In oncology, the seriousness of the disease amplifies the visibility of radiological errors, leading to both significant individual consequences and broader public health concerns. By leveraging quantitative approaches, the authors reframe the diagnostic process in radiology as a classification problem, a perspective aligned with recent neurocognitive theories on decision-making errors.
This structured model offers a practical framework for conducting root cause analysis of diagnostic errors in radiology and developing effective risk-management strategies.