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Artificial intelligence, as applied to medical images to detect, rule out, diagnose, and stage disease, has seen enormous growth over the last few years. There are multiple use cases of AI algorithms in medical imaging: first-reader (or concurrent) mode, second-reader mode, triage mode, and more recently prescreening mode as when an AI algorithm is applied to the worklist of images to identify obvious negative cases so that human readers do not need to review them and can focus on interpreting the remaining cases. In this paper we describe the statistical considerations for designing a study to test a new AI prescreening algorithm for identifying normal lung cancer screening CTs. We contrast agreement vs. accuracy studies, and retrospective vs. prospective designs. We evaluate various test performance metrics with respect to their sensitivity to changes in the AI algorithm's performance, as well as to shifts in reader behavior to a revised worklist. We consider sample size requirements for testing the AI prescreening algorithm. Keywords: Artificial intelligence, Diagnostic accuracy, Prescreening, Computer-aided detection, Diagnostic accuracy studies, Area under the ROC curve