Artificial intelligence for cardiology: from diagnosis to management

Area of Science:

• Cardiology
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) and machine learning (ML) are transforming cardiac electrophysiology.
• These technologies utilize diverse data sources for improved decision-making in diagnosing and treating cardiac arrhythmias.

Purpose of the Study:

• To explore the evolving role of AI in cardiology, focusing on diagnostics, predictive analytics, and procedural innovations.
• To examine interdisciplinary collaboration in addressing AI challenges in cardiology.
• To identify barriers and propose strategies for AI integration into routine clinical practice.

Main Methods:

• Scoping review of 142 studies.
• Analysis of AI applications in cardiac electrophysiology.
• Examination of interdisciplinary team dynamics and challenges.

Main Results:

• AI enhances diagnostic precision and patient outcomes in cardiology.
• AI integration streamlines healthcare delivery and supports preventive cardiology.
• Successful AI implementation requires interdisciplinary coordination and ethical considerations.

Conclusions:

• AI is a catalyst for innovation in cardiac care, improving risk assessment and interventions.
• Fostering collaboration and aligning AI with clinical needs are crucial for adoption.
• This review provides a roadmap for advancing AI development and application in cardiology to reduce cardiovascular disease globally.