Know what your health tools know.
And what they don't.
AI health tools don't work equally well for everyone. This project gives you the research, the data, and the framework to understand why — and to make better decisions about your own health.
Does the AI answering your health question work the same for everyone?
Peer-reviewed studies in Science, The Lancet, and npj Digital Medicine show that many AI health tools produce different results depending on a patient's race, skin tone, or background. These aren't edge cases — they're measured, documented, and published in the most respected journals in medicine.
The reasons trace back decades. Medical algorithms were built on data that reflected historical biases in clinical practice — biases that have since been identified and, in many cases, corrected at the institutional level. But the training data that AI models learned from still carries those patterns.
Understanding this isn't about fear. It's about being an informed consumer of the tools your generation uses more than any before it.
The data is clear. Here's what the science says.
These findings come from peer-reviewed research. Knowing them puts you ahead of most adults.
Stereotyping rate in one AI platform's diagnoses
One major AI platform attributed sarcoidosis to Black patients at a 97% rate — reflecting training data patterns, not medical reality.
of major AI platforms repeated race-based medical myths
When tested with race-sensitive medical questions, every major large language model perpetuated debunked biological claims.
This isn't about avoiding technology. It's about using it better.
AI health tools are useful. They're also imperfect — and the imperfections aren't random. When you understand where bias lives in these systems, you can make smarter choices about what to trust, what to question, and when to seek a second opinion.
Diagnose the Bias is a free, open-source curriculum designed for high school and college students. It's built on published research, grounded in real data, and focused on one goal: giving you the knowledge to be a more informed, more autonomous health consumer.
See the data
Real findings from peer-reviewed journals, translated for a student audience.
Understand the why
How historical patterns in medical practice became training data for modern AI.
Own your decisions
A practical framework for evaluating health information — on your terms.
Launching Spring 2026
Free curriculum, case studies, facilitator guides, and a downloadable toolkit for any educator, student group, or community organization. Built to be used, shared, and adapted.