ReviewJournal of conservative dentistry and endodontics2025
Revolutionizing the diagnosis of dental caries using artificial intelligence-based methods.
Review in Journal of conservative dentistry and endodontics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
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Who cites it
12 citing papers in PubMed.
- Comparative diagnostic accuracy of a You Only Look Once-11s deep learning model and human observers for detection of periapical lesions on intraoral periapical radiographs.Journal of conservative dentistry and endodontics · 2026Article
- Enhancing deep learning-based dental caries detection through feature pyramid network-based image segmentation.Journal of conservative dentistry and endodontics · 2026Article
- Comparative benchmark assessment of performance of six different large language models in clinical decision-making for vital pulp therapy.Journal of conservative dentistry and endodontics · 2026Article
- Artificial intelligence in dentistry: Training and testing a caries detection model with intraoral scans.Journal of conservative dentistry and endodontics · 2026Article
- Predicting outcomes of nonsurgical retreatment with artificial intelligence: A multicenter retrospective study using logistic regression, random forest, and extreme gradient boosting.Journal of conservative dentistry and endodontics · 2026Article
- Beta testing and clinical evaluation of an artificial intelligence-powered clinical decision support system for endodontic case assessment.Journal of conservative dentistry and endodontics · 2026Article
- Patient and dental practitioner acceptance of artificial intelligence in dental care: a cross-sectional study in Saudi Arabia's eastern province.Frontiers in oral health · 2026Article
- Artificial Intelligence and Innovation in Oral Health Care Sciences: A Conceptual Review.Healthcare (Basel, Switzerland) · 2025Review
- An end-to-end deep-learning system for segmentation and classification of dental caries from radiovisiography images.Journal of conservative dentistry and endodontics · 2025Article
- Artificial intelligence for dental caries detection: A mixup, fine-tuning, and quantization approach on the MobileNetV2 model.Journal of conservative dentistry and endodontics · 2025Article
- Indian Association of Conservative Dentistry and Endodontics consensus statement on deep caries management.Journal of conservative dentistry and endodontics · 2025Article
- Artificial Intelligence Integrated with Intraoral Digital Imaging in Dental Caries Detection, Treatment Planning, and Clinical Decision-Making: A Scoping Review.F1000Research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early diagnosis and carious lesion detection through artificial intelligence (AI) have transformed current standard methodologies because it generates precise results which work more efficiently and dependably. AI uses machine learning and deep learning technologies with computer-aided diagnostic systems to accomplish exceptional image evaluation of radiographic data and clinical records in dental caries detection through intraoral scans. This review discusses both prevailing challenges which limit and potential future uses of AI in dental diagnosis together with its ability to become a part of standard clinical work routines. Various researchers confirmed that AI works as a helpful tool which supports dental experts by improving diagnosis and minimizing human biases to enhance preventive care effects for patients.
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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 Socratic graph.