Editorial Views  |   October 2018
Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark
Author Notes
  • From the Departments of Anesthesiology (M.R.M., S.K.); Computational Medicine and Bioinformatics (K.N.); and Emergency Medicine (K.N.), University of Michigan Health System, Ann Arbor, Michigan.
  • Corresponding articles on pages 649, 663, and 675.
    Corresponding articles on pages 649, 663, and 675.×
  • Accepted for publication July 9, 2018.
    Accepted for publication July 9, 2018.×
  • Address correspondence to Dr. Mathis: mathism@med.umich.edu
Article Information
Editorial Views / Cardiovascular Anesthesia / Technology / Equipment / Monitoring
Editorial Views   |   October 2018
Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark
Anesthesiology 10 2018, Vol.129, 619-622. doi:10.1097/ALN.0000000000002384
Anesthesiology 10 2018, Vol.129, 619-622. doi:10.1097/ALN.0000000000002384
MACHINE learning, the quintessential tool currently driving forward the development of artificial intelligence, was discovered and developed decades ago. Nevertheless, it is only recently that machine learning has seen an exponential increase in growth, sophistication, and influence. Recent success stories outside of health care are numerous, including Facebook’s DeepFace, unveiled in 2014, which is a machine-learning technology capable of identifying faces with 97.25% accuracy (compared to human accuracy of 97.53%).1  In 2016, Google adopted a deep learning approach to language translation, using an algorithm that is fed massive amounts of data to effectively train itself to recognize patterns in speech, with translation errors reduced by 87%.2