Emergence of Artificial Intelligence in Multiple Domains of Neurology: A Review

Abstract

Artificial Intelligence (AI) has emerged as a transformative force across multiple domains of neurology, reshaping traditional paradigms in diagnosis, therapeutics, research, and patient care. This review explores the breadth and depth of AI applications within neurological sciences, emphasizing clinical innovations, translational potential, and ethical implications. In diagnostics, AI has significantly enhanced neuroimaging interpretation, disease classification, and multimodal data integration—enabling earlier and more accurate detection of conditions such as Alzheimer’s disease, stroke, and Parkinson’s disease. Therapeutically, AI-driven adaptive deep brain stimulation (aDBS) and closed-loop neuromodulation systems are setting new standards for personalized treatment, offering dynamic feedback-based modulation of neural activity. The review also addresses AI’s critical role in drug discovery and development, where deep learning and generative models have accelerated target identification, virtual screening, and molecule generation. Assistive technologies, particularly brain–computer interfaces (BCIs), have advanced rapidly, enabling communication and mobility for individuals with profound neurological disabilities. These innovations include speech prostheses, silent-speech decoding, and robotic sensory feedback systems. Despite these advancements, the integration of AI in neurology faces challenges including data bias, explainability, and privacy concerns. The future of AI-driven neurology will depend on interdisciplinary collaboration, ethical governance, and the continued development of interpretable, equitable, and human-centered technologies. This review synthesizes current progress while mapping trajectories for future research and clinical implementation.