Evidence map›Paper›PMID 41286821›Full record

ArticleBMC medical education2025

Attitudes of physical therapists toward AI diagnostics: barriers, enablers, and clinical implications.

Reem M Alwhaibi, Raghad B Alshammari, Nouf I Alrufayyiq, Jawaher Q Alenzi, Amirah H Alsumayli, Btool I Alrushud, Tahani J Alahmadi

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Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Reem M AlwhaibiDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. rmalwhaibi@pnu.edu.sa.
Raghad B AlshammariDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Nouf I AlrufayyiqDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Jawaher Q AlenziDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Amirah H AlsumayliDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Btool I AlrushudDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Tahani J AlahmadiDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project PNURSP2025R117
6 · The paper itself

Abstract

backgroundArtificial Intelligence (AI) is increasingly integrated into healthcare, yet its application in physical therapy remains limited. Unlike other medical fields, physical therapy relies heavily on hands-on assessments and individualized clinical reasoning, which may shape unique adoption challenges. Understanding physical therapists' perceptions of AI-powered diagnostic tools is essential for supporting their effective and ethical implementation.

objectivesThis study explored physical therapists' perception of AI-powered diagnostic tools and identified key factors influencing their adoption attitudes.

methodWe conducted semi-structured interviews with 27 licensed physical therapists in Saudi Arabia, representing diverse clinical settings and specialties. Transcripts were analyzed thematically to capture perceptions, barriers, and enablers of AI integration.

resultsA total of 27 licensed physical therapists participated (63% female, mean age 29 years, range 24-38; clinical experience 1-7 + years). Participants demonstrated varied perspectives. Ten (37%) emphasized AI's potential to improve diagnostic accuracy, treatment planning, and improve workflow efficiency, while seven (26%) expressed caution about overreliance and limited insight Ethical concerns were common, with 12 (44%) citing patient data privacy and 5 (19%) highlighting cultural sensitivities in female patient care. Barriers to adoption were identified by 14 (52%), including cost, workload, time, and space limitations. Training needs were also emphasized, with 9 (33%) calling for structured workshops and 6 (22%) noting gaps in foundational AI literacy. Overall, most participants viewed AI as a complementary tool rather than a replacement for clinical judgment.

conclusionPhysical therapists in Saudi Arabia recognize the potential benefits of AI-powered diagnostic tools but remain cautious due to ethical, educational, and systemic challenges. CLINICAL IMPLICATIONS: Addressing barriers through structured training, ethical guidelines, and supportive policies can foster responsible adoption of AI in rehabilitation practice.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelPhysical TherapistsAdultFemaleHumansInterviews as TopicMaleQualitative ResearchSaudi ArabiaYoung AdultArtificial intelligencePhysical therapyQualitative researchRehabilitationSaudi arabiaTechnology adoption

Identifiers

PMID41286821
PMCPMC12750540

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.