Title: Artificial intelligence in dentistry: Overcoming diagnostic challenges in modern practice
Abstract:
Artificial intelligence (AI) is rapidly reshaping dentistry, moving from a promising innovation to a practical clinical aid across multiple areas of diagnosis and treatment planning. In routine dental practice, diagnosis is often complicated by subtle radiographic findings, anatomical structures that resemble pathology, incidental findings, time pressure, and interpretive variability. Together, these challenges can contribute to misdiagnoses, missed diagnoses, inappropriate interventions, and inconsistent clinical decisions. AI-based systems are increasingly being developed to address these challenges across a wide range of dental imaging modalities, including panoramic, periapical, bitewing, cephalometric, and cone beam computed tomography (CBCT) examinations. By supporting the visualization of anatomical landmarks, highlighting abnormalities, performing automated measurements, and assisting with structured radiographic interpretation, AI can enhance diagnostic consistency and help clinicians distinguish normal anatomy from true pathology more effectively. Beyond diagnosis, these tools also offer value in patient communication by making radiographic findings more understandable and visually accessible during chairside discussions. This presentation explores how contemporary AI applications can help overcome some of the most common diagnostic barriers in dentistry and examines their potential to improve accuracy, efficiency, and clinical confidence while reducing overtreatment, missed incidental findings, and observer variability. It also considers the current limitations of AI adoption, including integration into clinical workflows, interpretability, and the need for responsible implementation. By positioning AI as an adjunct rather than a replacement for clinical judgment, this presentation highlights how innovation can be translated into meaningful clinical impact.
