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

Yıl 2025, Cilt: 10 Sayı: 2, 145 - 152, 30.06.2025

Güzin Kaya Aytutuldu , İlhan Aytutuldu , Tansu Birinci Olgun , Yusuf Sinan Akgül

https://doi.org/10.26453/otjhs.1659222

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

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