Value and Credibility of Meta-Analysis: Tutorial on Enhancing Methodological Rigor and AI-Powered Efficiency

The value of a meta-analysis is based on its methodological and statistical rigor, yet many published systematic reviews and meta-analyses contain statistical shortcomings that limit their utility for clinical practice and public health. This can make it challenging to aggregate data for treatment choices for patients as well as limit the extent to which policymakers can promote social change and improve public health. This challenge is compounded by the traditionally slow and resource-intensive nature of systematic reviews, which delays the translation of vital evidence. In this tutorial, we address both challenges. We first provide a primer on essential statistical techniques to help authors produce more robust and reliable meta-analyses. We then briefly discuss the growing role of artificial intelligence (AI) in automating tasks in systematic literature reviews and meta-analyses. Ethical use and disclosure of AI in supporting these essential tasks are also important considerations. This guide is intended to help authors enhance the rigor of their work and use new technologies to ensure their findings are both trustworthy and timely.