https://github.com/asreview/asreview
Active learning for systematic reviews
https://github.com/asreview/asreview
active-learning asreview deep-learning language-model learning-algorithms literature llm natural-language-processing neural-network research systematic-literature-reviews systematic-reviews utrecht-university
Last synced: 25 days ago
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Active learning for systematic reviews
- Host: GitHub
- URL: https://github.com/asreview/asreview
- Owner: asreview
- License: apache-2.0
- Created: 2019-01-09T14:09:32.000Z (over 7 years ago)
- Default Branch: main
- Last Pushed: 2026-03-27T13:56:54.000Z (about 1 month ago)
- Last Synced: 2026-03-28T00:02:52.146Z (about 1 month ago)
- Topics: active-learning, asreview, deep-learning, language-model, learning-algorithms, literature, llm, natural-language-processing, neural-network, research, systematic-literature-reviews, systematic-reviews, utrecht-university
- Language: Python
- Homepage: https://asreview.ai
- Size: 161 MB
- Stars: 864
- Watchers: 20
- Forks: 158
- Open Issues: 80
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
- Security: SECURITY.md
Awesome Lists containing this project
- awesome-ai-for-economists - ASReview - Active learning for systematic reviews, reducing screening time by up to 95% (Nature Machine Intelligence).  (Literature Review and Research Discovery / DSGE and Structural Models)
- awesome-utrecht-university - asreview - Active learning for systematic reviews (Projects / Research software)
README
🎉 ASReview LAB v3 is here! 🎉
Cleaner screening, smarter data handling, and more control over your reviews.
Automatic duplicate detection, editable tags, and a streamlined workflow.
## ASReview LAB: Active Learning for Systematic Reviews
**ASReview LAB** is an open-source machine learning tool for efficient,
transparent, and interactive screening of large textual datasets. It is widely
used for systematic reviews, meta-analyses, and any scenario requiring
systematic text screening.
The key features of **ASReview LAB** are:
- **Active Learning**: Interactively prioritize records using AI models that
learn from your labeling decisions.
- **Scientifically validated**: ASReview LAB has been scientifically validated
and published in [Nature Machine
Intelligence](https://doi.org/10.1038/s42256-020-00287-7).
- **Flexible AI Models**: Choose from pre-configured ELAS models or build your
own with custom components.
- **Simulation toolkit**: Assess model performance on fully labeled datasets.
- **Label Management**: All decisions are saved automatically; easily change
labels at any time.
- **User-Centric Design**: Humans are the oracle; the interface is transparent
and customizable.
- **Privacy First**: Everything is open source and no usage or user data is
collected.
---
### What's New in Version 3?
- **Automatic Duplicate Hiding**: Records with duplicate titles and texts are
automatically hidden during screening, keeping your workflow clean and tidy.
Need those records back? No problem — you can choose to include them when you
export your data.
- **Editable Tags in Collection**: Manage and edit tags directly from the
Collection screen, giving you more control over your data extraction and classification.
---
## Installation
Requires Python 3.10 or later.
```bash
pip install asreview
```
Upgrade:
```bash
pip install --upgrade asreview
```
For Docker and advanced installation, see the [installation
guide](https://asreview.readthedocs.io/en/stable/lab/installation.html).
Latest version of ASReview LAB: [](https://badge.fury.io/py/asreview)
## The ASReview LAB Workflow
1. **Import Data**: Load your dataset (CSV, RIS, XLSX, etc.).
2. **Create Project**: Set up a new review or simulation project.
3. **Select Prior Knowledge**: Optionally provide records you already know are
relevant or not relevant.
4. **Start Screening**: Label records as Relevant or Not Relevant; the AI model
continuously improves.
5. **Monitor Progress**: Use the dashboard to track your progress and decide
when to stop.
6. **Export Results**: Download your labeled dataset or project file.
[](https://asreview.readthedocs.io/en/stable/lab/about.html
"ASReview LAB")
---
## Documentation & Resources
- [Documentation](https://asreview.readthedocs.io/)
- [Video tutorials](https://www.youtube.com/@ASReview)
- [AI models of ASReview
LAB](https://asreview.readthedocs.io/en/latest/lab/models.html)
- [FAQ](https://github.com/asreview/asreview/discussions?discussions_q=sort%3Atop)
- [Live Demo](https://asreview.app)
## Citation
If you wish to cite the underlying methodology of the ASReview software, please
use the following publication in Nature Machine Intelligence:
> van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine
> learning framework for efficient and transparent systematic reviews. Nat Mach
> Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7
For citing the software, please refer to the specific release of the ASReview
software on Zenodo: https://doi.org/10.5281/zenodo.3345592. The menu on the
right can be used to find the citation format you need.
For more scientific publications on the ASReview software, go to
[asreview.ai/papers](https://asreview.ai/papers/).
## Community & Contact
The best resources to find an answer to your question or ways to get in contact
with the team are:
- [Newsletter](https://asreview.ai/newsletter/subscribe)
- [FAQ](https://github.com/asreview/asreview/discussions?discussions_q=sort%3Atop)
- [Community events](https://asreview.ai/events)
- [Issues or feature requests](https://github.com/asreview/asreview/issues)
- [Donate to ASReview](https://asreview.ai/donate)
- [Contact](mailto:asreview@uu.nl) (asreview@uu.nl)
## License
The ASReview software has an Apache 2.0 [LICENSE](LICENSE). The ASReview team
accepts no responsibility or liability for the use of the ASReview tool or any
direct or indirect damages arising out of the application of the tool.