Objective: To evaluate the appropriateness of responses generated by an online chat-based artificial intelligence (AI) model for diabetic retinopathy (DR) related questions.
Design: Cross-sectional study.
Methods: A set of 20 questions framed from the patient's perspective addressing DR-related queries, such as the definition of disease, symptoms, prevention methods, treatment options, diagnostic methods, visual impact, and complications, were formulated for input into ChatGPT-4.
Purpose: To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross-referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic.
Methods: This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician's clinic.
Background: To analyse the distribution of spectral domain optical coherence tomography (SD-OCT) biomarkers in different types of vitreomacular adhesion (VMA) associated visual impairment in diabetic macular oedema.
Methods: A total of 317 eyes of 202 patients were enrolled. Cases were divided into two groups focal VMA and broad VMA and subjects with no VMA were enrolled as controls.