Ophthalmic Diseases (Specific Types Not Restricted)
Conditions
Brief summary
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.
Interventions
None listed
Sponsors
Zhongshan Ophthalmic Center, Sun Yat-sen University
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Metrics for Evaluating Referral Accuracy of Large Language Models: Sensitivity, Specificity, Accuracy, Positive Predictive Value, Negative Predictive Value. | through study completion, up to 1 year. |
Contacts
Primary ContactHaotian Lin
Backup ContactMingjie Luo
Outcome results
None listed