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dc.contributor.author | Fatima, Anum | |
dc.contributor.author | Shafi, Imran | |
dc.contributor.author | Afzal, Hammad | |
dc.contributor.author | Torre Díez, Isabel de la | |
dc.contributor.author | Río Sola, María Lourdes del | |
dc.contributor.author | Breñosa, Jose | |
dc.contributor.author | Martínez Espinosa, Julio César | |
dc.contributor.author | Ashraf, Imran | |
dc.date.accessioned | 2023-06-28T08:53:17Z | |
dc.date.available | 2023-06-28T08:53:17Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Healthcare, 2022, Vol, 10, Nº. 11, 2188 | es |
dc.identifier.issn | 2227-9032 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/59977 | |
dc.description | Producción Científica | es |
dc.description.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. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Dentistry | es |
dc.subject | Odontología | es |
dc.subject | Artificial intelligence | es |
dc.subject | Inteligencia artificial | es |
dc.subject | Machine learning | es |
dc.subject | Aprendizaje automático | es |
dc.subject | Deep learning (Machine learning) | es |
dc.subject | Artificial intelligence - Medical applications | es |
dc.title | Advancements in dentistry with artificial intelligence: Current clinical applications and future perspectives | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2022 The Authors | es |
dc.identifier.doi | 10.3390/healthcare10112188 | es |
dc.relation.publisherversion | https://www.mdpi.com/2227-9032/10/11/2188 | es |
dc.identifier.publicationfirstpage | 2188 | es |
dc.identifier.publicationissue | 11 | es |
dc.identifier.publicationtitle | Healthcare | es |
dc.identifier.publicationvolume | 10 | es |
dc.peerreviewed | SI | es |
dc.identifier.essn | 2227-9032 | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 32 Ciencias Médicas | es |
dc.subject.unesco | 1203.04 Inteligencia Artificial | es |
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