Enhancing stroke diagnosis and detection through Artificial Intelligence

Stroke remains one of the most significant health concerns in the world that not only results in deaths but also in disabilities and the earlier a patient is diagnosed and treated, the better are the outcomes. Machine learning (ML) and deep learning (DL) are the components of Artificial Intelligence (AI) that have not yet reached their full potential in enhancing the diagnosis of the stroke because of gradually emerging medical applications. In the review, the functioning of AI technologies in stroke care was investigated with the approach to medical imaging methods as well as clinical decision support systems/symptom recognition tools and predictive models as concerns electronic health records (EHR). AI-enhanced medical imaging instruments have a high rate of ischemic and hemorrhagic stroke recognition, as well as the large vessel occlusion (and the volume of infarct core and penumbra). The same is true of medical imaging tools that can match the capacity of expert radiologists. The mobile health applications along with wearable devices are associated with real-time symptom monitoring that ensures early health intervention especially to patients who reside in isolate or underprivileged settings. The advantages of fastness, accuracy, and distant accessibility are continuously undermined by issues of bias in algorithms, along with the data quality, and also clinical integration and regulatory clearance procedures. AI holds significant promise in changing how stroke is diagnosed and treated but there is still a long way to get there and that will entail an ethical application and a powerful validation and that includes working jointly with practitioners and researchers and policymakers on behalf of an evenhanded and successful outcome.