ReviewThe Japanese dental science review2026
Diagnostic accuracy and feasibility of artificial intelligence-driven smartphone imaging for dental caries detection: A systematic review.
Review in The Japanese dental science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Assessing diagnostic performance of multimodal LLMs and a custom convolutional neural network in tooth-level caries detection and localization.BMC oral health · 2026Article
- Lightweight Deep Learning for Automated Dental Caries Screening from Pediatric Oral Photographs.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This systematic review assesses the diagnostic accuracy, feasibility, and clinical performance of artificial intelligence (AI)-based smartphone imaging tools for detecting dental caries. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) guidelines, five databases: PubMed, Scopus, Web of Science, Embase, and Cochrane Library, were searched up to March 26, 2025. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD420251047689). Diagnostic accuracy and feasibility of AI-driven analysis of smartphone-based dental images for the detection of dental caries were assessed. Risk of bias and applicability were evaluated using QUADAS-2. Results: Fourteen studies met the inclusion criteria. AI models, particularly YOLO variants, DenseNet201, and MobileNetV3, demonstrated high diagnostic accuracy, especially for cavitated lesions, with some outperforming junior dentists. Enhanced YOLO models achieved up to 85.5 % mean average precision. Tools were generally user-friendly and suitable for community or at-home screening. However, sensitivity for early or non-cavitated lesions varied. Conclusion: AI-driven smartphone imaging shows promise as an accessible and reliable tool for caries detection, particularly in low-resource or remote settings. Further research is needed to improve early lesion detection, ensure clinical validation, and support equitable implementation.
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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.