The role of machine learning in early detection, accurate diagnosis, and timely treatment of diseases.

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

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

The use of AI in cardiovascular care is expected to optimize resource allocation, reduce healthcare costs, and ultimately improve survival rates, despite ongoing challenges with data quality, model transparency, and ethical considerations.

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.

The expanding role of artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging

Artificial Intelligence can be very effective in increasing medical professionals’ knowledge and, consequently, improving patient outcomes, but its effective use in the clinic necessitates addressing concerns about data privacy, rigorous validation, and the development of methods to reduce bias caused by medical data.

Computational Intelligence for Heart Disease Prediction and Detection: A Comparative Study of Machine Learning, Deep Learning, and Quantum Algorithms

: Heart disease continues to be one of the most serious global health challenges, affecting people’s quality of life and placing a heavy burden on health-care systems. To improve early diagnosis and support better patient outcomes, this study examines a computational approach that brings together methods from machine learning, deep learning, and emerging quantum computing. Traditional machine learning models offer interpretable insights when working with structured clinical data, while deep learning techniques are well suited for identifying subtle patterns in complex sources such as ECG signals and cardiac imaging. In addition, quantum computing methods such as quantum support vector machines and variation quantum circuits show promise in exploring features more efficiently and speeding up optimization, potentially overcoming some of the limitations of classical computation. Early comparative results suggest that, when adequate quantum resources are available, hybrid quantum classical models can shorten training time and improve prediction accuracy. Overall, this integrated approach highlights how advanced computational techniques can support more effective health-care delivery, strengthen preventive care, and enable real-time, scalable detection of cardiac diseases.

AI-Enabled Smart Healthcare Systems: Innovative Approaches to Disease Prediction, Accurate Diagnosis, and Effective Public Health Management

It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve.