Artificial intelligence in dentistry

Dentistry Review

Pragya Rajpurohit¹  Mira Ghaly²  Mohamed E. Awad²  Mohamed M. Meghil³  Linah A. Shahoumi³  Joshua McKee⁴ 

[1] Department of Oral Biology and Diagnostic Sciences, Dental College of Georgia, Augusta University, Augusta, GA, USA;Department of Oral Biology and Diagnostic Sciences, Dental College of Georgia, Augusta University, Augusta, GA, USA;Department of Periodontics, Dental College of Georgia, Augusta University, Augusta, GA, USA;Michigan State University- Detroit Medical Center, Detroit, MI, USA;

关键词: Dentistry;    Machine learning;    Artificial intelligence;    Periodontics;    Orthodontics;    Endodontics;   

DOI  :  

来源: DOAJ

【 摘 要 】

Background: Artificial intelligence (AI) has received enormous attention and has gone through a transition stage from being a pure statistical tool to being one of the main drivers of modern medicine. The purpose of this systematic review was to determine the use of this technology in dental specialties. Types of studies reviewed: This systematic review was conducted according to the PRISMA protocol and the Cochrane Handbook for Systematic Reviews of Interventions. Online databases (PubMed, Ovid via Medline, and web of Science) and manual retrieval of cross references were searched. The selection process yielded 28 studies investigating the acceptability, effectiveness, or feasibility of AI models in various dental subspecialties. The methodological quality and risk of bias of the included studies were analyzed and appraised using the Prediction Study Risk of Bias Assessment Tool (PROBAST). Results: The authors included 28 studies that investigated the use of AI in the dental fields. Six studies in periodontics reported 480 training data sets and 171 test data sets. Eight studies in orthodontics reported 1336 training and 80 test data sets. Five studies in prosthodontics reported 4659 training and 1759 test data sets. Five studies in oral medicine and pathology reported 1151 training and 68 test data sets. Two studies in maxillofacial surgery reported 47 training data sets. Three studies in endodontics reported 142 training and 103 test data sets. Conclusions and practical implications: AI represents an effective approach to analyze clinical dental data. Further studies, including randomized clinical trials, are needed to confirm the value of this concept in dental practice with the goal of providing data-driven, high performance dental care that can rapidly improve the science, economics, and delivery of optimum treatment options for patients.

【 授权许可】

Unknown