Artificial intelligence in cardiovascular imaging and electrocardiography

Artificial intelligence (AI), particularly deep learning, is reshaping cardiovascular imaging and electrocardiography (ECG) by enabling automated acquisition support, quantification, disease detection, and risk prediction. Yet, high retrospective accuracy does not by itself establish transportability or clinical benefit. This narrative review first outlines the AI development lifecycle, with emphasis on data and label quality, architecture selection, patient-level data partitioning, calibration, external validation, and prospective evaluation. It then critically examines representative advances in ECG, echocardiography, cardiac magnetic resonance (CMR), cardiac computed tomography (CT), and nuclear cardiology. Particular attention is given to three rapidly evolving areas: AI-enhanced ECG, video- and vision-language echocardiography, and volumetric CT and CMR analysis. We compare convolutional, transformer-based, hybrid, and generative approaches; summarize explainable-AI methods, including gradient-weighted class activation mapping (Grad-CAM), SHapley Additive exPlanations (SHAP), integrated gradients, and occlusion testing; and analyze failure modes arising from label, sampling, demographic, device, and temporal bias. Quantitative examples show that performance can deteriorate across institutions, demographic groups, and acquisition settings despite apparently strong internal validation. Future progress will depend on prospective workflow-based trials, multimodal foundation models with transparent benchmarks, lifecycle governance and regulation, and continuous post-deployment surveillance.