Next-Generation AI in Neuro-development Multi-Omics Applications from Diagnosis to Care

Abstract

Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), intellectual disability (ID), and rare genetic syndromes, affect millions of children worldwide and impose a significant global health burden. Current diagnosis relies primarily on behavioral assessments, which are subjective, delayed, and poorly suited to capture comorbidities. Biological testing remains limited, leading to diagnostic delays of years in ASD, ADHD, and rare syndromes. Advances in multi-omics like genomics, transcriptomics, proteomics, metabolomics, and epigenomics, together with artificial intelligence (AI) provide a transformative path toward precision medicine in NDDs. Genomic studies highlight the role of copy number variants and polygenic risk scores in risk stratification, while transcriptomic and proteomic analyses reveal synaptic and neuroinflammatory pathways relevant to pathogenesis. Metabolomic profiling of biofluids identifies mitochondrial and microbiome-linked biomarkers, and epigenomics offers an environment-responsive regulatory layer. AI enables integration of these high-dimensional datasets, overcoming the “curse of dimensionality” through deep embedding, graph learning, and multimodal fusion. Case studies demonstrate promising accuracies in early prediction of ASD and ADHD from placental transcriptomics, DNA methylation, and newborn metabolomics, with reported AUCs approaching 1.00. Beyond diagnosis, AI-driven multi-omics supports stratified interventions, from metabolic modulation to pathway-specific pharmacology and neuromodulation, while adaptive monitoring systems linking omics to electronic health records and wearable biosensors enable continuous, individualized care. However, small cohorts, limited replication, high costs, and ethical issues around privacy, equity, and algorithmic bias remain critical barriers. Future progress is contingent on the independent validation of existing models, a shift toward explainable AI (XAI) to elucidate biological mechanisms, and the adoption of privacy-preserving federated learning platforms to enhance data diversity and model robustness. Future directions demand longitudinal biobanking, federated learning, XAI frameworks, and cross-disciplinary collaboration to ensure robust translation. Integrating AI with multi-omics holds unprecedented potential to reshape neurodevelopmental care from diagnosis to lifelong management.