Large Language Model Agents for Evidence Based Genetic Disease Severity Classification
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...
| Authors | Tohid Ghasemnejad, Ahmadreza Argha, Mark Grosser et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 17 September 2026 |
| Last updated at source | 18 September 2026 |
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