Authors : Sneha Ramakrishnan; Brindha Arun Kumar; Pavithra Sundaram

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/yvb3aa69

Scribd : https://tinyurl.com/3vhdvmrv

DOI : https://doi.org/10.38124/ijisrt/26feb1317

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : 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

Keywords : Artificial Intelligence, Specific Neurological Disorders, Clinical Diagnostic Application.

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