• Ward H van der Ven, Denise P Veelo, Marije Wijnberge, Björn J P van der Ster, VlaarAlexander P JAPJAmsterdam UMC, University of Amsterdam, Department of Intensive Care, Amsterdam, Netherlands; Amsterdam UMC, University of Amsterdam, Laboratory of Experimental Intensive Care and Anesthesiology, Amsterdam, Netherlands. Electronic add, and Bart F Geerts.
• Amsterdam UMC, University of Amsterdam, Department of Anesthesiology, Amsterdam, Netherlands.
• Surgery. 2021 Jun 1; 169 (6): 1300-1303.

AbstractThis review describes the steps and conclusions from the development and validation of an artificial intelligence algorithm (the Hypotension Prediction Index), one of the first machine learning predictive algorithms used in the operating room environment. The algorithm has been demonstrated to reduce intraoperative hypotension in two randomized controlled trials via real-time prediction of upcoming hypotensive events prompting anesthesiologists to act earlier, more often, and differently in managing impending hypotension. However, the algorithm entails no dynamic learning process that evolves from use in clinical patient care, meaning the algorithm is fixed, and furthermore provides no insight into the decisional process that leads to an early warning for intraoperative hypotension, which makes the algorithm a "black box." Many other artificial intelligence machine learning algorithms have these same disadvantages. Clinical validation of such algorithms is relatively new and requires more standardization, as guidelines are lacking or only now start to be drafted. Before adaptation in clinical practice, impact of artificial intelligence algorithms on clinical behavior, outcomes and economic advantages should be studied too.Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

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