AI-driven approaches in the management of early childhood caries: A path toward global oral health.

Background: The integration of Artificial Intelligence (AI) with paediatric dentistry has unveiled transformative possibilities, particularly in mitigating the global burden of a prevalent yet preventable oral health issue, namely early childhood caries (ECC). ECC affects millions of children worldwide, leading to significant health, developmental, and economic challenges. This paper explores the application of AI-driven technologies, including machine learning and deep learning, in the detection, diagnosis, risk assessment, and management of ECC. Brief summary: AI models leveraging dental radiographs and intraoral photographs have demonstrated high accuracy in caries detection, while predictive algorithms facilitate the identification of high-risk groups using patient demographics, behavioural data, and even genetic markers. Smartphone applications equipped with AI capabilities, such as AICaries, empower caregivers with tools for at-home caries screening, enhancing accessibility and fostering preventive care.Today, AI's role extends to optimizing healthcare utilization patterns and advancing personalized treatment strategies, particularly in underserved regions where traditional resources are scarce. Efforts to develop diverse training datasets have not eliminated biases leading to concerns about fairness, discrimination and privacy. Further, unregulated AI applications may worsen rather than reduce health disparities. Implications for future research: This review underscores the potential of AI to revolutionize ECC prevention and management, paving the way for equitable oral healthcare globally. It advocates for further interdisciplinary research to refine AI tools, address practical challenges, and support the development of evidence-based policies for widespread implementation. Ultimately, AI emerges as a pivotal advancement in transitioning from disease management to proactive oral health care strategies.