Abstract
Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009–2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.
Funder
European University of the Atlantic
Subject
Health Information Management,Health Informatics,Health Policy,Leadership and Management
Reference78 articles.
1. Haykin, S. Neural Networks and Learning Machines, 3/E, 2009.
2. Artificial neural networks: Fundamentals, computing, design, and application;Basheer;J. Microbiol. Methods,2000
3. Application of artificial neural networks to clinical medicine;Baxt;Lancet,1995
4. Artificial neural networks: A tutorial;Jain;Computer,1996
5. Deep learning for the radiographic detection of periodontal bone loss;Krois;Sci. Rep.,2019
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