Simple scoring systems using standard clinical MRI were developed and validated for predicting isocitrate dehydrogenase and 1p/19q codeletion status in adult-type diffuse gliomas lacking contrast enhancement.

It is unclear whether the visualization methods for artificial-intelligence-based computer-aided detection (AI-CAD) of chest radiographs influence the accuracy of readers’ interpretation. We aimed to evaluate the accuracy of radiologists’ interpretations of chest radiographs using different visualization methods for the same AI-CAD. Initial chest radiographs of patients with acute respiratory symptoms were retrospectively collected. A commercialized AI-CAD using three different methods of visualizing was applied: (a) closed-line method, (b) heat map method, and (c) combined method. A reader test was conducted with five trainee radiologists over three interpretation sessions. In each session, the chest radiographs were interpreted using AI-CAD with one of the three visualization methods in random order. Examination-level sensitivity and accuracy, and lesion-level detection rates for clinically significant abnormalities were evaluated for the three visualization methods. The sensitivity (p = 0.007) and accuracy (p = 0.037) of the combined method are significantly higher than that of the closed-line method. Detection rates using the heat map method (p = 0.043) and the combined method (p = 0.004) are significantly higher than those using the closed-line method. The methods for visualizing AI-CAD results for chest radiographs influenced the performance of radiologists’ interpretations. Combining the closed-line and heat map methods for visualizing AI-CAD results led to the highest sensitivity and accuracy of radiologists.

Song J, et al. Korean J Radiol. 2026 Jan;27:e11. https://doi.org/10.3348/kjr.2025.1272

Routine low-dose CT screening for lung cancer in individuals who have never smoked is not justified due to uncertain mortality benefits and risks of overdiagnosis, overtreatment, and radiation, highlighting the need for personalized, risk-based strategies.

Successful human–large language model collaboration is associated with model confidence and reader expertise in chest imaging, with rationale quality facilitating uptake of correct advice but increasing overreliance on incorrect suggestions.

송지영, et al. J Korean Soc Radiol. 2021 May;82(3):562-574. https://doi.org/10.3348/jksr.2020.0185