Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks

Ourang, Seyed AmirHossein, Sohrabniya, Fatemeh, Mohammad-Rahimi, Hossein, Dianat, Omid, Aminoshariae, Anita, Nagendrababu, Venkateshbabu, Dummer, Paul M.H. ORCID: https://orcid.org/0000-0002-0726-7467, Duncan, Henry F. and Nosrat, Ali 2024. Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks. International Endodontic Journal 57 (11) , pp. 1546-1565. 10.1111/iej.14127 Item availability restricted.

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Abstract

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Dentistry
Publisher:	Wiley
ISSN:	0143-2885
Date of First Compliant Deposit:	12 September 2024
Date of Acceptance:	16 July 2024
Last Modified:	13 Nov 2024 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/172053

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