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Article: Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging

TitlePersonalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging
Authors
Keywordsartificial intelligence
deep learning
dentistry
diagnostic imaging
personalized medicine
Issue Date2023
Citation
Dentomaxillofacial Radiology, 2023, v. 52, n. 1, article no. 20220335 How to Cite?
AbstractPersonalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one’s biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.
Persistent Identifierhttp://hdl.handle.net/10722/329907
ISSN
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 0.816
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHung, Kuo Feng-
dc.contributor.authorYeung, Andy Wai Kan-
dc.contributor.authorBornstein, Michael M.-
dc.contributor.authorSchwendicke, Falk-
dc.date.accessioned2023-08-09T03:36:21Z-
dc.date.available2023-08-09T03:36:21Z-
dc.date.issued2023-
dc.identifier.citationDentomaxillofacial Radiology, 2023, v. 52, n. 1, article no. 20220335-
dc.identifier.issn0250-832X-
dc.identifier.urihttp://hdl.handle.net/10722/329907-
dc.description.abstractPersonalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one’s biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.-
dc.languageeng-
dc.relation.ispartofDentomaxillofacial Radiology-
dc.subjectartificial intelligence-
dc.subjectdeep learning-
dc.subjectdentistry-
dc.subjectdiagnostic imaging-
dc.subjectpersonalized medicine-
dc.titlePersonalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1259/dmfr.20220335-
dc.identifier.pmid36472627-
dc.identifier.scopuseid_2-s2.0-85144589002-
dc.identifier.volume52-
dc.identifier.issue1-
dc.identifier.spagearticle no. 20220335-
dc.identifier.epagearticle no. 20220335-
dc.identifier.eissn1476-542X-
dc.identifier.isiWOS:000905725500006-

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