Open Source Software for Efficient and Transparent Reviews

TL;DR

This paper introduces ASReview, an open source machine learning tool that significantly improves the efficiency and transparency of screening studies for systematic reviews and meta-analyses through active learning.

Contribution

The paper presents a novel open source pipeline using active learning to enhance systematic review screening, demonstrating superior efficiency over manual methods.

Findings

ASReview outperforms manual screening in efficiency.

High-quality results comparable to traditional methods.

Open source software encourages community contributions.

Abstract

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.