AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs

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Xinyu Mao, Teerapong Leelanupab, Martin Potthast, Harrisen Scells, Guido Zuccon

Published: 2025, Last Modified: 25 Jan 2026SIGIR 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen, or assess, many studies for inclusion in the review. Existing tools help streamline this process, mostly using traditional machine learning. Large language models (LLMs) offer new opportunities to speed up screening, yet no tool currently enables users to directly apply LLMs or ensures systematic and transparent use of these methods. This paper presents (i) a flexible framework for using LLMs in systematic review tasks, especially title and abstract screening, and (ii) a web-based interface for LLM-assisted screening. Together, they form AiReview-a novel platform that connects cutting-edge LLM-assisted screening methods with real-world systematic review practice. The live tool is available at https://aireview.ielab.io. We also release the code publicly at https://github.com/ielab/ai-review.