# Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions arXiv: https://arxiv.org/abs/2504.15327 ยท HTML: https://mlsanity.com/paper/2504.15327/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 57 of its 188 references that are in the map. - First posted on arXiv 21 Apr 2025. ## Builds on (most cited first, 30 of 57) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023) - [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017) - [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023) - [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023) - [BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2305.17100/) (2023) - [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021) - [BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation](https://mlsanity.com/paper/2403.09227/) (2024) - [AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy](https://mlsanity.com/paper/2208.02049/) (2022) - [LoViT: Long Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2305.08989/) (2023) - [Retrospectives on the Embodied AI Workshop](https://mlsanity.com/paper/2210.06849/) (2022) - [Artificial General Intelligence for Medical Imaging Analysis](https://mlsanity.com/paper/2306.05480/) (2023) - [SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning](https://mlsanity.com/paper/2108.13035/) (2021) - [EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning](https://mlsanity.com/paper/2405.05237/) (2024) - [CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection](https://mlsanity.com/paper/2302.06294/) (2023) - [Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network](https://mlsanity.com/paper/2305.04208/) (2023) - [Robot Autonomy for Surgery](https://mlsanity.com/paper/1707.03080/) (2017) - [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021) - [Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation](https://mlsanity.com/paper/2005.13366/) (2020) - [Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations](https://mlsanity.com/paper/2307.03705/) (2023) - [Data-driven Methods Applied to Soft Robot Modeling and Control: A Review](https://mlsanity.com/paper/2305.12137/) (2023) - [DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation](https://mlsanity.com/paper/2406.01591/) (2024) - [Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion](https://mlsanity.com/paper/2210.07611/) (2022) - [CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography](https://mlsanity.com/paper/2402.00570/) (2024) - [Evolving embodied intelligence from materials to machines](https://mlsanity.com/paper/1901.05704/) (2019) - [Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet](https://mlsanity.com/paper/2006.14702/) (2020) - [CathSim: An Open-source Simulator for Endovascular Intervention](https://mlsanity.com/paper/2208.01455/) (2022) - [Deep reinforcement learning for guidewire navigation in coronary artery phantom](https://mlsanity.com/paper/2110.01840/) (2021) - [Dynamic Coronary Roadmapping via Catheter Tip Tracking in X-ray Fluoroscopy with Deep Learning Based Bayesian Filtering](https://mlsanity.com/paper/2001.03801/) (2020) - [Autonomous Navigation for Robot-assisted Intraluminal and Endovascular Procedures: A Systematic Review](https://mlsanity.com/paper/2305.04027/) (2023) ## Built on by (most cited first, 2 of 2) - [Real-Time Guidewire Tip Tracking Using a Siamese Network for Image-Guided Endovascular Procedures](https://mlsanity.com/paper/2507.00051/) (2025) - [VLA-InfoEntropy: A Training-Free Vision-Attention Information Entropy Approach for Vision-Language-Action Models Inference Acceleration and Success](https://mlsanity.com/paper/2604.05323/) (2026) ## Abstract (arXiv) 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.