AI-Generated Clinical Case Studies In Physiotherapy: Enhancing Education Through Integrated Artificial Intelligence

Abstract

Building on previous research, this study advances the use of AI-generated clinical case studies, specifically targeting various domains in physiotherapy. This work involved the creation of ten detailed clinical cases using a large language model (LLM) known as OpenAI's ChatGPT (Generative Pretrained Transformer; OpenAI). Each case was carefully designed to simulate real-world scenarios that physiotherapy students might encounter in their professional practice, covering diverse areas such as orthopedics, neurology, cardiopulmonary, and geriatrics. To ensure the generated cases adhered to high standards of educational quality, the prompts provided to ChatGPT were meticulously reformulated following established guidelines from the literature. Moreover, a classical physiotherapy textbook was employed as a reference for formatting and structuring the clinical reports. Preliminary feedback from physiotherapy educators and students suggests that the AI-generated content effectively mimics human-authored clinical cases, providing a valuable tool for enhancing clinical reasoning skills and bridging the gap between theoretical knowledge and practical application. Future research will focus on refining the AI prompts further and expanding the range of clinical scenarios to cover a broader spectrum of physiotherapy practice.

Keywords: Artificial Intelligence, Clinical Education, Physiotherapy, Large Language Model, Case Studies.

DOI: 10.54941/ahfe1005935