Organizations: Chair for Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · Medical Intelligence and Robotic Cognition Lab, The University of Hong Kong, Hong Kong, China
Robotic ultrasound offers advantages over free-hand scanning, including improved reproducibility and reduced operator dependency. In clinical practice, US acquisition relies heavily on the sonographer's experience and situational judgment. When transferring this process to robotic systems, such expertise is often encoded explicitly through fixed procedures and task-specific models, yielding pipelines that can be difficult to adapt to new scanning tasks. In this work, we propose a unified framework for autonomous robotic US scanning that leverages a LLM-based agent to interpret US scanning guidelines and execute scans by dynamically invoking a set of provided software tools. Instead of encoding fixed scanning procedures, the LLM agent retrieves and reasons over guideline steps from scanning handbooks and adapts its planning decisions based on observations and the current scanning state. This enables the system to handle variable and decision-dependent workflows, such as adjusting scanning strategies, repeating steps, or selecting the appropriate next tool call in response to image quality or anatomical findings. Because the reasoning underlying tool selection is also critical for transparent and trustworthy planning, we further fine tune the LLM agent using a RL based strategy to improve both its reasoning quality and the correctness of tool selection and parameterization, while maintaining robust generalization to unseen guidelines and related tasks. We first validate the approach via verbal execution on 10 US scanning guidelines, assessing reasoning as well as tool selection and parameterization, and showing the benefit of RL fine tuning. We then demonstrate real world feasibility on robotic scanning of the gallbladder, spine, and kidney. Overall, the framework follows diverse guidelines and enables reliable autonomous scanning across multiple anatomical targets within a unified system.
Figures & tables
Figure 1: Overview of the proposed guideline-driven agentic robotic US system. An RL fine tuned LLM retrieves guideline steps from the scanning handbook, reasons over the current state and observations, and generates structured tool calls with parameters. Executed tools update the state, enabling closed loop, non linear scanning workflows.
Figure 2: Trajectory planning pipeline for initial scan generation.
Base Model
After Fine Tuning
Step-wise Accuracy
0.6512
0.8973
Overall Success Rate
0.5384
0.9230
Table 1: Performance comparison before and after fine-tuning.
Artificial intelligence-assisted ultrasound scanning enhances diagnostic reliability and efficiency by providing real-time guidance for standardized image acquisition and reducing operator dependence. However, existing reinforcement learning and learning-assisted ultrasound scanning methods typically rely on carefully designed reward functions or extensive interaction data, which limits their generalization ability and stability across different devices, patient populations, and complex clinical scenarios. To address these challenges, we propose an ultrasound vision-language-action model (US-VLA) for automated ultrasound scanning that explicitly encodes clinical semantic goals and generates sequential probe manipulation actions under real-time ultrasound feedback. In particular, we first design an ultrasound-aware expert fusion module to jointly integrate ultrasound observations with auxiliary contextual information, enabling semantic ultrasound feedback to effectively guide the scanning process. Then, we construct US-VLA-Data, a real-world dataset covering liver and kidney examinations, which includes five clinically defined standard planes and comprises 320 expert scanning trajectories with approximately 80,000 synchronized timesteps. Extensive experiments demonstrate that US-VLA achieves competitive performance in ultrasound probe manipulation tasks, indicating its effectiveness and promising generalization within the evaluated abdominal ultrasound setting. The source code is available at https://github.com/VMVLab/US-VLA.
Cheng Zhang, Xingzheng Wu, Guihao Yan +4
Innovation School of Artificial Intelligence, Hefei University of Technology Hefei, China
Robotic ultrasound (US) has recently attracted increasing attention as a means to overcome the limitations of conventional US examinations, such as the strong operator dependence. However, the decision-making process of existing methods is often either rule-based or relies on end-to-end learning models that operate as black boxes. This has been seen as a main limit for clinical acceptance and raises safety concerns for widespread adoption in routine practice. To tackle this challenge, we introduce the RAG-RUSS, an interpretable framework capable of performing a full carotid examination in accordance with the clinical workflow while explicitly explaining both the current stage and the next planned action. Furthermore, given the scarcity of medical data, we incorporate retrieval-augmented generation to enhance generalization and reduce dependence on large-scale training datasets. The method was trained on data acquired from 28 volunteers, while an additional four volumetric scans recorded from previously unseen volunteers were reserved for testing. The results demonstrate that the method can explain the current scanning stage and autonomously plan probe motions to complete the carotid examination, encompassing both transverse and longitudinal planes.
Dianye Huang, Ziping Cong, Nassir Navab +1
Computer-Aided Medical Procedures and Augmented Reality (CAMP), Technical University of Munich (TUM), Germany · Munich Center for Machine Learning (MCML), Germany
In this paper, we present a robotic ultrasound acquisition system that integrates stereo vision, touch-based feedback, and expert-informed strategies to perform autonomous and adaptive abdominal scans. The system records freehand motion and force data from expert radiologists, creating a framework to capture transducer motion, applied forces, and anatomical scanning strategies. This expert data is replayed to replicate characteristic scans with the robot, forming a foundation for further autonomous capabilities. Using stereo vision, the system generates three-dimensional topography maps of the patient's abdomen, which are refined through stiffness measurements at key points to delineate the rib cage boundary. These combined techniques enable the robot to execute two distinct scanning paths: an upward-angled sweep beneath the rib cage to visualize structures near the upper abdomen and a perpendicular sweep across soft tissue regions. A compliant, torque-controlled seven degree-of-freedom robotic manipulator is controlled to maintain consistent probe contact through closed-loop force control over the varied anatomical surfaces. Physical experiments demonstrate that the system achieves high-quality imaging comparable to expert scans while dynamically adapting to patient-specific topographies. Furthermore, the robotic system surpasses expert capabilities by enabling three-dimensional volume acquisition, which enhances diagnostic potential and provides volumetric data for advanced analyses. This work highlights the integration of expert knowledge into autonomous robotic systems and underscores the potential of combining perception-based autonomy with physical reasoning for enhanced diagnostic performance.
Adrian Piedra, R Brooke Jeffrey, Oussama Khatib
Stanford Robotics Laboratory, Computer Science Department, Stanford University, Stanford, CA 94305 USA · Department of Radiology, School of Medicine, Stanford University, Stanford, CA 94305 USA