ReviewDiagnostics (Basel, Switzerland)2024
Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- An augmentation-enhanced transformer model for forensic identification using panoramic dental x-rays.International journal of legal medicine · 2026Article
- Accuracy and Error Patterns of References Generated by Large Language Models in Endodontics: The Role of Prompt Design and Model Selection.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Classification of periapical radiographic findings for root canal therapy decision support using deep neural networks.BMC medical informatics and decision making · 2026Article
- A benchmark multimodal oro-dental dataset for large vision-language models.Scientific data · 2026Article
- Teeth identification and numbering in mixed dentition: evaluating deep learning models for pediatric panoramic radiographs.BMC oral health · 2026Article
- Automated deep learning detection of orthodontically induced external apical root resorption in maxillary incisors on panoramic radiographs.Progress in orthodontics · 2026Article
- Advanced Deep Learning Models for Classifying Dental Diseases from Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Review
- Artificial Intelligence in Dentistry: A Concise Review of Reporting Checklists and Guidelines.International dental journal · 2026Review
- Oral implanomics: transitioning from experience-based discipline to AI-driven therapeutic paradigm - radiomics and artificial intelligence in oral implantology.Frontiers in oral health · 2026Article
- The Leeuwenhoek paradox in dental artificial intelligence: A narrative review of artificial intelligence-detected subclinical periapical changes and human-labeled false positives.Journal of dental sciences · 2026Review
- Use of artificial intelligence in oral radiology: a multicenter cross-sectional study in Egypt.BMC oral health · 2025Observational
- Evaluation of VITA shade-based tooth color categories using deep learning.Scientific reports · 2025Article
- AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers.Bioengineering (Basel, Switzerland) · 2025Review
- DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation.Scientific reports · 2025Article
- Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study.BMC oral health · 2025Article
- The Validation of an Artificial Intelligence-Based Software for the Detection and Numbering of Primary Teeth on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters.Medicina (Kaunas, Lithuania) · 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
2 authors.
Funding
Abstract
backgroundDental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes.
methodsAn extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library.
resultsThe studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications.
conclusionsBy providing a detailed overview of AI's role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.
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.