AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs

| Project page | Demo Paper | Website | Dense Reviewer

AiReview is a flexible, open platform for accelerating systematic reviews with large language models (LLMs). It enables researchers and librarians to LLM-assisted titles and abstract screening with three defined roles and seven pipelines composed of the roles, offering transparent, configurable, and interactive support for evidence-based screening tasks.

Features

• Flexible LLM Integration: Transpartent control over LLMs used, from configurations to prompts.
• Three LLM Roles Framework:
  • Pre-reviewer: Scores studies before human review, aiding initial prioritization
  • Co-reviewer: Real-time assistance during human screening with SR Assistant
  • Post-reviewer: Quality control and decision validation after screening
• Configurable Interaction Levels: Choose between low-support (on-demand) and high-support (always visible) LLM assistance for bias control
• Multiple Pipeline Modes: Seven different LLM usage patterns from single-role (e.g. Pre-Only) to full assistance workflows

Installation

Prerequisites:

Docker and Docker Compose

1. Get the Code

git clone https://github.com/ielab/ai-review.git

cd ai-review

2. Launch AiReview

docker-compose build backend --no-cache --build-arg BUILD_OS=$(uname -s)

# start the app
docker compose up -d

System Architecture

AiReview consists of two Docker containers:

1. Frontend: Vue.js-based web interface with Tailwind CSS styling, served via Nginx.
3. Backend: Python-based back end with REST and WebSocket APIs (Django), PostgreSQL database, RabbitMQ message queuing, and Celery task manager for for LLM-assisted screening.

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0) with additional clauses - see the LICENSE file for details.

Acknowledgments

We extend our gratitude to the engineering team of AI DETA Technologies Co. for their consultation and support in developing DenseReviewer.

Citation

If you find this repo useful for your research, please kindly cite the following paper:

@inproceedings{2025maoaireview,
  author       = {Xinyu Mao and Teerapong Leelanupab and Martin Potthast and Harrisen Scells and Guido Zuccon},
  title        = {AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs},
  booktitle    = {Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  series       = {SIGIR '25},
  year         = {2025},
  publisher    = {ACM},
  doi          = {10.1145/3726302.3730133}
}

Contact

For questions and feedback, please open an issue on GitHub or contact the authors directly.