A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization

TL;DR

Hygieia is a multi-modal AI system that improves rare disease diagnosis accuracy, reduces clinician workload, and supports clinical decision-making by integrating diverse data sources and providing confidence scores.

Contribution

This work introduces Hygieia, a novel AI agent that combines multi-modal data integration and knowledge-enhanced strategies for improved rare disease diagnosis.

Findings

Hygieia outperforms physicians with 12%-60% improvement in diagnostic accuracy.

It demonstrates state-of-the-art performance across multiple diagnostic benchmarks.

Hygieia reduces clinician workload and enhances interpretability in clinical settings.

Abstract

Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workflows often lead to prolonged assessment times and low accuracy. To address these limitations, we introduce Hygieia, a multi-modal AI agent system designed to support precision disease diagnosis by integrating diverse data sources, including phenotypic features, genetic profiles, and clinical records. Hygieia features a router-based and knowledge-enhanced framework that mitigates hallucination and tailors diagnostic strategies to different disease categories. Notably, it prioritizes risk-related genomic factors for rare diseases and provides confidence scores to assist clinical decision-making. We conducted a comprehensive evaluation demonstrating that Hygieia achieves state-of-the-art performance across multiple diagnostic benchmarks. In collaboration with clinical experts from Yale School of Medicine and Duke-NUS Medical School, we further validated its practical utility by showing (1) Hygieia's superior diagnostic performance compared to physicians with an improvement from 12%-60% and (2) its effectiveness in assisting clinicians with medical records for handling real-world cases. Our findings indicate that Hygieia not only enhances diagnostic accuracy and interpretability but also significantly reduces clinician workload, highlighting its potential as a valuable tool in clinical decision support systems.