HealthyPhase 2NCT06809634
Large Linguistic Model for Clinical Reaoning of Physical Therapy Students
A Phase 2 study testing Large Language Model and Conventional in people with Healthy and Artificial Intelligence (AI), run by Neuron, Spain at 1 site in Spain.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 30 yearsHealthy volunteers can take part
- Conditions the study is for
- Healthy, Artificial Intelligence (AI)
- What is being tested?
- Large Language Model and Conventional
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 36 participants
- Where
- 1 siteSpain
- Study dates
- 1 Sep 2023to 30 Jul 2026
- Phase
- Phase 2
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 30 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Spain1 site
Centro Superior de Estudios Universitarios La Salle
Madrid, Madrid, 28023
About this study
Official title: Feasibility of a Randomized Controlled Trial of Large Artificial Intelligence-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students. A Randomized Controlled Trial
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:
Read more
- Experimental Group - Students will receive LLM-based training, engaging with a conversational artificial intelligence model to solve clinical cases over an 4-week period. The model will provide real-time responses to their questions, allowing them to refine their diagnostic and treatment reasoning.
- Control Group - Students will follow the standard curriculum, participating in conventional case-based learning and supervised clinical reasoning exercises without AI-based assistance.
The primary outcome of the study is the improvement in clinical reasoning skills, assessed through standardized written case evaluations and structured practical examinations. Secondary outcomes include changes in digital competence, student engagement levels, overall satisfaction with the educational approach, and cost-effectiveness of the intervention.
By assessing the impact of LLMs on clinical reasoning training, this study seeks to determine whether AI-driven educational tools can effectively complement traditional physiotherapy education and improve student preparedness for real-world clinical practice.
Read the detailed description
Clinical reasoning is a key competency for physical therapy students, allowing them to assess, diagnose, and create treatment plans based on patient information. Despite its importance, traditional educational approaches often limit students' exposure to a broad variety of clinical cases, restricting their ability to develop comprehensive reasoning skills. Advances in artificial intelligence, particularly Large Language Models (LLMs) such as ChatGPT, offer a promising solution by simulating realistic and interactive clinical scenarios.
This randomized controlled trial (RCT) aims to evaluate the effectiveness of an LLM-based intervention compared to traditional training methods in improving clinical reasoning skills among physical therapy students. The third-year students will be randomly assigned to either the experimental group, receiving AI-driven case-based training, or the control group, following conventional curriculum-based case discussions.
The intervention will last 8 weeks, during which students in the experimental group will interact with an LLM to solve weekly clinical cases, mimicking real-world patient encounters. The model will function as a virtual patient, responding to students' inquiries and allowing them to refine their diagnostic reasoning and treatment planning. In contrast, the control group will participate in traditional written and tutor-led case discussions.
Statistical Analysis Plan
Data will be analyzed using SPSS version 29.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistics will be used to summarize baseline characteristics of participants, with continuous variables expressed as mean ± standard deviation (SD) or median [interquartile range], depending on normality, and categorical variables presented as frequency (n) and percentage (%). Normality of distributions will be assessed using the Kolmogorov-Smirnov test and Shapiro-Wilk test. Between-group comparisons will be performed using; Independent t-tests or Mann-Whitney U tests for continuous variables; Chi-square tests or Fisher's exact test for categorical variables; Repeated-measures ANOVA or linear mixed models will be used to evaluate changes over time in clinical reasoning scores, digital competence, and satisfaction levels.
Logistic regression models will be applied to explore predictors of engagement with the LLM-based intervention. Effect sizes (Cohen's d, Rosenthal's r) will be calculated to measure the magnitude of differences observed. A cost-effectiveness analysis will be conducted by comparing the cost of implementing the LLM-based intervention with the improvement in clinical reasoning scores and student engagement levels.
Statistical significance will be set at p < 0.05, and all analyses will be conducted using a two-tailed approach.
- Conditions
- Healthy, Artificial Intelligence (AI)
- Being tested
- Large Language Model, Conventional
- Keywords
- Clinical reasoning, Physical Therapy education, Artificial Intelligence, Large Language Models
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large Language ModelOther
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.
ConventionalOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Other: Conventional |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Assessed at the beginning and end of the 8-week intervention through case-based assessments and practical evaluations.
Secondary outcomes (3)
Digital competences
This outcome evaluates the students' ability to effectively use digital tools, particularly AI-driven platforms like ChatGPT, in the context of clinical reasoning. Students will complete a digital competence questionnaire assessing their skills across several domains, such as data management, health communication, and digital content creation. It will be assessed with the Digital competences questionnaire developed by Montero-Delgado et al. (2020)
Measured: Evaluated at the start and end of the 8-week intervention via the ad hoc digital competence questionnaire.
Student engagement with the intervention
This outcome measures the level of active engagement of students with the LLM-based platform (experimental group) and the traditional case-based learning (control group). Engagement will be assessed based on the frequency of interactions, duration of usage, and completion rates of the assigned cases.
Measured: Monitored throughout the 8-week intervention period with weekly tracking of student interactions and case completions.
Satisfaction with the educational approach
This outcome assesses overall student satisfaction with their learning experience, focusing on the effectiveness, usability, and perceived value of the intervention. Satisfaction will be measured using a Visual Analog Scale (VAS), where students rate their level of satisfaction with the training method.
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Feb 2025
- Study start1 Sep 2023
- Primary completion30 Jun 2026
- Study completion30 Jul 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for HealthyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 2
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Investigator, Outcomes assessor
- Enrolment
- 36 participants (actual)
Sponsor and investigators
- Lead sponsor
- Neuron, Spain
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Alfredo Lerín Calvo, Centro Universitario La Salle
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06809634
- Sponsor's protocol ID
- ALC001NR005
- Status last verified
- Oct 2026
- First submitted
- 30 Jan 2025
- Last updated
- 6 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: In this study, the individual participant data (IPD) will not be shared publicly. The data collected, including clinical reasoning assessments, digital competence evaluations, and satisfaction scores, will be used solely for the purposes of this research study. Access to participant data will be restricted to the research team and will not be made available for sharing with external parties. The privacy and confidentiality of the participants will be strictly maintained throughout the study. All data will be anonymized, and any identifiable information will be securely stored and protected in compliance with applicable data protection regulations. Additionally, the study's results will be shared in aggregate form, ensuring that no individual's data is disclosed or identifiable in any public reports or publications. As part of the ethical commitment to safeguarding participant privacy, any requests for access to IPD will not be granted.
Publications
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.
PubMed 18019107Milad 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.
PubMed 38365427
Study at a glance
From the registry record, last updated by the sponsor on 6 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Feasibility of a Randomized Controlled Trial of Large Artificial Intelligence-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students. A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT06809634
- Acronym / sponsor ID
- ALC001NR005
- Conditions
- Healthy, Artificial Intelligence (AI)
- Keywords
- Clinical reasoning, Physical Therapy education, Artificial Intelligence, Large Language Models
- Phase
- Phase 2
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 36 participants (actual)
- Lead sponsor
- Neuron, SpainUniversity, hospital or other
- Study start
- 1 Sep 2023 (actual)
- Primary completion
- 30 Jun 2026 (actual)
- Study completion
- 30 Jul 2026 (actual)
- First posted
- 5 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Experimental Group - Students will receive LLM-based training, engaging with a conversational artificial intelligence model to solve clinical cases over an 4-week period. The model will provide real-time responses to their questions, allowing them to refine their diagnostic and treatment reasoning.
- Control Group - Students will follow the standard curriculum, participating in conventional case-based learning and supervised clinical reasoning exercises without AI-based assistance.
The primary outcome of the study is the improvement in clinical reasoning skills, assessed through standardized written case evaluations and structured practical examinations. Secondary outcomes include changes in digital competence, student engagement levels, overall satisfaction with the educational approach, and cost-effectiveness of the intervention.
By assessing the impact of LLMs on clinical reasoning training, this study seeks to determine whether AI-driven educational tools can effectively complement traditional physiotherapy education and improve student preparedness for real-world clinical practice.
Detailed description
Clinical reasoning is a key competency for physical therapy students, allowing them to assess, diagnose, and create treatment plans based on patient information. Despite its importance, traditional educational approaches often limit students' exposure to a broad variety of clinical cases, restricting their ability to develop comprehensive reasoning skills. Advances in artificial intelligence, particularly Large Language Models (LLMs) such as ChatGPT, offer a promising solution by simulating realistic and interactive clinical scenarios.
This randomized controlled trial (RCT) aims to evaluate the effectiveness of an LLM-based intervention compared to traditional training methods in improving clinical reasoning skills among physical therapy students. The third-year students will be randomly assigned to either the experimental group, receiving AI-driven case-based training, or the control group, following conventional curriculum-based case discussions.
The intervention will last 8 weeks, during which students in the experimental group will interact with an LLM to solve weekly clinical cases, mimicking real-world patient encounters. The model will function as a virtual patient, responding to students' inquiries and allowing them to refine their diagnostic reasoning and treatment planning. In contrast, the control group will participate in traditional written and tutor-led case discussions.
Statistical Analysis Plan
Data will be analyzed using SPSS version 29.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistics will be used to summarize baseline characteristics of participants, with continuous variables expressed as mean ± standard deviation (SD) or median [interquartile range], depending on normality, and categorical variables presented as frequency (n) and percentage (%). Normality of distributions will be assessed using the Kolmogorov-Smirnov test and Shapiro-Wilk test. Between-group comparisons will be performed using; Independent t-tests or Mann-Whitney U tests for continuous variables; Chi-square tests or Fisher's exact test for categorical variables; Repeated-measures ANOVA or linear mixed models will be used to evaluate changes over time in clinical reasoning scores, digital competence, and satisfaction levels.
Logistic regression models will be applied to explore predictors of engagement with the LLM-based intervention. Effect sizes (Cohen's d, Rosenthal's r) will be calculated to measure the magnitude of differences observed. A cost-effectiveness analysis will be conducted by comparing the cost of implementing the LLM-based intervention with the improvement in clinical reasoning scores and student engagement levels.
Statistical significance will be set at p < 0.05, and all analyses will be conducted using a two-tailed approach.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (investigator, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| LLM group | Experimental | Other: Large Language ModelParticipants 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. |
| Conventional learning group | Active comparator | Other: ConventionalParticipants 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. |
Who may qualify
- Ages
- 18 to 30 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large Language ModelThe 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. | Other | LLM group |
| ConventionalThe 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. | Other | Conventional learning group |
Primary outcome
Clinical Reasoning Performance
Time frame: Assessed at the beginning and end of the 8-week intervention through case-based assessments and practical evaluations.
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.
3 secondary outcomes
Digital competences
Time frame: Evaluated at the start and end of the 8-week intervention via the ad hoc digital competence questionnaire.
Student engagement with the intervention
Time frame: Monitored throughout the 8-week intervention period with weekly tracking of student interactions and case completions.
Satisfaction with the educational approach
Time frame: 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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Oct 2026.
- Background: PubMed 18019107Lasater 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.
- Background: PubMed 38365427Milad 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.
- Individual participant dataSharing planned: No. In this study, the individual participant data (IPD) will not be shared publicly. The data collected, including clinical reasoning assessments, digital competence evaluations, and satisfaction scores, will be used solely for the purposes of this research study. Access to participant data will be restricted to the research team and will not be made available for sharing with external parties. The privacy and confidentiality of the participants will be strictly maintained throughout the study. All data will be anonymized, and any identifiable information will be securely stored and protected in compliance with applicable data protection regulations. Additionally, the study's results will be shared in aggregate form, ensuring that no individual's data is disclosed or identifiable in any public reports or publications. As part of the ethical commitment to safeguarding participant privacy, any requests for access to IPD will not be granted.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A Phase 2 study testing Large Language Model and Conventional in people with Healthy and Artificial Intelligence (AI), run by Neuron, Spain at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Phase 2 studies give the treatment to a larger group to see whether it has the intended effect and to keep checking safety.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical Reasoning Performance (time frame: Assessed at the beginning and end of the 8-week intervention through case-based assessments and practical evaluations.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06809634, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Oct 2026.