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Joined August 2013
New study suggests that the Epic Sepsis Model may only identify some high-risk patients after sepsis is clinically recognized, rather than before infection onset. - bit.ly/3UQrNWR
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Novel deep learning model leverages metabolic biomarkers to predict the likelihood that Alzheimer's disease will develop long before clinical symptom onset. - bit.ly/3uGzeVS
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New Center for Connected Medicine report highlights that oversight of artificial intelligence tools is of growing interest to healthcare executives. - bit.ly/3UJDC0O
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University of California San Diego researchers argue that healthcare AI regulations should require developers to demonstrate how these tools impact patient outcomes. - bit.ly/42zab3F
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Researchers leveraging a machine learning-based “fragmentomics” approach may be able to detect cancer sooner using smaller blood samples than traditional methods. - bit.ly/3vQ4py2
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UC San Diego Health’s artificial intelligence tool to predict sepsis infection risk in emergency department patients has reduced mortality by 17 percent. - bit.ly/3OmZjjd
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Research findings from Cleveland Clinic and IBM demonstrated how AI models can better show how immune systems recognize threats, which may improve immunotherapies. - bit.ly/3vQJLOn
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A machine learning model outperformed standard tools for predicting death and other serious complications in patients undergoing percutaneous coronary intervention. - bit.ly/3SvYd7l
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Cedars-Sinai’s Molecular Twin Precision Oncology Platform has outperformed the only FDA-approved test to predict pancreatic cancer survival. - bit.ly/42ryGjf
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