Artificial Intelligence (AI) is transforming healthcare, particularly in stroke diagnosis, management, and rehabilitation. Stroke, a leading cause of mortality and disability, requires rapid intervention for improved outcomes. AI’s ability to process vast amounts of data and detect patterns has revolutionized stroke care. Machine learning (ML) algorithms are now used to analyze Computerized Tomography (CT) scans and Magnetic Resonance Imaging (MRI) scans for early stroke detection, helping to determine the type, location, and severity of brain damage. AI is also integrated into predictive modeling systems, assessing stroke risk factors for better prevention management.
AI enhances clinical decision-making by providing personalized treatment recommendations, such as optimal therapies like thrombolysis or thrombectomy. Additionally, AI supports post-stroke rehabilitation by enabling adaptive learning in robotic-assisted therapy and virtual reality, offering personalized recovery plans. The rapid growth of AI in stroke care has the potential to improve patient outcomes, reduce diagnosis time, and provide continuous monitoring.
However, challenges like data privacy, model interpretability, and regulatory approval remain significant barriers. Future research should focus on enhancing AI system accuracy, ensuring generalization across diverse populations, and improving the integration of AI tools into clinical workflows to optimize stroke management.