Several tools are in place to gauge OR efciency, among them surgical checklists and the Metric for Evaluating Task Execution in the Operating Room (METEOR). These tools undertake data collection, analysis, evaluation, iterative correction, and dissemination to staff and institutions [35–37]. Drawing inspiration from the avia­tion industry’s use of a “black box” recording device to monitor extensive ight data for real-time and future analysis, similar approaches have been adapted for OR set­tings. Commercially available platforms like the OR Black Box (based in Toronto, ON, Canada) enable the capture of audio, visual, and other data related to various aspects of a procedure, both within the operative eld and the operating room itself (e.g., tracking team members). Articial intelligence and machine learning are employed to assist teams in enhancing quality and efciency. These systems are designed to detect and redact personal information of patients and providers while retaining clinically relevant data. Additionally, they have the potential to identify intraoperative errors, events, and distractions [38, 39].

In critical care settings, AI-driven tools have the potential to forecast clinical deterioration in patient hours before it occurs. Experimental systems have shown promise in autonomously determining appropriate treatment strategies for patients once they deteriorate [74, 75]. For instance, a machine learning-based hypotension prediction algorithm has been shown to reduce the time spent in a hypotensive state compared to standard care in a randomized single-center trial [76]. AI systems can be utilized to augment and lessen the burden on physician’s decision-making [77].