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麻醉学决策:人工智能会使术中护理更安全吗?

Decision-making in anesthesiology: will artificial intelligence make intraoperative care safer?

机构信息

University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.

Vanderbilt University Medical Center, Nashville, Tennessee, USA.

出版信息

Curr Opin Anaesthesiol. 2023 Dec 1;36(6):691-697. doi: 10.1097/ACO.0000000000001318. Epub 2023 Oct 13.

Abstract

PURPOSE OF REVIEW

This article explores the impact of recent applications of artificial intelligence on clinical anesthesiologists' decision-making.

RECENT FINDINGS

Naturalistic decision-making, a rich research field that aims to understand how cognitive work is accomplished in complex environments, provides insight into anesthesiologists' decision processes. Due to the complexity of clinical work and limits of human decision-making (e.g. fatigue, distraction, and cognitive biases), attention on the role of artificial intelligence to support anesthesiologists' decision-making has grown. Artificial intelligence, a computer's ability to perform human-like cognitive functions, is increasingly used in anesthesiology. Examples include aiding in the prediction of intraoperative hypotension and postoperative complications, as well as enhancing structure localization for regional and neuraxial anesthesia through artificial intelligence integration with ultrasound.

SUMMARY

To fully realize the benefits of artificial intelligence in anesthesiology, several important considerations must be addressed, including its usability and workflow integration, appropriate level of trust placed on artificial intelligence, its impact on decision-making, the potential de-skilling of practitioners, and issues of accountability. Further research is needed to enhance anesthesiologists' clinical decision-making in collaboration with artificial intelligence.

摘要

目的综述

本文探讨了人工智能在临床麻醉医师决策中的应用的影响。

最近的发现

自然决策是一个旨在理解认知工作在复杂环境中是如何完成的丰富研究领域,为我们理解麻醉医师的决策过程提供了线索。由于临床工作的复杂性和人类决策的局限性(例如疲劳、分心和认知偏差),人们越来越关注人工智能在支持麻醉医师决策方面的作用。人工智能是计算机执行类似人类认知功能的能力,在麻醉学中的应用日益广泛。例如,它可以帮助预测术中低血压和术后并发症,通过人工智能与超声的整合来增强区域和神经轴麻醉的结构定位。

总结

为了充分实现人工智能在麻醉学中的益处,必须考虑几个重要因素,包括其可用性和工作流程集成、对人工智能的适当信任水平、对决策的影响、从业者技能的潜在降低以及问责制问题。需要进一步研究如何与人工智能合作,以增强麻醉医师的临床决策能力。

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本文引用的文献

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Decision-Making During High-Risk Events: A Systematic Literature Review.高风险事件中的决策:一项系统文献综述
J Cogn Eng Decis Mak. 2023 Jun;17(2):188-212. doi: 10.1177/15553434221147415. Epub 2023 Jan 17.

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