  The use of machine learning to develop intelligent software tools for
interpretation of radiology images has gained widespread attention in recent
years. The development, deployment, and eventual adoption of these models in
clinical practice, however, remains fraught with challenges. In this paper, we
propose a list of key considerations that machine learning researchers must
recognize and address to make their models accurate, robust, and usable in
practice. Namely, we discuss: insufficient training data, decentralized
datasets, high cost of annotations, ambiguous ground truth, imbalance in class
representation, asymmetric misclassification costs, relevant performance
metrics, generalization of models to unseen datasets, model decay, adversarial
attacks, explainability, fairness and bias, and clinical validation. We
describe each consideration and identify techniques to address it. Although
these techniques have been discussed in prior research literature, by freshly
examining them in the context of medical imaging and compiling them in the form
of a laundry list, we hope to make them more accessible to researchers,
software developers, radiologists, and other stakeholders.
