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

Author:

Alsobhi MashaelORCID,Sachdev Harpreet Singh,Chevidikunnan Mohamed FaisalORCID,Basuodan Reem,K U Dhanesh Kumar,Khan FayazORCID

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.

Funder

Princess Nourah bint Abdulrahman University researchers supporting project

Publisher

MDPI AG

Subject

Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health

Reference47 articles.

1. The relationship between foot posture and lower limb kinematics during walking: A systematic review;Buldt;Gait Posture,2013

2. (2022, September 19). Gait Deviations of Patients with Ruptured Anterior Cruciate Ligament: A Cross-Sectional Gait Analysis Study on Male Patients|Knee Surgery & Related Research|Full Text. Available online: https://kneesurgrelatres.biomedcentral.com/articles/10.1186/s43019-021-00128-w.

3. Lee, M.H., Siewiorek, D.P., Smailagic, A., and Bernardino, A. (2020). Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment. arXiv, Available online: http://arxiv.org/abs/2002.12261.

4. The Role of the Sharing Economy and Artificial Intelligence in Health Care: Opportunities and Challenges;Wu;J. Med. Internet Res.,2019

5. Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy;Tack;Musculoskelet. Sci. Pract.,2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3