ReviewCureus2023
Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques.
Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Robot-Assisted Oral Examination: Challenges and Innovations.International dental journal · 2026Pooled it
- Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review.Frontiers in dental medicine · 2025Pooled it
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.Clinical and experimental dental research · 2024Pooled it
- Assessing diagnostic performance of multimodal AI and human experts in oral and maxillofacial radiography: a comparative analysis of ChatGPT, Grok, and MANUS.Annals of medicine · 2026Article
- Diagnostic accuracy of ChatGPT and teledentistry compared with dentist examination in detecting dental caries: preliminary diagnostic accuracy study.BMC oral health · 2026Article
- Dental image analysis and surgical decision support using modified generative adversarial networks.BMC oral health · 2026Article
- Classification of occlusal caries severity using spectrophotometric CIELAB measurements and machine learning algorithms.Clinical oral investigations · 2026Article
- Detection of Pediatric Dental Caries in Panoramic Radiograph Using Deep Learning: A Benchmark Study on MD-OPG.Sensors (Basel, Switzerland) · 2026Article
- Postbiotics and phage synergy in precision oral microbiome engineering: systems biology strategies targetingFrontiers in molecular biosciences · 2026Review
- Artificial intelligence and oral photography: an approach to the epidemiology of dental caries.Revista de saude publica · 2026Article
- Using artificial intelligence in the evaluation of periapical, caries, and restoration status: a new methodological and technological study.BMC medical imaging · 2025Article
- Prognostic Evaluation of Lower Third Molar Eruption Status from Panoramic Radiographs Using Artificial Intelligence-Supported Machine and Deep Learning Models.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Charting New Territory: AI Applications in Dental Caries Detection from Panoramic Imaging.Dentistry journal · 2025Review
- Classification Algorithms for Early Tooth Demineralization Assessment by Impedance Spectroscopy.Sensors (Basel, Switzerland) · 2025Article
- Revolutionizing the diagnosis of dental caries using artificial intelligence-based methods.Journal of conservative dentistry and endodontics · 2025Review
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- Predictive analysis of root canal morphology in relation to root canal treatment failures: a retrospective study.Frontiers in dental medicine · 2025Article
- Artificial intelligence-produced radiographic enhancements in dental clinical care: provider and patient perspectives.Frontiers in oral health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
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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.