Investigating the Neuroplasticity in the Era of Artificial Intelligence

• Published in: Iranian rehabilitation journal Vol. 22; no. 3; pp. 357 - 358
• Main Author: Chawla, Nistara S.
• Format: Journal Article
• Language: English
• Published: Negah Institute for Scientific Communication 01.09.2024
• Subjects: chronicity; motor recovery; neurological rehabilitation; neuroplasticity; rehabilitation; stroke
• ISSN: 1735-3602; 1735-3610; 1735-3610
• DOI: 10.32598/irj.22.3.2062.1

The relationship between rehabilitation and motor recovery after stroke, as well as the impact of time on recovery, remains unclear. The widely accepted theory of “critical window for recovery” suggests that the most substantial recovery occurs within 3 to 6 months post-stroke, leading to the cessation of physical therapy during the chronic stage. However, recent studies have shown that neuroplasticity and treatment responsiveness extend beyond this critical window. Patients in chronic and late chronic stages still exhibit enhanced sensitivity to therapy. Artificial intelligence (AI) plays a significant role in predicting and understanding neuroplasticity by analyzing complex data through advanced computational methods. AI algorithms identify patterns, develop predictive models, and uncover hidden relationships, shedding light on the dynamics of neuro-plastic changes. Personalized rehabilitation approaches can be optimized through AI by tailoring treatment plans based on individual characteristics. AI’s potential in predicting and understanding neuroplasticity can advance our knowledge of brain plasticity mechanisms and improve personalized treatment strategies for stroke and related conditions.