SynthesisDento maxillo facial radiology2026
Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.
Synthesis in Dento maxillo facial radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesThis systematic review aimed to investigate the current development and application landscape of generative artificial intelligence (Gen-AI) networks specifically designed for dental and maxillofacial radiology (DMFR) and summarize their potential applications for clinical practice, education, and research.
methodsFive electronic databases were searched to identify studies that developed and validated DMFR-specific Gen-AI networks. Data regarding the purpose and type of the Gen-AI model, dataset details, quantitative evaluation metrics, methods of subjective assessment, and key findings were extracted. Customized assessment criteria adapted from the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) checklist were used to evaluate the risk-of-bias for the included studies in 4 domains (dataset details, reporting of Gen-AI model architecture and training strategies, reliability of performance evaluation methods, and accessibility of code and developed models).
resultsForty-three studies were included from the initially identified 2060 records. Of these, 24 studies (55.8%) focused on image quality, addressing improvements in spatial resolution, artifact and noise reduction, image geometry and projection, quantitative accuracy of voxel values, 14 (32.6%) on image simulation (including the generation of bitewing, panoramic, and cephalometric radiographs, image-to-image translation, and post-treatment prediction simulation), 3 (7%) on 3D reconstruction from 2D images, and 2 (4.6%) on automated interpretation and reporting. Nearly all included studies (95.3%) reported objective evaluation metrics while about half (58.1%) incorporated subjective assessments using scoring systems or visual grading. Risk-of-bias was moderate for dataset details in 7 studies (16.3%) and performance evaluation in 20 (46.5%), and high for code and model accessibility in 35 studies (81.4%).
conclusionsWhile DMFR-specific Gen-AI models show promising potential for clinical practice, education, and research, their applicability requires overcoming challenges related to data quality, validation, integration, and ethical and legal considerations. Further clinical validation, increased transparency and accessibility, and thorough evaluation of cost-effectiveness in diagnostic workflows are essential to ensure their safe and effective use.
Indexed as
Identifiers
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.