Artificial Intelligence in Dental Health Informatics: Advancing Early Diagnosis, Clinical Decision-Making, and Economic Efficiency

Artificial intelligence (AI) is increasingly transforming dental health informatics by integrating advanced computational techniques into clinical decision-making and healthcare management. In dentistry, where diagnosis and treatment planning rely heavily on the interpretation of radiographic and clinical data, AI technologies—particularly machine learning and deep learning algorithms—have demonstrated considerable potential in improving diagnostic accuracy and efficiency. Recent studies have reported that deep learning models applied to dental radiographs can achieve diagnostic accuracies ranging from 85–94% in detecting dental caries and other radiographic pathologies, often performing at levels comparable to experienced clinicians. This review provides a comprehensive overview of the current applications of artificial intelligence in dental health informatics, with particular emphasis on diagnostic imaging, clinical decision-support systems, and predictive analytics for treatment planning. The review evaluates the performance of AI-assisted diagnostic tools in comparison with clinician interpretation and examines their role in improving workflow efficiency and reducing diagnostic variability in dental practice. In addition, key barriers to the clinical implementation of AI technologies are discussed, including challenges related to data quality, algorithm transparency, ethical considerations, patient privacy, and limitations in digital infrastructure. The economic implications of AI integration in dentistry are also examined, particularly with respect to cost–benefit considerations and the long-term value of early disease detection in preventive dental care. Evidence suggests that AI-assisted diagnostic systems may improve early detection of oral diseases, optimize resource utilization, and support evidence-based clinical decision-making. Although artificial intelligence holds significant promise for advancing dental health informatics and improving the quality of oral healthcare delivery, its most effective role is as a supportive tool that augments clinician expertise rather than replacing clinical judgment. Careful consideration of ethical, regulatory, and implementation challenges will therefore be essential for the safe and effective integration of AI technologies into routine dental practice. This review underscore a comprehensive understanding of how AI technologies can support clinicians, enhance diagnostic accuracy, and contribute to the advancement of dental health informatics.