ARTIFICIAL INTELLIGENCE IN RETINA: Edited by Judy E. Kim and Ehsan RahimyAutomated deep learning in ophthalmology: AI that can build AIO’Byrne, Ciaraa,b; Abbas, Abdallaha,c; Korot, Edwarda,d; Keane, Pearse A.a,eAuthor Information aMedical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK bTrinity College School of Medicine, Dublin, Ireland cUniversity College London Medical School, London, UK dByers Eye Institute, Stanford University, Stanford, California, USA eNIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation, London, UK Correspondence to Dr Pearse A. Keane, Moorfields Eye Hospital NHS Foundation Trust, London, UK;. e-mail: [email protected] Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website (www.co-ophthalmology.com). Current Opinion in Ophthalmology: September 2021 - Volume 32 - Issue 5 - p 406-412 doi: 10.1097/ICU.0000000000000779 Buy SDC Metrics Abstract Purpose of review The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning. Recent findings There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology. Summary Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education. http://links.lww.com/COOP/A44 Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.