ReviewCureus2023
Revolutionizing Dental Imaging: A Comprehensive Study on the Integration of Artificial Intelligence in Dental and Maxillofacial Radiology.
Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- The Leeuwenhoek paradox in dental artificial intelligence: A narrative review of artificial intelligence-detected subclinical periapical changes and human-labeled false positives.Journal of dental sciences · 2026Review
- Comparison of the reliability of ANB Sar and W measurements used in cephalometric diagnostics for the assessment of sagittal discrepancy.Scientific reports · 2025Article
- Toward Precision Diagnosis of Maxillofacial Pathologies by Artificial Intelligence Algorithms: A Systematic Review.Journal of maxillofacial and oral surgery · 2025Review
- Selected Indicators Used in Cephalometric Analysis and Their Predictive Value in Defining Sagittal Discrepancy Malocclusions: A Comparative Study.Journal of clinical medicine · 2025Article
- Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Comparison of the Repeatability and Reproducibility Levels of ANB, Tau and Yen Angle Measurements Used in Cephalometric Diagnostics in the Assessment of Sagittal Discrepancy: A Comparative Study.Journal of clinical medicine · 2025Article
- Automatic dental age estimation in adolescents via oral panoramic imaging.Frontiers in dental medicine · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in deep learning and artificial intelligence (AI) have profoundly impacted various fields, including diagnostic imaging. Integrating AI technologies such as deep learning and convolutional neural networks has the potential to drastically improve diagnostic methods in the field of dentistry and maxillofacial radiography. A systematic study that adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards was carried out to examine the efficacy and uses of AI in dentistry and maxillofacial radiography. Incorporating cohort studies, case-control studies, and randomized clinical trials, the study used an interdisciplinary methodology. A thorough search spanning peer-reviewed research papers from 2009 to 2023 was done in databases including MEDLINE/PubMed and EMBASE. The inclusion criteria were original clinical research in English that employed AI models to recognize anatomical components in oral and maxillofacial pictures, identify anomalies, and diagnose disorders. The study looked at numerous research that used cutting-edge technology to show how accurate and dependable dental imaging is. Among the tasks covered by these investigations were age estimation, periapical lesion detection, segmentation of maxillary structures, assessment of dentofacial abnormalities, and segmentation of the mandibular canal. The study revealed important developments in the precise definition of anatomical structures and the identification of diseases. The use of AI technology in dental imaging marks a revolutionary development that will usher in a time of unmatched accuracy and effectiveness. These technologies have not only improved diagnostic accuracy and enabled early disease detection but have also streamlined intricate procedures, significantly enhancing patient outcomes. The symbiotic collaboration between human expertise and machine intelligence promises a future of more sophisticated and empathetic oral healthcare.
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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.