| Artificial Intelligence In Physiotherapy: Narrative Review |
| Paper ID : 1049-ISCSR3 (R2) |
| Authors |
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Manar Farid Farouk *1, Arwa Amer Ahmed2, Marwa El-kawy Ghait Mohamed3 1Student of Physical Therapy at Sphinx University 2Student of physical therapy at sphinx university 3Assistant lecturer, Department of Physical Therapy for Pediatrics, Faculty of Physical Therapy, Sphinx University, Assiut, Egypt |
| Abstract |
| Background : Artificial intelligence has an important role in practical life and rehabilitation, proving successful compared to conventional methods by enhancing clinical decision-making, patient monitoring, and optimizing patient outcomes. AI-powered tools have demonstrated the ability to improve treatment precision, automate documentation, and provide personalized therapy based on real-time patient data. Moreover, AI-driven rehabilitation technologies offer a more interactive and adaptive approach, ensuring better engagement and adherence to treatment programs. Objectives : The purpose of this review is to highlight effectiveness of AI-driven tools in enhancing clinical documentation and in managing musculoskeletal conditions, with a focus on exercise correction and pain management . Methods : Data were collected and analyzed in February 2025 from PubMed and Google Scholar. A total of 16 studies were identified, of which 6 studies were selected for inclusion which published between 2019 and 2024 .Studies investigate Ai-driven tools: DAX Copilot, Sword and Kaia health app in the following areas: Documentation, Home program tracking and exercise correction. Results: A total of 6 studies with diverse designs : Cohort, longitudinal cohort, Randomized controlled trial, pilot and secondary analysis of prospective clinical study were included. Key findings from the studies are as follows: pain reduction different age group, AI motion coaching showed high agreement with physiotherapy assessments , proving Its ability to detect movement patterns and provide feedback and reduced time spent on electronic medical records while note length increased . Conclusion : Reviewed studies reported high adherence, engagement, Effective telerehabilitation outcomes, improved clinical documentation by reducing time spent on electronic medical Records and enhancing note accuracy |
| Keywords |
| Key words: Artificial intelligence; Ambient listening technology; documentation; Home program tracking; Telerehabilitation |
| Status: Abstract Accepted |