Background: Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews.Study design and setting: This protocol describes how we will answer two research questions using a retrospective study design: Is there a difference in resources used to produce reviews using recommended ML versus not using ML, and is there a difference in time-to-completion? We will also compare recommended ML use to non-recommended ML use that merely adds ML use to existing procedures. We will retrospectively include all reviews conducted at our institute from 1 August 2020, corresponding to the commission of the first review in our institute that used ML. We will use the results from this study to design a rigorous, multi-institutional, prospective study that will additionally explore review quality.Conclusion: We invite other evidence synthesis groups to adopt and adapt this protocol and to collaborate with us.

Background Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews. Methods This protocol describes how we will answer two research questions using a retrospective study design: Is there a difference in resources used to produce reviews using recommended ML versus not using ML, and is there a difference in time-to-completion? We will also compare recommended ML use to non-recommended ML use. We will retrospectively include all reviews conducted at our institute from 1 August 2020, corresponding to the commission of the first review in our institute that used ML. We will use the results from this study to design a rigorous, multi-institutional, prospective study that will additionally explore review quality. Conclusion We invite other evidence synthesis groups to adopt and adapt this protocol and to collaborate with us.