{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T05:38:02Z","timestamp":1792561082126,"version":"4.5.0"},"reference-count":85,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Australian Dental Journal"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>\n                      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\u2010Analyses Extension for Scoping Reviews (PRISMA\u2010ScR) 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\u2010nine studies were included. Computer vision studies (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200932) most often used panoramic radiographs (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200914), mainly for tooth detection, numbering and segmentation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910) and caries detection (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20099). Non\u2010imaging ML studies (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200918) primarily used clinical variables and questionnaire data (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20098) or salivary\/biological samples (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910). Accuracy ranged from 50% to 100%, with sensitivity reaching 100% in biomarker\u2010based studies. LLM accuracy varied widely (33%\u201396%). Computer vision and non\u2010imaging 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance<\/jats:title>\n                    <jats:p>AI\u2010based tools may support paediatric dentists by improving tooth detection, dental structure assessment, caries detection, disease prediction and risk stratification. Computer vision and non\u2010imaging 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/adj.70076","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T04:37:50Z","timestamp":1792557470000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Paediatric Dentistry: A Scoping Review"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9668-7684","authenticated-orcid":false,"given":"Rata","family":"Rokhshad","sequence":"first","affiliation":[{"name":"Pediatric Dentistry Department Loma Linda University School of Dentistry  Loma Linda California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6260-9992","authenticated-orcid":false,"given":"Antonin","family":"Tichy","sequence":"additional","affiliation":[{"name":"Clinic for Conservative Dentistry, Periodontology and Digital Dentistry LMU University Hospital  Munich Germany"},{"name":"Institute of Dental Medicine, First Faculty of Medicine Charles University  Prague Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8205-3453","authenticated-orcid":false,"given":"Jung\u2010Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Pediatric Dentistry Department Loma Linda University School of Dentistry  Loma Linda California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0985-5308","authenticated-orcid":false,"given":"Sadaf","family":"Taheri","sequence":"additional","affiliation":[{"name":"Loma Linda School of Dentistry  Loma Linda California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4025-6352","authenticated-orcid":false,"given":"Salar","family":"Motamedi","sequence":"additional","affiliation":[{"name":"Endodontics Department Loma Linda School of Dentistry  Loma Linda California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1223-1669","authenticated-orcid":false,"given":"Falk","family":"Schwendicke","sequence":"additional","affiliation":[{"name":"Clinic for Conservative Dentistry, Periodontology and Digital Dentistry LMU University Hospital  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,20]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1177\/0022034520915714","article-title":"Artificial Intelligence in Dentistry: Chances and Challenges","volume":"99","author":"Schwendicke F.","year":"2020","journal-title":"Journal of Dental Research"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2023.104430","article-title":"Deep Learning: A Primer for Dentists and Dental Researchers","volume":"130","author":"Mohammad\u2010Rahimi H.","year":"2023","journal-title":"Journal of Dentistry"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.hybadv.2024.100277","article-title":"AI Revolutionizing Industries Worldwide: A Comprehensive Overview of Its Diverse Applications","volume":"7","author":"Bin Rashid A.","year":"2024","journal-title":"Hybrid Advances"},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","volume":"2","author":"Sarker I. 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