Advancements in Artificial Intelligence for Enhanced Diagnosis and Prognosis in Cardiac CT: a Comprehensive Review and Future Perspectives.

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Abstract

Cardiovascular disease, with coronary artery disease (CAD) as its main culprit, remains a leading cause of death worldwide. Traditional diagnostic methods that focus on the degree of arterial narrowing (percent diameter stenosis, or %DS) can miss early signs of disease or the specific plaque features that indicate high risk. The purpose of this review is to examine how artificial intelligence (AI) can enhance the diagnostic and prognostic capabilities of cardiac computed tomography (CCT), and to address where AI can most meaningfully improve diagnostic accuracy, workflow efficiency, and patient care. AI is transforming cardiac CT across the entire imaging pathway. By applying AI algorithms, recent studies have refined the assessment of coronary artery calcium (CAC), improved plaque characterization, and detected subtle lesions that might evade traditional detection methods. AI further enables non-invasive assessment of ischemia (reduced blood flow), aids in the evaluation of myocardial infarction, analyzes adipose tissue surrounding the heart, improves image quality, and provides valuable prognostic insights for risk stratification. This comprehensive review explores the transformative potential of AI in cardiac CT, highlighting its role in accurate diagnosis, precise risk stratification, and personalized management of cardiovascular disease. Through collaboration between AI technology and clinical expertise, we envision a future where cardiac CT imaging is more powerful, efficient, and patient-centered, ultimately leading to improved outcomes in cardiovascular medicine. Continued standardization, external validation, and clinician education will be essential to realize this impact in routine practice.

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