Abstract
The growing number of published academic literature poses challenges to the research community which struggles to keep up with the vast amount of publications through traditional research methods that are highly manual in nature. Researchers are struggling to determine the most relevant research gaps, yielding insignificant publications that constitute a waste of resources. As a consequence, AI applications are being applied increasingly to automate and facilitate the review process of these vast amounts of papers. However, scholars have so far only addressed a limited number of scientific fields and focused their efforts on one end of the spectrum in automating systematic literature reviews (SLRs). Yet, these are not sufficient to cover the full range of research questions and available data sources. This paper offers a comparative study of systematic and semi-systematic literature reviews to determine the potential of AI applications in both types of literature review processes. The analysis addresses the status quo and discusses apparent limitations of AI to automate reviews. Results are synthesized in proposing a new tool integrating various AI applications along the research process that improve the speed, quality, and cost-efficiency of the overall research process. © 2022 IEEE.