Kang Su

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About

Kang Su is at the forefront of robotic autonomous ultrasound systems, with a primary research focus on human-robot interaction and medical robotics. His most impactful contribution is the development of a fully autonomous robotic ultrasound system for thyroid scanning (2024, 57 citations), which addresses the critical challenge of operator dependency in ultrasound imaging. This work eliminates the need for sonographer expertise by enabling robots to autonomously navigate and scan thyroid regions, reducing physical and cognitive strain on clinicians. Su further advanced the field with a reinforcement learning-based "Tissue-View Map" for robotic carotid artery scanning (2025), framing autonomous ultrasound as a sequential decision-making problem. Beyond diagnostic applications, he has innovated in medical education through a mobile natural human-robot interaction method for virtual Chinese acupuncture (2022, 11 citations), enabling large-area, intuitive interaction via automatic hand tracking. His work bridges robotics, AI, and clinical practice, tackling real-world bottlenecks in medical imaging and training. With a growing citation record and pioneering autonomous systems, Kang Su is shaping the future of accessible, operator-independent ultrasound diagnostics.