Explainable artificial intelligence in neurology: a holistic exploration of models for diagnosis, progression tracking, and treatment planning

Abstract Neurological disorders, including neurodegenerative diseases, are among the leading causes of disability and mortality worldwide, with profound effects on cognition, sensory function, motor performance, and overall quality of life. Despite decades of research, effective treatment options remain limited because of complex pathophysiological mechanisms, diagnostic uncertainty, and delayed or insufficiently robust detection. Recent advances in Artificial Intelligence (AI), particularly in Machine Learning (ML) and Deep Learning (DL), have enabled the development of powerful computational models for the diagnosis and management of neurological disorders. However, the opaque nature of many of these black-box models remains a major barrier to clinical adoption, underscoring the need for Explainable Artificial Intelligence (XAI) to enhance trust, transparency, and clinical interpretability. In response to this need, this review presents a comprehensive and task-oriented examination of recent XAI applications and frameworks in neurology. Unlike prior surveys that have focused on isolated neurological conditions, limited model categories, or narrow interpretabilit