Abstract
Pediatric intensive care units (PICUs) could transform due to artificial intelligence (AI), which could improve patient outcomes, increase diagnostic accuracy, and streamline repetitive procedures. The goal of this systematic review was to outline how AI can be used to enhance any health outcomes in pediatric intensive care. We searched four databases (PubMed, Scopus, Web of Science, and IEEE Xplore) for relevant studies using a predefined systematic search. We found 267 studies in these four databases. The studies were first screened to remove the duplicates and then screened by titles to remove irrelevant studies. The studies were further screened based on inclusion and exclusion criteria, in which 32 studies were found suitable for inclusion in this study. The studies were assessed for risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST) tool. After AI was implemented, almost 22% (n = 7) of studies showed an immediate effect and enhanced health outcomes. A small number of studies involved AI implementation in actual PICUs, while the majority focused on experimental testing. AI models outperformed conventional clinical modalities among the remaining 78% (n = 25) and might have indirectly impacted patient outcomes. Significant variation in metrics and standardization was found when health outcomes were quantitatively assessed using statistical measures, including specificity (38%; n = 12) and area under the receiver operating characteristic curve (AUROC) (56%; n = 18). There are not sufficient studies showing that AI has significantly enhanced pediatric critical care patients' health outcomes. To evaluate AI's impact, more prospective, experimental research is required, utilizing verified outcome measures, defined metrics, and established application frameworks.