Individualized Cardiovascular Therapeutics via Artificial Intelligence-Powered Cardiovascular Imaging

Abstract

Cardiovascular disease remains a leading cause of global illness and death, accounting for approximately 17.9 million deaths annually, according to the World Health Organization. There is a growing need for more precise and individualized therapeutic strategies to reduce this burden. Artificial intelligence (AI) has the potential to enhance cardiovascular outcomes by facilitating personalized risk stratification and informing treatment decisions, particularly through advancements in AI-powered cardiovascular imaging and machine learning models. Traditional approaches rely on population-level risk assessments and generalized treatment guidelines, which often fail to capture the complex and diverse nature of individual patient presentations. This review examines the transformative impact of AI-enhanced cardiovascular imaging modalities, including echocardiography, cardiac magnetic resonance imaging, computed tomography angiography, and nuclear cardiology, on optimizing medical therapy. Machine learning algorithms can rapidly and accurately analyze large volumes of imaging data, improving efficiency, standardizing image acquisition and interpretation, reducing inter-observer variability, and increasing diagnostic confidence. By integrating AI with cardiovascular imaging, clinicians can achieve personalized risk assessments, early disease detection, and tailored treatment plans that range from pharmacotherapy to interventions and lifestyle modifications. These advances hold promise for improving outcomes in patients with cardiovascular disease or at risk for its development. The review also addresses current challenges and future directions, highlighting the potential of AI to usher in a new era of individualized cardiovascular care.

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