  In this work, we propose an AI-based method that intends to improve the
conventional retinal disease treatment procedure and help ophthalmologists
increase diagnosis efficiency and accuracy. The proposed method is composed of
a deep neural networks-based (DNN-based) module, including a retinal disease
identifier and clinical description generator, and a DNN visual explanation
module. To train and validate the effectiveness of our DNN-based module, we
propose a large-scale retinal disease image dataset. Also, as ground truth, we
provide a retinal image dataset manually labeled by ophthalmologists to
qualitatively show, the proposed AI-based method is effective. With our
experimental results, we show that the proposed method is quantitatively and
qualitatively effective. Our method is capable of creating meaningful retinal
image descriptions and visual explanations that are clinically relevant.
