The role of artificial intelligence in post-stroke rehabilitation

Abstract

Stroke remains one of the leading causes of disability and mortality worldwide. It results from impaired cerebral blood supply and leads to pronounced neurological deficits that negatively affect patients’ quality of life. Artificial intelligence (AI) technologies, including machine learning, convolutional neural networks, and brain–computer interfaces, enable reproduction of mechanisms underlying natural neural recovery. AI-based rehabilitation systems are capable of analyzing individual patient characteristics and adapting therapeutic strategies in real time, which is analogous to the processes of biological neuroplasticity in the brain. The scientific data search was conducted using international and Russian electronic databases, including PubMed, Google Scholar, and eLibrary.ru. Search queries were formulated using keywords and phrases reflecting the key aspects of post-stroke rehabilitation with AI technologies: искусственный интеллект (artificial intelligence), реабилитация после инсульта (post stroke rehabilitation), инсульт (stroke), машинное обучение (machine learning), нейрореабилитация (neurorehabilitation), artificial intelligence, stroke rehabilitation, stroke, machine learning, neurorehabilitation, and telemedicine. The integration of advanced neuroimaging techniques enhanced by AI algorithms has contributed to the modernization of diagnostic approaches, particularly through the application of deep learning methods for the analysis of computed tomography and magnetic resonance imaging data, as well as for the automated identification of the ischemic penumbra. Prognostic modeling based on machine learning algorithms enables the prediction of functional recovery outcomes, the risk of complications, and the degree of disability. The implementation of AI in post-stroke care raises a number of ethical, legal, and regulatory challenges that must be addressed to ensure its effective use. AI is a tool capable of exerting a positive impact on the rehabilitation of patients after stroke, and its integration into the treatment process offers broad prospects; however, it is associated with a number of challenges that must be addressed to fully realize its potential. Despite such issues as data heterogeneity and the need for interdisciplinary collaboration, advances in artificial intelligence technologies may contribute to improved outcomes of post-stroke rehabilitation.

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