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

Lai, Honghao; Liu, Jiayi; Bai, Chunyang; Liu, Hui; Pan, Bei; Luo, Xufei; Hou, Liangying; Zhao, Weilong; Xia, Danni; Tian, Jinhui; Chen, Yaolong; Zhang, Lu; Estill, Janne; Liu, Jie; Liao, Xing; Shi, Nannan; Sun, Xin; Shang, Hongcai; Bian, Zhaoxiang; Yang, Kehu; Huang, Luqi; Ge, Long; On behalf of ADVANCED Working Group; Li, Haodong; Wang, Ye; Zhang, Huayu; Zhu, Di; Peng, Dongrui; Wang, Fan; Li, Yueyan; Tang, Shilin; Liu, Hanxiang; Li, Zeming; Yang, Zhenhua; Yu, Xuan; Qin, Yishan

doi:10.1038/s41746-025-01457-w

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

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 hig…