Performance of Large Language Models in Diagnosing Rare Hematologic Diseases and the Impact of Their Diagnostic Outputs on Physicians: Combined Retrospective and Prospective Study.

Background: Rare hematologic diseases are frequently underdiagnosed or misdiagnosed due to their clinical complexity. Whether new-generation large language models (LLMs), particularly those using chain-of-thought reasoning, can improve diagnostic accuracy remains unclear. Objective: This study aimed to evaluate the diagnostic performance of new-generation commercial LLMs in rare hematologic diseases and to determine whether the LLM output enhances physicians' diagnostic accuracy. Methods: We conducted a 2-phase study. In the retrospective phase, we evaluated 7 mainstream LLMs on 158 nonpublic real-world admission records covering 9 rare hematologic diseases, assessed diagnostic performance using top-10 accuracy and mean reciprocal rank (MRR), and evaluated ranking stability via Jaccard similarity and entropy. Spearman rank correlation was used to examine the association between physicians' diagnoses and LLM-generated outputs. In the prospective phase, 28 physicians with varying levels of experience diagnosed 5 cases each, gaining access to LLM-generated diagnoses across 3 sequential steps to assess whether LLMs can improve diagnostic accuracy. Results: In the retrospective phase, ChatGPT-o1-preview demonstrated the highest top-10 accuracy (70.3%) and MRR (0.577), and DeepSeek-R1 ranked second. Diagnostic performance was low for amyloid light-chain (AL) amyloidosis; Castleman disease; Erdheim-Chester disease; and polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes (POEMS) syndrome. Interestingly, higher accuracy often correlated with lower ranking stability across most LLMs. The physician performance showed a strong correlation with both top-10 accuracy (ρ=0.565) and MRR (ρ=0.650). In the prospective phase, LLMs significantly improved the diagnostic accuracy of less-experienced physicians; no significant benefit was observed for specialists. However, when LLMs generated biased responses, physician performance often failed to improve or even declined. Conclusions: Without fine-tuning, new-generation commercial LLMs, particularly those with chain-of-thought reasoning, can identify diagnoses of rare hematologic diseases with high accuracy and significantly enhance the diagnostic performance of less-experienced physicians. Nevertheless, biased LLM outputs may mislead clinicians, highlighting the need for critical appraisal and cautious clinical integration with appropriate safeguard systems.