  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.
