The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study

Abstract

Highlights Machine learning (ML) tools for evidence synthesis now exist, but little is known about whether they lead to decreased resource use and time-to-completion of reviews. We propose a protocol to systematically measure any resource savings of using machine learning to produce evidence syntheses. Co-primary analyses will compare “recommended” ML use (in which ML replaces some human activities) and no ML use. We will additionally explore the differences between “recommended” ML use and “non-recommended” ML use (in which ML is over-used or under-used).

Keywords

• Systematic review
• Machine learning
• Artificial intelligence
• Research waste
• Business process management