873
AD  - The Neuromuscular Rehabilitation Clinic Division, Teaching Emergency Hospital 
      "Bagdasar-Arseni", 041915 Bucharest, Romania.
AD  - Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", 020022 
      Bucharest, Romania.
FAU - Ciobanu, Ilinca
AU  - Ciobanu I
AD  - The Neuromuscular Rehabilitation Clinic Division, Teaching Emergency Hospital 
      "Bagdasar-Arseni", 041915 Bucharest, Romania.
FAU - Gheorghița, Ștefan
AU  - Gheorghița Ș
AD  - The Neuromuscular Rehabilitation Clinic Division, Teaching Emergency Hospital 
      "Bagdasar-Arseni", 041915 Bucharest, Romania.
FAU - Ciobanu, Vlad
AU  - Ciobanu V
AD  - Computer Science Department, Politehnica University of Bucharest, 060042 
      Bucharest, Romania.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230602
PL  - Switzerland
TA  - Biomedicines
JT  - Biomedicines
JID - 101691304
PMC - PMC10295843
OTO - NOTNLM
OT  - PRISMA-type systematic literature review
OT  - chatbot/GPT (artificial intelligence—AI)
OT  - deproteinized ultrafiltrate/hemodialysate compound Actovegin
OT  - ischemic stroke
OT  - meta-analysis
COIS- The authors confirm no conflict of interest. The producers of Actovegin(®) 
      (Nycomed/Takeda) are constant partners of our societies (the Romanian Society for 
      NeuroRehabilitation, the Romanian Spinal Cord Society, and the Romanian Society 
      of Physical and Rehabilitation Medicine & Balneoclimatology) and Clinic Division. 
      They did not interfere with the study process (data collection or processing, 
      concluding results, or editing endeavors).
EDAT- 2023/06/28 06:42
MHDA- 2023/06/28 06:43
CRDT- 2023/06/28 01:08
PHST- 2023/04/06 00:00 [received]
PHST- 2023/05/24 00:00 [revised]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/06/28 06:43 [medline]
PHST- 2023/06/28 06:42 [pubmed]
PHST- 2023/06/28 01:08 [entrez]
AID - biomedicines11061623 [pii]
AID - biomedicines-11-01623 [pii]
AID - 10.3390/biomedicines11061623 [doi]
PST - epublish
SO  - Biomedicines. 2023 Jun 2;11(6):1623. doi: 10.3390/biomedicines11061623.

PMID- 38046089
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2472-5390 (Electronic)
IS  - 2472-5390 (Linking)
VI  - 7
IP  - 6
DP  - 2023 Dec
TI  - Brain versus bot: Distinguishing letters of recommendation authored by humans 
      compared with artificial intelligence.
LID - 10.1002/aet2.10924 [doi]
AB  - OBJECTIVES: Letters of recommendation (LORs) are essential within academic 
      medicine, affecting a number of important decisions regarding advancement, yet 
      these letters take significant amounts of time and labor to prepare. The use of 
      generative artificial intelligence (AI) tools, such as ChatGPT, are gaining 
      popularity for a variety of academic writing tasks and offer an innovative 
      solution to relieve the burden of letter writing. It is yet to be determined if 
      ChatGPT could aid in crafting LORs, particularly in high-stakes contexts like 
      faculty promotion. To determine the feasibility of this process and whether there 
      is a significant difference between AI and human-authored letters, we conducted a 
      study aimed at determining whether academic physicians can distinguish between 
      the two. METHODS: A quasi-experimental study was conducted using a single-blind 
      design. Academic physicians with experience in reviewing LORs were presented with 
      LORs for promotion to associate professor, written by either humans or AI. 
      Participants reviewed LORs and identified the authorship. Statistical analysis 
      was performed to determine accuracy in distinguishing between human and 
      AI-authored LORs. Additionally, the perceived quality and persuasiveness of the 
      LORs were compared based on suspected and actual authorship. RESULTS: A total of 
      32 participants completed letter review. The mean accuracy of distinguishing 
      between human- versus AI-authored LORs was 59.4%. The reviewer's certainty and 
      time spent deliberating did not significantly impact accuracy. LORs suspected to 
      be human-authored were rated more favorably in terms of quality and 
      persuasiveness. A difference in gender-biased language was observed in our 
      letters: human-authored letters contained significantly more female-associated 
      words, while the majority of AI-authored letters tended to use more 
      male-associated words. CONCLUSIONS: Participants were unable to reliably 
      differentiate between human- and AI-authored LORs for promotion. AI may be able 
      to generate LORs and relieve the burden of letter writing for academicians. New 
      strategies, policies, and guidelines are needed to balance the benefits of AI 
      while preserving integrity and fairness in academic promotion decisions.
CI  - © 2023 The Authors. AEM Education and Training published by Wiley Periodicals LLC 
      on behalf of Society for Academic Emergency Medicine.
FAU - Preiksaitis, Carl
AU  - Preiksaitis C
AUID- ORCID: 0000-0002-3856-0068
AD  - Department of Emergency Medicine Stanford School of Medicine Stanford California 
      USA.
FAU - Nash, Christopher
AU  - Nash C
AUID- ORCID: 0000-0002-0738-409X
AD  - Department of Emergency Medicine Massachusetts General Hospital Boston 
      Massachusetts USA.
FAU - Gottlieb, Michael
AU  - Gottlieb M
AUID- ORCID: 0000-0003-3276-8375
AD  - Department of Emergency Medicine Rush University Medical Center Chicago Illinois 
      USA.
FAU - Chan, Teresa M
AU  - Chan TM
AUID- ORCID: 0000-0001-6104-462X
AD  - Division of Emergency Medicine, Department of Medicine McMaster University 
      Hamilton Ontario Canada.
FAU - Alvarez, Al'ai
AU  - Alvarez A
AUID- ORCID: 0000-0002-5438-2476
AD  - Department of Emergency Medicine Stanford School of Medicine Stanford California 
      USA.
FAU - Landry, Adaira
AU  - Landry A
AUID- ORCID: 0000-0002-5299-679X
AD  - Department of Emergency Medicine Harvard Medical School Boston Massachusetts USA.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - United States
TA  - AEM Educ Train
JT  - AEM education and training
JID - 101722142
PMC - PMC10688127
COIS- The authors declare no conflicts of interest.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 04:49
PHST- 2023/06/25 00:00 [received]
PHST- 2023/09/16 00:00 [revised]
PHST- 2023/09/18 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 04:49 [entrez]
AID - AET210924 [pii]
AID - 10.1002/aet2.10924 [doi]
PST - epublish
SO  - AEM Educ Train. 2023 Nov 30;7(6):10.1002/aet2.10924. doi: 10.1002/aet2.10924. 
      eCollection 2023 Dec.

PMID- 36999112
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230401
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Complicated Invasive Pneumococcal Meningitis: Highlighting the Value of 
      Implementing New Pneumococcal Vaccines.
PG  - e35482
LID - 10.7759/cureus.35482 [doi]
LID - e35482
AB  - Invasive Streptococcus pneumoniae disease (IPD) remains a serious cause of 
      morbidity and loss of life in children and adults worldwide. While pneumococcal 
      vaccines have reduced the frequency of invasive pneumococcal disease, the 
      emergence of invasive non-vaccine serotypes has mandated the development of novel 
      pneumococcal vaccines to further protect against these emerging serotypes. We 
      present a case of a non-vaccine serotype invasive pneumococcal disease-causing 
      septic shock, meningitis, and stroke in a previously healthy and appropriately 
      vaccinated 23-month-old male.
CI  - Copyright © 2023, Thacker et al.
FAU - Thacker, Stephen A
AU  - Thacker SA
AD  - Pediatrics, Medical University of South Carolina, Charleston, USA.
LA  - eng
PT  - Case Reports
DEP - 20230226
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10044317
OTO - NOTNLM
OT  - acute kidney injury
OT  - chatgpt
OT  - infectious disease
OT  - invasive pneumococcal disease
OT  - meningitis
OT  - pneumococcal vaccine
OT  - streptococcus pneumoniae meningitis
OT  - streptococcus sepsis
OT  - vaccine science and policy
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/01 06:00
MHDA- 2023/04/01 06:01
CRDT- 2023/03/31 02:15
PHST- 2023/02/25 00:00 [accepted]
PHST- 2023/04/01 06:01 [medline]
PHST- 2023/03/31 02:15 [entrez]
PHST- 2023/04/01 06:00 [pubmed]
AID - 10.7759/cureus.35482 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 26;15(2):e35482. doi: 10.7759/cureus.35482. eCollection 2023 
      Feb.

PMID- 37751243
OWN - NLM
STAT- MEDLINE
DCOM- 20230927
LR  - 20231013
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Sep 26
TI  - A Future of Smarter Digital Health Empowered by Generative Pretrained 
      Transformer.
PG  - e49963
LID - 10.2196/49963 [doi]
LID - e49963
AB  - Generative pretrained transformer (GPT) tools have been thriving, as ignited by 
      the remarkable success of OpenAI's recent chatbot product. GPT technology offers 
      countless opportunities to significantly improve or renovate current health care 
      research and practice paradigms, especially digital health interventions and 
      digital health-enabled clinical care, and a future of smarter digital health can 
      thus be expected. In particular, GPT technology can be incorporated through 
      various digital health platforms in homes and hospitals embedded with numerous 
      sensors, wearables, and remote monitoring devices. In this viewpoint paper, we 
      highlight recent research progress that depicts the future picture of a smarter 
      digital health ecosystem through GPT-facilitated centralized communications, 
      automated analytics, personalized health care, and instant decision-making.
CI  - ©Hongyu Miao, Chengdong Li, Jing Wang. Originally published in the Journal of 
      Medical Internet Research (https://www.jmir.org), 26.09.2023.
FAU - Miao, Hongyu
AU  - Miao H
AUID- ORCID: 0000-0002-4131-3164
AD  - College of Nursing, Florida State University, Tallahassee, FL, United States.
FAU - Li, Chengdong
AU  - Li C
AUID- ORCID: 0000-0001-5330-9624
AD  - College of Nursing, Florida State University, Tallahassee, FL, United States.
FAU - Wang, Jing
AU  - Wang J
AUID- ORCID: 0000-0002-4012-0977
AD  - College of Nursing, Florida State University, Tallahassee, FL, United States.
LA  - eng
GR  - UL1 TR001427/TR/NCATS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20230926
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Ecosystem
MH  - *Health Services Research
MH  - Hospitals
MH  - Software
MH  - Technology
PMC - PMC10565615
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - artificial intelligence
OT  - digital health
OT  - ethics
OT  - generative pretrained model
OT  - generative pretrained transformer
OT  - precision medicine
OT  - privacy
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/26 13:42
MHDA- 2023/09/27 06:43
CRDT- 2023/09/26 11:53
PHST- 2023/06/14 00:00 [received]
PHST- 2023/08/28 00:00 [accepted]
PHST- 2023/07/30 00:00 [revised]
PHST- 2023/09/27 06:43 [medline]
PHST- 2023/09/26 13:42 [pubmed]
PHST- 2023/09/26 11:53 [entrez]
AID - v25i1e49963 [pii]
AID - 10.2196/49963 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Sep 26;25:e49963. doi: 10.2196/49963.

PMID- 37644945
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230831
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - Harnessing the Power of Large Language Models (LLMs) for Electronic Health 
      Records (EHRs) Optimization.
PG  - e42634
LID - 10.7759/cureus.42634 [doi]
LID - e42634
AB  - This editorial discusses the potential benefits of integrating large language 
      models (LLMs), such as GPT-4, into electronic health records (EHRs) to optimize 
      patient care, improve clinical decision-making, and promote efficient healthcare 
      management. Artificial intelligence (AI)-driven LLMs can revolutionize healthcare 
      practices by streamlining the data input process, expediting information 
      extraction from unstructured narratives, and facilitating personalized patient 
      communication. However, concerns related to patient privacy, data security, and 
      potential biases must be addressed to ensure equitable healthcare for all. 
      Therefore, we encourage healthcare professionals and researchers to explore 
      innovative solutions that leverage AI capabilities while addressing the 
      challenges associated with privacy and equity.
CI  - Copyright © 2023, Nashwan et al.
FAU - Nashwan, Abdulqadir J
AU  - Nashwan AJ
AD  - Nursing, Hamad Medical Corporation, Doha, QAT.
FAU - AbuJaber, Ahmad A
AU  - AbuJaber AA
AD  - Nursing, Hamad Medical Corporation, Doha, QAT.
LA  - eng
PT  - Editorial
DEP - 20230729
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10461074
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - clinical decision-making
OT  - electronic health records
OT  - gpt-4
OT  - large language models
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/08/30 06:48
MHDA- 2023/08/30 06:49
CRDT- 2023/08/30 03:44
PHST- 2023/07/28 00:00 [accepted]
PHST- 2023/08/30 06:49 [medline]
PHST- 2023/08/30 06:48 [pubmed]
PHST- 2023/08/30 03:44 [entrez]
AID - 10.7759/cureus.42634 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 29;15(7):e42634. doi: 10.7759/cureus.42634. eCollection 2023 
      Jul.

PMID- 38023448
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 1662-5218 (Print)
IS  - 1662-5218 (Electronic)
IS  - 1662-5218 (Linking)
VI  - 17
DP  - 2023
TI  - A human-centered safe robot reinforcement learning framework with interactive 
      behaviors.
PG  - 1280341
LID - 10.3389/fnbot.2023.1280341 [doi]
LID - 1280341
AB  - Deployment of Reinforcement Learning (RL) algorithms for robotics applications in 
      the real world requires ensuring the safety of the robot and its environment. 
      Safe Robot RL (SRRL) is a crucial step toward achieving human-robot coexistence. 
      In this paper, we envision a human-centered SRRL framework consisting of three 
      stages: safe exploration, safety value alignment, and safe collaboration. We 
      examine the research gaps in these areas and propose to leverage interactive 
      behaviors for SRRL. Interactive behaviors enable bi-directional information 
      transfer between humans and robots, such as conversational robot ChatGPT. We 
      argue that interactive behaviors need further attention from the SRRL community. 
      We discuss four open challenges related to the robustness, efficiency, 
      transparency, and adaptability of SRRL with interactive behaviors.
CI  - Copyright © 2023 Gu, Kshirsagar, Du, Chen, Peters and Knoll.
FAU - Gu, Shangding
AU  - Gu S
AD  - Department of Computer Science, Technical University of Munich, Munich, Germany.
FAU - Kshirsagar, Alap
AU  - Kshirsagar A
AD  - Department of Computer Science, Technical University of Darmstadt, Darmstadt, 
      Germany.
FAU - Du, Yali
AU  - Du Y
AD  - Department of Informatics, King's College London, London, United Kingdom.
FAU - Chen, Guang
AU  - Chen G
AD  - College of Electronic and Information Engineering, Tongji University, Shanghai, 
      China.
FAU - Peters, Jan
AU  - Peters J
AD  - Department of Computer Science, Technical University of Darmstadt, Darmstadt, 
      Germany.
FAU - Knoll, Alois
AU  - Knoll A
AD  - Department of Computer Science, Technical University of Munich, Munich, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231109
PL  - Switzerland
TA  - Front Neurorobot
JT  - Frontiers in neurorobotics
JID - 101477958
PMC - PMC10665848
OTO - NOTNLM
OT  - bi-direction information
OT  - interactive behaviors
OT  - safe collaboration
OT  - safe exploration
OT  - value alignment
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest. The author(s) declared that they were an editorial board 
      member of Frontiers, at the time of submission. This had no impact on the peer 
      review process and the final decision.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:48
PHST- 2023/08/20 00:00 [received]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:48 [entrez]
AID - 10.3389/fnbot.2023.1280341 [doi]
PST - epublish
SO  - Front Neurorobot. 2023 Nov 9;17:1280341. doi: 10.3389/fnbot.2023.1280341. 
      eCollection 2023.

PMID- 38011011
OWN - NLM
STAT- Publisher
LR  - 20231127
IS  - 1557-7740 (Electronic)
IS  - 1557-7740 (Linking)
DP  - 2023 Nov 21
TI  - "You Are Not Alone": The Allure and Limitations of Artificial Intelligence in 
      Serious Illness Communication.
LID - 10.1089/jpm.2023.0471 [doi]
AB  - Artificial intelligence (AI) is changing the way clinicians practice medicine, 
      and recent technological advancements have resulted in consumer-facing products 
      that can respond to users with dynamic and nuanced language. Clinicians typically 
      struggle with serious illness communication, such as delivering news about a poor 
      prognosis. Palliative care clinicians receive extensive training in serious 
      illness communication, but there is a paucity of such highly trained specialists. 
      This article explores the allure of employing AI-powered chatbots to assist 
      nonspecialist clinicians with serious illness communication and highlights the 
      ethical and practical drawbacks. While outsourcing communication to new AI 
      chatbot technologies may be inappropriate, there is a role for AI in training 
      clinicians on effective language to use when discussing serious illness with 
      their patients.
FAU - Burry, Nicolas
AU  - Burry N
AD  - Adult Palliative Care Service, Department of Medicine, Columbia University Irving 
      Medical Center, New York, New York, USA.
FAU - Nakagawa, Shunichi
AU  - Nakagawa S
AD  - Adult Palliative Care Service, Department of Medicine, Columbia University Irving 
      Medical Center, New York, New York, USA.
FAU - Blinderman, Craig D
AU  - Blinderman CD
AD  - Adult Palliative Care Service, Department of Medicine, Columbia University Irving 
      Medical Center, New York, New York, USA.
LA  - eng
PT  - Journal Article
DEP - 20231121
PL  - United States
TA  - J Palliat Med
JT  - Journal of palliative medicine
JID - 9808462
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - ethics in palliative care
OT  - serious illness communication
EDAT- 2023/11/27 18:42
MHDA- 2023/11/27 18:42
CRDT- 2023/11/27 13:03
PHST- 2023/11/27 18:42 [medline]
PHST- 2023/11/27 18:42 [pubmed]
PHST- 2023/11/27 13:03 [entrez]
AID - 10.1089/jpm.2023.0471 [doi]
PST - aheadofprint
SO  - J Palliat Med. 2023 Nov 21. doi: 10.1089/jpm.2023.0471.

PMID- 37252579
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230531
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Abdominal Ecchymosis: Emergency, or Urgen-C?
PG  - e38091
LID - 10.7759/cureus.38091 [doi]
LID - e38091
AB  - Scurvy is a multisystem disease caused by vitamin C deficiency, historically 
      associated with lethargy, gingivitis, ecchymosis, edema, and death if left 
      untreated. Contemporary socioeconomic risk factors for scurvy include smoking, 
      alcohol abuse, fad diets, mental health conditions, social isolation, and 
      economic marginalization. Food insecurity is also a risk factor. This report 
      describes a case of a man in his 70s who presented with unexplained dyspnea, 
      abdominal pain, and abdominal ecchymosis. His plasma vitamin C level was 
      undetectable, and he improved with vitamin C supplementation. This case 
      highlights the significance of awareness of these risk factors and emphasizes the 
      need for a comprehensive social and dietary history to enable the timely 
      treatment of this rare but potentially fatal disease.
CI  - Copyright © 2023, Jones et al.
FAU - Jones, Riley
AU  - Jones R
AD  - Medicine, University of Florida College of Medicine, Gainesville, USA.
FAU - Shafiq, Leila
AU  - Shafiq L
AD  - Medicine, University of South Florida Morsani College of Medicine, Tampa, USA.
FAU - Idowu, Benmichael
AU  - Idowu B
AD  - Medicine, University of Florida College of Medicine, Gainesville, USA.
FAU - Radhakrishnan, Nila S
AU  - Radhakrishnan NS
AD  - Medicine, University of Florida College of Medicine, Gainesville, USA.
FAU - Fromm, Jason
AU  - Fromm J
AD  - Medicine, University of Florida College of Medicine, Gainesville, USA.
LA  - eng
PT  - Case Reports
DEP - 20230425
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10209748
OTO - NOTNLM
OT  - chatgpt
OT  - food insecurity
OT  - malnutrition
OT  - psychosocial factors
OT  - scurvy
OT  - vitamin c deficiency
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/30 13:07
MHDA- 2023/05/30 13:08
CRDT- 2023/05/30 11:55
PHST- 2023/04/17 00:00 [accepted]
PHST- 2023/05/30 13:08 [medline]
PHST- 2023/05/30 13:07 [pubmed]
PHST- 2023/05/30 11:55 [entrez]
AID - 10.7759/cureus.38091 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 25;15(4):e38091. doi: 10.7759/cureus.38091. eCollection 2023 
      Apr.

PMID- 37958843
OWN - NLM
STAT- MEDLINE
DCOM- 20231115
LR  - 20231122
IS  - 1422-0067 (Electronic)
IS  - 1422-0067 (Linking)
VI  - 24
IP  - 21
DP  - 2023 Nov 1
TI  - Deep Learning for Genomics: From Early Neural Nets to Modern Large Language 
      Models.
LID - 10.3390/ijms242115858 [doi]
LID - 15858
AB  - The data explosion driven by advancements in genomic research, such as 
      high-throughput sequencing techniques, is constantly challenging conventional 
      methods used in genomics. In parallel with the urgent demand for robust 
      algorithms, deep learning has succeeded in various fields such as vision, speech, 
      and text processing. Yet genomics entails unique challenges to deep learning, 
      since we expect a superhuman intelligence that explores beyond our knowledge to 
      interpret the genome from deep learning. A powerful deep learning model should 
      rely on the insightful utilization of task-specific knowledge. In this paper, we 
      briefly discuss the strengths of different deep learning models from a genomic 
      perspective so as to fit each particular task with proper deep learning-based 
      architecture, and we remark on practical considerations of developing deep 
      learning architectures for genomics. We also provide a concise review of deep 
      learning applications in various aspects of genomic research and point out 
      current challenges and potential research directions for future genomics 
      applications. We believe the collaborative use of ever-growing diverse data and 
      the fast iteration of deep learning models will continue to contribute to the 
      future of genomics.
FAU - Yue, Tianwei
AU  - Yue T
AD  - School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, 
      USA.
FAU - Wang, Yuanxin
AU  - Wang Y
AD  - School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, 
      USA.
FAU - Zhang, Longxiang
AU  - Zhang L
AUID- ORCID: 0009-0009-0111-832X
AD  - School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, 
      USA.
FAU - Gu, Chunming
AU  - Gu C
AUID- ORCID: 0000-0003-3568-2032
AD  - Department of Biomedical Engineering, School of Medicine, Johns Hopkins 
      University, Baltimore, MD 21218, USA.
FAU - Xue, Haoru
AU  - Xue H
AD  - The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
FAU - Wang, Wenping
AU  - Wang W
AUID- ORCID: 0000-0001-9665-3783
AD  - School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, 
      USA.
FAU - Lyu, Qi
AU  - Lyu Q
AD  - Department of Computational Mathematics, Science, and Engineering, Michigan State 
      University, East Lansing, MI 48824, USA.
FAU - Dun, Yujie
AU  - Dun Y
AUID- ORCID: 0000-0001-5213-1000
AD  - School of Information and Communications Engineering, Xi'an Jiaotong University, 
      Xi'an 710049, China.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231101
PL  - Switzerland
TA  - Int J Mol Sci
JT  - International journal of molecular sciences
JID - 101092791
SB  - IM
MH  - *Deep Learning
MH  - Genomics/methods
MH  - Algorithms
MH  - Language
MH  - Intelligence
PMC - PMC10649223
OTO - NOTNLM
OT  - computer vision
OT  - deep learning
OT  - genomics
OT  - large language model
OT  - multi-modal machine learning
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/14 06:42
MHDA- 2023/11/15 06:42
CRDT- 2023/11/14 02:10
PHST- 2023/09/30 00:00 [received]
PHST- 2023/10/24 00:00 [revised]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/11/15 06:42 [medline]
PHST- 2023/11/14 06:42 [pubmed]
PHST- 2023/11/14 02:10 [entrez]
AID - ijms242115858 [pii]
AID - ijms-24-15858 [pii]
AID - 10.3390/ijms242115858 [doi]
PST - epublish
SO  - Int J Mol Sci. 2023 Nov 1;24(21):15858. doi: 10.3390/ijms242115858.

PMID- 38050159
OWN - NLM
STAT- Publisher
LR  - 20231205
IS  - 1473-4257 (Electronic)
IS  - 0306-6800 (Linking)
DP  - 2023 Nov 23
TI  - Open AI meets open notes: surveillance capitalism, patient privacy and online 
      record access.
LID - jme-2023-109574 [pii]
LID - 10.1136/jme-2023-109574 [doi]
AB  - Patient online record access (ORA) is spreading worldwide, and in some countries, 
      including Sweden, and the USA, access is advanced with patients obtaining rapid 
      access to their full records. In the UK context, from 31 October 2023 as part of 
      the new NHS England general practitioner (GP) contract it will be mandatory for 
      GPs to offer ORA to patients aged 16 and older. Patients report many benefits 
      from reading their clinical records including feeling more empowered, better 
      understanding and remembering their treatment plan, and greater awareness about 
      medications including possible adverse effects. However, a variety of indirect 
      evidence suggests these benefits are unlikely to accrue without supplementation 
      from internet-based resources. Using such routes to augment interpretation of the 
      data and notes housed in electronic health records, however, comes with 
      trade-offs in terms of exposing sensitive patient information to internet 
      corporations. Furthermore, increased work burdens on clinicians, including the 
      unique demands of ORA, combined with the easy availability and capability of a 
      new generation of large language model (LLM)-powered chatbots, create a perfect 
      collision course for exposing sensitive patient information to private tech 
      companies. This paper surveys how ORA intersects with internet associated privacy 
      risks and offers a variety of multilevel suggestions for how these risks might be 
      better mitigated.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published 
      by BMJ.
FAU - Blease, Charlotte
AU  - Blease C
AUID- ORCID: 0000-0002-0205-1165
AD  - Women's and Children's Health, Uppsala Universitet, Uppsala, Sweden 
      charlotte.blease@uu.se.
AD  - Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20231123
PL  - England
TA  - J Med Ethics
JT  - Journal of medical ethics
JID - 7513619
SB  - IM
OTO - NOTNLM
OT  - Education
OT  - Ethics- Medical
OT  - Information Technology
OT  - Informed Consent
OT  - Policy
COIS- Competing interests: None declared.
EDAT- 2023/12/05 12:44
MHDA- 2023/12/05 12:44
CRDT- 2023/12/05 01:03
PHST- 2023/09/09 00:00 [received]
PHST- 2023/10/30 00:00 [accepted]
PHST- 2023/12/05 12:44 [medline]
PHST- 2023/12/05 12:44 [pubmed]
PHST- 2023/12/05 01:03 [entrez]
AID - jme-2023-109574 [pii]
AID - 10.1136/jme-2023-109574 [doi]
PST - aheadofprint
SO  - J Med Ethics. 2023 Nov 23:jme-2023-109574. doi: 10.1136/jme-2023-109574.

PMID- 38055323
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231206
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Dec 6
TI  - Performance Comparison of ChatGPT-4 and Japanese Medical Residents in the General 
      Medicine In-Training Examination: Comparison Study.
PG  - e52202
LID - 10.2196/52202 [doi]
AB  - BACKGROUND: The reliability of GPT-4, a state-of-the-art expansive language model 
      specializing in clinical reasoning and medical knowledge, remains largely 
      unverified across non-English languages. OBJECTIVE: This study aims to compare 
      fundamental clinical competencies between Japanese residents and GPT-4 by using 
      the General Medicine In-Training Examination (GM-ITE). METHODS: We used the GPT-4 
      model provided by OpenAI and the GM-ITE examination questions for the years 2020, 
      2021, and 2022 to conduct a comparative analysis. This analysis focused on 
      evaluating the performance of individuals who were concluding their second year 
      of residency in comparison to that of GPT-4. Given the current abilities of 
      GPT-4, our study included only single-choice exam questions, excluding those 
      involving audio, video, or image data. The assessment included 4 categories: 
      general theory (professionalism and medical interviewing), symptomatology and 
      clinical reasoning, physical examinations and clinical procedures, and specific 
      diseases. Additionally, we categorized the questions into 7 specialty fields and 
      3 levels of difficulty, which were determined based on residents' correct 
      response rates. RESULTS: Upon examination of 137 GM-ITE questions in Japanese, 
      GPT-4 scores were significantly higher than the mean scores of residents 
      (residents: 55.8%, GPT-4: 70.1%; P<.001). In terms of specific disciplines, GPT-4 
      scored 23.5 points higher in the "specific diseases," 30.9 points higher in 
      "obstetrics and gynecology," and 26.1 points higher in "internal medicine." In 
      contrast, GPT-4 scores in "medical interviewing and professionalism," "general 
      practice," and "psychiatry" were lower than those of the residents, although this 
      discrepancy was not statistically significant. Upon analyzing scores based on 
      question difficulty, GPT-4 scores were 17.2 points lower for easy problems 
      (P=.007) but were 25.4 and 24.4 points higher for normal and difficult problems, 
      respectively (P<.001). In year-on-year comparisons, GPT-4 scores were 21.7 and 
      21.5 points higher in the 2020 (P=.01) and 2022 (P=.003) examinations, 
      respectively, but only 3.5 points higher in the 2021 examinations (no significant 
      difference). CONCLUSIONS: In the Japanese language, GPT-4 also outperformed the 
      average medical residents in the GM-ITE test, originally designed for them. 
      Specifically, GPT-4 demonstrated a tendency to score higher on difficult 
      questions with low resident correct response rates and those demanding a more 
      comprehensive understanding of diseases. However, GPT-4 scored comparatively 
      lower on questions that residents could readily answer, such as those testing 
      attitudes toward patients and professionalism, as well as those necessitating an 
      understanding of context and communication. These findings highlight the 
      strengths and limitations of artificial intelligence applications in medical 
      education and practice.
CI  - ©Takashi Watari, Soshi Takagi, Kota Sakaguchi, Yuji Nishizaki, Taro Shimizu, Yu 
      Yamamoto, Yasuharu Tokuda. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 06.12.2023.
FAU - Watari, Takashi
AU  - Watari T
AUID- ORCID: 0000-0002-9322-8455
AD  - General Medicine Center, Shimane University Hospital, Izumo, Japan.
AD  - Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, 
      United States.
AD  - Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States.
FAU - Takagi, Soshi
AU  - Takagi S
AUID- ORCID: 0009-0004-3211-1626
AD  - Faculty of Medicine, Shimane University, Izuom, Japan.
FAU - Sakaguchi, Kota
AU  - Sakaguchi K
AUID- ORCID: 0000-0002-5169-6613
AD  - General Medicine Center, Shimane University Hospital, Izumo, Japan.
FAU - Nishizaki, Yuji
AU  - Nishizaki Y
AUID- ORCID: 0000-0002-6964-6702
AD  - Division of Medical Education, Juntendo University School of Medicine, Tokyo, 
      Japan.
FAU - Shimizu, Taro
AU  - Shimizu T
AUID- ORCID: 0000-0002-3788-487X
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University 
      Hospital, Tochigi, Japan.
FAU - Yamamoto, Yu
AU  - Yamamoto Y
AUID- ORCID: 0000-0003-2247-4963
AD  - Division of General Medicine, Center for Community Medicine, Jichi Medical 
      University, Tochigi, Japan.
FAU - Tokuda, Yasuharu
AU  - Tokuda Y
AUID- ORCID: 0000-0002-9325-7934
AD  - Muribushi Okinawa Project for Teaching Hospitals, Okinawa, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231206
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
OTO - NOTNLM
OT  - Asia
OT  - Asian
OT  - ChatGPT
OT  - ChatGPT-4
OT  - GM-ITE
OT  - Japan
OT  - Japanese
OT  - LLM
OT  - NLP
OT  - answer
OT  - answers
OT  - artificial intelligence
OT  - chatbot
OT  - chatbots
OT  - clinical
OT  - clinical training
OT  - conversational agent
OT  - conversational agents
OT  - exam
OT  - examination
OT  - examinations
OT  - exams
OT  - language model
OT  - language models
OT  - medical education
OT  - natural language processing
OT  - non-English language
OT  - performance
OT  - reasoning
OT  - residency programs
OT  - response
OT  - responses
OT  - self-assessment
EDAT- 2023/12/06 12:43
MHDA- 2023/12/06 12:44
CRDT- 2023/12/06 11:54
PHST- 2023/08/25 00:00 [received]
PHST- 2023/11/03 00:00 [accepted]
PHST- 2023/10/22 00:00 [revised]
PHST- 2023/12/06 12:44 [medline]
PHST- 2023/12/06 12:43 [pubmed]
PHST- 2023/12/06 11:54 [entrez]
AID - v9i1e52202 [pii]
AID - 10.2196/52202 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Dec 6;9:e52202. doi: 10.2196/52202.

PMID- 38045763
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Performance analysis of large language models in the domain of legal argument 
      mining.
PG  - 1278796
LID - 10.3389/frai.2023.1278796 [doi]
LID - 1278796
AB  - Generative pre-trained transformers (GPT) have recently demonstrated excellent 
      performance in various natural language tasks. The development of ChatGPT and the 
      recently released GPT-4 model has shown competence in solving complex and 
      higher-order reasoning tasks without further training or fine-tuning. However, 
      the applicability and strength of these models in classifying legal texts in the 
      context of argument mining are yet to be realized and have not been tested 
      thoroughly. In this study, we investigate the effectiveness of GPT-like models, 
      specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely 
      study the model's performance considering diverse prompt formulation and example 
      selection in the prompt via semantic search using state-of-the-art embedding 
      models from OpenAI and sentence transformers. We primarily concentrate on the 
      argument component classification task on the legal corpus from the European 
      Court of Human Rights. To address these models' inherent non-deterministic nature 
      and make our result statistically sound, we conducted 5-fold cross-validation on 
      the test set. Our experiments demonstrate, quite surprisingly, that relatively 
      small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for 
      premise and conclusion classes, with 1.9% and 12% improvements, respectively. We 
      hypothesize that the performance drop indirectly reflects the complexity of the 
      structure in the dataset, which we verify through prompt and data analysis. 
      Nevertheless, our results demonstrate a noteworthy variation in the performance 
      of GPT models based on prompt formulation. We observe comparable performance 
      between the two embedding models, with a slight improvement in the local model's 
      ability for prompt selection. This suggests that local models are as semantically 
      rich as the embeddings from the OpenAI model. Our results indicate that the 
      structure of prompts significantly impacts the performance of GPT models and 
      should be considered when designing them.
CI  - Copyright © 2023 Al Zubaer, Granitzer and Mitrović.
FAU - Al Zubaer, Abdullah
AU  - Al Zubaer A
AD  - Faculty of Computer Science and Mathematics, Chair of Data Science, University of 
      Passau, Passau, Germany.
FAU - Granitzer, Michael
AU  - Granitzer M
AD  - Faculty of Computer Science and Mathematics, Chair of Data Science, University of 
      Passau, Passau, Germany.
FAU - Mitrović, Jelena
AU  - Mitrović J
AD  - Faculty of Computer Science and Mathematics, Chair of Data Science, University of 
      Passau, Passau, Germany.
AD  - Group for Human Computer Interaction, Institute for Artificial Intelligence 
      Research and Development of Serbia, Novi Sad, Serbia.
LA  - eng
PT  - Journal Article
DEP - 20231117
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10691378
OTO - NOTNLM
OT  - ChatGPT
OT  - European Court of Human Rights (ECHR)
OT  - GPT-4
OT  - argument mining
OT  - large language models
OT  - legal data
OT  - natural language processing (NLP)
OT  - sequence classification
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 04:44
PHST- 2023/08/16 00:00 [received]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 04:44 [entrez]
AID - 10.3389/frai.2023.1278796 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Nov 17;6:1278796. doi: 10.3389/frai.2023.1278796. 
      eCollection 2023.

PMID- 38021810
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Assessing the Utilization of Large Language Models in Medical Education: Insights 
      From Undergraduate Medical Students.
PG  - e47468
LID - 10.7759/cureus.47468 [doi]
LID - e47468
AB  - Background Artificial intelligence (AI) has the potential to be integrated into 
      medical education. Among AI-based technology, large language models (LLMs) such 
      as ChatGPT, Google Bard, Microsoft Bing, and Perplexity have emerged as powerful 
      tools with capabilities in natural language processing. With this background, 
      this study investigates the knowledge, attitude, and practice of undergraduate 
      medical students regarding the utilization of LLMs in medical education in a 
      medical college in Jharkhand, India. Methods A cross-sectional online survey was 
      sent to 370 undergraduate medical students on Google Forms. The questionnaire 
      comprised the following three domains: knowledge, attitude, and practice, each 
      containing six questions. Cronbach's alphas for knowledge, attitude, and practice 
      domains were 0.703, 0.707, and 0.809, respectively. Intraclass correlation 
      coefficients for knowledge, attitude, and practice domains were 0.82, 0.87, and 
      0.78, respectively. The average scores in the three domains were compared using 
      ANOVA. Results A total of 172 students participated in the study (response rate: 
      46.49%). The majority of the students (45.93%) rarely used the LLMs for their 
      teaching-learning purposes (chi-square (3) = 41.44, p < 0.0001). The overall 
      score of knowledge (3.21±0.55), attitude (3.47±0.54), and practice (3.26±0.61) 
      were statistically significantly different (ANOVA F (2, 513) = 10.2, p < 0.0001), 
      with the highest score in attitude and lowest in knowledge. Conclusion While 
      there is a generally positive attitude toward the incorporation of LLMs in 
      medical education, concerns about overreliance and potential inaccuracies are 
      evident. LLMs offer the potential to enhance learning resources and provide 
      accessible education, but their integration requires further planning. Further 
      studies are required to explore the long-term impact of LLMs in diverse 
      educational contexts.
CI  - Copyright © 2023, Biri et al.
FAU - Biri, Sairavi Kiran
AU  - Biri SK
AD  - Biochemistry, Phulo Jhano Medical College, Dumka, IND.
FAU - Kumar, Subir
AU  - Kumar S
AD  - Pharmacology, Phulo Jhano Medical College, Dumka, IND.
FAU - Panigrahi, Muralidhar
AU  - Panigrahi M
AD  - Pharmacology, Bhima Bhoi Medical College and Hospital, Balangir, IND.
FAU - Mondal, Shaikat
AU  - Mondal S
AD  - Physiology, Raiganj Government Medical College & Hospital, Raiganj, IND.
FAU - Behera, Joshil Kumar
AU  - Behera JK
AD  - Physiology, Nagaland Institute of Medical Sciences and Research, Kohima, IND.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, IND.
LA  - eng
PT  - Journal Article
DEP - 20231022
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10662537
OTO - NOTNLM
OT  - artificial intelligence
OT  - attitude
OT  - chatgpt
OT  - intelligence
OT  - knowledge
OT  - medical students
OT  - practice
OT  - search engine
OT  - surveys and questionnaires
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:12
PHST- 2023/10/22 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:12 [entrez]
AID - 10.7759/cureus.47468 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 22;15(10):e47468. doi: 10.7759/cureus.47468. eCollection 2023 
      Oct.

PMID- 37692617
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare.
PG  - e43262
LID - 10.7759/cureus.43262 [doi]
LID - e43262
AB  - The integration of artificial intelligence (AI) into healthcare promises 
      groundbreaking advancements in patient care, revolutionizing clinical diagnosis, 
      predictive medicine, and decision-making. This transformative technology uses 
      machine learning, natural language processing, and large language models (LLMs) 
      to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated 
      LLM, holds immense potential in medical practice, research, and education. 
      However, as AI in healthcare gains momentum, it brings forth profound ethical 
      challenges that demand careful consideration. This comprehensive review explores 
      key ethical concerns in the domain, including privacy, transparency, trust, 
      responsibility, bias, and data quality. Protecting patient privacy in data-driven 
      healthcare is crucial, with potential implications for psychological well-being 
      and data sharing. Strategies like homomorphic encryption (HE) and secure 
      multiparty computation (SMPC) are vital to preserving confidentiality. 
      Transparency and trustworthiness of AI systems are essential, particularly in 
      high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical 
      aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity 
      becomes a pressing concern as AI's complexity creates vulnerabilities for 
      potential breaches. Determining responsibility in AI-driven outcomes raises 
      important questions, with debates on AI's moral agency and human accountability. 
      Shifting from data ownership to data stewardship enables responsible data 
      management in compliance with regulations. Addressing bias in healthcare data is 
      crucial to avoid AI-driven inequities. Biases present in data collection and 
      algorithm development can perpetuate healthcare disparities. A public-health 
      approach is advocated to address inequalities and promote diversity in AI 
      research and the workforce. Maintaining data quality is imperative in AI 
      applications, with convolutional neural networks showing promise in 
      multi-input/mixed data models, offering a comprehensive patient perspective. In 
      this ever-evolving landscape, it is imperative to adopt a multidimensional 
      approach involving policymakers, developers, healthcare practitioners, and 
      patients to mitigate ethical concerns. By understanding and addressing these 
      challenges, we can harness the full potential of AI in healthcare while ensuring 
      ethical and equitable outcomes.
CI  - Copyright © 2023, Jeyaraman et al.
FAU - Jeyaraman, Madhan
AU  - Jeyaraman M
AD  - Orthopedics, ACS Medical College and Hospital, Dr. MGR Educational and Research 
      Institute, Chennai, IND.
FAU - Balaji, Sangeetha
AU  - Balaji S
AD  - Orthopedics, Government Medical College, Omandurar Government Estate, Chennai, 
      IND.
FAU - Jeyaraman, Naveen
AU  - Jeyaraman N
AD  - Orthopedics, ACS Medical College and Hospital, Dr. MGR Educational and Research 
      Institute, Chennai, IND.
FAU - Yadav, Sankalp
AU  - Yadav S
AD  - Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230810
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10492220
OTO - NOTNLM
OT  - artificial intelligence (ai)
OT  - chatgpt
OT  - healthcare
OT  - homo-morphic encryption
OT  - large language models
OT  - secure multiparty computation
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 04:41
PHST- 2023/08/10 00:00 [accepted]
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 04:41 [entrez]
AID - 10.7759/cureus.43262 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 10;15(8):e43262. doi: 10.7759/cureus.43262. eCollection 2023 
      Aug.

PMID- 37780992
OWN - NLM
STAT- MEDLINE
DCOM- 20231004
LR  - 20231020
IS  - 1551-4056 (Electronic)
IS  - 0044-0086 (Print)
IS  - 0044-0086 (Linking)
VI  - 96
IP  - 3
DP  - 2023 Sep
TI  - Artificial Intelligence to Improve Patient Understanding of Radiology Reports.
PG  - 407-417
LID - 10.59249/NKOY5498 [doi]
AB  - Diagnostic imaging reports are generally written with a target audience of other 
      providers. As a result, the reports are written with medical jargon and technical 
      detail to ensure accurate communication. With implementation of the 21st Century 
      Cures Act, patients have greater and quicker access to their imaging reports, but 
      these reports are still written above the comprehension level of the average 
      patient. Consequently, many patients have requested reports to be conveyed in 
      language accessible to them. Numerous studies have shown that improving patient 
      understanding of their condition results in better outcomes, so driving 
      comprehension of imaging reports is essential. Summary statements, second 
      reports, and the inclusion of the radiologist's phone number have been proposed, 
      but these solutions have implications for radiologist workflow. Artificial 
      intelligence (AI) has the potential to simplify imaging reports without 
      significant disruptions. Many AI technologies have been applied to radiology 
      reports in the past for various clinical and research purposes, but patient 
      focused solutions have largely been ignored. New natural language processing 
      technologies and large language models (LLMs) have the potential to improve 
      patient understanding of their imaging reports. However, LLMs are a nascent 
      technology and significant research is required before LLM-driven report 
      simplification is used in patient care.
CI  - Copyright ©2023, Yale Journal of Biology and Medicine.
FAU - Amin, Kanhai
AU  - Amin K
AD  - Yale University, New Haven, CT, USA.
FAU - Khosla, Pavan
AU  - Khosla P
AD  - Yale School of Medicine, New Haven, CT, USA.
FAU - Doshi, Rushabh
AU  - Doshi R
AD  - Yale School of Medicine, New Haven, CT, USA.
FAU - Chheang, Sophie
AU  - Chheang S
AD  - Department of Radiology and Biomedical Imaging, Yale School of Medicine, New 
      Haven, CT, USA.
FAU - Forman, Howard P
AU  - Forman HP
AD  - Department of Radiology and Biomedical Imaging, Yale School of Medicine, New 
      Haven, CT, USA.
AD  - Yale School of Management, New Haven, CT, USA.
AD  - Department of Health Policy and Management, Yale School of Public Health, New 
      Haven, CT, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230929
PL  - United States
TA  - Yale J Biol Med
JT  - The Yale journal of biology and medicine
JID - 0417414
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Radiology/methods
MH  - Communication
PMC - PMC10524809
OTO - NOTNLM
OT  - 21st Century Cures Act
OT  - Artificial Intelligence
OT  - Imaging Report
OT  - Large Language Model
OT  - Natural Language Processing
OT  - Radiology Report
EDAT- 2023/10/02 06:42
MHDA- 2023/10/04 06:43
CRDT- 2023/10/02 04:37
PHST- 2023/10/04 06:43 [medline]
PHST- 2023/10/02 06:42 [pubmed]
PHST- 2023/10/02 04:37 [entrez]
AID - yjbm963407 [pii]
AID - 10.59249/NKOY5498 [doi]
PST - epublish
SO  - Yale J Biol Med. 2023 Sep 29;96(3):407-417. doi: 10.59249/NKOY5498. eCollection 
      2023 Sep.

PMID- 38077554
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2376-5992 (Electronic)
IS  - 2376-5992 (Linking)
VI  - 9
DP  - 2023
TI  - PluDG: enhancing task-oriented dialogue system with knowledge graph plug-in 
      module.
PG  - e1707
LID - 10.7717/peerj-cs.1707 [doi]
LID - e1707
AB  - Task-oriented dialogue systems continue to face significant challenges as they 
      require not only an understanding of dialogue history but also domain-specific 
      knowledge. However, knowledge is often dynamic, making it difficult to 
      effectively integrate into the learning process. Existing large language model 
      approaches primarily treat knowledge bases as textual resources, neglecting to 
      capture the underlying relationships between facts within the knowledge base. To 
      address this limitation, we propose a novel dialogue system called PluDG. We 
      regard the knowledge as a knowledge graph and propose a knowledge extraction 
      plug-in, Kg-Plug, to capture the features of the graph and generate prompt 
      entities to assist the system's dialogue generation. Besides, we propose Unified 
      Memory Integration, a module that enhances the comprehension of the sentence's 
      internal structure and optimizes the knowledge base's encoding location. We 
      conduct experiments on three public datasets and compare PluDG with several 
      state-of-the-art dialogue models. The experimental results indicate that PluDG 
      achieves significant improvements in both accuracy and diversity, outperforming 
      the current state-of-the-art dialogue system models and achieving 
      state-of-the-art performance.
CI  - ©2023 Dong and Chen.
FAU - Dong, Xuelian
AU  - Dong X
AD  - School of Computer Science, University of South China, Hunan, China.
FAU - Chen, Jiale
AU  - Chen J
AD  - School of Computer Science, University of South China, Hunan, China.
LA  - eng
PT  - Journal Article
DEP - 20231124
PL  - United States
TA  - PeerJ Comput Sci
JT  - PeerJ. Computer science
JID - 101660598
PMC - PMC10703105
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Data science
OT  - Dialogue systems
OT  - Graph neural networks
OT  - Natural language processing
COIS- The authors declare there are no competing interests.
EDAT- 2023/12/11 12:43
MHDA- 2023/12/11 12:44
CRDT- 2023/12/11 06:32
PHST- 2023/09/08 00:00 [received]
PHST- 2023/10/27 00:00 [accepted]
PHST- 2023/12/11 12:44 [medline]
PHST- 2023/12/11 12:43 [pubmed]
PHST- 2023/12/11 06:32 [entrez]
AID - cs-1707 [pii]
AID - 10.7717/peerj-cs.1707 [doi]
PST - epublish
SO  - PeerJ Comput Sci. 2023 Nov 24;9:e1707. doi: 10.7717/peerj-cs.1707. eCollection 
      2023.

PMID- 37624621
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231102
IS  - 1526-2359 (Electronic)
IS  - 1073-2748 (Print)
IS  - 1073-2748 (Linking)
VI  - 30
DP  - 2023 Jan-Dec
TI  - The Future of AI in Ovarian Cancer Research: The Large Language Models 
      Perspective.
PG  - 10732748231197915
LID - 10.1177/10732748231197915 [doi]
LID - 10732748231197915
AB  - Conversational large language model (LLM)-based chatbots utilize neural networks 
      to process natural language. By generating highly sophisticated outputs from 
      contextual input text, they revolutionize the access to further learning, leading 
      to the development of new skills and personalized interactions. Although they are 
      not developed to provide healthcare, their potential to address biomedical issues 
      is rather unexplored. Healthcare digitalization and documentation of electronic 
      health records is now developing into a standard practice. Developing tools to 
      facilitate clinical review of unstructured data such as LLMs can derive clinical 
      meaningful insights for ovarian cancer, a heterogeneous but devastating disease. 
      Compared to standard approaches, they can host capacity to condense results and 
      optimize analysis time. To help accelerate research in biomedical language 
      processing and improve the validity of scientific writing, task-specific and 
      domain-specific language models may be required. In turn, we propose a bespoke, 
      proprietary ovarian cancer-specific natural language using solely in-domain text, 
      whereas transfer learning drifts away from the pretrained language models to 
      fine-tune task-specific models for all possible downstream applications. This 
      venture will be fueled by the abundance of unstructured text information in the 
      electronic health records resulting in ovarian cancer research ultimately 
      reaching its linguistic home.
FAU - Laios, Alexandros
AU  - Laios A
AUID- ORCID: 0000-0002-4870-7393
AD  - Department of Gynaecologic Oncology, ESGO Center of Excellence for Ovarian Cancer 
      Surgery, St James's University Hospital, Leeds, UK. RINGGOLD: 98540
FAU - Theophilou, Georgios
AU  - Theophilou G
AD  - Department of Gynaecologic Oncology, ESGO Center of Excellence for Ovarian Cancer 
      Surgery, St James's University Hospital, Leeds, UK. RINGGOLD: 98540
FAU - De Jong, Diederick
AU  - De Jong D
AUID- ORCID: 0000-0003-0081-674X
AD  - Department of Gynaecologic Oncology, ESGO Center of Excellence for Ovarian Cancer 
      Surgery, St James's University Hospital, Leeds, UK. RINGGOLD: 98540
FAU - Kalampokis, Evangelos
AU  - Kalampokis E
AD  - Information Systems Lab, Department of Business Administration, University of 
      Macedonia, Thessaloniki, Greece. RINGGOLD: 68999
LA  - eng
PT  - Editorial
PL  - United States
TA  - Cancer Control
JT  - Cancer control : journal of the Moffitt Cancer Center
JID - 9438457
SB  - IM
MH  - Humans
MH  - Female
MH  - *Ovarian Neoplasms/diagnosis
MH  - Language
MH  - Communication
MH  - Electronic Health Records
PMC - PMC10467259
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - GPT-4
OT  - Large Language Models
OT  - Ovarian Cancer
COIS- The author(s) declared no potential conflicts of interest with respect to the 
      research, authorship, and/or publication of this article.
EDAT- 2023/08/25 12:42
MHDA- 2023/08/25 12:43
CRDT- 2023/08/25 11:43
PHST- 2023/08/25 12:43 [medline]
PHST- 2023/08/25 12:42 [pubmed]
PHST- 2023/08/25 11:43 [entrez]
AID - 10.1177_10732748231197915 [pii]
AID - 10.1177/10732748231197915 [doi]
PST - ppublish
SO  - Cancer Control. 2023 Jan-Dec;30:10732748231197915. doi: 
      10.1177/10732748231197915.

PMID- 37539466
OWN - NLM
STAT- Publisher
LR  - 20230809
IS  - 1366-5928 (Electronic)
IS  - 0049-8254 (Linking)
DP  - 2023 Aug 8
TI  - In silico ADME/tox comes of age: twenty years later.
PG  - 1-7
LID - 10.1080/00498254.2023.2245049 [doi]
AB  - In the early 2000s pharmaceutical drug discovery was beginning to use 
      computational approaches for absorption, distribution, metabolism, excretion and 
      toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction 
      was an effort to reduce the risk of later stage failures from ADME/Tox.Much has 
      been written in the intervening twenty plus years and significant expenditure has 
      occurred in companies developing these in silico capabilities which can be 
      gleaned from publications. It is therefore an appropriate time to briefly reflect 
      on what was proposed then and what the reality is today.20 years ago, we tended 
      to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously 
      pharmaceutical companies needed a whole infrastructure for models - in silico and 
      in vitro experts, IT, champions on a project team, educators and management 
      support. Now we are in the age of generative de novo design where bioactivity and 
      many ADME/Tox properties can be optimised and large language model technologies 
      are available.There are also some challenges such as the focus on very large 
      molecules which may be outside of current ADME/Tox models.We provide an 
      opportunity to look forward with the increasing public data for ADME/Tox as well 
      as expanded types of algorithms available.
FAU - Ekins, Sean
AU  - Ekins S
AD  - Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA.
FAU - Lane, Thomas R
AU  - Lane TR
AD  - Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA.
FAU - Urbina, Fabio
AU  - Urbina F
AD  - Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA.
FAU - Puhl, Ana C
AU  - Puhl AC
AD  - Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230808
PL  - England
TA  - Xenobiotica
JT  - Xenobiotica; the fate of foreign compounds in biological systems
JID - 1306665
SB  - IM
OTO - NOTNLM
OT  - ADME/Tox
OT  - de novo
OT  - in silico
EDAT- 2023/08/04 06:43
MHDA- 2023/08/04 06:43
CRDT- 2023/08/04 04:16
PHST- 2023/08/04 06:43 [pubmed]
PHST- 2023/08/04 06:43 [medline]
PHST- 2023/08/04 04:16 [entrez]
AID - 10.1080/00498254.2023.2245049 [doi]
PST - aheadofprint
SO  - Xenobiotica. 2023 Aug 8:1-7. doi: 10.1080/00498254.2023.2245049.

PMID- 37910408
OWN - NLM
STAT- Publisher
LR  - 20231213
IS  - 1941-0506 (Electronic)
IS  - 1077-2626 (Linking)
VI  - PP
DP  - 2023 Nov 1
TI  - Interactive Table Synthesis with Natural Language.
LID - 10.1109/TVCG.2023.3329120 [doi]
AB  - Tables are a ubiquitous data format for insight communication. However, 
      transforming data into consumable tabular views remains a challenging and 
      time-consuming task. To lower the barrier of such a task, research efforts have 
      been devoted to developing interactive approaches for data transformation, but 
      many approaches still presume that their users have considerable knowledge of 
      various data transformation concepts and functions. In this study, we leverage 
      natural language (NL) as the primary interaction modality to improve the 
      accessibility of average users to performing complex data transformation and 
      facilitate intuitive table generation and editing. Designing an NL-driven data 
      transformation approach introduces two challenges: a) NL-driven synthesis of 
      interpretable pipelines and b) incremental refinement of synthesized tables. To 
      address these challenges, we present NL2Rigel, an interactive tool that assists 
      users in synthesizing and improving tables from semi-structured text with NL 
      instructions. Based on a large language model and prompting techniques, NL2Rigel 
      can interpret the given NL instructions into a table synthesis pipeline 
      corresponding to Rigel specifications, a declarative language for tabular data 
      transformation. An intuitive interface is designed to visualize the synthesis 
      pipeline and the generated tables, helping users understand the transformation 
      process and refine the results efficiently with targeted NL instructions. The 
      comprehensiveness of NL2Rigel is demonstrated with an example gallery, and we 
      further confirmed NL2Rigel's usability with a comparative user study by showing 
      that the task completion time with NL2Rigel is significantly shorter than that 
      with the original version of Rigel with comparable completion rates.
FAU - Huang, Yanwei
AU  - Huang Y
FAU - Zhou, Yunfan
AU  - Zhou Y
FAU - Chen, Ran
AU  - Chen R
FAU - Pan, Changhao
AU  - Pan C
FAU - Shu, Xinhuan
AU  - Shu X
FAU - Weng, Di
AU  - Weng D
FAU - Wu, Yingcai
AU  - Wu Y
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - United States
TA  - IEEE Trans Vis Comput Graph
JT  - IEEE transactions on visualization and computer graphics
JID - 9891704
SB  - IM
EDAT- 2023/11/01 18:42
MHDA- 2023/11/01 18:42
CRDT- 2023/11/01 12:43
PHST- 2023/11/01 18:42 [pubmed]
PHST- 2023/11/01 18:42 [medline]
PHST- 2023/11/01 12:43 [entrez]
AID - 10.1109/TVCG.2023.3329120 [doi]
PST - aheadofprint
SO  - IEEE Trans Vis Comput Graph. 2023 Nov 1;PP. doi: 10.1109/TVCG.2023.3329120.

PMID- 37777997
OWN - NLM
STAT- MEDLINE
DCOM- 20231115
LR  - 20231207
IS  - 1552-146X (Electronic)
IS  - 0093-0334 (Linking)
VI  - 53
IP  - 5
DP  - 2023 Sep
TI  - Editors' Statement on the Responsible Use of Generative AI Technologies in 
      Scholarly Journal Publishing.
PG  - 3-6
LID - 10.1002/hast.1507 [doi]
AB  - Generative artificial intelligence (AI) has the potential to transform many 
      aspects of scholarly publishing. Authors, peer reviewers, and editors might use 
      AI in a variety of ways, and those uses might augment their existing work or 
      might instead be intended to replace it. We are editors of bioethics and 
      humanities journals who have been contemplating the implications of this ongoing 
      transformation. We believe that generative AI may pose a threat to the goals that 
      animate our work but could also be valuable for achieving those goals. In the 
      interests of fostering a wider conversation about how generative AI may be used, 
      we have developed a preliminary set of recommendations for its use in scholarly 
      publishing. We hope that the recommendations and rationales set out here will 
      help the scholarly community navigate toward a deeper understanding of the 
      strengths, limits, and challenges of AI for responsible scholarly work.
CI  - © 2023 The Hastings Center.
FAU - Kaebnick, Gregory E
AU  - Kaebnick GE
AD  - Editor of the Hastings Center Report.
FAU - Magnus, David Christopher
AU  - Magnus DC
AD  - Editor in chief of the American Journal of Bioethics.
FAU - Kao, Audiey
AU  - Kao A
AD  - Editor in chief of the AMA Journal of Ethics.
FAU - Hosseini, Mohammad
AU  - Hosseini M
AD  - Associate editors of Accountability in Research.
FAU - Resnik, David
AU  - Resnik D
AD  - Associate editors of Accountability in Research.
FAU - Dubljević, Veljko
AU  - Dubljević V
AD  - Editor in chief of the American Journal of Bioethics-Neuroscience.
FAU - Rentmeester, Christy
AU  - Rentmeester C
AD  - Managing editor of the AMA Journal of Ethics.
FAU - Gordijn, Bert
AU  - Gordijn B
AD  - Co-editor in chief of Medicine, Health Care and Philosophy.
FAU - Cherry, Mark J
AU  - Cherry MJ
AD  - Editor of the Journal of Medicine and Philosophy.
LA  - eng
GR  - Intramural Research Program of the National Institute of Environmental Health 
      Sciences (NIEHS) at the National Institutes of Health (NIH)/
GR  - UL1TR001422/National Center for Advancing Translational Sciences (NCATS)/
GR  - #2043612/National Science Foundation (NSF)/
PT  - Editorial
DEP - 20231001
PL  - United States
TA  - Hastings Cent Rep
JT  - The Hastings Center report
JID - 0410447
SB  - IM
EIN - Hastings Cent Rep. 2023 Dec 7;:. PMID: 38059922
MH  - Humans
MH  - *Publishing
MH  - Editorial Policies
MH  - Scholarly Communication
MH  - Artificial Intelligence
MH  - *Bioethics
OTO - NOTNLM
OT  - ChatGPT
OT  - accountability
OT  - bioethics
OT  - community of scholars
OT  - generative AI
OT  - humanities
OT  - journal publishing
OT  - large language models
OT  - transparency
EDAT- 2023/10/01 18:41
MHDA- 2023/11/15 06:42
CRDT- 2023/10/01 15:02
PHST- 2023/11/15 06:42 [medline]
PHST- 2023/10/01 18:41 [pubmed]
PHST- 2023/10/01 15:02 [entrez]
AID - 10.1002/hast.1507 [doi]
PST - ppublish
SO  - Hastings Cent Rep. 2023 Sep;53(5):3-6. doi: 10.1002/hast.1507. Epub 2023 Oct 1.

PMID- 37162998
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230904
DP  - 2023 Apr 24
TI  - Evaluating Large Language Models on Medical Evidence Summarization.
LID - 2023.04.22.23288967 [pii]
LID - 10.1101/2023.04.22.23288967 [doi]
AB  - Recent advances in large language models (LLMs) have demonstrated remarkable 
      successes in zero- and few-shot performance on various downstream tasks, paving 
      the way for applications in high-stakes domains. In this study, we systematically 
      examine the capabilities and limitations of LLMs, specifically GPT-3.5 and 
      ChatGPT, in performing zero-shot medical evidence summarization across six 
      clinical domains. We conduct both automatic and human evaluations, covering 
      several dimensions of summary quality. Our study has demonstrated that automatic 
      metrics often do not strongly correlate with the quality of summaries. 
      Furthermore, informed by our human evaluations, we define a terminology of error 
      types for medical evidence summarization. Our findings reveal that LLMs could be 
      susceptible to generating factually inconsistent summaries and making overly 
      convincing or uncertain statements, leading to potential harm due to 
      misinformation. Moreover, we find that models struggle to identify the salient 
      information and are more error-prone when summarizing over longer textual 
      contexts.
FAU - Tang, Liyan
AU  - Tang L
AD  - School of Information, The University of Texas at Austin, Austin, TX.
FAU - Sun, Zhaoyi
AU  - Sun Z
AD  - Department of Population Health Sciences, Weill Cornell Medicine, New York, NY.
FAU - Idnay, Betina
AU  - Idnay B
AD  - Department of Biomedical Informatics, Columbia University, New York, NY.
FAU - Nestor, Jordan G
AU  - Nestor JG
AD  - Department of Medicine, Columbia University, New York, NY.
FAU - Soroush, Ali
AU  - Soroush A
AD  - Department of Biomedical Informatics, Columbia University, New York, NY.
FAU - Elias, Pierre A
AU  - Elias PA
AD  - Department of Biomedical Informatics, Columbia University, New York, NY.
FAU - Xu, Ziyang
AU  - Xu Z
AD  - Department of Medicine, Massachusetts General Hospital, Boston, MA.
FAU - Ding, Ying
AU  - Ding Y
AD  - School of Information, The University of Texas at Austin, Austin, TX.
FAU - Durrett, Greg
AU  - Durrett G
AD  - Department of Computer Science, The University of Texas at Austin, Austin, TX.
FAU - Rousseau, Justin
AU  - Rousseau J
AD  - Departments of Population Health and Neurology, Dell Medical School, The 
      University of Texas at Austin, Austin, TX.
FAU - Weng, Chunhua
AU  - Weng C
AD  - Department of Biomedical Informatics, Columbia University, New York, NY.
FAU - Peng, Yifan
AU  - Peng Y
AD  - Department of Population Health Sciences, Weill Cornell Medicine, New York, NY.
LA  - eng
GR  - R00 LM013001/LM/NLM NIH HHS/United States
GR  - R01 LM009886/LM/NLM NIH HHS/United States
PT  - Preprint
DEP - 20230424
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
UIN - NPJ Digit Med. 2023 Aug 24;6(1):158. PMID: 37620423
PMC - PMC10168498
EDAT- 2023/05/10 12:42
MHDA- 2023/05/10 12:43
CRDT- 2023/05/10 11:22
PHST- 2023/05/10 12:43 [medline]
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 11:22 [entrez]
AID - 2023.04.22.23288967 [pii]
AID - 10.1101/2023.04.22.23288967 [doi]
PST - epublish
SO  - medRxiv. 2023 Apr 24:2023.04.22.23288967. doi: 10.1101/2023.04.22.23288967. 
      Preprint.

PMID- 37307079
OWN - NLM
STAT- MEDLINE
DCOM- 20231005
LR  - 20231211
IS  - 1462-0332 (Electronic)
IS  - 1462-0324 (Print)
IS  - 1462-0324 (Linking)
VI  - 62
IP  - 10
DP  - 2023 Oct 3
TI  - AI am a rheumatologist: a practical primer to large language models for 
      rheumatologists.
PG  - 3256-3260
LID - 10.1093/rheumatology/kead291 [doi]
AB  - Natural language processing (NLP), a subclass of artificial intelligence, large 
      language models (LLMs), and its latest applications, such as Generative 
      Pre-trained Transformers (GPT), ChatGPT, or LLAMA, have recently become one of 
      the most discussed topics. Up to now, artificial intelligence and NLP ultimately 
      impacted several areas, such as finance, economics and diagnostic/scoring systems 
      in healthcare. Another area that artificial intelligence has affected and will 
      continue to affect increasingly is academic life. This narrative review will 
      define NLP, LLMs and their applications, discuss the opportunities and challenges 
      that components of academic society will experience in rheumatology, and discuss 
      the impact of NLP and LLMs in rheumatology healthcare.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      British Society for Rheumatology.
FAU - Venerito, Vincenzo
AU  - Venerito V
AUID- ORCID: 0000-0002-2573-5930
AD  - Rheumatology Unit, Department of Precision and Regenerative Medicine-Ionian Area, 
      University of Bari 'Aldo Moro', Bari, Italy.
FAU - Bilgin, Emre
AU  - Bilgin E
AUID- ORCID: 0000-0002-2260-4660
AD  - Division of Rheumatology, Department of Internal Medicine, Hacettepe University 
      Faculty of Medicine, Ankara, Turkey.
FAU - Iannone, Florenzo
AU  - Iannone F
AUID- ORCID: 0000-0003-0474-5344
AD  - Rheumatology Unit, Department of Precision and Regenerative Medicine-Ionian Area, 
      University of Bari 'Aldo Moro', Bari, Italy.
FAU - Kiraz, Sedat
AU  - Kiraz S
AUID- ORCID: 0000-0003-2802-6061
AD  - Division of Rheumatology, Department of Internal Medicine, Hacettepe University 
      Faculty of Medicine, Ankara, Turkey.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - England
TA  - Rheumatology (Oxford)
JT  - Rheumatology (Oxford, England)
JID - 100883501
SB  - IM
CIN - Rheumatology (Oxford). 2023 Dec 1;62(12):e353. PMID: 37594746
MH  - Humans
MH  - *Rheumatologists
MH  - Artificial Intelligence
MH  - *Rheumatology
MH  - Natural Language Processing
PMC - PMC10547503
OTO - NOTNLM
OT  - Natural language processing (NLP)
OT  - academia
OT  - challenges
OT  - generative pre-trained transformers (GPT)
OT  - large language models (LLMs)
OT  - opportunities
OT  - rheumatology
EDAT- 2023/06/12 13:07
MHDA- 2023/10/05 06:43
CRDT- 2023/06/12 12:03
PHST- 2023/04/14 00:00 [received]
PHST- 2023/06/06 00:00 [accepted]
PHST- 2023/10/05 06:43 [medline]
PHST- 2023/06/12 13:07 [pubmed]
PHST- 2023/06/12 12:03 [entrez]
AID - 7194150 [pii]
AID - kead291 [pii]
AID - 10.1093/rheumatology/kead291 [doi]
PST - ppublish
SO  - Rheumatology (Oxford). 2023 Oct 3;62(10):3256-3260. doi: 
      10.1093/rheumatology/kead291.

PMID- 37777979
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231127
IS  - 2578-2363 (Electronic)
IS  - 2578-2355 (Linking)
VI  - 45
IP  - 5
DP  - 2023 Sep-Oct
TI  - Editors' Statement on the Responsible Use of Generative AI Technologies in 
      Scholarly Journal Publishing.
PG  - 39-43
LID - 10.1002/eahr.500182 [doi]
AB  - Generative artificial intelligence (AI) has the potential to transform many 
      aspects of scholarly publishing. Authors, peer reviewers, and editors might use 
      AI in a variety of ways, and those uses might augment their existing work or 
      might instead be intended to replace it. We are editors of bioethics and 
      humanities journals who have been contemplating the implications of this ongoing 
      transformation. We believe that generative AI may pose a threat to the goals that 
      animate our work but could also be valuable for achieving those goals. In the 
      interests of fostering a wider conversation about how generative AI may be used, 
      we have developed a preliminary set of recommendations for its use in scholarly 
      publishing. We hope that the recommendations and rationales set out here will 
      help the scholarly community navigate toward a deeper understanding of the 
      strengths, limits, and challenges of AI for responsible scholarly work.
CI  - © 2023 by The Hastings Center. All rights reserved.
FAU - Kaebnick, Gregory E
AU  - Kaebnick GE
AD  - Editor of the Hastings Center Report.
FAU - Magnus, David Christopher
AU  - Magnus DC
AD  - Editor in chief of the American Journal of Bioethics.
FAU - Kao, Audiey
AU  - Kao A
AD  - Editor in chief of the AMA Journal of Ethics.
FAU - Hosseini, Mohammad
AU  - Hosseini M
AD  - Associate editors of Accountability in Research.
FAU - Resnik, David
AU  - Resnik D
AD  - Associate editors of Accountability in Research.
FAU - Dubljević, Veljko
AU  - Dubljević V
AD  - Editor in chief of the American Journal of Bioethics-Neuroscience.
FAU - Rentmeester, Christy
AU  - Rentmeester C
AD  - Managing editor of the AMA Journal of Ethics.
FAU - Gordijn, Bert
AU  - Gordijn B
AD  - Co-editor in chief of Medicine, Health Care and Philosophy.
FAU - Cherry, Mark J
AU  - Cherry MJ
AD  - Editor of the Journal of Medicine and Philosophy.
FAU - Maschke, Karen J
AU  - Maschke KJ
AD  - Editor of Ethics & Human Research.
FAU - McMillan, John
AU  - McMillan J
AD  - Editor in chief of the Journal of Medical Ethics.
FAU - Rasmussen, Lisa M
AU  - Rasmussen LM
AD  - Editor in chief of Accountability in Research.
FAU - Haupt, Laura
AU  - Haupt L
AD  - Managing editor of the Hastings Center Report and Ethics & Human Research.
FAU - Schüklenk, Udo
AU  - Schüklenk U
AD  - Joint editor in chief of Bioethics and of Developing World Bioethics.
FAU - Chadwick, Ruth
AU  - Chadwick R
AD  - Joint editor in chief of Bioethics and the commissioning editor for ethics of the 
      British Medical Bulletin.
FAU - Diniz, Debora
AU  - Diniz D
AD  - Joint editor in chief of Developing World Bioethics.
LA  - eng
GR  - Intramural Research Program of the National Institute of Environmental Health 
      Sciences (NIEHS) at the National Institutes of Health (NIH)/
GR  - UL1TR001422/National Center for Advancing Translational Sciences (NCATS)/
GR  - #2043612/National Science Foundation (NSF)/
PT  - Editorial
PL  - United States
TA  - Ethics Hum Res
JT  - Ethics & human research
JID - 101738005
SB  - IM
EIN - Ethics Hum Res. 2023 Nov 27;:. PMID: 38011609
MH  - Humans
MH  - *Publishing
MH  - *Editorial Policies
MH  - Scholarly Communication
MH  - Artificial Intelligence
MH  - Technology
OTO - NOTNLM
OT  - ChatGPT
OT  - LLM
OT  - accountability
OT  - bioethics
OT  - community of scholars
OT  - generative AI
OT  - humanities
OT  - journal publishing
OT  - transparency
EDAT- 2023/10/01 18:41
MHDA- 2023/10/01 18:42
CRDT- 2023/10/01 15:01
PHST- 2023/10/01 18:42 [medline]
PHST- 2023/10/01 18:41 [pubmed]
PHST- 2023/10/01 15:01 [entrez]
AID - 10.1002/eahr.500182 [doi]
PST - ppublish
SO  - Ethics Hum Res. 2023 Sep-Oct;45(5):39-43. doi: 10.1002/eahr.500182.

PMID- 37362116
OWN - NLM
STAT- Publisher
LR  - 20230701
IS  - 2231-3796 (Print)
IS  - 0973-7707 (Electronic)
IS  - 2231-3796 (Linking)
VI  - 75
IP  - 3
DP  - 2023 Apr 26
TI  - The Urgent Need for Healthcare Workforce Upskilling and Ethical Considerations in 
      the Era of AI-Assisted Medicine.
PG  - 1-2
LID - 10.1007/s12070-023-03755-9 [doi]
AB  - This letter is in response to the article "Enhancing India's Health Care during 
      COVID Era: Role of Artificial Intelligence and Algorithms". While the integration 
      of AI has the potential to improve patient outcomes and reduce the workload of 
      healthcare professionals, there is a need for significant training and upskilling 
      of healthcare providers. There are ethical and privacy concerns related to the 
      use of AI in healthcare, which must be accompanied by rigorous guidelines. One 
      solution to the overburdened healthcare systems in India is the use of new 
      language generation models like ChatGPT to assist healthcare workers in writing 
      discharge summaries. By using these technologies responsibly, we can improve 
      healthcare outcomes and alleviate the burden on overworked healthcare 
      professionals.
CI  - © Association of Otolaryngologists of India 2023.
FAU - Rao, Divya
AU  - Rao D
AUID- ORCID: 0000-0001-8370-4959
AD  - 576104 Manipal, India Department of Information and Communication Technology, 
      Manipal Institute of Technology, Manipal Academy of Higher Education. GRID: 
      grid.411639.8. ISNI: 0000 0001 0571 5193
LA  - eng
PT  - Journal Article
DEP - 20230426
PL  - India
TA  - Indian J Otolaryngol Head Neck Surg
JT  - Indian journal of otolaryngology and head and neck surgery : official publication 
      of the Association of Otolaryngologists of India
JID - 9422551
PMC - PMC10132410
OTO - NOTNLM
OT  - Covid-19
OT  - Data Privacy
OT  - Ethical Considerations
OT  - Healthcare workforce
OT  - India
EDAT- 2023/06/26 19:07
MHDA- 2023/06/26 19:07
CRDT- 2023/06/26 13:01
PHST- 2023/03/27 00:00 [received]
PHST- 2023/03/30 00:00 [accepted]
PHST- 2023/06/26 19:07 [medline]
PHST- 2023/06/26 19:07 [pubmed]
PHST- 2023/06/26 13:01 [entrez]
AID - 3755 [pii]
AID - 10.1007/s12070-023-03755-9 [doi]
PST - aheadofprint
SO  - Indian J Otolaryngol Head Neck Surg. 2023 Apr 26;75(3):1-2. doi: 
      10.1007/s12070-023-03755-9.

PMID- 37128519
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230503
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - A Case of Delusional Disorder With Abuse of Isoniazid, Rifampicin, Pyrazinamide, 
      and Ethambutol, the First-Line Anti-tuberculosis Therapy Drugs in India.
PG  - e36893
LID - 10.7759/cureus.36893 [doi]
LID - e36893
AB  - Tuberculosis (TB) and mental illnesses frequently coexist and are both extremely 
      common worldwide. Through the National Program for Elimination of Tuberculosis 
      (NTEP), anti-tuberculosis therapy (ATT) medications are used to treat 
      tuberculosis in India. We want to report a case 45-year-old patient from the 
      state of Andhra Pradesh, India with comorbid delusional disorder leading to daily 
      ATT drug consumption for the past 20 years. This unusual presentation 
      demonstrates that abuse of a Schedule "H" substance like ATT is also conceivable. 
      To stop "Off-label" purchases, strict measures must be taken. Before beginning 
      ATT, evaluating the patient's mental health may be a wise move.
CI  - Copyright © 2023, Sathiyamoorthi et al.
FAU - Sathiyamoorthi, Sathiyanarayanan
AU  - Sathiyamoorthi S
AD  - Department of Community and Family Medicine, All India Institute of Medical 
      Sciences, Mangalagiri, IND.
FAU - Pentapati, Siva Santosh Kumar
AU  - Pentapati SSK
AD  - Department of Community and Family Medicine, All India Institute of Medical 
      Sciences, Mangalagiri, IND.
FAU - Vullanki, Sai Sreeja
AU  - Vullanki SS
AD  - Department of Psychiatry, All India Institute of Medical Sciences, Mangalagiri, 
      IND.
FAU - Avula, Vijaya Chandra Reddy
AU  - Avula VCR
AD  - Department of Psychiatry, All India Institute of Medical Sciences, Mangalagiri, 
      IND.
FAU - Aravindakshan, Rajeev
AU  - Aravindakshan R
AD  - Department of Community and Family Medicine, All India Institute of Medical 
      Sciences, Mangalagiri, IND.
LA  - eng
PT  - Case Reports
DEP - 20230329
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10147993
OTO - NOTNLM
OT  - abuse
OT  - anti-tuberculosis therapy
OT  - chatgpt
OT  - delusional disorder
OT  - isoniazid
OT  - rifampicin
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/02 06:42
MHDA- 2023/05/02 06:43
CRDT- 2023/05/02 01:58
PHST- 2023/03/28 00:00 [accepted]
PHST- 2023/05/02 06:43 [medline]
PHST- 2023/05/02 06:42 [pubmed]
PHST- 2023/05/02 01:58 [entrez]
AID - 10.7759/cureus.36893 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 29;15(3):e36893. doi: 10.7759/cureus.36893. eCollection 2023 
      Mar.

PMID- 37620423
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231121
IS  - 2398-6352 (Electronic)
IS  - 2398-6352 (Linking)
VI  - 6
IP  - 1
DP  - 2023 Aug 24
TI  - Evaluating large language models on medical evidence summarization.
PG  - 158
LID - 10.1038/s41746-023-00896-7 [doi]
LID - 158
AB  - Recent advances in large language models (LLMs) have demonstrated remarkable 
      successes in zero- and few-shot performance on various downstream tasks, paving 
      the way for applications in high-stakes domains. In this study, we systematically 
      examine the capabilities and limitations of LLMs, specifically GPT-3.5 and 
      ChatGPT, in performing zero-shot medical evidence summarization across six 
      clinical domains. We conduct both automatic and human evaluations, covering 
      several dimensions of summary quality. Our study demonstrates that automatic 
      metrics often do not strongly correlate with the quality of summaries. 
      Furthermore, informed by our human evaluations, we define a terminology of error 
      types for medical evidence summarization. Our findings reveal that LLMs could be 
      susceptible to generating factually inconsistent summaries and making overly 
      convincing or uncertain statements, leading to potential harm due to 
      misinformation. Moreover, we find that models struggle to identify the salient 
      information and are more error-prone when summarizing over longer textual 
      contexts.
CI  - © 2023. Springer Nature Limited.
FAU - Tang, Liyan
AU  - Tang L
AD  - School of Information, The University of Texas at Austin, Austin, TX, USA.
FAU - Sun, Zhaoyi
AU  - Sun Z
AUID- ORCID: 0009-0003-8197-1465
AD  - Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 
      USA.
FAU - Idnay, Betina
AU  - Idnay B
AUID- ORCID: 0000-0002-4318-5987
AD  - Department of Biomedical Informatics, Columbia University, New York, NY, USA.
FAU - Nestor, Jordan G
AU  - Nestor JG
AUID- ORCID: 0000-0003-1418-3103
AD  - Department of Medicine, Columbia University, New York, NY, USA.
FAU - Soroush, Ali
AU  - Soroush A
AUID- ORCID: 0000-0001-6900-5596
AD  - Department of Medicine, Columbia University, New York, NY, USA.
FAU - Elias, Pierre A
AU  - Elias PA
AUID- ORCID: 0000-0002-9643-3024
AD  - Department of Biomedical Informatics, Columbia University, New York, NY, USA.
FAU - Xu, Ziyang
AU  - Xu Z
AD  - Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
FAU - Ding, Ying
AU  - Ding Y
AD  - School of Information, The University of Texas at Austin, Austin, TX, USA.
FAU - Durrett, Greg
AU  - Durrett G
AD  - Department of Computer Science, The University of Texas at Austin, Austin, TX, 
      USA.
FAU - Rousseau, Justin F
AU  - Rousseau JF
AUID- ORCID: 0000-0002-2817-9124
AD  - Departments of Population Health and Neurology, Dell Medical School, The 
      University of Texas at Austin, Austin, TX, USA. 
      justin.rousseau@utsouthwestern.edu.
AD  - Department of Neurology, University of Texas Southwestern Medical Center, Dallas, 
      TX, USA. justin.rousseau@utsouthwestern.edu.
FAU - Weng, Chunhua
AU  - Weng C
AUID- ORCID: 0000-0002-9624-0214
AD  - Department of Biomedical Informatics, Columbia University, New York, NY, USA. 
      cw2384@cumc.columbia.edu.
FAU - Peng, Yifan
AU  - Peng Y
AUID- ORCID: 0000-0001-9309-8331
AD  - Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 
      USA. yip4002@med.cornell.edu.
LA  - eng
GR  - R01 LM014306/LM/NLM NIH HHS/United States
GR  - R00 LM013001/LM/NLM NIH HHS/United States
GR  - R01 LM009886/LM/NLM NIH HHS/United States
GR  - KL2 TR001874/TR/NCATS NIH HHS/United States
GR  - P30 CA013696/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20230824
PL  - England
TA  - NPJ Digit Med
JT  - NPJ digital medicine
JID - 101731738
UOF - medRxiv. 2023 Apr 24;:. PMID: 37162998
PMC - PMC10449915
COIS- The authors declare no competing interests.
EDAT- 2023/08/25 00:42
MHDA- 2023/08/25 00:43
CRDT- 2023/08/24 23:27
PHST- 2023/05/25 00:00 [received]
PHST- 2023/08/03 00:00 [accepted]
PHST- 2023/08/25 00:43 [medline]
PHST- 2023/08/25 00:42 [pubmed]
PHST- 2023/08/24 23:27 [entrez]
AID - 10.1038/s41746-023-00896-7 [pii]
AID - 896 [pii]
AID - 10.1038/s41746-023-00896-7 [doi]
PST - epublish
SO  - NPJ Digit Med. 2023 Aug 24;6(1):158. doi: 10.1038/s41746-023-00896-7.

PMID- 37971399
OWN - NLM
STAT- Publisher
LR  - 20231116
IS  - 1537-6591 (Electronic)
IS  - 1058-4838 (Linking)
DP  - 2023 Nov 16
TI  - Black Box Warning: Large Language Models and the Future of Infectious Diseases 
      Consultation.
LID - ciad633 [pii]
LID - 10.1093/cid/ciad633 [doi]
AB  - Large language models (LLMs) are artificial intelligence systems trained by deep 
      learning algorithms to process natural language and generate text responses to 
      user prompts. Some approach physician performance on a range of medical 
      challenges, leading some proponents to advocate for their potential use in 
      clinical consultation and prompting some consternation about the future of 
      cognitive specialties. However, LLMs currently have limitations that preclude 
      safe clinical deployment in performing specialist consultations, including 
      frequent confabulations, lack of contextual awareness crucial for nuanced 
      diagnostic and treatment plans, inscrutable and unexplainable training data and 
      methods, and propensity to recapitulate biases. Nonetheless, considering the 
      rapid improvement in this technology, growing calls for clinical integration, and 
      healthcare systems that chronically undervalue cognitive specialties, it is 
      critical that infectious diseases clinicians engage with LLMs to enable informed 
      advocacy for how they should-and shouldn't-be used to augment specialist care.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of 
      Infectious Diseases Society of America.
FAU - Schwartz, Ilan S
AU  - Schwartz IS
AUID- ORCID: 0000-0002-7522-0281
AD  - Division of Infectious Diseases, Department of Medicine, Duke University School 
      of Medicine, Durham, North Carolina, USA.
FAU - Link, Katherine E
AU  - Link KE
AD  - Department of Medical Education, Icahn School of Medicine at Mount Sinai, 
      NewYork, New York, USA.
AD  - Healthcare & Life Sciences Division, Hugging Face, Brooklyn, NewYork, USA.
FAU - Daneshjou, Roxana
AU  - Daneshjou R
AD  - Department of Dermatology, Stanford School of Medicine, Stanford, California, 
      USA.
AD  - Department of Biomedical Data Science, Stanford School of Medicine, Stanford, 
      California, USA.
FAU - Cortés-Penfield, Nicolás
AU  - Cortés-Penfield N
AD  - Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, 
      Nebraska, USA.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - United States
TA  - Clin Infect Dis
JT  - Clinical infectious diseases : an official publication of the Infectious Diseases 
      Society of America
JID - 9203213
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - chatbot
OT  - natural language processing
OT  - workforce
COIS- Potential conflicts of interest. I. S. S. reports an unpaid role as the Secretary 
      for the Mycoses Study Group Education & Research Consortium Board of Directors, 
      unrelated to this work. K. E. L. reports institutional grants from the 
      Neurosurgical Research & Educational Foundation (NREF) and the Radiological 
      Society of North America (RSNA); is a former employment at NYU Langone; had a 
      former internship at Google, Inc; holds a patent (16/902422) related to 
      personalized questionnaire development, a pending patent (16/891757) related to 
      understanding the underlying relationships between mental health symptoms, and a 
      pending patent (17/007193) related to processing electroencephalography data; and 
      is currently an employee of Hugging Face, a platform for open-source machine 
      learning, for which they receive stock options. R. D. has received personal fees 
      from DWA, Pfizer, L’Oreal, and VisualDx; stock options from MDAlgorithms and 
      Revea; a role on the American Academy of Dermatology Al Committee; holds a patent 
      pending for TrueImage (17/937714), a machine-learning algorithm for detecting 
      skin disease; and support from grant number 5T32AR007422-38, all outside the 
      submitted work. N.C.-P. reports no potential conflicts. All authors have 
      submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. 
      Conflicts that the editors consider relevant to the content of the manuscript 
      have been disclosed.
EDAT- 2023/11/17 15:32
MHDA- 2023/11/17 15:32
CRDT- 2023/11/16 10:23
PHST- 2023/05/27 00:00 [received]
PHST- 2023/11/17 15:32 [medline]
PHST- 2023/11/17 15:32 [pubmed]
PHST- 2023/11/16 10:23 [entrez]
AID - 7424520 [pii]
AID - 10.1093/cid/ciad633 [doi]
PST - aheadofprint
SO  - Clin Infect Dis. 2023 Nov 16:ciad633. doi: 10.1093/cid/ciad633.

PMID- 37848247
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20231103
LR  - 20231108
IS  - 1943-0264 (Electronic)
IS  - 1943-0264 (Linking)
VI  - 15
IP  - 11
DP  - 2023 Nov 1
TI  - Exploring the Protein Sequence Space with Global Generative Models.
LID - 10.1101/cshperspect.a041471 [doi]
LID - a041471
AB  - Recent advancements in specialized large-scale architectures for training images 
      and language have profoundly impacted the field of computer vision and natural 
      language processing (NLP). Language models, such as the recent ChatGPT and GPT-4, 
      have demonstrated exceptional capabilities in processing, translating, and 
      generating human language. These breakthroughs have also been reflected in 
      protein research, leading to the rapid development of numerous new methods in a 
      short time, with unprecedented performance. Several of these models have been 
      developed with the goal of generating sequences in novel regions of the protein 
      space. In this work, we provide an overview of the use of protein generative 
      models, reviewing (1) language models for the design of novel artificial 
      proteins, (2) works that use non-transformer architectures, and (3) applications 
      in directed evolution approaches.
CI  - Copyright © 2023 Cold Spring Harbor Laboratory Press; all rights reserved.
FAU - Romero-Romero, Sergio
AU  - Romero-Romero S
AUID- ORCID: 0000-0003-2144-7912
AD  - Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany.
FAU - Lindner, Sebastian
AU  - Lindner S
AUID- ORCID: 0009-0009-8251-2758
AD  - University of Heidelberg, 69047 Heidelberg, Germany.
FAU - Ferruz, Noelia
AU  - Ferruz N
AUID- ORCID: 0000-0003-4172-8201
AD  - Barcelona Institute of Molecular Biology, 08028 Barcelona, Spain 
      noelia.ferruz@ibmb.csic.es.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231101
PL  - United States
TA  - Cold Spring Harb Perspect Biol
JT  - Cold Spring Harbor perspectives in biology
JID - 101513680
SB  - IM
PMC - PMC10626256
EDAT- 2023/10/18 00:43
MHDA- 2023/10/18 00:44
PMCR- 2025/11/01
CRDT- 2023/10/17 20:52
PHST- 2025/11/01 00:00 [pmc-release]
PHST- 2023/10/18 00:44 [medline]
PHST- 2023/10/18 00:43 [pubmed]
PHST- 2023/10/17 20:52 [entrez]
AID - cshperspect.a041471 [pii]
AID - a041471 [pii]
AID - 10.1101/cshperspect.a041471 [doi]
PST - epublish
SO  - Cold Spring Harb Perspect Biol. 2023 Nov 1;15(11):a041471. doi: 
      10.1101/cshperspect.a041471.

PMID- 37071279
OWN - NLM
STAT- MEDLINE
DCOM- 20230615
LR  - 20230615
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 7
DP  - 2023 Jul
TI  - The Potential of GPT-4 as an AI-Powered Virtual Assistant for Surgeons 
      Specialized in Joint Arthroplasty.
PG  - 1366-1370
LID - 10.1007/s10439-023-03207-z [doi]
AB  - The emergence of artificial intelligence (AI) offers unprecedented opportunities 
      for joint arthroplasty surgery. Notably, on 14th March, 2023, the OpenAI company 
      officially launched its latest version GPT-4, which once again become the focus 
      of discussion on social media. Although more than 200 articles have reported the 
      potential role of ChatGPT/GPT-4 in various areas, there are no studies that 
      discussed the potential of GPT-4 as an AI-powered virtual assistant for surgeons 
      specialized in joint arthroplasty. In this study, we summarized the five major 
      roles of GPT-4 including scientific research, disease diagnosis, treatment 
      options, preoperative planning, intraoperative support, and postoperative 
      rehabilitation for arthroplasty doctors. Of note, in parallel to enjoy AI 
      dividend, it is also necessary to pay attention to protect the data from misuse 
      with ethical considerations in place.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China.
FAU - Li, Zhiyong
AU  - Li Z
AD  - Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, 
      Tianjin, China.
FAU - Li, Cheng
AU  - Li C
AD  - Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical 
      College of Peking University, Beijing, China.
FAU - Xie, Ruijie
AU  - Xie R
AD  - Department of Hand and Microsurgery, Hengyang Medical School, The Affiliated 
      Nanhua Hospital, University of South China Hengyang, Hengyang, China.
FAU - Guo, Qiang
AU  - Guo Q
AD  - Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, 
      Tianjin, China.
FAU - He, Yongbin
AU  - He Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China. heyongbin012@163.com.
AD  - University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 
      heyongbin012@163.com.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Graduate School, Tianjin Medical University, Tianjin, China. 
      wuhaiyang2021@tmu.edu.cn.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      NC, USA. wuhaiyang2021@tmu.edu.cn.
LA  - eng
GR  - 2022M720385/China Postdoctoral Science Foundation/
PT  - Letter
DEP - 20230418
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Surgeons
MH  - Arthroplasty
OTO - NOTNLM
OT  - Arthroplasty
OT  - Artificial intelligence
OT  - Assistant
OT  - Chatbot
OT  - GPT-4
EDAT- 2023/04/19 06:00
MHDA- 2023/06/15 06:42
CRDT- 2023/04/18 11:23
PHST- 2023/04/09 00:00 [received]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/06/15 06:42 [medline]
PHST- 2023/04/19 06:00 [pubmed]
PHST- 2023/04/18 11:23 [entrez]
AID - 10.1007/s10439-023-03207-z [pii]
AID - 10.1007/s10439-023-03207-z [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Jul;51(7):1366-1370. doi: 10.1007/s10439-023-03207-z. Epub 
      2023 Apr 18.

PMID- 37115365
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20231211
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 8
DP  - 2023 Aug
TI  - Exploring the Potential of GPT-4 in Biomedical Engineering: The Dawn of a New 
      Era.
PG  - 1645-1653
LID - 10.1007/s10439-023-03221-1 [doi]
AB  - Biomedical engineering is a relatively young interdisciplinary field based on 
      engineering, biology, and medicine. Of note, the rapid progress of artificial 
      intelligence (AI)-based technologies has made a significant impact on the 
      biomedical engineering field, and continuously bring innovations and 
      breakthroughs. Recently, ChatGPT, an AI chatbot developed by OpenAI company, has 
      gained tremendous attention due to its powerful natural language generation and 
      understanding ability. In this study, we explored potential of GPT-4 in the eight 
      branches of biomedical engineering including medical imaging, medical devices, 
      bioinformatics, biomaterials, biomechanics, gene and cell engineering, tissue 
      engineering, and neural engineering. Our results show that the application of 
      GPT-4 will bring new opportunities for the development of this field.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Cheng, Kunming
AU  - Cheng K
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China.
FAU - Guo, Qiang
AU  - Guo Q
AD  - Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, 
      Tianjin, China.
FAU - He, Yongbin
AU  - He Y
AD  - School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 
      China.
FAU - Lu, Yanqiu
AU  - Lu Y
AD  - Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou 
      University, Zhengzhou, Henan, China. yanqiul@163.com.
FAU - Gu, Shuqin
AU  - Gu S
AD  - Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC, USA. 
      shuqin.gu@duke.edu.
FAU - Wu, Haiyang
AU  - Wu H
AD  - Department of Graduate School, Tianjin Medical University, Tianjin, China. 
      wuhaiyang2021@tmu.edu.cn.
AD  - Department of Spine Surgery, Tianjin Huanhu Hospital, Tianjin Medical University, 
      Tianjin, China. wuhaiyang2021@tmu.edu.cn.
AD  - Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, 
      NC, USA. wuhaiyang2021@tmu.edu.cn.
LA  - eng
PT  - Letter
DEP - 20230428
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
RN  - 0 (Biocompatible Materials)
SB  - IM
MH  - *Biomedical Engineering
MH  - *Artificial Intelligence
MH  - Bioengineering
MH  - Tissue Engineering
MH  - Biocompatible Materials
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Biomedical engineering
OT  - Chatbot
OT  - GPT-4
OT  - New era
EDAT- 2023/04/28 12:43
MHDA- 2023/07/10 06:42
CRDT- 2023/04/28 11:10
PHST- 2023/04/23 00:00 [received]
PHST- 2023/04/24 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/04/28 12:43 [pubmed]
PHST- 2023/04/28 11:10 [entrez]
AID - 10.1007/s10439-023-03221-1 [pii]
AID - 10.1007/s10439-023-03221-1 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Aug;51(8):1645-1653. doi: 10.1007/s10439-023-03221-1. Epub 
      2023 Apr 28.

PMID- 37665620
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230921
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Sep 4
TI  - Artificial Intelligence in Medical Education: Comparative Analysis of ChatGPT, 
      Bing, and Medical Students in Germany.
PG  - e46482
LID - 10.2196/46482 [doi]
LID - e46482
AB  - BACKGROUND: Large language models (LLMs) have demonstrated significant potential 
      in diverse domains, including medicine. Nonetheless, there is a scarcity of 
      studies examining their performance in medical examinations, especially those 
      conducted in languages other than English, and in direct comparison with medical 
      students. Analyzing the performance of LLMs in state medical examinations can 
      provide insights into their capabilities and limitations and evaluate their 
      potential role in medical education and examination preparation. OBJECTIVE: This 
      study aimed to assess and compare the performance of 3 LLMs, GPT-4, Bing, and 
      GPT-3.5-Turbo, in the German Medical State Examinations of 2022 and to evaluate 
      their performance relative to that of medical students. METHODS: The LLMs were 
      assessed on a total of 630 questions from the spring and fall German Medical 
      State Examinations of 2022. The performance was evaluated with and without 
      media-related questions. Statistical analyses included 1-way ANOVA and 
      independent samples t tests for pairwise comparisons. The relative strength of 
      the LLMs in comparison with that of the students was also evaluated. RESULTS: 
      GPT-4 achieved the highest overall performance, correctly answering 88.1% of 
      questions, closely followed by Bing (86.0%) and GPT-3.5-Turbo (65.7%). The 
      students had an average correct answer rate of 74.6%. Both GPT-4 and Bing 
      significantly outperformed the students in both examinations. When media 
      questions were excluded, Bing achieved the highest performance of 90.7%, closely 
      followed by GPT-4 (90.4%), while GPT-3.5-Turbo lagged (68.2%). There was a 
      significant decline in the performance of GPT-4 and Bing in the fall 2022 
      examination, which was attributed to a higher proportion of media-related 
      questions and a potential increase in question difficulty. CONCLUSIONS: LLMs, 
      particularly GPT-4 and Bing, demonstrate potential as valuable tools in medical 
      education and for pretesting examination questions. Their high performance, even 
      relative to that of medical students, indicates promising avenues for further 
      development and integration into the educational and clinical landscape.
CI  - ©Jonas Roos, Adnan Kasapovic, Tom Jansen, Robert Kaczmarczyk. Originally 
      published in JMIR Medical Education (https://mededu.jmir.org), 04.09.2023.
FAU - Roos, Jonas
AU  - Roos J
AUID- ORCID: 0000-0001-8843-4695
AD  - Department of Orthopedics and Trauma Surgery, University Hospital of Bonn, Bonn, 
      Germany.
FAU - Kasapovic, Adnan
AU  - Kasapovic A
AUID- ORCID: 0000-0001-6273-207X
AD  - Department of Orthopedics and Trauma Surgery, University Hospital of Bonn, Bonn, 
      Germany.
FAU - Jansen, Tom
AU  - Jansen T
AUID- ORCID: 0009-0001-3842-1914
AD  - Department of Orthopedics and Trauma Surgery, University Hospital of Bonn, Bonn, 
      Germany.
FAU - Kaczmarczyk, Robert
AU  - Kaczmarczyk R
AUID- ORCID: 0000-0002-8570-1601
AD  - Department of Dermatology and Allergy, Technical University of Munich, Munich, 
      Germany.
LA  - eng
PT  - Journal Article
DEP - 20230904
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10507517
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - exams
OT  - large language models
OT  - medical education
OT  - state examinations
COIS- Conflicts of Interest: None declared.
EDAT- 2023/09/04 12:42
MHDA- 2023/09/04 12:43
CRDT- 2023/09/04 11:53
PHST- 2023/02/13 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/06/22 00:00 [revised]
PHST- 2023/09/04 12:43 [medline]
PHST- 2023/09/04 12:42 [pubmed]
PHST- 2023/09/04 11:53 [entrez]
AID - v9i1e46482 [pii]
AID - 10.2196/46482 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Sep 4;9:e46482. doi: 10.2196/46482.

PMID- 37095800
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230426
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Ventricular Septal Rupture and Artificial Intelligence (AI)-Assisted Healthcare.
PG  - e36581
LID - 10.7759/cureus.36581 [doi]
LID - e36581
AB  - This case report highlights the use of point-of-care ultrasound (POCUS) for the 
      diagnosis of ventricular septal rupture (VSR), a severe consequence of acute 
      myocardial infarction (AMI). VSR has a broad spectrum of signs and inconspicuous 
      symptoms, making the diagnosis difficult. POCUS offers non-invasive, real-time 
      cardiac imaging and has an advantage over other methods due to its ability to 
      identify VSR early. Here we present a 63-year-old female with a history of type 2 
      diabetes, hypothyroidism, hyperlipidemia, and a family history of cardiovascular 
      disease, who came to the ED with chest pain for three days, palpitations, and 
      dyspnea at rest. On examination, the patient was hypotensive, tachycardic, and 
      had crackles with a harsh holosystolic murmur. An EKG and elevated troponin 
      levels suggested acute on chronic anterior-lateral wall ST-elevation myocardial 
      infarction (STEMI). Resuscitation efforts were initiated, followed by a lung 
      ultrasound that revealed good lung sliding and multiple B lines without pleural 
      thickening, indicating pulmonary edema. Echocardiography revealed ischemic heart 
      disease with moderate left ventricle (LV) systolic dysfunction and a 14 mm apical 
      ventricular septal rupture (hypokinetic thinning of the anterior wall, septum, 
      apex, and anterolateral wall with a left ventricular ejection fraction (LVEF) of 
      39%). The presence of flow on color Doppler across the interventricular septum, 
      showing left-to-right shunting, led to a definitive diagnosis of acute-on-chronic 
      myocardial infarction (MI) with ventricular septal rupture. The case report also 
      emphasizes how modern AI applications like ChatGPT (OpenAI, San Francisco, 
      California, United States), aid in language and research, saving time and 
      redefining the healthcare and research industry. As a result, we are confident 
      that AI-assisted healthcare will be the next global breakthrough.
CI  - Copyright © 2023, Lal Vallath et al.
FAU - Lal Vallath, Aditya
AU  - Lal Vallath A
AD  - Emergency Medicine, Peerless Hospital and B. K. Roy Research Centre, Kolkata, 
      IND.
FAU - Sivasubramanian, Barath Prashanth
AU  - Sivasubramanian BP
AD  - Internal Medicine, Employees State Insurance Corporation Medical College & 
      Employees State Insurance Post Graduate Institute of Medical Sciences and 
      Research (ESIC MC & PGIMSR), Chennai, IND.
FAU - Chatterjee, Aryapriyo
AU  - Chatterjee A
AD  - Emergency Medicine, Peerless Hospital and B. K. Roy Research Centre, Kolkata, 
      IND.
FAU - Erva, Snigdha
AU  - Erva S
AD  - Internal Medicine, MNR (M. N. Raju) Medical College and Hospital, Fasalwadi, IND.
FAU - Ravikumar, Diviya Bharathi
AU  - Ravikumar DB
AD  - Internal Medicine, Employees State Insurance Corporation Medical College & 
      Employees State Insurance Post Graduate Institute of Medical Sciences and 
      Research (ESIC MC & PGIMSR), Chennai, IND.
FAU - Dasgupta, Indraneel
AU  - Dasgupta I
AD  - Emergency Medicine, Peerless Hospital and B. K. Roy Research Centre, Kolkata, 
      IND.
LA  - eng
PT  - Case Reports
DEP - 20230323
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10122442
OTO - NOTNLM
OT  - cardiogenic shock
OT  - chatgpt
OT  - myocardial infarction
OT  - point-of-care ultrasound (pocus)
OT  - ventricular septal rupture
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/25 06:42
MHDA- 2023/04/25 06:43
CRDT- 2023/04/25 02:04
PHST- 2023/02/06 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/04/25 06:43 [medline]
PHST- 2023/04/25 06:42 [pubmed]
PHST- 2023/04/25 02:04 [entrez]
AID - 10.7759/cureus.36581 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 23;15(3):e36581. doi: 10.7759/cureus.36581. eCollection 2023 
      Mar.

PMID- 37073180
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230421
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Closed Negative Suction Drain Entrapment in Total Knee Arthroplasty: A Report on 
      the Implications of a Broken Drain Based on the ChatGPT Outlook.
PG  - e36290
LID - 10.7759/cureus.36290 [doi]
LID - e36290
AB  - Total knee arthroplasty (TKA) has been the gold standard treatment for end stage 
      arthritis of knee. The advancements in techniques have allowed successful 
      outcomes. The use of closed negative suction drain in TKA has been controversial. 
      Entrapment of a drain following TKA with a broken drain has been reported rarely 
      though it has important implications.  An obese 65-year-old female presented with 
      painful bilateral knees. A clinic-radiological assessment confirmed an advanced 
      grade osteoarthritis (OA). A single stage bilateral TKA was done. The use of 
      closed negative suction drain for both knees was done as a routine protocol. The 
      left knee drain got entrapped and an inadvertent pull due to abnormal positioning 
      in flexed knee impinged and broke the drain. The right knee removal of drain on 
      the second post-operative day was uneventful. A radiological assessment confirmed 
      the position of the broken drain in left knee. A mini arthrotomy ensued with 
      removal of the drain piece. Subsequent post-operative period was uneventful. The 
      knee function recovered with painless full range of motion. There was no evidence 
      of infection or implant loosening noted at a two-year follow-up. The generative 
      text model ChatGPT (OpenAI, USA) was used to identify the implication with the 
      use of drains in TKA. The use of drains remains controversial with no clear 
      consensus on its regular use. The breakage of drain poses an immediate concern 
      with need for wound revision and removal of foreign body. The long-term 
      observation for any knee infection, stiffness or poor knee function needs 
      monitoring. The early identification can prevent late symptomatology.  The closed 
      negative suction drain in our practice has become selective and presently has an 
      infrequent use in TKA. An entrapment of a closed negative suction drain merits 
      urgent measures. The remedial measures may ensure to preserve the knee joint 
      function and maintain the ability to do activities of daily living.
CI  - Copyright © 2023, Tomar et al.
FAU - Tomar, Lavindra
AU  - Tomar L
AD  - Department of Orthopedics, Max Super Speciality Hospital, Patparganj, Delhi, IND.
FAU - Govil, Gaurav
AU  - Govil G
AD  - Department of Orthopedics, Max Super Speciality Hospital, Patparganj, Delhi, IND.
FAU - Dhawan, Pawan
AU  - Dhawan P
AD  - Department of Orthopedics, Max Super Speciality Hospital, Patparganj, Delhi, IND.
LA  - eng
PT  - Case Reports
DEP - 20230317
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10106098
OTO - NOTNLM
OT  - arthroplasty
OT  - chat gpt
OT  - closed drain
OT  - complication
OT  - entrapment
OT  - knee
OT  - suction drain
OT  - total knee replacement
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/19 06:41
MHDA- 2023/04/19 06:42
CRDT- 2023/04/19 01:44
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/04/19 06:42 [medline]
PHST- 2023/04/19 06:41 [pubmed]
PHST- 2023/04/19 01:44 [entrez]
AID - 10.7759/cureus.36290 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 17;15(3):e36290. doi: 10.7759/cureus.36290. eCollection 2023 
      Mar.

PMID- 37597659
OWN - NLM
STAT- In-Process
LR  - 20231209
IS  - 1878-8769 (Electronic)
IS  - 1878-8750 (Linking)
VI  - 179
DP  - 2023 Nov
TI  - GPT-4 Artificial Intelligence Model Outperforms ChatGPT, Medical Students, and 
      Neurosurgery Residents on Neurosurgery Written Board-Like Questions.
PG  - e160-e165
LID - S1878-8750(23)01144-0 [pii]
LID - 10.1016/j.wneu.2023.08.042 [doi]
AB  - BACKGROUND: Artificial intelligence (AI) and machine learning have transformed 
      health care with applications in various specialized fields. Neurosurgery can 
      benefit from artificial intelligence in surgical planning, predicting patient 
      outcomes, and analyzing neuroimaging data. GPT-4, an updated language model with 
      additional training parameters, has exhibited exceptional performance on 
      standardized exams. This study examines GPT-4's competence on neurosurgical 
      board-style questions, comparing its performance with medical students and 
      residents, to explore its potential in medical education and clinical 
      decision-making. METHODS: GPT-4's performance was examined on 643 Congress of 
      Neurological Surgeons Self-Assessment Neurosurgery Exam (SANS) board-style 
      questions from various neurosurgery subspecialties. Of these, 477 were text-based 
      and 166 contained images. GPT-4 refused to answer 52 questions that contained no 
      text. The remaining 591 questions were inputted into GPT-4, and its performance 
      was evaluated based on first-time responses. Raw scores were analyzed across 
      subspecialties and question types, and then compared to previous findings on Chat 
      Generative pre-trained transformer performance against SANS users, medical 
      students, and neurosurgery residents. RESULTS: GPT-4 attempted 91.9% of Congress 
      of Neurological Surgeons SANS questions and achieved 76.6% accuracy. The model's 
      accuracy increased to 79.0% for text-only questions. GPT-4 outperformed Chat 
      Generative pre-trained transformer (P < 0.001) and scored highest in 
      pain/peripheral nerve (84%) and lowest in spine (73%) categories. It exceeded the 
      performance of medical students (26.3%), neurosurgery residents (61.5%), and the 
      national average of SANS users (69.3%) across all categories. CONCLUSIONS: GPT-4 
      significantly outperformed medical students, neurosurgery residents, and the 
      national average of SANS users. The mode's accuracy suggests potential 
      applications in educational settings and clinical decision-making, enhancing 
      provider efficiency, and improving patient care.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Guerra, Gage A
AU  - Guerra GA
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA. Electronic address: gageguer@usc.edu.
FAU - Hofmann, Hayden
AU  - Hofmann H
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Sobhani, Sina
AU  - Sobhani S
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Hofmann, Grady
AU  - Hofmann G
AD  - Department of Biology, Stanford University, Palo Alto, California, USA.
FAU - Gomez, David
AU  - Gomez D
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Soroudi, Daniel
AU  - Soroudi D
AD  - School of Medicine, University of California San Francisco, San Francisco, 
      California, USA.
FAU - Hopkins, Benjamin S
AU  - Hopkins BS
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Dallas, Jonathan
AU  - Dallas J
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Pangal, Dhiraj J
AU  - Pangal DJ
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Cheok, Stephanie
AU  - Cheok S
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Nguyen, Vincent N
AU  - Nguyen VN
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Mack, William J
AU  - Mack WJ
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
FAU - Zada, Gabriel
AU  - Zada G
AD  - Department of Neurosurgery, University of Southern California, Los Angeles, 
      California, USA.
LA  - eng
PT  - Journal Article
DEP - 20230818
PL  - United States
TA  - World Neurosurg
JT  - World neurosurgery
JID - 101528275
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - ChatGPT
OT  - GPT-4
OT  - Machine learning
OT  - Neurosurgical boards
OT  - Neurosurgical training
OT  - SANS question bank
EDAT- 2023/08/20 00:41
MHDA- 2023/08/20 00:41
CRDT- 2023/08/19 19:24
PHST- 2023/05/02 00:00 [received]
PHST- 2023/08/08 00:00 [revised]
PHST- 2023/08/09 00:00 [accepted]
PHST- 2023/08/20 00:41 [pubmed]
PHST- 2023/08/20 00:41 [medline]
PHST- 2023/08/19 19:24 [entrez]
AID - S1878-8750(23)01144-0 [pii]
AID - 10.1016/j.wneu.2023.08.042 [doi]
PST - ppublish
SO  - World Neurosurg. 2023 Nov;179:e160-e165. doi: 10.1016/j.wneu.2023.08.042. Epub 
      2023 Aug 18.

PMID- 37113342
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230430
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Treatment Outcomes of Leiomyosarcoma Metastasis Affecting the Brachial Plexus: A 
      Comparative Case Report Using Chat Generative Pre-trained Transformer (ChatGPT).
PG  - e36715
LID - 10.7759/cureus.36715 [doi]
LID - e36715
AB  - Sarcomas are a rare type of cancer that can develop in various parts of the body, 
      including the brachial plexus. Leiomyosarcomas (LMs) are a subtype of sarcoma 
      that develops in smooth muscle tissue and can metastasize to different parts of 
      the body. In this case report, we present two patients with LM metastasized to 
      the brachial plexus, one treated with CyberKnife (Accuray, Sunnyvale, CA) 
      stereotactic radiosurgery (CK SRS) and the other with surgical resection. The aim 
      of this case report is to present the treatment outcomes and adverse effects of 
      CK SRS and surgical resection in brachial plexus LM metastasis. Patient 1 was a 
      39-year-old female who received CK SRS, and at three months of follow-up, the 
      lesion was smaller, and she reported symptomatic improvement. At 15 months, the 
      lesion was stable in size, and there was no evidence of local invasion of the 
      adjacent vascular structures or nerves. Patient 2 was a 52-year-old male who 
      underwent surgical resection, and at one-month follow-up, the patient was 
      asymptomatic with no recurrence of his symptoms. The size of the residual 
      axillary tumor was stable at three months and showed a slight interval decrease 
      in size at five months of follow-up. He was followed for over 12 months, with no 
      recurrence of his symptoms. Both treatments appear to have been effective in 
      controlling LM growth and relieving symptoms. CK SRS provides a non-invasive 
      option. However, more research is needed to fully understand the effectiveness 
      and safety of these treatments for brachial plexus sarcoma. This case report 
      highlights the importance of considering different treatment options for brachial 
      plexus sarcoma and the need for further studies to understand the best approach 
      for these rare cases.
CI  - Copyright © 2023, Zamarud et al.
FAU - Zamarud, Aroosa
AU  - Zamarud A
AD  - Neurosurgery, Stanford Health Care, Palo Alto, USA.
FAU - Marianayagam, Neelan
AU  - Marianayagam N
AD  - Neurosurgery, Stanford University, Stanford, USA.
FAU - Sekar, Vashisht
AU  - Sekar V
AD  - Neurosurgery, Stanford University School of Medicine, Stanford, USA.
FAU - Chang, Steven D
AU  - Chang SD
AD  - Neurosurgery, Stanford University School of Medicine, Stanford, USA.
FAU - Meola, Antonio
AU  - Meola A
AD  - Neurosurgery, Stanford University School of Medicine, Palo Alto, USA.
LA  - eng
PT  - Case Reports
DEP - 20230326
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10129366
OTO - NOTNLM
OT  - brachial plexus sarcoma
OT  - chat gpt
OT  - leiomyosarcoma
OT  - stereotactic radiosurgery
OT  - surgical resection
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/28 06:42
MHDA- 2023/04/28 06:43
CRDT- 2023/04/28 02:27
PHST- 2023/02/06 00:00 [received]
PHST- 2023/03/26 00:00 [accepted]
PHST- 2023/04/28 06:43 [medline]
PHST- 2023/04/28 06:42 [pubmed]
PHST- 2023/04/28 02:27 [entrez]
AID - 10.7759/cureus.36715 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 26;15(3):e36715. doi: 10.7759/cureus.36715. eCollection 2023 
      Mar.

PMID- 37814006
OWN - NLM
STAT- Publisher
LR  - 20231009
IS  - 2157-846X (Electronic)
IS  - 2157-846X (Linking)
DP  - 2023 Oct 9
TI  - Rapid discovery of high-affinity antibodies via massively parallel sequencing, 
      ribosome display and affinity screening.
LID - 10.1038/s41551-023-01093-3 [doi]
AB  - Developing therapeutic antibodies is laborious and costly. Here we report a 
      method for antibody discovery that leverages the Illumina HiSeq platform to, 
      within 3 days, screen in the order of 10(8) antibody-antigen interactions. The 
      method, which we named 'deep screening', involves the clustering and sequencing 
      of antibody libraries, the conversion of the DNA clusters into complementary RNA 
      clusters covalently linked to the instrument's flow-cell surface on the same 
      location, the in situ translation of the clusters into antibodies tethered via 
      ribosome display, and their screening via fluorescently labelled antigens. By 
      using deep screening, we discovered low-nanomolar nanobodies to a model antigen 
      using 4 × 10(6) unique variants from yeast-display-enriched libraries, and 
      high-picomolar single-chain antibody fragment leads for human interleukin-7 
      directly from unselected synthetic repertoires. We also leveraged deep screening 
      of a library of 2.4 × 10(5) sequences of the third complementarity-determining 
      region of the heavy chain of an anti-human epidermal growth factor receptor 2 
      (HER2) antibody as input for a large language model that generated new 
      single-chain antibody fragment sequences with higher affinity for HER2 than those 
      in the original library.
CI  - © 2023. The Author(s).
FAU - Porebski, Benjamin T
AU  - Porebski BT
AUID- ORCID: 0000-0003-1398-0442
AD  - MRC Laboratory of Molecular Biology, Cambridge, UK.
FAU - Balmforth, Matthew
AU  - Balmforth M
AUID- ORCID: 0000-0003-2651-9428
AD  - MRC Laboratory of Molecular Biology, Cambridge, UK.
AD  - UCB, Slough, UK.
FAU - Browne, Gareth
AU  - Browne G
AD  - Biologics Engineering, AstraZeneca, Cambridge, UK.
FAU - Riley, Aidan
AU  - Riley A
AD  - Biologics Engineering, AstraZeneca, Cambridge, UK.
FAU - Jamali, Kiarash
AU  - Jamali K
AUID- ORCID: 0009-0008-4805-9518
AD  - MRC Laboratory of Molecular Biology, Cambridge, UK.
FAU - Fürst, Maximillian J L J
AU  - Fürst MJLJ
AUID- ORCID: 0000-0001-7720-9214
AD  - MRC Laboratory of Molecular Biology, Cambridge, UK.
AD  - Groningen Biomolecular Sciences and Biotechnology Institute, University of 
      Groningen, Groningen, the Netherlands.
FAU - Velic, Mirko
AU  - Velic M
AD  - , Docklands, Victoria, Australia.
FAU - Buchanan, Andrew
AU  - Buchanan A
AUID- ORCID: 0000-0002-5191-7682
AD  - Biologics Engineering, AstraZeneca, Cambridge, UK.
FAU - Minter, Ralph
AU  - Minter R
AUID- ORCID: 0000-0001-5571-3102
AD  - Biologics Engineering, AstraZeneca, Cambridge, UK.
AD  - Alchemab Therapeutics, London, UK.
FAU - Vaughan, Tristan
AU  - Vaughan T
AD  - Biologics Engineering, AstraZeneca, Cambridge, UK.
FAU - Holliger, Philipp
AU  - Holliger P
AUID- ORCID: 0000-0002-3440-9854
AD  - MRC Laboratory of Molecular Biology, Cambridge, UK. ph1@mrc-lmb.cam.ac.uk.
LA  - eng
GR  - U105178804/RCUK | Medical Research Council (MRC)/
GR  - ALTF 648-202/European Molecular Biology Organization (EMBO)/
PT  - Journal Article
DEP - 20231009
PL  - England
TA  - Nat Biomed Eng
JT  - Nature biomedical engineering
JID - 101696896
SB  - IM
EDAT- 2023/10/10 00:42
MHDA- 2023/10/10 00:42
CRDT- 2023/10/09 23:30
PHST- 2022/12/05 00:00 [received]
PHST- 2023/08/23 00:00 [accepted]
PHST- 2023/10/10 00:42 [medline]
PHST- 2023/10/10 00:42 [pubmed]
PHST- 2023/10/09 23:30 [entrez]
AID - 10.1038/s41551-023-01093-3 [pii]
AID - 10.1038/s41551-023-01093-3 [doi]
PST - aheadofprint
SO  - Nat Biomed Eng. 2023 Oct 9. doi: 10.1038/s41551-023-01093-3.

PMID- 37100106
OWN - NLM
STAT- MEDLINE
DCOM- 20230605
LR  - 20230612
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Linking)
VI  - 142
DP  - 2023 Jun
TI  - Contextualized medication information extraction using Transformer-based deep 
      learning architectures.
PG  - 104370
LID - S1532-0464(23)00091-6 [pii]
LID - 10.1016/j.jbi.2023.104370 [doi]
AB  - OBJECTIVE: To develop a natural language processing (NLP) system to extract 
      medications and contextual information that help understand drug changes. This 
      project is part of the 2022 n2c2 challenge. MATERIALS AND METHODS: We developed 
      NLP systems for medication mention extraction, event classification (indicating 
      medication changes discussed or not), and context classification to classify 
      medication changes context into 5 orthogonal dimensions related to drug changes. 
      We explored 6 state-of-the-art pretrained transformer models for the three 
      subtasks, including GatorTron, a large language model pretrained using > 90 
      billion words of text (including > 80 billion words from > 290 million clinical 
      notes identified at the University of Florida Health). We evaluated our NLP 
      systems using annotated data and evaluation scripts provided by the 2022 n2c2 
      organizers. RESULTS: Our GatorTron models achieved the best F1-scores of 0.9828 
      for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 
      2nd), and the best micro-average accuracy of 0.9126 for context classification. 
      GatorTron outperformed existing transformer models pretrained using smaller 
      general English text and clinical text corpora, indicating the advantage of large 
      language models. CONCLUSION: This study demonstrated the advantage of using large 
      transformer models for contextual medication information extraction from clinical 
      narratives.
CI  - Copyright © 2023. Published by Elsevier Inc.
FAU - Chen, Aokun
AU  - Chen A
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, 
      University of Florida Health Cancer Center, Gainesville, FL, USA.
FAU - Yu, Zehao
AU  - Yu Z
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Yang, Xi
AU  - Yang X
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Guo, Yi
AU  - Guo Y
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, 
      University of Florida Health Cancer Center, Gainesville, FL, USA.
FAU - Bian, Jiang
AU  - Bian J
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, 
      University of Florida Health Cancer Center, Gainesville, FL, USA.
FAU - Wu, Yonghui
AU  - Wu Y
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, 
      University of Florida Health Cancer Center, Gainesville, FL, USA. Electronic 
      address: yonghui.wu@ufl.edu.
LA  - eng
GR  - R56 AG069880/AG/NIA NIH HHS/United States
GR  - R21 AG068717/AG/NIA NIH HHS/United States
GR  - R01 CA246418/CA/NCI NIH HHS/United States
GR  - R21 CA245858/CA/NCI NIH HHS/United States
GR  - R21 CA253394/CA/NCI NIH HHS/United States
GR  - R01 DA050676/DA/NIDA NIH HHS/United States
GR  - R01 MH121907/MH/NIMH NIH HHS/United States
GR  - U18 DP006512/DP/NCCDPHP CDC HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, P.H.S.
DEP - 20230424
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
MH  - *Deep Learning
MH  - Natural Language Processing
MH  - Information Storage and Retrieval
OTO - NOTNLM
OT  - Clinical natural language processing
OT  - Deep learning
OT  - Medication information extraction
OT  - Named entity recognition
OT  - Text classification
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/04/27 00:42
MHDA- 2023/06/05 06:42
CRDT- 2023/04/26 19:24
PHST- 2023/01/06 00:00 [received]
PHST- 2023/03/14 00:00 [revised]
PHST- 2023/04/19 00:00 [accepted]
PHST- 2023/06/05 06:42 [medline]
PHST- 2023/04/27 00:42 [pubmed]
PHST- 2023/04/26 19:24 [entrez]
AID - S1532-0464(23)00091-6 [pii]
AID - 10.1016/j.jbi.2023.104370 [doi]
PST - ppublish
SO  - J Biomed Inform. 2023 Jun;142:104370. doi: 10.1016/j.jbi.2023.104370. Epub 2023 
      Apr 24.

PMID- 37601525
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1658-354X (Print)
IS  - 0975-3125 (Electronic)
VI  - 17
IP  - 3
DP  - 2023 Jul-Sep
TI  - Artificial intelligence in medicine and research - the good, the bad, and the 
      ugly.
PG  - 401-406
LID - 10.4103/sja.sja_344_23 [doi]
AB  - Artificial intelligence (AI) broadly refers to machines that simulate intelligent 
      human behavior, and research into this field is exponential and worldwide, with 
      global players such as Microsoft battling with Google for supremacy and market 
      share. This paper reviews the "good" aspects of AI in medicine for individuals 
      who embrace the 4P model of medicine (Predictive, Preventive, Personalized, and 
      Participatory) to medical assistants in diagnostics, surgery, and research. The 
      "bad" aspects relate to the potential for errors, culpability, ethics, data loss 
      and data breaches, and so on. The "ugly" aspects are deliberate personal 
      malfeasances and outright scientific misconduct including the ease of plagiarism 
      and fabrication, with particular reference to the novel ChatGPT as well as AI 
      software that can also fabricate graphs and images. The issues pertaining to the 
      potential dangers of creating rogue, super-intelligent AI systems that lead to a 
      technological singularity and the ensuing perceived existential threat to mankind 
      by leading AI researchers are also briefly discussed.
CI  - Copyright: © 2023 Saudi Journal of Anesthesia.
FAU - Grech, Victor
AU  - Grech V
AD  - Mater Dei Hospital, Msida, Malta.
FAU - Cuschieri, Sarah
AU  - Cuschieri S
AD  - University of Malta, Msida, Malta.
FAU - Eldawlatly, Abdelazeem A
AU  - Eldawlatly AA
AD  - College of Medicine, King Saud University, Riyadh, Saudi Arabia.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230622
PL  - India
TA  - Saudi J Anaesth
JT  - Saudi journal of anaesthesia
JID - 101500601
PMC - PMC10435812
OTO - NOTNLM
OT  - Artificial intelligence
OT  - deep learning
OT  - fraud
OT  - machine learning
OT  - scientific misconduct
COIS- There are no conflicts of interest.
EDAT- 2023/08/21 06:42
MHDA- 2023/08/21 06:43
CRDT- 2023/08/21 05:01
PHST- 2023/04/26 00:00 [received]
PHST- 2023/04/26 00:00 [accepted]
PHST- 2023/08/21 06:43 [medline]
PHST- 2023/08/21 06:42 [pubmed]
PHST- 2023/08/21 05:01 [entrez]
AID - SJA-17-401 [pii]
AID - 10.4103/sja.sja_344_23 [doi]
PST - ppublish
SO  - Saudi J Anaesth. 2023 Jul-Sep;17(3):401-406. doi: 10.4103/sja.sja_344_23. Epub 
      2023 Jun 22.

PMID- 37351658
OWN - NLM
STAT- MEDLINE
DCOM- 20230713
LR  - 20231103
IS  - 1863-2661 (Electronic)
IS  - 1863-2653 (Linking)
VI  - 228
IP  - 6
DP  - 2023 Jul
TI  - The human cost of ethical artificial intelligence.
PG  - 1365-1369
LID - 10.1007/s00429-023-02662-7 [doi]
AB  - Foundational models such as ChatGPT critically depend on vast data scales the 
      internet uniquely enables. This implies exposure to material varying widely in 
      logical sense, factual fidelity, moral value, and even legal status. Whereas data 
      scaling is a technical challenge, soluble with greater computational resource, 
      complex semantic filtering cannot be performed reliably without human 
      intervention: the self-supervision that makes foundational models possible at 
      least in part presupposes the abilities they seek to acquire. This unavoidably 
      introduces the need for large-scale human supervision-not just of training input 
      but also model output-and imbues any model with subjectivity reflecting the 
      beliefs of its creator. The pressure to minimize the cost of the former is in 
      direct conflict with the pressure to maximise the quality of the latter. 
      Moreover, it is unclear how complex semantics, especially in the realm of the 
      moral, could ever be reduced to an objective function any machine could plausibly 
      maximise. We suggest the development of foundational models necessitates urgent 
      innovation in quantitative ethics and outline possible avenues for its 
      realisation.
CI  - © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Ruffle, James K
AU  - Ruffle JK
AD  - Queen Square Institute of Neurology, University College London, London, WC1N 3BG, 
      UK. j.ruffle@ucl.ac.uk.
FAU - Foulon, Chris
AU  - Foulon C
AD  - Queen Square Institute of Neurology, University College London, London, WC1N 3BG, 
      UK.
FAU - Nachev, Parashkev
AU  - Nachev P
AD  - Queen Square Institute of Neurology, University College London, London, WC1N 3BG, 
      UK.
LA  - eng
GR  - MR/X00046X/1/MRC_/Medical Research Council/United Kingdom
GR  - 213038/Z/18/Z/WT_/Wellcome Trust/United Kingdom
PT  - Letter
DEP - 20230623
PL  - Germany
TA  - Brain Struct Funct
JT  - Brain structure & function
JID - 101282001
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Morals
MH  - Semantics
MH  - Logic
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Ethical modelling
OT  - Philosophy and ethics
OT  - Policy
EDAT- 2023/06/23 13:07
MHDA- 2023/07/13 06:42
CRDT- 2023/06/23 11:07
PHST- 2023/04/21 00:00 [received]
PHST- 2023/06/01 00:00 [accepted]
PHST- 2023/07/13 06:42 [medline]
PHST- 2023/06/23 13:07 [pubmed]
PHST- 2023/06/23 11:07 [entrez]
AID - 10.1007/s00429-023-02662-7 [pii]
AID - 10.1007/s00429-023-02662-7 [doi]
PST - ppublish
SO  - Brain Struct Funct. 2023 Jul;228(6):1365-1369. doi: 10.1007/s00429-023-02662-7. 
      Epub 2023 Jun 23.

PMID- 37956196
OWN - NLM
STAT- MEDLINE
DCOM- 20231115
LR  - 20231116
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 18
IP  - 11
DP  - 2023
TI  - Enabling AI in synthetic biology through Construction File specification.
PG  - e0294469
LID - 10.1371/journal.pone.0294469 [doi]
LID - e0294469
AB  - The Construction File (CF) specification establishes a standardized interface for 
      molecular biology operations, laying a foundation for automation and enhanced 
      efficiency in experiment design. It is implemented across three distinct software 
      projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for 
      interactive parsing and simulating experiments; ConstructionFileSimulator, a 
      field-tested Java project that showcases 'Experiment' objects expressed as flat 
      files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps 
      Script, providing a user-friendly interface for authoring and simulation of CF. 
      The CF specification not only standardizes and modularizes molecular biology 
      operations but also promotes collaboration, automation, and reuse, significantly 
      reducing potential errors. The potential integration of CF with artificial 
      intelligence, particularly GPT-4, suggests innovative automation strategies for 
      synthetic biology. While challenges such as token limits, data storage, and 
      biosecurity remain, proposed solutions promise a way forward in harnessing AI for 
      experiment design. This shift from human-driven design to AI-assisted workflows, 
      steered by high-level objectives, charts a potential future path in synthetic 
      biology, envisioning an environment where complexities are managed more 
      effectively.
CI  - Copyright: © 2023 Ataii et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Ataii, Nassim
AU  - Ataii N
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Bakshi, Sanjyot
AU  - Bakshi S
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Chen, Yisheng
AU  - Chen Y
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Fernandez, Michael
AU  - Fernandez M
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Shao, Zihang
AU  - Shao Z
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Scheftel, Zachary
AU  - Scheftel Z
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Tou, Connor
AU  - Tou C
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Vega, Mia
AU  - Vega M
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Wang, Yuting
AU  - Wang Y
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Zhang, Hanxiao
AU  - Zhang H
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Zhao, Zexuan
AU  - Zhao Z
AUID- ORCID: 0000-0003-2410-0893
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
FAU - Anderson, J Christopher
AU  - Anderson JC
AUID- ORCID: 0000-0002-9115-7323
AD  - Department of Bioengineering, University of California, Berkeley, Berkeley, 
      California, United States of America.
AD  - QB3: California Institute for Quantitative Biological Research, University of 
      California, Berkeley, Berkeley, California, United States of America.
AD  - Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, 
      California, United States of America.
LA  - eng
PT  - Journal Article
DEP - 20231113
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Synthetic Biology
MH  - Software
MH  - Computer Simulation
MH  - Automation
PMC - PMC10642840
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/13 18:42
MHDA- 2023/11/15 06:43
CRDT- 2023/11/13 13:44
PHST- 2023/07/11 00:00 [received]
PHST- 2023/10/31 00:00 [accepted]
PHST- 2023/11/15 06:43 [medline]
PHST- 2023/11/13 18:42 [pubmed]
PHST- 2023/11/13 13:44 [entrez]
AID - PONE-D-23-21700 [pii]
AID - 10.1371/journal.pone.0294469 [doi]
PST - epublish
SO  - PLoS One. 2023 Nov 13;18(11):e0294469. doi: 10.1371/journal.pone.0294469. 
      eCollection 2023.

PMID- 36909080
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230314
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Recurrent Strokes in a Patient With Metastatic Lung Cancer.
PG  - e34699
LID - 10.7759/cureus.34699 [doi]
LID - e34699
AB  - A stroke, also known as cerebrovascular accident (CVA), is a medical condition 
      that occurs when the blood supply to the brain is interrupted, resulting in brain 
      cell death. Cancer-associated stroke (CAS) is a rare but serious complication of 
      cancer, where a malignant tumor or its metastases invade or compress the blood 
      vessels in the brain, resulting in a stroke. We describe a case of a 60-year-old 
      male patient recently diagnosed with lung cancer with metastasis to the liver, 
      esophagus, small intestine, and pancreas who has had at least three CVAs within a 
      span of three months. He developed sequelae such as hemiplegia and aphasia. He 
      was managed with dual antiplatelet therapy, neuroprotective drugs, and 
      physiotherapy. Patients with advanced cancers should be routinely screened for 
      neurovascular complications and risks. Prophylactic therapy should be started 
      wherever warranted.
CI  - Copyright © 2023, Karkra et al.
FAU - Karkra, Rohan
AU  - Karkra R
AD  - Medicine, JSS (Jagadguru Sri Shivarathreeshwara) Medical College and Hospital, 
      JSSAHER (JSS Academy of Higher Education and Research), Mysuru, IND.
FAU - Jain, Riya
AU  - Jain R
AD  - Medicine, JSS (Jagadguru Sri Shivarathreeshwara) Medical College and Hospital, 
      JSSAHER (JSS Academy of Higher Education and Research), Mysuru, IND.
FAU - Shivaswamy, Rajendra P
AU  - Shivaswamy RP
AD  - Internal Medicine, JSS (Jagadguru Sri Shivarathreeshwara) Medical College and 
      Hospital, JSSAHER (JSS Academy of Higher Education and Research), Mysuru, IND.
LA  - eng
PT  - Case Reports
DEP - 20230206
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC9995248
OTO - NOTNLM
OT  - chatgpt
OT  - hemiplegia
OT  - lung cancer
OT  - metastasis
OT  - recurrent
OT  - stroke
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/14 06:00
MHDA- 2023/03/14 06:01
CRDT- 2023/03/13 03:54
PHST- 2023/02/06 00:00 [accepted]
PHST- 2023/03/13 03:54 [entrez]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/14 06:01 [medline]
AID - 10.7759/cureus.34699 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 6;15(2):e34699. doi: 10.7759/cureus.34699. eCollection 2023 Feb.

PMID- 37885558
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231028
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Using Artificial Intelligence to Assess the Teratogenic Risk of Vitamin A 
      Supplements.
PG  - e45931
LID - 10.7759/cureus.45931 [doi]
LID - e45931
AB  - Vitamin A in high doses has been found to be highly teratogenic, leading to 
      severe fetal abnormalities if exposure occurs during pregnancy. Hence, 
      prescription vitamin A acne medications like isotretinoin are highly regulated 
      via programs such as iPledge, which intend to avert fetal exposure to 
      isotretinoin and to educate healthcare providers, pharmacists, and patients about 
      the significant risks associated with isotretinoin and its appropriate usage 
      conditions. However, over-the-counter (OTC) vitamin A supplements are not subject 
      to these requirements, and calculating the vitamin A content of these supplements 
      can be difficult due to the lack of Food and Drug Administration (FDA) 
      regulations and inconsistencies in labeling. If the necessary information is 
      provided, ChatGPT, a generative artificial intelligence (AI) tool, can help the 
      general public calculate the vitamin A content of supplements. Nonetheless, 
      supplement manufacturers do not always provide the data necessary for these 
      calculations.
CI  - Copyright © 2023, Holla et al.
FAU - Holla, Swathi
AU  - Holla S
AD  - Dermatology, Baylor College of Medicine, Houston, USA.
FAU - Zamil, Dina H
AU  - Zamil DH
AD  - Dermatology, Baylor College of Medicine, Houston, USA.
FAU - Paidisetty, Praneet S
AU  - Paidisetty PS
AD  - Dermatology, McGovern Medical School, The University of Texas Health Science 
      Center, Houston, USA.
FAU - Wang, Leonard K
AU  - Wang LK
AD  - Dermatology, John Sealy School of Medicine, The University of Texas Medical 
      Branch, Galveston, USA.
FAU - Katta, Rajani
AU  - Katta R
AD  - Dermatology, McGovern Medical School, The University of Texas Health Science 
      Center, Houston, USA.
LA  - eng
PT  - Editorial
DEP - 20230925
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10599455
OTO - NOTNLM
OT  - acne
OT  - ai & robotics in healthcare
OT  - ethics
OT  - isotretinoin
OT  - teratogenic medications
OT  - vitamin a supplements
COIS- The authors have declared financial relationships, which are detailed in the next 
      section.
EDAT- 2023/10/27 06:42
MHDA- 2023/10/27 06:43
CRDT- 2023/10/27 04:19
PHST- 2023/09/25 00:00 [accepted]
PHST- 2023/10/27 06:43 [medline]
PHST- 2023/10/27 06:42 [pubmed]
PHST- 2023/10/27 04:19 [entrez]
AID - 10.7759/cureus.45931 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 25;15(9):e45931. doi: 10.7759/cureus.45931. eCollection 2023 
      Sep.

PMID- 37544516
OWN - NLM
STAT- Publisher
LR  - 20230829
IS  - 1600-0641 (Electronic)
IS  - 0168-8278 (Linking)
DP  - 2023 Aug 5
TI  - ChatGPT: The transformative influence of generative AI on science and healthcare.
LID - S0168-8278(23)05039-0 [pii]
LID - 10.1016/j.jhep.2023.07.028 [doi]
AB  - In an age where technology is evolving at a sometimes incomprehensibly rapid 
      pace, the liver community must adjust and learn to embrace breakthroughs with an 
      open mind in order to benefit from potentially transformative influences on our 
      science and practice. The Journal of Hepatology has responded to novel 
      developments in artificial intelligence (AI) by recruiting experts in the field 
      to serve on the Editorial Board. Publications introducing novel AI technology are 
      no longer uncommon in our journal and are among the most highly debated and 
      possibly practice-changing papers across a broad range of scientific disciplines, 
      united by their focus on liver disease. As AI is rapidly evolving, this expert 
      paper will focus on educating our readership on large language models and their 
      possible impact on our research practice and clinical outlook, outlining both 
      challenges and opportunities in the field. "To improve is to change; to be 
      perfect is to change often." - Winston S. Churchill.
CI  - Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.
FAU - Varghese, Julian
AU  - Varghese J
AD  - Institute of Medical Informatics, University of Münster, Germany. Electronic 
      address: Julian.Varghese@ukmuenster.de.
FAU - Chapiro, Julius
AU  - Chapiro J
AD  - Department of Radiology and Biomedical Imaging, Department of Digestive Diseases; 
      Yale School of Medicine, Department of Biomedical Engineering; Yale School of 
      Engineering and Applied Sciences, New Haven, CT, USA.
LA  - eng
PT  - Journal Article
DEP - 20230805
PL  - Netherlands
TA  - J Hepatol
JT  - Journal of hepatology
JID - 8503886
SB  - IM
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Clinical Decision Support
OT  - Large Language Models
OT  - Public Health
EDAT- 2023/08/07 00:42
MHDA- 2023/08/07 00:42
CRDT- 2023/08/06 19:16
PHST- 2023/07/01 00:00 [received]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/08/07 00:42 [pubmed]
PHST- 2023/08/07 00:42 [medline]
PHST- 2023/08/06 19:16 [entrez]
AID - S0168-8278(23)05039-0 [pii]
AID - 10.1016/j.jhep.2023.07.028 [doi]
PST - aheadofprint
SO  - J Hepatol. 2023 Aug 5:S0168-8278(23)05039-0. doi: 10.1016/j.jhep.2023.07.028.

PMID- 38032442
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1535-1645 (Electronic)
IS  - 1523-3812 (Linking)
DP  - 2023 Nov 30
TI  - An Introduction to Generative Artificial Intelligence in Mental Health Care: 
      Considerations and Guidance.
LID - 10.1007/s11920-023-01477-x [doi]
AB  - PURPOSE OF REVIEW: This paper provides an overview of generative artificial 
      intelligence (AI) and the possible implications in the delivery of mental health 
      care. RECENT FINDINGS: Generative AI is a powerful technology that is changing 
      rapidly. As psychiatrists, it is important for us to understand generative AI 
      technology and how it may impact our patients and our practice of medicine. This 
      paper aims to build this understanding by focusing on GPT-4 and its potential 
      impact on mental health care delivery. We first introduce key concepts and 
      terminology describing how the technology works and various novel uses of it. We 
      then dive into key considerations for GPT-4 and other large language models 
      (LLMs) and wrap up with suggested future directions and initial guidance to the 
      field.
CI  - © 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - King, Darlene R
AU  - King DR
AD  - Department of Psychiatry, The University of Texas Southwestern Medical Center, 
      5323 Harry Hines Blvd, MC 8849, Dallas, TX 75390-8849, USA. 
      Darlene.King@UTSouthwestern.edu.
FAU - Nanda, Guransh
AU  - Nanda G
AD  - The University of Texas, Southwestern Medical School, 5323 Harry Hines Blvd, 
      Dallas, TX, 75390-8830, USA.
FAU - Stoddard, Joel
AU  - Stoddard J
AD  - Children's Hospital Colorado Anschutz Medical Campus, Child and Adolescent 
      Psychiatry, University of Colorado Anschutz Medical Campus, 13123 East 16th Ave, 
      Aurora, CO, 80045, USA.
AD  - Department of Psychiatry and Family Medicine, School of Medicine, Centers for 
      American Indian and Alaska Native Health, Colorado School of Public Health, 
      Telemedicine Helen and Arthur E. Johnson Depression Center, University of 
      Colorado Anschutz Medical Campus, Mail Stop F800, 13055 East 17th Avenue, Aurora, 
      CO 80045, USA.
FAU - Dempsey, Allison
AU  - Dempsey A
AD  - Department of Psychiatry and Family Medicine, School of Medicine, Centers for 
      American Indian and Alaska Native Health, Colorado School of Public Health, 
      Telemedicine Helen and Arthur E. Johnson Depression Center, University of 
      Colorado Anschutz Medical Campus, Mail Stop F800, 13055 East 17th Avenue, Aurora, 
      CO 80045, USA.
FAU - Hergert, Sarah
AU  - Hergert S
AD  - Department of Psychiatry, The University of Texas Southwestern Medical Center, 
      5323 Harry Hines Blvd, MC 8849, Dallas, TX 75390-8849, USA.
FAU - Shore, Jay H
AU  - Shore JH
AD  - Department of Psychiatry and Family Medicine, School of Medicine, Centers for 
      American Indian and Alaska Native Health, Colorado School of Public Health, 
      Telemedicine Helen and Arthur E. Johnson Depression Center, University of 
      Colorado Anschutz Medical Campus, Mail Stop F800, 13055 East 17th Avenue, Aurora, 
      CO 80045, USA.
FAU - Torous, John
AU  - Torous J
AD  - Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard 
      Medical School, Boston, MA, USA.
AD  - Massachusetts Mental Health Center, Beth Israel Deaconess Medical Center, Harvard 
      Medical School, Boston, MA, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231130
PL  - United States
TA  - Curr Psychiatry Rep
JT  - Current psychiatry reports
JID - 100888960
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence (AI)
OT  - Augmented intelligence (AI)
OT  - ChatGPT in mental health
OT  - Generative artificial intelligence
OT  - Mental health AI
EDAT- 2023/11/30 12:42
MHDA- 2023/11/30 12:42
CRDT- 2023/11/30 11:08
PHST- 2023/11/21 00:00 [accepted]
PHST- 2023/11/30 12:42 [medline]
PHST- 2023/11/30 12:42 [pubmed]
PHST- 2023/11/30 11:08 [entrez]
AID - 10.1007/s11920-023-01477-x [pii]
AID - 10.1007/s11920-023-01477-x [doi]
PST - aheadofprint
SO  - Curr Psychiatry Rep. 2023 Nov 30. doi: 10.1007/s11920-023-01477-x.

PMID- 37599779
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230823
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - How children learn to understand language meanings: a neural model of adult-child 
      multimodal interactions in real-time.
PG  - 1216479
LID - 10.3389/fpsyg.2023.1216479 [doi]
LID - 1216479
AB  - This article describes a biological neural network model that can be used to 
      explain how children learn to understand language meanings about the perceptual 
      and affective events that they consciously experience. This kind of learning 
      often occurs when a child interacts with an adult teacher to learn language 
      meanings about events that they experience together. Multiple types of 
      self-organizing brain processes are involved in learning language meanings, 
      including processes that control conscious visual perception, joint attention, 
      object learning and conscious recognition, cognitive working memory, cognitive 
      planning, emotion, cognitive-emotional interactions, volition, and goal-oriented 
      actions. The article shows how all of these brain processes interact to enable 
      the learning of language meanings to occur. The article also contrasts these 
      human capabilities with AI models such as ChatGPT. The current model is called 
      the ChatSOME model, where SOME abbreviates Self-Organizing MEaning.
CI  - Copyright © 2023 Grossberg.
FAU - Grossberg, Stephen
AU  - Grossberg S
AD  - Center for Adaptive Systems, Boston University, Boston, MA, United States.
LA  - eng
PT  - Journal Article
DEP - 20230803
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10435915
OTO - NOTNLM
OT  - circular reaction
OT  - joint attention
OT  - language development
OT  - learning
OT  - meaning
OT  - neural network
OT  - object recognition
OT  - visual perception
COIS- The author declares that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/08/21 06:42
MHDA- 2023/08/21 06:43
CRDT- 2023/08/21 04:36
PHST- 2023/05/15 00:00 [received]
PHST- 2023/06/28 00:00 [accepted]
PHST- 2023/08/21 06:43 [medline]
PHST- 2023/08/21 06:42 [pubmed]
PHST- 2023/08/21 04:36 [entrez]
AID - 10.3389/fpsyg.2023.1216479 [doi]
PST - epublish
SO  - Front Psychol. 2023 Aug 3;14:1216479. doi: 10.3389/fpsyg.2023.1216479. 
      eCollection 2023.

PMID- 37516790
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230801
IS  - 2398-6352 (Electronic)
IS  - 2398-6352 (Linking)
VI  - 6
IP  - 1
DP  - 2023 Jul 29
TI  - The shaky foundations of large language models and foundation models for 
      electronic health records.
PG  - 135
LID - 10.1038/s41746-023-00879-8 [doi]
LID - 135
AB  - The success of foundation models such as ChatGPT and AlphaFold has spurred 
      significant interest in building similar models for electronic medical records 
      (EMRs) to improve patient care and hospital operations. However, recent hype has 
      obscured critical gaps in our understanding of these models' capabilities. In 
      this narrative review, we examine 84 foundation models trained on non-imaging EMR 
      data (i.e., clinical text and/or structured data) and create a taxonomy 
      delineating their architectures, training data, and potential use cases. We find 
      that most models are trained on small, narrowly-scoped clinical datasets (e.g., 
      MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated 
      on tasks that do not provide meaningful insights on their usefulness to health 
      systems. Considering these findings, we propose an improved evaluation framework 
      for measuring the benefits of clinical foundation models that is more closely 
      grounded to metrics that matter in healthcare.
CI  - © 2023. The Author(s).
FAU - Wornow, Michael
AU  - Wornow M
AUID- ORCID: 0000-0003-2215-6527
AD  - Department of Computer Science, Stanford University, Stanford, CA, USA. 
      mwornow@stanford.edu.
FAU - Xu, Yizhe
AU  - Xu Y
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, USA.
FAU - Thapa, Rahul
AU  - Thapa R
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, USA.
FAU - Patel, Birju
AU  - Patel B
AUID- ORCID: 0000-0001-7673-6143
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, USA.
FAU - Steinberg, Ethan
AU  - Steinberg E
AUID- ORCID: 0000-0001-7166-5032
AD  - Department of Computer Science, Stanford University, Stanford, CA, USA.
FAU - Fleming, Scott
AU  - Fleming S
AUID- ORCID: 0000-0002-6047-7877
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, USA.
FAU - Pfeffer, Michael A
AU  - Pfeffer MA
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, USA.
AD  - Technology and Digital Services, Stanford Health Care, Palo Alto, CA, USA.
FAU - Fries, Jason
AU  - Fries J
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, USA.
FAU - Shah, Nigam H
AU  - Shah NH
AUID- ORCID: 0000-0001-9385-7158
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, USA.
AD  - Technology and Digital Services, Stanford Health Care, Palo Alto, CA, USA.
AD  - Department of Medicine, Stanford University School of Medicine, Stanford, CA, 
      USA.
AD  - Clinical Excellence Research Center, Stanford University School of Medicine, 
      Stanford, CA, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230729
PL  - England
TA  - NPJ Digit Med
JT  - NPJ digital medicine
JID - 101731738
PMC - PMC10387101
COIS- B.P. reports stock-based compensation from Google, LLC. Otherwise, the authors 
      declare that there are no competing interests.
EDAT- 2023/07/30 01:06
MHDA- 2023/07/30 01:07
CRDT- 2023/07/29 23:20
PHST- 2023/03/21 00:00 [received]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/07/30 01:07 [medline]
PHST- 2023/07/30 01:06 [pubmed]
PHST- 2023/07/29 23:20 [entrez]
AID - 10.1038/s41746-023-00879-8 [pii]
AID - 879 [pii]
AID - 10.1038/s41746-023-00879-8 [doi]
PST - epublish
SO  - NPJ Digit Med. 2023 Jul 29;6(1):135. doi: 10.1038/s41746-023-00879-8.

PMID- 37145177
OWN - NLM
STAT- MEDLINE
DCOM- 20230809
LR  - 20230809
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 9
DP  - 2023 Sep
TI  - Exploring the Potential of Chat GPT in Personalized Obesity Treatment.
PG  - 1887-1888
LID - 10.1007/s10439-023-03227-9 [doi]
AB  - Obesity has become a serious global health problem. For some patients who cannot 
      be treated with traditional methods, artificial intelligence technologies are a 
      new source of hope. Chat GPT is a language model that has become popular in 
      recent times and has many applications in natural language processing. This 
      article focuses on the potential use of Chat GPT in obesity treatment. Chat GPT 
      can provide personalized recommendations on topics, such as nutrition plans, 
      exercise programs, and psychological support. In this way, a personalized 
      treatment plan can be created based on the individual needs of patients and a 
      more effective approach to obesity treatment can be achieved. However, some 
      ethical and security concerns should also be considered regarding the use of this 
      technology. In conclusion, the potential of Chat GPT in obesity treatment is 
      promising, and with the effective use of this technology, better results can be 
      achieved in obesity treatment.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Arslan, Sedat
AU  - Arslan S
AUID- ORCID: 0000-0002-3356-7332
AD  - Nutrition and Dietetics Department, Faculty of Health Science, Bandirma Onyedi 
      Eylul University, Balıkesir, Turkey. sarslan@bandirma.edu.tr.
LA  - eng
PT  - Letter
DEP - 20230505
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Exercise
MH  - Obesity/therapy
MH  - Technology
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - Obesity
EDAT- 2023/05/05 12:42
MHDA- 2023/08/09 06:43
CRDT- 2023/05/05 11:05
PHST- 2023/04/27 00:00 [received]
PHST- 2023/04/29 00:00 [accepted]
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/05/05 12:42 [pubmed]
PHST- 2023/05/05 11:05 [entrez]
AID - 10.1007/s10439-023-03227-9 [pii]
AID - 10.1007/s10439-023-03227-9 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Sep;51(9):1887-1888. doi: 10.1007/s10439-023-03227-9. Epub 
      2023 May 5.

PMID- 38035200
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231202
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Guiding principles and proposed classification system for the responsible 
      adoption of artificial intelligence in scientific writing in medicine.
PG  - 1283353
LID - 10.3389/frai.2023.1283353 [doi]
LID - 1283353
AB  - The integration of large language models (LLMs) and artificial intelligence (AI) 
      into scientific writing, especially in medical literature, presents both 
      unprecedented opportunities and inherent challenges. This manuscript evaluates 
      the transformative potential of LLMs for the synthesis of information, linguistic 
      enhancements, and global knowledge dissemination. At the same time, it raises 
      concerns about unintentional plagiarism, the risk of misinformation, data biases, 
      and an over-reliance on AI. To address these, we propose governing principles for 
      AI adoption that ensure integrity, transparency, validity, and accountability. 
      Additionally, guidelines for reporting AI involvement in manuscript development 
      are delineated, and a classification system to specify the level of AI assistance 
      is introduced. This approach uniquely addresses the challenges of AI in 
      scientific writing, emphasizing transparency in authorship, qualification of AI 
      involvement, and ethical considerations. Concerns regarding access equity, 
      potential biases in AI-generated content, authorship dynamics, and accountability 
      are also explored, emphasizing the human author's continued responsibility. 
      Recommendations are made for fostering collaboration between AI developers, 
      researchers, and journal editors and for emphasizing the importance of AI's 
      responsible use in academic writing. Regular evaluations of AI's impact on the 
      quality and biases of medical manuscripts are also advocated. As we navigate the 
      expanding realm of AI in scientific discourse, it is crucial to maintain the 
      human element of creativity, ethics, and oversight, ensuring that the integrity 
      of scientific literature remains uncompromised.
CI  - Copyright © 2023 Hryciw, Seely and Kyeremanteng.
FAU - Hryciw, Brett N
AU  - Hryciw BN
AD  - Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, 
      ON, Canada.
FAU - Seely, Andrew J E
AU  - Seely AJE
AD  - Division of Thoracic Surgery, Department of Surgery, The Ottawa Hospital, Ottawa, 
      ON, Canada.
AD  - Clinical Epidemiology, Ottawa Hospital Research Institute, University of Ottawa, 
      Ottawa, ON, Canada.
FAU - Kyeremanteng, Kwadwo
AU  - Kyeremanteng K
AD  - Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, 
      ON, Canada.
AD  - Clinical Epidemiology, Ottawa Hospital Research Institute, University of Ottawa, 
      Ottawa, ON, Canada.
AD  - Institute du Savoir Montfort, Ottawa, ON, Canada.
LA  - eng
PT  - Journal Article
DEP - 20231116
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10687472
OTO - NOTNLM
OT  - artificial intelligence
OT  - ethics
OT  - guidelines and recommendations
OT  - innovation
OT  - large language model
OT  - medicine
OT  - natural language processing
OT  - scientific writing
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/30 18:45
MHDA- 2023/11/30 18:46
CRDT- 2023/11/30 17:46
PHST- 2023/08/25 00:00 [received]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/11/30 18:46 [medline]
PHST- 2023/11/30 18:45 [pubmed]
PHST- 2023/11/30 17:46 [entrez]
AID - 10.3389/frai.2023.1283353 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Nov 16;6:1283353. doi: 10.3389/frai.2023.1283353. 
      eCollection 2023.

PMID- 37865683
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231118
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 21
TI  - PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in 
      first opinion veterinary electronic health records.
PG  - 18015
LID - 10.1038/s41598-023-45155-7 [doi]
LID - 18015
AB  - Effective public health surveillance requires consistent monitoring of disease 
      signals such that researchers and decision-makers can react dynamically to 
      changes in disease occurrence. However, whilst surveillance initiatives exist in 
      production animal veterinary medicine, comparable frameworks for companion 
      animals are lacking. First-opinion veterinary electronic health records (EHRs) 
      have the potential to reveal disease signals and often represent the initial 
      reporting of clinical syndromes in animals presenting for medical attention, 
      highlighting their possible significance in early disease detection. Yet despite 
      their availability, there are limitations surrounding their free text-based 
      nature, inhibiting the ability for national-level mortality and morbidity 
      statistics to occur. This paper presents PetBERT, a large language model trained 
      on over 500 million words from 5.1 million EHRs across the UK. PetBERT-ICD is the 
      additional training of PetBERT as a multi-label classifier for the automated 
      coding of veterinary clinical EHRs with the International Classification of 
      Disease 11 framework, achieving F1 scores exceeding 83% across 20 disease codings 
      with minimal annotations. PetBERT-ICD effectively identifies disease outbreaks, 
      outperforming current clinician-assigned point-of-care labelling strategies up to 
      3 weeks earlier. The potential for PetBERT-ICD to enhance disease surveillance in 
      veterinary medicine represents a promising avenue for advancing animal health and 
      improving public health outcomes.
CI  - © 2023. Springer Nature Limited.
FAU - Farrell, Sean
AU  - Farrell S
AD  - Department of Computer Science, Durham University, Durham, UK. 
      sean.farrell2@durham.ac.uk.
FAU - Appleton, Charlotte
AU  - Appleton C
AD  - Centre for Health Informatics, Computing, and Statistics, Lancaster Medical 
      School, Lancaster University, Lancaster, UK.
FAU - Noble, Peter-John Mäntylä
AU  - Noble PM
AD  - Institute of Infection, Veterinary and Ecological Sciences, University of 
      Liverpool, Liverpool, UK.
FAU - Al Moubayed, Noura
AU  - Al Moubayed N
AD  - Department of Computer Science, Durham University, Durham, UK.
AD  - Evergreen Life Ltd, Manchester, UK.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20231021
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Animals
MH  - *International Classification of Diseases
MH  - *Electronic Health Records
MH  - Disease Outbreaks/veterinary
MH  - Public Health Surveillance
PMC - PMC10590382
COIS- The authors declare no competing interests.
EDAT- 2023/10/22 00:41
MHDA- 2023/11/02 12:45
CRDT- 2023/10/21 23:21
PHST- 2023/06/19 00:00 [received]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/11/02 12:45 [medline]
PHST- 2023/10/22 00:41 [pubmed]
PHST- 2023/10/21 23:21 [entrez]
AID - 10.1038/s41598-023-45155-7 [pii]
AID - 45155 [pii]
AID - 10.1038/s41598-023-45155-7 [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 21;13(1):18015. doi: 10.1038/s41598-023-45155-7.

PMID- 37742294
OWN - NLM
STAT- MEDLINE
DCOM- 20231009
LR  - 20231012
IS  - 1945-4589 (Electronic)
IS  - 1945-4589 (Linking)
VI  - 15
IP  - 18
DP  - 2023 Sep 22
TI  - Biomedical generative pre-trained based transformer language model for 
      age-related disease target discovery.
PG  - 9293-9309
LID - 10.18632/aging.205055 [doi]
AB  - Target discovery is crucial for the development of innovative therapeutics and 
      diagnostics. However, current approaches often face limitations in efficiency, 
      specificity, and scalability, necessitating the exploration of novel strategies 
      for identifying and validating disease-relevant targets. Advances in natural 
      language processing have provided new avenues for predicting potential 
      therapeutic targets for various diseases. Here, we present a novel approach for 
      predicting therapeutic targets using a large language model (LLM). We trained a 
      domain-specific BioGPT model on a large corpus of biomedical literature 
      consisting of grant text and developed a pipeline for generating target 
      prediction. Our study demonstrates that pre-training of the LLM model with 
      task-specific texts improves its performance. Applying the developed pipeline, we 
      retrieved prospective aging and age-related disease targets and showed that these 
      proteins are in correspondence with the database data. Moreover, we propose CCR5 
      and PTH as potential novel dual-purpose anti-aging and disease targets which were 
      not previously identified as age-related but were highly ranked in our approach. 
      Overall, our work highlights the high potential of transformer models in novel 
      target prediction and provides a roadmap for future integration of AI approaches 
      for addressing the intricate challenges presented in the biomedical field.
FAU - Zagirova, Diana
AU  - Zagirova D
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Pushkov, Stefan
AU  - Pushkov S
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Leung, Geoffrey Ho Duen
AU  - Leung GHD
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Liu, Bonnie Hei Man
AU  - Liu BHM
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Urban, Anatoly
AU  - Urban A
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Sidorenko, Denis
AU  - Sidorenko D
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Kalashnikov, Aleksandr
AU  - Kalashnikov A
AD  - Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, 
      Masdar City, Abu Dhabi, UAE.
FAU - Kozlova, Ekaterina
AU  - Kozlova E
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Naumov, Vladimir
AU  - Naumov V
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Pun, Frank W
AU  - Pun FW
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Ozerov, Ivan V
AU  - Ozerov IV
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
FAU - Aliper, Alex
AU  - Aliper A
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
AD  - Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, 
      Masdar City, Abu Dhabi, UAE.
FAU - Zhavoronkov, Alex
AU  - Zhavoronkov A
AD  - Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New 
      Territories, Hong Kong, China.
AD  - Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, 
      Masdar City, Abu Dhabi, UAE.
LA  - eng
PT  - Journal Article
DEP - 20230922
PL  - United States
TA  - Aging (Albany NY)
JT  - Aging
JID - 101508617
SB  - IM
MH  - Prospective Studies
MH  - Databases, Factual
MH  - *Language
PMC - PMC10564439
OTO - NOTNLM
OT  - aging biomarkers
OT  - deep learning
OT  - human aging
OT  - therapeutic target discovery
OT  - transformers
COIS- CONFLICTS OF INTEREST: DZ, SP, GL, BL, AU, DS, EK, AK, VN, FR, IO, AA, and AZ are 
      affiliated with Insilico Medicine, a commercial company developing AI solutions 
      for aging research, drug discovery, and longevity medicine.
EDAT- 2023/09/24 18:41
MHDA- 2023/10/09 06:42
CRDT- 2023/09/24 15:09
PHST- 2023/06/15 00:00 [received]
PHST- 2023/08/20 00:00 [accepted]
PHST- 2023/10/09 06:42 [medline]
PHST- 2023/09/24 18:41 [pubmed]
PHST- 2023/09/24 15:09 [entrez]
AID - 205055 [pii]
AID - 10.18632/aging.205055 [doi]
PST - ppublish
SO  - Aging (Albany NY). 2023 Sep 22;15(18):9293-9309. doi: 10.18632/aging.205055. Epub 
      2023 Sep 22.

PMID- 37955873
OWN - NLM
STAT- Publisher
LR  - 20231113
IS  - 2168-6114 (Electronic)
IS  - 2168-6106 (Linking)
DP  - 2023 Nov 13
TI  - Health Disinformation Use Case Highlighting the Urgent Need for Artificial 
      Intelligence Vigilance: Weapons of Mass Disinformation.
LID - 10.1001/jamainternmed.2023.5947 [doi]
AB  - IMPORTANCE: Although artificial intelligence (AI) offers many promises across 
      modern medicine, it may carry a significant risk for the mass generation of 
      targeted health disinformation. This poses an urgent threat toward public health 
      initiatives and calls for rapid attention by health care professionals, AI 
      developers, and regulators to ensure public safety. OBSERVATIONS: As an example, 
      using a single publicly available large-language model, within 65 minutes, 102 
      distinct blog articles were generated that contained more than 17 000 words of 
      disinformation related to vaccines and vaping. Each post was coercive and 
      targeted at diverse societal groups, including young adults, young parents, older 
      persons, pregnant people, and those with chronic health conditions. The blogs 
      included fake patient and clinician testimonials and obeyed prompting for the 
      inclusion of scientific-looking referencing. Additional generative AI tools 
      created an accompanying 20 realistic images in less than 2 minutes. This process 
      was undertaken by health care professionals and researchers with no specialized 
      knowledge in bypassing AI guardrails, relying solely on publicly available 
      information. CONCLUSIONS AND RELEVANCE: These observations demonstrate that when 
      the guardrails of AI tools are insufficient, the ability to rapidly generate 
      diverse and large amounts of convincing disinformation is profound. Beyond 
      providing 2 example scenarios, these findings demonstrate an urgent need for 
      robust AI vigilance. The AI tools are rapidly progressing; alongside these 
      advancements, emergent risks are becoming increasingly apparent. Key pillars of 
      pharmacovigilance-including transparency, surveillance, and regulation-may serve 
      as valuable examples for managing these risks and safeguarding public health.
FAU - Menz, Bradley D
AU  - Menz BD
AD  - Discipline of Clinical Pharmacology, College of Medicine and Public Health, 
      Flinders University, Adelaide, Australia.
FAU - Modi, Natansh D
AU  - Modi ND
AD  - Discipline of Clinical Pharmacology, College of Medicine and Public Health, 
      Flinders University, Adelaide, Australia.
FAU - Sorich, Michael J
AU  - Sorich MJ
AD  - Discipline of Clinical Pharmacology, College of Medicine and Public Health, 
      Flinders University, Adelaide, Australia.
FAU - Hopkins, Ashley M
AU  - Hopkins AM
AD  - Discipline of Clinical Pharmacology, College of Medicine and Public Health, 
      Flinders University, Adelaide, Australia.
LA  - eng
PT  - Journal Article
DEP - 20231113
PL  - United States
TA  - JAMA Intern Med
JT  - JAMA internal medicine
JID - 101589534
SB  - IM
EDAT- 2023/11/13 12:43
MHDA- 2023/11/13 12:43
CRDT- 2023/11/13 11:33
PHST- 2023/11/13 12:43 [medline]
PHST- 2023/11/13 12:43 [pubmed]
PHST- 2023/11/13 11:33 [entrez]
AID - 2811333 [pii]
AID - 10.1001/jamainternmed.2023.5947 [doi]
PST - aheadofprint
SO  - JAMA Intern Med. 2023 Nov 13. doi: 10.1001/jamainternmed.2023.5947.

PMID- 37883049
OWN - NLM
STAT- Publisher
LR  - 20231026
IS  - 1939-1285 (Electronic)
IS  - 0278-7393 (Linking)
DP  - 2023 Oct 26
TI  - The function/content word distinction and eye movements in reading.
LID - 10.1037/xlm0001301 [doi]
AB  - A substantial quantity of research has explored whether readers' eye movements 
      are sensitive to the distinction between function and content words. No clear 
      answer has emerged, in part due to the difficulty of accounting for differences 
      in length, frequency, and predictability between the words in the two classes. 
      Based on evidence that readers differentially overlook function word errors, we 
      hypothesized that function words may be more frequently skipped or may receive 
      shorter fixations. We present two very large-scale eyetracking experiments using 
      selected sentences from a corpus of natural text, with each sentence containing a 
      target function or content word. The target words in the two classes were 
      carefully matched on length, frequency, and predictability, with the latter 
      variable operationalized in terms of next-word probability obtained from the 
      large language model GPT-2. While the experiments replicated a range of expected 
      effects, word class did not have any clear influence on target word skipping 
      probability, and there was some evidence for a content word advantage in fixation 
      duration measures. These results indicate that readers' tendency to overlook 
      function word errors is not due to reduced time spent encoding these words. The 
      results also broadly support the implicit assumption in prominent models of eye 
      movement control in reading that a word's syntactic category does not play an 
      important role in decisions about when and where to move the eyes. (PsycInfo 
      Database Record (c) 2023 APA, all rights reserved).
FAU - Staub, Adrian
AU  - Staub A
AUID- ORCID: 0000-0002-9045-9610
AD  - Department of Psychological and Brain Sciences, University of Massachusetts 
      Amherst.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - United States
TA  - J Exp Psychol Learn Mem Cogn
JT  - Journal of experimental psychology. Learning, memory, and cognition
JID - 8207540
SB  - IM
EDAT- 2023/10/26 12:43
MHDA- 2023/10/26 12:43
CRDT- 2023/10/26 11:17
PHST- 2023/10/26 12:43 [medline]
PHST- 2023/10/26 12:43 [pubmed]
PHST- 2023/10/26 11:17 [entrez]
AID - 2024-20471-001 [pii]
AID - 10.1037/xlm0001301 [doi]
PST - aheadofprint
SO  - J Exp Psychol Learn Mem Cogn. 2023 Oct 26. doi: 10.1037/xlm0001301.

PMID- 38021866
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Evaluating the Usefulness of a Large Language Model as a Wholesome Tool for De 
      Novo Polymerase Chain Reaction (PCR) Primer Design.
PG  - e47711
LID - 10.7759/cureus.47711 [doi]
LID - e47711
AB  - This study aimed to assess the ability of language learning models (LLMs), 
      specifically GPT-3.5 (Chat Generative Pre-trained Transformer 3.5) and GPT-4 
      (Chat Generative Pre-trained Transformer 3.5), in designing primers for 
      diagnostic polymerase chain reaction (PCR) of the monkeypox virus (MPXV). Five 
      primer pairs were generated by each LLM, and their thermodynamic properties and 
      specificity were analysed post-hoc using commonly used software. The LLMs 
      demonstrated ability in sequence generation and predicting melting temperatures 
      (Tm), but their accuracy in predicting GC content was suboptimal, necessitating 
      further investigation. Results indicated that, of the total primer pairs, only 
      three designed by GPT-4 and two by GPT-3.5 could theoretically form a PCR 
      product, but only one pair demonstrated suitable parameters for experimental 
      validation. This preliminary exploration suggests that while LLMs have a 
      potential in aiding primer design, their accuracy needs improvement to match 
      current deterministic, rule-based tools used in the field. Consequently, manual 
      intervention remains a crucial step in PCR primer design.
CI  - Copyright © 2023, Jorapur et al.
FAU - Jorapur, Soham
AU  - Jorapur S
AD  - Department of Biological Sciences, Indian Institute of Science Education and 
      Research, Bhopal, Bhopal, IND.
FAU - Srivastava, Amisha
AU  - Srivastava A
AD  - Department of Electrical & Computer Engineering, University of Texas at Dallas, 
      Richardson, USA.
FAU - Kulkarni, Suyamindra
AU  - Kulkarni S
AD  - Department of Higher Education, Government of Karnataka, Karnataka Institute for 
      DNA Research (KIDNAR), Dharwad, IND.
LA  - eng
PT  - Journal Article
DEP - 20231026
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10676229
OTO - NOTNLM
OT  - chat gpt
OT  - diagnostics
OT  - large language models (llms)
OT  - molecular biology
OT  - monkeypox virus
OT  - pcr
OT  - primer
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:13
PHST- 2023/10/26 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:13 [entrez]
AID - 10.7759/cureus.47711 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 26;15(10):e47711. doi: 10.7759/cureus.47711. eCollection 2023 
      Oct.

PMID- 37755795
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231003
IS  - 2305-6304 (Electronic)
IS  - 2305-6304 (Linking)
VI  - 11
IP  - 9
DP  - 2023 Sep 15
TI  - Datasets Construction and Development of QSAR Models for Predicting Micronucleus 
      In Vitro and In Vivo Assay Outcomes.
LID - 10.3390/toxics11090785 [doi]
LID - 785
AB  - In silico (quantitative) structure-activity relationship modeling is an approach 
      that provides a fast and cost-effective alternative to assess the genotoxic 
      potential of chemicals. However, one of the limiting factors for model 
      development is the availability of consolidated experimental datasets. In the 
      present study, we collected experimental data on micronuclei in vitro and in 
      vivo, utilizing databases and conducting a PubMed search, aided by text mining 
      using the BioBERT large language model. Chemotype enrichment analysis on the 
      updated datasets was performed to identify enriched substructures. Additionally, 
      chemotypes common for both endpoints were found. Five machine learning models in 
      combination with molecular descriptors, twelve fingerprints and two data 
      balancing techniques were applied to construct individual models. The 
      best-performing individual models were selected for the ensemble construction. 
      The curated final dataset consists of 981 chemicals for micronuclei in vitro and 
      1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched 
      in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. 
      The ensemble model exhibited high accuracy and sensitivity when applied to an 
      external test set of in vitro data. A good balanced predictive performance was 
      also achieved for the micronucleus in vivo endpoint.
FAU - Khondkaryan, Lusine
AU  - Khondkaryan L
AD  - Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia.
AD  - Toxometris.ai, Yerevan 0009, Armenia.
FAU - Tevosyan, Ani
AU  - Tevosyan A
AD  - Toxometris.ai, Yerevan 0009, Armenia.
AD  - YerevaNN, Yerevan 0025, Armenia.
FAU - Navasardyan, Hayk
AU  - Navasardyan H
AD  - Toxometris.ai, Yerevan 0009, Armenia.
FAU - Khachatrian, Hrant
AU  - Khachatrian H
AD  - YerevaNN, Yerevan 0025, Armenia.
AD  - Department of Informatics and Applied Mathematics, Yerevan State University, 
      Yerevan 0025, Armenia.
FAU - Tadevosyan, Gohar
AU  - Tadevosyan G
AD  - Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia.
AD  - Toxometris.ai, Yerevan 0009, Armenia.
FAU - Apresyan, Lilit
AU  - Apresyan L
AD  - Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia.
AD  - Toxometris.ai, Yerevan 0009, Armenia.
FAU - Chilingaryan, Gayane
AU  - Chilingaryan G
AD  - YerevaNN, Yerevan 0025, Armenia.
FAU - Navoyan, Zaven
AU  - Navoyan Z
AD  - Toxometris.ai, Yerevan 0009, Armenia.
FAU - Stopper, Helga
AU  - Stopper H
AUID- ORCID: 0000-0003-4168-8211
AD  - Institute of Pharmacology and Toxicology, University of Würzburg, 97078 Würzburg, 
      Germany.
FAU - Babayan, Nelly
AU  - Babayan N
AUID- ORCID: 0000-0001-9205-7693
AD  - Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia.
AD  - Toxometris.ai, Yerevan 0009, Armenia.
LA  - eng
GR  - № 20TTCG-1F004/RA MES State Committee of Science/
PT  - Journal Article
DEP - 20230915
PL  - Switzerland
TA  - Toxics
JT  - Toxics
JID - 101639637
PMC - PMC10537630
OTO - NOTNLM
OT  - chemotypes analysis
OT  - ensemble
OT  - in vitro
OT  - in vivo
OT  - micronucleus
OT  - prediction
COIS- The authors declare no conflict of interest.
EDAT- 2023/09/27 12:41
MHDA- 2023/09/27 12:42
CRDT- 2023/09/27 11:33
PHST- 2023/08/16 00:00 [received]
PHST- 2023/09/07 00:00 [revised]
PHST- 2023/09/11 00:00 [accepted]
PHST- 2023/09/27 12:42 [medline]
PHST- 2023/09/27 12:41 [pubmed]
PHST- 2023/09/27 11:33 [entrez]
AID - toxics11090785 [pii]
AID - toxics-11-00785 [pii]
AID - 10.3390/toxics11090785 [doi]
PST - epublish
SO  - Toxics. 2023 Sep 15;11(9):785. doi: 10.3390/toxics11090785.

PMID- 38036543
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231205
IS  - 2041-1723 (Electronic)
IS  - 2041-1723 (Linking)
VI  - 14
IP  - 1
DP  - 2023 Nov 30
TI  - Augmenting interpretable models with large language models during training.
PG  - 7913
LID - 10.1038/s41467-023-43713-1 [doi]
LID - 7913
AB  - Recent large language models (LLMs), such as ChatGPT, have demonstrated 
      remarkable prediction performance for a growing array of tasks. However, their 
      proliferation into high-stakes domains and compute-limited settings has created a 
      burgeoning need for interpretability and efficiency. We address this need by 
      proposing Aug-imodels, a framework for leveraging the knowledge learned by LLMs 
      to build extremely efficient and interpretable prediction models. Aug-imodels use 
      LLMs during fitting but not during inference, allowing complete transparency and 
      often a speed/memory improvement of greater than 1000x for inference compared to 
      LLMs. We explore two instantiations of Aug-imodels in natural-language 
      processing: Aug-Linear, which augments a linear model with decoupled embeddings 
      from an LLM and Aug-Tree, which augments a decision tree with LLM feature 
      expansions. Across a variety of text-classification datasets, both outperform 
      their non-augmented, interpretable counterparts. Aug-Linear can even outperform 
      much larger models, e.g. a 6-billion parameter GPT-J model, despite having 
      10,000x fewer parameters and being fully transparent. We further explore 
      Aug-imodels in a natural-language fMRI study, where they generate interesting 
      interpretations from scientific data.
CI  - © 2023. The Author(s).
FAU - Singh, Chandan
AU  - Singh C
AUID- ORCID: 0000-0003-0318-2340
AD  - Microsoft Research, Redmond, WA, USA. chansingh@microsoft.com.
FAU - Askari, Armin
AU  - Askari A
AD  - University of California, Berkeley, Berkeley, CA, USA.
FAU - Caruana, Rich
AU  - Caruana R
AD  - Microsoft Research, Redmond, WA, USA.
FAU - Gao, Jianfeng
AU  - Gao J
AD  - Microsoft Research, Redmond, WA, USA.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - England
TA  - Nat Commun
JT  - Nature communications
JID - 101528555
SB  - IM
MH  - *Learning
MH  - *Knowledge
MH  - Language
MH  - Linear Models
MH  - Natural Language Processing
PMC - PMC10689442
COIS- The authors declare no competing interests.
EDAT- 2023/12/01 00:43
MHDA- 2023/12/04 12:43
CRDT- 2023/11/30 23:23
PHST- 2023/07/28 00:00 [received]
PHST- 2023/11/17 00:00 [accepted]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/12/01 00:43 [pubmed]
PHST- 2023/11/30 23:23 [entrez]
AID - 10.1038/s41467-023-43713-1 [pii]
AID - 43713 [pii]
AID - 10.1038/s41467-023-43713-1 [doi]
PST - epublish
SO  - Nat Commun. 2023 Nov 30;14(1):7913. doi: 10.1038/s41467-023-43713-1.

PMID- 37344394
OWN - NLM
STAT- MEDLINE
DCOM- 20230714
LR  - 20230718
IS  - 1881-7823 (Electronic)
IS  - 1881-7815 (Linking)
VI  - 17
IP  - 3
DP  - 2023 Jul 11
TI  - The potential of 'Segment Anything' (SAM) for universal intelligent ultrasound 
      image guidance.
PG  - 230-233
LID - 10.5582/bst.2023.01119 [doi]
AB  - Ultrasound image guidance is a method often used to help provide care, and it 
      relies on accurate perception of information, and particularly tissue 
      recognition, to guide medical procedures. It is widely used in various scenarios 
      that are often complex. Recent breakthroughs in large models, such as ChatGPT for 
      natural language processing and Segment Anything Model (SAM) for image 
      segmentation, have revolutionized interaction with information. These large 
      models exhibit a revolutionized understanding of basic information, holding 
      promise for medicine, including the potential for universal autonomous ultrasound 
      image guidance. The current study evaluated the performance of SAM on commonly 
      used ultrasound images and it discusses SAM's potential contribution to an 
      intelligent image-guided framework, with a specific focus on autonomous and 
      universal ultrasound image guidance. Results indicate that SAM performs well in 
      ultrasound image segmentation and has the potential to enable universal 
      intelligent ultrasound image guidance.
FAU - Ning, Guochen
AU  - Ning G
AD  - Department of Biomedical Engineering, School of Medicine, Tsinghua University, 
      Beijing, China.
FAU - Liang, Hanyin
AU  - Liang H
AD  - Department of Biomedical Engineering, School of Medicine, Tsinghua University, 
      Beijing, China.
FAU - Jiang, Zhongliang
AU  - Jiang Z
AD  - Computer Aided Medical Procedures, Technical University of Munich, Germany.
FAU - Zhang, Hui
AU  - Zhang H
AD  - Department of Biomedical Engineering, School of Medicine, Tsinghua University, 
      Beijing, China.
FAU - Liao, Hongen
AU  - Liao H
AD  - Department of Biomedical Engineering, School of Medicine, Tsinghua University, 
      Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20230622
PL  - Japan
TA  - Biosci Trends
JT  - Bioscience trends
JID - 101502754
SB  - IM
MH  - *Algorithms
MH  - Ultrasonography
OTO - NOTNLM
OT  - intelligent medical image guidance
OT  - large models
OT  - medical artificial intelligence
OT  - ultrasound image
EDAT- 2023/06/22 01:07
MHDA- 2023/07/14 13:05
CRDT- 2023/06/21 23:02
PHST- 2023/07/14 13:05 [medline]
PHST- 2023/06/22 01:07 [pubmed]
PHST- 2023/06/21 23:02 [entrez]
AID - 10.5582/bst.2023.01119 [doi]
PST - ppublish
SO  - Biosci Trends. 2023 Jul 11;17(3):230-233. doi: 10.5582/bst.2023.01119. Epub 2023 
      Jun 22.

PMID- 36941906
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230326
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Delayed Skin Allergy to Glucosamine Chondroitin Supplement.
PG  - e36310
LID - 10.7759/cureus.36310 [doi]
LID - e36310
AB  - Glucosamine chondroitin is a popular dietary supplement used for joint health and 
      osteoarthritis pain and is one of the dietary supplements commonly recommended by 
      chiropractors. Herein, we present the case of a 36-year-old woman who developed a 
      skin rash with delayed onset after taking glucosamine and chondroitin pills for 
      lumbar degenerative joint disease. Within 3 hours of taking the supplement, she 
      developed an itchy rash on her torso and legs. Over the next few hours, the rash 
      spread over her entire body, and facial swelling developed. Given the timing of 
      the symptoms after the administration of glucosamine chondroitin, an allergic 
      reaction was suspected. The supplement was withdrawn and the allergic reactions 
      were treated with antihistamines and steroids for several days. This case report 
      demonstrates the need to recognize delayed allergic reactions as a potential side 
      effect of widely used supplements, such as glucosamine chondroitin, which can 
      produce hypersensitivity reactions in sensitive individuals.
CI  - Copyright © 2023, Chu et al.
FAU - Chu, Eric Chun-Pu
AU  - Chu EC
AD  - Chiropractic Department, New York Medical Group, EC Healthcare, Hong Kong, HKG.
FAU - Huang, Kevin Hsu Kai
AU  - Huang KHK
AD  - Chiropractic Department, New York Medical Group, EC Healthcare, Yuen Long, HKG.
FAU - Cheung, Gordon
AU  - Cheung G
AD  - Chiropractic Department, New York Medical Group, EC Healthcare, Hong Kong, HKG.
FAU - Ng, Gabriel
AU  - Ng G
AD  - Chiropractic Department, New York Medical Group, EC Healthcare, Hong Kong, HKG.
FAU - Lin, Andy
AU  - Lin A
AD  - Chiropractic Department, New York Medical Group, EC Healthcare, Hong Kong, HKG.
LA  - eng
PT  - Case Reports
DEP - 20230317
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10024115
OTO - NOTNLM
OT  - chatgpt
OT  - chiropractic management
OT  - chiropractic therapy
OT  - degenerative spine disease
OT  - glucosamine
OT  - glucosamine sulfate
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/22 06:00
MHDA- 2023/03/22 06:01
CRDT- 2023/03/21 02:06
PHST- 2023/03/17 00:00 [accepted]
PHST- 2023/03/21 02:06 [entrez]
PHST- 2023/03/22 06:00 [pubmed]
PHST- 2023/03/22 06:01 [medline]
AID - 10.7759/cureus.36310 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 17;15(3):e36310. doi: 10.7759/cureus.36310. eCollection 2023 
      Mar.

PMID- 38012673
OWN - NLM
STAT- MEDLINE
DCOM- 20231129
LR  - 20231130
IS  - 1756-8722 (Electronic)
IS  - 1756-8722 (Linking)
VI  - 16
IP  - 1
DP  - 2023 Nov 27
TI  - Novel research and future prospects of artificial intelligence in cancer 
      diagnosis and treatment.
PG  - 114
LID - 10.1186/s13045-023-01514-5 [doi]
LID - 114
AB  - Research into the potential benefits of artificial intelligence for comprehending 
      the intricate biology of cancer has grown as a result of the widespread use of 
      deep learning and machine learning in the healthcare sector and the availability 
      of highly specialized cancer datasets. Here, we review new artificial 
      intelligence approaches and how they are being used in oncology. We describe how 
      artificial intelligence might be used in the detection, prognosis, and 
      administration of cancer treatments and introduce the use of the latest large 
      language models such as ChatGPT in oncology clinics. We highlight artificial 
      intelligence applications for omics data types, and we offer perspectives on how 
      the various data types might be combined to create decision-support tools. We 
      also evaluate the present constraints and challenges to applying artificial 
      intelligence in precision oncology. Finally, we discuss how current challenges 
      may be surmounted to make artificial intelligence useful in clinical settings in 
      the future.
CI  - © 2023. The Author(s).
FAU - Zhang, Chaoyi
AU  - Zhang C
AD  - Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 
      270 Dong'An Road, Shanghai, 200032, People's Republic of China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, People's Republic of China.
AD  - Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, 
      People's Republic of China.
AD  - Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's 
      Republic of China.
FAU - Xu, Jin
AU  - Xu J
AD  - Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 
      270 Dong'An Road, Shanghai, 200032, People's Republic of China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, People's Republic of China.
AD  - Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, 
      People's Republic of China.
AD  - Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's 
      Republic of China.
FAU - Tang, Rong
AU  - Tang R
AD  - Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 
      270 Dong'An Road, Shanghai, 200032, People's Republic of China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, People's Republic of China.
AD  - Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, 
      People's Republic of China.
AD  - Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's 
      Republic of China.
FAU - Yang, Jianhui
AU  - Yang J
AD  - Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 
      270 Dong'An Road, Shanghai, 200032, People's Republic of China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, People's Republic of China.
AD  - Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, 
      People's Republic of China.
AD  - Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's 
      Republic of China.
FAU - Wang, Wei
AU  - Wang W
AD  - Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 
      270 Dong'An Road, Shanghai, 200032, People's Republic of China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, People's Republic of China.
AD  - Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, 
      People's Republic of China.
AD  - Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's 
      Republic of China.
FAU - Yu, Xianjun
AU  - Yu X
AD  - Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 
      270 Dong'An Road, Shanghai, 200032, People's Republic of China. 
      yuxianjun@fudanpci.org.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, People's Republic of China. yuxianjun@fudanpci.org.
AD  - Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, 
      People's Republic of China. yuxianjun@fudanpci.org.
AD  - Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's 
      Republic of China. yuxianjun@fudanpci.org.
FAU - Shi, Si
AU  - Shi S
AD  - Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 
      270 Dong'An Road, Shanghai, 200032, People's Republic of China. 
      shisi@fudanpci.org.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, People's Republic of China. shisi@fudanpci.org.
AD  - Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, 
      People's Republic of China. shisi@fudanpci.org.
AD  - Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's 
      Republic of China. shisi@fudanpci.org.
LA  - eng
GR  - U21A20374/National Natural Science Foundation of China/
GR  - 82072698/National Natural Science Foundation of China/
GR  - 21JC1401500/Shanghai Municipal Science and Technology Major Project/
GR  - 2019-01-07-00-07-E00057/Scientific Innovation Project of Shanghai Education 
      Committee/
GR  - 23ZR1479300/Natural Science Foundation of Shanghai/
PT  - Journal Article
PT  - Review
DEP - 20231127
PL  - England
TA  - J Hematol Oncol
JT  - Journal of hematology & oncology
JID - 101468937
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Neoplasms/diagnosis/genetics/therapy
MH  - Precision Medicine
MH  - Machine Learning
MH  - Medical Oncology
PMC - PMC10680201
COIS- The authors declare that they have no competing interests.
EDAT- 2023/11/28 06:42
MHDA- 2023/11/29 06:42
CRDT- 2023/11/28 00:07
PHST- 2023/10/07 00:00 [received]
PHST- 2023/11/20 00:00 [accepted]
PHST- 2023/11/29 06:42 [medline]
PHST- 2023/11/28 06:42 [pubmed]
PHST- 2023/11/28 00:07 [entrez]
AID - 10.1186/s13045-023-01514-5 [pii]
AID - 1514 [pii]
AID - 10.1186/s13045-023-01514-5 [doi]
PST - epublish
SO  - J Hematol Oncol. 2023 Nov 27;16(1):114. doi: 10.1186/s13045-023-01514-5.

PMID- 37960458
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231117
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 21
DP  - 2023 Oct 27
TI  - A Study on Generative Models for Visual Recognition of Unknown Scenes Using a 
      Textual Description.
LID - 10.3390/s23218757 [doi]
LID - 8757
AB  - In this study, we investigate the application of generative models to assist 
      artificial agents, such as delivery drones or service robots, in visualising 
      unfamiliar destinations solely based on textual descriptions. We explore the use 
      of generative models, such as Stable Diffusion, and embedding representations, 
      such as CLIP and VisualBERT, to compare generated images obtained from textual 
      descriptions of target scenes with images of those scenes. Our research 
      encompasses three key strategies: image generation, text generation, and text 
      enhancement, the latter involving tools such as ChatGPT to create concise textual 
      descriptions for evaluation. The findings of this study contribute to an 
      understanding of the impact of combining generative tools with multi-modal 
      embedding representations to enhance the artificial agent's ability to recognise 
      unknown scenes. Consequently, we assert that this research holds broad 
      applications, particularly in drone parcel delivery, where an aerial robot can 
      employ text descriptions to identify a destination. Furthermore, this concept can 
      also be applied to other service robots tasked with delivering to unfamiliar 
      locations, relying exclusively on user-provided textual descriptions.
FAU - Martinez-Carranza, Jose
AU  - Martinez-Carranza J
AUID- ORCID: 0000-0002-8914-1904
AD  - Department of Computational Science, Instituto Nacional de Astrofisica, Optica y 
      Electronica (INAOE), Puebla 72840, Mexico.
FAU - Hernández-Farías, Delia Irazú
AU  - Hernández-Farías DI
AUID- ORCID: 0000-0003-2133-8716
AD  - Department of Computational Science, Instituto Nacional de Astrofisica, Optica y 
      Electronica (INAOE), Puebla 72840, Mexico.
FAU - Vazquez-Meza, Victoria Eugenia
AU  - Vazquez-Meza VE
AUID- ORCID: 0009-0000-8819-3676
AD  - Department of Computational Science, Instituto Nacional de Astrofisica, Optica y 
      Electronica (INAOE), Puebla 72840, Mexico.
FAU - Rojas-Perez, Leticia Oyuki
AU  - Rojas-Perez LO
AUID- ORCID: 0000-0002-9440-9944
AD  - Department of Computational Science, Instituto Nacional de Astrofisica, Optica y 
      Electronica (INAOE), Puebla 72840, Mexico.
FAU - Cabrera-Ponce, Aldrich Alfredo
AU  - Cabrera-Ponce AA
AUID- ORCID: 0000-0002-9998-7444
AD  - Faculty of Computer Science, Benemerita Universidad Autonoma de Puebla (BUAP), 
      Puebla 72570, Mexico.
LA  - eng
PT  - Journal Article
DEP - 20231027
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10649081
OTO - NOTNLM
OT  - CLIP
OT  - diffusion model
OT  - generative models
OT  - textual descriptions
OT  - visual scene recognition
OT  - visualBERT
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/14 06:43
MHDA- 2023/11/14 06:44
CRDT- 2023/11/14 02:19
PHST- 2023/09/29 00:00 [received]
PHST- 2023/10/23 00:00 [revised]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/11/14 06:44 [medline]
PHST- 2023/11/14 06:43 [pubmed]
PHST- 2023/11/14 02:19 [entrez]
AID - s23218757 [pii]
AID - sensors-23-08757 [pii]
AID - 10.3390/s23218757 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Oct 27;23(21):8757. doi: 10.3390/s23218757.

PMID- 37898699
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231031
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 28
TI  - Languages with more speakers tend to be harder to (machine-)learn.
PG  - 18521
LID - 10.1038/s41598-023-45373-z [doi]
LID - 18521
AB  - Computational language models (LMs), most notably exemplified by the widespread 
      success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range 
      of linguistic tasks, thus providing cognitive science and linguistics with a 
      computational working model to empirically study different aspects of human 
      language. Here, we use LMs to test the hypothesis that languages with more 
      speakers tend to be easier to learn. In two experiments, we train several 
      LMs-ranging from very simple n-gram models to state-of-the-art deep neural 
      networks-on written cross-linguistic corpus data covering 1293 different 
      languages and statistically estimate learning difficulty. Using a variety of 
      quantitative methods and machine learning techniques to account for phylogenetic 
      relatedness and geographical proximity of languages, we show that there is robust 
      evidence for a relationship between learning difficulty and speaker population 
      size. However, contrary to expectations derived from previous research, our 
      results suggest that languages with more speakers tend to be harder to learn.
CI  - © 2023. The Author(s).
FAU - Koplenig, Alexander
AU  - Koplenig A
AD  - Leibniz Institute for the German Language (IDS), Mannheim, Germany. 
      koplenig@ids-mannheim.de.
FAU - Wolfer, Sascha
AU  - Wolfer S
AD  - Leibniz Institute for the German Language (IDS), Mannheim, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231028
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - Phylogeny
MH  - *Language
MH  - *Linguistics
MH  - Neural Networks, Computer
MH  - Machine Learning
PMC - PMC10613286
COIS- The authors declare no competing interests.
EDAT- 2023/10/29 06:46
MHDA- 2023/10/30 06:46
CRDT- 2023/10/29 00:30
PHST- 2023/08/24 00:00 [received]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/10/30 06:46 [medline]
PHST- 2023/10/29 06:46 [pubmed]
PHST- 2023/10/29 00:30 [entrez]
AID - 10.1038/s41598-023-45373-z [pii]
AID - 45373 [pii]
AID - 10.1038/s41598-023-45373-z [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 28;13(1):18521. doi: 10.1038/s41598-023-45373-z.

PMID- 37663348
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230905
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - Intelligence across humans and machines: a joint perspective.
PG  - 1209761
LID - 10.3389/fpsyg.2023.1209761 [doi]
LID - 1209761
AB  - This paper aims to address the divergences and contradictions in the definition 
      of intelligence across different areas of knowledge, particularly in 
      computational intelligence and psychology, where the concept is of significant 
      interest. Despite the differences in motivation and approach, both fields have 
      contributed to the rise of cognitive science. However, the lack of a standardized 
      definition, empirical evidence, or measurement strategy for intelligence is a 
      hindrance to cross-fertilization between these areas, particularly for 
      semantic-based applications. This paper seeks to equalize the definitions of 
      intelligence from the perspectives of computational intelligence and psychology, 
      and offer an overview of the methods used to measure intelligence. We argue that 
      there is no consensus for intelligence, and the term is interchangeably used with 
      similar, opposed, or even contradictory definitions in many fields. This paper 
      concludes with a summary of its central considerations and contributions, where 
      we state intelligence is an agent's ability to process external and internal 
      information to find an optimum adaptation (decision-making) to the environment 
      according to its ontology and then decode this information as an output action.
CI  - Copyright © 2023 Da Silveira and Lopes.
FAU - Da Silveira, Tiago Buatim Nion
AU  - Da Silveira TBN
AD  - Computational Intelligence Laboratory (LABIC), Federal University of Technology - 
      Paraná, Curitiba, Brazil.
AD  - Polytechnic School, University of Vale do Itajaí, Itajaí, Brazil.
FAU - Lopes, Heitor Silvério
AU  - Lopes HS
AD  - Computational Intelligence Laboratory (LABIC), Federal University of Technology - 
      Paraná, Curitiba, Brazil.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230817
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10470035
OTO - NOTNLM
OT  - CHC model
OT  - ChatGPT
OT  - IQ
OT  - artificial intelligence
OT  - computational intelligence
OT  - human intelligence
OT  - intelligence measurement
OT  - semantics
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/09/04 06:42
MHDA- 2023/09/04 06:43
CRDT- 2023/09/04 04:59
PHST- 2023/04/21 00:00 [received]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/09/04 06:43 [medline]
PHST- 2023/09/04 06:42 [pubmed]
PHST- 2023/09/04 04:59 [entrez]
AID - 10.3389/fpsyg.2023.1209761 [doi]
PST - epublish
SO  - Front Psychol. 2023 Aug 17;14:1209761. doi: 10.3389/fpsyg.2023.1209761. 
      eCollection 2023.

PMID- 36999111
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230401
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - A Case of Radiation-Induced Aortitis in a Patient With Cervical Cancer.
PG  - e35484
LID - 10.7759/cureus.35484 [doi]
LID - e35484
AB  - Radiation-induced aortitis is a rare but potentially serious complication of 
      radiotherapy. We report the case of a 46-year-old female with a history of 
      cervical cancer who developed radiation-induced aortitis following two courses of 
      concurrent chemoradiation. The patient was asymptomatic, and the condition was 
      detected during a routine follow-up positron emission tomography (PET) scan. The 
      patient was referred to rheumatology for differential diagnosis, which ruled out 
      non-radiation-induced aortitis. The condition was managed conservatively, and a 
      follow-up computed tomography (CT) scan showed resolution of the aortitis but the 
      progression of aorto-iliac fibrosis. The patient was then started on prednisone, 
      which led to a regression of the aorto-iliac vessel thickening.
CI  - Copyright © 2023, Charrois-Durand et al.
FAU - Charrois-Durand, Cédric
AU  - Charrois-Durand C
AD  - Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal 
      (CHUM), Montréal, CAN.
FAU - Beauchemin, Marie-Claude
AU  - Beauchemin MC
AD  - Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal 
      (CHUM), Montréal, CAN.
FAU - Barkati, Maroie
AU  - Barkati M
AD  - Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal 
      (CHUM), Montréal, CAN.
LA  - eng
PT  - Case Reports
DEP - 20230226
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10044790
OTO - NOTNLM
OT  - abdominal aortitis
OT  - chatgpt
OT  - complication of treatment
OT  - radiation induced vasculitis
OT  - recurring cervical cancer
OT  - side effects of radiotherapy
COIS- The authors have declared financial relationships, which are detailed in the next 
      section.
EDAT- 2023/04/01 06:00
MHDA- 2023/04/01 06:01
CRDT- 2023/03/31 02:15
PHST- 2023/02/26 00:00 [accepted]
PHST- 2023/04/01 06:01 [medline]
PHST- 2023/03/31 02:15 [entrez]
PHST- 2023/04/01 06:00 [pubmed]
AID - 10.7759/cureus.35484 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 26;15(2):e35484. doi: 10.7759/cureus.35484. eCollection 2023 
      Feb.

PMID- 37781994
OWN - NLM
STAT- MEDLINE
DCOM- 20231003
LR  - 20231004
IS  - 2047-2986 (Electronic)
IS  - 2047-2978 (Print)
IS  - 2047-2978 (Linking)
VI  - 13
DP  - 2023 Oct 2
TI  - Scarcity of research on psychological or psychiatric states using validated 
      questionnaires in low- and middle-income countries: A ChatGPT-assisted 
      bibliometric analysis and national case study on some psychometric properties.
PG  - 04102
LID - 10.7189/jogh.13.04102 [doi]
LID - 04102
AB  - BACKGROUND: It is vital to assess whether research on psychological or 
      psychiatric states using validated questionnaires is still lagging in low- and 
      middle-income countries and to what degree, and to continue to assess the 
      psychometric properties of the most informative questionnaires. METHODS: We 
      performed a bibliometric analysis of Web of Science Core Collection for all years 
      to determine the number of studies performed in each country that used an 
      inventory or a questionnaire on aggression, anxiety, depression, borderline 
      personality, narcissism, self-harm, shame, or childhood trauma. We conducted a 
      simple observational analysis of distributions by countries to derive the main 
      overall conclusions, assisted by ChatGPT to test its ability to summarise and 
      interpret this type of information. We also carried out a study in Croatia to 
      examine some psychometric properties of five commonly used questionnaires, using 
      Cronbach's α coefficient and zero-order correlations. RESULTS: We observed a 
      concentration of research activity in a few high-income countries, primarily the 
      United States and several European nations, suggesting a robust research 
      infrastructure and a strong emphasis on studying psychological and psychiatric 
      states within their population. In contrast, low- and middle-income countries 
      were notably under-represented in research on psychological and psychiatric 
      states, although the gap seems to be closing in some countries. Turkey, Iran, 
      Brazil, South Africa, Mexico, India, Malaysia and Pakistan have been consistently 
      contributing an increasing number of studies and catching up with the most 
      research-intensive high-income countries. The national case study in Croatia 
      confirmed adequate psychometric properties of the most frequently used 
      questionnaires. CONCLUSIONS: Addressing research gaps in low- and middle-income 
      countries is crucial, because relying solely on research from high-income 
      countries may not fully capture the nuances of psychological and psychiatric 
      states within diverse populations. To bridge this gap, it is essential to 
      prioritise mental health research in low-resource settings, provide training and 
      resources to local researchers, and establish international collaborations. Such 
      efforts can lead to the development of culturally valid questionnaires, an 
      improved understanding of psychological and psychiatric states in diverse 
      contexts, and the creation of effective interventions to promote mental 
      well-being on a global scale.
CI  - Copyright © 2023 by the Journal of Global Health. All rights reserved.
FAU - Rudan, Duško
AU  - Rudan D
AD  - Department of Psychiatry and Psychological Medicine, University Hospital Centre 
      Zagreb, Zagreb, Croatia.
FAU - Marčinko, Darko
AU  - Marčinko D
AD  - Faculty of Medicine, University of Zagreb, Department of Psychiatry and 
      Psychological Medicine, University Hospital Centre Zagreb, Zagreb, Croatia.
FAU - Degmečić, Dunja
AU  - Degmečić D
AD  - Faculty of Medicine, J. J. Strossmayer University of Osijek, Department of 
      Psychiatry, University Hospital Centre Osijek, Osijek, Croatia.
FAU - Jakšić, Nenad
AU  - Jakšić N
AD  - Department of Psychiatry and Psychological Medicine, University Hospital Centre 
      Zagreb, Zagreb, Croatia.
LA  - eng
PT  - Journal Article
PT  - Observational Study
DEP - 20231002
PL  - Scotland
TA  - J Glob Health
JT  - Journal of global health
JID - 101578780
SB  - IM
MH  - Humans
MH  - *Developing Countries
MH  - Psychometrics
MH  - *Anxiety
MH  - Income
MH  - Surveys and Questionnaires
PMC - PMC10543016
COIS- Disclosure of interest: The authors completed the ICMJE Disclosure of Interest 
      Form (available upon request from the corresponding author) and disclose no 
      relevant activities and relationships.
EDAT- 2023/10/02 12:42
MHDA- 2023/10/03 06:47
CRDT- 2023/10/02 07:33
PHST- 2023/10/03 06:47 [medline]
PHST- 2023/10/02 12:42 [pubmed]
PHST- 2023/10/02 07:33 [entrez]
AID - jogh-13-04102 [pii]
AID - 10.7189/jogh.13.04102 [doi]
PST - epublish
SO  - J Glob Health. 2023 Oct 2;13:04102. doi: 10.7189/jogh.13.04102.

PMID- 38032660
OWN - NLM
STAT- Publisher
LR  - 20231130
IS  - 1538-3598 (Electronic)
IS  - 0098-7484 (Linking)
DP  - 2023 Nov 30
TI  - Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?
LID - 10.1001/jama.2023.25054 [doi]
AB  - IMPORTANCE: Since the introduction of ChatGPT in late 2022, generative artificial 
      intelligence (genAI) has elicited enormous enthusiasm and serious concerns. 
      OBSERVATIONS: History has shown that general purpose technologies often fail to 
      deliver their promised benefits for many years ("the productivity paradox of 
      information technology"). Health care has several attributes that make the 
      successful deployment of new technologies even more difficult than in other 
      industries; these have challenged prior efforts to implement AI and electronic 
      health records. However, genAI has unique properties that may shorten the usual 
      lag between implementation and productivity and/or quality gains in health care. 
      Moreover, the health care ecosystem has evolved to make it more receptive to 
      genAI, and many health care organizations are poised to implement the 
      complementary innovations in culture, leadership, workforce, and workflow often 
      needed for digital innovations to flourish. CONCLUSIONS AND RELEVANCE: The 
      ability of genAI to rapidly improve and the capacity of organizations to 
      implement complementary innovations that allow IT tools to reach their potential 
      are more advanced than in the past; thus, genAI is capable of delivering 
      meaningful improvements in health care more rapidly than was the case with 
      previous technologies.
FAU - Wachter, Robert M
AU  - Wachter RM
AD  - Department of Medicine, University of California, San Francisco.
FAU - Brynjolfsson, Erik
AU  - Brynjolfsson E
AD  - Digital Economy Lab and Institute for Human-Centered AI, Stanford University, 
      Palo Alto, California.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - United States
TA  - JAMA
JT  - JAMA
JID - 7501160
SB  - IM
EDAT- 2023/11/30 12:42
MHDA- 2023/11/30 12:42
CRDT- 2023/11/30 11:35
PHST- 2023/11/30 12:42 [medline]
PHST- 2023/11/30 12:42 [pubmed]
PHST- 2023/11/30 11:35 [entrez]
AID - 2812615 [pii]
AID - 10.1001/jama.2023.25054 [doi]
PST - aheadofprint
SO  - JAMA. 2023 Nov 30. doi: 10.1001/jama.2023.25054.

PMID- 37790417
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231019
DP  - 2023 Aug 2
TI  - Generative Methods for Pediatric Genetics Education.
LID - 2023.08.01.23293506 [pii]
LID - 10.1101/2023.08.01.23293506 [doi]
AB  - Artificial intelligence (AI) is used in an increasing number of areas, with 
      recent interest in generative AI, such as using ChatGPT to generate programming 
      code or DALL-E to make illustrations. We describe the use of generative AI in 
      medical education. Specifically, we sought to determine whether generative AI 
      could help train pediatric residents to better recognize genetic conditions. From 
      publicly available images of individuals with genetic conditions, we used 
      generative AI methods to create new images, which were checked for accuracy with 
      an external classifier. We selected two conditions for study, Kabuki (KS) and 
      Noonan (NS) syndromes, which are clinically important conditions that 
      pediatricians may encounter. In this study, pediatric residents completed 208 
      surveys, where they each classified 20 images following exposure to one of 4 
      possible educational interventions, including with and without generative AI 
      methods. Overall, we find that generative images perform similarly but appear to 
      be slightly less helpful than real images. Most participants reported that images 
      were useful, although real images were felt to be more helpful. We conclude that 
      generative AI images may serve as an adjunctive educational tool, particularly 
      for less familiar conditions, such as KS.
FAU - Waikel, Rebekah L
AU  - Waikel RL
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
FAU - Othman, Amna A
AU  - Othman AA
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
FAU - Patel, Tanviben
AU  - Patel T
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
FAU - Hanchard, Suzanna Ledgister
AU  - Hanchard SL
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
FAU - Hu, Ping
AU  - Hu P
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
FAU - Tekendo-Ngongang, Cedrik
AU  - Tekendo-Ngongang C
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
FAU - Duong, Dat
AU  - Duong D
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
FAU - Solomon, Benjamin D
AU  - Solomon BD
AD  - Medical Genetics Branch, National Human Genome Research Institute, Bethesda, 
      Maryland, United States of America.
LA  - eng
PT  - Preprint
DEP - 20230802
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10543060
EDAT- 2023/10/04 06:44
MHDA- 2023/10/04 06:45
CRDT- 2023/10/04 04:04
PHST- 2023/10/04 06:44 [pubmed]
PHST- 2023/10/04 06:45 [medline]
PHST- 2023/10/04 04:04 [entrez]
AID - 2023.08.01.23293506 [pii]
AID - 10.1101/2023.08.01.23293506 [doi]
PST - epublish
SO  - medRxiv. 2023 Aug 2:2023.08.01.23293506. doi: 10.1101/2023.08.01.23293506. 
      Preprint.

PMID- 37446520
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2079-4991 (Print)
IS  - 2079-4991 (Electronic)
IS  - 2079-4991 (Linking)
VI  - 13
IP  - 13
DP  - 2023 Jul 4
TI  - Contrast Agents of Magnetic Resonance Imaging and Future Perspective.
LID - 10.3390/nano13132003 [doi]
LID - 2003
AB  - In recent times, magnetic resonance imaging (MRI) has emerged as a highly 
      promising modality for diagnosing severe diseases. Its exceptional spatiotemporal 
      resolution and ease of use have established it as an indispensable clinical 
      diagnostic tool. Nevertheless, there are instances where MRI encounters 
      challenges related to low contrast, necessitating the use of contrast agents 
      (CAs). Significant efforts have been made by scientists to enhance the precision 
      of observing diseased body parts by leveraging the synergistic potential of MRI 
      in conjunction with other imaging techniques and thereby modifying the CAs. In 
      this work, our focus is on elucidating the rational designing approach of CAs and 
      optimizing their compatibility for multimodal imaging and other intelligent 
      applications. Additionally, we emphasize the importance of incorporating various 
      artificial intelligence tools, such as machine learning and deep learning, to 
      explore the future prospects of disease diagnosis using MRI. We also address the 
      limitations associated with these techniques and propose reasonable remedies, 
      with the aim of advancing MRI as a cutting-edge diagnostic tool for the future.
FAU - Lv, Jie
AU  - Lv J
AD  - School of Computer Science and Engineering, Yulin Normal University, Yulin 
      537000, China.
FAU - Roy, Shubham
AU  - Roy S
AUID- ORCID: 0000-0001-5245-3229
AD  - Shenzhen Key Laboratory of Flexible Printed Electronics Technology, School of 
      Science, Harbin Institute of Technology, Shenzhen 518055, China.
AD  - Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and 
      Comprehensive Application, School of Science, Harbin Institute of Technology, 
      Shenzhen 518055, China.
FAU - Xie, Miao
AU  - Xie M
AD  - School of Computer Science and Engineering, Yulin Normal University, Yulin 
      537000, China.
FAU - Yang, Xiulan
AU  - Yang X
AUID- ORCID: 0009-0000-8858-4087
AD  - School of Computer Science and Engineering, Yulin Normal University, Yulin 
      537000, China.
FAU - Guo, Bing
AU  - Guo B
AUID- ORCID: 0000-0001-7981-0644
AD  - Shenzhen Key Laboratory of Flexible Printed Electronics Technology, School of 
      Science, Harbin Institute of Technology, Shenzhen 518055, China.
AD  - Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and 
      Comprehensive Application, School of Science, Harbin Institute of Technology, 
      Shenzhen 518055, China.
LA  - eng
GR  - 2021A1515110086/Guangdong Basic and Applied Basic Research Foundation/
GR  - 2022A1515011781/General project of Guangdong Natural Science Foundation/
GR  - RCBS20200714114910141/the Science and Technology Innovation Commission of 
      Shenzhen/
GR  - JCYJ20210324132816039/JCYJ20210324132816039/
GR  - ZDSYS20220527171407017/Shenzhen Key Laboratory of Advanced Functional Carbon 
      Materials Research and Comprehensive Application/
PT  - Journal Article
PT  - Review
DEP - 20230704
PL  - Switzerland
TA  - Nanomaterials (Basel)
JT  - Nanomaterials (Basel, Switzerland)
JID - 101610216
PMC - PMC10343825
OTO - NOTNLM
OT  - ChatGPT
OT  - MRI
OT  - contrast agents
OT  - deep learning
OT  - machine learning
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/14 13:07
MHDA- 2023/07/14 13:08
CRDT- 2023/07/14 01:18
PHST- 2023/06/20 00:00 [received]
PHST- 2023/07/01 00:00 [revised]
PHST- 2023/07/03 00:00 [accepted]
PHST- 2023/07/14 13:08 [medline]
PHST- 2023/07/14 13:07 [pubmed]
PHST- 2023/07/14 01:18 [entrez]
AID - nano13132003 [pii]
AID - nanomaterials-13-02003 [pii]
AID - 10.3390/nano13132003 [doi]
PST - epublish
SO  - Nanomaterials (Basel). 2023 Jul 4;13(13):2003. doi: 10.3390/nano13132003.

PMID- 38051572
OWN - NLM
STAT- MEDLINE
DCOM- 20231207
LR  - 20231207
IS  - 1438-8871 (Electronic)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Dec 5
TI  - How Can the Clinical Aptitude of AI Assistants Be Assayed?
PG  - e51603
LID - 10.2196/51603 [doi]
AB  - Large language models (LLMs) are exhibiting remarkable performance in clinical 
      contexts, with exemplar results ranging from expert-level attainment in medical 
      examination questions to superior accuracy and relevance when responding to 
      patient queries compared to real doctors replying to queries on social media. The 
      deployment of LLMs in conventional health care settings is yet to be reported, 
      and there remains an open question as to what evidence should be required before 
      such deployment is warranted. Early validation studies use unvalidated surrogate 
      variables to represent clinical aptitude, and it may be necessary to conduct 
      prospective randomized controlled trials to justify the use of an LLM for 
      clinical advice or assistance, as potential pitfalls and pain points cannot be 
      exhaustively predicted. This viewpoint states that as LLMs continue to 
      revolutionize the field, there is an opportunity to improve the rigor of 
      artificial intelligence (AI) research to reward innovation, conferring real 
      benefits to real patients.
CI  - ©Arun James Thirunavukarasu. Originally published in the Journal of Medical 
      Internet Research (https://www.jmir.org), 05.12.2023.
FAU - Thirunavukarasu, Arun James
AU  - Thirunavukarasu AJ
AUID- ORCID: 0000-0001-8968-4768
AD  - Oxford University Clinical Academic Graduate School, University of Oxford, 
      Oxford, United Kingdom.
AD  - School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20231205
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Aptitude
MH  - *Artificial Intelligence
MH  - Prospective Studies
MH  - Language
MH  - Pain
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - LLM
OT  - artificial general intelligence
OT  - artificial intelligence
OT  - barrier
OT  - barriers
OT  - challenge
OT  - challenges
OT  - chatbot
OT  - chatbots
OT  - clinical decision aid
OT  - conversational agent
OT  - conversational agents
OT  - foundation models
OT  - implementation
OT  - language model
OT  - large language models
OT  - pain point
OT  - pain points
OT  - pitfall
OT  - pitfalls
OT  - validation
EDAT- 2023/12/05 17:44
MHDA- 2023/12/07 12:42
CRDT- 2023/12/05 11:54
PHST- 2023/08/04 00:00 [received]
PHST- 2023/11/20 00:00 [accepted]
PHST- 2023/09/28 00:00 [revised]
PHST- 2023/12/07 12:42 [medline]
PHST- 2023/12/05 17:44 [pubmed]
PHST- 2023/12/05 11:54 [entrez]
AID - v25i1e51603 [pii]
AID - 10.2196/51603 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Dec 5;25:e51603. doi: 10.2196/51603.

PMID- 38049305
OWN - NLM
STAT- MEDLINE
DCOM- 20231206
LR  - 20231206
IS  - 1938-2421 (Electronic)
IS  - 0148-4834 (Linking)
VI  - 62
IP  - 12
DP  - 2023 Dec
TI  - Comparative Analysis of NCLEX-RN Questions: A Duel Between ChatGPT and Human 
      Expertise.
PG  - 679-687
LID - 10.3928/01484834-20231006-07 [doi]
AB  - BACKGROUND: Artificial intelligence (AI) has the potential to revolutionize 
      nursing education. This study compared NCLEX-RN questions generated by AI and 
      those created by nurse educators. METHOD: Faculty of accredited baccalaureate 
      programs were invited to participate. Likert-scale items for grammar and clarity 
      of the item stem and distractors were compared using Mann-Whitney U, and yes/no 
      questions about clinical relevance and complex terminology were analyzed using 
      chi-square. A one-sample binomial test with confidence intervals evaluated 
      participants' question preference (AI-generated or educator-written). Qualitative 
      responses identified themes across faculty. RESULTS: Item clarity, grammar, and 
      difficulty were similar for AI and educator-created questions. Clinical relevance 
      and use of complex terminology was similar for all question pairs. Of the four 
      sets with preference for one item, three were generated by AI. CONCLUSION: AI can 
      assist faculty with item generation to prepare nursing students for the NCLEX-RN 
      examination. Faculty expertise is necessary to refine questions written using 
      both methods. [J Nurs Educ. 2023;62(12):679-687.].
FAU - Cox, Rachel L
AU  - Cox RL
FAU - Hunt, Karen L
AU  - Hunt KL
FAU - Hill, Rebecca R
AU  - Hill RR
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - United States
TA  - J Nurs Educ
JT  - The Journal of nursing education
JID - 7705432
SB  - IM
MH  - Humans
MH  - *Education, Nursing, Baccalaureate/methods
MH  - Artificial Intelligence
MH  - Educational Measurement/methods
MH  - Licensure, Nursing
MH  - *Education, Nursing
MH  - *Students, Nursing
EDAT- 2023/12/05 00:42
MHDA- 2023/12/06 06:42
CRDT- 2023/12/04 21:02
PHST- 2023/12/06 06:42 [medline]
PHST- 2023/12/05 00:42 [pubmed]
PHST- 2023/12/04 21:02 [entrez]
AID - 10.3928/01484834-20231006-07 [doi]
PST - ppublish
SO  - J Nurs Educ. 2023 Dec;62(12):679-687. doi: 10.3928/01484834-20231006-07. Epub 
      2023 Dec 1.

PMID- 38028660
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak 
      control.
PG  - 1266560
LID - 10.3389/frai.2023.1266560 [doi]
LID - 1266560
AB  - COVID-19 has brought significant changes to our political, social, and 
      technological landscape. This paper explores the emergence and global spread of 
      the disease and focuses on the role of Artificial Intelligence (AI) in containing 
      its transmission. To the best of our knowledge, there has been no scientific 
      presentation of the early pictorial representation of the disease's spread. 
      Additionally, we outline various domains where AI has made a significant impact 
      during the pandemic. Our methodology involves searching relevant articles on 
      COVID-19 and AI in leading databases such as PubMed and Scopus to identify the 
      ways AI has addressed pandemic-related challenges and its potential for further 
      assistance. While research suggests that AI has not fully realized its potential 
      against COVID-19, likely due to data quality and diversity limitations, we review 
      and identify key areas where AI has been crucial in preparing the fight against 
      any sudden outbreak of the pandemic. We also propose ways to maximize the 
      utilization of AI's capabilities in this regard.
CI  - Copyright © 2023 Farhat, Sohail, Alam, Ubaid, Shakil, Ashhad and Madsen.
FAU - Farhat, Faiza
AU  - Farhat F
AD  - Department of Zoology, Aligarh Muslim University, Aligarh, India.
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AD  - Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India.
FAU - Alam, Mohammed Talha
AU  - Alam MT
AD  - Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India.
FAU - Ubaid, Syed
AU  - Ubaid S
AD  - Faculty of Electronic and Information Technology, Warsaw University of 
      Technology, Warsaw, Poland.
FAU - Shakil
AU  - Shakil
AD  - Faculty of Electronic and Information Technology, Warsaw University of 
      Technology, Warsaw, Poland.
FAU - Ashhad, Mohd
AU  - Ashhad M
AD  - Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India.
FAU - Madsen, Dag Øivind
AU  - Madsen DØ
AD  - USN School of Business, University of South-Eastern Norway, Hønefoss, Norway.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231108
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10663297
OTO - NOTNLM
OT  - COVID-19
OT  - ChatGPT
OT  - Monkeypox
OT  - artificial intelligence
OT  - novel coronavirus
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 17:16
PHST- 2023/07/25 00:00 [received]
PHST- 2023/10/02 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 17:16 [entrez]
AID - 10.3389/frai.2023.1266560 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Nov 8;6:1266560. doi: 10.3389/frai.2023.1266560. 
      eCollection 2023.

PMID- 37378099
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 5
DP  - 2023 May
TI  - Embracing Large Language Models for Medical Applications: Opportunities and 
      Challenges.
PG  - e39305
LID - 10.7759/cureus.39305 [doi]
LID - e39305
AB  - Large language models (LLMs) have the potential to revolutionize the field of 
      medicine by, among other applications, improving diagnostic accuracy and 
      supporting clinical decision-making. However, the successful integration of LLMs 
      in medicine requires addressing challenges and considerations specific to the 
      medical domain. This viewpoint article provides a comprehensive overview of key 
      aspects for the successful implementation of LLMs in medicine, including transfer 
      learning, domain-specific fine-tuning, domain adaptation, reinforcement learning 
      with expert input, dynamic training, interdisciplinary collaboration, education 
      and training, evaluation metrics, clinical validation, ethical considerations, 
      data privacy, and regulatory frameworks. By adopting a multifaceted approach and 
      fostering interdisciplinary collaboration, LLMs can be developed, validated, and 
      integrated into medical practice responsibly, effectively, and ethically, 
      addressing the needs of various medical disciplines and diverse patient 
      populations. Ultimately, this approach will ensure that LLMs enhance patient care 
      and improve overall health outcomes for all.
CI  - Copyright © 2023, Karabacak et al.
FAU - Karabacak, Mert
AU  - Karabacak M
AD  - Neurological Surgery, Mount Sinai Health System, New York, USA.
FAU - Margetis, Konstantinos
AU  - Margetis K
AD  - Neurological Surgery, Mount Sinai Health System, New York, USA.
LA  - eng
PT  - Editorial
DEP - 20230521
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10292051
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatgpt
OT  - data privacy
OT  - domain adaptation
OT  - ethical considerations
OT  - generative ai
OT  - large language models
OT  - multimodal learning
OT  - reinforcement learning
OT  - transfer learning
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/06/28 13:08
MHDA- 2023/06/28 13:09
CRDT- 2023/06/28 09:28
PHST- 2023/05/21 00:00 [accepted]
PHST- 2023/06/28 13:09 [medline]
PHST- 2023/06/28 13:08 [pubmed]
PHST- 2023/06/28 09:28 [entrez]
AID - 10.7759/cureus.39305 [doi]
PST - epublish
SO  - Cureus. 2023 May 21;15(5):e39305. doi: 10.7759/cureus.39305. eCollection 2023 
      May.

PMID- 37009388
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230404
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Histoplasmosis of the External Auditory Canal.
PG  - e35644
LID - 10.7759/cureus.35644 [doi]
LID - e35644
AB  - This report describes the diagnosis and treatment of a patient with a rare fungal 
      infection of the external ear, as well as a review of the literature. A 
      76-year-old Caucasian gentleman from rural southern United States with diabetes 
      and hypertension was referred to our clinic for intractable left otalgia, 
      otorrhea, headaches, and an exophytic lesion in the left external ear since five 
      months. There was no pertinent travel history. Biopsy by an outside 
      otolaryngologist was inconclusive. Repeat biopsy under anesthesia revealed 
      morphological characteristics consistent with histoplasmosis. Intravenous 
      amphotericin B and later oral antifungal agent voriconazole led to improvement in 
      symptoms. The clinical presentation resembled a malignancy. A high index of 
      clinical suspicion, histologic confirmation with deep tissue biopsy, and culture 
      are essential for diagnostic confirmation followed by treatment with systemic 
      antifungals. A multidisciplinary team approach is necessary to manage this rare 
      condition.
CI  - Copyright © 2023, Rockwell et al.
FAU - Rockwell, Michael
AU  - Rockwell M
AD  - Medicine, Louisiana State University Health Sciences Center, Shreveport, USA.
FAU - Spiller, Patrick
AU  - Spiller P
AD  - Otolaryngology - Head and Neck Surgery, Louisiana State University Health 
      Sciences Center, Shreveport, USA.
FAU - Cuevas-Ocampo, Areli K
AU  - Cuevas-Ocampo AK
AD  - Pathology, Louisiana State University Health Sciences Center, Shreveport, USA.
FAU - Malek, Alexandre
AU  - Malek A
AD  - Infectious Diseases, Louisiana State University Health Sciences Center, 
      Shreveport, USA.
FAU - Mankekar, Gauri
AU  - Mankekar G
AD  - Otolaryngology - Head and Neck Surgery, Louisiana State University Health 
      Sciences Center, Shreveport, USA.
LA  - eng
PT  - Case Reports
DEP - 20230301
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10065131
OTO - NOTNLM
OT  - amphotericin
OT  - chatgpt
OT  - external auditory ear canal
OT  - histoplasmosis
OT  - liposomal amphotericin b
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/04 06:00
MHDA- 2023/04/04 06:01
CRDT- 2023/04/03 04:26
PHST- 2023/03/01 00:00 [accepted]
PHST- 2023/04/04 06:01 [medline]
PHST- 2023/04/03 04:26 [entrez]
PHST- 2023/04/04 06:00 [pubmed]
AID - 10.7759/cureus.35644 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 1;15(3):e35644. doi: 10.7759/cureus.35644. eCollection 2023 Mar.

PMID- 37892665
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2077-0383 (Print)
IS  - 2077-0383 (Electronic)
IS  - 2077-0383 (Linking)
VI  - 12
IP  - 20
DP  - 2023 Oct 14
TI  - Using Generative Artificial Intelligence Tools in Cosmetic Surgery: A Study on 
      Rhinoplasty, Facelifts, and Blepharoplasty Procedures.
LID - 10.3390/jcm12206524 [doi]
LID - 6524
AB  - Artificial intelligence (AI), notably Generative Adversarial Networks, has the 
      potential to transform medical and patient education. Leveraging GANs in medical 
      fields, especially cosmetic surgery, provides a plethora of benefits, including 
      upholding patient confidentiality, ensuring broad exposure to diverse patient 
      scenarios, and democratizing medical education. This study investigated the 
      capacity of AI models, DALL-E 2, Midjourney, and Blue Willow, to generate 
      realistic images pertinent to cosmetic surgery. We combined the generative powers 
      of ChatGPT-4 and Google's BARD with these GANs to produce images of various 
      noses, faces, and eyelids. Four board-certified plastic surgeons evaluated the 
      generated images, eliminating the need for real patient photographs. Notably, 
      generated images predominantly showcased female faces with lighter skin tones, 
      lacking representation of males, older women, and those with a body mass index 
      above 20. The integration of AI in cosmetic surgery offers enhanced patient 
      education and training but demands careful and ethical incorporation to ensure 
      comprehensive representation and uphold medical standards.
FAU - Lim, Bryan
AU  - Lim B
AD  - Department of Plastic and Reconstructive Surgery, Peninsula Health, Frankston, 
      VIC 3199, Australia.
AD  - Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC 
      3004, Australia.
FAU - Seth, Ishith
AU  - Seth I
AUID- ORCID: 0000-0001-5444-8925
AD  - Department of Plastic and Reconstructive Surgery, Peninsula Health, Frankston, 
      VIC 3199, Australia.
AD  - Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC 
      3004, Australia.
FAU - Kah, Skyler
AU  - Kah S
AD  - Department of Plastic and Reconstructive Surgery, Peninsula Health, Frankston, 
      VIC 3199, Australia.
FAU - Sofiadellis, Foti
AU  - Sofiadellis F
AD  - Department of Plastic and Reconstructive Surgery, Peninsula Health, Frankston, 
      VIC 3199, Australia.
FAU - Ross, Richard J
AU  - Ross RJ
AD  - Department of Plastic and Reconstructive Surgery, Peninsula Health, Frankston, 
      VIC 3199, Australia.
FAU - Rozen, Warren M
AU  - Rozen WM
AUID- ORCID: 0000-0002-4092-182X
AD  - Department of Plastic and Reconstructive Surgery, Peninsula Health, Frankston, 
      VIC 3199, Australia.
AD  - Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC 
      3004, Australia.
FAU - Cuomo, Roberto
AU  - Cuomo R
AUID- ORCID: 0000-0002-8396-095X
AD  - Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, 
      University of Siena, 53100 Siena, Italy.
LA  - eng
PT  - Journal Article
DEP - 20231014
PL  - Switzerland
TA  - J Clin Med
JT  - Journal of clinical medicine
JID - 101606588
PMC - PMC10607912
OTO - NOTNLM
OT  - AI
OT  - Generative Adversarial Networks
OT  - blepharoplasty
OT  - cosmetic surgery
OT  - facelifts
OT  - rhinoplasty
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/28 11:44
MHDA- 2023/10/28 11:45
CRDT- 2023/10/28 01:05
PHST- 2023/08/26 00:00 [received]
PHST- 2023/10/03 00:00 [revised]
PHST- 2023/10/13 00:00 [accepted]
PHST- 2023/10/28 11:45 [medline]
PHST- 2023/10/28 11:44 [pubmed]
PHST- 2023/10/28 01:05 [entrez]
AID - jcm12206524 [pii]
AID - jcm-12-06524 [pii]
AID - 10.3390/jcm12206524 [doi]
PST - epublish
SO  - J Clin Med. 2023 Oct 14;12(20):6524. doi: 10.3390/jcm12206524.

PMID- 37722904
OWN - NLM
STAT- MEDLINE
DCOM- 20230920
LR  - 20230920
IS  - 0001-5423 (Print)
IS  - 0001-5423 (Linking)
VI  - 65
IP  - 2
DP  - 2023 Summer
TI  - Objective and subjective assessment of Dupuytren's contracture.
PG  - 74-78
LID - 10.48095/ccachp202374 [doi]
AB  - This paper describes the evaluation options of Dupuytren's contracture by 
      subjective and objective methods. There are various classification schemes named 
      after their authors, including graphical representation for objective evaluation 
      of the disease. Subjective assessment was performed in the form of 
      a questionnaire for patients. The QuickDASH with a small specification for 
      Dupuytren's contracture is the most commonly used questionnaire. The Southampton 
      Dupuytren's Scoring Scheme questionnaire appears to be a higher specification. 
      The classifications allow evaluation of treatment success to determine prognosis 
      of the disease. The analysis of articles is based on PubMed search from the years 
      1967-2022, with 28 relevant articles were retrieved. Based on this analysis, the 
      Tubiana classification appears to be the most appropriate one for patients with 
      Dupuytren's contracture. Of patient questionnaires, the Southampton 
      Dupuytren's Scoring Scheme meets these parameters.
FAU - Menoušek, J
AU  - Menoušek J
FAU - Šácha, Z Dvořák T
AU  - Šácha ZDT
LA  - eng
PT  - Journal Article
PT  - Review
TT  - Objective and subjective assessment of Dupuytren's contracture.
PL  - Czech Republic
TA  - Acta Chir Plast
JT  - Acta chirurgiae plasticae
JID - 0370301
SB  - IM
MH  - Humans
MH  - *Dupuytren Contracture/diagnosis
MH  - PubMed
OTO - NOTNLM
OT  - ChatGPT
OT  - DASH
OT  - Dupuytren's contracture
OT  - Michigan Hand Questionnaire
OT  - OpenAI
OT  - QuickDASH
OT  - Southampton Dupuytren's Scoring Scheme (SDSS )
OT  - classification
OT  - objective and subjective scoring
EDAT- 2023/09/19 00:43
MHDA- 2023/09/20 06:42
CRDT- 2023/09/18 21:39
PHST- 2023/09/20 06:42 [medline]
PHST- 2023/09/19 00:43 [pubmed]
PHST- 2023/09/18 21:39 [entrez]
AID - 135197 [pii]
AID - 10.48095/ccachp202374 [doi]
PST - ppublish
SO  - Acta Chir Plast. 2023 Summer;65(2):74-78. doi: 10.48095/ccachp202374.

PMID- 37519863
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230731
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - An intelligence coordination system toward creating the super-intelligent law 
      firm.
PG  - 1145308
LID - 10.3389/frai.2023.1145308 [doi]
LID - 1145308
AB  - A large law firm typically exhibits a collective intelligence comprised of 
      hundreds or thousands of legal minds aimed at simultaneously engaging thousands 
      of active matters across scores of industries and dozens of practice specialties 
      with distinct doctrinal and procedural characteristics. The firm is challenged 
      not only to achieve successful, cost-effective outcomes for its clients, but must 
      also simultaneously, in competition with other firms and alternative service 
      providers, attract and cultivate talent, develop and coordinate capabilities 
      across multiple evolving areas of practice and continually improve a robust 
      collective intelligence to gain a competitive edge. As various types of machine 
      intelligences and tools are introduced, firms must also groom these into the 
      collective. In this paper we explore a human-machine hybrid system for addressing 
      this large scale, multi-dimensional, dynamic optimization challenge to coordinate 
      a collective intelligence of humans and machines. Machine intelligence is needed 
      to handle the computational complexity and it is complemented by human 
      intelligence to help handle exceptions and novel situations. We believe this 
      approach has potential for transforming the collective intelligence that is the 
      large law firm.
CI  - Copyright © 2023 Kaomea.
FAU - Kaomea, Peter
AU  - Kaomea P
AD  - Morrison & Foerster, LLP, San Francisco, CA, United States.
LA  - eng
PT  - Journal Article
DEP - 20230714
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10375011
OTO - NOTNLM
OT  - ChatGPT
OT  - collective-intelligence
OT  - generative artificial intelligence
OT  - machine intelligence
OT  - pareto
OT  - super-intelligence
OT  - work allocation
OT  - work coordination
COIS- PK was employed by Morrison & Foerster, LLP.
EDAT- 2023/07/31 06:43
MHDA- 2023/07/31 06:44
CRDT- 2023/07/31 04:41
PHST- 2023/01/16 00:00 [received]
PHST- 2023/05/09 00:00 [accepted]
PHST- 2023/07/31 06:44 [medline]
PHST- 2023/07/31 06:43 [pubmed]
PHST- 2023/07/31 04:41 [entrez]
AID - 10.3389/frai.2023.1145308 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Jul 14;6:1145308. doi: 10.3389/frai.2023.1145308. 
      eCollection 2023.

PMID- 37900654
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 0975-7651 (Print)
IS  - 0976-6952 (Electronic)
IS  - 0975-7651 (Linking)
VI  - 14
IP  - 3
DP  - 2023 Sep
TI  - From ChatGPT to Treatment: the Future of AI and Large Language Models in Surgical 
      Oncology.
PG  - 537-539
LID - 10.1007/s13193-023-01836-3 [doi]
AB  - This paper explores the transformative potential of Large Language Models (LLMs) 
      within the context of surgical oncology and outlines the foundational mechanisms 
      behind these models. LLMs, such as GPT-4, have rapidly evolved in terms of scale 
      and capabilities, with profound implications for their applications in 
      healthcare. These models, rooted in the Generative Pretrained Transformer 
      architecture, exhibit advanced natural language understanding and generation 
      skills. Within surgical oncology, LLMs, when integrated into a Generalist Medical 
      AI (GMAI) framework, hold great promise in offering real-time support throughout 
      the cancer journey. However, alongside these opportunities, this paper 
      underscores the importance of ethical, privacy, and efficacy considerations, 
      especially in light of issues like data drift and potential biases. Collaborative 
      efforts among healthcare providers, AI developers, and regulatory bodies are 
      pivotal in ensuring responsible and effective use of LLMs in surgical oncology, 
      thereby contributing to enhanced patient care and safety. As LLMs continue to 
      advance, they are poised to become indispensable tools in the delivery of 
      high-quality, efficient care in this specialized medical field.
CI  - © The Author(s), under exclusive licence to Indian Association of Surgical 
      Oncology 2023. Springer Nature or its licensor (e.g. a society or other partner) 
      holds exclusive rights to this article under a publishing agreement with the 
      author(s) or other rightsholder(s); author self-archiving of the accepted 
      manuscript version of this article is solely governed by the terms of such 
      publishing agreement and applicable law.
FAU - Ramamurthi, Adhitya
AU  - Ramamurthi A
AUID- ORCID: 0000-0001-8937-3310
AD  - Department of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI USA. 
      ROR: https://ror.org/00qqv6244. GRID: grid.30760.32. ISNI: 0000 0001 2111 8460
FAU - Are, Chandrakanth
AU  - Are C
AD  - Department of Surgery, University of Nebraska Medical Center, Omaha, NE USA. ROR: 
      https://ror.org/00thqtb16. GRID: grid.266813.8. ISNI: 0000 0001 0666 4105
FAU - Kothari, Anai N
AU  - Kothari AN
AD  - Department of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI USA. 
      ROR: https://ror.org/00qqv6244. GRID: grid.30760.32. ISNI: 0000 0001 2111 8460
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231017
PL  - India
TA  - Indian J Surg Oncol
JT  - Indian journal of surgical oncology
JID - 101532448
PMC - PMC10611626
OTO - NOTNLM
OT  - AI
OT  - LLMs
OT  - Surgical oncology
EDAT- 2023/10/30 06:47
MHDA- 2023/10/30 06:48
PMCR- 2024/09/01
CRDT- 2023/10/30 04:37
PHST- 2023/09/28 00:00 [received]
PHST- 2023/10/04 00:00 [accepted]
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/10/30 06:48 [medline]
PHST- 2023/10/30 06:47 [pubmed]
PHST- 2023/10/30 04:37 [entrez]
AID - 1836 [pii]
AID - 10.1007/s13193-023-01836-3 [doi]
PST - ppublish
SO  - Indian J Surg Oncol. 2023 Sep;14(3):537-539. doi: 10.1007/s13193-023-01836-3. 
      Epub 2023 Oct 17.

PMID- 37728811
OWN - NLM
STAT- Publisher
LR  - 20230920
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Sep 20
TI  - Revolutionizing Personalized Protein Energy Malnutrition Treatment: Harnessing 
      the Power of Chat GPT.
LID - 10.1007/s10439-023-03331-w [doi]
AB  - Protein energy malnutrition (PEM) is a global public health concern, and 
      personalized treatment approaches are crucial for improved outcomes. This study 
      explores the transformative potential of Chat GPT, an AI language model, in 
      revolutionizing personalized treatment for PEM. By providing accurate 
      information, personalized dietary recommendations, food choices, psychological 
      counseling of the patient and real-time monitoring and support, Chat GPT can 
      enhance the effectiveness of PEM interventions. Along with the benefits it is 
      also important to acknowledge its potential flaws and limitations. The study 
      emphasizes the importance of collaboration between AI technology and healthcare 
      professionals to leverage Chat GPT's capabilities effectively. By combining human 
      expertise with AI capabilities, personalized PEM treatment can be revolutionized, 
      leading to improved patient outcomes and a comprehensive approach to addressing 
      this global public health concern. The study highlights the significant impact of 
      Chat GPT in providing tailored guidance and continuous support throughout the 
      treatment process, empowering individuals and improving their overall well-being.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Khan, Urooj
AU  - Khan U
AUID- ORCID: 0000-0001-8291-0064
AD  - Department of Human Nutrition and Dietetics, Faculty of Allied Health Science, 
      Superior University, Sargodha Campus, Sargodha, Pakistan. uroojkhan162@gmail.com.
LA  - eng
PT  - Letter
DEP - 20230920
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - PEM
OT  - Protein deficiency
EDAT- 2023/09/20 12:53
MHDA- 2023/09/20 12:53
CRDT- 2023/09/20 11:29
PHST- 2023/07/18 00:00 [received]
PHST- 2023/07/22 00:00 [accepted]
PHST- 2023/09/20 12:53 [medline]
PHST- 2023/09/20 12:53 [pubmed]
PHST- 2023/09/20 11:29 [entrez]
AID - 10.1007/s10439-023-03331-w [pii]
AID - 10.1007/s10439-023-03331-w [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Sep 20. doi: 10.1007/s10439-023-03331-w.

PMID- 37181990
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230516
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Concurrent Occurrence of Port-Wine Stain and Glaucoma in Sturge-Weber Syndrome: A 
      Case Report.
PG  - e37451
LID - 10.7759/cureus.37451 [doi]
LID - e37451
AB  - Sturge-Weber syndrome (SWS) is a rare neurological disorder that is present at 
      birth. It is characterized by a reddish-purple birthmark on the face, typically 
      on one side of the forehead and upper eyelid, and sometimes involving the scalp 
      and ear. This birthmark, called a port-wine stain, is caused by an abnormal 
      buildup of blood vessels in the skin. SWS can also cause neurological problems 
      such as seizures, developmental delays, and problems with vision and 
      coordination. Treatment for SWS typically includes a combination of medications 
      to control seizures and other symptoms, as well as laser therapy or surgery to 
      reduce the appearance of the birthmark. Additionally, physical therapy and other 
      therapies can help improve vision and coordination. It is important to note that 
      the symptoms and severity of SWS can vary widely from person to person, and early 
      diagnosis and treatment can help improve outcomes.
CI  - Copyright © 2023, Rajaram Mohan et al.
FAU - Rajaram Mohan, Karthik
AU  - Rajaram Mohan K
AD  - Oral Medicine and Radiology, Vinayaka Mission's Sankarachariyar Dental College, 
      Vinayaka Mission's Research Foundation (Deemed to be University), Salem, IND.
FAU - Fenn, Saramma Mathew
AU  - Fenn SM
AD  - Oral Medicine and Radiology, Vinayaka Mission's Sankarachariyar Dental College, 
      Vinayaka Mission's Research Foundation (Deemed to be University), Salem, IND.
FAU - Pethagounder Thangavelu, Ravikumar
AU  - Pethagounder Thangavelu R
AD  - Oral Medicine and Radiology, Vinayaka Mission's Sankarachariyar Dental College, 
      Vinayaka Mission's Research Foundation (Deemed to be University), Salem, IND.
LA  - eng
PT  - Case Reports
DEP - 20230411
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10174712
OTO - NOTNLM
OT  - chatgpt
OT  - glaucoma
OT  - laser-therapy
OT  - port-wine stain
OT  - trigeminal nerve
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/14 19:13
MHDA- 2023/05/14 19:14
CRDT- 2023/05/14 12:26
PHST- 2023/02/02 00:00 [accepted]
PHST- 2023/05/14 19:14 [medline]
PHST- 2023/05/14 19:13 [pubmed]
PHST- 2023/05/14 12:26 [entrez]
AID - 10.7759/cureus.37451 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 11;15(4):e37451. doi: 10.7759/cureus.37451. eCollection 2023 
      Apr.

PMID- 37143645
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230507
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Mammographic and Sonographic Findings of Intraductal Papilloma of the Right 
      Breast: A Case Report.
PG  - e37034
LID - 10.7759/cureus.37034 [doi]
LID - e37034
AB  - Intraductal papillomas are tumors that arise in the epithelial cells of the 
      mammary duct. Common presenting symptoms for intraductal papilloma include serous 
      or serosanguinous nipple discharge or a palpable mass. We present a case of a 
      48-year-old woman who presented with spontaneous right breast nipple discharge 
      and a palpable mass. Diagnostic imaging for the patient included mammography and 
      ultrasound with color doppler imaging that revealed a mass at eight o'clock in 
      the right breast at a distance of 2 cm from the nipple and that corresponded to 
      the area of palpable concern. Percutaneous ultrasound-guided biopsy of the mass 
      confirmed a diagnosis of intraductal papilloma. Surgical excision may be required 
      in many cases of an intraductal papilloma due to the variety of diagnoses that 
      can be included on the differential, the increased risk for cellular atypia, and 
      the treatment for spontaneous nipple discharge.
CI  - Copyright © 2023, Hodge et al.
FAU - Hodge, Emily
AU  - Hodge E
AD  - Medicine, Central Michigan University College of Medicine, Mount Pleasant, USA.
FAU - Mirchandani, Anicia
AU  - Mirchandani A
AD  - Radiology, Detroit Medical Center/Wayne State University, Detroit, USA.
FAU - Shah, Biren
AU  - Shah B
AD  - Radiology, Detroit Medical Center/Wayne State University, Detroit, USA.
AD  - Radiology, Detroit Medical Center/Sinai-Grace Hospital, Detroit, USA.
AD  - Radiology, Western Michigan University Homer Stryker M.D. School of Medicine, 
      Kalamazoo, USA.
LA  - eng
PT  - Case Reports
DEP - 20230402
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10153652
OTO - NOTNLM
OT  - breast mass
OT  - breast mri
OT  - breast ultrasound
OT  - chatgpt
OT  - intraductal papilloma
OT  - mammogram
OT  - nipple discharge
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/05 06:42
MHDA- 2023/05/05 06:43
CRDT- 2023/05/05 03:49
PHST- 2023/04/02 00:00 [accepted]
PHST- 2023/05/05 06:43 [medline]
PHST- 2023/05/05 06:42 [pubmed]
PHST- 2023/05/05 03:49 [entrez]
AID - 10.7759/cureus.37034 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 2;15(4):e37034. doi: 10.7759/cureus.37034. eCollection 2023 Apr.

PMID- 37952122
OWN - NLM
STAT- Publisher
LR  - 20231112
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Linking)
DP  - 2023 Nov 10
TI  - Complementary and Integrative Health Information in the literature: its lexicon 
      and named entity recognition.
LID - ocad216 [pii]
LID - 10.1093/jamia/ocad216 [doi]
AB  - OBJECTIVE: To construct an exhaustive Complementary and Integrative Health (CIH) 
      Lexicon (CIHLex) to help better represent the often underrepresented physical and 
      psychological CIH approaches in standard terminologies, and to also apply 
      state-of-the-art natural language processing (NLP) techniques to help recognize 
      them in the biomedical literature. MATERIALS AND METHODS: We constructed the 
      CIHLex by integrating various resources, compiling and integrating data from 
      biomedical literature and relevant sources of knowledge. The Lexicon encompasses 
      724 unique concepts with 885 corresponding unique terms. We matched these 
      concepts to the Unified Medical Language System (UMLS), and we developed and 
      utilized BERT models comparing their efficiency in CIH named entity recognition 
      to well-established models including MetaMap and CLAMP, as well as the large 
      language model GPT3.5-turbo. RESULTS: Of the 724 unique concepts in CIHLex, 27.2% 
      could be matched to at least one term in the UMLS. About 74.9% of the mapped UMLS 
      Concept Unique Identifiers were categorized as "Therapeutic or Preventive 
      Procedure." Among the models applied to CIH named entity recognition, BLUEBERT 
      delivered the highest macro-average F1-score of 0.91, surpassing other models. 
      CONCLUSION: Our CIHLex significantly augments representation of CIH approaches in 
      biomedical literature. Demonstrating the utility of advanced NLP models, BERT 
      notably excelled in CIH entity recognition. These results highlight promising 
      strategies for enhancing standardization and recognition of CIH terminology in 
      biomedical contexts.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      American Medical Informatics Association.
FAU - Zhou, Huixue
AU  - Zhou H
AUID- ORCID: 0000-0002-6524-5506
AD  - Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 
      United States.
FAU - Austin, Robin
AU  - Austin R
AUID- ORCID: 0000-0003-1993-4623
AD  - School of Nursing, University of Minnesota, Minneapolis, MN, United States.
FAU - Lu, Sheng-Chieh
AU  - Lu SC
AD  - Department of Symptom Research, The University of Texas MD Anderson Cancer 
      Center, Houston, TX, United States.
FAU - Silverman, Greg Marc
AU  - Silverman GM
AD  - Department of Surgery, University of Minnesota, Minneapolis, MN, United States.
FAU - Zhou, Yuqi
AU  - Zhou Y
AD  - Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 
      United States.
AD  - Department of Pharmaceutical Care & Health Systems, University of Minnesota, 
      Minneapolis, MN, United States.
FAU - Kilicoglu, Halil
AU  - Kilicoglu H
AUID- ORCID: 0000-0003-3987-9393
AD  - School of Information Sciences, University of Illinois Urbana-Champaign, 
      Champaign, IL, United States.
FAU - Xu, Hua
AU  - Xu H
AD  - Section of Biomedical Informatics and Data Science, School of Medicine, Yale 
      University, New Haven, CT, United States.
FAU - Zhang, Rui
AU  - Zhang R
AUID- ORCID: 0000-0001-8258-3585
AD  - Department of Surgery, University of Minnesota, Minneapolis, MN, United States.
LA  - eng
GR  - R01AT009457/AT/NCCIH NIH HHS/United States
GR  - NH/NIH HHS/United States
PT  - Journal Article
DEP - 20231110
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
OTO - NOTNLM
OT  - Complementary and Integrative Health
OT  - Unified Medical Language System
OT  - named entity recognition
OT  - terminology
EDAT- 2023/11/12 06:42
MHDA- 2023/11/12 06:42
CRDT- 2023/11/12 05:26
PHST- 2023/06/15 00:00 [received]
PHST- 2023/10/20 00:00 [revised]
PHST- 2023/11/08 00:00 [accepted]
PHST- 2023/11/12 06:42 [medline]
PHST- 2023/11/12 06:42 [pubmed]
PHST- 2023/11/12 05:26 [entrez]
AID - 7407972 [pii]
AID - 10.1093/jamia/ocad216 [doi]
PST - aheadofprint
SO  - J Am Med Inform Assoc. 2023 Nov 10:ocad216. doi: 10.1093/jamia/ocad216.

PMID- 37986764
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
DP  - 2023 Nov 10
TI  - Development of a Liver Disease-Specific Large Language Model Chat Interface using 
      Retrieval Augmented Generation.
LID - 2023.11.10.23298364 [pii]
LID - 10.1101/2023.11.10.23298364 [doi]
AB  - BACKGROUND: Large language models (LLMs) have significant capabilities in 
      clinical information processing tasks. Commercially available LLMs, however, are 
      not optimized for clinical uses and are prone to generating incorrect or 
      hallucinatory information. Retrieval-augmented generation (RAG) is an enterprise 
      architecture that allows embedding of customized data into LLMs. This approach 
      "specializes" the LLMs and is thought to reduce hallucinations. METHODS: We 
      developed "LiVersa," a liver disease-specific LLM, by using our institution's 
      protected health information (PHI)-complaint text embedding and LLM platform, 
      "Versa." We conducted RAG on 30 publicly available American Association for the 
      Study of Liver Diseases (AASLD) guidelines and guidance documents to be 
      incorporated into LiVersa. We evaluated LiVersa's performance by comparing its 
      responses versus those of trainees from a previously published knowledge 
      assessment study regarding hepatitis B (HBV) treatment and hepatocellular 
      carcinoma (HCC) surveillance. RESULTS: LiVersa answered all 10 questions 
      correctly when forced to provide a "yes" or "no" answer. Full detailed responses 
      with justifications and rationales, however, were not completely correct for 
      three of the questions. DISCUSSIONS: In this study, we demonstrated the ability 
      to build disease-specific and PHI-compliant LLMs using RAG. While our LLM, 
      LiVersa, demonstrated more specificity in answering questions related to clinical 
      hepatology - there were some knowledge deficiencies due to limitations set by the 
      number and types of documents used for RAG. The LiVersa prototype, however, is a 
      proof of concept for utilizing RAG to customize LLMs for clinical uses and a 
      potential strategy to realize personalized medicine in the future.
FAU - Ge, Jin
AU  - Ge J
AUID- ORCID: 0000-0003-1574-1525
AD  - Division of Gastroenterology and Hepatology, Department of Medicine, University 
      of California - San Francisco, San Francisco, CA.
FAU - Sun, Steve
AU  - Sun S
AD  - UCSF Health Information Technology, University of California - San Francisco, San 
      Francisco, CA.
FAU - Owens, Joseph
AU  - Owens J
AD  - UCSF Health Information Technology, University of California - San Francisco, San 
      Francisco, CA.
FAU - Galvez, Victor
AU  - Galvez V
AD  - UCSF Health Information Technology, University of California - San Francisco, San 
      Francisco, CA.
FAU - Gologorskaya, Oksana
AU  - Gologorskaya O
AD  - UCSF Health Information Technology, University of California - San Francisco, San 
      Francisco, CA.
AD  - Bakar Computational Health Sciences Institute, University of California - San 
      Francisco, San Francisco, CA.
FAU - Lai, Jennifer C
AU  - Lai JC
AUID- ORCID: 0000-0003-2092-6380
AD  - Division of Gastroenterology and Hepatology, Department of Medicine, University 
      of California - San Francisco, San Francisco, CA.
FAU - Pletcher, Mark J
AU  - Pletcher MJ
AUID- ORCID: 0000-0002-6966-1312
AD  - Department of Epidemiology and Biostatistics, University of California - San 
      Francisco, San Francisco, CA.
FAU - Lai, Ki
AU  - Lai K
AD  - UCSF Health Information Technology, University of California - San Francisco, San 
      Francisco, CA.
LA  - eng
GR  - KL2 TR001870/TR/NCATS NIH HHS/United States
GR  - K24 AG080021/AG/NIA NIH HHS/United States
GR  - UL1 TR001872/TR/NCATS NIH HHS/United States
GR  - P30 DK026743/DK/NIDDK NIH HHS/United States
GR  - R01 AG059183/AG/NIA NIH HHS/United States
PT  - Preprint
DEP - 20231110
PL  - United States
TA  - medRxiv
JT  - medRxiv : the preprint server for health sciences
JID - 101767986
PMC - PMC10659484
COIS- Disclosures: The authors of this manuscript have the following potential 
      conflicts of interest to disclose: - Dr. Jin Ge receives research support from 
      Merck and Co; and consults for Astellas Pharmaceuticals/Iota Biosciences. - Dr. 
      Jennifer C. Lai receives research support from Lipocene and Vir Biotechnologies; 
      receives an education grant from Nestle Nutrition Sciences; serves on an advisory 
      board for Novo Nordisk; and consults for Genfit, Third Rock Ventures, and 
      Boehringer Ingelheim.
EDAT- 2023/11/21 06:42
MHDA- 2023/11/21 06:43
CRDT- 2023/11/21 05:05
PHST- 2023/11/21 06:42 [pubmed]
PHST- 2023/11/21 06:43 [medline]
PHST- 2023/11/21 05:05 [entrez]
AID - 2023.11.10.23298364 [pii]
AID - 10.1101/2023.11.10.23298364 [doi]
PST - epublish
SO  - medRxiv. 2023 Nov 10:2023.11.10.23298364. doi: 10.1101/2023.11.10.23298364. 
      Preprint.

PMID- 36572766
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230413
IS  - 2398-6352 (Electronic)
IS  - 2398-6352 (Linking)
VI  - 5
IP  - 1
DP  - 2022 Dec 26
TI  - A large language model for electronic health records.
PG  - 194
LID - 10.1038/s41746-022-00742-2 [doi]
LID - 194
AB  - There is an increasing interest in developing artificial intelligence (AI) 
      systems to process and interpret electronic health records (EHRs). Natural 
      language processing (NLP) powered by pretrained language models is the key 
      technology for medical AI systems utilizing clinical narratives. However, there 
      are few clinical language models, the largest of which trained in the clinical 
      domain is comparatively small at 110 million parameters (compared with billions 
      of parameters in the general domain). It is not clear how large clinical language 
      models with billions of parameters can help medical AI systems utilize 
      unstructured EHRs. In this study, we develop from scratch a large clinical 
      language model-GatorTron-using >90 billion words of text (including >82 billion 
      words of de-identified clinical text) and systematically evaluate it on five 
      clinical NLP tasks including clinical concept extraction, medical relation 
      extraction, semantic textual similarity, natural language inference (NLI), and 
      medical question answering (MQA). We examine how (1) scaling up the number of 
      parameters and (2) scaling up the size of the training data could benefit these 
      NLP tasks. GatorTron models scale up the clinical language model from 110 million 
      to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 
      9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI 
      systems to improve healthcare delivery. The GatorTron models are publicly 
      available at: 
      https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .
CI  - © 2022. The Author(s).
FAU - Yang, Xi
AU  - Yang X
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Cancer Informatics and eHealth core, University of Florida Health Cancer Center, 
      Gainesville, FL, USA.
FAU - Chen, Aokun
AU  - Chen A
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Cancer Informatics and eHealth core, University of Florida Health Cancer Center, 
      Gainesville, FL, USA.
FAU - PourNejatian, Nima
AU  - PourNejatian N
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Shin, Hoo Chang
AU  - Shin HC
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Smith, Kaleb E
AU  - Smith KE
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Parisien, Christopher
AU  - Parisien C
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Compas, Colin
AU  - Compas C
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Martin, Cheryl
AU  - Martin C
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Costa, Anthony B
AU  - Costa AB
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Flores, Mona G
AU  - Flores MG
AUID- ORCID: 0000-0002-7362-3044
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Zhang, Ying
AU  - Zhang Y
AUID- ORCID: 0000-0003-4210-2104
AD  - Research Computing, University of Florida, Gainesville, FL, USA.
FAU - Magoc, Tanja
AU  - Magoc T
AD  - Integrated Data Repository Research Services, University of Florida, Gainesville, 
      FL, USA.
FAU - Harle, Christopher A
AU  - Harle CA
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Integrated Data Repository Research Services, University of Florida, Gainesville, 
      FL, USA.
FAU - Lipori, Gloria
AU  - Lipori G
AD  - Integrated Data Repository Research Services, University of Florida, Gainesville, 
      FL, USA.
AD  - Lillian S. Wells Department of Neurosurgery, UF Clinical and Translational 
      Science Institute, University of Florida, Gainesville, FL, USA.
FAU - Mitchell, Duane A
AU  - Mitchell DA
AD  - Lillian S. Wells Department of Neurosurgery, UF Clinical and Translational 
      Science Institute, University of Florida, Gainesville, FL, USA.
FAU - Hogan, William R
AU  - Hogan WR
AUID- ORCID: 0000-0002-9881-1017
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Shenkman, Elizabeth A
AU  - Shenkman EA
AUID- ORCID: 0000-0003-4903-1804
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
FAU - Bian, Jiang
AU  - Bian J
AUID- ORCID: 0000-0002-2238-5429
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA.
AD  - Cancer Informatics and eHealth core, University of Florida Health Cancer Center, 
      Gainesville, FL, USA.
FAU - Wu, Yonghui
AU  - Wu Y
AUID- ORCID: 0000-0002-6780-6135
AD  - Department of Health Outcomes and Biomedical Informatics, College of Medicine, 
      University of Florida, Gainesville, FL, USA. yonghui.wu@ufl.edu.
AD  - Cancer Informatics and eHealth core, University of Florida Health Cancer Center, 
      Gainesville, FL, USA. yonghui.wu@ufl.edu.
LA  - eng
GR  - 1R01CA246418/U.S. Department of Health & Human Services | NIH | National Cancer 
      Institute (NCI)/
GR  - R21AG062884/U.S. Department of Health & Human Services | NIH | National Institute 
      on Aging (U.S. National Institute on Aging)/
GR  - UL1 TR001427/TR/NCATS NIH HHS/United States
GR  - R56AG069880/U.S. Department of Health & Human Services | NIH | National Institute 
      on Aging (U.S. National Institute on Aging)/
GR  - ME-2018C3-14754/Patient-Centered Outcomes Research Institute (PCORI)/
PT  - Journal Article
DEP - 20221226
PL  - England
TA  - NPJ Digit Med
JT  - NPJ digital medicine
JID - 101731738
PMC - PMC9792464
COIS- The authors declare no competing interests.
EDAT- 2022/12/27 06:00
MHDA- 2022/12/27 06:01
CRDT- 2022/12/26 23:27
PHST- 2022/06/21 00:00 [received]
PHST- 2022/12/13 00:00 [accepted]
PHST- 2022/12/26 23:27 [entrez]
PHST- 2022/12/27 06:00 [pubmed]
PHST- 2022/12/27 06:01 [medline]
AID - 10.1038/s41746-022-00742-2 [pii]
AID - 742 [pii]
AID - 10.1038/s41746-022-00742-2 [doi]
PST - epublish
SO  - NPJ Digit Med. 2022 Dec 26;5(1):194. doi: 10.1038/s41746-022-00742-2.

PMID- 37927888
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231107
IS  - 2589-9090 (Electronic)
IS  - 2589-9090 (Linking)
VI  - 7
DP  - 2023 Dec
TI  - Accuracy of generative artificial intelligence models in differential diagnoses 
      of familial Mediterranean fever and deficiency of Interleukin-1 receptor 
      antagonist.
PG  - 100213
LID - 10.1016/j.jtauto.2023.100213 [doi]
LID - 100213
AB  - With the increasing development of artificial intelligence, large language models 
      (LLMs) have been utilized to solve problems in natural language processing tasks. 
      More recently, LLMs have shown unique potential in numerous applications within 
      medicine but have been particularly investigated for their ability in clinical 
      reasoning. Although the diagnostic accuracy of LLMs in forming differential 
      diagnoses has been reviewed in general internal medicine applications, much is 
      unknown in autoinflammatory disorders. From the nature of autoinflammatory 
      diseases, forming a differential diagnosis is challenging due to the overlapping 
      symptoms between disorders and even more difficult without genetic screening. In 
      this work, the diagnostic accuracy of the Generative Pre-Trained Transformer 
      Model-4 (GPT-4), GPT-3.5, and Large Language Model Meta AI (LLaMa) were evaluated 
      in clinical vignettes of Deficiency of Interleukin-1 Receptor Antagonist (DIRA) 
      and Familial Mediterranean Fever (FMF). We then compared these models to a 
      control group including one internal medicine physician. It was found that GPT-4 
      did not significantly differ in correctly identifying DIRA and FMF patients 
      compared to the internist. However, the physician maintained a significantly 
      higher accuracy than GPT-3.5 and LLaMa 2 for either disease. Overall, we explore 
      and discuss the unique potential of LLMs in diagnostics for autoimmune diseases.
CI  - © 2023 The Authors.
FAU - Pillai, Joshua
AU  - Pillai J
AD  - Department of Biological Sciences, Irvine Unified School District. 5050 Barranca 
      Parkway, Irvine, 92604, CA, USA.
FAU - Pillai, Kathryn
AU  - Pillai K
AD  - Department of Medical Education, California University of Science and Medicine, 
      School of Medicine. 1501 Violet St, Colton, CA, USA.
LA  - eng
PT  - Journal Article
DEP - 20231014
PL  - Netherlands
TA  - J Transl Autoimmun
JT  - Journal of translational autoimmunity
JID - 101759413
PMC - PMC10622681
OTO - NOTNLM
OT  - Artificial intelligence
OT  - DIRA
OT  - Deficiency of Interleukin-1 receptor antagonist
OT  - FMF
OT  - Familial Mediterranean fever
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/11/06 06:42
MHDA- 2023/11/06 06:43
CRDT- 2023/11/06 04:24
PHST- 2023/08/14 00:00 [received]
PHST- 2023/09/18 00:00 [revised]
PHST- 2023/10/06 00:00 [accepted]
PHST- 2023/11/06 06:43 [medline]
PHST- 2023/11/06 06:42 [pubmed]
PHST- 2023/11/06 04:24 [entrez]
AID - S2589-9090(23)00026-6 [pii]
AID - 100213 [pii]
AID - 10.1016/j.jtauto.2023.100213 [doi]
PST - epublish
SO  - J Transl Autoimmun. 2023 Oct 14;7:100213. doi: 10.1016/j.jtauto.2023.100213. 
      eCollection 2023 Dec.

PMID- 38000386
OWN - NLM
STAT- Publisher
LR  - 20231124
IS  - 1362-4962 (Electronic)
IS  - 0305-1048 (Linking)
DP  - 2023 Nov 24
TI  - The Monarch Initiative in 2024: an analytic platform integrating phenotypes, 
      genes and diseases across species.
LID - gkad1082 [pii]
LID - 10.1093/nar/gkad1082 [doi]
AB  - Bridging the gap between genetic variations, environmental determinants, and 
      phenotypic outcomes is critical for supporting clinical diagnosis and 
      understanding mechanisms of diseases. It requires integrating open data at a 
      global scale. The Monarch Initiative advances these goals by developing open 
      ontologies, semantic data models, and knowledge graphs for translational 
      research. The Monarch App is an integrated platform combining data about genes, 
      phenotypes, and diseases across species. Monarch's APIs enable access to 
      carefully curated datasets and advanced analysis tools that support the 
      understanding and diagnosis of disease for diverse applications such as variant 
      prioritization, deep phenotyping, and patient profile-matching. We have migrated 
      our system into a scalable, cloud-based infrastructure; simplified Monarch's data 
      ingestion and knowledge graph integration systems; enhanced data mapping and 
      integration standards; and developed a new user interface with novel search and 
      graph navigation features. Furthermore, we advanced Monarch's analytic tools by 
      developing a customized plugin for OpenAI's ChatGPT to increase the reliability 
      of its responses about phenotypic data, allowing us to interrogate the knowledge 
      in the Monarch graph using state-of-the-art Large Language Models. The resources 
      of the Monarch Initiative can be found at monarchinitiative.org and its 
      corresponding code repository at github.com/monarch-initiative/monarch-app.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic 
      Acids Research.
FAU - Putman, Tim E
AU  - Putman TE
AUID- ORCID: 0000-0002-4291-0737
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Schaper, Kevin
AU  - Schaper K
AUID- ORCID: 0000-0003-3311-7320
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Matentzoglu, Nicolas
AU  - Matentzoglu N
AUID- ORCID: 0000-0002-7356-1779
AD  - Independent Consultant, Semanticly, Athens, Greece.
FAU - Rubinetti, Vincent P
AU  - Rubinetti VP
AUID- ORCID: 0000-0002-4655-3773
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Alquaddoomi, Faisal S
AU  - Alquaddoomi FS
AUID- ORCID: 0000-0003-4297-8747
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Cox, Corey
AU  - Cox C
AUID- ORCID: 0000-0001-9042-5982
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Caufield, J Harry
AU  - Caufield JH
AUID- ORCID: 0000-0001-5705-7831
AD  - Environmental Genomics and Systems Biology, Lawrence Berkeley National 
      Laboratory, Berkeley, CA 94720, USA.
FAU - Elsarboukh, Glass
AU  - Elsarboukh G
AUID- ORCID: 0000-0001-8272-7198
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Gehrke, Sarah
AU  - Gehrke S
AUID- ORCID: 0000-0003-3245-2880
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Hegde, Harshad
AU  - Hegde H
AUID- ORCID: 0000-0002-2411-565X
AD  - Environmental Genomics and Systems Biology, Lawrence Berkeley National 
      Laboratory, Berkeley, CA 94720, USA.
FAU - Reese, Justin T
AU  - Reese JT
AUID- ORCID: 0000-0002-2170-2250
AD  - Environmental Genomics and Systems Biology, Lawrence Berkeley National 
      Laboratory, Berkeley, CA 94720, USA.
FAU - Braun, Ian
AU  - Braun I
AUID- ORCID: 0000-0002-2389-9288
AD  - Data Collaboration Center, Critical Path Institute, Tucson, AZ 85718, USA.
FAU - Bruskiewich, Richard M
AU  - Bruskiewich RM
AUID- ORCID: 0000-0002-4447-5978
AD  - STAR Informatics, Delphinai Corporation, Sooke, BC V9Z 0M3, Canada.
FAU - Cappelletti, Luca
AU  - Cappelletti L
AUID- ORCID: 0000-0002-1269-2038
AD  - Biology, University of Fribourg, Fribourg, Switzerland.
FAU - Carbon, Seth
AU  - Carbon S
AUID- ORCID: 0000-0001-8244-1536
AD  - Environmental Genomics and Systems Biology, Lawrence Berkeley National 
      Laboratory, Berkeley, CA 94720, USA.
FAU - Caron, Anita R
AU  - Caron AR
AUID- ORCID: 0000-0002-6523-4866
AD  - European Bioinformatics Institute (EMBL-EBI), Hinxton CB10 1SD, UK.
FAU - Chan, Lauren E
AU  - Chan LE
AUID- ORCID: 0000-0002-7463-6306
AD  - College of Public Health and Human Sciences, Oregon State University, Corvallis, 
      OR 97331, USA.
FAU - Chute, Christopher G
AU  - Chute CG
AUID- ORCID: 0000-0001-5437-2545
AD  - Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, 
      Baltimore, MD 21205, USA.
FAU - Cortes, Katherina G
AU  - Cortes KG
AUID- ORCID: 0000-0002-9489-3158
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - De Souza, Vinícius
AU  - De Souza V
AUID- ORCID: 0000-0003-3961-0247
AD  - European Bioinformatics Institute (EMBL-EBI), Hinxton CB10 1SD, UK.
FAU - Fontana, Tommaso
AU  - Fontana T
AUID- ORCID: 0000-0002-9806-3493
AD  - Dipartimento di Informatica, Università degli Studi di Milano Statale, Milano, 
      Italy.
FAU - Harris, Nomi L
AU  - Harris NL
AUID- ORCID: 0000-0001-6315-3707
AD  - Environmental Genomics and Systems Biology, Lawrence Berkeley National 
      Laboratory, Berkeley, CA 94720, USA.
FAU - Hartley, Emily L
AU  - Hartley EL
AUID- ORCID: 0000-0001-5839-2535
AD  - Data Collaboration Center, Critical Path Institute, Tucson, AZ 85718, USA.
FAU - Hurwitz, Eric
AU  - Hurwitz E
AUID- ORCID: 0000-0002-6581-7754
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Jacobsen, Julius O B
AU  - Jacobsen JOB
AUID- ORCID: 0000-0002-3265-1591
AD  - William Harvey Research Institute, Queen Mary University of London, London EC1M 
      6BQ, UK.
FAU - Krishnamurthy, Madan
AU  - Krishnamurthy M
AUID- ORCID: 0000-0002-9767-3636
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Laraway, Bryan J
AU  - Laraway BJ
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - McLaughlin, James A
AU  - McLaughlin JA
AUID- ORCID: 0000-0002-8361-2795
AD  - European Bioinformatics Institute (EMBL-EBI), Hinxton CB10 1SD, UK.
FAU - McMurry, Julie A
AU  - McMurry JA
AUID- ORCID: 0000-0002-9353-5498
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Moxon, Sierra A T
AU  - Moxon SAT
AUID- ORCID: 0000-0002-8719-7760
AD  - Environmental Genomics and Systems Biology, Lawrence Berkeley National 
      Laboratory, Berkeley, CA 94720, USA.
FAU - Mullen, Kathleen R
AU  - Mullen KR
AUID- ORCID: 0000-0002-5002-8648
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - O'Neil, Shawn T
AU  - O'Neil ST
AUID- ORCID: 0000-0001-6220-7080
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Shefchek, Kent A
AU  - Shefchek KA
AUID- ORCID: 0000-0001-6439-2224
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Stefancsik, Ray
AU  - Stefancsik R
AUID- ORCID: 0000-0001-8314-2140
AD  - European Bioinformatics Institute (EMBL-EBI), Hinxton CB10 1SD, UK.
FAU - Toro, Sabrina
AU  - Toro S
AUID- ORCID: 0000-0002-4142-7153
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Vasilevsky, Nicole A
AU  - Vasilevsky NA
AUID- ORCID: 0000-0001-5208-3432
AD  - Data Collaboration Center, Critical Path Institute, Tucson, AZ 85718, USA.
FAU - Walls, Ramona L
AU  - Walls RL
AUID- ORCID: 0000-0001-8815-0078
AD  - Data Collaboration Center, Critical Path Institute, Tucson, AZ 85718, USA.
FAU - Whetzel, Patricia L
AU  - Whetzel PL
AUID- ORCID: 0000-0002-3458-4839
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Osumi-Sutherland, David
AU  - Osumi-Sutherland D
AD  - European Bioinformatics Institute (EMBL-EBI), Hinxton CB10 1SD, UK.
FAU - Smedley, Damian
AU  - Smedley D
AUID- ORCID: 0000-0002-5836-9850
AD  - William Harvey Research Institute, Queen Mary University of London, London EC1M 
      6BQ, UK.
FAU - Robinson, Peter N
AU  - Robinson PN
AUID- ORCID: 0000-0002-0736-9199
AD  - The Jackson Laboratory for Genomic Medicine, Farmington, CT 6032, USA.
FAU - Mungall, Christopher J
AU  - Mungall CJ
AUID- ORCID: 0000-0002-6601-2165
AD  - Environmental Genomics and Systems Biology, Lawrence Berkeley National 
      Laboratory, Berkeley, CA 94720, USA.
FAU - Haendel, Melissa A
AU  - Haendel MA
AUID- ORCID: 0000-0001-9114-8737
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
FAU - Munoz-Torres, Monica C
AU  - Munoz-Torres MC
AUID- ORCID: 0000-0001-8430-6039
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO 80045, USA.
LA  - eng
GR  - 5RM1HG010860-04/HG/NHGRI NIH HHS/United States
GR  - T15LM009451/LM/NLM NIH HHS/United States
PT  - Journal Article
DEP - 20231124
PL  - England
TA  - Nucleic Acids Res
JT  - Nucleic acids research
JID - 0411011
SB  - IM
EDAT- 2023/11/25 07:43
MHDA- 2023/11/25 07:43
CRDT- 2023/11/24 19:05
PHST- 2023/11/02 00:00 [accepted]
PHST- 2023/10/21 00:00 [revised]
PHST- 2023/09/18 00:00 [received]
PHST- 2023/11/25 07:43 [medline]
PHST- 2023/11/25 07:43 [pubmed]
PHST- 2023/11/24 19:05 [entrez]
AID - 7449493 [pii]
AID - 10.1093/nar/gkad1082 [doi]
PST - aheadofprint
SO  - Nucleic Acids Res. 2023 Nov 24:gkad1082. doi: 10.1093/nar/gkad1082.

PMID- 37904646
OWN - NLM
STAT- Publisher
LR  - 20231031
IS  - 1547-5069 (Electronic)
IS  - 1527-6546 (Linking)
DP  - 2023 Oct 31
TI  - The importance of transparency: Declaring the use of generative artificial 
      intelligence (AI) in academic writing.
LID - 10.1111/jnu.12938 [doi]
AB  - The integration of generative artificial intelligence (AI) into academic research 
      writing has revolutionized the field, offering powerful tools like ChatGPT and 
      Bard to aid researchers in content generation and idea enhancement. We explore 
      the current state of transparency regarding generative AI use in nursing academic 
      research journals, emphasizing the need for explicitly declaring the use of 
      generative AI by authors in the manuscript. Out of 125 nursing studies journals, 
      37.6% required explicit statements about generative AI use in their authors' 
      guidelines. No significant differences in impact factors or journal categories 
      were found between journals with and without such requirement. A similar 
      evaluation of medicine, general and internal journals showed a lower percentage 
      (14.5%) including the information about generative AI usage. Declaring generative 
      AI tool usage is crucial for maintaining the transparency and credibility in 
      academic writing. Additionally, extending the requirement for AI usage 
      declarations to journal reviewers can enhance the quality of peer review and 
      combat predatory journals in the academic publishing landscape. Our study 
      highlights the need for active participation from nursing researchers in 
      discussions surrounding standardization of generative AI declaration in academic 
      research writing.
CI  - © 2023 The Authors. Journal of Nursing Scholarship published by Wiley Periodicals 
      LLC on behalf of Sigma Theta Tau International.
FAU - Tang, Arthur
AU  - Tang A
AD  - School of Science, Engineering and Technology, RMIT University, Ho Chi Minh City, 
      Vietnam.
FAU - Li, Kin-Kit
AU  - Li KK
AD  - Department of Social and Behavioural Sciences, City University of Hong Kong, Hong 
      Kong, Hong Kong Special Administrative Region.
FAU - Kwok, Kin On
AU  - Kwok KO
AD  - JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 
      Hong Kong, Hong Kong Special Administrative Region.
AD  - Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of 
      Hong Kong, Hong Kong, Hong Kong Special Administrative Region.
AD  - Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, 
      Hong Kong, Hong Kong Special Administrative Region.
AD  - Department of Infectious Disease Epidemiology, School of Public Health, Imperial 
      College London, London, UK.
FAU - Cao, Liujiao
AU  - Cao L
AD  - West China School of Nursing/West China Hospital, Sichuan University, Chengdu, 
      China.
FAU - Luong, Stanley
AU  - Luong S
AD  - School of Science, Engineering and Technology, RMIT University, Ho Chi Minh City, 
      Vietnam.
FAU - Tam, Wilson
AU  - Tam W
AUID- ORCID: 0000-0003-0641-3060
AD  - Alice Lee Centre for Nursing Studies, National University of Singapore, 
      Singapore.
LA  - eng
PT  - Journal Article
DEP - 20231031
PL  - United States
TA  - J Nurs Scholarsh
JT  - Journal of nursing scholarship : an official publication of Sigma Theta Tau 
      International Honor Society of Nursing
JID - 100911591
SB  - IM
EDAT- 2023/10/31 06:42
MHDA- 2023/10/31 06:42
CRDT- 2023/10/31 03:48
PHST- 2023/08/21 00:00 [revised]
PHST- 2023/06/27 00:00 [received]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/10/31 06:42 [medline]
PHST- 2023/10/31 06:42 [pubmed]
PHST- 2023/10/31 03:48 [entrez]
AID - 10.1111/jnu.12938 [doi]
PST - aheadofprint
SO  - J Nurs Scholarsh. 2023 Oct 31. doi: 10.1111/jnu.12938.

PMID- 37336616
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20231005
IS  - 1532-8651 (Electronic)
IS  - 1098-7339 (Linking)
VI  - 48
IP  - 11
DP  - 2023 Nov
TI  - How large language models can augment perioperative medicine: a daring discourse.
PG  - 575-577
LID - 10.1136/rapm-2023-104637 [doi]
AB  - Interest in natural language processing, specifically large language models, for 
      clinical applications has exploded in a matter of several months since the 
      introduction of ChatGPT. Large language models are powerful and impressive. It is 
      important that we understand the strengths and limitations of this rapidly 
      evolving technology so that we can brainstorm its future potential in 
      perioperative medicine. In this daring discourse, we discuss the issues with 
      these large language models and how we should proactively think about how to 
      leverage these models into practice to improve patient care, rather than worry 
      that it may take over clinical decision-making. We review three potential major 
      areas in which it may be used to benefit perioperative medicine: (1) clinical 
      decision support and surveillance tools, (2) improved aggregation and analysis of 
      research data related to large retrospective studies and application in 
      predictive modeling, and (3) optimized documentation for quality measurement, 
      monitoring and billing compliance. These large language models are here to stay 
      and, as perioperative providers, we can either adapt to this technology or be 
      curtailed by those who learn to use it well.
CI  - © American Society of Regional Anesthesia & Pain Medicine 2023. No commercial 
      re-use. See rights and permissions. Published by BMJ.
FAU - Gabriel, Rodney A
AU  - Gabriel RA
AUID- ORCID: 0000-0003-4443-0021
AD  - Anesthesiology, University of California San Diego, La Jolla, California, USA 
      ragabriel@health.ucsd.edu.
FAU - Mariano, Edward R
AU  - Mariano ER
AUID- ORCID: 0000-0003-2735-248X
AD  - Anesthesiology and Perioperative Care Service, VA Palo Alto Health Care System, 
      Palo Alto, California, USA.
AD  - Department of Anesthesiology, Perioperative and Pain Medicine, Stanford 
      University School of Medicine, Stanford, California, USA.
FAU - McAuley, Julian
AU  - McAuley J
AD  - Computer Science and Engineering, University of California San Diego, La Jolla, 
      California, USA.
FAU - Wu, Christopher L
AU  - Wu CL
AUID- ORCID: 0000-0002-4484-0787
AD  - Anesthesiology, Hospital for Special Surgery, New York City, New York, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230619
PL  - England
TA  - Reg Anesth Pain Med
JT  - Regional anesthesia and pain medicine
JID - 9804508
SB  - IM
MH  - Humans
MH  - Retrospective Studies
MH  - *Perioperative Medicine
MH  - Forecasting
OTO - NOTNLM
OT  - Acute Pain
OT  - Analgesics, Opioid
OT  - CHRONIC PAIN
OT  - TECHNOLOGY
COIS- Competing interests: RG’s institution has received funding and/or product for 
      research purposes from Epimed, Infutronix, SPR Therapeutics, Merck, and Precision 
      Genetics. RG is a consultant for Avanos. ERM, JM, and CLW have no financial 
      conflicts of interest to disclose.
EDAT- 2023/06/20 01:09
MHDA- 2023/09/25 06:43
CRDT- 2023/06/19 21:02
PHST- 2023/04/27 00:00 [received]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2023/09/25 06:43 [medline]
PHST- 2023/06/20 01:09 [pubmed]
PHST- 2023/06/19 21:02 [entrez]
AID - rapm-2023-104637 [pii]
AID - 10.1136/rapm-2023-104637 [doi]
PST - ppublish
SO  - Reg Anesth Pain Med. 2023 Nov;48(11):575-577. doi: 10.1136/rapm-2023-104637. Epub 
      2023 Jun 19.

PMID- 38074090
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2296-889X (Print)
IS  - 2296-889X (Electronic)
IS  - 2296-889X (Linking)
VI  - 10
DP  - 2023
TI  - Ten questions to AI regarding the present and future of proteomics.
PG  - 1295721
LID - 10.3389/fmolb.2023.1295721 [doi]
LID - 1295721
AB  - The role of a scientist is at first not so different from a philosopher. They 
      both need to question common thinking and evaluate whether reality is not as we 
      always thought. Based on this, we need to design hypotheses, experiments, and 
      analyses to prove our alternative vision. Artificial Intelligence (AI) is rapidly 
      moving from an "assistant" into a proper "colleague" for literature mining, data 
      analysis and interpretation, and literally having (almost) real scientific 
      conversations. However, being AI based on existing information, if we rely on it 
      excessively will we still be able to question the status quo? In this article, we 
      are particularly interested in discussing the future of proteomics and mass 
      spectrometry with our new electronic collaborator. We leave to the reader the 
      judgement whether the answers we received are satisfactory or superficial. What 
      we were mostly interested in was laying down what we think are critical questions 
      that the proteomics community should occasionally ask to itself. Proteomics has 
      been around for more than 30 years, but it is still missing a few critical steps 
      to fully address its promises as being the new genomics for clinical diagnostics 
      and fundamental science, while becoming a user-friendly tool for every lab. Will 
      we get there with the help of AI? And will these answers change in a short 
      period, as AI continues to advance?
CI  - Copyright © 2023 Stransky, Sun, Shi and Sidoli.
FAU - Stransky, Stephanie
AU  - Stransky S
AD  - Department of Biochemistry, Albert Einstein College of Medicine, New York, NY, 
      United States.
FAU - Sun, Yan
AU  - Sun Y
AD  - Department of Biochemistry, Albert Einstein College of Medicine, New York, NY, 
      United States.
FAU - Shi, Xuyan
AU  - Shi X
AD  - Department of Biochemistry, Albert Einstein College of Medicine, New York, NY, 
      United States.
FAU - Sidoli, Simone
AU  - Sidoli S
AD  - Department of Biochemistry, Albert Einstein College of Medicine, New York, NY, 
      United States.
LA  - eng
PT  - Journal Article
DEP - 20231123
PL  - Switzerland
TA  - Front Mol Biosci
JT  - Frontiers in molecular biosciences
JID - 101653173
PMC - PMC10701410
OTO - NOTNLM
OT  - Bard
OT  - ChatGPT
OT  - artificial intelligence
OT  - mass spectrometry
OT  - proteomics
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/12/11 06:45
MHDA- 2023/12/11 06:46
CRDT- 2023/12/11 05:38
PHST- 2023/09/17 00:00 [received]
PHST- 2023/11/10 00:00 [accepted]
PHST- 2023/12/11 06:46 [medline]
PHST- 2023/12/11 06:45 [pubmed]
PHST- 2023/12/11 05:38 [entrez]
AID - 1295721 [pii]
AID - 10.3389/fmolb.2023.1295721 [doi]
PST - epublish
SO  - Front Mol Biosci. 2023 Nov 23;10:1295721. doi: 10.3389/fmolb.2023.1295721. 
      eCollection 2023.

PMID- 37540551
OWN - NLM
STAT- MEDLINE
DCOM- 20230807
LR  - 20230823
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Aug 4
TI  - Toward Community-Based Natural Language Processing (CBNLP): Cocreating With 
      Communities.
PG  - e48498
LID - 10.2196/48498 [doi]
LID - e48498
AB  - Rapid development and adoption of natural language processing (NLP) techniques 
      has led to a multitude of exciting and innovative societal and health care 
      applications. These advancements have also generated concerns around perpetuation 
      of historical injustices and that these tools lack cultural considerations. While 
      traditional health care NLP techniques typically include clinical subject matter 
      experts to extract health information or aid in interpretation, few NLP tools 
      involve community stakeholders with lived experiences. In this perspective paper, 
      we draw upon the field of community-based participatory research, which gathers 
      input from community members for development of public health interventions, to 
      identify and examine ways to equitably involve communities in developing health 
      care NLP tools. To realize the potential of community-based NLP (CBNLP), research 
      and development teams must thoughtfully consider mechanisms and resources needed 
      to effectively collaborate with community members for maximal societal and 
      ethical impact of NLP-based tools.
CI  - ©Malvika Pillai, Ashley C Griffin, Clair A Kronk, Terika McCall. Originally 
      published in the Journal of Medical Internet Research (https://www.jmir.org), 
      04.08.2023.
FAU - Pillai, Malvika
AU  - Pillai M
AUID- ORCID: 0000-0001-8739-189X
AD  - Center for Biomedical Informatics Research, Stanford University School of 
      Medicine, Stanford, CA, United States.
AD  - Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States.
FAU - Griffin, Ashley C
AU  - Griffin AC
AUID- ORCID: 0000-0002-1535-0797
AD  - Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States.
AD  - Department of Health Policy, Stanford University School of Medicine, Stanford, 
      CA, United States.
FAU - Kronk, Clair A
AU  - Kronk CA
AUID- ORCID: 0000-0001-8397-8810
AD  - Center for Medical Informatics, Yale School of Medicine, New Haven, CT, United 
      States.
FAU - McCall, Terika
AU  - McCall T
AUID- ORCID: 0000-0002-8143-5393
AD  - Center for Medical Informatics, Yale School of Medicine, New Haven, CT, United 
      States.
AD  - Division of Health Informatics, Department of Biostatistics, Yale School of 
      Public Health, New Haven, CT, United States.
AD  - Section of Biomedical Informatics and Data Science, Yale School of Medicine, New 
      Haven, CT, United States.
LA  - eng
GR  - R01 LM013477/LM/NLM NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20230804
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Natural Language Processing
MH  - *Community-Based Participatory Research
PMC - PMC10439463
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - co-creation
OT  - co-design
OT  - collaboration
OT  - collaborative
OT  - community based
OT  - community-based participatory research
OT  - lived experience
OT  - lived experiences
OT  - machine learning
OT  - natural language processing
OT  - participatory
OT  - research design
COIS- Conflicts of Interest: TM is a member of the Clinical Diversity Advisory Board at 
      Woebot Health, the Digital Wellbeing Advisory Board at Peer Health Exchange, and 
      the advisory board at RACE Space Inc. The aforementioned companies were not 
      involved in the writing of this article or the decision to submit it for 
      publication. TM is also an associate editor of the Journal of Medical Internet 
      Research.
EDAT- 2023/08/04 13:09
MHDA- 2023/08/07 06:41
CRDT- 2023/08/04 11:53
PHST- 2023/04/25 00:00 [received]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/06/15 00:00 [revised]
PHST- 2023/08/07 06:41 [medline]
PHST- 2023/08/04 13:09 [pubmed]
PHST- 2023/08/04 11:53 [entrez]
AID - v25i1e48498 [pii]
AID - 10.2196/48498 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Aug 4;25:e48498. doi: 10.2196/48498.

PMID- 36874336
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230307
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Pacemaker Malfunction in a Patient With Congestive Heart Failure and 
      Hypertension.
PG  - e34574
LID - 10.7759/cureus.34574 [doi]
LID - e34574
AB  - A pacemaker is a medical device commonly used to regulate a patient's heartbeat 
      in cases where the heart's natural electrical impulses may be erratic or 
      compromised. Failure of a pacemaker, or pacemaker malfunction, can be 
      life-threatening and requires immediate action to prevent serious 
      complications. This case report describes a 75-year-old male patient with a 
      history of ventricular tachycardia, congestive heart failure, hypertension, and 
      smoking who was admitted to the hospital with symptoms of palpitations, 
      dizziness, lightheadedness, and decreased level of alertness. The patient had a 
      single-chamber pacemaker implanted two years prior to the current admission. Upon 
      physical examination, the patient's pacemaker had failed, and he was diagnosed 
      with pacemaker failure. Differential diagnoses were ranked from most likely to 
      least likely, based on the patient's history and physical examination, and 
      included pacemaker failure, arrhythmia, myocardial infarction, and pulmonary 
      embolism. Treatment included the replacement of the pacemaker, and the patient 
      was discharged in stable condition.
CI  - Copyright © 2023, Saeed et al.
FAU - Saeed, Abdullah
AU  - Saeed A
AD  - Action Research, Ministry of Health, Abha, SAU.
FAU - AlShafea, Abdullah
AU  - AlShafea A
AD  - Resarch Unit, Ministry of Health, Abha, SAU.
FAU - A, Foton
AU  - A F
AD  - Public Health, King Khalid University, Khamis Mushait, SAU.
FAU - Bin Saeed, Abdulrahman
AU  - Bin Saeed A
AD  - Public Health, King Abdulaziz University, Khamis Mushait, SAU.
LA  - eng
PT  - Case Reports
DEP - 20230202
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC9981543
OTO - NOTNLM
OT  - chatgpt
OT  - heart failure
OT  - hypertension
OT  - malfunction
OT  - pacemaker
OT  - pacemaker complication
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/07 06:00
MHDA- 2023/03/07 06:01
CRDT- 2023/03/06 03:54
PHST- 2023/02/02 00:00 [accepted]
PHST- 2023/03/06 03:54 [entrez]
PHST- 2023/03/07 06:00 [pubmed]
PHST- 2023/03/07 06:01 [medline]
AID - 10.7759/cureus.34574 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 2;15(2):e34574. doi: 10.7759/cureus.34574. eCollection 2023 Feb.

PMID- 37908613
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231103
IS  - 0973-2063 (Print)
IS  - 0973-2063 (Electronic)
IS  - 0973-2063 (Linking)
VI  - 19
IP  - 8
DP  - 2023
TI  - Emergent Risk Group-4 (RG-4) Filoviruses: A paradox in progress.
PG  - 829-832
LID - 10.6026/97320630019829 [doi]
AB  - Filoviruses, categorized as World Health Organization (WHO) Risk Group 4 (RG-4) 
      pathogens, represent significant global health risks due to their extraordinary 
      virulence. The Filoviridae family encompasses Ebola strains such as Sudan, Zaire, 
      Bundibugyo, Tai Forest (formerly known as Ivory Coast), Reston, and Bombali, in 
      addition to the closely related Marburg and Ravn virus strains. Filoviruses 
      originated from a common ancestor about 10,000 years ago and displayed remarkable 
      consistency in genetic heterogeneity until the 20th century. However, they 
      overcame a genetic bottleneck by mid-century. Paradoxically, this resulted in the 
      emergence of boosted virulent strains from the 1970's onward. Filovirus research 
      is included in the NIAID Biodefense Program and utilizes the highest level 
      specialized protective laboratories, Biosafety Laboratory (BSL)-4. The spread of 
      Filoviruses as well as other RG-4 pathogens within Africa poses a significant 
      health threat increasingly both in Africa and out of Africa.
CI  - © 2023 Biomedical Informatics.
FAU - Sinnott, John T
AU  - Sinnott JT
AD  - Division of Infectious Diseases and International Health, Department of Internal 
      Medicine, Morsani College of Medicine, Tampa, Florida 33606. USA.
FAU - Kim, Kami
AU  - Kim K
AD  - Division of Infectious Diseases and International Health, Department of Internal 
      Medicine, Morsani College of Medicine, Tampa, Florida 33606. USA.
FAU - Somboonwit, Charurut
AU  - Somboonwit C
AD  - Division of Infectious Diseases and International Health, Department of Internal 
      Medicine, Morsani College of Medicine, Tampa, Florida 33606. USA.
FAU - Cosnett, Conor
AU  - Cosnett C
AD  - Wolfram Research Inc., Champaigne, Illinois 61820 USA.
FAU - Segal, David
AU  - Segal D
AD  - College of Health Sciences and Public Policy, Walden University, Minneapolis, 
      Minnesota 55401 USA.
FAU - Shapshak, Paul
AU  - Shapshak P
AD  - Division of Infectious Diseases and International Health, Department of Internal 
      Medicine, Morsani College of Medicine, Tampa, Florida 33606. USA.
LA  - eng
PT  - Editorial
DEP - 20230831
PL  - Singapore
TA  - Bioinformation
JT  - Bioinformation
JID - 101258255
PMC - PMC10613816
OTO - NOTNLM
OT  - Biosafety Laboratory (BSL)-4
OT  - Bombali
OT  - Bundibugyo
OT  - CDC
OT  - ChatGPT
OT  - Cueva
OT  - Ebola
OT  - Filoviridae
OT  - Marburg
OT  - NIAID
OT  - NIH
OT  - Ravn
OT  - Reston
OT  - Sudan
OT  - Tai forest (formerly Côte d'Ivoire)
OT  - Thamno
OT  - Wolfram Mathematica
OT  - World Health Organization (WHO) Risk Group 4 (RG-4) virus pathogens
OT  - Zaire
OT  - and Stria viruses
OT  - bats
OT  - biodefense
OT  - ecology
OT  - emergent virus
OT  - global warming
OT  - health-care setting
OT  - humans
OT  - monkeys
OT  - paradox
OT  - reservoir
OT  - rodents
OT  - sexual risk
OT  - vector
OT  - virulence
COIS- The authors report no conflicts of interest.
EDAT- 2023/11/01 06:43
MHDA- 2023/11/01 06:44
CRDT- 2023/11/01 03:57
PHST- 2023/08/01 00:00 [received]
PHST- 2023/08/31 00:00 [revised]
PHST- 2023/08/31 00:00 [accepted]
PHST- 2023/11/01 06:44 [medline]
PHST- 2023/11/01 06:43 [pubmed]
PHST- 2023/11/01 03:57 [entrez]
AID - 97320630019829 [pii]
AID - 10.6026/97320630019829 [doi]
PST - epublish
SO  - Bioinformation. 2023 Aug 31;19(8):829-832. doi: 10.6026/97320630019829. 
      eCollection 2023.

PMID- 37750374
OWN - NLM
STAT- MEDLINE
DCOM- 20230927
LR  - 20230928
IS  - 1598-6357 (Electronic)
IS  - 1011-8934 (Print)
IS  - 1011-8934 (Linking)
VI  - 38
IP  - 38
DP  - 2023 Sep 25
TI  - GPTZero Performance in Identifying Artificial Intelligence-Generated Medical 
      Texts: A Preliminary Study.
PG  - e319
LID - 10.3346/jkms.2023.38.e319 [doi]
LID - e319
AB  - BACKGROUND: With emergence of chatbots to help authors with scientific writings, 
      editors should have tools to identify artificial intelligence-generated texts. 
      GPTZero is among the first websites that has sought media attention claiming to 
      differentiate machine-generated from human-written texts. METHODS: Using 20 text 
      pieces generated by ChatGPT in response to arbitrary questions on various topics 
      in medicine and 30 pieces chosen from previously published medical articles, the 
      performance of GPTZero was assessed. RESULTS: GPTZero had a sensitivity of 0.65 
      (95% confidence interval, 0.41-0.85); specificity, 0.90 (0.73-0.98); accuracy, 
      0.80 (0.66-0.90); and positive and negative likelihood ratios, 6.5 (2.1-19.9) and 
      0.4 (0.2-0.7), respectively. CONCLUSION: GPTZero has a low false-positive 
      (classifying a human-written text as machine-generated) and a high false-negative 
      rate (classifying a machine-generated text as human-written).
CI  - © 2023 The Korean Academy of Medical Sciences.
FAU - Habibzadeh, Farrokh
AU  - Habibzadeh F
AUID- ORCID: 0000-0001-5360-2900
AD  - Past President, World Association of Medical Editors (WAME), Editorial 
      Consultant, The Lancet, Associate Editor, Frontiers in Epidemiology. 
      Farrokh.Habibzadeh@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230925
PL  - Korea (South)
TA  - J Korean Med Sci
JT  - Journal of Korean medical science
JID - 8703518
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Medicine
MH  - Writing
PMC - PMC10519776
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Classification
OT  - Journalism
OT  - Scientific Writing
OT  - Sensitivity and Specificity
COIS- The author has no potential conflicts of interest to disclose.
EDAT- 2023/09/26 06:44
MHDA- 2023/09/27 06:42
CRDT- 2023/09/26 05:43
PHST- 2023/06/25 00:00 [received]
PHST- 2023/07/19 00:00 [accepted]
PHST- 2023/09/27 06:42 [medline]
PHST- 2023/09/26 06:44 [pubmed]
PHST- 2023/09/26 05:43 [entrez]
AID - 38.e319 [pii]
AID - 10.3346/jkms.2023.38.e319 [doi]
PST - epublish
SO  - J Korean Med Sci. 2023 Sep 25;38(38):e319. doi: 10.3346/jkms.2023.38.e319.

PMID- 37450276
OWN - NLM
STAT- Publisher
LR  - 20230714
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
DP  - 2023 Jul 14
TI  - Towards Precision Medicine in Spinal Surgery: Leveraging AI Technologies.
LID - 10.1007/s10439-023-03315-w [doi]
AB  - This critique explores the implications of integrating artificial intelligence 
      (AI) technology, specifically OpenAI's advanced language model GPT-4 and its 
      interface, ChatGPT, into the field of spinal surgery. It examines the potential 
      effects of algorithmic bias, unique challenges in surgical domains, access and 
      equity issues, cost implications, global disparities in technology adoption, and 
      the concept of technological determinism. It posits that biases present in AI 
      training data may impact the quality and equity of healthcare outcomes. 
      Challenges related to the unique nature of surgical procedures, including 
      real-time decision-making, are also addressed. Concerns over access, equity, and 
      cost implications underscore the potential for exacerbated healthcare 
      disparities. Global disparities in technology adoption highlight the importance 
      of global collaboration, technology transfer, and capacity building. Finally, the 
      critique challenges the notion of technological determinism, emphasizing the 
      continued importance of human judgement and patient-care provider relationship in 
      healthcare. The critique calls for a comprehensive evaluation of AI technology 
      integration in healthcare to ensure equitable and quality care.
CI  - © 2023. The Author(s).
FAU - Lawson McLean, Aaron
AU  - Lawson McLean A
AUID- ORCID: 0000-0001-5528-6905
AD  - Department of Neurosurgery, Jena University Hospital - Friedrich Schiller 
      University Jena, Am Klinikum 1, 07747, Jena, Germany. 
      aaron.lawsonmclean@med.uni-jena.de.
LA  - eng
PT  - Letter
DEP - 20230714
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Data security
OT  - Healthcare technology
OT  - Patient outcomes
OT  - Spinal surgery
OT  - Surgical decision making
EDAT- 2023/07/14 13:08
MHDA- 2023/07/14 13:08
CRDT- 2023/07/14 11:18
PHST- 2023/07/04 00:00 [received]
PHST- 2023/07/06 00:00 [accepted]
PHST- 2023/07/14 13:08 [medline]
PHST- 2023/07/14 13:08 [pubmed]
PHST- 2023/07/14 11:18 [entrez]
AID - 10.1007/s10439-023-03315-w [pii]
AID - 10.1007/s10439-023-03315-w [doi]
PST - aheadofprint
SO  - Ann Biomed Eng. 2023 Jul 14. doi: 10.1007/s10439-023-03315-w.

PMID- 37369892
OWN - NLM
STAT- MEDLINE
DCOM- 20230919
LR  - 20230926
IS  - 1525-1497 (Electronic)
IS  - 0884-8734 (Print)
IS  - 0884-8734 (Linking)
VI  - 38
IP  - 12
DP  - 2023 Sep
TI  - Relief in Sight? Chatbots, In-baskets, and the Overwhelmed Primary Care 
      Clinician.
PG  - 2808-2815
LID - 10.1007/s11606-023-08271-8 [doi]
AB  - The recent emergence of publically facing artificial intelligence (AI) chatbots 
      has generated vigorous discussion in the lay public around the possibilities, 
      liabilities, and uncertainties of the integration of such technology into 
      everyday life. As primary care clinicians continue to struggle against 
      ever-increasing loads of asynchronous, electronic work, the potential for AI to 
      improve the quality and efficiency of this work looms large. In this essay, we 
      discuss the basic premise of open-access AI chatbots such as CHATGPT, review 
      prior applications of AI in healthcare, and preview some possible AI 
      chatbot-assisted in-basket assistance including scenarios of communicating test 
      results with patients, providing patient education, and clinical decision support 
      in history taking, review of prior diagnostic test characteristics, and common 
      management scenarios. We discuss important concerns related to the future 
      adoption of this technology including the transparency of the training data used 
      in developing these models, the level of oversight and trustworthiness of the 
      information generated, and possible impacts on equity, bias, and patient privacy. 
      A stepwise and balanced approach to simultaneously understand the capabilities 
      and address the concerns associated with these tools will be needed before these 
      tools can improve patient care.
CI  - © 2023. The Author(s), under exclusive licence to Society of General Internal 
      Medicine.
FAU - Matulis, John
AU  - Matulis J
AUID- ORCID: 0000-0001-9319-5457
AD  - Division of Community Internal Medicine, Geriatrics and Palliative Care, Mayo 
      Clinic Minnesota, Rochester, MN, USA. Matulis.john@mayo.edu.
FAU - McCoy, Rozalina
AU  - McCoy R
AD  - Division of Community Internal Medicine, Geriatrics and Palliative Care, Mayo 
      Clinic Minnesota, Rochester, MN, USA.
LA  - eng
PT  - Editorial
DEP - 20230627
PL  - United States
TA  - J Gen Intern Med
JT  - Journal of general internal medicine
JID - 8605834
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Electronics
MH  - Technology
MH  - Uncertainty
MH  - Primary Health Care
PMC - PMC10506981
COIS- None.
EDAT- 2023/06/28 01:06
MHDA- 2023/09/19 06:42
PMCR- 2024/09/01
CRDT- 2023/06/27 23:30
PHST- 2023/01/31 00:00 [received]
PHST- 2023/06/07 00:00 [accepted]
PHST- 2024/09/01 00:00 [pmc-release]
PHST- 2023/09/19 06:42 [medline]
PHST- 2023/06/28 01:06 [pubmed]
PHST- 2023/06/27 23:30 [entrez]
AID - 10.1007/s11606-023-08271-8 [pii]
AID - 8271 [pii]
AID - 10.1007/s11606-023-08271-8 [doi]
PST - ppublish
SO  - J Gen Intern Med. 2023 Sep;38(12):2808-2815. doi: 10.1007/s11606-023-08271-8. 
      Epub 2023 Jun 27.

PMID- 37261894
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230619
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Jun 1
TI  - Large Language Models in Medical Education: Opportunities, Challenges, and Future 
      Directions.
PG  - e48291
LID - 10.2196/48291 [doi]
LID - e48291
AB  - The integration of large language models (LLMs), such as those in the Generative 
      Pre-trained Transformers (GPT) series, into medical education has the potential 
      to transform learning experiences for students and elevate their knowledge, 
      skills, and competence. Drawing on a wealth of professional and academic 
      experience, we propose that LLMs hold promise for revolutionizing medical 
      curriculum development, teaching methodologies, personalized study plans and 
      learning materials, student assessments, and more. However, we also critically 
      examine the challenges that such integration might pose by addressing issues of 
      algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, 
      and copyright concerns in medical education. As we navigate the shift from an 
      information-driven educational paradigm to an artificial intelligence (AI)-driven 
      educational paradigm, we argue that it is paramount to understand both the 
      potential and the pitfalls of LLMs in medical education. This paper thus offers 
      our perspective on the opportunities and challenges of using LLMs in this 
      context. We believe that the insights gleaned from this analysis will serve as a 
      foundation for future recommendations and best practices in the field, fostering 
      the responsible and effective use of AI technologies in medical education.
CI  - ©Alaa Abd-alrazaq, Rawan AlSaad, Dari Alhuwail, Arfan Ahmed, Padraig Mark Healy, 
      Syed Latifi, Sarah Aziz, Rafat Damseh, Sadam Alabed Alrazak, Javaid Sheikh. 
      Originally published in JMIR Medical Education (https://mededu.jmir.org), 
      01.06.2023.
FAU - Abd-Alrazaq, Alaa
AU  - Abd-Alrazaq A
AUID- ORCID: 0000-0001-7695-4626
AD  - AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
FAU - AlSaad, Rawan
AU  - AlSaad R
AUID- ORCID: 0000-0002-3235-0860
AD  - AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
AD  - College of Computing and Information Technology, University of Doha for Science 
      and Technology, Doha, Qatar.
FAU - Alhuwail, Dari
AU  - Alhuwail D
AUID- ORCID: 0000-0001-5038-3044
AD  - Information Science Department, College of Life Sciences, Kuwait University, 
      Kuwait, Kuwait.
FAU - Ahmed, Arfan
AU  - Ahmed A
AUID- ORCID: 0000-0002-4025-5767
AD  - AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
FAU - Healy, Padraig Mark
AU  - Healy PM
AUID- ORCID: 0000-0002-5804-0342
AD  - Office of Educational Development, Division of Medical Education, Weill Cornell 
      Medicine-Qatar, Doha, Qatar.
FAU - Latifi, Syed
AU  - Latifi S
AUID- ORCID: 0000-0003-0505-609X
AD  - Office of Educational Development, Division of Medical Education, Weill Cornell 
      Medicine-Qatar, Doha, Qatar.
FAU - Aziz, Sarah
AU  - Aziz S
AUID- ORCID: 0000-0002-0861-9743
AD  - AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
FAU - Damseh, Rafat
AU  - Damseh R
AUID- ORCID: 0000-0001-6797-0448
AD  - Department of Computer Science and Software Engineering, United Arab Emirates 
      University, Abu Dhabi, United Arab Emirates.
FAU - Alabed Alrazak, Sadam
AU  - Alabed Alrazak S
AUID- ORCID: 0000-0002-8586-7564
AD  - Department of Mechanical & Industrial Engineering, Faculty of Applied Science and 
      Engineering, University of Toronto, Toronto, ON, Canada.
FAU - Sheikh, Javaid
AU  - Sheikh J
AUID- ORCID: 0000-0002-5762-4186
AD  - AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
LA  - eng
PT  - Journal Article
DEP - 20230601
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10273039
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-4
OT  - artificial intelligence
OT  - educators
OT  - generative AI
OT  - large language models
OT  - medical education
OT  - students
COIS- Conflicts of Interest: A Abd-alrazaq is an Associate Editor of JMIR Nursing at 
      the time of this publication. The other authors have no conflicts of interest to 
      declare.
EDAT- 2023/06/01 13:09
MHDA- 2023/06/01 13:10
CRDT- 2023/06/01 11:53
PHST- 2023/04/19 00:00 [received]
PHST- 2023/05/17 00:00 [accepted]
PHST- 2023/05/15 00:00 [revised]
PHST- 2023/06/01 13:10 [medline]
PHST- 2023/06/01 13:09 [pubmed]
PHST- 2023/06/01 11:53 [entrez]
AID - v9i1e48291 [pii]
AID - 10.2196/48291 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Jun 1;9:e48291. doi: 10.2196/48291.

PMID- 37230490
OWN - NLM
STAT- MEDLINE
DCOM- 20230725
LR  - 20230725
IS  - 1749-6632 (Electronic)
IS  - 0077-8923 (Linking)
VI  - 1525
IP  - 1
DP  - 2023 Jul
TI  - Holistic Evaluation of Language Models.
PG  - 140-146
LID - 10.1111/nyas.15007 [doi]
AB  - Language models (LMs) like GPT-3, PaLM, and ChatGPT are the foundation for almost 
      all major language technologies, but their capabilities, limitations, and risks 
      are not well understood. We present Holistic Evaluation of Language Models (HELM) 
      to improve the transparency of LMs. LMs can serve many purposes and their 
      behavior should satisfy many desiderata. To navigate the vast space of potential 
      scenarios and metrics, we taxonomize the space and select representative subsets. 
      We evaluate models on 16 core scenarios and 7 metrics, exposing important 
      trade-offs. We supplement our core evaluation with seven targeted evaluations to 
      deeply analyze specific aspects (including world knowledge, reasoning, 
      regurgitation of copyrighted content, and generation of disinformation). We 
      benchmark 30 LMs, from OpenAI, Microsoft, Google, Meta, Cohere, AI21 Labs, and 
      others. Prior to HELM, models were evaluated on just 17.9% of the core HELM 
      scenarios, with some prominent models not sharing a single scenario in common. We 
      improve this to 96.0%: all 30 models are now benchmarked under the same 
      standardized conditions. Our evaluation surfaces 25 top-level findings. For full 
      transparency, we release all raw model prompts and completions publicly. HELM is 
      a living benchmark for the community, continuously updated with new scenarios, 
      metrics, and models https://crfm.stanford.edu/helm/latest/.
CI  - © 2023 New York Academy of Sciences.
FAU - Bommasani, Rishi
AU  - Bommasani R
AD  - Center for Research on Foundation Models, Stanford University, Stanford, 
      California, USA.
FAU - Liang, Percy
AU  - Liang P
AD  - Center for Research on Foundation Models, Stanford University, Stanford, 
      California, USA.
FAU - Lee, Tony
AU  - Lee T
AD  - Center for Research on Foundation Models, Stanford University, Stanford, 
      California, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230525
PL  - United States
TA  - Ann N Y Acad Sci
JT  - Annals of the New York Academy of Sciences
JID - 7506858
SB  - IM
MH  - Humans
MH  - *Dietary Supplements
MH  - *Knowledge
MH  - Language
MH  - Problem Solving
MH  - Technology
OTO - NOTNLM
OT  - artificial intelligence
OT  - evaluation
OT  - foundation models
OT  - language models
OT  - natural language processing
OT  - transparency
EDAT- 2023/05/26 01:05
MHDA- 2023/07/25 06:43
CRDT- 2023/05/25 20:22
PHST- 2023/07/25 06:43 [medline]
PHST- 2023/05/26 01:05 [pubmed]
PHST- 2023/05/25 20:22 [entrez]
AID - 10.1111/nyas.15007 [doi]
PST - ppublish
SO  - Ann N Y Acad Sci. 2023 Jul;1525(1):140-146. doi: 10.1111/nyas.15007. Epub 2023 
      May 25.

PMID- 36912253
OWN - NLM
STAT- MEDLINE
DCOM- 20230518
LR  - 20230530
IS  - 1466-187X (Electronic)
IS  - 0142-159X (Linking)
VI  - 45
IP  - 6
DP  - 2023 Jun
TI  - Ethical use of Artificial Intelligence in Health Professions Education: AMEE 
      Guide No. 158.
PG  - 574-584
LID - 10.1080/0142159X.2023.2186203 [doi]
AB  - Health Professions Education (HPE) has benefitted from the advances in Artificial 
      Intelligence (AI) and is set to benefit more in the future. Just as any 
      technological advance opens discussions about ethics, so the implications of AI 
      for HPE ethics need to be identified, anticipated, and accommodated so that HPE 
      can utilise AI without compromising crucial ethical principles. Rather than 
      focussing on AI technology, this Guide focuses on the ethical issues likely to 
      face HPE teachers and administrators as they encounter and use AI systems in 
      their teaching environment. While many of the ethical principles may be familiar 
      to readers in other contexts, they will be viewed in light of AI, and some 
      unfamiliar issues will be introduced. They include data gathering, anonymity, 
      privacy, consent, data ownership, security, bias, transparency, responsibility, 
      autonomy, and beneficence. In the Guide, each topic explains the concept and its 
      importance and gives some indication of how to cope with its complexities. Ideas 
      are drawn from personal experience and the relevant literature. In most topics, 
      further reading is suggested so that readers may further explore the concepts at 
      their leisure. The aim is for HPE teachers and decision-makers at all levels to 
      be alert to these issues and to take proactive action to be prepared to deal with 
      the ethical problems and opportunities that AI usage presents to HPE.
FAU - Masters, Ken
AU  - Masters K
AUID- ORCID: 0000-0003-3425-5020
AD  - Medical Education and Informatics Department, College of Medicine and Health 
      Sciences, Sultan Qaboos University, Muscat, Sultanate of Oman.
LA  - eng
PT  - Journal Article
DEP - 20230313
PL  - England
TA  - Med Teach
JT  - Medical teacher
JID - 7909593
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Privacy
MH  - Health Occupations
OTO - NOTNLM
OT  - ChatGPT
OT  - Ethics
OT  - artificial intelligence
OT  - health professions education
OT  - medical education
EDAT- 2023/03/14 06:00
MHDA- 2023/05/18 06:42
CRDT- 2023/03/13 06:43
PHST- 2023/05/18 06:42 [medline]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/13 06:43 [entrez]
AID - 10.1080/0142159X.2023.2186203 [doi]
PST - ppublish
SO  - Med Teach. 2023 Jun;45(6):574-584. doi: 10.1080/0142159X.2023.2186203. Epub 2023 
      Mar 13.

PMID- 37914030
OWN - NLM
STAT- Publisher
LR  - 20231117
IS  - 1553-6467 (Electronic)
IS  - 0002-9459 (Linking)
DP  - 2023 Oct 30
TI  - A scoping review of artificial intelligence within pharmacy education.
PG  - 100615
LID - S0002-9459(23)04553-9 [pii]
LID - 10.1016/j.ajpe.2023.100615 [doi]
AB  - OBJECTIVES: This scoping review aimed to summarize the available literature on 
      the use of artificial intelligence (AI) in pharmacy education and identify gaps 
      where additional research is needed. FINDINGS: Seven studies specifically 
      addressing the use of AI in pharmacy education were identified. Of these 7 
      studies, 5 focused on AI use in the context of teaching and learning, 1 on the 
      prediction of academic performance for admissions, and the final study focused on 
      using AI text generation to elucidate the benefits and limitations of ChatGPT use 
      in pharmacy education. SUMMARY: There are currently a limited number of available 
      publications that describe AI use in pharmacy education. Several challenges exist 
      regarding the use of AI in pharmacy education, including the need for faculty 
      expertise and time, limited generalizability of tools, limited outcomes data, and 
      several legal and ethical concerns. As AI use increases and implementation 
      becomes more standardized, opportunities will be created for the inclusion of AI 
      in pharmacy education.
CI  - Copyright © 2023 American Association of Colleges of Pharmacy. Published by 
      Elsevier Inc. All rights reserved.
FAU - Abdel Aziz, May H
AU  - Abdel Aziz MH
AD  - University of Texas at Tyler, Ben and Maytee Fisch College of Pharmacy, 
      Department of Pharmaceutical Sciences and Health Outcomes, Tyler, TX, USA. 
      Electronic address: mabdelaziz@uttyler.edu.
FAU - Rowe, Casey
AU  - Rowe C
AD  - University of Florida College of Pharmacy, Department of Pharmacotherapy and 
      Translational Research, Orlando, FL, USA.
FAU - Southwood, Robin
AU  - Southwood R
AD  - University of Georgia, College of Pharmacy, Department of Clinical and 
      Administrative Pharmacy, Athens, GA, USA.
FAU - Nogid, Anna
AU  - Nogid A
AD  - Fairleigh Dickinson University, School of Pharmacy and Health Sciences, 
      Department of Pharmacy Practice, Florham Park, NJ, USA.
FAU - Berman, Sarah
AU  - Berman S
AD  - University of the Incarnate Word, Feik School of Pharmacy, Department of Pharmacy 
      Practice, San Antonio, TX, USA.
FAU - Gustafson, Kyle
AU  - Gustafson K
AD  - Northeast Ohio Medical University, Department of Pharmacy Practice, Rootstown, 
      OH, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231030
PL  - United States
TA  - Am J Pharm Educ
JT  - American journal of pharmaceutical education
JID - 0372650
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Deep learning
OT  - Machine learning
OT  - Pharmacy education
COIS- Declaration of Competing Interest None declared.
EDAT- 2023/11/02 00:42
MHDA- 2023/11/02 00:42
CRDT- 2023/11/01 20:30
PHST- 2023/07/25 00:00 [received]
PHST- 2023/10/19 00:00 [revised]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/11/02 00:42 [pubmed]
PHST- 2023/11/02 00:42 [medline]
PHST- 2023/11/01 20:30 [entrez]
AID - S0002-9459(23)04553-9 [pii]
AID - 10.1016/j.ajpe.2023.100615 [doi]
PST - aheadofprint
SO  - Am J Pharm Educ. 2023 Oct 30:100615. doi: 10.1016/j.ajpe.2023.100615.

PMID- 37729050
OWN - NLM
STAT- MEDLINE
DCOM- 20231129
LR  - 20231129
IS  - 1531-6963 (Electronic)
IS  - 1040-8711 (Linking)
VI  - 36
IP  - 1
DP  - 2024 Jan 1
TI  - Large language models and the future of rheumatology: assessing impact and 
      emerging opportunities.
PG  - 46-51
LID - 10.1097/BOR.0000000000000981 [doi]
AB  - PURPOSE OF REVIEW: Large language models (LLMs) have grown rapidly in size and 
      capabilities as more training data and compute power has become available. Since 
      the release of ChatGPT in late 2022, there has been growing interest and 
      exploration around potential applications of LLM technology. Numerous examples 
      and pilot studies demonstrating the capabilities of these tools have emerged 
      across several domains. For rheumatology professionals and patients, LLMs have 
      the potential to transform current practices in medicine. RECENT FINDINGS: Recent 
      studies have begun exploring capabilities of LLMs that can assist rheumatologists 
      in clinical practice, research, and medical education, though applications are 
      still emerging. In clinical settings, LLMs have shown promise in assist 
      healthcare professionals enabling more personalized medicine or generating 
      routine documentation like notes and letters. Challenges remain around 
      integrating LLMs into clinical workflows, accuracy of the LLMs and ensuring 
      patient data confidentiality. In research, early experiments demonstrate LLMs can 
      offer analysis of datasets, with quality control as a critical piece. Lastly, 
      LLMs could supplement medical education by providing personalized learning 
      experiences and integration into established curriculums. SUMMARY: As these 
      powerful tools continue evolving at a rapid pace, rheumatology professionals 
      should stay informed on how they may impact the field.
CI  - Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Mannstadt, Insa
AU  - Mannstadt I
AD  - Hospital for Special Surgery, New York, New York, USA.
FAU - Mehta, Bella
AU  - Mehta B
AD  - Weill Cornell Medicine.
AD  - Hospital for Special Surgery, New York, New York, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230918
PL  - United States
TA  - Curr Opin Rheumatol
JT  - Current opinion in rheumatology
JID - 9000851
SB  - IM
MH  - Humans
MH  - *Rheumatology
MH  - Rheumatologists
MH  - Dietary Supplements
MH  - Language
MH  - Precision Medicine
EDAT- 2023/09/20 18:42
MHDA- 2023/11/29 06:42
CRDT- 2023/09/20 12:32
PHST- 2023/11/29 06:42 [medline]
PHST- 2023/09/20 18:42 [pubmed]
PHST- 2023/09/20 12:32 [entrez]
AID - 00002281-990000000-00083 [pii]
AID - 10.1097/BOR.0000000000000981 [doi]
PST - ppublish
SO  - Curr Opin Rheumatol. 2024 Jan 1;36(1):46-51. doi: 10.1097/BOR.0000000000000981. 
      Epub 2023 Sep 18.

PMID- 37571587
OWN - NLM
STAT- MEDLINE
DCOM- 20230814
LR  - 20230814
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 15
DP  - 2023 Jul 30
TI  - KRP-DS: A Knowledge Graph-Based Dialogue System with Inference-Aided Prediction.
LID - 10.3390/s23156805 [doi]
LID - 6805
AB  - With the popularity of ChatGPT, there has been increasing attention towards 
      dialogue systems. Researchers are dedicated to designing a knowledgeable model 
      that can engage in conversations like humans. Traditional seq2seq dialogue models 
      often suffer from limited performance and the issue of generating safe responses. 
      In recent years, large-scale pretrained language models have demonstrated their 
      powerful capabilities across various domains. Many studies have leveraged these 
      pretrained models for dialogue tasks to address concerns such as safe response 
      generation. Pretrained models can enhance responses by carrying certain knowledge 
      information after being pre-trained on large-scale data. However, when specific 
      knowledge is required in a particular domain, the model may still generate bland 
      or inappropriate responses, and the interpretability of such models is poor. 
      Therefore, in this paper, we propose the KRP-DS model. We design a knowledge 
      module that incorporates a knowledge graph as external knowledge in the dialogue 
      system. The module utilizes contextual information for path reasoning and guides 
      knowledge prediction. Finally, the predicted knowledge is used to enhance 
      response generation. Experimental results show that our proposed model can 
      effectively improve the quality and diversity of responses while having better 
      interpretability, and outperforms baseline models in both automatic and human 
      evaluations.
FAU - He, Qiang
AU  - He Q
AD  - School of Information Science and Electrical Engineering, Shandong Jiaotong 
      University, Jinan 250357, China.
FAU - Xu, Shuobo
AU  - Xu S
AD  - School of Information Science and Electrical Engineering, Shandong Jiaotong 
      University, Jinan 250357, China.
FAU - Zhu, Zhenfang
AU  - Zhu Z
AUID- ORCID: 0000-0002-7217-3109
AD  - School of Information Science and Electrical Engineering, Shandong Jiaotong 
      University, Jinan 250357, China.
FAU - Wang, Peng
AU  - Wang P
AUID- ORCID: 0000-0003-4771-7984
AD  - School of Information Science and Electrical Engineering, Shandong Jiaotong 
      University, Jinan 250357, China.
FAU - Li, Kefeng
AU  - Li K
AD  - School of Information Science and Electrical Engineering, Shandong Jiaotong 
      University, Jinan 250357, China.
FAU - Zheng, Quanfeng
AU  - Zheng Q
AD  - School of Information Science and Electrical Engineering, Shandong Jiaotong 
      University, Jinan 250357, China.
FAU - Li, Yanshun
AU  - Li Y
AD  - School of Information Science and Electrical Engineering, Shandong Jiaotong 
      University, Jinan 250357, China.
LA  - eng
PT  - Journal Article
DEP - 20230730
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Humans
MH  - *Pattern Recognition, Automated
MH  - *Communication
MH  - Knowledge
MH  - Knowledge Bases
MH  - Language
PMC - PMC10422325
OTO - NOTNLM
OT  - chat bots
OT  - intelligent dialogue system
OT  - knowledge graph
OT  - knowledge-grounded dialogue
COIS- The authors declare no conflict of interest.
EDAT- 2023/08/12 10:45
MHDA- 2023/08/14 06:43
CRDT- 2023/08/12 01:23
PHST- 2023/06/15 00:00 [received]
PHST- 2023/07/13 00:00 [revised]
PHST- 2023/07/27 00:00 [accepted]
PHST- 2023/08/14 06:43 [medline]
PHST- 2023/08/12 10:45 [pubmed]
PHST- 2023/08/12 01:23 [entrez]
AID - s23156805 [pii]
AID - sensors-23-06805 [pii]
AID - 10.3390/s23156805 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Jul 30;23(15):6805. doi: 10.3390/s23156805.

PMID- 37425598
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 6
DP  - 2023 Jun
TI  - Radiology Gets Chatty: The ChatGPT Saga Unfolds.
PG  - e40135
LID - 10.7759/cureus.40135 [doi]
LID - e40135
AB  - As artificial intelligence (AI) continues to evolve and mature, it is 
      increasingly finding applications in the field of healthcare, particularly in 
      specialties like radiology that are data-heavy and image-focused. Language 
      learning models (LLMs) such as OpenAI's Generative Pre-trained Transformer-4 
      (GPT-4) are new in the field of medicine and there is a paucity of literature 
      regarding the possible utilities of GPT-4 given its novelty. We aim to present an 
      in-depth exploration of the role of GPT-4, an advanced language model, in 
      radiology. Giving the GPT-4 model prompts for generating reports, template 
      generation, enhancing clinical decision-making, and suggesting captivating titles 
      for research articles, patient communication, and education, can occasionally be 
      quite generic, and at times, it may present factually incorrect content, which 
      could lead to errors. The responses were then analyzed in detail regarding their 
      potential utility in day-to-day radiologist workflow, patient education, and 
      research processes. Further research is required to evaluate LLMs' accuracy and 
      safety in clinical practice and to develop comprehensive guidelines for their 
      implementation.
CI  - Copyright © 2023, Grewal et al.
FAU - Grewal, Harpreet
AU  - Grewal H
AD  - Radiology, Florida State University College of Medicine, Pensacola, USA.
FAU - Dhillon, Gagandeep
AU  - Dhillon G
AD  - Internal Medicine, Baltimore Washington Medical Center, Glen Burnie, USA.
FAU - Monga, Varun
AU  - Monga V
AD  - Psychiatry, Banner Health, Phoenix, USA.
FAU - Sharma, Pranjal
AU  - Sharma P
AD  - Nephrology, Northeast Ohio Medical University, Rootstown, USA.
FAU - Buddhavarapu, Venkata S
AU  - Buddhavarapu VS
AD  - Hospital Medicine, Banner Health, Phoenix, USA.
FAU - Sidhu, Gurmanpreet
AU  - Sidhu G
AD  - Pathology, Government Medical College, Patiala, Patiala, IND.
FAU - Kashyap, Rahul
AU  - Kashyap R
AD  - Medicine, Drexel University College of Medicine, Philadelphia, USA.
AD  - Global Clinical Scholars Research Training, Harvard Medical School, Boston, USA.
AD  - Research, Global Remote Research Scholars Program, Saint Paul, USA.
AD  - Critical Care Medicine, Mayo Clinic, Rochester, USA.
LA  - eng
PT  - Journal Article
DEP - 20230608
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10329466
OTO - NOTNLM
OT  - ai chatbot
OT  - artificial intelligence in radiology
OT  - chat gpt
OT  - general radiology
OT  - language learning model
OT  - openai
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/10 06:42
MHDA- 2023/07/10 06:43
CRDT- 2023/07/10 04:57
PHST- 2023/06/08 00:00 [accepted]
PHST- 2023/07/10 06:43 [medline]
PHST- 2023/07/10 06:42 [pubmed]
PHST- 2023/07/10 04:57 [entrez]
AID - 10.7759/cureus.40135 [doi]
PST - epublish
SO  - Cureus. 2023 Jun 8;15(6):e40135. doi: 10.7759/cureus.40135. eCollection 2023 Jun.

PMID- 37065362
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230418
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Acute Pneumonia Like Illness and Sepsis in India: Is it Time to Suspect Pulmonary 
      Melioidosis?
PG  - e36122
LID - 10.7759/cureus.36122 [doi]
LID - e36122
AB  - This article describes a case of melioidosis, a severe and potentially fatal 
      disease caused by the Gram-negative bacillus Burkholderia pseudomallei, in a 
      55-year-old female in India. The disease is endemic in Southeast Asia and 
      Northern Australia. Recently there has been an increased number of cases reported 
      in India. The source of B. pseudomallei in India is thought to be soil and water, 
      with the most common mode of infection being through skin contact. The clinical 
      presentation of melioidosis in India varies greatly, making diagnosis difficult. 
      The case presented here with a history of acute febrile illness and progressive 
      dyspnoea, with clinical worsening leading to intensive care unit (ICU) care. We 
      managed this acute pneumonia-like melioidosis with antibiotics and supportive 
      care which showed rapid recovery at follow-up. This case highlights the need for 
      a high index of suspicion and increased awareness of early diagnosis of 
      melioidosis in the Indian subcontinent to improve the patient.
CI  - Copyright © 2023, Sampath et al.
FAU - Sampath, Ananyan
AU  - Sampath A
AD  - General Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
FAU - Sukumar, M
AU  - Sukumar M
AD  - General Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
FAU - Tejaswini, Penuboina
AU  - Tejaswini P
AD  - General Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
FAU - Gupta, Ayush
AU  - Gupta A
AD  - Microbiology, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
FAU - Khadanga, Sagar
AU  - Khadanga S
AD  - General Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
LA  - eng
PT  - Case Reports
DEP - 20230314
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10099402
OTO - NOTNLM
OT  - acute pneumonia
OT  - artificial intelligence
OT  - burkholderia
OT  - chatgpt
OT  - fever
OT  - melioidosis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/18 06:00
MHDA- 2023/04/18 06:01
CRDT- 2023/04/17 03:54
PHST- 2023/03/14 00:00 [accepted]
PHST- 2023/04/18 06:01 [medline]
PHST- 2023/04/17 03:54 [entrez]
PHST- 2023/04/18 06:00 [pubmed]
AID - 10.7759/cureus.36122 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 14;15(3):e36122. doi: 10.7759/cureus.36122. eCollection 2023 
      Mar.

PMID- 37976064
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 2574-3805 (Electronic)
IS  - 2574-3805 (Linking)
VI  - 6
IP  - 11
DP  - 2023 Nov 1
TI  - Leveraging Large Language Models for Decision Support in Personalized Oncology.
PG  - e2343689
LID - 10.1001/jamanetworkopen.2023.43689 [doi]
LID - e2343689
AB  - IMPORTANCE: Clinical interpretation of complex biomarkers for precision oncology 
      currently requires manual investigations of previous studies and databases. 
      Conversational large language models (LLMs) might be beneficial as automated 
      tools for assisting clinical decision-making. OBJECTIVE: To assess performance 
      and define their role using 4 recent LLMs as support tools for precision 
      oncology. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study examined 10 
      fictional cases of patients with advanced cancer with genetic alterations. Each 
      case was submitted to 4 different LLMs (ChatGPT, Galactica, Perplexity, and 
      BioMedLM) and 1 expert physician to identify personalized treatment options in 
      2023. Treatment options were masked and presented to a molecular tumor board 
      (MTB), whose members rated the likelihood of a treatment option coming from an 
      LLM on a scale from 0 to 10 (0, extremely unlikely; 10, extremely likely) and 
      decided whether the treatment option was clinically useful. MAIN OUTCOMES AND 
      MEASURES: Number of treatment options, precision, recall, F1 score of LLMs 
      compared with human experts, recognizability, and usefulness of recommendations. 
      RESULTS: For 10 fictional cancer patients (4 with lung cancer, 6 with other; 
      median [IQR] 3.5 [3.0-4.8] molecular alterations per patient), a median (IQR) 
      number of 4.0 (4.0-4.0) compared with 3.0 (3.0-5.0), 7.5 (4.3-9.8), 11.5 
      (7.8-13.0), and 13.0 (11.3-21.5) treatment options each was identified by the 
      human expert and 4 LLMs, respectively. When considering the expert as a criterion 
      standard, LLM-proposed treatment options reached F1 scores of 0.04, 0.17, 0.14, 
      and 0.19 across all patients combined. Combining treatment options from different 
      LLMs allowed a precision of 0.29 and a recall of 0.29 for an F1 score of 0.29. 
      LLM-generated treatment options were recognized as AI-generated with a median 
      (IQR) 7.5 (5.3-9.0) points in contrast to 2.0 (1.0-3.0) points for manually 
      annotated cases. A crucial reason for identifying AI-generated treatment options 
      was insufficient accompanying evidence. For each patient, at least 1 LLM 
      generated a treatment option that was considered helpful by MTB members. Two 
      unique useful treatment options (including 1 unique treatment strategy) were 
      identified only by LLM. CONCLUSIONS AND RELEVANCE: In this diagnostic study, 
      treatment options of LLMs in precision oncology did not reach the quality and 
      credibility of human experts; however, they generated helpful ideas that might 
      have complemented established procedures. Considering technological progress, 
      LLMs could play an increasingly important role in assisting with screening and 
      selecting relevant biomedical literature to support evidence-based, personalized 
      treatment decisions.
FAU - Benary, Manuela
AU  - Benary M
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
AD  - Core Unit Bioinformatics, Berlin Institute of Health at 
      Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany.
FAU - Wang, Xing David
AU  - Wang XD
AD  - Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin, Berlin, 
      Germany.
FAU - Schmidt, Max
AU  - Schmidt M
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
AD  - Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin 
      Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie 
      Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
FAU - Soll, Dominik
AU  - Soll D
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
AD  - Department of Endocrinology and Metabolic Diseases, Charité Universitätsmedizin 
      Berlin, Department of Endocrinology and Metabolic Diseases, Corporate Member of 
      Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
FAU - Hilfenhaus, Georg
AU  - Hilfenhaus G
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
AD  - Department of Hematology, Oncology and Cancer Immunology, Campus Charité Mitte, 
      Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin 
      and Humboldt-Universität zu Berlin, Berlin, Germany.
FAU - Nassir, Mani
AU  - Nassir M
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
AD  - Department of Hematology, Oncology and Cancer Immunology, Campus Charité Mitte, 
      Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin 
      and Humboldt-Universität zu Berlin, Berlin, Germany.
FAU - Sigler, Christian
AU  - Sigler C
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
FAU - Knödler, Maren
AU  - Knödler M
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
FAU - Keller, Ulrich
AU  - Keller U
AD  - Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin 
      Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie 
      Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
AD  - German Cancer Consortium and German Cancer Research Center, Partner Site Berlin, 
      Germany.
FAU - Beule, Dieter
AU  - Beule D
AD  - Core Unit Bioinformatics, Berlin Institute of Health at 
      Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany.
FAU - Keilholz, Ulrich
AU  - Keilholz U
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
AD  - German Cancer Consortium and German Cancer Research Center, Partner Site Berlin, 
      Germany.
FAU - Leser, Ulf
AU  - Leser U
AD  - Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin, Berlin, 
      Germany.
FAU - Rieke, Damian T
AU  - Rieke DT
AD  - Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, 
      Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 
      Berlin, Germany.
AD  - Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin 
      Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie 
      Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
AD  - German Cancer Consortium and German Cancer Research Center, Partner Site Berlin, 
      Germany.
LA  - eng
PT  - Journal Article
DEP - 20231101
PL  - United States
TA  - JAMA Netw Open
JT  - JAMA network open
JID - 101729235
SB  - IM
MH  - Humans
MH  - *Precision Medicine
MH  - Medical Oncology
MH  - Language
MH  - *Lung Neoplasms
MH  - Communication
PMC - PMC10656647
COIS- Conflict of Interest Disclosures: Dr Schmidt reported receiving advisor fees from 
      Fosanis GmbH outside the submitted work. Dr Hilfenhaus reported receiving speaker 
      fees from AstraZeneca outside the submitted work. Dr Keller reported service on 
      advisory boards for Roche, Janssen-Cilag, Bristol-Myers Squibb/Celgene, Takeda, 
      Gilead, Pfizer, AstraZeneca, Lilly, and Pentixapharm; he reported receiving 
      clinical research support from Janssen-Cilag, Bristol-Myers Squibb, and Roche; 
      and he reported receiving travel support from Roche, Bristol-Myers 
      Squibb/Celgene, Gilead, Lilly, Takeda, and Janssen-Cilag outside the submitted 
      work. Dr Rieke reported receiving consulting, advisory board, or speaking fees 
      from Lilly, Bayer, Roche, and Bristol-Myers Squibb outside the submitted work. No 
      other disclosures were reported.
EDAT- 2023/11/17 15:28
MHDA- 2023/11/27 12:44
CRDT- 2023/11/17 11:33
PHST- 2023/11/27 12:44 [medline]
PHST- 2023/11/17 15:28 [pubmed]
PHST- 2023/11/17 11:33 [entrez]
AID - 2812097 [pii]
AID - zoi231267 [pii]
AID - 10.1001/jamanetworkopen.2023.43689 [doi]
PST - epublish
SO  - JAMA Netw Open. 2023 Nov 1;6(11):e2343689. doi: 
      10.1001/jamanetworkopen.2023.43689.

PMID- 37115527
OWN - NLM
STAT- MEDLINE
DCOM- 20230607
LR  - 20230612
IS  - 2168-6114 (Electronic)
IS  - 2168-6106 (Print)
IS  - 2168-6106 (Linking)
VI  - 183
IP  - 6
DP  - 2023 Jun 1
TI  - Comparing Physician and Artificial Intelligence Chatbot Responses to Patient 
      Questions Posted to a Public Social Media Forum.
PG  - 589-596
LID - 10.1001/jamainternmed.2023.1838 [doi]
AB  - IMPORTANCE: The rapid expansion of virtual health care has caused a surge in 
      patient messages concomitant with more work and burnout among health care 
      professionals. Artificial intelligence (AI) assistants could potentially aid in 
      creating answers to patient questions by drafting responses that could be 
      reviewed by clinicians. OBJECTIVE: To evaluate the ability of an AI chatbot 
      assistant (ChatGPT), released in November 2022, to provide quality and empathetic 
      responses to patient questions. DESIGN, SETTING, AND PARTICIPANTS: In this 
      cross-sectional study, a public and nonidentifiable database of questions from a 
      public social media forum (Reddit's r/AskDocs) was used to randomly draw 195 
      exchanges from October 2022 where a verified physician responded to a public 
      question. Chatbot responses were generated by entering the original question into 
      a fresh session (without prior questions having been asked in the session) on 
      December 22 and 23, 2022. The original question along with anonymized and 
      randomly ordered physician and chatbot responses were evaluated in triplicate by 
      a team of licensed health care professionals. Evaluators chose "which response 
      was better" and judged both "the quality of information provided" (very poor, 
      poor, acceptable, good, or very good) and "the empathy or bedside manner 
      provided" (not empathetic, slightly empathetic, moderately empathetic, 
      empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale 
      and compared between chatbot and physicians. RESULTS: Of the 195 questions and 
      responses, evaluators preferred chatbot responses to physician responses in 78.6% 
      (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were 
      significantly shorter than chatbot responses (52 [17-62] words vs 211 [168-245] 
      words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher 
      quality than physician responses (t = 13.3; P < .001). The proportion of 
      responses rated as good or very good quality (≥ 4), for instance, was higher for 
      chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 
      95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or 
      very good quality responses for the chatbot. Chatbot responses were also rated 
      significantly more empathetic than physician responses (t = 18.9; P < .001). The 
      proportion of responses rated empathetic or very empathetic (≥4) was higher for 
      chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 
      95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 
      times higher prevalence of empathetic or very empathetic responses for the 
      chatbot. CONCLUSIONS: In this cross-sectional study, a chatbot generated quality 
      and empathetic responses to patient questions posed in an online forum. Further 
      exploration of this technology is warranted in clinical settings, such as using 
      chatbot to draft responses that physicians could then edit. Randomized trials 
      could assess further if using AI assistants might improve responses, lower 
      clinician burnout, and improve patient outcomes.
FAU - Ayers, John W
AU  - Ayers JW
AD  - Qualcomm Institute, University of California San Diego, La Jolla.
AD  - Division of Infectious Diseases and Global Public Health, Department of Medicine, 
      University of California San Diego, La Jolla.
FAU - Poliak, Adam
AU  - Poliak A
AD  - Department of Computer Science, Bryn Mawr College, Bryn Mawr, Pennsylvania.
FAU - Dredze, Mark
AU  - Dredze M
AD  - Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
FAU - Leas, Eric C
AU  - Leas EC
AD  - Qualcomm Institute, University of California San Diego, La Jolla.
AD  - Herbert Wertheim School of Public Health and Human Longevity Science, University 
      of California San Diego, La Jolla.
FAU - Zhu, Zechariah
AU  - Zhu Z
AD  - Qualcomm Institute, University of California San Diego, La Jolla.
FAU - Kelley, Jessica B
AU  - Kelley JB
AD  - Human Longevity, La Jolla, California.
FAU - Faix, Dennis J
AU  - Faix DJ
AD  - Naval Health Research Center, Navy, San Diego, California.
FAU - Goodman, Aaron M
AU  - Goodman AM
AD  - Division of Blood and Marrow Transplantation, Department of Medicine, University 
      of California San Diego, La Jolla.
AD  - Moores Cancer Center, University of California San Diego, La Jolla.
FAU - Longhurst, Christopher A
AU  - Longhurst CA
AD  - Department of Biomedical Informatics, University of California San Diego, La 
      Jolla.
FAU - Hogarth, Michael
AU  - Hogarth M
AD  - Department of Biomedical Informatics, University of California San Diego, La 
      Jolla.
AD  - Altman Clinical Translational Research Institute, University of California San 
      Diego, La Jolla.
FAU - Smith, Davey M
AU  - Smith DM
AD  - Division of Infectious Diseases and Global Public Health, Department of Medicine, 
      University of California San Diego, La Jolla.
AD  - Altman Clinical Translational Research Institute, University of California San 
      Diego, La Jolla.
LA  - eng
GR  - K01 DA054303/DA/NIDA NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - JAMA Intern Med
JT  - JAMA internal medicine
JID - 101589534
SB  - IM
CIN - JAMA Intern Med. 2023 Jun 1;183(6):596-597. PMID: 37115531
MH  - Humans
MH  - Artificial Intelligence
MH  - Cross-Sectional Studies
MH  - *Social Media
MH  - *Physicians
MH  - Language
PMC - PMC10148230
COIS- Conflict of Interest Disclosures: Dr Ayers reported owning equity in companies 
      focused on data analytics, Good Analytics, of which he was CEO until June 2018, 
      and Health Watcher. Dr Dredze reported personal fees from Bloomberg LP and 
      Sickweather outside the submitted work and owning an equity position in Good 
      Analytics. Dr Leas reported personal fees from Good Analytics during the conduct 
      of the study. Dr Goodman reported personal fees from Seattle Genetics outside the 
      submitted work. Dr Hogarth reported being an adviser for LifeLink, a health care 
      chatbot company. Dr Longhurst reported being an adviser and equity holder at 
      Doximity. Dr Smith reported stock options from Linear Therapies, personal fees 
      from Arena Pharmaceuticals, Model Medicines, Pharma Holdings, Bayer 
      Pharmaceuticals, Evidera, Signant Health, Fluxergy, Lucira, and Kiadis outside 
      the submitted work. No other disclosures were reported.
EDAT- 2023/04/28 12:43
MHDA- 2023/06/07 06:42
PMCR- 2024/04/28
CRDT- 2023/04/28 11:33
PHST- 2024/04/28 00:00 [pmc-release]
PHST- 2023/06/07 06:42 [medline]
PHST- 2023/04/28 12:43 [pubmed]
PHST- 2023/04/28 11:33 [entrez]
AID - 2804309 [pii]
AID - ioi230030 [pii]
AID - 10.1001/jamainternmed.2023.1838 [doi]
PST - ppublish
SO  - JAMA Intern Med. 2023 Jun 1;183(6):589-596. doi: 10.1001/jamainternmed.2023.1838.

PMID- 37952206
OWN - NLM
STAT- Publisher
LR  - 20231112
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Linking)
DP  - 2023 Nov 11
TI  - AutoCriteria: a generalizable clinical trial eligibility criteria extraction 
      system powered by large language models.
LID - ocad218 [pii]
LID - 10.1093/jamia/ocad218 [doi]
AB  - OBJECTIVES: We aim to build a generalizable information extraction system 
      leveraging large language models to extract granular eligibility criteria 
      information for diverse diseases from free text clinical trial protocol 
      documents. We investigate the model's capability to extract criteria entities 
      along with contextual attributes including values, temporality, and modifiers and 
      present the strengths and limitations of this system. MATERIALS AND METHODS: The 
      clinical trial data were acquired from https://ClinicalTrials.gov/. We developed 
      a system, AutoCriteria, which comprises the following modules: preprocessing, 
      knowledge ingestion, prompt modeling based on GPT, postprocessing, and interim 
      evaluation. The final system evaluation was performed, both quantitatively and 
      qualitatively, on 180 manually annotated trials encompassing 9 diseases. RESULTS: 
      AutoCriteria achieves an overall F1 score of 89.42 across all 9 diseases in 
      extracting the criteria entities, with the highest being 95.44 for nonalcoholic 
      steatohepatitis and the lowest of 84.10 for breast cancer. Its overall accuracy 
      is 78.95% in identifying all contextual information across all diseases. Our 
      thematic analysis indicated accurate logic interpretation of criteria as one of 
      the strengths and overlooking/neglecting the main criteria as one of the 
      weaknesses of AutoCriteria. DISCUSSION: AutoCriteria demonstrates strong 
      potential to extract granular eligibility criteria information from trial 
      documents without requiring manual annotations. The prompts developed for 
      AutoCriteria generalize well across different disease areas. Our evaluation 
      suggests that the system handles complex scenarios including multiple arm 
      conditions and logics. CONCLUSION: AutoCriteria currently encompasses a diverse 
      range of diseases and holds potential to extend to more in the future. This 
      signifies a generalizable and scalable solution, poised to address the 
      complexities of clinical trial application in real-world settings.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      American Medical Informatics Association.
FAU - Datta, Surabhi
AU  - Datta S
AUID- ORCID: 0000-0002-5634-2005
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Lee, Kyeryoung
AU  - Lee K
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Paek, Hunki
AU  - Paek H
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Manion, Frank J
AU  - Manion FJ
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Ofoegbu, Nneka
AU  - Ofoegbu N
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Du, Jingcheng
AU  - Du J
AUID- ORCID: 0000-0002-0322-4566
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Li, Ying
AU  - Li Y
AD  - Regeneron Pharmaceuticals, Tarrytown, NY 10591, United States.
FAU - Huang, Liang-Chin
AU  - Huang LC
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Wang, Jingqi
AU  - Wang J
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Lin, Bin
AU  - Lin B
AD  - Melax Technologies, Houston, TX 77030, United States.
FAU - Xu, Hua
AU  - Xu H
AD  - Yale School of Medicine, New Haven, CT 06511, United States.
FAU - Wang, Xiaoyan
AU  - Wang X
AD  - Melax Technologies, Houston, TX 77030, United States.
LA  - eng
PT  - Journal Article
DEP - 20231111
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
OTO - NOTNLM
OT  - GPT-4
OT  - clinical trial eligibility criteria
OT  - information extraction
OT  - large language model
OT  - natural language processing
OT  - zero-shot prompting
EDAT- 2023/11/12 18:42
MHDA- 2023/11/12 18:42
CRDT- 2023/11/12 15:05
PHST- 2023/06/26 00:00 [received]
PHST- 2023/09/19 00:00 [revised]
PHST- 2023/11/08 00:00 [accepted]
PHST- 2023/11/12 18:42 [medline]
PHST- 2023/11/12 18:42 [pubmed]
PHST- 2023/11/12 15:05 [entrez]
AID - 7413158 [pii]
AID - 10.1093/jamia/ocad218 [doi]
PST - aheadofprint
SO  - J Am Med Inform Assoc. 2023 Nov 11:ocad218. doi: 10.1093/jamia/ocad218.

PMID- 37734067
OWN - NLM
STAT- Publisher
LR  - 20230921
IS  - 0034-8376 (Print)
IS  - 0034-8376 (Linking)
DP  - 2023 Sep 21
TI  - Is generative artificial intelligence the next step toward a personalized 
      hemodialysis?
LID - 10.24875/RIC.23000162 [doi]
AB  - Artificial intelligence (AI) generative models driven by the integration of AI 
      and natural language processing technologies, such as OpenAI's chatbot generative 
      pre-trained transformer large language model (LLM), are receiving much public 
      attention and have the potential to transform personalized medicine. Dialysis 
      patients are highly dependent on technology and their treatment generates a 
      challenging large volume of data that has to be analyzed for knowledge 
      extraction. We argue that, by integrating the data acquired from hemodialysis 
      treatments with the powerful conversational capabilities of LLMs, nephrologists 
      could personalize treatments adapted to patients' lifestyles and preferences. We 
      also argue that this new conversational AI integrated with a personalized 
      patient-computer interface will enhance patients' engagement and self-care by 
      providing them with a more personalized experience. However, generative AI models 
      require continuous and accurate updates of data, and expert supervision and must 
      address potential biases and limitations. Dialysis patients can also benefit from 
      other new emerging technologies such as Digital Twins with which patients' care 
      can also be addressed from a personalized medicine perspective. In this paper, we 
      will revise LLMs potential strengths in terms of their contribution to 
      personalized medicine, and, in particular, their potential impact, and 
      limitations in nephrology. Nephrologists' collaboration with AI academia and 
      companies, to develop algorithms and models that are more transparent, 
      understandable, and trustworthy, will be crucial for the next generation of 
      dialysis patients. The combination of technology, patient-specific data, and AI 
      should contribute to create a more personalized and interactive dialysis process, 
      improving patients' quality of life.
FAU - Hueso, Miguel
AU  - Hueso M
AD  - Department of Nephrology, Hospital Universitari Bellvitge and Institut 
      d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain.
AD  - BigData and Artificial Intelligence Group (BigSEN Working Group), Spanish Society 
      of Nephrology (SENEFRO), Santander, España.
FAU - Álvarez, Rafael
AU  - Álvarez R
AD  - Department of Nephrology, Hospital Universitari Bellvitge and Institut 
      d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain.
FAU - Marí, David
AU  - Marí D
AD  - Digital Health Unit, Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain.
FAU - Ribas-Ripoll, Vicent
AU  - Ribas-Ripoll V
AD  - Digital Health Unit, Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain.
FAU - Lekadir, Karim
AU  - Lekadir K
AD  - Artificial Intelligence in Medicine Lab (BCN-AIM), Department of Mathematics and 
      Computer Science, University of Barcelona, Barcelona, Spain.
FAU - Vellido, Alfredo
AU  - Vellido A
AD  - Intelligent Data Science and Artificial Intelligence Research Center, Universitat 
      Politècnica de Catalunya, (IDEAI-UPC), Barcelona, Spain.
AD  - Centro de Investigación Biomédica en Red (CIBER), Barcelona, Spain.
LA  - eng
PT  - Journal Article
DEP - 20230921
PL  - Mexico
TA  - Rev Invest Clin
JT  - Revista de investigacion clinica; organo del Hospital de Enfermedades de la 
      Nutricion
JID - 9421552
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Large Language Models
OT  - Natural language processing
OT  - Personalized hemodialysis
EDAT- 2023/09/21 18:42
MHDA- 2023/09/21 18:42
CRDT- 2023/09/21 17:01
PHST- 2023/09/21 18:42 [medline]
PHST- 2023/09/21 18:42 [pubmed]
PHST- 2023/09/21 17:01 [entrez]
AID - 10.24875/RIC.23000162 [doi]
PST - aheadofprint
SO  - Rev Invest Clin. 2023 Sep 21. doi: 10.24875/RIC.23000162.

PMID- 37547540
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230808
IS  - 2666-5204 (Electronic)
IS  - 2666-5204 (Linking)
VI  - 15
DP  - 2023 Sep
TI  - AI and machine learning in resuscitation: Ongoing research, new concepts, and key 
      challenges.
PG  - 100435
LID - 10.1016/j.resplu.2023.100435 [doi]
LID - 100435
AB  - AIM: Artificial intelligence (AI) and machine learning (ML) are important areas 
      of computer science that have recently attracted attention for their application 
      to medicine. However, as techniques continue to advance and become more complex, 
      it is increasingly challenging for clinicians to stay abreast of the latest 
      research. This overview aims to translate research concepts and potential 
      concerns to healthcare professionals interested in applying AI and ML to 
      resuscitation research but who are not experts in the field. MAIN TEXT: We 
      present various research including prediction models using structured and 
      unstructured data, exploring treatment heterogeneity, reinforcement learning, 
      language processing, and large-scale language models. These studies potentially 
      offer valuable insights for optimizing treatment strategies and clinical 
      workflows. However, implementing AI and ML in clinical settings presents its own 
      set of challenges. The availability of high-quality and reliable data is crucial 
      for developing accurate ML models. A rigorous validation process and the 
      integration of ML into clinical practice is essential for practical 
      implementation. We furthermore highlight the potential risks associated with 
      self-fulfilling prophecies and feedback loops, emphasizing the importance of 
      transparency, interpretability, and trustworthiness in AI and ML models. These 
      issues need to be addressed in order to establish reliable and trustworthy AI and 
      ML models. CONCLUSION: In this article, we overview concepts and examples of AI 
      and ML research in the resuscitation field. Moving forward, appropriate 
      understanding of ML and collaboration with relevant experts will be essential for 
      researchers and clinicians to overcome the challenges and harness the full 
      potential of AI and ML in resuscitation.
CI  - © 2023 The Author(s).
FAU - Okada, Yohei
AU  - Okada Y
AD  - Duke-NUS Medical School, National University of Singapore, Singapore.
AD  - Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
FAU - Mertens, Mayli
AU  - Mertens M
AD  - Antwerp Center for Responsible AI, Antwerp University, Belgium.
AD  - Centre for Ethics, Department of Philosophy, Antwerp University, Belgium.
FAU - Liu, Nan
AU  - Liu N
AD  - Duke-NUS Medical School, National University of Singapore, Singapore.
FAU - Lam, Sean Shao Wei
AU  - Lam SSW
AD  - Duke-NUS Medical School, National University of Singapore, Singapore.
FAU - Ong, Marcus Eng Hock
AU  - Ong MEH
AD  - Duke-NUS Medical School, National University of Singapore, Singapore.
AD  - Department of Emergency Medicine, Singapore General Hospital.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230728
PL  - Netherlands
TA  - Resusc Plus
JT  - Resuscitation plus
JID - 101774410
PMC - PMC10400904
OTO - NOTNLM
OT  - Emergency medicine
OT  - Feedback loop
OT  - Heterogeneity
OT  - Large language model
OT  - Natural language processing
OT  - Prediction model
OT  - Self-fulfilling prophecy
COIS- YO has received a research grant from the ZOLL Foundation and overseas 
      scholarships from the Japan Society for Promotion of Science, the FUKUDA 
      Foundation for medical technology, and the International medical research 
      foundation. These organizations have no role in conducting this research. MEHO 
      reports grants from the Laerdal Foundation, Laerdal Medical, and Ramsey Social 
      Justice Foundation for funding of the Pan-Asian Resuscitation Outcomes Study an 
      advisory relationship with Global Healthcare SG, a commercial entity that 
      manufactures cooling devices; and funding from Laerdal Medical on an observation 
      program to their Community CPR Training Centre Research Program in Norway. MEHO 
      is a Scientific Advisor to TIIM Healthcare SG and Global Healthcare SG.
EDAT- 2023/08/07 06:42
MHDA- 2023/08/07 06:43
CRDT- 2023/08/07 04:54
PHST- 2023/08/07 06:43 [medline]
PHST- 2023/08/07 06:42 [pubmed]
PHST- 2023/08/07 04:54 [entrez]
AID - S2666-5204(23)00078-4 [pii]
AID - 100435 [pii]
AID - 10.1016/j.resplu.2023.100435 [doi]
PST - epublish
SO  - Resusc Plus. 2023 Jul 28;15:100435. doi: 10.1016/j.resplu.2023.100435. 
      eCollection 2023 Sep.

PMID- 37426456
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231121
IS  - 2331-8422 (Electronic)
IS  - 2331-8422 (Linking)
DP  - 2023 Nov 14
TI  - HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution.
LID - arXiv:2306.15794v2
AB  - Genomic (DNA) sequences encode an enormous amount of information for gene 
      regulation and protein synthesis. Similar to natural language models, researchers 
      have proposed foundation models in genomics to learn generalizable features from 
      unlabeled genome data that can then be fine-tuned for downstream tasks such as 
      identifying regulatory elements. Due to the quadratic scaling of attention, 
      previous Transformer-based genomic models have used 512 to 4k tokens as context 
      (<0.001% of the human genome), significantly limiting the modeling of long-range 
      interactions in DNA. In addition, these methods rely on tokenizers or fixed 
      k-mers to aggregate meaningful DNA units, losing single nucleotide resolution 
      where subtle genetic variations can completely alter protein function via single 
      nucleotide polymorphisms (SNPs). Recently, Hyena, a large language model based on 
      implicit convolutions was shown to match attention in quality while allowing 
      longer context lengths and lower time complexity. Leveraging Hyena's new 
      long-range capabilities, we present HyenaDNA, a genomic foundation model 
      pretrained on the human reference genome with context lengths of up to 1 million 
      tokens at the single nucleotide-level - an up to 500x increase over previous 
      dense attention-based models. HyenaDNA scales sub-quadratically in sequence 
      length (training up to 160x faster than Transformer), uses single nucleotide 
      tokens, and has full global context at each layer. We explore what longer context 
      enables - including the first use of in-context learning in genomics. On 
      fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches 
      state-of-the-art (SotA) on 12 of 18 datasets using a model with orders of 
      magnitude less parameters and pretraining data. On the GenomicBenchmarks, 
      HyenaDNA surpasses SotA on 7 of 8 datasets on average by +10 accuracy points. 
      Code at https://github.com/HazyResearch/hyena-dna.
FAU - Nguyen, Eric
AU  - Nguyen E
FAU - Poli, Michael
AU  - Poli M
FAU - Faizi, Marjan
AU  - Faizi M
FAU - Thomas, Armin
AU  - Thomas A
FAU - Birch-Sykes, Callum
AU  - Birch-Sykes C
FAU - Wornow, Michael
AU  - Wornow M
FAU - Patel, Aman
AU  - Patel A
FAU - Rabideau, Clayton
AU  - Rabideau C
FAU - Massaroli, Stefano
AU  - Massaroli S
FAU - Bengio, Yoshua
AU  - Bengio Y
FAU - Ermon, Stefano
AU  - Ermon S
FAU - Baccus, Stephen A
AU  - Baccus SA
FAU - Ré, Chris
AU  - Ré C
LA  - eng
PT  - Preprint
DEP - 20231114
PL  - United States
TA  - ArXiv
JT  - ArXiv
JID - 101759493
PMC - PMC10327243
EDAT- 2023/07/10 06:42
MHDA- 2023/07/10 06:43
CRDT- 2023/07/10 05:10
PHST- 2023/07/10 06:42 [pubmed]
PHST- 2023/07/10 06:43 [medline]
PHST- 2023/07/10 05:10 [entrez]
AID - 2306.15794 [pii]
PST - epublish
SO  - ArXiv. 2023 Nov 14:arXiv:2306.15794v2. Preprint.

PMID- 37286606
OWN - NLM
STAT- MEDLINE
DCOM- 20230726
LR  - 20230726
IS  - 1476-4687 (Electronic)
IS  - 0028-0836 (Print)
IS  - 0028-0836 (Linking)
VI  - 619
IP  - 7969
DP  - 2023 Jul
TI  - Health system-scale language models are all-purpose prediction engines.
PG  - 357-362
LID - 10.1038/s41586-023-06160-y [doi]
AB  - Physicians make critical time-constrained decisions every day. Clinical 
      predictive models can help physicians and administrators make decisions by 
      forecasting clinical and operational events. Existing structured data-based 
      clinical predictive models have limited use in everyday practice owing to 
      complexity in data processing, as well as model development and deployment(1-3). 
      Here we show that unstructured clinical notes from the electronic health record 
      can enable the training of clinical language models, which can be used as 
      all-purpose clinical predictive engines with low-resistance development and 
      deployment. Our approach leverages recent advances in natural language 
      processing(4,5) to train a large language model for medical language (NYUTron) 
      and subsequently fine-tune it across a wide range of clinical and operational 
      predictive tasks. We evaluated our approach within our health system for five 
      such tasks: 30-day all-cause readmission prediction, in-hospital mortality 
      prediction, comorbidity index prediction, length of stay prediction, and 
      insurance denial prediction. We show that NYUTron has an area under the curve 
      (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with 
      traditional models. We additionally demonstrate the benefits of pretraining with 
      clinical text, the potential for increasing generalizability to different sites 
      through fine-tuning and the full deployment of our system in a prospective, 
      single-arm trial. These results show the potential for using clinical language 
      models in medicine to read alongside physicians and provide guidance at the point 
      of care.
CI  - © 2023. The Author(s).
FAU - Jiang, Lavender Yao
AU  - Jiang LY
AUID- ORCID: 0000-0003-2464-3281
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
AD  - Center for Data Science, New York University, New York, NY, USA.
FAU - Liu, Xujin Chris
AU  - Liu XC
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
AD  - Electrical and Computer Engineering, Tandon School of Engineering, New York, NY, 
      USA.
FAU - Nejatian, Nima Pour
AU  - Nejatian NP
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Nasir-Moin, Mustafa
AU  - Nasir-Moin M
AUID- ORCID: 0000-0002-0389-1852
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Wang, Duo
AU  - Wang D
AD  - Predictive Analytics Unit, NYU Langone Health, New York, NY, USA.
FAU - Abidin, Anas
AU  - Abidin A
AUID- ORCID: 0000-0003-0032-0664
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Eaton, Kevin
AU  - Eaton K
AD  - Department of Internal Medicine, NYU Langone Health, New York, NY, USA.
FAU - Riina, Howard Antony
AU  - Riina HA
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Laufer, Ilya
AU  - Laufer I
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Punjabi, Paawan
AU  - Punjabi P
AD  - Department of Internal Medicine, NYU Langone Health, New York, NY, USA.
FAU - Miceli, Madeline
AU  - Miceli M
AD  - Department of Internal Medicine, NYU Langone Health, New York, NY, USA.
FAU - Kim, Nora C
AU  - Kim NC
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Orillac, Cordelia
AU  - Orillac C
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Schnurman, Zane
AU  - Schnurman Z
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Livia, Christopher
AU  - Livia C
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Weiss, Hannah
AU  - Weiss H
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Kurland, David
AU  - Kurland D
AUID- ORCID: 0000-0002-4074-7497
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Neifert, Sean
AU  - Neifert S
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Dastagirzada, Yosef
AU  - Dastagirzada Y
AUID- ORCID: 0009-0003-3237-6037
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Kondziolka, Douglas
AU  - Kondziolka D
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Cheung, Alexander T M
AU  - Cheung ATM
AUID- ORCID: 0000-0003-0946-3493
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
FAU - Yang, Grace
AU  - Yang G
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
AD  - Center for Data Science, New York University, New York, NY, USA.
FAU - Cao, Ming
AU  - Cao M
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
AD  - Center for Data Science, New York University, New York, NY, USA.
FAU - Flores, Mona
AU  - Flores M
AUID- ORCID: 0000-0002-7362-3044
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Costa, Anthony B
AU  - Costa AB
AD  - NVIDIA, Santa Clara, CA, USA.
FAU - Aphinyanaphongs, Yindalon
AU  - Aphinyanaphongs Y
AD  - Predictive Analytics Unit, NYU Langone Health, New York, NY, USA.
AD  - Department of Population Health, NYU Langone Health, New York, NY, USA.
FAU - Cho, Kyunghyun
AU  - Cho K
AD  - Center for Data Science, New York University, New York, NY, USA.
AD  - Prescient Design, Genentech, New York, NY, USA.
AD  - Courant Institute of Mathematical Sciences, New York University, New York, NY, 
      USA.
AD  - Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
FAU - Oermann, Eric Karl
AU  - Oermann EK
AUID- ORCID: 0000-0002-1876-5963
AD  - Department of Neurosurgery, NYU Langone Health, New York, NY, USA. 
      eric.oermann@nyulangone.org.
AD  - Center for Data Science, New York University, New York, NY, USA. 
      eric.oermann@nyulangone.org.
AD  - Department of Radiology, NYU Langone Health, New York, NY, USA. 
      eric.oermann@nyulangone.org.
LA  - eng
GR  - P30 CA016087/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20230607
PL  - England
TA  - Nature
JT  - Nature
JID - 0410462
SB  - IM
MH  - Humans
MH  - *Electronic Health Records
MH  - *Natural Language Processing
MH  - *Physicians
MH  - *Clinical Decision-Making/methods
MH  - Patient Readmission
MH  - Hospital Mortality
MH  - Comorbidity
MH  - Length of Stay
MH  - Insurance Coverage
MH  - Area Under Curve
MH  - Point-of-Care Systems/trends
MH  - Clinical Trials as Topic
PMC - PMC10338337
COIS- E.K.O. reports consulting with Sofinnova and Google, income from Merck & Co. and 
      Mirati Therapeutics, and equity in Artisight. N.P.N., M.F. and A.B.C. are 
      employed by NVIDIA. D.K. reports consulting with Elekta. K.C. is employed by 
      Prescient Design, a Genentech accelerator, a subsidiary of Roche. There are no 
      other potential conflicts of interest. The work presented herein was performed 
      exclusively within the NYU Langone Health System.
EDAT- 2023/06/08 01:08
MHDA- 2023/07/14 13:08
CRDT- 2023/06/07 23:19
PHST- 2022/10/14 00:00 [received]
PHST- 2023/05/02 00:00 [accepted]
PHST- 2023/07/14 13:08 [medline]
PHST- 2023/06/08 01:08 [pubmed]
PHST- 2023/06/07 23:19 [entrez]
AID - 10.1038/s41586-023-06160-y [pii]
AID - 6160 [pii]
AID - 10.1038/s41586-023-06160-y [doi]
PST - ppublish
SO  - Nature. 2023 Jul;619(7969):357-362. doi: 10.1038/s41586-023-06160-y. Epub 2023 
      Jun 7.

PMID- 38069869
OWN - NLM
STAT- In-Process
LR  - 20231209
IS  - 0869-866X (Print)
IS  - 0869-866X (Linking)
VI  - 31
IP  - Special Issue 2
DP  - 2023 Oct
TI  - [Dialogues with neural networks about the present and future of medical 
      professions: risks and prospects].
PG  - 1097-1103
LID - 10.32687/0869-866X-2023-31-s2-1097-1103 [doi]
AB  - The article examines the risks to social perception of health professions in the 
      context of the growing popularity of generative textual and visual neural 
      networks. The article is consistent with the growing body of research that tends 
      to see more risks of this technology, including for healthcare. The second focus 
      of the article - the stereotypes about health professions - is critical, as they 
      have been associated with negative outcomes for the healthcare system. The study 
      primarily focuses on the perception of nursing professionals as most stereotyped 
      by the public. Using uploads from the chatGPT, Kandinsky, and Shedevrum networks, 
      the author has investigated the neural network's embedded perceptions of the 
      nursing profession. Reproduction of certain stereotypes has been identified in 
      all neural networks; the appearance stereotypes (gender, age, race, uniform) are 
      more often shared by visual neural networks, while the function stereotypes (lack 
      of autonomy, dependence on colleagues) - by text networks. Actually, we can 
      assume the risks of a vicious circle, when the network is trained on the largely 
      stereotypical data, and products based on its output increase the scale of 
      stereotypical representations during subsequent training of the neural network. 
      Developers' efforts to avoid reproducing stereotypes are acknowledged, however, 
      the uploads show that they are not fully successful. To prevent the spread of 
      perceptual distortions in neural networks, it is recommended to promote their 
      sociohumanistic evaluation. A «prompt-experiment» is proposed as a mechanism for 
      identifying such risks.
FAU - Aksenova, E I
AU  - Aksenova EI
AD  - Research Institute for Healthcare Organization and Medical Management, Moscow, 
      Russia.
AD  - Peoples' Friendship University of Russia named after Patrice Lumumba, Moscow, 
      Russia.
FAU - Bogdan, I V
AU  - Bogdan IV
AD  - Research Institute for Healthcare Organization and Medical Management, Moscow, 
      Russia, bogdaniv@zdrav.mos.ru.
LA  - rus
PT  - English Abstract
PT  - Journal Article
PL  - Russia (Federation)
TA  - Probl Sotsialnoi Gig Zdravookhranenniiai Istor Med
JT  - Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny
JID - 101270373
SB  - IM
OTO - NOTNLM
OT  - medical profession
OT  - midjourney
OT  - neural networks
OT  - nurse
OT  - perception
OT  - СhatGPT
EDAT- 2023/12/10 06:42
MHDA- 2023/12/10 06:42
CRDT- 2023/12/09 10:02
PHST- 2023/06/30 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/12/10 06:42 [medline]
PHST- 2023/12/10 06:42 [pubmed]
PHST- 2023/12/09 10:02 [entrez]
AID - 10.32687/0869-866X-2023-31-s2-1097-1103 [doi]
PST - ppublish
SO  - Probl Sotsialnoi Gig Zdravookhranenniiai Istor Med. 2023 Oct;31(Special Issue 
      2):1097-1103. doi: 10.32687/0869-866X-2023-31-s2-1097-1103.

PMID- 36901550
OWN - NLM
STAT- MEDLINE
DCOM- 20230314
LR  - 20230417
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 20
IP  - 5
DP  - 2023 Mar 3
TI  - Artificial Intelligence and Public Health: An Exploratory Study.
LID - 10.3390/ijerph20054541 [doi]
LID - 4541
AB  - Artificial intelligence (AI) has the potential to revolutionize research by 
      automating data analysis, generating new insights, and supporting the discovery 
      of new knowledge. The top 10 contribution areas of AI towards public health were 
      gathered in this exploratory study. We utilized the "text-davinci-003" model of 
      GPT-3, using OpenAI playground default parameters. The model was trained with the 
      largest training dataset any AI had, limited to a cut-off date in 2021. This 
      study aimed to test the ability of GPT-3 to advance public health and to explore 
      the feasibility of using AI as a scientific co-author. We asked the AI asked for 
      structured input, including scientific quotations, and reviewed responses for 
      plausibility. We found that GPT-3 was able to assemble, summarize, and generate 
      plausible text blocks relevant for public health concerns, elucidating valuable 
      areas of application for itself. However, most quotations were purely invented by 
      GPT-3 and thus invalid. Our research showed that AI can contribute to public 
      health research as a team member. According to authorship guidelines, the AI was 
      ultimately not listed as a co-author, as it would be done with a human 
      researcher. We conclude that good scientific practice also needs to be followed 
      for AI contributions, and a broad scientific discourse on AI contributions is 
      needed.
FAU - Jungwirth, David
AU  - Jungwirth D
AUID- ORCID: 0000-0002-4834-8909
AD  - Department of Environmental Health, Center for Public Health, Medical University 
      of Vienna, 1090 Vienna, Austria.
FAU - Haluza, Daniela
AU  - Haluza D
AUID- ORCID: 0000-0001-5619-2863
AD  - Department of Environmental Health, Center for Public Health, Medical University 
      of Vienna, 1090 Vienna, Austria.
LA  - eng
PT  - Journal Article
DEP - 20230303
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Public Health
MH  - Data Analysis
MH  - Knowledge
MH  - Parks, Recreational
PMC - PMC10002031
OTO - NOTNLM
OT  - ChatGPT
OT  - GPT-3
OT  - OpenAI
OT  - artificial intelligence
OT  - automation
OT  - chatbots
OT  - collaboration
OT  - digital health
OT  - human-AI interaction
OT  - open science
OT  - technological advancement
COIS- The authors declare no conflicts of interest.
EDAT- 2023/03/12 06:00
MHDA- 2023/03/15 06:00
CRDT- 2023/03/11 01:13
PHST- 2023/01/21 00:00 [received]
PHST- 2023/02/21 00:00 [revised]
PHST- 2023/03/02 00:00 [accepted]
PHST- 2023/03/11 01:13 [entrez]
PHST- 2023/03/12 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
AID - ijerph20054541 [pii]
AID - ijerph-20-04541 [pii]
AID - 10.3390/ijerph20054541 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2023 Mar 3;20(5):4541. doi: 
      10.3390/ijerph20054541.

PMID- 36875249
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230307
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Rectal Bezoar: A Rare Cause of Intestinal Obstruction.
PG  - e35726
LID - 10.7759/cureus.35726 [doi]
LID - e35726
AB  - Bezoars are conglomerates of undigested contents that accumulate in the 
      gastrointestinal tract. They can have different compositions, such as fibers, 
      seeds, vegetables (phytobezoars), hair (trichobezoars), and medication 
      (pharmacobezoars). Bezoars are typically caused by an impaired grinding mechanism 
      of the stomach or interdigestive migrating motor complex, but the composition of 
      ingested material can also play a role in their formation. Gastric dysmotility, 
      previous gastric surgery, and gastroparesis are some of the risk factors that can 
      increase the likelihood of developing bezoars. While bezoars are usually 
      asymptomatic and found in the stomach, they can sometimes migrate to the small 
      intestine or colon and cause complications such as intestinal obstruction or 
      perforation. Endoscopy is essential for diagnosis and etiology, and treatment 
      depends on the composition, which can include chemical dissolution or surgical 
      intervention. We present a case of an 86-year-old woman, who had a bezoar located 
      in an unusual location (rectum), most likely due to migration. This condition led 
      to symptoms of intermittent intestinal obstruction and rectal bleeding. However, 
      due to anal stenosis, the patient was unable to expel the bezoar. Its removal was 
      not possible through various endoscopic techniques. Therefore, it was removed via 
      fragmentation, using an anoscope and forceps, due to its hard/stone-like 
      consistency. This case highlights the importance of considering bezoars in the 
      differential diagnosis of gastrointestinal bleeding and illustrates the 
      importance of prompt diagnosis and appropriate techniques for the removal of 
      bezoars.
CI  - Copyright © 2023, Caleça Emidio et al.
FAU - Caleça Emidio, Fábio
AU  - Caleça Emidio F
AD  - Department of Internal Medicine, Centro Hospitalar Universitário do Algarve, 
      Hospital de Faro, Faro, PRT.
FAU - Pereira, Rafaela C
AU  - Pereira RC
AD  - Department of Internal Medicine, Centro Hospitalar Universitário do Algarve, 
      Hospital de Faro, Faro, PRT.
FAU - Blanco Saez, Rosário
AU  - Blanco Saez R
AD  - Department of Internal Medicine, Centro Hospitalar Universitário do Algarve, 
      Hospital de Faro, Faro, PRT.
FAU - Abegão, Teresa
AU  - Abegão T
AD  - Department of Internal Medicine, Centro Hospitalar Universitário do Algarve, 
      Hospital de Faro, Faro, PRT.
FAU - Ribeiro, Ana S
AU  - Ribeiro AS
AD  - Department of Internal Medicine, Centro Hospitalar Universitário do Algarve, 
      Hospital de Faro, Faro, PRT.
LA  - eng
PT  - Case Reports
DEP - 20230303
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC9983705
OTO - NOTNLM
OT  - adult
OT  - bezoars
OT  - chatgpt
OT  - endoscopy
OT  - intestinal obstruction
OT  - rectal bezoar
OT  - rectal diseases
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/07 06:00
MHDA- 2023/03/07 06:01
CRDT- 2023/03/06 04:07
PHST- 2023/03/03 00:00 [accepted]
PHST- 2023/03/06 04:07 [entrez]
PHST- 2023/03/07 06:00 [pubmed]
PHST- 2023/03/07 06:01 [medline]
AID - 10.7759/cureus.35726 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 3;15(3):e35726. doi: 10.7759/cureus.35726. eCollection 2023 Mar.

PMID- 37692629
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - AI-Powered Chatbots in Medical Education: Potential Applications and 
      Implications.
PG  - e43271
LID - 10.7759/cureus.43271 [doi]
LID - e43271
AB  - Artificial intelligence (AI) is anticipated to have a considerable impact on the 
      routine practice of medicine, spanning from medical education to clinical 
      practice across specialties and, ultimately, patient care. With the imminent 
      widespread adoption of AI in medical practice, it is imperative that medical 
      schools adapt to the use of these advanced technologies in their curriculum to 
      produce future healthcare professionals who can seamlessly integrate these tools 
      into practice. Chatbots, AI systems programmed to process and generate human 
      language, are currently being evaluated for various tasks in medical education. 
      This paper explores the potential applications and implications of chatbots in 
      medical education, specifically in learning and research. With their capability 
      to summarize, simplify complex concepts, automate the creation of memory aids, 
      and serve as an interactive tutor and point-of-care medical reference, chatbots 
      have the potential to enhance students' comprehension, retention, and application 
      of medical knowledge in real-time. While the integration of AI-powered chatbots 
      in medical education presents numerous advantages, it is crucial for students to 
      use these tools as assistive tools rather than relying on them entirely. Chatbots 
      should be programmed to reference evidence-based medical resources and produce 
      precise and trustworthy content that adheres to medical science standards, 
      scientific writing guidelines, and ethical considerations.
CI  - Copyright © 2023, Ghorashi et al.
FAU - Ghorashi, Nima
AU  - Ghorashi N
AD  - Department of Radiology, George Washington University School of Medicine and 
      Health Sciences, Washington, USA.
FAU - Ismail, Ahmed
AU  - Ismail A
AD  - Department of Radiology, George Washington University School of Medicine and 
      Health Sciences, Washington, USA.
FAU - Ghosh, Pritha
AU  - Ghosh P
AD  - Department of Neurology, George Washington University School of Medicine and 
      Health Sciences, Washington, USA.
FAU - Sidawy, Anton
AU  - Sidawy A
AD  - Department of Surgery, George Washington University School of Medicine and Health 
      Sciences, Washington, USA.
FAU - Javan, Ramin
AU  - Javan R
AD  - Department of Radiology, George Washington University School of Medicine and 
      Health Sciences, Washington, USA.
LA  - eng
PT  - Editorial
DEP - 20230810
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10492519
OTO - NOTNLM
OT  - artificial intelligence
OT  - chatbots
OT  - chatgpt
OT  - gpt-4
OT  - large language models
OT  - medical education
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/11 06:43
MHDA- 2023/09/11 06:44
CRDT- 2023/09/11 04:41
PHST- 2023/08/10 00:00 [accepted]
PHST- 2023/09/11 06:44 [medline]
PHST- 2023/09/11 06:43 [pubmed]
PHST- 2023/09/11 04:41 [entrez]
AID - 10.7759/cureus.43271 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 10;15(8):e43271. doi: 10.7759/cureus.43271. eCollection 2023 
      Aug.

PMID- 37318857
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231212
IS  - 1743-9159 (Electronic)
IS  - 1743-9191 (Print)
IS  - 1743-9159 (Linking)
VI  - 109
IP  - 10
DP  - 2023 Oct 1
TI  - Algorithmic bias and research integrity; the role of nonhuman authors in shaping 
      scientific knowledge with respect to artificial intelligence: a perspective.
PG  - 2987-2990
LID - 10.1097/JS9.0000000000000552 [doi]
AB  - Artificial intelligence technologies were developed to assist authors in 
      bettering the organization and caliber of their published papers, which are both 
      growing in quantity and sophistication. Even though the usage of artificial 
      intelligence tools in particular ChatGPT's natural language processing systems 
      has been shown to be beneficial in research, there are still concerns about 
      accuracy, responsibility, and transparency when it comes to the norms regarding 
      authorship credit and contributions. Genomic algorithms quickly examine large 
      amounts of genetic data to identify potential disease-causing mutations. By 
      analyzing millions of medications for potential therapeutic benefits, they can 
      quickly and relatively economically find novel approaches to treatment. 
      Researchers from several fields can collaborate on difficult tasks with the 
      assistance of nonhuman writers, promoting interdisciplinary research. Sadly, 
      there are a number of significant disadvantages associated with employing 
      nonhuman authors, including the potential for algorithmic prejudice. Biased data 
      may be reinforced by the algorithm since machine learning algorithms can only be 
      as objective as the data they are trained on. It is overdue that scholars bring 
      forth basic moral concerns in the fight against algorithmic prejudice. Overall, 
      even if the use of nonhuman authors has the potential to significantly improve 
      scientific research, it is crucial for scientists to be aware of these drawbacks 
      and take precautions to avoid bias and limits. To provide accurate and objective 
      results, algorithms must be carefully designed and implemented, and researchers 
      need to be mindful of the larger ethical ramifications of their usage.
CI  - Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Oduoye, Malik Olatunde
AU  - Oduoye MO
AD  - College of Medical Sciences, Ahmadu Bello University Teaching Hospital, Shika, 
      Kaduna State, Nigeria.
AD  - Department of Medical Research, Medical Research Circle, Bukavu, Democratic 
      Republic of Congo.
FAU - Javed, Binish
AU  - Javed B
AUID- ORCID: 0000-0002-0847-7757
AD  - Department of Medical Research, Medical Research Circle, Bukavu, Democratic 
      Republic of Congo.
AD  - Atal Bihari Vajpayee Institute of Medical Sciences and Dr Ram Manohar Lohia 
      Hospital, New Delhi, India.
FAU - Gupta, Nikhil
AU  - Gupta N
AD  - Atal Bihari Vajpayee Institute of Medical Sciences and Dr Ram Manohar Lohia 
      Hospital, New Delhi, India.
FAU - Valentina Sih, Che Mbali
AU  - Valentina Sih CM
AUID- ORCID: 0009-0008-6549-3631
AD  - Department of Nursing Science, University of Bamenda, Bambili, Cameroon.
LA  - eng
PT  - Journal Article
DEP - 20231001
PL  - United States
TA  - Int J Surg
JT  - International journal of surgery (London, England)
JID - 101228232
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Algorithms
MH  - Authorship
MH  - Awareness
MH  - Bias
PMC - PMC10583945
COIS- The authors have no competing interests to declare that are relevant to the 
      content of this article.
EDAT- 2023/06/15 19:14
MHDA- 2023/10/23 00:42
CRDT- 2023/06/15 12:25
PHST- 2023/04/15 00:00 [received]
PHST- 2023/06/02 00:00 [accepted]
PHST- 2023/10/23 00:42 [medline]
PHST- 2023/06/15 19:14 [pubmed]
PHST- 2023/06/15 12:25 [entrez]
AID - 01279778-202310000-00012 [pii]
AID - IJS-D-23-00684 [pii]
AID - 10.1097/JS9.0000000000000552 [doi]
PST - epublish
SO  - Int J Surg. 2023 Oct 1;109(10):2987-2990. doi: 10.1097/JS9.0000000000000552.

PMID- 37953349
OWN - NLM
STAT- Publisher
LR  - 20231112
IS  - 1362-4962 (Electronic)
IS  - 0305-1048 (Linking)
DP  - 2023 Nov 11
TI  - EVLncRNAs 3.0: an updated comprehensive database for manually curated functional 
      long non-coding RNAs validated by low-throughput experiments.
LID - gkad1057 [pii]
LID - 10.1093/nar/gkad1057 [doi]
AB  - Long noncoding RNAs (lncRNAs) have emerged as crucial regulators across diverse 
      biological processes and diseases. While high-throughput sequencing has enabled 
      lncRNA discovery, functional characterization remains limited. The EVLncRNAs 
      database is the first and exclusive repository for all experimentally validated 
      functional lncRNAs from various species. After previous releases in 2018 and 
      2021, this update marks a major expansion through exhaustive manual curation of 
      nearly 25 000 publications from 15 May 2020, to 15 May 2023. It incorporates 
      substantial growth across all categories: a 154% increase in functional lncRNAs, 
      160% in associated diseases, 186% in lncRNA-disease associations, 235% in 
      interactions, 138% in structures, 234% in circular RNAs, 235% in resistant 
      lncRNAs and 4724% in exosomal lncRNAs. More importantly, it incorporated 
      additional information include functional classifications, detailed interaction 
      pathways, homologous lncRNAs, lncRNA locations, COVID-19, phase-separation and 
      organoid-related lncRNAs. The web interface was substantially improved for 
      browsing, visualization, and searching. ChatGPT was tested for information 
      extraction and functional overview with its limitation noted. EVLncRNAs 3.0 
      represents the most extensive curated resource of experimentally validated 
      functional lncRNAs and will serve as an indispensable platform for unravelling 
      emerging lncRNA functions. The updated database is freely available at 
      https://www.sdklab-biophysics-dzu.net/EVLncRNAs3/.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic 
      Acids Research.
FAU - Zhou, Bailing
AU  - Zhou B
AUID- ORCID: 0000-0002-4824-347X
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Ji, Baohua
AU  - Ji B
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
AD  - College of Physics and Electronic Information, Dezhou University, Dezhou 253023, 
      China.
FAU - Shen, Congcong
AU  - Shen C
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Zhang, Xia
AU  - Zhang X
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Yu, Xue
AU  - Yu X
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Huang, Pingping
AU  - Huang P
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Yu, Ru
AU  - Yu R
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Zhang, Hongmei
AU  - Zhang H
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
AD  - College of Life Science, Dezhou University, Dezhou 253023, China.
FAU - Dou, Xianghua
AU  - Dou X
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Chen, Qingshuai
AU  - Chen Q
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Zeng, Qiangcheng
AU  - Zeng Q
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
AD  - College of Life Science, Dezhou University, Dezhou 253023, China.
FAU - Wang, Xiaoxin
AU  - Wang X
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
AD  - College of Physics and Electronic Information, Dezhou University, Dezhou 253023, 
      China.
FAU - Cao, Zanxia
AU  - Cao Z
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Hu, Guodong
AU  - Hu G
AUID- ORCID: 0000-0001-6770-0667
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Xu, Shicai
AU  - Xu S
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
FAU - Zhao, Huiying
AU  - Zhao H
AUID- ORCID: 0000-0001-9134-536X
AD  - Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen 
      University, Guangzhou 510120, China.
FAU - Yang, Yuedong
AU  - Yang Y
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
AD  - School of Computer Science and Engineering, Sun Yat-Sen University, 
      Guangzhou 510006, China.
FAU - Zhou, Yaoqi
AU  - Zhou Y
AUID- ORCID: 0000-0002-9958-5699
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
AD  - Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, 
      Shenzhen 518038, China.
FAU - Wang, Jihua
AU  - Wang J
AD  - Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou 
      University, Dezhou 253023, China.
LA  - eng
GR  - 62071085/National Natural Science Foundation of China/
GR  - ZR2023MC186/Natural Science Foundation of Shandong of China/
GR  - Youth Innovation Team Lead-education Project of Shandong Educational Committee/
GR  - 2020xjrc216/Talent Introduction Project of Dezhou University of China/
PT  - Journal Article
DEP - 20231111
PL  - England
TA  - Nucleic Acids Res
JT  - Nucleic acids research
JID - 0411011
SB  - IM
EDAT- 2023/11/13 00:41
MHDA- 2023/11/13 00:41
CRDT- 2023/11/12 23:44
PHST- 2023/11/01 00:00 [accepted]
PHST- 2023/10/23 00:00 [revised]
PHST- 2023/09/13 00:00 [received]
PHST- 2023/11/13 00:41 [medline]
PHST- 2023/11/13 00:41 [pubmed]
PHST- 2023/11/12 23:44 [entrez]
AID - 7416389 [pii]
AID - 10.1093/nar/gkad1057 [doi]
PST - aheadofprint
SO  - Nucleic Acids Res. 2023 Nov 11:gkad1057. doi: 10.1093/nar/gkad1057.

PMID- 37893850
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 20
DP  - 2023 Oct 20
TI  - Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for 
      Healthcare Integration.
LID - 10.3390/healthcare11202776 [doi]
LID - 2776
AB  - Generative artificial intelligence (AI) and large language models (LLMs), 
      exemplified by ChatGPT, are promising for revolutionizing data and information 
      management in healthcare and medicine. However, there is scant literature guiding 
      their integration for non-AI professionals. This study conducts a scoping 
      literature review to address the critical need for guidance on integrating 
      generative AI and LLMs into healthcare and medical practices. It elucidates the 
      distinct mechanisms underpinning these technologies, such as Reinforcement 
      Learning from Human Feedback (RLFH), including few-shot learning and 
      chain-of-thought reasoning, which differentiates them from traditional, 
      rule-based AI systems. It requires an inclusive, collaborative co-design process 
      that engages all pertinent stakeholders, including clinicians and consumers, to 
      achieve these benefits. Although global research is examining both opportunities 
      and challenges, including ethical and legal dimensions, LLMs offer promising 
      advancements in healthcare by enhancing data management, information retrieval, 
      and decision-making processes. Continued innovation in data acquisition, model 
      fine-tuning, prompt strategy development, evaluation, and system implementation 
      is imperative for realizing the full potential of these technologies. 
      Organizations should proactively engage with these technologies to improve 
      healthcare quality, safety, and efficiency, adhering to ethical and legal 
      guidelines for responsible application.
FAU - Yu, Ping
AU  - Yu P
AUID- ORCID: 0000-0002-7910-9396
AD  - School of Computing and Information Technology, University of Wollongong, 
      Wollongong, NSW 2522, Australia.
FAU - Xu, Hua
AU  - Xu H
AD  - Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 
      College Street, Fl 9, New Haven, CT 06510, USA.
FAU - Hu, Xia
AU  - Hu X
AD  - Department of Computer Science, Rice University, P.O. Box 1892, Houston, TX 
      77251-1892, USA.
FAU - Deng, Chao
AU  - Deng C
AUID- ORCID: 0000-0003-1147-5741
AD  - School of Medical, Indigenous and Health Sciences, University of Wollongong, 
      Wollongong, NSW 2522, Australia.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231020
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10606429
OTO - NOTNLM
OT  - LLM
OT  - ethics
OT  - generative AI
OT  - generative artificial intelligence
OT  - healthcare
OT  - large language models
OT  - medicine
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/28 11:44
MHDA- 2023/10/28 11:45
CRDT- 2023/10/28 01:12
PHST- 2023/09/18 00:00 [received]
PHST- 2023/10/13 00:00 [revised]
PHST- 2023/10/17 00:00 [accepted]
PHST- 2023/10/28 11:45 [medline]
PHST- 2023/10/28 11:44 [pubmed]
PHST- 2023/10/28 01:12 [entrez]
AID - healthcare11202776 [pii]
AID - healthcare-11-02776 [pii]
AID - 10.3390/healthcare11202776 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Oct 20;11(20):2776. doi: 10.3390/healthcare11202776.

PMID- 37271176
OWN - NLM
STAT- MEDLINE
DCOM- 20230606
LR  - 20230606
IS  - 1471-2962 (Electronic)
IS  - 1364-503X (Linking)
VI  - 381
IP  - 2251
DP  - 2023 Jul 24
TI  - Understanding computational dialogue understanding.
PG  - 20220049
LID - 10.1098/rsta.2022.0049 [doi]
AB  - In this paper, we first explain why human-like dialogue understanding is so 
      difficult for artificial intelligence (AI). We discuss various methods for 
      testing the understanding capabilities of dialogue systems. Our review of the 
      development of dialogue systems over five decades focuses on the transition from 
      closed-domain to open-domain systems and their extension to multi-modal, 
      multi-party and multi-lingual dialogues. From being somewhat of a niche topic of 
      AI research for the first 40 years, it has made newspaper headlines in recent 
      years and is now being discussed by political leaders at events such as the World 
      Economic Forum in Davos. We ask whether large language models are super-parrots 
      or a milestone towards human-like dialogue understanding and how they relate to 
      what we know about language processing in the human brain. Using ChatGPT as an 
      example, we present some limitations of this approach to dialogue systems. 
      Finally, we present some lessons learned from our 40 years of research in this 
      field about system architecture principles: symmetric multi-modality, no 
      presentation without representation and anticipation feedback loops. We conclude 
      with a discussion of grand challenges such as satisfying conversational maxims 
      and the European Language Equality Act through massive digital 
      multi-linguality-perhaps enabled by interactive machine learning with human 
      trainers. This article is part of a discussion meeting issue 'Cognitive 
      artificial intelligence'.
FAU - Wahlster, Wolfgang
AU  - Wahlster W
AUID- ORCID: 0000-0002-8785-5567
AD  - German Research Center for Artificial Intelligence (DFKI), Alt-Moabit 91c, 
      D-10559 Berlin, Germany.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230605
PL  - England
TA  - Philos Trans A Math Phys Eng Sci
JT  - Philosophical transactions. Series A, Mathematical, physical, and engineering 
      sciences
JID - 101133385
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Brain
MH  - Machine Learning
OTO - NOTNLM
OT  - anticipation feedback
OT  - chatbots
OT  - dialogue systems
OT  - human-like understanding
OT  - large language models
OT  - multi-modality
EDAT- 2023/06/05 00:41
MHDA- 2023/06/06 06:42
CRDT- 2023/06/04 19:13
PHST- 2023/06/06 06:42 [medline]
PHST- 2023/06/05 00:41 [pubmed]
PHST- 2023/06/04 19:13 [entrez]
AID - 10.1098/rsta.2022.0049 [doi]
PST - ppublish
SO  - Philos Trans A Math Phys Eng Sci. 2023 Jul 24;381(2251):20220049. doi: 
      10.1098/rsta.2022.0049. Epub 2023 Jun 5.

PMID- 36900687
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230313
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 5
DP  - 2023 Feb 25
TI  - Artificial Intelligence Systems Assisting in the Assessment of the Course and 
      Retention of Orthodontic Treatment.
LID - 10.3390/healthcare11050683 [doi]
LID - 683
AB  - This scoping review examines the contemporary applications of advanced artificial 
      intelligence (AI) software in orthodontics, focusing on its potential to improve 
      daily working protocols, but also highlighting its limitations. The aim of the 
      review was to evaluate the accuracy and efficiency of current AI-based systems 
      compared to conventional methods in diagnosing, assessing the progress of 
      patients' treatment and follow-up stability. The researchers used various online 
      databases and identified diagnostic software and dental monitoring software as 
      the most studied software in contemporary orthodontics. The former can accurately 
      identify anatomical landmarks used for cephalometric analysis, while the latter 
      enables orthodontists to thoroughly monitor each patient, determine specific 
      desired outcomes, track progress, and warn of potential changes in pre-existing 
      pathology. However, there is limited evidence to assess the stability of 
      treatment outcomes and relapse detection. The study concludes that AI is an 
      effective tool for managing orthodontic treatment from diagnosis to retention, 
      benefiting both patients and clinicians. Patients find the software easy to use 
      and feel better cared for, while clinicians can make diagnoses more easily and 
      assess compliance and damage to braces or aligners more quickly and frequently.
FAU - Strunga, Martin
AU  - Strunga M
AUID- ORCID: 0000-0002-6761-5531
AD  - Department of Orthodontics, Regenerative and Forensic Dentistry, Faculty of 
      Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia.
FAU - Urban, Renáta
AU  - Urban R
AD  - Department of Orthodontics, Regenerative and Forensic Dentistry, Faculty of 
      Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia.
FAU - Surovková, Jana
AU  - Surovková J
AD  - Department of Orthodontics, Regenerative and Forensic Dentistry, Faculty of 
      Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia.
FAU - Thurzo, Andrej
AU  - Thurzo A
AUID- ORCID: 0000-0002-7810-5721
AD  - Department of Orthodontics, Regenerative and Forensic Dentistry, Faculty of 
      Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia.
LA  - eng
GR  - KEGA Thurzo - grant No. xxx/0342/22/Slovak Grant Agency for Science/
PT  - Journal Article
PT  - Review
DEP - 20230225
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10000479
OTO - NOTNLM
OT  - AI
OT  - AI Treatment Assessment
OT  - ChatGPT
OT  - Teledentistry Cephalometrics
OT  - orthodontics
COIS- The authors declare no conflict of interest.
EDAT- 2023/03/12 06:00
MHDA- 2023/03/12 06:01
CRDT- 2023/03/11 01:08
PHST- 2022/12/31 00:00 [received]
PHST- 2023/02/17 00:00 [revised]
PHST- 2023/02/23 00:00 [accepted]
PHST- 2023/03/11 01:08 [entrez]
PHST- 2023/03/12 06:00 [pubmed]
PHST- 2023/03/12 06:01 [medline]
AID - healthcare11050683 [pii]
AID - healthcare-11-00683 [pii]
AID - 10.3390/healthcare11050683 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Feb 25;11(5):683. doi: 10.3390/healthcare11050683.

PMID- 38032930
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 1091-6490 (Electronic)
IS  - 0027-8424 (Print)
IS  - 0027-8424 (Linking)
VI  - 120
IP  - 49
DP  - 2023 Dec 5
TI  - Uncovering the semantics of concepts using GPT-4.
PG  - e2309350120
LID - 10.1073/pnas.2309350120 [doi]
LID - e2309350120
AB  - The ability of recent Large Language Models (LLMs) such as GPT-3.5 and GPT-4 to 
      generate human-like texts suggests that social scientists could use these LLMs to 
      construct measures of semantic similarity that match human judgment. In this 
      article, we provide an empirical test of this intuition. We use GPT-4 to 
      construct a measure of typicality-the similarity of a text document to a concept. 
      We evaluate its performance against other model-based typicality measures in 
      terms of the correlation with human typicality ratings. We conduct this 
      comparative analysis in two domains: the typicality of books in literary genres 
      (using an existing dataset of book descriptions) and the typicality of tweets 
      authored by US Congress members in the Democratic and Republican parties (using a 
      novel dataset). The typicality measure produced with GPT-4 meets or exceeds the 
      performance of the previous state-of-the art typicality measure we introduced in 
      a recent paper [G. Le Mens, B. Kovács, M. T. Hannan, G. Pros Rius, Sociol. Sci. 
      2023, 82-117 (2023)]. It accomplishes this without any training with the research 
      data (it is zero-shot learning). This is a breakthrough because the previous 
      state-of-the-art measure required fine-tuning an LLM on hundreds of thousands of 
      text documents to achieve its performance.
FAU - Le Mens, Gaël
AU  - Le Mens G
AUID- ORCID: 0000-0003-4800-0598
AD  - Department of Economics and Business, Universitat Pompeu Fabra (UPF), Barcelona 
      School of Economics (BSE), UPF-Barcelona School of Management, Barcelona 08005, 
      Spain.
FAU - Kovács, Balázs
AU  - Kovács B
AUID- ORCID: 0000-0001-6916-6357
AD  - Yale School of Management, Yale University, New Haven, CT 06520.
FAU - Hannan, Michael T
AU  - Hannan MT
AUID- ORCID: 0000-0002-7664-9565
AD  - Stanford Graduate School of Business, Stanford University, Stanford, CA 94305.
FAU - Pros, Guillem
AU  - Pros G
AUID- ORCID: 0009-0009-9937-4902
AD  - Department of Economics and Business, Universitat Pompeu Fabra (UPF), Barcelona 
      School of Economics (BSE), UPF-Barcelona School of Management, Barcelona 08005, 
      Spain.
LA  - eng
GR  - 772268/EC | European Research Council (ERC)/
GR  - PID2019-105249GB- I00/AEI/10.13039/501100011033/Ministerio de Ciencia e 
      Innovaci&#x00F3;n (MCIN)/
PT  - Journal Article
DEP - 20231130
PL  - United States
TA  - Proc Natl Acad Sci U S A
JT  - Proceedings of the National Academy of Sciences of the United States of America
JID - 7505876
SB  - IM
PMC - PMC10710071
OTO - NOTNLM
OT  - LLM
OT  - categories
OT  - chatGPT
OT  - deep learning
OT  - typicality
COIS- Competing interests statement:The authors declare no competing interest.
EDAT- 2023/11/30 18:45
MHDA- 2023/11/30 18:46
CRDT- 2023/11/30 13:44
PHST- 2023/11/30 18:46 [medline]
PHST- 2023/11/30 18:45 [pubmed]
PHST- 2023/11/30 13:44 [entrez]
AID - 202309350 [pii]
AID - 10.1073/pnas.2309350120 [doi]
PST - ppublish
SO  - Proc Natl Acad Sci U S A. 2023 Dec 5;120(49):e2309350120. doi: 
      10.1073/pnas.2309350120. Epub 2023 Nov 30.

PMID- 37645491
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230831
IS  - 2732-5121 (Electronic)
IS  - 2732-5121 (Linking)
VI  - 3
DP  - 2023
TI  - Investigating antiquities trafficking with generative pre-trained transformer 
      (GPT)-3 enabled knowledge graphs: A case study.
PG  - 100
LID - 10.12688/openreseurope.16003.1 [doi]
LID - 100
AB  - Background: There is a wide variety of potential sources from which insight into 
      the antiquities trade could be culled, from newspaper articles to auction 
      catalogues, to court dockets, to personal archives, if it could all be 
      systematically examined. We explore the use of a large language model, GPT-3, to 
      semi-automate the creation of a knowledge graph of a body of scholarship 
      concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to 
      identify knowledge statements around the trade. Given GPT-3's understanding of 
      the statistical properties of language, our prompt teaches GPT-3 to append text 
      to each article we feed it where the appended text summarizes the knowledge in 
      the article. The summary is in the form of a list of subject, predicate, and 
      object relationships, representing a knowledge graph. Previously we created such 
      lists by manually annotating the source articles. We compare the result of this 
      automatic process with a knowledge graph created from the same sources via hand. 
      When such knowledge graphs are projected into a multi-dimensional embedding model 
      using a neural network (via the Ampligraph open-source Python library), the 
      relative positioning of entities implies the probability of a connection; the 
      direction of the positioning implies the kind of connection. Thus, we can 
      interrogate the embedding model to discover new probable relationships. The 
      results can generate new insight about the antiquity trade, suggesting possible 
      avenues of research. Results: We find that our semi-automatic approach to 
      generating the knowledge graph in the first place produces comparable results to 
      our hand-made version, but at an enormous savings of time and a possible 
      expansion of the amount of materials we can consider. Conclusions: These results 
      have implications for working with other kinds of archaeological knowledge in 
      grey literature, reports, articles, and other venues via computational means.
CI  - Copyright: © 2023 Graham S et al.
FAU - Graham, Shawn
AU  - Graham S
AD  - Department of History, Carleton University, Ottawa, Ontario, Canada.
FAU - Yates, Donna
AU  - Yates D
AUID- ORCID: 0000-0002-9936-6461
AD  - Faculty of Law, Universiteit Maastricht, Maastricht, Limburg, 6211LH, The 
      Netherlands.
FAU - El-Roby, Ahmed
AU  - El-Roby A
AD  - School of Computer Science, Carleton University, Ottawa, Ontario, Canada.
LA  - eng
PT  - Journal Article
DEP - 20230620
PL  - Belgium
TA  - Open Res Eur
JT  - Open research Europe
JID - 9918230081006676
PMC - PMC10445804
OTO - NOTNLM
OT  - Large language models
OT  - antiquities trade
OT  - antiquities trafficking
OT  - art market
OT  - gpt3
OT  - illicit antiquities
OT  - knowledge graph
OT  - knowledge graph embedding model
COIS- No competing interests were disclosed.
EDAT- 2023/08/30 06:48
MHDA- 2023/08/30 06:49
CRDT- 2023/08/30 03:53
PHST- 2023/05/26 00:00 [accepted]
PHST- 2023/08/30 06:49 [medline]
PHST- 2023/08/30 06:48 [pubmed]
PHST- 2023/08/30 03:53 [entrez]
AID - 10.12688/openreseurope.16003.1 [doi]
PST - epublish
SO  - Open Res Eur. 2023 Jun 20;3:100. doi: 10.12688/openreseurope.16003.1. eCollection 
      2023.

PMID- 37644845
OWN - NLM
STAT- MEDLINE
DCOM- 20231005
LR  - 20231009
IS  - 1537-2995 (Electronic)
IS  - 0041-1132 (Linking)
VI  - 63
IP  - 10
DP  - 2023 Oct
TI  - Would doctors dream of electric blood bankers? Large language model-based 
      artificial intelligence performs well in many aspects of transfusion medicine.
PG  - 1833-1840
LID - 10.1111/trf.17526 [doi]
AB  - BACKGROUND: Large language models (LLMs) excel at answering knowledge-based 
      questions. Many aspects of blood banking and transfusion medicine involve no 
      direct patient care and require only knowledge and judgment. We hypothesized that 
      public LLMs could perform such tasks with accuracy and precision. STUDY DESIGN 
      AND METHODS: We presented three sets of tasks to three publicly-available LLMs 
      (Bard, GPT-3.5, and GPT-4). The first was to review short case presentations and 
      then decide if a red blood cell transfusion was indicated. The second task was to 
      answer a set of consultation questions common in clinical transfusion practice. 
      The third task was to take a multiple-choice test experimentally validated to 
      assess internal medicine postgraduate knowledge of transfusion practice (the 
      BEST-TEST). RESULTS: In the first task, the area under the receiver operating 
      characteristic curve for correct transfusion decisions was 0.65, 0.90, and 0.92, 
      respectively for Bard, GPT-3.5 and GPT-4. All three models had a modest rate of 
      acceptable responses to the consultation questions. Average scores on the 
      BEST-TEST were 55%, 40%, and 87%, respectively. CONCLUSION: When presented with 
      transfusion medicine tasks in natural language, publicly available LLMs 
      demonstrated a range of ability, but GPT-4 consistently scored very well in all 
      tasks. Research is needed to assess the utility of LLMs in transfusion medicine 
      practice. Transfusion Medicine physicians should consider their role alongside 
      such technologies, and how they might be used for the benefit and safety of 
      patients.
CI  - © 2023 The Authors. Transfusion published by Wiley Periodicals LLC on behalf of 
      AABB.
FAU - Hurley, Nathan C
AU  - Hurley NC
AUID- ORCID: 0000-0003-3055-8825
AD  - Department of Anesthesiology, University of Wisconsin-Madison, Madison, 
      Wisconsin, USA.
FAU - Schroeder, Kristopher M
AU  - Schroeder KM
AD  - Department of Anesthesiology, University of Wisconsin-Madison, Madison, 
      Wisconsin, USA.
FAU - Hess, Aaron S
AU  - Hess AS
AUID- ORCID: 0000-0003-2230-8367
AD  - Department of Anesthesiology, University of Wisconsin-Madison, Madison, 
      Wisconsin, USA.
AD  - Department of Pathology & Laboratory Medicine, University of Wisconsin-Madison, 
      Madison, Wisconsin, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230830
PL  - United States
TA  - Transfusion
JT  - Transfusion
JID - 0417360
SB  - IM
MH  - Humans
MH  - *Transfusion Medicine
MH  - Artificial Intelligence
MH  - Blood Transfusion
MH  - Erythrocyte Transfusion
MH  - *Physicians
OTO - NOTNLM
OT  - RBC transfusion
OT  - transfusion practices (adult)
OT  - transfusion service operations
EDAT- 2023/08/30 06:47
MHDA- 2023/10/05 06:43
CRDT- 2023/08/30 03:32
PHST- 2023/07/03 00:00 [revised]
PHST- 2023/05/25 00:00 [received]
PHST- 2023/07/31 00:00 [accepted]
PHST- 2023/10/05 06:43 [medline]
PHST- 2023/08/30 06:47 [pubmed]
PHST- 2023/08/30 03:32 [entrez]
AID - 10.1111/trf.17526 [doi]
PST - ppublish
SO  - Transfusion. 2023 Oct;63(10):1833-1840. doi: 10.1111/trf.17526. Epub 2023 Aug 30.

PMID- 37606733
OWN - NLM
STAT- Publisher
LR  - 20230822
IS  - 1432-2072 (Electronic)
IS  - 0033-3158 (Linking)
DP  - 2023 Aug 22
TI  - Psilocybin therapy for treatment resistant depression: prediction of clinical 
      outcome by natural language processing.
LID - 10.1007/s00213-023-06432-5 [doi]
AB  - RATIONALE: Therapeutic administration of psychedelics has shown significant 
      potential in historical accounts and recent clinical trials in the treatment of 
      depression and other mood disorders. A recent randomized double-blind phase-IIb 
      study demonstrated the safety and efficacy of COMP360, COMPASS Pathways' 
      proprietary synthetic formulation of psilocybin, in participants with 
      treatment-resistant depression. OBJECTIVE: While the phase-IIb results are 
      promising, the treatment works for a portion of the population and early 
      prediction of outcome is a key objective as it would allow early identification 
      of those likely to require alternative treatment. METHODS: Transcripts were made 
      from audio recordings of the psychological support session between participant 
      and therapist 1 day post COMP360 administration. A zero-shot machine learning 
      classifier based on the BART large language model was used to compute 
      two-dimensional sentiment (valence and arousal) for the participant and therapist 
      from the transcript. These scores, combined with the Emotional Breakthrough Index 
      (EBI) and treatment arm were used to predict treatment outcome as measured by 
      MADRS scores. (Code and data are available at 
      https://github.com/compasspathways/Sentiment2D .) RESULTS: Two multinomial 
      logistic regression models were fit to predict responder status at week 3 and 
      through week 12. Cross-validation of these models resulted in 85% and 88% 
      accuracy and AUC values of 88% and 85%. CONCLUSIONS: A machine learning algorithm 
      using NLP and EBI accurately predicts long-term patient response, allowing rapid 
      prognostication of personalized response to psilocybin treatment and insight into 
      therapeutic model optimization. Further research is required to understand if 
      language data from earlier stages in the therapeutic process hold similar 
      predictive power.
CI  - © 2023. The Author(s).
FAU - Dougherty, Robert F
AU  - Dougherty RF
AUID- ORCID: 0000-0002-1254-5943
AD  - COMPASS Pathways, London, UK. robert.dougherty@compasspathways.com.
FAU - Clarke, Patrick
AU  - Clarke P
AD  - COMPASS Pathways, London, UK.
FAU - Atli, Merve
AU  - Atli M
AD  - COMPASS Pathways, London, UK.
FAU - Kuc, Joanna
AU  - Kuc J
AD  - COMPASS Pathways, London, UK.
FAU - Schlosser, Danielle
AU  - Schlosser D
AD  - COMPASS Pathways, London, UK.
FAU - Dunlop, Boadie W
AU  - Dunlop BW
AD  - Emory University, Atlanta, GA, USA.
FAU - Hellerstein, David J
AU  - Hellerstein DJ
AD  - Columbia University, New York, NY, USA.
FAU - Aaronson, Scott T
AU  - Aaronson ST
AD  - Sheppard Pratt, Baltimore, MD, USA.
FAU - Zisook, Sidney
AU  - Zisook S
AD  - University of California, San Diego, CA, USA.
FAU - Young, Allan H
AU  - Young AH
AD  - King's College, London, UK.
FAU - Carhart-Harris, Robin
AU  - Carhart-Harris R
AD  - University of California, San Francisco, CA, USA.
FAU - Goodwin, Guy M
AU  - Goodwin GM
AD  - COMPASS Pathways, London, UK.
FAU - Ryslik, Gregory A
AU  - Ryslik GA
AD  - COMPASS Pathways, London, UK.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - Germany
TA  - Psychopharmacology (Berl)
JT  - Psychopharmacology
JID - 7608025
SB  - IM
OTO - NOTNLM
OT  - AI
OT  - Depression
OT  - Emotional breakthrough index
OT  - Machine learning
OT  - Natural language processing
OT  - Psilocybin
EDAT- 2023/08/22 13:42
MHDA- 2023/08/22 13:42
CRDT- 2023/08/22 11:14
PHST- 2022/10/25 00:00 [received]
PHST- 2023/07/21 00:00 [accepted]
PHST- 2023/08/22 13:42 [medline]
PHST- 2023/08/22 13:42 [pubmed]
PHST- 2023/08/22 11:14 [entrez]
AID - 10.1007/s00213-023-06432-5 [pii]
AID - 10.1007/s00213-023-06432-5 [doi]
PST - aheadofprint
SO  - Psychopharmacology (Berl). 2023 Aug 22. doi: 10.1007/s00213-023-06432-5.

PMID- 37923374
OWN - NLM
STAT- Publisher
LR  - 20231103
IS  - 1468-2079 (Electronic)
IS  - 0007-1161 (Linking)
DP  - 2023 Nov 3
TI  - Capabilities of GPT-4 in ophthalmology: an analysis of model entropy and progress 
      towards human-level medical question answering.
LID - bjo-2023-324438 [pii]
LID - 10.1136/bjo-2023-324438 [doi]
AB  - BACKGROUND: Evidence on the performance of Generative Pre-trained Transformer 4 
      (GPT-4), a large language model (LLM), in the ophthalmology question-answering 
      domain is needed. METHODS: We tested GPT-4 on two 260-question multiple choice 
      question sets from the Basic and Clinical Science Course (BCSC) Self-Assessment 
      Program and the OphthoQuestions question banks. We compared the accuracy of GPT-4 
      models with varying temperatures (creativity setting) and evaluated their 
      responses in a subset of questions. We also compared the best-performing GPT-4 
      model to GPT-3.5 and to historical human performance. RESULTS: GPT-4-0.3 (GPT-4 
      with a temperature of 0.3) achieved the highest accuracy among GPT-4 models, with 
      75.8% on the BCSC set and 70.0% on the OphthoQuestions set. The combined accuracy 
      was 72.9%, which represents an 18.3% raw improvement in accuracy compared with 
      GPT-3.5 (p<0.001). Human graders preferred responses from models with a 
      temperature higher than 0 (more creative). Exam section, question difficulty and 
      cognitive level were all predictive of GPT-4-0.3 answer accuracy. GPT-4-0.3's 
      performance was numerically superior to human performance on the BCSC (75.8% vs 
      73.3%) and OphthoQuestions (70.0% vs 63.0%), but the difference was not 
      statistically significant (p=0.55 and p=0.09). CONCLUSION: GPT-4, an LLM trained 
      on non-ophthalmology-specific data, performs significantly better than its 
      predecessor on simulated ophthalmology board-style exams. Remarkably, its 
      performance tended to be superior to historical human performance, but that 
      difference was not statistically significant in our study.
CI  - © Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and 
      permissions. Published by BMJ.
FAU - Antaki, Fares
AU  - Antaki F
AUID- ORCID: 0000-0001-6679-7276
AD  - Moorfields Eye Hospital NHS Foundation Trust, London, UK.
AD  - Institute of Ophthalmology, UCL, London, UK.
AD  - The CHUM School of Artificial Intelligence in Healthcare, Montreal, Quebec, 
      Canada.
AD  - Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal 
      (CHUM), Montreal, Quebec, Canada.
FAU - Milad, Daniel
AU  - Milad D
AUID- ORCID: 0000-0002-0693-3421
AD  - Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal 
      (CHUM), Montreal, Quebec, Canada.
AD  - Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, 
      Canada.
FAU - Chia, Mark A
AU  - Chia MA
AD  - Moorfields Eye Hospital NHS Foundation Trust, London, UK.
AD  - Institute of Ophthalmology, UCL, London, UK.
FAU - Giguère, Charles-Édouard
AU  - Giguère CÉ
AD  - Institut universitaire en santé mentale de Montréal (IUSMM), Montreal, Quebec, 
      Canada.
FAU - Touma, Samir
AU  - Touma S
AUID- ORCID: 0000-0002-6365-0946
AD  - Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal 
      (CHUM), Montreal, Quebec, Canada.
AD  - Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, 
      Canada.
FAU - El-Khoury, Jonathan
AU  - El-Khoury J
AUID- ORCID: 0000-0003-3186-2351
AD  - Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada.
AD  - Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal 
      (CHUM), Montreal, Quebec, Canada.
AD  - Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, 
      Canada.
FAU - Keane, Pearse A
AU  - Keane PA
AUID- ORCID: 0000-0002-9239-745X
AD  - Moorfields Eye Hospital NHS Foundation Trust, London, UK renaud.duval@gmail.com 
      p.keane@ucl.ac.uk.
AD  - Institute of Ophthalmology, UCL, London, UK.
AD  - NIHR Moorfields Biomedical Research Centre, London, UK.
FAU - Duval, Renaud
AU  - Duval R
AD  - Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada 
      renaud.duval@gmail.com p.keane@ucl.ac.uk.
AD  - Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, 
      Canada.
LA  - eng
PT  - Journal Article
DEP - 20231103
PL  - England
TA  - Br J Ophthalmol
JT  - The British journal of ophthalmology
JID - 0421041
SB  - IM
OTO - NOTNLM
OT  - Medical Education
COIS- Competing interests: None declared.
EDAT- 2023/11/06 03:54
MHDA- 2023/11/06 03:54
CRDT- 2023/11/03 21:13
PHST- 2023/08/24 00:00 [received]
PHST- 2023/10/01 00:00 [accepted]
PHST- 2023/11/06 03:54 [medline]
PHST- 2023/11/06 03:54 [pubmed]
PHST- 2023/11/03 21:13 [entrez]
AID - bjo-2023-324438 [pii]
AID - 10.1136/bjo-2023-324438 [doi]
PST - aheadofprint
SO  - Br J Ophthalmol. 2023 Nov 3:bjo-2023-324438. doi: 10.1136/bjo-2023-324438.

PMID- 38074472
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 2001-0370 (Print)
IS  - 2001-0370 (Electronic)
IS  - 2001-0370 (Linking)
VI  - 21
DP  - 2023
TI  - Improving antibody optimization ability of generative adversarial network through 
      large language model.
PG  - 5839-5850
LID - 10.1016/j.csbj.2023.11.041 [doi]
AB  - Generative adversarial networks (GANs) have successfully generated functional 
      protein sequences. However, traditional GANs often suffer from inherent 
      randomness, resulting in a lower probability of obtaining desirable sequences. 
      Due to the high cost of wet-lab experiments, the main goal of computer-aided 
      antibody optimization is to identify high-quality candidate antibodies from a 
      large range of possibilities, yet improving the ability of GANs to generate these 
      desired antibodies is a challenge. In this study, we propose and evaluate a new 
      GAN called the Language Model Guided Antibody Generative Adversarial Network 
      (AbGAN-LMG). This GAN uses a language model as an input, harnessing such models' 
      powerful representational capabilities to improve the GAN's generation of 
      high-quality antibodies. We conducted a comprehensive evaluation of the antibody 
      libraries and sequences generated by AbGAN-LMG for COVID-19 (SARS-CoV-2) and 
      Middle East Respiratory Syndrome (MERS-CoV). Results indicate that AbGAN-LMG has 
      learned the fundamental characteristics of antibodies and that it improved the 
      diversity of the generated libraries. Additionally, when generating sequences 
      using AZD-8895 as the target antibody for optimization, over 50% of the generated 
      sequences exhibited better developability than AZD-8895 itself. Through molecular 
      docking, we identified 70 antibodies that demonstrated higher affinity for the 
      wild-type receptor-binding domain (RBD) of SARS-CoV-2 compared to AZD-8895. In 
      conclusion, AbGAN-LMG demonstrates that language models used in conjunction with 
      GANs can enable the generation of higher-quality libraries and candidate 
      sequences, thereby improving the efficiency of antibody optimization. AbGAN-LMG 
      is available at http://39.102.71.224:88/.
CI  - © 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of 
      Computational and Structural Biotechnology.
FAU - Zhao, Wenbin
AU  - Zhao W
AD  - Academy of Military Medical Sciences, Beijing 100850, China.
FAU - Luo, Xiaowei
AU  - Luo X
AD  - Academy of Military Medical Sciences, Beijing 100850, China.
FAU - Tong, Fan
AU  - Tong F
AD  - Academy of Military Medical Sciences, Beijing 100850, China.
FAU - Zheng, Xiangwen
AU  - Zheng X
AD  - Academy of Military Medical Sciences, Beijing 100850, China.
FAU - Li, Jing
AU  - Li J
AD  - Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of 
      Pathogen and Biosecurity, Beijing 100071, China.
FAU - Zhao, Guangyu
AU  - Zhao G
AD  - Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of 
      Pathogen and Biosecurity, Beijing 100071, China.
FAU - Zhao, Dongsheng
AU  - Zhao D
AD  - Academy of Military Medical Sciences, Beijing 100850, China.
LA  - eng
PT  - Journal Article
DEP - 20231123
PL  - Netherlands
TA  - Comput Struct Biotechnol J
JT  - Computational and structural biotechnology journal
JID - 101585369
PMC - PMC10698008
OTO - NOTNLM
OT  - Antibody optimization
OT  - Generative Adversarial Network
OT  - Language model
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/12/11 06:45
MHDA- 2023/12/11 06:46
CRDT- 2023/12/11 05:44
PHST- 2023/10/07 00:00 [received]
PHST- 2023/11/21 00:00 [revised]
PHST- 2023/11/21 00:00 [accepted]
PHST- 2023/12/11 06:46 [medline]
PHST- 2023/12/11 06:45 [pubmed]
PHST- 2023/12/11 05:44 [entrez]
AID - S2001-0370(23)00455-5 [pii]
AID - 10.1016/j.csbj.2023.11.041 [doi]
PST - epublish
SO  - Comput Struct Biotechnol J. 2023 Nov 23;21:5839-5850. doi: 
      10.1016/j.csbj.2023.11.041. eCollection 2023.

PMID- 37643659
OWN - NLM
STAT- Publisher
LR  - 20231022
IS  - 1555-7162 (Electronic)
IS  - 0002-9343 (Linking)
VI  - 136
IP  - 11
DP  - 2023 Nov
TI  - Comparative Evaluation of Diagnostic Accuracy Between Google Bard and Physicians.
PG  - 1119-1123.e18
LID - S0002-9343(23)00536-3 [pii]
LID - 10.1016/j.amjmed.2023.08.003 [doi]
AB  - BACKGROUND: In this study, we evaluated the diagnostic accuracy of Google Bard, a 
      generative artificial intelligence (AI) platform. METHODS: We searched published 
      case reports from our department for difficult or uncommon case descriptions and 
      mock cases created by physicians for common case descriptions. We entered the 
      case descriptions into the prompt of Google Bard to generate the top 10 
      differential-diagnosis lists. As in previous studies, other physicians created 
      differential-diagnosis lists by reading the same clinical descriptions. RESULTS: 
      A total of 82 clinical descriptions (52 case reports and 30 mock cases) were 
      used. The accuracy rates of physicians were still higher than Google Bard in the 
      top 10 (56.1% vs 82.9%, P < .001), the top 5 (53.7% vs 78.0%, P = .002), and the 
      top differential diagnosis (40.2% vs 64.6%, P = .003). Even within the specific 
      context of case reports, physicians consistently outperformed Google Bard. When 
      it came to mock cases, the performances of the differential-diagnosis lists by 
      Google Bard were no different from those of the physicians in the top 10 (80.0% 
      vs 96.6%, P = .11) and the top 5 (76.7% vs 96.6%, P = .06), except for those in 
      the top diagnoses (60.0% vs 90.0%, P = .02). CONCLUSION: While physicians 
      excelled overall, and particularly with case reports, Google Bard displayed 
      comparable diagnostic performance in common cases. This suggested that Google 
      Bard possesses room for further improvement and refinement in its diagnostic 
      capabilities. Generative AIs, including Google Bard, are anticipated to become 
      increasingly beneficial in augmenting diagnostic accuracy.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Hirosawa, Takanobu
AU  - Hirosawa T
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan. Electronic address: hirosawa@dokkyomed.ac.jp.
FAU - Mizuta, Kazuya
AU  - Mizuta K
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
FAU - Harada, Yukinori
AU  - Harada Y
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
FAU - Shimizu, Taro
AU  - Shimizu T
AD  - Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 
      Tochigi, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230827
PL  - United States
TA  - Am J Med
JT  - The American journal of medicine
JID - 0267200
SB  - IM
OTO - NOTNLM
OT  - Clinical decision supporting system
OT  - Diagnosis
OT  - Diagnostic excellence
OT  - Generative artificial intelligence
OT  - Large language model
OT  - Natural language processing
EDAT- 2023/08/30 00:41
MHDA- 2023/08/30 00:41
CRDT- 2023/08/29 19:13
PHST- 2023/06/22 00:00 [received]
PHST- 2023/07/11 00:00 [revised]
PHST- 2023/08/24 00:00 [accepted]
PHST- 2023/08/30 00:41 [pubmed]
PHST- 2023/08/30 00:41 [medline]
PHST- 2023/08/29 19:13 [entrez]
AID - S0002-9343(23)00536-3 [pii]
AID - 10.1016/j.amjmed.2023.08.003 [doi]
PST - ppublish
SO  - Am J Med. 2023 Nov;136(11):1119-1123.e18. doi: 10.1016/j.amjmed.2023.08.003. Epub 
      2023 Aug 27.

PMID- 31265066
OWN - NLM
STAT- MEDLINE
DCOM- 20210129
LR  - 20210129
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Print)
IS  - 1067-5027 (Linking)
VI  - 26
IP  - 11
DP  - 2019 Nov 1
TI  - Enhancing clinical concept extraction with contextual embeddings.
PG  - 1297-1304
LID - 10.1093/jamia/ocz096 [doi]
AB  - OBJECTIVE: Neural network-based representations ("embeddings") have dramatically 
      advanced natural language processing (NLP) tasks, including clinical NLP tasks 
      such as concept extraction. Recently, however, more advanced embedding methods 
      and representations (eg, ELMo, BERT) have further pushed the state of the art in 
      NLP, yet there are no common best practices for how to integrate these 
      representations into clinical tasks. The purpose of this study, then, is to 
      explore the space of possible options in utilizing these new models for clinical 
      concept extraction, including comparing these to traditional word embedding 
      methods (word2vec, GloVe, fastText). MATERIALS AND METHODS: Both off-the-shelf, 
      open-domain embeddings and pretrained clinical embeddings from MIMIC-III (Medical 
      Information Mart for Intensive Care III) are evaluated. We explore a battery of 
      embedding methods consisting of traditional word embeddings and contextual 
      embeddings and compare these on 4 concept extraction corpora: i2b2 2010, i2b2 
      2012, SemEval 2014, and SemEval 2015. We also analyze the impact of the 
      pretraining time of a large language model like ELMo or BERT on the extraction 
      performance. Last, we present an intuitive way to understand the semantic 
      information encoded by contextual embeddings. RESULTS: Contextual embeddings 
      pretrained on a large clinical corpus achieves new state-of-the-art performances 
      across all concept extraction tasks. The best-performing model outperforms all 
      state-of-the-art methods with respective F1-measures of 90.25, 93.18 (partial), 
      80.74, and 81.65. CONCLUSIONS: We demonstrate the potential of contextual 
      embeddings through the state-of-the-art performance these methods achieve on 
      clinical concept extraction. Additionally, we demonstrate that contextual 
      embeddings encode valuable semantic information not accounted for in traditional 
      word representations.
CI  - © The Author(s) 2019. Published by Oxford University Press on behalf of the 
      American Medical Informatics Association. All rights reserved. For permissions, 
      please email: journals.permissions@oup.com.
FAU - Si, Yuqi
AU  - Si Y
AD  - School of Biomedical Informatics, University of Texas Health Science Center at 
      Houston, Houston, Texas, USA.
FAU - Wang, Jingqi
AU  - Wang J
AD  - School of Biomedical Informatics, University of Texas Health Science Center at 
      Houston, Houston, Texas, USA.
FAU - Xu, Hua
AU  - Xu H
AD  - School of Biomedical Informatics, University of Texas Health Science Center at 
      Houston, Houston, Texas, USA.
FAU - Roberts, Kirk
AU  - Roberts K
AD  - School of Biomedical Informatics, University of Texas Health Science Center at 
      Houston, Houston, Texas, USA.
LA  - eng
GR  - R00 LM012104/LM/NLM NIH HHS/United States
GR  - R01 LM010681/LM/NLM NIH HHS/United States
GR  - U24 CA194215/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
MH  - Big Data
MH  - Databases, Factual
MH  - Humans
MH  - Information Storage and Retrieval/*methods
MH  - *Natural Language Processing
MH  - *Neural Networks, Computer
MH  - Public Reporting of Healthcare Data
PMC - PMC6798561
OTO - NOTNLM
OT  - clinical concept extraction
OT  - contextual embeddings
OT  - language model
EDAT- 2019/07/03 06:00
MHDA- 2021/01/30 06:00
CRDT- 2019/07/03 06:00
PHST- 2019/03/18 00:00 [received]
PHST- 2019/05/10 00:00 [revised]
PHST- 2019/05/24 00:00 [accepted]
PHST- 2019/07/03 06:00 [pubmed]
PHST- 2021/01/30 06:00 [medline]
PHST- 2019/07/03 06:00 [entrez]
AID - 5527248 [pii]
AID - ocz096 [pii]
AID - 10.1093/jamia/ocz096 [doi]
PST - ppublish
SO  - J Am Med Inform Assoc. 2019 Nov 1;26(11):1297-1304. doi: 10.1093/jamia/ocz096.

PMID- 38070902
OWN - NLM
STAT- In-Process
LR  - 20231209
IS  - 2044-6055 (Electronic)
IS  - 2044-6055 (Linking)
VI  - 13
IP  - 12
DP  - 2023 Dec 9
TI  - Large language model-based information extraction from free-text radiology 
      reports: a scoping review protocol.
PG  - e076865
LID - 10.1136/bmjopen-2023-076865 [doi]
AB  - INTRODUCTION: Radiological imaging is one of the most frequently performed 
      diagnostic tests worldwide. The free-text contained in radiology reports is 
      currently only rarely used for secondary use purposes, including research and 
      predictive analysis. However, this data might be made available by means of 
      information extraction (IE), based on natural language processing (NLP). 
      Recently, a new approach to NLP, large language models (LLMs), has gained 
      momentum and continues to improve performance of IE-related tasks. The objective 
      of this scoping review is to show the state of research regarding IE from 
      free-text radiology reports based on LLMs, to investigate applied methods and to 
      guide future research by showing open challenges and limitations of current 
      approaches. To our knowledge, no systematic or scoping review of IE from 
      radiology reports based on LLMs has been published. Existing publications are 
      outdated and do not comprise LLM-based methods. METHODS AND ANALYSIS: This 
      protocol is designed based on the JBI Manual for Evidence Synthesis, chapter 
      11.2: 'Development of a scoping review protocol'. Inclusion criteria and a search 
      strategy comprising four databases (PubMed, IEEE Xplore, Web of Science Core 
      Collection and ACM Digital Library) are defined. Furthermore, we describe the 
      screening process, data charting, analysis and presentation of extracted data. 
      ETHICS AND DISSEMINATION: This protocol describes the methodology of a scoping 
      literature review and does not comprise research on or with humans, animals or 
      their data. Therefore, no ethical approval is required. After the publication of 
      this protocol and the conduct of the review, its results are going to be 
      published in an open access journal dedicated to biomedical informatics/digital 
      health.
CI  - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No 
      commercial re-use. See rights and permissions. Published by BMJ.
FAU - Reichenpfader, Daniel
AU  - Reichenpfader D
AUID- ORCID: 0000-0002-8052-3359
AD  - Institute for Patient-centered Digital Health, Bern University of Applied 
      Sciences, Bern, Switzerland daniel.reichenpfader@bfh.ch.
FAU - Müller, Henning
AU  - Müller H
AUID- ORCID: 0000-0001-6800-9878
AD  - Department of Radiology and Medical Informatics, Université de Genève, Genève, 
      Switzerland.
AD  - Informatics Institute, HES-SO Valais-Wallis, Sierre, Switzerland.
FAU - Denecke, Kerstin
AU  - Denecke K
AUID- ORCID: 0000-0001-6691-396X
AD  - Institute for Patient-centered Digital Health, Bern University of Applied 
      Sciences, Bern, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20231209
PL  - England
TA  - BMJ Open
JT  - BMJ open
JID - 101552874
SB  - IM
OTO - NOTNLM
OT  - information extraction
OT  - natural language processing
OT  - radiology & imaging
COIS- Competing interests: None declared.
EDAT- 2023/12/10 06:43
MHDA- 2023/12/10 06:43
CRDT- 2023/12/09 20:52
PHST- 2023/12/10 06:43 [medline]
PHST- 2023/12/10 06:43 [pubmed]
PHST- 2023/12/09 20:52 [entrez]
AID - bmjopen-2023-076865 [pii]
AID - 10.1136/bmjopen-2023-076865 [doi]
PST - epublish
SO  - BMJ Open. 2023 Dec 9;13(12):e076865. doi: 10.1136/bmjopen-2023-076865.

PMID- 38020426
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 2352-3409 (Electronic)
IS  - 2352-3409 (Linking)
VI  - 51
DP  - 2023 Dec
TI  - HexAI-TJAtxt: A textual dataset to advance open scientific research in total 
      joint arthroplasty.
PG  - 109738
LID - 10.1016/j.dib.2023.109738 [doi]
LID - 109738
AB  - Total joint arthroplasty (TJA) is the most common and fastest inpatient surgical 
      procedure in the elderly, nationwide. Due to the increasing number of TJA 
      patients and advancements in healthcare, there is a growing number of scientific 
      articles being published in a daily basis. These articles offer important 
      insights into TJA, covering aspects like diagnosis, prevention, treatment 
      strategies, and epidemiological factors. However, there has been limited effort 
      to compile a large-scale text dataset from these articles and make it publicly 
      available for open scientific research in TJA. Rapid yet, utilizing computational 
      text analysis on these large columns of scientific literatures holds great 
      potential for uncovering new knowledge to enhance our understanding of joint 
      diseases and improve the quality of TJA care and clinical outcomes. This work 
      aims to build a dataset entitled HexAI-TJAtxt, which includes more than 61,936 
      scientific abstracts collected from PubMed using MeSH (Medical Subject Headings) 
      terms within "MeSH Subheading" and "MeSH Major Topic," and Publication Date from 
      01/01/2000 to 12/31/2022. The current dataset is freely and publicly available at 
      https://github.com/pitthexai/HexAI-TJAtxt, and it will be updated frequently in 
      bi-monthly manner from new abstracts published at PubMed.
CI  - © 2023 The Author(s).
FAU - Amirian, Soheyla
AU  - Amirian S
AD  - School of Computing, University of Georgia, Athens, GA, USA.
FAU - Ghazaleh, Husam
AU  - Ghazaleh H
AD  - Department of Computer Science, Quincy University, Quincy, IL, USA.
FAU - Carlson, Luke A
AU  - Carlson LA
AD  - Department of Orthopaedic Surgery, School of Medicine, University of Pittsburgh, 
      Pittsburgh, PA, USA.
FAU - Gong, Matthew
AU  - Gong M
AD  - Department of Orthopaedic Surgery, School of Medicine, University of Pittsburgh, 
      Pittsburgh, PA, USA.
FAU - Finger, Logan
AU  - Finger L
AD  - Department of Orthopaedic Surgery, School of Medicine, University of Pittsburgh, 
      Pittsburgh, PA, USA.
FAU - Plate, Johannes F
AU  - Plate JF
AD  - Department of Orthopaedic Surgery, School of Medicine, University of Pittsburgh, 
      Pittsburgh, PA, USA.
FAU - Tafti, Ahmad P
AU  - Tafti AP
AD  - Department of Health Information Management, School of Health and Rehabilitation 
      Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
AD  - Intelligent Systems Program, School of Computing and Information, University of 
      Pittsburgh, Pittsburgh, PA, USA.
LA  - eng
PT  - Journal Article
DEP - 20231031
PL  - Netherlands
TA  - Data Brief
JT  - Data in brief
JID - 101654995
PMC - PMC10661650
OTO - NOTNLM
OT  - ChatGPT
OT  - Computational text analytics
OT  - Large scale textual dataset
OT  - Total joint arthroplasty
EDAT- 2023/11/29 18:41
MHDA- 2023/11/29 18:42
CRDT- 2023/11/29 14:43
PHST- 2023/07/11 00:00 [received]
PHST- 2023/10/19 00:00 [revised]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/11/29 18:42 [medline]
PHST- 2023/11/29 18:41 [pubmed]
PHST- 2023/11/29 14:43 [entrez]
AID - S2352-3409(23)00807-7 [pii]
AID - 109738 [pii]
AID - 10.1016/j.dib.2023.109738 [doi]
PST - epublish
SO  - Data Brief. 2023 Oct 31;51:109738. doi: 10.1016/j.dib.2023.109738. eCollection 
      2023 Dec.

PMID- 37900350
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2059-8661 (Electronic)
IS  - 2059-8661 (Linking)
VI  - 7
IP  - 1
DP  - 2023
TI  - An approach for collaborative development of a federated biomedical knowledge 
      graph-based question-answering system: Question-of-the-Month challenges.
PG  - e214
LID - 10.1017/cts.2023.619 [doi]
LID - e214
AB  - Knowledge graphs have become a common approach for knowledge representation. Yet, 
      the application of graph methodology is elusive due to the sheer number and 
      complexity of knowledge sources. In addition, semantic incompatibilities hinder 
      efforts to harmonize and integrate across these diverse sources. As part of The 
      Biomedical Translator Consortium, we have developed a knowledge graph-based 
      question-answering system designed to augment human reasoning and accelerate 
      translational scientific discovery: the Translator system. We have applied the 
      Translator system to answer biomedical questions in the context of a broad array 
      of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, 
      multiple sclerosis, and others. A variety of collaborative approaches have been 
      used to research and develop the Translator system. One recent approach involved 
      the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. 
      Herein, we describe the structure of the QotM Challenge; the six challenges that 
      have been conducted to date on drug-induced liver injury, cannabidiol toxicity, 
      coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related 
      phenotypes; the scientific insights that have been gleaned during the challenges; 
      and the technical issues that were identified over the course of the challenges 
      and that can now be addressed to foster further development of the prototype 
      Translator system. We close with a discussion on Large Language Models such as 
      ChatGPT and highlight differences between those models and the Translator system.
CI  - © The Author(s) 2023.
FAU - Fecho, Karamarie
AU  - Fecho K
AUID- ORCID: 0000-0002-6704-9306
AD  - Renaissance Computing Institute (RENCI), University of North Carolina at Chapel 
      Hill, Chapel Hill, NC, USA.
AD  - Copperline Professional Solutions, Pittsboro, NC, USA.
FAU - Bizon, Chris
AU  - Bizon C
AUID- ORCID: 0000-0002-9491-7674
AD  - Renaissance Computing Institute (RENCI), University of North Carolina at Chapel 
      Hill, Chapel Hill, NC, USA.
FAU - Issabekova, Tursynay
AU  - Issabekova T
AUID- ORCID: 0000-0003-0842-7335
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO, USA.
FAU - Moxon, Sierra
AU  - Moxon S
AUID- ORCID: 0000-0002-8719-7760
AD  - Biosystems Data Science Department, Lawrence Berkeley National Laboratory, 
      Berkeley, CA, USA.
FAU - Thessen, Anne E
AU  - Thessen AE
AUID- ORCID: 0000-0002-2908-3327
AD  - Department of Biomedical Informatics, University of Colorado Anschutz Medical 
      Campus, Aurora, CO, USA.
FAU - Abdollahi, Shervin
AU  - Abdollahi S
AUID- ORCID: 0000-0002-2444-0014
AD  - Division of Preclinical Innovation, National Center for Advancing Translational 
      Sciences, National Institutes of Health, Rockville, MD, USA.
FAU - Baranzini, Sergio E
AU  - Baranzini SE
AUID- ORCID: 0000-0003-0067-194X
AD  - Department of Neurology, Weill Institute for Neuroscience, University of 
      California - San Francisco, San Francisco, CA, USA.
FAU - Belhu, Basazin
AU  - Belhu B
AD  - Institute for Systems Biology, Seattle, WA, USA.
FAU - Byrd, William E
AU  - Byrd WE
AUID- ORCID: 0000-0003-4730-5293
AD  - The Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, 
      Birmingham, AL, USA.
FAU - Chung, Lawrence
AU  - Chung L
AUID- ORCID: 0000-0002-6823-9275
AD  - The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
FAU - Crouse, Andrew
AU  - Crouse A
AUID- ORCID: 0000-0003-3499-6902
AD  - The Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, 
      Birmingham, AL, USA.
FAU - Duby, Marc P
AU  - Duby MP
AUID- ORCID: 0000-0002-3884-6680
AD  - The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
FAU - Ferguson, Stephen
AU  - Ferguson S
AUID- ORCID: 0000-0003-3172-8347
AD  - National Institute of Environmental Health Sciences, National Institutes of 
      Health, Research Triangle Park, NC, USA.
FAU - Foksinska, Aleksandra
AU  - Foksinska A
AUID- ORCID: 0000-0002-1035-3294
AD  - The Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, 
      Birmingham, AL, USA.
FAU - Forero, Laura
AU  - Forero L
AD  - Rady Children's Institute for Genomic Medicine, Rady Children's Hospital, San 
      Diego, CA, USA.
AD  - University of California at San Diego, San Diego, CA, USA.
FAU - Friedman, Jennifer
AU  - Friedman J
AD  - Rady Children's Institute for Genomic Medicine, Rady Children's Hospital, San 
      Diego, CA, USA.
AD  - University of California at San Diego, San Diego, CA, USA.
FAU - Gardner, Vicki
AU  - Gardner V
AD  - Renaissance Computing Institute (RENCI), University of North Carolina at Chapel 
      Hill, Chapel Hill, NC, USA.
FAU - Glusman, Gwênlyn
AU  - Glusman G
AUID- ORCID: 0000-0001-8060-5955
AD  - Institute for Systems Biology, Seattle, WA, USA.
FAU - Hadlock, Jennifer
AU  - Hadlock J
AUID- ORCID: 0000-0001-6103-7606
AD  - Institute for Systems Biology, Seattle, WA, USA.
FAU - Hanspers, Kristina
AU  - Hanspers K
AUID- ORCID: 0000-0001-5410-599X
AD  - Gladstone Institutes, University of California - San Francisco, San Francisco, 
      CA, USA.
FAU - Hinderer, Eugene
AU  - Hinderer E
AUID- ORCID: 0000-0001-8045-7083
AD  - Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, 
      MA, USA.
FAU - Hobbs, Charlotte
AU  - Hobbs C
AUID- ORCID: 0000-0002-3964-5952
AD  - Rady Children's Institute for Genomic Medicine, Rady Children's Hospital, San 
      Diego, CA, USA.
FAU - Hyde, Gregory
AU  - Hyde G
AUID- ORCID: 0000-0001-5608-5224
AD  - Thayer School of Engineering at Dartmouth College, Hanover, NH, USA.
FAU - Huang, Sui
AU  - Huang S
AUID- ORCID: 0000-0002-3545-4665
AD  - Institute for Systems Biology, Seattle, WA, USA.
FAU - Koslicki, David
AU  - Koslicki D
AUID- ORCID: 0000-0002-0640-954X
AD  - Departments of Computer Science and Engineering, Biology, and the Huck Institutes 
      of the Life Sciences, Penn State University, University Park, PA, USA.
FAU - Mease, Philip
AU  - Mease P
AUID- ORCID: 0000-0002-6620-0457
AD  - Swedish Medical Center, St. Joseph Health, Seattle, WA, USA.
AD  - University of Washington, Seattle, WA, USA.
FAU - Muller, Sandrine
AU  - Muller S
AUID- ORCID: 0000-0001-5998-3003
AD  - The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
FAU - Mungall, Christopher J
AU  - Mungall CJ
AUID- ORCID: 0000-0002-6601-2165
AD  - Biosystems Data Science Department, Lawrence Berkeley National Laboratory, 
      Berkeley, CA, USA.
FAU - Ramsey, Stephen A
AU  - Ramsey SA
AUID- ORCID: 0000-0002-2168-5403
AD  - Oregon State University, Corvallis, OR, USA.
FAU - Roach, Jared
AU  - Roach J
AUID- ORCID: 0000-0003-4524-0107
AD  - Institute for Systems Biology, Seattle, WA, USA.
FAU - Rubin, Irit
AU  - Rubin I
AUID- ORCID: 0000-0003-2377-3498
AD  - Institute for Systems Biology, Seattle, WA, USA.
FAU - Schurman, Shepherd H
AU  - Schurman SH
AUID- ORCID: 0000-0002-9133-7906
AD  - National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
FAU - Shalev, Anath
AU  - Shalev A
AUID- ORCID: 0000-0003-2869-7228
AD  - The Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, 
      Birmingham, AL, USA.
FAU - Smith, Brett
AU  - Smith B
AUID- ORCID: 0000-0002-2586-5014
AD  - Institute for Systems Biology, Seattle, WA, USA.
FAU - Soman, Karthik
AU  - Soman K
AUID- ORCID: 0000-0002-3490-9306
AD  - Department of Neurology, Weill Institute for Neuroscience, University of 
      California - San Francisco, San Francisco, CA, USA.
FAU - Stemann, Sarah
AU  - Stemann S
AD  - Division of Preclinical Innovation, National Center for Advancing Translational 
      Sciences, National Institutes of Health, Rockville, MD, USA.
FAU - Su, Andrew I
AU  - Su AI
AUID- ORCID: 0000-0002-9859-4104
AD  - The Scripps Research Institute, La Jolla, CA, USA.
FAU - Ta, Casey
AU  - Ta C
AUID- ORCID: 0000-0002-4679-805X
AD  - Columbia University Irving Medical Center, New York, NY, USA.
FAU - Watkins, Paul B
AU  - Watkins PB
AUID- ORCID: 0000-0001-9541-8917
AD  - Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of 
      Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
FAU - Williams, Mark D
AU  - Williams MD
AUID- ORCID: 0000-0001-8020-916X
AD  - Division of Preclinical Innovation, National Center for Advancing Translational 
      Sciences, National Institutes of Health, Rockville, MD, USA.
FAU - Wu, Chunlei
AU  - Wu C
AUID- ORCID: 0000-0002-2629-6124
AD  - The Scripps Research Institute, La Jolla, CA, USA.
FAU - Xu, Colleen H
AU  - Xu CH
AUID- ORCID: 0000-0003-2975-882X
AD  - The Scripps Research Institute, La Jolla, CA, USA.
CN  - Biomedical Data Translator Consortium
LA  - eng
PT  - Journal Article
DEP - 20230914
PL  - England
TA  - J Clin Transl Sci
JT  - Journal of clinical and translational science
JID - 101689953
PMC - PMC10603356
OTO - NOTNLM
OT  - Translational research
OT  - bioinformatics
OT  - knowledge graphs
OT  - semantic technology
OT  - team science
COIS- JF receives additional funding from the Rady Children’s Institute for Genomic 
      Medicine, and her spouse is Founder and Principal of Friedman Bioventure. JH 
      receives grant/contract support (paid to institution) from: Pfizer; Novartis; 
      Janssen; BMS; and Gilead. PJM receives grant/research support from: AbbVie; 
      Amgen; Bristol Myers Squibb; Eli Lilly; Galapagos; Gilead; Janssen; Novartis; 
      Pfizer; Sun Pharma; and UCB. PJM also serves as a consultant at: AbbVie; 
      Acelyrin; Aclaris; Amgen; Boehringer Ingelheim; Bristol Myers Squibb; Eli Lilly; 
      Galapagos; Gilead; GlaxoSmithKline; Inmagene; Janssen; Pfizer; Moonlake Pharma; 
      Novartis; Sun Pharma; and UCB. In addition, PJM receives speakers’ bureau fees 
      from: AbbVie; Amgen; Eli Lilly; Janssen; Novartis; Pfizer; and Union Chimique 
      Belge. SHS is supported by the National Institute on Aging, Intramural Research 
      Program. All other primary authors have no conflicts of interest to declare.
EDAT- 2023/10/30 06:47
MHDA- 2023/10/30 06:48
CRDT- 2023/10/30 04:34
PHST- 2023/07/10 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/10/30 06:48 [medline]
PHST- 2023/10/30 06:47 [pubmed]
PHST- 2023/10/30 04:34 [entrez]
AID - S2059866123006192 [pii]
AID - 10.1017/cts.2023.619 [doi]
PST - epublish
SO  - J Clin Transl Sci. 2023 Sep 14;7(1):e214. doi: 10.1017/cts.2023.619. eCollection 
      2023.

PMID- 37007334
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230404
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - Cochlear Implant After Sudden Onset Sensorineural Hearing Loss: A Case Report.
PG  - e35559
LID - 10.7759/cureus.35559 [doi]
LID - e35559
AB  - We present an uncommon case of a pediatric patient with sudden-onset 
      sensorineural hearing loss (SSNHL), a medical condition in which a person 
      experiences a rapid loss of 30 or more decibels within a matter of hours or days. 
      The patient is a nine-year-old female who, two years prior, suddenly lost hearing 
      in her left ear after a 24-hour episode of nausea, vomiting, and left ear pain. 
      She presented to our clinic two years after the episode, long after the window 
      for evidence-based treatment for acute SSNHL, such as corticosteroid therapy or 
      antivirals, had passed. However, she remembered the moment of her hearing loss 
      vividly, an uncommon occurrence in pediatric patients. CT, MRI, family history, 
      and physical exam were unremarkable. The patient had a brief hearing aid trial 
      where she described being able to hear the sound but did not have any clarity in 
      understanding the sound. The patient was ultimately treated with a unilateral 
      cochlear implant and showed excellent subjective and audiogram responses. 
      Continued research on the management of SSNHL in pediatric patients who present 
      outside of the acute therapeutic window is needed.
CI  - Copyright © 2023, Curtis et al.
FAU - Curtis, Deven
AU  - Curtis D
AD  - Otolaryngology, Northeast Ohio Medical University, Rootstown, USA.
FAU - Shaw, Avi
AU  - Shaw A
AD  - Otolaryngology - Head and Neck Surgery, Mercy Health, Youngstown, USA.
FAU - Malik, Noorullah Z
AU  - Malik NZ
AD  - Biomedical Sciences, The University of Akron, Akron, USA.
FAU - Baumann, Anthony
AU  - Baumann A
AD  - Rehabilitation Services, University Hospitals Cleveland Medical Center, 
      Cleveland, USA.
FAU - Jeyakumar, Anita
AU  - Jeyakumar A
AD  - Otolaryngology - Head and Neck Surgery, Mercy Health, Youngstown, USA.
LA  - eng
PT  - Case Reports
DEP - 20230227
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10060074
OTO - NOTNLM
OT  - chat gpt
OT  - chatgpt
OT  - cochlear implant
OT  - otolaryngology
OT  - pediatric hearing loss
OT  - sudden hearing loss
OT  - sudden onset hearing loss
OT  - sudden sensorineural hearing loss
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/04 06:00
MHDA- 2023/04/04 06:01
CRDT- 2023/04/03 03:58
PHST- 2023/02/27 00:00 [accepted]
PHST- 2023/04/04 06:01 [medline]
PHST- 2023/04/03 03:58 [entrez]
PHST- 2023/04/04 06:00 [pubmed]
AID - 10.7759/cureus.35559 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 27;15(2):e35559. doi: 10.7759/cureus.35559. eCollection 2023 
      Feb.

PMID- 38091290
OWN - NLM
STAT- In-Process
LR  - 20231213
IS  - 1091-6490 (Electronic)
IS  - 0027-8424 (Linking)
VI  - 120
IP  - 51
DP  - 2023 Dec 19
TI  - Systematic testing of three Language Models reveals low language accuracy, 
      absence of response stability, and a yes-response bias.
PG  - e2309583120
LID - 10.1073/pnas.2309583120 [doi]
AB  - Humans are universally good in providing stable and accurate judgments about what 
      forms part of their language and what not. Large Language Models (LMs) are 
      claimed to possess human-like language abilities; hence, they are expected to 
      emulate this behavior by providing both stable and accurate answers, when asked 
      whether a string of words complies with or deviates from their next-word 
      predictions. This work tests whether stability and accuracy are showcased by 
      GPT-3/text-davinci-002, GPT-3/text-davinci-003, and ChatGPT, using a series of 
      judgment tasks that tap on 8 linguistic phenomena: plural attraction, anaphora, 
      center embedding, comparatives, intrusive resumption, negative polarity items, 
      order of adjectives, and order of adverbs. For every phenomenon, 10 sentences (5 
      grammatical and 5 ungrammatical) are tested, each randomly repeated 10 times, 
      totaling 800 elicited judgments per LM (total n = 2,400). Our results reveal 
      variable above-chance accuracy in the grammatical condition, below-chance 
      accuracy in the ungrammatical condition, a significant instability of answers 
      across phenomena, and a yes-response bias for all the tested LMs. Furthermore, we 
      found no evidence that repetition aids the Models to converge on a processing 
      strategy that culminates in stable answers, either accurate or inaccurate. We 
      demonstrate that the LMs' performance in identifying (un)grammatical word 
      patterns is in stark contrast to what is observed in humans (n = 80, tested on 
      the same tasks) and argue that adopting LMs as theories of human language is not 
      motivated at their current stage of development.
FAU - Dentella, Vittoria
AU  - Dentella V
AUID- ORCID: 0000-0001-6697-9184
AD  - Departament d'Estudis Anglesos i Alemanys, Universitat Rovira i Virgili, 
      Tarragona 43002, Spain.
FAU - Günther, Fritz
AU  - Günther F
AUID- ORCID: 0000-0002-9205-6786
AD  - Institut für Psychologie, Humboldt-Universitat zu Berlin, Berlin 10099, Germany.
FAU - Leivada, Evelina
AU  - Leivada E
AUID- ORCID: 0000-0003-3181-1917
AD  - Departament de Filologia Catalana, Universitat Autònoma de Barcelona, Barcelona 
      08193, Spain.
AD  - Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, 
      Spain.
LA  - eng
GR  - 945413/Horizon Europe 2020 | Marie Sklodowska-Curie Actions/
GR  - 945413/Universitat Rovira i Virgili (Rovira i Virgili University)/
GR  - 459717703/Deutsche Forschungsgemeinschaft (DFG)/
GR  - PID2021-124399NA-I00/Spanish Ministry of Science and Innovation/
PT  - Journal Article
DEP - 20231213
PL  - United States
TA  - Proc Natl Acad Sci U S A
JT  - Proceedings of the National Academy of Sciences of the United States of America
JID - 7505876
SB  - IM
OTO - NOTNLM
OT  - Language Models
OT  - bias
OT  - cognitive models
OT  - language
COIS- Competing interests statement:The authors declare no competing interest.
EDAT- 2023/12/13 18:42
MHDA- 2023/12/13 18:42
CRDT- 2023/12/13 13:38
PHST- 2023/12/13 18:42 [medline]
PHST- 2023/12/13 18:42 [pubmed]
PHST- 2023/12/13 13:38 [entrez]
AID - 10.1073/pnas.2309583120 [doi]
PST - ppublish
SO  - Proc Natl Acad Sci U S A. 2023 Dec 19;120(51):e2309583120. doi: 
      10.1073/pnas.2309583120. Epub 2023 Dec 13.

PMID- 37355478
OWN - NLM
STAT- MEDLINE
DCOM- 20230809
LR  - 20230809
IS  - 1573-9686 (Electronic)
IS  - 0090-6964 (Linking)
VI  - 51
IP  - 9
DP  - 2023 Sep
TI  - GPT Technology to Help Address Longstanding Barriers to Care in Free Medical 
      Clinics.
PG  - 1906-1909
LID - 10.1007/s10439-023-03256-4 [doi]
AB  - The implementation of technology in healthcare has revolutionized 
      patient-centered decision making by providing contextualized information about a 
      patient's healthcare journey, leading to increased efficiency (Keyworth et al. in 
      BMC Med Inform Decis Mak 18:93, 2018, https://doi.org/10.1186/s12911-018-0661-3 
      ). Artificial intelligence has been integrated within Electronic Health Records 
      (EHR) to prompt screenings or diagnostic tests based on a patient's holistic 
      health profile. While larger hospitals have already widely adopted these 
      technologies, free clinics hold lower utilization of these advanced capability 
      EHRs. The patient population at a free clinic faces a multitude of factors that 
      limits their access to comprehensive care, thus requiring necessary efforts and 
      measures to close the gap in healthcare disparities. Emerging Artificial 
      Intelligence (AI) technology, such as OpenAI's ChatGPT, GPT-4, and other large 
      language models (LLMs) have remarkable potential to improve patient care 
      outcomes, promote health equity, and enhance comprehensive and holistic care in 
      resource-limited settings. This paper aims to identify areas in which integrating 
      these LLM AI advancements into free clinics operations can optimize and 
      streamline healthcare delivery to underserved patient populations. This paper 
      also identifies areas of improvements in GPT that are necessary to deliver those 
      services.
CI  - © 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
FAU - Ong, Hannah
AU  - Ong H
AUID- ORCID: 0000-0002-6041-0322
AD  - College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. 
      Hannah.ong@osumc.edu.
FAU - Ong, Joshua
AU  - Ong J
AD  - Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
FAU - Cheng, Rebekah
AU  - Cheng R
AD  - Department of Physical Therapy, Virginia Commonwealth University, Richmond, VA, 
      USA.
FAU - Wang, Calvin
AU  - Wang C
AD  - College of Medicine - Robert Wood Johnson, Rutgers University, New Brunswick, NJ, 
      USA.
FAU - Lin, Murong
AU  - Lin M
AD  - Distinguished Engineer, Verizon, San Jose, CA, USA.
FAU - Ong, Dennis
AU  - Ong D
AD  - Amazon Web Services, Amazon, Seattle, WA, USA.
LA  - eng
PT  - Letter
DEP - 20230624
PL  - United States
TA  - Ann Biomed Eng
JT  - Annals of biomedical engineering
JID - 0361512
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - *Health Promotion
MH  - Hospitals
MH  - Technology
MH  - Health Services Accessibility
OTO - NOTNLM
OT  - Barriers to care
OT  - Electronic Health Records
OT  - Free medical clinic
OT  - GPT
EDAT- 2023/06/25 01:08
MHDA- 2023/08/09 06:43
CRDT- 2023/06/24 23:03
PHST- 2023/05/23 00:00 [received]
PHST- 2023/05/25 00:00 [accepted]
PHST- 2023/08/09 06:43 [medline]
PHST- 2023/06/25 01:08 [pubmed]
PHST- 2023/06/24 23:03 [entrez]
AID - 10.1007/s10439-023-03256-4 [pii]
AID - 10.1007/s10439-023-03256-4 [doi]
PST - ppublish
SO  - Ann Biomed Eng. 2023 Sep;51(9):1906-1909. doi: 10.1007/s10439-023-03256-4. Epub 
      2023 Jun 24.

PMID- 37187652
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230517
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Discordance of the Urinary and Pleural Fluid Antigen Test and False Positive for 
      Streptococcus pneumoniae in Empyema Secondary to Necrotizing Bacterial Pneumonia.
PG  - e37458
LID - 10.7759/cureus.37458 [doi]
LID - e37458
AB  - Empyema is a severe complication of pneumonia with high morbidity and mortality 
      rates. Rapid diagnosis and tailoring of antibiotic therapy are crucial to 
      treatment success for these severe bacterial lung infections. A Streptococcus 
      pneumoniae (S. pneumonia) antigen test drawn from the pleural fluid rather than a 
      urine sample has been found to have equivalent diagnostic utility to the urinary 
      antigen test. Discordance between these tests is rare. We report a case of a 
      69-year-old female with CT imaging findings consistent with empyema and a 
      bronchopulmonary fistula. A rapid S. pneumonia antigen test was negative from the 
      urinary sample but positive when drawn from a patient's pleural fluid sample. 
      Final pleural fluid cultures resulted in Streptococcus constellatus 
      (S. constellatus). This case demonstrates discordance between the results of 
      urinary and pleural fluid S. pneumoniae antigen tests, representing a potential 
      pitfall in using rapid antigen testing on pleural fluid samples. False positives 
      for the S. pneumoniae antigen in patients with viridans streptococci infections 
      have been documented due to the cross-reactivity of cell wall proteins in 
      different streptococcal species. Physicians encountering bacterial pneumonia of 
      unknown etiology complicated by empyema should understand the potential for 
      discordance and false positives using this diagnostic method.
CI  - Copyright © 2023, Shumway et al.
FAU - Shumway, David O
AU  - Shumway DO
AD  - Internal Medicine, Keesler Medical Center, Biloxi, USA.
FAU - Kriege, Kevin
AU  - Kriege K
AD  - Internal Medicine, Keesler Medical Center, Biloxi, USA.
FAU - Wood, Stuart T
AU  - Wood ST
AD  - Infectious Disease, Keesler Medical Center, Biloxi, USA.
LA  - eng
PT  - Case Reports
DEP - 20230411
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10175080
OTO - NOTNLM
OT  - bronchopulmonary fistula
OT  - chatgpt
OT  - empyema
OT  - pleural effusion
OT  - pneumococcal pneumonia
OT  - rapid antigen detection test
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/16 01:09
MHDA- 2023/05/16 01:10
CRDT- 2023/05/15 19:29
PHST- 2023/04/01 00:00 [accepted]
PHST- 2023/05/16 01:10 [medline]
PHST- 2023/05/16 01:09 [pubmed]
PHST- 2023/05/15 19:29 [entrez]
AID - 10.7759/cureus.37458 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 11;15(4):e37458. doi: 10.7759/cureus.37458. eCollection 2023 
      Apr.

PMID- 38018708
OWN - NLM
STAT- MEDLINE
DCOM- 20231201
LR  - 20231201
IS  - 2245-1919 (Electronic)
IS  - 2245-1919 (Linking)
VI  - 70
IP  - 12
DP  - 2023 Nov 23
TI  - A comparison of cover letters written by ChatGPT-4 or humans.
LID - A06230412 [pii]
AB  - INTRODUCTION: Artificial intelligence has started to become a part of scientific 
      studies and may help researchers with a wide range of tasks. However, no 
      scientific studies have been published on its ussefulness in writing cover 
      letters for scientific articles. This study aimed to determine whether Generative 
      Pre-Trained Transformer (GPT)-4 is as good as humans in writing cover letters for 
      scientific papers. METHODS: In this randomised non-inferiority study, we included 
      two parallel arms consisting of cover letters written by humans and by GPT-4. 
      Each arm had 18 cover letters, which were assessed by three different blinded 
      assessors. The assessors completed a questionnaire in which they had to assess 
      the cover letters with respect to impression, readability, criteria satisfaction, 
      and degree of detail. Subsequently, we performed readability tests with Lix score 
      and Flesch Kincaid grade level. RESULTS: No significant or relevant difference 
      was found on any parameter. A total of 61% of the blinded assessors guessed 
      correctly as to whether the cover letter was written by GPT-4 or a human. GPT-4 
      had a higher score according to our objective readability tests. Nevertheless, it 
      performed better than human writing on readability in the subjective assessments. 
      CONCLUSION: We found that GPT-4 was non-inferior at writing cover letters 
      compared to humans. This may be used to streamline cover letters for researchers, 
      providing an equal chance to all researchers for advancement to peer-review. 
      FUNDING: This study received no financial support from external sources. TRIAL 
      REGISTRATION: This study was not registered before the study commenced.
CI  - Published under Open Access CC-BY-NC-BD 4.0. 
      https://creativecommons.org/licenses/by-nc-nd/4.0/.
FAU - Deveci, Can Deniz
AU  - Deveci CD
AD  - Center for Perioperative Optimization, Department of Surgery, Copenhagen 
      University Hospital - Herlev Hospital, Denmark.
FAU - Baker, Jason Joe
AU  - Baker JJ
AD  - Center for Perioperative Optimization, Department of Surgery, Copenhagen 
      University Hospital - Herlev Hospital, Denmark.
FAU - Sikander, Binyamin
AU  - Sikander B
AD  - Center for Perioperative Optimization, Department of Surgery, Copenhagen 
      University Hospital - Herlev Hospital, Denmark.
FAU - Rosenberg, Jacob
AU  - Rosenberg J
AD  - Center for Perioperative Optimization, Department of Surgery, Copenhagen 
      University Hospital - Herlev Hospital, Denmark.
LA  - eng
PT  - Journal Article
PT  - Randomized Controlled Trial
DEP - 20231123
PL  - Denmark
TA  - Dan Med J
JT  - Danish medical journal
JID - 101576205
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Comprehension
MH  - *Writing
EDAT- 2023/11/29 12:43
MHDA- 2023/11/30 06:43
CRDT- 2023/11/29 07:03
PHST- 2023/11/30 06:43 [medline]
PHST- 2023/11/29 12:43 [pubmed]
PHST- 2023/11/29 07:03 [entrez]
AID - A06230412 [pii]
PST - epublish
SO  - Dan Med J. 2023 Nov 23;70(12):A06230412.

PMID- 37738186
OWN - NLM
STAT- MEDLINE
DCOM- 20231206
LR  - 20231206
IS  - 2168-2208 (Electronic)
IS  - 2168-2194 (Linking)
VI  - 27
IP  - 12
DP  - 2023 Dec
TI  - Large AI Models in Health Informatics: Applications, Challenges, and the Future.
PG  - 6074-6087
LID - 10.1109/JBHI.2023.3316750 [doi]
AB  - Large AI models, or foundation models, are models recently emerging with massive 
      scales both parameter-wise and data-wise, the magnitudes of which can reach 
      beyond billions. Once pretrained, large AI models demonstrate impressive 
      performance in various downstream tasks. A prime example is ChatGPT, whose 
      capability has compelled people's imagination about the far-reaching influence 
      that large AI models can have and their potential to transform different domains 
      of our lives. In health informatics, the advent of large AI models has brought 
      new paradigms for the design of methodologies. The scale of multi-modal data in 
      the biomedical and health domain has been ever-expanding especially since the 
      community embraced the era of deep learning, which provides the ground to 
      develop, validate, and advance large AI models for breakthroughs in 
      health-related areas. This article presents a comprehensive review of large AI 
      models, from background to their applications. We identify seven key sectors in 
      which large AI models are applicable and might have substantial influence, 
      including: 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) 
      medical informatics; 5) medical education; 6) public health; and 7) medical 
      robotics. We examine their challenges, followed by a critical discussion about 
      potential future directions and pitfalls of large AI models in transforming the 
      field of health informatics.
FAU - Qiu, Jianing
AU  - Qiu J
FAU - Li, Lin
AU  - Li L
FAU - Sun, Jiankai
AU  - Sun J
FAU - Peng, Jiachuan
AU  - Peng J
FAU - Shi, Peilun
AU  - Shi P
FAU - Zhang, Ruiyang
AU  - Zhang R
FAU - Dong, Yinzhao
AU  - Dong Y
FAU - Lam, Kyle
AU  - Lam K
FAU - Lo, Frank P-W
AU  - Lo FP
FAU - Xiao, Bo
AU  - Xiao B
FAU - Yuan, Wu
AU  - Yuan W
FAU - Wang, Ningli
AU  - Wang N
FAU - Xu, Dong
AU  - Xu D
FAU - Lo, Benny
AU  - Lo B
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231205
PL  - United States
TA  - IEEE J Biomed Health Inform
JT  - IEEE journal of biomedical and health informatics
JID - 101604520
SB  - IM
MH  - Humans
MH  - *Medical Informatics
MH  - Computational Biology
MH  - Imagination
MH  - Public Health
MH  - *Robotics
EDAT- 2023/09/22 18:42
MHDA- 2023/12/06 06:42
CRDT- 2023/09/22 13:03
PHST- 2023/12/06 06:42 [medline]
PHST- 2023/09/22 18:42 [pubmed]
PHST- 2023/09/22 13:03 [entrez]
AID - 10.1109/JBHI.2023.3316750 [doi]
PST - ppublish
SO  - IEEE J Biomed Health Inform. 2023 Dec;27(12):6074-6087. doi: 
      10.1109/JBHI.2023.3316750. Epub 2023 Dec 5.

PMID- 37443536
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2075-4418 (Print)
IS  - 2075-4418 (Electronic)
IS  - 2075-4418 (Linking)
VI  - 13
IP  - 13
DP  - 2023 Jun 22
TI  - Evaluating Scoliosis Severity Based on Posturographic X-ray Images Using a 
      Contrastive Language-Image Pretraining Model.
LID - 10.3390/diagnostics13132142 [doi]
LID - 2142
AB  - Assessing severe scoliosis requires the analysis of posturographic X-ray images. 
      One way to analyse these images may involve the use of open-source artificial 
      intelligence models (OSAIMs), such as the contrastive language-image pretraining 
      (CLIP) system, which was designed to combine images with text. This study aims to 
      determine whether the CLIP model can recognise visible severe scoliosis in 
      posturographic X-ray images. This study used 23 posturographic images of patients 
      diagnosed with severe scoliosis that were evaluated by two independent 
      neurosurgery specialists. Subsequently, the X-ray images were input into the CLIP 
      system, where they were subjected to a series of questions with varying levels of 
      difficulty and comprehension. The predictions obtained using the CLIP models in 
      the form of probabilities ranging from 0 to 1 were compared with the actual data. 
      To evaluate the quality of image recognition, true positives, false negatives, 
      and sensitivity were determined. The results of this study show that the CLIP 
      system can perform a basic assessment of X-ray images showing visible severe 
      scoliosis with a high level of sensitivity. It can be assumed that, in the 
      future, OSAIMs dedicated to image analysis may become commonly used to assess 
      X-ray images, including those of scoliosis.
FAU - Fabijan, Artur
AU  - Fabijan A
AUID- ORCID: 0000-0002-0998-4435
AD  - Department of Neurosurgery, Polish-Mother's Memorial Hospital Research Institute, 
      93-338 Lodz, Poland.
FAU - Fabijan, Robert
AU  - Fabijan R
AD  - Independent Researcher, Luton, LU2 0GS, UK.
FAU - Zawadzka-Fabijan, Agnieszka
AU  - Zawadzka-Fabijan A
AUID- ORCID: 0000-0002-4031-0930
AD  - Department of Rehabilitation Medicine, Medical University of Lodz, 90-419 Lodz, 
      Poland.
FAU - Nowosławska, Emilia
AU  - Nowosławska E
AD  - Department of Neurosurgery, Polish-Mother's Memorial Hospital Research Institute, 
      93-338 Lodz, Poland.
FAU - Zakrzewski, Krzysztof
AU  - Zakrzewski K
AD  - Department of Neurosurgery, Polish-Mother's Memorial Hospital Research Institute, 
      93-338 Lodz, Poland.
FAU - Polis, Bartosz
AU  - Polis B
AD  - Department of Neurosurgery, Polish-Mother's Memorial Hospital Research Institute, 
      93-338 Lodz, Poland.
LA  - eng
PT  - Journal Article
DEP - 20230622
PL  - Switzerland
TA  - Diagnostics (Basel)
JT  - Diagnostics (Basel, Switzerland)
JID - 101658402
PMC - PMC10340165
OTO - NOTNLM
OT  - CLIP
OT  - ChatGPT
OT  - OpenAI
OT  - artificial intelligence
OT  - contrastive language–image pretraining
OT  - open-source artificial intelligence models
OT  - scoliosis
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/14 13:05
MHDA- 2023/07/14 13:06
CRDT- 2023/07/14 01:01
PHST- 2023/05/11 00:00 [received]
PHST- 2023/06/10 00:00 [revised]
PHST- 2023/06/20 00:00 [accepted]
PHST- 2023/07/14 13:06 [medline]
PHST- 2023/07/14 13:05 [pubmed]
PHST- 2023/07/14 01:01 [entrez]
AID - diagnostics13132142 [pii]
AID - diagnostics-13-02142 [pii]
AID - 10.3390/diagnostics13132142 [doi]
PST - epublish
SO  - Diagnostics (Basel). 2023 Jun 22;13(13):2142. doi: 10.3390/diagnostics13132142.

PMID- 37279048
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Jun 6
TI  - The Advent of Generative Language Models in Medical Education.
PG  - e48163
LID - 10.2196/48163 [doi]
LID - e48163
AB  - Artificial intelligence (AI) and generative language models (GLMs) present 
      significant opportunities for enhancing medical education, including the 
      provision of realistic simulations, digital patients, personalized feedback, 
      evaluation methods, and the elimination of language barriers. These advanced 
      technologies can facilitate immersive learning environments and enhance medical 
      students' educational outcomes. However, ensuring content quality, addressing 
      biases, and managing ethical and legal concerns present obstacles. To mitigate 
      these challenges, it is necessary to evaluate the accuracy and relevance of 
      AI-generated content, address potential biases, and develop guidelines and 
      policies governing the use of AI-generated content in medical education. 
      Collaboration among educators, researchers, and practitioners is essential for 
      developing best practices, guidelines, and transparent AI models that encourage 
      the ethical and responsible use of GLMs and AI in medical education. By sharing 
      information about the data used for training, obstacles encountered, and 
      evaluation methods, developers can increase their credibility and trustworthiness 
      within the medical community. In order to realize the full potential of AI and 
      GLMs in medical education while mitigating potential risks and obstacles, ongoing 
      research and interdisciplinary collaboration are necessary. By collaborating, 
      medical professionals can ensure that these technologies are effectively and 
      responsibly integrated, contributing to enhanced learning experiences and patient 
      care.
CI  - ©Mert Karabacak, Burak Berksu Ozkara, Konstantinos Margetis, Max Wintermark, 
      Sotirios Bisdas. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 06.06.2023.
FAU - Karabacak, Mert
AU  - Karabacak M
AUID- ORCID: 0000-0002-9263-9893
AD  - Department of Neurosurgery, Mount Sinai Health System, New York, NY, United 
      States.
FAU - Ozkara, Burak Berksu
AU  - Ozkara BB
AUID- ORCID: 0000-0002-8769-3342
AD  - Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, United 
      States.
FAU - Margetis, Konstantinos
AU  - Margetis K
AUID- ORCID: 0000-0002-3715-8093
AD  - Department of Neurosurgery, Mount Sinai Health System, New York, NY, United 
      States.
FAU - Wintermark, Max
AU  - Wintermark M
AUID- ORCID: 0000-0002-6726-3951
AD  - Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, United 
      States.
FAU - Bisdas, Sotirios
AU  - Bisdas S
AUID- ORCID: 0000-0001-9930-5549
AD  - Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, United 
      States.
AD  - Department of Neuroradiology, The National Hospital for Neurology and 
      Neurosurgery, University College London NHS Foundation Trust, London, United 
      Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230606
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10282912
OTO - NOTNLM
OT  - AI-driven feedback
OT  - ChatGPT
OT  - academic integrity
OT  - artificial intelligence
OT  - evaluation
OT  - generative language model
OT  - learning environment
OT  - medical education
OT  - medical student
OT  - stimulation
OT  - technology
COIS- Conflicts of Interest: None declared.
EDAT- 2023/06/06 13:09
MHDA- 2023/06/06 13:10
CRDT- 2023/06/06 11:53
PHST- 2023/04/13 00:00 [received]
PHST- 2023/05/24 00:00 [accepted]
PHST- 2023/05/22 00:00 [revised]
PHST- 2023/06/06 13:10 [medline]
PHST- 2023/06/06 13:09 [pubmed]
PHST- 2023/06/06 11:53 [entrez]
AID - v9i1e48163 [pii]
AID - 10.2196/48163 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Jun 6;9:e48163. doi: 10.2196/48163.

PMID- 37131884
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2331-8422 (Electronic)
IS  - 2331-8422 (Linking)
DP  - 2023 May 16
TI  - GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access 
      to Biomedical Information.
LID - arXiv:2304.09667v3
AB  - While large language models (LLMs) have been successfully applied to various 
      tasks, they still face challenges with hallucinations. Augmenting LLMs with 
      domain-specific tools such as database utilities can facilitate easier and more 
      precise access to specialized knowledge. In this paper, we present GeneGPT, a 
      novel method for teaching LLMs to use the Web APIs of the National Center for 
      Biotechnology Information (NCBI) for answering genomics questions. Specifically, 
      we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context 
      learning and an augmented decoding algorithm that can detect and execute API 
      calls. Experimental results show that GeneGPT achieves state-of-the-art 
      performance on eight tasks in the GeneTuring benchmark with an average score of 
      0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), 
      biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 
      (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1) API 
      demonstrations have good cross-task generalizability and are more useful than 
      documentations for in-context learning; (2) GeneGPT can generalize to longer 
      chains of API calls and answer multi-hop questions in GeneHop, a novel dataset 
      introduced in this work; (3) Different types of errors are enriched in different 
      tasks, providing valuable insights for future improvements.
FAU - Jin, Qiao
AU  - Jin Q
AD  - National Library of Medicine, National Institutes of Health.
FAU - Yang, Yifan
AU  - Yang Y
AD  - National Library of Medicine, National Institutes of Health.
AD  - University of Maryland, College Park.
FAU - Chen, Qingyu
AU  - Chen Q
AD  - National Library of Medicine, National Institutes of Health.
FAU - Lu, Zhiyong
AU  - Lu Z
AD  - National Library of Medicine, National Institutes of Health.
LA  - eng
PT  - Preprint
DEP - 20230516
PL  - United States
TA  - ArXiv
JT  - ArXiv
JID - 101759493
PMC - PMC10153281
EDAT- 2023/05/03 06:42
MHDA- 2023/05/03 06:43
CRDT- 2023/05/03 02:00
PHST- 2023/05/03 06:42 [pubmed]
PHST- 2023/05/03 06:43 [medline]
PHST- 2023/05/03 02:00 [entrez]
AID - 2304.09667 [pii]
PST - epublish
SO  - ArXiv. 2023 May 16:arXiv:2304.09667v3. Preprint.

PMID- 37713022
OWN - NLM
STAT- Publisher
LR  - 20230915
IS  - 1867-108X (Electronic)
IS  - 1867-1071 (Linking)
DP  - 2023 Sep 15
TI  - Preliminary assessment of automated radiology report generation with generative 
      pre-trained transformers: comparing results to radiologist-generated reports.
LID - 10.1007/s11604-023-01487-y [doi]
AB  - PURPOSE: In this preliminary study, we aimed to evaluate the potential of the 
      generative pre-trained transformer (GPT) series for generating radiology reports 
      from concise imaging findings and compare its performance with 
      radiologist-generated reports. METHODS: This retrospective study involved 28 
      patients who underwent computed tomography (CT) scans and had a diagnosed disease 
      with typical imaging findings. Radiology reports were generated using GPT-2, 
      GPT-3.5, and GPT-4 based on the patient's age, gender, disease site, and imaging 
      findings. We calculated the top-1, top-5 accuracy, and mean average precision 
      (MAP) of differential diagnoses for GPT-2, GPT-3.5, GPT-4, and radiologists. Two 
      board-certified radiologists evaluated the grammar and readability, image 
      findings, impression, differential diagnosis, and overall quality of all reports 
      using a 4-point scale. RESULTS: Top-1 and Top-5 accuracies for the different 
      diagnoses were highest for radiologists, followed by GPT-4, GPT-3.5, and GPT-2, 
      in that order (Top-1: 1.00, 0.54, 0.54, and 0.21, respectively; Top-5: 1.00, 
      0.96, 0.89, and 0.54, respectively). There were no significant differences in 
      qualitative scores about grammar and readability, image findings, and overall 
      quality between radiologists and GPT-3.5 or GPT-4 (p > 0.05). However, 
      qualitative scores of the GPT series in impression and differential diagnosis 
      scores were significantly lower than those of radiologists (p < 0.05). 
      CONCLUSIONS: Our preliminary study suggests that GPT-3.5 and GPT-4 have the 
      possibility to generate radiology reports with high readability and reasonable 
      image findings from very short keywords; however, concerns persist regarding the 
      accuracy of impressions and differential diagnoses, thereby requiring 
      verification by radiologists.
CI  - © 2023. The Author(s).
FAU - Nakaura, Takeshi
AU  - Nakaura T
AUID- ORCID: 0000-0002-9010-0341
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan. 
      kff00712@nifty.com.
FAU - Yoshida, Naofumi
AU  - Yoshida N
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
FAU - Kobayashi, Naoki
AU  - Kobayashi N
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
FAU - Shiraishi, Kaori
AU  - Shiraishi K
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
FAU - Nagayama, Yasunori
AU  - Nagayama Y
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
FAU - Uetani, Hiroyuki
AU  - Uetani H
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
FAU - Kidoh, Masafumi
AU  - Kidoh M
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
FAU - Hokamura, Masamichi
AU  - Hokamura M
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
FAU - Funama, Yoshinori
AU  - Funama Y
AD  - Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, 
      Honjo 1-1-1, Kumamoto, 860-8556, Japan.
FAU - Hirai, Toshinori
AU  - Hirai T
AD  - Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto 
      University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - Japan
TA  - Jpn J Radiol
JT  - Japanese journal of radiology
JID - 101490689
SB  - IM
OTO - NOTNLM
OT  - Computed tomography
OT  - Deep learning
OT  - Generative pre-trained transformer
OT  - Large language model
OT  - Radiology report
EDAT- 2023/09/15 12:43
MHDA- 2023/09/15 12:43
CRDT- 2023/09/15 11:09
PHST- 2023/07/07 00:00 [received]
PHST- 2023/08/29 00:00 [accepted]
PHST- 2023/09/15 12:43 [medline]
PHST- 2023/09/15 12:43 [pubmed]
PHST- 2023/09/15 11:09 [entrez]
AID - 10.1007/s11604-023-01487-y [pii]
AID - 10.1007/s11604-023-01487-y [doi]
PST - aheadofprint
SO  - Jpn J Radiol. 2023 Sep 15. doi: 10.1007/s11604-023-01487-y.

PMID- 37899961
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - A review of the explainability and safety of conversational agents for mental 
      health to identify avenues for improvement.
PG  - 1229805
LID - 10.3389/frai.2023.1229805 [doi]
LID - 1229805
AB  - Virtual Mental Health Assistants (VMHAs) continuously evolve to support the 
      overloaded global healthcare system, which receives approximately 60 million 
      primary care visits and 6 million emergency room visits annually. These systems, 
      developed by clinical psychologists, psychiatrists, and AI researchers, are 
      designed to aid in Cognitive Behavioral Therapy (CBT). The main focus of VMHAs is 
      to provide relevant information to mental health professionals (MHPs) and engage 
      in meaningful conversations to support individuals with mental health conditions. 
      However, certain gaps prevent VMHAs from fully delivering on their promise during 
      active communications. One of the gaps is their inability to explain their 
      decisions to patients and MHPs, making conversations less trustworthy. 
      Additionally, VMHAs can be vulnerable in providing unsafe responses to patient 
      queries, further undermining their reliability. In this review, we assess the 
      current state of VMHAs on the grounds of user-level explainability and safety, a 
      set of desired properties for the broader adoption of VMHAs. This includes the 
      examination of ChatGPT, a conversation agent developed on AI-driven models: 
      GPT3.5 and GPT-4, that has been proposed for use in providing mental health 
      services. By harnessing the collaborative and impactful contributions of AI, 
      natural language processing, and the mental health professionals (MHPs) 
      community, the review identifies opportunities for technological progress in 
      VMHAs to ensure their capabilities include explainable and safe behaviors. It 
      also emphasizes the importance of measures to guarantee that these advancements 
      align with the promise of fostering trustworthy conversations.
CI  - Copyright © 2023 Sarkar, Gaur, Chen, Garg and Srivastava.
FAU - Sarkar, Surjodeep
AU  - Sarkar S
AD  - Department of Computer Science and Electrical Engineering, University of 
      Maryland, Baltimore County, Baltimore, MD, United States.
FAU - Gaur, Manas
AU  - Gaur M
AD  - Department of Computer Science and Electrical Engineering, University of 
      Maryland, Baltimore County, Baltimore, MD, United States.
FAU - Chen, Lujie Karen
AU  - Chen LK
AD  - Department of Information Systems, University of Maryland, Baltimore County, 
      Baltimore, MD, United States.
FAU - Garg, Muskan
AU  - Garg M
AD  - Department of AI & Informatics, Mayo Clinic, Rochester, MN, United States.
FAU - Srivastava, Biplav
AU  - Srivastava B
AD  - AI Institute, University of South Carolina, Columbia, SC, United States.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231012
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10601652
OTO - NOTNLM
OT  - conversational AI
OT  - evaluation metrics
OT  - explainable AI
OT  - knowledge-infused learning
OT  - mental health
OT  - safety
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/10/30 06:47
MHDA- 2023/10/30 06:48
CRDT- 2023/10/30 04:27
PHST- 2023/05/27 00:00 [received]
PHST- 2023/08/29 00:00 [accepted]
PHST- 2023/10/30 06:48 [medline]
PHST- 2023/10/30 06:47 [pubmed]
PHST- 2023/10/30 04:27 [entrez]
AID - 10.3389/frai.2023.1229805 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Oct 12;6:1229805. doi: 10.3389/frai.2023.1229805. 
      eCollection 2023.

PMID- 37090406
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230425
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Breaking Bias: The Role of Artificial Intelligence in Improving Clinical 
      Decision-Making.
PG  - e36415
LID - 10.7759/cureus.36415 [doi]
LID - e36415
AB  - This case report reflects on a delayed diagnosis for a 27-year-old woman who 
      reported chest pain and shortness of breath to the emergency department. The 
      treating clinician reflects upon how cognitive biases influenced their diagnostic 
      process and how multiple missed opportunities resulted in missteps. Using 
      artificial intelligence (AI) tools for clinical decision-making, we suggest how 
      AI could augment the clinician, and in this case, delayed diagnosis avoided. 
      Incorporating AI tools into clinical decision-making brings potential benefits, 
      including improved diagnostic accuracy and addressing human factors contributing 
      to medical errors. For example, they may support a real-time interpretation of 
      medical imaging and assist clinicians in generating a differential diagnosis in 
      ensuring that critical diagnoses are considered. However, it is vital to be aware 
      of the potential pitfalls associated with the use of AI, such as automation bias, 
      input data quality issues, limited clinician training in interpreting AI methods, 
      and the legal and ethical considerations associated with their use. The report 
      draws attention to the utility of AI clinical decision-support tools in 
      overcoming human cognitive biases. It also emphasizes the importance of 
      clinicians developing skills needed to steward the adoption of AI tools in 
      healthcare and serve as patient advocates, ensuring safe and effective use of 
      health data.
CI  - Copyright © 2023, Brown et al.
FAU - Brown, Chris
AU  - Brown C
AD  - Internal Medicine, Jersey General Hospital, St Helier, JEY.
FAU - Nazeer, Rayiz
AU  - Nazeer R
AD  - Internal Medicine, Jersey General Hospital, St Helier, JEY.
FAU - Gibbs, Austin
AU  - Gibbs A
AD  - Cardiology, Jersey General Hospital, St Helier, JEY.
FAU - Le Page, Pierre
AU  - Le Page P
AD  - Cardiology, Jersey General Hospital, St Helier, JEY.
FAU - Mitchell, Andrew Rj
AU  - Mitchell AR
AD  - Cardiology, Jersey General Hospital, St Helier, JEY.
LA  - eng
PT  - Case Reports
DEP - 20230320
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10115193
OTO - NOTNLM
OT  - chatgpt
OT  - clinical artificial intelligence
OT  - cognitive bias
OT  - human factors
OT  - medical errors
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/24 06:42
MHDA- 2023/04/24 06:43
CRDT- 2023/04/24 03:40
PHST- 2023/03/20 00:00 [accepted]
PHST- 2023/04/24 06:43 [medline]
PHST- 2023/04/24 06:42 [pubmed]
PHST- 2023/04/24 03:40 [entrez]
AID - 10.7759/cureus.36415 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 20;15(3):e36415. doi: 10.7759/cureus.36415. eCollection 2023 
      Mar.

PMID- 37073214
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230421
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Uncovering the Unusual: A Case of Mixed Connective Tissue Disease With Rare 
      Presentation, Atypical Complications, and Therapeutic Dilemmas.
PG  - e36298
LID - 10.7759/cureus.36298 [doi]
LID - e36298
AB  - Mixed connective tissue disease (MCTD) is an overlap syndrome characterized by 
      features of systemic lupus erythematosus, scleroderma, and polymyositis, along 
      with the presence of the U1RNP antibody. A 46-year-old female patient presented 
      with severe anemia, cough, and breathlessness, and was diagnosed with cold 
      agglutinin disease, a type of autoimmune hemolytic anemia (AIHA). Autoimmune 
      workup revealed MCTD by positive antinuclear and U1RNP antibodies. She had 
      bilateral miliary mottling on X-ray and a tree-in-bud appearance on 
      high-resolution computed tomography of the thorax, which were suggestive of 
      pulmonary tuberculosis. Standard therapy with steroids was not advisable. She was 
      subsequently started on anti-tuberculosis treatment (anti-Koch's therapy), 
      followed by steroid therapy and immunosuppressive therapy after three weeks. The 
      patient responded well to treatment, but after two months, she developed 
      cytomegalovirus (CMV) retinitis. Adult-onset CMV disease may occur as a result of 
      primary infection, reinfection, or activation of a latent infection. Although not 
      directly related, it can occur as an atypical association in the setting of 
      immunosuppressive therapy. Morbidity and mortality are significantly increased in 
      this population secondary to infectious potentiation: immunosuppression causes 
      infections, and infections cause AIHA. The management of MCTD and secondary AIHA 
      and immunosuppression poses a therapeutic challenge.
CI  - Copyright © 2023, Khot et al.
FAU - Khot, Rajashree S
AU  - Khot RS
AD  - General Medicine, All India Institute of Medical Sciences, Nagpur, IND.
FAU - Patil, Adarsh
AU  - Patil A
AD  - General Medicine, All India Institute of Medical Sciences, Nagpur, IND.
FAU - Rathod, Bharatsing D
AU  - Rathod BD
AD  - General Medicine, All India Institute of Medical Sciences, Nagpur, IND.
FAU - Patidar, Madan
AU  - Patidar M
AD  - General Medicine, All India Institute of Medical Sciences, Nagpur, IND.
FAU - Joshi, Prashant P
AU  - Joshi PP
AD  - General Medicine, All India Institute of Medical Sciences, Nagpur, IND.
LA  - eng
PT  - Case Reports
DEP - 20230317
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10106112
OTO - NOTNLM
OT  - autoimmune haemolytic anaemia
OT  - chatgpt
OT  - cytomegalovirus
OT  - cytomegalovirus retinitis
OT  - mixed connective tissue disease
OT  - pulmonary tuberculosis
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/19 06:41
MHDA- 2023/04/19 06:42
CRDT- 2023/04/19 01:44
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/04/19 06:42 [medline]
PHST- 2023/04/19 06:41 [pubmed]
PHST- 2023/04/19 01:44 [entrez]
AID - 10.7759/cureus.36298 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 17;15(3):e36298. doi: 10.7759/cureus.36298. eCollection 2023 
      Mar.

PMID- 38034697
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231202
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 11
DP  - 2023 Nov
TI  - Towards enhancing security of IoT-Enabled healthcare system.
PG  - e22336
LID - 10.1016/j.heliyon.2023.e22336 [doi]
LID - e22336
AB  - The Internet-of-Things (IoT)-based healthcare systems are comprised of a large 
      number of networked medical devices, wearables, and sensors that collect and 
      transmit data to improve patient care. However, the enormous number of networked 
      devices renders these systems vulnerable to assaults. To address these 
      challenges, researchers advocated reducing execution time, leveraging 
      cryptographic protocols to improve security and avoid assaults, and utilizing 
      energy-efficient algorithms to minimize energy consumption during computation. 
      Nonetheless, these systems still struggle with long execution times, assaults, 
      excessive energy usage, and inadequate security. We present a novel whale-based 
      attribute encryption scheme (WbAES) that empowers the transmitter and receiver to 
      encrypt and decrypt data using asymmetric master key encryption. The proposed 
      WbAES employs attribute-based encryption (ABE) using whale optimization algorithm 
      behaviour, which transforms plain data to ciphertexts and adjusts the whale 
      fitness to generate a suitable master public and secret key, ensuring security 
      against unauthorized access and manipulation. The proposed WbAES is evaluated 
      using patient health record (PHR) datasets collected by IoT-based sensors, and 
      various attack scenarios are established using Python libraries to validate the 
      suggested framework. The simulation outcomes of the proposed system are compared 
      to cutting-edge security algorithms and achieved finest performance in terms of 
      reduced 11 s of execution time for 20 sensors, 0.121 mJ of energy consumption, 
      850 Kbps of throughput, 99.85 % of accuracy, and 0.19 ms of computational cost.
CI  - © 2023 The Authors.
FAU - Irshad, Reyazur Rashid
AU  - Irshad RR
AD  - Department of Computer Science, College of Science and Arts, Sharurah-68341, 
      Najran University, Kingdom of Saudi Arabia.
FAU - Sohail, Shahab Saquib
AU  - Sohail SS
AD  - Department of Computer Science and Engineering, School of Engineering Sciences 
      and Technology, Jamia Hamdard, New Delhi, India.
FAU - Hussain, Shahid
AU  - Hussain S
AD  - Innovation Value Institute (IVI), School of Business, National University of 
      Ireland,Maynooth (NUIM), Maynooth, Co. kildare, W23, F2H6 Ireland.
FAU - Madsen, Dag Øivind
AU  - Madsen DØ
AD  - USN School of Business, University of South-Eastern Norway, 3511 Hønefoss, 
      Norway.
FAU - Zamani, Abu Sarwar
AU  - Zamani AS
AD  - Department of Computer and Self Development, Preparatory Year Deanship, Prince 
      Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
FAU - Ahmed, Abdallah Ahmed Alzupair
AU  - Ahmed AAA
AD  - Department of Computer Science, College of Science and Arts, Sharurah-68341, 
      Najran University, Kingdom of Saudi Arabia.
FAU - Alattab, Ahmed Abdu
AU  - Alattab AA
AD  - Department of Computer Science, College of Science and Arts, Sharurah-68341, 
      Najran University, Kingdom of Saudi Arabia.
FAU - Badr, Mohamed Mahdi
AU  - Badr MM
AD  - Department of Computer Science, College of Science and Arts, Sharurah-68341, 
      Najran University, Kingdom of Saudi Arabia.
FAU - Alwayle, Ibrahim M
AU  - Alwayle IM
AD  - Department of Computer Science, College of Science and Arts, Sharurah-68341, 
      Najran University, Kingdom of Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20231115
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10687057
OTO - NOTNLM
OT  - Asymmetric key
OT  - Attribute based encryption
OT  - ChatGPT
OT  - IoT-enabled healthcare system
OT  - Patient health record
OT  - Whale-based attribute encryption
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/11/30 18:45
MHDA- 2023/11/30 18:46
CRDT- 2023/11/30 17:37
PHST- 2023/03/31 00:00 [received]
PHST- 2023/10/29 00:00 [revised]
PHST- 2023/11/09 00:00 [accepted]
PHST- 2023/11/30 18:46 [medline]
PHST- 2023/11/30 18:45 [pubmed]
PHST- 2023/11/30 17:37 [entrez]
AID - S2405-8440(23)09544-0 [pii]
AID - e22336 [pii]
AID - 10.1016/j.heliyon.2023.e22336 [doi]
PST - epublish
SO  - Heliyon. 2023 Nov 15;9(11):e22336. doi: 10.1016/j.heliyon.2023.e22336. 
      eCollection 2023 Nov.

PMID- 37822918
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2666-688X (Electronic)
IS  - 2666-688X (Linking)
VI  - 60
DP  - 2023
TI  - Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery.
PG  - 57-63
LID - 10.1016/j.ejvsvf.2023.09.002 [doi]
AB  - OBJECTIVE: The use of Natural Language Processing (NLP) has attracted increased 
      interest in healthcare with various potential applications including 
      identification and extraction of health information, development of chatbots and 
      virtual assistants. The aim of this comprehensive literature review was to 
      provide an overview of NLP applications in vascular surgery, identify current 
      limitations, and discuss future perspectives in the field. DATA SOURCES: The 
      MEDLINE database was searched on April 2023. REVIEW METHODS: The database was 
      searched using a combination of keywords to identify studies reporting the use of 
      NLP and chatbots in three main vascular diseases. Keywords used included Natural 
      Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery 
      disease, vascular, and vascular surgery. RESULTS: Given the heterogeneity of 
      study design, techniques, and aims, a comprehensive literature review was 
      performed to provide an overview of NLP applications in vascular surgery. By 
      enabling identification and extraction of information on patients with vascular 
      diseases, such technology could help to analyse data from healthcare information 
      systems to provide feedback on current practice and help in optimising patient 
      care. In addition, chatbots and NLP driven techniques have the potential to be 
      used as virtual assistants for both health professionals and patients. 
      CONCLUSION: While Artificial Intelligence and NLP technology could be used to 
      enhance care for patients with vascular diseases, many challenges remain 
      including the need to define guidelines and clear consensus on how to evaluate 
      and validate these innovations before their implementation into clinical 
      practice.
CI  - © 2023 The Author(s).
FAU - Lareyre, Fabien
AU  - Lareyre F
AD  - Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France.
AD  - Université Côte d'Azur, Inserm, U1065, C3M, Nice, France.
FAU - Nasr, Bahaa
AU  - Nasr B
AD  - Department of Vascular and Endovascular Surgery, Brest University Hospital, 
      Brest, France.
AD  - INSERM, UMR 1101, LaTIM, Brest, France.
FAU - Chaudhuri, Arindam
AU  - Chaudhuri A
AD  - Bedfordshire - Milton Keynes Vascular Centre, Bedfordshire Hospitals, NHS 
      Foundation Trust, Bedford, UK.
FAU - Di Lorenzo, Gilles
AU  - Di Lorenzo G
AD  - Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France.
FAU - Carlier, Mathieu
AU  - Carlier M
AD  - Department of Urology, University Hospital of Nice, Nice, France.
FAU - Raffort, Juliette
AU  - Raffort J
AD  - Université Côte d'Azur, Inserm, U1065, C3M, Nice, France.
AD  - Institute 3IA Côte d'Azur, Université Côte d'Azur, France.
AD  - Clinical Chemistry Laboratory, University Hospital of Nice, France.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230917
PL  - England
TA  - EJVES Vasc Forum
JT  - EJVES vascular forum
JID - 101766732
PMC - PMC10562666
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - Literature search
OT  - Natural Language Processing
OT  - Vascular surgery
COIS- None.
EDAT- 2023/10/12 06:43
MHDA- 2023/10/12 06:44
CRDT- 2023/10/12 04:14
PHST- 2023/05/11 00:00 [received]
PHST- 2023/07/13 00:00 [revised]
PHST- 2023/09/08 00:00 [accepted]
PHST- 2023/10/12 06:44 [medline]
PHST- 2023/10/12 06:43 [pubmed]
PHST- 2023/10/12 04:14 [entrez]
AID - S2666-688X(23)00075-8 [pii]
AID - 10.1016/j.ejvsvf.2023.09.002 [doi]
PST - epublish
SO  - EJVES Vasc Forum. 2023 Sep 17;60:57-63. doi: 10.1016/j.ejvsvf.2023.09.002. 
      eCollection 2023.

PMID- 37375838
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 1424-8247 (Print)
IS  - 1424-8247 (Electronic)
IS  - 1424-8247 (Linking)
VI  - 16
IP  - 6
DP  - 2023 Jun 18
TI  - The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies.
LID - 10.3390/ph16060891 [doi]
LID - 891
AB  - Artificial intelligence (AI) has the potential to revolutionize the drug 
      discovery process, offering improved efficiency, accuracy, and speed. However, 
      the successful application of AI is dependent on the availability of high-quality 
      data, the addressing of ethical concerns, and the recognition of the limitations 
      of AI-based approaches. In this article, the benefits, challenges, and drawbacks 
      of AI in this field are reviewed, and possible strategies and approaches for 
      overcoming the present obstacles are proposed. The use of data augmentation, 
      explainable AI, and the integration of AI with traditional experimental methods, 
      as well as the potential advantages of AI in pharmaceutical research, are also 
      discussed. Overall, this review highlights the potential of AI in drug discovery 
      and provides insights into the challenges and opportunities for realizing its 
      potential in this field. Note from the human authors: This article was created to 
      test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in 
      terms of assisting human authors in writing review articles. The text generated 
      by the AI following our instructions (see Supporting Information) was used as a 
      starting point, and its ability to automatically generate content was evaluated. 
      After conducting a thorough review, the human authors practically rewrote the 
      manuscript, striving to maintain a balance between the original proposal and the 
      scientific criteria. The advantages and limitations of using AI for this purpose 
      are discussed in the last section.
FAU - Blanco-González, Alexandre
AU  - Blanco-González A
AUID- ORCID: 0000-0001-8593-5881
AD  - Department of Organic Chemistry, Center for Research in Biological Chemistry and 
      Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago 
      de Compostela, Spain.
AD  - Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty 
      of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, 
      Spain.
AD  - MD.USE Innovations S.L., Edificio Emprendia, 15782 Santiago de Compostela, Spain.
FAU - Cabezón, Alfonso
AU  - Cabezón A
AD  - Department of Organic Chemistry, Center for Research in Biological Chemistry and 
      Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago 
      de Compostela, Spain.
AD  - Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty 
      of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, 
      Spain.
FAU - Seco-González, Alejandro
AU  - Seco-González A
AD  - Department of Organic Chemistry, Center for Research in Biological Chemistry and 
      Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago 
      de Compostela, Spain.
AD  - Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty 
      of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, 
      Spain.
FAU - Conde-Torres, Daniel
AU  - Conde-Torres D
AD  - Department of Organic Chemistry, Center for Research in Biological Chemistry and 
      Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago 
      de Compostela, Spain.
AD  - Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty 
      of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, 
      Spain.
FAU - Antelo-Riveiro, Paula
AU  - Antelo-Riveiro P
AD  - Department of Organic Chemistry, Center for Research in Biological Chemistry and 
      Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago 
      de Compostela, Spain.
AD  - Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty 
      of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, 
      Spain.
FAU - Piñeiro, Ángel
AU  - Piñeiro Á
AD  - Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty 
      of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, 
      Spain.
FAU - Garcia-Fandino, Rebeca
AU  - Garcia-Fandino R
AD  - Department of Organic Chemistry, Center for Research in Biological Chemistry and 
      Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago 
      de Compostela, Spain.
LA  - eng
GR  - RTI2018-098795-A-I00, PID2019-111327GB-I00 and PDC2022-133402-I00/Agencia Estatal 
      de Investigación (Spain)/
GR  - ED431F 2020/05, ED431B 2022/36, and Centro singular de investigación de Galicia 
      accreditation 2016-2019, ED431G/09/Xunta de Galicia/
PT  - Journal Article
PT  - Review
DEP - 20230618
PL  - Switzerland
TA  - Pharmaceuticals (Basel)
JT  - Pharmaceuticals (Basel, Switzerland)
JID - 101238453
PMC - PMC10302890
OTO - NOTNLM
OT  - AI-assisted content generation
OT  - AI-limitations
OT  - artificial intelligence
OT  - drug discovery
COIS- Author Alexandre Blanco-González was employed by the company MD.USE Innovations 
      SL. The remaining authors declare that the research was conducted in the absence 
      of any commercial or financial relationships that could be construed as a 
      potential conflict of interest.
EDAT- 2023/06/28 06:43
MHDA- 2023/06/28 06:44
CRDT- 2023/06/28 01:35
PHST- 2023/05/22 00:00 [received]
PHST- 2023/06/14 00:00 [revised]
PHST- 2023/06/15 00:00 [accepted]
PHST- 2023/06/28 06:44 [medline]
PHST- 2023/06/28 06:43 [pubmed]
PHST- 2023/06/28 01:35 [entrez]
AID - ph16060891 [pii]
AID - pharmaceuticals-16-00891 [pii]
AID - 10.3390/ph16060891 [doi]
PST - epublish
SO  - Pharmaceuticals (Basel). 2023 Jun 18;16(6):891. doi: 10.3390/ph16060891.

PMID- 38019961
OWN - NLM
STAT- Publisher
LR  - 20231129
IS  - 1437-4331 (Electronic)
IS  - 1434-6621 (Linking)
DP  - 2023 Nov 30
TI  - Rising adoption of artificial intelligence in scientific publishing: evaluating 
      the role, risks, and ethical implications in paper drafting and review process.
LID - 10.1515/cclm-2023-1136 [doi]
AB  - BACKGROUND: In the rapid evolving landscape of artificial intelligence (AI), 
      scientific publishing is experiencing significant transformations. AI tools, 
      while offering unparalleled efficiencies in paper drafting and peer review, also 
      introduce notable ethical concerns. CONTENT: This study delineates AI's dual role 
      in scientific publishing: as a co-creator in the writing and review of scientific 
      papers and as an ethical challenge. We first explore the potential of AI as an 
      enhancer of efficiency, efficacy, and quality in creating scientific papers. A 
      critical assessment follows, evaluating the risks vs. rewards for researchers, 
      especially those early in their careers, emphasizing the need to maintain a 
      balance between AI's capabilities and fostering independent reasoning and 
      creativity. Subsequently, we delve into the ethical dilemmas of AI's involvement, 
      particularly concerning originality, plagiarism, and preserving the genuine 
      essence of scientific discourse. The evolving dynamics further highlight an 
      overlooked aspect: the inadequate recognition of human reviewers in the academic 
      community. With the increasing volume of scientific literature, tangible metrics 
      and incentives for reviewers are proposed as essential to ensure a balanced 
      academic environment. SUMMARY: AI's incorporation in scientific publishing is 
      promising yet comes with significant ethical and operational challenges. The role 
      of human reviewers is accentuated, ensuring authenticity in an AI-influenced 
      environment. OUTLOOK: As the scientific community treads the path of AI 
      integration, a balanced symbiosis between AI's efficiency and human discernment 
      is pivotal. Emphasizing human expertise, while exploit artificial intelligence 
      responsibly, will determine the trajectory of an ethically sound and efficient 
      AI-augmented future in scientific publishing.
CI  - © 2023 Walter de Gruyter GmbH, Berlin/Boston.
FAU - Carobene, Anna
AU  - Carobene A
AUID- ORCID: 0000-0003-0147-9378
AD  - Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy.
FAU - Padoan, Andrea
AU  - Padoan A
AUID- ORCID: 0000-0003-1284-7885
AD  - Department of Medicine-DIMED, University of Padova, Padova, Italy.
AD  - Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy.
FAU - Cabitza, Federico
AU  - Cabitza F
AD  - DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy.
AD  - IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.
FAU - Banfi, Giuseppe
AU  - Banfi G
AD  - IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.
AD  - University Vita-Salute San Raffaele, Milan, Italy.
FAU - Plebani, Mario
AU  - Plebani M
AUID- ORCID: 0000-0002-0270-1711
AD  - Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy.
AD  - University of Padova, Padova, Italy.
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - Germany
TA  - Clin Chem Lab Med
JT  - Clinical chemistry and laboratory medicine
JID - 9806306
SB  - IM
OTO - NOTNLM
OT  - ChatGPT
OT  - artificial intelligence
OT  - authorship
OT  - ethical implications
OT  - peer review
OT  - scientific writing
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:42
CRDT- 2023/11/29 14:32
PHST- 2023/10/12 00:00 [received]
PHST- 2023/11/13 00:00 [accepted]
PHST- 2023/11/29 18:42 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 14:32 [entrez]
AID - cclm-2023-1136 [pii]
AID - 10.1515/cclm-2023-1136 [doi]
PST - aheadofprint
SO  - Clin Chem Lab Med. 2023 Nov 30. doi: 10.1515/cclm-2023-1136.

PMID- 37993519
OWN - NLM
STAT- MEDLINE
DCOM- 20231124
LR  - 20231125
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Nov 22
TI  - Evaluation of the performance of GPT-3.5 and GPT-4 on the Polish Medical Final 
      Examination.
PG  - 20512
LID - 10.1038/s41598-023-46995-z [doi]
LID - 20512
AB  - The study aimed to evaluate the performance of two Large Language Models (LLMs): 
      ChatGPT (based on GPT-3.5) and GPT-4 with two temperature parameter values, on 
      the Polish Medical Final Examination (MFE). The models were tested on three 
      editions of the MFE from: Spring 2022, Autumn 2022, and Spring 2023 in two 
      language versions-English and Polish. The accuracies of both models were compared 
      and the relationships between the correctness of answers with the answer's 
      metrics were investigated. The study demonstrated that GPT-4 outperformed GPT-3.5 
      in all three examinations regardless of the language used. GPT-4 achieved mean 
      accuracies of 79.7% for both Polish and English versions, passing all MFE 
      versions. GPT-3.5 had mean accuracies of 54.8% for Polish and 60.3% for English, 
      passing none and 2 of 3 Polish versions for temperature parameter equal to 0 and 
      1 respectively while passing all English versions regardless of the temperature 
      parameter value. GPT-4 score was mostly lower than the average score of a medical 
      student. There was a statistically significant correlation between the 
      correctness of the answers and the index of difficulty for both models. The 
      overall accuracy of both models was still suboptimal and worse than the average 
      for medical students. This emphasizes the need for further improvements in LLMs 
      before they can be reliably deployed in medical settings. These findings suggest 
      an increasing potential for the usage of LLMs in terms of medical education.
CI  - © 2023. The Author(s).
FAU - Rosoł, Maciej
AU  - Rosoł M
AD  - Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, 
      Warsaw University of Technology, Boboli 8 Street, 02-525, Warsaw, Poland. 
      maciej.rosol.dokt@pw.edu.pl.
FAU - Gąsior, Jakub S
AU  - Gąsior JS
AD  - Department of Pediatric Cardiology and General Pediatrics, Medical University of 
      Warsaw, Warsaw, Poland.
FAU - Łaba, Jonasz
AU  - Łaba J
AD  - Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, 
      Warsaw University of Technology, Boboli 8 Street, 02-525, Warsaw, Poland.
FAU - Korzeniewski, Kacper
AU  - Korzeniewski K
AD  - Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, 
      Warsaw University of Technology, Boboli 8 Street, 02-525, Warsaw, Poland.
FAU - Młyńczak, Marcel
AU  - Młyńczak M
AD  - Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, 
      Warsaw University of Technology, Boboli 8 Street, 02-525, Warsaw, Poland.
LA  - eng
PT  - Journal Article
DEP - 20231122
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - Poland
MH  - *Benchmarking
MH  - *Education, Medical
MH  - Language
MH  - Physical Examination
PMC - PMC10665355
COIS- The authors declare no competing interests.
EDAT- 2023/11/23 00:42
MHDA- 2023/11/24 06:42
CRDT- 2023/11/22 23:33
PHST- 2023/09/14 00:00 [received]
PHST- 2023/11/07 00:00 [accepted]
PHST- 2023/11/24 06:42 [medline]
PHST- 2023/11/23 00:42 [pubmed]
PHST- 2023/11/22 23:33 [entrez]
AID - 10.1038/s41598-023-46995-z [pii]
AID - 46995 [pii]
AID - 10.1038/s41598-023-46995-z [doi]
PST - epublish
SO  - Sci Rep. 2023 Nov 22;13(1):20512. doi: 10.1038/s41598-023-46995-z.

PMID- 37981579
OWN - NLM
STAT- MEDLINE
DCOM- 20231121
LR  - 20231121
IS  - 1975-5937 (Electronic)
IS  - 1975-5937 (Linking)
VI  - 20
DP  - 2023
TI  - Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing 
      Medical Examination: a cross-sectional study.
PG  - 30
LID - 10.3352/jeehp.2023.20.30 [doi]
AB  - PURPOSE: We aimed to describe the performance and evaluate the educational value 
      of justifications provided by artificial intelligence chatbots, including 
      GPT-3.5, GPT-4, Bard, Claude, and Bing, on the Peruvian National Medical 
      Licensing Examination (P-NLME). METHODS: This was a cross-sectional analytical 
      study. On July 25, 2023, each multiple-choice question (MCQ) from the P-NLME was 
      entered into each chatbot (GPT-3, GPT-4, Bing, Bard, and Claude) 3 times. Then, 4 
      medical educators categorized the MCQs in terms of medical area, item type, and 
      whether the MCQ required Peru-specific knowledge. They assessed the educational 
      value of the justifications from the 2 top performers (GPT-4 and Bing). RESULTS: 
      GPT-4 scored 86.7% and Bing scored 82.2%, followed by Bard and Claude, and the 
      historical performance of Peruvian examinees was 55%. Among the factors 
      associated with correct answers, only MCQs that required Peru-specific knowledge 
      had lower odds (odds ratio, 0.23; 95% confidence interval, 0.09-0.61), whereas 
      the remaining factors showed no associations. In assessing the educational value 
      of justifications provided by GPT-4 and Bing, neither showed any significant 
      differences in certainty, usefulness, or potential use in the classroom. 
      CONCLUSION: Among chatbots, GPT-4 and Bing were the top performers, with Bing 
      performing better at Peru-specific MCQs. Moreover, the educational value of 
      justifications provided by the GPT-4 and Bing could be deemed appropriate. 
      However, it is essential to start addressing the educational value of these 
      chatbots, rather than merely their performance on examinations.
FAU - Torres-Zegarra, Betzy Clariza
AU  - Torres-Zegarra BC
AD  - Escuela de Medicina, Universidad Cientifica del Sur, Lima, Peru.
FAU - Rios-Garcia, Wagner
AU  - Rios-Garcia W
AD  - Sociedad Científica de Estudiantes de Medicina de Ica, Universidad Nacional San 
      Luis Gonzaga, Ica, Peru.
FAU - Ñaña-Cordova, Alvaro Micael
AU  - Ñaña-Cordova AM
AD  - Escuela de Medicina, Universidad Cientifica del Sur, Lima, Peru.
FAU - Arteaga-Cisneros, Karen Fatima
AU  - Arteaga-Cisneros KF
AD  - Escuela de Medicina, Universidad Cientifica del Sur, Lima, Peru.
FAU - Chalco, Xiomara Cristina Benavente
AU  - Chalco XCB
AD  - Escuela de Medicina, Universidad Cientifica del Sur, Lima, Peru.
FAU - Ordoñez, Marina Atena Bustamante
AU  - Ordoñez MAB
AD  - Escuela de Medicina, Universidad Cientifica del Sur, Lima, Peru.
FAU - Rios, Carlos Jesus Gutierrez
AU  - Rios CJG
AD  - Escuela de Medicina, Universidad Cientifica del Sur, Lima, Peru.
FAU - Godoy, Carlos Alberto Ramos
AU  - Godoy CAR
AD  - Universidad Nacional de Cajamarca, Cajamarca, Peru.
AD  - Academic Department, USAMEDIC, Lima, Peru.
FAU - Quezada, Kristell Luisa Teresa Panta
AU  - Quezada KLTP
AD  - Academic Department, USAMEDIC, Lima, Peru.
FAU - Gutierrez-Arratia, Jesus Daniel
AU  - Gutierrez-Arratia JD
AD  - Academic Department, USAMEDIC, Lima, Peru.
AD  - Neurogenetics Research Center, Instituto Nacional de Ciencias Neurologicas, Lima, 
      Peru.
FAU - Flores-Cohaila, Javier Alejandro
AU  - Flores-Cohaila JA
AD  - Escuela de Medicina, Universidad Cientifica del Sur, Lima, Peru.
AD  - Academic Department, USAMEDIC, Lima, Peru.
LA  - eng
PT  - Journal Article
DEP - 20231120
PL  - Korea (South)
TA  - J Educ Eval Health Prof
JT  - Journal of educational evaluation for health professions
JID - 101490061
SB  - IM
MH  - Humans
MH  - Cross-Sectional Studies
MH  - Peru
MH  - *Artificial Intelligence
MH  - Educational Status
MH  - *Knowledge
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Educational measurement
OT  - Medical education
OT  - Peru
EDAT- 2023/11/20 00:43
MHDA- 2023/11/21 06:42
CRDT- 2023/11/19 23:07
PHST- 2023/10/06 00:00 [received]
PHST- 2023/11/07 00:00 [accepted]
PHST- 2023/11/21 06:42 [medline]
PHST- 2023/11/20 00:43 [pubmed]
PHST- 2023/11/19 23:07 [entrez]
AID - jeehp.2023.20.30 [pii]
AID - 10.3352/jeehp.2023.20.30 [doi]
PST - ppublish
SO  - J Educ Eval Health Prof. 2023;20:30. doi: 10.3352/jeehp.2023.20.30. Epub 2023 Nov 
      20.

PMID- 37168194
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230514
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Cyberknife Radiosurgery for Synovial Sarcoma Metastasizing to the Spine: 
      Illustrative Case Reports.
PG  - e37087
LID - 10.7759/cureus.37087 [doi]
LID - e37087
AB  - Synovial sarcoma (SS) is a rare and aggressive type of soft tissue sarcoma that 
      commonly affects young adults. Metastasis in the spine is a rare complication, 
      and the management of these lesions is challenging. Radiosurgery is an 
      increasingly popular treatment option for spinal metastasis due to its ability to 
      deliver high doses of radiation to the target volume with minimal exposure to 
      surrounding healthy tissues. In this paper, we present two cases of SS with 
      spinal metastasis that were treated with CyberKnife radiosurgery (CKRS). The 
      first case was a 52-year-old female with a history of multiple thoracotomies and 
      lobectomies for lung metastases, who was diagnosed with T6-T8 and T4 spinal 
      metastasis. The second case was a 53-year-old female with Down syndrome, who was 
      diagnosed with T12-L1 spinal metastasis. Both patients experienced an improvement 
      in their symptoms following CKRS treatment and showed stable or decreasing lesion 
      sizes on follow-up imaging. The progression-free survival (PFS) in the first case 
      was 37 months and overall survival (OS) was 79 months. In the second case, the 
      PFS was 12 months and OS was 18 months. These cases highlight the potential 
      benefits of CKRS as a treatment option for SS with spinal metastasis and support 
      its use in the management of this challenging condition.
CI  - Copyright © 2023, Zamarud et al.
FAU - Zamarud, Aroosa
AU  - Zamarud A
AD  - Department of Neurosurgery, Stanford University School of Medicine, Stanford, 
      USA.
FAU - Park, David J
AU  - Park DJ
AD  - Department of Neurosurgery, Stanford University School of Medicine, Stanford, 
      USA.
FAU - Haider, Ghani
AU  - Haider G
AD  - Department of Neurosurgery, Stanford University School of Medicine, Stanford, 
      USA.
FAU - Chang, Steven D
AU  - Chang SD
AD  - Department of Neurosurgery, Stanford University School of Medicine, Stanford, 
      USA.
FAU - Meola, Antonio
AU  - Meola A
AD  - Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, 
      USA.
LA  - eng
PT  - Case Reports
DEP - 20230404
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10166278
OTO - NOTNLM
OT  - chatgpt
OT  - cyberknife
OT  - openai
OT  - sarcoma
OT  - sarcoma spine metastases
OT  - stereotactic radiosurgery
OT  - synovial sarcoma
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/12 01:07
MHDA- 2023/05/12 01:08
CRDT- 2023/05/11 19:16
PHST- 2023/03/01 00:00 [received]
PHST- 2023/04/03 00:00 [accepted]
PHST- 2023/05/12 01:08 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 19:16 [entrez]
AID - 10.7759/cureus.37087 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 4;15(4):e37087. doi: 10.7759/cureus.37087. eCollection 2023 Apr.

PMID- 37153307
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230509
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Prior Restorative Procedures to Endodontic Treatment.
PG  - e37106
LID - 10.7759/cureus.37106 [doi]
LID - e37106
AB  - Tooth loss due to fracture and the failure of endodontic treatment (ET) are 
      common situations in teeth with extensive tissue destruction. This is due to the 
      fragility of the remaining dental structure and the difficulty in sealing 
      cavities, which is sometimes associated with the violation of the supracrestal 
      insertion tissue. The previous restoration of marginal ridges or cusps with 
      composite resin (CR) restores their fracture resistance, due to the adhesive 
      characteristics of the restorative material, while also protecting the quality of 
      endodontic treatment through better sealing. However, the protocol adopted in 
      teeth requiring endodontic treatment involves performing the restorative 
      procedure only after the endodontic procedures. The objective of this study was 
      to report a case in which restoration of marginal ridges and/or cusps was 
      performed prior to endodontic treatment, focusing on maintaining the tooth in 
      function without dental fracture. The restoration was performed with an inverted 
      operative sequence before the endodontic treatment. There was a violation of the 
      supracrestal insertion tissue, requiring crown lengthening surgery (CLS) prior to 
      the restorative procedure. Clinical and radiographic evaluations were performed 
      postoperatively at seven days, three, six, and nine months, and five years. Tooth 
      function was maintained without dental fractures or restoration loss. 
      Periradicular space healing occurred with the disappearance of the lesion. 
      Performing the restorative procedure prior to endodontic treatment in teeth with 
      extensive coronal destruction is an alternative technique that facilitates 
      clinical procedures, reduces the likelihood of dental loss due to fracture, and 
      promotes endodontic treatment success.
CI  - Copyright © 2023, Moreira et al.
FAU - Moreira, Mário A
AU  - Moreira MA
AD  - Dentistry, Ceará Federal University, Sobral, BRA.
FAU - Silveira, Virgínia R
AU  - Silveira VR
AD  - Dentistry, Ceará Federal University, Sobral, BRA.
FAU - Alcantara, Veronica O
AU  - Alcantara VO
AD  - Dentistry, Ceará Federal University, Sobral, BRA.
FAU - Sousa, Frederico B
AU  - Sousa FB
AD  - Morphology, Paraíba Federal University, João Pessoa, BRA.
FAU - Sousa, Bruno C
AU  - Sousa BC
AD  - Dentistry, Ceará Federal University, Sobral, BRA.
LA  - eng
PT  - Case Reports
DEP - 20230404
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10158847
OTO - NOTNLM
OT  - chatgpt
OT  - clinical success
OT  - composite resin restorations
OT  - dental fracture
OT  - endodontics
OT  - periodontics
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/08 06:41
MHDA- 2023/05/08 06:42
CRDT- 2023/05/08 04:13
PHST- 2023/04/04 00:00 [accepted]
PHST- 2023/05/08 06:42 [medline]
PHST- 2023/05/08 06:41 [pubmed]
PHST- 2023/05/08 04:13 [entrez]
AID - 10.7759/cureus.37106 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 4;15(4):e37106. doi: 10.7759/cureus.37106. eCollection 2023 Apr.

PMID- 37113369
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230430
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Giant Meckel's Diverticulum as a Cause of Post-traumatic Hemoperitoneum in a 
      25-Year-Old Male: An Extremely Rare Phenomenon.
PG  - e36688
LID - 10.7759/cureus.36688 [doi]
LID - e36688
AB  - Following the "rule of 2", Meckel's Diverticulum (MD) is 2 inches or 5cm long. 
      However, we report the case of an extremely large MD. To the best of our 
      elucidated literature search, it is the first case of Giant Meckel's Diverticulum 
      (GMD) from Pakistan presenting with post-traumatic hemoperitoneum. A 25-year-old 
      Pakistani male presented to a surgical emergency with a two-hour history of 
      generalized abdominal pain after blunt abdominal trauma. An exploratory 
      laparotomy was carried out due to the deranged hemodynamic parameters and free 
      fluid in the abdominopelvic cavity, revealing a 35 centimeters long MD with a 
      bleeding vessel on its tip. Diverticulectomy with the repair of a small 
      intestinal defect was performed after the evacuation of 2.5 liters of clotted 
      blood. Histologic evaluation revealed ectopic gastric tissue. He had an 
      uneventful post-operative stay and was discharged home. The current English 
      scientific literature has adequate case reports documenting the complications of 
      perforation, intestinal obstruction, and diverticulitis of Meckel's Diverticulum 
      (MD) of normal length. However, this case report highlights the significance of 
      an MD with an abnormal length which put the patient's life at risk of death in 
      the setting of normal intra-operative anatomy of all other abdominal organs.
CI  - Copyright © 2023, Nasir et al.
FAU - Nasir, Muhammad
AU  - Nasir M
AD  - General Surgery, Lahore General Hospital, Lahore, PAK.
FAU - Gohar, Ali
AU  - Gohar A
AD  - General Surgery, Lahore General Hospital, Lahore, PAK.
FAU - Mehmood Qadri, Haseeb
AU  - Mehmood Qadri H
AD  - General Surgery, Lahore General Hospital, Lahore, PAK.
FAU - Qayyum, Fahad
AU  - Qayyum F
AD  - General Surgery, Jinnah Hospital, Lahore, PAK.
FAU - Rehman, Rabia
AU  - Rehman R
AD  - Surgery, Sharif Medical City Hospital, Lahore, PAK.
FAU - Chaudhry, Hassan
AU  - Chaudhry H
AD  - Surgery, Rashid Latif Medical College, Lahore, PAK.
FAU - Iqbal, Kashif
AU  - Iqbal K
AD  - Surgery, Azra Naheed Medical College, Lahore, PAK.
LA  - eng
PT  - Case Reports
DEP - 20230326
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10127553
OTO - NOTNLM
OT  - blunt abdominal injury
OT  - chatgpt
OT  - diverticulectomy
OT  - explorative laparotomy
OT  - giant meckel’s diverticulum
OT  - hemoperitoneum
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/04/28 06:42
MHDA- 2023/04/28 06:43
CRDT- 2023/04/28 02:27
PHST- 2023/03/26 00:00 [accepted]
PHST- 2023/04/28 06:43 [medline]
PHST- 2023/04/28 06:42 [pubmed]
PHST- 2023/04/28 02:27 [entrez]
AID - 10.7759/cureus.36688 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 26;15(3):e36688. doi: 10.7759/cureus.36688. eCollection 2023 
      Mar.

PMID- 38058478
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231208
IS  - 0973-1075 (Print)
IS  - 1998-3735 (Electronic)
IS  - 0973-1075 (Linking)
VI  - 29
IP  - 4
DP  - 2023 Oct-Dec
TI  - Can Artificial Intelligence aid communication? Considering the possibilities of 
      GPT-3 in Palliative care.
PG  - 418-425
LID - 10.25259/IJPC_155_2023 [doi]
AB  - OBJECTIVES: This article reviews the developments in artificial intelligence (AI) 
      technologies and their current and prospective applications in endof-life 
      communications. It uses Open AI's generative pre-trained transformer 3 (GPT-3) as 
      a case study to understand the possibilities of AI-aided communication in 
      Palliative Care. MATERIAL AND METHODS: Open AI's GPT-3 was taken as a case study 
      where responses were generated through the GPT-3 beta playground (Davinci engine) 
      and were scrutinised by six mental health professionals (MHPs) working in a 
      palliative care setting in India. They were tasked to evaluate the responses 
      generated by the AI (the identity was not revealed until a part of the study was 
      completed) in a simulated palliative care conversation with another MHP posing as 
      a patient. The aim was to undermine whether the professionals were able to detect 
      that the responses were indeed generated by a machine and did they approve or 
      disapprove of the responses. RESULTS: The GPT-3 playground with the right prompts 
      produced remarkable, often surprising texts and responses that imitated human 
      interaction. However, glitches such as redundancy were noticed along with 
      strongly held opinions in certain questions related to faith, death, and life 
      after death. CONCLUSION: AI-assisted communication in palliative care could be 
      used to train professionals in the palliative care field using it as a simulation 
      in training. It could also be used as a therapeutic intervention for the purpose 
      of engagement and philosophical dialogue after certain modifications. However, it 
      would have its own limitations such as it cannot replace a human agent just yet.
CI  - © 2023 Published by Scientific Scholar on behalf of Indian Journal of Palliative 
      Care.
FAU - Srivastava, Rajashree
AU  - Srivastava R
AD  - Department of Psychology, School of Liberal Education, Galgotias University, 
      Greater Noida, Uttar Pradesh, India.
FAU - Srivastava, Shikha
AU  - Srivastava S
AD  - Department of Psychology, School of Liberal Education, Galgotias University, 
      Greater Noida, Uttar Pradesh, India.
LA  - eng
PT  - Journal Article
DEP - 20231018
PL  - United States
TA  - Indian J Palliat Care
JT  - Indian journal of palliative care
JID - 101261221
PMC - PMC10696352
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - ChatGPT
OT  - Communication
OT  - Generative pre-trained transformer 3
OT  - Palliative Care
COIS- There are no conflicts of interest.
EDAT- 2023/12/07 06:42
MHDA- 2023/12/07 06:43
CRDT- 2023/12/07 04:04
PHST- 2023/06/19 00:00 [received]
PHST- 2023/08/21 00:00 [accepted]
PHST- 2023/12/07 06:43 [medline]
PHST- 2023/12/07 06:42 [pubmed]
PHST- 2023/12/07 04:04 [entrez]
AID - 10.25259/IJPC_155_2023 [pii]
AID - 10.25259/IJPC_155_2023 [doi]
PST - ppublish
SO  - Indian J Palliat Care. 2023 Oct-Dec;29(4):418-425. doi: 10.25259/IJPC_155_2023. 
      Epub 2023 Oct 18.

PMID- 37729376
OWN - NLM
STAT- MEDLINE
DCOM- 20230922
LR  - 20230923
IS  - 1806-9282 (Electronic)
IS  - 0104-4230 (Print)
IS  - 0104-4230 (Linking)
VI  - 69
IP  - 9
DP  - 2023
TI  - The use of artificial intelligence to improve the scientific writing of 
      non-native english speakers.
PG  - e20230560
LID - S0104-42302023000900626 [pii]
LID - 10.1590/1806-9282.20230560 [doi]
LID - e20230560
AB  - OBJECTIVE: Scientific writing in English is a daunting task for non-native 
      English speakers. The challenges of writing in a foreign language are evident in 
      the scientific literature where texts by non-native English-speaking scientists 
      tend to be less clear and succinct, contain grammatical errors, and are often 
      rejected by prestigious journals. METHODS: We conducted a non-systematic review 
      of the most recent literature using the terms "Artificial Intelligence," 
      "Scientific Writing," and "Non-English Speaking" to create a narrative review. 
      RESULTS: Artificial intelligence can be a solution to improve scientific writing, 
      especially for non-native English-speaking scientists. Artificial intelligence 
      can assist in the search for pertinent scientific papers, generate summaries, and 
      help with the writing of different sections of the manuscript, including the 
      abstract, introduction, methods, results, and discussion. Artificial 
      intelligence-based programs can correct grammatical errors and improve writing 
      style, both of which are particularly helpful for non-native English speakers. 
      Two artificial intelligence programs that can help with the search for pertinent 
      scientific papers on the internet are Elicit and ResearchRabbit. Scispace Copilot 
      can be used to summarize the retrieved reference. The artificial intelligence 
      software programs such as Grammarly and Paperpal can correct grammatical and 
      spelling errors, while ChatGPT can also restructure sentences and paragraphs, 
      reword text, and suggest appropriate words and phrases. CONCLUSION: Overall, 
      artificial intelligence can be an effective tool to improve the clarity, style, 
      and coherence of scientific writing, helping non-native English-speaking 
      scientists to communicate their research more effectively.
FAU - Giglio, Auro Del
AU  - Giglio AD
AUID- ORCID: 0000-0002-2009-824X
AD  - Fundação do ABC, Faculdade de Medicina, Disciplina de Hematologia e Oncologia - 
      Santo André (SP), Brazil.
FAU - Costa, Mateus Uerlei Pereira da
AU  - Costa MUPD
AUID- ORCID: 0000-0003-1570-3967
AD  - Biblioteca da Sociedade Brasileira de Oncologia Clínica - São Paulo (SP), Brazil.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230918
PL  - Brazil
TA  - Rev Assoc Med Bras (1992)
JT  - Revista da Associacao Medica Brasileira (1992)
JID - 9308586
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Internet
MH  - Language
MH  - *Physicians
MH  - Writing
PMC - PMC10508892
COIS- Conflicts of interest: the authors declare there is no conflicts of interest.
EDAT- 2023/09/20 18:42
MHDA- 2023/09/22 06:42
CRDT- 2023/09/20 13:47
PHST- 2023/05/19 00:00 [received]
PHST- 2023/05/28 00:00 [accepted]
PHST- 2023/09/22 06:42 [medline]
PHST- 2023/09/20 18:42 [pubmed]
PHST- 2023/09/20 13:47 [entrez]
AID - S0104-42302023000900626 [pii]
AID - 10.1590/1806-9282.20230560 [doi]
PST - epublish
SO  - Rev Assoc Med Bras (1992). 2023 Sep 18;69(9):e20230560. doi: 
      10.1590/1806-9282.20230560. eCollection 2023.

PMID- 38046096
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2632-1297 (Electronic)
IS  - 2632-1297 (Linking)
VI  - 5
IP  - 6
DP  - 2023
TI  - Characterizing phonemic fluency by transfer learning with deep language models.
PG  - fcad318
LID - 10.1093/braincomms/fcad318 [doi]
LID - fcad318
AB  - Though phonemic fluency tasks are traditionally indexed by the number of correct 
      responses, the underlying disorder may shape the specific choice of words-both 
      correct and erroneous. We report the first comprehensive qualitative analysis of 
      incorrect and correct words generated on the phonemic ('S') fluency test, in a 
      large sample of patients (n = 239) with focal, unilateral frontal or posterior 
      lesions and healthy controls (n = 136). We conducted detailed qualitative 
      analyses of the single words generated in the phonemic fluency task using 
      categorical descriptions for different types of errors, low-frequency words and 
      clustering/switching. We further analysed patients' and healthy controls' entire 
      sequences of words by employing stochastic block modelling of Generative 
      Pretrained Transformer 3-based deep language representations. We conducted 
      predictive modelling to investigate whether deep language representations of word 
      sequences improved the accuracy of detecting the presence of frontal lesions 
      using the phonemic fluency test. Our qualitative analyses of the single words 
      generated revealed several novel findings. For the different types of errors 
      analysed, we found a non-lateralized frontal effect for profanities, left frontal 
      effects for proper nouns and permutations and a left posterior effect for 
      perseverations. For correct words, we found a left frontal effect for 
      low-frequency words. Our novel large language model-based approach found five 
      distinct communities whose varied word selection patterns reflected 
      characteristic demographic and clinical features. Predictive modelling showed 
      that a model based on Generative Pretrained Transformer 3-derived word sequence 
      representations predicted the presence of frontal lesions with greater fidelity 
      than models of native features. Our study reveals a characteristic pattern of 
      phonemic fluency responses produced by patients with frontal lesions. These 
      findings demonstrate the significant inferential and diagnostic value of 
      characterizing qualitative features of phonemic fluency performance with large 
      language models and stochastic block modelling.
CI  - © The Author(s) 2023. Published by Oxford University Press on behalf of the 
      Guarantors of Brain.
FAU - Mole, Joe
AU  - Mole J
AUID- ORCID: 0000-0003-0092-1128
AD  - Department of Neuropsychology, National Hospital for Neurology and Neurosurgery, 
      London WC1N 3BG, UK.
AD  - Institute of Neurology, University College London, London WC1N 3BG, UK.
FAU - Nelson, Amy
AU  - Nelson A
AD  - Institute of Neurology, University College London, London WC1N 3BG, UK.
FAU - Chan, Edgar
AU  - Chan E
AD  - Department of Neuropsychology, National Hospital for Neurology and Neurosurgery, 
      London WC1N 3BG, UK.
AD  - Institute of Neurology, University College London, London WC1N 3BG, UK.
FAU - Cipolotti, Lisa
AU  - Cipolotti L
AD  - Department of Neuropsychology, National Hospital for Neurology and Neurosurgery, 
      London WC1N 3BG, UK.
AD  - Institute of Neurology, University College London, London WC1N 3BG, UK.
FAU - Nachev, Parashkev
AU  - Nachev P
AUID- ORCID: 0000-0002-2718-4423
AD  - Institute of Neurology, University College London, London WC1N 3BG, UK.
LA  - eng
PT  - Journal Article
DEP - 20231128
PL  - England
TA  - Brain Commun
JT  - Brain communications
JID - 101755125
PMC - PMC10691875
OTO - NOTNLM
OT  - executive functions
OT  - fluency
OT  - frontal lobes
OT  - language modelling
OT  - machine learning
COIS- The authors report no competing interests.
EDAT- 2023/12/04 06:41
MHDA- 2023/12/04 06:42
CRDT- 2023/12/04 04:49
PHST- 2023/03/15 00:00 [received]
PHST- 2023/10/06 00:00 [revised]
PHST- 2023/11/27 00:00 [accepted]
PHST- 2023/12/04 06:42 [medline]
PHST- 2023/12/04 06:41 [pubmed]
PHST- 2023/12/04 04:49 [entrez]
AID - fcad318 [pii]
AID - 10.1093/braincomms/fcad318 [doi]
PST - epublish
SO  - Brain Commun. 2023 Nov 28;5(6):fcad318. doi: 10.1093/braincomms/fcad318. 
      eCollection 2023.

PMID- 37902018
OWN - NLM
STAT- Publisher
LR  - 20231110
IS  - 2326-5205 (Electronic)
IS  - 2326-5191 (Linking)
DP  - 2023 Oct 30
TI  - Doctor versus AI: Patient and physician evaluation of large language model 
      responses to rheumatology patient questions, a cross sectional study.
LID - 10.1002/art.42737 [doi]
AB  - OBJECTIVE: To assess the quality of LLM-chatbot vs. physician-generated responses 
      to patient-generated rheumatology questions. METHODS: We conducted a single 
      center cross-sectional survey of rheumatology patients (n=17) in Edmonton, 
      Canada. Patients evaluated LLM-chatbot vs. physician-generated responses for 
      comprehensiveness and readability, with four rheumatologists also evaluated 
      accuracy, using a Likert scale of 1-10 (1 being poor, 10 being excellent). 
      RESULTS: Patients rated no significant difference between AI vs. 
      physician-generated responses in comprehensiveness (7.12±0.99 vs. 7.52±1.16, 
      p=0.1962) nor readability (7.90±0.90 vs.7.80±0.75, p=0.5905). Rheumatologists 
      rated AI responses significantly poorer than physician responses on 
      comprehensiveness (AI 5.52±2.13 vs. physician 8.76±1.07, p<0.0001); readability 
      (AI 7.85±0.92 vs. physician 8.75±0.57, p=0.0003); and accuracy (AI 6.48±2.07 vs. 
      physician 9.08±0.64, p<0.0001). The proportion of preference to AI vs. 
      physician-generated responses by patients and physicians was 0.45 ±0.18 and 
      0.15±0.08, respectively (p=0.0106). After learning that one answer for each 
      question was AI-generated, patients were able to correctly identify AI-generated 
      answers at a lower proportion compared to physicians (0.49 ± 0.26 vs.0.97 ± 0.04, 
      p=0.0183). The average word count of AI answers was 69.10 ± 25.35 words, as 
      compared to 98.83 ± 34.58 words for physician-generated responses (p=0.0008). 
      CONCLUSION: Rheumatology patients rated AI-generated responses to patient 
      questions similarly to physician-generated responses in terms of 
      comprehensiveness, readability, and overall preference. However, rheumatologists 
      rated AI-responses significantly poorer than physician-generated responses, 
      suggesting that LLM-chatbot responses are inferior to physician responses, a 
      difference of which patients may not be aware.
CI  - This article is protected by copyright. All rights reserved.
FAU - Ye, Carrie
AU  - Ye C
AUID- ORCID: 0000-0002-9858-5398
AD  - Department of Medicine, University of Alberta, Edmonton, Canada.
FAU - Zweck, Elric
AU  - Zweck E
AD  - Department of Cardiology, Pulmonology and Vascular Medicine, University Hospital 
      Düsseldorf, Düsseldorf, Germany.
FAU - Ma, Zechen
AU  - Ma Z
AD  - Department of Medicine, University of Alberta, Edmonton, Canada.
FAU - Smith, Justin
AU  - Smith J
AD  - Department of Medicine, University of Alberta, Edmonton, Canada.
FAU - Katz, Steven
AU  - Katz S
AUID- ORCID: 0000-0001-6718-1393
AD  - Department of Medicine, University of Alberta, Edmonton, Canada.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - United States
TA  - Arthritis Rheumatol
JT  - Arthritis & rheumatology (Hoboken, N.J.)
JID - 101623795
SB  - IM
EDAT- 2023/10/30 06:47
MHDA- 2023/10/30 06:47
CRDT- 2023/10/30 05:28
PHST- 2023/10/13 00:00 [revised]
PHST- 2023/08/02 00:00 [received]
PHST- 2023/10/22 00:00 [accepted]
PHST- 2023/10/30 06:47 [pubmed]
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/10/30 05:28 [entrez]
AID - 10.1002/art.42737 [doi]
PST - aheadofprint
SO  - Arthritis Rheumatol. 2023 Oct 30. doi: 10.1002/art.42737.

PMID- 37722445
OWN - NLM
STAT- Publisher
LR  - 20231012
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Linking)
VI  - 146
DP  - 2023 Oct
TI  - A self-supervised language model selection strategy for biomedical question 
      answering.
PG  - 104486
LID - S1532-0464(23)00207-1 [pii]
LID - 10.1016/j.jbi.2023.104486 [doi]
AB  - Large neural-based Pre-trained Language Models (PLM) have recently gained much 
      attention due to their noteworthy performance in many downstream Information 
      Retrieval (IR) and Natural Language Processing (NLP) tasks. PLMs can be 
      categorized as either general-purpose, which are trained on resources such as 
      large-scale Web corpora, and domain-specific which are trained on in-domain or 
      mixed-domain corpora. While domain-specific PLMs have shown promising performance 
      on domain-specific tasks, they are significantly more computationally expensive 
      compared to general-purpose PLMs as they have to be either retrained or trained 
      from scratch. The objective of our work in this paper is to explore whether it 
      would be possible to leverage general-purpose PLMs to show competitive 
      performance to domain-specific PLMs without the need for expensive retraining of 
      the PLMs for domain-specific tasks. By focusing specifically on the recent BioASQ 
      Biomedical Question Answering task, we show how different general-purpose PLMs 
      show synergistic behaviour in terms of performance, which can lead to overall 
      notable performance improvement when used in tandem with each other. More 
      concretely, given a set of general-purpose PLMs, we propose a self-supervised 
      method for training a classifier that systematically selects the PLM that is most 
      likely to answer the question correctly on a per-input basis. We show that 
      through such a selection strategy, the performance of general-purpose PLMs can 
      become competitive with domain-specific PLMs while remaining computationally 
      light since there is no need to retrain the large language model itself. We run 
      experiments on the BioASQ dataset, which is a large-scale biomedical 
      question-answering benchmark. We show that utilizing our proposed selection 
      strategy can show statistically significant performance improvements on 
      general-purpose language models with an average of 16.7% when using only lighter 
      models such as DistilBERT and DistilRoBERTa, as well as 14.2% improvement when 
      using relatively larger models such as BERT and RoBERTa and so, their performance 
      become competitive with domain-specific large language models such as PubMedBERT.
CI  - Copyright © 2023 Elsevier Inc. All rights reserved.
FAU - Arabzadeh, Negar
AU  - Arabzadeh N
AD  - University of Waterloo, Waterloo, ON, Canada. Electronic address: 
      narabzad@uwaterloo.ca.
FAU - Bagheri, Ebrahim
AU  - Bagheri E
AD  - Toronto Metropolitan University, Toronto, ON, Canada.
LA  - eng
PT  - Journal Article
DEP - 20230916
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
OTO - NOTNLM
OT  - Biomedical question answering
OT  - Domain-specific language model
OT  - General-purpose language model
OT  - Self-supervised learning
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/09/19 00:43
MHDA- 2023/09/19 00:43
CRDT- 2023/09/18 19:12
PHST- 2023/01/24 00:00 [received]
PHST- 2023/08/14 00:00 [revised]
PHST- 2023/09/01 00:00 [accepted]
PHST- 2023/09/19 00:43 [pubmed]
PHST- 2023/09/19 00:43 [medline]
PHST- 2023/09/18 19:12 [entrez]
AID - S1532-0464(23)00207-1 [pii]
AID - 10.1016/j.jbi.2023.104486 [doi]
PST - ppublish
SO  - J Biomed Inform. 2023 Oct;146:104486. doi: 10.1016/j.jbi.2023.104486. Epub 2023 
      Sep 16.

PMID- 38005425
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231127
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 22
DP  - 2023 Nov 8
TI  - Detection of AI-Created Images Using Pixel-Wise Feature Extraction and 
      Convolutional Neural Networks.
LID - 10.3390/s23229037 [doi]
LID - 9037
AB  - Generative AI has gained enormous interest nowadays due to new applications like 
      ChatGPT, DALL E, Stable Diffusion, and Deepfake. In particular, DALL E, Stable 
      Diffusion, and others (Adobe Firefly, ImagineArt, etc.) can create images from a 
      text prompt and are even able to create photorealistic images. Due to this fact, 
      intense research has been performed to create new image forensics applications 
      able to distinguish between real captured images and videos and artificial ones. 
      Detecting forgeries made with Deepfake is one of the most researched issues. This 
      paper is about another kind of forgery detection. The purpose of this research is 
      to detect photorealistic AI-created images versus real photos coming from a 
      physical camera. Id est, making a binary decision over an image, asking whether 
      it is artificially or naturally created. Artificial images do not need to try to 
      represent any real object, person, or place. For this purpose, techniques that 
      perform a pixel-level feature extraction are used. The first one is Photo 
      Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on 
      the camera sensor that is used for source camera identification. The underlying 
      idea is that AI images will have a different PRNU pattern. The second one is 
      error level analysis (ELA). This is another type of feature extraction 
      traditionally used for detecting image editing. ELA is being used nowadays by 
      photographers for the manual detection of AI-created images. Both kinds of 
      features are used to train convolutional neural networks to differentiate between 
      AI images and real photographs. Good results are obtained, achieving accuracy 
      rates of over 95%. Both extraction methods are carefully assessed by computing 
      precision/recall and F(1)-score measurements.
FAU - Martin-Rodriguez, Fernando
AU  - Martin-Rodriguez F
AUID- ORCID: 0000-0002-5323-7744
AD  - AtlanTTic Research Center for Telecommunication Technologies, University of Vigo, 
      36310 Vigo, Spain.
FAU - Garcia-Mojon, Rocio
AU  - Garcia-Mojon R
AUID- ORCID: 0009-0007-8084-7402
AD  - AtlanTTic Research Center for Telecommunication Technologies, University of Vigo, 
      36310 Vigo, Spain.
FAU - Fernandez-Barciela, Monica
AU  - Fernandez-Barciela M
AUID- ORCID: 0000-0002-3650-6068
AD  - AtlanTTic Research Center for Telecommunication Technologies, University of Vigo, 
      36310 Vigo, Spain.
LA  - eng
PT  - Journal Article
DEP - 20231108
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10674908
OTO - NOTNLM
OT  - AI images
OT  - CCN
OT  - ELA
OT  - PRNU
OT  - artificial intelligence
OT  - deep learning
OT  - photographs
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/25 12:42
MHDA- 2023/11/25 12:43
CRDT- 2023/11/25 01:27
PHST- 2023/09/29 00:00 [received]
PHST- 2023/10/31 00:00 [revised]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/11/25 12:43 [medline]
PHST- 2023/11/25 12:42 [pubmed]
PHST- 2023/11/25 01:27 [entrez]
AID - s23229037 [pii]
AID - sensors-23-09037 [pii]
AID - 10.3390/s23229037 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Nov 8;23(22):9037. doi: 10.3390/s23229037.

PMID- 37693657
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230913
IS  - 2215-0161 (Print)
IS  - 2215-0161 (Electronic)
IS  - 2215-0161 (Linking)
VI  - 11
DP  - 2023 Dec
TI  - A method for analyzing text using VOSviewer.
PG  - 102339
LID - 10.1016/j.mex.2023.102339 [doi]
LID - 102339
AB  - The need for technical support for data handling and visualization solutions has 
      increased in tandem with the complexity of today's data and information, that is 
      of multiple sources, huge in size and of different formats. This study focuses on 
      handling and analyzing text-based data. Despite many available text analysis 
      tools, there is a high demand among researchers for easy- to-use tools yet 
      scalable and with incomparable visualization features. Of recent, there has been 
      a significant focus on utilizing VOSviewer, an open-source software for 
      bibliometric analysis. This software is able to analyze a significant amount of 
      data and provide excellent network data mapping. However, there is a lack of 
      existing work in evaluating this sophisticated tool for text analysis. Thus, this 
      article explores the capability of VOSviewer and presents evidence-based 
      implementation of this software for text analysis. Specifically, this study 
      demonstrates the usage of VOSviewer to analyze text based on YouTube interviews 
      related to ChatGPT. Hence, this study significantly contributes by processing 
      textual data and producing visualization network maps that are different from 
      bibliometric data. The study recognizes VOSviewer as a powerful tool for data 
      visualization in mapping text data and illustrates the potential of this software 
      for analyzing text networks in various fields. •The study illustrates how text 
      analysis and visualization can be realized using VOSviewer, an open-source 
      software mostly used for biblio- metric analysis.•The study presents the workflow 
      indicating how the dataset can be prepared as input for VOSviewer for text 
      analysis.•The study proves that VOSviewer is a powerful tool for data 
      visualization and network mapping for any type of network data including 
      transcripts from social media.
CI  - © 2023 The Authors. Published by Elsevier B.V.
FAU - Bukar, Umar Ali
AU  - Bukar UA
AD  - Centre for Intelligent Cloud Computing (CICC), Faculty of Information Science and 
      Technology, Multimedia University, Melaka, Malaysia.
FAU - Sayeed, Md Shohel
AU  - Sayeed MS
AD  - Centre for Intelligent Cloud Computing (CICC), Faculty of Information Science and 
      Technology, Multimedia University, Melaka, Malaysia.
FAU - Razak, Siti Fatimah Abdul
AU  - Razak SFA
AD  - Centre for Intelligent Cloud Computing (CICC), Faculty of Information Science and 
      Technology, Multimedia University, Melaka, Malaysia.
FAU - Yogarayan, Sumendra
AU  - Yogarayan S
AD  - Centre for Intelligent Cloud Computing (CICC), Faculty of Information Science and 
      Technology, Multimedia University, Melaka, Malaysia.
FAU - Amodu, Oluwatosin Ahmed
AU  - Amodu OA
AD  - Department of Electrical, Electronics Systems Engineering, Universiti Kebangsaan 
      Malaysia (UKM), Bangi, Selangor 43600, Malaysia.
AD  - Information and Communication Engineering Department, Elizade University, 
      Ilara-Mokin, Ondo State, Nigeria.
FAU - Mahmood, Raja Azlina Raja
AU  - Mahmood RAR
AD  - Department of Communication Technology and Network, Faculty of Computer Science 
      and Information Technology, Universiti Putra Malaysia, Serdang, Selangor Darul 
      Ehsan 43400, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - Netherlands
TA  - MethodsX
JT  - MethodsX
JID - 101639829
PMC - PMC10491643
OTO - NOTNLM
OT  - Analysis and visualization of text data
OT  - Dataset
OT  - Text analysis
OT  - VOSviewer
OT  - Visualization
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/09/11 06:42
MHDA- 2023/09/11 06:43
CRDT- 2023/09/11 04:57
PHST- 2023/05/05 00:00 [received]
PHST- 2023/08/20 00:00 [accepted]
PHST- 2023/09/11 06:43 [medline]
PHST- 2023/09/11 06:42 [pubmed]
PHST- 2023/09/11 04:57 [entrez]
AID - S2215-0161(23)00336-9 [pii]
AID - 102339 [pii]
AID - 10.1016/j.mex.2023.102339 [doi]
PST - epublish
SO  - MethodsX. 2023 Aug 22;11:102339. doi: 10.1016/j.mex.2023.102339. eCollection 2023 
      Dec.

PMID- 37567827
OWN - NLM
STAT- Publisher
LR  - 20230811
IS  - 1873-7560 (Electronic)
IS  - 0302-2838 (Linking)
DP  - 2023 Aug 9
TI  - How Well Do Artificial Intelligence Chatbots Respond to the Top Search Queries 
      About Urological Malignancies?
LID - S0302-2838(23)02972-X [pii]
LID - 10.1016/j.eururo.2023.07.004 [doi]
AB  - Artificial intelligence (AI) chatbots are becoming a popular source of 
      information but there are limited data on the quality of information on 
      urological malignancies that they provide. Our objective was to characterize the 
      quality of information and detect misinformation about prostate, bladder, kidney, 
      and testicular cancers from four AI chatbots: ChatGPT, Perplexity, Chat Sonic, 
      and Microsoft Bing AI. We used the top five search queries related to prostate, 
      bladder, kidney, and testicular cancers according to Google Trends from January 
      2021 to January 2023 and input them into the AI chatbots. Responses were 
      evaluated for quality, understandability, actionability, misinformation, and 
      readability using published instruments. AI chatbot responses had moderate to 
      high information quality (median DISCERN score 4 out of 5, range 2-5) and lacked 
      misinformation. Understandability was moderate (median Patient Education Material 
      Assessment Tool for Printable Materials [PEMAT-P] understandability 66.7%, range 
      44.4-90.9%) and actionability was moderate to poor (median PEMAT-P actionability 
      40%, range 0-40%The responses were written at a fairly difficult reading level. 
      AI chatbots produce information that is generally accurate and of moderate to 
      high quality in response to the top urological malignancy-related search queries, 
      but the responses lack clear, actionable instructions and exceed the reading 
      level recommended for consumer health information. PATIENT SUMMARY: Artificial 
      intelligence chatbots produce information that is generally accurate and of 
      moderately high quality in response to popular Google searches about urological 
      cancers. However, their responses are fairly difficult to read, are moderately 
      hard to understand, and lack clear instructions for users to act on.
CI  - Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All 
      rights reserved.
FAU - Musheyev, David
AU  - Musheyev D
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York, NY, USA.
FAU - Pan, Alexander
AU  - Pan A
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York, NY, USA.
FAU - Loeb, Stacy
AU  - Loeb S
AD  - Department of Urology, New York University and Manhattan Veterans Affairs, New 
      York, NY, USA; Department of Population Health, New York University, New York, 
      NY, USA.
FAU - Kabarriti, Abdo E
AU  - Kabarriti AE
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York, NY, USA. Electronic address: abdo.kabarriti@downstate.edu.
LA  - eng
PT  - Journal Article
DEP - 20230809
PL  - Switzerland
TA  - Eur Urol
JT  - European urology
JID - 7512719
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Misinformation
OT  - Urological malignancies
EDAT- 2023/08/12 10:42
MHDA- 2023/08/12 10:42
CRDT- 2023/08/11 21:57
PHST- 2023/04/20 00:00 [received]
PHST- 2023/06/18 00:00 [revised]
PHST- 2023/07/17 00:00 [accepted]
PHST- 2023/08/12 10:42 [medline]
PHST- 2023/08/12 10:42 [pubmed]
PHST- 2023/08/11 21:57 [entrez]
AID - S0302-2838(23)02972-X [pii]
AID - 10.1016/j.eururo.2023.07.004 [doi]
PST - aheadofprint
SO  - Eur Urol. 2023 Aug 9:S0302-2838(23)02972-X. doi: 10.1016/j.eururo.2023.07.004.

PMID- 36942193
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230322
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 2
DP  - 2023 Feb
TI  - The Gridlock Between Chronic Cerebral Venous Thrombosis and Dural Arteriovenous 
      Fistulas.
PG  - e35035
LID - 10.7759/cureus.35035 [doi]
LID - e35035
AB  - Cerebral venous thrombosis (CVT) is a rare condition that occurs when a blood 
      clot forms in the veins that drain blood from the brain. The incidence of CVT is 
      estimated to be 1.3-1.6 cases per 100,000 adults, with a higher prevalence in 
      females than males. Dural arteriovenous fistulas (DAVFs) are abnormal connections 
      between the dural sinuses and the venous system, which can occur as a 
      complication of CVT. The incidence of DAVFs after a diagnosis of CVT is reported 
      to be between 0.9 and 13%. It is believed that a thrombus in the cerebral venous 
      system causes stagnation of blood flow leading to an increase in venous pressure 
      and causing enlargement of pre-existing physiological arteriovenous shunts or 
      neoangiogenesis, resulting in the development of a DAVF. However, it is difficult 
      to establish a causal relationship between CVT and DAVF as most low-grade DAVFs 
      are asymptomatic and lack evidence. High-grade DAVFs are considered to be a more 
      serious form of the condition, as they are associated with a higher risk of 
      intracerebral hemorrhage (ICH) and other neurological complications. In our case 
      series of a total of two cases, all were diagnosed first with CVT, and later in 
      the follow-up imaging, chronic CVT with dural AV fistula was observed. This 
      highlights the importance of long-term follow-up imaging in patients with CVT to 
      detect any potential complications such as DAVF, especially in high-risk 
      patients, and to ensure the prompt treatment to prevent serious complications.
CI  - Copyright © 2023, Kosinepalli et al.
FAU - Kosinepalli, Sai Siddartha
AU  - Kosinepalli SS
AD  - Radiology, Jagadguru Sri Shivarathreeshwara Medical College, Mysuru, IND.
FAU - Das, Sudha Kiran
AU  - Das SK
AD  - Radiology, Jagadguru Sri Shivarathreeshwara Medical College, Mysuru, IND.
FAU - B, Gurumurthy
AU  - B G
AD  - Radiology, Jagadguru Sri Shivarathreeshwara Medical College, Mysuru, IND.
FAU - Chittaragi, Kavitha
AU  - Chittaragi K
AD  - Radiology, Jagadguru Sri Shivarathreeshwara Medical College, Mysuru, IND.
LA  - eng
PT  - Case Reports
DEP - 20230215
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10023285
OTO - NOTNLM
OT  - cerebral venous thrombosis
OT  - chatgpt
OT  - complications
OT  - dural arteriovenous fistula
OT  - intracerebral hemorrhage
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/03/22 06:00
MHDA- 2023/03/22 06:01
CRDT- 2023/03/21 02:16
PHST- 2023/02/15 00:00 [accepted]
PHST- 2023/03/21 02:16 [entrez]
PHST- 2023/03/22 06:00 [pubmed]
PHST- 2023/03/22 06:01 [medline]
AID - 10.7759/cureus.35035 [doi]
PST - epublish
SO  - Cureus. 2023 Feb 15;15(2):e35035. doi: 10.7759/cureus.35035. eCollection 2023 
      Feb.

PMID- 37934833
STAT- Publisher
PB  - Canadian Agency for Drugs and Technologies in Health
CTI - CADTH Horizon Scans
DP  - 2023 Aug
BTI - Artificial Intelligence in Prehospital Emergency Health Care: CADTH Horizon Scan
AB  - WHAT IS THE ISSUE? Health care systems in Canada, including prehospital emergency 
      health care services, are facing staffing shortages and increased patient 
      volumes, and are looking for creative solutions to ensure the provision of 
      high-quality care. WHAT IS THE TECHNOLOGY? Artificial intelligence (AI) combines 
      computer science and data to mimic human thought processes, problem solving, and 
      responses. AI relies on statistical models, algorithms, data analysis, and 
      machine learning to make predictions based on past behaviours or results 
      (predictive AI) or to learn from past data to generate new content (generative 
      AI). Chatbots, such as ChatGPT, are examples of generative AI. WHAT IS THE 
      POTENTIAL IMPACT? In health care settings, AI can be used to automate some 
      clinical and administrative processes to create efficiencies and reduce burden on 
      health care providers and health administrators. In a prehospital setting, this 
      can include AI used at the dispatch stage (e.g., analyzing the conversation and 
      prompting a dispatcher with additional questions, or translating speech to help a 
      dispatcher understand a caller in real time) or in an ambulance (e.g., providing 
      traffic analysis or information to support optimal patient management). WHAT ELSE 
      DO WE KEED TO KNOW? The use of AI in prehospital emergency health care is still 
      in the early stages of development and implementation. Early implementation of AI 
      programs such as those to detect out of hospital cardiac events and to triage 
      emergency calls during peak times are underway in some countries. . More 
      real-world clinical trials and research are required before widespread 
      implementation will take place.
CN  - Authors
FAU - Clark, Michelle
AU  - Clark M
FAU - Severn, Melissa
AU  - Severn M
LA  - eng
PT  - Review
PT  - Book
PL  - Ottawa (ON)
RF  - 22
EDAT- 2023/08/01 00:00
CRDT- 2023/08/01 00:00
AID - NBK596747 [bookaccession]

PMID- 37709612
OWN - NLM
STAT- Publisher
LR  - 20230914
IS  - 1878-4046 (Electronic)
IS  - 1076-6332 (Linking)
DP  - 2023 Sep 12
TI  - Assessing AI-Powered Patient Education: A Case Study in Radiology.
LID - S1076-6332(23)00439-7 [pii]
LID - 10.1016/j.acra.2023.08.020 [doi]
AB  - RATIONALE AND OBJECTIVES: With recent advancements in the power and accessibility 
      of artificial intelligence (AI) Large Language Models (LLMs) patients might 
      increasingly turn to these platforms to answer questions regarding radiologic 
      examinations and procedures, despite valid concerns about the accuracy of 
      information provided. This study aimed to assess the accuracy and completeness of 
      information provided by the Bing Chatbot-a LLM powered by ChatGPT-on patient 
      education for common radiologic exams. MATERIALS AND METHODS: We selected three 
      common radiologic examinations and procedures: computed tomography (CT) abdomen, 
      magnetic resonance imaging (MRI) spine, and bone biopsy. For each, ten questions 
      were tested on the chatbot in two trials using three different chatbot settings. 
      Two reviewers independently assessed the chatbot's responses for accuracy and 
      completeness compared to an accepted online resource, radiologyinfo.org. RESULTS: 
      Of the 360 reviews performed, 336 (93%) were rated "entirely correct" and 24 (7%) 
      were "mostly correct," indicating a high level of reliability. Completeness 
      ratings showed that 65% were "complete" and 35% were "mostly complete." The "More 
      Creative" chatbot setting produced a higher proportion of responses rated 
      "entirely correct" but there were otherwise no significant difference in ratings 
      based on chatbot settings or exam types. The readability level was rated 
      eighth-grade level. CONCLUSION: The Bing Chatbot provided accurate responses 
      answering all or most aspects of the question asked of it, with responses tending 
      to err on the side of caution for nuanced questions. Importantly, no responses 
      were inaccurate or had potential to cause harm or confusion for the user. Thus, 
      LLM chatbots demonstrate potential to enhance patient education in radiology and 
      could be integrated into patient portals for various purposes, including exam 
      preparation and results interpretation.
CI  - Copyright © 2023 The Association of University Radiologists. Published by 
      Elsevier Inc. All rights reserved.
FAU - Kuckelman, Ian J
AU  - Kuckelman IJ
AD  - University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, 
      Madison, WI 53705 (I.J.K., M.B., I.O., J.A.A., A.B.R.). Electronic address: 
      kuckelman@wisc.edu.
FAU - Yi, Paul H
AU  - Yi PH
AD  - University of Maryland School of Medicine, Baltimore, Maryland (P.H.Y.).
FAU - Bui, Molinna
AU  - Bui M
AD  - University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, 
      Madison, WI 53705 (I.J.K., M.B., I.O., J.A.A., A.B.R.).
FAU - Onuh, Ifeanyi
AU  - Onuh I
AD  - University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, 
      Madison, WI 53705 (I.J.K., M.B., I.O., J.A.A., A.B.R.).
FAU - Anderson, Jade A
AU  - Anderson JA
AD  - University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, 
      Madison, WI 53705 (I.J.K., M.B., I.O., J.A.A., A.B.R.).
FAU - Ross, Andrew B
AU  - Ross AB
AD  - University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, 
      Madison, WI 53705 (I.J.K., M.B., I.O., J.A.A., A.B.R.).
LA  - eng
PT  - Journal Article
DEP - 20230912
PL  - United States
TA  - Acad Radiol
JT  - Academic radiology
JID - 9440159
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Bing Chatbot
OT  - Large language models
OT  - Patient education
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/09/15 00:42
MHDA- 2023/09/15 00:42
CRDT- 2023/09/14 21:55
PHST- 2023/07/17 00:00 [received]
PHST- 2023/08/08 00:00 [revised]
PHST- 2023/08/19 00:00 [accepted]
PHST- 2023/09/15 00:42 [medline]
PHST- 2023/09/15 00:42 [pubmed]
PHST- 2023/09/14 21:55 [entrez]
AID - S1076-6332(23)00439-7 [pii]
AID - 10.1016/j.acra.2023.08.020 [doi]
PST - aheadofprint
SO  - Acad Radiol. 2023 Sep 12:S1076-6332(23)00439-7. doi: 10.1016/j.acra.2023.08.020.

PMID- 38045764
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231204
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - The unreasonable effectiveness of large language models in zero-shot semantic 
      annotation of legal texts.
PG  - 1279794
LID - 10.3389/frai.2023.1279794 [doi]
LID - 1279794
AB  - The emergence of ChatGPT has sensitized the general public, including the legal 
      profession, to large language models' (LLMs) potential uses (e.g., document 
      drafting, question answering, and summarization). Although recent studies have 
      shown how well the technology performs in diverse semantic annotation tasks 
      focused on legal texts, an influx of newer, more capable (GPT-4) or 
      cost-effective (GPT-3.5-turbo) models requires another analysis. This paper 
      addresses recent developments in the ability of LLMs to semantically annotate 
      legal texts in zero-shot learning settings. Given the transition to mature 
      generative AI systems, we examine the performance of GPT-4 and 
      GPT-3.5-turbo(-16k), comparing it to the previous generation of GPT models, on 
      three legal text annotation tasks involving diverse documents such as 
      adjudicatory opinions, contractual clauses, or statutory provisions. We also 
      compare the models' performance and cost to better understand the trade-offs. We 
      found that the GPT-4 model clearly outperforms the GPT-3.5 models on two of the 
      three tasks. The cost-effective GPT-3.5-turbo matches the performance of the 20× 
      more expensive text-davinci-003 model. While one can annotate multiple data 
      points within a single prompt, the performance degrades as the size of the batch 
      increases. This work provides valuable information relevant for many practical 
      applications (e.g., in contract review) and research projects (e.g., in empirical 
      legal studies). Legal scholars and practicing lawyers alike can leverage these 
      findings to guide their decisions in integrating LLMs in a wide range of 
      workflows involving semantic annotation of legal texts.
CI  - Copyright © 2023 Savelka and Ashley.
FAU - Savelka, Jaromir
AU  - Savelka J
AD  - School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United 
      States.
FAU - Ashley, Kevin D
AU  - Ashley KD
AD  - School of Law, University of Pittsburgh, Pittsburgh, PA, United States.
LA  - eng
PT  - Journal Article
DEP - 20231117
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10690809
OTO - NOTNLM
OT  - large language models (LLM)
OT  - legal text analytics
OT  - semantic annotation
OT  - text annotation
OT  - zero-shot classification
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 04:44
PHST- 2023/08/18 00:00 [received]
PHST- 2023/10/16 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 04:44 [entrez]
AID - 10.3389/frai.2023.1279794 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Nov 17;6:1279794. doi: 10.3389/frai.2023.1279794. 
      eCollection 2023.

PMID- 37781703
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231004
IS  - 1663-9812 (Print)
IS  - 1663-9812 (Electronic)
IS  - 1663-9812 (Linking)
VI  - 14
DP  - 2023
TI  - Approach to machine learning for extraction of real-world data variables from 
      electronic health records.
PG  - 1180962
LID - 10.3389/fphar.2023.1180962 [doi]
LID - 1180962
AB  - Background: As artificial intelligence (AI) continues to advance with 
      breakthroughs in natural language processing (NLP) and machine learning (ML), 
      such as the development of models like OpenAI's ChatGPT, new opportunities are 
      emerging for efficient curation of electronic health records (EHR) into 
      real-world data (RWD) for evidence generation in oncology. Our objective is to 
      describe the research and development of industry methods to promote transparency 
      and explainability. Methods: We applied NLP with ML techniques to train, 
      validate, and test the extraction of information from unstructured documents 
      (e.g., clinician notes, radiology reports, lab reports, etc.) to output a set of 
      structured variables required for RWD analysis. This research used a nationwide 
      electronic health record (EHR)-derived database. Models were selected based on 
      performance. Variables curated with an approach using ML extraction are those 
      where the value is determined solely based on an ML model (i.e. not confirmed by 
      abstraction), which identifies key information from visit notes and documents. 
      These models do not predict future events or infer missing information. Results: 
      We developed an approach using NLP and ML for extraction of clinically meaningful 
      information from unstructured EHR documents and found high performance of output 
      variables compared with variables curated by manually abstracted data. These 
      extraction methods resulted in research-ready variables including initial cancer 
      diagnosis with date, advanced/metastatic diagnosis with date, disease stage, 
      histology, smoking status, surgery status with date, biomarker test results with 
      dates, and oral treatments with dates. Conclusion: NLP and ML enable the 
      extraction of retrospective clinical data in EHR with speed and scalability to 
      help researchers learn from the experience of every person with cancer.
CI  - Copyright © 2023 Adamson, Waskom, Blarre, Kelly, Krismer, Nemeth, Gippetti, 
      Ritten, Harrison, Ho, Linzmayer, Bansal, Wilkinson, Amster, Estola, Benedum, 
      Fidyk, Estévez, Shapiro and Cohen.
FAU - Adamson, Blythe
AU  - Adamson B
AD  - Flatiron Health, Inc., New York, NY, United States.
AD  - The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, 
      Department of Pharmacy, University of Washington, Seattle, WA, United States.
FAU - Waskom, Michael
AU  - Waskom M
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Blarre, Auriane
AU  - Blarre A
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Kelly, Jonathan
AU  - Kelly J
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Krismer, Konstantin
AU  - Krismer K
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Nemeth, Sheila
AU  - Nemeth S
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Gippetti, James
AU  - Gippetti J
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Ritten, John
AU  - Ritten J
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Harrison, Katherine
AU  - Harrison K
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Ho, George
AU  - Ho G
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Linzmayer, Robin
AU  - Linzmayer R
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Bansal, Tarun
AU  - Bansal T
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Wilkinson, Samuel
AU  - Wilkinson S
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Amster, Guy
AU  - Amster G
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Estola, Evan
AU  - Estola E
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Benedum, Corey M
AU  - Benedum CM
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Fidyk, Erin
AU  - Fidyk E
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Estévez, Melissa
AU  - Estévez M
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Shapiro, Will
AU  - Shapiro W
AD  - Flatiron Health, Inc., New York, NY, United States.
FAU - Cohen, Aaron B
AU  - Cohen AB
AD  - Flatiron Health, Inc., New York, NY, United States.
AD  - Department of Medicine, NYU Grossman School of Medicine, New York, NY, United 
      States.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - Switzerland
TA  - Front Pharmacol
JT  - Frontiers in pharmacology
JID - 101548923
PMC - PMC10541019
OTO - NOTNLM
OT  - artificial intelligence
OT  - cancer
OT  - electronic health records
OT  - machine learning
OT  - natural language processing
OT  - oncology
OT  - real-world data
COIS- Authors BA, MW, AB, JK, KK, SN, JG, JR, KH, GH, RL, TB, SW, GA, EE, CB, EF, ME, 
      WS, and AB are employees of Flatiron Health, Inc., which is an independent member 
      of the Roche group, and own stock in Roche.
EDAT- 2023/10/02 06:42
MHDA- 2023/10/02 06:43
CRDT- 2023/10/02 04:51
PHST- 2023/03/06 00:00 [received]
PHST- 2023/08/25 00:00 [accepted]
PHST- 2023/10/02 06:43 [medline]
PHST- 2023/10/02 06:42 [pubmed]
PHST- 2023/10/02 04:51 [entrez]
AID - 1180962 [pii]
AID - 10.3389/fphar.2023.1180962 [doi]
PST - epublish
SO  - Front Pharmacol. 2023 Sep 15;14:1180962. doi: 10.3389/fphar.2023.1180962. 
      eCollection 2023.

PMID- 37130009
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231024
IS  - 1938-808X (Electronic)
IS  - 1040-2446 (Linking)
VI  - 98
IP  - 9
DP  - 2023 Sep 1
TI  - Medical School Admissions: Focusing on Producing a Physician Workforce That 
      Addresses the Needs of the United States.
PG  - 983-986
LID - 10.1097/ACM.0000000000005262 [doi]
AB  - The aging population, burnout, and earlier retirement of physicians along with 
      the static number of training positions are likely to worsen the current 
      physician shortage. There is an urgent need to transform the process for 
      selecting medical students. In this Invited Commentary, the authors suggest that 
      to build the physician workforce that the United States needs for the future, 
      academic medicine should focus on building capacity in 3 overarching areas. 
      First, medical schools need to develop a more diverse pool of capable applicants 
      that better matches the demographic characteristics of health care trainees with 
      those of the population, and they need to nurture applicants with diverse career 
      aspirations. Second, medical schools should recalibrate their student selection 
      process, aligning criteria for admission with competencies expected of medical 
      school graduates, whether they choose to become practicing clinicians, 
      physician-scientists, members of the public health workforce, or policy makers. 
      Selection criteria that overweight the results of standardized test scores should 
      be replaced by assessments that value and predict academic capacity, adaptive 
      learning skills, curiosity, compassion, empathy, emotional maturity, and superior 
      communication skills. Finally, to improve the equity and effectiveness of the 
      selection processes, medical schools should leverage innovations in data science 
      and generative artificial intelligence platforms. The ability of ChatGPT to pass 
      the United States Medical Licensing Examination (USMLE) demonstrates the 
      decreasing importance of memorization in medicine in favor of critical thinking 
      and problem-solving skills. The 2022 change in the USMLE Step 1 to pass/fail plus 
      the exodus of several prominent medical schools from the U.S. News and World 
      Report rankings have exposed limitations of the current selection processes. 
      Newer approaches that use precision education systems to leverage data and 
      technology can help address these limitations.
CI  - Copyright © 2023 by the Association of American Medical Colleges.
FAU - Prober, Charles G
AU  - Prober CG
AD  - C.G. Prober is professor of pediatrics, microbiology, and immunology, and senior 
      associate vice provost for health education, Stanford University, Stanford, 
      California.
FAU - Desai, Sanjay V
AU  - Desai SV
AD  - S.V. Desai is chief academic officer, American Medical Association, Washington, 
      DC, and professor of medicine, Johns Hopkins University, Baltimore, Maryland.
LA  - eng
PT  - Journal Article
DEP - 20230427
PL  - United States
TA  - Acad Med
JT  - Academic medicine : journal of the Association of American Medical Colleges
JID - 8904605
SB  - IM
MH  - Humans
MH  - United States
MH  - Aged
MH  - *Schools, Medical
MH  - Artificial Intelligence
MH  - *Physicians
MH  - Workforce
MH  - School Admission Criteria
EDAT- 2023/05/02 18:42
MHDA- 2023/10/23 12:42
CRDT- 2023/05/02 12:23
PHST- 2023/10/23 12:42 [medline]
PHST- 2023/05/02 18:42 [pubmed]
PHST- 2023/05/02 12:23 [entrez]
AID - 00001888-990000000-00437 [pii]
AID - 10.1097/ACM.0000000000005262 [doi]
PST - ppublish
SO  - Acad Med. 2023 Sep 1;98(9):983-986. doi: 10.1097/ACM.0000000000005262. Epub 2023 
      Apr 27.

PMID- 37065505
OWN - NLM
STAT- MEDLINE
DCOM- 20230418
LR  - 20230421
IS  - 2046-1402 (Electronic)
IS  - 2046-1402 (Linking)
VI  - 12
DP  - 2023
TI  - Open Data and transparency in artificial intelligence and machine learning: A new 
      era of research.
PG  - 387
LID - 10.12688/f1000research.133019.1 [doi]
LID - 387
AB  - Artificial Intelligence (AI) and machine learning are the current forefront of 
      computer science and technology. AI and related sub-disciplines, including 
      machine learning, are essential technologies which have enabled the widespread 
      use of smart technology, such as smart phones, smart home appliances and even 
      electric toothbrushes. It is AI that allows the devices used day-to-day across 
      people's personal lives, working lives and in industry to better anticipate and 
      respond to our needs. However, the use of AI technology comes with a range of 
      ethical questions - including issues around privacy, security, reliability, 
      copyright/plagiarism and whether AI is capable of independent, conscious thought. 
      We have seen several issues related to racial and sexual bias in AI in the recent 
      times, putting the reliability of AI in question. Many of these issues have been 
      brought to the forefront of cultural awareness in late 2022, early 2023, with the 
      rise of AI art programs (and the copyright issues arising from the deep-learning 
      methods employed to train this AI), and the popularity of ChatGPT alongside its 
      ability to be used to mimic human output, particularly in regard to academic 
      work. In critical areas like healthcare, the errors of AI can be fatal. With the 
      incorporation of AI in almost every sector of our everyday life, we need to keep 
      asking ourselves- can we trust AI, and how much? This Editorial outlines the 
      importance of openness and transparency in the development and applications of AI 
      to allow all users to fully understand both the benefits and risks of this 
      ubiquitous technology, and outlines how the Artificial Intelligence and Machine 
      Learning  Gateway on F1000Research meets these needs.
CI  - Copyright: © 2023 Rodgers CM et al.
FAU - Rodgers, Caellin M
AU  - Rodgers CM
AUID- ORCID: 0009-0003-6927-9222
AD  - F1000, London, UK.
FAU - Ellingson, Sally R
AU  - Ellingson SR
AD  - UK College of Medicine, Markey Cancer Center, Lexington, Kentucky, USA.
FAU - Chatterjee, Parag
AU  - Chatterjee P
AUID- ORCID: 0000-0001-6760-4704
AD  - Universidad Tecnologica Nacional Facultad Regional Buenos Aires, Buenos Aires, 
      Autonomous City of Buenos Aires, Argentina.
AD  - Universidad de la Republica Uruguay, Montevideo, Montevideo Department, Uruguay.
LA  - eng
PT  - Editorial
DEP - 20230412
PL  - England
TA  - F1000Res
JT  - F1000Research
JID - 101594320
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Reproducibility of Results
MH  - *Machine Learning
MH  - Health Facilities
MH  - Industry
PMC - PMC10098385
OTO - NOTNLM
OT  - Artificial Intelligence and Machine Learning Gateway
OT  - artificial intelligence
OT  - machine learning
OT  - open data
OT  - open research
OT  - sharing
COIS- Competing interests: Caellin M Rodgers helped in preparing the first draft of 
      this article. Caellin M Rodgers is a Publishing Executive at F1000. She did not 
      handle the editorial processing of this article in any way.
EDAT- 2023/04/18 06:00
MHDA- 2023/04/18 06:42
CRDT- 2023/04/17 03:56
PHST- 2023/04/04 00:00 [accepted]
PHST- 2023/04/18 06:42 [medline]
PHST- 2023/04/17 03:56 [entrez]
PHST- 2023/04/18 06:00 [pubmed]
AID - 10.12688/f1000research.133019.1 [doi]
PST - epublish
SO  - F1000Res. 2023 Apr 12;12:387. doi: 10.12688/f1000research.133019.1. eCollection 
      2023.

PMID- 37899405
OWN - NLM
STAT- MEDLINE
DCOM- 20231031
LR  - 20231101
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 29
TI  - Comparative performance of humans versus GPT-4.0 and GPT-3.5 in the 
      self-assessment program of American Academy of Ophthalmology.
PG  - 18562
LID - 10.1038/s41598-023-45837-2 [doi]
LID - 18562
AB  - To compare the performance of humans, GPT-4.0 and GPT-3.5 in answering 
      multiple-choice questions from the American Academy of Ophthalmology (AAO) Basic 
      and Clinical Science Course (BCSC) self-assessment program, available at 
      https://www.aao.org/education/self-assessments . In June 2023, text-based 
      multiple-choice questions were submitted to GPT-4.0 and GPT-3.5. The AAO provides 
      the percentage of humans who selected the correct answer, which was analyzed for 
      comparison. All questions were classified by 10 subspecialties and 3 practice 
      areas (diagnostics/clinics, medical treatment, surgery). Out of 1023 questions, 
      GPT-4.0 achieved the best score (82.4%), followed by humans (75.7%) and GPT-3.5 
      (65.9%), with significant difference in accuracy rates (always P < 0.0001). Both 
      GPT-4.0 and GPT-3.5 showed the worst results in surgery-related questions (74.6% 
      and 57.0% respectively). For difficult questions (answered incorrectly by > 50% 
      of humans), both GPT models favorably compared to humans, without reaching 
      significancy. The word count for answers provided by GPT-4.0 was significantly 
      lower than those produced by GPT-3.5 (160 ± 56 and 206 ± 77 respectively, 
      P < 0.0001); however, incorrect responses were longer (P < 0.02). GPT-4.0 
      represented a substantial improvement over GPT-3.5, achieving better performance 
      than humans in an AAO BCSC self-assessment test. However, ChatGPT is still 
      limited by inconsistency across different practice areas, especially when it 
      comes to surgery.
CI  - © 2023. The Author(s).
FAU - Taloni, Andrea
AU  - Taloni A
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Borselli, Massimiliano
AU  - Borselli M
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Scarsi, Valentina
AU  - Scarsi V
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Rossi, Costanza
AU  - Rossi C
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Coco, Giulia
AU  - Coco G
AD  - Department of Clinical Sciences and Translational Medicine, University of Rome 
      Tor Vergata, Rome, Italy.
FAU - Scorcia, Vincenzo
AU  - Scorcia V
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy.
FAU - Giannaccare, Giuseppe
AU  - Giannaccare G
AUID- ORCID: 0000-0003-2617-0289
AD  - Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro, 
      Italy. giuseppe.giannaccare@gmail.com.
AD  - Department of Surgical Sciences, Eye Clinic, University of Cagliari, Via 
      Università 40, 09124, Cagliari, Italy. giuseppe.giannaccare@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231029
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - *Ophthalmology
MH  - Self-Assessment
MH  - Academies and Institutes
PMC - PMC10613606
COIS- The authors declare no competing interests.
EDAT- 2023/10/30 06:46
MHDA- 2023/10/31 06:42
CRDT- 2023/10/30 00:07
PHST- 2023/07/26 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/10/31 06:42 [medline]
PHST- 2023/10/30 06:46 [pubmed]
PHST- 2023/10/30 00:07 [entrez]
AID - 10.1038/s41598-023-45837-2 [pii]
AID - 45837 [pii]
AID - 10.1038/s41598-023-45837-2 [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 29;13(1):18562. doi: 10.1038/s41598-023-45837-2.

PMID- 37795455
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231030
IS  - 2639-8028 (Electronic)
IS  - 2639-8028 (Linking)
VI  - 5
IP  - 10
DP  - 2023 Oct
TI  - Clinical Research With Large Language Models Generated Writing-Clinical Research 
      with AI-assisted Writing (CRAW) Study.
PG  - e0975
LID - 10.1097/CCE.0000000000000975 [doi]
LID - e0975
AB  - IMPORTANCE: The scientific community debates Generative Pre-trained Transformer 
      (GPT)-3.5's article quality, authorship merit, originality, and ethical use in 
      scientific writing. OBJECTIVES: Assess GPT-3.5's ability to craft the background 
      section of critical care clinical research questions compared to medical 
      researchers with H-indices of 22 and 13. DESIGN: Observational cross-sectional 
      study. SETTING: Researchers from 20 countries from six continents evaluated the 
      backgrounds. PARTICIPANTS: Researchers with a Scopus index greater than 1 were 
      included. MAIN OUTCOMES AND MEASURES: In this study, we generated a background 
      section of a critical care clinical research question on "acute kidney injury in 
      sepsis" using three different methods: researcher with H-index greater than 20, 
      researcher with H-index greater than 10, and GPT-3.5. The three background 
      sections were presented in a blinded survey to researchers with an H-index range 
      between 1 and 96. First, the researchers evaluated the main components of the 
      background using a 5-point Likert scale. Second, they were asked to identify 
      which background was written by humans only or with large language 
      model-generated tools. RESULTS: A total of 80 researchers completed the survey. 
      The median H-index was 3 (interquartile range, 1-7.25) and most (36%) researchers 
      were from the Critical Care specialty. When compared with researchers with an 
      H-index of 22 and 13, GPT-3.5 was marked high on the Likert scale ranking on main 
      background components (median 4.5 vs. 3.82 vs. 3.6 vs. 4.5, respectively; p < 
      0.001). The sensitivity and specificity to detect researchers writing versus 
      GPT-3.5 writing were poor, 22.4% and 57.6%, respectively. CONCLUSIONS AND 
      RELEVANCE: GPT-3.5 could create background research content indistinguishable 
      from the writing of a medical researcher. It was marked higher compared with 
      medical researchers with an H-index of 22 and 13 in writing the background 
      section of a critical care clinical research question.
CI  - Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf 
      of the Society of Critical Care Medicine.
FAU - Huespe, Ivan A
AU  - Huespe IA
AD  - Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
AD  - Universidad de Buenos Aires, Buenos Aires, Argentina.
FAU - Echeverri, Jorge
AU  - Echeverri J
AD  - Universidad Javeriana, Bogotá, Colombia.
FAU - Khalid, Aisha
AU  - Khalid A
AD  - Harvard Medical School, Boston, MA.
FAU - Carboni Bisso, Indalecio
AU  - Carboni Bisso I
AD  - Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
FAU - Musso, Carlos G
AU  - Musso CG
AD  - Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
AD  - Facultad de Ciencias de la Salud, Universidad Simon Bolivar, Barranquilla, 
      Colombia.
FAU - Surani, Salim
AU  - Surani S
AD  - Mayo Clinic, Rochester, MN.
AD  - Texas A&M University, College Station, TX.
FAU - Bansal, Vikas
AU  - Bansal V
AD  - Mayo Clinic, Rochester, MN.
FAU - Kashyap, Rahul
AU  - Kashyap R
AD  - Mayo Clinic, Rochester, MN.
AD  - WellSpan Health, York, PA.
LA  - eng
PT  - Journal Article
DEP - 20231002
PL  - United States
TA  - Crit Care Explor
JT  - Critical care explorations
JID - 101746347
PMC - PMC10547240
OTO - NOTNLM
OT  - Generative Pre-trained Transformer-3.5
OT  - article writing
OT  - artificial intelligence
OT  - clinical research
OT  - medical research
COIS- The authors have disclosed that they do not have any potential conflicts of 
      interest.
EDAT- 2023/10/05 06:44
MHDA- 2023/10/05 06:45
CRDT- 2023/10/05 04:12
PHST- 2023/10/05 06:45 [medline]
PHST- 2023/10/05 06:44 [pubmed]
PHST- 2023/10/05 04:12 [entrez]
AID - 10.1097/CCE.0000000000000975 [doi]
PST - epublish
SO  - Crit Care Explor. 2023 Oct 2;5(10):e0975. doi: 10.1097/CCE.0000000000000975. 
      eCollection 2023 Oct.

PMID- 37837968
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231116
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Electronic)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Oct 30
TI  - The Potential of GPT-4 as a Support Tool for Pharmacists: Analytical Study Using 
      the Japanese National Examination for Pharmacists.
PG  - e48452
LID - 10.2196/48452 [doi]
LID - e48452
AB  - BACKGROUND: The advancement of artificial intelligence (AI), as well as machine 
      learning, has led to its application in various industries, including health 
      care. AI chatbots, such as GPT-4, developed by OpenAI, have demonstrated 
      potential in supporting health care professionals by providing medical 
      information, answering examination questions, and assisting in medical education. 
      However, the applicability of GPT-4 in the field of pharmacy remains unexplored. 
      OBJECTIVE: This study aimed to evaluate GPT-4's ability to answer questions from 
      the Japanese National Examination for Pharmacists (JNEP) and assess its potential 
      as a support tool for pharmacists in their daily practice. METHODS: The question 
      texts and answer choices from the 107th and 108th JNEP, held in February 2022 and 
      February 2023, were input into GPT-4. As GPT-4 cannot process diagrams, questions 
      that included diagram interpretation were not analyzed and were initially given a 
      score of 0. The correct answer rates were calculated and compared with the 
      passing criteria of each examination to evaluate GPT-4's performance. RESULTS: 
      For the 107th and 108th JNEP, GPT-4 achieved an accuracy rate of 64.5% (222/344) 
      and 62.9% (217/345), respectively, for all questions. When considering only the 
      questions that GPT-4 could answer, the accuracy rates increased to 78.2% 
      (222/284) and 75.3% (217/287), respectively. The accuracy rates tended to be 
      lower for physics, chemistry, and calculation questions. CONCLUSIONS: Although 
      GPT-4 demonstrated the potential to answer questions from the JNEP and support 
      pharmacists' capabilities, it also showed limitations in handling highly 
      specialized questions, calculation questions, and questions requiring diagram 
      recognition. Further evaluation is necessary to explore its applicability in 
      real-world clinical settings, considering the complexities of patient scenarios 
      and collaboration with health care professionals. By addressing these 
      limitations, GPT-4 could become a more reliable tool for pharmacists in their 
      daily practice.
CI  - ©Yuki Kunitsu. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 30.10.2023.
FAU - Kunitsu, Yuki
AU  - Kunitsu Y
AUID- ORCID: 0000-0003-0331-6417
AD  - Department of Pharmacy, Shiga University of Medical Science Hospital, Otsu, 
      Shiga, Japan.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10644185
OTO - NOTNLM
OT  - AI
OT  - ChatGPT
OT  - GPT-4
OT  - artificial intelligence
OT  - chatbot
OT  - generative pretrained transformer
OT  - natural language processing
OT  - pharmacist
OT  - pharmacy
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/15 05:46
MHDA- 2023/10/15 05:47
CRDT- 2023/10/14 18:42
PHST- 2023/04/25 00:00 [received]
PHST- 2023/10/14 00:00 [accepted]
PHST- 2023/07/06 00:00 [revised]
PHST- 2023/10/15 05:47 [medline]
PHST- 2023/10/15 05:46 [pubmed]
PHST- 2023/10/14 18:42 [entrez]
AID - v9i1e48452 [pii]
AID - 10.2196/48452 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Oct 30;9:e48452. doi: 10.2196/48452.

PMID- 37153269
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230509
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Identifying Group A Streptococcal Pharyngitis in Children Through Clinical 
      Variables Using Machine Learning.
PG  - e37141
LID - 10.7759/cureus.37141 [doi]
LID - e37141
AB  - Background Group A Streptococcus (GAS) is the most common bacterial cause of 
      pharyngitis in children. GAS pharyngitis requires antimicrobial agents, and rapid 
      antigen detection tests (RADTs) are currently considered useful for diagnosis. 
      However, the decision to perform the test is based on the pediatrician's 
      examination findings, but the indicators are not clear. Therefore, we used 
      machine learning (ML) to create a model to identify GAS pharyngitis from clinical 
      findings and to explore important features. Methods ML with Python programming 
      language was used for this study. Data from the included study involved 676 
      children aged 3 to 15 years diagnosed with pharyngitis, with positive results on 
      the RADT serving as exposures, and negative results serving as controls. The ML 
      performances served as the outcome. We utilized six types of ML classifiers, 
      namely, logistic regression, support vector machine, k-nearest neighbor 
      algorithm, random forest, an ensemble of them, Voting Classifier, and the eXtreme 
      Gradient Boosting (XGBoost) algorithm. Additionally, we used SHapley Additive 
      exPlanations (SHAP) values to identify important features. Results Moderately 
      performing models were generated for all six ML classifiers. XGBoost produced the 
      best model, with an area under the receiver operating characteristics curve of 
      0.75 ± 0.01. The order of important features in the model was palatal petechiae, 
      followed by scarlatiniform rash, tender cervical lymph nodes, and age. Conclusion 
      Through this study, we have demonstrated that ML models can predict childhood GAS 
      pharyngitis with moderate accuracy using only commonly recorded clinical 
      variables in children diagnosed with pharyngitis. We have also identified four 
      important clinical variables. These findings may serve as a reference for 
      considering indicators under the current guidelines recommended for selective 
      RADTs.
CI  - Copyright © 2023, Miyagi et al.
FAU - Miyagi, Yoshifumi
AU  - Miyagi Y
AD  - Department of Pediatrics, Haibara General Hospital, Shizuoka, JPN.
LA  - eng
PT  - Journal Article
DEP - 20230404
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10160315
OTO - NOTNLM
OT  - auc
OT  - chatgpt
OT  - clinical symptoms
OT  - group a streptococcal pharyngitis
OT  - machine learning (ml)
OT  - xgboost
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/08 06:41
MHDA- 2023/05/08 06:42
CRDT- 2023/05/08 04:13
PHST- 2023/04/04 00:00 [accepted]
PHST- 2023/05/08 06:42 [medline]
PHST- 2023/05/08 06:41 [pubmed]
PHST- 2023/05/08 04:13 [entrez]
AID - 10.7759/cureus.37141 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 4;15(4):e37141. doi: 10.7759/cureus.37141. eCollection 2023 Apr.

PMID- 38046496
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231205
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 10
DP  - 2023 Oct
TI  - Machine Learning Applications in Spine Surgery.
PG  - e48078
LID - 10.7759/cureus.48078 [doi]
LID - e48078
AB  - This literature review sought to identify and evaluate the current applications 
      of artificial intelligence (AI)/machine learning (ML) in spine surgery that can 
      effectively guide clinical decision-making and surgical planning. By using 
      specific keywords to maximize search sensitivity, a thorough literature research 
      was conducted in several online databases: Scopus, PubMed, and Google Scholar, 
      and the findings were filtered according to the Preferred Reporting Items for 
      Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 46 studies 
      met the requirements and were included in this review. According to this study, 
      AI/ML models were sufficiently accurate with a mean overall value of 74.9%, and 
      performed best at preoperative patient selection, cost prediction, and length of 
      stay. Performance was also good at predicting functional outcomes and 
      postoperative mortality. Regression analysis was the most frequently utilized 
      application whereas deep learning/artificial neural networks had the highest 
      sensitivity score (81.5%). Despite the relatively brief history of engagement 
      with AI/ML, as evidenced by the fact that 77.5% of studies were published after 
      2018, the outcomes have been promising. In light of the Big Data era, the 
      increasing prevalence of National Registries, and the wide-ranging applications 
      of AI, such as exemplified by ChatGPT (OpenAI, San Francisco, California), it is 
      highly likely that the field of spine surgery will gradually adopt and integrate 
      AI/ML into its clinical practices. Consequently, it is of great significance for 
      spine surgeons to acquaint themselves with the fundamental principles of AI/ML, 
      as these technologies hold the potential for substantial improvements in overall 
      patient care.
CI  - Copyright © 2023, Tragaris et al.
FAU - Tragaris, Themistoklis
AU  - Tragaris T
AD  - 1st Department of Orthopaedic Surgery, National and Kapodistrian University of 
      Athens School of Medicine, KAT Hospital, Athens, GRC.
FAU - Benetos, Ioannis S
AU  - Benetos IS
AD  - 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of 
      Athens School of Medicine, KAT Hospital, Athens, GRC.
FAU - Vlamis, John
AU  - Vlamis J
AD  - 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of 
      Athens School of Medicine, KAT Hospital, Athens, GRC.
FAU - Pneumaticos, Spyridon
AU  - Pneumaticos S
AD  - 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of 
      Athens School of Medicine, KAT Hospital, Athens, GRC.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231031
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10689893
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - artificial intelligence
OT  - healthcare improvement
OT  - machine learning
OT  - patient reported outcomes
OT  - spine
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/04 06:42
MHDA- 2023/12/04 06:43
CRDT- 2023/12/04 05:00
PHST- 2023/10/31 00:00 [accepted]
PHST- 2023/12/04 06:43 [medline]
PHST- 2023/12/04 06:42 [pubmed]
PHST- 2023/12/04 05:00 [entrez]
AID - 10.7759/cureus.48078 [doi]
PST - epublish
SO  - Cureus. 2023 Oct 31;15(10):e48078. doi: 10.7759/cureus.48078. eCollection 2023 
      Oct.

PMID- 37862079
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231106
IS  - 2369-3762 (Print)
IS  - 2369-3762 (Linking)
VI  - 9
DP  - 2023 Oct 20
TI  - Opportunities, Challenges, and Future Directions of Generative Artificial 
      Intelligence in Medical Education: Scoping Review.
PG  - e48785
LID - 10.2196/48785 [doi]
AB  - BACKGROUND: Generative artificial intelligence (AI) technologies are increasingly 
      being utilized across various fields, with considerable interest and concern 
      regarding their potential application in medical education. These technologies, 
      such as Chat GPT and Bard, can generate new content and have a wide range of 
      possible applications. OBJECTIVE: This study aimed to synthesize the potential 
      opportunities and limitations of generative AI in medical education. It sought to 
      identify prevalent themes within recent literature regarding potential 
      applications and challenges of generative AI in medical education and use these 
      to guide future areas for exploration. METHODS: We conducted a scoping review, 
      following the framework by Arksey and O'Malley, of English language articles 
      published from 2022 onward that discussed generative AI in the context of medical 
      education. A literature search was performed using PubMed, Web of Science, and 
      Google Scholar databases. We screened articles for inclusion, extracted data from 
      relevant studies, and completed a quantitative and qualitative synthesis of the 
      data. RESULTS: Thematic analysis revealed diverse potential applications for 
      generative AI in medical education, including self-directed learning, simulation 
      scenarios, and writing assistance. However, the literature also highlighted 
      significant challenges, such as issues with academic integrity, data accuracy, 
      and potential detriments to learning. Based on these themes and the current state 
      of the literature, we propose the following 3 key areas for investigation: 
      developing learners' skills to evaluate AI critically, rethinking assessment 
      methodology, and studying human-AI interactions. CONCLUSIONS: The integration of 
      generative AI in medical education presents exciting opportunities, alongside 
      considerable challenges. There is a need to develop new skills and competencies 
      related to AI as well as thoughtful, nuanced approaches to examine the growing 
      use of generative AI in medical education.
CI  - ©Carl Preiksaitis, Christian Rose. Originally published in JMIR Medical Education 
      (https://mededu.jmir.org), 20.10.2023.
FAU - Preiksaitis, Carl
AU  - Preiksaitis C
AUID- ORCID: 0000-0002-3856-0068
AD  - Department of Emergency Medicine, Stanford University School of Medicine, Palo 
      Alto, CA, United States.
FAU - Rose, Christian
AU  - Rose C
AUID- ORCID: 0000-0002-5115-649X
AD  - Department of Emergency Medicine, Stanford University School of Medicine, Palo 
      Alto, CA, United States.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20231020
PL  - Canada
TA  - JMIR Med Educ
JT  - JMIR medical education
JID - 101684518
PMC - PMC10625095
OTO - NOTNLM
OT  - AI
OT  - Bard
OT  - ChatGPT
OT  - artificial intelligence
OT  - educator
OT  - generative
OT  - learner
OT  - medical education
OT  - review
OT  - scoping
EDAT- 2023/10/20 12:44
MHDA- 2023/10/20 12:45
CRDT- 2023/10/20 11:54
PHST- 2023/05/07 00:00 [received]
PHST- 2023/09/28 00:00 [accepted]
PHST- 2023/07/28 00:00 [revised]
PHST- 2023/10/20 12:45 [medline]
PHST- 2023/10/20 12:44 [pubmed]
PHST- 2023/10/20 11:54 [entrez]
AID - v9i1e48785 [pii]
AID - 10.2196/48785 [doi]
PST - epublish
SO  - JMIR Med Educ. 2023 Oct 20;9:e48785. doi: 10.2196/48785.

PMID- 38022995
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231201
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - The impact of human-AI collaboration types on consumer evaluation and usage 
      intention: a perspective of responsibility attribution.
PG  - 1277861
LID - 10.3389/fpsyg.2023.1277861 [doi]
LID - 1277861
AB  - Despite the widespread availability of artificial intelligence (AI) products and 
      services, consumer evaluations and adoption intentions have not met expectations. 
      Existing research mainly focuses on AI's instrumental attributes from the 
      consumer perspective, along with negative impacts of AI failures on evaluations 
      and willingness to use. However, research is lacking on AI as a collaborative 
      agent, investigating the impact of human-AI collaboration on AI acceptance under 
      different outcome expectations. This study examines the interactive effects of 
      human-AI collaboration types (AI-dominant vs. AI-assisted) and outcome 
      expectations (positive vs. negative) on AI product evaluations and usage 
      willingness, along with the underlying mechanisms, from a human-AI relationship 
      perspective. It also investigates the moderating role of algorithm transparency 
      in these effects. Using three online experiments with analysis of variance and 
      bootstrap methods, the study validates these interactive mechanisms, revealing 
      the mediating role of attribution and moderating role of algorithm transparency. 
      Experiment 1 confirms the interactive effects of human-AI collaboration types and 
      outcome expectations on consumer evaluations and usage willingness. Under 
      positive outcome expectations, consumers evaluate and express willingness to use 
      AI-dominant intelligent vehicles with autonomous driving capabilities higher than 
      those with emergency evasion capabilities (AI-assisted). However, under negative 
      outcome expectations, consumers rate autonomous driving capabilities lower 
      compared to emergency evasion capabilities. Experiment 2 examines the mediating 
      role of attribution through ChatGPT's dominant or assisting role under different 
      outcome expectations. Experiment 3 uses a clinical decision-making system to 
      study algorithm transparency's moderating role, showing higher transparency 
      improves evaluations and willingness to use AI products and services under 
      negative outcome expectations. Theoretically, this study advances consumer 
      behavior research by exploring the human-AI relationship within artificial 
      intelligence, enhancing understanding of consumer acceptance variations. 
      Practically, it offers insights for better integrating AI products and services 
      into the market.
CI  - Copyright © 2023 Yue and Li.
FAU - Yue, Beibei
AU  - Yue B
AD  - School of Business, Qingdao University, Qingdao, Shandong, China.
FAU - Li, Hu
AU  - Li H
AD  - School of Business, Qingdao University, Qingdao, Shandong, China.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10643528
OTO - NOTNLM
OT  - algorithm transparency
OT  - artificial intelligence
OT  - evaluation
OT  - human-AI collaboration
OT  - outcome expectation
OT  - responsibility attribution
OT  - usage intention
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/11/29 18:42
MHDA- 2023/11/29 18:43
CRDT- 2023/11/29 15:37
PHST- 2023/08/15 00:00 [received]
PHST- 2023/10/09 00:00 [accepted]
PHST- 2023/11/29 18:43 [medline]
PHST- 2023/11/29 18:42 [pubmed]
PHST- 2023/11/29 15:37 [entrez]
AID - 10.3389/fpsyg.2023.1277861 [doi]
PST - epublish
SO  - Front Psychol. 2023 Oct 30;14:1277861. doi: 10.3389/fpsyg.2023.1277861. 
      eCollection 2023.

PMID- 37424120
OWN - NLM
STAT- MEDLINE
DCOM- 20230826
LR  - 20230913
IS  - 0974-7559 (Electronic)
IS  - 0019-6061 (Linking)
VI  - 60
IP  - 7
DP  - 2023 Jul 15
TI  - Pediatrics in Artificial Intelligence Era: A Systematic Review on Challenges, 
      Opportunities, and Explainability.
PG  - 561-569
AB  - BACKGROUND: The emergence of artificial intelligence (AI) tools such as ChatGPT 
      and Bard is disrupting a broad swathe of fields, including medicine. In pediatric 
      medicine, AI is also increasingly being used across multiple subspecialties. 
      However, the practical application of AI still faces a number of key challenges. 
      Consequently, there is a requirement for a concise overview of the roles of AI 
      across the multiple domains of pediatric medicine, which the current study seeks 
      to address. AIM: To systematically assess the challenges, opportunities, and 
      explainability of AI in pediatric medicine. METHODOLOGY: A systematic search was 
      carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, 
      and grey literature using search terms related to machine learning (ML) and AI 
      for the years 2016 to 2022 in the English language. A total of 210 articles were 
      retrieved that were screened with PRISMA for abstract, year, language, context, 
      and proximal relevance to research aims. A thematic analysis was carried out to 
      extract findings from the included studies. RESULTS: Twenty articles were 
      selected for data abstraction and analysis, with three consistent themes emerging 
      from these articles. In particular, eleven articles address the current 
      state-of-the-art application of AI in diagnosing and predicting health conditions 
      such as behavioral and mental health, cancer, syndromic and metabolic diseases. 
      Five articles highlight the specific challenges of AI deployment in pediatric 
      medicines: data security, handling, authentication, and validation. Four articles 
      set out future opportunities for AI to be adapted: the incorporation of Big Data, 
      cloud computing, precision medicine, and clinical decision support systems. These 
      studies collectively critically evaluate the potential of AI in overcoming 
      current barriers to adoption. CONCLUSION: AI is proving disruptive within 
      pediatric medicine and is presently associated with challenges, opportunities, 
      and the need for explainability. AI should be viewed as a tool to enhance and 
      support clinical decision-making rather than a substitute for human judgement and 
      expertise. Future research should consequently focus on obtaining comprehensive 
      data to ensure the generalizability of research findings.
FAU - Balla, Yashaswini
AU  - Balla Y
AD  - Neurosciences Department, Alder Hey Children's NHS Foundation Trust, Liverpool, 
      United Kingdom.
FAU - Tirunagari, Santosh
AU  - Tirunagari S
AD  - Department of Psychology, Middlesex University, London, United Kingdom. 
      Correspondence to: Dr Santosh Tirunagari, Department of Psychology, Middlesex 
      University, London, United Kingdom. s.tirunagari@mdx.ac.uk.
FAU - Windridge, David
AU  - Windridge D
AD  - Department of Computer Science, Middlesex University, London, United Kingdom.
LA  - eng
PT  - Journal Article
PT  - Systematic Review
PL  - India
TA  - Indian Pediatr
JT  - Indian pediatrics
JID - 2985062R
SB  - IM
MH  - *Artificial Intelligence
MH  - *Pediatrics
MH  - *Clinical Decision-Making
MH  - Humans
MH  - Child, Preschool
MH  - Child
MH  - Deep Learning
EDAT- 2023/07/10 06:42
MHDA- 2023/07/11 06:42
CRDT- 2023/07/10 01:59
PHST- 2023/07/11 06:42 [medline]
PHST- 2023/07/10 06:42 [pubmed]
PHST- 2023/07/10 01:59 [entrez]
PST - ppublish
SO  - Indian Pediatr. 2023 Jul 15;60(7):561-569.

PMID- 37868075
OWN - NLM
STAT- MEDLINE
DCOM- 20231031
LR  - 20231103
IS  - 2212-277X (Electronic)
IS  - 2212-2761 (Print)
IS  - 2212-2761 (Linking)
VI  - 12
IP  - 1
DP  - 2023
TI  - Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature 
      and Practices.
PG  - 399-410
LID - 10.5334/pme.954 [doi]
AB  - INTRODUCTION: The increasing use of Artificial Intelligence (AI) in medicine has 
      raised ethical concerns, such as patient autonomy, bias, and transparency. Recent 
      studies suggest a need for teaching AI ethics as part of medical curricula. This 
      scoping review aimed to represent and synthesize the literature on teaching AI 
      ethics as part of medical education. METHODS: The PRISMA-SCR guidelines and JBI 
      methodology guided a literature search in four databases (PubMed, Embase, Scopus, 
      and Web of Science) for the past 22 years (2000-2022). To account for the release 
      of AI-based chat applications, such as ChatGPT, the literature search was updated 
      to include publications until the end of June 2023. RESULTS: 1384 publications 
      were originally identified and, after screening titles and abstracts, the full 
      text of 87 publications was assessed. Following the assessment of the full text, 
      10 publications were included for further analysis. The updated literature search 
      identified two additional relevant publications from 2023 were identified and 
      included in the analysis. All 12 publications recommended teaching AI ethics in 
      medical curricula due to the potential implications of AI in medicine. 
      Anticipated ethical challenges such as bias were identified as the recommended 
      basis for teaching content in addition to basic principles of medical ethics. 
      Case-based teaching using real-world examples in interactive seminars and small 
      groups was recommended as a teaching modality. CONCLUSION: This scoping review 
      reveals a scarcity of literature on teaching AI ethics in medical education, with 
      most of the available literature being recent and theoretical. These findings 
      emphasize the importance of more empirical studies and foundational definitions 
      of AI ethics to guide the development of teaching content and modalities. 
      Recognizing AI's significant impact of AI on medicine, additional research on the 
      teaching of AI ethics in medical education is needed to best prepare medical 
      students for future ethical challenges.
CI  - Copyright: © 2023 The Author(s).
FAU - Weidener, Lukas
AU  - Weidener L
AUID- ORCID: 0000-0002-7132-8826
AD  - UMIT TIROL - Private University for Health Sciences and Health Technology, 
      Eduard-Wallnöfer-Zentrum 1, 6060 Hall in Tirol, Austria.
FAU - Fischer, Michael
AU  - Fischer M
AUID- ORCID: 0000-0002-2952-7645
AD  - Head of the Research Unit for Quality and Ethics in Health Care, UMIT TIROL - 
      Private University for Health Sciences and Health Technology, Austria.
LA  - eng
PT  - Journal Article
PT  - Review
PT  - Systematic Review
DEP - 20231016
PL  - Netherlands
TA  - Perspect Med Educ
JT  - Perspectives on medical education
JID - 101590643
SB  - IM
MH  - Humans
MH  - Artificial Intelligence
MH  - *Education, Medical
MH  - *Medicine
MH  - Curriculum
MH  - Ethics, Medical
PMC - PMC10588522
COIS- The authors have no competing interests to declare.
EDAT- 2023/10/23 06:48
MHDA- 2023/10/31 06:42
CRDT- 2023/10/23 04:32
PHST- 2023/03/03 00:00 [received]
PHST- 2023/10/03 00:00 [accepted]
PHST- 2023/10/31 06:42 [medline]
PHST- 2023/10/23 06:48 [pubmed]
PHST- 2023/10/23 04:32 [entrez]
AID - 10.5334/pme.954 [doi]
PST - epublish
SO  - Perspect Med Educ. 2023 Oct 16;12(1):399-410. doi: 10.5334/pme.954. eCollection 
      2023.

PMID- 37840574
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231020
IS  - 2396-8923 (Electronic)
IS  - 2396-8923 (Linking)
VI  - 8
IP  - 1
DP  - 2023
TI  - Comprehensive microbiome causal mediation analysis using MiMed on user-friendly 
      web interfaces.
PG  - bpad023
LID - 10.1093/biomethods/bpad023 [doi]
LID - bpad023
AB  - It is a central goal of human microbiome studies to see the roles of the 
      microbiome as a mediator that transmits environmental, behavioral, or medical 
      exposures to health or disease outcomes. Yet, mediation analysis is not used as 
      much as it should be. One reason is because of the lack of carefully planned 
      routines, compilers, and automated computing systems for microbiome mediation 
      analysis (MiMed) to perform a series of data processing, diversity calculation, 
      data normalization, downstream data analysis, and visualizations. Many 
      researchers in various disciplines (e.g. clinicians, public health practitioners, 
      and biologists) are not also familiar with related statistical methods and 
      programming languages on command-line interfaces. Thus, in this article, we 
      introduce a web cloud computing platform, named as MiMed, that enables 
      comprehensive MiMed on user-friendly web interfaces. The main features of MiMed 
      are as follows. First, MiMed can survey the microbiome in various spheres (i) as 
      a whole microbial ecosystem using different ecological measures (e.g. alpha- and 
      beta-diversity indices) or (ii) as individual microbial taxa (e.g. phyla, 
      classes, orders, families, genera, and species) using different data 
      normalization methods. Second, MiMed enables covariate-adjusted analysis to 
      control for potential confounding factors (e.g. age and gender), which is 
      essential to enhance the causality of the results, especially for observational 
      studies. Third, MiMed enables a breadth of statistical inferences in both 
      mediation effect estimation and significance testing. Fourth, MiMed provides 
      flexible and easy-to-use data processing and analytic modules and creates nice 
      graphical representations. Finally, MiMed employs ChatGPT to search for what has 
      been known about the microbial taxa that are found significantly as mediators 
      using artificial intelligence technologies. For demonstration purposes, we 
      applied MiMed to the study on the mediating roles of oral microbiome in 
      subgingival niches between e-cigarette smoking and gingival inflammation. MiMed 
      is freely available on our web server (http://mimed.micloud.kr).
CI  - © The Author(s) 2023. Published by Oxford University Press.
FAU - Jang, Hyojung
AU  - Jang H
AD  - Department of Applied Mathematics and Statistics, The State University of New 
      York, Korea, Incheon, South Korea.
FAU - Park, Solha
AU  - Park S
AD  - Department of Applied Mathematics and Statistics, The State University of New 
      York, Korea, Incheon, South Korea.
FAU - Koh, Hyunwook
AU  - Koh H
AUID- ORCID: 0000-0001-6893-7164
AD  - Department of Applied Mathematics and Statistics, The State University of New 
      York, Korea, Incheon, South Korea.
LA  - eng
PT  - Journal Article
DEP - 20231004
PL  - England
TA  - Biol Methods Protoc
JT  - Biology methods & protocols
JID - 101693064
PMC - PMC10576642
OTO - NOTNLM
OT  - causal inference
OT  - causal mediation analysis
OT  - human microbiome
OT  - microbiome data analysis
OT  - web cloud computing
COIS- The authors declare that they have no competing interests.
EDAT- 2023/10/16 06:48
MHDA- 2023/10/16 06:49
CRDT- 2023/10/16 04:17
PHST- 2023/07/26 00:00 [received]
PHST- 2023/09/19 00:00 [revised]
PHST- 2023/10/02 00:00 [accepted]
PHST- 2023/10/16 06:49 [medline]
PHST- 2023/10/16 06:48 [pubmed]
PHST- 2023/10/16 04:17 [entrez]
AID - bpad023 [pii]
AID - 10.1093/biomethods/bpad023 [doi]
PST - epublish
SO  - Biol Methods Protoc. 2023 Oct 4;8(1):bpad023. doi: 10.1093/biomethods/bpad023. 
      eCollection 2023.

PMID- 38037784
OWN - NLM
STAT- MEDLINE
DCOM- 20231204
LR  - 20231204
IS  - 1117-1936 (Print)
VI  - 30
IP  - 4
DP  - 2023 Oct-Dec
TI  - Exploring artificial intelligence in the Nigerian medical educational space: An 
      online cross-sectional study of perceptions, risks and benefits among students 
      and lecturers from ten universities.
PG  - 285-292
LID - 10.4103/npmj.npmj_186_23 [doi]
AB  - BACKGROUND: The impact of artificial intelligence (AI) has been compared to that 
      of the Internet and printing, evoking both apprehension and anticipation in an 
      uncertain world. OBJECTIVE: This study aimed to explore the perceptions of 
      medical students and faculty members from ten universities across Nigeria 
      regarding AI. METHODS: Using Google Forms and WhatsApp, a cross-sectional online 
      survey was administered to clinical year medical students and their lecturers 
      from ten medical schools representing all the six geopolitical zones of Nigeria. 
      RESULTS: The survey received 1003 responses, of which 708 (70.7%) were from 
      students and 294 (29.3%) were from lecturers. Both groups displayed an average 
      level of knowledge, with students (Median:4, range -5 to 12) significantly 
      outperforming lecturers (Median:3, range -5 to 15). Social media (61.2%) was the 
      most common form of first contact with AI. Participants demonstrated a favourable 
      attitude towards AI, with a median score of 6.8 out of 10. Grammar checkers 
      (62.3%) were the most commonly reported AI tool used, while ChatGPT (43.6%) was 
      the most frequently mentioned dedicated AI tool. Students were significantly more 
      likely than lecturers to have used AI tools in the past but <5% of both groups 
      had received prior AI training. Excitement about the potential of AI slightly 
      outweighed concerns regarding future risks. A significantly higher proportion of 
      students compared to lecturers believed that AI could dehumanise health care 
      (70.6% vs. 60.8%), render physicians redundant (57.6% vs. 34.7%), diminish 
      physicians' skills (79.3% vs. 71.3%) and ultimately harm patients (28.6% vs. 
      20.6%). CONCLUSION: The simultaneous fascination and apprehension with AI 
      observed among both lecturers and students in our study mirrors the global trend. 
      This finding was particularly evident in students who, despite possessing greater 
      knowledge of AI compared to their lecturers, did not exhibit a corresponding 
      reduction in their fear of AI.
FAU - Oluwadiya, Kehinde Sunday
AU  - Oluwadiya KS
AD  - Department of Surgery, Ekiti State University, Ado-Ekiti, Nigeria.
FAU - Adeoti, Adekunle Olatayo
AU  - Adeoti AO
AD  - Department of Medicine, Ekiti State University, Ado-Ekiti, Nigeria.
FAU - Agodirin, Sulaiman Olayide
AU  - Agodirin SO
AD  - Department of Surgery, University of Ilorin, Ilorin, Nigeria.
FAU - Nottidge, Timothy Eyo
AU  - Nottidge TE
AD  - Department of Orthopaedics and Traumatology, University of Uyo, Uyo, Akwa-Ibom 
      State, Nigeria.
FAU - Usman, Mustapha Ibrahim
AU  - Usman MI
AD  - Department of Surgery, Bayero University, Kano, Kano State, Nigeria.
FAU - Gali, Mtaku Bata
AU  - Gali MB
AD  - Department of Surgery, Nile University of Nigeria, Abuja, Nigeria.
FAU - Onyemaechi, Ndubuisi Onu
AU  - Onyemaechi NO
AD  - Department of Surgery, University of Nigeria, Enugu, Enugu State, Nigeria.
FAU - Ramat, Ali Mohammed
AU  - Ramat AM
AD  - Department of Orthopaedics, University of Maiduguri Teaching Hospital, Maiduguri, 
      Borno State, Nigeria.
FAU - Adedire, Adejare
AU  - Adedire A
AD  - Department of Surgery, Osun State University, Osogbo, Nigeria.
FAU - Zakari, Lawal Ya'u
AU  - Zakari LY
AD  - Department of Trauma and Orthopaedic Surgery, Ahmadu Bello University, Zaria, 
      Nigeria.
LA  - eng
PT  - Journal Article
PL  - Nigeria
TA  - Niger Postgrad Med J
JT  - The Nigerian postgraduate medical journal
JID - 9613595
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Cross-Sectional Studies
MH  - Universities
MH  - Nigeria
MH  - *Students, Medical
MH  - Risk Assessment
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Nigeria
OT  - digitalisation
OT  - education
OT  - medicine
EDAT- 2023/12/01 06:44
MHDA- 2023/12/04 12:41
CRDT- 2023/12/01 05:04
PHST- 2023/12/04 12:41 [medline]
PHST- 2023/12/01 06:44 [pubmed]
PHST- 2023/12/01 05:04 [entrez]
AID - NigerPostgradMedJ_2023_30_4_285_389967 [pii]
AID - 10.4103/npmj.npmj_186_23 [doi]
PST - ppublish
SO  - Niger Postgrad Med J. 2023 Oct-Dec;30(4):285-292. doi: 10.4103/npmj.npmj_186_23.

PMID- 37864817
OWN - NLM
STAT- Publisher
LR  - 20231021
IS  - 1930-613X (Electronic)
IS  - 0026-4075 (Linking)
DP  - 2023 Oct 20
TI  - The Need for Artificial Intelligence Curriculum in Military Medical Education.
LID - usad412 [pii]
LID - 10.1093/milmed/usad412 [doi]
AB  - The success of deep-learning algorithms in analyzing complex structured and 
      unstructured multidimensional data has caused an exponential increase in the 
      amount of research devoted to the applications of artificial intelligence (AI) in 
      medicine in the past decade. Public release of large language models like ChatGPT 
      the past year has generated an unprecedented storm of excitement and rumors of 
      machine intelligence finally reaching or even surpassing human capability in 
      detecting meaningful signals in complex multivariate data. Such enthusiasm, 
      however, is met with an equal degree of both skepticism and fear over the social, 
      legal, and moral implications of such powerful technology with relatively little 
      safeguards or regulations on its development. The question remains in medicine of 
      how to harness the power of AI to improve patient outcomes by increasing the 
      diagnostic accuracy and treatment precision provided by medical professionals. 
      Military medicine, given its unique mission and resource constraints,can benefit 
      immensely from such technology. However, reaping such benefits hinges on the 
      ability of the rising generations of military medical professionals to understand 
      AI algorithms and their applications. Additionally, they should strongly consider 
      working with them as an adjunct decision-maker and view them as a colleague to 
      access and harness relevant information as opposed to something to be feared. 
      Ideas expressed in this commentary were formulated by a military medical student 
      during a two-month research elective working on a multidisciplinary team of 
      computer scientists and clinicians at the National Library of Medicine advancing 
      the state of the art of AI in medicine. A motivation to incorporate AI in the 
      Military Health System is provided, including examples of applications in 
      military medicine. Rationale is then given for inclusion of AI in education 
      starting in medical school as well as a prudent implementation of these 
      algorithms in a clinical workflow during graduate medical education. Finally, 
      barriers to implementation are addressed along with potential solutions. The end 
      state is not that rising military physicians are technical experts in AI; but 
      rather that they understand how they can leverage its rapidly evolving 
      capabilities to prepare for a future where AI will have a significant role in 
      clinical care. The overall goal is to develop trained clinicians that can 
      leverage these technologies to improve the Military Health System.
CI  - Published by Oxford University Press on behalf of the Association of Military 
      Surgeons of the United States 2023. This work is written by (a) US Government 
      employee(s) and is in the public domain in the US.
FAU - Spirnak, Jonathan R
AU  - Spirnak JR
AUID- ORCID: 0009-0007-2579-669X
AD  - Uniformed Services University of the Health Sciences (USUHS) School of Medicine, 
      Bethesda, MD 20814, USA.
FAU - Antani, Sameer
AU  - Antani S
AUID- ORCID: 0000-0002-0040-1387
AD  - Computational Health Research Branch, National Library of Medicine, National 
      Institutes of Health, Bethesda, MD 20894, USA.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - England
TA  - Mil Med
JT  - Military medicine
JID - 2984771R
SB  - IM
EDAT- 2023/10/21 19:43
MHDA- 2023/10/21 19:43
CRDT- 2023/10/21 14:32
PHST- 2023/09/04 00:00 [received]
PHST- 2023/09/25 00:00 [revised]
PHST- 2023/10/04 00:00 [accepted]
PHST- 2023/10/21 19:43 [medline]
PHST- 2023/10/21 19:43 [pubmed]
PHST- 2023/10/21 14:32 [entrez]
AID - 7326531 [pii]
AID - 10.1093/milmed/usad412 [doi]
PST - aheadofprint
SO  - Mil Med. 2023 Oct 20:usad412. doi: 10.1093/milmed/usad412.

PMID- 37179470
OWN - NLM
STAT- Publisher
LR  - 20230514
IS  - 0974-7559 (Electronic)
IS  - 0019-6061 (Linking)
DP  - 2023 May 14
TI  - Machine Learning in Pediatrics: Evaluating Challenges, Opportunities, and 
      Explainability.
LID - S097475591600533 [pii]
AB  - BACKGROUND: The emergence of Artificial Intelligence (AI) tools such as ChatGPT 
      and Bard is disrupting a broad swathe of fields, including medicine. In pediatric 
      medicine, AI is also increasingly being used across multiple subspecialties. 
      However, the practical application of AI still faces a number of key challenges. 
      Consequently, there is a requirement for a concise overview of the roles of AI 
      across the multiple domains of pediatric medicine that the current study seeks to 
      address. AIM: To systematically assess the challenges, opportunities, and 
      explainability of AI in pediatric medicine. METHODOLOGY: A systematic search was 
      carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, 
      and grey literature using search terms related to machine learning (ML) and AI 
      for the years 2016 to 2022 in the English language. A total of 210 articles were 
      retrieved that were screened with PRISMA for abstract, year, language, context, 
      and proximal relevance to research aims. A thematic analysis was carried out to 
      extract findings from the included studies. RESULTS: Twenty articles were 
      selected for data abstraction and analysis, with three consistent themes emerging 
      from these articles. In particular, eleven articles address the current 
      state-of-the-art application of AI in diagnosing and predicting health conditions 
      such as behavioral and mental health, cancer, syndromic and metabolic diseases. 
      Five articles highlight the specific challenges of AI deployment in pediatric 
      medicines: data security, handling, authentication, and validation. Four articles 
      set out future opportunities for AI to be adapted: the incorporation of Big Data, 
      cloud computing, precision medicine, and clinical decision support systems. These 
      studies collectively critically evaluate the potential of AI in overcoming 
      current barriers to adoption. CONCLUSION: AI is proving disruptive within 
      pediatric medicine and is presently associated with challenges, opportunities, 
      and the need for explainability. AI should be viewed as a tool to enhance and 
      support clinical decision-making rather than a substitute for human judgement and 
      expertise. Future research should consequently focus on obtaining comprehensive 
      data to ensure the generalizability of research findings.
FAU - Balla, Yashaswini
AU  - Balla Y
AD  - Neurosciences Department, Alder Hey Children's NHS Foundation Trust, Liverpool, 
      United Kingdom.
FAU - Tirunagari, Santosh
AU  - Tirunagari S
AD  - Department of Psychology, Middlesex University, London, United Kingdom. 
      Correspondence to: Dr Santosh Tirunagari, Department of Psychology, Middlesex 
      University, London, United Kingdom. s.tirunagari@mdx.ac.uk.
FAU - Windridge, David
AU  - Windridge D
AD  - Department of Computer Science, Middlesex University, London, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230514
PL  - India
TA  - Indian Pediatr
JT  - Indian pediatrics
JID - 2985062R
SB  - IM
EDAT- 2023/05/14 06:42
MHDA- 2023/05/14 06:42
CRDT- 2023/05/14 05:52
PHST- 2023/05/14 06:42 [medline]
PHST- 2023/05/14 06:42 [pubmed]
PHST- 2023/05/14 05:52 [entrez]
AID - S097475591600533 [pii]
PST - aheadofprint
SO  - Indian Pediatr. 2023 May 14:S097475591600533.

PMID- 37809482
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 9
DP  - 2023 Sep
TI  - Are paid tools worth the cost? A prospective cross-over study to find the right 
      tool for plagiarism detection.
PG  - e19194
LID - 10.1016/j.heliyon.2023.e19194 [doi]
LID - e19194
AB  - BACKGROUND: The increasing pressure to publish research has led to a rise in 
      plagiarism incidents, creating a need for effective plagiarism detection 
      software. The importance of this study lies in the high cost variation amongst 
      the available options for plagiarism detection. By uncovering the advantages of 
      these low-cost or free alternatives, researchers could access the appropriate 
      tools for plagiarism detection. This is the first study to compare four 
      plagiarism detection tools and assess factors impacting their effectiveness in 
      identifying plagiarism in AI-generated articles. METHODOLOGY: A prospective 
      cross-over study was conducted with the primary objective to compare Overall 
      Similarity Index(OSI) of four plagiarism detection software(iThenticate, 
      Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT 
      was used to generate 100 articles, ten from each of ten general domains affecting 
      various aspects of life. These were run through four software, recording the OSI. 
      Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade 
      Level(FKGL) were used to assess how factors, such as article length and language 
      complexity, impact plagiarism detection. RESULTS: The study found significant 
      variation in OSI(p < 0.001) among the four software, with Grammarly having the 
      highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise 
      analyses revealed significant differences(p < 0.001) between all pairs except for 
      Small SEO Tools-DupliChecker. Number of words showed a significant correlation 
      with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a 
      positive correlation, and GFI had a negative correlation with OSI by 
      DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and 
      DupliChecker. CONCLUSION: Grammarly is unexpectedly most effective in detecting 
      plagiarism in AI-generated articles compared to the other tools. This could be 
      due to different softwares using diverse data sources. This highlights the 
      potential for lower-cost plagiarism detection tools to be utilized by 
      researchers.
CI  - © 2023 The Authors.
FAU - Anil, Abhishek
AU  - Anil A
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Saravanan, Aswini
AU  - Saravanan A
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Singh, Surjit
AU  - Singh S
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Shamim, Muhammad Aaqib
AU  - Shamim MA
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Tiwari, Krishna
AU  - Tiwari K
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Lal, Hina
AU  - Lal H
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Seshatri, Shanmugapriya
AU  - Seshatri S
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Gomaz, Simi Bridjit
AU  - Gomaz SB
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Karat, Thoyyib P
AU  - Karat TP
AD  - Department of Dermatology, Venereology and Leprosy, All India Institute of 
      Medical Sciences, Jodhpur, Rajasthan-342005, India.
FAU - Dwivedi, Pradeep
AU  - Dwivedi P
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Varthya, Shoban Babu
AU  - Varthya SB
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Kaur, Rimple Jeet
AU  - Kaur RJ
AD  - Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, 
      Rajasthan-342005, India.
FAU - Satapathy, Prakasini
AU  - Satapathy P
AD  - Global Center for Evidence Synthesis, Chandigarh-160036, India.
FAU - Padhi, Bijaya Kumar
AU  - Padhi BK
AD  - Department of Community Medicine and School of Public Health, Postgraduate 
      Institute of Medical Education and Research, Chandigarh - 160012, India.
FAU - Gaidhane, Shilpa
AU  - Gaidhane S
AD  - One Health Centre (COHERD), Jawaharlal Nehru Medical College, Datta Meghe 
      Institute of Higher Education, Wardha - 442001, India.
FAU - Patil, Manoj
AU  - Patil M
AD  - Division of Evidence Synthesis, School of Epidemiology and Public Health and 
      Research, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher 
      Education, Wardha - 442001, India.
FAU - Khatib, Mahalaqua Nazli
AU  - Khatib MN
AD  - Division of Evidence Synthesis, Global Consortium of Public Health and Research, 
      Datta Meghe Institute of Higher Education and Research, Wardha - 442001, India.
FAU - Barboza, Joshuan J
AU  - Barboza JJ
AD  - Escuela de Medicina, Universidad Cesar Vallejo, Trujillo, 13007, Peru.
FAU - Sah, Ranjit
AU  - Sah R
AD  - Tribhuvan University Teaching Hospital, Kathmandu - 46000, Nepal.
AD  - Department of Clinical Microbiology, DY Patil Medical College, Hospital and 
      Research Centre, DY Patil Vidyapeeth, Pune - 411000, Maharashtra, India.
AD  - Department of Public Health Dentistry, Dr. D.Y. Patil Dental College and 
      Hospital, Dr. D.Y. Patil Vidyapeeth, Pune - 411018, Maharashtra, India.
LA  - eng
PT  - Journal Article
DEP - 20230824
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10558310
OTO - NOTNLM
OT  - DupliChecker
OT  - Grammarly
OT  - Overall similarity index
OT  - Plagiarism
OT  - Small SEO tools
OT  - iThenticate
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/10/09 06:42
MHDA- 2023/10/09 06:43
CRDT- 2023/10/09 05:55
PHST- 2023/06/01 00:00 [received]
PHST- 2023/08/14 00:00 [revised]
PHST- 2023/08/15 00:00 [accepted]
PHST- 2023/10/09 06:43 [medline]
PHST- 2023/10/09 06:42 [pubmed]
PHST- 2023/10/09 05:55 [entrez]
AID - S2405-8440(23)06402-2 [pii]
AID - e19194 [pii]
AID - 10.1016/j.heliyon.2023.e19194 [doi]
PST - epublish
SO  - Heliyon. 2023 Aug 24;9(9):e19194. doi: 10.1016/j.heliyon.2023.e19194. eCollection 
      2023 Sep.

PMID- 37153326
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230509
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 4
DP  - 2023 Apr
TI  - Cat Eye Syndrome with a Unique Liver and Dermatological Presentation.
PG  - e37142
LID - 10.7759/cureus.37142 [doi]
LID - e37142
AB  - Cat eye syndrome (CES), also known as Schmid-Fraccaro syndrome, is a complex 
      genetic syndrome with a highly variable phenotype that includes ocular coloboma, 
      anal atresia, preauricular skin tags and pits, heart defects, kidney 
      malformations, dysmorphic facial features, and mild to moderate intellectual 
      disability. We describe a case of a 23-year-old male with a past medical history 
      of CES with short stature, mild learning disability, and some dysmorphic facial 
      features who presented with recurrent pruritus and rashes and had mild liver 
      dysfunction. Furthermore, the patient did not have the classic presentation of 
      CES but a clinically milder expression of the phenotypes. Abnormalities in the 
      abdominal ultrasound prompted an ultrasound-guided liver biopsy, which showed 
      bile ductular proliferation with mild portal inflammation composed of lymphocytes 
      and plasma cells, and bridging fibrosis. The patient's labs showed elevated 
      immunoglobulins with the highest increase observed in IgG, along with negative 
      antinuclear antibodies (ANA), negative anti-mitochondrial antibody, and negative 
      hepatitis A/B/C but a weak positive anti-smooth muscle antibody (ASMA). These 
      findings indicated that the patient most likely had autoimmune hepatitis (AIH) or 
      an overlap syndrome with primary sclerosing cholangitis (PSC). The patient was 
      initially treated with steroids and antihistamines for pruritus, which led to 
      some clinical improvement. After dermatological evaluation, the patient was 
      diagnosed with atopic dermatitis and was recently started on a dupilumab 600 mg 
      loading dose and would continue with biweekly dupilumab 300 mg injections. This 
      dermatological finding may require additional examination and can be a unique 
      presentation in patients with CES. This case illustrates that even patients with 
      milder CES expression can experience intense dermatological complications if not 
      effectively managed. CES is a multifactorial disease that requires intervention 
      from multiple specialists. Therefore, primary care physicians must be aware of 
      the potential complications of CES and make adequate referrals to closely monitor 
      patients' symptoms.
CI  - Copyright © 2023, Mansur et al.
FAU - Mansur, Maureen
AU  - Mansur M
AD  - Family Medicine, Nassau University Medical Center, New York, USA.
FAU - Jacob, Thomas J
AU  - Jacob TJ
AD  - Internal Medicine, New York Institute of Technology College of Osteopathic 
      Medicine, Old Westbury, USA.
FAU - Wong, Helen
AU  - Wong H
AD  - Internal Medicine, New York Institute of Technology College of Osteopathic 
      Medicine, Old Westbury, USA.
FAU - Tarascin, Ilya
AU  - Tarascin I
AD  - Family Medicine, Nassau University Medical Center, New York, USA.
LA  - eng
PT  - Case Reports
DEP - 20230404
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10160316
OTO - NOTNLM
OT  - atopic dermatitis
OT  - autoimmune hepatitis
OT  - cat eye syndrome
OT  - chatgpt
OT  - livedo racemosa
OT  - primary biliary sclerosis
OT  - psoriasis
OT  - schmid-fraccaro syndrome
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/08 06:41
MHDA- 2023/05/08 06:42
CRDT- 2023/05/08 04:13
PHST- 2023/03/30 00:00 [accepted]
PHST- 2023/05/08 06:42 [medline]
PHST- 2023/05/08 06:41 [pubmed]
PHST- 2023/05/08 04:13 [entrez]
AID - 10.7759/cureus.37142 [doi]
PST - epublish
SO  - Cureus. 2023 Apr 4;15(4):e37142. doi: 10.7759/cureus.37142. eCollection 2023 Apr.

PMID- 37664372
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230905
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 8
DP  - 2023 Aug
TI  - Analysis of Predictive Factors for Return to Sports in Female Athletes With 
      Stress Urinary Incontinence.
PG  - e44364
LID - 10.7759/cureus.44364 [doi]
LID - e44364
AB  - Introduction This study aimed to identify predictive factors for successful 
      return to sports among elite female athletes (EFAs) experiencing stress urinary 
      incontinence (SUI). We used machine learning to analyze these predictors. Methods 
      This study was conducted at Yokosuka Urogynecology and Urology Clinic, located in 
      Yokosuka City, Kanagawa, Japan. A total of 153 EFAs with postpartum SUI were 
      included in this retrospective cohort study. Information regarding the frequency 
      of pelvic floor muscle training (PFMT), treatment approaches, rates of return to 
      sports after one year, and one-hour pad test (1HrPadtest) at three months were 
      collected. Results At three months, 26.8% of the EFAs improved in SUI; after one 
      year, 28.1% returned to their respective sports successfully. The equation for 
      predicting return to sports (logit(p)) involved several factors: (a) serum total 
      testosterone, (b) PFMT frequency per week, (c) 1HrPadtest at three months, and 
      (d) vaginal erbium-doped yttrium aluminum garnet laser (VEL) + urethral EL (UEL) 
      treatment. The equation was as follows: -126 - 0.07276a + 25.98b - 1.947c - 
      25.32d, with a logit(p) cutoff point at 0.5. The optimal cutoff values and the 
      four influential factors were determined through a receiver operating 
      characteristic (ROC) analysis and the random forest model, respectively. 
      Conclusions For EFAs with severe SUI to successfully return to their sports 
      activities, the PFMT frequency was paramount. Patients who exhibited 
      unsatisfactory results in the 1HrPadtest at the three-month mark benefited from 
      the VEL+UEL treatment. Serum total testosterone proved to be an effective 
      discerning criterion.
CI  - Copyright © 2023, Okui et al.
FAU - Okui, Nobuo
AU  - Okui N
AD  - Dentistry, Kanagawa Dental University, Yokosuka, JPN.
FAU - Erel, Tamer
AU  - Erel T
AD  - Department of Obstetrics and Gynecology, Istanbul University, Cerrahpasa School 
      of Medicine, Istanbul, TUR.
FAU - Okui, Machiko Aurora
AU  - Okui MA
AD  - Department of Urogynecology, Yokosuka Urogynecology Clinic, Yokosuka, JPN.
LA  - eng
PT  - Journal Article
DEP - 20230830
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10469950
OTO - NOTNLM
OT  - ai and robotics in healthcare
OT  - chatgpt
OT  - elite female athletes
OT  - pelvic floor muscle training
OT  - serum total testosterone
OT  - stress urinary incontinence
OT  - urethral erbium laser for urinary incontinence
OT  - vaginal erbium laser for urinary incontinance
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/09/04 06:43
MHDA- 2023/09/04 06:44
CRDT- 2023/09/04 05:13
PHST- 2023/08/30 00:00 [accepted]
PHST- 2023/09/04 06:44 [medline]
PHST- 2023/09/04 06:43 [pubmed]
PHST- 2023/09/04 05:13 [entrez]
AID - 10.7759/cureus.44364 [doi]
PST - epublish
SO  - Cureus. 2023 Aug 30;15(8):e44364. doi: 10.7759/cureus.44364. eCollection 2023 
      Aug.

PMID- 37190427
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230517
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 25
IP  - 4
DP  - 2023 Apr 10
TI  - Text Matching in Insurance Question-Answering Community Based on an Integrated 
      BiLSTM-TextCNN Model Fusing Multi-Feature.
LID - 10.3390/e25040639 [doi]
LID - 639
AB  - Along with the explosion of ChatGPT, the artificial intelligence 
      question-answering system has been pushed to a climax. Intelligent 
      question-answering enables computers to simulate people's behavior habits of 
      understanding a corpus through machine learning, so as to answer questions in 
      professional fields. How to obtain more accurate answers to personalized 
      questions in professional fields is the core content of intelligent 
      question-answering research. As one of the key technologies of intelligent 
      question-answering, the accuracy of text matching is related to the development 
      of the intelligent question-answering community. Aiming to solve the problem of 
      polysemy of text, the Enhanced Representation through Knowledge Integration 
      (ERNIE) model is used to obtain the word vector representation of text, which 
      makes up for the lack of prior knowledge in the traditional word vector 
      representation model. Additionally, there are also problems of homophones and 
      polyphones in Chinese, so this paper introduces the phonetic character sequence 
      of the text to distinguish them. In addition, aiming at the problem that there 
      are many proper nouns in the insurance field that are difficult to identify, 
      after conventional part-of-speech tagging, proper nouns are distinguished by 
      especially defining their parts of speech. After the above three types of 
      text-based semantic feature extensions, this paper also uses the Bi-directional 
      Long Short-Term Memory (BiLSTM) and TextCNN models to extract the global features 
      and local features of the text, respectively. It can obtain the feature 
      representation of the text more comprehensively. Thus, the text matching model 
      integrating BiLSTM and TextCNN fusing Multi-Feature (namely MFBT) is proposed for 
      the insurance question-answering community. The MFBT model aims to solve the 
      problems that affect the answer selection in the insurance question-answering 
      community, such as proper nouns, nonstandard sentences and sparse features. 
      Taking the question-and-answer data of the insurance library as the sample, the 
      MFBT text-matching model is compared and evaluated with other models. The 
      experimental results show that the MFBT text-matching model has higher evaluation 
      index values, including accuracy, recall and F1, than other models. The model 
      trained by historical search data can better help users in the insurance 
      question-and-answer community obtain the answers they need and improve their 
      satisfaction.
FAU - Li, Zhaohui
AU  - Li Z
AUID- ORCID: 0000-0002-1824-4826
AD  - School of Maritime Economics and Management, Dalian Maritime University, Dalian 
      116026, China.
FAU - Yang, Xueru
AU  - Yang X
AD  - School of Maritime Economics and Management, Dalian Maritime University, Dalian 
      116026, China.
FAU - Zhou, Luli
AU  - Zhou L
AD  - School of Maritime Economics and Management, Dalian Maritime University, Dalian 
      116026, China.
FAU - Jia, Hongyu
AU  - Jia H
AD  - School of Maritime Economics and Management, Dalian Maritime University, Dalian 
      116026, China.
FAU - Li, Wenli
AU  - Li W
AD  - School of Economics and Management, Dalian University of Technology, Dalian 
      116024, China.
LA  - eng
GR  - 71874022/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20230410
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC10137988
OTO - NOTNLM
OT  - ERNIE
OT  - deep learning
OT  - feature extraction
OT  - insurance question answering community
OT  - text matching
COIS- The authors declare no conflict of interest.
EDAT- 2023/05/16 06:42
MHDA- 2023/05/16 06:43
CRDT- 2023/05/16 01:14
PHST- 2023/02/20 00:00 [received]
PHST- 2023/03/27 00:00 [revised]
PHST- 2023/04/07 00:00 [accepted]
PHST- 2023/05/16 06:43 [medline]
PHST- 2023/05/16 06:42 [pubmed]
PHST- 2023/05/16 01:14 [entrez]
AID - e25040639 [pii]
AID - entropy-25-00639 [pii]
AID - 10.3390/e25040639 [doi]
PST - epublish
SO  - Entropy (Basel). 2023 Apr 10;25(4):639. doi: 10.3390/e25040639.

PMID- 37761715
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231003
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 18
DP  - 2023 Sep 12
TI  - Enhancing Kidney Transplant Care through the Integration of Chatbot.
LID - 10.3390/healthcare11182518 [doi]
LID - 2518
AB  - Kidney transplantation is a critical treatment option for end-stage kidney 
      disease patients, offering improved quality of life and increased survival rates. 
      However, the complexities of kidney transplant care necessitate continuous 
      advancements in decision making, patient communication, and operational 
      efficiency. This article explores the potential integration of a sophisticated 
      chatbot, an AI-powered conversational agent, to enhance kidney transplant 
      practice and potentially improve patient outcomes. Chatbots and generative AI 
      have shown promising applications in various domains, including healthcare, by 
      simulating human-like interactions and generating contextually appropriate 
      responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, 
      and Bard AI by Google exhibit significant potential in supporting evidence-based 
      research and healthcare decision making. The integration of chatbots in kidney 
      transplant care may offer transformative possibilities. As a clinical decision 
      support tool, it could provide healthcare professionals with real-time access to 
      medical literature and guidelines, potentially enabling informed decision making 
      and improved knowledge dissemination. Additionally, the chatbot has the potential 
      to facilitate patient education by offering personalized and understandable 
      information, addressing queries, and providing guidance on post-transplant care. 
      Furthermore, under clinician or transplant pharmacist supervision, it has the 
      potential to support post-transplant care and medication management by analyzing 
      patient data, which may lead to tailored recommendations on dosages, monitoring 
      schedules, and potential drug interactions. However, to fully ascertain its 
      effectiveness and safety in these roles, further studies and validation are 
      required. Its integration with existing clinical decision support systems may 
      enhance risk stratification and treatment planning, contributing to more informed 
      and efficient decision making in kidney transplant care. Given the importance of 
      ethical considerations and bias mitigation in AI integration, future studies may 
      evaluate long-term patient outcomes, cost-effectiveness, user experience, and the 
      generalizability of chatbot recommendations. By addressing these factors and 
      potentially leveraging AI capabilities, the integration of chatbots in kidney 
      transplant care holds promise for potentially improving patient outcomes, 
      enhancing decision making, and fostering the equitable and responsible use of AI 
      in healthcare.
FAU - Garcia Valencia, Oscar A
AU  - Garcia Valencia OA
AUID- ORCID: 0000-0003-0186-9448
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Thongprayoon, Charat
AU  - Thongprayoon C
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Jadlowiec, Caroline C
AU  - Jadlowiec CC
AUID- ORCID: 0000-0001-7860-9519
AD  - Division of Transplant Surgery, Department of Surgery, Mayo Clinic, Phoenix, AZ 
      85054, USA.
FAU - Mao, Shennen A
AU  - Mao SA
AUID- ORCID: 0000-0002-7571-2542
AD  - Division of Transplant Surgery, Department of Transplantation, Mayo Clinic, 
      Jacksonville, FL 32224, USA.
FAU - Miao, Jing
AU  - Miao J
AUID- ORCID: 0000-0003-0642-9740
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
FAU - Cheungpasitporn, Wisit
AU  - Cheungpasitporn W
AUID- ORCID: 0000-0001-9954-9711
AD  - Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 
      Rochester, MN 55905, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230912
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10530762
OTO - NOTNLM
OT  - AI-powered chatbot
OT  - clinical decision support
OT  - decision making
OT  - end-stage kidney disease
OT  - ethical considerations
OT  - healthcare professionals
OT  - kidney transplantation
OT  - operational efficiency
OT  - patient communication
OT  - patient outcomes
COIS- The authors declare no conflict of interest.
EDAT- 2023/09/28 06:43
MHDA- 2023/09/28 06:44
CRDT- 2023/09/28 01:14
PHST- 2023/08/20 00:00 [received]
PHST- 2023/09/03 00:00 [revised]
PHST- 2023/09/09 00:00 [accepted]
PHST- 2023/09/28 06:44 [medline]
PHST- 2023/09/28 06:43 [pubmed]
PHST- 2023/09/28 01:14 [entrez]
AID - healthcare11182518 [pii]
AID - healthcare-11-02518 [pii]
AID - 10.3390/healthcare11182518 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Sep 12;11(18):2518. doi: 10.3390/healthcare11182518.

PMID- 37419509
OWN - NLM
STAT- Publisher
LR  - 20230707
IS  - 1532-8651 (Electronic)
IS  - 1098-7339 (Linking)
DP  - 2023 Jul 6
TI  - Tracking persistent postoperative opioid use: a proof-of-concept study 
      demonstrating a use case for natural language processing.
LID - rapm-2023-104629 [pii]
LID - 10.1136/rapm-2023-104629 [doi]
AB  - BACKGROUND: Large language models have been gaining tremendous popularity since 
      the introduction of ChatGPT in late 2022. Perioperative pain providers should 
      leverage natural language processing (NLP) technology and explore pertinent use 
      cases to improve patient care. One example is tracking persistent postoperative 
      opioid use after surgery. Since much of the relevant data may be 'hidden' within 
      unstructured clinical text, NLP models may prove to be advantageous. The primary 
      objective of this proof-of-concept study was to demonstrate the ability of an NLP 
      engine to review clinical notes and accurately identify patients who had 
      persistent postoperative opioid use after major spine surgery. METHODS: Clinical 
      documents from all patients that underwent major spine surgery during July 
      2015-August 2021 were extracted from the electronic health record. The primary 
      outcome was persistent postoperative opioid use, defined as continued use of 
      opioids greater than or equal to 3 months after surgery. This outcome was 
      ascertained via manual clinician review from outpatient spine surgery follow-up 
      notes. An NLP engine was applied to these notes to ascertain the presence of 
      persistent opioid use-this was then compared with results from clinician manual 
      review. RESULTS: The final study sample consisted of 965 patients, in which 705 
      (73.1%) were determined to have persistent opioid use following surgery. The NLP 
      engine correctly determined the patients' opioid use status in 92.9% of cases, in 
      which it correctly identified persistent opioid use in 95.6% of cases and no 
      persistent opioid use in 86.1% of cases. DISCUSSION: Access to unstructured data 
      within the perioperative history can contextualize patients' opioid use and 
      provide further insight into the opioid crisis, while at the same time improve 
      care directly at the patient level. While these goals are in reach, future work 
      is needed to evaluate how to best implement NLP within different healthcare 
      systems for use in clinical decision support.
CI  - © American Society of Regional Anesthesia & Pain Medicine 2023. No commercial 
      re-use. See rights and permissions. Published by BMJ.
FAU - Seng, Eri C
AU  - Seng EC
AD  - Division of Perioperative Informatics, Department of Anesthesiology, University 
      of California San Diego, La Jolla, California, USA.
FAU - Mehdipour, Soraya
AU  - Mehdipour S
AUID- ORCID: 0000-0002-5132-4297
AD  - Division of Perioperative Informatics, Department of Anesthesiology, University 
      of California San Diego, La Jolla, California, USA.
FAU - Simpson, Sierra
AU  - Simpson S
AD  - Division of Perioperative Informatics, Department of Anesthesiology, University 
      of California San Diego, La Jolla, California, USA.
FAU - Gabriel, Rodney A
AU  - Gabriel RA
AUID- ORCID: 0000-0003-4443-0021
AD  - Division of Perioperative Informatics, Department of Anesthesiology, University 
      of California San Diego, La Jolla, California, USA ragabriel@health.ucsd.edu.
AD  - Division of Regional Anesthesia, Department of Anesthesiology, University of 
      California, San Diego, La Jolla, California, USA.
LA  - eng
PT  - Journal Article
DEP - 20230706
PL  - England
TA  - Reg Anesth Pain Med
JT  - Regional anesthesia and pain medicine
JID - 9804508
SB  - IM
OTO - NOTNLM
OT  - analgesics, opioid
OT  - chronic pain
OT  - pain management
COIS- Competing interests: RG’s institution has received funding and/or product for 
      research purposes from Epimed, Infutronix, SPR Therapeutics, Merck, and Precision 
      Genetics. RG is a consultant for Avanos. SS is Founder of BrilliantBiome. ECS was 
      a paid intern at KAIDHealth from 2022 to 2023.
EDAT- 2023/07/08 10:42
MHDA- 2023/07/08 10:42
CRDT- 2023/07/07 20:43
PHST- 2023/04/27 00:00 [received]
PHST- 2023/06/24 00:00 [accepted]
PHST- 2023/07/08 10:42 [medline]
PHST- 2023/07/08 10:42 [pubmed]
PHST- 2023/07/07 20:43 [entrez]
AID - rapm-2023-104629 [pii]
AID - 10.1136/rapm-2023-104629 [doi]
PST - aheadofprint
SO  - Reg Anesth Pain Med. 2023 Jul 6:rapm-2023-104629. doi: 10.1136/rapm-2023-104629.

PMID- 37838021
OWN - NLM
STAT- MEDLINE
DCOM- 20231205
LR  - 20231205
IS  - 1877-0568 (Electronic)
IS  - 1877-0568 (Linking)
VI  - 109
IP  - 8
DP  - 2023 Dec
TI  - Detecting generative artificial intelligence in scientific articles: Evasion 
      techniques and implications for scientific integrity.
PG  - 103706
LID - S1877-0568(23)00224-4 [pii]
LID - 10.1016/j.otsr.2023.103706 [doi]
AB  - BACKGROUND: Artificial intelligence (AI) tools, although beneficial for data 
      collection and analysis, can also facilitate scientific fraud. AI detectors can 
      help resolve this problem, but their effectiveness depends on their ability to 
      track AI progress. In addition, many methods of evading AI detection exist and 
      their constantly evolving sophistication can make the task more difficult. Thus, 
      from an AI-generated text, we wanted to: (1) evaluate the AI detection sites on a 
      text generated entirely by the AI, (2) test the methods described for evading AI 
      detection, and (3) evaluate the effectiveness of these methods to evade AI 
      detection on the sites tested previously. HYPOTHESIS: Not all AI detection tools 
      are equally effective in detecting AI-generated text and some techniques used to 
      evade АI detection can make an AI-produced text almost undetectable. MATERIALS 
      AND METHODS: We created a text with ChatGPT-4 (Chat Gеnеrаtivе Prе-trained 
      Transformer) and submitted it to 11 АI detection web tools (Оriginаlity, ZеrоGPT, 
      Writеr, Cоpylеаks, Crоssplag, GPTZеrо, Sapling, Cоntеnt аt scаlе, Cоrrеctоr, 
      Writеfull еt Quill), bеfоrе аnd аftеr applying strategies to minimise AI 
      detection. The strategies used to minimize AI detection were the improvement of 
      command messages in ChatPGT, the introduction of minor grammatical errors such as 
      comma deletion, paraphrasing, and the substitution of Latin letters with similar 
      Cyrillic letters (a and о) which is also a method used elsewhere to evade the 
      detection of plagiarism. We have also tested the effectiveness of these tools in 
      correctly identifying a scientific text written by a human in 1960. RESULTS: From 
      the initial text generated by the AI, 7 of the 11 detectors concluded that the 
      text was mainly written by humans. Subsequently, the introduction of simple 
      modifications, such as the removal of commas or paraphrasing can effectively 
      reduce AI detection and make the text appear human for all detectors. In 
      addition, replacing certain Latin letters with Cyrillic letters can make an AI 
      text completely undetectable. Finally, we observe that in a paradoxical way, 
      certain sites detect a significant proportion of AI in a text written by a human 
      in 1960. DISCUSSION: AI detectors have low efficiency, and simple modifications 
      can allow even the most robust detectors to be easily bypassed. The rapid 
      development of generative AI raises questions about the future of scientific 
      writing but also about the detection of scientific fraud, such as data 
      fabrication. LEVEL OF EVIDENCE: III Control case study.
CI  - Copyright © 2023 Elsevier Masson SAS. All rights reserved.
FAU - Оdri, Guillаumе-Аnthоny
AU  - Оdri GА
AD  - Sеrvicе dе chirurgiе оrthоpédiquе еt trаumаtоlоgiquе, cеntrе hоspitаliеr 
      univеrsitаirе Lаribоisièrе, 2, ruе Аmbrоisе-Pаré, 75010 Pаris, Frаncе; Insеrm 
      U1132 BIОSCАR, univеrsité Pаris-Cité, 75010 Pаris, Frаncе. Electronic address: 
      guillaume.odri@aphp.fr.
FAU - Ji Yun Yооn, Diаnе
AU  - Ji Yun Yооn D
AD  - Insеrm U1132 BIОSCАR, univеrsité Pаris-Cité, 75010 Pаris, Frаncе.
LA  - eng
PT  - Journal Article
DEP - 20231012
PL  - France
TA  - Orthop Traumatol Surg Res
JT  - Orthopaedics & traumatology, surgery & research : OTSR
JID - 101494830
SB  - IM
MH  - *Artificial Intelligence
MH  - *Writing
OTO - NOTNLM
OT  - Gеnеrаtivе аrtificiаl intеlligеncе
OT  - Sciеntific frаud
OT  - Аcаdеmic writing
EDAT- 2023/10/15 05:46
MHDA- 2023/12/04 12:43
CRDT- 2023/10/14 19:24
PHST- 2023/07/15 00:00 [received]
PHST- 2023/09/06 00:00 [revised]
PHST- 2023/09/19 00:00 [accepted]
PHST- 2023/12/04 12:43 [medline]
PHST- 2023/10/15 05:46 [pubmed]
PHST- 2023/10/14 19:24 [entrez]
AID - S1877-0568(23)00224-4 [pii]
AID - 10.1016/j.otsr.2023.103706 [doi]
PST - ppublish
SO  - Orthop Traumatol Surg Res. 2023 Dec;109(8):103706. doi: 
      10.1016/j.otsr.2023.103706. Epub 2023 Oct 12.

PMID- 38060223
OWN - NLM
STAT- MEDLINE
DCOM- 20231211
LR  - 20231211
IS  - 2574-3805 (Electronic)
IS  - 2574-3805 (Linking)
VI  - 6
IP  - 12
DP  - 2023 Dec 1
TI  - Performance of Large Language Models on a Neurology Board-Style Examination.
PG  - e2346721
LID - 10.1001/jamanetworkopen.2023.46721 [doi]
LID - e2346721
AB  - IMPORTANCE: Recent advancements in large language models (LLMs) have shown 
      potential in a wide array of applications, including health care. While LLMs 
      showed heterogeneous results across specialized medical board examinations, the 
      performance of these models in neurology board examinations remains unexplored. 
      OBJECTIVE: To assess the performance of LLMs on neurology board-style 
      examinations. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study was 
      conducted between May 17 and May 31, 2023. The evaluation utilized a question 
      bank approved by the American Board of Psychiatry and Neurology and was validated 
      with a small question cohort by the European Board for Neurology. All questions 
      were categorized into lower-order (recall, understanding) and higher-order 
      (apply, analyze, synthesize) questions based on the Bloom taxonomy for learning 
      and assessment. Performance by LLM ChatGPT versions 3.5 (LLM 1) and 4 (LLM 2) was 
      assessed in relation to overall scores, question type, and topics, along with the 
      confidence level and reproducibility of answers. MAIN OUTCOMES AND MEASURES: 
      Overall percentage scores of 2 LLMs. RESULTS: LLM 2 significantly outperformed 
      LLM 1 by correctly answering 1662 of 1956 questions (85.0%) vs 1306 questions 
      (66.8%) for LLM 1. Notably, LLM 2's performance was greater than the mean human 
      score of 73.8%, effectively achieving near-passing and passing grades in the 
      neurology board examination. LLM 2 outperformed human users in behavioral, 
      cognitive, and psychological-related questions and demonstrated superior 
      performance to LLM 1 in 6 categories. Both LLMs performed better on lower-order 
      than higher-order questions, with LLM 2 excelling in both lower-order and 
      higher-order questions. Both models consistently used confident language, even 
      when providing incorrect answers. Reproducible answers of both LLMs were 
      associated with a higher percentage of correct answers than inconsistent answers. 
      CONCLUSIONS AND RELEVANCE: Despite the absence of neurology-specific training, 
      LLM 2 demonstrated commendable performance, whereas LLM 1 performed slightly 
      below the human average. While higher-order cognitive tasks were more challenging 
      for both models, LLM 2's results were equivalent to passing grades in specialized 
      neurology examinations. These findings suggest that LLMs could have significant 
      applications in clinical neurology and health care with further refinements.
FAU - Schubert, Marc Cicero
AU  - Schubert MC
AD  - Neurology Clinic and National Center for Tumor Diseases, University Hospital 
      Heidelberg, Heidelberg, Germany.
AD  - Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer 
      Research Center, Heidelberg, Germany.
FAU - Wick, Wolfgang
AU  - Wick W
AD  - Neurology Clinic and National Center for Tumor Diseases, University Hospital 
      Heidelberg, Heidelberg, Germany.
AD  - Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer 
      Research Center, Heidelberg, Germany.
FAU - Venkataramani, Varun
AU  - Venkataramani V
AD  - Neurology Clinic and National Center for Tumor Diseases, University Hospital 
      Heidelberg, Heidelberg, Germany.
AD  - Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer 
      Research Center, Heidelberg, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231201
PL  - United States
TA  - JAMA Netw Open
JT  - JAMA network open
JID - 101729235
SB  - IM
MH  - Humans
MH  - Cross-Sectional Studies
MH  - Reproducibility of Results
MH  - Neurologic Examination
MH  - *Language
MH  - *Neurology
PMC - PMC10704278
COIS- Conflict of Interest Disclosures: Prof Wick reported having patent 
      WO2017020982A1, agents for use in the treatment of glioma. No other disclosures 
      were reported.
EDAT- 2023/12/07 12:43
MHDA- 2023/12/11 12:42
CRDT- 2023/12/07 11:33
PHST- 2023/12/11 12:42 [medline]
PHST- 2023/12/07 12:43 [pubmed]
PHST- 2023/12/07 11:33 [entrez]
AID - 2812620 [pii]
AID - zoi231362 [pii]
AID - 10.1001/jamanetworkopen.2023.46721 [doi]
PST - epublish
SO  - JAMA Netw Open. 2023 Dec 1;6(12):e2346721. doi: 
      10.1001/jamanetworkopen.2023.46721.

PMID- 37787655
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231006
IS  - 2291-9694 (Print)
IS  - 2291-9694 (Electronic)
VI  - 11
DP  - 2023 Sep 19
TI  - Large Language Models for Epidemiological Research via Automated Machine 
      Learning: Case Study Using Data From the British National Child Development 
      Study.
PG  - e43638
LID - 10.2196/43638 [doi]
LID - e43638
AB  - BACKGROUND: Large language models have had a huge impact on natural language 
      processing (NLP) in recent years. However, their application in epidemiological 
      research is still limited to the analysis of electronic health records and social 
      media data. OBJECTIVES: To demonstrate the potential of NLP beyond these domains, 
      we aimed to develop prediction models based on texts collected from an 
      epidemiological cohort and compare their performance to classical regression 
      methods. METHODS: We used data from the British National Child Development Study, 
      where 10,567 children aged 11 years wrote essays about how they imagined 
      themselves as 25-year-olds. Overall, 15% of the data set was set aside as a test 
      set for performance evaluation. Pretrained language models were fine-tuned using 
      AutoTrain (Hugging Face) to predict current reading comprehension score (range: 
      0-35) and future BMI and physical activity (active vs inactive) at the age of 33 
      years. We then compared their predictive performance (accuracy or discrimination) 
      with linear and logistic regression models, including demographic and lifestyle 
      factors of the parents and children from birth to the age of 11 years as 
      predictors. RESULTS: NLP clearly outperformed linear regression when predicting 
      reading comprehension scores (root mean square error: 3.89, 95% CI 3.74-4.05 for 
      NLP vs 4.14, 95% CI 3.98-4.30 and 5.41, 95% CI 5.23-5.58 for regression models 
      with and without general ability score as a predictor, respectively). Predictive 
      performance for physical activity was similarly poor for the 2 methods (area 
      under the receiver operating characteristic curve: 0.55, 95% CI 0.52-0.60 for 
      both) but was slightly better than random assignment, whereas linear regression 
      clearly outperformed the NLP approach when predicting BMI (root mean square 
      error: 4.38, 95% CI 4.02-4.74 for NLP vs 3.85, 95% CI 3.54-4.16 for regression). 
      The NLP approach did not perform better than simply assigning the mean BMI from 
      the training set as a predictor. CONCLUSIONS: Our study demonstrated the 
      potential of using large language models on text collected from epidemiological 
      studies. The performance of the approach appeared to depend on how directly the 
      topic of the text was related to the outcome. Open-ended questions specifically 
      designed to capture certain health concepts and lived experiences in combination 
      with NLP methods should receive more attention in future epidemiological studies.
CI  - © Rasmus Wibaek, Gregers Stig Andersen, Christina C Dahm, Daniel R Witte, Adam 
      Hulman. Originally published in JMIR Medical Informatics 
      (https://medinform.jmir.org).
FAU - Wibaek, Rasmus
AU  - Wibaek R
AUID- ORCID: 0000-0002-4989-2084
AD  - Steno Diabetes Center Copenhagen, Herlev, Denmark.
FAU - Andersen, Gregers Stig
AU  - Andersen GS
AUID- ORCID: 0000-0001-6125-300X
AD  - Steno Diabetes Center Copenhagen, Herlev, Denmark.
FAU - Dahm, Christina C
AU  - Dahm CC
AUID- ORCID: 0000-0003-0481-2893
AD  - Department of Public Health, Aarhus University, Aarhus, Denmark.
FAU - Witte, Daniel R
AU  - Witte DR
AUID- ORCID: 0000-0002-0769-2922
AD  - Department of Public Health, Aarhus University, Aarhus, Denmark.
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
FAU - Hulman, Adam
AU  - Hulman A
AUID- ORCID: 0000-0002-3969-1000
AD  - Department of Public Health, Aarhus University, Aarhus, Denmark.
AD  - Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
LA  - eng
PT  - Journal Article
DEP - 20230919
PL  - Canada
TA  - JMIR Med Inform
JT  - JMIR medical informatics
JID - 101645109
PMC - PMC10547934
OTO - NOTNLM
OT  - LLM
OT  - NLP
OT  - child development
OT  - cohort study
OT  - deep learning
OT  - epidemiology
OT  - language model
OT  - large language model
OT  - natural language processing
OT  - prediction
OT  - prediction model
OT  - regression model
COIS- GSA owns shares in Novo Nordisk A/S. GSA is currently employed by Novo Nordisk 
      A/S. During contribution to the manuscript, GSA was employed by Steno Diabetes 
      Center Copenhagen.
EDAT- 2023/10/03 12:43
MHDA- 2023/10/03 12:44
CRDT- 2023/10/03 10:03
PHST- 2022/10/21 00:00 [received]
PHST- 2023/06/29 00:00 [revised]
PHST- 2023/07/22 00:00 [accepted]
PHST- 2023/10/03 12:44 [medline]
PHST- 2023/10/03 12:43 [pubmed]
PHST- 2023/10/03 10:03 [entrez]
AID - v11i1e43638 [pii]
AID - 43638 [pii]
AID - 10.2196/43638 [doi]
PST - epublish
SO  - JMIR Med Inform. 2023 Sep 19;11:e43638. doi: 10.2196/43638.

PMID- 37123782
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230502
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Mar
TI  - Laboratory Findings of Benign Convulsions With Mild Gastroenteritis: A 
      Meta-Analysis.
PG  - e36784
LID - 10.7759/cureus.36784 [doi]
LID - e36784
AB  - Investigating factors associated with benign convulsions with mild 
      gastroenteritis (CwG) is important for early detection and treatment. In previous 
      studies, uric acid (UA) has been reported to be associated with CwG. However, the 
      association between CwG and abnormal laboratory values remains inconclusive. We 
      performed a meta-analysis of recent reports to determine the association between 
      CwG and laboratory findings, including UA, in patients with acute gastroenteritis 
      without convulsions. We conducted electronic searches of three databases (PubMed, 
      EMBASE, and Cochrane Library) and one scholarly search engine (Google Scholar 
      (Google, Inc., Mountain View, CA, USA)) up to February 2023 for studies on CwG. 
      Eligible studies were observational studies that assessed patients with CwG, 
      reported laboratory data, and stated the presence or absence of convulsions 
      during illness episodes. Patients were children with mild gastroenteritis, with 
      the exposure group developing convulsions and the control group not. The outcome 
      was a comparison of laboratory data between the two groups. The effect size was 
      calculated using the standardized mean difference (SMD), and random-effects 
      models were used for the analysis because of high heterogeneity. In total, 148 
      articles were included in this study. After the screening, nine studies, 
      including 8,367 patients, were selected for the meta-analysis. The most prevalent 
      laboratory finding was an increased serum UA level, with an SMD of 1.42 (N = 
      6,411; 95% confidence interval (CI): (1.12, 1.72); Z = 9.242, p< 0.001; I (2) = 
      81.68%, p= 0.002). The optimal serum UA cutoff value was 7.21 mg/dL, with an area 
      under the receiver operating characteristic (ROC) curve (AUC) of 0.827 (95% CI: 
      (0.807, 0.846)). This meta-analysis suggests that CwG is strongly associated with 
      increased serum UA levels. These results demonstrate that more attention should 
      be paid when interpreting laboratory findings in pediatric patients with acute 
      gastroenteritis.
CI  - Copyright © 2023, Miyagi et al.
FAU - Miyagi, Yoshifumi
AU  - Miyagi Y
AD  - Department of Pediatrics, Haibara General Hospital, Shizuoka, JPN.
FAU - Sasano, Tomoyuki
AU  - Sasano T
AD  - Department of Obstetrics and Gynecology, Osaka Saiseikai Nakatsu Hospital, Osaka, 
      JPN.
FAU - Kin, Kentoku
AU  - Kin K
AD  - Department of Pediatrics, Haibara General Hospital, Shizuoka, JPN.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230328
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10145619
OTO - NOTNLM
OT  - benign convulsions with mild gastroenteritis
OT  - chatgpt
OT  - child
OT  - convulsion
OT  - gastroenteritis
OT  - hyperuricemia
OT  - laboratory finding
OT  - meta-analysis
OT  - seizure
OT  - uric acid
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/05/01 06:42
MHDA- 2023/05/01 06:43
CRDT- 2023/05/01 03:34
PHST- 2023/03/28 00:00 [accepted]
PHST- 2023/05/01 06:43 [medline]
PHST- 2023/05/01 06:42 [pubmed]
PHST- 2023/05/01 03:34 [entrez]
AID - 10.7759/cureus.36784 [doi]
PST - epublish
SO  - Cureus. 2023 Mar 28;15(3):e36784. doi: 10.7759/cureus.36784. eCollection 2023 
      Mar.

PMID- 38010803
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231213
IS  - 2291-9694 (Print)
IS  - 2291-9694 (Electronic)
VI  - 11
DP  - 2023 Nov 27
TI  - A Large Language Model Screening Tool to Target Patients for Best Practice 
      Alerts: Development and Validation.
PG  - e49886
LID - 10.2196/49886 [doi]
LID - e49886
AB  - BACKGROUND: Best Practice Alerts (BPAs) are alert messages to physicians in the 
      electronic health record that are used to encourage appropriate use of health 
      care resources. While these alerts are helpful in both improving care and 
      reducing costs, BPAs are often broadly applied nonselectively across entire 
      patient populations. The development of large language models (LLMs) provides an 
      opportunity to selectively identify patients for BPAs. OBJECTIVE: In this paper, 
      we present an example case where an LLM screening tool is used to select patients 
      appropriate for a BPA encouraging the prescription of deep vein thrombosis (DVT) 
      anticoagulation prophylaxis. The artificial intelligence (AI) screening tool was 
      developed to identify patients experiencing acute bleeding and exclude them from 
      receiving a DVT prophylaxis BPA. METHODS: Our AI screening tool used a 
      BioMed-RoBERTa (Robustly Optimized Bidirectional Encoder Representations from 
      Transformers Pretraining Approach; AllenAI) model to perform classification of 
      physician notes, identifying patients without active bleeding and thus 
      appropriate for a thromboembolism prophylaxis BPA. The BioMed-RoBERTa model was 
      fine-tuned using 500 history and physical notes of patients from the MIMIC-III 
      (Medical Information Mart for Intensive Care) database who were not prescribed 
      anticoagulation. A development set of 300 MIMIC patient notes was used to 
      determine the model's hyperparameters, and a separate test set of 300 patient 
      notes was used to evaluate the screening tool. RESULTS: Our MIMIC-III test set 
      population of 300 patients included 72 patients with bleeding (ie, were not 
      appropriate for a DVT prophylaxis BPA) and 228 without bleeding who were 
      appropriate for a DVT prophylaxis BPA. The AI screening tool achieved impressive 
      accuracy with a precision-recall area under the curve of 0.82 (95% CI 0.75-0.89) 
      and a receiver operator curve area under the curve of 0.89 (95% CI 0.84-0.94). 
      The screening tool reduced the number of patients who would trigger an alert by 
      20% (240 instead of 300 alerts) and increased alert applicability by 14.8% (218 
      [90.8%] positive alerts from 240 total alerts instead of 228 [76%] positive 
      alerts from 300 total alerts), compared to nonselectively sending alerts for all 
      patients. CONCLUSIONS: These results show a proof of concept on how language 
      models can be used as a screening tool for BPAs. We provide an example AI 
      screening tool that uses a HIPAA (Health Insurance Portability and Accountability 
      Act)-compliant BioMed-RoBERTa model deployed with minimal computing power. Larger 
      models (eg, Generative Pre-trained Transformers-3, Generative Pre-trained 
      Transformers-4, and Pathways Language Model) will exhibit superior performance 
      but require data use agreements to be HIPAA compliant. We anticipate LLMs to 
      revolutionize quality improvement in hospital medicine.
CI  - ©Thomas Savage, John Wang, Lisa Shieh. Originally published in JMIR Medical 
      Informatics (https://medinform.jmir.org), 27.11.2023.
FAU - Savage, Thomas
AU  - Savage T
AUID- ORCID: 0000-0003-4828-5802
AD  - Division of Hospital Medicine, Department of Medicine, Stanford University, Palo 
      Alto, CA, United States.
FAU - Wang, John
AU  - Wang J
AUID- ORCID: 0000-0002-8515-5014
AD  - Divison of Gastroenterology and Hepatology, Department of Medicine, Stanford 
      University, Palo Alto, CA, United States.
FAU - Shieh, Lisa
AU  - Shieh L
AUID- ORCID: 0000-0003-2729-315X
AD  - Division of Hospital Medicine, Department of Medicine, Stanford University, Palo 
      Alto, CA, United States.
LA  - eng
PT  - Journal Article
DEP - 20231127
PL  - Canada
TA  - JMIR Med Inform
JT  - JMIR medical informatics
JID - 101645109
PMC - PMC10714262
OTO - NOTNLM
OT  - Artificial Intelligence
OT  - EHR
OT  - Natural Language Processing
OT  - health record
OT  - health records
OT  - language model
OT  - language models
OT  - large language models
OT  - quality improvement
COIS- Conflicts of Interest: None declared.
EDAT- 2023/11/27 12:44
MHDA- 2023/11/27 12:45
CRDT- 2023/11/27 11:55
PHST- 2023/06/12 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/08/04 00:00 [revised]
PHST- 2023/11/27 12:45 [medline]
PHST- 2023/11/27 12:44 [pubmed]
PHST- 2023/11/27 11:55 [entrez]
AID - v11i1e49886 [pii]
AID - 10.2196/49886 [doi]
PST - epublish
SO  - JMIR Med Inform. 2023 Nov 27;11:e49886. doi: 10.2196/49886.

PMID- 37885499
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231028
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 9
DP  - 2023 Sep
TI  - Comparison of Cardiovascular Response to Lower Body and Whole Body Exercise Among 
      Sedentary Young Adults.
PG  - e45880
LID - 10.7759/cureus.45880 [doi]
LID - e45880
AB  - Background Cardiovascular responses to exercise are essential indicators of 
      cardiovascular health and fitness. Understanding how different types of exercise, 
      such as lower-body and whole-body exercises, impact these responses is crucial 
      for designing effective fitness programs and assessing cardiovascular function. 
      Aim This study aimed to compare the cardiovascular response of young adults 
      during lower-body exercise using a bicycle ergometer and whole-body exercise on a 
      treadmill. Methods Thirty-two healthy young adults participated in this study. 
      Each participant completed two exercise sessions on separate days: lower-body 
      exercise on a bicycle ergometer with a fixed cadence of 60 rpm with a breaking 
      resistance of 1.75 kg and whole-body exercise on a treadmill with a speed of 1.7 
      mph and a 10% grade. Heart rate (HR), systolic blood pressure (BP), and diastolic 
      BP were measured at rest and immediately after a three-minute exercise. Data were 
      analyzed using paired t-tests to compare the cardiovascular responses between the 
      two exercise modalities. Results A total of 17 male and 15 female young adults 
      with a mean age of 20.87±1.43 years participated in the study. The male and 
      female participants had similar ages (p =0.56) and body mass indexes (p = 0.1). 
      There was a higher HR (129.16±2.67 versus 150.87±3.23, p<0.0001) and systolic BP 
      (127.29±2.34 versus 144.9±4.16, p<0.0001) and lower diastolic BP (68.97±2.41 
      versus 62.97±2.31, p<0.0001) in whole body exercise on treadmill compared to 
      lower body exercise in bicycle ergometer. The effect size was large enough as 
      Cohen's d was 7.33, 5.13, and 2.54 for HR, systolic BP, and diastolic BP, 
      respectively. Conclusion In sedentary young adults, treadmill exercise led to 
      higher HR, systolic BP, and lower diastolic BP than bicycle ergometer exercise. 
      Increased muscle recruitment might result in higher energy expenditure, 
      increasing the HR and systolic BP to deliver oxygen and nutrients to the working 
      muscles. Further research is needed to understand the mechanisms and long-term 
      implications for precise exercise recommendations and better cardiovascular 
      health management.
CI  - Copyright © 2023, Kumari et al.
FAU - Kumari, Anita
AU  - Kumari A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, IND.
FAU - Sinha, Swati
AU  - Sinha S
AD  - Physiology, Bhagwan Mahavir Institute of Medical Sciences, Pawapuri, IND.
FAU - Kumari, Amita
AU  - Kumari A
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, IND.
FAU - Dhanvijay, Anup Kumar D
AU  - Dhanvijay AKD
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, IND.
FAU - Singh, Sanjeet Kumar
AU  - Singh SK
AD  - Pathology, All India Institute of Medical Sciences, Deoghar, IND.
FAU - Mondal, Himel
AU  - Mondal H
AD  - Physiology, All India Institute of Medical Sciences, Deoghar, IND.
LA  - eng
PT  - Journal Article
DEP - 20230924
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10599171
OTO - NOTNLM
OT  - bicycle ergometer
OT  - blood pressure
OT  - chatgpt
OT  - exercise
OT  - exercise physiology
OT  - heart rate
OT  - lower body exercise
OT  - treadmill
OT  - whole-body exercise
OT  - young adult males
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/10/27 06:42
MHDA- 2023/10/27 06:43
CRDT- 2023/10/27 04:18
PHST- 2023/09/24 00:00 [accepted]
PHST- 2023/10/27 06:43 [medline]
PHST- 2023/10/27 06:42 [pubmed]
PHST- 2023/10/27 04:18 [entrez]
AID - 10.7759/cureus.45880 [doi]
PST - epublish
SO  - Cureus. 2023 Sep 24;15(9):e45880. doi: 10.7759/cureus.45880. eCollection 2023 
      Sep.

PMID- 37394024
OWN - NLM
STAT- MEDLINE
DCOM- 20230811
LR  - 20230815
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Linking)
VI  - 144
DP  - 2023 Aug
TI  - Increasing physical activity using an just-in-time adaptive digital assistant 
      supported by machine learning: A novel approach for hyper-personalised mHealth 
      interventions.
PG  - 104435
LID - S1532-0464(23)00156-9 [pii]
LID - 10.1016/j.jbi.2023.104435 [doi]
AB  - OBJECTIVE: Physical inactivity is a leading modifiable cause of death and disease 
      worldwide. Population-based interventions to increase physical activity are 
      needed. Existing automated expert systems (e.g., computer-tailored interventions) 
      have significant limitations that result in low long-term effectiveness. 
      Therefore, innovative approaches are needed. This special communication aims to 
      describe and discuss a novel mHealth intervention approach that proactively 
      offers participants with hyper-personalised intervention content adjusted in 
      real-time. METHODS: Using machine learning approaches, we propose a novel 
      physical activity intervention approach that can learn and adapt in real-time to 
      achieve high levels of personalisation and user engagement, underpinned by a 
      likeable digital assistant. It will consist of three major components: (1) 
      conversations: to increase user's knowledge on a wide range of activity-related 
      topics underpinned by Natural Language Processing; (2) nudge engine: to provide 
      users with hyper-personalised cues to action underpinned by reinforcement 
      learning (i.e., contextual bandit) and integrating real-time data from activity 
      tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users 
      asking any physical activity related questions underpinned by generative AI 
      (e.g., ChatGPT, Bard) for content generation. RESULTS: The detailed concept of 
      the proposed physical activity intervention platform demonstrates the practical 
      application of a just-in-time adaptive intervention applying various machine 
      learning techniques to deliver a hyper-personalised physical activity 
      intervention in an engaging way. Compared to traditional interventions, the novel 
      platform is expected to show potential for increased user engagement and 
      long-term effectiveness due to: (1) using new variables to personalise content 
      (e.g., GPS, weather), (2) providing behavioural support at the right time in 
      real-time, (3) implementing an engaging digital assistant and (4) improving the 
      relevance of content through applying machine learning algorithms. CONCLUSION: 
      The use of machine learning is on the rise in every aspect of today's society, 
      however few attempts have been undertaken to harness its potential to achieve 
      health behaviour change. By sharing our intervention concept, we contribute to 
      the ongoing dialogue on creating effective methods for promoting health and 
      well-being in the informatics research community. Future research should focus on 
      refining these techniques and evaluating their effectiveness in controlled and 
      real-world circumstances.
CI  - Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Vandelanotte, Corneel
AU  - Vandelanotte C
AD  - Appleton Institute, Central Queensland University, Bruce Highway, Rockhampton, 
      Queensland 4702, Australia. Electronic address: c.vandelanotte@cqu.edu.au.
FAU - Trost, Stewart
AU  - Trost S
AD  - School of Human Movement and Nutrition Science, The University of Queensland, St 
      Lucia, Queensland 4072, Australia. Electronic address: s.trost@uq.edu.au.
FAU - Hodgetts, Danya
AU  - Hodgetts D
AD  - Appleton Institute, Central Queensland University, Bruce Highway, Rockhampton, 
      Queensland 4702, Australia. Electronic address: d.hodgetts@cqu.edu.au.
FAU - Imam, Tasadduq
AU  - Imam T
AD  - School of Business and Law, Central Queensland University, 120 Spencer Street, 
      Melbourne, Victoria 3000, Australia. Electronic address: t.imam@cqu.edu.au.
FAU - Rashid, Mamunur
AU  - Rashid M
AD  - School of Engineering and Technology, Central Queensland University, 120 Spencer 
      Street, Melbourne, Victoria 3000, Australia. Electronic address: 
      m.rashid@cqu.edu.au.
FAU - To, Quyen G
AU  - To QG
AD  - Appleton Institute, Central Queensland University, Bruce Highway, Rockhampton, 
      Queensland 4702, Australia. Electronic address: q.to@cqu.edu.au.
FAU - Maher, Carol
AU  - Maher C
AD  - Allied Health and Human Performance, University of South Australia, City East 
      Campus, Adelaide, South Australia 5001, Australia. Electronic address: 
      Carol.Maher@unisa.edu.au.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230701
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
MH  - Humans
MH  - *Exercise
MH  - Health Behavior
MH  - *Telemedicine/methods
MH  - Machine Learning
MH  - Algorithms
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Behaviour change
OT  - Chatbot
OT  - Conversational agent
OT  - Exercise
OT  - Intervention
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/07/03 00:41
MHDA- 2023/08/11 06:43
CRDT- 2023/07/02 19:24
PHST- 2023/01/19 00:00 [received]
PHST- 2023/06/26 00:00 [revised]
PHST- 2023/06/28 00:00 [accepted]
PHST- 2023/08/11 06:43 [medline]
PHST- 2023/07/03 00:41 [pubmed]
PHST- 2023/07/02 19:24 [entrez]
AID - S1532-0464(23)00156-9 [pii]
AID - 10.1016/j.jbi.2023.104435 [doi]
PST - ppublish
SO  - J Biomed Inform. 2023 Aug;144:104435. doi: 10.1016/j.jbi.2023.104435. Epub 2023 
      Jul 1.

PMID- 37615960
OWN - NLM
STAT- Publisher
LR  - 20231019
IS  - 2374-2445 (Electronic)
IS  - 2374-2437 (Print)
IS  - 2374-2437 (Linking)
VI  - 9
IP  - 10
DP  - 2023 Oct 1
TI  - Assessment of Artificial Intelligence Chatbot Responses to Top Searched Queries 
      About Cancer.
PG  - 1437-1440
LID - 10.1001/jamaoncol.2023.2947 [doi]
AB  - IMPORTANCE: Consumers are increasingly using artificial intelligence (AI) 
      chatbots as a source of information. However, the quality of the cancer 
      information generated by these chatbots has not yet been evaluated using 
      validated instruments. OBJECTIVE: To characterize the quality of information and 
      presence of misinformation about skin, lung, breast, colorectal, and prostate 
      cancers generated by 4 AI chatbots. DESIGN, SETTING, AND PARTICIPANTS: This 
      cross-sectional study assessed AI chatbots' text responses to the 5 most commonly 
      searched queries related to the 5 most common cancers using validated 
      instruments. Search data were extracted from the publicly available Google Trends 
      platform and identical prompts were used to generate responses from 4 AI 
      chatbots: ChatGPT version 3.5 (OpenAI), Perplexity (Perplexity.AI), Chatsonic 
      (Writesonic), and Bing AI (Microsoft). EXPOSURES: Google Trends' top 5 search 
      queries related to skin, lung, breast, colorectal, and prostate cancer from 
      January 1, 2021, to January 1, 2023, were input into 4 AI chatbots. MAIN OUTCOMES 
      AND MEASURES: The primary outcomes were the quality of consumer health 
      information based on the validated DISCERN instrument (scores from 1 [low] to 5 
      [high] for quality of information) and the understandability and actionability of 
      this information based on the understandability and actionability domains of the 
      Patient Education Materials Assessment Tool (PEMAT) (scores of 0%-100%, with 
      higher scores indicating a higher level of understandability and actionability). 
      Secondary outcomes included misinformation scored using a 5-item Likert scale 
      (scores from 1 [no misinformation] to 5 [high misinformation]) and readability 
      assessed using the Flesch-Kincaid Grade Level readability score. RESULTS: The 
      analysis included 100 responses from 4 chatbots about the 5 most common search 
      queries for skin, lung, breast, colorectal, and prostate cancer. The quality of 
      text responses generated by the 4 AI chatbots was good (median [range] DISCERN 
      score, 5 [2-5]) and no misinformation was identified. Understandability was 
      moderate (median [range] PEMAT Understandability score, 66.7% [33.3%-90.1%]), and 
      actionability was poor (median [range] PEMAT Actionability score, 20.0% 
      [0%-40.0%]). The responses were written at the college level based on the 
      Flesch-Kincaid Grade Level score. CONCLUSIONS AND RELEVANCE: Findings of this 
      cross-sectional study suggest that AI chatbots generally produce accurate 
      information for the top cancer-related search queries, but the responses are not 
      readily actionable and are written at a college reading level. These limitations 
      suggest that AI chatbots should be used supplementarily and not as a primary 
      source for medical information.
FAU - Pan, Alexander
AU  - Pan A
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York.
FAU - Musheyev, David
AU  - Musheyev D
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York.
FAU - Bockelman, Daniel
AU  - Bockelman D
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York.
FAU - Loeb, Stacy
AU  - Loeb S
AD  - Department of Urology, New York University School of Medicine, New York.
AD  - Department of Population Health, New York University School of Medicine, New 
      York.
AD  - Department of Surgery, VA New York Harbor Health Care, New York.
FAU - Kabarriti, Abdo E
AU  - Kabarriti AE
AD  - Department of Urology, State University of New York Downstate Health Sciences 
      University, New York.
LA  - eng
PT  - Comment
PT  - Journal Article
PL  - United States
TA  - JAMA Oncol
JT  - JAMA oncology
JID - 101652861
SB  - IM
CON - JAMA Oncol. 2023 Oct 1;9(10):1341-1342. PMID: 37615950
PMC - PMC10450581
COIS- Conflict of Interest Disclosures: Dr Loeb is supported by a grant from the 
      National Cancer Institute (R01CA278997) and reported having a family member with 
      equity in Gilead Sciences outside the submitted work. No other disclosures were 
      reported.
EDAT- 2023/08/24 13:43
MHDA- 2023/08/24 13:43
PMCR- 2024/08/24
CRDT- 2023/08/24 11:44
PHST- 2024/08/24 00:00 [pmc-release]
PHST- 2023/08/24 13:43 [pubmed]
PHST- 2023/08/24 13:43 [medline]
PHST- 2023/08/24 11:44 [entrez]
AID - 2808733 [pii]
AID - cbr230013 [pii]
AID - 10.1001/jamaoncol.2023.2947 [doi]
PST - ppublish
SO  - JAMA Oncol. 2023 Oct 1;9(10):1437-1440. doi: 10.1001/jamaoncol.2023.2947.

PMID- 37449291
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2168-8184 (Print)
IS  - 2168-8184 (Electronic)
IS  - 2168-8184 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Jul
TI  - Laser Treatment for Patients With Vulvodynia and Interstitial Cystitis/Bladder 
      Pain Syndrome: A Case Series (The UNICORN-3 Study).
PG  - e41786
LID - 10.7759/cureus.41786 [doi]
LID - e41786
AB  - Introduction Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic 
      pain disorder characterized by urgency, frequency of urination, and pelvic pain. 
      Women with IC/BPS often experience sexual dysfunction, vulvodynia, and vaginal 
      health issues. Combined erbium and neodymium yttrium aluminum garnet (YAG) laser 
      treatments targeting the vagina and vulva have shown promise in improving 
      symptoms. Our study aims to investigate the effectiveness of these combined laser 
      treatments in women with IC/BPS and vulvodynia. Methods Women diagnosed with 
      vulvodynia and IC/BPS underwent combined laser treatment using vaginal erbium:YAG 
      laser (VEL) and neodymium:YAG laser (Nd:YAG). Various parameters were evaluated, 
      including the vulvodynia test, numeric rating scale (NRS-11) for pain, 
      interstitial cystitis symptom index and problem index (ICSI and ICPI), pelvic 
      pain and urgency/frequency symptom score (PUF), and mean urination volume/daily 
      urination frequency in a three-day urination diary. Treatment was administered 
      three times, with intervals of one month between each session, and follow-up 
      evaluations were conducted at six and 12 months. All statistical analyses were 
      designed and programmed by the AI chatbot GPT-4 (chatGPT-4). Results Fifteen 
      female patients diagnosed with vulvodynia and IC/BPS were treated with three 
      sessions of VEL + Nd:YAG. Significant improvements were observed in the 
      vulvodynia test, NRS-11 scores, PUF, ICSI scores, ICPI scores, mean urination 
      volume, and daily urination frequency at six and 12 months (p<0.01). Short-term 
      improvements in IC/BPS pain scores correlated with improvements in the vulvodynia 
      test (p=0.007), suggesting a synergistic effect. However, no significant 
      correlations were found at 12 months. Conclusion Combined laser treatments 
      targeting the vagina and vulva showed significant therapeutic effects in women 
      with IC/BPS and vulvodynia. The addition of Nd:YAG to the VEL treatment enhanced 
      outcomes. Short-term improvements in IC/BPS pain scores correlated with 
      improvements in the vulvodynia test, indicating a synergistic effect. Long-term 
      improvements in both vulvodynia and IC/BPS symptoms may occur 
      independently. These findings highlight the importance of comprehensive 
      approaches for treating coexisting vulvodynia and IC/BPS.
CI  - Copyright © 2023, Okui et al.
FAU - Okui, Nobuo
AU  - Okui N
AD  - Dentistry, Kanagawa Dental University, Kanagawa, JPN.
FAU - Okui, Machiko Aurora
AU  - Okui MA
AD  - Urogynecology, Yokosuka Urogynecology Clinic, Kanagawa, JPN.
FAU - Kouno, Yuko
AU  - Kouno Y
AD  - Urology, Dr Okui's Urogynecology and Urology, Yokosuka, JPN.
FAU - Nakano, Kaori
AU  - Nakano K
AD  - Urology, Dr Okui's Urogynecology and Urology, Yokosuka, JPN.
LA  - eng
PT  - Journal Article
DEP - 20230712
PL  - United States
TA  - Cureus
JT  - Cureus
JID - 101596737
PMC - PMC10338019
OTO - NOTNLM
OT  - ai & robotics in healthcare
OT  - ai chatbot gpt-4
OT  - erbium:yag laser
OT  - interstitial cystitis symptom index and problem index
OT  - interstitial cystitis/bladder pain syndrome
OT  - neodymium:yag laser
OT  - numeric rating scale
OT  - pelvic pain and urgency/frequency symptom score
OT  - vulvodynia
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/07/14 13:06
MHDA- 2023/07/14 13:07
CRDT- 2023/07/14 04:01
PHST- 2023/07/12 00:00 [accepted]
PHST- 2023/07/14 13:07 [medline]
PHST- 2023/07/14 13:06 [pubmed]
PHST- 2023/07/14 04:01 [entrez]
AID - 10.7759/cureus.41786 [doi]
PST - epublish
SO  - Cureus. 2023 Jul 12;15(7):e41786. doi: 10.7759/cureus.41786. eCollection 2023 
      Jul.

PMID- 37902826
OWN - NLM
STAT- MEDLINE
DCOM- 20231101
LR  - 20231116
IS  - 1438-8871 (Electronic)
IS  - 1439-4456 (Print)
IS  - 1438-8871 (Linking)
VI  - 25
DP  - 2023 Oct 30
TI  - Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: 
      Comparative Study.
PG  - e49324
LID - 10.2196/49324 [doi]
LID - e49324
AB  - BACKGROUND: As advancements in artificial intelligence (AI) continue, large 
      language models (LLMs) have emerged as promising tools for generating medical 
      information. Their rapid adaptation and potential benefits in health care require 
      rigorous assessment in terms of the quality, accuracy, and safety of the 
      generated information across diverse medical specialties. OBJECTIVE: This study 
      aimed to evaluate the performance of 4 prominent LLMs, namely, 
      Claude-instant-v1.0, GPT-3.5-Turbo, Command-xlarge-nightly, and Bloomz, in 
      generating medical content spanning the clinical specialties of ophthalmology, 
      orthopedics, and dermatology. METHODS: Three domain-specific physicians evaluated 
      the AI-generated therapeutic recommendations for a diverse set of 60 diseases. 
      The evaluation criteria involved the mDISCERN score, correctness, and potential 
      harmfulness of the recommendations. ANOVA and pairwise t tests were used to 
      explore discrepancies in content quality and safety across models and 
      specialties. Additionally, using the capabilities of OpenAI's most advanced 
      model, GPT-4, an automated evaluation of each model's responses to the diseases 
      was performed using the same criteria and compared to the physicians' assessments 
      through Pearson correlation analysis. RESULTS: Claude-instant-v1.0 emerged with 
      the highest mean mDISCERN score (3.35, 95% CI 3.23-3.46). In contrast, Bloomz 
      lagged with the lowest score (1.07, 95% CI 1.03-1.10). Our analysis revealed 
      significant differences among the models in terms of quality (P<.001). Evaluating 
      their reliability, the models displayed strong contrasts in their falseness 
      ratings, with variations both across models (P<.001) and specialties (P<.001). 
      Distinct error patterns emerged, such as confusing diagnoses; providing vague, 
      ambiguous advice; or omitting critical treatments, such as antibiotics for 
      infectious diseases. Regarding potential harm, GPT-3.5-Turbo was found to be the 
      safest, with the lowest harmfulness rating. All models lagged in detailing the 
      risks associated with treatment procedures, explaining the effects of therapies 
      on quality of life, and offering additional sources of information. Pearson 
      correlation analysis underscored a substantial alignment between physician 
      assessments and GPT-4's evaluations across all established criteria (P<.01). 
      CONCLUSIONS: This study, while comprehensive, was limited by the involvement of a 
      select number of specialties and physician evaluators. The straightforward 
      prompting strategy ("How to treat…") and the assessment benchmarks, initially 
      conceptualized for human-authored content, might have potential gaps in capturing 
      the nuances of AI-driven information. The LLMs evaluated showed a notable 
      capability in generating valuable medical content; however, evident lapses in 
      content quality and potential harm signal the need for further refinements. Given 
      the dynamic landscape of LLMs, this study's findings emphasize the need for 
      regular and methodical assessments, oversight, and fine-tuning of these AI tools 
      to ensure they produce consistently trustworthy and clinically safe medical 
      advice. Notably, the introduction of an auto-evaluation mechanism using GPT-4, as 
      detailed in this study, provides a scalable, transferable method for 
      domain-agnostic evaluations, extending beyond therapy recommendation assessments.
CI  - ©Theresa Isabelle Wilhelm, Jonas Roos, Robert Kaczmarczyk. Originally published 
      in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2023.
FAU - Wilhelm, Theresa Isabelle
AU  - Wilhelm TI
AUID- ORCID: 0000-0002-7462-1868
AD  - Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, 
      Freiburg, Germany.
AD  - Medical Graduate Center, School of Medicine, Technical University of Munich, 
      Munich, Germany.
FAU - Roos, Jonas
AU  - Roos J
AUID- ORCID: 0000-0001-8843-4695
AD  - Department of Orthopedics and Trauma Surgery, University Hospital of Bonn, Bonn, 
      Germany.
FAU - Kaczmarczyk, Robert
AU  - Kaczmarczyk R
AUID- ORCID: 0000-0002-8570-1601
AD  - Department of Dermatology and Allergy, School of Medicine, Technical University 
      of Munich, Munich, Germany.
AD  - Division of Dermatology and Venerology, Department of Medicine Solna, Karolinska 
      Institutet, Solna, Sweden.
LA  - eng
PT  - Journal Article
DEP - 20231030
PL  - Canada
TA  - J Med Internet Res
JT  - Journal of medical Internet research
JID - 100959882
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Quality of Life
MH  - Reproducibility of Results
MH  - *Medicine
MH  - Language
PMC - PMC10644179
OTO - NOTNLM
OT  - ChatGPT
OT  - LLM
OT  - accuracy
OT  - artificial intelligence
OT  - chatbot
OT  - chatbots
OT  - dermatology
OT  - health information
OT  - large language models
OT  - medical advice
OT  - medical information
OT  - ophthalmology
OT  - orthopedic
OT  - orthopedics
OT  - quality
OT  - recommendation
OT  - recommendations
OT  - reliability
OT  - reliable
OT  - safety
OT  - therapy
COIS- Conflicts of Interest: None declared.
EDAT- 2023/10/30 12:42
MHDA- 2023/11/01 12:44
CRDT- 2023/10/30 11:54
PHST- 2023/05/24 00:00 [received]
PHST- 2023/09/29 00:00 [accepted]
PHST- 2023/09/12 00:00 [revised]
PHST- 2023/11/01 12:44 [medline]
PHST- 2023/10/30 12:42 [pubmed]
PHST- 2023/10/30 11:54 [entrez]
AID - v25i1e49324 [pii]
AID - 10.2196/49324 [doi]
PST - epublish
SO  - J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324.

PMID- 37782499
OWN - NLM
STAT- MEDLINE
DCOM- 20231003
LR  - 20231128
IS  - 2574-3805 (Electronic)
IS  - 2574-3805 (Linking)
VI  - 6
IP  - 10
DP  - 2023 Oct 2
TI  - Accuracy and Reliability of Chatbot Responses to Physician Questions.
PG  - e2336483
LID - 10.1001/jamanetworkopen.2023.36483 [doi]
LID - e2336483
AB  - IMPORTANCE: Natural language processing tools, such as ChatGPT (generative 
      pretrained transformer, hereafter referred to as chatbot), have the potential to 
      radically enhance the accessibility of medical information for health 
      professionals and patients. Assessing the safety and efficacy of these tools in 
      answering physician-generated questions is critical to determining their 
      suitability in clinical settings, facilitating complex decision-making, and 
      optimizing health care efficiency. OBJECTIVE: To assess the accuracy and 
      comprehensiveness of chatbot-generated responses to physician-developed medical 
      queries, highlighting the reliability and limitations of artificial 
      intelligence-generated medical information. DESIGN, SETTING, AND PARTICIPANTS: 
      Thirty-three physicians across 17 specialties generated 284 medical questions 
      that they subjectively classified as easy, medium, or hard with either binary 
      (yes or no) or descriptive answers. The physicians then graded the 
      chatbot-generated answers to these questions for accuracy (6-point Likert scale 
      with 1 being completely incorrect and 6 being completely correct) and 
      completeness (3-point Likert scale, with 1 being incomplete and 3 being complete 
      plus additional context). Scores were summarized with descriptive statistics and 
      compared using the Mann-Whitney U test or the Kruskal-Wallis test. The study 
      (including data analysis) was conducted from January to May 2023. MAIN OUTCOMES 
      AND MEASURES: Accuracy, completeness, and consistency over time and between 2 
      different versions (GPT-3.5 and GPT-4) of chatbot-generated medical responses. 
      RESULTS: Across all questions (n = 284) generated by 33 physicians (31 faculty 
      members and 2 recent graduates from residency or fellowship programs) across 17 
      specialties, the median accuracy score was 5.5 (IQR, 4.0-6.0) (between almost 
      completely and complete correct) with a mean (SD) score of 4.8 (1.6) (between 
      mostly and almost completely correct). The median completeness score was 3.0 
      (IQR, 2.0-3.0) (complete and comprehensive) with a mean (SD) score of 2.5 (0.7). 
      For questions rated easy, medium, and hard, the median accuracy scores were 6.0 
      (IQR, 5.0-6.0), 5.5 (IQR, 5.0-6.0), and 5.0 (IQR, 4.0-6.0), respectively (mean 
      [SD] scores were 5.0 [1.5], 4.7 [1.7], and 4.6 [1.6], respectively; P = .05). 
      Accuracy scores for binary and descriptive questions were similar (median score, 
      6.0 [IQR, 4.0-6.0] vs 5.0 [IQR, 3.4-6.0]; mean [SD] score, 4.9 [1.6] vs 4.7 
      [1.6]; P = .07). Of 36 questions with scores of 1.0 to 2.0, 34 were requeried or 
      regraded 8 to 17 days later with substantial improvement (median score 2.0 [IQR, 
      1.0-3.0] vs 4.0 [IQR, 2.0-5.3]; P < .01). A subset of questions, regardless of 
      initial scores (version 3.5), were regenerated and rescored using version 4 with 
      improvement (mean accuracy [SD] score, 5.2 [1.5] vs 5.7 [0.8]; median score, 6.0 
      [IQR, 5.0-6.0] for original and 6.0 [IQR, 6.0-6.0] for rescored; P = .002). 
      CONCLUSIONS AND RELEVANCE: In this cross-sectional study, chatbot generated 
      largely accurate information to diverse medical queries as judged by academic 
      physician specialists with improvement over time, although it had important 
      limitations. Further research and model development are needed to correct 
      inaccuracies and for validation.
FAU - Goodman, Rachel S
AU  - Goodman RS
AD  - Vanderbilt University School of Medicine, Nashville, Tennessee.
FAU - Patrinely, J Randall
AU  - Patrinely JR
AD  - Department of Dermatology, Vanderbilt University Medical Center, Nashville, 
      Tennessee.
FAU - Stone, Cosby A Jr
AU  - Stone CA Jr
AD  - Department of Allergy, Pulmonology, and Critical Care, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Zimmerman, Eli
AU  - Zimmerman E
AD  - Department of Neurology, Vanderbilt University Medical Center, Nashville, 
      Tennessee.
FAU - Donald, Rebecca R
AU  - Donald RR
AD  - Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, 
      Tennessee.
FAU - Chang, Sam S
AU  - Chang SS
AD  - Department of Urology, Vanderbilt University Medical Center, Nashville, 
      Tennessee.
FAU - Berkowitz, Sean T
AU  - Berkowitz ST
AD  - Vanderbilt Eye Institute, Department of Ophthalmology, Vanderbilt University 
      Medical, Nashville, Tennessee.
FAU - Finn, Avni P
AU  - Finn AP
AD  - Vanderbilt Eye Institute, Department of Ophthalmology, Vanderbilt University 
      Medical, Nashville, Tennessee.
FAU - Jahangir, Eiman
AU  - Jahangir E
AD  - Department of Cardiovascular Medicine, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Scoville, Elizabeth A
AU  - Scoville EA
AD  - Department of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Reese, Tyler S
AU  - Reese TS
AD  - Department of Rheumatology and Immunology, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Friedman, Debra L
AU  - Friedman DL
AD  - Department of Pediatric Hematology/Oncology, Vanderbilt University Medical 
      Center, Nashville, Tennessee.
FAU - Bastarache, Julie A
AU  - Bastarache JA
AD  - Department of Allergy, Pulmonology, and Critical Care, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - van der Heijden, Yuri F
AU  - van der Heijden YF
AD  - Department of Infectious Disease, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Wright, Jordan J
AU  - Wright JJ
AD  - Department of Diabetes, Endocrinology, and Metabolism, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Ye, Fei
AU  - Ye F
AD  - Department of Biostatistics, Vanderbilt University Medical Center, Nashville, 
      Tennessee.
FAU - Carter, Nicholas
AU  - Carter N
AD  - Division of Trauma and Surgical Critical Care, University of Miami Miller School 
      of Medicine, Miami, Florida.
FAU - Alexander, Matthew R
AU  - Alexander MR
AD  - Department of Cardiovascular Medicine and Clinical Pharmacology, Vanderbilt 
      University Medical Center, Nashville, Tennessee.
FAU - Choe, Jennifer H
AU  - Choe JH
AD  - Department of Hematology/Oncology, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Chastain, Cody A
AU  - Chastain CA
AD  - Department of Infectious Disease, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Zic, John A
AU  - Zic JA
AD  - Department of Dermatology, Vanderbilt University Medical Center, Nashville, 
      Tennessee.
FAU - Horst, Sara N
AU  - Horst SN
AD  - Department of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Turker, Isik
AU  - Turker I
AD  - Department of Cardiology, Washington University School of Medicine in St Louis, 
      St Louis, Missouri.
FAU - Agarwal, Rajiv
AU  - Agarwal R
AD  - Department of Hematology/Oncology, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Osmundson, Evan
AU  - Osmundson E
AD  - Department of Radiation Oncology, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Idrees, Kamran
AU  - Idrees K
AD  - Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Kiernan, Colleen M
AU  - Kiernan CM
AD  - Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Padmanabhan, Chandrasekhar
AU  - Padmanabhan C
AD  - Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Bailey, Christina E
AU  - Bailey CE
AD  - Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Schlegel, Cameron E
AU  - Schlegel CE
AD  - Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University 
      Medical Center, Nashville, Tennessee.
FAU - Chambless, Lola B
AU  - Chambless LB
AD  - Department of Neurological Surgery, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Gibson, Michael K
AU  - Gibson MK
AD  - Department of Hematology/Oncology, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Osterman, Travis J
AU  - Osterman TJ
AD  - Department of Biomedical Informatics, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
FAU - Wheless, Lee E
AU  - Wheless LE
AD  - Department of Dermatology, Vanderbilt University Medical Center, Nashville, 
      Tennessee.
FAU - Johnson, Douglas B
AU  - Johnson DB
AD  - Department of Hematology/Oncology, Vanderbilt University Medical Center, 
      Nashville, Tennessee.
LA  - eng
GR  - T32 CA217834/CA/NCI NIH HHS/United States
GR  - UG3 CA260318/CA/NCI NIH HHS/United States
GR  - R01 CA227481/CA/NCI NIH HHS/United States
GR  - R01 CA240093/CA/NCI NIH HHS/United States
GR  - U54 CA163072/CA/NCI NIH HHS/United States
GR  - P30 CA068485/CA/NCI NIH HHS/United States
GR  - U01 HL156620/HL/NHLBI NIH HHS/United States
GR  - R01 CA225005/CA/NCI NIH HHS/United States
GR  - K12 CA090625/CA/NCI NIH HHS/United States
GR  - UG3 CA265846/CA/NCI NIH HHS/United States
GR  - R01 HL151523/HL/NHLBI NIH HHS/United States
GR  - K08 DK133691/DK/NIDDK NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20231002
PL  - United States
TA  - JAMA Netw Open
JT  - JAMA network open
JID - 101729235
SB  - IM
CIN - doi: 10.1001/jamanetworkopen.2023.35924
MH  - Humans
MH  - *Artificial Intelligence
MH  - Cross-Sectional Studies
MH  - Reproducibility of Results
MH  - *Physicians
MH  - Software
PMC - PMC10546234
COIS- Conflict of Interest Disclosures: Ms Goodman reported receiving research support 
      from the SCRIPS Foundation and the Burroughs Wellcome Fund. Dr Stone reported 
      receiving grants from the American Academy of Allergy, Asthma, and the Immunology 
      Foundation Faculty Development Award during the conduct of the study. Dr Finn 
      reported being on the advisory boards of Eyepoint, Apellis, Allergan, Alimera, 
      and Iveric bio and consulting for and being on the advisory board of Genentech 
      outside the submitted work. Dr Horst reported receiving personal fees from 
      AbbVie, Takeda, BMS, and Janssen outside the submitted work. Dr Agarwal reported 
      receiving personal fees from the American Society of Clinical Oncology (ASCO) 
      (honoraria for faculty speaker at ASCO Advantage Course), the National 
      Comprehensive Cancer Network (NCCN) (honoraria for faculty speaker at NCCN annual 
      conference), the Great Debates and Updates in Gastrointestinal Malignancies 
      (honoraria for faculty speaker), and OncLive (honoraria for faculty speaker) 
      outside the submitted work. Dr Osmundson reported receiving grants from the 
      National Institutes of Health (NIH) unrelated to the present study and grants 
      from Varian Medical Systems unrelated outside the submitted work. Dr Chambless 
      reported receving consulting fees from Integra outside the submitted work. Dr 
      Osterman reported receiving grants from Microsoft, IBM, and GE Healthcare outside 
      the submitted work. Dr Wheless reported receiving grants from the Department of 
      Veterans Affairs during the conduct of the study. Dr Johnson reported receiving 
      grants from BMS and Incyte outside the submitted work and being on the advisory 
      boards of BMS, Catalyst, Merck, Iovance, Novartis, Pfizer. No other disclosures 
      were reported.
EDAT- 2023/10/02 12:42
MHDA- 2023/10/03 06:47
CRDT- 2023/10/02 11:33
PHST- 2023/10/03 06:47 [medline]
PHST- 2023/10/02 12:42 [pubmed]
PHST- 2023/10/02 11:33 [entrez]
AID - 2809975 [pii]
AID - zoi231053 [pii]
AID - 10.1001/jamanetworkopen.2023.36483 [doi]
PST - epublish
SO  - JAMA Netw Open. 2023 Oct 2;6(10):e2336483. doi: 
      10.1001/jamanetworkopen.2023.36483.
