Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

Artificial Intelligence Speeds Up The Planet's Financial System Both the financial crisis of 2008 and the COVID-19 pandemic stressed the financial markets. They resulted in uncertainties, market declines, and negative economic growth. Yet the financial market recovered much faster after the COVID-19 outbreak than the 2008 crisis. The main differences in recovery speeds between the two crises are the timing of the Fed's support, fintech innovations, and technology developments on trading and on general productivity. These factors change the data flow, market dynamics, and recovery speed.

The prediction of molecular reactions triggered by light is to date extremely time-consuming and therefore costly. A team led by Philipp Marquetand from the Faculty of Chemistry at the University of Vienna has now presented a method using artificial neural networks that drastically accelerates the simulation of light-induced processes. The method provides new possibilities for a better understanding of biological processes such as the first steps of carcinogenesis or ageing processes of matter. The study appeared in the current issue of the journal "Chemical Science", also including an accompanying illustration on one of its covers. Machine learning plays an increasingly important role in chemical research, e.g. in the discovery and development of new molecules and materials.