Advancements in AI Medical Education: Assessing ChatGPT's Performance on USMLE-Style Questions Across Topics and...

Background  AI language models have been shown to achieve a passing score on certain imageless diagnostic tests of the USMLE. However, they have failed certain specialty-specific examinations. This suggests there may be a difference in AI ability by medical topic or question difficulty. This study evaluates the performance of two versions of ChatGPT, a popular language-based AI model, on USMLE-style questions across various medical topics.  Methods  A total of 900 USMLE-style multiple-choice questions were equally divided into 18 topics, categorized by exam type (step 1 vs. step 2), and copied from AMBOSS, a medical learning resource with large question banks. Questions that contained images, charts, and tables were excluded due to current AI capabilities. The questions were entered into ChatGPT-3.5 (version September 25, 2023) and ChatGPT-4 (version April 2023) for multiple trials, and performance data were recorded. The two AI models were compared against human test takers (AMBOSS users) by medical topic and question difficulty.  Results  Chat-GPT-4, AMBOSS users, and Chat-GPT-3.5 had accuracies of 71.33%, 54.38%, and 46.23% respectively. When comparing models, GPT-4 was a significant improvement demonstrating a 25% greater accuracy and 8% higher concordance between trials than GPT-3 (p<.001). The performance of GPT models was similar between step 1 and step 2 content. Both GPT-3.5 and GPT-4 varied performance by medical topic (p=.027, p=.002). However, there was no clear pattern of variation. Performance for both GPT models and AMBOSS users declined as question difficulty increased (p<.001). However, the decline in accuracy was less pronounced for GPT-4. The accuracy of the GPT models showed less variability with question difficulty compared to AMBOSS users, with the average drop in accuracy from the easiest to hardest questions being 45% and 62%, respectively. Discussion  ChatGPT-4 shows significant improvement over its predecessor, ChatGPT-3.5, in the medical education setting. It is the first ChatGPT model to surpass human performance on modified AMBOSS USMLE tests. While there was variation in performance by medical topic for both models, there was no clear pattern of discrepancy. ChatGPT-4's improved accuracy, concordance, performance on difficult questions, and consistency across topics are promising for its reliability and utility for medical learners.  Conclusion  ChatGPT-4's improvements highlight its potential as a valuable tool in medical education, surpassing human performance in some areas. The lack of a clear performance pattern by medical topic suggests that variability is more related to question complexity than specific knowledge gaps.