Evaluating ChatGPT-4 in the development of family medicine residency examinations

Abstract

Creating high-quality medical examinations is challenging due to time, cost, and training requirements. This study evaluates the use of ChatGPT 4.0 (ChatGPT-4) in generating medical exam questions for postgraduate family medicine (FM) trainees. Develop a standardized method for postgraduate multiple-choice medical exam question creation using ChatGPT-4 and compare the effectiveness of large language model (LLM) generated questions to those created by human experts. Eight academic FM physicians rated multiple-choice questions (MCQs) generated by humans and ChatGPT-4 across four categories: 1) human-generated, 2) ChatGPT-4 cloned, 3) ChatGPT-4 novel, and 4) ChatGPT-4 generated questions edited by a human expert. Raters scored each question on 17 quality domains. Quality scores were compared using linear mixed effect models. ChatGPT-4 and human-generated questions were rated as high quality, addressing higher-order thinking. Human-generated questions were less likely to be perceived as artificial intelligence (AI) generated, compared to ChatGPT-4 generated questions. For several quality domains ChatGPT-4 was non-inferior (at a 10% margin), but not superior, to human-generated questions. ChatGPT-4 can create medical exam questions that are high quality, and with respect to certain quality domains, non-inferior to those developed by human experts. LLMs can assist in generating and appraising educational content, leading to potential cost and time savings.