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

arXiv: https://arxiv.org/abs/2609.18431 · HTML: https://mlsanity.com/paper/2609.18431/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://mlsanity.com/paper/2406.00922/) (2024)
- [An Agentic System for Rare Disease Diagnosis with Traceable Reasoning](https://mlsanity.com/paper/2506.20430/) (2025)
- [RareBench: Can LLMs Serve as Rare Diseases Specialists?](https://mlsanity.com/paper/2402.06341/) (2024)
- [Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning](https://mlsanity.com/paper/2506.13474/) (2025)
- [BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis](https://mlsanity.com/paper/2012.01065/) (2020)
- [MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring](https://mlsanity.com/paper/2512.24181/) (2025)
- [MedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questions](https://mlsanity.com/paper/2602.17308/) (2026)
- [MeDxAgent: Multi-Agent Consultation for Interactive Medical Diagnosis](https://mlsanity.com/paper/2606.03416/) (2026)
- [Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time](https://mlsanity.com/paper/2003.14127/) (2020)
- [MedAction: Towards Active Multi-turn Clinical Diagnostic LLMs](https://mlsanity.com/paper/2605.07305/) (2026)
- [Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs](https://mlsanity.com/paper/2607.03426/) (2026)

## 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.