Towards objective and systematic evaluation of bias in medical imaging
  AI
Emma A. M. Stanley Raissa Souza Anthony Winder Vedant Gulve Kimberly Amador Matthias Wilms Nils D. Forkert
Artificial intelligence (AI) models trained using medical images for clinical
tasks often exhibit bias in the form of disparities in performance between
subgroups. Since not all sources of biases in real-world medical imaging data
are easily identifiable, it is challenging to comprehensively assess how those
biases are encoded in models, and how capable bias mitigation methods are at
ameliorating performance disparities. In this article, we introduce a novel
analysis framework for systematically and objectively investigating the impact
of biases in medical images on AI models. We developed and tested this
framework for conducting controlled in silico trials to assess bias in medical
imaging AI using a tool for generating synthetic magnetic resonance images with
known disease effects and sources of bias. The feasibility is showcased by
using three counterfactual bias scenarios to measure the impact of simulated
bias effects on a convolutional neural network (CNN) classifier and the
efficacy of three bias mitigation strategies. The analysis revealed that the
simulated biases resulted in expected subgroup performance disparities when the
CNN was trained on the synthetic datasets. Moreover, reweighing was identified
as the most successful bias mitigation strategy for this setup, and we
demonstrated how explainable AI methods can aid in investigating the
manifestation of bias in the model using this framework. Developing fair AI
models is a considerable challenge given that many and often unknown sources of
biases can be present in medical imaging datasets. In this work, we present a
novel methodology to objectively study the impact of biases and mitigation
strategies on deep learning pipelines, which can support the development of
clinical AI that is robust and responsible.