Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis

Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis

BackgroundConcise synopses of clinical evidence support treatment decision-making but are time-consuming to curate. Large language models (LLMs) offer potential but they may provide inaccurate information. We objectively assessed the abilities of four commercially available LLMs to generate synopses...

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Bibliographic Details
Main Authors: Samuel Rubinstein, Aleenah Mohsin, Rahul Banerjee, Will Ma, Sanjay Mishra, Mary Kwok, Peter Yang, Jeremy L. Warner, Andrew J. Cowan
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Digital Health
Subjects:
large language models
clinical evidence summarization
cancer treatment synopses
multiple myeloma
comparative analysis
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2025.1569554/full
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https://www.frontiersin.org/articles/10.3389/fdgth.2025.1569554/full

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