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OpenTrials
Completed

NCT Number: NCT07154680

Ophthalmic Diseases and AI: an 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:

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.

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Key information

Conditions

Sex eligibility

All sexes

Study type

Observational

Primary location

Affiliated Hospital of North Sichuan Medical College

Nanchong, Sichuan, 637000, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • There are patient complaints

Exclusion criteria

  • No patient complaints

Treatment and study plan

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

Diagnostic Test

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

Primary outcomes

  1. Large Language Model Diagnostics

    Time frame: 1 week

    The accuracy of the large language model in diagnosing eye diseases

Secondary outcomes

  1. Large Language Model Medical Assistance

    Time frame: 1 week

    The time of diagnosis of eye diseases and other information of the large language model

Other outcomes

  1. Large Language Model Medical Explanation

    Time frame: 1 week

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

Sponsors and collaborators

Lead sponsor

North Sichuan Medical College

Other

Collaborators

  • Affiliated Hospital of North Sichuan Medical College

Registry information

Official study title

Ophthalmic Diseases and AI: an Parallel Comparison RCT Study

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Sep 4, 2025
Registry last updated
Sep 4, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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