Journals / Journal of Health Sciences and Management / 2026 / Cilt: 6 Sayı: 1

Use of Artificial Intelligence and Technology by Physiotherapists in Spine Rehabilitation

Omurga Rehabilitasyonunda Fizyoterapistlerin Yapay Zekâ ve Teknoloji Kullanımı

Aleyna Karahan(Marmara Üniversitesi)0009-0000-2059-2101
Büşra Duran(MARMARA ÜNİVERSİTESİ)0009-0003-7067-4473
Eray Yüceel(MARMARA ÜNİVERSİTESİ)0009-0004-3464-4266
Yağmur Deniz Erol(MARMARA ÜNİVERSİTESİ)0009-0008-1928-9082
Gönül Acar(MARMARA ÜNİVERSİTESİ)0000-0002-6964-6614
Tuğba Kuru Çolak(MARMARA ÜNİVERSİTESİ)0000-0002-3263-2278
Pages
18–24
Publication Date
2026-01-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This highlights the need for physiotherapists to adapt to technological advancements and underscores the importance of further education and awareness initiatives in this area.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: Artificial Intelligence (AI) has emerged as a transformative technology in modern medicine, particularly in the fields of physiotherapy and rehabilitation in spine rehabilitation.
📊
Key Findings & Data:The study included 88 physiotherapists working in public and private healthcare facilities.
💡
Conclusion & Impact:This highlights the need for physiotherapists to adapt to technological advancements and underscores the importance of further education and awareness initiatives in this area.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Fizyoterapist · Omurga · Rehabilitasyon

Abstract

Aim: Artificial Intelligence (AI) has emerged as a transformative technology in modern medicine, particularly in the fields of physiotherapy and rehabilitation in spine rehabilitation. This descriptive study aims to evaluate the knowledge, attitudes, and usage levels of AI and advanced technologies among physiotherapists across Türkiye. Methods: The data collection process was conducted using an online survey method. Participants provided data by responding to a questionnaire created by the researcher and administered via the Google Forms platform. The study included 88 physiotherapists working in public and private healthcare facilities. Results: In clinical practice, exercise games (37.5%) and robotic devices (26.1%) were identified as the most commonly used technologies. Physiotherapists generally expressed a positive attitude toward the potential impact of AI on future treatment processes. A majority (54.5%) believe that technology will assist in treatment processes, but ultimate decisions should be made by humans. Key benefits of AI in treatment include improved time management (75%), enhanced patient monitoring (64.8%), and the development of personalized treatment plans (52.3%). However, physiotherapists remain cautious about the ability of technology to fully address individual patient needs. Conclusions: While AI and advanced technologies hold significant potential in spine rehabilitation, their adoption in clinical practice remains limited. This highlights the need for physiotherapists to adapt to technological advancements and underscores the importance of further education and awareness initiatives in this area.

Özet

Amaç: Yapay zekâ (YZ), modern tıpta, özellikle fizyoterapi ve rehabilitasyonu alanlarında dönüştürücü bir teknoloji olarak dikkat çekmektedir. Bu çalışma, Türkiye genelindeki fizyoterapistlerin omurga rehabilitasyonun alanında YZ ve ileri teknolojilere dair bilgi, tutum ve kullanım düzeylerini değerlendirmeyi amaçlayan tanımlayıcı bir araştırmadır. Yöntemler: Bu araştırmada veri toplama süreci, çevrimiçi anket yöntemi kullanılarak gerçekleştirilmiştir. Katılımcılar, araştırmacı tarafından oluşturulan ve Google Forms platformu üzerinden sunulan anket formunu yanıtlayarak veri sağlamıştır. Araştırmaya, kamu ve özel sağlık kuruluşlarında çalışan 88 fizyoterapist katılmıştır. Bulgular: Klinik uygulamalarda ise egzersiz oyunları (%37,5) ve robotik cihazlar (%26,1) en yaygın kullanılan teknolojiler arasında yer almaktadır. YZ’nin gelecekte tedavi süreçlerine etkisi konusunda fizyoterapistler genel olarak olumlu bir yaklaşım sergilemiştir. Katılımcıların %54,5’i teknolojinin tedavi sürecine yardımcı olacağını, ancak asıl kararların insanlar tarafından verilmesi gerektiğini düşünmektedir. Tedaviye yönelik faydalar arasında zaman yönetiminin iyileştirilmesi (%75), hasta takibinin kolaylaştırılması (%64,8) ve kişiselleştirilmiş tedavi planlarının geliştirilmesi (%52,3) öne çıkmaktadır. Bununla birlikte, fizyoterapistler, teknolojinin bireysel hasta ihtiyaçlarına her zaman tam olarak uyum sağlayamayacağı konusunda dikkatli bir yaklaşım sergilemektedir. Sonuç: YZ ve ileri teknolojiler, omurga rehabilitasyonunda önemli bir potansiyele sahip olsa da klinik uygulamalarda benimsenmesi sınırlıdır. Bu durum, fizyoterapistlerin teknolojik yeniliklere uyum sağlamasını ve bu alanda daha fazla eğitim ve farkındalık çalışmalarını gerekli kılmaktadır.

🔖

Cite This Article

Karahan, A., Duran, B., Yüceel, E., Erol, Y. D., Khaselouei, L. A., Acar, G., Hüseyinsinoğlu, B. E., Çolak, T. K. (2026). Use of Artificial Intelligence and Technology by Physiotherapists in Spine Rehabilitation. Journal of Health Sciences and Management, Cilt: 6 Sayı: 1, 18–24. https://ajindex.com/articles/1592315

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (22)

  1. Akalın, B., & Demirbaş, M. B. (2022). Rehabilitasyon hizmetlerinde yapay zekâ uygulamaları. Acta Infologica, 6(2), 141–161. https://doi.org/10.26650/acin.1068576
  2. Alsobhi, M., Khan, F., Chevidikunnan, M. F., Basuodan, R., Shawli, L., & Neamatallah, Z. (2022). Physical therapists’ knowledge and attitudes regarding artificial intelligence applications in health care and rehabilitation: Cross-sectional study. Journal of Medical Internet Research, 24(10), e39565. https://doi.org/10.2196/39565
  3. Alsobhi, M., Sachdev, H. S., Chevidikunnan, M. F., Basuodan, R., KU, D. K., & Khan, F. (2022). Facilitators and barriers of artificial intelligence applications in rehabilitation: A mixed-method approach. International Journal of Environmental Research and Public Health, 19(23), 15919. https://doi.org/10.3390/ijerph192315919
  4. Bell, E., Harley, B., & Bryman, A. (2022). Business research methods. Oxford University Press.
  5. Bhardwaj, S., Khan, A. A., & Muzammil, M. (2021). Lower limb rehabilitation robotics: The current understanding and technology. Work, 69(3), 775–793. https://doi.org/10.3233/WOR-205012
  6. Bocanegra-Becerra, J. E., Ferreira, J. S. N., Simoni, G., Hong, A., Rios-Garcia, W., Eraghi, M. M., Castilla-Encinas, A. M., Colan, J. A., Rojas-Apaza, R., & Trevejo, E. E. F. P. (2025). Machine learning algorithms for neurosurgical preoperative planning: A scoping review. World Neurosurgery, 194, 123465. https://doi.org/10.1016/j.wneu.2024.11.048
  7. Brandes, G. I. G., D’Ippolito, G., Azzolini, A. G., & Meirelles, G. (2020). Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo. Radiologia Brasileira, 53, 167–170. https://doi.org/10.1590/0100-3984.2019.0101
  8. Davids, J., Lidströmer, N., & Ashrafian, H. (2022). Artificial intelligence for physiotherapy and rehabilitation. In Artificial Intelligence in Medicine (pp. 1789–1807). Springer Publishing.
  9. Low, X. Z., Furqan, M. S., Makmur, A., Lim, D. S. W., Liu, R. W., Lim, X., Chan, Y. H., Tan, J. H., Lau, L. L., & Hallinan, J. T. P. D. (2024). Automated Cobb angle measurement in scoliosis radiographs: A deep learning approach for screening. Annals of the Academy of Medicine, Singapore, 53(10), 635–637. https://doi.org/10.47102/annals-acadmedsg.2023300
  10. Lowe, S. W. (2024). The role of artificial intelligence in physical therapy education. Bulletin of Faculty of Physical Therapy, 29(1), 13. https://doi.org/10.1186/s43161-024-00177-8
  11. Luchmann, D., Jecklin, S., Cavalcanti, N. A., Laux, C. J., Massalimova, A., Esfandiari, H., Farshad, M., & Fürnstahl, P. (2024). Spinal navigation with AI-driven 3D-reconstruction of fluoroscopy images: An ex-vivo feasibility study. BMC Musculoskeletal Disorders, 25(1), 925. https://doi.org/10.1186/s12891-024-08052-2
  12. Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328–2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19
  13. Mehta, N., Harish, V., Bilimoria, K., Morgado, F., Ginsburg, S., Law, M., & Das, S. (2021). Knowledge of and attitudes on artificial intelligence in healthcare: A provincial survey study of medical students. medRxiv. https://doi.org/10.1101/2021.01.14.21249830
  14. Mobbs, R. J. (2024). Artificial intelligence in spine care: A paradigm shift in diagnosis, surgery, and rehabilitation. Journal of Spine Surgery, 10(4), 775. https://doi.org/10.21037/jss-24-156
  15. Mousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023). Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Science Reports, 6(3), e1138. https://doi.org/10.1002/hsr2.1138
  16. Raghunathan, K., Morris, M. E., Wani, T. A., Edvardsson, K., Peiris, C., Fowler-Davis, S., McKercher, J. P., Bourke, S., Danish, S., & Johnston, J. (2025). Using artificial intelligence to improve healthcare delivery in select allied health disciplines: A scoping review protocol. BMJ Open, 15(3), e098290. https://doi.org/10.1136/bmjopen-2024-098290
  17. Rouse, M., Spohrer J.C. (2017). AUTOMATING VERSUS AUGMENTING INTELLIGENCE. Journal of Enterprise Transformation.
  18. Tack, C. (2019). Artificial intelligence and machine learning applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice, 39, 164–169. https://doi.org/10.1016/j.msksp.2018.11.012
  19. Tarakçı, D. (2021). Rehabilitasyonda yapay zekâ. SD Platform. Retrieved June 3, 2021.
  20. Truong, N. M., Vo, T. Q., Tran, H. T. B., Nguyen, H. T., & Pham, V. N. H. (2023). Healthcare students’ knowledge, attitudes, and perspectives toward artificial intelligence in southern Vietnam. Heliyon, 9(12), e22653. https://doi.org/10.1016/j.heliyon.2023.e22653
  21. Veras, M., Dyer, J.-O., & Kairy, D. (2024). Artificial intelligence and digital divide in physiotherapy education. Cureus, 16(1), e52617. https://doi.org/10.7759/cureus.52617
  22. Verma, M. (2018). Artificial intelligence and its scope in different areas with special reference to the field of education. Online Submission, 3(1), 5–10.