CAIHL read · Jun 12, 2026
AI use is surging across HHS, jumping 148% at the FDA in 2025, Bipartisan Policy Center data finds
Framework
What CAIHL does
Critical AI Health Literacy (CAIHL) is an analytical lens — Hugo Campos and Liz Salmi's 2025 National Academy of Medicine commentary, "Critical AI Health Literacy as Liberation Technology." It applies Paulo Freire's theory of critical literacy to health AI.
The central question CAIHL asks is whose interests does this AI actually serve? Four dimensions answer it: who is the primary user, where is it hosted, whose interests does it advance, and does it expand or constrain patient agency.
This deep-read separates the four dimensions on a single item from the day's scan, so you can see the specific structural shape of the AI in question — not just the bucket it landed in.
The four dimensions
How this item reads through CAIHL
Primary user
mixed
Both patients and clinicians interact directly with this AI.
Hosting
government
Hosted or controlled by a government agency or program.
Interests
mixed
Multiple stakeholder interests in tension; the alignment is not stable.
Agency
constraining
Channels patients toward predetermined pathways or substitutes for patient capabilities.
Editor's CAIHL read
One-sentence synthesis
Federal agency AI deployment outpacing oversight architecture; constraining agency where the deployment outruns the disclosure.
In the scan
How this item appeared in the daily scan
Editor's note: Federal-agency AI adoption running at 148% YoY at the FDA is the deployment trajectory the AMA's just-adopted transparency policy is trying to match. Whichever side scales faster determines which side patients will be operating inside next year.
Summary: Fierce Healthcare: Bipartisan Policy Center analysis showing AI use across HHS surged in 2025 — FDA deployment jumped 148% year-over-year — with documentation of which agency sub-units expanded most aggressively.
methodology
Limitations
CAIHL is a lens, not a verdict. The four dimensions are conditions of use — reassess them when a tool's business model, deployment context, or patient behavior changes. See the NAM commentary for the full framework.