MyopiaNCT06607822
Development and Validation of a Large Language Model-based Myopia Assistant System
A study testing A patient-centered assistant system based on Large-Language Model (LLM) in people with Myopia and Large Language Model, run by The Hong Kong Polytechnic University at 1 site in China.
Trial summary
Myopia is a rapidly growing global health concern, and there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. Read more about this study
- Who is it for?
- Ages 6 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Myopia, Large Language Model
- What is being tested?
- A patient-centered assistant system based on Large-Language Model (LLM)
- Type of treatment
- Pill or oral medicine, Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 70 participants
- Where
- 1 siteChina
- Study dates
- 21 Sep 2024to 26 Oct 2024
- Phase
- Interventional
- 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 6 to 75 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Outpatient participants aged 6 to 75.
- Participants who undergo ophthalmic examinations for medical purposes.
- Participants who can produce clear ophthalmic images in both eyes.
- No prior experience in research involving digital medicine
- Agree to participate in this study with written informed consent
Who cannot join
- Participants who are reluctant to participate in this study
- Participants who are unable to understand the study.
- Participants who have recently undergone ocular surgery or those with severe ocular conditions that may affect the interpretation of imaging results related to myopia evaluation (e.g., severe vitreous hemorrhage, cataracts, corneal leukoma, etc.) will be excluded from the study.
- Participants with poor quality of ophthalmic images, including blurriness, artifacts, underexposure, or overexposure.
- Other unsuitable reasons determined by the evaluators.
Trial locations and contacts
1 site in 1 country.
About this study
Myopia is a rapidly growing global health concern, and there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. In this study, investigators aim to validate a patient-centered Large Language Model (LLM)-based Myopia Assistant System with the following key objectives: 1) evaluate the ability of the LLM models to generate high-level reports and help self-evaluation of myopia for patients in primary care; 2) evaluate its performance in answering evidence-based medicine-oriented questions and improving overall satisfaction within clinics for myopic patients.
Read the detailed description
Myopia is a rapidly growing global health concern particularly affecting children and adolescents. The progression of myopia can lead to severe complications such as myopic macular degeneration, significantly impacting visual acuity and quality of life. With the rising prevalence of myopia, there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. Nevertheless, their effectiveness and safety in real clinical environments have not been fully explored.
In this study, investigators aim to validate a patient-centered Large Language Model (LLM)-based Myopia Assistant System with the following key objectives: 1) evaluate the ability of the LLM models to generate high-level reports and help self-evaluation of myopia for patients in primary care; 2) evaluate its performance in answering evidence-based medicine-oriented questions and improving overall satisfaction within clinics for myopic patients. The findings of this study will provide valuable insights for the application of the GPT model in the healthcare field, making a significant contribution to improving the accessibility and quality of medical services.
- Conditions
- Myopia, Large Language Model
- Being tested
- A patient-centered assistant system based on Large-Language Model (LLM)
- Keywords
- Myopia, Large language model, Assistance
- Medical subject headings
- Myopia, Helping Behavior
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
A patient-centered assistant system based on Large-Language Model (LLM)Device
Participants will engage in a 10-minute medical consultation using LLM model interface embedded in a tablet device before their regular face-to-face consulation with physicians. During the trials, participants could engage in free conversations covering aspects including risk factors, symptoms, diagnosis, examinations, treatment, advice and caution, etc. Participants who have completed the ophthalmic imaging examination will be asked to input results into the assistant model to generate structured reports.
| Study group | Role | What they receive |
|---|---|---|
| Patient-centered assistant system | Experimental | Participants engaged in the outpatient clinic visit procedure with a patient-centered assistant system based on Large-Language Model (LLM) for 10 minutes. Receives: Device: A patient-centered assistant system based on Large-Language Model (LLM) |
| Control group | No intervention | Participants engaged in the outpatient clinic visit procedure without the support of patient-centered assistant system based on Large-Language Model (LLM) or any similar artificial intelligence assistance for 10 minutes. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Satisfaction level
Participants satisfaction level of the clinical experience with or without the use of a patient-centered assistant system based on a large language model (LLM) was assessed. The total satisfaction score was reported using the questionnaire (Patient User Satisfaction Scale), which evaluated the participant satisfaction with the clinical experience and the effectiveness of resolving their own issues. The questionnaire was measured on a 5-point Likert scale, where 1 represents strongly disagree; and 5 represents strongly agree; with higher scores indicating greater satisfaction.
Measured: Immediately after the outpatient clinic visit procedure
Secondary outcomes (1)
Whether participants adopt the myopia management advice from the physician
It is a binary outcome that assesses whether participants follow the recommendations from the physician for myopia management. It focuses on whether participants implement the prescribed treatments or interventions provided to control or manage their myopia.
Measured: Immediately after the outpatient clinic visit procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Sep 2024
- Study start21 Sep 2024
- Primary completion26 Oct 2024
- Study completion26 Oct 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for MyopiaStudies 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 N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 70 participants (actual)
Sponsor and investigators
- Lead sponsor
- The Hong Kong Polytechnic University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Mingguang He, M.D, Ph.D, Principal investigator, The Hong Kong Polytechnic University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06607822
- Sponsor's protocol ID
- HSEARS20240229009
- Status last verified
- Feb 2025
- First submitted
- 11 Sep 2024
- Last updated
- 13 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 13 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Large Language Model-based Myopia Assistant System
- ClinicalTrials.gov ID
- NCT06607822
- Acronym / sponsor ID
- HSEARS20240229009
- Conditions
- Myopia, Large Language Model
- Keywords
- Myopia, Large language model, Assistance
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 70 participants (actual)
- Lead sponsor
- The Hong Kong Polytechnic UniversityUniversity, hospital or other
- Study start
- 21 Sep 2024 (actual)
- Primary completion
- 26 Oct 2024 (actual)
- Study completion
- 26 Oct 2024 (actual)
- First posted
- 23 Sep 2024 (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.
Myopia is a rapidly growing global health concern, and there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. In this study, investigators aim to validate a patient-centered Large Language Model (LLM)-based Myopia Assistant System with the following key objectives: 1) evaluate the ability of the LLM models to generate high-level reports and help self-evaluation of myopia for patients in primary care; 2) evaluate its performance in answering evidence-based medicine-oriented questions and improving overall satisfaction within clinics for myopic patients.
Detailed description
Myopia is a rapidly growing global health concern particularly affecting children and adolescents. The progression of myopia can lead to severe complications such as myopic macular degeneration, significantly impacting visual acuity and quality of life. With the rising prevalence of myopia, there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. Nevertheless, their effectiveness and safety in real clinical environments have not been fully explored.
In this study, investigators aim to validate a patient-centered Large Language Model (LLM)-based Myopia Assistant System with the following key objectives: 1) evaluate the ability of the LLM models to generate high-level reports and help self-evaluation of myopia for patients in primary care; 2) evaluate its performance in answering evidence-based medicine-oriented questions and improving overall satisfaction within clinics for myopic patients. The findings of this study will provide valuable insights for the application of the GPT model in the healthcare field, making a significant contribution to improving the accessibility and quality of medical services.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patient-centered assistant system | Experimental | Device: A patient-centered assistant system based on Large-Language Model (LLM)Participants engaged in the outpatient clinic visit procedure with a patient-centered assistant system based on Large-Language Model (LLM) for 10 minutes. |
| Control group | No intervention | –Participants engaged in the outpatient clinic visit procedure without the support of patient-centered assistant system based on Large-Language Model (LLM) or any similar artificial intelligence assistance for 10 minutes. |
Who may qualify
- Ages
- 6 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (5 listed)
- Outpatient participants aged 6 to 75.
- Participants who undergo ophthalmic examinations for medical purposes.
- Participants who can produce clear ophthalmic images in both eyes.
- No prior experience in research involving digital medicine
- Agree to participate in this study with written informed consent
Key exclusion criteria (5 listed)
- Participants who are reluctant to participate in this study
- Participants who are unable to understand the study.
- Participants who have recently undergone ocular surgery or those with severe ocular conditions that may affect the interpretation of imaging results related to myopia evaluation (e.g., severe vitreous hemorrhage, cataracts, corneal leukoma, etc.) will be excluded from the study.
- Participants with poor quality of ophthalmic images, including blurriness, artifacts, underexposure, or overexposure.
- Other unsuitable reasons determined by the evaluators.
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 |
|---|---|---|
| A patient-centered assistant system based on Large-Language Model (LLM)Participants will engage in a 10-minute medical consultation using LLM model interface embedded in a tablet device before their regular face-to-face consulation with physicians. During the trials, participants could engage in free conversations covering aspects including risk factors, symptoms, diagnosis, examinations, treatment, advice and caution, etc. Participants who have completed the ophthalmic imaging examination will be asked to input results into the assistant model to generate structured reports. | Device | Patient-centered assistant system |
Primary outcome
Satisfaction level
Time frame: Immediately after the outpatient clinic visit procedure
Participants satisfaction level of the clinical experience with or without the use of a patient-centered assistant system based on a large language model (LLM) was assessed. The total satisfaction score was reported using the questionnaire (Patient User Satisfaction Scale), which evaluated the participant satisfaction with the clinical experience and the effectiveness of resolving their own issues. The questionnaire was measured on a 5-point Likert scale, where 1 represents strongly disagree; and 5 represents strongly agree; with higher scores indicating greater satisfaction.
1 secondary outcome
Whether participants adopt the myopia management advice from the physician
Time frame: Immediately after the outpatient clinic visit procedure
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 13 Mar 2025.
- Individual participant dataSharing planned: Undecided
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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.
Mingguang He, M.D, Ph.D principal investigator
The Hong Kong Polytechnic University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing A patient-centered assistant system based on Large-Language Model (LLM) in people with Myopia and Large Language Model, run by The Hong Kong Polytechnic University at 1 site in China.
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 5 inclusion and 5 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 13 Mar 2025. 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 China. 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.
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?
Satisfaction level (time frame: Immediately after the outpatient clinic visit procedure). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 13 Mar 2025.