ArticleMedical education online2025
Large language models in medical education: a comparative cross-platform evaluation in answering histological questions.
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 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 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
- Application of artificial intelligence-generated clinical cases in case-based learning of histology and embryology.Anatomical sciences education · 2026Article
- Performance of Seven Large Language Models on Anatomy Examination Questions.Clinical anatomy (New York, N.Y.) · 2026Article
- Comparative evaluation of radiological anatomy knowledge and accuracy of ChatGPT-5, Gemini 2.5, and Grok 4 across normal and thinking modes.Anatomical sciences education · 2026Article
- Is artificial intelligence getting better at anatomy? A two-year review of ChatGPT's free public versions.Anatomical sciences education · 2026Article
- Large Language Models in German Continuing Medical Education Assessments: Protocol for a Fully Crossed Experimental Study.JMIR research protocols · 2026Article
- Medical students' attitudes, usage patterns, and associated factors with DeepSeek adoption in education: a cross-sectional study in China.BMC medical education · 2026Article
- Performance of Large Language Models in Answering Healthcare Delivery Questions: A Quantitative Cross-Sectional Study.Health science reports · 2026Article
- Foundation models in healthcare: a comprehensive review from technical advances to clinical translation.Journal of translational medicine · 2026Review
- Evaluating large language models for pharmacotherapy simulations: a mixed-methods study.NPJ digital medicine · 2026Article
- Awareness, attitudes, and educational use of artificial intelligence among medical students: a large cross-sectional survey.BMC medical education · 2026Article
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- Article
- Visual recognition limitations in multimodal large language models: A comparative analysis of histological image interpretation.PLOS digital health · 2026Article
- Performance of large language models on neuroanatomy-based medical riddles: a comparative study.Surgical and radiologic anatomy : SRA · 2026Article
- Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.BMC medical informatics and decision making · 2026Article
- Critical evaluation of large language models for human cross-sectional anatomy identification: implications for collaborative intelligence.BMC research notes · 2026Article
- A Comparative Analysis of AI-Language Models' MCQ Performance versus Medical Students Across Different Pediatric Topics.Advances in medical education and practice · 2026Article
- Accuracy, Reliability, and Bloom's Taxonomy Performance of Seven Large Language Models on Microbiology Questions.Advances in medical education and practice · 2026Article
- Value systems of artificial intelligence and university students: theoretical dominance in large language models and religious priority in humans.Frontiers in psychology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have shown promising capabilities across medical disciplines, yet their performance in basic medical sciences remains incompletely characterized. Medical histology, requiring factual knowledge and interpretative skills, provides a unique domain for evaluating AI capabilities in medical education. To evaluate and compare the performance of five current LLMs: GPT-4.1, Claude 3.7 Sonnet, Gemini 2.0 Flash, Copilot, and DeepSeek R1 on correctly answering medical histology multiple choice questions (MCQs). This cross-sectional comparative study used 200 USMLE-style histology MCQs across 20 topics. Each LLM completed all the questions in three separate attempts. Performance metrics included accuracy rates, test-retest reliability (ICC), and topic-specific analysis. Statistical analysis employed ANOVA with post-hoc Tukey's tests and two-way mixed ANOVA for system-topic interactions. All LLMs achieved exceptionally high accuracy (Mean 91.1%, SD 7.2). Gemini performed best (92.0%), followed by Claude (91.5%), Copilot (91.0%), GPT-4 (90.8%), and DeepSeek (90.3%), with no significant differences between systems (
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Registered trials
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.