Prognostic Clinical Predictive Models for Dental Caries Using Artificial Intelligence: Methodological Considerations.

backgroundArtificial intelligence (AI) is currently used to develop clinical predictive models for dental caries. However, most prognostic models lack key methodological components. Few reach clinical practice, and fewer demonstrate clinical benefit. SUMMARY: This narrative methodological review examines considerations for developing, validating, and implementing AI-based caries prognostic models. Based on established prediction model frameworks (TRIPOD+AI, PROBAST+AI, and PROGRESS), we contextualize eight critical phases for caries-specific application. Each phase addresses challenges specific to caries, e.g., clustering of teeth within patients, dominance of baseline caries experience as a predictor, and frequent absence of external validation. Addressing these phases reduces bias, improves reproducibility, and supports meaningful evaluation of clinical impact. AI-based prognostic models should serve as decision-support tools that inform clinician and patient choices rather than substitutes for clinical judgment. KEY MESSAGES: (i) Most AI-based caries prognostic models are never implemented and fewer of those implemented demonstrate clinical benefit. Methodological deficiencies explain this gap. (ii) Caries prognostic research faces field-specific challenges such as retrospective design, clustering of teeth within patients, and dominance of baseline caries experience. Valid caries prognostic models require attention to eight phases, from problem selection through deployment and ongoing monitoring. (iii) Better discrimination (higher AUC) does not indicate clinical usefulness. Calibration and decision curve analysis are essential yet frequently absent from published studies. (iv) Researchers should report regulatory considerations and plan prospective impact assessment from study inception. Models generating risk estimates that influence clinical decisions qualify as software as a medical device, requiring regulatory compliance and demonstrated clinical impact before deployment.