The AI Revolution in Conservative Dentistry and Endodontics: A Narrative Review
Blessie R
*
Department of Conservative Dentistry and Endodontics, Adhiparasakthi Dental College and Hospital affiliated to the Tamil Nadu Dr.M.G.R Medical University, Melmaruvathur, India.
Sriram S
Department of Conservative Dentistry and Endodontics, Adhiparasakthi Dental College and Hospital affiliated to the Tamil Nadu Dr.M.G.R Medical University, Melmaruvathur, India.
Bharath N
Department of Conservative Dentistry and Endodontics, Adhiparasakthi Dental College and Hospital affiliated to the Tamil Nadu Dr.M.G.R Medical University, Melmaruvathur, India.
Veni Ashok B
Department of Conservative Dentistry and Endodontics, Adhiparasakthi Dental College and Hospital affiliated to the Tamil Nadu Dr.M.G.R Medical University, Melmaruvathur, India.
*Author to whom correspondence should be addressed.
Abstract
Background: Artificial Intelligence (AI), introduced by John McCarthy in 1956, simulates human cognitive functions such as reasoning and decision-making. Its integration in dentistry, particularly in conservative dentistry and endodontics, has significantly advanced diagnostics, treatment planning, and patient care.
Objective: This review explore the applications and clinical utility of AI in conservative dentistry and endodontics, highlighting its current impact and potential.
Methods: This narrative review synthesizes evidence from studies, AI model applications, and dental technologies. It categorizes AI by capability and functionality and explores its key subdomains—machine learning and deep learning. The clinical workflow and role of AI in various procedures are described.
Results: In conservative dentistry, AI is utilized for early caries detection, shade matching, caries risk prediction, restorative material selection, crown failure prediction, and finish line detection. Techniques like convolutional neural networks (CNNs), support vector machines (SVMs), and case-based reasoning systems demonstrate high diagnostic accuracy (up to 100%). In endodontics, AI aids in detecting periapical lesions and root fractures, determining working length, analyzing root morphology, supporting regenerative therapies, and predicting retreatment needs. CNNs applied to radiographic images have shown accuracy levels up to 97%, often exceeding human performance.
Conclusion: AI represents a transformative tool in conservative and endodontic dentistry, significantly enhancing diagnostic precision, predictive capability, and clinical workflow efficiency. However, limitations such as dependency on large datasets, lack of transparency in deep learning models, integration challenges, and ethical concerns must be addressed. Future advancements should focus on standardization, interdisciplinary collaboration, and clinician training to ensure AI serves as an effective adjunct to professional expertise.
Keywords: Artificial intelligence, machine learning, deep learning, artificial neural network, convolutional neural network