Explore more about: Machine Learning

April 20, 2022
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NIBIB-funded researchers are developing an autonomous robot that can perform bowel surgery with minimal assistance from a surgeon. In preclinical models, the robot outperformed expert surgeons when compared head-to-head.
March 2, 2022
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NIH-funded researchers are investigating how to use smartwatches to predict clinical test results, which could potentially serve as an early warning signal for underlying health issues.
November 22, 2021
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Washington University in St. Louis is joining a major international effort to advance data science, catalyze innovation and spur health discoveries across Africa. The program is supported by the National Institutes of Health’s (NIH) Common Fund, which will invest nearly $75 million over five years to fund the Harnessing Data Science for Health Discovery and Innovation in Africa (DS-I Africa) program. Source: Washington University in St. Louis
May 24, 2022
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The Medical Imaging and Data Resource Center (MIDRC), a collaboration of leading medical imaging organizations funded by NIBIB, has launched a repository of imaging data to facilitate medical imaging research on COVID-19.
September 8, 2021
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Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture.
February 8, 2022
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A new article looks at the use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the coronavirus disease (COVID-19) pandemic.
May 24, 2022
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NIBIB-funded researchers at NYU Langone Health worked with Facebook AI researchers to develop a method to speed up MRI scans.
May 24, 2022
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NIBIB-funded researchers have developed a way to use artificial intelligence to speed up MRI imaging without sacrificing quality.
March 25, 2022
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The National Institutes of Health has launched an ambitious effort to use artificial intelligence, computation, and medical imaging to enable early disease detection, inform successful treatment strategies, and predict individual disease outcomes of COVID-19.
August 6, 2021
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NIBIB-funded researchers at Stanford University have created an artificial neural network that analyzes lung CT scans to provide information about lung cancer severity that can guide treatment options.