Artificial Intelligence in Operating Room Management

Abstract

This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence’s transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.

Transformative Landscape of Anesthesia Education: Simulation, AI Integration, and Learner-Centric Reforms: A Narrative Review

“…Anesthesiologists entering the era of AI need to develop non-technical skills, including clinical judgment skills related to risk management, not only to maximize the benefits of AI but also to address potential challenges. Third-generation AI, through its sophisticated algorithms referring to neural networks, can engage in complex tasks comparable to human cognitive functions [14]. This includes intricate processes such as reasoning, pattern recognition, data analysis, discovery of hidden insights within vast datasets, and learning from previous experiences.…”