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Scoping review finds AI shows promise for enhancing dental education across multiple domainsAI shows promise for improving dental education but evidence remains limited

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Key Takeaway
Consider AI in dental education as promising but early-stage, with evidence limited by methodological heterogeneity.

This scoping review synthesized evidence from 17 empirical studies examining the applications, benefits, and challenges of artificial intelligence (AI) in dental education. The review mapped AI integration across dental curricula, though specific study populations, comparators, and follow-up periods were not reported. The analysis found AI demonstrated promising applications across multiple educational domains, including improvements in procedural accuracy, diagnostic consistency, assessment workflows, and learning material generation. No quantitative effect sizes, absolute numbers, or statistical measures were reported for these outcomes.

Safety and tolerability data were not reported in the included studies. The review identified substantial methodological limitations across the evidence base, including heterogeneous study designs, frequently limited 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 processes.

For practice, the review suggests AI offers substantial opportunities to enhance dental education but requires standardized definitions, stronger methodological rigor, ethical governance, and improved faculty readiness. The authors emphasize that clinician-led, collaborative AI development will be critical to ensuring safe, pedagogically aligned integration. Given the early-stage, heterogeneous nature of the evidence, these findings should be interpreted as preliminary indications of potential rather than established educational benefits.

Researchers reviewed 17 studies about how artificial intelligence (AI) is being used in dental education. They looked at how AI tools might help train future dentists across different areas of learning. The review found that AI shows promise for improving several aspects of dental training, including helping students practice procedures more accurately, making assessments more consistent, and creating learning materials.

The studies showed AI could help with procedural accuracy, diagnostic consistency, assessment workflows, and generating educational content. However, the researchers noted important limitations in the current evidence. Most studies were small, evaluated AI tools for only short periods, and often relied on participants' own reports rather than objective measurements.

Readers should understand this is early-stage research exploring possibilities rather than proven educational methods. The review highlights that while AI offers interesting opportunities for dental education, much more rigorous testing is needed. For now, these findings suggest AI might become a helpful teaching tool in the future, but dental schools will need to carefully evaluate any AI systems before adopting them widely.

What this means for you:
AI shows early promise for dental education, but more research is needed to understand its real-world effectiveness.

Study Details

Study typeSystematic review
EvidenceLevel 1
PublishedApr 2026
View Original Abstract ↓
BackgroundArtificial 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.MethodsFollowing 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.ResultsAI 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.ConclusionAI 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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