The Healthcare AI Adoption Gap Is A Trust Gap

Every week another health system announces an artificial intelligence partnership. Every week I talk to a clinician who has quietly stopped using the tool their organization announced last year.

The gap between those two facts is not technical. The models work well enough. The gap is trust — and trust in healthcare is earned in the least convenient moments, not in the demo.

Organizations that succeed here do three things consistently. They deploy where the pain is worst rather than where the data is cleanest. They name a clinical owner before a technical owner. And they publish what the tool got wrong, on purpose, early.

That last one is uncomfortable. It is also the only thing I have seen reliably convert skeptics. Clinicians do not need a perfect tool. They need an honest one, and a clear answer to a simple question: when this is wrong, who is accountable?

Until that question has a name attached to it, adoption numbers will keep looking impressive in the first quarter and disappointing in the fourth.