A Chronological Narrative Review of AI Evolution in Dentistry

Abstract

Artificial intelligence (AI) has catalyzed a paradigm shift in dentistry, progressively transforming diagnostic accuracy, treatment planning and clinical work flows through advanced computational models, real-time image analysis, predictive analytics and autonomous robotic systems.This study presents a comprehensive chronological review of AI's integration into dental practice, delineating its evolution across five developmental stages.Early applications in the 1980s and 1990s were confined to rule-based expert systems and rudimentary CAD/CAM technologies, providing a nascent foundation for computational dentistry.The subsequent decade witnessed the adoption of machine learning (ML) with artificial neural networks (ANNs) surpassing human performance in caries detection.The proliferation of deep learning (DL) in the early 2010s marked a significant inflection point, as convolutional neural networks (CNNs) demonstrated superior precision in radiographic lesion detection, cephalometric landmarking and oral cancer screening.Between 2016 and 2020, AI achieved clinical validation, exemplified by FDA-cleared diagnostic systems and teledentistry applications, reinforcing its credibility for real-world deployment.The current era (2021-present) has expanded AI's role beyond imaging, introducing predictive analytics, natural language processing (NLP) for automated dental charting and AI-assisted robotic surgery with sub-millimetric precision.Despite these advancements, ethical concerns persist, particularly regarding dataset bias, regulatory oversight and algorithmic accountability.This study calls for the need for interdisciplinary collaboration between dental professionals, computer scientists and policymakers to optimize AI integration while ensuring ethical compliance and clinical reliability.Future research should prioritize AI model generalizability across diverse populations, regulatory standardization and the development of transparent, interpretable AI frameworks to enhance patient outcomes, optimize resource allocation and redefine precision-driven dental care in the digital age.