Rehabilitation, neuroplasticity, and machine learning: Approaching artificial intelligence for equitable health systems

Abstract

Recently, technology has evolved significantly in the rehabilitation process for neurological disorders and neurodegenerative diseases, focusing on neuroplasticity. Neuroplasticity, as a fundamental base of brain rehabilitation, is the change in the output of neural circuits in response to the stimulus of the input activity. The physiological and anatomical changes that occur following a brain insult compel the brain to rewire for the sake of reacquiring lost functions or behaviors in a driven form of neural plasticity called neurorehabilitation. One of the main challenges in neuroscience research is the accurate visualization of the brain structure and brain connectivity related to behaviors and memory. Building machine learning predictive models for brain disorders associated with neural circuits and brain plasticity defects is a promising early detection tool for some mental health disorders. Machine learning is becoming more impactful in neuroimaging because it can discern intricate patterns in multidimensional and multimodal data. This technology may then be utilized to generate data-driven classifications and predictions for specific patients. In this review, we discuss main ideologies of neuroplasticity concepts as well as the concept of neuroplasticity induction and neural circuit defects associated with neurodegenerative disorders, in addition to discussing some applications of artificial intelligence of machine learning (ML) models for the future vision of early mental health detection in low- and middle-income countries. This review considers how the health system stratifies rehabilitation and utilizes large data sets for strengthening health systems.