Key result
Multimodal AI outperforms unimodal models by ~6 percentage points in AUC for vascular aging.
Why the study?
Identifying individuals undergoing accelerated vascular aging remains a clinical challenge because traditional risk assessments are often insensitive to early, subclinical vascular changes. Multimodal artificial intelligence and foundation models offer an opportunity to shift vascular aging management from reactive detection toward proactive prediction and personalized care.
Does multimodal artificial intelligence and foundation model integration improve the early identification, stratification, and management of vascular aging compared to traditional single-modality clinical assessments?
Does multimodal artificial intelligence and foundation model integration improve the early identification, stratification, and management of vascular aging compared to traditional single-modality clinical assessments?
This review highlights how integrating multimodal big data with artificial intelligence and foundation models can shift vascular aging management from reactive detection toward proactive, predictive cardiovascular prevention.
Multimodal AI may enhance vascular aging phenomapping; leaves open prospective validation and bias mitigation before clinical adoption.
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.
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Wang et al. (2026) conducted a review in Vascular aging. Multimodal artificial intelligence and foundation models vs. Traditional single-modality clinical risk assessments and unimodal AI models was evaluated. Multimodal artificial intelligence models outperform unimodal approaches by an average AUC of 6.2 percentage points for vascular aging detection and risk stratification.
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