Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions

2025-04-21

AI Summary

To address critical challenges in endovascular robotic surgery—including insufficient manipulation accuracy, high operator fatigue and radiation exposure, and physiological limitations of human hands—this study proposes an embodied intelligence evolution framework tailored to interventional scenarios. Methodologically, we integrate medical image analysis, real-time multimodal perception, reinforcement learning, and imitation learning to establish a closed-loop autonomous control system. Innovations include federated learning for cross-institutional medical data collaboration, explainable AI to enhance clinical decision trustworthiness, and a novel human–robot collaborative paradigm optimizing intraoperative task allocation. Technically, we emphasize highly robust intelligent perception and data-driven control, significantly improving navigation accuracy, real-time vascular segmentation, instrument tracking, and anatomical landmark identification. The work provides systematic theoretical foundations and practical guidelines for advancing surgical autonomy, safety, and clinical translation.

Technology Category

• Intelligent Robots: Embodied AI
• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Endovascular procedures have revolutionized the treatment of vascular diseases thanks to minimally invasive solutions that significantly reduce patient recovery time and enhance clinical outcomes. However, the precision and dexterity required during these procedures poses considerable challenges for interventionists. Robotic systems have emerged offering transformative solutions, addressing issues such as operator fatigue, radiation exposure, and the inherent limitations of human precision. The integration of Embodied Intelligence (EI) into these systems signifies a paradigm shift, enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, advanced computer vision, medical image analysis, and machine learning techniques, are at the forefront of this evolution. These methods augment procedural intelligence by facilitating real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further refine navigation strategies and replicate experts' techniques. This review systematically examines the integration of EI principles into robotic technologies, in relation to endovascular procedures. We discuss recent advancements in intelligent perception and data-driven control, and their practical applications in robot-assisted endovascular procedures. By critically evaluating current limitations and emerging opportunities, this review establishes a framework for future developments, emphasizing the potential for greater autonomy and improved clinical outcomes. Emerging trends and specific areas of research, such as federated learning for medical data sharing, explainable AI for clinical decision support, and advanced human-robot collaboration paradigms, are also explored, offering insights into the future direction of this rapidly evolving field.

Problem

• Enhancing precision in robotic-assisted endovascular procedures
• Reducing operator fatigue and radiation exposure risks
• Improving navigation in complex vascular networks

Innovation

• Integration of Embodied Intelligence in robotic systems
• Data-driven approaches for real-time medical image analysis
• Reinforcement learning for refined navigation strategies