Optimizing AI implementation for surgery: recommendations for infrastructure and deployment in the operating room

Abstract

Recent years have seen a surge in the development of Artificial Intelligence (AI) technologies to enhance intraoperative decision-making and improve patient safety. Yet, real-world evidence on their implementation remains limited. This study evaluated the impact of AI deployment in the operating room across key implementation domains-usability, cost, and carbon footprint-using cloud and edge-based infrastructures. Twenty-three end-users (surgeons, residents, fellows, operating room nurses) from a multi-site teaching hospital in Canada participated in system usability testing, assessed with validated scales and open-ended feedback. Cost and carbon footprint were assessed based on operational metrics. Results showed greater deployment task completion with Cloud but increased physical strain, carbon emissions, and operational costs compared to Edge after 396 h of use. Despite similar usability ratings, participants suggested improvements to the Cloud setup and both systems' interface. Findings provide evidence-based recommendations toward the implementation of sustainable, user-centered AI systems in surgical practice.

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