Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Faculty of Medicine , University of Belgrade , Belgrade , Serbia
Faculty of Medicine , University of Belgrade , Belgrade , Serbia
Clinic for ORL and MFH, Klinički centar Srbije , Belgrade , Serbia
Faculty of Medicine , University of Belgrade , Belgrade , Serbia
Clinic for eye diseases, Klinički centar Srbije , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Faculty of Medicine , University of Belgrade , Belgrade , Serbia
Faculty of Medicine , University of Belgrade , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Faculty of Medicine , University of Belgrade , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Faculty of Medicine , University of Banja Luka , Banja Luka , Bosnia and Herzegovina
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
Faculty of Medicine , University of Banja Luka , Banja Luka , Bosnia and Herzegovina
PHI Hospital „Sveti Vračevi” , Bijeljina, Republic of Srpska, , Bosnia and Herzegovina
Faculty of Medicine Foča, University of East Sarajevo , Lukavica , Bosnia and Herzegovina
Institut za kardiovaskularne bolesti Dedinje , Belgrade , Serbia
The integration of Artificial Intelligence (AI) and wearable technologies in healthcare is revolutionizing preventive medicine, particularly in cardiovascular disease (CVD) prevention. With CVD being the leading cause of global mortality, these innovations offer transformative potential in addressing the disease through a multi-level prevention strategy. Capabilities of AI, supported by wearables, enhance data collection and analysis, allowing for tailored, patient-specific interventions. Primary prevention focuses on mitigating risk factors, while secondary prevention enables early detection through real-time monitoring, and tertiary prevention optimizes management of existing conditions to improve quality of life. This review explores the roles of AI and wearables in each level of prevention, highlighting advancements in predictive analytics, patient-centered care, and personalized treatment planning. Ethical considerations surrounding data privacy and security are also discussed, as well as the importance of accessible technology to reduce health disparities, particularly in low- and middle-income countries. As AI algorithms and wearable data improve, they will become increasingly effective in proactive health management, marking a shift from reactive treatment to preventive care. The successful implementation of these technologies depends on robust ethical frameworks and interdisciplinary collaboration, fostering a future in which preventive healthcare is more personalized, accessible, and impactful.
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