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

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Technology

Background:

• Neuroplasticity is key to brain rehabilitation after neurological insults.
• Accurate brain visualization and connectivity mapping are crucial research challenges.
• Machine learning (ML) excels at analyzing complex neuroimaging data.

Purpose of the Study:

• To review neuroplasticity concepts and neurorehabilitation strategies.
• To explore ML applications in neuroimaging for early mental health detection.
• To discuss the integration of AI in healthcare systems for improved rehabilitation.

Main Methods:

• Literature review of neuroplasticity, neurorehabilitation, and ML in neuroscience.
• Analysis of AI's role in identifying patterns in multidimensional neuroimaging data.
• Discussion of ML-driven predictive models for brain disorder detection.

Main Results:

• ML models can discern intricate patterns in neuroimaging, aiding in data-driven classifications.
• AI applications show potential for early detection of mental health disorders.
• The review highlights the use of large datasets for health system strengthening in rehabilitation.

Conclusions:

• Neuroplasticity and ML are transformative in neurorehabilitation and early detection.
• AI-powered predictive models offer a future vision for mental health screening.
• Integrating advanced technologies can significantly strengthen global health systems for neurological care.