Findings

What do systematic reviews say?

6 questions · 6 findings · 1 reviews · Dental caries

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No studies found 0 · 0%GRADE not reported 6 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%

AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard)

Sensitivity · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities)

AI algorithms have high pooled sensitivity (0.85) for detecting dental caries; ungraded, with very high heterogeneity (I2>95%) and evidence of publication bias.

1 finding from 1 review

Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging… 2025 All outcomes
Cross-sectional diagnostic accuracy (index test vs reference standard) · Dental imaging: intraoral photographs, periapical, bitewing, panoramic radiographs, CBCT, near-infrared transillumination · Overall (all imaging modalities pooled)
Sensitivity 0.85 [0.83, 0.93]
20 studies · 29423 participants
GRADE not reported

AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard)

Specificity · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities)

AI algorithms have high pooled specificity (0.90) for detecting dental caries; ungraded, with very high heterogeneity (I2>95%).

1 finding from 1 review

Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging… 2025 All outcomes
Cross-sectional diagnostic accuracy (index test vs reference standard) · Dental imaging: intraoral photographs, periapical, bitewing, panoramic radiographs, CBCT, near-infrared transillumination · Overall (all imaging modalities pooled)
Specificity 0.90 [0.85, 0.95]
20 studies · 29423 participants
GRADE not reported

AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard)

Area under the summary ROC curve · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities)

The area under the summary ROC curve for AI caries detection is 0.86, indicating high overall diagnostic accuracy; ungraded.

1 finding from 1 review

Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging… 2025 All outcomes
Cross-sectional diagnostic accuracy (index test vs reference standard) · Dental imaging: intraoral photographs, periapical, bitewing, panoramic radiographs, CBCT, near-infrared transillumination · Overall (all imaging modalities pooled)
AUC 0.86
20 studies · 29423 participants
GRADE not reported

AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard)

Positive likelihood ratio · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities)

A positive AI test raises caries probability substantially (LR+ 10.44; positive post-test probability 79%); ungraded.

1 finding from 1 review

Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging… 2025 All outcomes
Cross-sectional diagnostic accuracy (index test vs reference standard) · Dental imaging: intraoral photographs, periapical, bitewing, panoramic radiographs, CBCT, near-infrared transillumination · Overall (all imaging modalities pooled)
LR+ 10.443 [7.505, 14.531]
20 studies · 29423 participants
GRADE not reported

AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard)

Log diagnostic odds ratio · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities)

AI algorithms show strong pooled discriminative accuracy for dental caries (log DOR 4.37; DOR 70.9); ungraded.

1 finding from 1 review

Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging… 2025 All outcomes
Cross-sectional diagnostic accuracy (index test vs reference standard) · Dental imaging: intraoral photographs, periapical, bitewing, panoramic radiographs, CBCT, near-infrared transillumination · Overall (all imaging modalities pooled)
Log DOR 4.37 [3.16, 6.27]
20 studies · 29423 participants
GRADE not reported

AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard)

Negative likelihood ratio · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities)

A negative AI test lowers caries probability substantially (LR- 0.17; negative post-test probability 6%); ungraded.

1 finding from 1 review

Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging… 2025 All outcomes
Cross-sectional diagnostic accuracy (index test vs reference standard) · Dental imaging: intraoral photographs, periapical, bitewing, panoramic radiographs, CBCT, near-infrared transillumination · Overall (all imaging modalities pooled)
LR- 0.168 [0.138, 0.205]
20 studies · 29423 participants
GRADE not reported