ArticleOrphanet journal of rare diseases2025
Comparative analysis of large language models on rare disease identification.
Article in Orphanet journal of rare diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Large language models in the diagnosis and treatment of immunosuppression-related infections in rheumatic diseases: a comparative, vignette-based study.Rheumatology international · 2026Article
- Review
- Stepwise Diagnostic Evaluation of Chinese Large Language Models: Comparative Study of Common and Rare Diseases.Journal of medical Internet research · 2026Article
- Article
- Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy.Health and technology · 2026Article
- Scaling medical AI across clinical contexts.Nature medicine · 2026Review
- Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach.Journal of personalized medicine · 2025Review
- AI-Based Facial Phenotyping Supports a Shared Molecular Axis inInternational journal of molecular sciences · 2025Article
- Article
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6 authors.
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Abstract
Diagnosing rare diseases is challenging due to their low prevalence, diverse presentations, and limited recognition, often leading to diagnostic delays and errors. This study evaluates the effectiveness of multiple large language models (LLMs) in identifying rare diseases, comparing their performance with that of human physicians using real clinical cases. We analyzed 152 rare disease cases from the Chinese Medical Case Repository using four LLMs: ChatGPT-4o, Claude 3.5 Sonnet, Gemini Advanced, and Llama 3.1 405B. Overall, the LLMs performed better than human physicians, and Claude 3.5 Sonnet achieved the highest accuracy at 78.9%, significantly surpassing the accuracy of human physicians, which was 26.3%. These findings suggest that LLMs can improve rare disease diagnosis and serve as valuable tools in clinical settings, particularly in regions with limited resources. However, further validation and careful consideration of ethical and privacy issues are necessary for their effective integration into medical practice.
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