Abstract

Background

Artificial intelligence (AI) is rapidly transforming vascular care by enhancing diagnostic accuracy, streamlining pre-interventional planning, and improving patient outcomes. Given the inherent complexity of vascular conditions and the emergence of big data, AI has emerged as a promising tool to address longstanding challenges in clinical decision-making, surgical precision, and health care efficiency.

Methods

This review synthesizes recent literature on the integration of AI into vascular care, focusing on its clinical applications, financial implications, and ethical considerations. Specific attention is given to the use of machine learning and deep learning in imaging analysis, AI-driven predictive analytics for patient stratification and risk modeling, and the evolution of robotic-assisted surgical techniques. The review also explores cost-effectiveness data, resource optimization, and challenges such as algorithmic bias and data privacy.

Results

AI applications in vascular care have demonstrated high accuracy in image interpretation, enhanced risk prediction for postoperative outcomes, and greater precision in robotic-assisted interventions. Machine learning models have improved workflow efficiency, reduced diagnostic errors, and enabled early identification of vascular pathology. Financial models suggest that AI implementation can reduce hospital readmissions, operating time, and resource waste, whereas wearable technology and digital twin models show promise for personalized, real-time patient monitoring. Despite these advances, concerns remain about equitable access, transparency, and clinical integration.

Conclusions

AI holds significant promise in revolutionizing vascular care by enabling personalized treatment plans, improving procedural outcomes, and optimizing system-level efficiency. However, broader adoption will require ongoing interdisciplinary collaboration, robust data governance, and ethical oversight to ensure that AI-driven solutions are both effective and equitable in clinical practice.

	Year	Citations

About