  Artificial intelligence (AI) has been successful at solving numerous problems
in machine perception. In radiology, AI systems are rapidly evolving and show
progress in guiding treatment decisions, diagnosing, localizing disease on
medical images, and improving radiologists' efficiency. A critical component to
deploying AI in radiology is to gain confidence in a developed system's
efficacy and safety. The current gold standard approach is to conduct an
analytical validation of performance on a generalization dataset from one or
more institutions, followed by a clinical validation study of the system's
efficacy during deployment. Clinical validation studies are time-consuming, and
best practices dictate limited re-use of analytical validation data, so it is
ideal to know ahead of time if a system is likely to fail analytical or
clinical validation. In this paper, we describe a series of sanity tests to
identify when a system performs well on development data for the wrong reasons.
We illustrate the sanity tests' value by designing a deep learning system to
classify pancreatic cancer seen in computed tomography scans.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning