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

Area of Science:

• Preventive Cardiology
• Artificial Intelligence in Healthcare
• Digital Health

Background:

• Cardiovascular diseases (CVDs) are a leading global cause of death, necessitating advanced prevention strategies.
• Traditional risk assessment methods have limitations in capturing dynamic individual risk profiles.
• Artificial intelligence (AI) presents a transformative opportunity for enhancing CVD prevention.

Purpose of the Study:

• To explore the role and impact of AI in preventive cardiology.
• To highlight AI's potential in risk assessment, early detection, and personalized interventions for CVDs.
• To discuss the future directions and challenges of integrating AI into clinical practice for cardiovascular care.

Main Methods:

• Review of AI-driven models integrating multidimensional, longitudinal data.
• Analysis of machine learning and deep learning for subclinical CVD identification.
• Examination of AI-powered wearable devices and digital health solutions for continuous monitoring and interventions.

Main Results:

• AI models outperform traditional risk scores in cardiovascular risk prediction.
• AI facilitates earlier identification of subclinical CVD.
• AI-powered digital health solutions enable continuous monitoring and personalized interventions, especially for high-risk populations.

Conclusions:

• AI is poised to shift preventive cardiology towards predictive, participatory, and precision-based strategies.
• Successful clinical integration requires addressing data literacy, validation, ethical oversight, and workflow integration.
• AI offers a promising avenue to reduce the global burden of CVD through more patient-centered and equitable care.