Pediatric neurodevelopmental disorders (NDDs) encompass a highly heterogeneous group of conditions characterized by complex interactions among genetic, molecular, developmental, and environmental factors. Growing evidence increasingly supports an important role for neuroglial dysfunction, including disturbances in astrocytic, microglial, and oligodendroglial biology, in the pathophysiology of disorders such as autism spectrum disorder, global developmental delay, intellectual disability, and rare neurogenetic syndromes. At the same time, artificial intelligence (AI)-assisted analytical approaches are becoming increasingly relevant in pediatric diagnostics through integration of multidimensional datasets, including clinical phenotypes, neuroimaging, genomic sequencing, and molecular biomarkers. This review examines the evolving intersection of neuroglial biology and AI-based analytical methods in pediatric NDDs. Current understanding of neuroglial mechanisms underlying disease vulnerability and developmental heterogeneity is discussed alongside emerging applications of machine learning, deep phenotyping platforms, radiogenomics, and large language models in diagnostic interpretation and clinical decision support. Important translational and ethical challenges, including algorithmic bias, interpretability limitations, data governance, and disparities in data accessibility, are also considered. Overall, integration of neuroglial research with AI-assisted analytical frameworks may contribute to more biologically informed interpretation of pediatric neurodevelopmental disorders and support ongoing development of increasingly individualized diagnostic approaches.
Ilić et al. (Fri,) studied this question.