Ultrasound robots are increasingly used in medical diagnostics and early disease screening. However, current ultrasound robots lack the intelligence to understand human intentions and instructions, hindering autonomous ultrasound scanning. To solve this problem, we propose a novel Ultrasound Embodied Intelligence system that equips ultrasound robots with the large language model (LLM) and domain knowledge, thereby improving the efficiency of ultrasound robots. Specifically, we first design an ultrasound operation knowledge database to add expertise in ultrasound scanning to the LLM, enabling the LLM to perform precise motion planning. Furthermore, we devise a dynamic ultrasound scanning strategy based on a \textit{think-observe-execute} prompt engineering, allowing LLMs to dynamically adjust motion planning strategies during the scanning procedures. Extensive experiments demonstrate that our system significantly improves ultrasound scan efficiency and quality from verbal commands. This advancement in autonomous medical scanning technology contributes to non-invasive diagnostics and streamlined medical workflows.

翻译：超声机器人正越来越多地应用于医学诊断和早期疾病筛查。然而，当前超声机器人缺乏理解人类意图和指令的智能能力，这阻碍了自主超声扫查的实现。为解决此问题，我们提出了一种新型超声具身智能系统，该系统为超声机器人配备大语言模型（LLM）和领域知识，从而提升超声机器人的效率。具体而言，我们首先设计了一个超声操作知识数据库，将超声扫查的专业知识注入LLM，使其能够执行精确的运动规划。此外，我们基于\textit{思考-观察-执行}提示工程范式设计了一种动态超声扫查策略，使LLM能够在扫查过程中动态调整运动规划策略。大量实验表明，我们的系统通过语音指令显著提升了超声扫查的效率与质量。这项自主医学扫查技术的进步为非侵入性诊断和优化医疗工作流程做出了贡献。