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

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 interpretability issues, this study provides a unified and clinically grounded taxonomy across five major application domains: early diagnosis, disease classification, progression tracking, risk prediction, and treatment planning. The review systematically categorizes existing interpretability techniques and examines their usage trends, evaluation strategies, ethical considerations, clinical relevance, and implementation challenges. It also highlights critical gaps in the current literature, including the absence of explicit security protocols in 96.3% of the reviewed studies. By bridging current research with practical clinical needs, this review aims to provide researchers and clinicians with a clear and structured reference for the development and deployment of explainable models in real-world neurological settings. In doing so, it not only clarifies the current strengths and limitations of XAI in neurology but also identifies important directions for future innovation and responsible clinical integration.