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Related Experiment Video

Updated: Jun 9, 2025

Author Spotlight: Rehabilitation of Stroke Patients With a Digital Occupational Training System
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AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.

Isuru Senadheera1,2, Prasad Hettiarachchi1,2, Brendon Haslam2,3

  • 1Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Melbourne, VIC 3086, Australia.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances stroke rehabilitation by improving care quality and precision. This review of 704 studies shows AI applications progressing from basic concepts to advanced techniques for motor function, robotic assistance, and predictive modeling.

Keywords:
artificial intelligencemachine learningneurorehabilitationrecoverystroke rehabilitationtherapy

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Area of Science:

  • Neuroscience and Rehabilitation Medicine

Background:

  • Stroke is a primary cause of adult disability globally.
  • Advancements in AI and sensor technology offer potential to improve stroke recovery and rehabilitation outcomes.
  • Existing research on AI applications in adult stroke rehabilitation requires comprehensive characterization.

Purpose of the Study:

  • To identify and categorize AI applications in adult stroke recovery and rehabilitation.
  • To analyze the progression of AI technologies in this field over time.
  • To summarize key findings from a large body of relevant research.

Main Methods:

  • A scoping review of peer-reviewed articles from electronic databases up to January 2024.
  • AI-enhanced multi-method, data-driven techniques, including theme and topic clustering, were used for data extraction.
  • Analysis of 704 studies to identify common themes and temporal patterns.

Main Results:

  • Four major themes emerged: impairment, assisted intervention, prediction and imaging, and neuroscience.
  • AI applications evolved from initial concepts to sophisticated supervised learning, artificial neural networks (ANN), and natural language processing (NLP).
  • Specific focus on upper limb rehabilitation utilizing machine learning (ML), deep learning, and sensors like inertial measurement units (IMU) for movement analysis.

Conclusions:

  • AI demonstrates significant potential to personalize therapeutic delivery in stroke rehabilitation.
  • Optimized rehabilitation strategies driven by AI can lead to improved patient outcomes.
  • AI facilitates sustained recovery, bridging the gap between rehabilitation and real-world functional reintegration.