Efficacy of Using Large Language Model to Assist in Diabetic Retinopathy Detection

"Following the baseline assessment, participants will be guided to use a self-evaluation tool independently to assess their risk of diabetic retinopathy (DR). This tool is a fusion of a conversational AI system based on LLM and an existing logistic diagnostic model.~The AI system is designed to collect clinical variables, including age, duration of diabetes, Body Mass Index (BMI), and insulin usage. Additionally, clinical test data such as mean arterial pressure, HbA1c, serum creatinine, and microalbuminuria will be extracted from a local dataset using the patient's name and ID. Once collected, these data will be transmitted to a server-based diagnostic model for further analysis to determine the presence of DR."