A woman with late-stage breast cancer came to a city hospital, fluids already flooding her lungs. She saw two doctors and got a radiology scan. The hospital’s computers read her vitals signs and estimated a 9.3 percent chance she would die during her stay. Then, Google came with a new algorithm created by the company read up on the woman 175,639 data points. Google rendered its assessment of her death risk: 19.9 percent. She passed away in a matter of days. Google had created a tool that could forecast a host of patient outcomes. It includes how long people may stay in hospitals, their odds of re-admission and chances they will soon die.
Medical experts were impressed with Google’ ability to sift through data previously out of reach. It included notes buried in PDFs or scribbled on odd charts. The neural net gobbled up all this unruly information then spat out predictions. And it did it far faster and more accurately than existing techniques. The system even displayed which records led it to conclusions.
Google’s future plan
Google’s next step is moving this predictive system into clinics. It is working on a slew of AI tools that can predict symptoms and disease with a level of accuracy that is being met with hope as well as alarm.
Inside the company, there’s a lot of excitement about the initiative. “They’ve finally found a new application for AI that has commercial promise,” one Googler says. Since Alphabet Inc.’s Google declared itself an “AI-first” company in 2016, much of its work in this area has gone to improve existing internet services.
Another Google researcher said existing models miss obvious medical events, including whether a patient had prior surgery. The person described existing hand-coded models as “an obvious, gigantic roadblock” in health care. The person asked not to be identified discussing work in progress. AI models could include information on local weather and traffic and other factors that influence patient outcomes.