# Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

arXiv: https://arxiv.org/abs/2210.12220 · HTML: https://mlsanity.com/paper/2210.12220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)

## Abstract (arXiv)

Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in this setting in an actionable, trustworthy way. To this end, we conduct a qualitative study with cardiac critical care clinicians. Our results reveal that clinician trust may be impacted most not by the degree of uncertainty, but rather by how transparent the visualization of what the sources of uncertainty are. Our results show a clear connection between feature interpretability and clinical actionability.