How Is Artificial Intelligence Transforming the Intersection of Pediatric and Special Care Dentistry? A Scoping Review of Current Applications and Ethical Considerations

Abstract

Background: Artificial intelligence (AI) is influencing pediatric dentistry by supporting diagnostic accuracy, optimizing treatment planning, and improving patient care, especially for children with special needs. Previous studies explored various aspects of AI in pediatric dentistry and special care dentistry, predominantly focusing on clinical implementation or technical advancements. However, no prior review has specifically addressed its application at the intersection of pediatric dentistry and special care dentistry, particularly with respect to ethical and environmental perspectives. Objective: This scoping review provides a comprehensive synthesis of AI technologies in pediatric dentistry with a dedicated focus on children with special health care needs. It aims to critically evaluate current applications and examine the clinical, ethical, and environmental implementation challenges unique to these populations. Methods: A structured literature search was conducted in PubMed, Scopus, and Web of Science from inception to August 2025, using predefined inclusion and exclusion criteria. Eligible studies investigated AI applications in pediatric dental care or special needs contexts. Studies were synthesized narratively according to thematic domains. Results: Sixty-five studies met the inclusion criteria. Thematic synthesis identified nine domains of AI application: (1) diagnostic imaging and caries detection, (2) three-dimensional imaging, (3) interceptive and preventive orthodontics, (4) chatbots and teledentistry, (5) decision support, patient engagement and predictive analytics, (6) pain assessment and discomfort monitoring, (7) behavior management, (8) behavior modeling, and (9) ethical considerations and challenges. The majority of studies were conducted in general pediatric populations, with relatively few specifically addressing children with special health care needs. Conclusions: AI in pediatric dentistry is most developed in diagnostic imaging and caries detection, while applications in teledentistry and predictive analytics remain emerging, and areas such as pain assessment, behavior management, and behavior modelling are still exploratory. Evidence for children with special health care needs is limited and seldom validated, highlighting the need for focused research in this group. Ethical deployment of AI in pediatric dentistry requires safeguarding data privacy, minimizing algorithmic bias, preventing overtreatment, and reducing the carbon footprint of cloud-based technologies.

1. Introduction

The integration of Artificial Intelligence (AI) into health care has introduced early innovations [1,2,3]. In recent years, AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) have shown early potential in enhancing diagnostic precision, optimizing treatment plans, improving patient outcomes, and increasing the efficiency of dental practices [4]. Within pediatric dentistry, children with special needs refers to pediatric patients with intellectual, physical, behavioral, or sensory impairments that may require adaptations in dental care delivery [5]. Addressing these diverse clinical demands, the application of AI is emerging as a complementary tool to support improvements in both clinical outcomes and patient experiences [6]. AI applications in health care include the automation of complex tasks, support for clinical decision-making, and enhanced predictive modeling [7]. In pediatric dentistry, where the early detection of dental problems such as dental caries, malocclusion, or developmental anomalies is crucial, AI tools may assist professionals in diagnosing conditions more efficiently. ML algorithms, particularly DL models, are trained to analyze a variety of diagnostic data, including radiographic images, to detect issues that may be otherwise difficult for a clinician to observe. Preliminary studies suggest that AI systems may improve diagnostic accuracy and potentially support earlier intervention, which can be important in preventing the progression of carious lesions in pediatric patients [8,9,10].

AI is also being explored for its potential to personalize treatment for pediatric patients. AI tools offer the possibility of developing more tailored treatment plans based on individual dental history and risk factors, which may enhance personalization in pediatric care [11]. Personalized treatment, which has traditionally been a challenge due to the variability in patient responses and the need for individualized approaches, is now becoming more feasible with AI algorithms that take a data-driven approach to treatment planning [12]. Through analyzing large datasets, AI may help predict how a child responds to certain treatments, assist in selecting the most effective interventions, and provide recommendations for post-treatment care [13]. Additionally, AI’s potential to support preventive care in pediatric dentistry is receiving growing attention. In pediatric dentistry, prevention is often the cornerstone of care, with emphasis placed on avoiding the onset of conditions including caries and orthodontic malocclusion. Emerging AI models have been used to analyze dental and health data to estimate risks such as caries development or orthodontic needs, although their integration into routine practice is still at an early stage [14]. These predictive tools may assist in the formulation of individualized preventive care plans, which can significantly reduce the incidence of prevalent oral health conditions in pediatric populations.

While the potential benefits of AI in pediatric dentistry are vast, the integration of AI into clinical practice does not come without its challenges [15]. One of the primary concerns is the quality and availability of data. AI systems are only as effective as the data they are trained on, and in pediatric dentistry, comprehensive, high-quality datasets that cover diverse patient populations may be limited [14]. This can hinder the development and performance of AI models. Furthermore, the adaptation of pediatric dentists to AI technologies also presents a challenge. Dentists need to trust AI systems, which requires proper training, validation, and clinical integration. It is essential that AI tools are seen not as replacements for human clinicians but as complementary technologies that can enhance decision-making and improve patient care [16]. In addition to these technical and educational hurdles, ethical concerns around the use of AI in pediatric dentistry must be addressed. Issues such as data privacy, the potential for bias in AI algorithms, and the impact of AI on the doctor-patient relationship are crucial considerations. The use of AI requires careful management to ensure that patient data remains secure and that AI tools are fair and non-discriminatory, particularly when dealing with a diverse patient base [17]. Additionally, the feasibility of AI integration in pediatric dentistry depends on practical considerations such as implementation costs, infrastructure requirements, regulatory approval processes, and the capacity for seamless integration into existing clinical workflows. These factors are critical in determining whether promising technologies can transition from research settings into routine patient care.

Despite these challenges, the growing body of research and development in AI technologies is encouraging. As the field of pediatric dentistry continues to evolve, AI is likely to play a growing role, although current applications remain in early stages. Recent reviews have examined AI in pediatric dentistry, but with narrower emphases. Rokhshad et al. [18] conducted a systematic review and meta-analysis that primarily assessed diagnostic and treatment-planning applications, including caries detection, tooth identification, and prediction of early childhood caries. While methodologically rigorous, their review focused on technical accuracy and did not address special care populations, ethical concerns, or environmental impacts. Similarly, Vishwanathaiah et al. [19] provided a narrative overview of AI in pediatric dentistry, highlighting diagnosis, prevention, and treatment planning, but without systematic appraisal of special health care needs populations or broader ethical and environmental considerations. Nevertheless, no prior review has specifically addressed AI application at the intersection of pediatric dentistry and special care dentistry, particularly with respect to ethical and environmental perspectives. Therefore, this scoping review aimed to explore the current applications of AI in pediatric dentistry and its implications for pediatric patients with special needs, evaluate the ethical and environmental challenges associated with these technologies, and discuss the future prospects of AI in shaping the field of pediatric dental care.

2. Methods

2.1. Search Strategy

A comprehensive literature search was conducted in PubMed, Web of Science, and Scopus. The search included articles published in English from inception up to August 11, 2025. The complete search strings for each database are provided in

Table 1. Search strings applied across databases.

Database	Applied Search Strategy
PubMed	(“Artificial Intelligence” [Mesh] OR “Machine Learning” [Mesh] OR “Neural Networks, Computer” [Mesh] OR “Natural Language Processing” [Mesh] OR “Robotics” [Mesh] OR “artificial intelligence” [tiab] OR “machine learning” [tiab] OR “deep learning” [tiab] OR “neural network*” [tiab] OR “natural language processing” [tiab] OR “large language model*” [tiab] OR “computer vision” [tiab] OR “predictive analytic*” [tiab] OR “teledentistry” [tiab]) AND (“Pediatric Dentistry” [Mesh] OR “Dentistry” [Mesh] OR “Orthodontics” [Mesh] OR “pediatric dent*” [tiab] OR “paediatric dent*” [tiab] OR “child dent*” [tiab] OR orthodontic* [tiab]) AND (“Child” [Mesh] OR “Adolescent” [Mesh] OR “Disabled Children” [Mesh] OR “Intellectual Disability” [Mesh] OR “Developmental Disabilities” [Mesh] OR “Autism Spectrum Disorder” [Mesh] OR child* [tiab] OR pediatric* [tiab] OR paediatric* [tiab] OR adolescent* [tiab] OR “special need*” [tiab] OR disability [tiab] OR disabilities [tiab] OR neurodivergen* [tiab]) AND (diagnos* [tiab] OR detection [tiab] OR screening [tiab] OR “treatment planning” [tiab] OR “decision support” [tiab] OR “behavior management” [tiab] OR “behaviour management” [tiab] OR triage [tiab] OR monitoring [tiab] OR “remote monitoring” [tiab] OR “cone beam” [tiab] OR CBCT [tiab] OR radiograph* [tiab] OR chatbot* [tiab] OR “virtual assistant*” [tiab] OR robotics [tiab] OR robot-assist* [tiab])
Web of Science	TS = ((“artificial intelligence” OR “machine learning” OR “deep learning” OR “neural network*” OR “natural language processing” OR “large language model*” OR “computer vision” OR robotics OR “predictive analytic*” OR teledentistry) AND (“pediatric dent*” OR “paediatric dent*” OR “child dent*” OR orthodontic* OR dentistry) AND (child* OR pediatric* OR paediatric* OR adolescent* OR “special need*” OR disability OR disabilities OR neurodivergen*) AND (diagnos* OR detection OR screening OR “treatment planning” OR “decision support” OR “behavior management” OR “behaviour management” OR triage OR monitoring OR “remote monitoring” OR radiograph* OR CBCT OR “cone beam” OR chatbot* OR “virtual assistant*” OR robot*))
Scopus	TITLE-ABS-KEY(“artificial intelligence” OR “machine learning” OR “deep learning” OR “neural network*” OR “natural language processing” OR “large language model*” OR “computer vision” OR robotics OR “predictive analytic*” OR teledentistry) AND TITLE-ABS-KEY(“pediatric dent*” OR “paediatric dent*” OR “child dent*” OR orthodontic* OR dentistry) AND TITLE-ABS-KEY(child* OR pediatric* OR paediatric* OR adolescent* OR “special need*” OR disability OR disabilities OR neurodivergen*) AND TITLE-ABS-KEY(diagnos* OR detection OR screening OR “treatment planning” OR “decision support” OR “behavior management” OR “behaviour management” OR triage OR monitoring OR “remote monitoring” OR radiograph* OR CBCT OR “cone beam” OR chatbot* OR “virtual assistant*” OR robot*)

.

2.2. Inclusion Criteria

Studies were included if they met the following criteria:

• Focused on pediatric patients, including children with intellectual, developmental, or physical disabilities.
• Described the application of AI technologies in any domain of pediatric dentistry.
• Addressed AI use in clinical or behavioral settings, including diagnostics, treatment planning, preventive care, patient monitoring, behavior management, or teledentistry.
• Included original research articles, narrative reviews, systematic reviews, scoping reviews, or technology-focused overviews.

2.3. Exclusion Criteria

Studies were excluded if they:

• Focused exclusively on adult populations.
• Mentioned AI superficially without describing practical applications in pediatric dental contexts.
• Were non-peer-reviewed formats (editorials, letters, conference abstracts).
• Were published in languages other than English.

2.4. Study Selection and Data Extraction

Titles and abstracts were screened for relevance by two independent reviewers, and full texts of eligible articles were reviewed to extract information regarding AI applications including diagnostic imaging, predictive analytics, interceptive and preventive orthodontics, treatment planning, patient monitoring, behavior management including virtual and augmented reality interventions, teledentistry, AI-powered chatbots, and robotic-assisted procedures. The review also incorporated studies addressing specialized populations, particularly children with special health care needs and neurodivergent patients. Any disagreements were resolved through discussion, with a third reviewer available to adjudicate if needed. The selection process considered both thematic relevance to pediatric dentistry and methodological soundness, with particular attention to studies discussing ethical considerations such as data privacy, algorithmic bias, clinician autonomy, risk of overtreatment, and environmental impacts associated with AI technologies. Thematic synthesis was conducted collaboratively through group discussions to reach consensus on key domains. Although this work is a scoping review rather than a systematic review, selected elements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were incorporated [20]. The protocol has been registered with the Open Science Framework (osf.io/tvk73).

3. Results

3.1. Study Selection

A total of 393 records were identified through database searching. After removal of 141 duplicates, 252 titles and abstracts were screened, of which 186 were excluded. Sixty-six full-text articles were assessed for eligibility, and one was excluded for not evaluating AI in pediatric dentistry or special needs contexts. Accordingly, 65 studies were included in the final synthesis. A PRISMA flow diagram summarizing the selection process is depicted in Figure 1.

3.2. Overview of the Evidence Base

The included studies spanned nine thematic domains. Diagnostic imaging and caries detection (n = 9) and three-dimensional imaging (n = 4) represented the most established research areas, supported by systematic reviews, meta-analyses, and large annotated datasets. Interceptive and preventive orthodontics (n = 5), chatbots and teledentistry (n = 8), and decision support and predictive analytics (n = 8) reflected growing but still heterogeneous evidence bases. Pain assessment and discomfort monitoring (n = 7), behavior management (n = 4), and behavior modeling (n = 3) were less developed, typically relying on small feasibility trials, observational cohorts, or technical validations. Ethical and environmental considerations (n = 17) were primarily addressed in conceptual reviews or methodological discussions rather than original clinical research. Importantly, the majority of studies addressed general pediatric populations, with only a minority explicitly including children with special health care needs or neurodivergent conditions. In those that did, evidence was often descriptive, limited in sample size, and rarely stratified outcomes by subgroup. Consequently, data specific to special needs populations remain sparse across all domains.

Table 2. Summary of evidence maturity and inclusion of children with special health care needs across domains.

Thematic Domain	Evidence Maturity	Children with Special Health Care Needs-Specific Evidence
Diagnostic imaging and caries detection	Established (systematic reviews, large annotated datasets)	Explored conceptually, but lacking SHCN-specific validation
Three-dimensional imaging	Emerging (feasibility and optimization studies)	No SHCN-focused validation; adult/mixed datasets dominate
Interceptive and preventive orthodontics	Emerging (growth prediction, treatment timelines)	No SHCN-focused validation; entirely general pediatric populations
Chatbots and teledentistry	Developing (pilot studies, scoping reviews)	Some early SHCN-tailored applications improved triage and follow-up, but limited overall
Decision support and predictive analytics	Emerging (ML-based risk prediction, systematic review)	Sparse
Pain assessment and discomfort monitoring	Emerging (diagnostic accuracy, multimodal feasibility)	Directly relevant to nonverbal/neurodivergent children, but validation remains limited
Behavior management	Emerging (small RCTs and intervention trials)	Trials in ASD and mild intellectual disability show reduced anxiety and better cooperation, but replication sparse
Behavior modeling	Early-stage (conceptual or prototype systems)	Entirely SHCN-focused (ASD, ADHD, profound disabilities), but unvalidated
Ethical and environmental issues	Conceptual (reviews, methodological perspectives)	No empirical SHCN-focused studies; implications inferred

SHCN, Special Health Care Needs; ML, Machine Learning; RCT, Randomized Controlled Trial; ASD, Autism Spectrum Disorder; ADHD, Attention-Deficit/Hyperactivity Disorder.

outlines the maturity of evidence and the contributions specific to children with special health care needs across domains.

3.3. Diagnostic Imaging and Caries Detection

AI has the potential to enhance the accuracy, efficiency, and consistency of diagnostic imaging in pediatric dentistry, particularly when compared to conventional methods. A cross-sectional study compared AI-assisted versus conventional radiographic evaluation in children aged 6–14 [21]. The AI-based analysis of standardized bitewing radiographs demonstrated significantly higher diagnostic performance, with a sensitivity of 88.3%, specificity of 90.8%, and overall accuracy of 89.6%, all notably superior to conventional interpretation. Similarly, a systematic review and meta-analysis evaluated deep learning models on bitewing radiographs, identifying moderate mean sensitivity (77%) [22]. Performance of individual models varied based on architecture and training data. However, AI was found to supplement clinical diagnosis rather than replace it. Dentists still achieved higher overall performance, yet the integration of AI did enhance consistency, especially in challenging or equivocal cases. Further, an advanced deep learning study processed over 1500 annotated pediatric bitewing radiographs [23]. The model achieved a precision of 96.0% for enamel caries and 80.1% for dentin caries. Another feasibility study deployed an artificial neural network on a mobile device, yielding a sensitivity of 75% and precision of 84.6% in detecting caries on bitewing radiographs in real-time clinical scenarios [24]. This highlights the growing potential for accessible and rapid AI-supported diagnostics in everyday pediatric dental practice. Several studies also stressed that AI-based tools decrease inter-observer variability and increase diagnostic confidence among clinicians, by yielding valuable decision support, particularly where clinical expertise varies or case volume is high [25,26].

Pediatric patients with special health care needs consistently report a higher burden of positioning and motion errors on panoramic radiography, which in turn drives retake imaging and added radiation exposure. A retrospective study analyzed panoramic images from children with special health care needs and documented frequent technical errors that compromise diagnostic quality, underscoring the clinical need to lower repeats in this group [27]. In parallel, emerging AI quality-assessment tools for dental panoramics can automatically flag suboptimal contrast and positioning, improving consistency and offering a pathway to fewer technical repeats [28]. This mirrors evidence from general radiology, suggesting a plausible mechanism for repeat-reduction when similar systems are deployed in dental imaging workflows that serve special-needs children [29]. Nonetheless, limitations exist. Most studies utilize data from single centers or retrospective samples, limiting broader generalizability. Performance metrics are often not stratified by age, dentition stage, or special needs subgroups, and evidence for special needs populations remains sparse, with published studies generally having small sample sizes. There is also a lack of research regarding the real-world cost-effectiveness of AI-supported imaging in pediatric dentistry. Figure 2 summarizes the diagnostic imaging workflow consistently described across included diagnostic imaging studies, starting with radiographic acquisition and preprocessing, progressing through AI-based model training and pattern recognition, and concluding with automated or clinician-assisted interpretation.

3.4. Three-Dimensional Imaging

AI-enhanced Cone Beam Computed Tomography (CBCT) has shown promise for improving diagnostic precision while reducing radiation dose. In a dose-optimization study using dental CBCT datasets, a super-resolution generative adversarial network reconstruction maintained diagnostic quality for fine structures such as the lamina dura and periodontal ligament while reducing radiation dose by up to 50% compared with conventional reconstruction protocols [30]. This reduction is especially important in children and in special health care needs populations who may require multiple imaging sessions. Motion artifacts are a frequent challenge in pediatric patients and children with special health care needs due to limited tolerance for long acquisition times. A feasibility study of sparse-view CBCT combined with AI-based reconstruction reported shorter scan times and fewer motion-related image degradations without loss of diagnostic acceptability, suggesting an avenue to improve imaging compliance in these populations [31]. Artifact suppression has also been explored. Although most evidence comes from mixed-age datasets, an artifact suppression evaluation of an AI noise- and artifact-reduction algorithm demonstrated significant improvement in contrast-to-noise ratios and reduced streak artifacts from metallic restorations, thereby improving visibility of adjacent structures [32]. These improvements are likely to be beneficial in pediatric patients and children with special health care where orthodontic appliances and restorations are common sources of artifacts.

Automated diagnostic support is another emerging application. A diagnostic performance study of an AI platform for CBCT interpretation reported that clinician sensitivity for detecting pathologies increased from 76.7% without AI to 85.4% with AI assistance, while specificity rose slightly from 96.2% to 96.7% [33]. Although this study was not pediatric-specific, the improved detection of periapical lesions and root fractures is directly relevant to trauma and infection management in children. Despite these promising results, there is a striking absence of pediatric and special health care needs-focused CBCT AI studies. Most datasets are adult or mixed-age, with no stratification by dentition stage.

3.5. Interceptive and Preventive Orthodontics

AI has been utilized to predict mandibular growth trends in pediatric patients, allowing early identification of children at risk for developing prognathic mandibles. A growth-prediction study applied a deep learning model to cephalometric radiographs from children with anterior crossbite, achieving 85% accuracy, significantly outperforming junior orthodontists who averaged 54.2% [34]. This indicated AI’s potential to guide early clinical decision-making in interceptive care. Similarly, a longitudinal growth-model study using CNN-based prediction on serial lateral cephalograms achieved low mean absolute error for mandibular length estimation, reinforcing AI’s potential to enhance early orthodontic intervention [35]. AI has also been applied to optimize orthodontic treatment timelines. A treatment-duration prediction study trained several machine learning algorithms on pre-treatment variables to forecast total treatment time [36]. The most accurate models achieved a mean absolute error of 7.27 months, outperforming clinicians. Key contributors included extraction decisions, malocclusion type, and appliance use. Automated cephalometric landmark detection has shown performance approaching that reported in clinical settings. In a landmark identification study, AI models identified over 90% of cephalometric landmarks within clinical tolerance, significantly reducing human error and analysis time, and offering scalable utility in pediatric screening contexts [37].

More recently, a mobile AI screening tool was developed to detect skeletal malocclusion from a single facial photograph. A preclinical validation study showed that it streamlined early referrals, supporting early interceptive orthodontic assessment in underserved pediatric populations [38]. Despite these promising developments, direct evidence of AI-driven interceptive orthodontics in special health care needs children remains lacking. Existing models are trained on general pediatric populations and often lack validation in special health care needs contexts where development patterns and compliance differ markedly. Moreover, data on AI-driven improvements in appointment timing, treatment efficiency, or long-term outcomes remain sparse.

3.6. Chatbots and Teledentistry

With the help of AI, pediatric dentists can offer virtual consultations and monitor patients remotely [39]. This is particularly valuable in underserved areas, where access to pediatric dental care may be limited. Through virtual consultations, AI can help dental professionals remotely assess the patient’s condition and provide guidance for home care, follow-up visits, or immediate treatments. AI-powered chatbots are also used to support triage and follow-up in pediatric dentistry, with particularly significant benefits for children with special needs who face access barriers and communication challenges. A recent study evaluating AI chatbots in pediatric dental triage found that these systems achieve accuracy rates exceeding 90% when matching clinicians’ triage decisions in routine pediatric dental cases [40]. This included urgent symptom assessment and appointment prioritization. For comparison, standard non-AI digital triage tools yielded only 60–75% concordance in similar settings, demonstrating the superior reliability of modern AI systems. For special needs children, AI chatbots may provide tailored symptom screening and scheduling support. In situations where verbal communication is limited, chatbots equipped with alternative communication modes (icons, pictograms, or simple yes/no queries) have improved the identification of pain and dental emergencies by up to 40% compared to manual phone triage protocols [41]. Importantly, these tools are reported by clinicians to reduce appointment delays and mis-triage errors in special needs populations, supporting more timely access to care.

Programs using automated chatbot reminders and individualized instructions have reported follow-up compliance rates above 85%, with caregivers noting the ease of access and timely support as key factors [40]. One comparative investigation showed that when special needs pediatric dental patients received AI-enhanced navigation and monitoring, health outcome disparities were reduced [42]. This contrasted with conventional systems where children from underserved backgrounds or with disabilities had significantly lower rates of completed treatment [43]. Patient and caregiver satisfaction with virtual pediatric dental services appears to be generally high. Studies reported that over 80% of respondents are highly satisfied, citing convenience and fewer access barriers as key benefits [44,45]. Additionally, research indicated that teledentistry platforms used for outreach and remote assessment may increase preventive dental screening rates by around 30% [46,47]. Nonetheless, surveys also found that approximately 48% of dental professionals express concerns about privacy, data security, and regulatory challenges when using AI-enabled teledentistry [46].

3.7. Decision Support, Patient Engagement, and Predictive Analytics

ML is increasingly utilized for Early Childhood Caries (ECC) risk prediction. A risk-prediction study applied AI to mother–child paired data from a longitudinal birth cohort [48]. The study incorporated variables such as maternal oral health status, dietary habits, and fluoride exposure. The approach enabled stratification of children into high- and low-risk groups for early childhood caries, highlighting AI’s potential for targeted preventive interventions and personalized anticipatory guidance in pediatric dentistry. A systematic review of ECC prediction further confirmed strong diagnostic metrics—accuracy, sensitivity, specificity, and AUC—across multiple ML studies, underscoring reliability when integrated into clinical workflows [49]. On the patient engagement front, a pre-post chatbot intervention study using the 30-Day FunDee oral health chatbot reported a rise in tooth-brushing compliance from 72.4% to 93.1%, alongside high usability and satisfaction scores [50]. Similarly, a pilot emotional-support chatbot study for pediatric and adolescent/young adult cancer patients showed reduced anxiety and increased treatment engagement [51]. Furthermore, a cross-sectional risk factor modeling study identified poor oral hygiene, high sugary diet, and low fluoride exposure as significant caries predictors in young children, using Random Forest models [52]. However, a scoping review of ML applications in dental decision-making noted important implementation gaps: 22.9% of models lacked comparison to a clinical standard, and reports often lacked bias assessment, calibration analysis, or data reproducibility—issues that need to be addressed before AI tools can be safely adopted in pediatric contexts [53]. In special-needs populations, children with autism spectrum disorder exhibit significantly higher caries severity than neurotypical peers, as quantified by the DMFS index [54]. In another example, the ORIENTATE machine learning toolkit was applied to a group of children with special health care needs receiving dental treatment under deep sedation [55]. The system was effective in correctly identifying which patients were most likely to require a second sedation session. This balance between correctly identifying true cases and minimizing false alarms indicates its potential value in helping clinicians plan ahead for high-risk patients, optimize treatment strategies, and reduce the need for unnecessary repeat sedation.

3.8. Pain Assessment and Discomfort Monitoring

ML techniques are also being applied to pediatric pain assessment, particularly for children with limited verbal communication or neurodevelopmental disorders. Convolutional neural networks have been used to detect pain-related facial microexpressions [56]. Similarly, methods utilizing NLP and acoustic feature extraction can identify distress-related changes in vocalizations during dental procedures, supplementing existing approaches to evaluate pain in nonverbal populations [57]. Additionally, analyses of physiological parameters such as heart rate variability and skin conductance have been explored to infer autonomic responses associated with discomfort and anxiety [58]. Recent developments include multimodal systems that integrate behavioral and physiological data—such as facial analysis combined with heart rate measurement—using advanced ML models to improve the detection of acute pain in pediatric patients [59]. These approaches may serve as useful, non-invasive adjuncts to clinical observation, supporting clinicians in assessing discomfort and informing management strategies, especially for patients who have difficulty communicating their experiences.

Recent investigations have broadened the scope of AI applications in pediatric pain assessment. A validation study of an AI-powered facial expression analysis tool during in children showed high agreement with gold-standard observational pain scales and identified specific facial action units linked to greater pain intensity [60]. A systematic review reinforced these findings, reporting that technology-assisted facial recognition systems generally outperform manual scoring in sensitivity, specificity, and inter-rater reliability, though it emphasized the need for standardized protocols and improved generalizability [61]. Likewise, an evaluation of a rapid-assessment tool for infants demonstrated that pain-related facial features could be detected within seconds, offering a fast and objective supplement to caregiver-dependent scoring [62]. It appears that current evidence supports AI-based pain assessment as a standardized, non-invasive, and rapid method for identifying discomfort. However, adoption in clinical practice should remain cautious due to variability in algorithm performance across populations, reliance on high-quality input data, and privacy concerns when capturing facial or physiological signals.

3.9. Behavior Management

AI-assisted behavior management tools are emerging as valuable adjuncts in pediatric dentistry, offering innovative ways to improve cooperation, reduce anxiety, and create more positive treatment experiences—particularly for children with special needs. These tools include social robots, virtual reality (VR) systems, and AI-powered monitoring platforms that adapt interactions in real time based on the child’s behavioral and physiological responses. In a randomized clinical trial, the use of a humanoid robot during dental treatment significantly reduced children’s dental anxiety, lowered physiological stress markers, and improved behavioral ratings compared to conventional guidance methods [63]. Similarly, randomized and clinical intervention studies have shown that VR distraction—sometimes integrated with AI emotion-recognition systems—can enhance cooperation and reduce disruptive behaviors in children with autism spectrum disorder (ASD) [64,65]. Another study focusing on children with mild intellectual disabilities found that both audio and VR distraction strategies were effective in managing behavior and lowering anxiety during restorative procedures [66].

These approaches demonstrate that AI and robotics can play an important role in behavior management by offering tailored, responsive, and non-invasive support. However, successful integration into routine pediatric dental practice will require further standardized protocols and ethical safeguards to ensure safety, accessibility, and equitable use across different patient populations. Robust clinical validation through large-scale, multi-center trials is essential to confirm their safety and effectiveness in diverse patient populations, including those with complex medical and behavioral profiles.

3.10. Behavior Modeling

While much of the current literature emphasizes AI in diagnostic imaging and predictive analytics, an underexplored domain involves AI-driven behavior-modelling systems designed specifically for neurodivergent pediatric populations. These children, including those with ASD, attention-deficit/hyperactivity disorder (ADHD), and other developmental or sensory processing disorders, often experience heightened anxiety, altered sensory thresholds, or communication difficulties during dental care encounters [67]. Such challenges can lead to increased treatment times, higher reliance on sedation, and reduced tolerance for conventional care approaches. Behavior-modelling AI seeks to address these barriers by understanding, predicting, and responding to a child’s unique behavioral cues. This may allow for personalized care strategies that are responsive to the child’s needs before distress escalates, potentially improving cooperation and overall treatment outcomes.

Unlike conventional AI systems that rely primarily on static radiographic or clinical datasets, behavior-modelling AI tools incorporate real-time multimodal inputs to assess emotional and sensory states. These systems can dynamically adapt the dental environment or practitioner response to mitigate stress and improve patient cooperation. Wearable biosensors integrated with AI algorithms were found to predict distress based on physiological changes, prompting immediate interventions such as audiovisual distraction through VR or ambient lighting adjustments to match individual sensory preferences [68]. This approach shifts AI from a passive diagnostic role to an active, personalized facilitator of behavioral and emotional support. In neurodivergent populations, these applications may reduce the reliance on pharmacological sedation, shorten treatment times, and enhance the overall dental experience for both the child and caregiver. Moreover, by using adaptive algorithms, these systems can learn from each patient’s past behavioral patterns, gradually improving care delivery across repeated visits [69]. Despite their promise, such behavior-modelling applications remain relatively underrepresented in both the dental AI literature and clinical implementation. Their integration into pediatric dental practice will require further validation, clinician training, and robust ethical safeguards, especially regarding privacy and informed consent for the use of continuous behavioral monitoring. A consolidated overview of the included studies across all thematic domains is presented in

Table 3. Summary of included studies on AI applications in pediatric dentistry and special care dentistry.

Study	Study Type	Overview and Principal Findings
Diagnostic Imaging and Caries Detection
Malik et al., 2025 [21]	Cross-sectional study	Compared AI-based and conventional radiographic methods for caries detection in pediatric patients; AI achieved higher diagnostic accuracy, improving early lesion identification.
Silva-Filho et al., 2024 [22]	Systematic review and meta-analysis	Evaluated sensitivity of deep learning models for caries detection in bitewing radiographs; pooled results indicated consistently high sensitivity, supporting AI as a reliable adjunct to clinical diagnosis.
Bayati et al., 2025 [23]	Experimental study	Applied YOLOv8 deep learning model for interproximal caries detection; achieved rapid processing with high accuracy, outperforming earlier AI architectures.
Dhanak et al., 2024 [24]	Technical validation	Tested a smartphone app integrating AI for real-time caries detection on bitewing radiographs; showed strong diagnostic agreement with conventional readings, enabling point-of-care use.
Panyarak et al., 2023 [25]	Experimental study	Used YOLOv7 to enhance caries detection in bitewing radiographs; reported improved detection performance and processing efficiency over baseline models.
Albano et al., 2024 [26]	Systematic review	Synthesized evidence on AI for radiographic caries detection; concluded that AI achieves high diagnostic accuracy but stressed need for diverse, multi-center datasets.
Fux-Noy et al., 2023 [27]	Observational study	Documented high rates of panoramic imaging errors in pediatric patients with special needs, leading to increased repeat exposures; emphasized strategies to minimize repeats.
Ameli et al., 2025 [28]	Experimental study	Developed a deep learning system to automatically assess panoramic radiograph quality; successfully identified positioning/coverage issues, offering potential to reduce repeat imaging.
Eby et al., 2023 [29]	Observational study	Evaluated AI-driven quality improvement in mammography; demonstrated reduced technical repeat and recall rates, providing indirect evidence for similar benefits in dental radiography.
Three-Dimensional Imaging
Thummerer et al., 2025 [30]	Experimental study	Compared AI-based super-resolution CBCT reconstruction with standard protocols; AI maintained fine anatomical detail while reducing radiation dose by up to 50%, improving safety for pediatric and SHCN patients.
Usui et al., 2024 [31]	Experimental study	Compared AI-based sparse projection CBCT reconstruction with conventional methods; AI reduced streak artifacts and preserved diagnostic accuracy, enabling shorter scan times for pediatric and SHCN patients.
Wajer et al., 2024 [32]	Experimental study	Compared AI-based artifact reduction with unprocessed CBCT images; AI improved contrast-to-noise ratios and reduced metal artifacts, enhancing image quality in cases with orthodontic appliances or restorations.
Ezhov et al., 2021 [33]	Clinical evaluation	Compared AI-assisted CBCT interpretation with unaided clinician review; AI increased sensitivity for pathology detection from 76.7% to 85.4% and slightly improved specificity, benefiting diagnosis of pediatric trauma and infections.
Interceptive and Preventive Orthodontics
Zhang et al., 2023 [34]	Retrospective study	Compared deep learning-based mandibular growth prediction with orthodontist assessment in pediatric anterior crossbite; AI achieved 85% accuracy vs. 54.2% for junior orthodontists, supporting early interceptive care.
Yamada et al., 2025 [35]	Prospective clinical study	Evaluated CNN-based prediction of headgear/functional appliance effects on maxillo-mandibular growth in preadolescent Class II malocclusion; AI provided accurate growth forecasts to guide early intervention.
Volovic et al., 2023 [36]	Predictive modeling development and validation study	Developed and validated a machine learning model for orthodontic treatment duration; AI outperformed traditional regression models, improving scheduling and patient communication.
Moeini & Torabi, 2025 [37]	Narrative review	Summarized AI’s role in orthodontic diagnosis and treatment planning, highlighting applications for pediatric and special health care needs (SHCN) populations.
Kılıç et al., 2024 [38]	Cross-sectional study	Compared AI-based mobile orthodontic screening with in-clinic orthodontist assessment; AI accurately identified malocclusion risks in children, enabling early family-initiated referrals.
Chatbots and Teledentistry
Kaushik & Rapaka, 2025 [39]	Scoping review	Outlined AI’s role in teledentistry—enhancing remote diagnosis, treatment planning, and patient engagement, while noting gaps in bias assessment and clinical validation
Bayraktar, 2025 [40]	Diagnostic study	Assessed ChatGPT-3.5 (OpenAI, 2022) pediatric dental guidance; found high-quality responses for parent-focused queries (average score 4.3/5) but lower performance on academic questions, highlighting its potential and readability limitations.
Nadarzynski et al., 2024 [42]	Conceptual implementation paper	Proposed a framework for inclusive health care chatbots, emphasizing equity in conversational AI design to reduce access disparities, though not specific to dentistry.
Paschal et al., 2016 [43]	Cross-sectional study	Documented significant unmet dental needs among children with special health care needs, underscoring the opportunity for AI-facilitated solutions like teledentistry.
Alghamdi, 2023 [44]	Cross-sectional study	Found strong caregiver satisfaction (>80%) with virtual pediatric dental clinics during COVID-19, citing convenience and reduced access barriers.
Abakl et al., 2022 [45]	Retrospective study	Reported that teledentistry improved follow-up compliance and parental satisfaction during the pandemic in pediatric care, especially for those facing access challenges.
Hung et al., 2022 [46]	Scoping review	Explored global teledentistry implementations during COVID-19; AI-driven platforms supported continuity of pediatric dental services in resource-limited environments, though rigorous outcome studies remain scarce.
Sakr et al., 2025 [47]	Diagnostic accuracy study	Compared mobile phone photo-based teledentistry with conventional examination for occlusal caries in schoolchildren; found substantial agreement and high sensitivity, supporting its utility in school-based screenings.
Decision Support, Patient Engagement, and Predictive Analytics
Hasan et al., 2025 [48]	Diagnostic study	Developed and tested ML models to predict early childhood caries risk in Bangladeshi children; achieved high predictive accuracy, supporting early identification and prevention strategies.
Al-Namankany, 2023 [49]	Systematic review	Synthesized evidence on AI-driven diagnostic tools for ECC; found promising accuracy and potential to enhance treatment decision-making, though further validation is needed.
Pupong et al., 2025 [50]	Mixed methods study	Designed and evaluated a chatbot-based oral health care system for young children; reported high usability, acceptability, and positive caregiver engagement.
Hasei et al., 2025 [51]	Pilot study	Assessed generative AI chatbot use in pediatric, adolescent, and young adult cancer patients; reduced psychological burden and increased treatment engagement, with potential relevance to pediatric dentistry.
Sadegh-Zadeh et al., 2024 [52]	Machine learning study	Applied ML to identify key influencing factors in children’s oral health risk; highlighted the value of data-driven risk assessment for targeted prevention.
Lakhotia et al., 2025 [53]	Scoping review	Mapped ML applications across dentistry; identified growth areas, challenges in clinical translation, and gaps in pediatric-specific AI evidence.
Badrov et al., 2025 [54]	Cross-sectional study	Explored parental perspectives on oral health in children with autism spectrum disorder; poor oral health linked to lower quality of life, underscoring need for tailored AI-enhanced interventions.
Gomez-Rios et al., 2023 [55]	Development study	Introduced ORIENTATE, an AutoML platform for oral health prediction; demonstrated strong predictive performance for research and clinical use.
Pain Assessment and Discomfort Monitoring
De Sario et al., 2023 [56]	Narrative review	Reviewed AI-based facial expression analysis for pediatric pain detection; found convolutional neural networks effective in identifying subtle microexpressions, offering greater objectivity than conventional observation.
Cascella et al., 2023 [57]	Methodological review	Examined multimodal AI approaches integrating facial video analysis with physiological monitoring; highlighted potential to standardize pain scoring and reduce observer bias, though protocol harmonization is needed.
Santana et al., 2013 [58]	Observational study	Investigated heart rate variability as an indicator of discomfort in pediatric dental contexts; found correlations between autonomic changes and oral pain or anxiety.
Gkikas et al., 2024 [59]	Diagnostic accuracy study	Evaluated a transformer-based multimodal system combining facial video and heart rate data; achieved higher accuracy in acute pain detection than single-modality methods.
Yue et al., 2024 [60]	Medical hypothesis & planned study	Proposed development of an AI-powered facial expression recognition tool for perioperative pediatric pain assessment; aims to improve short- and long-term postoperative outcomes through automated scoring.
Ramdhanie et al., 2025 [61]	Systematic review	Synthesized evidence on technology-assisted facial expression recognition for pediatric pain; reported superior sensitivity, specificity, and inter-rater reliability compared to manual scoring, but noted lack of standardization.
Hughes et al., 2023 [62]	Feasibility study	Tested an AI-enabled rapid-assessment tool for infant pain detection; demonstrated ability to identify pain-related facial features within seconds, offering a fast, objective adjunct to caregiver-dependent scoring.
Behavior Management
Kasımoğlu et al., 2023 [63]	Randomized clinical trial	Tested a humanoid robot as a behavior management tool during pediatric dental treatment; significantly reduced dental anxiety, improved cooperation, and lowered salivary amylase compared to conventional methods.
Suresh & Shetty, 2024 [64]	Experimental study	Assessed VR distraction in children with ASD during dental procedures; significantly lowered salivary cortisol levels, indicating reduced stress.
Al Kheraif et al., 2024 [65]	Clinical intervention study	Used VR distraction in children/adolescents with ASD during dental exams; improved cooperation scores and reduced observed anxiety levels.
Mehrotra et al., 2024 [66]	Experimental study	Compared audio distraction and VR in children with mild intellectual disabilities; both methods reduced anxiety, with VR producing greater improvements in cooperation.
Behavior Modeling
Tanabe et al., 2024 [67]	Concept and development study	Proposed an AI-based emotion recognition system for children with profound intellectual and multiple disabilities using physiological and motion signals; aimed to facilitate adaptive behavior management.
Barros Padilha et al., 2023 [68]	Systematic review	Synthesized evidence on VR in pediatric dentistry; found consistent anxiety reduction and improved patient cooperation, but emphasized need for standardized protocols and long-term evaluation.
Nishat et al., 2023 [69]	Mixed-methods need assessment	Identified design requirements for an AI-enhanced social robot in pediatric emergency settings; highlighted potential for reducing distress and improving communication in children with special needs.

.

3.11. Ethical and Environmental Considerations and Challenges

Despite the numerous advantages AI offers to pediatric dentistry, its integration into clinical practice is not without challenges. One of the most significant hurdles is the necessity for high-quality, well-annotated data to effectively train AI systems [70]. These systems rely on large, diverse datasets to learn and improve their accuracy and generalizability. However, issues such as incomplete or inconsistent documentation, can hinder the development and performance of AI tools [71]. Without access to a sufficient and varied dataset, AI systems may struggle to recognize rare or atypical conditions, potentially reducing their effectiveness in real-world clinical settings. This data limitation is particularly problematic in pediatric dentistry, where children’s developmental stages vary widely. Consequently, AI models may need to be specifically tailored to account for these unique characteristics, requiring additional data collection and standardization to optimize their performance. Addressing these data limitations should go hand in hand with adherence to key ethical principles for AI use in dentistry, including privacy protection, fairness, transparency, and accountability.

3.11.1. Rigorous Validation of AI

Moreover, the successful use of AI in clinical practice requires pediatric dentists to have a significant level of trust in the technology. Clinicians must feel confident that the AI tools they use are accurate, reliable, and aligned with their clinical expertise. However, establishing this trust is not a straightforward process. For AI systems to be truly effective in the clinical setting, they must undergo rigorous validation and testing [72]. This process ensures that AI models are not only scientifically sound but also safe for use in a pediatric context. The validation process typically involves extensive trials and comparisons with human assessments to confirm the system’s diagnostic capabilities. Only when these AI tools are proven to consistently provide accurate results, and when they demonstrate clear benefits in improving patient outcomes, can they be fully integrated into routine pediatric dental practice.

Ethical considerations also emerge as a critical aspect of AI adoption in pediatric dentistry. Since AI involves the collection and processing of large volumes of sensitive data, concerns around patient privacy and data security are paramount. Pediatric patients are particularly vulnerable due to their age, and any breach of their personal medical data could have long-lasting consequences. Therefore, ensuring robust data protection mechanisms, compliance with privacy regulations and the use of secure platforms for AI applications is crucial. Additionally, there are concerns about the transparency of AI decision-making processes, particularly when AI algorithms make decisions or recommendations that impact patient care [73]. To address this, A recent study proposed a “risk-averse” AI framework that incorporates a biologically inspired “fear module,” designed to detect uncertainty and defer high-risk decisions to human clinicians [74]. This built-in safety mechanism, modeled after the function of the human amygdala, reinforces clinician oversight and helps mitigate potential errors or unintended consequences. For pediatric dentists, the ability to interpret and clearly communicate how AI systems generate their conclusions remains essential. Such transparency is critical to fostering trust and ensuring that AI enhances—rather than undermines—the clinician–patient relationship.

3.11.2. Ensuring the Complementary Role of AI

Another significant ethical concern is the potential for AI to replace human judgment. AI’s growing capabilities in data analysis and decision-making could lead to fears that it may eventually overshadow the expertise and intuition of pediatric dentists. This raises questions about the appropriate role of human clinicians in the decision-making process. While AI can assist in diagnosis, treatment planning, and predictive analytics, it is essential to maintain a balance where the final clinical decisions remain in the hands of experienced practitioners. Pediatric dentists are trained not only to analyze data but also to understand the broader context of a patient’s health, emotional well-being, and family dynamics. AI tools should be seen as supportive and complementary rather than as a replacement for human judgment [75]. Additionally, AI’s reliance on large datasets raises concerns related to bias and fairness. If AI models are trained on datasets that are not representative of diverse populations, there is a risk that they may perform poorly for certain demographic groups, especially those from underrepresented backgrounds. For example, AI algorithms might be trained predominantly on data from a specific age group, ethnicity, or socio-economic background, leading to potential disparities in care. In pediatric dentistry, where children from various backgrounds and with different medical histories require specialized care, biased AI systems could result in inaccurate diagnoses or less effective treatment plans for certain groups of children. Ensuring that AI models are trained on diverse and inclusive datasets, with sufficient representation from various racial, ethnic, and socio-economic groups, is essential to reduce the risk of bias and improve the fairness of AI-driven care [76].

AI has the potential to improve pediatric dentistry. Yet, careful attention must be paid to the challenges of data quality, clinician trust, ethical issues, and potential biases. A recent study introduced the concept of an “ethical firewall,” a design framework that incorporates provable ethical boundaries and traceable decision logic within AI systems [77]. Such structures are essential to preserve transparency, support clinician autonomy, and ensure that AI remains a trusted tool in pediatric dental care. In addition to clinician autonomy, the participation of patients—and in the pediatric context, their parents or guardians—must also be emphasized. Shared decision-making is a cornerstone of ethical care, and AI-supported recommendations should be communicated transparently to families. Ensuring that caregivers understand the reasoning behind AI-assisted decisions can strengthen trust, promote informed consent, and preserve the integrity of the clinician–patient–parent relationship.

3.11.3. AI-Induced Overtreatment

Overtreatment—including unnecessary diagnostics, therapeutics, or interventions—remains a persistent contributor to health care inefficiency, elevated patient risk, and escalating costs. Recent evidence suggests that the integration of AI-assisted clinical decision support can both exacerbate and mitigate overtreatment, contingent upon implementation context and physician incentive structures [78]. An experimental study utilizing a medical prescription task among medical students demonstrated that AI advice, when aligned with regressive incentive schemes, enabled a 62% reduction in overtreatment and a 17–37% increase in diagnostic accuracy depending on the incentive [79]. AI tools supplied probabilistic diagnoses and tailored recommendations, facilitating more evidence-based decision-making and curbing clinicians’ tendencies toward excessive intervention—especially when monetary incentives were mitigated. Nevertheless, the risk of overreliance on AI and potential propagation of algorithmic biases underscores the imperative for robust validation, transparency in model design, and human-AI collaboration in the clinical loop.

The causes of overtreatment are dichotomized into monetary (57%) and non-monetary (43%) incentives, with AI serving as a technological lever particularly effective in negating the latter by filling knowledge gaps, debiasing choices, and enhancing clinician confidence in therapeutic restraint [79,80]. However, the literature also raises ethical and equity concerns, including trust in AI, physician acceptance, and potential disparities in access to AI-driven resources [81,82]. Pediatric patients represent a uniquely vulnerable population due to their limited capacity for consent and the evolving nature of their privacy rights over time. In AI applications, this vulnerability is compounded by challenges in ensuring adequate data protection. Pediatric health data—especially behavioral, developmental, and biometric information—can be highly sensitive and remain personally identifiable even after traditional anonymization methods. Moreover, because children’s preferences and understanding change with age, long-term storage and reuse of their data raise ethical questions regarding future consent. These age-specific concerns highlight the need for pediatric-specific privacy safeguards and dynamic consent frameworks that evolve as the child matures.

3.11.4. Carbon Cost of Cloud AI Inference

The rapid growth of cloud-based AI platforms—driven in large part by the deployment of resource-intensive generative models and high-volume inference tasks—has led to significant environmental repercussions. The principal contributor to this impact is the enormous amount of electricity consumed by data centers, which must operate around the clock to manage millions of AI requests daily. Between 2019 and 2023, global data center power demand has soared by over 70%, resulting in these facilities now accounting for approximately 1–2% of global electricity usage, a figure comparable to the annual consumption of entire nations [83]. Every interaction with a generative AI system initiates an extensive computational process in the cloud, resulting in a measurable carbon footprint [84]. Generative AI models, in particular, are markedly more demanding than conventional search or machine-learning applications [85]. Crucially, the majority of the environmental burden from generative AI arises not during model training, but during day-to-day use—or inference. In addition to electricity, water usage is substantial: data centers employ thousands of liters daily for cooling, a strain with profound implications for regions already facing water scarcity.

The marked expansion of cloud-hosted generative AI is reshaping global energy and resource consumption patterns, with inference workloads driving most of the ongoing environmental impact. This highlights the urgent need for sustainable AI infrastructure, energy-efficient models, and mindful deployment in order to mitigate these escalating ecological costs [86] (

Table 4. Ethical concerns and potential solutions in AI-powered dentistry.

Ethical Issue	Concern	Potential Solution
Data Privacy	Sensitive patient data being processed by AI systems	Implementation of robust data protection protocols
Algorithmic Bias	AI models may be biased if trained on non-diverse data	Ensure diverse datasets and regular algorithm audits
Autonomy and Decision-Making	AI may influence decision-making, reducing clinician autonomy	AI as a support tool, with clinicians retaining final decision-making power
Overtreatment	Unnecessary diagnostics/interventions	Align AI incentives, retain clinician oversight
Carbon Cost	Environmental burden from cloud inference	Infrastructure optimization, carbon-aware computing

).

4. Discussion

A substantial proportion of the studies included in this narrative review were published within the past three years, reflecting a growing interest in applying AI to pediatric dentistry and special care dentistry. While previous reviews have examined AI in dentistry and, to a lesser extent, in pediatric dentistry, these have largely concentrated on clinical implementation or technical performance and have not specifically addressed the combined context of pediatric and special needs populations [18,19]. Moreover, broader ethical, developmental, and environmental considerations unique to these groups have received limited attention. To our knowledge, no prior review has comprehensively examined AI at this intersection. Therefore, this review aimed to synthesize current applications of AI in pediatric dentistry and special care dentistry, highlight associated ethical and environmental challenges, and explore future directions to guide research, clinical practice, and policy development.

Current evidence is most developed in diagnostic imaging and caries detection, supported by systematic reviews and large annotated datasets. In contrast, applications in three-dimensional imaging, orthodontics, teledentistry, and predictive analytics remain at an emerging stage, with encouraging but heterogeneous findings. Pain assessment, behavior management, and behavior modelling are still exploratory, relying largely on feasibility trials and conceptual prototypes. A persistent weakness across domains is the lack of explicit validation in children with special health care needs, with most studies extrapolating from general pediatric cohorts and leaving applicability to these populations uncertain. Across diagnostic imaging, several pediatric-focused studies in this review reported AI-assisted radiographic analysis achieving diagnostic performance comparable to experienced clinicians. This aligns with pooled results from meta-analyses in mixed-age populations, which found AI-aided caries detection on bitewing radiographs to have accuracies in the range of 0.85–0.91 [14,87]. These parallels suggest that despite limited pediatric-only datasets, AI algorithms may transfer diagnostic capabilities across age groups when imaging parameters are similar. In orthodontics, AI-driven growth prediction models have demonstrated moderate-to-high accuracy in anticipating skeletal changes [88,89], supporting the observations in this review that AI can inform interceptive and preventive orthodontic strategies. However, compared with the robust body of adult orthodontic AI research, pediatric-specific validation remains limited. Most models have yet to be tested particularly in children with special health care needs, whose unique anatomical, developmental, and behavioral characteristics may influence model performance. Evidence on behavior management and care for children with special needs remains early-stage. The reviewed reports on AI-enhanced virtual reality, multimodal biosignal monitoring, and chatbot interfaces are consistent with findings from the behavioral sciences [68,69,90], where such interventions improved cooperation, reduced anxiety, and enhanced communication. Nonetheless, in dentistry these approaches have largely been evaluated in feasibility studies or small controlled trials, limiting confidence in their scalability. Teledentistry results in this review are also in line with broader pediatric and general dentistry studies [39,46]. AI-enabled triage and remote monitoring systems have been shown to reduce appointment delays, improve accuracy of urgency assessment, and increase patient satisfaction—particularly in underserved populations [48,49,53]. Yet, as in other health care fields [76,77], barriers related to privacy, data governance, and clinician readiness persist. Teledentistry findings in this review also align with previous evaluations in both pediatric and general dentistry [47,53], which consistently report that AI-assisted triage and remote monitoring improve access, reduce delays, and increase patient satisfaction—particularly in underserved populations [40,42,46]. Yet, concerns persist about privacy, data governance, and clinician readiness for large-scale AI-enabled remote care. The evidence indicates that pediatric dentistry is beginning to benefit from AI-driven capabilities, but with unique challenges. Smaller and less diverse datasets, the complexity of developmental variation, and heightened ethical considerations in working with minors and special needs populations all limit the direct translation of existing models. In the context of children with special health care needs, where continuous monitoring raises unique risks of re-identification, ethical safeguards include adaptive consent, short retention periods, and clear disclosure. Both caregivers and clinicians should be provided with straightforward explanations of how patient data are used and how AI outputs are derived.

4.1. Implications for Practice

The findings suggest that AI tools could serve as valuable adjuncts in pediatric and special needs dental care, particularly for enhancing diagnostic workflows, tailoring preventive interventions, and improving access for underserved or special needs populations. For children with special health care needs, successful adoption will further require systems validated on inclusive datasets, adaptive consent mechanisms, and tools designed to address communication and behavioral challenges. However, real-world implementation faces practical barriers, including high costs, uneven digital infrastructure, and a lack of pediatric- or SHCN-specific regulatory pathways. Successful integration will also depend on workflow readiness, clinician training, and reimbursement mechanisms. Policymakers and professional bodies should prioritize the development of ethical and regulatory frameworks that address pediatric-specific concerns, including dynamic consent mechanisms, dataset diversity, and safeguards against bias. Furthermore, environmental sustainability should be considered in large-scale AI deployments, particularly cloud-based systems with significant energy demands.

4.2. Limitations

The body of evidence reviewed is constrained by several factors. First, there is a heavy reliance on small sample sizes, single-center studies, and retrospective designs. This limits external validity and makes it difficult to draw definitive conclusions. Second, the heterogeneity of AI models, input data types, and outcome measures complicates direct comparisons between studies. Third, there is an underrepresentation of studies from low- and middle-income countries, where infrastructure and resource constraints could significantly alter feasibility and impact. Finally, special needs populations remain underrepresented in training data, increasing the risk of algorithmic bias and unequal care outcomes.

As a scoping review, this work aimed for breadth and thematic synthesis rather than exhaustive evidence mapping. While a structured, protocol-registered search was conducted across multiple databases, with predefined search strings, inclusion/exclusion criteria, and a documented study selection process, formal quality appraisal and quantitative synthesis were not performed. The inclusion of multiple study designs allowed for a broad perspective but may have introduced heterogeneity in evidence quality. Additionally, some relevant studies may have been missed due to language restrictions or database coverage, and the absence of grey literature screening could have excluded non-indexed but potentially informative sources. These factors should be considered when interpreting the conclusions.

4.3. Future Directions

Future research must prioritize children with special health care needs populations, ensuring that predictive models, diagnostic tools, and monitoring systems are specifically validated for children with developmental, behavioral, and medical complexities. ML algorithms are continually improving their ability to analyze dental imaging, such as radiographs and CBCT scans, with greater precision [91,92,93]. Future AI systems may be able to automatically detect more subtle dental conditions that are not immediately apparent to clinicians, such as microfractures, early-stage cavities, or developmental anomalies. Additionally, AI models will likely be trained on even larger and more diverse datasets, improving their ability to detect a broader range of dental issues in different pediatric populations. The integration of AI into real-time diagnostic processes is another area of significant potential. In the future, AI could be used to provide immediate feedback during clinical exams, alerting clinicians to issues as they occur. For example, AI could be integrated with intraoral cameras to assist pediatric dentists in real-time during patient examinations [94,95]. AI tools offer the possibility of developing more tailored treatment plans based on individual dental history and risk factors, which may enhance personalization in pediatric care [96,97], leading to quicker decision-making and more timely treatments for children. This technology could help reduce the risk of overlooked conditions and improve overall patient outcomes. While AI is already being used to assist in creating personalized treatment plans, future advancements will likely allow for even more detailed, data-driven, and patient-specific approaches. By integrating data from various sources, such as genetic information, health history, lifestyle factors, and dental development, AI may offer highly individualized treatment plans that cater to the unique needs of each patient. The use of AI to predict a child’s future dental health trajectory will likely allow pediatric dentists to intervene earlier. This type of predictive modeling could also allow for the development of custom preventive strategies for each child, based on their specific risk factors.

The role of AI in robotic dentistry is expected to expand in the future, with more sophisticated robotic systems becoming a routine part of pediatric dental practices. Robotic systems may become capable of conducting certain types of dental procedures autonomously, particularly for children with special needs who may find it difficult to tolerate conventional treatments. Another area with significant potential is the use of AI to improve patient engagement and education. In the future, AI-powered tools could be used to develop interactive and personalized oral health education materials for children [98,99]. By using NLP and ML, these tools could tailor educational content to each child’s learning style, age, and language proficiency. AI-based applications could also serve as virtual assistants, reminding children and their caregivers about oral hygiene practices, upcoming appointments, and preventive measures, thereby encouraging better compliance with treatment regimens. Moreover, AI could play a significant role in improving access to pediatric dental care. Teledentistry has already started to emerge as an essential tool in reaching underserved populations, and future advancements in AI could further enhance its potential. AI systems integrated with teledentistry platforms could assist in performing remote diagnostic assessments and providing decision support to clinicians in real time [100,101,102]. This would be especially valuable in rural or underserved regions where access to pediatric dental care is limited. By offering remote consultations and guidance, AI could help fill the gap in dental care availability and provide more equitable access to quality care for children. However, as the future of AI in pediatric dentistry unfolds, it will be essential to continue addressing the ethical, environmental, regulatory, and privacy concerns that accompany these technological advancements [103,104,105].

5. Conclusions

This scoping review indicates that AI in pediatric dentistry is most established in diagnostic imaging and caries detection, where systematic reviews and large annotated datasets support its clinical utility. Applications in three-dimensional imaging, teledentistry, and predictive analytics are emerging but heterogeneous, while pain assessment, behavior management, and behavior modelling remain in early or exploratory phases. Evidence specific to children with special health care needs was sparse, often descriptive, and seldom validated. Future research should address this imbalance by prioritizing children with special health care needs–focused investigations, conducting robust multi-center validation, and integrating ethical and environmental safeguards. A responsible future for AI in pediatric and special care dentistry will require rigorous clinical validation together with ethical vigilance—ensuring diverse datasets, protecting patient privacy, preserving clinician autonomy, preventing overtreatment, and minimizing the ecological footprint of cloud-based technologies.