Analyzing the Accuracy of Large Language Models in United States Medical Licensing Exam Social-Science Preparation Question Banks

Abstract

Introduction: Artificial intelligence (AI) emergence has changed the medical education landscape. The United States Medical Licensing Exams increasingly include questions in the “Social Sciences (Ethics/Legal/Professional)” domain, which often require reasoning through complex scenarios. This study evaluates three AI platforms in answering such preparation questions. Methods: Social-science questions from UWorld and Amboss were accumulated for Steps 1 and 2. Multiple-choice questions were entered into ChatGPT, Gemini, and Perplexity, yielding a correct/incorrect response. Percentages of correct responses were compared between platforms and to student averages. Analysis of variance and t-tests conducted determined statistical significance, the upper threshold being P = 0.05. As this study focused on quantitative performance, outcomes were limited to accuracy on multiple-choice items, rather than direct measures of empathetic reasoning. Results: One hundred nine UWorld and 63 Amboss questions were available for Step 1. A total of 189 UWorld and 185 Amboss questions were available for Step 2. Google Gemini had the highest accuracy for Step 1 (86.6%), while ChatGPT had the highest accuracy for Step 2 (83.6%). All platforms outperformed the student average for Step 1 (68.5%) and Step 2 (68.9%), with ChatGPT and Gemini doing so significantly for Step 1 (p < 0.01), and ChatGPT doing so significantly for Step 2 (p < 0.01). Conclusion: It is critical to understand whether empathetic thinking can be replicated by technology or prepare students. This study highlights the ability for AI to solve ethical dilemmas that students may struggle with.

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