ASReview LAB v.2 introduces an advancement in AI-assisted systematic reviewing by enabling collaborative screening with multiple experts (“a crowd of oracles”) using a shared AI model. The platform supports multiple AI agents within the same project, allowing users to switch between fast general-purpose models and domain-specific, semantic, or multilingual transformer models. Leveraging the SYNERGY benchmark dataset, performance has improved significantly, showing a 24.1% reduction in loss compared to version 1 through model improvements and hyperparameter tuning. ASReview LAB v.2 follows user-centric design principles and offers reproducible, transparent workflows. It logs key configuration and annotation data while balancing full model traceability with efficient storage. Future developments include automated model switching based on performance metrics, noise-robust learning, and ensemble-based decision-making.
@article{bruin2025asreview,title={ASReview LAB v.2: Open-source text screening with multiple agents and a crowd of experts},journal={Patterns},pages={101318},year={2025},issn={2666-3899},doi={https://doi.org/10.1016/j.patter.2025.101318},url={https://www.sciencedirect.com/science/article/pii/S2666389925001667},author={{de Bruin}, Jonathan and Lombaers, Peter and Kaandorp, Casper and Teijema, Jelle and {van der Kuil}, Timo and Yazan, Berke and Dong, Angie and {van de Schoot}, Rens}}
2024
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