Artificial intelligence in cardiovascular imaging and intervention.

Area of Science:

• Cardiovascular Imaging and Interventions
• Artificial Intelligence in Medicine
• Medical Image Analysis

Background:

• Recent advancements in artificial intelligence (AI) offer transformative potential for medical applications.
• Generative models, multimodal foundation models, and federated learning represent key AI breakthroughs.
• These technologies are poised to revolutionize cardiac image analysis and cardiovascular interventions.

Purpose of the Study:

• To provide a comprehensive overview of recent AI developments in cardiovascular imaging and intervention.
• To highlight the capabilities of novel AI technologies, such as vision-language models and federated learning.
• To offer a future outlook on the integration of AI in cardiovascular medicine.

Main Methods:

• Review of recent literature on artificial intelligence applications in cardiovascular imaging and interventions.
• Focus on generative models, multimodal foundation models, and federated learning.
• Discussion of vision-language transformer models for concurrent text and image analysis.

Main Results:

• AI enables novel applications like automatic image report generation and image synthesis.
• Vision-language models facilitate visual questioning, answering, and image retrieval based on textual descriptions.
• Federated learning supports multi-centric collaborative training, enabling access to large clinical cohorts.

Conclusions:

• AI, particularly generative and multimodal models, is rapidly advancing cardiovascular imaging and interventions.
• These technologies offer powerful tools for data analysis, clinical decision support, and research collaboration.
• The integration of AI promises significant improvements in patient care and cardiovascular medicine.