• Karisa Anand, Suk Hong, Kapil Anand, and Joseph Hendrix.
• University of Texas Southwestern.
• Curr Opin Anaesthesiol. 2024 Dec 1; 37 (6): 619623619-623.

Purpose Of ReviewThis review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care.Recent FindingsMachine learning models have demonstrated the ability to accurately forecast case durations, Post-Anesthesia Care Unit (PACU) lengths of stay, and risk of hospital transfers based on preoperative patient and procedural factors. These models can inform case scheduling, resource allocation, and preoperative evaluation. Additionally, machine learning can standardize assessments, predict outcomes, improve handoff communication, and enrich patient education.SummaryMachine learning has the potential to revolutionize ambulatory anesthesia practice by optimizing efficiency, personalizing care, and improving quality and safety. However, limitations such as algorithmic opacity, data biases, reproducibility issues, and adoption barriers must be addressed through transparent, participatory design principles and ongoing validation to ensure responsible innovation and incremental adoption.Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.

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