Systematic review and meta-analysis constitute a staple of evidence-based medicine, an obligatory step in developing the guideline and recommendation document. It is a formalized process aiming at extracting and summarizing knowledge from the published work, grading, and considering the quality of the included studies. It is very laborious and time-consuming. Therefore, the meta-analyses are rarely updated and seldom living, decreasing their utility with time. Here, we present a framework for integrating the large language models and natural language processing techniques applied to the previously published systematic review and meta-analysis of the diagnostic test accuracy of the point of care tests. We show that the framework can be used to automate the screening step of the existing meta-analyses with minimal costs to quality and, to a large extent, the extraction step while maintaining the strict nature of the systematic review process.