Entrepreneur-turned-venture capitalist Vinod Khosla made big headlines almost six years ago when he wrote a blog post called “Do We Need Doctors or Algorithms?” In it, he said medicine needed to be reinvented and he predicted a new era in which artificial intelligence might replace most of the functions that doctors do now—and do it much better, leaving physicians free to concentrate on the human element of care.
It’s been quite a ride since then. Along the way, Khosla has invested in a range of startup companies—including several tackling radiology, cardiology, and mental health (see slide and list at bottom)—that are using data and artificial intelligence to reimagine healthcare, hopefully lowering costs, improving quality, and making the best care accessible to all. And almost exactly a year ago, in September 2016, he published a 110-page paper on the subject called “20-percent doctor included” & Dr. Algorithm: Speculations and musings of a technology optimist that spelled out his thoughts and speculations (not predictions) in far greater detail.
I recently visited with Khosla, one of the founders of Sun Microsystems, at the offices of Khosla Ventures in Menlo Park, CA, for a discussion on A.I. and healthcare and beyond, including a not totally optimistic picture of the future of work and jobs. “There’s no reason an oncologist should be a human being,” is one of the things Khosla told me. “There’s nothing that requires human judgment that machines don’t have a chance at doing much better,” and “People don’t need to work, for those who don’t want to” are a few others.
What follows is an edited transcript of our conversation.
Xconomy: I’m curious–what got you into A.I. in healthcare?
Vinod Khosla. Well, I’m always looking for where the large changes are, and where the large problems are. If you look at healthcare, we all know how big a problem it is—there’s no rocket science. Nobody had ever looked at unique, highly leveraged ways to change healthcare and how one would do healthcare if it started from scratch. Of course, it has to at some point fit back into the old healthcare system.
I originally looked at it to see if I could do some nonprofit efforts in India. And you couldn’t scale enough doctors. If you had unlimited budget, you couldn’t start enough medical schools and get enough professors to teach the number of students who in 10 years would [make] enough doctors. The math didn’t work.
Then in January 2012, I wrote a piece called Do We Need Doctors or Algorithms?
X: That got a lot of attention.
VK: That was in TechCrunch. I didn’t intend to write it. I was skiing for two weeks [in Deer Valley, near Park City, UT], and the day before Christmas, I tore my ACL skiing. It’s a bummer when you’re planning on skiing and you have to stay in bed. I did an MRI and I took it to three different docs, and they recommended three different things. I said, ‘This is stupid. There’s one right answer.’ And when I talked to them about probability, they didn’t understand probability. And these are really good docs. The U.S. ski team is based in Deer Valley so they’re the best docs, and they see probably hundreds, if not thousands, of patients every season. So I was in bed, I was dealing with different opinions, and I’d been thinking about the India problem—looking at scaling medicine. That’s when I wrote that blog. Came from my frustration with the inconsistency of advice.
And frankly, there’s two types of advice in medicine, one that doesn’t matter. If you’ve got the flu, it doesn’t matter what advice you get—you’ll get better in the same timeframe. You might feel a little better if they give you Advil, a little worse if they don’t. And then when it really matters, you get a lot of opinions, but no science.
There’s a number in medicine that almost no doctor knows, but it’s well-established. It’s called NNT. You probably never heard of it. It’s amazing. [Editor’s note: NNT is Number Needed to Treat to avoid or prevent one additional bad result.] That’s the real number that matters. NNT is an incredibly important number that few doctors are aware of.
Look, medicine is better than it has ever been, and every year it’s improving. But it’s still the practice of medicine. It’s not a science. If it was a science, for any given patient you’d always have the same answer no matter who you ask, even if it is a probability distribution of outcomes. So my goal became to change the practice of medicine, which is pretty damn good, into the science of medicine. And the science of medicine needs science—and it can take a good system and make it much better.
X: I can guess where artificial intelligence figures into this, but please take us through your thinking.
VK: We don’t even measure the stuff that doctors can’t directly understand. We’re starting to run into this a little bit because in genomics, you might get a thousand data points, and no doctor can look at a thousand data points. And so in the past we didn’t measure anything humans couldn’t consume, which meant they can at best look at a few numbers. We should have thousands of numbers per patient, per episode.
So my approach was to look at how we reinvent medicine if
