Akademik Veri Yönetim Sistemi
THE FUTURE OF DENTISTRY AI APPLICATIONS AND CHALLENGES, Prof. Dr. FUNDA BAYINDIR, Editör, Turkiye Klinikleri, Ankara, ss.3-14, 2026
The integration of artificial intelligence (AI) in modern dentistry represents a paradigm shift, sig-nificantly enhancing diagnostic capabilities, refining treatment planning and optimizing opera-tional efficiency. This chapter outlines the historical development and underlying principles of AI, tracing its evolution from fundamental theoretical concepts in computer science to its current ap-plications in healthcare. This transformation is primarily driven by advances in machine learning (ML), particularly through deep learning (DL) systems that utilize convolutional neural networks (CNNs). These tools enable the automated analysis of complex dental imaging data, including panoramic radiographs and CBCT scans. They can identify issues such as caries, periodontal dis-ease and periapical lesions with an accuracy comparable to that of expert clinicians. They are also used in other areas, such as identifying landmarks in orthodontics, planning dental implants, and forensic dentistry, thus supporting a wide range of dental specialties. The discussion also examines specific AI-powered platforms such as Overjet, VideaHealth and Denti.AI. These systems demon-strate how such algorithms are being translated into real-world clinical use, helping to standardize scan interpretation and reduce subjectivity in diagnosis. This improves the consistency of radio-graphic evaluations and reduces differences in judgement between practitioners, leading to more uniform, evidence-based patient care. However, integrating this technology presents considerable ethical and practical challenges. The most pressing concerns include algorithmic bias, which is often caused by training data that is not fully representative, the inherent lack of transparency in complex ‘black box’ models, and the risk that clinicians might become overly dependent on them. Strong countermeasures are required to address these challenges, such as adopting explainable AI (XAI) principles and undergoing validation by independent third parties. Looking ahead, potential future developments include federated learning, which enables collaborative algorithm training while preserving privacy. AI has significant potential to work synergistically with teledentistry to improve access to care and to transform the education of future dentists through AI-powered vir-tual reality (VR). These innovations not only promise to elevate clinical standards, but also to ad-dress long-standing disparities in oral healthcare delivery. While AI holds immense potential to advance patient care and operational efficiency, the chapter concludes that its responsible integra-tion demands a critically aware, ethically-grounded approach to navigate its limitations. To harness the full benefits of AI across the spectrum of dental public health and clinical practice, interdisci-plinary collaboration, continuous validation and equitable implementation strategies are essential, ensuring that these technological advancements translate into meaningful improvements in patient outcomes and accessibility.