ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS, TREATMENT AND PROGNOSIS OF CARDIOVASCULAR DISEASES
Maria A. Soboleva,
Oleg G. Kargaev,
Marta A. Chekurishvili,
Milana V. Tedeeva,
Igor V. Gopantsov,
Norayr G. Zakaryan,
Arsen A. Babayan,
Dzerassa A. Tsogoeva,
Alan B. Tsagaraev,
Victoria E. Yuzbasheva,
Lana A. Valieva,
Kamila A. Egamova,
Sofia A. Nikolaenko,
Anastasia S. Grishina Abstract
Highlights
- Artificial intelligence is transforming cardiology by enabling more accurate diagnosis, personalized therapy, and prediction of cardiovascular complications.
- The study provides a comprehensive systematization of current approaches to machine learning, neural networks, and big data analytics in clinical cardiology.
- Key directions for integrating AI into Russian healthcare are highlighted, considering ethical, legal, and organizational aspects.
Abstract
Modern cardiology is undergoing a rapid digital transformation driven by artificial intelligence (AI). The application of machine learning and deep learning algorithms provides unprecedented opportunities for diagnosis, monitoring, and prediction of cardiovascular diseases (CVDs). This review summarizes current evidence on the integration of AI across key domains of cardiovascular care–from data acquisition and analysis to personalized treatment optimization. The article highlights successful applications of AI in electrocardiogram interpretation, cardiovascular imaging, hemodynamic assessment, and prediction of heart failure exacerbations. Particular attention is paid to ethical, legal, and organizational aspects of AI implementation, including transparency, data security, and clinical accountability. International and national frameworks, such as the EU Artificial Intelligence Act, GDPR, and Russian federal regulations, are discussed as foundations for safe and equitable adoption of AI in healthcare. The review also outlines the Russian Federation’s initiatives in digital health transformation, including the development of domestic diagnostic algorithms and unified medical data repositories. Future perspectives include the use of quantum computing, emotional AI, and integration of digital competencies into medical education. Artificial intelligence is viewed as a transformative tool to enhance diagnostic accuracy, treatment efficiency, and preventive strategies in cardiology, provided that human oversight and clinical validation remain central.
About the Authors
Maria A. Soboleva
Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
student, 6th year, Institute of Maternal and Child Health, Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
Oleg G. Kargaev
North Ossetian State Medical Academy
Russian Federation
Russian Federation
student, 6th year, Faculty of Pediatrics, North Ossetian State Medical Academy, Vladikavkaz, Russian Federation
Marta A. Chekurishvili
North Ossetian State Medical Academy
Russian Federation
Russian Federation
student, 6th year, Faculty of General Medicine, North Ossetian State Medical Academy, Vladikavkaz, Russian Federation
Milana V. Tedeeva
North Ossetian State Medical Academy
Russian Federation
Russian Federation
student, 6th year, Faculty of General Medicine, North Ossetian State Medical Academy, Vladikavkaz, Russian Federation
Igor V. Gopantsov
Krasnoyarsk State Medical University Named After Prof. V.F. Voino-Yasenetsky
Russian Federation
Russian Federation
student, 6th year, Faculty of General Medicine, Krasnoyarsk State Medical University Named After Prof. V.F. Voino-Yasenetsky, Krasnoyarsk, Russian Federation
Norayr G. Zakaryan
Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
student, 6th year, Institute of Clinical Medicine, Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
Arsen A. Babayan
Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
student, 6th year, Institute of Clinical Medicine, Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
Dzerassa A. Tsogoeva
North Ossetian State Medical Academy
Russian Federation
Russian Federation
student, 6th year, Faculty of General Medicine, North Ossetian State Medical Academy, Vladikavkaz, Russian Federation
Alan B. Tsagaraev
North Ossetian State Medical Academy
Russian Federation
Russian Federation
student, 6th year, Faculty of General Medicine, North Ossetian State Medical Academy, Vladikavkaz, Russian Federation
Victoria E. Yuzbasheva
Stavropol State Medical University
Russian Federation
Russian Federation
student, 6th year, Faculty of Pediatrics, Stavropol State Medical University, Stavropol, Russian Federation
Lana A. Valieva
North Ossetian State Medical Academy
Russian Federation
Russian Federation
student, 6th year, Faculty of General Medicine, North Ossetian State Medical Academy, Vladikavkaz, Russian Federation
Kamila A. Egamova
Kazan Federal University
Russian Federation
Russian Federation
student, 6th year, Faculty of General Medicine, Kazan Federal University, Kazan, Russian Federation
Sofia A. Nikolaenko
Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
student, 6th year, Institute of Maternal and Child Health, Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
Anastasia S. Grishina
Pacific State Medical University
Russian Federation
Russian Federation
student, Pacific State Medical University, Vladivostok, Russian Federation
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For citations:
Soboleva M.A., Kargaev O.G., Chekurishvili M.A., Tedeeva M.V., Gopantsov I.V., Zakaryan N.G., Babayan A.A., Tsogoeva D.A., Tsagaraev A.B., Yuzbasheva V.E., Valieva L.A., Egamova K.A., Nikolaenko S.A., Grishina A.S. ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS, TREATMENT AND PROGNOSIS OF CARDIOVASCULAR DISEASES. Complex Issues of Cardiovascular Diseases. (In Russ.)
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