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• W3180408064 abstract "The use of machine learning to develop intelligent software tools for the 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. We discuss insufficient training data, decentralized data sets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen data sets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify the techniques used to address it. Although these techniques have been discussed in prior research, 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." @default.
• W3180408064 created "2021-07-19" @default.
• W3180408064 creator A5016796365 @default.
• W3180408064 creator A5040554782 @default.
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• W3180408064 date "2021-09-09" @default.
• W3180408064 modified "2023-09-25" @default.
• W3180408064 title "Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice" @default.
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• W3180408064 doi "https://doi.org/10.2196/28776" @default.
• W3180408064 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8461525" @default.
• W3180408064 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34499049" @default.
• W3180408064 hasPublicationYear "2021" @default.
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