Keywords
Electrocardiogram
Intelligent system
Extreme learning machines
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Abstract
Objective: To evaluate, through recent bibliographic references (2015–2025), the effectiveness of Artificial Intelligence (AI) in the diagnostic interpretation of electrocardiograms (ECGs). Literature review: The evidence indicates that AI has the potential to transform the ECG into a powerful diagnostic and prognostic tool by detecting subtle, subclinical patterns that are often imperceptible to the human eye. Through deep learning and machine learning algorithms, AI can enhance the precision, speed, and scalability of ECG interpretation, contributing to earlier and more accurate diagnosis of a wide range of cardiovascular conditions. The review also emphasizes the importance of AI and machine learning literacy among healthcare professionals to ensure effective clinical adoption. Moreover, AI-augmented ECGs have demonstrated superior performance in some cases compared to conventional methods and even expert interpretation, highlighting their value in both acute and preventive settings. Final considerations: While the integration of AI into clinical practice still faces barriers such as model explainability, data security, and the need for external validation, its application in ECG analysis holds significant potential to improve cardiovascular care through early detection, personalized risk assessment, and efficient clinical decision-making.
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