Artificial Intelligence in Cardiovascular Disease Prevention: Current Applications and Future Perspectives

dc.contributor.authorKeser, Nurgul
dc.contributor.authorKivrak, Tarik
dc.contributor.authorSekban, Ahmet
dc.contributor.authorBozyel, Serdar
dc.date.accessioned2026-08-12T17:25:32Z
dc.date.issued2026
dc.departmentFırat Üniversitesi
dc.description.abstractCardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, emphasizing the ongoing need for effective and scalable primary and secondary prevention strategies. In this evolving landscape, artificial intelligence (AI) has emerged as a transformative force in preventive cardiology, with the potential to reshape risk assessment, early disease detection, and personalized preventive care. Artificial intelligence-driven models consistently outperform traditional risk scores by integrating large-scale, multidimensional, and longitudinal data derived from various platforms. These capabilities enable dynamic and time-adaptive cardiovascular risk prediction that more accurately reflects the evolving nature of individual risk profiles. Advances in machine learning and deep learning have facilitated the earlier identifica-tion of subclinical CVD often preceding clinical manifestation by several years. In parallel, AI-powered wearable devices and digital health (DH) solutions support continuous physi-ological monitoring, real-time feedback, and personalized lifestyle and behavioral interventions, thereby extending preventive care beyond traditional clinic-based settings. Such approaches appear particularly beneficial for high-risk populations by promoting sustained engagement, early intervention, and improved clinical outcomes. Looking ahead, emerging innovations such as multimodal AI systems, digital twin technologies, and AI-guided clinical guidelines signal a paradigm shift toward predictive, participa-tory, precision-based, and continuously learning prevention strategies. Nevertheless, the successful translation of AI into routine clinical practice will depend on increasing DH literacy, rigorous prospective validation, ethical and regulatory oversight, data transparency, and seamless integration into clinical workflows. When thoughtfully implemented, AI holds the promise to fundamentally advance preventive cardiology, enabling more patient-centered, participatory, and equitable cardiovascular care while reducing the global burden of CVD.
dc.identifier.doi10.14744/AnatolJCardiol.2026.6274
dc.identifier.issn2149-2263
dc.identifier.issn2149-2271
dc.identifier.pmid41983337
dc.identifier.urihttps://doi.org/10.14744/AnatolJCardiol.2026.6274
dc.identifier.urihttps://hdl.handle.net/11508/54396
dc.identifier.wosWOS:001744475200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherKare Publ
dc.relation.ispartofAnatolian Journal of Cardiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260511
dc.subjectArtifical intelligence
dc.subjectcardiovascular disease
dc.subjectprevention
dc.titleArtificial Intelligence in Cardiovascular Disease Prevention: Current Applications and Future Perspectives
dc.typeReview Article

Dosyalar