Papers
7
Total Citations
27
H-Index
3
About
Guanglin Cao is at the forefront of embodied intelligence for surgical robotics, with a focused mission to transform autonomous ultrasound systems. His research integrates robotics, computer vision, and large language models (LLMs) to create intelligent, context-aware medical instruments. Cao’s major contributions include pioneering the concept of “Ultrasound Embodied Intelligence,” enabling robots to understand human intentions and execute autonomous scanning—a critical step toward addressing the global shortage of professional sonographers. He developed the USPilot system, an LLM-enhanced graph planner that bridges high-level language commands with precise robotic actions. To overcome real-world clinical challenges, Cao introduced an ultra-fast intrinsic contact sensing method for arbitrarily shaped instruments, and a vision-haptic fusion control system that compensates for respiratory motion during 3D ultrasound acquisitions. His work on inverse kinematics-embedded networks for reconstructing patient anatomy from occluded multimodal data further advances surgical navigation and robotized scanning. With over 27 citations across his recent publications (2023–2025), Cao’s research is rapidly gaining recognition for its practical impact. His 2024 paper on transforming surgical interventions with embodied intelligence has already garnered 9 citations, signaling a rising influence in the field.
Research Focus
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Top Papers
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