An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Rare diseases collectively affect over 300 million individuals worldwide, yet
timely and accurate diagnosis remains a pervasive challenge. This is largely
due to their clinical heterogeneity, low individual prevalence, and the limited
familiarity most clinicians have with rare conditions. Here, we introduce
DeepRare, the first rare disease diagnosis agentic system powered by a large
language model (LLM), capable of processing heterogeneous clinical inputs. The
system generates ranked diagnostic hypotheses for rare diseases, each
accompanied by a transparent chain of reasoning that links intermediate
analytic steps to verifiable medical evidence.
  DeepRare comprises three key components: a central host with a long-term
memory module; specialized agent servers responsible for domain-specific
analytical tasks integrating over 40 specialized tools and web-scale,
up-to-date medical knowledge sources, ensuring access to the most current
clinical information. This modular and scalable design enables complex
diagnostic reasoning while maintaining traceability and adaptability. We
evaluate DeepRare on eight datasets. The system demonstrates exceptional
diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013
diseases. In HPO-based evaluations, DeepRare significantly outperforms other 15
methods, like traditional bioinformatics diagnostic tools, LLMs, and other
agentic systems, achieving an average Recall@1 score of 57.18% and surpassing
the second-best method (Reasoning LLM) by a substantial margin of 23.79
percentage points. For multi-modal input scenarios, DeepRare achieves 70.60% at
Recall@1 compared to Exomiser's 53.20% in 109 cases. Manual verification of
reasoning chains by clinical experts achieves 95.40% agreements. Furthermore,
the DeepRare system has been implemented as a user-friendly web application
http://raredx.cn/doctor.
685e119b71131fa43be08adf
Weike Zhao
claimed_verified
2025-07-10T07:10:19.528Z
64365addfae287005149dd24
/avatars/bd6d4512d66fd9fd7fd5476ea7a44b46.svg
Weike Zhao
user
Angelakeke
685e119b71131fa43be08ae0
Chaoyi Wu
685e119b71131fa43be08ae1
Yanjie Fan
685e119b71131fa43be08ae2
Xiaoman Zhang
685e119b71131fa43be08ae3
Pengcheng Qiu
685e119b71131fa43be08ae4
Yuze Sun
685e119b71131fa43be08ae5
Xiao Zhou
685e119b71131fa43be08ae6
Yanfeng Wang
685e119b71131fa43be08ae7
Ya Zhang
685e119b71131fa43be08ae8
Yongguo Yu
685e119b71131fa43be08ae9
Kun Sun
685e119b71131fa43be08aea
Weidi Xie
An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Rare diseases collectively affect over 300 million individuals worldwide, yet
timely and accurate diagnosis remains a pervasive challenge. This is largely
due to their clinical heterogeneity, low individual prevalence, and the limited
familiarity most clinicians have with rare conditions. Here, we introduce
DeepRare, the first rare disease diagnosis agentic system powered by a large
language model (LLM), capable of processing heterogeneous clinical inputs. The
system generates ranked diagnostic hypotheses for rare diseases, each
accompanied by a transparent chain of reasoning that links intermediate
analytic steps to verifiable medical evidence.
  DeepRare comprises three key components: a central host with a long-term
memory module; specialized agent servers responsible for domain-specific
analytical tasks integrating over 40 specialized tools and web-scale,
up-to-date medical knowledge sources, ensuring access to the most current
clinical information. This modular and scalable design enables complex
diagnostic reasoning while maintaining traceability and adaptability. We
evaluate DeepRare on eight datasets. The system demonstrates exceptional
diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013
diseases. In HPO-based evaluations, DeepRare significantly outperforms other 15
methods, like traditional bioinformatics diagnostic tools, LLMs, and other
agentic systems, achieving an average Recall@1 score of 57.18% and surpassing
the second-best method (Reasoning LLM) by a substantial margin of 23.79
percentage points. For multi-modal input scenarios, DeepRare achieves 70.60% at
Recall@1 compared to Exomiser's 53.20% in 109 cases. Manual verification of
reasoning chains by clinical experts achieves 95.40% agreements. Furthermore,
the DeepRare system has been implemented as a user-friendly web application
http://raredx.cn/doctor.
DeepRare, a large language model-based system, provides accurate rare disease diagnoses using heterogeneous clinical inputs and outperforms other diagnostic methods across various datasets.
large language model LLm diagnostic hypotheses chain of reasoning long-term memory module domain-specific analytical tasks medical knowledge sources HPO-based evaluations Recall@1 score multi-modal input scenarios web application