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Journals / İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi / 2026 / Sayı: 28

The Use of Artificial Intelligence in Physiotherapy, Advantages and Disadvantages: Literature Review

Yapay Zekânın Fizyoterapide Kullanımı, Avantajlar ve Dezavantajlar: Literatür İncelemesi

Ali Ceylan(KARAMANOGLU MEHMETBEY UNIVERSITY)0000-0001-7440-6714
Pages
361–377
Publication Date
2026-04-29
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AI Summary & Key Takeaways

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

We think that with the elimination of the difficulties encountered towards AI, AI-supported technologies will become widespread in the provision of physiotherapy and rehabilitation services and qualified research will increase.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial intelligence (AI) is known as technology, computer, and machine learning based systems that produce solutions to problems that human intelligence cannot overcome.
📊
Key Findings & Data:We believe that increasing the number of qualified personnel in the field of artificial intelligence, allocating more resources to the sector, revising the curriculum according to the developing technology, integration of information systems and health services will increase awareness of AI.
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Conclusion & Impact:We think that with the elimination of the difficulties encountered towards AI, AI-supported technologies will become widespread in the provision of physiotherapy and rehabilitation services and qualified research will increase.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zekâ · farkındalık · fizyoterapi ve rehabilitasyon

Abstract

Artificial intelligence (AI) is known as technology, computer, and machine learning based systems that produce solutions to problems that human intelligence cannot overcome. Apart from the military industry, science, and engineering, it is seen that the use of AI in different fields of health sciences is becoming increasingly widespread. There are scientific studies on AI in many health fields from orthopedics to neurology, radiology to cardiology, endocrinology to physiotherapy. Web-based systems such as wearable devices, robots, systems created with virtual reality, mobile applications, specially developed designs, gait analysis systems, tele-rehabilitation are AI-supported technologies widely used in physiotherapy services in treatment and diagnosis, evaluation, patient care and follow-up. Despite its benefits, there are also some disadvantages of AI. We think that these disadvantages are the most important reasons why qualified studies on AI in our country have not reached the required level. We believe that increasing the number of qualified personnel in the field of artificial intelligence, allocating more resources to the sector, revising the curriculum according to the developing technology, integration of information systems and health services will increase awareness of AI. We think that with the elimination of the difficulties encountered towards AI, AI-supported technologies will become widespread in the provision of physiotherapy and rehabilitation services and qualified research will increase.

Özet

Yapay zekâ (YZ), insan zekasının üstesinden gelemediği sorunlara çözüm üreten teknoloji, bilgisayar ve makine öğrenimi tabanlı sistemler olarak bilinmektedir. Savunma sanayi, fen ve mühendislik bilimi dışında sağlık bilimlerinin farklı alanlarında da YZ kullanımı giderek yaygınlaşmaktadır. Ortopediden nörolojiye, radyolojiden kardiyolojiye, endokrinolojiden fizyoterapiye kadar birçok sağlık alanında YZ ile ilgili bilimsel çalışmalar yapılmaktadır. Giyilebilir cihazlar, robotlar, sanal gerçeklik ile oluşturulan sistemler, mobil uygulamalar, özel geliştirilmiş tasarımlar, yürüme analiz sistemleri, tele-rehabilitasyon gibi web tabanlı sistemler fizyoterapi hizmetlerinde tedavi ve tanı, değerlendirme, hasta bakımı ve takibinde yaygın olarak kullanılan YZ destekli teknolojilerdir. Faydalarına rağmen, YZ'nin bazı dezavantajları da vardır. Bu dezavantajlar, ülkemizde YZ konusunda nitelikli çalışmaların yetersizliğinde önemli bir unsurdur. Yapay zekâ alanında nitelikli personel sayısının artırılması, sektöre daha fazla kaynak ayrılması, müfredatın gelişen teknolojiye göre yenilenmesi, bilişim sistemleri ile sağlık hizmetlerinin entegrasyonunun YZ konusunda farkındalığı artıracağına inanıyoruz. Yapay zekaya yönelik karşılaşılan zorlukların ortadan kalkması ile fizyoterapi ve rehabilitasyon hizmetlerinin sunumunda YZ destekli teknolojilerin yaygınlaşacağını ve nitelikli araştırmaların artacağını düşünüyoruz.

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Cite This Article

Ceylan, A. (2026). The Use of Artificial Intelligence in Physiotherapy, Advantages and Disadvantages: Literature Review. İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi, Sayı: 28, 361–377. https://doi.org/10.38079/igusabder.1598673

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References (81)

  1. Li X, Dunn J, Salins D, et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Bio. 2017;15(1):e2001402.
  2. Rghioui A, Lloret J, Harane M, et al. A smart glucose monitoring system for diabetic patient. Electronics. 2020;9(4):678. doi: 10.3390/electronics9040678.
  3. Burnell K, George MJ, Jensen M, et al. Associations between adolescents’ daily digital technology use and sleep. Journal of Adolescent Health. 2022;70(3):450-456.
  4. George MJ, Russell MA, Piontak JR, et al. Concurrent and subsequent associations between daily digital technology use and high‐risk adolescents’ mental health symptoms. Child Development. 2018;89(1):78-88.
  5. Cascianelli S, Scialpi M, Amici S, et al. Role of artificial intelligence techniques (automatic classifiers) in molecular imaging modalities in neurodegenerative diseases. Curr Alzheimer Res. 2017;14(2):198-207. doi: 10.2174/1567205013666160620122926.
  6. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410. doi: 10.1001/jama.2016.17216.
  7. Ekins S. The next era: deep learning in pharmaceutical research. Pharm Res. 2016;33(11):2594-2603. doi: 10.1007/s11095-016-2029-7.
  8. Griebel L, Prokosch HU, Köpcke F, et al. A scoping review of cloud computing in healthcare. BMC Med Inform Decis Mak. 2015;15:1-16.
  9. Aoun JE. Robot-Proof: Higher Education in the Age of Artificial Intelligence. Cambridge: MIT Press. 2017.
  10. Phillips SM, Cadmus-Bertram L, Rosenberg D, et al. Wearable technology and physical activity in chronic disease: opportunities and challenges. Am J Prev Med. 2018;54(1):144-150.
  11. Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836-1842.
  12. Houssami N, Turner RM, Morrow M. Meta-analysis of pre-operative magnetic resonance imaging (MRI) and surgical treatment for breast cancer. Breast Cancer Res Treat. 2017;165:273-283.
  13. Ceylan A. COVID-19 süreci ve Fizyoterapide telerehabilitasyon uygulamaları: derleme. Adnan Menderes Üniversitesi Sağlık Bilimleri Fakültesi Dergisi. 2021;5(3):617-627.
  14. Aggarwal R, Ganvir SS. Artificial intelligence in physiotherapy. Physiotherapy-The Journal of Indian Association of Physiotherapists. 2021;15(2):55-57.
  15. Ashfaq MH, Khan ARA, Magsi SK, et al. Revolutionizing Rehabilitation: The Role of artificial intelligence in modern Physiotherapy. Journal of Medical & Health Sciences Review. 2025;2(2).
  16. Baker JD. The purpose, process, and methods of writing a literature review. AORN Journal. 2016;103(3):265–269.
  17. Cronin P, Ryan F, Coughlan M. Undertaking a literature review: a step-by-step approach. British Journal of Nursing. 2008;17(1): 38-43.
  18. Noble H, Smith J. Reviewing the literature: Choosing a review design. Evidence Based Nursing. 2018;21(2): 39-41.
  19. Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ Digit Med. 2018;1(1):5.
  20. Frankish K, Ramsey WM (Eds.). The Cambridge handbook of artificial intelligence. Cambridge University Press. 2014.
  21. Bourne RR, Flaxma SR, Braithwaite T, et al. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob Health. 2017;5(9):e888-e897.
  22. Anderson D. Artificial intelligence and applications in PM&R. Am J Phys Med Rehabil. 2019;98(11):e128-e129.
  23. Ramanandi VH. Role and scope of artificial intelligence in physiotherapy: A scientific review of literature. IJASR. 2021;6(1):11-14.
  24. Zhang W. Blockchain-based solutions for clinical trial data management: a systematic review. Metaverse Basic and Applied Research. 2022;1:17-17.
  25. Weissler EH, Naumann T, Andersson T, et al. Correction to: The role of machine learning in clinical research: transforming the future of evidence generation. Trials. 2021;22:537.
  26. Inastrilla CRA. Data visualization in the information society. Seminars in Medical Writing and Education. 2023;2:25.
  27. Pool D, Valentine J, Taylor NF, et al. Locomotor and robotic assistive gait training for children with cerebral palsy. Dev Med Child Neurol. 2021;63(3):328-335.
  28. Menon SP, Shukla PK, Sethi P, et al. An intelligent diabetic patient tracking system based on machine learning for E-health applications. Sensors. 2023;23(6);3004.
  29. Söderlund A. Artificial intelligence and physiotherapy–editorial. European Journal of Physiotherapy. 2019;21(1):1.
  30. Rowe M. Artificial intelligence in clinical practice: Implications for physiotherapy education. OpenPhysio J. 2019;1-6.
  31. Godse SP, Singh S, Khule S, et al. Musculoskeletal physiotherapy using artificial intelligence and machine learning. Int J Innov Sci Res Technol. 2019;4(11):592-598.
  32. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98.
  33. Rabia A, Habiba S, Firdaus J, et al. Embracing technological innovations: the integration of AI in physiotherapy. Recent Trends in Multidisciplinary Research. 2023;9:275-288.
  34. Pignolo L. Robotics in neuro-rehabilitation. J Rehabil Med. 2009;41(12):955-960.
  35. Vélez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S. Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: A Review. Sensors. 2021;21:2146.
  36. Riener R, Lünenburger L, Colombo G. Human-centered robotics applied to gait training and assessment. J Rehabil Res Dev. 2006;43(5):679- 694.
  37. Mehrholz J, Pohl M, Platz T, et al. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018;5:114-123.
  38. Choi C, Schwarting W, DelPreto J, et al. Learning object grasping for soft robot hands. IEEE Robot Autom Lett. 2018;3:2370-2377.
  39. Ozdemir F, Ari A, Kilcik MH, et al. Prediction of neuropathy, neuropathic pain and kinesiophobia in patients with type 2 diabetes and design of computerized clinical decision support systems by using artificial intelligence. Med Hypotheses. 2020;143:110070.
  40. Holden M, Esfahani AH, Scerri A. Facilitated and emergent social learning in sustainable urban redevelopment: exposing a mismatch and moving towards convergence. Urban Research & Practice. 2014;7(1):1-19.
  41. Lo WLA, Lei D, Li L, et al. The perceived benefits of an artificial intelligence–embedded mobile app implementing evidence-based guidelines for the self-management of chronic neck and back pain: observational study. JMIR Mhealth Uhealth. 2018;6(11):e198.
  42. Correia FD, Nogueira A, Magalhães I, et al. Medium-term outcomes of digital versus conventional home-based rehabilitation after total knee arthroplasty: prospective, parallel-group feasibility study. JMIR Rehabil. Assist. Technol. 2019;6(1):e13111.
  43. McCabe J, Monkiewicz M, Holcomb J, et al. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96:981-990.
  44. Knight E, Stuckey MI, Prapavessis H, et al. Public health guidelines for physical activity: Is there an app for that? A review of android and apple app stores. JMIR Mhealth Uhealth. 2015;3(2):43.
  45. Stütz T, Emsenhuber G, Huber D, et al. Mobile phone–supported physiotherapy for frozen shoulder: feasibility assessment based on a usability study. JMIR Rehabil Assist Technol. 2017;4(2):e6.
  46. Blumenthal J, Wilkinson A, Chignell M. Physiotherapists’ and physiotherapy students’ perspectives on the use of mobile or wearable technology in their practice. Physiother Can. 2018;70(3):251–261.
  47. Sarsak HI. Telerehabilitation services: a successful paradigm for occupational therapy clinical services. Int Phys Med Rehab J. 2020;5(2):93‒98.
  48. Gazendam A, Zhu M, Chang Y, et al. Virtual reality rehabilitation following total knee arthroplasty: a systematic review and meta‐analysis of randomized controlled trials. Knee Surg Sports Traumatol Arthrosc. 2022;30(8):2548-2555.
  49. Barradell S. Moving forth: Imagining physiotherapy education differently. Physiotherapy Theory and Practice. 2017;33(6):439-447.
  50. Kakkad AD, Rathod PV. A study to find out strongest predictive factor for functional outcome after stroke: an exploratory study. Indian Journal of Public Health Research & Development. 2020;11(2):582-587.
  51. Sumner J, Lim HW, Chong LS, Bundele A, Mukhopadhyay A, Kayambu G. Artificial intelligence in physical rehabilitation: A systematic review. Artif Intell Med. 2023;102693.
  52. Kahile M, Deshmukh N, Makhija LH, et al. Artificial intelligence (AI) and machine learning (ML) in clinical practice and physiotherapy. Annals of Medical and Health Science Research. 2021;11(3):158–159.
  53. Russell S, Norvig P. Artificial intelligence (A modern approach). In 2010 The 2nd International Conference on Computer and Automation Engineering. ICCAE. 2010;4.
  54. Akalin B, Demirbas MB. Rehabilitasyon hizmetlerinde yapay zekâ uygulamaları. Acta Infologica. 2022;6(2):141-161.
  55. Burhani T, Naqvi WM. Impact of artificial ıntelligence in the physiotherapy rehabilitation of distal radial fracture patients: a review. Journal of Pharmaceutical Research International. 2021;33(60):1982-1988.
  56. Ravali RS, Vijayakumar TM, Lakshmi KS, et al. systematic review of artificial intelligence for pediatric physiotherapy practice: Past, present, and future. Neurosci Inform. 2022;2:100045.
  57. Rowe M, Nicholls DA, Shaw J. How to replace a physiotherapist: artificial intelligence and the redistribution of expertise. Physiother Theory Pract. 2022;38(13):2275-2283.
  58. Rajkomar A, Hardt M, Howell MD, et al. Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine. 2018;169(12):866–872.
  59. Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320:2199-200.
  60. Tonekaboni S, Joshi S, McCradden MD, et al. 2019 What clinicians want: Contextualizing explainable machine learning for clinical end use. ArXiv. 2019;1905.05134.
  61. Pearl RM, Fogel AL. New physicians will need business school skills. NEJM Catalyst. 2017;3(4).
  62. Babbott S, Manwell LB, Brown R, et al. Electronic medical records and physician stress in primary care: results from the MEMO Study. J Am Med Inform Assoc. 2014;21(e1):e100-e106. doi: 10.1136/amiajnl-2013-001875.
  63. Rathore FA, Rathore MA. The emerging role of artificial intelligence in healthcare. J Pak Med Assoc. 2023;73:1368–1369.
  64. Zafar HM, Ip IK, Mills AM, et al. Effect of clinical decision supportgenerated report cards versus real-time alerts on primary care provider guideline adherence for low back pain outpatient lumbar spine MRI orders. Am J Roentgenol. 2019;212:386–94.
  65. Salomon I, Olivier S. Artificial intelligence in medicine: advantages and disadvantages for today and the future. Int J Surg Open. 2024;62(4):471-473.
  66. Powell J. Trust me, I’m a chatbot: how artificial intelligence in health care fails the turing test. J Med Internet Res. 2019;21(10):e16222.
  67. Thomas A, Kuppasanı, PR. Advantages and Disadvantages of Artificial Intelligence. In: Artificial Intelligence and Biological Sciences. CRC Press, 2025;93-110.
  68. Khan B, Fatima H, Qureshi A, et al. Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Biomedical Materials & Devices. 2023;2:731-738.
  69. Yunusbek A. Advantages and Disadvantages of Medical Diagnosis Using Artificial Intelligence. In Russian-Uzbekistan Conference. 2025;323-326.
  70. Nambi G, Alghadier M, Mohamed SHP, et al. Clinical usefulness of artificial intelligence in physiotherapy–a practice-based review. SBV Journal of Basic, Clinical and Applied Health Science. 2024;7(4):184-188.
  71. Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Front Artif Intell. 2020;3:578983.
  72. Abdullah R, Fakieh B. Health care employees’ perceptions of the use of artificial intelligence applications: survey study. J Med Internet Res. 2020;22(5):e17620.
  73. Scheetz J, Rothschild P, McGuinness M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11(1):5193.
  74. Wong K, Gallant F, Szumacher E, Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology-national survey. J. Med. Imag. Radiat. Sci. 2021;2:44-48.
  75. Abuzaid MM, Elshami W, Hegazy F, et al. The impact of artificial intelligence (AI) in physiotherapy practice: a study of physiotherapist willingness and readiness. Journal of Hunan University Natural Sciences. 2022;49(3).
  76. Alsobhi M, Sachdev HS, Chevidikunnan MF, 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.
  77. Alsobhi M, Khan F, Chevidikunnan MF, et al. Physical therapists’ knowledge and attitudes regarding artificial intelligence applications in health care and rehabilitation: cross-sectional study. J Med Internet Res. 2022;24(10):e39565.
  78. Gunn H, Hunter H, Haas B. Problem–based learning in physiotherapy education: a practice perspective. Physiotherapy. 2012;98(4):330–5.
  79. Hui J, Li Y, Zhang R, et al. The effectiveness of ChatGPT-assisted problembased learning in clinical education: A randomized controlled study. BMC Med Educ. 2025;25(1):102.
  80. Wang J, Fan W. The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanit Soc Sci Commun. 2025;12:621.
  81. Feigerlova E, Hani H, Hothersall-Davies E. A systematic review of the impact of artificial intelligence on educational outcomes in health professions education. BMC Med Educ. 2025;25:129.