In the next 24 h, countless patients around the world will complain of chest pain, have a rhythm disturbance, feel lightheaded, lose consciousness, or suffer from a variety of other conditions resulting in a provider ordering an electrocardiogram. A piece of paper will ceremoniously print, with all 12 leads presenting for that provider’s interpretation. The providers will examine the tracings, and they will likely then shift their focus to the computer’s interpretation at the top of the page, looking for confirmation of their initial thoughts. Occasionally, this automated, digital review will result in a reconsideration of the provider’s initial interpretation and result in an urgent call to a cardiologist and/or some other intervention. While there is limited evidence that such technology affects outcomes and virtually no randomized trials showing outcome differences, the technology is widely implemented and utilized presumably because providers find the computer- generated electrocardiogram interpretations useful in certain...
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Editorial|
December 2022
Collaborative Artificial Intelligence in Practice: The Next Steps
James M. Blum, M.D., F.C.C.M.;
James M. Blum, M.D., F.C.C.M.
1Department of Anesthesia, University of Iowa Carver College of Medicine, Iowa City, Iowa; Department of Computer Science, University of Iowa College of Liberal Arts and Sciences, Iowa City, Iowa.
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David M. Kuehn, M.D., F.A.C.R.
David M. Kuehn, M.D., F.A.C.R.
2Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa.
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This editorial accompanies the article on p. 704.
Accepted for publication October 6, 2022.
Address correspondence to Dr. Blum: james-blum@uiowa.edu
Anesthesiology December 2022, Vol. 137, 664–665.
Citation
James M. Blum, David M. Kuehn; Collaborative Artificial Intelligence in Practice: The Next Steps. Anesthesiology 2022; 137:664–665 doi: https://doi.org/10.1097/ALN.0000000000004412
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