Intel and Penn Medicine are working on a huge, institution-spanning AI that will help identify brain tumors but without overstepping on strict medical privacy rules. The cross-location AI will use a technique known as "federated learning" as it spans 29 different healthcare and research institutions. Training artificial intelligences with data sets of illnesses, so that they can act as a filter on large numbers of cases, has been shown effective in a number of ways. However the downside is that for the most effective performance those data sets need to be considerable. An individual healthcare institution or research lab would likely struggle to feed a developing machine learning computer with all the information it requires.

We've seen AI outperform doctors in spotting breast cancer, lung cancer and skin cancer. Now, researchers from Intel and the University of Pennsylvania are turning their attention to brain tumors. Using Intel's AI hardware and software, Penn Medicine will lead 29 international healthcare and research institutions in creating an AI model trained on the largest brain tumor dataset ever -- and will do so without sharing sensitive patient data. The project is based on a technique called federated learning, which trains an algorithm across decentralized servers, so that hospitals can work together without actually sharing patient data. This will allow the institutions -- from the US, Canada, the UK, Germany, the Netherlands, Switzerland and India -- to create a much larger data set than any one institution would be able to on its own.