USPilot: An Embodied Robotic Assistant Ultrasound System With a Large Language Model Enhanced Graph Planner

Abstract

In the era of Large Language Models (LLMs), embodied artificial intelligence presents transformative opportunities for robotic manipulation tasks. Ultrasound imaging, a widely used and cost-effective medical diagnostic procedure, faces. challenges due to the global shortage of professional sonographers. To address this issue, we propose USPilot, an embodied robotic assistant ultrasound system powered by an LLM-based framework to enable autonomous ultrasound acquisition. USPilot is designed to function as a virtual sonographer, capable of responding to patients' ultrasound-related queries and performing ultrasound scans based on user intent. By fine-tuning the LLM, USPilot demonstrates a deep understanding of ultrasound-specific questions and tasks. Furthermore, USPilot incorporates an LLM-enhanced Graph Neural Network (GNN) to manage ultrasound robotic APIs and serve as a task planner. Experimental results show that the LLM-enhanced GNN achieves unprecedented accuracy in task planning on public datasets with an accuracy of 78.64%, 60.8% and 59.6%. Additionally, the system demonstrates significant potential in autonomously understanding and executing ultrasound scan procedures with a successful demonstration of our physical setup with the robot. These advancements bring us closer to achieving potentially autonomous robotic ultrasound systems, addressing critical resource gaps in medical imaging.