Diagnostic Performance of Large Language Models for Orthopedic-Related Rare Diseases and Their Impact on Physicians’ Diagnostic Accuracy: 2-Stage Comparative Evaluation Study Based on the Chinese Rare Disease Catalog

Background: Orthopedic-related rare diseases are difficult to diagnose because of their low prevalence, heterogeneous phenotypes, and fragmented knowledge. Large language models (LLMs) can serve as dynamic knowledge-support tools, but their diagnostic performance and effect on physicians’ decision-making remain unclear. Objective: This study aims to compare the diagnostic performance of advanced LLMs for orthopedic-related rare diseases and to evaluate the effect of a 2-stage LLM-assisted diagno

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ethics.ai (24 July 2026), “Diagnostic Performance of Large Language Models for Orthopedic-Related Rare Diseases and Their Impact on Physicians’ Diagnostic Accuracy: 2-Stage Comparative Evaluation Study Based on the Chinese Rare Disease Catalog,” evidence record 13211, https://ethics.ai/record/13211 (originally published by JMIR (Journal of Medical Internet Research)).

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