A recent qualitative study identified potential barriers and facilitators to implementing AI-based interventions in surgery. The authors suggested that these findings can be aligned with standard implementation strategies to better align the expectations of AI-based interventions in surgery with their actual capabilities. This approach establishes a foundation for more successful implementation, thereby increasing the likelihood that surgeons will effectively utilize this new technology to enhance patient outcomes.
This qualitative study is published in January 2026 in JAMA Surgery
Applying Artificial Intelligence (AI) Potential in the Operation Theatre (OT) Room
Artificial intelligence (AI) is a transformational tool for surgery that improves outcomes. However, the gaps between expectations and reality can hinder implementation. Given the high costs of implementing AI, understanding barriers and facilitators is critical to developing implementation packages and strategies that optimize the likelihood of successfully delivering and sustaining AI interventions that improve perioperative outcomes in surgical patients.
The Operating Room Black Box (ORBB) is a proprietary product developed by Surgical Safety Technologies and serves as a valuable case study for examining the impact of expectation gaps on the implementation of intraoperative AI interventions. ORBB is among the pioneering programs that utilize ambient AI within the operating room to capture and process audiovisual data related to team communication and performance, alongside intraoperative patient data. Evidence shows that ORBB can improve outcomes by identifying errors, enhancing team performance, ensuring safety, and providing training data.
Study Overview
This qualitative study was conducted at 3 large academic centres via semi-structured interviews with surgeons and implementation leaders of the AI intervention to identify barriers and facilitators to implementing AI–based interventions that improve intra- and postoperative care. Through a screening survey, 30 surgeons and 17 implementation leaders from 3 centres that implemented the AI intervention were interviewed. The intervention consisted of surgical video content management, which builds and stores intraoperative videos for evaluation; surgical case review, which automatically flags cases for review based on outlier identification or user request; and surgical safety checklist compliance, which automatically rates adherence to intraoperative safety protocols. The primary outcome was misalignment between participants' expectations of the AI intervention technology and the programme's deliverables.
Key Findings
In this study, 57% of the surgeons maintained a neutral perspective on the technology, 37% expressed favourable opinions, and 7% expressed negative views.
Interviewees identified the following 4 major themes that highlighted misalignment between user expectations and the experience of using the technology
- The AI model needed considerable additional training to be usable
- Accessing data on surgical cases was difficult and time-consuming
- The program showed limited ability to predict postoperative complications
- The program generated a few academic deliverables
Potential Learnings for Stakeholders
AI-derived data may not be immediately actionable, and slow turnaround times can limit its use in real time. To adapt AI models for healthcare settings, the implementation team must be prepared to reduce frustration and abandonment. Small pilot projects allow users to provide feedback that improves implementation and acceptance, because users can shut down the programme at minimal cost if it proves ineffective. For academic surgeons, AI may be more valuable for studying team communication and training than for studying rare complications. Other common barriers to implementation include poor workflow integration, limited stakeholder engagement, and insufficient institutional support. These challenges particularly affect smaller hospitals with fewer resources. Less-resourced institutions should consider implementing proven, ready-made programmes rather than adapting interventions and should focus their resources on technical support.
Reference: Thornton M, Cher BAY, Macdonald C, et al. Expectations vs. Reality of an Intraoperative Artificial Intelligence Intervention. JAMA Surg. Published online January 14, 2026. doi:10.1001/jamasurg.2025.6029
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