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Abstract Personalized clinical management of idiopathic pulmonary fibrosis (IPF) is hindered by its complex longitudinal course and fragmented, unstructured data. Although large language models (LLMs) and retrieval-augmented generation (RAG) show promise in clinical decision support, current methods face challenges in addressing dynamic disease progression due to static knowledge corpora, a lack of temporal awareness, and susceptibility to semantic noise and hallucinations.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
