  To help researchers conduct a systematic review or meta-analysis as
efficiently and transparently as possible, we designed a tool (ASReview) to
accelerate the step of screening titles and abstracts. For many tasks -
including but not limited to systematic reviews and meta-analyses - the
scientific literature needs to be checked systematically. Currently, scholars
and practitioners screen thousands of studies by hand to determine which
studies to include in their review or meta-analysis. This is error prone and
inefficient because of extremely imbalanced data: only a fraction of the
screened studies is 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 providing high quality. Furthermore, we describe the
options of the free and open source research software and present the results
from user experience tests. We invite the community to contribute to open
source projects such as our own that provide measurable and reproducible
improvements over current practice.
