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Purpose: Artificial intelligence (AI) is often trained on images without ocular co‑morbidities, limiting its generalizability. This study aims to evaluate the accuracy of a convolutional neural network (CNN) applied to color fundus photos (CFPs) with simulated cataracts (SCs) in detecting diabetic retinopathy (DR). Methods: A database of 3662 CFPs (from Asia Pacific Tele‑Ophthalmology Society (APTOS) 2019) was used. Using transfer learning, a CNN was trained to classify the training images as either DR or non‑DR. The CNN was then applied to classify the testing images after an SC was applied, using varying degrees of Gaussian blur. Results: Accuracy without SC was 97.0%, sensitivity (Sn) 95.7%, specificity (Sp) 98.3%. For mild SC, accuracy was 93.1%, Sn 91.8%, Sp 94.3%. For moderate SC, accuracy was 62.8%, Sn 31.4%, Sp 95.2%. For severe SC, accuracy was 53.5%, Sn 11.8%, Sp 96.5%. Conclusion: SCs significantly impaired AI accuracy. To prepare AI for clinical use, cataracts and other real‑world clinical challenges affecting image quality must be accounted for. [ABSTRACT FROM AUTHOR]