Evidence map›Paper›PMID 39997225›Full record

ArticleHealthcare (Basel, Switzerland)2025

Adoption of Artificial Intelligence in Rehabilitation: Perceptions, Knowledge, and Challenges Among Healthcare Providers.

Monira I Aldhahi, Amal I Alorainy, Mohamed M Abuzaid, Awadia Gareeballah, Naifah F Alsubaie, Anwar S Alshamary, Zuhal Y Hamd

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Monira I AldhahiDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0002-5255-4860
Amal I AlorainyDepartment of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Mohamed M AbuzaidMedical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.ORCID 0000-0002-6320-9878
Awadia GareeballahDepartment of Diagnostic Radiology, College of Applied Medical Science, Taibah University, P.O. Box 344, Al-Madinah Al-Munawwarah 41477, Saudi Arabia.ORCID 0000-0001-7320-2795
Naifah F AlsubaieDepartment of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0009-0005-0405-9098
Anwar S AlshamaryIntegrated Treatment Center, Riyadh 12281, Saudi Arabia.
Zuhal Y HamdDepartment of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0003-2895-2284

Funding

program of Research Project Funding After Publication RPFAP-44-1445
6 · The paper itself

Abstract

BACKGROUND/

objectivesThe current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness of rehabilitation healthcare providers to implement AI in practice.

methodsThis study was conducted in Saudi Arabia, with data collected from 430 physical therapy professionals via an online SurveyMonkey questionnaire between January and March 2024. The survey assessed demographics, AI knowledge and skills, and perceived challenges. Data were analyzed using Statistical Package for the Social Science (SPSS 27) and DATAtab (version 2025), with frequencies, percentages, and nonparametric tests used to examine the relationships between the variables.

resultsThe majority of respondents (80.9%) believed that AI would be integrated into physical therapy in future, with 78.6% seeing AI as significantly impacting their work. While 61.4% thought that AI would reduce workload and enhance productivity, only 30% expressed concerns about AI endangering their profession. A lack of formal AI training has commonly been reported, with social media platforms being respondents' primary source of AI knowledge. Despite these challenges, 85.1% expressed an eagerness to learn and use AI. Organizational preparedness was a significant barrier, with 45.6% of respondents reporting that their organizations lacked AI strategies. There were insignificant differences in the mean rank of AI perceptions or knowledge based on the gender, years of experience, and qualification degree of the respondents.

conclusionsThe results demonstrated a strong interest in AI implementation in physical therapy. The majority of respondents expressed confidence in AI's future utility and readiness to incorporate it into their practice. However, challenges, such as a lack of formal training and organizational preparedness, were identified. Overall, the findings highlight AI's potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential.

Indexed as

AI knowledgehealthcareorganizational preparednessrehabilitation

Identifiers

PMID39997225
PMCPMC11855079

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.