AI ASSOCIATED CHALLENGES TO PHYSICAL THERAPY PROFESSION

Background: The integration of Artificial Intelligence (AI) into physical therapy is transforming traditional practices, presenting a range of ethical, professional, and technical challenges. These include concerns about patient data security, algorithmic bias, job displacement, the need for continuous education, and system reliability. While AI has the potential to enhance diagnostic accuracy and treatment personalization, its adoption necessitates significant adaptations in clinical workflows and professional roles, emphasizing the need for a strategic approach to address these challenges and optimize its application. Objective: The objective of this systematic review was to analyze the ethical, professional, and technical challenges associated with implementing AI in physical therapy and to propose strategic recommendations for mitigating risks while maximizing the potential benefits. Methods: A systematic review was conducted following PRISMA guidelines, with a comprehensive search across PubMed, Scopus, Web of Science, and IEEE Xplore databases up to July 2024. The search terms included "Artificial Intelligence," "physical therapy," "challenges," "ethics," "professional development," and "technical reliability." Studies were included if they addressed ethical, professional, or technical barriers to AI integration in physical therapy. Titles and abstracts were screened independently by two reviewers, with disagreements resolved by a third reviewer. Data were extracted on study characteristics, AI applications, identified challenges, and suggested solutions. Quality appraisal was conducted using the CASP tool. Results: A total of 12 studies met the inclusion criteria. Ethical concerns, including patient data security and algorithmic bias, were reported in 60% of studies. Professional challenges, such as job displacement and the need for continuous education, were highlighted in 50%. Technical issues, including system reliability and integration into clinical workflows, were evident in 75%. The findings also showed that 40% of studies emphasized the importance of adapting clinical training programs to include AI-focused education. Conclusion: AI holds transformative potential for physical therapy by improving diagnostic precision and personalized care. However, its integration is hindered by significant ethical, professional, and technical challenges. Addressing these requires the establishment of robust ethical frameworks, continuous education and training for professionals, and rigorous technical validation processes to ensure AI's safe and effective application in clinical practice.