[Artificial intelligence-enhanced ECG interpretation: a new era for electrocardiography?]

Area of Science:

• Cardiology
• Artificial Intelligence
• Medical Diagnostics

Background:

• Traditional electrocardiogram (ECG) analysis faces limitations in accuracy and adaptability.
• Artificial intelligence (AI) is transforming ECG interpretation into a dynamic, predictive tool.
• AI models, particularly machine learning and deep learning, show improved diagnostic performance for various cardiovascular diseases.

Purpose of the Study:

• To explore the evolving role of AI in ECG interpretation.
• To highlight AI-ECG's capabilities in early detection, risk stratification, and decision support.
• To address the challenges and future directions of AI in ECG analysis.

Main Methods:

• Review of AI-driven models (machine learning, deep learning) applied to ECG interpretation.
• Analysis of AI-ECG's performance in diagnosing cardiovascular diseases and predicting adverse events.
• Discussion of multiparametric approaches and wearable device integration.
• Consideration of federated learning for model refinement and data privacy.

Main Results:

• AI-ECG demonstrates superior diagnostic accuracy for conditions like atrial fibrillation and myocardial infarction.
• AI-ECG can detect subclinical dysfunction, stratify long-term risk, and predict adverse events.
• AI-ECG serves as a decision support tool in complex cases and optimizes resource allocation.
• Wearable AI-ECG facilitates continuous monitoring and arrhythmia detection.

Conclusions:

• AI-enhanced ECG significantly advances cardiovascular diagnostics and personalized medicine.
• Addressing challenges like explainability, bias, and regulation is crucial for responsible implementation.
• Federated learning and regulatory frameworks can foster transparency, equity, and clinical validity.
• AI-ECG complements, rather than replaces, clinical expertise, enhancing patient care.