Ophthalmic Diseases (Specific Types Not Restricted)NCT06966882
Research on the Real-World Community Application of Large Language Models
An observational study of Ophthalmic Diseases (Specific Types Not Restricted), run by Zhongshan Ophthalmic Center, Sun Yat-sen University.
Trial summary
There is an imbalance between the supply and demand of eye care services, especially in local communities and remote areas. To address this, it's important to use new intelligent technologies to expand the reach of eye disease screening and treatment. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Ophthalmic Diseases (Specific Types Not Restricted)
- How many people
- 314 participants
- Where
- No sites listed yet
- Study dates
- May 2025to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: community sample or primary care clinic
Who may be able to join
- Participants of any age and gender
- Belonging to one of the following ophthalmic categories: Patients requiring specialist referral；Patients manageable at community level；Individuals without ocular pathology
- Voluntary participation with written informed consent
Who cannot join
- Investigator-determined clinical contraindications
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
There is an imbalance between the supply and demand of eye care services, especially in local communities and remote areas. To address this, it's important to use new intelligent technologies to expand the reach of eye disease screening and treatment. Large language models (LLMs) are a type of deep learning technology that can learn from large amounts of text and generate human-like language to help with medical tasks such as diagnosing diseases and answering health-related questions. The investigator's team has previously developed a localized LLM capable of answering ophthalmology-related medical questions. Building on this, this study plans to use a screening-based trial design to explore how accurately the LLM can make referral decisions for eye diseases, diagnose conditions, recommend appropriate tests, and receive user feedback in real-world community settings. The goal is to improve the ability to screen for eye diseases in grassroots and regional areas.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Negative group | Patients manageable at community level；Individuals without ocular pathology | |
| Positive group | Patients requiring specialist referral |
What is being measured
The primary outcome is the main question the study is designed to answer.
Metrics for Evaluating Referral Accuracy of Large Language Models: Sensitivity, Specificity, Accuracy, Positive Predictive Value, Negative Predictive Value.
Measured: through study completion, up to 1 year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 May 2025
- Study startMay 2025 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ophthalmic Diseases (Specific Types Not Restricted)Studies of the same condition, open and under way.
- More trials run by Zhongshan Ophthalmic Center, Sun Yat-sen UniversityThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 314 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06966882
- Sponsor's protocol ID
- 2025KYPJ020
- Status last verified
- May 2025
- First submitted
- 23 Apr 2025
- Last updated
- 13 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on the Real-World Community Application of Large Language Models
- ClinicalTrials.gov ID
- NCT06966882
- Acronym / sponsor ID
- 2025KYPJ020
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 314 participants (estimated)
- Lead sponsor
- Zhongshan Ophthalmic Center, Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- May 2025 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 13 May 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.
There is an imbalance between the supply and demand of eye care services, especially in local communities and remote areas. To address this, it's important to use new intelligent technologies to expand the reach of eye disease screening and treatment. Large language models (LLMs) are a type of deep learning technology that can learn from large amounts of text and generate human-like language to help with medical tasks such as diagnosing diseases and answering health-related questions. The investigator's team has previously developed a localized LLM capable of answering ophthalmology-related medical questions. Building on this, this study plans to use a screening-based trial design to explore how accurately the LLM can make referral decisions for eye diseases, diagnose conditions, recommend appropriate tests, and receive user feedback in real-world community settings. The goal is to improve the ability to screen for eye diseases in grassroots and regional areas.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Negative group | – | –Patients manageable at community level；Individuals without ocular pathology |
| Positive group | – | –Patients requiring specialist referral |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- community sample or primary care clinic
Key inclusion criteria (3 listed)
- Participants of any age and gender
- Belonging to one of the following ophthalmic categories: Patients requiring specialist referral；Patients manageable at community level；Individuals without ocular pathology
- Voluntary participation with written informed consent
Key exclusion criteria (1 listed)
- Investigator-determined clinical contraindications
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Metrics for Evaluating Referral Accuracy of Large Language Models: Sensitivity, Specificity, Accuracy, Positive Predictive Value, Negative Predictive Value.
Time frame: through study completion, up to 1 year.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 May 2025.
Related trials for Ophthalmic Diseases (Specific Types Not Restricted)
Not yet recruitingObservational study
Research on a New Intelligent Mobile Screening and Diagnosis Pattern for Ocular Diseases
Ophthalmic Diseases (Specific Types Not Restricted), Cataract
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.
Questions about this trial
What is this clinical trial studying?
An observational study of Ophthalmic Diseases (Specific Types Not Restricted), run by Zhongshan Ophthalmic Center, Sun Yat-sen University.
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 3 inclusion and 1 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: opening soon, as of the sponsor's last update on 13 May 2025. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Haotian Lin (86-13802793086, haot.lin@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Metrics for Evaluating Referral Accuracy of Large Language Models: Sensitivity, Specificity, Accuracy, Positive Predictive Value, Negative Predictive Value. (time frame: through study completion, up to 1 year.). 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 May 2025.