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.
Fernando Vega Lara, Lisa Koopmans, Christian Roest et al.· Abdominal Radiology· 0 citations
Simple Summary Deciding between active surveillance and treatment for prostate cancer patients often requires accurate assessment of prostate tumors. Independent fast quantitative analysis may provide a check and support for radiologists who conventionally visually inspect MRI and may follow protocols such as PI-RADS. Artificial intelligence is increasingly being applied to help clinical assessment of prostate tumors detected with multi-parametric MRI, just as in other fields. However, implementation of artificial intelligence and deep learning is computationally intensive, requires fast, high-end processing boards and, therefore, uses considerable energy and water resources for cooling. A less intensive, less wasteful, cheaper, and less environmentally destructive assessment approach is needed. The simpler spectral/statistics approach that mimics color vision was previously successfully applied in retrospective pilot studies of bi-parametric MRI of prostate cancer. The novel approach needs far fewer resources, is less computationally intensive, and is simpler than artificial intelligence to evaluate prostate tumors. However, these earlier spectral/statistics pilot studies required the intervention of an analyst and too much time for implementation in future large patient studies that are needed to validate the novel approach. This study developed, applied, and tested new automation tools to expedite the spectral/statistics approach. Automating the novel approach resulted in sufficiently high AUCs and a reduction in processing time, warranting future applications in large patient cohorts.
Rulon Mayer, Yuan Yuan, J. Udupa et al.· Cancers· 0 citations
The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.
Lisa Koopmans, Fernando Vega Lara, Christian Roest et al.· Abdominal Radiology· 0 citations
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