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.

翻译：超声成像技术已彻底革新了无创诊断方法，显著提升了多个医疗领域的患者治疗效果。尽管取得了这些进展，将超声技术与机器人系统集成以实现自动化扫描仍面临诸多挑战，包括指令理解有限和动态执行能力不足。为解决这些挑战，本文提出了一种新型超声具身智能系统，该系统通过将超声机器人与大型语言模型（LLMs）及领域特定知识增强技术协同结合，提升了超声机器人的智能水平和操作效率。我们的方法采用双重策略：首先，将LLMs与超声机器人集成，通过全面理解超声领域知识（包括API和操作手册），将医生的口头指令解析为精确的运动规划；其次，引入动态执行机制，允许根据患者移动或操作错误实时调整扫描计划。我们通过大量实验（包括消融研究和多种模型的对比）证明了该系统的有效性，展示了其在执行来自口头指令的医疗程序方面的显著改进。我们的研究结果表明，所提出的系统不仅提高了超声扫描的效率和质量，而且为自主医疗扫描技术的进一步发展铺平了道路，具有变革无创诊断和优化医疗工作流程的潜力。