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- Finishing Fellowship Earlyby Lauren M. Harry, Alexa Petrucciani, Richard B. Gunderman on August 22, 2025 at 12:00 am
To what extent should educational programs resemble prison sentences—that is, should learners be deemed to have completed their training by virtue of time served? Or should other criteria for completion predominate? In many public-school systems, it has long been the practice to allow outstanding students to skip grades or advance beyond typical levels of study in specific disciplines such as mathematics and language arts, based not on the amount of time they have spent at a particular level but on their level of educational attainment.
- Development and Validation of an Interpretable Machine Learning Model for Predicting Adverse Clinical Outcomes in Placenta Accreta Spectrum: A Multicenter Studyby Hongliang Li, Yueyue Zhang, Hangru Mei, Yangguang Yuan, Lei Wang, Weihao Liu, Haoyang Zeng, Jinjie Huang, Xiaowei Chai, Kai Wu, Hanlin Liu on August 22, 2025 at 12:00 am
Placenta accreta spectrum (PAS) is a serious perinatal complication. Accurate preoperative identification of patients at high risk for adverse clinical outcomes is essential for developing personalized treatment strategies. This study aimed to develop and validate a high-performance, interpretable machine learning model that integrates MRI morphological indicators and clinical features to predict adverse outcomes in PAS, and to build an online prediction tool to enhance its clinical applicability.
- Evaluating Large Language Models for Radiology Systematic Review Title and Abstract Screeningby Siddhant Dogra, Soroush Arabshahi, Jason Wei, Emmy Hu, Lucia Saidenberg, Sonali Sharma, Zehui Gu, Mutita Siriruchatanon, Stella K. Kang on August 22, 2025 at 12:00 am
To evaluate the performance, stability, and decision-making behavior of large language models (LLMs) for title and abstract screening for radiology systematic reviews, with attention to prompt framing, confidence calibration, and model robustness under disagreement.
- Tips on Teaching at the Point-of-careby Atul Agarwal, Brooke Stephanian, Albab S. Uddin, Danielle V. Hill, Jordan H. Cuskaden, Matthew Krosin, Mohsin Mukhtar, Scott D. Steenburg, Richard B. Gunderman on August 21, 2025 at 12:00 am
As clinical productivity demands on radiologists continue to grow, time, energy, and enthusiasm for teaching become ever more precious. Yet with every challenge comes an opportunity, and point-of-care teaching in radiology is no exception. In our department, we have identified faculty members who are especially highly esteemed in this regard, and in this article, we share some of their suggestions on how to perform better. Excellent point-of-care teaching benefits both learners, who get a better education, and educators, who derive more fulfillment from this vital professional mission (1).
- Deep Learning-Enhanced Single Breath-Hold Abdominal MRI at 0.55 T—Technical Feasibility and Image Quality Assessmentby Alina Carolin Seifert, Hanns-Christian Breit, Markus M. Obmann, Anastassia Korolenko, Marcel Dominik Nickel, Matthias Fenchel, Daniel T. Boll, Jan Vosshenrich on August 21, 2025 at 12:00 am
Inherently lower signal-to-noise ratios hamper the broad clinical use of low-field abdominal MRI. This study aimed to investigate the technical feasibility and image quality of deep learning (DL)-enhanced T2 HASTE and T1 VIBE-Dixon abdominal MRI at 0.55 T.
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