Technology in Physiotherapy: A Bibliometric Analysis of Artificial Intelligence in Physiotherapy and Rehabilitation

Öz

Objective: This study aimed to perform quantitative and qualitative evaluations of the state of artificial intelligence (AI) for physiotherapy and rehabilitation. Materials and Methods: The bibliometric data have been collected using title and abstract keyword searches from the Web of Science database for AI applications in the physiotherapy field. A total of 187 articles were identified using keywords such as machine learning, deep learning, artificial neural network, artificial intelligence, natural language processing, and physiotherapy. Results: A total of 187 articles published between 2001 and 2024 were analyzed. The year 2023 had the highest publication volume (47 articles). “Engineering Electrical Electronic” was the most productive research field. Frequently occurring terms included “Machine Learning,” “Rehabilitation,” and “Artificial Intelligence.” Conclusions: Publications on artificial intelligence and physiotherapy have significantly increased in recent years. These findings underscore the increasing relevance of AI-driven technologies for clinical practice, therapeutic decision-making, and rehabilitation research. For physiotherapists, healthcare professionals, and interdisciplinary researchers, this study provides valuable insight into emerging trends and areas of concentration. Future work can benefit from bibliometric analyses across different databases to support multidisciplinary research.

Anahtar Kelimeler

• Artificial intelligence (AI)
• deep learning
• machine learning
• physiotherapy and rehabilitation
• Web of Science (WoS)

Fizyoterapide Teknoloji: Fizyoterapi ve Rehabilitasyonda Yapay Zekâya Dair Bibliyometrik Bir Analiz

Öz

Amaç: Bu çalışma, fizyoterapi ve rehabilitasyon alanında yapay zekânın mevcut durumunu nicel ve nitel olarak değerlendirmeyi amaçlamaktadır. Materyal ve Metot: Bibliyometrik veriler, Web of Science veri tabanında başlık ve özet anahtar kelime aramaları yapılarak toplanmıştır. “Makine öğrenimi,” “derin öğrenme,” “yapay sinir ağı,” “yapay zekâ,” “doğal dil işleme” ve “fizyoterapi” gibi anahtar kelimeler kullanılarak toplam 187 makaleye ulaşılmıştır. Bulgular: 2001–2024 yılları arasında yayımlanan toplam 187 makale analiz edilmiştir. En fazla yayının yapıldığı yıl 2023 olup, bu yıl içinde 47 makale yayımlanmıştır. En üretken araştırma alanı “Elektrik Elektronik Mühendisliği” olarak belirlenmiştir. En sık karşılaşılan terimler arasında “Makine Öğrenimi,” “Rehabilitasyon” ve “Yapay Zekâ” yer almaktadır. Sonuç: Yapay zekâ ve fizyoterapi üzerine yapılan yayınlar son yıllarda önemli ölçüde artmıştır. Bu bulgular, klinik uygulamalar, tedaviye yönelik karar verme süreçleri ve rehabilitasyon araştırmaları açısından yapay zekâ destekli teknolojilerin artan önemini vurgulamaktadır. Fizyoterapistler, sağlık profesyonelleri ve disiplinler arası araştırmacılar için bu çalışma, yükselen eğilimler ve odaklanılan alanlar hakkında değerli içgörüler sunmaktadır. Multidisipliner çalışma yapan araştırmacılar için Scopus ve PubMed gibi farklı veri tabanlarından çıkarılacak bibliyometrik analizler gelecekteki çalışmalara yön verebilir.

Anahtar Kelimeler

• Derin öğrenme
• fizyoterapi ve rehabilitasyon
• makine öğrenimi
• yapay zekâ
• WoS

Kaynakça

1. Jakhar D, Kaur I. Artificial intelligence, machine learning and deep learning: definitions and differences. Clin Exp Dermatol. 2020;45(1):131-132. doi: 10.1111/ced.14029
2. Park CW, Lee J, Lee JH, et al. Artificial intelligence in health care: current applications and issues. J Korean Med Sci. 2020;35(42). doi: 10.3346/jkms.2020.35.e379
3. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-1318. doi: 10.1001/jama.2017.18391
4. Hinton G. Deep learning—a technology with the potential to transform health care. JAMA. 2018;320(11):1101-1102. doi: 10.1001/jama.2018.11100
5. Smye SW, Frangi AF. Interdisciplinary research: shaping the healthcare of the future. Future Healthc J. 2021;8(2):e218-e223. doi: 10.7861/fhj.2021-0025
6. Bhatt C, Naik N, Bhatia MS, et al. The state of the art of deep learning models in medical science and their challenges. Multimedia Syst. 2021;27(4):599-613. doi: 10.1007/s00530-020-00694-1
7. Khosravi M, Frizzo-Barker J, Nguyen Q, et al. Artificial intelligence and decision-making in healthcare: a thematic analysis of a systematic review of reviews. Health Serv Res Manag Epidemiol. 2024;11:23333928241234863. doi: 10.1177/23333928241234863
8. Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):016-026. doi: 10.1055/s-0039-1677908

9. Kocyigit BF, Assylbek MI, Yessirkepov M. Telerehabilitation: lessons from the COVID-19 pandemic and future perspectives. Rheumatol Int. 2024;44(4):577-582. doi: 10.1007/s00296-024-05537-0
10. Sardari S, Marzbanrad F, Toosizadeh N, et al. Artificial intelligence for skeleton-based physical rehabilitation action evaluation: a systematic review. Comput Biol Med. 2023;158:106835. doi: 10.1016/j.compbiomed.2023.106835
11. Debnath B, Dhar M, Mondal B, et al. A review of computer vision-based approaches for physical rehabilitation and assessment. Multimedia Syst. 2022;28(1):209-239. doi: 10.1007/s10462-025-11180-3
12. Kulkarni SA, Ganu P, Acharya B, et al. A brief analysis of key machine learning methods for predicting Medicare payments related to physical therapy practices in the United States. Information. 2021;12(2):57. doi: 10.3390/info12020057
13. Bai Y, Liu F, Zhang H. Artificial intelligence limb rehabilitation system on account of virtual reality technology on long-term health management of stroke patients in the context of the internet. Comput Math Methods Med. 2022;2022:2688003. doi: 10.1155/2022/2688003
14. Zhang X, Rong X, Luo H. Optimizing lower limb rehabilitation: the intersection of machine learning and rehabilitative robotics. Front Rehabil Sci. 2024;5:1246773. doi: 10.3389/fresc.2024.1246773
15. Aderinto N, Onwusuru I, Chukwuneke F, et al. Exploring the efficacy of virtual reality-based rehabilitation in stroke: a narrative review of current evidence. Ann Med. 2023;55(2):2285907. doi: 10.1080/07853890.2023.2285907
16. Godse SP, Patel S, Bhatt B, et al. Musculoskeletal physiotherapy using artificial intelligence and machine learning. Int J Innov Sci Res Technol. 2019;4(11):592-598.
17. Kaczmarczyk K, Wit A, Krawczyk M, Zaborski L. Artificial neural networks (ANN) applied for gait classification and physiotherapy monitoring in post-stroke patients. In: Artificial neural networks—methodological advances and biomedical applications. InTech; 2011. doi: 10.5772/15363
18. Tack C. Artificial intelligence and machine learning: applications in musculoskeletal physiotherapy. Musculoskelet Sci Pract. 2019;39:164-169. doi: 10.1016/j.msksp.2018.11.012
19. Ramanandi VH. Role and scope of artificial intelligence in physiotherapy: a scientific review of literature. Int J Adv Sci Res. 2021;6(1):11-14. doi: 10.36478/makrjms.2024.12.399.412
20. Donthu N, Kumar S, Mukherjee D, et al. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res. 2021;133:285-296. doi:10.1016/j.jbusres.2021.04.070
21. Aria M, Cuccurullo C, Aria MM. Package ‘bibliometrix’. CRAN. https://cran.r-project.org/web/packages/bibliometrix/bibliometrix.pdf. Published 2022. Accessed January 2, 2025.
22. Lord Ferguson S. Is the end of the pandemic the end of telerehabilitation? Phys Ther. 2022;102(4). doi: 10.1093/ptj/pzac004
23. Nogales A, Rodríguez-Aragón M, García-Tejedor ÁJ. A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies. Comput Biol Med. 2024;172:108082. doi: 10.1016/j.compbiomed.2024.108082
24. Sam RY, Nordin NAM, Kaur H, et al. Types, functions and mechanisms of robot-assisted intervention for fall prevention: a systematic scoping review. Arch Gerontol Geriatr. 2023:105117. doi: 10.1016/j.archger.2023.105117.
25. Usmani S, Khusro S, Arif M, et al. Latest research trends in fall detection and prevention using machine learning: a systematic review. Sensors (Basel). 2021;21(15):5134. doi: 10.3390/s21155134
26. Yenişehir S. Artificial intelligence based on falling in older people: a bibliometric analysis. Aging Med (Milton). 2024;7(2):162-170. doi: 10.1002/agm2.12302
27. Fernandes FG, de Santana EM. Machine learning applied to neurorehabilitation: a systematic mapping. J Adv Theor Appl Inform. 2020;5(1). doi: 10.26729/jadi.v5i1.3072
28. Díaz-Mohedo E, Odriozola Aguirre I, Molina García E, Infantes-Rosales MA, Hita-Contreras F. Functional exercise versus specific pelvic floor exercise: observational pilot study in female university students. Healthcare (Basel). 2023;11(4):561. doi:10.3390/healthcare11040561
29. 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. doi:10.1016/j.artmed.2023.102693
30. Abedi A, Colella TJ, Pakosh M, Khan SS. Artificial intelligence-driven virtual rehabilitation for people living in the community: a scoping review. NPJ Digit Med. 2024;7(1):25. doi:10.1038/s41746-024-00939-4