ArticleMedical education online2025
When AI models take the exam: large language models vs medical students on multiple-choice course exams.
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 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Performance of Reasoning and Comparator Large Language Models on Nephrology Multiple-choice Questions.JMA journal · 2026Article
- Performance of large language models on undergraduate endodontic multiple-choice questions.BMC oral health · 2026Article
- Comparing clinical decision-making between colposcopists and large language models in cervical dysplasia management: a pilot prospective multicenter study.Archives of gynecology and obstetrics · 2026Article
- Evaluating large language models for endodontic case difficulty assessment: accuracy, consistency, temporal stability, and agreement.BMC oral health · 2026Article
- Comparison of the performance of ChatGPT-5, Gemini 3, Copilot, Perplexity, and medical students in answering neurology questions: a cross-sectional study.Scientific reports · 2026Article
- Comprehensive Evaluation of Large Language Models on Four Core Medical School Courses: A Cross-Sectional Comparative Study.Advances in medical education and practice · 2026Article
- Assessment of vaccine information accuracy across large language models.Frontiers in public health · 2026Article
- Benchmarking large language models on a Chinese radiation oncology technology examination-preparation question set: accuracy, consensus, and efficiency for AI assisted education.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are increasingly used in healthcare and medical education, but their performance on institution-authored multiple-choice questions (MCQs), particularly with negative marking, remains unclear. To compare the examination performance of five contemporary LLMs with enrolled medical students on final multiple-choice (MCQ-style) course exams across four clinical courses. We conducted a comparative cross-sectional study at Miguel Hernández University (Spain) in 2025. Final exams in Infectious Diseases, Neurology, Respiratory Medicine, and Cardiovascular Medicine were administered under routine conditions in Spanish. Five LLMs (OpenAI o1, GPT-4o, DeepSeek R1, Microsoft Copilot, and Google Gemini 1.5 Flash) completed all MCQs in two independent runs. Scores were averaged and test-retest was estimated with Gwet's AC1. Student scores (
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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.