Artificial intelligence and technology in paediatric dentistry: a review

INTRODUCTION

The behavioral and developmental requirements of children, particularly those with complicated health issues, are the main emphasis of paediatric dentistry. It recommends establishing a "Dental Home" and conducting treatments by the age of one to improve preventive, parental, developmental and comprehensive oral health measures.¹ The early years are crucial for preventing tooth cavities. Early intervention lessens the long-term consequences of children's tooth decay, encourages proactive care and supports lifetime oral health behaviors.²

THE DIGITAL REVOLUTION

Artificial intelligence

AI is revolutionizing healthcare by utilizing machine learning and massive datasets to improve diagnosis accuracy, optimize processes and strike a balance between clinical efficiency and ethical norms.³ It serves as a crucial, data-driven co-pilot rather than replacing human therapists. Its ideal function is to automate heavy data triage and administrative duties, reducing clinician fatigue and freeing up doctors to spend more concentrated time managing patient care.⁴

Machine learning

By using dynamic algorithms to evaluate massive biomedical information, ML improves diagnostic precision, customizes treatment regimens and speeds up clinical decision-making.⁵

It transforms medical robotics beyond software applications into adaptive clinical systems with responsive physical rehabilitation, tremor reduction and real-time surgical navigation. Surgical accuracy is increased and human error is reduced through integrated computer vision and continuous sensory input, ultimately creating a highly accurate environment for patient-centered care.⁶

Machine robotics

By using dynamic algorithms to evaluate massive biomedical information, ML improves diagnostic precision, customizes treatment regimens and speeds up clinical decision-making.⁷

These technologies, which are powered by high-precision, AI-integrated systems, completely change surgical procedures by improving dexterity, maximizing visualization and reducing human error to guarantee less invasive execution and quicker patient recovery.⁸

In the end, three fundamental medical domains autonomous surgical navigation, sensory-driven physical rehabilitation and automated hospital logistics use this operational safety and clinical accuracy.⁷

Why do we need AI in pediatric dentistry?

Diagnostic subjectivity, observer tiredness and the difficulties of controlling patient behavior and dental fear are persistent problems in traditional paediatric dentistry. AI has become a crucial clinical tool to optimize diagnostic accuracy and enable the ultra-early diagnosis of carious lesions to address these vulnerabilities.⁹

Additionally, by analyzing multi-factorial information, AI frameworks are able to accurately forecast treatment trajectories and child behavior patterns, which minimizes chair time, lessens practitioner bias and promotes a safer, more efficient dental experience for paediatric cohorts.¹⁰

The primary aim is to transition pediatric dentistry from reactive treatment to a proactive, precision-based model by integrating AI and advanced Digital Technologies (DT).

This review is structured to achieve the following academic objectives define AI and robotics applications in pediatric dentistry. Evaluate diagnostic accuracy improvements in caries and growth monitoring. Assess technology-led behavioral management techniques. Identify practical workflows for digital treatment and prevention. Critique the ethical implications and future of digital pediatric care.

A systematic literature search was conducted across PubMed, Scopus, Cochrane Library and Google Scholar using six core keywords combined with Boolean operators: ("artificial intelligence", "robotics", "digital therapeutics", "pediatric dentistry", "diagnostics", "3D printing".

The search was limited to articles published between 2015 and 2026; however, seminal studies published before 2015 were included when they provided foundational concepts or technical frameworks relevant to the review.

Peer-reviewed studies on AI and DT in pediatric dentistry were included if they reported clinical or quantitative outcomes related to diagnosis, treatment, behavior or prevention. Studies focused on adult populations, non-digital workflows, non-English publications, conference abstracts, duplicates or lacking relevant outcome data were excluded.

Following eligibility assessment, 38 studies published between 2015 and 2026 were included and categorized into four domains: AI-assisted diagnostics, digital behavioral therapeutics, smart preventive technologies and computer-aided orthodontic planning and appliance fabrication.

AI IN DIAGNOSTICS AND RADIOGRAPHY

Caries detection

Tichý et al conclude that diagnostic assistance of DL models was objective in nature thanks to the quality of training data and was superior to conventional analysis of bitewing radiographs in terms of detecting incipient and proximal lesions.¹¹

A research by Bayati et al claims that the YOLOv8 model lowers diagnostic subjectivity and mistakes, improves early-stage caries detection on bitewing radiographs and permits precise real-time diagnosis of interproximal caries.¹² According to Lee et al by providing sensitive, objective detection and trustworthy decision support, the inception residual network version 2 (Inception-ResNet-v2) model was able to detect early-stage enamel caries on bitewing radiographs with clinician-level accuracy.¹³

DL decreases subjectivity and error while attaining clinician-level accuracy in caries detection with strong sensitivity for early and proximal lesions, according to an exciting meta-analysis research by Shindé et al, However, clinical integration and data quality are critical to performance.¹⁴

Pathology identification

Septina et al, found that DL models achieve high accuracy in detecting periapical lesions, especially on cone beam computed tomography (CBCT) and periapical radiographs, minimizing diagnostic subjectivity, albeit performance varies on imaging modality and data quality.¹⁵

Furthermore, Uzel et al found that DL models accurately detect additional teeth on paediatric panoramic radiographs, reducing diagnostic mistakes and encouraging early dental abnormality treatment.¹⁶

Interestingly, Zheng et al found that both YOLOv8 and Faster R-convolutional neural network (CNN) can accurately detect a large number of additional teeth, with YOLOv8 providing faster clinical findings, reducing human error and improving preoperative planning accuracy.¹⁷

According to research by Makrygiannakis et al AI-based software detects extra and missing teeth on panoramic radiographs with clinician-level accuracy, reducing diagnostic monitoring and supporting early orthodontic intervention.¹⁸

Issa et al, found that AI can identify periapical periodontitis with specialist-level accuracy, reducing diagnostic variability and boosting sensitivity on 2D radiographs as a useful tool for doctors.¹⁹

Bone age assessment

The study by Kavasoglu et al confirmed that AI-driven wrist radiography accurately classifies skeletal maturation stages, reducing diagnostic subjectivity and helping with growth prediction and the optimal timing for orthodontic treatment.²⁰

AI models achieve specialist-level accuracy in detecting bone age through cervical vertebral growth, reducing diagnostic variability and supporting correct orthodontic treatment scheduling as a reliable therapeutic tool, according to a study by Kazimierczak et al.²¹

A fascinating meta-analysis was carried out by Sadeghi et al 2025 showed that AI effectively detects cervical vertebral development, reducing inter-observer variability and enabling precise scheduling of orthodontic therapy, even if rigorous training data are still needed.²² Cervical vertebrae and frontal sinus measures were used in a study conducted by Alfawzan to establish AI as a reliable technique for evaluating skeletal maturity in Saudi patients, enhancing growth prediction and treatment time.²³

BEHAVIORAL MANAGEMENT AND PATIENT ENGAGEMENT

Virtual reality and augmented reality

According to a review by Acharya et al VR reduces discomfort and anxiety during dental procedures, while AR promotes children's compliance with oral hygiene. These non-invasive techniques enhance behavior management and clinical experience.²⁴

The review research by Kalikanov et al verified that although VR and AR use non-invasive, technology-based techniques to improve behavioral control and patient outcomes, VR decreases pain perception through immersive distraction.²⁵

Similarly, Ciubean et al's review study confirmed that VR reduces procedure pain through cognitive diversion, while AR and gamification boost patient engagement and compliance and improve treatment outcomes using non-pharmacological digital strategies.²⁶

Social robots

Akyazi and Bastemur's study demonstrated how interactive robots improve teamwork, reduce children's fear and enable kid-friendly behavioral control during medical operations.²⁷

According to Ferrari et al social robots enhance rehabilitation by lowering children's anxiety, pain perception and discomfort through interesting, kid-centered interactions.²⁸

Meta-analysis study conducted by Ruo-yan et al found that social robots effectively reduce paediatric anxiety, promote teamwork and enhance the therapeutic environment through interactive help and distraction.²⁹

However, via interactive support and diversion, social robots promote emotional coping, lessen paediatric pain and anxiety and improve the quality of paediatric care, according to Jeong et al's study.³⁰

Humanoid robots promote paediatric teamwork, reduce preoperative anxiety and enhance mental wellness through interactive distraction and training, according to fascinating study by Franconi et al.³¹

PREVENTIVE DENTISTRY

Smart toothbrushes

In their review research, Maini et al verified that AI-powered smart toothbrushes enhance kids' dental hygiene by tracking brushing patterns, offering immediate feedback and promoting preventative behaviors through gamified instruction.³²

Additionally, a research by Adeghe et al confirmed that Internet of Things) IoT (-integrated smart toothbrushes improve early intervention, lifelong oral hygiene behaviors and real-time monitoring to support paediatric preventive care.³³

In order to promote paediatric oral health, Goodwin et al verified that smart toothbrushes offer objective monitoring of children's brushing behaviors, overcoming errors in self-reported data and providing customized educational interventions.³⁴

According to Choi et al's survey, smart toothbrushes provide for precise telemonitoring of kids' oral hygiene, enhancing remote supervision, objective habit tracking and preventative treatment through improved clinician-family cooperation.³⁵

Risk prediction models

By analyzing nutrition, socioeconomic characteristics and saliva composition, AI predicts future caries, allowing for early risk diagnosis and individualized preventative therapy, according to the scoping study conducted by Bhatia et al.³⁶

Abbott et al's meta-analysis, which looked at diet, socioeconomic factors and saliva composition, confirmed that AI predicts caries risk. This promotes tailored, data-driven preventative dental treatment and enables early risk detection.³⁷

By combining clinical, sociodemographic and behavioural data, light gradient boosting machine (LightGBM)-based ML correctly predicts paediatric caries, providing early detection and individualized prevention, according to intriguing research by Bahammamm.³⁸

Ramos-Gomez et al confirmed that random forest models predict caries in young children using behavioural, socioeconomic and dietary factors, enabling early identification of high-risk patients and supporting tailored prevention.³⁹

Interestingly, by looking at carer’s knowledge, diet and socioeconomic status, Yuan et al, confirmed that eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlantation (SHAP) accurately predicts paediatric caries, enabling precise early risk classification and customized prevention.⁴⁰

ORTHODONTIC AND SURGICAL PLANNING

Digital impressions: the shift from physical molds to intraoral scanners

Digital intraoral scanners provide real-time 3D visualization, decrease mistakes in dental restoration fabrication and increase accuracy, patient comfort and workflow efficiency, according to a review research by Divya et al.⁴¹

Digital imprints offer precise, dependable substitutes for physical moulds, lowering distortion and enhancing computer-aided design and computer-aided manufacturing (CAD/CAM) workflow efficiency and predictability, according to a systematic review research by Shah et al.⁴²

Intraoral scanners are more precise, pleasant and time-efficient than conventional moulds, according to Palomino et al. They strengthen the basis for high-precision restorative dentistry by removing material distortions and integrating directly with CAD/CAM technologies.⁴³

According to Rupan et al intraoral scanners perform better than physical imprints in terms of accuracy, workflow efficiency and diagnostic capabilities. They also eliminate material-related mistakes and improve patient comfort.⁴⁴

3D printing: fabrication of space maintainers, surgical guides and orthodontic appliances

Through digital scanning and additive manufacture for paediatric appliances and surgical guides, Zarean et al, systematic review discovered that 3D printing increases accuracy, decreases chair time, improves biocompatibility and enables a more predictable and efficient workflow.⁴⁵

According to Trivedi et al 3D-printed lingual arches eliminate fabrication mistakes, improve fit, comfort and overall, CAD/CAM workflow efficiency in paediatric orthodontics and are more accurate, long-lasting and time-efficient than traditional space maintainers.⁴⁶

Pawar verified that 3D-printed band-and-loop space maintainers are more precise, solder-free and effective than traditional appliances, providing better fit and longevity via additive manufacturing and digital design.⁴⁷

Whitley et al verified that employing desktop stereolithography (SLA) equipment and digital planning software, in-office 3D printing provides high precision and optimized processes for the accurate, economical and efficient manufacturing of surgical guides.⁴⁸

AI IN DIAGNOSTICS AND RADIOGRAPHY

Caries detection

When it comes to identifying early and interproximal caries, DL models typically perform better than traditional radiography interpretation. CNN-based systems increase sensitivity while decreasing diagnostic subjectivity, YOLOv8 allows real-time lesion identification and Inception-ResNet-v2 reaches clinician-level accuracy in enamel caries. However, clinical integration and dataset quality continue to be key factors in success.^11-14

Pathology identification

AI does a great job of detecting more common dental conditions such periapical lesions, additional teeth and developmental anomalies. Advanced models (YOLOv8, Faster R-CNN) improve surgical planning and detection time while reducing human error and variability in panoramic and CBCT interpretation.^15-19

Bone age assessment

Orthodontic timing accuracy is increased and inter-observer variability is decreased by AI-based skeletal maturation evaluation utilizing wrist radiographs and cervical vertebral analysis. Reliable yet data-dependent performance for growth prediction and treatment scheduling is supported by meta-analytic evidence.^20-23

BEHAVIORAL MANAGEMENT & PATIENT ENGAGEMENT

Virtual reality and augmented reality

While AR improves long-term engagement through gamification and interactive dental hygiene teaching, VR offers immersive distraction that dramatically lowers procedural pain and anxiety. When combined, they enhance both at-home compliance and in-clinic behavior.^24-26

Social robots

Through interactive communication and behavioral modeling, social and humanoid robots enhance children's collaboration, emotional comfort and anxiety reduction. Meta-analytic data supports non-pharmacological behavior control by demonstrating consistent advantages in paediatric dentistry settings.^27-31

PREVENTIVE DENTISTRY

Smart toothbrushes

IoT and AI-enabled toothbrushes improve preventative care by overcoming the constraints of self-reported compliance and offering real-time feedback, gamification and objective monitoring of brushing behavior.^32-35

Risk prediction models

Early caries risk prediction and individualized prophylaxes are made possible by ML models (LightGBM, Random Forest, XGBoost with SHAP) that incorporate clinical, behavioral and socioeconomic aspects. These strategies change dental treatment from being reactive to being proactive and data-driven.^36-40

ORTHODONTIC AND SURGICAL PLANNING

Digital impressions

By removing distortion, enabling real-time 3D visualization and increasing accuracy, patient comfort and CAD/CAM workflow efficiency, intraoral scanners take the role of traditional impressions.^41-44

3D printing

Orthodontic appliances, space maintainers and surgical guides may be precisely made with more precision, less chair time and better biocompatibility thanks to additive manufacturing. It improves predictability and clinical efficiency by supporting completely digital processes from scanning to manufacture.^45-48

Clinical outlook and limitations

While these innovations could significantly streamline operations and reduce the clinician’s workload, this technology is designed to serve as an advanced “co-pilot,” assisting professionals rather than replacing them. The value and accuracy of such programs undoubtedly rely on the quality, variety and consistency of the data used for training.

The long-term success of this endeavor depends on a combination of responsible data utilization, multi-center research and clinician collaboration. By integrating such technologies with robotics, digital dentistry has the potential to provide a safe and highly accurate solution, offering each patient a unique and personalized experience in managing their oral health care needs.

CONCLUSION

The integration of AI, ML and cutting-edge technology in pediatric dentistry can lead to a paradigm shift in the approach to managing patients. The use of convolutional neural networks allows for reducing diagnostic subjectivity to a minimum, thus improving the quality of diagnostics, primarily of interproximal caries detection, dental anomaly recognition or growth assessment.

At the same time, the implementation of VR, augmented reality and robotic-assisted systems into the daily practice provides dentists with an opportunity to reduce the stress of the patients and manage their behavior during treatment. Other emerging technologies, such as smart toothbrushes, also play a role in creating a better experience for patients, as well as an essential role in preventing caries.

In addition, the use of intraoral scanners and 3D printers allows dentists to make the process of taking dental impressions more comfortable and create more accurate pediatric dental prostheses. Thus, AI and other DT can indeed become a valuable tool for managing patient care.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: Not required

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