Ophthalmic Diseases and AI: an RCT Study

Completed

• Conditions: Eye Diseases

• Registration Number: NCT07154680
• Lead Sponsor: North Sichuan Medical College

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2024-08-15
• Study First Submitted: 2024-12-29
• Primary Completion: 2025-01-15
• Study Completion: 2025-01-30
• Status Verified: 2025-08
• Study First Submitted QC: 2025-08-26
• Last Update Submitted: 2025-08-26
• Study First Posted: 2025-09-04
• Last Update Posted: 2025-09-04

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 2000

Inclusion Criteria

• There are patient complaints

Exclusion Criteria

• No patient complaints

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Large Language Model Diagnostics	1 week	The accuracy of the large language model in diagnosing eye diseases

Secondary Outcome Measures

Name	Time	Method
Large Language Model Medical Assistance	1 week	The time of diagnosis of eye diseases and other information of the large language model

Trial Locations

Locations (1)

Affiliated Hospital of North Sichuan Medical College; 🇨🇳 Nanchong, Sichuan, China