IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i23p15919-d987829.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/23/15919/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/23/15919/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charlotte Blease & Anna Kharko & Cosima Locher & Catherine M DesRoches & Kenneth D Mandl, 2020. "US primary care in 2029: A Delphi survey on the impact of machine learning," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
    2. Lepore, Dominique & Dolui, Koustabh & Tomashchuk, Oleksandr & Shim, Heereen & Puri, Chetanya & Li, Yuan & Chen, Nuoya & Spigarelli, Francesca, 2023. "Interdisciplinary research unlocking innovative solutions in healthcare," Technovation, Elsevier, vol. 120(C).
    3. Lily Popova Zhuhadar & Miltiadis D. Lytras, 2023. "The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    4. Delahunty, Fionn & Arcan, Mihael & Johansson, Robert, 2019. "Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System," Thesis Commons 98c3q, Center for Open Science.
    5. Carl B. Roth & Andreas Papassotiropoulos & Annette B. Brühl & Undine E. Lang & Christian G. Huber, 2021. "Psychiatry in the Digital Age: A Blessing or a Curse?," IJERPH, MDPI, vol. 18(16), pages 1-32, August.
    6. Dessislava Pachamanova & Vera Tilson & Keely Dwyer-Matzky, 2022. "Case Article—Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations," INFORMS Transactions on Education, INFORMS, vol. 22(3), pages 178-187, May.
    7. Esra Zihni & Vince Istvan Madai & Michelle Livne & Ivana Galinovic & Ahmed A Khalil & Jochen B Fiebach & Dietmar Frey, 2020. "Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-15, April.
    8. Ignat Drozdov & Daniel Forbes & Benjamin Szubert & Mark Hall & Chris Carlin & David J Lowe, 2020. "Supervised and unsupervised language modelling in Chest X-Ray radiological reports," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-16, March.
    9. Morley, Jessica & Machado, Caio C.V. & Burr, Christopher & Cowls, Josh & Joshi, Indra & Taddeo, Mariarosaria & Floridi, Luciano, 2020. "The ethics of AI in health care: A mapping review," Social Science & Medicine, Elsevier, vol. 260(C).
    10. repec:thr:techub:10024:y:2021:i:1:p:696-706 is not listed on IDEAS
    11. Thomas Molala & Jabulani Makhubele, 2021. "A conceptual framework for the ethical deployment of Artificial Intelligence in addressing mental health challenges: Guidelines for Social Workers," Technium Social Sciences Journal, Technium Science, vol. 24(1), pages 696-706, October.
    12. Siala, Haytham & Wang, Yichuan, 2022. "SHIFTing artificial intelligence to be responsible in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 296(C).
    13. Zahlan, Ahmed & Ranjan, Ravi Prakash & Hayes, David, 2023. "Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research," Technology in Society, Elsevier, vol. 74(C).
    14. Mohammad I. Merhi, 2023. "An Assessment of the Barriers Impacting Responsible Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(3), pages 1147-1160, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15919-:d:987829. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.