Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems

Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems

Objective The practice of evidence-based medicine can be challenging when relevant data are lacking or difficult to contextualize for a specific patient. Large language models (LLMs) could potentially address both challenges by summarizing published literature or generating new studies using real-wo...

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Main Authors: Yen Sia Low, Michael L Jackson, Rebecca J Hyde, Robert E Brown, Neil M Sanghavi, Julian D Baldwin, C William Pike, Jananee Muralidharan, Gavin Hui, Natasha Alexander, Hadeel Hassan, Rahul V Nene, Morgan Pike, Courtney J Pokrzywa, Shivam Vedak, Adam Paul Yan, Dong-han Yao, Amy R Zipursky, Christina Dinh, Philip Ballentine, Dan C Derieg, Vladimir Polony, Rehan N Chawdry, Jordan Davies, Brigham B Hyde, Nigam H Shah, Saurabh Gombar
Format: Article
Language:English
Published: SAGE Publishing 2025-06-01
Series:Digital Health
Online Access:https://doi.org/10.1177/20552076251348850
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https://doi.org/10.1177/20552076251348850

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