What question did this study set out to answer?

This review aims to evaluate the integration of AI with intraoral digital imaging for detecting dental caries and assess its methodological quality and clinical applicability.

March 7, 2026F1000ResearchOpen Access

Artificial Intelligence Integrated with Intraoral Digital Imaging in Dental Caries Detection, Treatment Planning, and Clinical Decision-Making: A Scoping Review

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Authors

SKSarah Kayali AGAli Golkari MMM. S. Mathur

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Overview

A scoping review examines AI's role in detecting dental caries in adults, suggesting significant clinical implications.

Key Points

This review aims to evaluate the integration of AI with intraoral digital imaging for detecting dental caries and assess its methodological quality and clinical applicability.
Conducted a literature search across PubMed, Scopus, and IEEE Xplore from 2015 to 2025.
Included studies meeting predefined eligibility criteria.
Performed thematic analysis to identify key themes.
Applied CASP quality appraisal to ensure rigorous methodology.
AI systems detected more carious lesions than human clinicians, especially early-stage caries.
Reported metrics included 88% sensitivity, 91% specificity, and 89% accuracy for AI detection.
Some models showed F1-scores up to 89% and AUC around 95%.
Identified implementation challenges, including limited real-world validation and ethical considerations.

Cite This Study

Kayali et al. (2026) studied this question.

synapsesocial.com/papers/69abc2615af8044f7a4ebf54 https://doi.org/10.12688/f1000research.172671.2

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March 7, 2026F1000ResearchOpen Access

Artificial Intelligence Integrated with Intraoral Digital Imaging in Dental Caries Detection, Treatment Planning, and Clinical Decision-Making: A Scoping Review

View Full Paper

Ask AI

Bookmark

Authors

SKSarah Kayali AGAli Golkari MMM. S. Mathur

Discussion

Member takes

Overview

A scoping review examines AI's role in detecting dental caries in adults, suggesting significant clinical implications.

Key Points

This review aims to evaluate the integration of AI with intraoral digital imaging for detecting dental caries and assess its methodological quality and clinical applicability.
Conducted a literature search across PubMed, Scopus, and IEEE Xplore from 2015 to 2025.
Included studies meeting predefined eligibility criteria.
Performed thematic analysis to identify key themes.
Applied CASP quality appraisal to ensure rigorous methodology.
AI systems detected more carious lesions than human clinicians, especially early-stage caries.
Reported metrics included 88% sensitivity, 91% specificity, and 89% accuracy for AI detection.
Some models showed F1-scores up to 89% and AUC around 95%.
Identified implementation challenges, including limited real-world validation and ethical considerations.

Cite This Study

Kayali et al. (2026) studied this question.

synapsesocial.com/papers/69abc2615af8044f7a4ebf54 https://doi.org/10.12688/f1000research.172671.2

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