ArticleBMC medical education2025
GPT-4 generated answer rationales to multiple choice assessment questions in undergraduate medical education.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of ChatGPT and other large language models on multiple-choice questions in biomedical disciplines: A meta-analysis.Anatomical sciences education · 2026Pooled it
- Performance of Cloud-Hosted Large Vision-Language Models on the Japanese National Examination for Clinical Laboratory Technicians: Comparative Benchmarking Study.JMIR medical education · 2026Article
- Evaluating the clinical decision-making performance of large language models in clinically oriented thoracic anatomy scenarios: a comparative evaluation study.BMC medical education · 2026Article
- Article
- What the performance of multimodal LLMs on a national licensing exam teaches us about occupational therapy education.BMC medical education · 2026Article
- Evaluating large language models for diabetic retinopathy multiple-choice question generation in clinical ophthalmic education.Frontiers in medicine · 2026Article
- Evaluating Multiple Input Strategies of Large Language Models for Gallbladder Polyps on Ultrasound: Comparative Study.JMIR medical informatics · 2025Article
- Large language models in medical education: a comparative cross-platform evaluation in answering histological questions.Medical education online · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPre-clerkship medical students benefit from practice questions that provide rationales for answer choices. Creating these rationales is a time-intensive endeavor. Therefore, not all practice multiple choice questions (MCQ) have corresponding explanations to aid learning. The authors examined artificial intelligence's (AI) potential to create high-quality answer rationales for clinical vignette-style MCQs.
methodsThe authors conducted a single-center pre-post intervention survey study in August 2023 assessing 8 pre-clerkship course director (CD) attitudes towards GPT-4 generated answer rationales to clinical vignette style MCQs. Ten MCQs from each course's question bank were selected and input into GPT-4 with instructions to select the best answer and generate rationales for each answer choice. CDs were provided their unmodified GPT-4 interactions to assess the accuracy, clarity, appropriateness, and likelihood of implementation of the rationales. CDs were asked about time spent reviewing and making necessary modifications, satisfaction, and receptiveness in using GPT-4 for this purpose.
resultsGPT-4 correctly answered 75/80 (93.8%) questions on the first attempt. CDs were receptive to using GPT-4 for rationale generation and all were satisfied with the generated rationales. CDs determined that the majority of rationales were very accurate (77.5%), very clear (83.8%) and very appropriate (93.8%). Most rationales could be implemented with little or no modification (88.3%). All CDs would implement AI-generated answer rationales with CD editorial insights. Most CDs (75%) took ≤ 4 min to review a set of generated rationales for a question.
conclusionGPT-4 is an acceptable and feasible tool for generating accurate, clear and appropriate answer rationales for MCQs in medical education. Future studies should examine students' feedback to generated rationales and further explore generating rationales for question with media. The authors plan to explore the implementation of this technological application at their medical school, including logistics and training to create a streamlined process that benefits both learners and educators. CLINICAL TRIAL: Not applicable; not a clinical trial.
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