Stanford Advisors

Contact

  • Academic
    jwbkim@stanford.edu
    University - Scholar Department: Computer Science Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 9025

All Publications

  • Will your next surgeon be a robot? Autonomy and AI in robotic surgery SCIENCE ROBOTICS Schmidgall, S., Opfermann, J. D., Kim, J., Krieger, A. 2025; 10 (104): eadt0187

    Abstract

    State-of-the-art surgery is performed robotically under direct surgeon control. However, surgical outcome is limited by the availability, skill, and day-to-day performance of the operating surgeon. What will it take to improve surgical outcomes independent of human limitations? In this Review, we explore the technological evolution of robotic surgery and current trends in robotics and artificial intelligence that could lead to a future generation of autonomous surgical robots that will outperform today's teleoperated robots.

    View details for DOI 10.1126/scirobotics.adt0187

    View details for Web of Science ID 001533518500002

    View details for PubMedID 40700524

  • SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning. Science robotics Kim, J. W., Chen, J. T., Hansen, P., Shi, L. X., Goldenberg, A., Schmidgall, S., Scheikl, P. M., Deguet, A., White, B. M., Tsai, D. R., Cha, R. J., Jopling, J., Finn, C., Krieger, A. 2025; 10 (104): eadt5254

    Abstract

    Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and robust generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning strategies. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach uses a high-level policy for task planning and a low-level policy for generating low-level trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and help recover from errors made by the low-level policy. We validated our framework through ex vivo experiments on cholecystectomy, a commonly practiced minimally invasive procedure, and conducted ablation studies to evaluate key components of the system. Our method achieves a 100% success rate across eight different ex vivo gallbladders, operating fully autonomously without human intervention. The hierarchical approach improved the policy's ability to recover from suboptimal states that are inevitable in the highly dynamic environment of realistic surgical applications. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems.

    View details for DOI 10.1126/scirobotics.adt5254

    View details for PubMedID 40632876