From task-specific AI to cardiovascular foundation models: a new era of multimodal interpretation.

Abstract

Artificial intelligence (AI) is reshaping cardiovascular medicine, evolving from task-specific supervised models towards multitask and foundation models capable of broader, more scalable interpretation. Early AI systems achieved high performance in electrocardiographic interpretation, echocardiographic measurement and disease diagnosis but their dependence on predefined outputs and labelled datasets limits flexibility and confines them to individual tasks. Foundation models address these limitations by learning reusable representations from large-scale data, often through self-supervised or contrastive pretraining, and adapting them to multiple downstream tasks. Emerging models in electrocardiography, echocardiography, chest radiography, cardiac MR and cardiac CT demonstrate capabilities including disease classification, quantitative measurement, segmentation, image-text retrieval, report generation and risk prediction. Generalist and multimodal biomedical foundation models further suggest a future in which cardiovascular AI integrates signals, imaging, clinical text, laboratory data and medical history to support patient-level diagnosis and decision-making. This review synthesises the transition from task-specific AI to cardiovascular foundation models and outlines opportunities for multimodal human-centred implementation.