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

• URL: http://arxiv.org/abs/2605.06226v2
• Date: Sat, 09 May 2026 22:50:37 GMT
• Title: A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization
• Abstract summary: Hygieia is a multi-modal AI system designed to support precision disease diagnosis.<n>It integrates phenotypic features, genetic profiles, and clinical records.<n>It achieves state-of-the-art performance across multiple diagnostic benchmarks.
• Score: 41.13722391440127
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases [59.5328455571863]
  ClinMM-Bench is the largest multi-turn multimodal clinical diagnostic evaluation benchmark to date.<n>It contains 1,089 challenging real-world clinical cases and 3,760 medical images across eight specialties.<n> proprietary models achieved the highest overall diagnostic accuracy, but the proportion of completely correct diagnoses remained limited.
  arXiv  Detail & Related papers  (2026-07-28T16:19:03Z)
• MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning [66.94527468532843]
  We propose MultiDx, a two-stage diagnostic reasoning framework.<n>It first generates suspected diagnoses and reasoning paths by leveraging knowledge from web search, SOAP-formatted case, and clinical case database.<n>Then it integrates multi-perspective evidence through matching, voting, and differential diagnosis to generate the final prediction.
  arXiv  Detail & Related papers  (2026-04-27T08:46:29Z)
• Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning [16.709358805587506]
  Clinical diagnosis is a complex reasoning process in which clinicians gather evidence, form hypotheses, and test them against alternative explanations.<n>We propose a counterfactual multi-agent diagnostic framework inspired by clinician training that makes hypothesis testing explicit and evidence-grounded.
  arXiv  Detail & Related papers  (2026-03-29T19:14:26Z)
• AI-Powered Dermatological Diagnosis: From Interpretable Models to Clinical Implementation A Comprehensive Framework for Accessible and Trustworthy Skin Disease Detection [0.0]
  Family history influences skin disease susceptibility and treatment responses, but is often underutilized in diagnostic processes.<n>We developed a comprehensive multi-modal AI framework that combines deep learning-based image analysis with structured clinical data.<n>Our approach employs interpretable convolutional neural networks integrated with clinical decision trees that incorporate hereditary risk factors.
  arXiv  Detail & Related papers  (2025-12-18T06:28:51Z)
• Evolving Diagnostic Agents in a Virtual Clinical Environment [75.59389103511559]
  We present a framework for training large language models (LLMs) as diagnostic agents with reinforcement learning.<n>Our method acquires diagnostic strategies through interactive exploration and outcome-based feedback.<n>DiagAgent significantly outperforms 10 state-of-the-art LLMs, including DeepSeek-v3 and GPT-4o.
  arXiv  Detail & Related papers  (2025-10-28T17:19:47Z)
• End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning [52.12425911708585]
  Deep-DxSearch is an agentic RAG system trained end-to-end with reinforcement learning (RL)<n>In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources.<n> Experiments demonstrate that our end-to-end RL training framework consistently outperforms prompt-engineering and training-free RAG approaches.
  arXiv  Detail & Related papers  (2025-08-21T17:42:47Z)
• An Agentic System for Rare Disease Diagnosis with Traceable Reasoning [69.46279475491164]
  We introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM)<n>DeepRare generates ranked diagnostic hypotheses for rare diseases, each accompanied by a transparent chain of reasoning.<n>The system demonstrates exceptional diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013 diseases.
  arXiv  Detail & Related papers  (2025-06-25T13:42:26Z)
• DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models [25.13622249539088]
  DiagnosisArena is a benchmark designed to rigorously assess professional-level diagnostic competence.<n> DiagnosisArena consists of 1,113 pairs of segmented patient cases and corresponding diagnoses, spanning 28 medical specialties.<n>Our study reveals that even the most advanced reasoning models, o3, o1, and DeepSeek-R1, achieve only 51.12%, 31.09%, and 17.79% accuracy, respectively.
  arXiv  Detail & Related papers  (2025-05-20T09:14:53Z)
• OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis [11.775648630734949]
  Alzheimer's disease (AD) cannot be reversed or cured, but timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task. We propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings.
  arXiv  Detail & Related papers  (2023-07-03T12:35:03Z)
• An Expert System to Diagnose Spinal Disorders [0.0]
  Traditional invasive approaches have been the only means being leveraged to diagnose spinal disorders. We develop an expert system based on a hybrid inference algorithm and comprehensive integrated knowledge.
  arXiv  Detail & Related papers  (2023-02-07T17:28:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.