Wearable and Portable Cardiac Technologies for Early Detection of Cardiomyopathies: A Clinically Oriented Review

Abstract

Cardiomyopathies are a heterogeneous group of myocardial disorders that may lead to heart failure, arrhythmias, and sudden cardiac death. Early detection remains challenging, particularly in asymptomatic individuals and regions with limited access to advanced imaging modalities. Recent advances in wearable and portable cardiac technologies provide new opportunities for early identification of electrical and structural abnormalities associated with cardiomyopathies. This clinically oriented review examines the role of wearable and portable cardiac technologies—including smartwatch-based single-lead electrocardiography, continuous ECG patch monitoring, portable echocardiography, and artificial intelligence (AI)–enabled diagnostic tools—in the early detection of cardiomyopathies. A structured literature search of PubMed, MEDLINE, Embase, and Google Scholar identified clinically relevant studies published between January 2010 and September 2025. The review emphasizes real-world clinical applicability by highlighting the strengths, limitations, and appropriate clinical contexts of these technologies in detecting hypertrophic, dilated, arrhythmogenic, and restrictive cardiomyopathies. Unlike previous reviews focused primarily on diagnostic accuracy or individual technologies, this review integrates wearable ECG, portable echocardiography, AI-enabled diagnostics, and disease-specific applications into a practical clinician-oriented framework. Although these technologies demonstrate promising diagnostic sensitivity and improved accessibility, they should be regarded as complementary rather than replacement diagnostic tools. Integration into clinical workflows, clinician training, and validation across diverse populations remain essential. When thoughtfully implemented, these technologies may facilitate earlier detection, improved risk stratification, and timely referral for definitive cardiac evaluation. Their continued integration with AI is expected to further advance precision cardiology and population-based cardiovascular screening.