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

arXiv: https://arxiv.org/abs/2311.05245 · HTML: https://mlsanity.com/paper/2311.05245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications](https://mlsanity.com/paper/2006.15172/) (2020)
- [A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models](https://mlsanity.com/paper/2201.03263/) (2022)
- [Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning](https://mlsanity.com/paper/2305.14872/) (2023)

## Abstract (arXiv)

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.