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.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Metrics for Evaluating Referral Accuracy of Large Language Models: Sensitivity, Specificity, Accuracy, Positive Predictive Value, Negative Predictive Value.
Timeframe: through study completion, up to 1 year.