ArticleAdvances in medical education and practice2026
Comprehensive Evaluation of Large Language Models on Four Core Medical School Courses: A Cross-Sectional Comparative Study.
Article in Advances in medical education and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Large language models (LLMs) have demonstrated remarkable potential in medical education, yet their performance on discipline-specific medical school course examinations remains incompletely characterized. This study evaluated six contemporary LLMs on final examinations for four core medical school courses: Surgery, Musculoskeletal System Diseases, Digestive System Diseases, and Respiratory System Diseases. Methods: A total of 400 multiple-choice questions (100 per course) were administered to each model. Responses were scored against official answer keys and compared to the performance of 312 medical students. Each question was tested three times per model to assess response consistency and reproducibility. Results: All six LLMs achieved mean scores exceeding 93% across all four courses, substantially outperforming the student cohort mean of 71.8%, with all models scoring above the 99.5 Conclusions: Contemporary LLMs can achieve near-perfect performance on medical school course examinations, substantially exceeding average student performance. While this capability suggests significant potential for LLMs as supplementary educational tools, it also raises important concerns regarding assessment integrity and the appropriate role of AI in medical training.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.