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- John C Alexander and Girish P Joshi.
- Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, Texas.
- Proc (Bayl Univ Med Cent). 2018 Jan 1; 31 (1): 117-119.
AbstractThere have been many attempts to incorporate automation into the practice of anesthesiology, though none have been successful. Fundamentally, these failures are due to the underlying complexity of anesthesia practice and the inability of rule-based feedback loops to fully master it. Recent innovations in artificial intelligence, especially machine learning, may usher in a new era of automation across many industries, including anesthesiology. It would be wise to consider the implications of such potential changes before they have been fully realized.
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