Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy.
J Med Syst. 2024 Feb 14;48(1):19. doi: 10.1007/s10916-024-02038-2.
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.
本系统评价考察了人工智能，特别是机器学习在手术室管理中的最新应用。分析了 2019 年 2 月至 2023 年 9 月期间的 22 项选定研究。本综述强调了人工智能在预测手术持续时间、优化麻醉后护理单元资源分配以及检测手术取消方面的重大影响。XGBoost、随机森林和神经网络等机器学习算法已证明其在提高预测准确性和资源利用率方面的有效性。然而，数据访问和隐私问题等挑战也得到了承认。本综述强调了人工智能在围手术期医学研究中的不断发展性质，以及需要不断创新，以利用人工智能为医疗保健管理人员、从业者和患者带来变革性潜力。最终，人工智能在手术室管理中的整合有望提高医疗效率和患者预后。
