Detecting false exclusions in single-reviewer literature screening by using AI tools as secondary reviewers

Abstract

BACKGROUND: Systematic reviews are fundamental to evidence-based medicine, but the process of screening studies is time-consuming and prone to errors, especially when conducted by a single reviewer. False exclusions of relevant studies can significantly impact the quality and reliability of reviews. Artificial intelligence (AI) tools have emerged as secondary reviewers in detecting these false exclusions, yet empirical evidence comparing their performance is limited.

METHODS: This study protocol outlines a comprehensive evaluation of four AI tools (ASReview, DistillerSR Artificial Intelligence System [DAISY], Evidence for Policy and Practice Information [EPPI]-Reviewer, and Rayyan) in their capacity to act as secondary reviewers during single-reviewer title and abstract screening for systematic reviews. Utilizing a database of single-reviewer screening decisions from two published systematic reviews, we will assess how effective AI tools are at detecting false exclusions while assisting single-reviewer screening compared to the dual-reviewer reference standard. Additionally, we aim to determine the overall screening performance of AI tools in assisting single-reviewer screening.

DISCUSSION: This research seeks to provide valuable insights into the potential of AI-assisted screening for detecting falsely excluded studies during single screening. By comparing the performance of multiple AI tools, we aim to guide researchers in selecting the most effective assistive technologies for their review processes.

SYSTEMATIC REVIEW REGISTRATION: (Open Science Framework): https://osf.io/dky26.