Artificial intelligence and enabled technologies for neurological disorders: a narrative review and critical analysis

Neurological disorders, encompassing a diverse array of conditions affecting the brain, spinal cord, and peripheral nerves, have emerged as significant contributors to global disability and mortality. Recent studies indicate a marked increase in the burden of these disorders, necessitating innovative, scalable solutions for diagnosis and management. This review explores the translational potential of advanced artificial intelligence (AI) and machine learning (ML) technologies in the assessment and treatment of neurological and neurodevelopmental disorders, including epilepsy, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder, and neurodegenerative diseases like Alzheimer’s, Parkinson’s, Huntington’s, stroke, multiple sclerosis, etc. By integrating AI-driven tools—ranging from diagnostic algorithms to wearable technologies—this research highlights their capabilities in early detection, personalized treatment, and improved patient outcomes. The AI-powered technologies aid clinicians in understanding the complex nonlinear neurological features of an individual for better diagnosis of the neurological disorders. The review covers the recent developments in the learning algorithms and feature selection methodologies for better accuracy in disease prediction. The application of deep learning algorithms for brain image-based data analysis, feature extraction, and pattern learning for developing a diagnostic tool are highlighted. We have explored the integration of these AI models in wearables, ChatGPT, and gaming-based apps for real-time monitoring and patient-specific therapies. The paper discusses real-world applications, ethical considerations, and challenges associated with AI implementation in healthcare while providing a comprehensive overview of current methodologies and statistical insights. We have discussed the importance of data volume along with variety for reducing the misdiagnosis/overdiagnosis due to biasness/variance, which will improve the diagnostic precision with an increase in the trust of users. The review serves as a resource for researchers, clinicians, and policymakers aiming to advance global brain health initiatives and optimize neurological care. Through systematic literature analysis, the findings highlight the critical role of AI in reshaping the landscape of neurological disorder management, emphasizing the need for continued research and development in this rapidly evolving field.