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AI investors are running the numbers

The ideas and innovators shaping health care
Jan 22, 2024 View in browser
 
Future Pulse

By Daniel Payne, Ruth Reader, Carmen Paun and Erin Schumaker

INNOVATORS

Bertrand

Bertrand | Digital Diagnostics

Digital Diagnostics created the first FDA-cleared artificial intelligence to diagnose diabetic retinopathy — a potentially blinding eye condition linked to diabetes — without a doctor’s input.

So John Bertrand, co-founder and CEO of the Iowa-based company, is well positioned to evaluate the business and regulatory challenges facing AI developers in health care.

He spoke with Daniel about how the market — and the government — are receiving new artificial intelligence products.

This interview has been edited for length and clarity.

How is AI shaping the health care landscape?

We’ve really crossed over from a novel technology mindset to one of validated use cases. We’re at a point where providers are ready to adopt AI.

There are signals that things have gone mainstream. But many of the old conversations are still there, around bias, ethics and validation. Many of the principles from before still matter and will be discussed for some time.

Are investors driving innovation?

There’s a higher level of sophistication around AI from investors. Two years ago, if you said “AI” and waved your hands, someone would write you a check.

Now you have investors asking really smart questions. I’d say we’re in inning two of the game, and people are now starting to understand some fundamentals: What is your revenue model? Are you going to be a reimbursed product?

I find that to be good for the industry as a whole because we’re going to start to see products that deliver real value in the way health care needs it delivered.

Will regulation affect what products succeed?

We haven’t seen any major changes on the regulatory side, honestly.

In the last few years, there’s been some forward-looking directional statements made by the White House and the FDA. There hasn’t been a ton of revolutionary change in how they’re approaching it.

These regulatory bodies have done a good job of collaborating with the early technologists, getting the first several products through and getting feedback.

But we’re at a point where there’s more innovation out there than there’s capacity to regulate. So I think more streamlining and cross-agency work is necessary.

 

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Share any thoughts, news, tips and feedback with Carmen Paun at cpaun@politico.com, Daniel Payne at dpayne@politico.com, Ruth Reader at rreader@politico.com or Erin Schumaker at eschumaker@politico.com.

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CHECKUP

An anesthesiologist places a mask on patient, with surgeons in the background.

AI's not replacing doctors anytime soon, new research says. | SCIENCE PHOTO LIBRARY via AP Images

Replacing people with artificial intelligence isn’t yet cost-effective in most cases, according to a new paper from researchers at the Massachusetts Institute of Technology, IBM’s Institute for Business Value and the Productivity Institute, a U.K.-based research organization.

That should be a relief to many in the health sector who’ve seen their employers move to adopt AI systems.

The MIT group found that while 36 percent of nonfarm jobs have at least one feature that could be automated with computer vision — a field of computer science related to artificial intelligence — only 8 percent of those tasks are worth replacing with AI.

Take the job of a nurse anesthetist. “Their whole thing is to look at someone when they’re under anesthesia and say…are they in a healthy state or not,” Neil Thompson, director of MIT’s FutureTech research project and the lead researcher on the study, said.

“If a computer vision system is doing it, but it’s only 60 percent accurate, that’s a problem, right?” he asked.

In theory, the company or health system could dump more money into scaling up the data or fine-tuning the algorithm, but the cost of improving the AI may be a lot higher than the salary of a nurse anesthetist.

What’s next? Thompson told Ruth he and his team are looking at whether there’s a cap to how much AI will continue to improve and whether there will come a point of diminishing returns.

“You can start to make these predictions about, well, what is that point where the economics makes it unattractive?” he said.

Thompson and his colleagues have submitted the paper to the peer-reviewed journal Management Science.

 

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