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Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach
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 investigat...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737928/ https://www.ncbi.nlm.nih.gov/pubmed/36497993 http://dx.doi.org/10.3390/ijerph192315919 |
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author | Alsobhi, Mashael Sachdev, Harpreet Singh Chevidikunnan, Mohamed Faisal Basuodan, Reem K U, Dhanesh Kumar Khan, Fayaz |
author_facet | Alsobhi, Mashael Sachdev, Harpreet Singh Chevidikunnan, Mohamed Faisal Basuodan, Reem K U, Dhanesh Kumar Khan, Fayaz |
author_sort | Alsobhi, Mashael |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9737928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97379282022-12-11 Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach Alsobhi, Mashael Sachdev, Harpreet Singh Chevidikunnan, Mohamed Faisal Basuodan, Reem K U, Dhanesh Kumar Khan, Fayaz Int J Environ Res Public Health Article 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. MDPI 2022-11-29 /pmc/articles/PMC9737928/ /pubmed/36497993 http://dx.doi.org/10.3390/ijerph192315919 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Alsobhi, Mashael Sachdev, Harpreet Singh Chevidikunnan, Mohamed Faisal Basuodan, Reem K U, Dhanesh Kumar Khan, Fayaz Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach |
title | Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach |
title_full | Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach |
title_fullStr | Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach |
title_full_unstemmed | Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach |
title_short | Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach |
title_sort | facilitators and barriers of artificial intelligence applications in rehabilitation: a mixed-method approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737928/ https://www.ncbi.nlm.nih.gov/pubmed/36497993 http://dx.doi.org/10.3390/ijerph192315919 |
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