This review demonstrates improved risk stratification using AI in cardiovascular diagnostics, highlighting its clinical implications from randomized controlled trials.
Key Points
AI identifies paroxysmal atrial fibrillation during normal sinus rhythm, enabling timely interventions.
Deep learning algorithms can analyze high-dimensional data directly from ECG signals effectively.
Randomized controlled trials confirm AI enhances diagnostic criteria and reduces intervention times significantly.
The integration of AI in electrocardiography marks a promising future for cardiovascular risk management.