Artificial Intelligence in Paediatric Dentistry: A Scoping Review

This review scoped artificial intelligence (AI) in paediatric dentistry to summarize current applications and limitations. A comprehensive literature search was completed across PubMed, Google Scholar, Scopus, Embase, IEEE Xplore, and medRxiv/aArXiv. The preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist was used for reporting the present study. Studies concerning paediatric dentistry and utilizing AI, machine learning (ML) or large language models (LLMs) were included. Two independent reviewers screened and three extracted data on AI methodologies, performance metrics, and clinical applications. Fifty-nine studies were included. Computer vision studies (n = 32) most often used panoramic radiographs (n = 14), mainly for tooth detection, numbering and segmentation (n = 10) and caries detection (n = 9). Non-imaging ML studies (n = 18) primarily used clinical variables and questionnaire data (n = 8) or salivary/biological samples (n = 10). Accuracy ranged from 50% to 100%, with sensitivity reaching 100% in biomarker-based studies. LLM accuracy varied widely (33%-96%). Computer vision and non-imaging ML show promise for paediatric dentistry, particularly in the detection of dental structures and diseases, as well as risk stratification. Variable LLM performance underscores the need for standardized evaluation before clinical integration. CLINICAL RELEVANCE: AI-based tools may support paediatric dentists by improving tooth detection, dental structure assessment, caries detection, disease prediction and risk stratification. Computer vision and non-imaging machine learning approaches appear to have potential, but their clinical value depends on robust external validation, transparent reporting and integration into paediatric dental workflows. Given their variable performance, AI tools should be used only as adjunctive tools or a second reviewer.