TY - JOUR
T1 - One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making
AU - van der Ven, Ward H.
AU - Veelo, Denise P.
AU - Wijnberge, Marije
AU - van der Ster, Björn J. P.
AU - Vlaar, Alexander P. J.
AU - Geerts, Bart F.
N1 - Funding Information: Dr. Veelo reports having received personal fees and other from Edwards Lifesciences outside the submitted work as well as consultancy fees and research grants from Philips and Hemologic. Dr. Wijnberge reports having received consultancy fees from Edwards Lifesciences outside the submitted work. Dr. Vlaar reports having received grants from Edwards Lifesciences and Philips and personal fees from AKPA and InflaRx. Dr. Geerts reports having received grants from Edwards Lifesciences outside the submitted work and consultancy fees and research grants from Philips. The other authors have no related conflicts of interest to declare. Publisher Copyright: © 2020 The Authors
PY - 2021/6
Y1 - 2021/6
N2 - This 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.
AB - This 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.
UR - http://www.scopus.com/inward/record.url?scp=85097752467&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.surg.2020.09.041
DO - https://doi.org/10.1016/j.surg.2020.09.041
M3 - Article
C2 - 33309616
SN - 0039-6060
VL - 169
SP - 1300
EP - 1303
JO - Surgery (United States)
JF - Surgery (United States)
IS - 6
ER -