Jingao Li

Papers

Total Citations

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About

Jingao Li is a pioneering researcher in surgical robotics and teleoperation systems, with a focus on enhancing precision and safety in robot-assisted minimally invasive surgery (RMIS). His work centers on two key areas: automated surgical skill assessment and force estimation in teleoperation. Li’s most cited paper (2023, 12 citations) introduces a groundbreaking framework that uses visual motion signals and deep neural networks to quantify surgical instrument tip motion, enabling objective, automated evaluation of surgical quality—a critical step toward improving operative accuracy and training. His 2024 paper (4 citations) tackles the challenge of force feedback in teleoperation, proposing a graph robot network that estimates contact forces without traditional sensors, overcoming size and environmental constraints to reduce risks in remote procedures. Though early in his career, Li’s contributions are already shaping the future of intelligent surgical systems, offering scalable solutions for skill assessment and haptic feedback. His innovative use of deep learning and graph-based models positions him as a rising leader in medical robotics, with potential to transform surgical training and teleoperated interventions.