As advances in deep learning have quickly permeated into the field of ophthalmology, supervised deep learning approaches have achieved great performance in eye disease classification tasks such as diabetic retinopathy, age-related macular degeneration, and glaucoma.1-3 However, disease-specific classification models are limited in scope with regard to detecting other ocular diseases that are not included in the training set. One solution to this is to develop a multiclass model trained on a broad array of ocular diseases. However, this solution would require large data sets comprising comprehensive and balanced distributions of many ocular diseases, as well as necessity for labor- and cost-intensive sample annotations. A more scalable solution would be to use a semisupervised or unsupervised approach in order to train a general anomaly detection algorithm. In addition to reducing the cost of image annotation, AD models could also screen large populations ahead of a more comprehensive workup.