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

Artificial Intelligence (AI) in Medical Education (129) Chatbots and Conversational Agents (648) Human Factors and Usability Case Studies (903) Generative Language Models Including ChatGPT (526) Decision Support for Health Professionals (1367) Development and Evaluation of Research Methods, Instruments and Tools (650) Natural Language Processing (812) Cardiology (203)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51282, first published .
Assessing GPT-4’s Performance in Delivering Medical Advice: Comparative Analysis With Human Experts

Assessing GPT-4’s Performance in Delivering Medical Advice: Comparative Analysis With Human Experts

Assessing GPT-4’s Performance in Delivering Medical Advice: Comparative Analysis With Human Experts

Authors of this article:

Eunbeen Jo1 Author Orcid Image ;   Sanghoun Song2 Author Orcid Image ;   Jong-Ho Kim3, 4 Author Orcid Image ;   Subin Lim5 Author Orcid Image ;   Ju Hyeon Kim5 Author Orcid Image ;   Jung-Joon Cha5 Author Orcid Image ;   Young-Min Kim6 Author Orcid Image ;   Hyung Joon Joo1, 3, 4 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

Journals

  1. Leon M, Ruaengsri C, Pelletier G, Bethencourt D, Shibata M, Flores M, Shudo Y. Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions. Journal of Clinical Medicine 2024;13(21):6543 View
  2. Janumpally R, Nanua S, Ngo A, Youens K. Generative artificial intelligence in graduate medical education. Frontiers in Medicine 2025;11 View
  3. Adarkwah M, Badu S, Osei E, Adu-Gyamfi E, Odame J, Schneider K. ChatGPT in healthcare education: a double-edged sword of trends, challenges, and opportunities. Discover Education 2025;4(1) View
  4. Weisman D, Sugarman A, Huang Y, Gelberg L, Ganz P, Comulada W. Development of a GPT-4–Powered Virtual Simulated Patient and Communication Training Platform for Medical Students to Practice Discussing Abnormal Mammogram Results With Patients: Multiphase Study. JMIR Formative Research 2025;9:e65670 View
  5. Sav N. Assessing the performance of large language models (GPT-3.5 and GPT-4) and accurate clinical information for pediatric nephrology. Pediatric Nephrology 2025 View
  6. Abu-Faty H, Kafafy A, Hadhoud M, Abdel-Raouf O. Integrating generative AI and machine learning classifiers for solving heterogenous MCGDM: a case of employee churn prediction. Scientific Reports 2025;15(1) View
  7. Chen R, Zhang S, Zheng Y, Yu Q, Wang C. Enhancing treatment decision-making for low back pain: a novel framework integrating large language models with retrieval-augmented generation technology. Frontiers in Medicine 2025;12 View
  8. Zhou S, Xu Z, Zhang M, Xu C, Guo Y, Zhan Z, Fang Y, Ding S, Wang J, Xu K, Xia L, Yeung J, Zha D, Cai D, Melton G, Lin M, Zhang R. Large language models for disease diagnosis: a scoping review. npj Artificial Intelligence 2025;1(1) View