Advances in the Application of Artificial Intelligence in Clinical Anesthesia
10.11714/jsysu.med.YX20250087
- VernacularTitle:人工智能在临床麻醉中的应用进展
- Author:
Liming YANG
1
;
Shihong WEN
1
Author Information
1. Department of Anesthesiology The First Affiliated Hospital， Sun Yat-sen University， Guangzhou 510080， China
- Publication Type:Review
- Keywords:
artificial intelligence;
deep learning;
anesthesia;
perioperative management;
pain diagnosis treatment
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2026;47(1):77-83
- CountryChina
- Language:Chinese
-
Abstract:
In recent years， with the emergence of large language models like ChatGPT and DeepSeek into the public domain， artificial intelligence （AI） has become one of the most rapidly developing fields of the 21st century. As AI continues to evolve， new models are constantly emerging， particularly through the integration of multimodal data that enables more comprehensive information capture and analysis， demonstrating significant value in supporting clinical anesthesia decision-making. Preoperatively， AI can assist in evaluating patients’ overall health status， aid in selecting appropriate anesthesia methods， and predict potential risks such as difficult airways. Intraoperatively， AI can collaborate with anesthesiologists to monitor physiological parameters and optimize anesthesia management strategies， especially in predicting hypotension and improving automated drug infusion control systems. Postoperatively， AI can predict pulmonary complications， postoperative delirium， and major adverse cardiovascular events， thereby accelerating recovery and improving long-term survival.This article analyzes recent advances in AI technology in medicine worldwide， systematically reviews its application scenarios in clinical anesthesia， and provides detailed accounts of progress in preoperative risk assessment， formulation of personalized anesthesia plans， intraoperative monitoring and decision support， postoperative recovery， and long-term follow-up. It further highlights the need for future research to strengthen multimodal database construction， enhance model generalizability， develop explainable frameworks， and improve ethical governance. By summarizing current achievements and challenges， this review offers valuable reference for anesthesiologists and researchers， underscores the role of AI in driving the intelligent transformation of anesthesiology， and provides guidance for subsequent research directions.
