ArticleNPJ digital medicine2025
Characteristics, licensing, and ethical considerations of openly accessible oral-maxillofacial imaging datasets: a systematic review.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Artificial Intelligence in Dentistry: A Concise Review of Reporting Checklists and Guidelines.International dental journal · 2026Review
- Contemporary Applications of Robotic Systems in Dental Implantology: A Review.International dental journal · 2026Review
- Impact of Generative AI-Enhanced Low-Dose Cone-Beam Computed Tomography on Diagnosis and Treatment Planning for Impacted Mandibular Third Molars.International dental journal · 2026Article
- BRAR-anchored multimodal dataset of panoramic radiographs for periodontal bone resorption grading.Scientific data · 2025Article
- Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different Tooth Numbering Systems.Diagnostics (Basel, Switzerland) · 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
13 authors.
Funding
Abstract
Several open-source oral-maxillofacial imaging datasets have been created but their characteristics, ethical clearance, and licensing for reuse remain unclear. This study aimed to systematically identify these datasets and investigate their characteristics, ethical approvals, and licensing requirements for reuse. Open-source oral-maxillofacial imaging datasets were identified through electronic databases and dataset platforms. 105 datasets with 437538 images and 100 intraoral videos from patients across twenty-one countries were included. The datasets comprise imaging modalities, including photographs, periapical, panoramic, and cephalometric radiographs, CBCT, MRI, surface scans, videos, and histopathological images. Nearly 80% of them provide annotations, but only 25.7% specified the annotators' qualification. The majority (83.8%) did not disclose whether ethical approval was obtained, while 61.9% specified terms or licenses for dataset reuse. There is an urgent need to develop standardized guidelines for reusing image datasets and to establish AI-specific consents to fully inform patients about potential uses of their data in AI projects.
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.