UPTON, NY—Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have successfully demonstrated that autonomous methods can discover new materials. The artificial intelligence (AI)-driven technique led to the discovery of three new nanostructures, including a first-of-its-kind nanoscale “ladder.
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.
A study published today in the Journal of the American College of Cardiology (JACC): Cardiovascular Imaging shows that artificial intelligence tools can more rapidly, and objectively, determine calcium scores in computed tomographic (CT) and positron emission tomographic (PET) images than physicians, even when obtained from very-low-radiation CT attenuation scans.
In a first-of-its-kind randomized clinical trial led by researchers at the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine at Cedars-Sinai, artificial intelligence (AI) proved more successful in assessing and diagnosing cardiac function when compared to echocardiogram assessments made by sonographers.
Black patients have a dramatically higher risk of advanced vision loss after a new diagnosis of primary open angle glaucoma (POAG) when compared to white patients, according to a new study from New York Eye and Ear Infirmary of Mount Sinai (NYEE).
Article title: Gut microbiota of wild fish as reporters of compromised aquatic environments sleuthed through machine learning Authors: John W. Turner Jr., Xi Cheng, Nilanjana Saferin, Ji-Youn Yeo, Tao Yang Bina Joe From the authors: “Overall, this study represents the…
Chula Engineering and Chula Medicine co-invent an innovative device for a rapid gastrointestinal cancer detection that yields accurate results hoping to foster preventive medicine in gastrointestinal malignancy and reduce the number of cancer patients.
An innovative mobile phone application was found to be as good as expert gastroenterologists at characterizing stool specimens, according to a study by Cedars-Sinai. The artificial intelligence (AI) used in the smartphone app also outperformed reports by patients describing their stool specimens.