Artificial Intelligence in Cardiovascular Imaging and Risk Stratification: From Algorithmic Discovery to Meaningful...

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.