Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged diagnostic odyssey exceeding five years, marked by repeated referrals, misdiagnoses, and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burdens. Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support powered by large language models, integrating over 40 specialized tools and up-to-date knowledge sources. DeepRare processes heterogeneous clinical inputs, including free-text descriptions, structured Human Phenotype Ontology terms, and genetic testing results, to generate ranked diagnostic hypotheses with transparent reasoning linked to verifiable medical evidence. Evaluated across nine datasets from literature, case reports and clinical centres across Asia, North America and Europe spanning 14 medical specialties, DeepRare demonstrates exceptional performance on 3,134 diseases. In human-phenotype-ontology-based tasks, it achieves an average Recall@1 of 57.18%, outperforming the next-best method by 23.79%; in multi-modal tests, it reaches 69.1% compared with Exomiser's 55.9% on 168 cases. Expert review achieved 95.4% agreement on its reasoning chains, confirming their validity and traceability. Our work not only advances rare disease diagnosis but also demonstrates how the latest powerful large-language-model-driven agentic systems can reshape current clinical workflows.

翻译：罕见病影响全球超过3亿人，然而及时准确的诊断仍是一项紧迫挑战。患者通常需经历超过五年的漫长诊断历程，其间反复转诊、误诊和不必要的干预屡见不鲜，导致治疗延误并造成巨大的情感与经济负担。本文提出DeepRare——一个由大语言模型驱动的多智能体罕见病鉴别诊断决策支持系统，该系统整合了超过40种专业工具及最新知识源。DeepRare能够处理异构临床输入数据（包括自由文本描述、结构化人类表型本体术语及基因检测结果），生成带有透明推理过程的排序诊断假设，且所有推理均关联可验证的医学证据。通过在涵盖亚洲、北美和欧洲地区14个医学专科的文献、病例报告及临床中心提供的九个数据集上进行评估，DeepRare在3,134种疾病上展现出卓越性能。在基于人类表型本体的任务中，其平均Recall@1达到57.18%，较次优方法提升23.79%；在多模态测试中，针对168个案例的评估结果显示其诊断准确率为69.1%，优于Exomiser的55.9%。专家评审对其推理链的认可度达95.4%，证实了推理的有效性与可追溯性。本研究不仅推动了罕见病诊断的发展，更展示了当前最先进的大语言模型驱动智能体系统如何重塑临床工作流程。