Artificial Intelligence in Stroke Care: Enhancing Diagnostic Accuracy, Personalizing Treatment, and Addressing Implementation Challenges - Yahya Abdul Rehman Shah; Qureshi, Sara Mudassir; Hamza Ahmed Qureshi; Saad Ur Rehman Shah; Shiwlani, Ashish; Ahmad, Ahsan (International Journal of Applied Research and Sustainable Sciences, 2024)
Objective: Stroke remains a leading cause of global disability, and with ageing populations, there is a growing need for advanced medical interventions. This literature review aims to assess how Artificial Intelligence (AI) and Machine Learning (ML) technologies have transformed the diagnosis, treatment, and long-term care of stroke patients. Methods: A comprehensive literature review was conducted using databases such as PubMed, IEEE Xplore, and Scopus, covering articles published from January 2018 to August 2024. The review focused on studies related to the application of AI/ML in stroke diagnosis, treatment, and management, including ethical, technical, and regulatory issues. Results: AI and ML technologies have significantly enhanced stroke diagnosis, primarily through advanced deep learning models that analyze imaging data more accurately and rapidly than traditional methods. These AI-based models have demonstrated high precision in detecting ischemic and hemorrhagic strokes, reducing diagnosis time by up to 50% and markedly improving patient outcomes. Predictive models utilizing big data have consistently surpassed traditional risk assessments in forecasting stroke outcomes and customizing treatments. AI-driven decision-support systems have improved patient selection for thrombolysis and mechanical thrombectomy, optimizing treatment strategies. Conclusion: While AI and ML offer substantial advancements in stroke management, including improved diagnosis, personalized therapy, and prognosis, challenges remain. Issues such as data quality, algorithmic transparency, integration into clinical workflows, algorithmic bias, and patient privacy must be addressed. Further research is needed to overcome these technical, ethical, and regulatory obstacles to fully integrate AI and ML into healthcare systems and enhance stroke management and patient outcomes.