From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital; and the Department of Physiology and Biophysics, Boston University, Boston, Massachusetts.
Anesthesiology. 2019 Dec;131(6):1346-1359. doi: 10.1097/ALN.0000000000002694.
Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them-perhaps bringing anesthesiology into an era of machine-assisted discovery.
最近，人工智能和机器学习的商业应用取得了显著的进展，特别是在图像识别、自然语言处理、语言翻译、文本分析和自学等领域。这些领域的历史发展缓慢，以至于这些技能似乎与智慧难以分割。然而，这些商业上的进步在单任务应用中表现最佳，在这些应用中，不完美的输出和偶尔的明显错误是可以容忍的。麻醉学的实践则不同。它体现了对高可靠性的要求，以及解释、身体动作和反应的压力循环，而不是任何单一的认知行为。这篇综述涵盖了人工智能和机器学习对执业麻醉师的基本含义，描述了决策行为如何从简单的方程式中产生。引入了相关的临床问题来说明机器学习如何帮助解决这些问题——也许将麻醉学带入了一个机器辅助发现的时代。
