ASReview

What is ASReview?

ASReview is an open-source software designed to assist researchers in conducting systematic literature reviews more efficiently. It employs artificial intelligence algorithms to prioritize and rank research papers, enabling users to focus on the most relevant studies first. The tool supports the entire screening process, from initial data import to final review, by offering features that automate repetitive tasks and reduce manual effort. Researchers can upload their literature data in various formats and interact with the AI's suggestions through a user-friendly interface.

The core functionality of ASReview revolves around its active learning approach. The AI learns from the user's labeling decisions (e.g., including or excluding a paper) to improve its ranking of the remaining unlabeled studies. This iterative process helps to quickly identify papers that are likely to be relevant, significantly speeding up the screening phase compared to traditional manual methods. The software also provides tools for managing the review process, tracking progress, and exporting results for further analysis or reporting.

This tool is particularly beneficial for researchers dealing with a large number of articles, a common challenge in systematic reviews across various academic disciplines. By automating the initial screening, ASReview frees up valuable researcher time and resources. Its open-source nature means it is freely available and can be adapted or extended by the community. The goal is to make systematic review processes more accessible and less time-consuming for academics worldwide.

Key features

• AI-powered relevance ranking
• Active learning loop
• Data import options
• User labeling interface
• Progress tracking
• Exportable results
• Open-source code

Use cases

• Screening thousands of abstracts for systematic reviews.
• Accelerating meta-analysis literature searches.
• Identifying relevant papers in large datasets.
• Reducing time spent on initial literature filtering.
• Assisting PhD students with literature reviews.

Pros & cons

FAQ