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Prognostic Clinical Predictive Models for Dental Caries Using Artificial Intelligence: Methodological Considerations
Caries Research
|July 16, 2026
Summary
Developing artificial intelligence (AI) models for dental caries prediction requires rigorous methodology. This review outlines eight critical phases to ensure AI models reduce bias and improve clinical impact as decision-support tools.
Area of Science:
- Dental research
- Artificial intelligence in healthcare
- Predictive modeling
Background:
- Artificial intelligence (AI) is increasingly used for clinical predictive models in dentistry, particularly for dental caries.
- Many existing AI prognostic models for dental caries lack essential methodological rigor.
- Limited implementation and demonstrated clinical benefits hinder the widespread adoption of these AI models.
Purpose of the Study:
- To review and contextualize key considerations for developing, validating, and implementing AI-based prognostic models for dental caries.
- To provide a framework for improving the quality, reproducibility, and clinical utility of AI caries models.
- To emphasize the role of AI models as decision-support tools in clinical practice.
Main Methods:
- A narrative methodological review of AI-based prognostic models for dental caries.
- Adaptation of established prediction model frameworks (TRIPOD+AI, PROBAST+AI, PROGRESS) for dental caries.
- Identification and contextualization of eight critical phases for AI model development and implementation.
Main Results:
- Eight critical phases for AI caries model application were identified: problem selection, data quality, study design, model development, validation, performance assessment, transparent reporting, and deployment/maintenance.
- Addressing these phases is crucial for minimizing bias and enhancing the reproducibility of AI models.
- Successful implementation requires a focus on clinical impact and meaningful evaluation.
Conclusions:
- Rigorous methodological approaches are essential for developing reliable AI-based prognostic models for dental caries.
- AI models should function as decision-support tools, augmenting rather than replacing clinical judgment.
- Adherence to established frameworks and transparent reporting will improve the clinical value and adoption of AI in dental caries management.
