This paper is in the following e-collection/theme issue:

Letters to the Editor (17) Theme Issue: ChatGPT and Generative Language Models in Medical Education (69) Artificial Intelligence (1214) Chatbots and Conversational Agents (473) e-Learning and Medical Education (658) Natural Language Processing (650) Continuing Medical Education (CME) for Doctors (120) Testing and Assessment in Medical Education (40) Artificial Intelligence (AI) in Medical Education (59) Generative Language Models Including ChatGPT (218)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48305, first published .
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”

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”

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”

Authors of this article:

Richard H Epstein1 Author Orcid Image ;   Franklin Dexter2 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

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  1. Velásquez-Henao J, Franco-Cardona C, Cadavid-Higuita L. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA 2023;90(230):9 View
  2. Gilson A, Safranek C, Huang T, Socrates V, Chi L, Taylor R, Chartash D. Authors’ Reply to: Variability in Large Language Models’ Responses to Medical Licensing and Certification Examinations. JMIR Medical Education 2023;9:e50336 View
  3. Guillen-Grima F, Guillen-Aguinaga S, Guillen-Aguinaga L, Alas-Brun R, Onambele L, Ortega W, Montejo R, Aguinaga-Ontoso E, Barach P, Aguinaga-Ontoso I. Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine. Clinics and Practice 2023;13(6):1460 View
  4. Meyer A, Riese J, Streichert T. Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study. JMIR Medical Education 2024;10:e50965 View
  5. Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu N, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea R, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Medical Teacher 2024;46(4):446 View
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  7. Pohl N, Derector E, Rivlin M, Bachoura A, Tosti R, Kachooei A, Beredjiklian P, Fletcher D. A quality and readability comparison of artificial intelligence and popular health website education materials for common hand surgery procedures. Hand Surgery and Rehabilitation 2024;43(3):101723 View