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

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...

Full description

Saved in:
Bibliographic Details
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 achieved high accuracy (≥95%), with LLM-assisted methods performing better (≥97%). LLM-assisted methods significantly reduced processing time (14.7 and 5.9 min vs. 86.9 and 10.4 min for conventional methods). These findings highlight LLMs’ potential when integrated with human expertise.
ISSN:2398-6352

Similar Items