Integrating AI in Cardiovascular Systems: Innovations in Diagnosis, Risk Prediction, and Management.

ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS, TREATMENT AND PROGNOSIS OF CARDIOVASCULAR DISEASES

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.

From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, driven by the increasing availability of large-scale clinical data and advances in machine learning. Early computational applications in cardiology were primarily limited to rule-based electrocardiogram interpretation systems. Over time, these approaches have evolved into sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data. In parallel with the broader development of digital health technologies, including wearable devices, electronic health records, and remote monitoring systems, AI applications have expanded across multiple domains of cardiovascular care. These now include electrocardiographic (ECG) and electrophysiological analysis, cardiovascular imaging, surgical planning, and multimodal risk prediction. More recently, multimodal AI models have emerged that integrate heterogeneous data sources such as imaging, physiological signals, clinical records, and genomic information, enabling more comprehensive characterisation of cardiovascular disease. Beyond diagnostic applications, AI is increasingly influencing system-level aspects of cardiovascular medicine, including clinical decision support, workflow optimisation, medical education, and clinical trial design. This narrative review traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation systems to contemporary multimodal and system-level applications. It highlights key technological developments, current clinical applications, translational challenges, and the emerging role of AI within digital cardiovascular health ecosystems, with particular emphasis on early disease detection, risk stratification, prognostic modelling, and personalised cardiovascular care.

Artificial Intelligence in Cardiovascular Imaging and Risk Stratification: From Algorithmic Discovery to Meaningful Clinical Implication.

It is emphasized that successful integration of AI into cardiovascular care requires rigorous prospective validation, transparent algorithmic governance, equitable data representation, and human-AI collaborative frameworks, provided its meaningful clinical implication is demonstrated through improved patient outcomes.

Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records.

[Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis].

Cardiovascular diseases remain the leading cause of mortality and morbidity worldwide, with substantial impact in Italy. Cardiovascular prevention is a strategic priority, yet a significant gap persists between evidence-based guideline recommendations and their actual implementation in clinical practice. Artificial intelligence (AI), through machine learning and deep learning models, is emerging as a potentially transformative technology to bridge this gap, enabling more precise, dynamic, and personalized cardiovascular risk stratification compared with traditional risk scores. This review examines the most recent evidence on the application of AI in cardiovascular prevention, with a specific focus on risk stratification, early detection of subclinical disease, and identification of patients most likely to benefit from targeted interventions. It addresses the limitations of conventional risk scores and the contribution of emerging risk determinants, including digital biomarkers, genetic data, and wearable devices. It discusses the role of AI-enabled electrocardiography in the early detection of subclinical atrial fibrillation, left ventricular dysfunction, and coronary artery disease; the potential of opportunistic imaging (chest radiography, chest and coronary computed tomography, mammography) for subclinical atherosclerosis; and the integration of AI into clinical care pathways, electronic health records, clinical decision support systems, and telemonitoring networks. Overall, AI outlines the transition from a reactive cardiology model toward a predictive, proactive, and precision-based approach. Translation into routine clinical practice requires robust prospective evidence, randomized controlled trials, validation in heterogeneous populations, improved model interpretability, and adequate digital and regulatory infrastructures.

Enhancing Cardiovascular Disease Diagnosis With Data-Driven Predictive Systems

The proposed approach uses a Quantum Neural Network for machine learning for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system that has the highest sensitivity and specificity in the current literature, matching exact expert opinions.