Physical Therapists' Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study

Mashael Alsobhi, Fayaz Khan, Mohamed Faisal Chevidikunnan, Reem Basuodan, Lama Shawli, Ziyad Neamatallah, Mashael Alsobhi, Fayaz Khan, Mohamed Faisal Chevidikunnan, Reem Basuodan, Lama Shawli, Ziyad Neamatallah

Abstract

Background: The use of artificial intelligence (AI) in the field of rehabilitation is growing rapidly. Therefore, there is a need to understand how physical therapists (PTs) perceive AI technologies in clinical practice.

Objective: This study aimed to investigate the knowledge and attitude of PTs regarding AI applications in rehabilitation based on multiple explanatory factors.

Methods: A web-based Google Form survey, which was divided into 4 sections, was used to collect the data. A total of 317 PTs participated voluntarily in the study.

Results: The PTs' knowledge about AI applications in rehabilitation was lower than their knowledge about AI in general. We found a statistically significant difference in the PTs' knowledge regarding AI applications in the rehabilitation field based on sex (odds ratio [OR] 2.43, 95% CI 1.53-3.87; P<.001). In addition, experience (OR 1.79, 95% CI 1.11-2.87; P=.02) and educational qualification (OR 1.68, 95% CI 1.05-2.70; P=.03) were found to be significant predictors of knowledge about AI applications. PTs who work in the nonacademic sector and who had <10 years of experience had positive attitudes regarding AI.

Conclusions: AI technologies have been integrated into many physical therapy practices through the automation of clinical tasks. Therefore, PTs are encouraged to take advantage of the widespread development of AI technologies and enrich their knowledge about, and enhance their practice with, AI applications.

Keywords: artificial intelligence; clinicians’ attitudes; digital health; health care; machine learning; physical therapy; rehabilitation; survey.

Conflict of interest statement

Conflicts of Interest: None declared.

©Mashael Alsobhi, Fayaz Khan, Mohamed Faisal Chevidikunnan, Reem Basuodan, Lama Shawli, Ziyad Neamatallah. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2022.

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