Eye Diseases
Conditions
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.
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.
Sponsors
Affiliated Hospital of North Sichuan Medical College
North Sichuan Medical College
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* There are patient complaints
Exclusion criteria
* No patient complaints
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Large Language Model Diagnostics | 1 week | The accuracy of the large language model in diagnosing eye diseases |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Large Language Model Medical Assistance | 1 week | The time of diagnosis of eye diseases and other information of the large language model |
Other
| Measure | Time frame | Description |
|---|---|---|
| Large Language Model Medical Explanation | 1 week | Large language models diagnose the process of eye disease, attitude to patients, etc |
Countries
China
Outcome results
None listed