i-Sight uses a novel semi-supervised pseudo learning method called Advanced Meta Pseudo Labels (AMPL) to make diagnosis on a patient's eye health. 

i-Sight solves the most prominent issue in medical AI: the lack of labeled data.

By using the novel semi-supervised training method Advanced Meta Pseudo Labels (AMPL), the i-Sight architecture expands past just retinal images, to CT scans, X-Rays, MRI scans and more.

i-Sight can classify, detect, or segment any medical image when given labeled and unlabeled data. i-Sight can also combine multiple images together to create holistic diagnoses taking into account multiple factors (age, height, ethnicity, etc.) that may play into account on the health of the individual.