This study, presented by Antel et al., provided a meta-analysis on the use of AI in pediatric anaesthesia, with the goals of characterizing the use of AI and identifying barriers to the successful integration of AI in the pediatric anesthesia field. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed in this study, and the study was registered with the international systematic review registry (PROSPERO). 3313 articles were initially identified. Through a screening process, where two authors analyzed the studies independently, 40 of the studies were deemed relevant to AI use in pediatric anaesthesia and fit the criteria. All of the studies had a mean participant age of less than 18 and were in English or French. Additionally, it’s important to note that linear and logistic regression models were included, as they were identified as a basic form of machine learning. All of the studies identified were then moved to Microsoft Excel for data extraction and organization. Through analysis, the investigators found that AI was used for patient risk factor prediction in 60% of the studies. Closely following, AI was used for physiological variable monitoring in 15% of the studies, and for airway device selection in 7.5% of the studies. Barriers to the use of AI in the field of paediatric anaesthesia were poor provider understanding, a lack of external validation, and limited knowledge of the use of AI in specific surgical procedures.