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      Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review

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          Abstract

          Objective

          The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.

          Materials and Methods

          Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.

          Results

          The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.

          Conclusion

          The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

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          Most cited references53

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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            Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm

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              Is Open Access

              Artificial Intelligence in Dentistry: Chances and Challenges

              The term “artificial intelligence” (AI) refers to the idea of machines being capable of performing human tasks. A subdomain of AI is machine learning (ML), which “learns” intrinsic statistical patterns in data to eventually cast predictions on unseen data. Deep learning is a ML technique using multi-layer mathematical operations for learning and inferring on complex data like imagery. This succinct narrative review describes the application, limitations and possible future of AI-based dental diagnostics, treatment planning, and conduct, for example, image analysis, prediction making, record keeping, as well as dental research and discovery. AI-based applications will streamline care, relieving the dental workforce from laborious routine tasks, increasing health at lower costs for a broader population, and eventually facilitate personalized, predictive, preventive, and participatory dentistry. However, AI solutions have not by large entered routine dental practice, mainly due to 1) limited data availability, accessibility, structure, and comprehensiveness, 2) lacking methodological rigor and standards in their development, 3) and practical questions around the value and usefulness of these solutions, but also ethics and responsibility. Any AI application in dentistry should demonstrate tangible value by, for example, improving access to and quality of care, increasing efficiency and safety of services, empowering and enabling patients, supporting medical research, or increasing sustainability. Individual privacy, rights, and autonomy need to be put front and center; a shift from centralized to distributed/federated learning may address this while improving scalability and robustness. Lastly, trustworthiness into, and generalizability of, dental AI solutions need to be guaranteed; the implementation of continuous human oversight and standards grounded in evidence-based dentistry should be expected. Methods to visualize, interpret, and explain the logic behind AI solutions will contribute (“explainable AI”). Dental education will need to accompany the introduction of clinical AI solutions by fostering digital literacy in the future dental workforce.

                Author and article information

                Contributors
                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi
                2314-6133
                2314-6141
                2021
                22 June 2021
                : 2021
                : 9751564
                Affiliations
                1Prosthodontics Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kota Bharu, Kelantan, Malaysia
                2Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
                3Undergraduate Student Bachelor of Dental Surgery, Dow Dental College, Dow University of Health Sciences, Karachi 74200, Pakistan
                4Research Intern, Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
                5Conservative Dentistry Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kota Bharu, Kelantan, Malaysia
                6Department of Oral Pathology, Bahria University Medical and Dental College, Karachi 75530, Pakistan
                7Department of Preventive Dentistry, College of Dentistry, Jouf University, Sakaka, Al Jouf, 72345, Saudi Arabia
                Author notes

                Academic Editor: Vincenzo Grassia

                Author information
                https://orcid.org/0000-0002-0960-1123
                https://orcid.org/0000-0003-2050-0988
                https://orcid.org/0000-0001-5769-5649
                https://orcid.org/0000-0002-0423-8070
                https://orcid.org/0000-0001-7131-1752
                Article
                10.1155/2021/9751564
                8245240
                34258283
                eae45e5b-69c2-4224-99f0-4b1f6239a81e
                Copyright © 2021 Naseer Ahmed et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 6 May 2021
                : 30 May 2021
                : 5 June 2021
                Funding
                Funded by: Altamash Institute of Dental Medicine
                Funded by: Jouf University
                Funded by: Universiti Sains Malaysia
                Categories
                Review Article

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