Comparing Artificial Intelligence and manual methods in systematic review processes: protocol for a systematic review

Abstract

<h2>Abstract</h2><h3>Objectives</h3> This systematic review aims to evaluate the effectiveness of automated methods using artificial intelligence (AI) in conducting systematic reviews, with a focus on both performance and resource utilization compared to human reviewers. <h3>Study Design and Setting</h3> This systematic review and meta-analysis protocol follows the Cochrane Methodology protocol and review guidance. We searched five bibliographic databases to identify potential studies published in English from 2005. Two independent reviewers will screen the titles and abstracts, followed by a full-text review of the included articles. Any discrepancies will be resolved through discussion and, if necessary, referral to a third reviewer. The risk of bias (RoB) in included studies will be assessed at the outcome level using the revised Cochrane risk-of-bias tool for randomized trials and the RoB In Non-randomized Studies - of Interventions for non-randomized studies. Where appropriate, we plan to conduct meta-analysis using random-effects models to obtain pooled estimates. We will explore the sources of heterogeneity and conduct sensitivity analyses based on prespecified characteristics. Where meta-analysis is not feasible, a narrative synthesis will be performed. <h3>Results</h3> We will present the results of this review, focusing on performance and resource utilization metrics. <h3>Conclusion</h3> This systematic review will evaluate the effectiveness of automated methods, especially AI tools in systematic reviews, aiming to synthesize current evidence on their performance, resource utilization, and impact on review quality. The findings will inform evidence-based recommendations for systematic review authors and developers on implementing automation tools to optimize review efficiency while maintaining methodological rigor. In addition, we will identify key research gaps to guide future development of AI-assisted systematic review methods. <h3>Plain Language Summary</h3> A systematic review is a thorough and organized summary of all relevant studies on a specific topic. These reviews are important for gathering evidence to guide health care decisions, but they often take a lot of time and effort. Recently, tools using artificial intelligence (AI) have been developed to speed up this process. We will conduct a systematic review to see how well these AI tools perform compared to human reviewers. We will examine studies from 2005 that have used AI to conduct systematic reviews. We will assess how well AI tools find the right information, how much time and work they save, and how easy and reliable they are for users. This study aims to help researchers choose the best AI tools to make systematic reviews faster and more efficient without losing quality.

Ähnliche Arbeiten