arXiv:2608. 07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret.
By Fakrul Islam Tushar, Stephen Adamo, Geoffrey D. Rubin
arXiv:2512. 14732v3 Announce Type: replace-cross Abstract: Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines.
By Idan Tankel, Nir Mazor, Rafi Brada, Christina LeBedis, Guy ben-Yosef
arXiv:2607. 05880v1 Announce Type: cross Abstract: Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone.
By Suneeta Mall, Vladimir Nekrasov, Ashnil Kumar, Sajith Karunasena, Aiden Nibali, Alix Bird, Mateo Diaz Shine, Jarrel Seah
arXiv:2502. 21187v4 Announce Type: replace Abstract: AI-based lung cancer screening is constrained by scarce, annotated CT data, particularly for rare nodule presentations.
By Fakrul Islam Tushar, Lavsen Dahal, Paul Segars, Joseph Y. Lo
arXiv:2607. 01001v1 Announce Type: cross Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness.
By Nils Neukirch, Martin Maurer, Nils Strodthoff
arXiv:2604. 15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT).
By M\'elanie Roschewitz, Kenneth Styppa, Yitian Tao, Jiwoong Sohn, Jean-Benoit Delbrouck, Benjamin Gundersen, Nicolas Deperrois, Christian Bluethgen, Julia E. Vogt, Bjoern Menze, Farhad Nooralahzadeh, Michael Krauthammer, Michael Moor