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

Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems