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Published on: February 23, 2024
Artificial intelligence for dental caries detection: An umbrella review
Ahmad Bittar1, Rafael Dascanio2, Mutlu Özcan3
1Department of Restorative Dentistry, Faculty of Dentistry, Istinye University, Istanbul, Türkiye.
Journal of Dentistry
|July 31, 2026
Summary
Artificial intelligence (AI) shows promise for detecting dental caries but lacks clinical validation. Current evidence suggests AI tools are not yet reliable for standalone diagnosis, needing further research for clinical use.
Area of Science:
- Dentistry
- Medical Imaging
- Artificial Intelligence
Background:
- Artificial intelligence (AI) is increasingly explored for enhancing dental caries detection.
- The clinical utility of AI in dental diagnostics remains uncertain.
- Systematic reviews are needed to consolidate evidence on AI for caries detection.
Purpose of the Study:
- To synthesize and critically appraise existing systematic reviews on AI for dental caries detection and diagnosis.
- To evaluate the methodological quality and evidence reliability of AI in dental imaging.
- To determine the current clinical applicability of AI tools in dentistry.
Main Methods:
- Umbrella review methodology following PRIOR guidelines.
- Comprehensive literature search across major databases (MEDLINE, Embase, Scopus, Web of Science, Google Scholar) up to March 2026.
- Methodological quality assessed using AMSTAR 2; study overlap quantified using CCA.
Main Results:
- Seventeen systematic reviews were included; five performed meta-analyses on diagnostic test accuracy.
- Pooled sensitivity (0.76-0.94) and specificity (0.85-0.91) were reported for AI models, primarily deep learning on radiographs and intraoral photos.
- Significant heterogeneity in methods, limitations in external validation, and retrospective data use were identified, weakening evidence reliability.
Conclusions:
- AI demonstrates high diagnostic performance in experimental settings for dental caries detection.
- Current evidence does not support AI as a standalone diagnostic tool in clinical practice.
- Further prospective validation is required to establish AI's clinical impact and guide implementation in decision-support contexts.
