Artificial Intelligence in Physical Therapy: A Narrative Review of Artificial Intelligence Applications Across the Patient and Client Management Model

ABSTRACT Background and Aims Artificial intelligence (AI) is rapidly transforming healthcare and has increasing relevance for physical therapy practice. This review aims to examine how AI can be integrated across the continuum of care within the Physical Therapy Patient and Client Management Model. Methods This narrative review synthesizes current literature and emerging applications of AI in rehabilitation and discusses their relevance to the six components of the Physical Therapy Patient and Client Management Model: examination, evaluation, diagnosis, prognosis, intervention, and outcomes. Examples from recent research and clinical innovations are used to illustrate how AI tools may support physical therapy practice. Results AI technologies have the potential to enhance multiple stages of patient management. During examination, AI‐driven systems can automate patient intake and assist with triage by identifying clinical red and yellow flags. In evaluation and diagnosis, computer vision and wearable sensor systems enable objective movement analysis and functional assessment. For prognosis, machine learning models can support risk stratification and predict rehabilitation outcomes using multidimensional clinical data. In intervention, large language models and AI‐enabled platforms may assist clinical decision‐making and facilitate remote therapeutic monitoring. AI can also improve outcome measurement and benchmarking by analyzing large datasets and developing efficient assessment tools. Conclusion AI presents significant opportunities to enhance efficiency, personalization, and data‐informed decision‐making in physical therapy. By thoughtfully integrating AI into each phase of care, physical therapists can enhance decision‐making, improve health outcomes, and strengthen the therapeutic alliance.