A recent study by Cedars-Sinai reveals that AI accurately assesses cardiovascular risk in standard chest CT scans without contrast, potentially revolutionizing risk identification with non-invasive methods.
The Radiological Society of North America (RSNA) is launching a new AI certificate course focused on emergency imaging, the third in their series, aimed at helping radiology professionals improve efficiency and patient outcomes by leveraging AI in emergency clinical settings.
The Medical Imaging & Technology Alliance (MITA) is praising President Biden's executive order on Safe, Secure, and Trustworthy Artificial Intelligence for its focus on reaping AI benefits while addressing risks.
An AI chest X-ray model showed racial and gender biases, emphasizing the need for diverse data and thorough bias checks in medical AI, researchers say.
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In a study analyzing more than 2,000 chest X-rays, radiologists were more accurate than AI in detecting three common lung diseases, with AI often producing false-positive results, especially in complex cases or when multiple issues were present.
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Researchers have developed an AI model that enhances the accuracy of determining if all cancerous tissue has been removed during breast cancer surgeries, potentially reducing the need for subsequent operations.
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Using a standardized assessment, researchers in the U.K. compared the performance of a commercially available artificial intelligence algorithm with human readers of screening mammograms.
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Combining artificial intelligence systems for short- and long-term breast cancer risk results in an improved cancer risk assessment, according to a study published in Radiology.
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