Uncertainty Wrapper in the medical domain: Establishing transparent
  uncertainty quantification for opaque machine learning models in practice
Lisa Jöckel Michael Kläs Georg Popp Nadja Hilger Stephan Fricke
When systems use data-based models that are based on machine learning (ML),
errors in their results cannot be ruled out. This is particularly critical if
it remains unclear to the user how these models arrived at their decisions and
if errors can have safety-relevant consequences, as is often the case in the
medical field. In such cases, the use of dependable methods to quantify the
uncertainty remaining in a result allows the user to make an informed decision
about further usage and draw possible conclusions based on a given result. This
paper demonstrates the applicability and practical utility of the Uncertainty
Wrapper using flow cytometry as an application from the medical field that can
benefit from the use of ML models in conjunction with dependable and
transparent uncertainty quantification.