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.

翻译：当系统使用基于机器学习（ML）的数据驱动模型时，其结果错误难以完全排除。若用户无法理解决策过程的机理，且错误可能产生安全相关后果（这在医学领域尤为常见），该问题便显得尤为关键。在此类场景中，采用可靠方法量化结果中残留的不确定性，可使用户在知情前提下决定后续使用方式，并基于给定结果得出合理推论。本文以流式细胞术作为医学领域的应用案例，展示了不确定性封装器（Uncertainty Wrapper）的适用性与实际效用——该领域可通过结合ML模型与可靠且透明的不确定性量化方法而受益。