Large Linguistic Model for Clinical Reaoning of Physical Therapy Students

NCT06809634 | Recruiting Phase 2

• 1 other identifier: ALC001NR005
• Study Type: interventional
• Target: 60
• Locations: 1 country
• Sites: 1

Brief Summary

Clinical reasoning is a fundamental skill for physical therapy students, enabling them to collect and interpret patient information to make accurate diagnoses and treatment decisions. Traditional training methods often limit students' exposure to a diverse range of clinical cases, which can restrict the development of these skills. The integration of Large Language Models (LLMs), such as ChatGPT, into physical therapy education offers a novel approach to enhance clinical reasoning by simulating interactive and realistic patient scenarios. This randomized controlled trial aims to evaluate the effectiveness of an LLM-based educational intervention in improving clinical reasoning skills in physical therapy students. The study will recruit a total of 200 third-year physiotherapy students from multiple university institutions. Participants will be randomly assigned to one of two groups:

1. 1.Experimental Group - Students will receive LLM-based training, engaging with a conversational artificial intelligence model to solve clinical cases over an 8-week period. The model will provide real-time responses to their questions, allowing them to refine their diagnostic and treatment reasoning.
2. 2.Control Group - Students will follow the standard curriculum, participating in conventional case-based learning and supervised clinical reasoning exercises without AI-based assistance.

Conditions

Healthy; Artificial Intelligence (AI)

Keywords

Clinical reasoning; Physical Therapy education; Artificial Intelligence; Large Language Models

Outcome Measures

Primary Outcomes (1)

• Clinical Reasoning Performance

  This outcome measures the improvement in students' clinical reasoning skills after the intervention. Students will be assessed based on their ability to collect, interpret, and analyze patient information, and formulate accurate diagnoses and treatment plans. This will be evaluated through both written case studies and practical exams using the Lasater rubric, being this scale the instrument used for evaluating this outcome.

  Assessed at the beginning and end of the 8-week intervention through case-based assessments and practical evaluations.

Secondary Outcomes (3)

• Digital competences

  Evaluated at the start and end of the 8-week intervention via the ad hoc digital competence questionnaire.

• Student engagement with the intervention

  Monitored throughout the 8-week intervention period with weekly tracking of student interactions and case completions.

• Satisfaction with the educational approach

  Calculated at the end of the intervention period, using the costs associated with providing access to the LLM-based platform and comparing it to the improvements observed in other outcomes.

Study Arms (2)

LLM group

EXPERIMENTAL

Participants in the experimental group will undergo an 8-week intervention incorporating Large Language Model (LLM)-based training into their clinical reasoning education. Students will engage in weekly clinical case simulations using an LLM-powered platform (ChatGPT), where they will interact with the model to obtain patient information, formulate diagnoses, and propose treatment plans. The LLM will provide real-time responses, simulating a virtual patient encounter. The training will complement the standard curriculum, allowing students to practice clinical reasoning skills in a structured and interactive AI-assisted environment. At the end of the intervention, participants will complete a final case-based assessment to evaluate improvements in clinical reasoning, digital competence, and engagement with the technology.

Other: Large Language Model

Conventional learning group

ACTIVE COMPARATOR

Participants in the control group will follow the standard curriculum for clinical reasoning training over an 8-week period, without exposure to the LLM-based intervention. Students will engage in weekly case-based discussions using traditional learning methods, including written case analyses and supervised discussions with instructors. These sessions will follow the usual educational framework used in physical therapy training programs, emphasizing diagnostic reasoning and treatment planning through instructor-led guidance. At the end of the training period, participants will complete a final case-based assessment to evaluate their clinical reasoning skills, digital competence, and overall engagement with the learning process.

Other: Conventional

Interventions

Large Language Model OTHER

The intervention in the experimental group is distinguished by the integration of a Large Language Model (LLM)-based interactive platform (ChatGPT) into clinical reasoning training for physical therapy students. Unlike traditional educational approaches, this intervention provides real-time, AI-generated patient interactions, allowing students to actively engage in virtual clinical case simulations.

LLM group

Conventional OTHER

The intervention in the control group follows a traditional case-based learning approach, which is commonly used in physical therapy education. Unlike the experimental group, this training method relies solely on human-led instruction and written case analysis, without the integration of artificial intelligence or interactive digital tools.

Conventional learning group

Eligibility Criteria

• Age: 18 Years - 30 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• Students enrolled in the third year of the Physiotherapy program at La Salle Centre for Higher University Studies (LCHUS)
• Participants must be between 18 and 30 years old.
• Students must agree to participate in the study by signing an informed consent form after being briefed about the study's objectives, procedures, and potential risks.
• Participants must be willing to engage with the LLM-based platform (for the experimental group) or participate in traditional learning activities (for the control group) for the duration of the study.

You may not qualify if:

• Students with previous clinical experience beyond the third year of physiotherapy education.
• Physical or cognitive disabilities that may interfere with the ability to participate in or benefit from the intervention (e.g., vision, hearing, or motor impairments).
• Students who do not provide informed consent to participate in the study.
• Students who do not possess sufficient proficiency in Spanish or English to understand the materials and the intervention.

Sponsors & Collaborators

• Neuron, Spain: lead

Study Sites (1)

Centro Superior de Estudios Universitarios La Salle

Madrid, Madrid, 28023, Spain

RECRUITING

Related Publications (2)

• Lasater K. Clinical judgment development: using simulation to create an assessment rubric. J Nurs Educ. 2007 Nov;46(11):496-503. doi: 10.3928/01484834-20071101-04.

  PMID: 18019107 BACKGROUND

• Milad D, Antaki F, Milad J, Farah A, Khairy T, Mikhail D, Giguere CE, Touma S, Bernstein A, Szigiato AA, Nayman T, Mullie GA, Duval R. Assessing the medical reasoning skills of GPT-4 in complex ophthalmology cases. Br J Ophthalmol. 2024 Sep 20;108(10):1398-1405. doi: 10.1136/bjo-2023-325053.

  PMID: 38365427 BACKGROUND

MeSH Terms

Interventions

Large Language Models; Congresses as Topic

Intervention Hierarchy (Ancestors)

Deep Learning; Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts; Neural Networks, Computer; Organizations; Health Care Economics and Organizations

Central Study Contacts

Alfredo Lerín Calvo, Professor

CONTACT

+34620187457; alfredo.lerin@lasallecampus.es

Study Design

• Study Type: interventional
• Phase: phase 2
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: January 30, 2025
• First Posted: February 5, 2025
• Study Start: September 1, 2023
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): July 30, 2026
• Last Updated: December 4, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)