INTEGRATION OF ARTIFICIAL INTELLIGENCE IN CARDIOVASCULAR RISK PREDICTION: A PARADIGM SHIFT IN PREVENTIVE CARDIOLOGY
PDF

Ключевые слова

Artificial intelligence, cardiovascular diseases, risk prediction, machine learning, preventive cardiology, deep learning, clinical decision support systems.

Аннотация

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide. Traditional risk assessment models, while valuable, often lack the precision needed for individualized patient care. The advent of artificial intelligence (AI) offers a transformative approach to risk prediction, enabling the analysis of complex datasets to identify patterns and risk factors with unprecedented accuracy. This study explores the integration of AI in cardiovascular risk prediction, examining various AI models, their applications in clinical settings, and the potential benefits and challenges associated with their implementation. By analyzing recent studies and clinical trials, we aim to provide a comprehensive overview of how AI is reshaping preventive cardiology.

PDF

Библиографические ссылки

Khera AV, et al. "Deep learning of retinal images for cardiovascular risk prediction." Nature Biomedical Engineering, 2018.

Attia ZI, et al. "An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm." The Lancet, 2019.

Weng SF, et al. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?" PLOS ONE, 2017.

Nour M, et al. "Artificial intelligence in cardiovascular medicine." Nature Reviews Cardiology, 2020.

Topol EJ. "High-performance medicine: the convergence of human and artificial intelligence." Nature Medicine, 2019.

Johnson KW, et al. "Artificial intelligence in cardiology." Journal of the American College of Cardiology, 2018.

Esteva A, et al. "A guide to deep learning in healthcare." Nature Medicine, 2019.

Rajpurkar P, et al. "Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists." PLOS Medicine, 2018.

Shickel B, et al. "Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis." Journal of Biomedical Informatics, 2018.

Dilsizian SE, et al. "Artificial intelligence in nuclear cardiology: A review of the literature and future directions." Journal of Nuclear Cardiology, 2019.