Anar Aidarkhanova

Background/Objectives: Clinicians’ expectations of artificial intelligence (AI) for periodontal care in Central Asia are largely unknown. We surveyed dentists and physicians in Kazakhstan on preliminary requirements for a future decision-support system and explored patients’ self-reported periodontal signs. Methods: Cross-sectional online surveys (July–September 2026; non-probability recruitment) included 33 dentists, 91 primary health care physicians and medical specialists, and 455 adult patients whose periodontal eligibility was not clinically verified; the patient questionnaire did not address AI. Analyses used Wilson 95% confidence intervals (CI), exact tests with Holm adjustment and exploratory logistic regression. Results: Dentists prioritised history-taking (81.8%) and early gingivitis diagnosis (69.7%). Autonomy choices were spread across five clinician-controlled configurations (low autonomy most frequent, 30.3%); no autonomous option was offered. Lack of a legal framework and liability insurance was the most cited barrier (60.6%). Physicians and specialists selected early gingivitis screening (80.2%) but cited insufficient competence (53.8%). Self-reported signs occurred in 49.0% of patients. Associations with high stress (adjusted odds ratio 2.11; 95% CI 1.14–3.89) and family history (1.61; 1.02–2.55) lost significance after excluding 204 responses collected via an inter-university platform. Conclusions: Clinician responses suggest preliminary requirements; patient associations remain exploratory. Instrument and clinical outcome validation should precede system design.

Background/Objectives: Artificial intelligence (AI) tools for periodontal diagnosis are proliferating, yet little is known about what clinicians and patients in Central Asia expect from such systems. We elicited requirements for an AI-enabled periodontal decision-support system from three stakeholder groups in Kazakhstan and examined which patient-reported risk factors are associated with self-reported signs of periodontal disease. Methods: A cross-sectional online survey (July–September 2026) enrolled 33 dentists, 91 primary health care (PHC) physicians and medical specialists, and 455 patients with periodontal disease. Proportions were reported with Wilson 95% confidence intervals (CI); groups were compared with Fisher’s exact, χ², Mann–Whitney and Kruskal–Wallis tests with effect sizes; multivariable logistic regression identified factors associated with self-reported periodontal signs. Results: Dentists prioritised automation of history-taking (81.8%) and visual gingival assessment (72.7%), and named early gingivitis diagnosis as the stage where AI is most needed (69.7%). Only human-controlled autonomy levels were chosen; the most frequently cited barrier was the absence of a legal framework and liability insurance (60.6%), and dentists with >5 years of experience more often demanded mandatory verification or a prohibition of full autonomy (exact p = 0.045; Cramér’s V = 0.54). PHC physicians considered early gingivitis screening their most effective contribution (80.2%) but cited insufficient competence as the main barrier (53.8%). Self-reported periodontal signs were present in 49.0% of patients and were independently associated with high stress (adjusted odds ratio 2.11; 95% CI 1.14–3.89) and a positive family history (1.61; 1.02–2.55); six symptom items did not form a reliable scale (Cronbach’s α = 0.56). Conclusions: Stakeholders in Kazakhstan favour a low-autonomy, clinician-verified AI system focused on early gingivitis detection, interprofessional data sharing and clearly regulated accountability. These requirements provide an evidence base for the design and governance of periodontal AI in emerging health systems.

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.

Технологическое развитие искусственного интеллекта постепенно упростило его внедрение в различные области. В частности, интеграция искусственного интеллекта в медицинское образование стала значимой тенденцией, сопровождающейся важными научными открытиями. В связи с этим возникает необходимость комплексного обзора и анализа современного состояния исследований в данной сфере для понимания дальнейших перспектив. Медицинское образование должно адаптироваться к требованиям общества и современным моделям практики. В XXI веке система высшего медицинского образования претерпевает значительные изменения, направленные на подготовку компетентных специалистов. Ожидается, что искусственный интеллект окажет существенное влияние не только на содержание образовательных программ, но и на методы их реализации. Среди таких методов можно выделить использование интеллектуальных обучающих систем, симуляционных платформ на основе искусственного интеллекта, автоматизированного анализа прогресса студентов, чат-ботов для обратной связи, а также адаптивных электронных курсов, подстраивающихся под индивидуальные особенности обучающегося. Применение искусственного интеллекта в учебном процессе позволяет не только персонализировать обучение, но и оперативно выявлять пробелы в знаниях, оптимизировать работу преподавателя и формировать гибкие образовательные траектории. С учетом потенциала искусственного интеллекта в снижении затрат на медицинское образование и повышении эффективности обучения студентов важно продолжать изучение его роли как инструмента академической поддержки в образовательном процессе. В нашем исследовании рассматриваются современные стратегии внедрения искусственного интеллекта в образовательные программы бакалавриата, а также возможные вызовы, связанные с его использованием. Ключевые слова: искусственный интеллект, персонализированное обучение, этика, медицинское образование, адаптивные технологии.

Background/Objectives: Periodontitis is the sixth most prevalent disease worldwide, affecting over 740 million people and representing a growing public health burden, particularly in transitional economies. While artificial intelligence (AI) technologies -including deep learning (DL) and machine learning (ML) -have demonstrated high diagnostic accuracy in controlled research settings, a critical implementation gap persists between proof-of-concept studies and real-world clinical deployment, especially in countries with nascent regulatory frameworks such as Kazakhstan. This systematic scoping review synthesizes global evidence on AI methodologies applied to periodontal diagnosis, risk prediction, and patient monitoring; evaluates regulatory and governance frameworks; and proposes an evidence-based five-stage implementation model contextualized for emerging health systems. Methods: Following PRISMA 2020 guidelines, a systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library, supplemented by regulatory and grey literature. Search terms combined (‘artificial intelligence’ OR ‘machine learning’ OR ‘deep learning’) AND (‘periodontitis’ OR ‘periodontal disease’ OR ‘gingivitis’). Studies published from January 2015 to April 2025 were considered. Forty-five sources (36 peer-reviewed empirical and review studies plus 9 regulatory and grey-literature documents) meeting predefined inclusion criteria were included in qualitative synthesis. Results: Two dominant AI paradigms were identified: (1) image-based deep learning -particularly convolutional neural networks (CNNs) and Vision Transformers -achieving diagnostic accuracies of 73–98.6% for periodontal bone loss detection on panoramic and periapical radiographs; and (2) ML-based non-clinical predictive screening models using patient-reported data and salivary biomarkers. Digital patient management tools (smart toothbrushes, chatbots, IoT platforms) represent a third emerging application domain. Significant external validation performance drops were consistently observed across models, underscoring data quality, dataset heterogeneity, and sample-size constraints as primary barriers. Regulatory analysis revealed convergence of the EU AI Act, U.S. FDA framework, WHO guidance, and the emerging Kazakhstan AI Development Concept (2024–2029) around risk-based classification, algorithmic transparency, and post-market surveillance mandates. Conclusions: Safe, ethical, and clinically effective integration of AI in periodontology requires a phased implementation approach: establishing national multimodal databases, performing locally validated clinical trials, integrating certified tools into digital workflows, deploying continuous monitoring systems, and developing robust legal governance including liability and insurance mechanisms. Kazakhstan’s rapidly evolving regulatory infrastructure and digitalization strategy create a concrete opportunity to establish a replicable model for AI adoption in periodontal care across Central Asian and transitional economies.

Periodontal diseases represent a major global health burden and are closely associated with systemic conditions such as cardiovascular diseases and diabetes, making early detection and screening essential priorities in public health. The rapid development of digital dentistry has led to the accumulation of diverse clinical and non-clinical datasets, including intraoral images, radiographic data, salivary biomarkers, and electronic health records. These developments create opportunities for the application of AI technologies in improving diagnostic accuracy, standardization, and accessibility of periodontal care. This review aims to systematically analyze current approaches to the application of AI in periodontology and to propose a structured framework for integrating AI tools into clinical practice and population-based screening programs. A structured literature search was conducted in major databases including PubMed, Scopus, and Google Scholar, covering publications from 2005 to 2025. The review includes studies focusing on image-based analysis, radiographic interpretation, predictive modeling, natural language processing, and telemedicine applications. The results demonstrate that AI models, particularly deep learning architectures such as convolutional neural networks, show high accuracy in detecting gingival inflammation, dental plaque, and alveolar bone loss. Machine learning methods are also effectively used for risk prediction and stratification based on clinical parameters and biomarkers. Additionally, natural language processing techniques enable automated extraction of diagnostic information from electronic dental records, enhancing data usability and clinical decision-making. Despite promising results, the evidence remains heterogeneous, with common limitations including small sample sizes, lack of external validation, and variability in reference standards. Implementation challenges include data standardization, regulatory requirements, and integration into clinical workflows. In conclusion, AI has significant potential to transform periodontal diagnostics and screening through improved accuracy, scalability, and accessibility. However, successful implementation requires robust validation, standardized data protocols, and alignment with clinical and ethical guidelines.

Periodontal diseases represent a major global health burden and are closely associated with systemic conditions such as cardiovascular diseases and diabetes, making early detection and screening essential priorities in public health. The rapid development of digital dentistry has led to the accumulation of diverse clinical and non-clinical datasets, including intraoral images, radiographic data, salivary biomarkers, and electronic health records. These developments create opportunities for the application of AItechnologies in improving diagnostic accuracy, standardization, and accessibility of periodontal care.This review aims to systematically analyze current approaches to the application of AIin periodontology and to propose a structured framework for integrating AItools into clinical practice and population-based screening programs. A structured literature search was conducted in major databases including PubMed, Scopus, and Google Scholar, covering publications from 2005 to 2025. The review includes studies focusing on image-based analysis, radiographic interpretation, predictive modeling, natural language processing, and telemedicine applications.The results demonstrate that AImodels, particularly deep learning architectures such as convolutional neural networks, show high accuracy in detecting gingival inflammation, dental plaque, and alveolar bone loss. Machine learning methods are also effectively used for risk prediction and stratification based on clinical parameters and biomarkers. Additionally, natural language processing techniques enable automated extraction of diagnostic information from electronic dental records, enhancing data usability and clinical decision-making.Despite promising results, the evidence remains heterogeneous, with common limitations including small sample sizes, lack of external validation, and variability in reference standards. Implementation challenges include data standardization, regulatory requirements, and integration into clinical workflows.In conclusion, AIhas significant potential to transform periodontal diagnostics and screening through improved accuracy, scalability, and accessibility. However, successful implementation requires robust validation, standardized data protocols, and alignment with clinical and ethical guidelines.Keywords:artificial intelligence,periodontitis,screening,panoramic radiography,Cone beam computed tomography,intraoral images,teledentistry.