Abstract

Background: Artificial intelligence (AI) is increasingly being integrated into healthcare, including dentistry, with promising applications in diagnostics, treatment planning, and patient management. Paediatric dentistry presents unique challenges that may benefit from AI-driven solutions. Objective: This systematic review aims to evaluate the current applications, accuracy, and effectiveness of artificial intelligence in paediatric dentistry, and to identify existing limitations and future directions. Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore for studies published, using keywords related to “artificial intelligence,” “machine learning,” and “paediatric dentistry.” Studies were screened and selected according to PRISMA guidelines. Inclusion criteria encompassed peer-reviewed original research articles involving AI applications in children’s dental care. Data on study characteristics, AI techniques used, target dental conditions, performance metrics, and outcomes were extracted and synthesized. Results: A total of 15 studies met the inclusion criteria. AI applications in paediatric dentistry primarily focused on diagnostic imaging (e.g., caries detection, growth assessment), behaviour prediction, and treatment planning. Convolutional neural networks (CNNs) and other machine learning algorithms showed high accuracy in image-based diagnostics, with reported accuracies ranging from 87%-96%. However, heterogeneity in study design and small sample sizes limited the generalizability of findings. Conclusions: AI demonstrates considerable potential in improving diagnostic accuracy and clinical decision-making in paediatric dentistry. Nevertheless, further high-quality, standardized studies are needed to validate AI tools and ensure their safety, ethical integration into clinical practice.

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