Machine learning for identifying randomised controlled trials when conducting systematic reviews: Development and evaluation of its impact on practice.

ABSTRACT: Machine learning (ML) models have been developed to identify randomised controlled trials (RCTs) to accelerate systematic reviews (SRs). However, their use has been limited due to concerns about their performance and practical benefits. We developed a high-recall ensemble learning model using Cochrane RCT data to enhance the identification of RCTs for rapid title and abstract screening in SRs and evaluated the model externally with our annotated RCT datasets. Additionally, we assessed the practical impact in terms of labour time savings and recall improvement under two scenarios: ML-assisted double screening (where ML and one reviewer screened all citations in parallel) and ML-assisted stepwise screening (where ML flagged all potential RCTs, and at least two reviewers subsequently filtered

SUBMITTER: Qin X 

PROVIDER: S-EPMC12527483 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature