ArticleJournal of clinical medicine2021
Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Educational Applications of ChatGPT in University-Based Dental Education. A Systematic Review.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Pooled it
- Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study.BMC oral health · 2025Trial
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Educational gaps and factors associated with artificial intelligence adoption among Egyptian periodontists: a multicenter cross-sectional study.Scientific reports · 2026Article
- Development and validation of the Dental Artificial Intelligence Readiness and Acceptance Instrument (DAI-RAI) for dental professionals.BMC oral health · 2026Article
- Artificial Intelligence in Dental Imaging Practice among Oral Radiologists from the City of Chennai: A Qualitative Analysis.The Indian journal of radiology & imaging · 2026Article
- Prescribing a dose of transparency: a qualitative evaluation of AI explanations with cardiovascular healthcare professionals.Frontiers in artificial intelligence · 2026Article
- Can artificial intelligence in orthopantomography advance dental diagnostics through automated image analysis?Frontiers in radiology · 2026Review
- Can ChatGPT be trusted? Evaluating AI responses to oral health questions among pregnant Arabic-speaking women.BMC oral health · 2025Article
- AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers.Bioengineering (Basel, Switzerland) · 2025Review
- Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss.BMC oral health · 2025Article
- A Comprehensive Guide to Implement Artificial Intelligence Cloud Solutions in a Dental Clinic: A Review.Cureus · 2025Review
- A Pilot Investigation Into the Use of Teledentistry and Artificial Intelligence to Assess Dental Erosion in Competitive Swimmers.Clinical and experimental dental research · 2024Article
- Article
- Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review.Imaging science in dentistry · 2023Article
- Knowledge, Attitude and Perception Regarding Artificial Intelligence in Periodontology: A Questionnaire Study.Cureus · 2023Article
- The implementation of artificial intelligence in radiology: a narrative review of patient perspectives.Future healthcare journal · 2023Review
- Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis.BMJ open · 2023Article
- Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging.Dento maxillo facial radiology · 2023Review
- Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aimed to identify barriers and enablers for the implementation of artificial intelligence (AI) in dental, specifically radiographic, diagnostics. Semi-structured phone interviews with dentists and patients were conducted between the end of May and the end of June 2020 (convenience/snowball sampling). A questionnaire developed along the Theoretical Domains Framework (TDF) and the Capabilities, Opportunities and Motivations influencing Behaviors model (COM-B) was used to guide interviews. Mayring's content analysis was employed to point out barriers and enablers. We identified 36 barriers, conflicting themes or enablers, covering nine of the fourteen domains of the TDF and all three determinants of behavior (COM). Both stakeholders emphasized chances and hopes for AI. A range of enablers for implementing AI in dental diagnostics were identified (e.g., the chance for higher diagnostic accuracy, a reduced workload, more comprehensive reporting and better patient-provider communication). Barriers related to reliance on AI and responsibility for medical decisions, as well as the explainability of AI and the related option to de-bug AI applications, emerged. Decision-makers and industry may want to consider these aspects to foster implementation of AI in dentistry.
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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.