Optimizing Human-Machine Interface for Real-Time AI Support in the Operating Room: the CVS Copilot

2026-06-25

AI Summary

This study addresses the challenge of AI-assisted critical view of safety (CVS) assessment during laparoscopic cholecystectomy, where conventional human–AI interaction often disrupts surgical workflow. The authors propose a surgeon-centered, role-adaptive human–machine interaction (HMI) design paradigm integrating an AI-driven CVS recognition algorithm with a configurable visualization interface. Through interviews with 17 surgeons of varying experience levels and mixed-methods evaluation—including reflexive thematic analysis and human factors heuristics—the system implements a “minimalist default display” coupled with “on-demand anatomical overlay.” The resulting CVS Copilot design guidelines indicate strong surgeon preference for minimal overlay (94%) and on-demand segmentation (76%), while explicitly rejecting disruptive interaction modalities such as persistent visual coverage, haptic feedback, and numerical confidence scores, thereby effectively balancing AI support with clinical autonomy.

Abstract

Artificial intelligence (AI) systems for automated Critical View of Safety (CVS) assessment in laparoscopic cholecystectomy are nearing clinical translation. Beyond algorithmic performance, clinical safety and effectiveness depend on the quality of the human-machine interface (HMI). This work examines how AI-generated predictions should be presented and controlled intraoperatively. Seventeen surgeons, including residents, attending surgeons, and professors, took part in a mixed-methods, user-centered design study to optimize an intraoperative HMI for AI-assisted safe laparoscopic cholecystectomy. Interviews explored interaction modalities, timing of assistance, visualization strategies, and control mechanisms across surgical roles, and were analyzed using reflexive thematic analysis and human-factors heuristics. Most surgeons (16/17) supported the use of AI for intraoperative decision support while rejecting autonomous decision-making. Attendings preferred minimal AI feedback at decisive moments (13/14), whereas residents favored optional guidance (3/3) with confidence indicators and on-demand anatomical overlays. Across interviews, surgeons consistently prioritized visual, surgeon-controlled, minimally intrusive displays, with the strongest support for a minimal overlay (16/17) and on-demand anatomical segmentation (13/17). Recurrent concerns included persistent overlays, haptic feedback, and numeric confidence displays, although these were not uniformly raised across the cohort. These findings informed the design of CVS Copilot, a surgeon-controlled, role-adaptive HMI that provides AI-based CVS assessment with minimal default visualization and optional overlays.

Problem

• Human-Machine Interface
• Artificial Intelligence
• Intraoperative Decision Support
• Laparoscopic Cholecystectomy
• Critical View of Safety

Innovation

• human-machine interface
• AI-assisted surgery
• user-centered design
• intraoperative decision support
• role-adaptive HMI