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Universiteit Utrecht (the Netherlands)ASReview LAB
Dutch open-source tool from Universiteit Utrecht that speeds up the screening of titles and abstracts for systematic reviews using active learning: the model learns from your decisions and puts the most relevant studies first. Runs locally or on your own server.

- Provider based in the Netherlands
- Publishes certification/GDPR safeguards
Last checked: 27 October 2026 · How we check
Is this your tool? Fill in missing detailsWhat it does
Server & cloud: self-hostable, via browser
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WISKIon requestopen source and runnable locally on your own laptop or institutional server, so your dataset never leaves your own environment; uses active-learning models that you can choose and simulate in advance. The makers explicitly cite GDPR compliance and no tracking cookies. Many tools build on a large model from, for example, OpenAI or Anthropic. If in doubt, ask for the data processing agreement: it states where your data goes.
Questions about ASReview LAB
- Which AI model does ASReview LAB use?
- The maker of ASReview LAB does not publicly disclose which AI model sits underneath. Open source and runnable locally on your own laptop or institutional server, so your dataset never leaves your own environment; uses active-learning models you can choose and simulate in advance. The makers explicitly state GDPR compliance and no tracking cookies.
- How much does ASReview LAB cost?
- free (open source); server/institutional variant can be self-hosted. Check the current price on the provider's website; rates change often.
- Who is ASReview LAB for?
- ASReview LAB is aimed at research & science. It fits work such as literature searching and exploration with semantic search and citation networks, screening titles, abstracts and full texts for systematic reviews, and extracting and summarising data and study characteristics from papers.
- What should I look out for with AI in research & science?
- Never use AI as a source: every reference must be traceable to the original article, as fabricated or wrongly attributed citations are the most common error. Unpublished data, research proposals and peer-review manuscripts are confidential or commercially sensitive, so do not enter them into a consumer account without a data processing agreement, and check whether the provider stores your input or uses it for training (publishers usually prohibit this explicitly for peer review). If you work with personal data from research participants, the GDPR and your own ethics review apply: running models locally or using an EU-hosted tool with data minimisation is then the safe route. Finally, document your search strategy and your AI use: journals and funders increasingly ask for this, and a review must remain reproducible.
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