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.

Keywords: Artificial intelligence; Machine learning; Management; Operating room; Perioperative.

Fig. 1

Literature search flow diagram based on PRISMA.

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Publication per year since 2019. Note: the timeline counts all publication dates for a citation as supplied by the publisher. These dates may span more than one year. This means the sum of results represented in the timeline may differ from the search results count

Fig. 3

Learning curve of artificial intelligence and publications

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Number of publications per area