Aya Dhahri
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Aya Dhahri has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Angelo Aliberti
Department of Neuroscience, Reproductive Science and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini, 5, 80138 Naples, Italy
1 shared publication - Faten Ben Amor
Oral and Facial Rehabilitation Research Laboratory, Faculty of Dentistry, University of Monastir, Monastir 5000, Tunisia
1 shared publication - Faten Khanfir
Oral and Facial Rehabilitation Research Laboratory, Faculty of Dentistry, University of Monastir, Monastir 5000, Tunisia
1 shared publication - Ghada Neji
Oral and Facial Rehabilitation Research Laboratory, Faculty of Dentistry, University of Monastir, Monastir 5000, Tunisia
1 shared publication - Gilberto Sammartino
Department of Neuroscience, Reproductive Science and Dentistry, University of Naples Federico II, 80131 Naples, Italy
1 shared publication - Maria Domenica Campana
Department of Neuroscience, Reproductive Science and Dentistry, University of Naples Federico II, 80131 Naples, Italy
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 27 December 2025
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 15 (1) , 228
AI-Powered Predictive Models in Implant Dentistry: Planning, Risk Assessment, and Outcomes
Artificial intelligence (AI) is rapidly transforming the landscape of dental implantology by enhancing every stage of treatment, from diagnostics and digital planning to intraoperative navigation, outcome prediction, and long-term follow-up. This narrative review explores the current and emerging applications of AI technologies in implant dentistry, with a focus on machine learning, neural networks, and computer vision. It examines how AI is utilized in digital implant planning, surgical navigation, peri-implant disease monitoring, risk assessment, and the prediction of treatment outcomes such as peri-implantitis and implant failure. These innovations contribute to more efficient workflows, more personalized treatment strategies, and improved cost-effectiveness of care. Finally, future perspectives and educational implications of AI integration in clinical implantology are discussed.
- 27 December 2025
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 15 (1) , 228
Top Cited PapersOpen AccessAI-Powered Predictive Models in Implant Dentistry: Planning, Risk Assessment, and Outcomes
Artificial intelligence (AI) is rapidly transforming the landscape of dental implantology by enhancing every stage of treatment, from diagnostics and digital planning to intraoperative navigation, outcome prediction, and long-term follow-up. This narrative review explores the current and emerging applications of AI technologies in implant dentistry, with a focus on machine learning, neural networks, and computer vision. It examines how AI is utilized in digital implant planning, surgical navigation, peri-implant disease monitoring, risk assessment, and the prediction of treatment outcomes such as peri-implantitis and implant failure. These innovations contribute to more efficient workflows, more personalized treatment strategies, and improved cost-effectiveness of care. Finally, future perspectives and educational implications of AI integration in clinical implantology are discussed.