Background: Timely prehospital management is critical for survival after traumatic injury. In rapidly growing metropolises, emergency medical service (EMS) systems often struggle to provide equitable care amid urban sprawl and traffic congestion. This study investigated spatiotemporal inequalities in trauma-related EMS response in a rapidly expanding capital city (Astana, Kazakhstan) to inform healthcare optimization and urban health equity. Methods: We analyzed a five-year population-based dataset of 26,073 trauma-related EMS calls recorded between 2020 and 2024. Spatial patterns were examined using Kernel Density Estimation (KDE) and Getis–Ord Gi* hotspot analysis. Road-network modeling assessed accessibility at 3, 5, and 10 min thresholds using a GIS-based network analyst framework. Results: Males accounted for 60.1% of utilization and had higher clinical severity (hospitalization rate: 45.5% vs. 40.3%, p < 0.001). Demand peaked at 20:00, coinciding with peak traffic. The mean total response time was 21.63 min, and only 16.9% of calls met the 10 min benchmark. Significant accessibility gaps were found in the Baikonur district (61.4% delay rate). Conclusions: The findings demonstrate that while the EMS system provides broad geographic coverage, it suffers from systemic spatiotemporal bottlenecks. Targeted infrastructure expansion in underserved peripheral districts and the implementation of dynamic deployment models are necessary to enhance urban health equity and reduce preventable mortality in expanding metropolitan areas.

Introduction: Few local-level studies have examined population needs for emergency medical service (EMS) within specific districts during pandemics. This study used geographic information systems (GISs) to analyze 312,707 EMS calls in Astana, Kazakhstan, examining changes before, during, and at the peak of the COVID-19 pandemic. Methods: This study retrospectively evaluated EMS data in Astana, Kazakhstan, to assess the impact of the COVID-19 pandemic. Data were extracted from two automated systems used by the Astana ambulance station: “Komek 103” (introduced in late 2019) and the ADIS information system. These systems collect comprehensive call data, including caller demographics, address, time, seasonality, and emergency outcome. Moreover, we detected the geographical distribution of EMS calls during COVID-19. Results: The results show a substantial increase in EMS calls during the COVID-19 pandemic in Astana, Kazakhstan. Before the pandemic (Jan 1–Mar 13, 2020), there were 50,488 calls, compared to 126,308 calls at the onset and 135,911 calls at the peak. The peak period saw the highest call volume on Mondays (21,329 calls), a 35.7% increase in calls from individuals aged 65+, and a significant rise (51%) in complaints of fever. Within the 65+ age-group, men accounted for a 26.6% larger proportion of EMS users compared to women. Moreover, the number of requests for emergency medical care varied by district in Astana. Conclusion: The main trends in EMS calls showed that during the peak of the pandemic, the number of calls significantly increased. GIS technologies made it possible to determine the main flow and needs of EMS calls.

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: The new coronavirus infection (coronavirus disease [COVID]-19) has become a serious obstacle on the health-care systems in Kazakhstan, as well as throughout the world. Certainly, the extent of emergency medical services (EMSs) use at some stage within the COVID-19 pandemic has been increased. However, the impact on the use of EMS in Kazakhstan during the COVID-19 pandemic has not been studied. AIM: The aim of this study was to analyze the dynamics of changes in the number of emergency medical calls during the COVID-19 pandemic, depending on the time of year, day of the week, and the causes for the calls. METHODS: This study retrospectively evaluated the Nur-Sultan (capital of Kazakhstan) EMS data for the period after the pandemic was first seen in Kazakhstan. The necessary permissions for the study were obtained from the Nur-Sultan City Public Health Department. EMS data for the same time interval (February 1–May 1) in the past 3 years (2018, 2019, and 2020) were compared. The data were taken from the database of the automated control system “Komek 103,” which is used in the ambulance station since the end of 2019, and the automated dispatch information systems “ADIS” used by the ambulance station in Nur-Sultan. RESULTS: During the COVID-19 pandemic, EMS call rates had been maximum common on Mondays and Thursdays by 13, 75%. The evaluation suggests that the quantity of male sufferers extended by 14, 05% in the course of the pandemic length, as compared to the pre-pandemic length. The quantity of patients with inside the 65+ age group in the course of the pandemic length has extended by 31.5%. During the pandemic, the signs and symptoms of fever enlarged significantly by 83%. Moreover, suicides elevated by 61% during the pandemic period. During the COVID‐19 pandemic, using EMS particularly for time touchy illnesses, which include hypertension (−31%), surgical emergencies (acute abdomen) (−40%), cerebrovascular illnesses (−11%), and other heart illnesses (−29%) decreased. CONCLUSIONS: During the pandemic period, there was an increase in the calls from male patients, compared to the pre-pandemic period. Moreover, during the COVID-19 pandemic, EMS call rates had been increased on Mondays and Thursdays. We found an alteration with inside the sample of obtained calls. Furthermore, the quantity of patients with inside the 65+ age group in the course of the pandemic length has extended.

Background/Objectives: Ischemic heart disease (IHD) is a major cause of cardiovascular mortality, and emergency medical service (EMS) crews often make the first decision on whether an IHD-coded patient should be transported to hospital or managed at the scene. Evidence from Central Asian EMS systems is sparse, and it remains unclear whether routinely coded comorbidity information adds decision-relevant information beyond acute presentation. We aimed to identify predictors of the EMS hospitalization/transport decision among IHD calls in Astana, Kazakhstan. Materials and Methods: We conducted a retrospective call-level cohort study of 9985 consecutive EMS calls coded as IHD (ICD-10 I20-I25) over a five-year period. The endpoint was field disposition—hospitalization/transport versus being left at the scene—and should not be interpreted as confirmed ACS, mortality, clinical appropriateness or any other patient outcome. Group comparisons used the Mann–Whitney U and Pearson chi-square tests, and independent associations were estimated using explanatory multivariable logistic regression. Results: Overall, 2676 calls (26.8%) resulted in hospitalization/transport. The strongest independent predictors were cardiogenic shock (aOR 15.06), acute/unstable IHD versus chronic I25 (aOR 8.52) and heart failure (aOR 2.46). Other arrhythmias (aOR 1.84), atrial fibrillation (aOR 1.60), male sex (aOR 1.65) and age <45 years (aOR 1.88) were also associated with higher transport odds, whereas age ≥75 years (aOR 0.61), specialized crews (aOR 0.84) and high dispatch urgency (categories 1–2; aOR 0.84) were associated with lower odds. Model discrimination was moderate (AUC 0.69; optimism-corrected AUC 0.69), plausibly reflecting the absence of ECG findings, vital signs, symptom severity and hospital outcome data. Conclusions: The expected acuity markers dominated EMS transport decisions, but routinely coded cardiac comorbidities were independently associated with disposition in this understudied setting. The model is not deployable for individual triage; these variables should be considered candidate inputs for future models that incorporate richer clinical data, outcome linkage and 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.