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HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition

arXiv 2609.18431 ↗First posted 16 Sep 2026cs.AI cs.LG q-bio.GNcited by 0

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Abstract (arXiv)

More than 300 million people worldwide are affected by one of over 7,000 known rare diseases, yet diagnosis remains difficult because patients initially present with incomplete and heterogeneous phenotypes. We present HPOQuest, a training-free framework for sequential phenotype acquisition in rare-disease diagnosis. Starting from a small set of observed patient phenotypes, HPOQuest maintains a probabilistic disease ranking and iteratively selects informative follow-up questions to support clinicians during patient assessment. Confirmed phenotypes update the disease ranking, while all responses update the candidate question set. Across four benchmark cohorts, HPOQuest substantially improves diagnosis from sparse initial phenotypes, with gains of up to 30% points at Recall@1 and 45% points at Recall@5. These results demonstrate that sequential phenotype acquisition can substantially improve rare-disease diagnosis from limited initial clinical evidence.

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References · 12

  1. 2024GPT-4o System Cardcited by 4,588
  2. 2024MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoningcited by 154
  3. 2025An Agentic System for Rare Disease Diagnosis with Traceable Reasoningcited by 52
  4. 2024RareBench: Can LLMs Serve as Rare Diseases Specialists?cited by 45
  5. 2025Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learningcited by 15
  6. 2020BSODA: A Bipartite Scalable Framework for Online Disease Diagnosiscited by 14
  7. 2025MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiringcited by 7
  8. 2026MedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questionscited by 4
  9. 2026MeDxAgent: Multi-Agent Consultation for Interactive Medical Diagnosiscited by 4
  10. 2020Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Timecited by 3
  11. 2026MedAction: Towards Active Multi-turn Clinical Diagnostic LLMscited by 3
  12. 2026Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMscited by 2

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