Bloomberg said that quacks are rather impressed with Google’s ability to sift through data previously out of reach: notes buried in PDFs or scribbled on old 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.
As much as 80 percent of the time spent on today’s predictive models goes to making the data presentable, said Nigam Shah, an associate professor at Stanford University, who co-authored Google’s research paper, published in the journal Nature. Google’s approach avoids this. "You can throw in the kitchen sink and not have to worry about it”, Shah said.
Google’s approach, where machines learn to parse data on their own, “can just leapfrog everything else”, said Vik Bajaj, a former executive at Verily, an Alphabet health care arm, and managing director of investment firm Foresite Capital. “They understand what problems are worth solving", he said. "They’ve now done enough small experiments to know exactly what the fruitful directions are.”
The plan is to let the AI system steering doctors toward certain medications and diagnoses.
For all the optimism over Google’s potential, harnessing AI to improve health care outcomes remains a huge challenge. Other outfits, notably IBM’s Watson unit, have tried to apply AI to medicine but have had limited success saving money.
Over time, Google could license its systems to clinics, or sell them through the company’s cloud-computing division as a sort of diagnostics-as-a-service. Microsoft, a top cloud rival, is also working on predictive AI services. To commercialise an offering, Google would first need to get its paws on more records, which tend to vary widely across health providers. Google could buy them, but that may not sit as well with regulators or consumers. The deals with UCSF and the University of Chicago aren’t commercial.