Language models for data extraction and risk of bias assessment in complementary medicine

Abstract Large language models (LLMs) have the potential to enhance evidence synthesis efficiency and accuracy. This study assessed LLM-only and LLM-assisted methods in data extraction and risk of bias assessment for 107 trials on complementary medicine. Moonshot-v1-128k and Claude-3.5-sonnet achiev...

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Main Authors:	Honghao Lai, Jiayi Liu, Chunyang Bai, Hui Liu, Bei Pan, Xufei Luo, Liangying Hou, Weilong Zhao, Danni Xia, Jinhui Tian, Yaolong Chen, Lu Zhang, Janne Estill, Jie Liu, Xing Liao, Nannan Shi, Xin Sun, Hongcai Shang, Zhaoxiang Bian, Kehu Yang, Luqi Huang, Long Ge, On behalf of ADVANCED Working Group
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-01-01
Series:	npj Digital Medicine
Online Access:	https://doi.org/10.1038/s41746-025-01457-w
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Language models for data extraction and risk of bias assessment in complementary medicine

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