Why the study?
Machine learning technology is increasingly applied in perioperative management to enhance surgical safety, improve outcomes, reduce healthcare costs, and optimize anesthetic management.
Answers are grounded in this paper and cite their sources.
Machine learning technology shows potential in perioperative risk prediction, real-time monitoring, and prescription assessment, gradually changing clinical practice in anesthesiology.
The application of machine learning (ML) technology in perioperative management is increasing, with its importance lying in enhancing surgical safety, improving patient outcomes, reducing healthcare costs, and optimizing anesthetic management. Research progress indicates that ML technology has shown great potential in perioperative risk prediction, real-time monitoring, and rationality assessment of prescriptions, and is gradually changing clinical practice in anesthesiology. We will introduce the perioperative application of ML from the aspects of preoperative assessment, intraoperative management, and postoperative recovery. In addition, we will discuss the progress and challenges of ML in recent years, as well as the future use and research directions of ML.
No takes yet. Share an insight, caveat, or question.
Zhou et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: