Machine learning for accelerating screening in evidence reviews

Abstract

Evidence reviews are important for informing decision-making and primary research, but they can be time-consuming and costly. With the advent of artificial intelligence, including machine learning, there is an opportunity to accelerate the review process at many stages, with study screening identified as a prime candidate for assistance.Despite the availability of a large number of tools promising to assist with study screening, these are not consistently used in practice and there is skepticism about their application. Single-arm evaluations suggest the potential for tools to reduce screening burden. However, their integration into practice may need further investigation through evaluations of outcomes such as overall resource use and impact on review findings and recommendations. Because the literature lacks comparative studies, it is not currently possible to determine their relative accuracy.In this commentary, we outline the published research and discuss options for incorporating tools into the review workflow, considering the needs and requirements of different types of review.

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Efficiency and Workload Reduction of Semi-automated Citation Screening Software for Creating Clinical Practice Guidelines: A Prospective Observational Study

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