Behind every reliable medical AI model is high-quality data. Explore why accurate ophthalmic image annotation is fundamental to developing trustworthy AI systems.

Artificial intelligence learns from data. In medical imaging, that data often needs to be carefully labelled before it can be used to train an AI model.

Medical image annotation involves identifying and marking important structures, abnormalities, lesions, or regions of interest within an image. In ophthalmology, accurate annotation of retinal and ocular images can play an important role in developing and evaluating AI-based systems.

The quality of the annotation directly influences the quality of the dataset—and consequently, the reliability of the resulting AI model.

This makes annotation more than a technical task. It requires an understanding of both medical imaging and the objectives of the AI application.

At METARES 3.0 – AVISHKAR, participants will gain hands-on exposure to ophthalmic image annotation and the processes involved in creating reliable datasets for AI research.