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Endovascular therapies have transformed cardiovascular medicine, yet restenosis, thrombosis, and device failure remain common and poorly predictable complications. Increasing evidence suggests that immunothrombotic processes critically shape vascular recovery after device implantation. This includes neutrophil extracellular trap (NET) formation, innate immune polarization, and endothelial damage responses. Concurrently, advances in artificial intelligence (AI) are increasingly enabling continuous multimodal monitoring and adaptive clinical decision-making throughout the medical device life cycle. Here, we propose the concept of immunologically adaptive endovascular devices: a closed-loop paradigm in which patient immune status informs device selection, device-tissue interactions are interpreted via mechanistic biomarkers, and real-world monitoring dynamically updates risk and management. The study introduces (i) an immune-device interaction phenotype taxonomy linking device design features to measurable thrombo-inflammatory trajectories, (ii) a mechanistic framework defining interface signaling processes that enhance or resolve NET-driven responses, (iii) a minimum evidence model encompassing preclinical testing, clinical validation, and post-market surveillance, and (iv) a reference AI architecture for risk prediction, drift detection, and safety monitoring. This study also outlined testable predictions and a translational roadmap toward precision endovascular intervention and next-generation adaptive cardiovascular devices.