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May 22, 2024
Early traumatic experiences can affect ongoing development, especially in the first five years of life. Here are resources to help support healthy growth.
April 19, 2024
With this collaborative effort, the profile of UAB within biomedical data science and future data-driven medicine will be significantly raised on a national stage. Written by: Ryan McCain Media contact: Savannah Koplon J...
January 25, 2024
/PRNewswire/ -- In collaboration with 11 federal agencies, alongside private sector, nonprofit, and philanthropic organizations, the NAIRR pilot is set to...
January 18, 2024
Stanford University Professor of Radiology, Medicine and Biomedical Data Science; Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center); Associate Director of Stanford’s Institute for Human-Centered Artificial Intelligence (HAI); and Associate Chair for Information Systems, Medical Informatics Director for Stanford Health Care
November 29, 2023
Curtis P. Langlotz, MD, PhD, has taken the helm as 2023-2024 president of the Radiological Society of North America, RSNA, during the organization’s Scientific Assembly and Annual Meeting being held in Chicago this week. A news update referred to Langlotz, who has been with Stanford University since 2014, as a renowned imaging informatics leader and committed advocate for improved clinical communication.
November 13, 2023
The rankings seemed to indicate a trend in certain regions of the country.
October 12, 2023
Researchers at the Icahn School of Medicine at Mount Sinai and the UCSD have been awarded an $8.5M grant to create a data integration hub.
August 14, 2023
Chest x-rays can reveal early warning signs for diabetes, even in patients who don't meet guidelines for elevated risk, researchers say.
August 4, 2023
A new artificial intelligence model finds that x-ray images collected during routine medical care can provide warning signs for diabetes, even in patients who don’t meet the guidelines for elevated risk.
May 8, 2023
Researchers identify potential sources of bias in AI models used in medical imaging, opening doors to their mitigation