Artificial Intelligence in Assessing Cardiovascular Disease

Abstract

Cardiovascular disease (CVD) assessment has long relied on traditional methods that are often subjective, time-consuming, and limited in predictive accuracy, creating a significant gap for more objective and scalable tools. To address this gap, artificial intelligence (AI) is profoundly transforming the assessment and management of CVD, enabling a shift from these conventional approaches to data-driven, precise, and predictive paradigms. This review comprehensively summarizes the latest advancements in AI applications across major CVD diagnostic modalities, including cardiac imaging (echocardiography and cardiac computed tomography), physiological signal processing (electrocardiogram and photoplethysmography from wearable devices), and multimodal risk prediction. AI algorithms demonstrate expert-level performance in automating the quantification of key metrics such as ejection fraction and coronary calcium, detecting subtle arrhythmias, and identifying early signs of cardiac dysfunction. Furthermore, by integrating multimodal data—such as electronic health records (EHRs) and retinal images—AI models excel in predicting individual risks of major adverse cardiovascular events, heart failure (HF) hospitalization, and mortality, outperforming conventional risk stratification tools. Despite these significant developments, challenges related to data quality, model interpretability, and clinical integration still exist. The future focus will be on three major directions: introducing large language models (LLMs) to build a more intelligent patient management system, developing adaptive analysis systems with continuous learning capabilities, and expanding the application of AI in auxiliary diagnosis and treatment. Through a human-machine collaboration model, to provide clinical solutions for CVD prevention and treatment that are both personalized and preventive.