Opportunities and challenges for data extraction with a large language model

Webinar 3 of the Cochrane Learning Live webinar series "Artificial Intelligence (AI) methods in evidence synthesis"

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.

Presenter:

Prof. Gerald Gartlehner is a professor for evidence-based medicine and clinical epidemiology and the co-director of Cochrane Austria. He is also a co-convenor of the Cochrane Rapid Review Methods Groups. With over two decades of experience in conducting evidence synthesis, Gartlehner has recently focused on evaluating the performance of LLMs for semi-automating various steps in the evidence synthesis processes.

 

Upcoming Learning Live events

9. April 2025
(How well) can large language models and AI-based automation tools assist in Risk of Bias Assessment?
Dr. Angelika Eisele-Metzger, Cochrane Germany & Institute for Evidence in Medicine, Freiburg.

7. Mai 2025
How effectively do large language models and AI-based automation tools assist in writing and summarizing evidence syntheses?
Dr. Riaz Qureshi, Assistant Professor, Department of Ophthalmology, University of Colorado Anschutz Medical Campus.

3. Juni 2025
Recommendations and guidance on responsible AI in evidence synthesis
Ella Flemyng, Head of Editorial Policy and Research Integrity, Cochrane.
Dr. Anna Noel-Storr, Head of Evidence Pipeline and Data Curation, Cochrane.
Prof. James Thomas, Principle Investigator, Evidence Reviews Facility, Department of Health and Social Care, England.

 
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