Artificial intelligence (AI)-powered prognostic tools and clinical decision support systems can predict the outcome of certain diseases based on a multitude of patient data at high speed, facilitating decisions by healthcare professionals. In acute ischemic stroke, the overall treatment effect and population-wide outcome benefit of treatments such as IV thrombolysis and mechanical thrombectomy are well established. However, in individual patients it is difficult to predict the prognosis in the acute phase of stroke: some patients are candidates for these treatments, but may have poor clinical outcomes (no improvement of stroke or even worsening) Our aim in this study is to validate an artificial intelligence (AI)-based prognostic tool to provide accurate real-time outcome prediction in patients with acute ischemic stroke. During the study, all patients admitted to the emergency room with an acute ischemic stroke will receive the usual treatment for acute stroke in accordance with the stroke neurologists in charge. A shadow clinical researcher, without interaction with treating physicians, will collect the data required by the AI model in vivo. These data will be obtained by filling in clinical data through an App on a hospital mobile/tablet, and by a connection with your electronic medical record. The AI models will estimate the outcome of the acute stroke patient, and this prediction will be compared with the real outcome of the patient after 3 months of follow-up.