The Role and Impact of Artificial Intelligence in Preventive Dentistry: A Scoping Review.

OBJECTIVE: This scoping review aims to identify the current applications of artificial intelligence (AI) in preventive dentistry for primary disease prevention and synthesises evidence regarding their impact on oral disease prevention. METHODS: A comprehensive search strategy was conducted across PubMed, Scopus, Embase, and Web of Science to include research published before January 1, 2026. Inclusion criteria were original studies employing AI for disease prevention in dentistry, including risk prediction, risk factor identification, self-monitoring of oral hygiene, or oral health education. Studies focusing exclusively on disease diagnosis, nonoral diseases, or nonclinical prevention were excluded. Data on study characteristics, including AI methodologies, data sources, clinical applications, and key outcomes were extracted and charted. RESULTS: Forty-three included studies reported AI application in preventive dentistry of the following domains: risk prediction for dental caries (23/43) and periodontal diseases (6/43), self-monitoring of oral health through automated plaque detection (7/43), toothbrushing analysis (2/43) and oral bite force monitoring (1/43), and patient education via chatbots or AI-guided videos (4/43). AI models were predominantly trained using structured data (electronic health records, questionnaires), image data (photographs, radiographs), or molecular data (saliva, genetic samples). Study designs consisted mainly of cross-sectional and cohort studies for model development, with sample sizes ranging from single participants to over 43,000. Across these applications, AI tools demonstrated comparable accuracy with human experts in risk stratification, enabled personalised preventive strategies and empowered patient engagement. Key challenges include technical barriers such as the lack of standardised, interoperable datasets; practical hurdles like high costs and insufficient professional training; ethical concerns over data privacy; and the fundamental difficulty of translating AI-driven knowledge into sustained patient behaviour modification. CONCLUSION: AI has established a diverse and effective role in preventive dentistry by providing powerful tools for objective risk stratification, personalised education, and patient self-monitoring. These intelligent systems enable a proactive approach by identifying high-risk individuals for targeted intervention. While challenges exist, the future integration of these technologies into clinical workflows and personal health applications promises to establish a more predictive, preventive, and participatory paradigm for managing global oral health.