Clinician ai collaboration improves robotic stroke surgery navigation success
Clinician-AI Collaboration in Endovascular Neurointerventions: In-Vitro User Study of Copilot Assistance Modes for Robotic Navigation Across the Autonomy Spectrum
Robotics
Summary
Mechanical thrombectomy is a treatment for strokes but requires expert doctors, which are not always available. This paper studies how doctors and AI systems can work together to guide robot-assisted stroke surgery tools. The researchers tested different ways of sharing control between doctors and AI in realistic models, finding that teamwork led to better success rates than AI alone. This suggests that combining human skills with AI is more reliable than just increasing automation for these surgeries.
What this means in practice
- •For interventional neurovascular teams: Use shared control modes integrating clinician input and AI assistance to improve robotic navigation success during stroke treatment procedures.
- •For robotic system developers: Design robot navigation interfaces that balance human and AI control to reduce performance drop from simulation to real patient environments.
Authors
Nikola Fischer, Harry Robertshaw, S. M. Hadi Sadati, Xingyu Chen, Weiyuan Deng, Benjamin Jackson, Michael Adofo Kwakye, Sara Sciacca, Naga Kandasamy, Parthiban Balasundaram, Sahan Guruge, Charles Cummins, Joe Leyon, Nasr Abdelsalam, Matthew Benger, Andrew Falzon, Alejandro Granados, Christos Bergeles, Thomas Booth
Abstract
Mechanical thrombectomy (MT) is an effective treatment for acute ischemic stroke, yet access remains limited by the availability of neurointerventional expertise. Telerobotic and AI-assisted systems may help address this challenge, but it remains unclear how clinicians interact with different levels of autonomy and how autonomous navigation systems perform in realistic environments. This work introduces Copilot Assistance Modes (CAMs), a framework for clinician-AI collaboration in robotic endovascular navigation spanning the autonomy spectrum from manual operation to full autonomy. In the first in-vitro user study comparing clinician-AI collaboration strategies, 11 experts and two students evaluated six navigation modes in a patient-specific vascular phantom. Objective performance metrics were combined with subjective assessments, including workload and usability. Autonomous performance was additionally evaluated both in silico and in vitro to investigate sim-to-real transfer. Hands-on manual navigation achieved the strongest overall performance (100% success). While fully autonomous navigation achieved 81% success in simulation, performance dropped to 23% in vitro. In contrast, several clinician-AI shared-autonomy modes achieved success rates of 92-97%, approximately four times higher than full autonomy and comparable to direct robotic control (95%), demonstrating mitigation of sim-to-real domain shift through clinician-AI collaboration. Among the evaluated AI-driven approaches, user-directed task delegation and rotational shared control demonstrated the most favorable balance between performance and clinician acceptance. These findings suggest that effective clinician-AI collaboration and allocation of control authority may currently be more important for reliable robotic neurointerventional navigation than increasing autonomy alone.