Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness

Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness

Abstract Large Language Models (LLMs) hold promise for medical applications but often lack domain-specific expertise. Retrieval Augmented Generation (RAG) enables customization by integrating specialized knowledge. This study assessed the accuracy, consistency, and safety of LLM-RAG models in determ...

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Main Authors: Yu He Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Chang-Fu Kuo, Shao-Chun Wu, Vesela P. Kovacheva, Daniel Shu Wei Ting
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01519-z
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https://doi.org/10.1038/s41746-025-01519-z

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