The purpose of this structured literature review was to outline key applications of AI and ML in physical therapy (PT) practice. The purpose of this structured literature review was to outline key applications of AI and ML in physical therapy (PT) practice.
A review of the most relevant published articles, including, clinical trials and reviews was conducted. six electronic databases, Medline, CINAHL, Scopus, Embase, ERIC Web of Science, Google Scholar, and the grey literature were searched up to September 2024, to identify relevant literature. Two independent reviewers screened for empirical peer-reviewed articles. Studies that investigated AI and ML applications in one of the patient management model’s components, including examination, evaluation, diagnosis, prognosis, intervention, and outcomes, were included. The findings were critically reviewed using an inductive approach and the final version was approved by both authors.
The investigation indicates that the use of AI and ML in clinical settings is relatively new however, the literature in this field is emerging. The United States, China, and South Korea contributed the highest number of studies. The literature reveals several AI applications in PT practice. For instance: 1) the potential of AI in clinical decision-making on diagnosis and treatment combined with human decision-making will be more rigorous than human decision-making; 2) AI could be valuable during functional task movement analysis and prescription of personalized interventions; 3) AI can also be used to predict outcomes and develop a personalized plan of care; and 4) AI may be able to provide an effective method to monitor the type and dosage of home exercises.
Several clinical applications for AI and ML-based tools were identified in PT practice, including diagnosis, prognosis, treatment outcomes prediction, clinical decision support, movement analysis, patient monitoring, and personalized care plan. Future empirical research on AI applications in PT practice is required to enhance clinical practices, improve patient outcomes, and ensure the ethical integration of technology in PT practice.
With the advancement of AI and ML and the potential for implementation in PT practice, PT clinicians and educators need to be aware of this evolution and collaborate with other disciplines such as engineers, and data scientists to develop AI applications so that they can be used to complement physical therapists' clinical decision-making skills to deliver patient care that meets the patient's desires and goals. Also, the effectiveness and outcomes of PT treatment can then be better assessed.
machine learning
Physical Therapy Practice
