Large language models in medical education: a comparative cross-platform evaluation in answering histological questions

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 (p > 0.05). Claude showed the highest reliability (ICC = 0.931), followed by GPT-4 (ICC = 0.882). Complete accuracy and reproducibility (100%) were detected in Histological Methods, Blood and Hemopoiesis, and Circulatory System, while Muscle tissue (76.0%) and Lymphoid System (84.7%) presented the greatest challenges. LLMs demonstrate exceptional accuracy and reliability in answering histological MCQs, significantly outperforming other medical disciplines. Minimal inter-system variability suggests technological maturity, though topic-specific challenges and reliability concerns indicate the continued need for human expertise. These findings reflect rapid AI advancement and identify histology as particularly suitable for AI-assisted medical education.Clinical trial number: The clinical trial number is not pertinent to this study as it does not involve medicinal products or therapeutic interventions.

Keywords: ChatGPT; Claude; Copilot; DeepSeek; Gemini; Large language models; artificial intelligence; histology; medical education.

Figure 1.

Performance comparison of large language models in medical histology MCQs. Bars represent mean accuracy percentages, and error bars indicate standard deviations. Sample size = 200 questions, with three attempts per question.

Figure 2.

Topic-specific performance heatmap of LLMs in medical histology. The visualization displays accuracy percentages for each combination of histological topic (vertical axis) and LLM system (horizontal axis), with a “mean” column showing average performance across all systems.