ReviewInternational dental journal2026
Artificial Intelligence in Dentistry: A Concise Review of Reporting Checklists and Guidelines.
Review in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- From Clinic to Community: An Interpretable Artificial Intelligence Framework for Enamel Caries Detection to Support Public Health Dentistry.European journal of dentistry · 2026Article
- Reply to the Comment: Comparing ChatGPT and Dental Students' Performance in an Introduction to Dental Anatomy Examination.European journal of dentistry · 2026Article
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Review
- 3D reconstruction of the target tooth occlusal surface from a single-view image for root canal surgical navigation.Quantitative imaging in medicine and surgery · 2026Article
- Integration of Artificial Intelligence in Designing Removable Partial Dentures.International dental journal · 2026Article
- Three-Dimensional Radiographic and Morphological Features of Ameloblastoma and Odontogenic Keratocyst: A Comparative Cone Beam Computed Tomography Study Using Automatic Segmentation.International dental journal · 2026Article
- Artificial intelligence for skeletal classification in orthodontics: A systematic review and meta-analysis of lateral cephalometric studies.Journal of Taibah University Medical Sciences · 2026Review
- From Framework Inventory to Reporting Architecture: Next Steps for Dental AI Reporting Standards.International dental journal · 2026Article
- Article
- Predicting 3D Post-Orthodontic Facial Outcomes With a Diffusion Model Trained on Unpaired Datasets.International dental journal · 2026Article
- EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.International dental journal · 2026Article
- Machine Learning Models for Identifying Dental Pain in Adolescents.International dental journal · 2026Article
- AI Chatbots vs. Traditional Sources: Dental Health Literacy and Confidence Among Dental Patients-A Cross-Sectional Study.International dental journal · 2026Article
- Quantum-inspired fused explainable deep learning framework for early enamel caries classification in intraoral photographs.Odontology · 2026Article
- Diagnostic Capabilities of Large Language Models in Paediatric Dentistry.International dental journal · 2026Article
- Performance of Enhanced Large Language Models on Prosthodontic Multiple-Choice Questions.International dental journal · 2026Article
- Dual Framework for Classification and Detection of Third Molar Impaction in Panoramic Radiographs.International dental journal · 2026Article
- Preparing the AI-Ready Dentist: A Call for a Competency Framework in Dental Education.International dental journal · 2026Article
- Comparison of Artificial Intelligence Models for Automatic Segmentation of the Mandibular Canals and Branches.International dental journal · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has become increasingly integrated into dental diagnostics, imaging and treatment planning. However, despite this growing adoption, adherence to standardised reporting frameworks remains inconsistent. Insufficient use of established checklists continues to impede reproducibility, transparency and regulatory credibility. This review systematically examines existing AI reporting frameworks relevant to dental research, mapping their methodological domains, areas of overlap and persistent implementation gaps. We analysed established medical reporting guidelines for artificial intelligence in healthcare covering trials, protocols, prediction models, bias assessment, decision-support systems, imaging and systematic reviews alongside dentistry-specific checklists and ethical frameworks. While dental AI research is expanding rapidly, its reporting remains fragmented and inconsistent. Existing frameworks provide a comprehensive foundation for transparency and methodological rigour, but are underutilised. Harmonising these frameworks and promoting active adherence through journal policies, regulatory integration and quantitative compliance tracking are essential to bridge the gap between algorithmic performance and trustworthy clinical adoption.
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.