Precision Medicine for Electrocardiogram Interpretation: Clinical Relevance, Challenges, and Advances

Area of Science:

• Cardiology
• Medical Diagnostics
• Artificial Intelligence in Medicine

Background:

• Electrocardiograms (ECGs) are fundamental, non-invasive tools for cardiovascular diagnostics.
• A standard 12-lead ECG provides insights into heart rhythm, conduction, injury, and remodeling.
• ECG abnormalities can indicate various cardiac and non-cardiac conditions.

Purpose of the Study:

• To provide a structured framework for ECG interpretation.
• To review cardiac and non-cardiac conditions associated with ECG abnormalities.
• To explore the role of artificial intelligence (AI) in enhancing ECG analysis and clinical decision-making.

Main Methods:

• Systematic review of ECG interpretation principles.
• Discussion of general approaches to rate, rhythm, axis, intervals, and repolarization.
• Exploration of AI innovations, particularly deep learning, for ECG analysis.

Main Results:

• AI algorithms demonstrate performance rivaling and, in some cases, exceeding expert clinicians.
• AI excels in detecting conditions like left ventricular dysfunction and myocardial infarction.
• AI integration facilitates earlier disease recognition and refined risk stratification.

Conclusions:

• AI-enhanced ECG interpretation improves diagnostic accuracy and patient outcomes.
• AI optimizes clinical decision-making in both acute and chronic cardiovascular care.
• This review guides readers through fundamental ECG interpretation augmented by AI capabilities.