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CompletedNCT07154680Updated Sep 4, 2025

Ophthalmic Diseases and AI: an RCT Study

An observational study in Eye Diseases, sponsored by North Sichuan Medical College. Completed at 1 site in China. Per ClinicalTrials.gov, last updated 2025-09-04.

Sponsored by North Sichuan Medical College · Observational

Study type
Observational
Model
Other
Time perspective
Retrospective
Enrollment
2,000
Sex
All
01

Study 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). 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
02

Conditions studied

  • Eye Diseases

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03

In context

Eye Diseases

740 studies on the registry are indexed under Eye Diseases; 118 are open to participants now.

This study's enrollment of 2,000 is above the median of 120 across 172 observational studies indexed under Eye Diseases.

Browse Eye Diseases studies →

Lead sponsor

North Sichuan Medical College is the lead sponsor of 3 studies on the registry; 1 is open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
Child (0–17), Adult (18–64), Older adult (65+)
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Probability sample

Study population

Patients going to the hospital for regular eye check-ups

Inclusion criteria

  • There are patient complaints

Exclusion criteria

Exclusion Criteria:

  • No patient complaints
05

Study design

Observational model
Other
Time perspective
Retrospective
Enrollment
2,000 participants (actual)
Patient registry
No

Groups and cohorts

  • 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

Interventions

  • Diagnostic testGPT-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

    Input all the patient's information into the large language model and process it using a pre-defined prompt.

06

What researchers measure

Primary outcomes

  1. Large Language Model Diagnostics

    The accuracy of the large language model in diagnosing eye diseases

    Time frame: 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

    Time frame: 1 week

Other outcomes

  1. Large Language Model Medical Explanation

    Large language models diagnose the process of eye disease, attitude to patients, etc

    Time frame: 1 week

07

Study locations

1 site
  • Affiliated Hospital of North Sichuan Medical College
    Nanchong, Sichuan 637000, China
08

References and documents

Individual participant data

Plan to share: Yes

Supporting information: Study protocol, Sap, Icf, Csr, Analytic code

No publications or documents are linked to this record.

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Sep 4, 2025, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07154680
Lead sponsor
North Sichuan Medical College
Collaborators
Affiliated Hospital of North Sichuan Medical College
Responsible party
Zining Luo (Principal Investigator, North Sichuan Medical College) — Principal investigator
First posted
Sep 4, 2025
Start date
Aug 15, 2024
Primary completion
Jan 15, 2025
Completion
Jan 30, 2025
Last update
Sep 4, 2025

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Aug 2025. You cannot join it, but the record below documents what was studied.

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