EARLY STROKE DETECTION ALGORITHMS USING AI

Abstract

Stroke remains one of the leading causes of morbidity and mortality worldwide, making early detection and timely intervention critical for improving patient outcomes. Artificial intelligence (AI) has emerged as a promising tool to assist in the rapid identification of stroke events through the analysis of medical imaging, physiological signals, and patient data. This paper provides an overview of AI-based algorithms for early stroke detection, including machine learning models, deep learning approaches, and hybrid techniques. The study discusses the accuracy, sensitivity, and clinical applicability of these models, highlighting their potential to reduce diagnostic delays and support emergency decision-making. Challenges such as data heterogeneity, real-time processing requirements, and integration into clinical workflows are also addressed. By examining current trends and methodologies, this paper emphasizes the transformative potential of AI in enhancing stroke diagnosis and improving patient care.