SynthesisClinical oral investigations2025
Exploring a decade of deep learning in dentistry: A comprehensive mapping review.
Synthesis in Clinical oral investigations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Review
- Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions.Dentistry journal · 2026Review
- Comparing Deep Learning Models for Identifying Maxillary Transverse Deficiency from Intraoral Photographs.International dental journal · 2026Article
- Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning.International dental journal · 2026Article
- Automated detection of periapical lesions in pediatric panoramic radiographs using YOLOv7: a retrospective internal validation study.BMC pediatrics · 2026Article
- Diagnostic and localization performance of multimodal large language models in the interpretation of dental periapical radiographs.BMC oral health · 2026Article
- Quantum-inspired fused explainable deep learning framework for early enamel caries classification in intraoral photographs.Odontology · 2026Article
- Artificial Intelligence-Assisted Detection of Canine Impaction, Localization, and Classification from Panoramic Images: A Diagnostic Accuracy Comparative Study with CBCT.Children (Basel, Switzerland) · 2026Article
- Combining 3D Multi-Object Reconstruction with Finite Element Analysis for Accurate Dental Modelling and Restoration.International dental journal · 2026Article
- An Exploration of Nanobiotechnology Bridging Patho-Therapeutics with Regenerative and Clinical Perspectives in Periodontitis.Journal of functional biomaterials · 2026Review
- Artificial intelligence and its impact on oral health care education. A randomized study.Journal of education and health promotion · 2026Article
- Personalized Learning With Artificial Intelligence in Dental Education: An Integrative Review.International journal of dentistry · 2026Review
- Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data.Scientific reports · 2025Article
- DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation.Scientific reports · 2025Article
- Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss.Clinical oral investigations · 2025Article
- AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments-a narrative review.Frontiers in dental medicine · 2025Review
- Editorial: Oral health and quality of life in vulnerable populations.Frontiers in oral health · 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
11 authors.
Funding
Abstract
objectivesArtificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance. MATERIALS AND
methodsFollowing a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis.
resultsFrom 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty.
conclusionThis review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice. CLINICAL RELEVANCE: This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.
Indexed as
Identifiers
39969623What 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.