Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations

By Daniel A. Hashimoto, E. Witkowski, Lei Gao, Ozanan R. Meireles, Guy Rosman. Anesthesiology, 2020

344 citations recorded by the source corpus.

Artificial 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. This scoping review of artificial intelligence in anesthesiology summarizes six areas of research: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event/risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Supplemental Digital Content is available in the text.

Subfields: Cardiology and Cardiovascular Medicine, Health Informatics, Surgery

View Paper: https://www.semanticscholar.org/paper/94ae261ee01bd6be58c48cce83c1f4d927432c3a