Foundation models for cardiovascular diseases and medicine
Abstract
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. This narrative review synthesizes evidence on 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. Cardiovascular FMs show promising but unevenly validated capabilities across multiple applications. Electrocardiogram (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 support 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. Foundation models 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…
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.