Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and
Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the
patient’s illness cycle, assist with diagnosis, and offer appropriate therapy alternatives. Each approach
employed may address one or more AI problems, such as segmentation, prediction, recognition,
classification, and regression. However, the amount of AI-featured research on Inherited Retinal
Diseases (IRDs) is currently limited. Thus, this study aims to examine artificial intelligence approaches
used in managing Inherited Retinal Disorders, from diagnosis to treatment. A total of 20,906 articles
were identified using the Natural Language Processing (NLP) method from the IEEE Xplore, Springer,
Elsevier, MDPI, and PubMed databases, and papers submitted from 2010 to 30 October 2021 are
included in this systematic review. The resultant study demonstrates the AI approaches utilized on
images from different IRD patient categories and the most utilized AI architectures and models with
their imaging modalities, identifying the main benefits and challenges of using such methods.