Question · asked by 1 review

AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard): Area under the summary ROC curve

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

What each review found

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FindingEffectStudiesGRADE certainty
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) · 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)
The area under the summary ROC curve for AI caries detection is 0.86, indicating high overall diagnostic accuracy; ungraded.
Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) All outcomes
AUC 0.8620 studies · 29423 participantsGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

Studies behind the findings

None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.

How reviews are matched to this question

Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.