Comparative evaluation of large language models performance in medical education using urinary system histology assessment

In recent evaluations of large language models (LLMs) within medical education, the assessment of urinary system histology has emerged as an important focus. This analysis investigates the performance of various LLMs in addressing medical multiple-choice questions (MCQs) and in generating clinical scenarios related to urinary system histology.

### Overview of Findings

A total of thirteen models were evaluated, yielding noticeable variations in performance marked by both accuracy in MCQ responses and effectiveness in clinical scenario generation. ChatGPT-o1 led the MCQ assessment with an impressive accuracy of 96.31%, followed closely by ChatGPT-4.5 at 92.62%, with DeepSeek showing commendable consistency (ICC = 0.986). These LLMs significantly outperformed random guessing, reinforcing their potential as effective tools in medical education (p < 0.0001).For clinical scenario generation, Claude 3.5 achieved the highest score overall at 91.4%, demonstrating the abilities of advanced LLMs to generate coherent and contextual clinical scenarios. Statistical analyses confirmed considerable differences across models, with both tasks (MCQs and scenarios) revealing strong statistical significance (MCQs: F = 6.243, p < 0.000001; scenarios: F(12,455) = 8.48, p = 1.39 x 10⁻¹⁴).### Performance in MCQ AssessmentThe accuracy of models in tackling medical MCQs varied significantly, with performance rates ranging between 72.31% and 96.31%. Not only did ChatGPT-o1 exhibit the overall highest accuracy, but it also maintained excellent consistency (ICC = 0.971), demonstrating a variation across attempts of just 0.8%, underscoring its reliability. Other LLMs, including QwQ-3B and Qwen-2.5-M, revealed lower accuracy levels and greater variability.The analysis of consistency using Fleiss’ Kappa further indicated that all assessed models maintained robust agreement across repeated measures, with the highest-rated model consistently providing reliable outputs.### Explanation QualityThe quality of explanations offered by the models was evaluated based on parameters such as comprehensiveness and relevance. ChatGPT-4.5 delivered the highest-quality explanations, with significant correlations established between explanation quality and accuracy in most models.In an academic context, the ability of an LLM to provide not only accurate answers but also comprehensive explanations is crucial. The relationship between clarity in explanation and correctness of answers aids in reinforcing learning environments wherein students can gain insights beyond rote memorization.### Clinical Scenario GenerationWhen it came to generating clinical scenarios, Claude 3.5 again distinguished itself with the top score, while ChatGPT-4.5 however achieved moderate performance in this category, ranking seventh overall. Various models displayed differential strengths across five key evaluation dimensions: Quality, Complexity, Relevance, Correctness, and Variety. Here, correctness was identified as a pivotal dimension, leading to variations across models regarding their performance levels.The data indicated that most models, including QwQ-3B, found it easier to generate coherent clinical scenarios than formulate appropriate assessment questions—a finding that emphasizes the complex nature of question formulation in medical education.### Domain-Specific InsightsDifferential performance was also noted regarding specific histological topics, such as renal corpuscles and the nephron. Models like Claude-3.5 showed consistent excellence across all topics, while others were subject to notable variability. This topic-specific performance suggests that while models may generalize well, they can also exhibit strengths and weaknesses based on the complexity of the content.### Comparative Analysis of Model FamiliesWhen examining LLMs by their provider families, Claude models demonstrated superior overall performance compared to others, including ChatGPT, Grok, and Qwen families. The Claude family’s balance across evaluation dimensions—exhibiting both high correctness and quality—set them apart as the more reliable choice for medical assessments.### Challenges and ConsiderationsWhile the findings predominantly highlight the strengths of advanced LLMs in medical education, challenges remain. Issues such as variability in performance, the need for context in generating clinical scenarios, and the continuous evolution of these models necessitate ongoing evaluation. Other factors such as data privacy, ethical implications, and the necessity for instructor oversight should not be overlooked.Furthermore, the models under evaluation reflect the rapidly evolving landscape of artificial intelligence and its implications for learning environments.### ConclusionOverall, the comparative evaluation of large language models shows promise for their integration into medical education, particularly within the domain of urinary system histology assessment. The insights derived from this study reinforce the capability of LLMs to enhance educational strategies, facilitate student learning, and serve as effective assessment tools. Continuous innovation in LLM architectures will likely contribute to even richer resources in medical education, warranting further exploration and critical analysis.The integration of such models could potentially transform educational methodologies, providing students with enhanced accessibility to knowledge and practical applications of their learnings—an evolution that can contribute beneficially to the fields of medicine and beyond.Ongoing assessments will be imperative in harnessing the ideal balance of accuracy, consistency, and explanation quality that learners require for effective education outcomes in complex fields such as medicine.
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