Artificial intelligence in arterial healing, remodeling, and prediction: its implications in vascular surgery

The artificial intelligence (AI) is revolutionizing vascular surgery by enhancing arterial healing, remodeling, and predictive analytics. The AI-driven models, particularly machine learning (ML) and deep learning (DL) algorithms, provide unprecedented insights into endothelial recovery, smooth muscle migration, and extracellular matrix remodeling, optimizing preoperative planning, intraoperative guidance, and postoperative monitoring. Advanced imaging techniques, such as convolutional neural networks (CNNs) for ultrasound and MRI analysis, improve early detection of restenosis, thrombosis, and atherosclerotic plaque vulnerability. Additionally, AI-powered computational fluid dynamics (CFD) simulations facilitate personalized hemodynamic modeling, predicting aneurysm growth, rupture risk, and post-intervention outcomes. In vascular surgery, AI enhances precision medicine through tailored therapeutics, robotic-assisted procedures, and AI-enabled intraoperative navigation, improving surgical accuracy and efficiency. The AI-driven remote monitoring systems employ recurrent neural networks (RNNs) and biosensors to detect early complications, reducing adverse events and improving patient prognosis. Despite these advancements, challenges remain, including the need for high-quality annotated datasets, algorithmic interpretability issues, and ethical concerns regarding data privacy. Future research should focus on multimodal AI integration, validation through clinical trials, and standardized regulatory frameworks to ensure safe and effective implementation. AI continues to evolve, promising to redefine vascular interventions by advancing diagnostic accuracy, therapeutic innovation, and patient-specific care in cardiovascular medicine.