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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 ifit remains unclear to the user how these models arrived at their decisions andif errors can have safety-relevant consequences, as is often the case in themedical field. In such cases, the use of dependable methods to quantify theuncertainty remaining in a result allows the user to make an informed decisionabout further usage and draw possible conclusions based on a given result. Thispaper demonstrates the applicability and practical utility of the UncertaintyWrapper using flow cytometry as an application from the medical field that canbenefit from the use of ML models in conjunction with dependable andtransparent uncertainty quantification.

Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice