Agenda:
Data extraction in evidence synthesis is labour-intensive, costly, and prone to errors. The use of large language models (LLMs) presents a promising approach for AI-assisted data extraction, potentially enhancing both efficiency and accuracy. In this session, part of the Artificial Intelligence (AI) methods in evidence synthesis series, the presenter will give an overview of the current research landscape concerning data extraction using LLMs. The presenter will also show findings from a study within reviews (SWAR) that validated the workflow of employing an LLM for semi-automating data extraction within systematic reviews. Additionally, the webinar will address current methodological challenges in evaluating LLMs for data extraction tasks. This session is aimed at anyone conducting evidence synthesis.