Artificial Intelligence Applications in Pediatric Dentistry: A Systematic Scoping Review With Visual Evidence Mapping.
14 affiliations
- 1Otolaryngology Department of Taleghani Hospital, Students Research Committee Shahid Beheshti University of Medical Sciences Tehran Iran.
- 2School of Medicine Iran University of Medical Sciences Tehran Iran.
- 3School of Medicine Kerman University of Medical Sciences Tehran Iran.
- 4School of Dentistry Shahid Beheshti University of Medical Sciences Tehran Iran.
- 5Student Research Committee, Dental School Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran.
- 6Department of Nursing, Faculty of Nursing and Midwifery, Mashhad Medical Sciences Islamic Azad University Mashhad Iran.
- 7Oral and Dental Diseases Research Center Kerman University of Medical Sciences, Kerman, Iran.
- 8Student Research Committee, School of Dentistry Semnan University of Medical Sciences Semnan Iran.
- 9Department of Pediatric Dentistry, School of Dentistry Tehran University of Medical Sciences Tehran Iran.
- 10School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran.
- 11Restorative Department, Dental School Tehran University of Medical Sciences Tehran Iran.
- 12Student Research Committee, School of Medicine Guilan University of Medical Sciences Rasht Iran.
- 13Student Research Committee Fasa University of Medical Sciences Fasa Iran.
- 14School of Dentistry Belarusian State Medical University (BSMU), Minsk, Belarus.
Abstract
Background and aims
Artificial 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
PubMed, 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
Thirty-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
AI 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.
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BibTeX
@article{zarei2026artificial,
title = {{Artificial Intelligence Applications in Pediatric Dentistry: A Systematic Scoping Review With Visual Evidence Mapping.}},
author = {Aref Zarei and Amirhossein Ehsani and Amir Ebrahimzedeh Pasha and Mansour Gorjizad and Majid Aryannejad and Seyedeh Sara Rezazadeh Shojaee and Maedeh Barkhordari Dashtkhaki and Sharif Ebrahimi and Mohamad Mahdi Aghajany and Hamidreza Mousavi and Masoud Kiany and Mahsa Asadi Anar and Farzaneh Sadeghi Mahounak and Farbod Khosravi and Sara Zamani and Sepehr Ramezanipour and Nima Dehghan Banadaki},
journal = {Health science reports},
year = {2026},
month = nov,
doi = {10.1002/hsr2.73490},
url = {https://europepmc.org/article/MED/43011790},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial Intelligence Applications in Pediatric Dentistry: A Systematic Scoping Review With Visual Evidence Mapping.
AU - Aref Zarei
AU - Amirhossein Ehsani
AU - Amir Ebrahimzedeh Pasha
AU - Mansour Gorjizad
AU - Majid Aryannejad
AU - Seyedeh Sara Rezazadeh Shojaee
AU - Maedeh Barkhordari Dashtkhaki
AU - Sharif Ebrahimi
AU - Mohamad Mahdi Aghajany
AU - Hamidreza Mousavi
AU - Masoud Kiany
AU - Mahsa Asadi Anar
AU - Farzaneh Sadeghi Mahounak
AU - Farbod Khosravi
AU - Sara Zamani
AU - Sepehr Ramezanipour
AU - Nima Dehghan Banadaki
PY - 2026
DA - 2026/11/10
JO - Health science reports
DO - 10.1002/hsr2.73490
UR - https://europepmc.org/article/MED/43011790
AB - Background and aims Artificial 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 PubMed, 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 Thirty-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 AI 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.
SN - 2398-8835
ER -