©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Crit Care Med. Jun 5, 2020; 9(2): 13-19
Published online Jun 5, 2020. doi: 10.5492/wjccm.v9.i2.13
Published online Jun 5, 2020. doi: 10.5492/wjccm.v9.i2.13
Artificial intelligence and computer simulation models in critical illness
Amos Lal, Ognjen Gajic, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Rochester, Mayo Clinic, MN 55905, United States
Amos Lal, Yuliya Pinevich, Ognjen Gajic, Vitaly Herasevich, Brian Pickering, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
Yuliya Pinevich, Vitaly Herasevich, Brian Pickering, Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, United States
Author contributions: All authors equally contributed to this paper.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Amos Lal, MBBS, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, United States. lal.amos@mayo.edu
Received: December 31, 2019
Peer-review started: December 31, 2019
First decision: March 28, 2020
Revised: April 21, 2020
Accepted: May 12, 2020
Article in press: May 12, 2020
Published online: June 5, 2020
Processing time: 156 Days and 12.6 Hours
Peer-review started: December 31, 2019
First decision: March 28, 2020
Revised: April 21, 2020
Accepted: May 12, 2020
Article in press: May 12, 2020
Published online: June 5, 2020
Processing time: 156 Days and 12.6 Hours
Core Tip
Core tip: Widespread implementation of electronic health records coupled with increased computer power has led to the increased use of artificial intelligence and computer modeling in clinical medicine. To be clinically useful, artificial intelligence models need to be built on accurate data, take into consideration causal mechanisms, and provide actionable information at the point of care.