Abstract

Delayed diagnosis, limited access to specialist care and variability in ECG interpretation contribute to suboptimal cardiovascular risk assessment and preventable adverse outcomes. Tele-ECG has helped address some of these barriers by enabling remote ECG acquisition and transmission. However, its clinical potential is substantially enhanced when integrated with Artificial Intelligence (AI). This review highlights the manner in which AI-assisted tele-ECG can revolutionize the cardiovascular sector by early risk stratification, real-time monitoring and personalized clinical decision support. AI technologies such as deep learning-based ECG interpretation, predictive risk models and wearable ECG devices contribute to the precision of the diagnosis, reducing the variability between different readers and enabling the medical intervention to be timely even in places that are not traditionally covered by the healthcare services. At the same time, challenges such as model interpretability, data privacy, algorithmic bias, infrastructure limitations and the need for clinician oversight remain significant. Overcoming these challenges will demand joint efforts that include the creation of regulatory frameworks, standardized validation, clinician training and the devising of ethical deployment strategies. Ultimately, AI-enabled tele-ECG represents more than a technological advancement. It is a critical approach to improving early cardiovascular risk detection, optimizing preventive care and reducing disparities in access to highquality cardiovascular services.

Keywords:Artificial intelligence; Tele-ECG; Cardiovascular risk stratification; Electrocardiography; Deep learning; Remote monitoring; Digital cardiology