ReviewDentistry journal2025
Artificial Intelligence and Dentomaxillofacial Radiology Education: Innovations and Perspectives.
Review in Dentistry journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 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
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluating AI-powered instruction on Computed Tomography Angiography (CTA) teaching outcomes: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Development of AI competencies within the medical curriculum.Frontiers in medicine · 2026Pooled it
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Assessing diagnostic performance of multimodal AI and human experts in oral and maxillofacial radiography: a comparative analysis of ChatGPT, Grok, and MANUS.Annals of medicine · 2026Article
- Review
- Artificial Intelligence in Dental Education: A Pilot Study of Caries Detection Accuracy and Instructor Agreement.Journal of dental education · 2026Article
- Exploring the landscape of artificial intelligence in dental and maxillofacial radiology: a bibliometric analysis of studies and trends.Annals of medicine and surgery (2012) · 2026Review
- Influence of Oral and Maxillofacial Radiology Training and Experience on Attitudes Toward Artificial Intelligence in Dentistry: A Cross-Sectional Questionnaire Study.Healthcare (Basel, Switzerland) · 2026Article
- Digital intelligence in dental technology education: curriculum and practical teaching reform.Frontiers in medicine · 2026Review
- Educational aspects of artificial intelligence in oral and maxillofacial radiology: insights from a scoping review.BMC medical education · 2025Article
- Comparison of Conventional Root Tip Resection with Digitally Guided Resection-An In Vitro Study.Dentistry journal · 2025Article
- Examination of the Frequency of Soft Tissue Ossification and Calcifications in Panoramic Radiographs: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Integration of Artificial Intelligence in Nursing Clinical Education: Enhancing the Mini-CEX Model.Journal of multidisciplinary healthcare · 2025Article
- Perceptions of dental students on the integration of artificial intelligence in radiology clinical education.Frontiers in dental medicine · 2025Article
- Visualization of artificial intelligence applications in oral disease diagnosis: A bibliometric analysis.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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is transforming dentomaxillofacial radiology education by enabling adaptive, personalized, and data-driven learning experiences. This review critically examines the pedagogical potential of AI within dental curricula, focusing on its ability to enhance student engagement, improve diagnostic competencies, and streamline clinical decision-making processes. Key innovations include real-time feedback systems, AI-guided simulations, automated assessments, and clinical decision support tools. Through these resources, AI transforms static learning into dynamic, interactive, and competency-based education. Additionally, this review discusses the integration of AI into formative assessment frameworks, such as OSCEs and mini-CEX, and its impact on student confidence, performance tracking, and educational scalability. Although primarily narrative in structure, this review synthesizes the current literature on dentomaxillofacial radiology education, supported by selected insights from medical radiology, to provide a comprehensive and up-to-date perspective on the educational applications of AI. Challenges (including ethical implications and other practical considerations) are addressed, alongside future directions for research and curriculum development. Overall, AI has the potential to significantly enhance radiology education by fostering clinically competent, ethically grounded, and technologically literate dental professionals.
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.