CAIHL read · Jun 12, 2026
The big ideas from Stanford Health AI week
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
institutional
Hosted inside a health system, insurer, or large employer. Access controlled by the institution.
Interests
mixed
Multiple stakeholder interests in tension; the alignment is not stable.
Agency
neutral
Neither clearly expanding nor constraining patient agency.
Editor's CAIHL read
One-sentence synthesis
Academic-medical-center industry-facing convening output; agency direction depends on which partnership stack the patient encounter is downstream of.
In the scan
How this item appeared in the daily scan
Editor's note: The Stanford Health AI week roundup is the academic-medical-center version of the announcement layer. Whichever timelines and partnerships are named here become the deployment baseline that the next twelve months of patient encounters are calibrated against.
Summary: Stanford Medicine: Roundup of the major themes and announcements from Stanford Health AI week — the academic-medical-center industry-facing convening where deployment timelines are publicly stated.
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.