Application of artificial intelligence in pediatric dentistry: a systematic review

The use of artificial intelligence in medicine is controversial. Objective: to synthesize and evaluate current evidence on AI use in pediatric dentistry. Materials and methods. A comprehensive literature search was conducted across PubMed/MEDLINE, eLibrary, etc. The PRISMA framework guided the selection process. Forty-one studies were included in the final analysis. Results. AI applications clustered into: radiographic analysis; intraoral photography interpretation; caries-risk prediction; and virtual assistants for decision support and education. Diagnostic efficiency of programs ranged broadly: accuracy 72—99%, sensitivity 20—100%, specificity 49—100%. Key barriers include heterogeneous datasets, limited real-world validation, and unresolved legal/ethical governance. Conclusion. AI can augment diagnostics, prevention, and training in pediatric dentistry, but should be considered as complement and not replace clinical judgment. Future work should prioritize large multicentre datasets, prospective clinical validation, and robust ethical—legal frameworks to ensure safe, equitable deployment AI in pediatric dentistry.