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Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach

Author

Listed:
  • Mashael Alsobhi

    (Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia)

  • Harpreet Singh Sachdev

    (Department of Neurology, All India Institute of Medical Sciences, New Delhi 110029, India)

  • Mohamed Faisal Chevidikunnan

    (Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia)

  • Reem Basuodan

    (Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Dhanesh Kumar K U

    (Nitte Institute of Physiotherapy, Nitte University, Deralaktte, Mangalore 575022, India)

  • Fayaz Khan

    (Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia)

Abstract

Artificial intelligence (AI) has been used in physical therapy diagnosis and management for various impairments. Physical therapists (PTs) need to be able to utilize the latest innovative treatment techniques to improve the quality of care. The study aimed to describe PTs’ views on AI and investigate multiple factors as indicators of AI knowledge, attitude, and adoption among PTs. Moreover, the study aimed to identify the barriers to using AI in rehabilitation. Two hundred and thirty-six PTs participated voluntarily in the study. A concurrent mixed-method design was used to document PTs’ opinions regarding AI deployment in rehabilitation. A self-administered survey consisting of several aspects, including demographic, knowledge, uses, advantages, impacts, and barriers limiting AI utilization in rehabilitation, was used. A total of 63.3% of PTs reported that they had not experienced any kind of AI applications at work. The major factors predicting a higher level of AI knowledge among PTs were being a non-academic worker (OR = 1.77 [95% CI; 1.01 to 3.12], p = 0.04), being a senior PT (OR = 2.44, [95%CI: 1.40 to 4.22], p = 0.002), and having a Master/Doctorate degree (OR = 1.97, [95%CI: 1.11 to 3.50], p = 0.02). However, the cost and resources of AI were the major reported barriers to adopting AI-based technologies. The study highlighted a remarkable dearth of AI knowledge among PTs. AI and advanced knowledge in technology need to be urgently transferred to PTs.

Suggested Citation

  • Mashael Alsobhi & Harpreet Singh Sachdev & Mohamed Faisal Chevidikunnan & Reem Basuodan & Dhanesh Kumar K U & Fayaz Khan, 2022. "Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach," IJERPH, MDPI, vol. 19(23), pages 1-21, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15919-:d:987829
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    References listed on IDEAS

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    1. Effy Vayena & Alessandro Blasimme & I Glenn Cohen, 2018. "Machine learning in medicine: Addressing ethical challenges," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-4, November.
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