Join the movement towards fast, open, and transparent systematic reviews

ASReview LAB v1.0 is out!

Free and open source software for systematic reviewing.


ASReview uses state-of-the-art active learning techniques to solve one of the most interesting challenges in screening large amounts of text: there’s not enough time to read everything!  Learn more about the free and open source software for systematic reviewing: ASReview LAB.



ASReview has grown into a vivid community of researchers, users, and developers from around the world. Join the community today by taking part in discussions, submitting ideas for new features, or by joining the development-fund and support ASReview to continue its open-source mission. 

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ASReview LAB

Screening with ASReview LAB

Free, Open and Transparent

The software is installed on your device locally. This ensures that nobody else has access to your data, except when you share it with others. Nice, isn’t it?

  • Free and open source
  • Local or server installation
  • Full control over your own data
  • Install via docker

Download now!

In 2 minutes up and running

With the smart project setup features, you can start a new project in minutes.
Ready, set, start screening!

  • Create as many projects as you want
  • Choose your own or an existing dataset
  • Select prior knowledge
  • Select your favorite active learning algoritm


Class 101
Setting up a new ASReview project


A challenge and a solution

How to screen (tens of) thousands of papers by hand for inclusion in your systematic review, meta-analysis, medical guideline, or overview? As truly relevant records are very sparse (often <5%), this is an extremely time-intensive and error-prone task. The research focuses on:

  • Powerful active learning algorithms; 
  • Relevant records are shown to you first;
  • Save time or increase the number of records to screen;

Curious to how this works? See the Nature Machine Intelligence paper or the introduction video.

Nature Machine Intelligence Paper The Case of Systematic Reviewing
ASReview LAB search
Scientist ELAS, mascotte of ASReview

The Research Team continuously explores and investigates new ways for improvement through simulation studies, use-cases, and more. The team doesn’t do this all alone, but often together with other organizations, research groups, and developers. The findings are published in peer-reviewed journals like Nature Machine Intelligence. By publishing data, scripts and underlying code, all work becomes fully transparent too.