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Artificial intelligence in perioperative medicine: a narrative review

Cited 0 time in Web of Science Cited 6 time in Scopus
Authors

Yoon, Hyun-Kyu; Yang, Hyun-Lim; Jung, Chul-Woo; Lee, Hyung-Chul

Issue Date
2022-06
Publisher
대한마취통증의학회
Citation
Korean Journal of Anesthesiology, Vol.75 No.3, pp.202-215
Abstract
Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.
ISSN
2005-6419
URI
https://hdl.handle.net/10371/184309
DOI
https://doi.org/10.4097/kja.22157
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