用AI多智能体系统辅助罕见病诊断，推理过程可追溯且准确率高。
An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
- 构建基于大模型的多智能体系统，整合40多个专业工具与最新医学知识。
- 在14个专科3134种疾病上，推理召回率达57.18%，优于现有方法23.79%。
- 适合临床医生、研究者及医疗AI开发者，推动精准诊疗流程变革。
罕见病影响全球超3亿人，但及时准确诊断仍是严峻挑战。患者常经历超过五年的诊断延迟，伴随反复转诊、误诊和无效治疗，导致治疗延误及巨大身心与经济负担。本文提出DeepRare，一种基于大语言模型的多智能体罕见病鉴别诊断决策支持系统，集成40余种专业工具与实时医学知识源。该系统可处理自由文本描述、结构化人类表型本体术语及基因检测结果，生成带可追溯医学证据的排序诊断假设。在来自亚洲、北美、欧洲九个文献、病例报告及临床中心的数据集上评估，覆盖14个医学专科的3134种疾病。在人类表型本体任务中，平均Recall@1达57.18%，领先次优方法23.79%；在多模态测试中，准确率达69.1%，优于Exomiser的55.9%（168例）。专家评审对推理链一致性达成95.4%认可，证实其有效性和可追溯性。本工作不仅提升罕见病诊断水平，也展示了先进大模型驱动的智能体系统重塑临床流程的巨大潜力。
原文摘要 · Abstract (English)
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.
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