AI Papers in Ophthalmology Made Simple

Recently, EYE has published few manuscripts on artificial intelligence (AI) systems based on deep learning (DL). In ophthalmology, with the exponential growth in computational power, ocular imaging quality, and increasing capabilities, several groups have applied AI productively to interpret ocular images for diagnosis, referral management, risk stratification, and prognostication. Clinical implementation has also begun with the first FDA-cleared AI-equipped fundus camera for DR screening in 2018 (IDx-DR; IDx Technologies Inc, Coralville, IA, USA). Many general ophthalmologists may not have a computer science background, and traditional critical analysis skills for clinical studies do not always directly apply to AI studies. This editorial outlines a stepwise approach to help readers critically read the introduction, methods, results, and discussion components of an AI paper, with a view towards how these technologies can potentially be applied in routine clinical practice.