ReviewFrontiers in medicine2026
Artificial intelligence for teaching, training, and assessment in dental education: a domain-based scoping review.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is rapidly transforming dental education by enhancing preclinical skill development, clinical diagnostic training, assessment processes, and content generation. Despite increasing interest, the scope and methodological characteristics of AI integration across dental curricula remain unclear. This review aimed to map current applications, benefits, and challenges associated with AI in dental education. Methods: Following the Arksey and O'Malley framework and PRISMA-ScR guidelines, a systematic search was conducted across major databases in December 2025. Seventeen empirical studies met the inclusion criteria. Data were charted using a structured extraction tool and synthesized descriptively. Studies were categorized into four thematic domains: preclinical training, clinical and diagnostic training, assessment and feedback systems, and AI-generated educational content. Methodological characteristics and commonly reported limitations (e.g., sample size, outcome type, comparator presence, and validation approach) were mapped descriptively to contextualize the evidence. Results: AI demonstrated promising applications across domains, including improvements in procedural accuracy, diagnostic consistency, assessment workflows, and learning material generation. However, the evidence base was heterogeneous and frequently limited by small sample sizes, short evaluation periods, reliance on self-reported outcomes, and limited external validation. Key gaps included limited real-time procedural assessment and insufficient educator involvement in AI design. Conclusion: AI offers substantial opportunities to enhance dental education but requires standardized definitions, stronger methodological rigor, ethical governance, and improved faculty readiness. Clinician-led, collaborative AI development will be critical to ensuring safe, pedagogically aligned integration.
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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.