This research evaluates the effectiveness of different AI-driven large language models (Claude, ChatGPT, Copilot, and Gemini) compared to medical students in answering neuroscience questions. The study offers insights into the specific areas of neuroscience in which these chatbots may excel or have limitations, providing a comprehensive analysis of chatbots’ current capabilities in processing and interacting with certain topics of the basic medical sciences curriculum.

The assessment of short-answer questions (SAQs) in medical education is resource-intensive, requiring significant expert time. Large Language Models (LLMs) offer potential for automating this process, but their efficacy in specialized medical education assessment remains understudied. To evaluate the capability of five LLMs to grade medical SAQs compared to expert human graders across four distinct medical disciplines. This study analyzed 804 student responses across anatomy, histology, embryology, and physiology. Three faculty members graded all responses. Five LLMs (GPT-4.1, Gemini, Claude, Copilot, DeepSeek) evaluated responses twice: first using their learned representations to generate their own grading criteria (A1), then using expert-provided rubrics (A2). Agreement was measured using Cohen's Kappa and Intraclass Correlation Coefficient (ICC). Expert-expert agreement was substantial across all questions (average Kappa: 0.69, ICC: 0.86), ranging from moderate (SAQ2: 0.57) to almost perfect (SAQ4: 0.87). LLM performance varied dramatically by question type and model. The highest expert-LLM agreement was observed for Claude on SAQ3 (Kappa: 0.61) and DeepSeek on SAQ2 (Kappa: 0.53). Providing expert criteria had inconsistent effects, significantly improving some model-question combinations while decreasing others. No single LLM consistently outperformed others across all domains. LLM strictness in grading unsatisfactory responses varied substantially from experts. LLMs demonstrated domain-specific variations in grading capabilities. The provision of expert criteria did not consistently improve performance. While LLMs show promise for supporting medical education assessment, their implementation requires domain-specific considerations and continued human oversight.

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.

Background: Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have started a new era of innovation across various fields, with medicine at the forefront of this technological revolution. Many studies indicated that at the current level of development, LLMs can pass different board exams. However, the ability to answer specific subject-related questions requires validation. Objective: The objective of this study was to conduct a comprehensive analysis comparing the performance of advanced LLM chatbots—Claude (Anthropic), GPT-4 (OpenAI), Gemini (Google), and Copilot (Microsoft)—against the academic results of medical students in the medical biochemistry course. Methods: We used 200 USMLE (United States Medical Licensing Examination)–style multiple-choice questions (MCQs) selected from the course exam database. They encompassed various complexity levels and were distributed across 23 distinctive topics. The questions with tables and images were not included in the study. The results of 5 successive attempts by Claude 3.5 Sonnet, GPT-4‐1106, Gemini 1.5 Flash, and Copilot to answer this questionnaire set were evaluated based on accuracy in August 2024. Statistica 13.5.0.17 (TIBCO Software Inc) was used to analyze the data’s basic statistics. Considering the binary nature of the data, the chi-square test was used to compare results among the different chatbots, with a statistical significance level of P<.05. Results: On average, the selected chatbots correctly answered 81.1% (SD 12.8%) of the questions, surpassing the students’ performance by 8.3% (P=.02). In this study, Claude showed the best performance in biochemistry MCQs, correctly answering 92.5% (185/200) of questions, followed by GPT-4 (170/200, 85%), Gemini (157/200, 78.5%), and Copilot (128/200, 64%). The chatbots demonstrated the best results in the following 4 topics: eicosanoids (mean 100%, SD 0%), bioenergetics and electron transport chain (mean 96.4%, SD 7.2%), hexose monophosphate pathway (mean 91.7%, SD 16.7%), and ketone bodies (mean 93.8%, SD 12.5%). The Pearson chi-square test indicated a statistically significant association between the answers of all 4 chatbots (P<.001 to P<.04). Conclusions: Our study suggests that different AI models may have unique strengths in specific medical fields, which could be leveraged for targeted support in biochemistry courses. This performance highlights the potential of AI in medical education and assessment.