Name: A “Nutrition Label” for AI Tools in Healthcare
Uploaded: 2023-12-07T14:22:00Z
Duration: 5 min 58 s

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Doctors and hospitals are using AI tools to communicate with patients and help make diagnoses. Except, the tools don’t always work as promised. They can introduce errors in patient notes or incorrectly predict which patients will be diagnosed with certain conditions. Now federal regulators are suggesting that AI tools come with a “nutrition label” to help healthcare providers decide how or whether to use them. WSJ tech policy reporter Ryan Tracy explains. Photo: Leah Willingham/Associated Press

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A “Nutrition Label” for AI Tools in Healthcare

Alex: Welcome to Tech News Briefing. It's Wednesday, December 6th. I'm Alex Ossola for the Wall Street Journal. AI is already making its way into healthcare. It's taking notes for doctors and patient meetings and even helping them with diagnoses. But these AI tools aren't perfect, and sometimes errors can be dangerous. So, the Biden Administration is proposing a new labeling system, sort of a nutrition label for AI apps in medicine. Here now with more on how that could help clinicians avoid mistakes is WSJ Tech Policy Reporter Ryan Tracy. Ryan, why might people want more transparency around AI in medicine, specifically?

Ryan: AI is being pretty widely used in the healthcare sector already, and there have been some studies about problems that have cropped up with that. So there have been studies showing that AI systems had racial bias embedded in them, or spit out results that were racially biased without the designers anticipating or realizing that that was what was happening. There's also been issues of accuracy. There was a algorithm that was being used to predict whether patients would go into sepsis, which is a really serious condition that can happen when you're in the hospital, and it was very good at first, but then when they started using it in more and more hospitals over time, it turned out it wasn't as good. And so, the idea here is let's give the hospitals and doctors who are buying these AI tools more information about them so that they can make better decisions and vet these tools for whether they've been checked for these kind of problems.

Alex: So this nutrition label that could contain some of this information, who exactly is suggesting it?

So there's this agency called the Office of the National Coordinator for Health IT. And this agency was created more than a decade ago as part of an effort to get the electronic health record system in this country into better shape and more interoperable and all that kind of thing. And they come up with these certification requirements that if you're making this kind of software, you need to be certified by them.

Alex: What would that nutrition label have in it?

Ryan: They haven't specified what it has to look like, but they've said the things that it needs to say. So, one category is measures of "Validity and fairness." Some kind of quantitative measure of is this thing valid? Does it work? It would also say how you should use this algorithm, and importantly, how shouldn't you use it? You know, it's been trained for this purpose, but not necessarily for this other related purpose where it might not be as accurate. And then they also want description of risk assessments. Did you test it for racial bias and that sort of thing?

Alex: What decision has to be made for it to actually be implemented?

Ryan: So this idea has been proposed by this agency, the ONC, and they've taken comments on it this year. Sometime by the end of this year or maybe early next year, they're expected to finalize the proposal and make some changes. And to be clear, this is really primarily a voluntary thing. Not all of the software that's used in healthcare has to go through this agency and get certified. And all the proposal says is that there has to be the ability for the app developer to make these disclosures, not necessarily that they have to do it. Now, what the agency's hoping is that if a doctor or a hospital is looking for an app or looking at buying an AI app and they see that there's no nutrition label that that should raise some questions. So it's voluntary, but the idea is that like a blank field will be informative to the buyer as well.

Alex: What is the cost associated with this and who would be shouldering that?

Ryan: The agency estimates, you know, between 80,000,300 $30 million over the next decade in terms of just the software developers writing the code to make this work and other things that'll have to happen, and that would be spread across the industry. One question here is how big the scope is gonna be. Is this gonna be like any software that has any little bit of automation in it or is it gonna be narrower than that? Some of that'll get worked out in the final rule, and we'll see how far this reaches and whether that cost figure is at the high end or the low end that could get passed on to patients, potentially these are businesses and when their costs go up, they do have to pass those costs on.

Alex: Does everyone agree that this kind of a label is like a good or useful idea? And I'm particularly interested in the opinions of AI developers or other companies.

Ryan: Everyone agrees that transparency is a good idea. And what's so interesting about this rule is that it really is one of the few areas where the government is taking this high level idea about how to make AI safe and actually trying to write a regulation that's going to operate in the real world. And there's some tension over that. What does transparency mean? How much transparency is enough and how much transparency is too much? So, when we talked to developers about this proposal, they all said, "Great, we love the idea of a label." But what they're asking us to disclose is going too far. So if I, the developer have to disclose all the training data sets that I use to create my algorithm, well then some other developer could look at those training data sets and then go copy my work. There are other people in the academic world who push back on that and say, "No, you know, we're not asking for that level of disclosure." And so this is the type of thing that it's gonna be interesting to see how it actually operates in the real world, and whether the agency strikes the right balance between disclosing too little or too much.

Alex: That was WSJ Tech Policy Reporter Ryan Tracy. And that's it for Tech News Briefing. Today's show was produced by Julie Chang with supervising producer Katherine Milsop. I'm Alex Ossola for the Wall Street Journal. Thanks for listening.

A 'Nutrition Label' for AI Tools in Healthcare

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