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Purpose: Previous research has demonstrated that the clinical effectiveness of computer aided diagnostic (CAD) systems may differ significantly from the results obtained through carefully controlled research trials. Our goal was to conduct a case study of a deep learning (DL) based CAD system deployed in a clinical setting, to understand the real world factors that may affect the performance, use, and evaluation of these tools in practice.

Methods: A prospective single-reader study with n= 1000 patients, using a DL system designed to assist in the diagnosis of Diabetic Retinopathy (DR). For each patient, three non-mydriatic fundus images of each eye were taken and sent electronically to an experienced reader (clinician) for diagnosis. After initial grading (five-point DR scale), the reader viewed the diagnosis provided by the DL system (five-point scale) and was asked to provide their final diagnosis. At any point the …