JMIR Publications

ABSTRACT

Background:

Oral diseases have been described by the World Health Organization (WHO) as the most prevalent diseases globally, affecting some 3.5 billion people. This leads to significant health and economic burdens and can impact the quality of life of affected individuals. Therefore, dentists have a great responsibility to efficiently diagnose and determine the best treatment option. However, some do not have the experience and knowledge to make the right clinical decisions. For this reason, artificial intelligence (AI) techniques, mainly rule-based systems, have been used in dentistry to aid physicians in making faster and more reliable decisions.

Objective:

This scoping review aims to explore and summarize the application of rule-based systems widely employed in dentistry and to evaluate their performance and practical significance.

Methods:

We conducted a scoping review following the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 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.

Results:

Of 303 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 rule-based system (n = 16) and (ii) integrating machine learning techniques with the rule-based system (n = 5). The vast majority of included publications used fuzzy logic to manage uncertainty (n = 11). A hybrid fuzzy rule-based system and neural network achieved the highest accuracy of 96%. From a medical perspective, the articles were aimed at diagnosis (n = 11), treatment (n = 3), and both diagnosis and treatment (n = 4), while less attention was paid to detection and classification (n = 1). The review also found that periodontology was the most commonly addressed specialty.

Conclusions:

In an analysis of the current literature, rule-based systems were found reliable to assist dental practitioners in decision-making. Clinical decision-making involves a high level of uncertainty, which explains the tendency to use fuzzy logic in rule-based systems. These systems can also be used as educational tools primarily for both dental interns and less experienced general dentists to aid in making reliable decisions.