Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care

Artificial intelligence (AI) has stimulated extensive research in clinical neurology, but relatively few AI systems have meaningfully changed bedside decision-making. This translational gap is not explained solely by inadequate algorithms. More often, AI models in neurology remain outside routine practice because they are trained on unstable labels, validated in narrow or single-center datasets, evaluated primarily by discrimination metrics, disconnected from clinical workflow, and deployed without prospective monitoring, reimbursement frameworks, or accountability. In this review, we propose the NEURAL framework for practice-changing neurological AI: Novel clinical insight, External and prospective validation, Utility over accuracy, Real-time workflow integration, Algorithmic transparency, and Long-term outcome linkage. Using this framework, we examine evidence across acute stroke, epilepsy and electroencephalography (EEG), sleep medicine, neurodegenerative disease, movement disorders, headache, vestibular disorders, neuroimmunology, rehabilitation, and neurocritical care. The most clinically advanced examples come from acute stroke, where imaging-based selection, large-vessel occlusion detection, and automated notification are linked to urgent, pathway-defined interventions. Automated EEG triage, focal cortical dysplasia detection, selected sleep-analysis tools, quantitative biomarker pipelines, and AI-assisted longitudinal monitoring may also become clinically meaningful if tested prospectively in real-world workflows. Many high-performing models for dementia progression, prodromal Parkinson disease, outpatient treatment response, and long-horizon risk prediction remain pre-implementation tools because they do not yet define a validated clinical action. Future neurological AI should therefore be judged less by whether it recognizes complex patterns and more by whether it improves the right decision for the right patient at the right time. A clinically useful AI system must demonstrate not only accuracy, but also actionability, workflow fit, equity, safety, sustainability, and measurable benefit for patients and health systems.