The human body is frail and people end up in intensive care units for all kinds of reasons. Whatever brings them there, more than half of adults admitted to an ICU end up sharing the same potentially life-threatening condition: kidney damage known as acute kidney injury.

The Department of Veterans Affairs thinks artificial intelligence could reduce the toll. In a project that drew on roughly 700,000 medical records from US veterans, the agency worked with Google parent Alphabet’s DeepMind unit to create software that attempts to predict which patients are likely to develop AKI. The VA hopes to test whether those predictions can help doctors prevent people from developing the condition. AKI manifests as a sudden failure of the kidneys to properly remove waste from the body, and often occurs as a complication of surgery, infection, or other stresses of hospitalization.

The project is an example of the worldwide push to save lives using the AI techniques that power internet companies’ virtual assistants and facial recognition. The spread of digital health records offers a torrent of data about patients, including subtle patterns that algorithms can interpret in ways doctors cannot. In the US and other rich countries, AI is seen as a way to improve care and cut costs. In places like India and China with chronic shortages of medical specialists, the technology could improve access to care.

DeepMind’s collaboration with the VA fits into a broader push into health care by Alphabet. The company hopes to use AI to diversify beyond advertising, which supplies nearly 90 percent of its revenue. Other Alphabet projects are training algorithms to detect eye disease and cancer. Google recently hired veteran health system executive David Feinberg to take charge of its health projects.

The VA collaboration also illustrates a challenge to Alphabet’s health care ambitions. The company has a world-beating roster of AI researchers. But in health care it lacks the kind of data troves that power Google’s dominance in search and online ads. Only by teaming up with organizations willing to share piles of medical data can Alphabet get the feedstock needed to train machine learning algorithms. The VA’s millions of electronic health records represent one of the largest collections in the US. A DeepMind spokesperson cited the VA's leadership in kidney disease and health analytics, and the fact it has “one of the most comprehensive electronic datasets covering patient care.”

The VA’s engagement with DeepMind began a few years ago, when the agency’s director of predictive analytics, Christopher Nielsen, received an unexpected phone call. “It’s not uncommon to get calls from people saying I can solve all your problems with AI,” Nielsen says. He has learned to be wary of out-of-the-blue AI pitches.

But this call came from Mustafa Suleyman, who cofounded DeepMind before it was acquired by Google in 2014. The company has a track record of breaking new ground in machine learning, including bots that beat Atari games and masters of the board game Go. Early in 2018, the VA announced that it had signed a formal research agreement with DeepMind.

Right away, Nielsen and his VA colleagues had to tackle a common hurdle for AI health care projects. The machine learning algorithms driving the AI boom need large amounts of example data to learn from; typically, the more data, the better the results. But when the data consists of people’s most private information, it must be treated with special care.

VA researchers and engineers developed a process that uses cryptographic hashes to obscure lab results and other data in a health record, Nielsen says. It was used to give DeepMind access to a sanitized collection of hundreds of thousands of health records from a 10-year period. AI experts at the company used some of Alphabet’s US computing infrastructure to train neural networks—the guts of much of today’s machine learning—to predict when a patient is likely to develop AKI.