Artificial Intelligence in Cardiac Amyloidosis: A State-of-the-Art Review

Cardiac amyloidosis (CA) remains substantially under-recognized because its clinical and imaging manifestations overlap with common cardiovascular conditions, including heart failure with preserved ejection fraction, hypertrophic cardiomyopathy, hypertensive heart disease, and aortic stenosis, while traditional diagnostic “red flags” on electrocardiography and imaging are often absent, subtle, or inconsistently recognized, particularly in early disease, and atypical phenotypes. As a result, CA is frequently diagnosed late, and at an advanced stage. Artificial intelligence (AI) offers a compelling strategy to address these challenges by identifying subtle, multidimensional disease signatures distributed across electrocardiography, echocardiography, cardiac magnetic resonance imaging, and nuclear scintigraphy, and by integrating multimodal and longitudinal data beyond the limits of human pattern recognition. Contemporary AI approaches in CA include deep learning models operating directly on raw signals and imaging data, classical machine learning using engineered features, radiomics-based analysis of myocardial tissue characteristics, and increasingly, multimodal fusion frameworks that aggregate complementary information across modalities. AI-enhanced electrocardiography has emerged as a particularly scalable screening tool, demonstrating high diagnostic discrimination for both transthyretin and light-chain CA and enabling opportunistic detection months before clinical diagnosis. In parallel, echocardiography AI has evolved from feature-based models toward end-to-end video analysis, improving standardization and reducing inter-reader variability, while cardiac magnetic resonance- and scintigraphy-based AI applications enable automated quantification, reduced technical variability, and more reproducible assessment of amyloid burden. Beyond diagnosis, emerging AI models aim to support disease phenotyping, prognostication, and longitudinal treatment monitoring. However, important challenges remain, including dataset shift, heterogeneity in diagnostic reference standards, spectrum bias, and the need for prospective validation and workflow-aware implementation. Thoughtful integration of AI into multidisciplinary amyloidosis care pathways will be essential to translate technical advances into earlier diagnosis and improved clinical outcomes.