Facilitators and Barriers to the Use of Artificial Intelligence in Rehabilitation: A Narrative Review

Umair Mahmood; Tehreem Akhtar; Ahmed Saffee Ali; Musab Bin Amir; Amina Shahzad; Sajida Butt; Hafiz Muddassir Riaz

doi:10.61919/zcge8f82

Authors

Umair Mahmood University of Health Sciences, Lahore, Pakistan Author https://orcid.org/0009-0001-9583-8126
Tehreem Akhtar Superior University, Lahore, Pakistan Author https://orcid.org/0000-0002-8213-7742
Ahmed Saffee Ali University of Health Sciences, Lahore, Pakistan Author https://orcid.org/0009-0009-9844-2547
Musab Bin Amir Ghurki Trust Teaching Hospital, Lahore, Pakistan Author https://orcid.org/0000-0003-4070-3600
Amina Shahzad Superior University, Lahore, Pakistan Author https://orcid.org/0009-0005-5648-7123
Sajida Butt Superior University, Lahore, Pakistan Author https://orcid.org/0009-0009-9122-4563
Hafiz Muddassir Riaz University of Health Sciences, Lahore, Pakistan, Superior University, Lahore, Pakistan Author https://orcid.org/0000-0001-8389-4449

DOI:

https://doi.org/10.61919/zcge8f82

Keywords:

Artificial Intelligence; Machine Learning; Rehabilitation; Physical Therapy; Technology Adoption; Implementation Barriers; Clinical Decision Support

Abstract

Background: Artificial intelligence is increasingly being applied to movement assessment, clinical decision support, remote monitoring, robotic rehabilitation, virtual assistance, and administrative functions. Its routine integration into rehabilitation nevertheless depends on professional, technological, ethical, organisational, and patient-related factors. Objective: To synthesise current evidence concerning AI applications, clinician perceptions, and barriers and facilitators influencing its adoption in rehabilitation and related healthcare settings. Methods: A narrative review with a structured literature search was conducted using PubMed, CINAHL, Scopus, Google Scholar, and ScienceDirect for English-language publications from January 2010 to April 2026. Eligible qualitative, quantitative, mixed-method, and review studies addressing AI adoption or implementation were synthesised thematically. Results: Nineteen publications were included. Recurrent barriers involved limited AI literacy and specialised training, uncertain clinical validity, lack of trust, privacy and ethical concerns, financial and infrastructural constraints, poor workflow integration, limited organisational readiness, and insufficient patient involvement. Facilitators included structured education, representative clinical validation, transparent governance, user-friendly design, technical and leadership support, local adaptation, clinician and patient co-design, and continued human oversight. Conclusion: AI may improve rehabilitation assessment, personalisation, monitoring, access, and service efficiency, but evidence for sustainable routine implementation remains limited. Adoption requires clinically validated, ethical, secure, contextually appropriate, and patient-centred systems that complement professional judgement

References

1. Copeland BJ. Artificial intelligence. Encyclopaedia Britannica. Available from: https://www.britannica.com/technology/artificial-intelligence

2. Stryker C, Kavlakoglu E. What is artificial intelligence? IBM. Available from: https://www.ibm.com/think/topics/artificial-intelligence

3. Aldhahi MI, Alorainy AI, Abuzaid MM, Gareeballah A, Alsubaie NF, Alshamary AS, et al. Adoption of artificial intelligence in rehabilitation: perceptions, knowledge, and challenges among healthcare providers. Healthcare. 2025;13(4):350.

4. Mir BA, Nishwa DE, Lee SW. Embodied artificial intelligence in healthcare: a systematic review of robotic perception, decision-making, and clinical impact. Healthcare. 2026;14(5):572.

5. Stanford Institute for Human-Centered Artificial Intelligence. The AI Index 2026 annual report. Stanford (CA): Stanford University; 2026.

6. World Health Organization. Rehabilitation. Available from: https://www.who.int/news-room/fact-sheets/detail/rehabilitation

7. Olawade DB, Adeleye KK, Egbon E, Nwabuoku US, David-Olawade AC, Boussios S, et al. Enhancing home rehabilitation through AI-driven virtual assistants: a narrative review. Ann Transl Med. 2025;13(5):61.

8. Hassan M, Kushniruk A, Borycki E. Barriers to and facilitators of artificial intelligence adoption in health care: a scoping review. JMIR Hum Factors. 2024;11:e48633.

9. Syed H, Abbas DS, Ranjan R, Maurya B, Warsi A, Khan S. Evaluating healthcare providers’ perceptions, expertise, and barriers regarding the adoption of AI in rehabilitation. 2025;54:4423-39.

10. Alsobhi M, Sachdev HS, Chevidikunnan MF, Basuodan R, K U, DK, et al. Facilitators and barriers of artificial intelligence applications in rehabilitation: a mixed-method approach. Int J Environ Res Public Health. 2022;19(23):15919.

11. Ghosh M. Artificial intelligence and ethical concerns: a review and research agenda. Cogent Bus Manag. 2025;12(1):2551809.

12. Stahl BC. Ethical issues of AI. In: Artificial intelligence for a better future. 2021. p. 35-53.

13. Nambi G, Alghadier M, Mohamed SHP, Aldhafian OR, Alshahrani NA, Albarakati AJA. Clinical usefulness of artificial intelligence in physiotherapy: a practice-based review. SBV J Basic Clin Appl Health Sci. 2024;7(4):184-8.

14. Scipion CEA, Manchester MA, Federman A, Wang Y, Arias JJ. Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review. BMJ Open. 2025;15(4):e092624.

15. Wenderott K, Krups J, Weigl M, Wooldridge AR. Facilitators and barriers to implementing AI in routine medical imaging: systematic review and qualitative analysis. J Med Internet Res. 2025;27:e63649.

16. Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A systematic review of the barriers to the implementation of artificial intelligence in healthcare. Cureus. 2023;15(10):e46454.

17. Bianchini S, Müller M, Pelletier P. Drivers and barriers of AI adoption and use in scientific research. Technol Forecast Soc Change. 2025;220:124303.

18. Marocco S, Barbieri B, Talamo A. Exploring facilitators and barriers to managers’ adoption of AI-based systems in decision-making: a systematic review. AI. 2024;5(4):2538-67.

19. Sahoo RK, Sahoo KC, Negi S, Baliarsingh SK, Panda B, Pati S. Health professionals’ perspectives on the use of artificial intelligence in healthcare: a systematic review. Patient Educ Couns. 2025;134:108680.

20. Lefebvre K, Leuther K, Schmalzl K. Understanding the role of artificial intelligence in rehabilitative practice: a qualitative study. Top Geriatr Rehabil. 2025;41(3):144-53.

21. Sumner J, Lim HW, Chong LS, Bundele A, Mukhopadhyay A, Kayambu G. Artificial intelligence in physical rehabilitation: a systematic review. Artif Intell Med. 2023;146:102693.

22. Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: a systematic review. J Med Internet Res. 2021;23(4):e25759.

23. Katonai G, Arvai N, Mesko B. AI and primary care: a scoping review. J Med Internet Res. 2025;27:e65950.

24. Fujimori R, Liu K, Soeno S, Naraba H, Ogura K, Hara K, et al. Acceptance, barriers, and facilitators to implementing artificial intelligence-based decision-support systems in emergency departments: quantitative and qualitative evaluation. JMIR Form Res. 2022;6(6):e36501.

25. N A, Chowdhury RR, L P, Peter RM, Vv A. Perception of the adoption of artificial intelligence in healthcare practices among healthcare professionals in a tertiary-care hospital: a cross-sectional study. Cureus. 2024;16(9):e69910.

26. Shawli L, Alsobhi M, Chevidikunnan MF, Rosewilliam S, Basuodan R, Khan F. Physical therapists’ perceptions and attitudes towards artificial intelligence in healthcare and rehabilitation: a qualitative study. Musculoskelet Sci Pract. 2024;73:103152.

27. Marco-Ruiz L, Hernández MAT, Ngo PD, Makhlysheva A, Svenning TO, Dyb K, et al. A multinational study on artificial intelligence adoption: clinical implementers’ perspectives. Int J Med Inform. 2024;184:105377.

28. Moy S, Irannejad M, Manning SJ, Farahani M, Ahmed Y, Gao E, et al. Patient perspectives on the use of artificial intelligence in health care: a scoping review. J Patient Cent Res Rev. 2024;11(1):51-62.

29. Mennella C, Maniscalco U, Di Pietro G, Esposito M. The role of artificial intelligence in future rehabilitation services: a systematic literature review. IEEE Access. 2023;11:11024-43.