Research Trends in Artificial Intelligence Applications in Dental Hygiene and Dentistry in Korean Academic Journals: A Scoping Review

Abstract

Background: This scoping review examined research on artificial intelligence (AI) published in Korean academic journals in the fields of dental hygiene and dentistry.The Context-Concept-Maturity framework was applied to compare research trends and identify future directions.Methods: Following the Arksey and O'Malley framework, with extensions by Levac et al., the review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines.Domestic academic journals published between January 1, 2020, and September 30, 2025, were searched in KISS, RISS, ScienceON, and DBpia.Studies were screened, extracted, and coded according to population, concept, and context criteria, yielding 70 eligible articles (26 in dental hygiene and 44 in dentistry).Results: In dental hygiene, most research focused on education (38.5%) and community/public oral health (38.5%), while clinical applications were limited (7.7%).Major conceptual domains included education/training tools (42.3%), workflow/automation (38.5%), prediction (15.4%), and classification (3.8%).Dentistry studies were predominantly clinically oriented and imagingbased, emphasizing classification, automation, and segmentation.Maturity analysis indicated that dentistry primarily occupied Stage 2 (application; 84.1%), whereas dental hygiene showed a dual distribution across Stage 1 (exploration; 46.2%) and Stage 2 (application; 46.2%), with limited Stage 3 (integration; 7.7%) studies.Conclusion: AI research in Korea exhibits divergent developmental trajectories.Dental hygiene research emphasizes education, prevention, and public oral health, combining exploratory and applied approaches, while dentistry demonstrates greater clinical integration and application-level maturity.Advancing AI maturity in dental hygiene will require expansion of clinical data-driven studies, incorporation of AI and data literacy into curricula, standardized integration of unstructured and clinical data, multicenter long-term validation, and the development of ethical and performance governance frameworks.

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