Performance of ChatGPT on free-response, clinical reasoning exams

Abstract

Importance: Studies show that ChatGPT, a general purpose large language model chatbot, could pass the multiple-choice US Medical Licensing Exams, but the model's performance on open-ended clinical reasoning is unknown. Objective: To determine if ChatGPT is capable of consistently meeting the passing threshold on free-response, case-based clinical reasoning assessments. Design: Fourteen multi-part cases were selected from clinical reasoning exams administered to pre-clerkship medical students between 2019 and 2022. For each case, the questions were run through ChatGPT twice and responses were recorded. Two clinician educators independently graded each run according to a standardized grading rubric. To further assess the degree of variation in ChatGPT's performance, we repeated the analysis on a single high-complexity case 20 times. Setting: A single US medical school. Participants: ChatGPT. Main Outcomes and Measures: Passing rate of ChatGPT's scored responses and the range in model performance across multiple run throughs of a single case. Results: 12 out of the 28 ChatGPT exam responses achieved a passing score (43%) with a mean score of 69% (95% CI: 65% to 73%) compared to the established passing threshold of 70%. When given the same case 20 separate times, ChatGPT's performance on that case varied with scores ranging from 56% to 81%. Conclusions and Relevance: ChatGPT's ability to achieve a passing performance in nearly half of the cases analyzed demonstrates the need to revise clinical reasoning assessments and incorporate artificial intelligence (AI)-related topics into medical curricula and practice.

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