Eye DiseasesNCT07154680
Ophthalmic Diseases and AI: an RCT Study
An observational study of Eye Diseases, run by North Sichuan Medical College at 1 site in China.
Trial summary
Ophthalmic diseases are a major category of conditions affecting visual health, including but not limited to cataracts, glaucoma, retinal and choroidal diseases, and refractive errors (such as myopia, hyperopia, and astigmatism). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Eye Diseases
- What is being tested?
- GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 15 Aug 2024to 30 Jan 2025
- Phase
- Observational
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients going to the hospital for regular eye check-ups
Who may be able to join
- There are patient complaints
Who cannot join
- No patient complaints
Trial locations and contacts
1 site in 1 country.
China1 site
Affiliated Hospital of North Sichuan Medical College
Nanchong, Sichuan, 637000
About this study
Official title: Ophthalmic Diseases and AI: an Parallel Comparison RCT Study
Ophthalmic diseases are a major category of conditions affecting visual health, including but not limited to cataracts, glaucoma, retinal and choroidal diseases, and refractive errors (such as myopia, hyperopia, and astigmatism). With the advancement of technology, artificial intelligence (AI) is being increasingly applied in the field of ophthalmology. This clinical trial aims to evaluate the potential of large language models (LLMs) in ophthalmology.
The main questions to be addressed are:
- Assessing the effectiveness of large language models (LLMs) in the diagnosis and treatment of ophthalmic diseases: Through randomized controlled trials (RCTs), evaluate the diagnostic and treatment effectiveness of LLMs in the field of ophthalmic diseases, exploring their potential to improve the quality and efficiency of ophthalmic care.
- Investigating the role of LLMs in medical consultations: Explore the role and effectiveness of LLMs in medical consultations for ophthalmic diseases, including their ability to provide medical advice, explain diagnostic results, and help patients understand treatment plans.
- Examining the ability of LLMs to adhere to ethical standards: Study how to ensure that LLMs comply with ethical standards and moral principles in ophthalmic medical consultations, safeguarding patient privacy and rights.
- Providing new technological support for the field of ophthalmology: Through research on the application of LLMs in ophthalmic diseases, offer new technological support and innovations to enhance the quality and efficiency of ophthalmic care.
- Exploring the differences between LLMs and ophthalmologists: By utilizing multiple large language models, compare the differences between LLMs and ophthalmologists in diagnostic outcomes, case analysis processes, and patient experiences during diagnosis and treatment.
- Evaluating the effectiveness of LLMs in ophthalmic diseases: Collect patient complaints, fundus images, doctors' diagnoses, and diagnosis times from offline doctor consultations, as well as gather AI-generated medical advice, diagnostic efficiency, and diagnostic accuracy online. Ultimately, conduct comprehensive data analysis to determine the feasibility and effectiveness of LLMs in diagnosing and treating ophthalmic diseases.
- Conditions
- Eye Diseases
- Being tested
- GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B
- Medical subject headings
- Eye Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5BDiagnostic test
Input all the patient's information into the large language model and process it using a pre-defined prompt.
| Study group | Role | What they receive |
|---|---|---|
| Large Language Model Diagnostics Group | Not described Receives: Diagnostic Test: GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B | |
| Large Language Model Medical Assistance Group | Not described Receives: Diagnostic Test: GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B | |
| Large Language Model Medical Explanation Team | Not described Receives: Diagnostic Test: GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B |
What is being measured
The primary outcome is the main question the study is designed to answer.
Large Language Model Diagnostics
The accuracy of the large language model in diagnosing eye diseases
Measured: 1 week
Secondary outcomes (1)
Large Language Model Medical Assistance
The time of diagnosis of eye diseases and other information of the large language model
Measured: 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Sep 2025
- Study start15 Aug 2024
- Primary completion15 Jan 2025
- Study completion30 Jan 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Eye DiseasesStudies 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
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 2,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- North Sichuan Medical College
- Sponsor type
- University, hospital or other
- Collaborators
- Affiliated Hospital of North Sichuan Medical College
- Responsible party
- Principal investigator, Zining Luo, North Sichuan Medical College
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07154680
- Sponsor's protocol ID
- 2765326471-2024-1
- Status last verified
- Aug 2025
- First submitted
- 29 Dec 2024
- Last updated
- 4 Sep 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Study at a glance
From the registry record, last updated by the sponsor on 4 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Ophthalmic Diseases and AI: an Parallel Comparison RCT Study
- ClinicalTrials.gov ID
- NCT07154680
- Acronym / sponsor ID
- 2765326471-2024-1
- Conditions
- Eye Diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,000 participants (actual)
- Lead sponsor
- North Sichuan Medical CollegeUniversity, hospital or other
- Collaborators
- Affiliated Hospital of North Sichuan Medical College
- Study start
- 15 Aug 2024 (actual)
- Primary completion
- 15 Jan 2025 (actual)
- Study completion
- 30 Jan 2025 (actual)
- First posted
- 4 Sep 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.
Ophthalmic diseases are a major category of conditions affecting visual health, including but not limited to cataracts, glaucoma, retinal and choroidal diseases, and refractive errors (such as myopia, hyperopia, and astigmatism). With the advancement of technology, artificial intelligence (AI) is being increasingly applied in the field of ophthalmology. This clinical trial aims to evaluate the potential of large language models (LLMs) in ophthalmology.
The main questions to be addressed are:
- Assessing the effectiveness of large language models (LLMs) in the diagnosis and treatment of ophthalmic diseases: Through randomized controlled trials (RCTs), evaluate the diagnostic and treatment effectiveness of LLMs in the field of ophthalmic diseases, exploring their potential to improve the quality and efficiency of ophthalmic care.
- Investigating the role of LLMs in medical consultations: Explore the role and effectiveness of LLMs in medical consultations for ophthalmic diseases, including their ability to provide medical advice, explain diagnostic results, and help patients understand treatment plans.
- Examining the ability of LLMs to adhere to ethical standards: Study how to ensure that LLMs comply with ethical standards and moral principles in ophthalmic medical consultations, safeguarding patient privacy and rights.
- Providing new technological support for the field of ophthalmology: Through research on the application of LLMs in ophthalmic diseases, offer new technological support and innovations to enhance the quality and efficiency of ophthalmic care.
- Exploring the differences between LLMs and ophthalmologists: By utilizing multiple large language models, compare the differences between LLMs and ophthalmologists in diagnostic outcomes, case analysis processes, and patient experiences during diagnosis and treatment.
- Evaluating the effectiveness of LLMs in ophthalmic diseases: Collect patient complaints, fundus images, doctors' diagnoses, and diagnosis times from offline doctor consultations, as well as gather AI-generated medical advice, diagnostic efficiency, and diagnostic accuracy online. Ultimately, conduct comprehensive data analysis to determine the feasibility and effectiveness of LLMs in diagnosing and treating ophthalmic diseases.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Large Language Model Diagnostics Group | – | Diagnostic Test: GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B |
| Large Language Model Medical Assistance Group | – | Diagnostic Test: GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B |
| Large Language Model Medical Explanation Team | – | Diagnostic Test: GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients going to the hospital for regular eye check-ups
Key inclusion criteria (1 listed)
- There are patient complaints
Key exclusion criteria (1 listed)
- No patient complaints
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 |
|---|---|---|
| GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5BInput all the patient's information into the large language model and process it using a pre-defined prompt. | Diagnostic test | Large Language Model Diagnostics Group, Large Language Model Medical Assistance Group, Large Language Model Medical Explanation Team |
Primary outcome
Large Language Model Diagnostics
Time frame: 1 week
The accuracy of the large language model in diagnosing eye diseases
1 secondary outcome
Large Language Model Medical Assistance
Time frame: 1 week
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 4 Sep 2025.
- Individual participant dataSharing planned: Yes
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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?
An observational study of Eye Diseases, run by North Sichuan Medical College 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 1 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: finished, as of the sponsor's last update on 4 Sep 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?
Large Language Model Diagnostics (time frame: 1 week). 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 4 Sep 2025.