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...

Fuld beskrivelse

Bibliografiske detaljer

Principais autores:	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:	Artigo
Sprog:	Inglês
Udgivet:	SAGE Publishing 2025-06-01
Serier:	Digital Health
Online adgang:	https://doi.org/10.1177/20552076251348850
Tags:	Tilføj Tag Ingen Tags, Vær først til at tagge denne postø!