Overview

Uncertainty quantification provides measures of confidence for AI outputs to guide clinician trust. It distinguishes between confident and uncertain predictions. This information supports decision making and risk management.

Methods

Bayesian neural networks ensemble methods and Monte Carlo dropout estimate predictive uncertainty. Calibration techniques align predicted probabilities with observed outcomes. Visualization of uncertainty aids interpretation.

Clinical Use

Uncertainty flags cases requiring human review or additional testing. It improves safety by reducing overreliance on automated outputs. Thresholds for action are defined in clinical governance frameworks.

Validation

Evaluating uncertainty requires datasets with known ground truth and diverse conditions. Metrics assess calibration sharpness and utility in triage. Continuous monitoring ensures reliability in practice.