Explore more about: Artificial Intelligence

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The lungs are one of the most difficult organs for physicians to navigate. A collaborative team of NIH-funded researchers have built a compact robotic system that can autonomously steer around anatomical obstacles within the lungs of live animals.
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This interview with Maryellen Giger, PhD, delves into the creation of the MIDRC imaging repository, how its data can be used to develop and evaluate AI algorithms, ways that bias can be introduced—and potentially mitigated—in medical imaging models, and what the future may hold.
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Researchers have shown that an automated cancer diagnostic method, which pairs cutting-edge ultrasound techniques with artificial intelligence, can accurately diagnose thyroid cancer, of which there are more than 40,000 new cases every year.
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In recent years, artificial intelligence (AI) has been recognized as a powerful tool in the field of medical imaging. However, these models can be subject to several biases, leading to inequities in how they benefit both doctors and patients. Understanding these biases and how to mitigate them is the first step towards a fair and trustworthy AI. Source: SPIE
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Doctors at the University of Florida Health Center are using artificial intelligence to help monitor their patients. The findings will help them develop algorithms that will soon provide real-time health care recommendations. NBC News’ Dr. John Torres on the future of technology in healthcare. Source: NBC News
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NIBIB has established the Center for Biomedical Engineering Technology Acceleration—BETA Center, a new intramural research program to solve a range of medicine’s most pressing problems. The BETA Center will serve the wider NIH intramural research program as a biotechnology resource and catalyst for NIH research discoveries.
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NIBIB researchers and their collaborators introduce several novel image restoration strategies that create sharp images with significantly reduced processing time and computing power.
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Researchers have found that AI models could accurately predict self-reported race in several types of medical images, suggesting that race information could be unknowingly incorporated into image analysis models.
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One team helped develop the first photon-counting detector (PCD)-CT system, which is superior to current CT technology. Another team has also been using artificial intelligence to lower the dose of radiation given to a patient when they are undergoing a conventional CT brain scan.
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After less than two years of data collection and processing, RSNA has successfully delivered over 30,000 de-identified imaging studies to the Medical Imaging and Data Resource Center (MIDRC) project, an open-access platform which publishes data to be used for research. MIDRC is funded by NIBIB. Source: RSNA News