Mitigating Bias in Radiology Machine Learning: 2. Model Development.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 36204532.
- Also identified by DOI 10.1148/ryai.220010 and PMC identifier 9530765.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
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. <b>Keywords:</b> Model, Bias, Machine Learning, Deep Learning, Radiology © RSNA, 2022.