  Deep learning has the potential to automate many clinically useful tasks in
medical imaging. However translation of deep learning into clinical practice
has been hindered by issues such as lack of the transparency and
interpretability in these "black box" algorithms compared to traditional
statistical methods. Specifically, many clinical deep learning models lack
rigorous and robust techniques for conveying certainty (or lack thereof) in
their predictions -- ultimately limiting their appeal for extensive use in
medical decision-making. Furthermore, numerous demonstrations of algorithmic
bias have increased hesitancy towards deployment of deep learning for clinical
applications. To this end, we explore how conformal predictions can complement
existing deep learning approaches by providing an intuitive way of expressing
uncertainty while facilitating greater transparency to clinical users. In this
paper, we conduct field interviews with radiologists to assess possible
use-cases for conformal predictors. Using insights gathered from these
interviews, we devise two clinical use-cases and empirically evaluate several
methods of conformal predictions on a dermatology photography dataset for skin
lesion classification. We show how to modify conformal predictions to be more
adaptive to subgroup differences in patient skin tones through equalized
coverage. Finally, we compare conformal prediction against measures of
epistemic uncertainty.
