Abstract / Summary
Abstract Acute ischemic stroke is a major cause of disability and death worldwide, and the key to its treatment lies in seizing every minute. In recent years, artificial intelligence (AI) technologies represented by deep learning and machine learning have shown potential in improving the efficiency and accuracy of diagnosing and treating this disease. This paper aims to systematically review the application progress of AI throughout the whole chain of diagnosing and treating acute ischemic stroke, including early identification and early—warning, precise imaging evaluation (such as distinguishing the infarct core from the ischemic penumbra), optimization of treatment decisions, as well as prognosis and rehabilitation management. This paper analyzes the applications and clinical values of current AI models, and deeply explores the problems it faces in terms of data quality, model generalization, clinical interpretability, and ethics and regulations, providing a theoretical basis and guiding direction for promoting acute ischemic stroke into an era of intelligent and personalized diagnosis and treatment.