  Recently, the research community of computerized medical imaging has started
to discuss and address potential fairness issues that may emerge when
developing and deploying AI systems for medical image analysis. This chapter
covers some of the pressing challenges encountered when doing research in this
area, and it is intended to raise questions and provide food for thought for
those aiming to enter this research field. The chapter first discusses various
sources of bias, including data collection, model training, and clinical
deployment, and their impact on the fairness of machine learning algorithms in
medical image computing. We then turn to discussing open challenges that we
believe require attention from researchers and practitioners, as well as
potential pitfalls of naive application of common methods in the field. We
cover a variety of topics including the impact of biased metrics when auditing
for fairness, the leveling down effect, task difficulty variations among
subgroups, discovering biases in unseen populations, and explaining biases
beyond standard demographic attributes.
