Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal.
Surgery and Physiology Department, Faculty of Medicine, University of Porto, Porto, Portugal.
J Clin Monit Comput. 2024 Apr;38(2):247-259. doi: 10.1007/s10877-023-01088-0. Epub 2023 Oct 21.
Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.
A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.
A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.
AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.
人工智能（AI）在医学中的应用正在迅速扩展。尽管已经有了大量的证据和有前景的结果，但仍需要对 AI 在麻醉学临床实践中的现状进行全面综述。因此，我们的研究旨在系统地回顾 AI 在这方面的应用。
根据系统评价和荟萃分析的首选报告项目（PRISMA）指南进行系统综述。我们在 Medline 和 Web of Science 上搜索了截至 2022 年 11 月与 AI 和麻醉学临床实践相关的文章，使用了与 AI 和麻醉学临床实践相关的术语。排除了涉及动物、社论、综述和样本量低于 10 例的文章。从每项研究中提取了特征和准确性度量。
本综述共纳入 46 篇文章。我们根据其临床适用性将其分为 4 类：（1）麻醉深度监测；（2）与麻醉相关的图像引导技术；（3）麻醉相关事件/风险预测；（4）药物管理控制。对每个组进行了分析，并总结了主要发现。在所有领域，与传统方法相比，大多数经过测试的 AI 方法都显示出了更优异的性能结果。
AI 系统正在被整合到麻醉学临床实践中，提高了医疗专业人员的决策、诊断准确性和治疗反应技能。
