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“This efficiency in detecting errors may hint at a future where AI can help optimize the workflow within radiology departments," a lead author of the study said.
ChatGPT-4 matched radiologists when tasked with detecting errors in imaging reports, with the study suggesting the technology may be useful for improving reporting accuracy, according to a study published April 16 in Radiology. A team at University Hospital Cologne in Germany compared the performance of ChatGPT-4 and six radiologists in detecting common errors in 200 radiology x-ray, CT, and MRI reports and found the large language model (LLM) was not only “on par” with radiologists, but require
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Interventional procedures play an integral role in managing traumatic pediatric injuries nonoperatively, though specific guidelines for the use of endovascular interventions in this setting have not been fully defined prior to these latest recommendations.
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Physicians with the University of Texas Medical Branch made their case for such an approach at the Radiology Business Management Association’s annual meeting in Las Vegas on Monday.
A deep-learning model based on mammographic images can determine tumor staging and sentinel lymph node metastasis in breast cancer patients, a study published April 15 in Informatics in Medicine Unlocked found. A team led by Elaheh Tarighati Sereshkeh from the Iran University of Medical Science in Tehran found that its convolutional neural network (CNN) model achieved high performance marks in these areas.
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Alan Matsumoto, MD. The American College of Radiology (ACR) has named Alan Matsumoto, MD, as chair of its board of chancellors. Most recently, Matsumoto served as vice chair of the board. Previously, he was chair of the ACR's Board of Chancellors (BOC) Commission on Interventional and Cardiovascular Imaging and a member of the ACR Council Steering Committee.
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A CT-based deep learning model can identify individuals with clinical stage IA non–small cell lung cancer (NSCLC) at high risk of further disease, researchers have found. A model developed by a team led by Kwon Joong Na, MD, of Seoul National University Hospital and College of Medicine in South Korea demonstrated an area under the receiver operating curve (AUC) of 0.86 for predicting recurrence of disease at two-years post procedure.
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A new study shows that GPT-4 may offer comparable error detection rates to those of attendings and radiology residents in reviewing radiology reports, but researchers noted key caveats as well.
Large language model GPT-4 matched the performance of radiologists in detecting errors in radiology reports, according to research published in Radiology.
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