ARTIFICIAL INTELLIGENCE IN RETINA: Edited by Judy E. Kim and Ehsan RahimyClinician-driven artificial intelligence in ophthalmology: resources enabling democratizationKorot, Edwarda,b; Gonçalves, Mariana B.b,c,d; Khan, Saad M.e; Struyven, Robbertb,f; Wagner, Siegfried K.b; Keane, Pearse A.bAuthor Information aStanford University Byers Eye Institute, Palo Alto, California, USA bMoorfields Eye Hospital, London, UK cFederal University of São Paulo (UNIFESP) dVision Institute (IPEPO), Sao Paulo, Brazil eRoyal Berkshire Hospital, Reading fUniversity College London, London, UK Correspondence to Edward Korot, 2450 Watson Ct, Palo Alto, CA 94303, USA. Tel: +650 723 6995; fax: +650 320 9443; e-mail: [email protected] Current Opinion in Ophthalmology: September 2021 - Volume 32 - Issue 5 - p 445-451 doi: 10.1097/ICU.0000000000000785 Buy Metrics Abstract Purpose of review This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology Recent findings Open datasets, efficient labeling techniques, code-free automated machine learning (AutoML) and cloud-based platforms for deployment are resources that enable clinicians with scarce resources to drive their own AI projects. Summary Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-relevant outcome measures. Taken together, open datasets, efficient labeling techniques, code-free AutoML and cloud platforms break the barriers for clinician-driven AI. As AI becomes increasingly democratized through such tools, clinicians and patients stand to benefit greatly. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.