Bioengineering· 2026Q2· Review
From Technical Performance to Clinical Readiness: A Scoping Review of Artificial Intelligence in Pediatric Dentistry
- 0citations
- Q2SCImago
- 2026year
Short summary
A scoping review of 179 reports reveals that while AI shows promise in pediatric dentistry for tasks like tooth detection and caries imaging, only 5.6% were externally tested for pediatric applicability, highlighting a significant gap between technical performance and clinical readiness.
AI-generated from the title and abstract; the full text is not read.
Key points
- The review analyzed 179 reports on AI in pediatric dentistry, with tooth detection, risk assessment, and caries imaging being the most common applications.
- Only 5.6% of the included reports (10 out of 179) documented external testing for pediatric applicability.
- Evidence directly linking AI's technical performance to improved pediatric patient care is largely missing.
- Three reports evaluated clinician-AI assistance, with two focusing on technical segmentation and one on diagnosis.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Bioengineering, 2026 · DOI ↗
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