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Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains,

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Evidence & attribution

First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.

Observed changes

AIIC observation times, not verified publisher revision times. Up to eight recent revisions.

2026-09-23T17:51:24.264Z

  • url: https://arxiv.org/abs/2609.19569v1 → https://arxiv.org/abs/2609.19569