Healthcare AI
Building and scaling healthcare AI that survives production: workflow fit, trusted data, compliance-aware design, and ecosystem adoption.
The Healthcare AI Evidence Ladder: Why Benchmarks Don’t Tell You What Happens to Patients
Healthcare AI is advancing faster than the tests used to judge it; the scarce resource is credible evidence that a system improves care, and the evidence bar should rise with the consequence.
The Excluded Middle: Where the Next Healthcare AI Companies Come From
The next generation of healthcare AI companies will come from the excluded middle: care between visits, built by founders who start with a bounded workflow and design it to run continuously.
When AI Passes the Test but Fails the Job
As AI moves from predicting to acting, the binding constraint is no longer model capability but the gap between what we can specify and what we actually mean.
Where AI Is Actually Working in Healthcare — and What Physician Entrepreneurs Should Build
Healthcare AI succeeds where it automates the work around the medicine, and physician entrepreneurs should find their startup by asking where healthcare still does valuable work badly.
Why Health AI Is Hard to Build—and What It Takes to Scale
In health AI, the model is only one part of the product — companies endure when workflow, data, trust, and economics are designed together from the start.