A Multimodal artificial intelligence framework for mechanistic discovery and digital twin construction in cardiovascular medicine

Abstract

Multimodal artificial intelligence (AI) is reshaping contemporary cardiovascular medicine. Its contribution has moved far beyond early successes in image recognition, arrhythmia detection, and automated risk stratification, and is now converging toward a deeper scientific transformation. This article anticipates two strategic frontiers that are likely to define the next decade of the field: mechanistic discovery driven by multimodal integration, and the construction of high‑fidelity, patient‑specific cardiovascular digital twins. We believe the core potential of multimodal AI lies in its ability to bridge the gap between data modalities, moving from revealing statistical “correlations” to elucidating biological “causality”. By systematically integrating imaging omics and multi-omics data, it drives a deeper understanding of the fundamental mechanisms underlying cardiovascular diseases. Simultaneously, as a core technological engine, it is transforming static anatomical models into dynamic, physically simulated personalized cardiovascular digital twins, paving the way for “predictive, preventive, personalized, and participatory” precision medicine. This article will explore the transformative potential of these two directions, key technological breakthroughs, representative cases, and systematically analyze the challenges of data, algorithms, clinical validation, and ethical regulation encountered in their implementation pathways.

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