AI in healthcare: where it is reaching patients and clinicians, what it changes in the workflow, and what still does not work.
4 articles · Updated
The public imagines diagnostic AI catching what doctors miss. The actual deployment win is documentation, and meanwhile half the cleared diagnostic devices were never shown to work in a clinical study.
ARPA-H is funding a push for the first FDA-authorized agentic AI for clinical care, deliberately starting with a high-risk, well-defined disease so regulators are forced to set the precedent.
NHS England is putting Microsoft Copilot in front of 505,000 staff after its own pilot reported saving 43 minutes a day. The open question is whether that time becomes patient care or just moves somewhere else.
Clearance was supposed to be the hard part for medical AI. It isn't. The gap between a cleared algorithm and a bedside is a billing code, and most tools lack one. Reimbursement, not accuracy, is the bottleneck.
HHS is running five years of audit reports through ChatGPT across all 50 states. A first pass over public documents is defensible; as the basis for cutting a state's funding, the model's confident errors are the catch.
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