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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 Michael Howell; Duncan, Henry F; Nosrat, Ali.
Afiliación
  • Mohammad-Rahimi H; Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Sohrabniya F; Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Ourang SA; Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Dianat O; Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, Maryland, USA.
  • Aminoshariae A; Private Practice, Irvine Endodontics, Irvine, California, USA.
  • Nagendrababu V; Department of Endodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
  • Dummer PMH; Department of Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, UAE.
  • Duncan HF; School of Dentistry, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
  • Nosrat A; Division of Restorative Dentistry, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland.
Int Endod J ; 2024 Jul 29.
Article en En | MEDLINE | ID: mdl-39075670
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.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Int Endod J Año: 2024 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Int Endod J Año: 2024 Tipo del documento: Article País de afiliación: Alemania