Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review

The application of artificial intelligence (AI) technologies in pediatric dentistry has expanded rapidly and is assuming an increasingly important role in the assessment of oral health and diagnostic processes in children. In particular, image-based analyses and data-driven predictive models offer significant potential to enhance diagnostic accuracy and reduce observer-dependent variability. This review addresses AI applications in pediatric dentistry within a structured framework, summarizing the methodological foundations of machine learning and deep learning approaches while critically evaluating the current body of evidence. It further identifies key limitations in the existing literature and outlines future research priorities, aiming to provide a perspective for the safe and sustainable clinical integration of AI in pediatric dentistry.

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Publication Details

Journal
Biomimetics
Published
2026-05-14
DOI
https://doi.org/10.3390/biomimetics11050339
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review

Mehmet Bani, Ecem Elif Çege, Zeliha Hatipoğlu Palaz, Ümmühan Karademir et al.
Biomimetics
Dental Radiography and Imaging
article

Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review

Mehmet Bani, Ecem Elif Çege, Zeliha Hatipoğlu Palaz, Ümmühan Karademir, Yasemin Akın
article en

Abstract

The application of artificial intelligence (AI) technologies in pediatric dentistry has expanded rapidly and is assuming an increasingly important role in the assessment of oral health and diagnostic processes in children. In particular, image-based analyses and data-driven predictive models offer significant potential to enhance diagnostic accuracy and reduce observer-dependent variability. This review addresses AI applications in pediatric dentistry within a structured framework, summarizing the methodological foundations of machine learning and deep learning approaches while critically evaluating the current body of evidence. It further identifies key limitations in the existing literature and outlines future research priorities, aiming to provide a perspective for the safe and sustainable clinical integration of AI in pediatric dentistry.

BiomimeticsVol. 11(5)
Karabük University (TR), Gazi University (TR)
Openalex Percentile: Top 3%
Dental Radiography and Imaging
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Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review — Mehmet Bani, Ecem Elif Çege, et al. · Biomimetics (2026) | TGRS Research Map | TGRS