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Each row is one question a systematic review asked: who, what, compared with what, and which outcome, with the effect it found and how certain that is. Words match anywhere in the field.
515 questions in 19 reviews · cluster CL0076
current versionsNo studies 19 · 3.7%Ungraded 449 · 87.2%Very low 17 · 3.3%Low 23 · 4.5%Moderate 7 · 1.4%High 0 · 0%
Ungraded means an effect was reported without a GRADE rating; it is not a gap. No studies means the review found nothing to answer the question. The six categories are a display order, not a score.
Intervention categories, outcome domains and clusters are a proposed key pending human validation. Clear all filters
| Question | Effect | Evidence | Status / GRADE |
|---|---|---|---|
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: specificity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.80 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Specificity · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Overall (all studies pooled) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | Proportion (weighted mean specificity, %) 87.90 | 7 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: sensitivity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 5 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology, radiography) Proximal caries detection: sensitivity · Extracted human teeth (in vitro); permanent teeth, proximal (approximal) surfaces · Overall (all studies permanent) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 3 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: specificity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.81 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Digital bitewing X-rays (Fig 4a) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | ~80 | 1 study | Ungraded |
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) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Sensitivity 0.85 [0.83, 0.93] | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: sensitivity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Primary dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.63 | 2 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · Permanent dentition (all 3 included studies) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 3 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: specificity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.66 | 3 studies | Ungraded |
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 · Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Post-test probability 0.06 | 20 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.86 | 4 studies | Ungraded |
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) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Specificity 0.90 [0.85, 0.95] | 20 studies · 29423 participants | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 3 studies | Ungraded |
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), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | AUC 0.86 | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.86 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Periapical X-rays (Fig 4c) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | ~76 | NR | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: specificity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 5 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of occlusal caries · human primary or permanent teeth with dental caries lesions · permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.80 | 3 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · Permanent dentition (all 3 included studies) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 3 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 3 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology, radiography) Proximal caries detection: specificity · Extracted human teeth (in vitro); permanent teeth, proximal (approximal) surfaces · Overall (all studies permanent) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 3 studies | Ungraded |
Artificial intelligence algorithms for caries detection vs Dental expert or visual-tactile/clinical assessment as reference standard Positive post-test probability · Dental images or diagnostic tests for dental caries detection · Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Post-test probability 0.79 | 20 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Sensitivity · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Overall (all studies pooled) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | OR 1.258 [0.493, 2.540] | 9 studies · 17190 participants | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of occlusal caries · human primary or permanent teeth with dental caries lesions · all dentitions Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.82 | 4 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: specificity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.82 | 4 studies | Ungraded |
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) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | LR+ 10.443 [7.505, 14.531] | 20 studies · 29423 participants | Ungraded |
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) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 4.37 [3.16, 6.27] | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.74 | 4 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: sensitivity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.91 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Digital panoramic X-rays (Fig 4b) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | ~70 | 1 study | Ungraded |
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) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | LR- 0.168 [0.138, 0.205] | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.76 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Overall (all X-ray types pooled) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | OR 2.72 [14.65, 14.65] | 14 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: specificity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Primary dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.81 | 2 studies | Ungraded |
Artificial intelligence algorithms for caries detection vs Dental expert or visual-tactile/clinical assessment as reference standard Diagnostic odds ratio · Dental images or diagnostic tests for dental caries detection · Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | DOR 70.9 [44.9, 111.9] | 20 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: specificity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.82 | 4 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Intra-examiner reliability · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting intra-examiner reliability; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Intra-examiner reliability (kappa) reported in 6 studies, ranging from 0.52 to 1.00. No pooled estimate. | 6 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Positive predictive value for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | no estimate extracted | 10 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Negative predictive value for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | no estimate extracted | 10 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Overall diagnostic accuracy (agreement) · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting accuracy; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Accuracy 92.9% to 97.6% | 5 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Specificity · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting specificity; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Specificity ranged from 83% to 100% across studies (reported in 13 of 19 studies). No pooled estimate; meta-analysis not performed. | 13 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Positive predictive value · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting PPV; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | PPV 41.8% to 99% | 10 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Sensitivity · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting sensitivity; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Sensitivity Overall 48% to 98.3% across 13 studies; sensitivity is lesion-stage dependent: Kohara reported <40% for initial/moderate caries (75-100% for extensive) and early enamel lesions were less sensitive | 13 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Inter-examiner reliability · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting inter-examiner reliability; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Inter-examiner reliability (kappa) reported in 13 studies, ranging from 0.44 to 0.91. No pooled estimate. | 13 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Negative predictive value · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting NPV; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Negative predictive value reported in 10 of 19 studies; per-study values ranged from about 31% to 99.5% (Table 2). No pooled estimate. | 10 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Sensitivity for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Sensitivity Overall 48% to 98.3% across 13 studies; sensitivity is lesion-stage dependent: Kohara reported <40% for initial/moderate caries (75-100% for extensive) and early enamel lesions were less sensitive | 13 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Specificity for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | no estimate extracted | 13 studies | Ungraded |
Visual or visual-tactile classification (e.g., ICDAS, ERK) vs Reference standard (histology, operative excavation, or enhanced visual examination) Sensitivity for detecting enamel caries · Asymptomatic children or adults suspected of enamel caries (clinical studies); extracted teeth (in vitro studies) · Analysis 1: fully within-person comparison (studies evaluating >=3 index tests) Enamel Caries Detection and Diagnosis: An Analysis of Systematic Reviews. (2022) | Sensitivity 0.82 [0.73, 0.89] | 19 studies · 2849 participants | Ungraded |
Machine-learning algorithms for dental caries detection vs Different machine-learning algorithms for caries prediction Caries detection accuracy on intraoral images · Dental images of human beings · Kühnisch et al., 2022; convolutional neural networks Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. (2022) | Accuracy 93.3 | 1 study · 2417 participants | Ungraded |