Artificial intelligence and technology in paediatric dentistry: a review
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20262763Keywords:
3D printing, Artificial intelligence, Digital therapeutics, Diagnostics, Pediatric dentistry, RoboticsAbstract
The field of pediatric dentistry is undergoing a paradigm shift, moving from a reactive model to one that embraces precision, prevention and the power of Artificial Intelligence (AI) and Digital Technologies (DT). These innovations offer exciting opportunities to transform diagnosis, management and treatment of paediatric oral health conditions. This study evaluates the role of AI and DT in pediatric dentistry, with emphasis on diagnostics, behavioral management, prevention and orthodontic planning. A thorough evaluation of the literature was carried out using Google Scholar, PubMed, Scopus and the Cochrane Library. A comprehensive review was conducted of peer-reviewed publications, clinical trials and meta-analyses published between 2015 and 2026 and included 38 studies. Four areas of evidence were combined: computer-aided orthodontic planning, digital behavioral therapies, smart preventative tracking and automated diagnostics. Deep learning (DL) models, including convolutional neural network (CNN), you only look once version 8 (YOLOv8) and inception residual network version 2 (Inception-ResNet-v2), demonstrated specialist-level accuracy in early caries detection, pathology mapping and cervical vertebral maturation staging. Virtual reality (VR) reduced procedural anxiety, while social robotics improved patient cooperation. Internet of Things (IoT)-enabled smart toothbrushes and machine learning models (ML) supported remote monitoring and personalized prevention; whereas intraoral scanning combined with three-dimensional (3D) printing enabled rapid fabrication of high-precision pediatric dental appliances. AI and DT are transforming pediatric dentistry by improving diagnostic accuracy, patient engagement, preventive care and treatment efficiency. However, their wider adoption requires robust datasets, standardized validation and multicenter clinical studies to ensure safe and effective implementation.
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