ArticleBioengineering (Basel, Switzerland)2025
Assessing the Efficacy of Ortho GPT: A Comparative Study with Medical Students and General LLMs on Orthopedic Examination Questions.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- LLM-Assisted Interpretation of Kinematic Gait Data in Children with Cerebral Palsy: A Pilot Study on Gait Deviation Detection and Surgical Group Recommendations.Bioengineering (Basel, Switzerland) · 2026Article
- PMR-Q&A: Development of a Bilingual Expert-Evaluated Question-Answer Dataset for Large Language Models in Physical Medicine and Rehabilitation.Bioengineering (Basel, Switzerland) · 2026Article
- Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence.Frontiers in medicine · 2026Review
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Authors and funding
7 authors.
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Abstract
backgroundDomain-specific large language models (LLMs) like Ortho GPT have potential advantages over general-purpose models in medical education, offering improved factual accuracy and contextual relevance. This study evaluates the performance of Ortho GPT against general LLMs and senior medical students on validated orthopedic examination questions.
methodsSix LLMs (Ortho GPT 4o, ChatGPT 4o, ChatGPT 3.5, Perplexity AI, DeepSeek-R1, and Llama 3.3-70B) were tested using multiple-choice items from final-year medical student orthopedic exams in German language. Each model answered identical questions under standardized zero-shot conditions; accuracy rates and item-level results were compared using McNemar's test, Jaccard similarity, and point-biserial correlation with student difficulty ratings.
resultsOrtho GPT achieved the highest accuracy across models. McNemar's tests revealed the significant superiority of Ortho GPT over DeepSeek (
conclusionsThese findings illustrate the superiority of Ortho GPT in orthopedic exam accuracy and context relevance, attributed to its specialized training data. The domain-specific approach enables performance matching or exceeding top general LLMs in orthopedics, emphasizing the importance of domain specialization for reliable, curriculum-aligned support in medical education.
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