Key Technology Considerations in Developing and Deploying Machine\n Learning Models in Clinical Radiology Practice

Abstract

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

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