Robotic Ultrasound Imaging

Vollständiger Abstract

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Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inherently restricts its reproducibility and global accessibility. Robotic ultrasound systems (RUSS) have evolved over the past 2 decades to mitigate these limitations by mechanically decoupling the human operator from the patient. This comprehensive review examines the historical trajectory of medical ultrasonography and robotics, highlighting their convergence into modern RUSS. We detail the taxonomies of robotic autonomy and evaluate the clinical impact of teleoperated systems (telesonography), which increasingly leverage ultra‐low‐latency 5G networks to project diagnostic expertise globally. Furthermore, we dissect the enabling hardware and control algorithms essential for autonomous acquisition, including compliant force control, probe orientation optimization, and dynamic path generation. The contemporary integration of artificial intelligence (AI), particularly deep learning, physics‐inspired neural networks, and reinforcement learning, has catalyzed a paradigm shift toward fully autonomous systems capable of semantic reasoning, motion‐aware imaging, and deformation compensation. This review explores emerging frontiers, such as soft robotics, wearable ultrasound patches, and large language model (LLM) graph planners, while addressing the critical regulatory and ethical frameworks required for the future clinical translation of intelligent robotic sonographers.