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 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.

SUBMITTER: Lai H 

PROVIDER: S-EPMC11785717 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature