Living systematic reviews are made possible by collaboration in large teams of researchers. Crowdsourcing is another tool researchers use to keep living systematic reviews up to date. Crowdsourcing gets the general public involved to complete specific tasks within the systematic review process. One example is the Cochrane crowd initiative, where volunteers can do assignments like identifying randomized controlled trials.

Machine learning is another tool researchers can use to make conducting a living systematic review more doable. You can use generic machine learning models to identify records based on specific in- or exclusion criteria for screening. One example of this is the model from RobotReviewer to identify RCTs that we have implemented in our tool.

Another possibility is to train a review specific machine learning models. An algorithm is trained, using active learning, to predict which records are probably relevant based on previous in- and exclusion decisions made by the reviewer. The algorithm produces a ranking of articles it indicates are most relevant. We also are researching machine learning for data extraction and are developing models that predict sentences containing relevant population or outcome data.