Considerations for Visualizing Uncertainty in Clinical Machine Learning Models
This addresses the problem of improving clinician trust in predictive models for healthcare, though it is incremental as it focuses on visualization methods rather than new model development.
The study investigated how to visually communicate uncertainty in clinical machine learning models to enhance clinician trust and actionability, finding that transparency about uncertainty sources is more impactful than the degree of uncertainty itself.
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.