Bridging the Implementation Gap: Artificial Intelligence in Periodontology from Proof-of-Concept to Clinical...

Background/Objectives: Periodontitis, the sixth most prevalent disease worldwide, affects over 740 million people and disproportionately burdens transitional economies. Although artificial intelligence (AI)-including deep learning (DL) and machine learning (ML)-achieves high diagnostic accuracy in research settings, a gap persists between proof-of-concept and real-world deployment, especially where regulation is nascent, as in Kazakhstan. This scoping review maps global evidence on AI for periodontal diagnosis, risk prediction, and monitoring; evaluates governance frameworks; and proposes a contextualised implementation model for emerging health systems. Methods: Following PRISMA-ScR and PRISMA 2020 guidance, PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library were searched (January 2015-April 2025), supplemented by regulatory and grey literature; ten additional sources published after the search closure were subsequently identified through citation checking and expert peer review during revision, as a targeted amendment rather than a re-executed database search. Forty sources in total (29 peer-reviewed empirical and review studies plus 11 regulatory, grey literature, and patent documents) met the inclusion criteria and were charted thematically. Results: Two dominant paradigms emerged: image-based DL (convolutional neural networks and Vision Transformers), achieving 73-98.6% accuracy for radiographic bone loss detection, and ML-based non-clinical screening using patient-reported data and salivary biomarkers. Digital tools (smart toothbrushes, chatbots, and IoT platforms) form a third domain. Performance dropped consistently on external validation, reflecting data quality and sample size constraints. Regulatory analysis showed convergence of the EU AI Act, U.S. FDA framework, WHO guidance, and Kazakhstan's AI Development Concept (2024-2029) around risk-based classification, transparency, and post-market surveillance. Conclusions: Safe, effective AI integration in periodontology requires a phased approach: national multimodal databases, local clinical validation, certified workflow integration, continuous monitoring, population-level surveillance, and legal governance covering liability and insurance. Kazakhstan's evolving regulatory and digitalisation strategy may offer a context-specific case for evaluating AI adoption pathways across Central Asia and transitional economies, pending prospective validation.