An Agentic System for Rare Disease Diagnosis with Traceable Reasoning

TL;DR

DeepRare is a multi-agent system utilizing large language models and specialized tools to improve rare disease diagnosis accuracy, transparency, and traceability across diverse clinical data and international datasets.

Contribution

This work introduces DeepRare, a novel multi-agent system that integrates large language models with specialized tools for traceable and accurate rare disease diagnosis.

Findings

Achieves 57.18% Recall@1 on nine datasets, outperforming previous methods.

Reaches 69.1% accuracy in multi-modal tests, surpassing Exomiser.

Expert review confirms 95.4% agreement on reasoning traceability.

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.