RESUMO
From a clinical viewpoint, there are enormous obstacles to early detection and diagnosis as well as treatment interventions for multiple sclerosis (MS). With the growing application of methods based on artificial intelligence (AI) to medical problems, there might be an opportunity for MS specialists and their patients. However, to develop accurate AI models, researchers must first examine large quantities of patient data (demographics, genetics-based information, clinical and radiological presentation) to identify the characteristics that distinguish illness from health. These are seen as promising approaches toward improved disease diagnosis, treatment individualization, and prognosis prediction. When applied to imaging data, the application of AI subdomains, such as machine learning (ML), deep learning (DL), and neural networks, have proven their value in healthcare. The application of AI in MS management marks a milestone within the healthcare sector. Now, as research and applications of AI continue to advance steadily, breakthroughs are coming at an ever-accelerating pace. As MS continues to develop, the integration of AI is more and more necessary for continuing progress in diagnosis and treatment as well as patient outcomes. In the field of multiple sclerosis, these algorithms have been used for many purposes, such as disease monitoring and therapy.