Application of Artificial Intelligence in Predicting and Managing Anesthesia Complications

Background: The safe administration of anesthesia is a cornerstone of modern surgical care. However, complications arising during anesthesia can significantly impact patient outcomes, particularly in high-risk scenarios or resource-limited settings. Traditional methods of monitoring and decision-making often struggle to predict and manage these complications in real time. Advances in artificial intelligence (AI) offer new opportunities to enhance patient safety by improving prediction accuracy and decision support during anesthesia. This study aims to evaluate the application of AI in identifying and managing anesthesia-related complications, emphasizing its potential to increase the safety and efficiency of perioperative care. Materials and Methods: A systematic review was conducted using PubMed, Scopus and Web of Science to identify studies published between January ۲۰۱۵ and December ۲۰۲۴. Keywords included "Artificial Intelligence," "Anesthesia Complications," "Predictive Modeling," "Machine Learning in Healthcare," and "Perioperative Safety." Studies focusing on AI applications in surgical or anesthesia settings were included, while those addressing non-clinical use cases were excluded. Results: Out of ۷۶ studies initially identified, ۱۵ met the inclusion criteria. Key findings indicated that machine learning models, particularly neural networks and support vector machines, demonstrated high accuracy in predicting complications such as hypotension, hypoxia, and delayed emergence. The integration of real-time data from multi-parameter monitors with AI algorithms improved the detection of early warning signs, enabling timely interventions. Decision-support systems leveraging AI showed promise in recommending optimal drug dosages and ventilator settings, reducing the incidence of adverse events. However, challenges such as data standardization, algorithm transparency, and integration into clinical workflows remain barriers to widespread adoption. Conclusion: The application of AI in anesthesia care has the potential to revolutionize complication management through real-time decision support and predictive analytics. Addressing current challenges requires interdisciplinary collaboration to refine algorithms, standardize data inputs, and ensure ethical deployment. Future research should focus on validating AI systems in diverse clinical settings and exploring their cost-effectiveness to support global implementation.

کلیدواژه ها:

Artificial Intelligence ، Anesthesia Complications ، Predictive Modeling ، Machine Learning in Healthcare ، Perioperative Safety