TY  - JOUR
AU  - Bicknell, Brenton T
AU  - Butler, Danner
AU  - Whalen, Sydney
AU  - Ricks, James
AU  - Dixon, Cory J
AU  - Clark, Abigail B
AU  - Spaedy, Olivia
AU  - Skelton, Adam
AU  - Edupuganti, Neel
AU  - Dzubinski, Lance
AU  - Tate, Hudson
AU  - Dyess, Garrett
AU  - Lindeman, Brenessa
AU  - Lehmann, Lisa Soleymani
PY  - 2024
DA  - 2024/11/6
TI  - ChatGPT-4 Omni Performance in USMLE Disciplines and Clinical Skills: Comparative Analysis
JO  - JMIR Med Educ
SP  - e63430
VL  - 10
KW  - large language model
KW  - ChatGPT
KW  - medical education
KW  - USMLE
KW  - AI in medical education
KW  - medical student resources
KW  - educational technology
KW  - artificial intelligence in medicine
KW  - clinical skills
KW  - LLM
KW  - medical licensing examination
KW  - medical students
KW  - United States Medical Licensing Examination
KW  - ChatGPT 4 Omni
KW  - ChatGPT 4
KW  - ChatGPT 3.5
AB  - Background: Recent studies, including those by the National Board of Medical Examiners, have highlighted the remarkable capabilities of recent large language models (LLMs) such as ChatGPT in passing the United States Medical Licensing Examination (USMLE). However, there is a gap in detailed analysis of LLM performance in specific medical content areas, thus limiting an assessment of their potential utility in medical education. Objective: This study aimed to assess and compare the accuracy of successive ChatGPT versions (GPT-3.5, GPT-4, and GPT-4 Omni) in USMLE disciplines, clinical clerkships, and the clinical skills of diagnostics and management. Methods: This study used 750 clinical vignette-based multiple-choice questions to characterize the performance of successive ChatGPT versions (ChatGPT 3.5 [GPT-3.5], ChatGPT 4 [GPT-4], and ChatGPT 4 Omni [GPT-4o]) across USMLE disciplines, clinical clerkships, and in clinical skills (diagnostics and management). Accuracy was assessed using a standardized protocol, with statistical analyses conducted to compare the models’ performances. Results: GPT-4o achieved the highest accuracy across 750 multiple-choice questions at 90.4%, outperforming GPT-4 and GPT-3.5, which scored 81.1% and 60.0%, respectively. GPT-4o’s highest performances were in social sciences (95.5%), behavioral and neuroscience (94.2%), and pharmacology (93.2%). In clinical skills, GPT-4o’s diagnostic accuracy was 92.7% and management accuracy was 88.8%, significantly higher than its predecessors. Notably, both GPT-4o and GPT-4 significantly outperformed the medical student average accuracy of 59.3% (95% CI 58.3‐60.3). Conclusions: GPT-4o’s performance in USMLE disciplines, clinical clerkships, and clinical skills indicates substantial improvements over its predecessors, suggesting significant potential for the use of this technology as an educational aid for medical students. These findings underscore the need for careful consideration when integrating LLMs into medical education, emphasizing the importance of structured curricula to guide their appropriate use and the need for ongoing critical analyses to ensure their reliability and effectiveness. 
SN  - 2369-3762
UR  - https://mededu.jmir.org/2024/1/e63430
UR  - https://doi.org/10.2196/63430
DO  - 10.2196/63430
ID  - info:doi/10.2196/63430
ER  -