HPOQuest is a training‑free framework that improves rare‑disease diagnosis by actively acquiring additional phenotypes. Starting from a few observed patient traits, it maintains a probabilistic ranking of possible diseases and iteratively selects follow‑up questions that are most informative. When clinicians confirm new phenotypes, the disease ranking is updated, and the set of candidate questions is refined, leading to significant gains—up to 30% in Recall@1 and 45% in Recall@5—across four benchmark cohorts.
arXiv:2607. 00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space.
arXiv:2608. 14683v1 Announce Type: new Abstract: Given a patient's clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for review.
arXiv:2606. 16149v1 Announce Type: new Abstract: Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases.
arXiv:2606.16149v5 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer. Large language models rank the correct disease first i...
The paper proposes a behavior-based fusion model that combines large language models (LLMs) with ontology rankers to improve rare-disease diagnosis. By examining ranked lists, agreement, and ontology support, the model learns how much to rely on each system per case, achieving significant recall gains on Phenopacket Store and RAMEDIS benchmarks. Importantly, the fused diagnoses retain candidate-level ontology evidence for inspection.
arXiv:2607. 23290v1 Announce Type: new Abstract: Rare diseases collectively affect an estimated 3.
The paper presents an autonomous AI agent that combines ReAct and Retrieval-Augmented Generation to classify the severity of genetic diseases using 10,211 Human Phenotype Ontology terms. It applies ACMG severity guidelines and ACOG quality-of-life criteria, retrieving PubMed literature to produce interpretable reasoning chains and verify claims. The agent achieved 93.55% accuracy on phenotype-level classification and identified 3,283 autosomal recessive gene pairs with severe presentations, with 95.2% concordance in external validation.
arXiv:2606. 24510v1 Announce Type: new Abstract: Rare diseases affect millions of individuals worldwide, yet timely diagnosis remains a major public health challenge due to scarcity of specialized clinical expertise.
arXiv:2609.35549v2 Announce Type: replace Abstract: Rare-disease diagnosis is a long-tail reasoning problem: phenotypes are incomplete, individual disorders are sparsely documented, and relevant evid...
arXiv:2606. 18933v1 Announce Type: new Abstract: Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision.
The paper introduces Frequency-Based Probabilistic Ranker (FBPR), a lightweight method that uses smoothed Naive Bayes on concept‑diagnosis co‑occurrence statistics to score multiple‑choice clinical diagnosis questions. When trained on corpora such as Dolma and RedPajama, FBPR matches the performance of large language models (LLMs) on MedQA benchmarks. FBPR and LLMs answer largely different subsets of questions, and a simple hybrid of the two yields a notable performance gain.
The paper introduces CHIP 2026 Shared Task 1, which focuses on patient‑specific phenotype concept recognition in full‑text biomedical literature. The task is split into two subtasks—full‑text phenotype concept recognition and patient‑specific phenotype association—using 200 annotated articles from the PatientPheX dataset that include 506 patients, 15,193 phenotype mentions, and 5,656 patient‑phenotype associations. A large community of 455 teams participated, with the best system scoring 0.7389, surpassing the baseline by 8.37 percentage points, and demonstrating the effectiveness of biomedical language models, large language models, and ensemble strategies while highlighting ongoing challenges in patient attribution and phenotype normalization.