Research on the Real-World Community Application of Large Language Models

Sun Yat-sen University

Status: Not yet enrolling

Conditions: Ophthalmic Diseases (Specific Types Not Restricted)

Study type: Observational

Funder types: Other

Identifiers

NCT06966882

2025KYPJ020

Details and patient eligibility

About

There is an imbalance between the supply and demand of eye care services, especially in local communities and remote areas. To address this, it's important to use new intelligent technologies to expand the reach of eye disease screening and treatment. Large language models (LLMs) are a type of deep learning technology that can learn from large amounts of text and generate human-like language to help with medical tasks such as diagnosing diseases and answering health-related questions. The investigator's team has previously developed a localized LLM capable of answering ophthalmology-related medical questions. Building on this, this study plans to use a screening-based trial design to explore how accurately the LLM can make referral decisions for eye diseases, diagnose conditions, recommend appropriate tests, and receive user feedback in real-world community settings. The goal is to improve the ability to screen for eye diseases in grassroots and regional areas.

Enrollment: 314 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Participants of any age and gender
• Belonging to one of the following ophthalmic categories: Patients requiring specialist referral；Patients manageable at community level；Individuals without ocular pathology
• Voluntary participation with written informed consent

Exclusion criteria

• Investigator-determined clinical contraindications

Trial design

314 participants in 2 patient groups

Negative group

Description:

Patients manageable at community level；Individuals without ocular pathology

Positive group

Description:

Patients requiring specialist referral