{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:33:40Z","timestamp":1786116820721,"version":"build-2736575974"},"reference-count":72,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Dent. J.","Dentistry Journal"],"abstract":"<jats:p>Background\/Objective: Artificial intelligence (AI) is increasingly transforming healthcare and has emerged as a promising tool in pediatric dentistry. This narrative review examines the methodological foundations, current applications, limitations, and future directions of AI in pediatric dental practice. Methods: A structured literature search was conducted in PubMed\/MEDLINE, Scopus, Web of Science, and Google Scholar for relevant English-language publications from database inception through to May 2026, using predefined search terms and eligibility criteria. Following screening and full-text assessment, 72 unique publications were retained for the narrative synthesis. Results: AI applications in pediatric dentistry include early childhood caries detection and risk prediction, dental plaque assessment, identification of mesiodens and supernumerary teeth, dental age estimation, automated tooth detection, fissure-sealant evaluation, and craniofacial growth prediction. Several imaging-based models demonstrated performance comparable to experienced clinicians. However, many studies relied on retrospective, single-center datasets and lacked external or prospective validation. Additional concerns included algorithmic bias, data privacy, limited interpretability, and infrastructure requirements. Conclusions: AI has considerable potential to improve diagnostic efficiency, consistency, and personalized preventive care in pediatric dentistry. However, it should be used as a clinical decision-support tool rather than a replacement for professional judgment. Future research should prioritize prospective multicenter validation, multimodal and explainable systems, standardized reporting, and ethical clinical implementation.<\/jats:p>","DOI":"10.3390\/dj14080493","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T11:43:02Z","timestamp":1786016582000},"page":"493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1069-6374","authenticated-orcid":false,"given":"Omar A.","family":"El Meligy","sequence":"first","affiliation":[{"name":"Pediatric Dentistry and Dental Public Health Department, Faculty of Dentistry, Alexandria University, Alexandria 21131, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5969-1323","authenticated-orcid":false,"given":"Ahmed O.","family":"Elmeligy","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0G4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"ref_1","first-page":"12","article-title":"A proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955","volume":"27","author":"McCarthy","year":"2006","journal-title":"AI Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100171","DOI":"10.1016\/j.caeo.2024.100171","article-title":"What are artificial intelligence literacy and competency? 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