Transforming Surgical Interventions with Embodied Intelligence for Ultrasound Robotics

TL;DR

This paper presents a novel ultrasound embodied intelligence system that combines large language models with ultrasound robots to interpret verbal instructions and dynamically adapt scanning procedures, enhancing autonomous medical diagnostics.

Contribution

Introduces a new system integrating LLMs with ultrasound robots and domain knowledge for improved interpretation and real-time adjustment of ultrasound procedures.

Findings

Significant improvement in ultrasound scan accuracy and efficiency.

Enhanced understanding of verbal commands through LLM integration.

Effective real-time adjustment to patient movements and procedural errors.

Abstract

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.