Premed-written breakdowns on where AI and medicine actually meet, no jargon required.
Our first articles are still in the works. Here's the lineup we're writing toward:
Cutting through the hype to explain what's actually deployed in hospitals today versus what's still research.
Read more →An honest look at what's working, what isn't, and what we've had to rebuild on the detection model.
Read more →The actual path our members took to get useful at this, not a generic "learn to code" list.
Read more →Sickle cell disproportionately affects populations that are historically underrepresented in medical datasets. We break down why that matters for anyone building a screening model, including us.
Read more →Sensitivity and specificity mean something very different in a hospital than they do in a machine learning paper. A guide for premed students evaluating model performance.
Read more →A plain-language walkthrough of the regulatory pathway every clinical AI tool, including ours eventually, has to go through before it touches a real patient.
Read more →It's rarely the model that kills a clinical AI project. It's workflow integration, liability, and trust. What that means for student-built tools like ours.
Read more →A postmortem on our earliest approach to sickle cell detection, what didn't generalize, and why we rebuilt it instead of patching it.
Read more →Why publishing early, even something small, matters more for med school applications and your own thinking than waiting until you feel "ready."
Read more →If one of these topics is calling your name, or you've got a better one, we want to hear about it.
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