Surgeon-Derived Design Insights Regarding Real-Time Artificial Intelligence Decision Support for Endoscopic Cancer...

IntroductionReal-time artificial intelligence (AI) decision-support systems are increasingly proposed for use in endoscopic and minimally invasive surgery although how best to present such information intraoperatively is unclear. This study explored surgeon perspectives regarding the user-interface, interpretation and workflow integration of a prototype AI system (CLASSICA-OR) for real-time endoscopic cancer characterization during surgery.MethodsSurgeons specialising in minimally invasive gastrointestinal surgery were asked for their first impressions in their evaluation of the CLASSICA-OR system within a high-fidelity surgical simulation environment. For this, participants viewed prerecorded colonoscopic imagery of significant rectal neoplasia while the AI analysis was applied in real-time to the video. Commentary and responses to semi-structured interview were recorded audiovisually for transcription and analysis using qualitative thematic analysis supported by NVivo (Lumivero).ResultsEighteen surgeons participated, generating rich qualitative data. Four major themes emerged: (1) AI was viewed as a valuable but evolving decision-support tool; (2) institutional culture, governance and commercial realities were recognised as important determinants of adoption; (3) real-time perfusion-based lesion characterisation (foundational to CLASSICA-OR) was considered promising but with specific challenges; and (4) clarity, usability, transparency and workflow compatibility are surgeon priorities in decision-support displays. Participants emphasised minimised cognitive load, preserved surgeon autonomy, clear communication of uncertainty and seamless workflow integration. Preferences differed between simplified binary outputs and more detailed numerical displays supporting the need for adaptable interfaces.ConclusionsSurgeons perceive real-time intraoperative AI decision support as promising but requiring thoughtful user-centred design. They articulate practical development and implementation insights for intraoperative AI systems.