JMIR Publications

ABSTRACT

Background:

Clinical reasoning is a critical skill for physical therapists, involving the collection and interpretation of patient information to form accurate diagnoses. Traditional training often lacks the diversity of clinical cases necessary for students to develop these skills comprehensively. Large Language Models (LLMs) like GPT-4 have the potential to simulate a wide range of clinical scenarios, offering a novel approach to enhance clinical reasoning in physical therapy education.

Objective:

The aim of this study is to explore the main barriers and facilitators that could be encountered in conducting a randomised clinical trial to study the effectiveness of the implementation of LLM models as tools to work on the clinical reasoning of physiotherapy students.

Methods:

This pilot randomized parallel-group study involved 46 third-year physiotherapy students at La Salle Centre for Higher University Studies. Participants were randomly assigned to either the experimental group, which received LLM training, or the control group, which followed the usual curriculum. The intervention lasted for four weeks, during which the experimental group used LLM to solve weekly clinical cases. Digital competences, satisfaction, and costs were evaluated in order to explore the feasibility of this intervention.

Results:

The recruitment and participation rates were high, but active engagement with the LLM was low, with only 5.75% of the experimental group actively using the model. No significant difference in overall satisfaction was found between the groups and the cost analysis reflected an initial cost of 460€ for completing the study.

Conclusions:

While LLMs have the potential to enhance specific digital competences in physical therapy students, their practical integration into the curriculum faces challenges. Future studies should focus on improving student engagement with LLMs and extending the training period to maximize benefits.