ReviewBioengineering (Basel, Switzerland)2024
The Use of Artificial Intelligence in Caries Detection: A Review.
Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- DentSeg3: A Three-Branch Specialized Fusion Network for Dental Lesion Segmentation in Color Images.Journal of imaging informatics in medicine · 2026Article
- Objective Measurement of Tooth Color Using Spectrophotometry and ICDAS: Quantifying CIELAB Color Changes Across Caries Severity.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026Article
- Diagnostic Accuracy of Novel AI-Based Software in the Detection of Dental Caries on Bitewing and Intraoral Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Classification of occlusal caries severity using spectrophotometric CIELAB measurements and machine learning algorithms.Clinical oral investigations · 2026Article
- Deep learning model development and clinical validation for radiographic surrogate markers of implant esthetic risk.NPJ digital medicine · 2026Article
- Evaluating the Managerial Feasibility of an AI-Based Tooth-Percussion Signal Screening Concept for Dental Caries: An In Silico Study.Diagnostics (Basel, Switzerland) · 2026Article
- Assessing the Efficacy of Artificial Intelligence Platforms in Answering Dental Caries Multiple-Choice Questions: A Comparative Study of ChatGPT and Google Gemini Language Models.Dentistry journal · 2026Article
- Illuminating the invisible: translational biophotonics for contemporary caries detection and biological decision-making.Frontiers in oral health · 2026Review
- Integrating Numerical Data with AI-Based Image Processing Techniques to Improve the Diagnostic Accuracy of Detecting Dental Caries in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Using artificial intelligence in the evaluation of periapical, caries, and restoration status: a new methodological and technological study.BMC medical imaging · 2025Article
- Role of Artificial Intelligence and Machine Learning in Conservative Dentistry and Endodontics: A Review.Cureus · 2025Review
- Minimally Invasive Techniques for Managing Dental Caries in Children: Efficacy, Applications, and Future Directions.Cureus · 2025Review
- Revolutionizing the diagnosis of dental caries using artificial intelligence-based methods.Journal of conservative dentistry and endodontics · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice.
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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.