Artificial Intelligence applications in magnetic resonance imaging for automated stroke detection segmentation and outcome prediction

Stroke is a leading cause of death and long-term disability worldwide, making a timely and accurate diagnosis crucial for effective treatment. MRI plays a key role in stroke evaluation; however, its manual interpretation is time-consuming, necessitates expertise, and can vary between observers. Recent advancements in artificial intelligence promise to enhance stroke imaging by offering quicker and more reliable diagnostic support. This narrative review combines material published between 2015 and 2025, sourced from PubMed, IEEE Xplore, Science Direct, and other important sources. The focus was on publications describing AI applications in MRI-based stroke diagnosis, focusing on DWI, FLAIR, and perfusion MRI studies, lesion identification, tissue segmentation, outcome prediction, and clinical workflow integration. AI has shown promising results in improving interpretations through automated lesion segmentation, stroke subtype categorization, and prognosis prediction. CNN-based models perform better in lesion detection, although classic machine learning techniques, such as support vector machines, are more resilient across smaller or diverse datasets. Commercial systems such as Rapid AI and Brainomix demonstrate translation in action; however, dataset diversity and model explainability remain challenges, alongside persistent issues regarding algorithm transparency and standardization. Artificial intelligence represents a paradigm change in MRI-based stroke examination, with and opportunity to increase diagnosis accuracy, speed, and tailored therapy. To gain widespread clinical acceptance, future research must focus on explainable AI, large-scale multicentre datasets, and effortless incorporation into stroke therapy pathways. These approaches will ensure that AI supports radiologists and neurologists, resulting in more reasonable and rapid stroke care.