Abstract:
: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.
journal_name
J Dent Resjournal_title
Journal of dental researchauthors
Schwendicke F,Samek W,Krois Jdoi
10.1177/0022034520915714subject
Has Abstractpub_date
2020-07-01 00:00:00pages
769-774issue
7eissn
0022-0345issn
1544-0591journal_volume
99pub_type
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abstract::Bone marrow-derived mesenchymal stem/progenitor cells (BMSCs) are commonly used in regeneration therapy. The current primary source of BMSCs is the iliac crest; however, the procedure is associated with various burdens on the patient, including the risk of pain and infection. Hence, the possibility to collect BMSCs fr...
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abstract::A number of studies have been made investigating the properties of dental prosthodontic soft polymer materials. In such materials, which are used as short-term denture soft liners, the polymer component is complexed with one or more plasticizers. This lowers the glass transition temperature, Tg, of the polymer by allo...
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journal_title:Journal of dental research
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