Challenges in the diagnosis and management of hypertrophic cardiomyopathy and the promise of artificial intelligence.

Hypertrophic cardiomyopathy (HCM), the most prevalent inherited cardiomyopathy, is characterized by left ventricular hypertrophy that typically manifests with asymmetric wall thickening and is not caused by a pressure overload state or systemic disease. Despite its considerable prevalence-estimated to affect up to 1 in 200 individuals based on imaging data-it often goes undiagnosed or misdiagnosed, particularly in general clinical settings. Traditional tools, such as the electrocardiogram, although widely used, frequently yield nonspecific findings that complicate the early identification or screening of HCM. In recent years, artificial intelligence (AI) and machine learning have emerged as powerful tools with the potential to revolutionize HCM diagnosis and management. AI-driven algorithms trained on ECG and imaging data are being developed to improve early detection, risk stratification, and therapeutic monitoring in patients with or at risk for HCM. Additionally, AI has shown utility in biomarker-based prediction models, further enhancing diagnostic precision and clinical decision-making. Harnessing the power of AI may help close critical diagnostic gaps and optimize outcomes for individuals affected by HCM.