In many health systems around the world, running a hospital is a knife-edge balancing act. On one side are the doctors and nurses providing urgent, even critical care to patients. Their job is to do all they can. On the other side are the hospital administrators making sure there are enough resources – medicines, equipment, beds, staff – to cope. Their job is to ensure spending on patient care stays efficient so that the hospital can stay open.

The NHS has started trials of a machine-learning system designed to help hospitals in England anticipate the demand on resources caused by COVID-19. The COVID-19 Capacity Planning and System (CPAS) is being piloted at four acute hospitals in England to demonstrate whether it can help the NHS predict the demand for equipment like ICU beds and ventilators. If successful, CPAS will be rolled out nationally. From cancelled conferences to disrupted supply chains, not a corner of the global economy is immune to the spread of COVID-19. NHS Digital told ZDNet the initiative marked the "first time any project of this scale and scope using machine learning has been rolled out in the NHS."

Nvidia unveiled a new federated learning edge computing reference application for radiology to help hospitals crunch medical data for better disease detection while protecting patient privacy. Called Clara Federal Learning, the system relies on Nvidia EGX, a computing platform which was announced earlier in 2019. It uses the Jetson Nano low wattage computer which can provide up to one-half trillion operations per second of processing for tasks like image recognition. EGX allows low-latency artificial intelligence at the edge to act on data, in this case images from MRIs, CT scans and more. Nvidia made its announcement of Clara on Sunday at the Radiological Society of North America conference in Chicago.

More people around the world are being diagnosed with cancer every year. Thankfully, researchers and clinicians continue to devise ever more effective and sophisticated techniques to combat the disease, resulting in better patient outcomes and improved survival rates for most forms of cancer. But both of these important trends have created a data challenge in the clinic: how to collect, process and analyse increasing amounts of data to extract the useful information that will deliver the most effective treatment plans and the best outcomes for patients. Artificial intelligence is emerging as a vital tool to tackle this data deluge. Using computer algorithms to search for important signals in the noise can reduce treatment times, improve the quality of care, and make the best use of valuable resources.