AI‐Driven Dentistry and Public Health Surveillance: Opportunities and Challenges

Abstract

ABSTRACT The integration of artificial intelligence (AI) into dentistry is reshaping clinical workflows, opening new possibilities for population‐level public health monitoring. Machine learning approaches, such as convolutional neural networks and other deep learning architectures, are becoming more and more capable to exhibit diagnostic performances, which are close to those of expert human readers. Beyond single‐clinic decision support, aggregated outputs that have been collected from AI diagnostic systems could be used as real‐time signals for population surveillance: When combined with geospatial and socioeconomic datasets, they may help reveal structural barriers to care. This review presents opportunities and constraints at the intersection of diagnostic AI and dental public health surveillance and outlines a five‐stage framework (data ingestion, spatiotemporal aggregation, socioeconomic enrichment, predictive modeling, and dashboard deployment) for turning de‐identified AI outputs into actionable, equity‐focused public health intelligence. This article also examines methodological, privacy, and governance challenges—including bias, interpretability, and accountability—as well as ethical issues and proposes safeguards to support equitable deployment.