Role of Artificial Intelligence and Machine Learning in Conservative Dentistry and Endodontics: A Review

Abstract

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in conservative dentistry and endodontics, revolutionizing diagnostic accuracy, treatment planning, and procedural efficiency. This narrative review explores the applications, methodologies, advantages, and challenges of AI in these fields. AI-driven systems, such as convolutional neural networks (CNNs), excel in analyzing dental imaging, including radiographs and cone-beam computed tomography, to detect caries, periapical lesions, and root canal morphologies with high precision. These technologies streamline tasks such as tooth shade determination and working length measurement, reducing human error and enhancing clinical outcomes. Predictive models utilize patient data to assess the risks of caries progression and endodontic complications, thereby enabling the development of personalized treatment plans. Natural language processing aids in extracting insights from clinical records, while generative adversarial networks enhance dataset quality by creating synthetic images. Despite these advancements, challenges persist, including limited availability of diverse, annotated datasets, which affects model generalizability across populations. The opaque nature of some AI algorithms raises concerns about interpretability, potentially undermining clinician trust. High computational requirements and implementation costs limit accessibility, particularly in resource-constrained settings. Ethical issues, such as patient data privacy and the risk of over-reliance on AI, further complicate adoption. Addressing these barriers requires standardized dental imaging databases, transparent algorithms, and collaboration between dental professionals and data scientists. Future research should focus on improving model explainability, expanding dataset diversity, and integrating AI seamlessly into clinical workflows. By overcoming these challenges, AI and ML hold the potential to become indispensable in conservative dentistry and endodontics, offering precise, efficient, and patient-centered solutions that enhance diagnostic reliability and treatment success, ultimately advancing the quality of dental care. This narrative review aimed to explore the theoretical foundations, historical evolution, and practical applications of AI and ML in conservative dentistry and endodontics, with a focus on their types, methodologies, advantages, and limitations.

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