Mitigating Bias in Radiology Machine Learning: 2. Model Development

Kuan Zhang, Bardia Khosravi, Sanaz Vahdati, Shahriar Faghani, Fred Nugen, Seyed Moein Rassoulinejad-Mousavi, Mana Moassefi, Jaidip Manikrao M. Jagtap, Yashbir Singh, Pouria Rouzrokh, Bradley J. Erickson

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numbere220010
JournalRadiology: Artificial Intelligence
Issue number5
StatePublished - Sep 2022


  • Bias
  • Deep Learning
  • Machine Learning
  • Model
  • Radiology

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Artificial Intelligence


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