Nineteen people die every day waiting for an organ transplant in the United States. Not because the organs don't exist — because the pipeline that gets an organ from donor to recipient is broken at almost every step.
In Part 1, I traced the root causes: siloed teams, chaotic workups, and a system that treats transplant evaluation like a loose series of handoffs. But diagnosing the problem was only half the work. Part 2 is about the fix.
The organ transplant process has five discrete failure points — and each one maps to an AI capability that already exists in adjacent areas of medicine. We're not talking about moonshots. We're talking about tools that are already deployed elsewhere, waiting to be pointed at the right problem.
I break down exactly where AI fits in: from smarter organ matching that accounts for trajectory (not just a static MELD score), to predicting which patients are quietly sliding toward "status seven" before a donor even arrives.
