ArticleBMC medical education2025
Generative AI in medical education: feasibility and educational value of LLM-generated clinical cases with MCQs.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Pedagogical Efficacy of LLM-Generated Synthetic Data Versus Real-World Clinical Records: A Randomized Controlled Non-Inferiority Trial.Perspectives on medical education · 2026Trial
- From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment Programs.Journal of medical Internet research · 2026Article
- Sufficient and necessary conditions for ChatGPT adoption in medical education: a combined partial least square-structural equation modelling and necessary condition analysis.BMC medical education · 2026Article
- Catalysts of Change: Using AI To Lower the Activation Energy for Developing Gamified Learning Experiences in Health Profession Education.Medical science educator · 2026Article
- Quality of Large Language Model-Generated MCQs Across Three Medical Disciplines: An Expert Rater-Based Comparison of Gemini, GPT-4 and Perplexity Pro.Advances in medical education and practice · 2026Article
- Bridging the gap between AI and traditional teaching methods in medical education: opportunities, challenges, and solutions.Frontiers in medicine · 2026Review
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Comparative Performance Evaluation of Large Language Models and Human Teachers in Answering Optometry Questions from Medical Undergraduates.Journal of medical education and curricular developmentArticle
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the feasibility and educational value of employing large language models (LLMs) to generate clinical case scenario with multiple-choice questions (MCQs) for undergraduate medical education.
methodsTwelve ophthalmology clinical case scenarios with MCQs generated by ChatGPT 4.0 were assessed for quality by eight teachers. High-scoring cases with MCQs were selected for review classes to test students' learning. Student perceptions were collected via in-class and after-class questionnaires using a 5-point Likert scale.
resultsThe average quality score of the 12 cases with MCQs was 52.33 ± 5.44 (range: 48-54.25; max = 60). There were statistical differences in the teachers' scores for identical clinical cases (F = 16.050, P < 0.001). Among 20 students, 95% agreed AI-generated cases enriched learning resources, 80% reported improved interdisciplinary integration and learning efficiency, while 85% used LLMs for post-class practice but raised concerns about content accuracy and difficulty calibration.
conclusionLLMs like ChatGPT can rapidly generate clinically relevant case scenarios and MCQs under precise prompts, offering a novel tool for educators and learners. However, expert review remains critical to mitigate risks of AI hallucinations (observed in 16.67% of cases, 2/12) and ensure alignment with curricular standards. Key issues included contradictions in imaging descriptions (e.g., inappropriate use of high-frequency ultrasound for chalazion) and diagnostic logic (e.g., inconsistent gonioscopy findings), underscoring the necessity of human oversight to refine content accuracy and educational utility.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.