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An Agentic System for Rare Disease Diagnosis with Traceable Reasoning

Weike Zhao, Chaoyi Wu, Yanjie Fan, Xiaoman Zhang, Pengcheng Qiu, Yuze Sun, Xiao Zhou, Yanfeng Wang, Xin Sun, Ya Zhang, Yongguo Yu, Kun Sun, Weidi Xie · Jun 25, 2025 · Citations: 0

Automatic Metrics Expert Verification Medicine Multi Agent
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

High trust

Use this as a practical starting point for protocol research, then validate against the original paper.

Best use

Primary protocol reference for eval design

What to verify

Validate the exact study setup in the full paper before operational use.

Evidence quality

High

Derived from extracted protocol signals and abstract evidence.

Abstract

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.

Should You Rely On This Paper?

This paper has strong direct human-feedback and evaluation protocol signal and is suitable as a primary eval pipeline reference.

Best use

Primary protocol reference for eval design

Use if you need

A concrete protocol example with enough signal to inform rater workflow design.

Main weakness

No major weakness surfaced.

Trust level

High

Usefulness score

75/100 • High

Use this as a primary source when designing or comparing eval protocols.

Human Feedback Signal

Detected

Evaluation Signal

Detected

Usefulness for eval research

High-confidence candidate

Extraction confidence 80%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Expert Verification

Directly usable for protocol triage.

"Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge."

Quality Controls

strong

Adjudication

Calibration/adjudication style controls detected.

"Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge."

Reported Metrics

strong

Recall, Agreement, Recall@1

Useful for evaluation criteria comparison.

"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."

Rater Population

strong

Domain Experts

Helpful for staffing comparability.

"Expert review achieved 95.4% agreement on its reasoning chains, confirming their validity and traceability."

Human Feedback Details

  • Uses human feedback: Yes
  • Feedback types: Expert Verification
  • Rater population: Domain Experts
  • Unit of annotation: Ranking
  • Expertise required: Medicine

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: Multi Agent
  • Quality controls: Adjudication
  • Evidence quality: High
  • Use this page as: Primary protocol reference for eval design

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

recallagreementrecall@1

Research Brief

Metadata summary

Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • 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.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • 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…
  • 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.

Why It Matters For Eval

  • 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.
  • 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.

Researcher Checklist

  • Pass: Human feedback protocol is explicit

    Detected: Expert Verification

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Pass: Quality control reporting appears

    Detected: Adjudication

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: recall, agreement, recall@1

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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