Data Science x Public Health
This podcast discusses the concepts of data science and public health, and then delves into their intersection, exploring the connection between the two fields in greater detail.
Data Science x Public Health
Symbolic AI in Public Health: When Rules Beat Neural Networks
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Everyone talks about neural networks.
But the systems quietly running public health? They don’t learn — they follow rules.
In this episode, we break down symbolic AI — the rule-based systems behind clinical decision support, disease surveillance, and health regulations.
You’ll learn:
What symbolic AI actually is
Where it’s already used in healthcare and public health
Why neural networks fall short in high-stakes systems
Why the future is hybrid (neuro-symbolic AI)
This is the side of AI most people overlook — but it’s the one systems trust.
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So, um, if a hospital's AI suddenly told a doctor to prescribe a fatal dose of medication, how would we actually know why it made that mistake?
SPEAKER_00Right. I mean, that is a terrifying thought.
SPEAKER_01Yeah, exactly. Welcome to today's deep dive. We are looking at a really fascinating article called The Architecture of Trust: Symbolic AI in Public Health. And our mission today is to uncover the, you know, the other side of AI that secretly runs our healthcare systems.
SPEAKER_00It's the stuff doing all the heavy lifting behind the scenes.
SPEAKER_01Okay, let's unpack this. When most people think of AI right now, they picture neural networks, which um, if you think about it, are kind of like these creative, experimental chefs. They find their own way.
SPEAKER_00Yeah, or like an off-road vehicle just tearing through the wilderness.
SPEAKER_01You know, they are super capable, but they can unpredictably drive right off a cliff. Symbolic AI, on the other hand, is the strict health inspector, or I guess a train on fixed steel tracks. You're all about the flashy chef, but the inspector is the one actually keeping you safe.
SPEAKER_00And that fixed track architecture is actually operating the background of almost every single clinic you visit. Like every time a physician logs an ICD diagnosis code.
SPEAKER_01Wait, the billing shorthand stuff?
SPEAKER_00Yeah. Just the standard medical billing shorthand for your illness. Or um when a pharmacy system flags a dangerous drug combination before a prescription is finalized, that is symbolic AI at work.
SPEAKER_01Aaron Powell Which brings up a pretty obvious tension. I mean, why do we rely on the inspector instead of just letting the chef handle everything?
SPEAKER_00Aaron Powell Well, it comes down to reliability.
SPEAKER_01Aaron Powell Right. Because if we have these incredibly smart neural networks that can like write poetry and pass the bar exam, why are we still forcing our healthcare systems to run on rigid train tracks? How does symbolic AI actually differ from something like ChatGPT?
SPEAKER_00Aaron Powell The difference lies entirely in how the code arrives at its conclusion. So neural networks operate using probabilistic math and millions of, well, invisible weight adjustments. They guess based on pattern.
SPEAKER_01Do you just guess?
SPEAKER_00Yeah, basically. But symbolic AI operates on deterministic logic gates. It uses explicit human-encoded rules mapped out in what are called ontologies.
SPEAKER_01Ontologies, okay.
SPEAKER_00Right. You can think of an ontology as this massive digital dictionary that explicitly defines how medical concepts physically and logically relate to one another. The source gives a great example with pneumonia.
SPEAKER_01Aaron Powell Where the system looks for very specific triggers in a patient's chart, right?
SPEAKER_00Aaron Powell Well it doesn't just look for them, it mandates them. The rule might be programmed simply as if the patient's temperature is over 101 degrees and their white blood cell count is elevated and they have a productive cough, then trigger a pneumonia flag.
SPEAKER_01Aaron Powell So it's not guessing based on historical trends.
SPEAKER_00Not even a little bit. It traces a hard-corded, predefined path. And what's fascinating here is that unlike a neural network, this structure provides 100% explainability.
SPEAKER_01Which is huge for medicine.
SPEAKER_00Exactly. When making life or death public health decisions, you can't tell regulators like the FDA or the CDC that the AI just, you know, felt like it based on a statistical probability. They require concrete mathematical proof of how a decision was made.
SPEAKER_01Aaron Powell But aren't neural nets infinitely more powerful? I mean, holding on to older, rigid if-then technology when things like ChatGPT exist feels a bit like insisting on using a typewriter just because you prefer understanding how the ink hits the paper.
SPEAKER_00Aaron Powell I get that, yeah. The raw power of a neural network is undeniable, but it comes with structural flaws that are fatal in public health. Because they rely on those invisible, shifting mathematical weights, they are essentially black boxes. They completely lack transparency. Aaron Powell Oh, wow.
SPEAKER_01So you really can't see the math.
SPEAKER_00No, you can't. Mathematical ambiguity makes them inconsistent. I mean, they can generate wildly different outputs for nearly identical inputs, which is an absolute nightmare for medical surveillance. Furthermore, they are entirely dependent on massive lakes of historical training data.
SPEAKER_01Aaron Powell Oh, I see. So if a brand new, completely unknown virus hits the motto, the off-road vehicle is stuck because there's no map and no historical data to learn from.
SPEAKER_00It wouldn't even know what to look for. With symbolic AI, an epidemiologist can sit down and immediately write a new if-then rule into the system the very day a novel virus is discovered. But if we connect this to the bigger picture, the goal in modern computer science isn't actually to choose one over the other. The frontier is neurosymbolic AI.
SPEAKER_01Oh, here's where it gets really interesting. How do those two fundamentally different architectures actually communicate? I kind of picture it like a police force where the neural net is the bloodhound sniffing out a suspicious ER cluster.
SPEAKER_00That is a great way to put it. The neural network acts as the scout. It can churn through millions of messy, unstructured emergency room notes, things a rigid system just can't easily read, and spot a subtle, anomalous cluster of symptoms.
SPEAKER_01And then instead of just acting on that guess, it hands it off to the detective.
SPEAKER_00It translates its findings into structured data and feeds the variables to the symbolic AI. The detective then strictly applies epidemiological rules to decide if an official alert should be issued. Wow. You get the raw pattern matching power of the neural network combined with the verifiable trust and accountability of the symbolic layer.
SPEAKER_01So what does this all mean for you, the listener? Well, the next time you see a medical system instantly flag a dosing error, you'll know there is a traceable human-encoded logic tree working behind the scenes. The fixed tracks are still there, basically keeping the powerful engines from driving off the cliff.
SPEAKER_00It's a vital safeguard, absolutely. However, this raises an important question. Since symbolic AI requires human experts to explicitly encode every single rule and definition, what happens when those experts unwittingly hard code their own historical medical biases directly into this supposedly unquestionable logic?