Assessing selective interaction for reliable and trustworthy robotic surgical assistance

Achieving reliable robotic assistance in collaborative surgery requires balancing the precision of autonomy with human oversight. This study assesses a Selective interaction strategy, in which the robot initiates communication only when task confidence is low, to explore whether uncertainty-triggered interaction may support reliability and operator trust relative to conventional Autonomous and Supervised modalities.

We implemented a collaborative framework for soft tissue resection capable of three interaction levels: (1) Autonomous, executing tasks without feedback; (2) Supervised, requiring confirmation for every critical action; and (3) Selective, which triggers queries based on internal uncertainty thresholds. We conducted a preliminary user study on resection of phantom tissue, assessing quantitative reliability (cutting path error, task completion time) and qualitative metrics (NASA-TLX, Trust/Distrust questionnaires).

Results suggest that the Selective modality optimizes the performance-workload trade-off. It showed a trend toward lower mean path error and shorter mean completion time compared to the Supervised modality. It also demonstrated lower mean distrust and mental demand scores alongside similar positive trust scores.

The preliminary data suggest that uncertainty-triggered communication can provide a favorable balance between task performance, workload, and operator perception. A larger study is required to determine whether the observed trends represent statistically reliable effects.