Foundation Models for Cardiovascular Disease and Medicine

Abstract Background Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, accounting for more than 17 million deaths annually. Foundation models (FMs) are large-scale artificial intelligence systems pre-trained on broad datasets and increasingly studied for cardiovascular diagnosis, risk prediction, workflow support, and treatment personalization. Methods This narrative review synthesized FMs applied to cardiovascular medicine, including text-based, image-based, multimodal, and time-series models. The review examined model architectures, training strategies, performance metrics, clinical applications, implementation challenges, and evidence maturity using a three-tier framework. Results Cardiovascular FMs show promising but unevenly validated capabilities across multiple applications. ECG-based models such as ECGFounder have reported strong external validation performance for rhythm and conduction diagnoses, whereas imaging models such as Echo-Vision-FM supports cardiac function assessment and pathology classification. Multimodal models, including CardioGPT and EchoCLIP, illustrate the potential value of integrating clinical text, imaging, and physiological signals, although several remain at a conceptual or proof-of-concept stage. Conclusions FMs may become important tools in cardiovascular medicine by supporting earlier diagnosis, risk stratification, and clinical decision-making. However, the current evidence base remains heterogeneous, and major barriers persist, including limited interpretability, underrepresentation of diverse populations in training data, regulatory uncertainty, computational demands, and workflow-integration challenges. Inclusive data development, transparent reporting, external validation, prospective clinical evaluation, and interdisciplinary implementation research are needed before these models can be deployed safely and equitably in routine cardiovascular care.