Artificial Intelligence in Stroke Patient Care A Systematic Review

Stroke remains a leading cause of global mortality and disability, often due to a failure to recognize early symptoms and inconsistent post-stroke care. The integration of Artificial Intelligence (AI) into healthcare presents a transformative opportunity to address these challenges. This study aims to systematically review and synthesize the current applications of AI in the comprehensive management of stroke patients, from early prevention to post-stroke rehabilitation. The focus is on the utility of various AI technologies including machine learning, deep learning, natural language processing, and computer vision in stroke detection, diagnosis, treatment, and medical record management. A systematic literature review was conducted, analyzing 16 peer-reviewed articles from journals and conference proceedings published between 2016 and 2025. The findings demonstrate that AI is extensively applied for predicting stroke mortality, functioning as virtual medical assistants, enhancing the analysis of brain CT scans, and enabling intelligent speech technology for medical documentation. Platforms such as Viz.ai and RAPID.AI are becoming integral to clinical practice. The review also identifies a strong global research landscape, with significant contributions from Europe and North America. This synthesis confirms that AI provides significant benefits to stroke care by improving diagnostic speed and accuracy, facilitating personalized rehabilitation, and optimizing clinical workflows. The results lay the groundwork for future research, including the development of novel, non-invasive diagnostic tools such as facial image-based stroke detection systems, ultimately pointing toward a more proactive and efficient healthcare paradigm.