Comparative analysis of large language models on rare disease identification

Ao, G., Chen, M., Li, J. et al. Comparative analysis of large language models on rare disease identification. Orphanet J Rare Dis 20, 150 (2025). doi:10.1186/s13023-025-03656-w 

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.

Comparative analysis of large language models on rare disease identification