{"ID":23474948,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.19569","arxiv_id":"2609.19569","title":"Large Language Model Agents for Evidence Based Genetic Disease Severity Classification","abstract":"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, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2609.19569","url_pdf":"https://arxiv.org/pdf/2609.19569v1","authors":"[\"Tohid Ghasemnejad\",\"Ahmadreza Argha\",\"Mark Grosser\",\"John Wang\",\"Min Yang\",\"Thantrira Porntaveetus\",\"Tony Roscioli\",\"Nigel H. Lovell\",\"Mahmoud Aarabi\",\"Hamid Alinejad-Rokny\"]","published":"2026-09-17T01:56:38Z","proceeding":"q-bio.GN","tasks":"[\"q-bio.GN\",\"cs.AI\",\"cs.CL\"]","methods":"[\"RAG\",\"Language Model\"]","has_code":false}