Artificial Intelligence in Cardiology: General Perspectives and Focus on Interventional Cardiology

Area of Science:

• Cardiology
• Medical Artificial Intelligence
• Clinical Implementation

Background:

• Artificial intelligence (AI) is increasingly utilized in cardiology for diagnostics, risk prediction, treatment planning, and procedures.
• Current AI tools, including ECG interpretation and image analysis, offer improved accuracy and efficiency.
• However, significant barriers hinder widespread real-world adoption.

Purpose of the Study:

• To critically appraise the current applications of AI in cardiology.
• To identify and analyze the limitations and challenges of AI implementation in cardiovascular medicine.
• To outline future directions for responsible AI integration into clinical practice.

Main Methods:

• Review of current AI applications in cardiology.
• Analysis of challenges including generalizability, regulatory hurdles, and workflow integration.
• Examination of limitations such as algorithmic bias and lack of explainability.
• Assessment of AI in interventional cardiology and decision support systems.
• Consideration of ethical and regulatory factors.

Main Results:

• AI has improved diagnostic accuracy and workflow efficiency in areas like ECG interpretation and imaging.
• Major obstacles include AI generalizability, regulatory approval, clinical workflow integration, algorithmic bias, and lack of explainability.
• Evidence for robotic-assisted procedures' superiority is limited, and AI decision support requires validation.
• Ethical concerns and varying regulatory frameworks present further barriers.

Conclusions:

• Responsible AI integration in cardiology requires addressing challenges in validation, transparency, and interpretability.
• Interdisciplinary collaboration is essential for overcoming implementation hurdles.
• Future efforts must focus on developing robust, ethical, and clinically validated AI solutions for cardiovascular care.