Artificial Intelligence-Powered Electrocardiography: New Frontiers in Cardiovascular Disease Detection and Prognosis

The field of cardiovascular diagnostics is undergoing a transformative shift with the advent of artificial intelligence (AI)-enhanced electrocardiography (ECG). While the ECG has long served as a fundamental tool for detecting and monitoring a broad spectrum of heart diseases, traditional approaches often struggle to identify subtle abnormalities that may signal early disease, stratify risk accurately, or predict outcomes with high precision. Recent years have seen the rapid development of deep learning algorithms capable of parsing complex, high-dimensional ECG data, thereby uncovering hidden biomarkers, improving risk assessment, and exceeding the performance of conventional analytical methods. This evolution is powered by access to large-scale ECG datasets and computational advances that enable both supervised and unsupervised learning approaches, sparking new enthusiasm for leveraging AI in the early identification and management of cardiovascular disease.This Research Topic aims to consolidate cutting-edge studies that harness artificial intelligence and machine learning to advance ECG-based diagnosis, risk prediction, and clinical decision support in cardiovascular medicine. The key objective is to explore how AI-driven analysis enables earlier, more accurate detection of disease phenotypes, enhances risk stratification, and drives personalized care strategies. By integrating deep learning with standard and advanced ECG technologies, researchers have the opportunity to develop novel diagnostic biomarkers, translate predictive models into real-world practice, and evaluate the impact of algorithmic tools on patient outcomes. The initiative also seeks to address practical challenges, such as ensuring algorithm interpretability, overcoming obstacles to clinical adoption, and maintaining standards of data privacy and ethical AI deployment.To gather further insights into AI-powered ECG and its translation into cardiovascular care, we welcome articles addressing, but not limited to, the following themes:o Application of deep learning and AI models to ECG datasets for disease detection, phenotyping, and risk predictiono Development of novel ECG-derived digital biomarkers for early and subclinical cardiovascular diseaseo Validation and real-world implementation of AI–ECG tools as clinical decision support systems across patient populationso Comparative studies of AI-powered ECG analysis versus traditional interpretation in diverse cardiovascular conditionso Strategies to enhance algorithm transparency, interpretability, and acceptance in clinical workflowso Ethical, legal, and privacy implications of AI-driven ECG diagnosticsThe scope of this Research Topic covers all article types. By fostering innovation at the interface of AI and cardiovascular diagnostics, the aim is to advance detection, precision risk assessment, and effective integration of AI–ECG tools into daily cardiology practice.*Prof. Yong-Soo Baek is the co-founder and Chief Technology Officer (CTO) of DeepCardio Inc. Topic editors: Simon W. Rabkin, Yong-Soo Baek Source: https://www.frontiersin.org/research-topics/77817/artificial-intelligence-powered-electrocardiography-new-frontiers-in-cardiovascular-disease-detection-and-prognosis