Newly Published
Review Article  |   April 2019
Artificial Intelligence and Machine Learning in Anesthesiology
Author Notes
  • From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital; and the Department of Physiology and Biophysics, Boston University, Boston, Massachusetts.
  • Submitted for publication September 2, 2018. Accepted for publication February 21, 2019.
    Submitted for publication September 2, 2018. Accepted for publication February 21, 2019.×
  • Correspondence: Address correspondence to Dr. Connor: Department of Anesthesiology, Perioperative and Pain Medicine, 75 Francis Street, CWN-L1, Boston, Massachusetts 02115. cconnor@bwh.harvard.edu. Information on purchasing reprints may be found at www.anesthesiology.org or on the masthead page at the beginning of this issue. Anesthesiology’s articles are made freely accessible to all readers, for personal use only, 6 months from the cover date of the issue.
Article Information
Review Article / Airway Management / Cardiovascular Anesthesia / Central and Peripheral Nervous Systems / Critical Care / Respiratory System / Technology / Equipment / Monitoring
Review Article   |   April 2019
Artificial Intelligence and Machine Learning in Anesthesiology
Anesthesiology Newly Published on April 10, 2019. doi:10.1097/ALN.0000000000002694
Anesthesiology Newly Published on April 10, 2019. doi:10.1097/ALN.0000000000002694
Abstract

Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated. The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them—perhaps bringing anesthesiology into an era of machine-assisted discovery.