Simulation-based Active Learning for Systematic Reviews: A Scoping Review of Literature

Abstract

This scoping review investigated the utility of active learning in accelerating the systematic review process. We reviewed literature from 2006 onwards and selected 60 relevant articles out of 1887 screened, while incorporating 238 out of 336 collected datasets. Our analysis of the articles found that, despite some limitations, all 60 articles provided positive recommendations for using active learning to improve the efficiency of the screening phase in systematic reviews. Future research should focus on standardizing metrics, promoting open data, and diversifying models to advance active learning in systematic reviews.

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.