  Deep Learning (DL) has made significant strides in various medical
applications in recent years, achieving remarkable results. In the field of
medical imaging, DL models can assist doctors in disease diagnosis by
classifying pathologies in Chest X-ray images. However, training on new data to
expand model capabilities and adapt to distribution shifts is a notable
challenge these models face. Continual Learning (CL) has emerged as a solution
to this challenge, enabling models to adapt to new data while retaining
knowledge gained from previous experiences. Previous studies have analyzed the
behavior of CL strategies in medical imaging regarding classification
performance. However, when considering models that interact with sensitive
information, such as in the medical domain, it is imperative to disaggregate
the performance of socially salient groups. Indeed, DL algorithms can exhibit
biases against certain sub-populations, leading to discrepancies in predictive
performance across different groups identified by sensitive attributes such as
age, race/ethnicity, sex/gender, and socioeconomic status. In this study, we go
beyond the typical assessment of classification performance in CL and study
bias evolution over successive tasks with domain-specific fairness metrics.
Specifically, we evaluate the CL strategies using the well-known CheXpert (CXP)
and ChestX-ray14 (NIH) datasets. We consider a class incremental scenario of
five tasks with 12 pathologies. We evaluate the Replay, Learning without
Forgetting (LwF), LwF Replay, and Pseudo-Label strategies. LwF and Pseudo-Label
exhibit optimal classification performance, but when including fairness metrics
in the evaluation, it is clear that Pseudo-Label is less biased. For this
reason, this strategy should be preferred when considering real-world scenarios
in which it is crucial to consider the fairness of the model.
