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Collaborative AI for precision neurorehabilitation: a roadmap.

Sook-Lei Liew1, R James Cotton2,3, Etienne Burdet4

  • 1Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Department of Neurology, Chan Division of Occupational Science and Occupational Therapy, Department of Biomedical Engineering, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA. sliew@chan.usc.edu.

Journal of Neuroengineering and Rehabilitation
|November 28, 2025
PubMed
Summary
This summary is machine-generated.

Collaborative artificial intelligence (AI) can enhance precision rehabilitation by integrating complex data for personalized patient care. This approach merges AI

Keywords:
Artificial intelligenceBayesian modelingCollaborative AIDigital twinLarge language modelsNeurorehabilitationPrecision medicineReinforcement learning

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Area of Science:

  • Rehabilitation Medicine
  • Artificial Intelligence in Healthcare
  • Data Science

Background:

  • Precision rehabilitation aims to personalize treatments for improved efficacy and efficiency.
  • Artificial intelligence (AI) offers potential but requires tailored approaches for rehabilitation's complexity.
  • Existing AI models in healthcare, like precision oncology, differ from rehabilitation's dynamic nature.

Purpose of the Study:

  • To explore how collaborative AI can advance precision rehabilitation.
  • To outline a roadmap for implementing collaborative AI in rehabilitation.
  • To discuss challenges, existing frameworks, and future directions for AI in rehabilitation.

Main Methods:

  • Reviewing the current landscape of precision rehabilitation.
  • Proposing a roadmap for collaborative AI systems in rehabilitation, identifying key challenges.
  • Examining existing precision rehabilitation frameworks and their common elements.
  • Describing current AI applications in specific rehabilitation scenarios.
  • Discussing data requirements and ethical considerations for AI-based rehabilitation.

Main Results:

  • Collaborative AI, where humans and AI work together, can distill complex data for nuanced clinical decision-making.
  • Four key challenges and four common elements across existing precision rehabilitation frameworks were identified.
  • AI has been applied to specific rehabilitation aspects, with potential for integration into larger models.
  • Large datasets and ethical considerations are crucial for developing accurate AI-based precision rehabilitation.

Conclusions:

  • Collaborative AI promises to revolutionize rehabilitation by creating individualized digital profiles for in silico intervention simulation.
  • Merging AI-driven personalized strategies with clinical expertise can optimize patient care and outcomes.
  • AI-based precision rehabilitation requires careful development, addressing data needs and ethical implications.