AI Medical Compendium Journal:
Clinical ophthalmology (Auckland, N.Z.)

PURPOSE: This study aims to evaluate the inter-observer variability in assessing the optic disc in fundus photographs and its implications for establishing ground truth in AI research.

BACKGROUND: The rise of large language models (LLM) promises to widely impact healthcare providers and patients alike. As these tools reflect the biases of currently available data on the internet, there is a risk that increasing LLM use will prolife...

PURPOSE: Compare large language models (LLMs) in analyzing and responding to a difficult series of ophthalmic cases.

PURPOSE: To evaluate the ability of an artificial intelligence (AI) model, ChatGPT, in predicting the diabetic retinopathy (DR) risk.

PURPOSE: To evaluate preoperative risk factors (mainly those related to corneal topography/tomography) for post-LASIK ectasia development.

Artificial Intelligence (AI) has found rapidly growing applications in ophthalmology, achieving robust recognition and classification in most kind of ocular diseases. Ophthalmic surgery is one of the most delicate microsurgery, requiring high finenes...

PURPOSE: We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows.