ArticleMedical education online2025
Assessing LLM-generated vs. expert-created clinical anatomy MCQs: a student perception-based comparative study in medical education.
Article in Medical education online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Psychometric performance and student perceptions of AI- versus student-generated multiple-choice questions: a single-center randomized controlled trial.BMC medical education · 2026Trial
- Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.Scientific reports · 2026Article
- Fine-Tuned Large Language Models for Generating Multiple-Choice Questions in Anesthesiology: Psychometric Comparison With Faculty-Written Items.JMIR formative research · 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
- Comparative performance of ChatGPT-4o, ChatGPT-5, and gemini 2.5 flash on Persian internal medicine subspecialty board exams.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) such as ChatGPT and Gemini are increasingly used to generate educational content in medical education, including multiple-choice questions (MCQs), but their effectiveness compared to expert-written questions remains underexplored, particularly in anatomy. We conducted a cross-sectional, mixed-methods study involving Year 2-4 medical students at Qatar University, where participants completed and evaluated three anonymized MCQ sets-authored by ChatGPT, Google-Gemini, and a clinical anatomist-across 17 quality criteria. Descriptive and chi-square analyses were performed, and optional feedback was reviewed thematically. Among 48 participants, most rated the three MCQ sources as equally effective, although ChatGPT was more often preferred for helping students identify and confront their knowledge gaps through challenging distractors and diagnostic insight, while expert-written questions were rated highest for deeper analytical thinking. A significant variation in preferences was observed across sources (χ² (64) = 688.79,
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