Artificial intelligence (AI) holds significant promise for improving pediatric diabetes management, but its clinical adoption hinges on transparency and validity. Despite growing interest in AI applications, systematic evaluations of these critical aspects remain scarce. This systematic review examines the transparency and validity of AI applications in pediatric diabetes, assessing methodological rigor, reporting standards, and clinical readiness. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we searched Scopus, PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, Web of Science, and Embase for studies employing AI in pediatric diabetes. Ten studies met the inclusion criteria after screening 308 records. Data were extracted on AI methodologies, transparency indicators, and validation approaches. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Included studies addressed diverse AI applications, including glucose prediction, hypoglycemia risk assessment, and insulin dosing optimization. Transparency varied widely: 60% of studies disclosed algorithm details, while others omitted critical methodological information. Validation methods ranged from in silico (computer-based) simulations to independent cohorts, but only 30% incorporated external validation. Performance metrics included area under the curve (AUC) and clinical accuracy. Risk of bias was low in 60% of studies, though concerns arose from algorithmic opacity and small validation cohorts. While AI demonstrates potential in pediatric diabetes, inconsistent transparency and insufficient validation limit clinical translation. Future research must prioritize standardized reporting, multicenter validation, and diverse populations to ensure reliability and equity.
Yousif et al. (Wed,) studied this question.