Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice

Abstract

Dental caries remains the most prevalent chronic disease worldwide, affecting more than two billion people and driving substantial healthcare costs. While early detection is central to minimally invasive dentistry, traditional diagnostic methods suffer from limited sensitivity and high inter-examiner variability, especially for incipient lesions. Artificial Intelligence (AI) has shown promise in overcoming these limitations, and many studies have reported expert-level performance under controlled conditions. However, a substantial translational gap persists between algorithmic success in silico and reliable performance in real-world clinical environments. This review synthesizes the full development pipeline of AI for caries diagnosis—from data curation and ground-truth construction to model design, validation, and clinical deployment. We highlight persistent bottlenecks including domain shift across imaging devices and clinical settings, subjective and inconsistent annotation practices, limited multimodal datasets, and heterogeneous reporting standards. Emerging strategies such as multi-center data collection, probabilistic labeling, self-supervised learning, domain adaptation, and test-time augmentation offer partial solutions but remain underutilized. We argue for a paradigm shift from binary detection toward quantitative, risk-based staging that aligns with minimally invasive dentistry and the WHO Global Oral Health Action Plan 2023–2030. By advocating for standardized multimodal datasets, rigorous external validation, explainable interfaces, and human-centered clinical integration, this review outlines a roadmap for translating AI innovation into trustworthy, equitable, and clinically meaningful decision-support systems capable of reducing the global burden of untreated caries.