Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review
TL;DR
This review aims to identify current evidence concerning AI use during preliminary SLR reference screening and describes characteristics such as the different metrics used for reporting performance and how the different algorithms, pipelines, workflows or web applications are validated.
Abstract
Artificial intelligence (AI) is a branch of technology enabling machines to emulate complex human skills; it can also entail problem‐solving using bioinspired methods. It is used for automating systematic literature reviews (SLR), that is, defining a clinical question, locating relevant literature, preliminary screening, study evaluation, data extraction and analysis. Title and abstract screening is one of the most time‐consuming and error‐prone phases involved in developing a systematic review. While AI promises to expedite this process, adopting it faces challenges due to concerns about compatibility and transparency. This review aims to identify current evidence concerning AI use during preliminary SLR reference screening; it describes characteristics such as the different metrics used for reporting performance and how the different algorithms, pipelines, workflows or web applications are validated. AI resource users' reflections regarding SLR screening automation have also been summarized.