ASReview vs RobotReviewer
ASReview
asreview.nlRobotReviewer
robotreviewer.netSide by side
| Figure | ASReview | RobotReviewer |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 2 |
| Spec score · AI Literature Review Software | 9.0 (#1) | 8.4 (#6) |
| API | ASReview documentation includes a Python API, and the institutional page also mentions a REST API and CLI for automated workflows. | A REST API exposes the underlying models for integration into systematic-review software. |
| Documentation depth | The documentation covers user, server and technical guides, including authentication, command-line use, APIs and extension development. | The README includes Docker, manual installation, configuration, startup, API, testing, and troubleshooting instructions. |
| Integrations | The site describes compatibility with EndNote, PubMed, and other reference managers, and says RIS files can be imported and exported. | The self-hosted setup uses Grobid for document processing, RabbitMQ and Celery for worker processing, and SciBERT through bert-as-a-service. |
| License | The ASReview software repository is released under the Apache License 2.0. | RobotReviewer is open source under the GNU GPL version 3.0 license. |
| Purpose | ASReview uses AI-aided active learning to prioritize records for systematic review screening while keeping a human screener in the decision loop. | RobotReviewer automatically annotates clinical trial PDFs with PICO characteristics and likely bias assessments using the Cochrane Risk of Bias tool. |
| Self-hosting | ASReview can run locally, on a server or cloud instance, and through Docker. | Users can run the web interface and API locally from the source repository. |
| Support | The site offers free online documentation, tutorials, videos, and community discussion support, and describes in-house workshops and custom consultancy for organizations. | The project directs users to an email address for questions and integration assistance. |
| What it does | ASReview uses active learning to reorder large collections of textual records so likely-relevant items appear earlier during systematic-review screening. | RobotReviewer analyzes clinical-trial PDFs and automatically extracts trial information and risk-of-bias assessments. |
Plans
In their own words
ASReview
ASReview is a free, open-source tool that uses AI-aided active learning to prioritize records for systematic review screening, with a human making screening decisions. Its site says the approach can reduce screening workload by up to 95%. Users can simulate AI-aided review to compare models, while Crowdscreen supports parallel screening with access managed by administrators through a private login system. The described model families are ELAS-Ultra, ELAS-Heavy, and language-agnostic ELAS-L; ELAS-Heavy and ELAS-L require the Dory package. ASReview works with reference managers including EndNote and PubMed, and imports and exports CSV, RIS, and XLSX files. Documentation includes a Python API, while the institutional workflow also describes a REST API and CLI for automation. In a local setup, data stays on the user's computer; server deployments keep it in the organization's environment and follow its policies. Support includes documentation, tutorials, videos, community discussion, and organizational workshops and consultancy. ASReview LAB costs nothing with no licensing fees or subscriptions, though running hardware or a server may incur costs.
RobotReviewer
RobotReviewer analyzes clinical trial reports by annotating PDFs with PICO characteristics and likely bias assessments using the Cochrane Risk of Bias tool. Uploading PDFs on the website extracts key data and presents a summary table. Its REST API can process groups of clinical trial reports to synthesize results across trials. The project is open source under GNU GPL version 3.0 and is free to use. It can run locally as Docker services, with a web app and API server exposed on local ports. The self-hosted processing setup uses Grobid, RabbitMQ, Celery, and SciBERT through bert-as-a-service. Docker instructions cover GPU use, and the worker can also run CPU-only. Manual setup calls for Python 3.6, TensorFlow 1.12.0, and the associated processing services. Installation is documented as tested on Ubuntu and Mac OS; Windows works with substantial installation difficulty. Maintainers ask users to cite the 2017 ACL paper and invite questions by email, including requests for help integrating RobotReviewer into other software.