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.
Frontiers in Digital Health (2021)
Paper
Bibtex
@ARTICLE{10.3389/fdgth.2021.671015,
AUTHOR={Mahmood, Usman and Shrestha, Robik and Bates, David D. B. and Mannelli, Lorenzo and Corrias, Giuseppe and Erdi, Yusuf Emre and Kanan, Christopher},
TITLE={Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems},
JOURNAL={Frontiers in Digital Health},
VOLUME={3},
YEAR={2021},
URL={https://www.frontiersin.org/article/10.3389/fdgth.2021.671015},
DOI={10.3389/fdgth.2021.671015},
ISSN={2673-253X},
ABSTRACT={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.}
}