Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data acces...

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Bibliographic Details
Main Authors: Austin A. Barr, Joshua Quan, Eddie Guo, Emre Sezgin
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Artificial Intelligence
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Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1533508/full
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