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.

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.

Background: Advances in neonatal intensive care in Kazakhstan have improved the survival of preterm infants, including those with very low birth weight (VLBW) and extremely low birth weight (ELBW). However, after hospital discharge, these medically vulnerable children may remain insufficiently protected against vaccine-preventable infections because of prolonged temporary medical exemptions, clinical caution, parental concerns, and fragmented coordination between specialized follow-up services and primary health care. Objective: This study aimed to assess vaccination coverage in a 10-year cohort of high-risk preterm infants and to identify clinical, parental, and organizational factors associated with delayed immunization. Methods: We conducted a retrospective cohort study of 331 high-risk preterm infants followed at a specialized pediatric center in Astana, Kazakhstan, between 2015 and 2024. Vaccination status at hospital discharge and at 24 months of chronological age, documented temporary medical exemptions, reasons for delayed or missed vaccination, and confirmed cases of pertussis and measles were analyzed. A supplementary cross-sectional survey of parents, physicians, and health care managers was conducted to further explore barriers to catch-up immunization. Results: At hospital discharge, 259 infants (78.2%) had not received the BCG or hepatitis B vaccine. By 24 months of age, 34.1% (n = 113) were fully vaccinated according to age, whereas 49.8% (n = 165) remained completely unvaccinated. During the observation period, 30 cases of measles and 17 cases of pertussis were recorded within the cohort, primarily during nationwide outbreaks. Among the 10 children with a history of measles before 12 months of age, 2 developed subacute sclerosing panencephalitis (SSPE), 1 developed profound hearing loss, and 1 required prolonged oxygen therapy following pneumonia. In addition, 2 children with periventricular leukomalacia (PVL) experienced clinical deterioration, while 1 infant with pertussis required hospitalization during the acute phase of illness. Conclusion: High-risk preterm infants in this cohort experienced substantial and persistent gaps in immunization after hospital discharge. These findings underscore the importance of regular reassessment of temporary medical exemptions, improved coordination between specialized follow-up services and primary health care, and strengthened catch-up immunization strategies for medically vulnerable children.

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: The global healthcare landscape is increasingly shaped by two pivotal trends: the adoption of international quality frameworks, such as Joint Commission International (JCI) accreditation, and the strategic shift towards resource-efficient models like outpatient surgery. While JCI is the gold standard for hospital risk management and outpatient surgery is a key driver of efficiency, a critical disconnect exists in both research and policy. The applicability and impact of hospital-centric JCI standards within the unique operational context of ambulatory surgery remain a critical blind spot. The present scoping review aims to systematically map the evidence regarding the association between JCI accreditation and outpatient surgery, identify critical knowledge gaps, and highlight implications for healthcare contexts. Methods: The scoping review was performed in accordance with the PRISMA-ScR guidelines. A comprehensive search of major databases (i.e., PubMed/MEDLINE, Scopus, and Web of Science) was conducted for the period 2000–2025. Data extraction was centered on study design, interventions, and key findings related to quality and risk management. Results: Our analysis of 80 included publications revealed a key finding: the literature on JCI accreditation and outpatient surgery exists in two separate, non-overlapping silos. A complete absence of empirical studies directly evaluating the implementation or effectiveness of JCI accreditation within outpatient surgery centers was identified. Conclusions: The review revealed a critical evidence gap concerning integrated risk management in ambulatory settings. This finding highlights an urgent need for further research to inform evidence-based healthcare policy and ensure patient safety in the growing field of outpatient surgery.

Background: The global healthcare landscape is being increasingly shaped by two pivotal trends: the adoption of international quality frameworks, such as Joint Commission International (JCI) accreditation, and the strategic shift towards resource-efficient models like outpatient surgery. While both areas have been extensively addressed in the healthcare literature, empirical evidence concerning their intersection remains limited. This scoping review aimed to systematically map the available literature, identify critical evidence gaps at the intersection of JCI accreditation and outpatient surgery, and examine implications for healthcare risk management and policy development. Methods: The scoping review was performed in accordance with the PRISMA-ScR guidelines. A dual-track evidence-mapping strategy was implemented across seven electronic databases and platforms (PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, eLIBRARY.ru, CyberLeninka, and Google Scholar) to search for publications published between January 2000 and May 2026. The final database searches were conducted on 15 May 2026. Data extraction was centered on study design, interventions, and key findings related to quality and risk management. Results: A total of 80 publications were included. The evidence comprised two complementary streams: literature concerning JCI accreditation and healthcare quality and safety, predominantly in hospital and general healthcare settings, and literature concerning outpatient, ambulatory, and day-surgery quality, safety, and hospital-substituting technologies. No empirical study directly evaluating the implementation or impact of JCI accreditation within outpatient surgery centers was identified. Conclusions: This scoping review identified a critical empirical evidence gap concerning the integration of accreditation and quality and risk-management frameworks in outpatient surgical settings. A hypothesis-generating conceptual framework is proposed to guide future empirical research evaluating whether and how JCI accreditation influences quality, safety, organizational, and economic outcomes in outpatient surgical settings.