Artificial intelligence (AI) holds transformative potential for advancing oral health surveillance by streamlining data collection, integration, and dissemination. This review critically synthesizes AI applications in oral health surveillance, highlighting its roles in 1) mapping population-level trends and oral health inequities using machine learning on epidemiological data; 2) enabling remote screening of oral diseases/conditions, including caries, oral hygiene, gingivitis, oral cancer, and malocclusion from intraoral images via computer vision models; and 3) integrating multimodal data through emerging large language models (LLMs) to enhance precision public health. We clarify the comparative strengths of distinct AI modeling for processing the primary data types in surveillance: structured clinical records, unstructured images, and integrated multimodal data. Traditional machine learning methods have been effectively applied to map population-level oral health disparities and identify risk factors but are constrained to structured data. Computer vision methods excel in individual-level diagnostics using intraoral photographs. To translate such capability into scalable surveillance, it is recommended to establish standardized imaging protocols for nonclinical settings, develop scalable models for fine-grained feature extraction, and implement reliable evaluation. These steps are essential to address pervasive challenges, including inconsistent image quality, domain shift, prevalence imbalance, and cost-effectiveness constraints. The future of AI-driven oral health surveillance lies in developing dental-adapted multimodal LLMs (MLLMs). Such MLLMs are uniquely capable of synthesizing disparate data streams, from structured clinical data to heterogeneous imaging modalities (e.g., intraoral photographs and radiographs), or even biomolecular data. This integration capacity facilitates a paradigm shift, moving current applications in dental consultation and clinical decision support toward a novel, tiered system for population-level monitoring. Such a system would provide actionable insights for public health policymaking via spatiotemporal analysis and causal inference. Next-generation AI-driven oral health surveillance systems can only succeed when built on a strong foundation of rigorous ethical principles and safeguards.
Chen et al. (Fri,) studied this question.