Blockchain and AI Synergy in Vascular Data Management: Enhancing Trust, Traceability, and Diagnostic Accuracy in Healthcare Systems
Keywords:
Blockchain; Artificial intelligence; Vascular imaging; Healthcare data management; Federated learning; Explainable AI.Abstract
The rapid growth of multimodal vascular data – including high-resolution angiography, intravascular ultrasound, optical coherence tomography, perfusion imaging, and hemodynamic waveforms – has created unprecedented opportunities for AI-driven diagnosis and risk stratification in cardiovascular and peripheral vascular disease. However, current data pipelines remain fragmented, opaque, and vulnerable to security breaches, limiting the reproducibility and clinical adoption of AI models. This paper investigates the synergy between blockchain and artificial intelligence (AI) for vascular data management, with a focus on enhancing trust, traceability, and diagnostic accuracy in healthcare systems. We conceptualize a layered reference architecture in which a permissioned blockchain network underpins provenance-aware vascular data lakes, federated and transfer-learning–based AI pipelines, and explainable diagnostic services. On-chain smart contracts govern fine-grained consent management, access control, and audit trails, while off-chain encrypted storage and edge nodes support scalable handling of large imaging datasets. We analyze how blockchain-enabled federated learning, verifiable model updates, and tamper-evident logs can mitigate data-silo, bias, and accountability challenges that currently affect vascular AI models. Drawing on emerging applications in cardiovascular disease screening, vascular surgery, and imaging-centric Internet of Medical Things ecosystems, we derive a set of design principles and performance, security, and governance metrics for blockchain–AI systems in vascular care. The paper concludes by outlining open research directions, including cross-chain interoperability for multi-institution vascular registries, integration of explainable AI with on-chain verifiability, and evaluation frameworks that jointly quantify diagnostic performance, privacy preservation, and system-level trust.
