Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.
TL;DR
Although UQ has the potential to improve the safety and interpretability of AI-assisted screening, challenges remain, including calibration, threshold selection, computational cost, and the need for prospective clinical validation.
Abstract
Artificial intelligence (AI) is rapidly integrating into clinical radiology. As primary diagnosticians, radiologists increasingly interpret AI-generated analyses and are expected to oversee the monitoring and governance of deployed AI systems. Although AI literacy among radiologists is improving, several technical aspects of AI remain insufficiently accessible. One such concept is uncertainty quantification (UQ), which estimates the reliability of AI predictions and can signal when outputs should be interpreted with caution. This review introduces key UQ concepts relevant to radiology, distinguishing between aleatoric uncertainty and epistemic uncertainty arising from data variability and knowledge gaps. We summarize commonly used UQ approaches in current research and practice. Furthermore, through a narrative review of selected recent AI imaging studies, we illustrate how UQ methods are applied in practice and highlight methodological trends, findings, and limitations. Although UQ has the potential to improve the safety and interpretability of AI-assisted screening, challenges remain, including calibration, threshold selection, computational cost, and the need for prospective clinical validation.