AIM Review Tool: Artificial Intelligence for Smarter Systematic Review Screening

In this study, we present and evaluate AIM Review Tool, a modern web-based application that combines active and supervised machine learning strategies to accelerate the screening of publications for systematic reviews. AIM Review incorporates state-of-the-art text vectorization methods and machine learning models implemented directly on the web browser. Unlike existing tools, it is the first application to integrate nested-cross-validated and semi-automated methods for systematic review screening, enhancing both efficiency and precision of paper selection, thus accelerating evidence synthesis. Using three real-world case studies, we tested the capabilities of both active learning and supervised learning pipelines. Active learning prioritization of relevant studies showed a reduction in screening workload (i.e., percentage of publications that did not need to be screened) from 92% to 20% depending on the characteristics of the systematic review. Supervised learning pipelines using a subset of the screenings predict the relevance of unscreened publications with balanced accuracies between 75% and 87%, using nested cross-validation. AIM Review offers a flexible, efficient solution for large-scale literature screening, allowing for seamless integration with the manual review processes. Future work will focus on automating subsequent stages, including data extraction, to further improve the speed and scalability of systematic reviews.