{
  "abstract": "Background and aims\n\nArtificial intelligence is increasingly applied to pediatric dental imaging, caries detection, orthodontic decision support, age estimation, and risk prediction. However, pediatric evidence remains fragmented across clinical tasks, model types, validation strategies, and reported metrics. This systematic scoping review mapped the clinical applications, methodological characteristics, and reported performance of AI in pediatric dentistry.Methods\n\nPubMed, Scopus, Web of Science, and Google Scholar were searched for English-language studies published from January 2010 to May 2026. Original studies evaluating AI, machine learning, or deep learning in pediatric dentistry or pediatric-relevant dental tasks were included. Screening was performed using Rayyan. Data extraction captured study characteristics, pediatric relevance, AI method, clinical task, outcomes, and validation approach. Risk of bias was assessed using QUADAS-3 for diagnostic/classification studies and PROBAST + AI for prediction and model-development studies. Findings were synthesized descriptively according to PRISMA-ScR.Results\n\nThirty-five studies were included, comprising 19 (54.3%) pediatric-only studies and 16 (45.7%) broader or mixed-age studies with separately identifiable pediatric or pediatric-relevant evidence. By primary application, 15 (42.9%) addressed orthodontic or craniofacial assessment and decision support, eight (22.9%) risk prediction or biomarker modeling, five (14.3%) diagnostic imaging or dental-anomaly detection, five (14.3%) dental age estimation, and two (5.7%) caries or plaque detection. Validation was internal or test-set based in 26 studies (74.3%), observer- or manual-comparative in five (14.3%), and not clearly defined in four (11.4%); none reported independent external validation. For QUADAS-3, non-low-risk judgments were most frequent in the analysis domain (8/19, 42.1%), whereas for PROBAST + AI they were most frequent in the participants and data-sources domain (5/16, 31.3%).Conclusion\n\nAI shows promising pediatric dental applications, especially in image-based and decision-support tasks, but evidence remains heterogeneous, task-specific, and limited by variable validation, incomplete reporting, and scarce external testing.",
  "abstract_shown": true,
  "affiliations": [
    "Otolaryngology Department of Taleghani Hospital, Students Research Committee Shahid Beheshti University of Medical Sciences Tehran Iran.",
    "School of Medicine Iran University of Medical Sciences Tehran Iran.",
    "School of Medicine Kerman University of Medical Sciences Tehran Iran.",
    "School of Dentistry Shahid Beheshti University of Medical Sciences Tehran Iran.",
    "Student Research Committee, Dental School Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran.",
    "Department of Nursing, Faculty of Nursing and Midwifery, Mashhad Medical Sciences Islamic Azad University Mashhad Iran.",
    "Oral and Dental Diseases Research Center Kerman University of Medical Sciences, Kerman, Iran.",
    "Student Research Committee, School of Dentistry Semnan University of Medical Sciences Semnan Iran.",
    "Department of Pediatric Dentistry, School of Dentistry Tehran University of Medical Sciences Tehran Iran.",
    "School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran.",
    "Restorative Department, Dental School Tehran University of Medical Sciences Tehran Iran.",
    "Student Research Committee, School of Medicine Guilan University of Medical Sciences Rasht Iran.",
    "Student Research Committee Fasa University of Medical Sciences Fasa Iran.",
    "School of Dentistry Belarusian State Medical University (BSMU), Minsk, Belarus."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 17,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Aref Zarei"
    },
    {
      "affs": [
        1
      ],
      "name": "Amirhossein Ehsani"
    },
    {
      "affs": [
        2
      ],
      "name": "Amir Ebrahimzedeh Pasha"
    },
    {
      "affs": [
        3
      ],
      "name": "Mansour Gorjizad"
    },
    {
      "affs": [
        4
      ],
      "name": "Majid Aryannejad"
    },
    {
      "affs": [
        5
      ],
      "name": "Seyedeh Sara Rezazadeh Shojaee",
      "orcid": "0000-0002-6736-4306"
    },
    {
      "affs": [
        6
      ],
      "name": "Maedeh Barkhordari Dashtkhaki"
    },
    {
      "affs": [
        7
      ],
      "name": "Sharif Ebrahimi"
    },
    {
      "affs": [
        7
      ],
      "name": "Mohamad Mahdi Aghajany"
    },
    {
      "affs": [
        7
      ],
      "name": "Hamidreza Mousavi"
    },
    {
      "affs": [
        8
      ],
      "name": "Masoud Kiany"
    },
    {
      "affs": [
        9
      ],
      "name": "Mahsa Asadi Anar",
      "orcid": "0000-0002-5772-2472"
    },
    {
      "affs": [
        10
      ],
      "name": "Farzaneh Sadeghi Mahounak"
    },
    {
      "affs": [
        9
      ],
      "name": "Farbod Khosravi"
    },
    {
      "affs": [
        11
      ],
      "name": "Sara Zamani"
    },
    {
      "affs": [
        12
      ],
      "name": "Sepehr Ramezanipour"
    },
    {
      "affs": [
        13
      ],
      "name": "Nima Dehghan Banadaki",
      "orcid": "0009-0003-9600-8233"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/45phnhnjn2y82.bib",
  "cited_by": 0,
  "doi": "10.1002/hsr2.73490",
  "fulltext_url": "https://europepmc.org/articles/PMC13749430",
  "id": "45phnhnjn2y82",
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  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
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  "native_subjects": [
    [
      "heading",
      "Systematic Review"
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      "3504|Dental Radiography and Imaging"
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  "pdf_url": "https://europepmc.org/articles/PMC13749430?pdf=render",
  "pmcid": "PMC13749430",
  "pmid": "43011790",
  "published": "2026-11-10",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/45phnhnjn2y82.ris",
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    {
      "first_seen": "2026-11-12T15:23:59Z",
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  "subjects": [
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  "title": "Artificial Intelligence Applications in Pediatric Dentistry: A Systematic Scoping Review With Visual Evidence Mapping.",
  "title_text": "Artificial Intelligence Applications in Pediatric Dentistry: A Systematic Scoping Review With Visual Evidence Mapping.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-14T12:22:13Z",
  "url": "https://research.draconitic.com/paper/45phnhnjn2y82",
  "venue": "Health science reports",
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