ReviewDiagnostics (Basel, Switzerland)2023
A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- Radiographic choice for skeletal maturation assessment: a systematic review of Cervical Vertebrae Method and Middle Phalanx Method.Oral radiology · 2026Pooled it
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study.BMC oral health · 2026Article
- Artificial Intelligence in Australian Dental and General Healthcare: A Scoping Review.Australian dental journal · 2025Article
- The Expanding Role of Artificial Intelligence in Dentistry: A Cross-Specialty Chairside Perspective.Cureus · 2025Review
- CBMNet: a dual-attention enhanced ConvNeXt model for accurate G.V. Black type I-III classification in intraoral periapical radiographs.Scientific reports · 2025Article
- AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers.Bioengineering (Basel, Switzerland) · 2025Review
- Deep learning for automated dental plaque index assessment: validation against expert evaluations.BMC oral health · 2025Article
- Determination of the Stage of Periodontitis with 20 ng/mL Cut-Off aMMP-8 Mouth Rinse Test and Polynomial Functions in a Mobile Application.Diagnostics (Basel, Switzerland) · 2025Article
- Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes.Diagnostics (Basel, Switzerland) · 2025Article
- AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters.Medicina (Kaunas, Lithuania) · 2025Article
- Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O'Leary Index.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence Integrated with Intraoral Digital Imaging in Dental Caries Detection, Treatment Planning, and Clinical Decision-Making: A Scoping Review.F1000Research · 2025Article
- Enhancement of early proximal caries annotations in radiographs: introducing the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset.BMC oral health · 2024Article
- Oral Health Status and Treatment Needs Based on Artificial Intelligence (AI) Dental Panoramic Radiograph (DPR) Analysis: A Cross-Sectional Study.Journal of clinical medicine · 2024Article
- Segmented X-ray image data for diagnosing dental periapical diseases using deep learning.Data in brief · 2024Article
- IDJ Pioneers Efforts to Reframe Dental Health Care Through Artificial Intelligence (AI).International dental journal · 2024Article
- Review
- Review
- A CNN-GRU framework for stroke-heart attack prediction using IMOWPA-tuned SMOTE and LZMA compression.Digital healthArticle
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 at 5 institutions in 5 countries.
Funding
Abstract
Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.
Indexed as
Identifiers
What Socratic 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 Socratic graph.