Evaluating large language models for automated meta-analysis generation

Large language models (LLMs) are increasingly used to support evidence synthesis, including literature interpretation, data extraction, and statistical analysis [ 1 , 2 , 3 ]. However, their ability to autonomously perform meta-analysis from primary studies remains unclear. We evaluated the accuracy of contemporary LLMs in extracting study-level data and generating meta-analysis estimates against published statistical reference standard.