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.