Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings

Inherited cardiomyopathies are a significant global cause of sudden cardiac death, particularly among younger individuals and those in under-resourced regions. Despite progress in diagnostics and therapeutics, screening and risk stratification remain challenging due to genetic complexity, variable clinical presentation, and the interpretive limitations of current electrophysiological and imaging tools. Artificial intelligence (AI)-particularly machine learning, deep learning, and natural language processing offers transformative potential by enabling large-scale analysis of complex data and detecting subtle disease patterns which could potentially improve diagnostic accuracy and cost-effectiveness, particularly in low-resource environments. This review evaluates the limitations of existing risk models, synthesizes disease-specific AI applications within a unified framework, and explores the role of AI in advancing personalized care and risk prediction in underserved populations.