Artificial Intelligence (AI) is revolutionizing healthcare, and its application in pediatric dentistry is showing significant promise in improving diagnostic accuracy, efficiency, and early detection of dental conditions in children. This review explores the current landscape of AI-driven technologies employed in the identification of pediatric dental diseases, including dental caries, malocclusion, developmental anomalies, and periodontal conditions. Various AI techniques, such as machine learning, deep learning, and convolutional neural networks (CNNs), are examined for their diagnostic potential and performance relative to traditional methods. The review also examines the integration of AI with radiographic imaging, intraoral scanners, and other diagnostic tools commonly used in pediatric dental practice. While AI presents considerable advantages such as speed, objectivity, and the potential to reduce human error, limitations, including data privacy, lack of standardized datasets, and ethical considerations, are also highlighted. Overall, this review underscores the ground-breaking potential of AI in pediatric dentistry and emphasizes the need for further research, validation, and clinical integration to realize its benefits fully.
Prathibha Prasad (2026) studied this question.