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.
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?
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With the smart project setup features, you can start a new project in minutes.
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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:
Curious to how this works? See the Nature Machine Intelligence paper or the introduction video.
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.
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
The ASReview research team conducted a systematic review on the implementation of AI-aided Systematic Reviews within Clinical Guideline Development.
The following PROSPERO pre-registration was created for a review the factors, including their interactions, that contribute to the onset, maintenance and relapse of substance use, anxiety and depressive disorders. This large review study will use ASReview for the screening of the title and abstracts.
In a time of exponential growth of new evidence supporting clinical decision making, combined with a labor-intensive process of selecting this evidence, there is a need for methods to speed up current processes in order to keep medical guidelines up-to-date.
This dataset contains an overview of 117 systematic reviews published by corresponding authors affiliated to Utrecht University (UU) or UMC Utrecht in 2020.
We show that by using active learning, ASReview can lead to far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, the presented software is fully transparent and open source.
Explore the systematic review dataset that was used for the publication “Psychological theories of depressive relapse and recurrence” from Brouwer et al., 2019. From pre-processing to the final dataset, a look into the complete systematic review process behind this publication.
Recreate the simulation study on the systematic review of Smid et al.. From pre-processing to the final dataset, dive into the complete process behind this publication.
After manually screening 5050 studies, Smid et al included only 27 studies for their review. In the current study, both the Bayesian and logistic regression models found more than 80% of relevant publications after screening only 10% of all publications.
To determine the defaults we performed a simulation study and Naive Bayes + TF-IDF model performed the best.
Combining human intelligence and machine learning into Researcher-in-the-loop machine learning. An effective technique for training models.
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