Mitigating Bias in Radiology Machine Learning: 2. Model Development
Kuan Zhang(Mayo Clinic), Bardia Khosravi(Mayo Clinic), Sanaz Vahdati(Mayo Clinic), Shahriar Faghani(Mayo Clinic), Fred Nugen(Mayo Clinic), Seyed Moein Rassoulinejad-Mousavi(Mayo Clinic), Mana Moassefi(Mayo Clinic), Jaidip Jagtap(Mayo Clinic), Yashbir Singh(Mayo Clinic), Pouria Rouzrokh(Mayo Clinic), Bradley J. Erickson(Mayo Clinic)
Cited by 78Open Access
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
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