Artificial Intelligence Across the Cardiac Amyloidosis Diagnostic and Management Pathway: From Single-Modality Detection to Multimodal Clinical Integration
Cardiac amyloidosis (CA) is increasingly recognized but remains substantially underdiagnosed because its clinical and imaging phenotype overlaps with more common cardiomyopathies. Definitive subtype assignment and management further require integration of multimodal evidence to distinguish transthyretin from light-chain disease. Machine learning and deep learning have been applied across the diagnostic and management pathway. These applications span electrocardiography (ECG), echocardiography, and health record-based case finding, as well as cardiac magnetic resonance (CMR) and nuclear interpretation, including single-photon emission computed tomography/computed tomography (SPECT/CT) biomarker quantification, prognostic modeling, and treatment response assessment. This narrative review synthesizes these studies by clinical tasks, namely screening, detection, quantification, prognosis, and longitudinal assessment after treatment initiation, rather than by input modality. This task-based organization clarifies why apparently similar AI models require different cohorts, reference standards, evaluation metrics, and implementation thresholds. The evidence reveals a maturity gradient. Binary detection and AI-assisted interpretation of cardiac scintigraphy with bone-avid tracers and SPECT/CT currently represent one of the more mature AI applications in cardiac amyloidosis, supporting standardized image interpretation and quantitative biomarker extraction. However, patient-level diagnosis still requires integration with monoclonal protein testing, SPECT/CT localization, clinical context, and biopsy or tissue typing when indicated.