Endovascular procedures have revolutionized vascular disease treatment, yet their manual execution is challenged by the demands for high precision, operator fatigue, and radiation exposure. Robotic systems have emerged as transformative solutions to mitigate these inherent limitations. A pivotal moment has arrived, where a confluence of pressing clinical needs and breakthroughs in AI creates an opportunity for a paradigm shift toward Embodied Intelligence (EI), enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, leveraging advanced computer vision, medical image analysis, and machine learning, drive this evolution by enabling real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further enhance navigation strategies and replicate expert techniques. This review systematically analyzes the integration of EI into endovascular robotics, identifying profound systemic challenges such as the heterogeneity in validation standards and the gap between human mimicry and machine-native capabilities. Based on this analysis, a conceptual roadmap is proposed that reframes the ultimate objective away from systems that supplant clinical decision-making. This vision of augmented intelligence, where the clinician's role evolves into that of a high-level supervisor, provides a principled foundation for the future of the field.

翻译：血管内手术已彻底改变了血管疾病的治疗方式，但其手动执行仍面临高精度要求、操作者疲劳及辐射暴露等挑战。机器人系统作为变革性解决方案应运而生，以缓解这些固有局限。当前正处于关键转折点：迫切的临床需求与人工智能领域的突破性进展汇聚，为向具身智能（EI）的范式转变创造了契机，使机器人能够导航复杂血管网络并适应动态生理条件。数据驱动方法通过利用先进的计算机视觉、医学图像分析与机器学习技术，实现了实时血管分割、器械跟踪和解剖标志物检测，从而推动这一演进。强化学习与模仿学习进一步优化了导航策略并复现了专家技术。本综述系统分析了具身智能在血管内机器人领域的集成应用，识别出深层次的系统性挑战，如验证标准的异质性以及人类模仿能力与机器原生能力之间的差距。基于此分析，本文提出了一条概念性路线图，将最终目标重新定义为不再替代临床决策的系统。这一增强智能愿景将临床医生的角色提升为高级监督者，为该领域的未来发展奠定了原则性基础。