Leveraging Open-Source Large Language Models to Identify Undiagnosed Patients with Rare Genetic Aortopathies.

ABSTRACT: Rare genetic aortopathies are frequently undiagnosed due to phenotypic heterogeneity, and delayed diagnosis can lead to fatal cardiac outcomes. While genetic testing can enable early proactive interventions, it relies on primary care physicians to recognize a genetic basis for symptoms and then refer patients to clinical genetics. Broad-scale screening methods are needed to identify cases that do not fit an obvious diagnostic pattern. Clinical notes, rich in narrative details, may support the automated flagging of patients for genetic testing. Given the strength of Large Language Models (LLMs) in processing unstructured text, we developed an open-source LLM-enabled genetic testing recommendation pipeline, which leverages retrieval augmented generation (RAG) on curated genetic aortopathy-re

SUBMITTER: Singhal P 

PROVIDER: S-EPMC12424886 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature