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.

Successful outcome of pulp therapy depends on good chemomechanical preparation of the canals. This is completed with the help of various upcoming rotary and hand files. However, during this preparation, there might be an apical extrusion of the debris which may result in postoperative complications. The aim of this study was to evaluate and compare the number of debris apically extruded during canal preparation using two different pediatric rotary file systems and conventional hand file systems in primary teeth. 60 primary maxillary central incisors that were extracted due to trauma or untreated dental caries with no signs of resorption were taken. Canal preparation was executed using three different file systems: Group A: Group A hand K file system, Group B Kedo S Plus, Group C Kedo SG Blue. For each of these files using the Myers and Montgomery model, the pre- and post-weight of the eppendorf tube was assessed to quantify the number of apical debris. The maximum extrusion of apical debris was noticed with the Hand K-file system. The least debris was noticed in the Kedo S Plus file system. Statistical analysis revealed that there were highly significant differences in apical extrusion and debris when comparing hand files and rotary files and also between the two rotary files used. Apical debris collection is an unavoidable outcome of canal instrumentation. Among the file systems compared, rotary files had lesser extrusion when compared to hand files. Among the rotary files, Kedo S plus showed normal extrusion compared to SG Blue.