Work

AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs

Year: 2025

Type: review

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 LLMassisted 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.

Source: International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

DOI: 10.1145/3726302.3730133

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

Institutions: The University of Queensland, University of Kassel, Center for Scalable Data Analytics and Artificial Intelligence, University of Tübingen

Language: English

Topic: Meta-analysis and systematic reviews

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funder: Universitas Brawijaya

Award: G4032089403