Applications of Rule-based Systems in Dental Decision Making: Scoping Review

Abstract

This scoping review aims to explore and summarize the application of rule-based systems (RBSs) widely employed
in dentistry and to evaluate their performance and practical significance. We conducted a scoping review following the
methodology of PRISMA Extension for Scoping Reviews (PRISMA-ScR) on five databases: Web of Science, Scopus, Google
Scholar, Saudi Digital Library, and the IEEE Xplore. We searched for literature published in English up to October 2021. Two
reviewers evaluated each potentially relevant study for inclusion/exclusion criteria, and any discrepancies were resolved by a third researcher. Out of 303 searched studies, 19 fulfilled this review’s inclusion criteria. We identified two domains based on the methodology used in the included studies: (i) uncertainty management approaches employed in the RBS (n = 16) and (ii)
integrating machine learning techniques with the RBS (n = 5). The vast majority of included publications used fuzzy logic to
manage uncertainty (n = 11). A hybrid fuzzy RBS and neural network achieved the highest accuracy of 96%. The review also
found that periodontology was the most commonly addressed specialty. In an analysis of the current literature, RBSs were found reliable in assisting dental practitioners in decision-making. Clinical decision-making involves a high level of uncertainty, which explains the tendency to use fuzzy logic in RBSs. These systems can also be used as educational tools primarily for both
undergraduate dental students and less experienced dentists (e.g., dental interns, postgraduate, and junior dentists) to aid in making reliable decisions.