Artificial Intelligence in Cardiology-A Narrative Review of Current Status

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is crucial for clinical decision support systems (CDSS).
• Machine learning (ML) techniques, like Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), are key components.
• Deep learning (DL), utilizing deep Artificial Neural Networks (DNNs), represents a significant advancement in AI complexity.

Purpose of the Study:

• To review the growing impact of AI in cardiology.
• To highlight recent achievements and future potential of AI in clinical practice.
• To underscore the increasing evidence for AI's central role in cardiology.

Main Methods:

• Review of recent literature on AI, ML, and DL in cardiology.
• Analysis of AI's application in approximating human reasoning for clinical decisions.
• Examination of ML techniques for extracting medical knowledge from data.

Main Results:

• AI, ML, and DL show significant achievements across nearly all areas of cardiology.
• The computational capacity for complex ML systems is rapidly increasing.
• Despite current limitations, AI's impact on clinical practice is poised for substantial growth.

Conclusions:

• AI is set to become a central tool in cardiology.
• The increasing volume of research signifies a paradigm shift towards AI-driven healthcare.
• Advanced AI techniques will likely revolutionize clinical decision-making and patient outcomes.