Machine learning, the tool currently driving the development of artificial Intelligence, has recently seen exponential growth in its sophistication and influence. In this issue of Anesthesiology, we explore three examples of machine learning applied to our field. Lee et al. use machine-learning techniques to predict postoperative mortality from electronic health record data. Kendale et al predict hypotension by leveraging data available during induction, and Hatib et al. predict hypotension using data from high-fidelity arterial line waveforms. In an accompanying Editorial View, Mathis et al. describe what the practicing anesthesiologist needs to know about artificial intelligence for anesthesia. Illustration by Annemarie Johnson, Vivo Visuals.

  • Lee et al.: Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality, p. 649

  • Kendale et al.: Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension, p. 675

  • Hatib et al.: Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis, p. 663

  • Mathis et al.: Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark, p. 619