Abstract
Artificial intelligence (AI) in medical imaging is in its infancy. However, ongoing advances in hardware and software as well as increasing access to ever-expanding datasets for training, validation, and testing purposes are likely to make AI an increasingly prevalent and powerful tool. Of course issues, such as the need to protect the privacy of sensitive health data, remain; nevertheless, it is likely the average imager will need to develop an evidence-based approach to assessing AI in medical imaging. We hope this article will provide insight into just how this can be conducted by applying 5 simple questions, specifically: (1) Who was in the training sample, (2) How was the model trained, (3) How reliable is the algorithm, (4) How was the model validated, and (5) How useable is the algorithm.
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