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Position statement on artificial intelligence (AI) use in evidence synthesis across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025

Ella Flemyng,Anna Noel-Storr,Biljana Macura,Gerald Gartlehner,James Thomas,Joerg J Meerpohl,Zoe Jordan,Jan Minx,Angelika Eisele-Metzger,Candyce Hamel,Paweł Jemioło,K. Porritt,M. Grainger

Published 2025 in JBI Evidence Synthesis

ABSTRACT

Evidence syntheses, including systematic reviews, are a type of research that uses systematic, replicable methods to evaluate all available evidence on a specific question. They are built on the principles of research integrity, including rigor, transparency, and reproducibility. There is wide recognition that artificial intelligence (AI) and automation have the potential to transform the way we produce evidence syntheses, making the process significantly more efficient. However, this technology is potentially disruptive, characterized by opaque decision ‐ making and black ‐ box predictions, susceptible to overfitting, potentially embedded with algorithmic biases

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