Artificial Intelligence in Neurology

The purpose of this paper is to provide a concise analysis of the role of artificial intelligence (AI) in the diagnosis of two major neurological disorders: stroke and multiple sclerosis. In the case of stroke, AI plays a pivotal role in enabling rapid diagnosis, which can significantly impact patient outcomes. Convolutional neural networks (CNNs) are employed to detect large vessel occlusions, calculate the ASPECT score, and assess prognosis. Other AI models contribute by enhancing image quality, reducing both patient exposure time and radiation dose. In multiple sclerosis, one of the main purposes of AI is the analysis of various serum or cerebrospinal fluid biomarkers to help differentiate it from other neurological diseases. Additionally, in the imaging field, AI allows the establishment of correlations between specific lesion patterns and clinical outcomes, including disease progression and response to treatment.