The SMART-OR Framework for Implementing Artificial Intelligence in the Operating Room

Background Intraoperative artificial intelligence (AI) decision support systems hold promise for improving surgical outcomes, yet significant barriers impede their translation from development to clinical deployment. A systematic implementation framework informed by stakeholder perspectives is needed to guide responsible adoption in operating room (OR) settings. Methods We conducted a qualitative study using the Consolidated Framework for Implementation Research at a single North American academic institution. Phase I involved semi-structured interviews with OR personnel (surgeons, trainees, nurses, biomedical engineers) recruited through purposive maximum variation and snowball sampling until thematic saturation. Phase II comprised focus groups with patients recruited via convenience sampling. Interview and focus group transcripts underwent iterative thematic analysis using both deductive and inductive coding approaches. Results Twenty-two stakeholder interviews and two patient focus groups (n = 8) identified unique barriers and facilitators that coalesced into five major themes defining implementation requirements for intraoperative AI decision support: intuitive design, adequate training, maximizing adaptability, ongoing support, and fostering buy-in. These themes were contextualized across the surgical timeline: pre-implementation, implementation, and post-implementation phases, to create a comprehensive SMART-OR framework. Key barriers included overreliance concerns, automation bias risks, workflow disruption, team coordination challenges, and medico-legal ambiguity. Facilitators included perceived accuracy improvements, real-time guidance utility, and enhanced educational opportunities. Conclusions This study provides the first comprehensive implementation framework for intraoperative AI decision support, offering practical guidance across the technology lifecycle. The framework addresses critical gaps between AI development and clinical deployment by integrating diverse stakeholder perspectives into actionable recommendations. Future implementation efforts should prioritize transparent validation, coordinated training, and clear governance structures to ensure responsible adoption.