麻醉学、自动化与人工智能。
Anesthesiology, automation, and artificial intelligence.
作者信息
Alexander John C, Joshi Girish P
机构信息
Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, Texas.
出版信息
Proc (Bayl Univ Med Cent). 2017 Dec 5;31(1):117-119. doi: 10.1080/08998280.2017.1391036. eCollection 2018 Jan.
There have been many attempts to incorporate automation into the practice of anesthesiology, though none have been successful. Fundamentally, these failures are due to the underlying complexity of anesthesia practice and the inability of rule-based feedback loops to fully master it. Recent innovations in artificial intelligence, especially machine learning, may usher in a new era of automation across many industries, including anesthesiology. It would be wise to consider the implications of such potential changes before they have been fully realized.
人们曾多次尝试将自动化融入麻醉学实践,但均未成功。从根本上说,这些失败是由于麻醉实践的内在复杂性以及基于规则的反馈回路无法完全掌握它。人工智能领域的最新创新,尤其是机器学习,可能会在包括麻醉学在内的许多行业开创一个自动化的新时代。在这些潜在变化完全实现之前考虑其影响是明智的。
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