This paper is in the following e-collection/theme issue:

Artificial Intelligence in Surgery and Perioperative Medicine (13) Machine Learning (1056)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41056, first published .
Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence–Based Tools in Critical Care: Web-Based Survey Study

Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence–Based Tools in Critical Care: Web-Based Survey Study

Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence–Based Tools in Critical Care: Web-Based Survey Study

Authors of this article:

Eric Mlodzinski 1 Author Orcid Image ;   Gabriel Wardi 1, 2 Author Orcid Image ;   Clare Viglione 3 Author Orcid Image ;   Shamim Nemati 4 Author Orcid Image ;   Laura Crotty Alexander 1, 5 Author Orcid Image ;   Atul Malhotra 1 Author Orcid Image
Article Authors Cited by (2) Tweetations (9) Metrics

Eric Mlodzinski   1 , MD ;   Gabriel Wardi   1, 2 , MD, MPH ;   Clare Viglione   3 , MPH, RD ;   Shamim Nemati   4 , PhD ;   Laura Crotty Alexander   1, 5 , MD ;   Atul Malhotra   1 , MD

1 Division of Pulmonary, Critical Care, Sleep and Physiology, University of California, San Diego, CA, US

2 Department of Emergency Medicine, University of California, San Diego, CA, US

3 Dissemination and Implementation Science Center, Altman Clinical & Translational Research Institute, University of California, San Diego, CA, US

4 Department of Biomedical Informatics, University of California, San Diego, CA, US

5 Section of Pulmonary and Critical Care, Veterans Affairs San Diego Healthcare System, San Diego, CA, US

Corresponding Author:

  • Eric Mlodzinski, MD
  • Division of Pulmonary, Critical Care, Sleep and Physiology
  • University of California
  • 9300 Campus Point Drive
  • San Diego, CA, 92037
  • US
  • Phone: 1 858-657-7118
  • Email: emlodz@gmail.com