ArticleBMC medical education2025
Virtual case reasoning and AI-assisted diagnostic instruction: an empirical study based on body interact and large language models.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Bridging the theory-practice gap in laboratory medicine: development and application of a standardized case library for fostering clinical reasoning in postgraduate training.Frontiers in medicine · 2026Article
- Artificial Intelligence and Multidisciplinary Oncology Decision-Making: A Systematic Review of LLM Concordance with MDT Recommendations and Implications for Clinical Reasoning Education.Advances in medical education and practice · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntegrating large language models (LLMs) with virtual patient platforms offers a novel approach to teaching clinical reasoning. This study evaluated the performance and educational value of combining Body Interact with two AI models, ChatGPT-4 and DeepSeek-R1, across acute care scenarios.
methodsThree standardized cases (coma, stroke, trauma) were simulated by two medical researchers. Structured case summaries were input into both models using identical prompts. Outputs were assessed for diagnostic and treatment consistency, alignment with clinical reasoning stages, and educational quality using expert scoring, AI self-assessment, text readability indices, and Grammarly analysis.
resultsChatGPT-4 performed best in stroke scenarios but was less consistent in coma and trauma cases. DeepSeek-R1 showed more stable diagnostic and therapeutic output across all cases. While both models received high expert and self-assessment scores, ChatGPT-4 produced more readable outputs, and DeepSeek-R1 demonstrated greater grammatical precision.
conclusionsOur findings suggest that ChatGPT-4 and DeepSeek-R1 each offer unique strengths for AI-assisted instruction. ChatGPT-4's accessible language may better support early learners, whereas DeepSeek-R1 may be more aligned with formal clinical reasoning. Selecting models based on specific teaching goals can enhance the effectiveness of AI-driven medical education.
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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.