AI-Driven Stroke Prediction and Outcome Forecasting Using Neuroimaging and Clinical Stream Data

In the world, stroke is one of the top causes of death and permanent disability. The ability to quickly identify patients at risk for stroke is paramount in improving patient outcomes and in decreasing healthcare costs. The clinical experience of the doctor and the reading of the picture is the basis of current diagnosis methods, so there is a time delay in the diagnosis process. With the development and advancement of Artificial Intelligence (AI) in recent years, it has become possible to collect and integrate several health care information sources and offer better predictive power. The paper introduces a conceptual explainable multimodal AI framework that integrates the neuroimaging information and clinical data for early prediction of stroke risk. The framework combines structured clinical data with the deep learning features derived from the CT/MRI scans, leverages an attention mechanism for the fusion, and adds explainable AI techniques for increased transparency and trust with the clinical team. The architecture of the proposed system is different from the conventional black-box models in terms of giving emphasis to interpretability, scalability, and clinical usability. It can assist health care workers with timely diagnosis, developing individual treatment plans and better handling of patients.