ASReview: Open Source Software for Efficient and Transparent Active Learning for Systematic Reviews

For many tasks - including but not limited to systematic reviews for research fields - the scientific literature needs to be checked systematically. Currently, scholars and practitioners might screen thousands of studies by hand to determine which studies to include in their review. This process is error prone and inefficient, because of the extremely imbalanced data: only a very small fraction of the studies screened will be relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that ASReview can yield far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, we describe the different options of the free and open source research software, we show how it can be used for screening the COVID19 literature, and we present the results from a series of user experience tests. We invite the community to contribute to open source projects such as our own, that provide measurable and reproducible improvement over current practice.