Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury

Abstract

Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management. The application of AI, specifically machine learning, has revolutionized the diagnosis, treatment, prognosis, and rehabilitation of patients with SCI. By leveraging large datasets and identifying complex patterns, AI contributes to improved diagnostic accuracy, optimizes surgical procedures, and enables the personalization of therapeutic interventions. AI-driven prognostic models provide accurate predictions of recovery, facilitating improved planning and resource allocation. Additionally, AI-powered rehabilitation systems, including robotic devices and brain-computer interfaces, increase the effectiveness and accessibility of therapy. However, realizing the full potential of AI in SCI care requires ongoing research, interdisciplinary collaboration, and the development of comprehensive datasets. As AI continues to evolve, it is expected to play an increasingly vital role in enhancing the outcomes of patients with SCI.

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