AI-Powered Physiotherapy: Evaluating LLMs Against Students in Clinical Rehabilitation Scenarios

Abstract

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) like ChatGPT and DeepSeek, is transforming healthcare by enhancing clinical decision-making, education, and patient interaction. This study compares ChatGPT (GPT-4) and DeepSeek against 60 final-year physiotherapy students in Greece answering 60 clinical questions across four rehabilitation domains: low back pain, multiple sclerosis, frozen shoulder, and knee osteoarthritis (15 questions per domain). Questions spanned basic knowledge, diagnosis, alternative treatments, and rehabilitation practices. Responses were evaluated for relevance, accuracy, clarity, completeness, and consistency with clinical practice guidelines (CPGs), emphasizing conceptual understanding. Results indicate LLMs outperformed students in most domains, particularly in global response quality and conceptual depth, raising questions about AI’s role in physiotherapy. This manuscript explores these findings, compares them with related work, and discusses whether GenAI could transform or threaten physiotherapy. Ethical considerations, limitations, and future directions, including AI voice assistants and AI characters, are addressed.

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