Multimodal artificial intelligence for vascular aging: toward proactive detection and management

Vascular aging is a fundamental driver of cardiovascular diseases (CVD), yet identifying individuals undergoing accelerated vascular aging - and therefore at elevated cardiovascular risk - remains a significant clinical challenge. The traditional “one-size-fits-all” health risk assessments are often insensitive to early, subclinical vascular changes. The recent explosion of medical big data, coupled with the advent of Artificial Intelligence (AI) and Multimodal Foundation Models (MFMs), offer a transformative opportunity to shift vascular aging management from reactive detection toward proactive, predictive protection. This review systematically explores the integration of multimodal artificial intelligence (MMAI) and large-scale foundation models in the identification, stratification, and personalized management of vascular aging. We first delineate the multidimensional data ecosystem of vascular aging, including electronic health records (EHRs), imaging, omics, and continuous physiological signals from wearables, and analyze how deep learning architectures enable the detection of vascular aging by capturing subclinical structural and functional alterations. We then focus on the emerging role of MFMs, which transcend task-specific AI by integrating heterogeneous data types to provide holistic phenomapping. Furthermore, we discuss the utility of Large Language Models (LLMs) in clinical workflows, enhancing patient adherence through natural language interaction, and accelerating research through knowledge synthesis. Despite the potential of foundation models, their clinical translation faces technical and institutional barriers. We analyze four systemic challenges: algorithmic fairness, regulatory uncertainty, explainable AI (XAI) and the lack of real-world validation. We propose a strategic roadmap aligned with international frameworks that progresses from standardized performance reporting for elderly subgroups in the near term to cost-effective, individualized interventions that intercept the health-to-disease transition in the long term. The convergence of big data and foundation models represents a shift from reactive cardiovascular care to proactive, precision-based longevity management, positioning early, AI-enabled identification of high-risk vascular aging phenotypes as a primary target for the next generation of preventive medicine.