Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality

Author(s): Aleena Dost, Raneem Alaraj, Rabeeya Mayet, Devendra K Agrawal

Artificial intelligence (AI) is transforming anesthesiology, showcasing applications that address patient monitoring, closed-loop anesthetic delivery, risk forecasting, customized management, and workflow betterment. This review highlights modern developments, analyzing the role of AI from early rule-based systems to machine learning and deep learning models, aided by the foundational role of anesthesia information Management Systems. AI processes depict strong performance of the clinical team and allowing anesthesiologists to intervene earlier in cases of intraoperative hypotension, acute kidney injury, tissue hypoxia, and giving them more time to focus on complex patient cases. Closed-loop systems guided by the physiologic and electroencephalogram feedback exemplify the ability of AI to maintain anesthetic stability while reducing clinician workload. Predictive models help with the American Society of Anesthesiologists’ classification in categorizing the patients, airway risk stratification, and customized treatment planning with improving preoperative evaluation. A move toward precision anesthetic administration is indicated by new developments in pharmacogenomics, perioperative pain characterization, and AI-assisted ultrasonography. Beyond clinical gains, AI guarantees improved operating room efficiency through organized scheduling, natural language processing documentation. Yet, widespread integration of AI in anesthesia still faces barriers regarding ethical concerns, clinical doubt, including replicability amongst healthcare systems, and a lack of in-depth data regarding the topic. Addressing these concerns demands data from multicenters, interdisciplinary education, and integration of explainable AI frameworks that are palatable to the clinical world. Overall, AI has the potential to behave as an adjunct, instead of replacing anesthesiologists by aiding in decision making, improving patient safety, and preparing for perioperative care.