Topic
16 facts from 1 episode, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
Undated
Ylan Kazi says healthcare organizations work with some of the most sensitive data in the world, including patient and member health information.
“we're working with some of the most sensitive data in the world. Patient data, member data, health conditions.”
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Ylan Kazi contrasts retail, where an AI error might be an incorrectly targeted ad, with healthcare, where the stakes are much higher.
“if we look at at retail, for instance, one of the worst things that could happen is somebody gets ad targeted incorrectly and, you know, they don't wanna buy that T-shirt.”
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Ylan Kazi says harm and death from medical errors number in the hundreds of thousands of cases, potentially more, yet medicine and surgery continue with safeguards.
“medical errors and and harm and death from medical errors, it's pretty high. It's it's in the hundreds of thousands of use cases, you know, potentially even more.”
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Ylan Kazi uses driving as an example of an everyday risky activity people accept with sensible precautions, as an analogy for AI adoption.
“Driving is inherently more dangerous, more risky than most of the things that we do on a daily or weekly basis.”
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Ylan Kazi lists driving precautions such as speed limits, seatbelts and lane indicators as the model for AI guardrails that reduce risk without stopping use.
“We we try to drive the speed limit, we wear our seatbelts, you know, we use lane indicators, we use on-ramps, off-ramps.”
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Ylan Kazi says people often reject an AI with 95% accuracy because it is wrong 5% of the time, while tolerating comparable human error.
“an AI has 95% accuracy, you can bet that whoever you're talking with, they're gonna say, well, it's it's gonna be wrong 5% of the time”
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Ylan Kazi recommends a cross-functional team including legal, finance, HR and governance to discuss AI before policies and procedures are created.
“You want to have legal at the table, you wanna have finance, HR, governance, et cetera at the table, so you can have these robust discussions before you're creating many of the policies and procedures.”
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Ylan Kazi says regulators and AI safety experts dominate the discussion even though many of them have never implemented AI themselves.
“You hear from them a majority of the time, but the problem is that many of those individuals have never actually implemented AI.”
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Ylan Kazi describes himself as a vocal proponent of what he calls the AI Hippocratic oath.
“a big proponent of what I call the AI Hippocratic oath, and I've, uh, it's been no secret, I mean, on on this podcast, on others, um, very vocal.”
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Ylan Kazi recommends starting AI adoption with easier, more manageable use cases rather than moonshot ideas.
“what I would really recommend is starting with those easier use cases or the ones that are more manageable.”
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Ylan Kazi says making mistakes in small, safe use cases lets organizations resolve problems before attempting broader or higher-risk projects.
“if you're doing that in a safe setting and, you know, in small enough use cases, you can iron out a lot of those, you know, issues and challenges that come up.”
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Ylan Kazi uses the tiny risk of being struck by lightning to argue that rare downside risks should be quantified rather than used to shut down an AI use case.
“if I if I walk outside, my risk of being struck by lightning is extremely small.”
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Ylan Kazi says many patients now paste their lab results or clinical notes into ChatGPT to get a simpler explanation.
“many people now when they go to the doctor, if they receive their lab results or clinical notes, they literally take them, they copy and paste them into ChatGPT”
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Ylan Kazi says patients are more demanding because they expect healthcare to match the experience they get from Amazon and other retailers.
“patients are becoming more demanding around it because they've had amazing experiences, you know, from Amazon and other retail companies.”
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Ylan Kazi believes AI could be applied more intensely to address affordability challenges in healthcare.
“I think that AI could really be applied more intensely to help with some of the affordability challenges.”
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Ylan Kazi predicts AI will cause more disintermediation, meaning fewer go-betweens, within the healthcare field.
“I think I think there's going to be, you know, more disintermediation within the healthcare field, particularly from from AI.”
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