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About

Xiaoyu Tan is a leading researcher at the intersection of artificial intelligence and robotic surgery, with a primary focus on minimally invasive procedures such as laparoscopy and radiofrequency ablation (RFA). His most significant contribution lies in pioneering the application of deep reinforcement learning (DRL) to automate and enhance surgical training and execution. In his landmark 2019 work, "Robot-Assisted Training in Laparoscopy Using Deep Reinforcement Learning" (59 citations), Tan demonstrated how AI could reduce the steep learning curve for surgeons, making complex instrument handling more intuitive. He further advanced the field by developing universal distributional DRL for robot-assisted flexible needle insertion (35 citations), enabling safer, more precise targeting of deep-seated tumors. Tan also addressed a critical clinical challenge by creating overlapping ablation planning methods for large liver tumors (24 citations), integrating robotic needle insertion from a single incision port. His recent exploration into using large language models for reward guidance (LMGT framework) signals a forward-looking approach to generalizing AI in surgery. With a career spanning cognitive engine design for patient-specific robotic control, Tan’s work has accumulated over 150 citations, establishing him as a key innovator in making robotic surgery more accessible, precise, and intelligent.