# Transforming Surgical Interventions with Embodied Intelligence for Ultrasound Robotics

arXiv: https://arxiv.org/abs/2406.12651 · HTML: https://mlsanity.com/paper/2406.12651/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives](https://mlsanity.com/paper/2307.05545/) (2023)
- [Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback](https://mlsanity.com/paper/2011.00099/) (2020)
- [Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation](https://mlsanity.com/paper/2209.07193/) (2022)
- [Motion-Aware Robotic 3D Ultrasound](https://mlsanity.com/paper/2107.05998/) (2021)

## Built on by (most cited first, 7 of 7)

- [ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding](https://mlsanity.com/paper/2407.19435/) (2024)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [SurgTrack: CAD-Free 3D Tracking of Real-world Surgical Instruments](https://mlsanity.com/paper/2409.02598/) (2024)
- [F2PASeg: Feature Fusion for Pituitary Anatomy Segmentation in Endoscopic Surgery](https://mlsanity.com/paper/2508.05465/) (2025)
- [MM2CT: MR-to-CT translation for multi-modal image fusion with mamba](https://mlsanity.com/paper/2508.05476/) (2025)
- [EmbodiedUS-FS: Fast Slow Intelligence for Ultrasound Robotics](https://mlsanity.com/paper/2606.22319/) (2026)

## Abstract (arXiv)

Ultrasonography has revolutionized non-invasive diagnostic methodologies, significantly enhancing patient outcomes across various medical domains. Despite its advancements, integrating ultrasound technology with robotic systems for automated scans presents challenges, including limited command understanding and dynamic execution capabilities. To address these challenges, this paper introduces a novel Ultrasound Embodied Intelligence system that synergistically combines ultrasound robots with large language models (LLMs) and domain-specific knowledge augmentation, enhancing ultrasound robots' intelligence and operational efficiency. Our approach employs a dual strategy: firstly, integrating LLMs with ultrasound robots to interpret doctors' verbal instructions into precise motion planning through a comprehensive understanding of ultrasound domain knowledge, including APIs and operational manuals; secondly, incorporating a dynamic execution mechanism, allowing for real-time adjustments to scanning plans based on patient movements or procedural errors. We demonstrate the effectiveness of our system through extensive experiments, including ablation studies and comparisons across various models, showcasing significant improvements in executing medical procedures from verbal commands. Our findings suggest that the proposed system improves the efficiency and quality of ultrasound scans and paves the way for further advancements in autonomous medical scanning technologies, with the potential to transform non-invasive diagnostics and streamline medical workflows.