ReviewThe Japanese dental science review2024
Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis.
Review in The Japanese dental science review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07027189 (DECIDE-AI), which is not on this map. Cited by 26 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
DECIDE-AI:The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection: A Randomized Controlled Study
Who cites it
26 citing papers in PubMed, 5 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Advancements in Caries Diagnostics Using Bitewing Radiography: A Systematic Review of Deep Learning Approaches.Caries research · 2026Pooled it
- Deep convolutional neural networks for early detection of interproximal caries using bitewing radiographs: A systematic review.Evidence-based dentistry · 2025Pooled it
- Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis.Head & face medicine · 2025Pooled it
- Pooled it
- Review
- Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection Across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis.Diagnostics (Basel, Switzerland) · 2026Review
- Diagnostic Accuracy of Novel AI-Based Software in the Detection of Dental Caries on Bitewing and Intraoral Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions.Dentistry journal · 2026Review
- The Cariostatic Mechanisms of Fluoride-An Updated Review.Dentistry journal · 2026Review
- Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs.Clinical oral investigations · 2026Article
- Assessment of posterior interproximal surfaces on bitewing and panoramic radiographs by dental interns and an artificial intelligence application.Oral radiology · 2026Article
- Rethinking Diagnostic Performance Metrics in Imaging Artificial Intelligence: Lessons from Caries Detection on Radiographs.Caries research · 2026Review
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- AI-assisted versus traditional radiographic interpretation for early caries detection.Bioinformation · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Evaluation of a YOLOv5x-based deep learning model for interproximal caries segmentation on bitewing radiographs across primary and permanent teeth.BMC oral health · 2025Article
- Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice.Clinical oral investigations · 2025Article
- Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence.Dentistry journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accuracy of artificial intelligence-aided (AI) caries diagnosis can vary considerably depending on numerous factors. This review aimed to assess the diagnostic accuracy of AI models for caries detection and classification on bitewing radiographs. Publications after 2010 were screened in five databases. A customized risk of bias (RoB) assessment tool was developed and applied to the 14 articles that met the inclusion criteria out of 935 references. Dataset sizes ranged from 112 to 3686 radiographs. While 86 % of the studies reported a model with an accuracy of ≥80 %, most exhibited unclear or high risk of bias. Three studies compared the model's diagnostic performance to dentists, in which the models consistently showed higher average sensitivity. Five studies were included in a bivariate diagnostic random-effects meta-analysis for overall caries detection. The diagnostic odds ratio was 55.8 (95 % CI= 28.8 - 108.3), and the summary sensitivity and specificity were 0.87 (0.76 - 0.94) and 0.89 (0.75 - 0.960), respectively. Independent meta-analyses for dentin and enamel caries detection were conducted and showed sensitivities of 0.84 (0.80 - 0.87) and 0.71 (0.66 - 0.75), respectively. Despite the promising diagnostic performance of AI models, the lack of high-quality, adequately reported, and externally validated studies highlight current challenges and future research needs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.