AI-Guided Systems Neurogenomics in Neurodevelopmental Disorders

Abstract

Despite substantial advances in genomic testing, many individuals with neurodevelopmental disorders remain without a molecular diagnosis, while others receive a genetic diagnosis that does not fully explain phenotypic variability, developmental trajectory or tissue-specific consequences. Artificial intelligence (AI)-assisted methods are increasingly used for phenotyping, variant prioritisation, splice prediction, protein modelling, DNA methylation episignature classification and multi-omic analysis. However, these approaches differ substantially in evidentiary status and are often applied as separate prediction tasks rather than as components of an explicit mechanistic model. In this targeted narrative review, focused primarily on rare and genetically enriched neurodevelopmental disorders, we examine how AI-assisted methods may contribute to systems-level interpretation while remaining anchored to established molecular diagnosis and variant-classification frameworks. We propose a hypothesis-generating load-capacity framework comprising regulatory load, network capacity, developmental buffering and regulatory network instability. These are treated as operationalisable but currently unvalidated constructs. Regulatory instability is distinguished from stable disease-associated dysregulation, and threshold-like behaviour is presented as an empirical possibility rather than an assumed property of neurodevelopmental disease. We formulate five falsifiable predictions, consider how genomic, transcriptomic, epigenomic, single-cell, spatial, imaging, neurophysiological and longitudinal phenotypic evidence can provide complementary mechanistic constraints, and outline an auditable workflow following nondiagnostic genomic testing. We distinguish clinically implemented approaches from translational, emerging and conceptual applications, and emphasise calibration, evidence traceability, domain validity, prospective validation and appropriate abstention. Finally, we describe the Instability Twin as a prospective architecture composed of independently testable patient-specific sub-models rather than an existing clinical platform. The central proposition is that systems neurogenomics should be evaluated by whether mechanistically constrained integration provides reproducible information beyond established gene-level and simpler multimodal approaches.