Can artificial intelligence transform cardiomyopathy diagnosis and risk stratification in the Philippines?

I read with great interest the article by Alahwany et al., which provides a rigorous synthesis of how artificial intelligence (AI) can transform the diagnosis and management of inherited cardiomyopathies in low-resource settings [1]. The integration of machine learning (ML), deep learning (DL), and natural language processing (NLP) offers a unified framework for screening and prediction in hypertrophic (HCM), dilated (DCM), and arrhythmogenic cardiomyopathies (ACM). The reported diagnostic accuracy of AI-enhanced electrocardiograms (ECG) and point-of-care imaging, achieving sensitivities above 97% for high-risk HCM features, underscores their potential for widespread clinical use [1].