ReviewAnnals of medicine and surgery (2012)2025
Artificial intelligence in dental radiology: a narrative review.
Review in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 2026Pooled it
- Educational Applications of ChatGPT in University-Based Dental Education. A Systematic Review.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Pooled it
- Deep Learning-Based Automated Diagnostic Charting on Panoramic Radiography: Comparison of YOLOv11 and YOLOv12.Odontology · 2026Article
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Review
- Driving emerging technology to clinical impact.Journal of the American Dental Association (1939) · 2026Review
- Exploring the landscape of artificial intelligence in dental and maxillofacial radiology: a bibliometric analysis of studies and trends.Annals of medicine and surgery (2012) · 2026Review
- Influence of Oral and Maxillofacial Radiology Training and Experience on Attitudes Toward Artificial Intelligence in Dentistry: A Cross-Sectional Questionnaire Study.Healthcare (Basel, Switzerland) · 2026Article
- AI-Based Detection of Osteoporosis on Dental Radiographs: Influence of Region-of-Interest Selection on Classification Performance.Journal of imaging · 2026Article
- Artificial intelligence in the detection of dental pulp calcifications: a scoping review.BMC oral health · 2026Article
- Automated Classification of Maxillary Sinus Ostium Patency Using a ConvNeXt-Tiny + DeiT Gated MLP-Based Hybrid Deep Learning Model: A Retrospective CBCT Study.Diagnostics (Basel, Switzerland) · 2026Article
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- Deep Learning-Based Dental Caries Diagnosis: A Modality-Stratified Systematic Review and Meta-Analysis of Faster R-CNN and Mask R-CNN.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence Versus Human Dental Expertise in Diagnosing Periapical Pathosis on Periapical Radiographs: A Multicenter Study.Bioengineering (Basel, Switzerland) · 2026Article
- Evaluation of artificial intelligence based prosthetic innovation on dental implants: A database research.Bioinformation · 2026Article
- Diode Laser-Guided Protocol for Endo-Perio Lesions: Toward a Multi-Stage Therapeutic Strategy-A Case Series and Brief Literature Review.Medicina (Kaunas, Lithuania) · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article examines how artificial intelligence (AI) is revolutionizing dental radiology, a vital aspect of dental diagnosis and treatment planning. AI improves diagnosis accuracy through sophisticated applications like automated anomaly identification, image segmentation, and treatment planning, whereas traditional imaging techniques like periapical and panoramic radiography have limits. Clinical procedures are streamlined, and accurate dental condition diagnosis is made possible by methods such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). AI contributes to improved patient outcomes by lowering radiation exposure and improving picture quality. Dental radiography has a bright future despite current obstacles including data collection and algorithm training; nonetheless, further study and cooperation are required to maximize AI's incorporation into clinical practice. AI has the potential to revolutionize dental diagnostics, despite obstacles in data collection and the requirement for strong algorithm training. The creation of innovative imaging modalities, further research on AI applications, and cooperative efforts between scientists, physicians, and industry participants are some of the future directions. The dentistry community may better utilize AI to enhance patient care and diagnostic skills by creating clear criteria for its integration. In the long run, AI has the potential to transform dental radiology, resulting in better treatment outcomes and a more effective 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.