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• W4293004928 abstract "There are increasing concerns about the bias and fairness of artificial intelligence (AI) models as they are put into clinical practice. Among the steps for implementing machine learning tools into clinical workflow, model development is an important stage where different types of biases can occur. This report focuses on four aspects of model development where such bias may arise: data augmentation, model and loss function, optimizers, and transfer learning. This report emphasizes appropriate considerations and practices that can mitigate biases in radiology AI studies. Keywords: Model, Bias, Machine Learning, Deep Learning, Radiology © RSNA, 2022." @default.
• W4293004928 created "2022-08-25" @default.
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• W4293004928 date "2022-09-01" @default.
• W4293004928 modified "2023-10-17" @default.
• W4293004928 title "Mitigating Bias in Radiology Machine Learning: 2. Model Development" @default.
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• W4293004928 doi "https://doi.org/10.1148/ryai.220010" @default.
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