• Daniel A Hashimoto, Elan Witkowski, Lei Gao, Ozanan Meireles, and Guy Rosman.
• From the Surgical Artificial Intelligence and Innovation Laboratory (D.A.H., E.W., O.M., G.R.) Department of Anesthesia, Critical Care, and Pain Medicine (L.G.), Massachusetts General Hospital, Boston, Massachusetts Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts (G.R.).
• Anesthesiology. 2020 Feb 1; 132 (2): 379-394.

AbstractArtificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

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