Big data and AI for precision panvascular aging management and healthy longevity

Abstract

Panvascular aging-related diseases, including coronary artery disease, ischemic stroke, and peripheral artery disease, are leading global causes of death and disability, yet their management remains fragmented. Emerging technologies offer solutions to this challenge. Big data integration across imaging, multi-omics, wearables, and environmental exposures provides opportunities for cross-organ insights but faces issues of heterogeneity and privacy. Artificial intelligence enables early detection and refined risk prediction by recognizing subtle vascular changes and integrating biomarkers, though adoption is limited by interpretability and bias. Foundation models, through cross-modal learning, offer a unifying framework for mechanism discovery, personalized management, and digital twin applications. By linking technological innovation with clinical practice, these approaches can transform panvascular aging management and promote healthy longevity. Importantly, translating these innovations into policy and practice will be essential for advancing equitable vascular health and achieving population-level impact.

Keywords

Panvascular aging / ischemic stroke / peripheral artery disease / big data integration / artificial intelligence

References

[1] Zhou X,Yu L,Zhao Y,Ge J. Panvascular medicine: an emerging discipline focusing on atherosclerotic diseases Eur Heart J. 2022 43 4528 31

[2] Guo W,Yi X. Advancements and future prospects in the study of panvascular disease Clin Hemorheol Microcirc. 2025 89 198 204

[3] Hu Y,Zhao Y,Dai N,Lu H,Ge J. Unwavering excellence: how to be a competent cardiovascular doctor in “panvascular medicine +” Innovation. 2023 4 100489 PMC10448321

[4] Hu Y,Zhao Y,Li P,Lu H,Li H,Ge J. Hypoxia and panvascular diseases: exploring the role of hypoxia-inducible factors in vascular smooth muscle cells under panvascular pathologies Sci Bull. 2023 68 1954 74

[5] Hu Y,Zhou Y,Dai N.et al. Enhancing panvascular medicine: unveiling the nexus of pan-cardio-oncology and expanding therapeutic frontiers Sci Bull. 2025 70 798 800

[6] Yousefi B,Khansari M,Trask R.et al. Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine IEEE Trans Instrum Meas. 2025 74 4503911 PMC12393170

[7] Nam Y,Kim J,Jung SH.et al. Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine Annu Rev Biomed Data Sci. 2024 7 225 50 PMC11972123

[8] Ran X,Shi J,Chen Y,Jiang K. Multimodal neuroimage data fusion based on multikernel learning in personalized medicine Front Pharmacol. 2022 13 947657 PMC9428611

[9] Glynn P,Greenland P. Contributions of the UK biobank high impact papers in the era of precision medicine Eur J Epidemiol. 2020 35 5 10

[10] Arai H,Okamura N,Furukawa K,Kudo Y. Geriatric medicine, Japanese Alzheimer’s disease neuroimaging initiative and biomarker development Tohoku J Exp Med. 2010 221 87 95

[11] Huang R,Kartsonaki C,Turnbull I.et al. Incidence and mortality rates of 14 site-specific infectious diseases in 10 diverse areas of China: findings from China Kadoorie Biobank, 2006-2018 Int J Infect Dis. 2024 147 107169

[12] Braunack-Mayer A,Carolan L,Street J,Ha T,Fabrianesi B,Carter S. Ethical issues in big data: a qualitative study comparing responses in the health and higher education sectors PLoS ONE. 2023 18 e0282285 PMC10129011

[13] Al-Ani A,Rayyan A,Maswadeh A.et al. Evaluating the understanding of the ethical and moral challenges of big data and AI among Jordanian medical students, physicians in training, and senior practitioners: a cross-sectional study BMC Med Ethics. 2024 25 18 PMC10873950

[14] Rijken L,Zwetsloot S,Smorenburg S.et al. Developing trustworthy artificial intelligence models to predict vascular disease progression: the VASCUL-AID-RETRO study protocol J Endovasc Ther. 2025 15266028251313963

[15] Sinitca AM,Lyanova AI,Kaplun DI.et al. Microscopy image dataset for deep learning-based quantitative assessment of pulmonary vascular changes Sci Data. 2024 11 635 PMC11180164

[16] Lee GY,Choi YH,Kim D.et al. Deep learning analysis of white matter hyperintensity and its association with comprehensive vascular factors in two large general populations J Imaging Inform Med. 2025 38 2761 78 PMC12572439

[17] Skovbo JS,Obel LM,Lindholt JS. Artificial intelligence in vascular surgery: bridging technology and clinical practice J Vasc Surg. 2025 81 1232 3

[18] Ranisch R,Haltaufderheide J. Foundation models in medicine are a social experiment: time for an ethical framework NPJ Digit Med. 2025 8 525 PMC12357904

[19] Khan W,Leem S,See KB,Wong JK,Zhang S,Fang R. A comprehensive survey of foundation models in medicine IEEE Rev Biomed Eng. 2026 19 283 304

[20] Alsaedi S,Gao X,Gojobori T. Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine FEBS Open Bio. 2025 15 1192 208 PMC12319712

[21] Silva-Rodríguez J,Dolz J,Ben Ayed I. Towards foundation models and few-shot parameter-efficient fine-tuning for volumetric organ segmentation Med Image Anal. 2025 103 103596

[22] Ramprasad P,Pai N,Pan W. Enhancing personalized gene expression prediction from DNA sequences using genomic foundation models HGG Adv. 2024 5 100347 PMC11416237

[23] Laleci Erturkmen GB,Yuksel M,Sarigul B,Lilja M,Chen R,Arvanitis TN. Personalised care plan management utilizing guideline-driven clinical decision support systems. Stud Health Technol Inform. 2018;247:750-754

[24] Harrisson S,Myers H,Wynne-Jones G.et al. Clinical and cost-effectiveness of a personalised guided consultation versus usual physiotherapy care in people presenting with shoulder pain: a protocol for the PANDA-S cluster randomised controlled trial and process evaluation BMJ Open. 2025 15 e100501 PMC12056648

[25] Guan G,Michel K,Corke C,Ranmuthugala G. A clinical prediction model for personalised emergency department discharge decisions for residential care facility residents post-fall J Pers Med. 2025 15: 332 PMC12387276

[26] Yuan Z,Zhang S,Zhang H,Xie P,Jia Y. Optimized drug-drug interaction extraction with BioGPT and focal loss-based attention IEEE J Biomed Health Inform. 2025 29 4560 70

[27] Luo R,Sun L,Xia Y.et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining Brief Bioinform. 2022 23: bbac409

[28] Boudi AL,Boudi M,Chan C,Boudi FB. Ethical challenges of artificial intelligence in medicine Cureus. 2024 16 e74495 PMC11670736