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

Vascular aging

Vascular aging is a physiological phenomenon in which large arteries undergo structural and functional changes with advancing age^[1]. Early vascular aging refers to, in populations with increased cardiovascular risk, the acceleration of these age-related changes^[2,3]. Vascular aging is the result of multidimensional interactions between intrinsic and extrinsic factors across the life course. These interactions are associated with both structural and functional changes in the arterial wall^[4]. Structural changes include vascular smooth muscle cell proliferation, irreversible elastin fragmentation, progressive collagen deposition, and advanced glycation end-product-mediated collagen cross-linking^[5-7]. Functional changes include Vascular Smooth Muscle Cell (VSMC) phenotype switching, endothelial dysfunction, and perivascular adipose tissue dysfunction^[8,9]. Large artery vascular aging leads to a range of adverse hemodynamic consequences: loss of arterial compliance increases pulsatility, which is transmitted to high-flow, low-resistance end organs, triggering vascular remodeling, perfusion imbalance, and organ-specific injury cascades in the heart, brain, and kidney that further accelerate disease progression^[10-12]. Collectively, these processes represent a continuum from risk exposure to molecular and cellular dysfunction, culminating in clinical manifestations and measurable changes in diagnostic biomarkers [Figure 1]. Vascular aging has become one of the most fundamental drivers of cardiovascular disease, which remains the leading cause of death globally, making accurate assessment of vascular health essential for effective management and prevention strategies^[13,14]. Traditional cardiovascular aging assessment relies on static, single-modality clinical indicators: structural changes are measured primarily by ultrasound assessment of arterial wall thickness and luminal diameter^[15]; functional changes are assessed through flow-mediated dilation to measure endothelium-dependent vasodilation^[16], and through arterial stiffness indices including the widely applied carotid-femoral pulse wave velocity and augmentation index^[17]; serological markers can also reflect the degree of vascular aging to some extent^[18,19]. However, vascular aging unfolds as a continuous and gradual process - a form of suboptimal health, or reversible, pre-pathological state preceding overt cardiovascular disease - rather than a discrete clinical event^[20]. Because of this dynamic trajectory, these static clinical indicators are inherently limited in their ability to capture this progression, underscoring the rationale for early, artificial intelligence (AI)-enabled detection before structural damage becomes irreversible.

Figure 1. Schematic flow chart of the pathophysiological processes in vascular aging. The schematic illustrates the transition from risk factors (e.g., chronic inflammation, lifestyle behaviors, and metabolic dysregulation) to the molecular and cellular mechanisms. These processes ultimately culminate in clinical manifestations and alterations in diagnostic biomarkers. Created in BioRender. Wang Z (2026) https://BioRender.com/77p8ijz. VSMC: Vascular smooth muscle cell; ECM: extracellular matrix; PVAT: perivascular adipose tissue.

The digital revolution

Over the past decade, the rapid development of AI has profoundly transformed the landscape of biomedical research and clinical practice. Multimodal AI is reshaping the framework of next-generation healthcare by integrating large-scale data from diverse sources, including electronic health records (EHRs), medical imaging, wearable devices, and genomic data^[21]. AI can reveal complex patterns and relationships that traditional methods struggle to detect^[22], enabling precise quantification and prediction of cardiovascular aging^[23,24]. Vascular aging is shaped by the interaction of multiple factors across biological scales, including genetic, metabolic, inflammatory, environmental, and behavioral dimensions^[18,25], making assessment through the single clinical indicators typically used in practice prone to bias, yet well suited to a multimodal AI data-driven analytical paradigm^[24]. Deep learning and large-scale foundation models are capable of processing these heterogeneous, high-dimensional data streams and extracting latent features and non-linear relationships that are imperceptible to human observers^[26,27]. This capacity is progressively repositioning health risk assessment from a static, one-time classification exercise into a continuous, predictive process capable of anticipating the health-to-disease transition before clinical symptoms emerge.

Literature search strategy

A literature search was conducted in PubMed (MEDLINE) through April 2026, using combinations of the following search terms: “vascular aging”, “arterial stiffness”, “early vascular aging”, “biological age”, “epigenetic clock”, “proteomic aging”, “artificial intelligence” combined with “cardiovascular”, “electrocardiographic age”, “photoplethysmography age”, “wearable sensors”, “heart rate variability”, “multimodal artificial intelligence”, “large language models”, “explainable artificial intelligence”, and “sex differences” combined with “cardiovascular aging”. Google Scholar was used as a supplementary source to identify additional relevant articles, particularly recent preprints and conference proceedings not yet indexed in PubMed. No language restriction was applied beyond a preference for English-language publications. While the search focused primarily on literature published in the past decade (2015-2026) to capture rapid advances in AI-based vascular aging assessment, seminal earlier studies establishing foundational concepts and measurement techniques (e.g., carotid intima-media thickness ultrasound methodology) were also included regardless of publication date. Reference lists of key reviews were additionally screened to identify further relevant studies.

Imaging modalities

Different imaging modalities capture the structural and functional features of vascular aging from complementary dimensions, providing indirect but clinically validated measures of age-related vascular changes.

Biological omics

Biological omics data offer intrinsic molecular-level signatures of aging processes, capable of providing mechanistic insights into vascular and systemic senescence that routine imaging may not fully capture.

Digital biomarkers

Digital biomarkers derived from wearable devices and mobile health terminals offer the core advantage of capturing the dynamic fluctuation characteristics of the vasculature under the load of daily life, thereby describing a continuous vascular aging trajectory.

Clinical records and administrative data

Clinical medical record text and administrative data contain patients’ long-term health narratives and medical behavior trajectories, capturing subjective symptom descriptions, treatment adherence, and social determinant information that structured imaging and omics data cannot readily quantify. Compared with structured data, the unstructured components of cardiovascular disease-related EHRs - such as progress notes, discharge summaries, and imaging reports - typically contain richer contextualized clinical information, making this data source particularly well suited to natural language processing (NLP) applications in cardiology^[65].

Architectural foundations I: discriminative AI

The evolution of AI in cardiovascular medicine marks a paradigm shift from “specialized tools” toward “general intelligence”. Early research relied primarily on discriminative AI, in which convolutional neural networks (CNNs) dominated medical image feature extraction. Deep learning algorithms have since achieved automated segmentation and quantification of carotid intima-media complex from ultrasound^[70] and fully automated CAC scoring from both ECG-gated and non-gated low-dose chest CT, providing quantitative vascular aging information without dedicated scanning protocols^[71]. Concurrently, recurrent neural networks (RNNs) and their long short-term memory (LSTM) variants were widely applied to time-series signals such as the ECG to estimate individual “cardiac age” - a biological age surrogate derived from the divergence between AI-estimated and chronological age^[72]. This approach was further extended to echocardiography-derived cardiac age estimation as an independent cardiovascular risk marker^[73]. Lima et al. demonstrated in 1,558,415 individuals that an ECG age gap exceeding eight years independently predicted all-cause mortality (HR = 1.79), establishing a large-scale proof that discriminative AI could extract clinically actionable biological aging signals from a single physiological signal^[74].

However, these models were designed for single tasks and struggled to integrate heterogeneous patient data across modalities. They confronted a fundamental “data silo” problem: institutions could not pool training data without compromising patient privacy and no individual site possessed sufficient data diversity to achieve population-level generalization. Federated learning emerged as a partial solution. Simulated multi-center studies in cardiovascular imaging demonstrated that federatively trained models achieved substantially more consistent performance across repeated initializations than centrally pooled models, reflecting increased robustness in output stability; however, under out-of-domain (leave-center-out) evaluation, federated models proved more sensitive to domain shift, with performance declining once shape and intensity augmentations were introduced^[75]. Yet the core constraint remained: discriminative AI optimized for one task could not transfer its learned representations to another, limiting its utility in the face of the true complexity of cardiovascular aging phenotypes.

Architectural foundations II: the transformer revolution

The advent of Transformer architecture resolved the cross-modal representation problem that constrained discriminative AI. Unlike CNNs and RNNs, Transformers employ self-attention mechanisms that capture long-range dependencies by computing pairwise relationships between all sequence elements simultaneously, enabling context-sensitive representations irrespective of the distance between positions^[76]. It is a property particularly advantageous for genomic^[77] and EHR data^[78] where a feature's importance is inseparable from its context^[26]. For vascular aging research, cross-modal learning enables imaging, omics and text data to be projected into a unified latent space, where non-linear associations that are invisible within any single modality become detectable^[79].

The quantitative evidence for this architectural advance is now consolidated. A scoping review of 432 deep learning-based multimodal AI papers published between 2018 and 2024 found that multimodal AI models consistently outperformed unimodal counterparts, with an average AUC improvement of 6.2 percentage points^[80]. This gain reflects genuine information complementarity across modalities rather than model complexity alone. Soenksen et al. benchmarked 14,324 independent models derived from HAIM-MIMIC-MM - a multimodal clinical database comprising 34,537 samples across 7,279 unique hospitalizations and 6,485 patients - spanning 4 data modalities (tabular, time-series, text, and images), 11 data sources, and 12 clinical prediction tasks; multimodal inputs improved performance by 6%-33% over single-source approaches^[81], with Shapley value attribution confirming that different modalities contributed differentially across tasks. Together, these findings established the empirical foundation for multimodal AI as a necessary, not merely aspirational, architectural upgrade.

Training paradigm shift I: pre-training and fine-tuning

Building on the Transformer architecture introduced above, the power of MFMs further derives from the pre-training and fine-tuning paradigm. General representations acquired from large-scale corpora are subsequently refined on targeted cardiovascular datasets, enabling detection of subtle vascular aging signals that would be unattainable through task-specific training alone^[26]. The canonical biomedical instantiation is BiomedCLIP, a multimodal foundation model pre-trained on PMC-15M - 15 million biomedical image-text pairs collected from 4.4 million PubMed Central articles - via contrastive learning with domain-specific adaptations tailored to biomedical vision-language processing^[82]. Benchmarked across retrieval, classification, and visual question-answering tasks, BiomedCLIP achieved state-of-the-art results across a wide range of standard biomedical datasets, substantially outperforming prior approaches^[82]. This substantially alleviates the annotation bottleneck that previously limited discriminative AI: rather than requiring large-scale labeled vascular aging cohorts, a fine-tuned foundation model can identify clinically relevant biological signals from a substantially smaller number of well-characterized examples.

The clinical impact of this paradigm is demonstrated by foundation models pre-trained on large-scale imaging repositories and adapted to novel downstream tasks. Nielsen et al. applied RETFound - a vision transformer pre-trained via masked autoencoding on large-scale retinal fundus images - to retinal age prediction by fine-tuning only a lightweight regression head, achieving performance on par with fully centralized training even under federated and distributed learning settings^[83]. Together, these studies confirm that the pre-training and fine-tuning paradigm transfers effectively to vascular biological age estimation, producing accurate and scalable biomarkers without requiring full model retraining.

Training paradigm shift II: generative intelligence

Within this same Transformer-based paradigm, generative intelligence further endows MFMs with the capacity to synthesize information, moving beyond the discriminative outputs described in Section Architectural foundations I: discriminative AI. Rather than returning a single “vascular age” value, a generative model can integrate findings across imaging, laboratory, and wearable data with relevant medical literature to produce structured diagnostic reports, transforming AI from a passive classifier into an active clinical reasoning tool^[84,85].

The LLM represents the primary vehicle for this transition. Singhal et al. demonstrated that Med-PaLM encoded sufficient clinical knowledge to achieve performance comparable to physicians on medical licensing benchmarks, establishing that LLMs could serve as the integrative reasoning layer in a multimodal clinical system^[86]. When augmented with Retrieval-Augmented Generation (RAG), LLMs can dynamically retrieve patient-specific evidence from unstructured clinical records, converting heterogeneous EHR data^[87] into a structured vascular health timeline - a transformation that would require prohibitive manual annotation using discriminative approaches alone.

Characteristics and limitations of current clinical applications

Existing MFMs can achieve high detection accuracy under controlled conditions and consistently outperform traditional risk scores in multi-center validation^[80]. However, the current clinical landscape is characterized by a clear “laboratory ahead of clinic” pattern, driven by three unresolved institutional barriers.

First, the “black-box” interpretability problem: clinicians are frequently unable to trace the reasoning pathway by which an MFM reaches an early vascular aging (EVA) conclusion. Explainability methods such as SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) exist but lack standardized evaluation frameworks, and their impact on clinical trust and decision quality has rarely been assessed in real care environments^[88].

Second, the hallucination risk of generative models: when fabricated clinical details are embedded in prompts, LLMs elaborate on the false information in 50%-83% of cases across leading models, with mitigation prompts halving but not eliminating the error rate^[89]. In a structured clinical summarization framework, hallucination and omission rates of 1.47% and 3.45%, respectively, were observed across 12,999 clinician-annotated sentences^[90] - rates that, while low in absolute terms, carry serious patient safety implications when deployed at scale in cardiovascular risk management.

Third, and most critically, the randomized controlled trials (RCTs) evidence gap: a systematic review of cardiovascular AI trials identified only 11 RCTs meeting inclusion criteria, conducted between 2021 and 2024. Among these, 6 studies (54.5%) reported improvements in diagnostic accuracy and early detection, 5 (45.5%) demonstrated improvements in clinical events, and 3 (27.3%) showed enhanced resource utilization^[91]. No prospective RCT has yet demonstrated that MFM-guided vascular aging interventions improve long-term cardiovascular outcomes, leaving a critical evidence gap specific to MFM-guided EVA interventions.

Precision screening: quantifying the biological age gap and identifying accelerated aging subgroups

The core challenge of cardiovascular aging screening is to capture the discrepancy between biological age and chronological age in the asymptomatic stage and convert it into actionable clinical risk signals.

The first pathway is evidence from single-modality biological age clocks. Deep learning models based on 12-lead ECG have been confirmed in large-scale cohorts to produce an AI-estimated ECG age that is an independent predictor of mortality in cardiovascular and acute care patients, outperforming traditional clinical scores^[74]. A systematic review and meta-analysis of 17 studies further confirmed that an elevated heart delta age is independently associated with all-cause mortality (HR = 1.62, 95%CI: 1.49-1.77) and cardiovascular mortality (HR = 2.12, 95%CI: 1.71-2.63)^[92]. Individuals with an AI-PPG age gap exceeding nine years show significantly elevated MACCE risk (HR = 2.37) in the UK Biobank cohort^[50]. Both signal types are acquirable through standard clinical devices or consumer-grade wearable devices, making the screening potentially low-cost^[93]. At the retinal microvascular level, the AI-derived retinal biological age marker RetiPhenoAge has been independently validated across three cohorts for its association with all-cause mortality (HR = 1.92) and cardiovascular mortality (HR = 1.97), with predictive performance exceeding traditional aging markers such as grip strength, telomere length, and physical activity^[37].

The second pathway is multimodal feature fusion for risk stratification. MMAI approaches integrating large biobanks, EHRs, medical imaging, wearable sensors, and genomic data can capture the inherent complexity of human health and disease, demonstrating systematic advantages over single biomarkers in personalized medicine and risk stratification^[81,84]. Validation of an early vascular aging construct model using external risk indicators confirmed that multidimensional integration of clinical and biochemical biomarkers improves comprehensive cardiovascular risk assessment beyond traditional single-marker approaches^[94]. However, the performance advantage of multimodal models, an average AUC improvement of 6.2 percentage points, remains infrequently confirmed in external validation studies, with most existing research relying on internal validation^[80], indicating that the generalization capacity of current screening models awaits systematic evaluation.

Dynamic monitoring and early intervention: empirical foundation and limitations of closed-loop management

MMAI holds potential in preventive medicine by integrating demographic characteristics, social determinants, immune status, omics, imaging, and pharmacogenomic data to identify high-risk individuals who can benefit from early intervention, enabling continuous health trajectory tracking based on real-time data streams.

In the area of dynamic monitoring combining wearable devices with AI, the evidence chain is forming but remains incomplete. AI-driven real-time monitoring systems have been demonstrated to provide early warning hours before events such as atrial fibrillation or ischemia occur, with a scoping review of 47 studies confirming that AI-based wearable monitoring significantly shortens the time from anomaly detection to clinical intervention across multiple cardiovascular conditions^[95]. New-generation flexible bioelectronic devices can now attach stably to the skin for extended periods, acquiring high-fidelity cardiovascular signals without compromising patient quality of life and providing high-quality input data for AI algorithms^[96]. In heart failure management, a systematic review confirmed that remote monitoring combined with AI analysis effectively reduces rehospitalization rates by identifying early subtle changes in fluid retention and hemodynamics^[97]. However, this evidence derives predominantly from single-disease cohorts, and long-term closed-loop management data specifically targeting the continuous process of early vascular aging remain absent.

At the level of therapeutic decision support, AI-based tools have shown auxiliary value in personalizing blood pressure targets^[98] and continuously monitoring treatment adherence and response^[99] and in management of complex dyslipidemia, where AI-assisted systems demonstrate more rigorous adherence to current guidelines and recommendation of more aggressive combination lipid-lowering regimens compared with human experts^[100]. In cardiovascular pharmacotherapy more broadly, AI has shown potential in predicting drug interactions, assessing adverse reaction risks, and optimizing dosing regimens in complex antithrombotic and lipid-lowering therapies^[101]. However, as of November 2024, only 11 RCTs meeting inclusion criteria have been identified in cardiovascular AI, of which 54.5% showed enhanced diagnostic accuracy and early detection and 45.5% reported improvements in clinical events, mostly concentrated on single-modality scenarios such as ECG analysis and CT assessment^[91]. High-quality RCT evidence that multimodal AI-driven intervention strategies improve long-term vascular aging outcomes remains entirely absent.

Integrated assessment of evidence maturity

Foundation model-driven MMAI systems such as LLaVA-Med and BiomedCLIP provide flexible and potentially generalizable reasoning capabilities, but their clinical translation still requires overcoming four challenges: computational resource demands, domain adaptation, interpretability, and rigorous real-world validation^[82,102].

Synthesizing the available evidence, the maturity of MMAI in clinical integration for early vascular aging can be summarized as a gradient: biological age quantification and risk stratification - evidence relatively robust, with consistent conclusions supporting multimodal superiority over unimodal approaches, as supported by previous reviews^[22,84] - progressing to dynamic monitoring and early warning - evidence accumulating, single-disease data relatively sufficient but vascular aging-specific data limited^[72,95,103] - and finally to personalized therapeutic decision support - proof-of-concept stage, lacking prospective RCTs specifically targeting early vascular aging^[21,104]. This gradient suggests that the highest-value clinical translation entry point at present is to position MMAI as a biological age screening tool for asymptomatic high-risk populations rather than as a direct therapeutic decision system. Table 2 summarizes the clinical evidence supporting AI-driven vascular aging assessment across major clinical domains, including study design, cohort characteristics, validation status, and an overall assessment of evidence maturity for each application area.

Algorithmic fairness and digital ageism

MMAI in vascular aging assessment faces a unique internal paradox: its core target population - elderly individuals - is precisely the group most likely to be systematically underrepresented in training data. Digital ageism can occur throughout the full lifecycle of medical AI: historical bias arises from training data originating from an era of low digital participation among older adults; representational bias arises from the longstanding systematic exclusion of elderly individuals from clinical trials, resulting in models trained on younger populations being applied to older ones^[106,107].

At the empirical level, earlier research found that a commercial risk prediction algorithm assigned similar risk scores to Black patients with more severe disease and White patients with less severe disease, resulting in the systematic under-referral of the former to complex care programs^[108]. This bias carries particular harm in the vascular aging context: if models systematically misestimate biological age in elderly populations, the consequence is failure of precision screening in the highest-risk group, not merely reduced generalization performance. The STANDING Together international consensus recommendations - developed with input from over 350 representatives from 58 countries - explicitly identify the core pathway for addressing dataset bias as mandating transparent reporting of training dataset limitations and prospective evaluation of performance across population subgroups^[109], a framework directly applicable to data governance for vascular aging AI.

Beyond age and race, sex remains an underexamined axis of bias in vascular aging AI. Cardiovascular aging is sex-dimorphic: women present atypically before menopause but converge toward male-typical presentation afterward, and their age-specific mortality rises steadily across the lifespan even as men’s declines after midlife^[110]. Guidelines have been slow to reflect this - a 2002-2024 audit of 34 European Society of Cardiology (ESC) guidelines found menopause largely overlooked despite its cardiovascular significance, and blood pressure targets remain sex-neutral^[111].

AI offers a practical path to close this gap. Natural language processing and federated learning can enable sex-stratified reanalysis of existing study data, while multimodal machine-learning models can integrate imaging, hemodynamic, and clinical variables into sex-specific risk scores. A UK Biobank study exemplifies this approach: by combining cardiac MRI, pulse wave analysis, ECG, and carotid ultrasound data, the resulting model outperformed the traditional Framingham Risk Score and quantified the extent of cardiovascular disease underdiagnosis in women^[112]. Yet most vascular age AI models still treat sex only as an adjustment covariate rather than a stratification variable^[113]. Sex-specific modeling can reveal genuinely distinct risk targets: an AI model of epicardial adipose tissue found density-based features drove risk more in women and volume-based features more in men, consistent with women’s earlier, inflammatory-stage disease^[114]; the HUNT Study trained separate sex-specific models motivated by female-specific risk factors like premature menopause^[115]; and the ongoing CARAMEL protocol targets AI-based risk tools specifically for menopausal women^[116]. These efforts show sex-specific AI modeling is feasible and informative but still far from standard - a gap that MMAI development in vascular aging should close by reporting sex-stratified performance and modeling inflection points like menopause explicitly.

Regulatory framework: institutional challenges for foundation models

As of December 20, 2024, the Food and Drug Administration (FDA) had granted 1,016 AI/Machine Learning (ML) medical device authorizations encompassing 736 unique devices. Among signal-based devices, cardiovascular medicine represents the largest specialty, accounting for 64.5% of this category^[117]. As of April 3, 2026, the FDA’s AI/ML-Enabled Medical Devices list included 1,431 authorized devices, of which 1,377 (96.2%) were cleared via the 510(k) substantial equivalence pathway. The remaining 54 devices (3.8%) received authorization through more rigorous regulatory routes, including De Novo classification (n = 37, 2.6%) and Premarket Approval (PMA) (n = 17, 1.2%)^[118]. This reveals the core tension in the current regulatory landscape: the threshold for AI medical device market entry is relatively low, while the potential clinical impact may be far-reaching.

The FDA’s Total Product Life Cycle (TPLC) regulatory framework requires manufacturers to submit Predetermined Change Control Plans (PCCPs) that pre-specify the boundaries of permissible algorithm modifications in pre-market applications, representing a shift toward proactive lifecycle management of AI-enabled medical devices. Transparency in the premarket development process is equally critical; sponsors must be transparent about AI use and regulators must develop proficiency in evaluating AI-based submissions. However, the emergence of generative AI and LLMs fundamentally challenges existing frameworks, necessitating special evaluation mechanisms that current regulatory structures were not designed to accommodate^[119]. Compounding these challenges, deep learning models are widely characterized as “black box” systems that lack transparency and interpretability, creating a gap between algorithmic outputs and the understanding required by clinicians and regulators^[120]. These limitations underscore the need for explainable AI (XAI) approaches that render model outputs both transparent and understandable to end users^[88]. At the international level, regulatory frameworks across countries differ substantially, posing institutional barriers to cross-national data sharing and international market access^[121].

Explainability: from technical tool to clinical institutional need

The value of XAI in MMAI vascular aging applications lies not only in improving physician understanding, but in satisfying three institutional requirements simultaneously: regulatory compliance, patient right to information, and clinical judgment^[122,123]. However, current XAI practice falls substantially short of these requirements^[88]. A systematic review of XAI methods in medical image analysis found that while explainability approaches such as SHAP and Grad-CAM are increasingly adopted to address transparency and trust requirements in clinical AI, standardized evaluation frameworks remain absent, and the medicolegal and clinical implications of explanation quality are rarely systematically assessed^[124,125]. To address this standardization gap, the first clinician-informed XAI evaluation checklist with metrics (CLIX-M) has been proposed, comprising 14 items across four categories - purpose, clinical attributes, decision attributes, and model attributes^[126]. By aiding authors and reviewers in the transparent reporting and evaluation of XAI methods in AI-powered clinical decision support systems, CLIX-M represents a critical step in the evolution of XAI from a technical tool to an institutional standard.

In the specific context of vascular aging, XAI encounters challenges that extend beyond those faced in conventional diagnostic AI. Biological age is a continuous, multidimensional construct shaped by the interplay of hemodynamic, structural, and metabolic factors^[127]. Although XAI can identify the individual biomarker signatures that most strongly influence a vascular age prediction for a given patient, a single feature importance ranking remains insufficient for clinical interpretation. Vascular aging reflects systemic interdependencies - across hemodynamic, metabolic, and inflammatory pathways - that require integrated, multi-biomarker explanations rather than isolated feature rankings, a challenge that current XAI methods have yet to fully resolve^[128].

Clinical translation roadmap: from evidence to institutional embedding

In light of the above challenges, the clinical translation roadmap for MMAI in early vascular aging should encompass the following institutional milestones.

Near term (1-3 years): Standardization and Regulatory Foundations. Establish standardized reporting norms for MMAI vascular aging assessment datasets drawing on the STANDING Together framework^[109], mandating stratified performance reporting for elderly subgroups; drive FDA/European Medicines Agency (EMA) clarification of device definitions for generative AI-assisted vascular risk reporting^[119]; and promote adoption of the CLIX-M checklist as a minimum XAI reporting standard for cardiovascular AI submissions^[126].

Medium term (3-5 years): Prospective Evidence and Federated Infrastructure. Conduct multi-center prospective cohort studies^[74]; establish cross-institutional federated learning networks to accumulate training data from diverse elderly populations under a privacy-protective framework^[129,75].

Long-term (beyond 5 years): Clinical Integration and Global Harmonization. Design RCTs with long-term cardiovascular event improvement as the endpoint to verify the net clinical benefit of MMAI-driven personalized intervention strategies^[91]; embed AI vascular aging scores into electronic health record systems for seamless integration with routine clinical workflows^[78]; and establish cross-national regulatory mutual recognition mechanisms to advance global market access coordination for MMAI products^[122].

The three horizons are deliberately sequential yet overlapping: near-term standardization creates the conditions for medium-term evidence generation, which in turn provides the outcome data necessary to justify long-term systemic integration. Progress across all three domains is interdependent, and delays in regulatory clarification or federated infrastructure development will propagate forward, compressing the timeline available for the high-quality RCT evidence that long-term clinical embedding ultimately requires.

Beyond these procedural dependencies, the roadmap represents a broader clinical ambition: to reorient cardiovascular preventive medicine from reactive disease management toward proactive, biology-informed risk stratification, in which vascular aging trajectories are identified and modulated years before irreversible damage accumulates [Figure 3]. At the population level, scalable MMAI vascular aging screening could translate individual-level precision into measurable reductions in cardiovascular disease burden.

Figure 3. Roadmap for the clinical integration of multimodal AI. The schematic identifies primary obstacles to clinical implementation, including algorithmic fairness, regulatory hurdles, model explainability and real-world validation. A tiered roadmap is proposed over a horizon extending beyond 5 years, transitioning from the establishment of dataset and Explainable AI (XAI) standards in the near term to large-scale randomized controlled trials (RCTs) and seamless EHR integration in the long term. The ultimate objective of this pipeline is to enhance clinical outcomes while simultaneously reducing provider workload. Created in BioRender. Wang Z (2026) https://BioRender.com/o1iqjcw. AI: Artificial intelligence; EHR: electronic health record.