Enhancing Surgical Robots with Embodied Intelligence for Autonomous Ultrasound Scanning
This work addresses the need for more autonomous and efficient ultrasound scanning in medical diagnostics, representing an incremental advancement in robotic medical technology.
The paper tackles the problem of ultrasound robots lacking intelligence for autonomous scanning by proposing an Ultrasound Embodied Intelligence system that integrates large language models with domain knowledge, resulting in significant improvements in scan efficiency and quality from verbal commands.
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.