Artificial Intelligence in Major Neurological Disorders: A Comprehensive Review

Background Artificial Intelligence (AI) has emerged as a promising computational approach in clinical neurology for improving diagnostic accuracy, disease monitoring, and prognostic assessment. Neurological disorders present significant challenges due to clinical variability and limitations in conventional diagnostic techniques.  Methods A review was done using PubMed, Scopus and other related databases focusing on AI-based techniques including Machine Learning, Deep Learning, Artificial Neural Networks, Natural Language Processing, Reinforcement Learning, and Transfer Learning in neurological research and clinical datasets such as neuroimaging and electrophysiological recordings.  Results AI-driven models demonstrated improved accuracy in seizure detection, stroke lesion segmentation, neurodegenerative biomarker identification, lesion quantification in demyelinating disorders, and predictive modelling of disease progression across multiple neurological conditions.  Conclusion AI integration in neurology offers significant potential for enhancing clinical decision-making and personalised care; however, challenges such as data heterogeneity, algorithm transparency, and validation remain critical for future clinical implementation