Machine-learning assisted screening for evidence synthesis: methodological case study of the ASReview tool

ASReview is a software that can potentially reduce the workload of literature screening in systematic reviews by ranking the retrieved records. We assessed the tool’s feasibility, advantages, and limitations, to populate a database of cancer immunotherapy trials. ASReview is easy to use, and it efficiently identified relevant records. It may save resources compared to traditional systematic reviews using two human reviewers. Predefined procedures are necessary to maintain a transparent and reproducible workflow. Limitations include that adding references to existing projects is difficult and that the algorithm learns from every decision, even when this may not be appropriate.