Fast-Track Your Abstract Screening: Mastering ASReview for Accelerating Abstract Screening and Evaluating Decisions From Automatic-Screening Methods

Research syntheses, such as systematic reviews and meta-analyses, are crucial for synthesizing research to support evidence-based decision-making. However, the abstract-screening phase, during which researchers evaluate titles and abstracts for inclusion, is highly time-consuming and often results in cognitive biases and fatigue. To address these challenges, machine-learning-assisted tools, particularly those using active learning, have gained prominence. One such tool is Active Screening Review (ASReview), an open-source software for semiautomating title and abstract screening in systematic reviews. ASReview incorporates user feedback to prioritize relevant studies, reducing screening time and improving efficiency. Despite its potential, many researchers remain uncertain about integrating ASReview into their workflows and making evidence-based decisions regarding the tool’s configuration, training, and stopping criteria. In this tutorial, we provide a step-by-step guide to using ASReview, including practical examples from psychological research. We demonstrate the software’s application in two use cases: screening unlabeled abstracts using active learning and verifying results from automated-screening methods. In the tutorial, we also offer evidence-based recommendations for selecting stopping rules to balance sensitivity and efficiency. We also outline strategies for prescreening, data-set preparation, model setup, and progress monitoring to ensure that researchers can maximize the tool’s benefits while maintaining scientific rigor. By offering evidence-based guidance at each stage of the process for practitioners without coding skills, in this tutorial, we aim to help researchers harness artificial-intelligence-aided screening to enhance the quality and efficiency of research syntheses across disciplines.