AI can now write like an experienced epidemiologist.
Clear. Structured. Confident.
But what happens when it’s wrong?

In this episode, we break down how large language models (LLMs) are being used in public health — from surveillance summaries to clinical decision support — and why their biggest strength is also their biggest risk.

You’ll learn:
What LLMs actually are (and what they’re not)
Where they’re already used in public health
Why hallucinations happen — and why they’re dangerous
The guardrails (RAG, validation, governance) that make them usable
The future of public health will use AI.
The real question is whether it can be trusted.

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Healthcare is producing more data than ever before — electronic health records, wearables, genomic data, insurance claims, and real-time patient monitoring.

But data alone doesn’t solve problems.

In this episode, we break down health data science — the field where biostatistics, machine learning, and modern healthcare systems come together. You’ll learn what health data science actually is, how it differs from traditional biostatistics, and why it’s one of the fastest-growing careers in public health and data science.

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We’ve known the major causes of heart disease for decades.

Smoking. Cholesterol. Blood pressure. Diabetes.

So why is it still the leading cause of death worldwide?

In this episode, we break down cardiovascular epidemiology — from the Framingham Heart Study to modern advances in genetics, wearables, and AI.

You’ll learn:

• How risk factors were discovered
• Why “residual risk” still exists
• How genetics and polygenic risk scores are changing the field
• The role of policy, prevention, and health disparities

This isn’t just history — it’s where the future of public health is being built.

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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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What if you could run a clinical trial… without randomizing anyone?

In this episode, we break down propensity score methods — one of the most important tools in biostatistics for turning messy observational data into something closer to a fair comparison.

You’ll learn:

• Why observational studies are biased by default
• How propensity scores balance treated vs untreated groups
• The 4 major methods (matching, weighting, stratification, adjustment)
• When they work… and when they fail

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Cancer cases are still rising…

But cancer deaths have dropped 34% since 1991 — preventing millions of deaths.

So what changed?

In this episode, we break down cancer epidemiology — the hidden system of data, registries, and research that reveals who gets cancer, why it happens, and how we prevent it at scale.

From risk factors like smoking to screening programs and health disparities, this is the science quietly shaping the fight against cancer.

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AI models in healthcare are making critical decisions every day…
Who gets flagged as high-risk.
Where resources are sent.
Who gets care first.

But there’s a problem:
Many of these models can’t explain their decisions.
In this episode, we break down Explainable AI (XAI) and why black-box models are a serious risk in public health. You’ll learn how tools like SHAP and LIME reveal what’s really happening inside AI — and why transparency is becoming non-negotiable.

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Before any study begins, one question determines everything:

How many participants do you need?

Get it wrong, and your study can miss real effects… or waste time, money, and lives.

In this episode, we break down sample size and power analysis — the foundation of every clinical trial and research study. You’ll learn how effect size, power, variability, and significance level all come together to determine the one number that decides whether your study succeeds or fails.

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Every year, millions of people die from injuries — and most of us call them “accidents.”

But what if that’s completely wrong?

In this episode, we break down injury epidemiology — the science of understanding and preventing harm before it happens. From car crashes to falls to workplace injuries, these events follow patterns, risk factors, and can be systematically reduced.

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AI models trained in top hospitals often fail when deployed elsewhere. Different populations. Different data. Different outcomes.

So how do you make AI work in places with limited data?

In this episode, we break down transfer learning — the technique that lets models reuse what they’ve already learned and adapt to new environments. From rural clinics to global health systems, this method is helping close the gap between data-rich and data-limited settings.

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