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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.
555 questions in 21 reviews · intervention: Caries detection/diagnosis · outcome domain: Diagnostic accuracy
current versionsNo studies 19 · 3.4%Ungraded 489 · 88.1%Very low 17 · 3.1%Low 23 · 4.1%Moderate 7 · 1.3%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 |
Intervention NR Log diagnostic odds ratio · Imaging modality: Bitewing radiographs Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 3.96 [3.20, 4.72] | 5 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 |
Intervention NR Postoperative hypersensitivity · Potassium nitrate liner vs polycarboxylate cement (Hodosh et al.) Efficacy of different cavity liners compared to no cavity lining in managing deep caries in vital permanent… (2025) | Postoperative hypersensitivity reduction Significant reduction with potassium nitrate; p<0.05 (p<0.01 in subgroup section) | 1 study | Ungraded |
Intervention NR Specificity · Imaging modality: Periapical radiographs Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Specificity 0.88 [0.82, 0.93] | 12 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 |
Intervention NR Log diagnostic odds ratio · Imaging modality: Panoramic radiographs Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 5.08 [4.15, 6.02] | 8 studies | Ungraded |
Intervention NR Sensitivity (trim-and-fill adjusted for publication bias) · Overall; trim-and-fill (20 observed + 6 imputed studies) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Sensitivity 0.818 [0.778, 0.858] | 26 studies | Ungraded |
Intervention NR Log diagnostic odds ratio · Imaging modality: CBCT Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 6.33 [5.95, 6.71] | 1 study | 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 |
Intervention NR Sensitivity · Imaging modality: Intraoral photographic images Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Sensitivity 0.80 [0.69, 0.90] | 7 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) | 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 |
Intervention NR Specificity · Imaging modality: Panoramic radiographs Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Specificity 0.97 [0.96, 0.98] | 8 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 · 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 |
Intervention NR Log diagnostic odds ratio · Imaging modality: Periapical radiographs Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 4.72 [3.16, 6.27] | 12 studies | Ungraded |
Intervention NR Log diagnostic odds ratio (trim-and-fill adjusted for publication bias) · Overall; trim-and-fill (20 observed + 6 imputed studies) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 3.913 [3.219, 4.606] | 26 studies | Ungraded |
Intervention NR Log diagnostic odds ratio · Imaging modality: Intraoral photographic images Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 3.69 [2.80, 4.59] | 7 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 |
Intervention NR Sensitivity · Imaging modality: CBCT Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Sensitivity 0.73 [0.68, 0.77] | 1 study | 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 |
Intervention NR Specificity · Imaging modality: Intraoral photographic images Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Specificity 0.84 [0.77, 0.91] | 7 studies | Ungraded |
Intervention NR Sensitivity · Imaging modality: Panoramic radiographs Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Sensitivity 0.83 [0.72, 0.94] | 8 studies | Ungraded |
Intervention NR Specificity · Imaging modality: CBCT Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Specificity 1.00 [0.99, 1.00] | 1 study | 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 |
Intervention NR Sensitivity · Imaging modality: Periapical radiographs Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Sensitivity 0.86 [0.83, 0.90] | 12 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 |