Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention

Abstract

<b>Objectives</b>: With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledge on the use of AI in the analysis of electrocardiographic (ECG) signals obtained from wearable devices, particularly smartwatches, and to outline perspectives for future clinical applications. <b>Methods</b>: A narrative literature review was conducted using PubMed, Web of Science, and Scopus databases. The search focused on combinations of keywords related to AI, ECG, and wearable technologies. After screening and applying inclusion criteria, 152 publications were selected for final analysis. <b>Conclusions</b>: Modern AI algorithms-especially deep neural networks-show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, and other cardiovascular conditions. Smartwatches without ECG sensors, using photoplethysmography (PPG) and machine learning, show potential as supportive tools for preliminary atrial fibrillation (AF) screening at the population level, although further validation in diverse real-world settings is needed. This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. Regulatory, technical, and ethical challenges are also discussed, along with the issue of limited clinical evidence. Artificial intelligence enables a significant enhancement of personalized, mobile, and preventive cardiology. Its integration into smartwatch ECG analysis opens a path toward early detection of cardiac disorders and the implementation of population-scale screening approaches.

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