Question · asked by 1 review
Machine-learning algorithms for dental caries detection vs Dental expert / visual-tactile clinical assessment (reference standard): Negative post-test probability
- Population
- Dental images or diagnostic tests for dental caries detection
- Intervention
- Machine-learning algorithms for dental caries detection
- Comparison
- Dental expert / visual-tactile clinical assessment (reference standard)
- Outcome
- Negative post-test probability
The negative post-test probability was 6%; certainty was not graded.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Artificial intelligence algorithms for caries detection vs Dental expert or visual-tactile/clinical assessment as reference standard Negative post-test probability · Dental images or diagnostic tests for dental caries detection · Dental imaging across modalities, Overall (all imaging modalities pooled) The negative post-test probability was 6%; certainty was not graded. Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) All outcomes | Post-test probability 0.06 | 20 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging modalities: An umbrella… 2025 All outcomes | October 15, 2024 | 20 | Not applicable |
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.