TY  - JOUR
AU  - Knoedler, Leonard
AU  - Alfertshofer, Michael
AU  - Knoedler, Samuel
AU  - Hoch, Cosima C
AU  - Funk, Paul F
AU  - Cotofana, Sebastian
AU  - Maheta, Bhagvat
AU  - Frank, Konstantin
AU  - Brébant, Vanessa
AU  - Prantl, Lukas
AU  - Lamby, Philipp
PY  - 2024
DA  - 2024/1/5
TI  - Pure Wisdom or Potemkin Villages? A Comparison of ChatGPT 3.5 and ChatGPT 4 on USMLE Step 3 Style Questions: Quantitative Analysis
JO  - JMIR Med Educ
SP  - e51148
VL  - 10
KW  - ChatGPT
KW  - United States Medical Licensing Examination
KW  - artificial intelligence
KW  - USMLE
KW  - USMLE Step 1
KW  - OpenAI
KW  - medical education
KW  - clinical decision-making
AB  - Background: The United States Medical Licensing Examination (USMLE) has been critical in medical education since 1992, testing various aspects of a medical student’s knowledge and skills through different steps, based on their training level. Artificial intelligence (AI) tools, including chatbots like ChatGPT, are emerging technologies with potential applications in medicine. However, comprehensive studies analyzing ChatGPT’s performance on USMLE Step 3 in large-scale scenarios and comparing different versions of ChatGPT are limited. Objective: This paper aimed to analyze ChatGPT’s performance on USMLE Step 3 practice test questions to better elucidate the strengths and weaknesses of AI use in medical education and deduce evidence-based strategies to counteract AI cheating. Methods: A total of 2069 USMLE Step 3 practice questions were extracted from the AMBOSS study platform. After including 229 image-based questions, a total of 1840 text-based questions were further categorized and entered into ChatGPT 3.5, while a subset of 229 questions were entered into ChatGPT 4. Responses were recorded, and the accuracy of ChatGPT answers as well as its performance in different test question categories and for different difficulty levels were compared between both versions. Results: Overall, ChatGPT 4 demonstrated a statistically significant superior performance compared to ChatGPT 3.5, achieving an accuracy of 84.7% (194/229) and 56.9% (1047/1840), respectively. A noteworthy correlation was observed between the length of test questions and the performance of ChatGPT 3.5 (ρ=–0.069; P=.003), which was absent in ChatGPT 4 (P=.87). Additionally, the difficulty of test questions, as categorized by AMBOSS hammer ratings, showed a statistically significant correlation with performance for both ChatGPT versions, with ρ=–0.289 for ChatGPT 3.5 and ρ=–0.344 for ChatGPT 4. ChatGPT 4 surpassed ChatGPT 3.5 in all levels of test question difficulty, except for the 2 highest difficulty tiers (4 and 5 hammers), where statistical significance was not reached. Conclusions: In this study, ChatGPT 4 demonstrated remarkable proficiency in taking the USMLE Step 3, with an accuracy rate of 84.7% (194/229), outshining ChatGPT 3.5 with an accuracy rate of 56.9% (1047/1840). Although ChatGPT 4 performed exceptionally, it encountered difficulties in questions requiring the application of theoretical concepts, particularly in cardiology and neurology. These insights are pivotal for the development of examination strategies that are resilient to AI and underline the promising role of AI in the realm of medical education and diagnostics. 
SN  - 2369-3762
UR  - https://mededu.jmir.org/2024/1/e51148
UR  - https://doi.org/10.2196/51148
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38180782
DO  - 10.2196/51148
ID  - info:doi/10.2196/51148
ER  -