AI-Enabled Medical Device Software Functions: FDA Guidance

Host 1: Hey everyone, and welcome back to The Deep Dive. Today we are going

Host 2: Going deep?

Host 1: Yeah, going deep into the future of healthcare.

Host 2: That's right.

Host 1: And we're talking about a future where medical devices are learning and evolving right alongside us.

Host 2: It's incredible, yeah.

Host 1: Thanks to AI, um

Host 2: Big topic.

Host 1: Huge.

Host 2: And it's already happening.

Host 1: Yeah. So, to help us unpack all of this, we're taking a deep dive into a draft guidance document from the FDA.

Host 2: Okay. It's called Big title.

Host 1: Yeah. Artificial Intelligence-Enabled Device Software Functions:

Host 2: Okay.

Host 1: Lifecycle Management and Marketing Submission Recommendations.

Host 2: Wow.

Host 1: It's a mouthful. But essentially it's a roadmap for developers and manufacturers who are trying to navigate this exciting

Host 2: Mhm. Very exciting.

Host 1: but complex world of AI in healthcare.

Host 2: It's really a brave new world.

Host 1: It is.

Host 2: And I think this document is really important because it's going to help ensure that these devices

Host 1: Right.

Host 2: are safe and effective

Host 1: For all of us.

Host 2: for patients, for doctors, everyone it touches.

Host 1: So, before we get too far ahead, let's back up.

Host 2: Sure.

Host 1: What exactly are we talking about when we say AI-enabled device software functions or AIDSSs for short?

Host 2: Okay. So,

Host 1: This is for those of us who don't speak fluent FDA.

Host 2: right. Um, think of it this way. You have an app on your phone that analyzes a photo of a mole

Host 1: Okay.

Host 2: to see if you might have skin cancer.

Host 1: Mhm.

Host 2: The app itself isn't the AI.

Host 1: Right.

Host 2: It's the software running it, that's using these complex algorithms,

Host 1: Okay.

Host 2: AI model,

Host 1: To actually look at that image and tell you what it thinks.

Host 2: to understand it and give you that insight. That's an AIDSSF in action, software

Host 1: Okay.

Host 2: inside a medical device,

Host 1: Got it.

Host 2: using AI to do something amazing.

Host 1: Okay.

Host 2: Yeah.

Host 1: So, it's not the hardware. It's the brains behind it. The software that's using the AI to make decisions.

Host 2: No. It's the brain. And the FDA is very specific

Host 1: Okay.

Host 2: about how these AI models are developed. They've to be transparent. Developers need to explain how they work,

Host 1: Okay.

Host 2: what kind of data they were trained on,

Host 1: So, it's not just about being smart. It's about being responsible.

Host 2: Responsible AI

Host 1: Yeah.

Host 2: that we can understand and trust.

Host 1: Exactly. Right. Which brings us to a critical piece of the puzzle, data.

Host 2: Yeah. Data is like the fuel

Host 1: Right.

Host 2: that powers these AI models.

Host 1: Absolutely.

Host 2: Garbage in, garbage out.

Host 1: Yeah. Right. So, if the AI is trained on bad data,

Host 2: You're going to get bad results.

Host 1: you're not going to get good results.

Host 2: Exactly.

Host 1: So, what is the FDA's take on all this?

Host 2: They are really focused on this

Host 1: Okay.

Host 2: because they're saying that the data that's used to train these models

Host 1: Mhm.

Host 2: needs to be representative

Host 1: Okay.

Host 2: of the entire patient population.

Host 1: The whole enchilada.

Host 2: The whole enchilada. You can't just

Host 1: skew it towards one age group.

Host 2: Right. Or gender or ethnicity.

Host 1: Right.

Host 2: It has to work for everyone.

Host 1: Everyone. So, they're basically saying like, "Hey, if you're going to create an AI that's going to diagnose heart disease,

Host 2: Yes.

Host 1: don't just train it on data from men in their 50s."

Host 2: Right. Exactly. It needs to account for

Host 2: women, different age groups,

Host 1: Different ethnicities,

Host 2: different ethnicities,

Host 1: The whole spectrum.

Host 2: the whole spectrum.

Host 1: Okay. So, we've got our AI models.

Host 2: Right.

Host 1: They're trained on good data,

Host 2: Good data.

Host 1: diverse data.

Host 2: Diverse data.

Host 1: Now, how does the FDA actually evaluate

Host 2: Okay. So, these devices?

Host 2: one of the key things is performance validation.

Host 1: Okay.

Host 2: They need objective evidence that the device actually does what it claims to do.

Host 1: So, you can't just say that it works.

Host 2: No, you have to prove it.

Host 1: You have to prove it.

Host 2: Yeah. And this might involve clinical trials

Host 1: Okay.

Host 2: or studies where the AIDSS is tested in the real world.

Host 1: So, they're really putting it through its paces.

Host 2: And it's not just about performance either.

Host 1: True.

Host 2: They're also looking at how easy these devices are to use

Host 1: Right.

Host 2: for doctors and nurses.

Host 1: Right, because what good is a brilliant AI

Host 2: Exactly.

Host 1: if nobody can figure out how to use it?

Host 2: It's got to be usable.

Host 1: Usable.

Host 2: Yeah, intuitive.

Host 1: Intuitive.

Host 2: Easy to understand.

Host 1: They also want to make sure that it's protected

Host 2: Yes.

Host 1: against all the bad stuff out there.

Host 2: Cybersecurity is huge.

Host 1: Cybersecurity huge.

Host 2: Cuz we're talking about sensitive medical information.

Host 1: Exactly.

Host 2: So, these devices have to be protected from hacking and data breaches.

Host 1: It's like we're building this amazing car, but forgetting to install seat belts.

Host 2: Yeah, that's a good analogy.

Host 1: Cybersecurity

Host 2: Critical, yeah.

Host 1: So, this is all pretty mind-boggling.

Host 2: It is.

Host 1: And it's clear that the FDA is taking this very seriously,

Host 2: They are.

Host 1: trying to walk that line between

Host 2: Yeah.

Host 1: encouraging this innovation,

Host 2: Right.

Host 1: but also making sure that it's safe.

Host 2: Safe and effective.

Host 1: For all of us.

Host 2: For all of us. That's the key.

Host 1: Yeah.

Host 2: And part of that is transparency.

Host 1: Okay.

Host 2: They want developers to be open about how these AI models work.

Host 1: So not just to the FDA.

Host 2: To the public as well.

Host 1: To the public.

Host 2: Yeah. And that's where the idea of model cards comes in.

Host 1: Model cards.

Host 2: Model cards.

Host 1: I like that.

Host 2: Yeah.

Host 1: Okay, tell me more.

Host 2: Okay, so think of a model card as a summary

Host 1: Okay.

Host 2: that explains

Host 1: Okay.

Host 2: key aspects of the AI model,

Host 1: Okay.

Host 2: but in plain language.

Host 1: In plain language.

Host 2: That everyone can understand.

Host 1: So it's not this black box.

Host 2: Black box. Yeah.

Host 2: It's not a mystery.

Host 1: Yeah.

Host 2: You can look inside

Host 1: Okay.

Host 2: and see how it thinks.

Host 1: So it's like the AI's resume.

Host 2: It's a good way to put it. It shows its strengths,

Host 1: Yeah.

Host 1: Okay.

Host 2: its limitations.

Host 1: What it's good at, what it's bad at.

Host 2: Exactly. And the FDA thinks this is really important

Host 1: Okay.

Host 2: for building trust in these devices.

Host 1: Yeah, I mean, think about it. You're more likely to trust something if you understand how it works.

Host 2: Yeah. That's right.

Host 1: I mean, how many times have I blindly trusted my GPS

Host 2: Oh, yeah.

Host 1: only to end up

Host 2: In the middle of nowhere.

Host 1: in the middle of nowhere. I wish my GPS had a model card.

Host 2: Right. A little more transparency.

Host 1: Exactly.

Host 2: And when it comes to our health,

Host 1: Yeah.

Host 2: we deserve that.

Host 1: Absolutely.

Host 2: We need to know what these devices are doing

Host 1: Mhm.

Host 2: and how they're affecting our lives.

Host 1: Okay, so we've covered a lot.

Host 2: We have.

Host 1: I feel like my brain needs a software update to keep up.

Host 2: Yeah, it's a lot to take in.

Host 1: But let's try to recap

Host 2: Okay.

Host 1: what we've learned so far about these AI-enabled

Host 2: AIDSSs.

Host 1: Yeah, AIDSSs.

Host 2: Okay, so we've talked about how they're basically software inside medical devices

Host 1: Right.

Host 2: that use AI models to analyze data

Host 1: Okay.

Host 2: and help doctor make better decisions.

Host 1: Right.

Host 2: We talked about how the FDA is taking a really deep look

Host 1: Mhm.

Host 2: at everything from the data used to train the AI

Host 1: Yeah.

Host 2: to the cyber security measures

Host 1: To protect that data.

Host 2: Exactly. And it all boils down to making sure that these devices are safe,

Host 1: Safe

Host 2: effective, and accessible

Host 1: and effective, for all of us.

Host 2: to everyone.

Host 1: Everyone. That's the goal.

Host 1: That's a great goal.

Host 2: Yeah.

Host 1: Now, I'm curious to learn more about the specific recommendations that the FDA is giving.

Host 2: Okay.

Host 1: What are some of the things that developers need to keep in mind?

Host 2: Let's start with a device description.

Host 1: Device description.

Host 2: It's basically an overview of the device.

Host 1: Okay.

Host 2: It outlines what it does,

Host 1: Okay.

Host 2: who it's for,

Host 1: Got it.

Host 2: how it works.

Host 1: So, it's like an instruction manual for the FDA?

Host 2: You could say that.

Host 1: Okay.

Host 2: But it's more than just technical.

Host 1: Okay.

Host 2: The FDA is saying these descriptions need to be clear

Host 1: Clear.

Host 2: and concise.

Host 1: Concise.

Host 2: Because not everyone

Host 1: Right.

Host 2: reading these documents is going to have a PhD in computer science.

Host 1: Exactly.

Host 2: So, it's got to be easy to understand.

Host 1: So, it's not just about the AI being smart. It's about the information being

Host 2: Accessible.

Host 1: understandable.

Host 2: To everyone involved, from the developers to the doctors, to the patients.

Host 1: Yeah. And that clarity extends to the user interface as well.

Host 2: Yes, it does. The FDA wants to see that the device is intuitive,

Host 1: Intuitive, easy to use.

Host 2: easy to use. So, even if it's doing

Host 1: Complex calculations.

Host 2: complex calculations behind the scenes,

Host 1: Yeah.

Host 2: it needs to be simple

Host 1: Simple.

Host 2: for the people actually using it.

Host 1: That's right. I don't need to understand

Host 2: I don't need to understand

Host 1: Yeah.

Host 2: how a car engine works

Host 1: Exactly.

Host 2: to be able to drive.

Host 1: That's a good analogy.

Host 2: Okay. So,

Host 1: So, we've got

Host 2: So, we've got device description.

Host 1: device description.

Host 2: Let's move on to data management.

Host 1: Data management, yes. This is where the guidance gets into

Host 2: Okay.

Host 1: the types of data

Host 2: that should be used.

Host 1: that should be used to train and validate the AI models,

Host 2: Okay.

Host 1: and how this data should be managed

Host 2: to make sure that it's good data.

Host 1: Good data, yeah.

Host 2: So, it's not just about having a lot of data. It's about having the right kind of data,

Host 1: the right kind of data that represents

Host 2: the diversity

Host 1: of the patient population.

Host 2: the patient population. And they're really focused on this idea of bias.

Host 1: Yes, bias is a big concern.

Host 2: Bias.

Host 1: Because it can really skew the results.

Host 2: Can you give an example

Host 1: Sure.

Host 2: of how bias might creep into the data?

Host 1: Okay, so let's say you're developing an AI

Host 2: Mhm.

Host 1: to analyze medical images.

Host 2: Okay.

Host 1: But all the images you're using to train it

Host 2: Yeah.

Host 1: are from these really high quality,

Host 2: Okay.

Host 1: state-of-the-art scanners.

Host 2: Fancy scanners.

Host 1: And so the AI might become overly reliant

Host 2: Right. on those perfect images.

Host 1: Okay.

Host 2: And then when it encounters images

Host 1: Right.

Host 2: from an older scanner

Host 1: In the real world.

Host 2: In the real world, it might not know what to do

Host 1: Cuz it's never seen that before.

Host 2: Exactly.

Host 1: So, the FDA is basically saying don't train your AI on a silver platter

Host 2: Yeah.

Host 1: of perfect data

Host 2: Right.

Host 1: and then expect it to work

Host 2: in the real world.

Host 1: in the real world.

Host 2: Where things are messy.

Host 1: Where things are messy.

Host 2: And complicated.

Host 1: Yeah, exactly.

Host 2: It's about being

Host 1: Realistic.

Host 2: realistic. This is all so important.

Host 1: Yeah.

Host 2: It is.

Host 1: It's reassuring to see

Host 2: Yeah.

Host 1: how carefully the FDA is considering all these different factors

Host 2: to ensure that these devices are used responsibly.

Host 1: Responsibly.

Host 2: That's the key. Okay, so we've covered the price description,

Host 1: Mhm.

Host 2: we've covered data management.

Host 1: Yes.

Host 2: What's next?

Host 1: Model description and development.

Host 2: Model description and development. This is where it gets technical.

Host 1: Okay, I'm ready.

Host 2: Okay, so developers need to provide a detailed explanation of their model.

Host 1: Okay.

Host 2: This includes things like the architecture.

Host 1: So, it's like opening up the hood.

Host 2: It's like showing the FDA the engine.

Host 1: Yeah.

Host 2: Exactly. The FDA wants to see that developers really understand their model,

Host 1: How it was built.

Host 2: how it was built,

Host 1: How it makes decisions.

Host 2: how it makes decisions.

Host 1: And it's not just about showing off

Host 2: No.

Host 1: how smart the AI is.

Host 2: They also need to be up front about its limitations.

Host 1: So, they need to say,

Host 2: "Here's what it can do

Host 1: Yeah.

Host 2: and here's what it can't do."

Host 1: Right.

Host 2: At least, not yet.

Host 1: Right.

Host 2: So, it's about setting realistic expectations

Host 1: Right.

Host 2: and making sure that the model is used appropriately.

Host 1: Appropriately.

Host 2: You wouldn't use a hammer

Host 1: Yeah.

Host 2: to screw in a light bulb.

Host 1: Exactly.

Host 2: So, each tool has its strengths and limitations.

Host 1: Okay, so we've got

Host 2: Device description.

Host 1: device description,

Host 2: Data management.

Host 1: data management,

Host 2: model description and development.

Host 1: And what's next?

Host 2: Validation.

Host 1: Validation.

Host 2: That's where we put it all to the test.

Host 1: Okay.

Host 2: We'll dive into that next time.

Host 1: I'm already on the edge of my seat. Until then, thanks for joining us on The Deep Dive. We'll see you next time for part two, where we explore the exciting world of AI-enabled device validation.

Host 1: Welcome back to The Deep Dive. Last time, we were talking all about the FDA's guidance on AI-powered devices.

Host 2: AI in healthcare.

Host 1: AI in healthcare, yeah. It's amazing how fast it's moving.

Host 2: It really is.

Host 1: And we're just at the beginning.

Host 2: Yeah, we really are just getting started.

Host 1: It's exciting.

Host 2: Yeah, it is.

Host 1: So, last time we went pretty deep

Host 2: We did.

Host 1: into how these AI models are actually developed

Host 2: and managed,

Host 1: and managed. Yes.

Host 2: All the details.

Host 1: All those details. But before we jump back into that,

Host 2: Okay.

Host 1: I'm curious how these devices are actually being used.

Host 2: Yeah.

Host 1: Out there in the real world.

Host 2: Out in the real world. Are we talking about robot surgeons?

Host 1: Robot surgeons!

Host 2: Is it that futuristic?

Host 1: Yeah. Is it like a sci-fi movie?

Host 2: Well, we're not quite there yet,

Host 1: Okay.

Host 2: though I do think it's a possibility.

Host 1: Okay.

Host 2: But the reality is

Host 1: Yeah.

Host 2: AI is already being used in a lot of different medical specialties

Host 1: Okay.

Host 2: in ways you might not even realize.

Host 1: Like what? Give me some examples.

Host 2: Okay, so let's start with radiology,

Host 1: Okay.

Host 2: a field where AI is already having a big impact.

Host 1: So like X-rays,

Host 2: That's right.

Host 1: CT scans, MRIs.

Host 2: Exactly.

Host 1: So, is it actually reading those images?

Host 2: Well, imagine an AI that's assisting radiologists

Host 1: Okay.

Host 2: in reading those images.

Host 1: Like an extra set of eyes?

Host 2: Like a super-powered set of eyes. With AI,

Host 1: With AI.

Host 2: Okay.

Host 1: they can go through tons of images

Host 2: Okay.

Host 1: really quickly

Host 2: Okay.

Host 1: and flag anything that looks unusual

Host 2: Okay.

Host 1: or that the radiologist should take a closer look at.

Host 2: So, like highlighting the important parts.

Host 1: It's like having an assistant

Host 2: Okay.

Host 1: that's highlighting

Host 2: all the important clues,

Host 1: all the important clues.

Host 2: Okay.

Host 1: So, the radiologist can focus on those.

Host 2: That makes sense. And that can lead to earlier

Host 1: Diagnosis.

Host 2: earlier diagnosis and more accurate diagnosis.

Host 1: Which is better for everyone.

Host 2: Absolute, it's a win-win.

Host 1: A win-win for everyone.

Host 2: So, where else are we seeing this?

Host 1: Yeah, what other areas of medicine?

Host 2: Well, cardiology is another one.

Host 1: Okay.

Host 2: So, think about an AI that can analyze

Host 1: EKGs.

Host 2: EKGs,

Host 1: Those squiggly lines.

Host 2: the squiggly lines that track your heart's electrical activity,

Host 1: Yeah. Right.

Host 2: and they can see patterns

Host 1: That humans might miss.

Host 2: that humans might miss.

Host 1: That could indicate a heart problem.

Host 2: Exactly. So, it's like

Host 1: a super-powered cardiologist,

Host 2: Yeah.

Host 1: constantly monitoring your heart.

Host 2: Which, I mean, that sounds

Host 1: Yeah.

Host 2: pretty good.

Host 1: Pretty amazing.

Host 2: Especially for someone with a heart condition.

Host 1: Yeah, especially for people with heart conditions.

Host 2: So, it's not just spotting problems,

Host 1: No.

Host 2: it could actually

Host 1: It can predict future events.

Host 2: Okay. Now, that's getting really sci-

Host 1: Yeah. It's like a crystal ball for your heart.

Host 2: Okay. What other

Host 1: Other specialties.

Host 2: Yeah.

Host 1: Oncology is another one.

Host 2: Okay. Oncol- so cancer.

Host 1: Cancer, yeah.

Host 2: So these AIDSSs,

Host 1: They're analyzing biopsies,

Host 2: Okay.

Host 1: predicting

Host 2: how likely the cancer is to come back.

Host 1: Exactly.

Host 2: And they're even being used

Host 1: To personalize treatment?

Host 2: personalized treatments based on a patient's genetic makeup.

Host 1: Wow. So, it's not one size fits all?

Host 2: No, not anymore. It's real tailored medicine,

Host 1: Tailored medicine

Host 2: to each individual.

Host 1: that's incredible.

Host 2: And in neurology.

Host 1: Okay. Neurology, the brain.

Host 2: The brain. AI is being used to detect abnormalities,

Host 1: AI is being used to detect abnormalities,

Host 2: Yes.

Host 1: diagnose conditions,

Host 2: and even predict the progression of diseases like Alzheimer's.

Host 1: So, it's like every area of medicine is

Host 2: Every area of medicine is being touched by AI.

Host 1: It's like we're on the verge of a revolution.

Host 2: A revolution in healthcare.

Host 1: in healthcare. But we need to be careful.

Host 2: We do. We need to make sure

Host 1: That these devices are used responsibly.

Host 2: responsibly and ethically.

Host 1: Ethically, yeah.

Host 2: Because we're talking about people's lives.

Host 1: Absolutely. So, that brings us back

Host 2: Back to the FDA guidance.

Host 1: to the FDA guidance

Host 2: And this idea of validation.

Host 1: Validation.

Host 2: Which is where the rubber meets the road.

Host 1: So, how does the FDA actually

Host 2: validate these devices?

Host 1: validate these devices?

Host 2: Well, they want to see that the AIDSSF actually does

Host 1: What it's supposed to do.

Host 2: what it claims to do.

Host 1: So, like if it says

Host 2: Yeah.

Host 1: it can diagnose

Host 2: Exactly.

Host 1: a certain condition,

Host 2: It better be able to.

Host 1: and do it reliably and consistently.

Host 2: Accurately. Yeah.

Host 1: So, they're looking for proof.

Host 2: Proof.

Host 1: So, this might include things like clinical trials.

Host 2: So, like real world,

Host 1: Real world testing.

Host 2: testing,

Host 1: To show that it actually works.

Host 2: so it's not just the developers saying,

Host 1: "Take our word for it."

Host 2: "Take our word for it."

Host 1: They have to show the data.

Host 2: They have to show the data.

Host 1: And the FDA really scrutinizes that data.

Host 2: They look at it with a fine-tooth comb.

Host 1: They want to make sure it's all there.

Host 2: And it's accurate.

Host 1: And that it supports their claims.

Host 2: It's a rigorous process.

Host 1: It is. It has to be.

Host 2: And they're also looking at

Host 1: how easy these devices are to use.

Host 2: How easy they are to use. Yeah.

Host 1: Usability.

Host 2: Usability.

Host 1: So, can a doctor actually use this device

Host 2: In a real world setting?

Host 1: in a real world setting

Host 2: Mhm.

Host 1: without pulling their hair out?

Host 2: Because if it's too complicated,

Host 1: Right.

Host 2: it's not going to be effective.

Host 1: No, it defeats the purpose.

Host 2: It's got to be intuitive,

Host 1: Intuitive.

Host 2: easy to understand.

Host 1: Yeah.

Host 2: So, it's like the difference between a smartphone

Host 1: Okay.

Host 2: that's really easy to use,

Host 1: User-friendly.

Host 2: user-friendly,

Host 1: versus one that's just

Host 2: clunky

Host 1: clunky and you know.

Host 2: Yeah.

Host 1: Drives you crazy.

Host 2: Drives you crazy.

Host 1: You want something that makes your life easier.

Host 2: Yeah. Exactly.

Host 1: And in healthcare,

Host 2: That's really important.

Host 1: that's critical.

Host 2: You don't want to add to the stress.

Host 1: Right, so you don't want to add to the cognitive load.

Host 2: Yeah.

Host 1: So, we've got device description, data management,

Host 2: model description and development,

Host 1: validation,

Host 2: validation,

Host 1: performance monitoring?

Host 2: Performance monitoring, yes.

Host 1: So, this is like once the device is out in the world,

Host 2: it's not over.

Host 1: it's not over.

Host 2: You can't just set it and forget it.

Host 1: Okay.

Host 2: The FDA wants to

Host 1: that you're still keeping an eye on things.

Host 2: that the device is still working as intended.

Host 1: Because things change.

Host 2: Things change.

Host 1: The AI is learning.

Host 2: They could already pick up bad habits.

Host 1: Pick up bad habits.

Host 2: Pick up bad habits, yeah.

Host 1: So, it's like having a student,

Host 2: you know, you send them off to college,

Host 1: Right.

Host 2: you want to make sure they're still studying.

Host 1: They're not just partying.

Host 2: Yeah, not just partying, yeah.

Host 1: So, you're checking in.

Host 2: Checking in.

Host 1: Making sure they're on the right path.

Host 2: Exactly. And that's what performance monitoring is all about.

Host 1: So, it's about being proactive.

Host 2: Proactive.

Host 1: Not reactive.

Host 2: Not waiting for something to go wrong.

Host 1: Right, constantly monitoring,

Host 2: Making adjustments.

Host 1: making adjustments so that the AI is always

Host 2: performing at its best.

Host 1: performing at its best.

Host 2: And it's not being biased.

Host 1: Not being biased, yeah.

Host 2: Because we want to make sure

Host 1: that it's treating everyone fairly.

Host 2: Regardless of background, ethnicity,

Host 1: Everything, everything.

Host 2: gender. Right. Description, data management,

Host 1: model development, validation, performance monitoring.

Host 2: What's next?

Host 1: Cybersecurity.

Host 2: Cybersecurity. This is a big one.

Host 1: This is a big one.

Host 2: Because we're talking about sensitive medical information.

Host 1: sensitive data,

Host 2: You need to make sure

Host 1: it's protected.

Host 2: it's protected. And the FDA is taking this very seriously.

Host 1: Okay.

Host 2: They have a whole section in the guidance

Host 1: Okay.

Host 2: that talks about cybersecurity.

Host 1: So, they're looking at potential vulnerabilities

Host 2: It's

Host 1: in the software, in the data management systems.

Host 2: It's like building a fortress.

Host 1: Okay.

Host 2: You don't just build the walls,

Host 1: you got to think about the windows,

Host 2: windows, the doors,

Host 1: the roof,

Host 2: the roof, any potential weak points.

Host 1: So, what kind of threats

Host 2: are we talking about?

Host 1: Yeah, what are the bad guys trying to do?

Host 2: Well, one thing is data breaches.

Host 1: Where they steal the data.

Host 2: They steal patient information,

Host 1: Like medical records.

Host 2: medical records, insurance details, genetic data.

Host 1: That's all valuable stuff.

Host 2: Very valuable.

Host 1: On the black market.

Host 2: It's like a gold mine for hackers.

Host 1: So, it's not just about stealing data, though.

Host 2: No, it's also about disrupting

Host 1: Disrupting the device.

Host 2: Imagine if someone hacked into an AI

Host 1: and messed with it.

Host 2: that's controlling a patient's vital signs.

Host 1: That's scary.

Host 2: That's really scary.

Host 1: It could be life or death.

Host 2: It could be life or death.

Host 1: So, it's not just about data privacy.

Host 2: No.

Host 1: It's about patient safety.

Host 2: Patient safety is paramount.

Host 1: So, the FDA is saying,

Host 2: "You need to be proactive

Host 1: Proactive.

Host 2: about cybersecurity.

Host 1: It's like building a house.

Host 2: Yeah.

Host 1: You don't wait till it's finished to put locks on the doors.

Host 2: to put locks on the doors. You do it from the beginning.

Host 1: From the beginning.

Host 2: You build it into the design.

Host 1: Okay. So,

Host 2: Device description, data management,

Host 1: model description and development,

Host 2: validation, performance monitoring,

Host 1: cybersecurity.

Host 2: Cybersecurity.

Host 1: My brain is full.

Host 2: Mine, too. It's a lot.

Host 1: But it's good stuff.

Host 2: It's all really important for understanding

Host 1: how these devices are being developed

Host 2: and regulated

Host 1: so they can be safe and

Host 2: for everyone.

Host 1: for everyone. Now, before we wrap up this part of our deep dive,

Host 2: Okay.

Host 1: I want to go back to

Host 2: those model cards.

Host 1: model cards.

Host 2: I think we need to spend a little more time.

Host 1: I'm still a little fuzzy on

Host 2: Yeah.

Host 1: what they are and how they work.

Host 2: Model cards are all about making those AI models less mysterious,

Host 1: Okay.

Host 2: more understandable,

Host 1: More understandable.

Host 2: more trustworthy.

Host 1: More trustworthy.

Host 2: So, we'll dive into that next time on The Deep Dive.

Host 1: Welcome back to The Deep Dive. We've spent the last two episodes exploring this world of AI-enabled medical devices.

Host 2: A lot to talk about.

Host 1: Yeah, it's been a wild ride.

Host 2: Yeah, it has.

Host 1: We've talked about how they're developed, how they're regulated, the potential benefits,

Host 2: The challenges and

Host 1: the challenges, yeah.

Host 2: It's a complex landscape.

Host 1: It really is.

Host 2: But we're just at the beginning.

Host 1: Yeah, we are.

Host 2: The field of AI in healthcare,

Host 1: Yeah.

Host 2: it's constantly evolving.

Host 1: New discoveries, new innovations all the time.

Host 2: It's exciting.

Host 1: It really is. So, in this final part of our deep dive,

Host 2: I want to kind of look ahead

Host 1: Okay.

Host 2: to the future.

Host 1: To the future. What's next for AI?

Host 2: What's next

Host 1: In healthcare?

Host 2: That's the big question.

Host 1: What are the emerging trends,

Host 2: Yeah.

Host 1: the possibilities,

Host 2: The challenges.

Host 1: It's like we've climbed one mountain

Host 2: Yeah.

Host 1: and now we see

Host 2: a whole range

Host 1: a whole range of new mountains to climb.

Host 2: I like that.

Host 1: So, what are we looking at?

Host 2: Yeah, what are we looking at? Well, one of the most exciting things on the horizon

Host 1: Mhm.

Host 2: is this concept of federated learning.

Host 1: Federated learning.

Host 2: It's a mouthful.

Host 1: It is.

Host 2: But it's basically a way to train AI models on huge amounts of data

Host 1: Okay.

Host 2: from lots of different sources,

Host 1: Without actually having to

Host 2: move the data

Host 1: move the data

Host 2: to a central location.

Host 1: Okay, I'm already lost.

Host 2: Okay, so imagine

Host 1: Yeah.

Host 2: you're trying to train an AI

Host 1: Okay.

Host 2: to diagnose a rare disease.

Host 1: Okay.

Host 2: You need a lot of data to do that,

Host 1: Right.

Host 2: ideally from patients all over the world.

Host 1: Okay.

Host 2: But getting access to all that data,

Host 1: That's a challenge.

Host 2: it's almost impossible,

Host 1: Yeah.

Host 2: because of privacy concerns and regulations.

Host 1: You can't just go asking hospitals for their patient records.

Host 2: That would be a disaster.

Host 1: Exactly.

Host 2: So, that's where federated learning comes in.

Host 1: Okay.

Host 2: Instead of bringing the data to the model,

Host 1: Okay.

Host 2: we bring the model to the data.

Host 1: Okay.

Host 2: So, the AI can travel

Host 1: Like a digital nomad?

Host 2: Like a digital nomad.

Host 1: The AI is a digital nomad.

Host 2: It goes from hospital to hospital,

Host 1: Learning from each data set.

Host 2: and it never actually sees

Host 1: Any personal information.

Host 2: It's just learning from the patterns.

Host 1: So, it's like sending the AI on a world tour.

Host 2: A world tour

Host 1: To learn without ever leaving home.

Host 2: Exactly.

Host 1: And this protects patient privacy.

Host 2: Yes.

Host 1: So, it's a win-win.

Host 2: It's a win-win for everyone.

Host 1: For every, yeah.

Host 2: Innovation and privacy.

Host 1: What other trends are you seeing out?

Host 2: Well, another really interesting one is explainable AI.

Host 1: Explainable AI, or XAI.

Host 2: XAI.

Host 1: We've touched on this a little bit,

Host 2: We have.

Host 1: but I'm still a little fuzzy on what it actually means.

Host 2: Yeah.

Host 2: So, it's about creating AI models

Host 1: Okay.

Host 2: that can actually explain their decisions.

Host 1: So not just giving an answer.

Host 2: Not just spitting out a diagnosis,

Host 1: Actually saying,

Host 2: "Here's how I got to that.

Host 1: Okay.

Host 2: Here's why."

Host 1: So the why,

Host 2: The why is important.

Host 1: because if an AI is going to recommend a treatment,

Host 2: Right.

Host 1: I want to know.

Host 2: How did it come to that conclusion?

Host 1: Yeah, how did it get there?

Host 2: What factors did it consider?

Host 1: What are the risks?

Host 2: What are the benefits?

Host 1: Yeah.

Host 2: So, it's about transparency,

Host 1: Transparency.

Host 2: building trust, it's like the AI showing its work.

Host 1: Yeah, showing its work.

Host 2: So, you can understand how it got to the answer.

Host 1: Okay. So, what else is out there? What other possibilities are you seeing?

Host 2: Well, personalized healthcare.

Host 1: Personalized healthcare.

Host 2: AI could tailor treatments

Host 1: Okay.

Host 2: to your specific needs,

Host 1: Wow.

Host 2: taking into account your genes,

Host 1: Okay.

Host 2: your lifestyle.

Host 1: So, it's not

Host 2: One-size-fits-all.

Host 1: one-size-fits-all.

Host 2: It's personalized.

Host 1: Personalized. So, how would that actually work?

Host 2: Well, the AI could look at all sorts of data.

Host 1: Okay.

Host 2: Your medical history,

Host 1: Data from your wearables?

Host 2: even your social media activity,

Host 1: If you're comfortable sharing that.

Host 2: if you're comfortable, a holistic picture

Host 1: Holistic picture, yeah.

Host 2: of your health.

Host 1: And then what?

Host 2: And then it uses that information

Host 1: To create a plan.

Host 2: a personalized plan.

Host 1: Personalized plan for me.

Host 2: For you.

Host 1: So, instead of getting

Host 2: generic advice,

Host 1: generic advice that everyone gets,

Host 2: I can get advice that's specific

Host 1: to your body and your life.

Host 2: to my body and my life.

Host 1: Exactly.

Host 2: Okay. I'm on board with this.

Host 1: It sounds pretty good.

Host 2: This sounds amazing.

Host 1: But there are challenges.

Host 2: There are always challenges.

Host 1: So, one of the big ones

Host 2: Okay.

Host 1: is making sure that these technologies are accessible.

Host 2: Accessible to everyone.

Host 1: Not just a select few.

Host 2: Cuz it wouldn't be fair if only the wealthy

Host 1: Few people with access to the best docs

Host 2: the best doctors

Host 1: could benefit. That creates a whole

Host 2: a whole other set of problems.

Host 1: a whole other set of problems.

Host 2: So, we have to be mindful of that.

Host 1: What else?

Host 2: Well, we also need to think about the ethical implications.

Host 1: The ethics of AI.

Host 2: The ethics of AI.

Host 1: That's a big one.

Host 2: It's a big one.

Host 1: So, things like bias,

Host 2: Bias,

Host 1: data privacy,

Host 2: data privacy,

Host 1: making sure that humans are still in control.

Host 2: Humans are still in the driver's seat

Host 1: of their own healthcare.

Host 2: It's a balancing act.

Host 1: Yeah, we want to harness the power of AI,

Host 2: We do.

Host 1: but we also need to be careful.

Host 2: And this is a conversation that everyone needs to be involved in.

Host 1: Not just the technologists.

Host 2: The technologists, ethicists, policymakers,

Host 1: Patients,

Host 2: patients, everyone.

Host 1: everyone.

Host 2: Because it's going to affect all of us.

Host 1: We all need to be part of shaping this future.

Host 2: So, where do we go from here?

Host 1: That's a great question.

Host 2: Um, we've covered a lot.

Host 1: We have.

Host 2: The FDA guidance,

Host 1: Federated learning,

Host 2: explainable AI,

Host 1: personalized medicine.

Host 2: It's clear that AI is going to play a huge role

Host 1: It already is.

Host 2: in shaping the future of healthcare.

Host 1: It's up to all of us.

Host 2: Yeah.

Host 1: To make sure that it's used responsibly,

Host 2: Use it ethically and

Host 1: to stay informed.

Host 2: Stay informed.

Host 1: Ask questions.

Host 2: Ask questions.

Host 1: Engage in the conversation.

Host 2: Don't be afraid to speak up.

Host 1: Yeah. Your voice matters.

Host 2: Your voice matters.

Host 1: Thanks for joining us on this deep dive.

Host 2: It's been a journey.

Host 1: It has.

Host 2: But it's just the beginning.

Host 1: Just the beginning.

Host 2: Stay curious.

Host 1: Stay engaged.

Host 2: Stay engaged.

Host 1: And we'll see you next time.

Host 2: For another deep rise into the world of science and technology.