AI-Driven Detection of Inherited Neurological Disorders Using Genomic and Multi-Omics Data

Advances in Genomics and Multi-Omics Technology have opened new opportunities to understand the molecular basis for genetic forms of neurological disorders. To meet the challenges of scale, heterogeneity and complexity, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as effective tools for integrating and analysing large datasets across different omics. This systematic review summarises current evidence on AI-based methods for detecting, classifying and interpreting inherited forms of neurological disease using multi-omics data. Deep learning and graph-based frameworks consistently outperform traditional statistical methods in variant prioritization, disease prediction and genotype-phenotype association. Integration of multiple omics significantly improves diagnostic accuracy and biological understanding, enabling better disease stratification and earlier detection. Explainable AI techniques are increasingly incorporated to improve clinician interpretability. Critical limitations persist, including small sample sizes for rare disorders, data heterogeneity across platforms, limited external validation, and ethical concerns around data privacy and algorithmic bias. The review highlights federated learning, standardized data harmonization pipelines, and clinically guided model development as pathways toward more robust applications, positioning AI-enabled multi-omics integration as a promising paradigm for precision neurology.