LASER AI vs RobotReviewer
LASER AI
laser.aiRobotReviewer
robotreviewer.netSide by side
| Figure | LASER AI | RobotReviewer |
|---|---|---|
| Price from | $250/mo | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| Android | Yes | — |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 3 | 2 |
| Spec score · AI Literature Review Software | 8.8 (#2) | 8.4 (#6) |
| Funding | Evidence Prime says it has secured more than €11 million in competitive grants. | The project acknowledges NIH National Library of Medicine and UK Medical Research Council grant support. |
| Integrations | GetApp lists third-party integrations as a supported feature and rates that feature 5.0 from one review. | The self-hosted setup uses Grobid for document processing, RabbitMQ and Celery for worker processing, and SciBERT through bert-as-a-service. |
| Purpose | LASER AI is an AI-supported platform for accelerating literature reviews and evidence synthesis. | RobotReviewer automatically annotates clinical trial PDFs with PICO characteristics and likely bias assessments using the Cochrane Risk of Bias tool. |
| Support | GetApp lists email/help desk, phone support, knowledge base and FAQ/forum support, plus live online, webinar, documentation and video training. | The project directs users to an email address for questions and integration assistance. |
| What it does | LASER AI is an AI-supported platform for systematic literature reviews and evidence synthesis. | RobotReviewer analyzes clinical-trial PDFs and automatically extracts trial information and risk-of-bias assessments. |
Plans
LASER AI
- Per project$3,000, paid once
- Per user$3,000 a year
- EnterprisePriced on request
In their own words
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.