ReviewFrontiers in radiology2026
Can artificial intelligence in orthopantomography advance dental diagnostics through automated image analysis?
Review in Frontiers in radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Reimagining Dental Education: Integrating Digital Technologies for a Global, Digitally Native Generation-A Perspective.European journal of dentistry · 2026Article
- Automated Detection and Ordinal Severity Grading of Periapical Lesions on Panoramic Radiographs Using a Lesion-Preserving Tiling and Ordinal-Aware YOLO Framework.Diagnostics (Basel, Switzerland) · 2026Article
- Does Lower Regression Error Mean Stronger Forensic Evidence? Machine Learning Regression Versus Demirjian and Willems Methods for Dental Age Estimation at 12- and 15-Year Legal Thresholds.Diagnostics (Basel, Switzerland) · 2026Article
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
Artificial Intelligence (AI) is rapidly transforming dental education and clinical practice, as deep learning-especially convolutional neural networks-brings unprecedented accuracy to interpreting orthopantomograms (OPGs). This review illuminates the cutting-edge frontiers of AI-driven dental imaging, tracing how recent breakthroughs are transforming the detection, classification, and segmentation of complex dental anatomy and pathology. Notably, state-of-the-art AI models have reached remarkably high accuracy in tooth identification, while commercial solutions demonstrate promising-though variable-performance in diagnosing complex conditions, such as the adequacy of endodontic procedures. Yet, bringing AI into routine dental care remains fraught with obstacles - demanding vast annotated datasets, coping with population variability, and confronting persistent medicolegal and trust concerns. Growing collaborations between regulators and professional bodies in the United States and European Union are now shaping ethical and legal frameworks to guide its safe use. This narrative review goes beyond summarizing technological progress in AI-driven dental radiology - it uniquely integrates diagnostic breakthroughs with the rapidly evolving regulatory and ethical landscape. By bridging innovation with implementation, it offers educators, practitioners, and learners a forward-looking roadmap that positions AI not as a distant promise, but as a transformative force already reshaping the future of dental diagnostics and training.
Indexed as
Identifiers
What Socratic 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 Socratic graph.