"We can improve predictions for medical events that might happen to you," said Katherine Chou, the head of product at Google Brain, in an interview with CNBC. "We have validated the data and seen promising results." Those results will not be released until a formal review process.

The company is applying its machine learning expertise, which it originally developed for consumer products like Translate and Image Search, to health care. To get there, it worked with hospitals, including Stanford Medicine, UC San Francisco and The University of Chicago Medicine, which stripped millions of patient medical records of personally identifying data and shared them with Google's research team, Google Brain .

Hospitals are increasingly under the gun to keep patients healthy and out of the emergency room. Increasingly, health systems are shifting away from "fee for service" models, in which they get paid for pricey tests and procedures, to "value-based care," where they're rewarded for improving health outcomes. That shift is a big opportunity for Silicon Valley's technology companies and startups, which are working with existing data to help hospitals take proactive steps to keep their patients healthy.

So, for instance, a computer might soon determine the likelihood that certain patients will acquire a potentially life-threatening disease like sepsis, or end up being readmitted after being discharged from the hospital.

The advance also addresses a big problem in medical specialties like radiology and pathology, where clinicians are saddled with a massive amount of information and too little time. Even a well-trained human eye can occasionally miss something.

Many of the top hospitals have their own technology teams, but they pale in comparison to the computing talent at Google. For Atul Butte, director of the Institute of Computational Health Sciences at UCSF, the draw was Google's amazing in-house machine learning expertise.

Butte said the project "bubbled up" because UCSF has a wealth of medical data, including admissions reports, medical records, diagnoses, lab results and so on, but it has not yet mined this information to make predictions about patient outcomes.

"This isn't a research project," he said. "It's more of a scientific collaboration around improving the quality of care for patients."

Watch: Google's latest AI chip