A deep learning model developed using standard radiographs can detect fatty liver disease from chest X-rays.
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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Ultrasound and Maternal Data Predict Prolonged NICU Stay
Combining maternal factors, 36-week ultrasound scan findings, and delivery data improves the prediction of prolonged NICU admission. Medscape News UK
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Stenting shows little benefit over medical therapy for reducing stroke
Stent placement doesn’t reduce risk of recurrent stroke more than medical therapy in patients with narrowing of arteries in the…
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MRI model predicts treatment response for triple-negative breast cancer
Post-treatment MRI can accurately predict treatment response for early triple-negative breast cancer, according to recent research…
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MRI predicts treatment response for triple-negative breast cancer
Post-treatment MRI can accurately predict treatment response for early triple-negative breast cancer, according to recent research…
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Cardiac MRI shows heart damage caused by air pollution
Cardiac MRI shows the effects of air pollution on the heart — and the findings aren’t good.
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A Hitchhiker's Guide to Good Prompting Practices for Large Language Models in Radiology
Large language models (LLMs) are reshaping radiology through their advanced capabilities in tasks such as medical report generation and clinical decision support. However, their effectiveness is heavily influenced by prompt engineering—the design of input prompts that guide the model’s responses. This review aims to illustrate how different prompt engineering techniques, including zero-shot, one-shot, few-shot, chain of thought, and tree of thought, affect LLM performance in a radiology context.