From Technical Performance to Clinical Readiness: A Scoping Review of Artificial Intelligence in Pediatric Dentistry

This scoping review mapped artificial intelligence (AI) applications in pediatric dentistry and evidence supporting clinical use. Searches covering 2022–2026 combined 957 records from six databases, 109 from a focused corrective search and 22 supplementary reports. Screening and record reconciliation yielded 179 included reports. Eight evaluation characteristics were mapped using six reporting states, with descriptive performance comparisons and exploratory sensitivity analyses. The largest domains were tooth detection, numbering and segmentation (38; 21.2%), risk, biomarkers and prevention (34; 19.0%), and caries imaging and developmental/age assessment (26 each; 14.5%). Ten reports (5.6%) documented external testing of varying independence and pediatric applicability; 22 included human or traditional-method comparators. Three evaluated clinician–AI assistance: two in technical segmentation and one in diagnosis. Ten examined workflow or use, three probability evaluation, eighteen subgroup performance or fairness, and seven patient-related outcomes. Main-article evidence was available for 153 reports; 26 relied on abstracts or excerpts. The main translational gap is evidence connecting technical performance to improved pediatric care. Priorities are prospective evaluation of diagnostic decisions and correction workload, independent calibration of risk models, and sustained patient outcomes. Revision-stage search restrictions and uneven source access limit generalization beyond the mapped evidence.