Related Experiment Videos
Artificial intelligence in cardiovascular imaging and risk stratification: From algorithmic discovery to meaningful
Xu Xia1, Wasim Ullah Khan2, Qaisar Khan3
1School of Nursing, Shandong XieHe University, Jinan, Shandong, China.
Insights
Artificial intelligence (AI) is revolutionizing cardiovascular disease (CVD) diagnosis and risk assessment by enabling automated, quantitative, and patient-specific evaluations. Overcoming challenges like bias and regulatory hurdles is key for AI integration to improve patient outcomes.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiovascular disease (CVD) is a leading cause of death globally.
- Traditional methods for CVD diagnosis and risk assessment are limited by subjectivity and time.
- Advancements in AI, deep learning, foundation models, and multimodal integration offer new possibilities.
Purpose of the Study:
- To review the evolution and application of AI in cardiovascular imaging.
- To evaluate AI-driven tools across various imaging modalities and digital biomarkers.
- To discuss challenges and future directions for AI integration in cardiovascular care.
Main Methods:
- Review of AI advancements in cardiovascular imaging from 2023-2026.
- Analysis of AI applications in echocardiography, cardiac MRI, CT angiography, and nuclear imaging.
- Assessment of AI-enhanced ECG and polygenic risk prediction models.
Main Results:
- AI tools are progressing from proof-of-concept to clinical validation across imaging modalities.
- AI-enhanced ECG and risk prediction models show superior performance to conventional methods.
- Significant hurdles remain, including algorithmic bias, generalizability, regulatory issues, and workflow integration.
Conclusions:
- Successful AI integration requires rigorous validation, transparent governance, and equitable data.
- Human-AI collaboration is essential for precision diagnostics in cardiovascular medicine.
- AI has the potential to augment interpretation, risk stratification, and decision-making, improving patient outcomes.
Abstract:
Cardiovascular disease (CVD) remains the leading global cause of mortality, with timely diagnosis and precise risk stratification serving as cornerstones of effective management. Traditional cardiovascular imaging and risk assessment have long been constrained by operator-dependent interpretation, time-intensive manual quantification, and a reliance on static, population-derived diagnostic thresholds. The rapid maturation of artificial intelligence (AI), particularly deep learning (DL), foundation models (FMs), and multimodal integration (MI), has catalyzed a paradigm shift toward automated, quantitative, and patient-specific cardiovascular evaluation. Between 2023 and 2026, AI-driven tools have progressed from retrospective proof-of-concept studies to early prospective clinical validations across echocardiography (EchoCG), cardiac magnetic resonance (CMR), coronary computed tomography angiography (CCTA), and nuclear imaging. Concurrently, AI-enhanced electrocardiography (ECG) and polygenic risk integration have enabled dynamic, longitudinal risk prediction models that outperform conventional scores. Despite these advances, clinical adoption faces substantial hurdles including algorithmic bias, limited generalizability across diverse populations, regulatory fragmentation, workflow integration challenges, and unresolved questions regarding clinical utility and cost-effectiveness. This review critically examines the technological evolution of AI in cardiovascular imaging, evaluates modality-specific applications and emerging digital biomarkers, appraises regulatory and implementation landscapes, and outlines priority research directions. We emphasize that successful integration of AI into cardiovascular care requires rigorous prospective validation, transparent algorithmic governance, equitable data representation, and human-AI collaborative frameworks. As cardiovascular medicine enters the era of precision diagnostics, AI will increasingly serve as a powerful augmentative partner in imaging interpretation, risk stratification, and therapeutic decision-making, provided its meaningful clinical implication is demonstrated through improved patient outcomes.
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:28Imaging Studies for Cardiovascular System V: CT
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...