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

Abstract

BACKGROUND: Artificial 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. OBJECTIVES: This study explored physical therapists' perception of AI-powered diagnostic tools and identified key factors influencing their adoption attitudes. METHOD: We 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. RESULTS: A 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. CONCLUSION: Physical 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.

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