Utility of AI models in critical care: union of man and the machine

Affiliations

¹ Division of Pulmonary and Critical Care Medicine, Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. manavamos@gmail.com.
² Division of Critical Care, Department of Anesthesiology and Perioperative Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
³ Division of Pulmonary and Critical Care Medicine, Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.

Comment

Amos Lal et al. Crit Care. 2021.

. 2021 Feb 2;25(1):46.

doi: 10.1186/s13054-021-03478-9.

¹ Division of Pulmonary and Critical Care Medicine, Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. manavamos@gmail.com.
² Division of Critical Care, Department of Anesthesiology and Perioperative Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
³ Division of Pulmonary and Critical Care Medicine, Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.

No abstract available

Conflict of interest statement

The authors declare that they have no competing interests.

The future of AI in critical care is augmented, not artificial, intelligence.
Liu VX. Liu VX. Crit Care. 2020 Dec 2;24(1):673. doi: 10.1186/s13054-020-03404-5. Crit Care. 2020. PMID: 33267905 Free PMC article. No abstract available.

1. Liu VX. The future of AI in critical care is augmented, not artificial, intelligence. Crit Care. 2020;24(1):673. doi: 10.1186/s13054-020-03404-5. - DOI - PMC - PubMed
1. Tomasev N, Glorot X, Rae JW, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116–119. doi: 10.1038/s41586-019-1390-1. - DOI - PMC - PubMed
1. Lal A, Pinevich Y, Gajic O, et al. Artificial intelligence and computer simulation models in critical illness. World J Crit Care Med. 2020;9(2):13–19. doi: 10.5492/wjccm.v9.i2.13. - DOI - PMC - PubMed
1. Lal A LG, Cubro E, Chalmers S, Li H, Herasevich V, Yue D, Pickering B, Kilickaya O, Gajic O. Development and verification of a digital twin patient model to predict specific treatment response during the first 24 hours of sepsis. Crit Care Explor. 2020;Ahead of Print. - PMC - PubMed
1. Eddy DM, Schlessinger L. Archimedes: a trial-validated model of diabetes. Diabetes Care. 2003;26(11):3093–3101. doi: 10.2337/diacare.26.11.3093. - DOI - PubMed