We scan the top radiology sources so you don’t have to.
From AI breakthroughs to imaging trends, we serve up real-time radiology insights.
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Deep Learning Model with Nodule Indexing Tailored to Early-Stage Lung Cancer Detection
To evaluate whether a deep learning–based AI system with suspected nodule indexing and malignancy risk stratification improves radiologist performance in detecting pulmonary nodules on CT, using a dataset enriched with challenging early-stage lung cancers.
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Radiology board-style exams and LLMs: a scoping review of model performance
Large Language Models (LLMs) are increasingly being evaluated for their ability to answer official radiology board-style examination questions. Understanding their accuracy, limitations, and potential applications in education is essential for assessing their utility in the field.
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More Than One Path: Recent Cross-Application Trends Among Radiology Residency Applicants, 2020-2023
Cross-application is increasingly common in radiology yet remains understudied. We aimed to quantify the prevalence, characterize patterns, and identify predictors of cross-application among diagnostic radiology (DR) and interventional radiology (IR) applicants.
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Misinformation and Overestimation of Computed Tomography Lung Cancer Screening Harms—Methodology Matters: A Joint Statement from The Society of Thoracic Surgeons, the American Society for Radiation Oncology, and the American College of Radiology
A number of recent peer-reviewed articles pertinent to CT lung cancer screening (LCS) contain substantial methodological flaws that contribute to the propagation of misinformation. Herein we highlight three important examples of misinformation regarding LCS: (1) the overestimation of downstream imaging and procedural complications after LCS, (2) the misrepresentation of LCS false-positive rate (FPR), and (3) the flawed analysis of oncogenic risk associated with radiation from CT scans.