Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions

Mohammad-Rahimi, Hossein, Sohrabniya, Fatemeh, Ourang, Seyed AmirHossein, 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: Data preparation, clinical applications, ethical considerations, limitations, and future directions. International Endodontic Journal 57 (11) , pp. 1566-1595. 10.1111/iej.14128 Item availability restricted.

PDF - Accepted Post-Print Version Restricted to Repository staff only until 29 July 2025 due to copyright restrictions. Download (893kB)

Abstract

Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.

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:	13 July 2024
Last Modified:	13 Nov 2024 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/172058

Actions (repository staff only)

Edit Item