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About

Zhongliang Jiang is a pioneering researcher at the intersection of medical robotics, ultrasound imaging, and artificial intelligence, whose work is reshaping the landscape of autonomous clinical diagnostics and surgical assistance. His research focuses primarily on robotic ultrasound systems (RUSS), robot-assisted surgery, and machine learning-driven medical imaging — fields where he has established himself as a leading voice through consistently high-impact publications. Jiang's most celebrated contributions include developing fully autonomous methods for robotic ultrasound probe positioning, leveraging confidence map optimization, force feedback, and real-time imaging to achieve reproducible, high-quality scans without human intervention. His landmark 2023 survey on robotic ultrasound imaging has already garnered 153 citations, underscoring its value as a defining reference in the field. His work on autonomous vascular screening and deformation-aware 3D ultrasound further demonstrates his ability to bridge theoretical innovation with clinical applicability. Beyond imaging, Jiang has made notable strides in robot-assisted laminectomy surgery, proposing intelligent planning frameworks and force-based cutting depth monitoring to enhance procedural safety. His more recent explorations into deep reinforcement learning for trajectory planning and simulation-to-real transfer reflect a forward-looking research vision. Collectively, his publications — accumulating nearly 800 citations — position him as an indispensable contributor to the future of intelligent, autonomous medical robotics.