Open LLM-based actionable incidental finding extraction from [18F]fluorodeoxyglucose PET-CT radiology reports

IntroductionWe developed an open, large language model (LLM)-based pipeline to extract actionable incidental findings (AIFs) from [18F]fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) reports. This imaging modality often uncovers AIFs, which can affect a patient'...

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Principais autores:	Stephen H. Barlow, Sugama Chicklore, Yulan He, Sebastien Ourselin, Thomas Wagner, Anna Barnes, Gary J. R. Cook
Formato:	Artigo
Idioma:	Inglês
Publicado:	Frontiers Media S.A. 2025-11-01
Series:	Frontiers in Digital Health
Assuntos:	incidental findings natural language processing diagnostic imaging artificial intelligence positron emission tomography-computed tomography
Acceso en liña:	https://www.frontiersin.org/articles/10.3389/fdgth.2025.1702082/full
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