REHABILITATION THROUGH NEUROPLASTICITY AND ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.55640/Keywords:
Neuroplasticity, artificial intelligence, rehabilitation, machine learning, brain–computer interface, virtual reality, robotics, motor recovery, adaptive therapy, neural modulationAbstract
Neuroplasticity—the brain’s intrinsic ability to reorganize its structure and function in response to injury, learning, and experience—forms the fundamental basis of modern neurological rehabilitation. Recent advances in artificial intelligence (AI) have introduced new possibilities to quantify, stimulate, and optimize neuroplastic processes through personalized, adaptive therapeutic interventions. This article explores the conceptual, empirical, and clinical intersections between neuroplasticity and AI-assisted rehabilitation, highlighting how machine learning, robotics, virtual reality (VR), wearable sensors, and intelligent neurofeedback systems can accelerate functional recovery after neurological disorders such as stroke, traumatic brain injury, and neurodegenerative diseases.
AI technologies enable continuous monitoring of patients’ physiological, behavioral, and cognitive responses, allowing for real-time analysis and customization of rehabilitation protocols. These capabilities address traditional limitations of rehabilitation, including variability in patient engagement, therapist workload, and the difficulty of accurately assessing subtle neurobehavioral changes. By leveraging large datasets and predictive algorithms, AI systems can recommend individualized training intensities, detect plateaus early, and identify patterns of neural adaptation that might otherwise go unnoticed.
This paper reviews key literature on neuroplasticity and AI-supported rehabilitation, outlines current methodologies used to integrate intelligent technologies with neurorehabilitation, and presents typical results observed across clinical trials—particularly improvements in motor recovery, cognitive performance, and functional independence. Despite promising outcomes, ethical and technical challenges remain, including data privacy, algorithmic transparency, biased datasets, and the need for long-term, large-scale clinical validation.
Overall, combining neuroplasticity-based therapeutic frameworks with AI-driven systems offers a powerful approach for enhancing recovery trajectories across neurological conditions. The paper concludes that AI-assisted rehabilitation is poised to fundamentally reshape traditional therapy models, making them more precise, adaptive, and accessible. As interdisciplinary research accelerates, this hybrid paradigm has the potential to become a central pillar of next-generation neurorehabilitation.
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