Language models for data extraction and risk of bias assessment in complementary medicine.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 39890970.
- Also identified by DOI 10.1038/s41746-025-01457-w and PMC identifier 11785717.
- Licence recorded as CC BY-NC-ND.
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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.