ReviewDiagnostics (Basel, Switzerland)2022
Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)-A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 4 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Applications of AI-based deep learning models for detecting dental caries on intraoral images - a systematic review.Evidence-based dentistry · 2025Pooled it
- Pooled it
- Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.Clinical and experimental dental research · 2024Pooled it
- Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.BMC oral health · 2024Pooled it
- The Impact of Training Dental Students to Use an Artificial Intelligence-Based Platform for Pulp Exposure Prediction Prior to Deep Caries Excavation: A Proof-of-Concept Randomised Controlled Trial.International endodontic journal · 2026Trial
- Development of Leading Enhancement Assistive Planning: A three-dimensional malocclusion classification system for orthodontic treatment with clear aligners.Korean journal of orthodontics · 2026Article
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Artificial intelligence and oral photography: an approach to the epidemiology of dental caries.Revista de saude publica · 2026Article
- Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x.BMC oral health · 2025Article
- The Application and Performance of Artificial Intelligence (AI) Models in the Diagnosis, Classification, and Prediction of Periodontal Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Automated Detection, Localization, and Severity Assessment of Proximal Dental Caries from Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8.Scientific reports · 2025Article
- The relationships of personality traits on perceptions and attitudes of dentistry students towards AI.BMC medical education · 2025Article
- Automated Classification of Dental Caries in Bitewing Radiographs Using Machine Learning and the ICCMS Framework.International journal of dentistry · 2025Article
- Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study.Journal of imaging informatics in medicine · 2024Article
- Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis.The Japanese dental science review · 2024Review
- Enhancing predictive analytics in mandibular third molar extraction using artificial intelligence: A CBCT-Based study.The Saudi dental journal · 2024Article
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis.Imaging science in dentistry · 2024Review
- Developing the Benchmark: Establishing a Gold Standard for the Evaluation of AI Caries Diagnostics.Journal of clinical medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC.
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What OpenQuestion holds
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.