A Comprehensive Systematic Review and Meta-Analysis: Evaluating the Effectiveness and Integration Obstacles of Artificial Intelligence (AI) within Anesthesia Departments.

Abstract Background Artificial intelligence (AI) is a multidisciplinary field focusing on expanding and generating intelligent computer algorithms to carry out simple to more complex tasks traditionally performed using human intelligence. In anesthesia, AI is rapidly becoming a transformative technology. However, its efficacy in anesthesia is still unknown. Therefore, the current study analyzed the efficacy of AI in anesthesia by studying two main applications of AI, i.e., predicting events related to anesthesia and assisting anesthesia-related procedures. Furthermore, this study explored some of the challenges of integrating AI in the anesthesia field. Methods PubMed, Google Scholar, IEEE Xplore, and Web of Science databases were thoroughly searched for articles relevant to the objective of the current study. The Comprehensive Meta-analysis software and STATA 16.0 were used for statistical analyses, while the Newcastle Ottawa Scale was used for quality evaluation. Results Twenty studies satisfying the eligibility criteria were used for review and analysis. A subgroup analysis showed that models incorporating machine learning algorithms were superior in predicting postinduction hypotension (AUROC: 0.93). ANN and SANN models also showed a good discriminatory capacity in predicting postinduction hypotension (AUROC: 0.82 and 0.80, respectively). Similarly, the subgroup analysis showed that ANN and GBM models had a good discriminatory capacity when predicting hypoxemia (AUROC: 0.8 and 0.81, respectively). Furthermore, SVM, ANN, and fuzzy logic models had a relatively good differentiation ability in predicting postoperative nausea and vomiting (AUROC: 0.93, 0.77, and 0.72, respectively). On the other hand, the subgroup analysis showed that robotically-assisted tracheal intubations were highly successful in both mannikins and humans (success rate: 98% and 92%, respectively). Similarly, robotically-assisted ultrasound-guided nerve blocks were highly successful in mannikins and humans (Success rate: 96% for humans and mannikins, respectively). Conclusion The current study suggests that AI is useful in predicting anesthesia-related events and automating procedures such as tracheal intubation and ultrasound-guided nerve block. However, there are multiple barriers hindering the integration of AI in anesthesia that need to be addressed.