Objectives: This review aimed to explore the emerging role and applications of artificial intelligence (AI) in paediatric dentistry, focusing on its potential to enhance diagnostic accuracy, optimize treatment planning, and improve the overall patient experience in children. Methods: A narrative review of the literature was conducted, focusing on studies utilizing machine learning and deep learning techniques in paediatric dental care. Key areas reviewed included AI applications in caries detection, enamel defect identification, radiographic analysis, malocclusion prediction, and behaviour management. The role of AI in clinical workflows, including documentation, triage, tele-dentistry, and administrative automation, was also examined. Results: AI technologies, particularly convolutional neural networks (CNNs) and other deep learning models, have demonstrated high accuracy in detecting early carious lesions and enamel defects from bitewing radiographs and intraoral images. Advanced segmentation models have improved interpretation of CBCT and panoramic imaging, while predictive algorithms have shown promise in early malocclusion screening and treatment timing. Additionally, AI-driven tools support personalized treatment planning based on individual risk profiles and enhance patient engagement through interactive technologies such as virtual reality for anxiety management. Administrative applications, including scheduling and resource optimization, further streamline paediatric dental practice. Conclusions: Artificial intelligence is transforming paediatric dentistry by integrating data-driven precision into clinical decision-making and patient care. Its applications extend beyond diagnostics to include treatment planning, patient management, and workflow optimization. Continued advancements and integration of AI technologies hold significant promise for improving clinical outcomes, enhancing patient experiences, and shaping the future of paediatric dental practice.
Babu et al. (2026) studied this question.