CAIHL read · Jun 13, 2026
How Wisconsin doctors and patients are using AI to assist with healthcare
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
patient-aligned
Interest structure prioritizes patients. Operates on a philanthropic, public-service, or advocacy footing.
Agency
neutral
Neither clearly expanding nor constraining patient agency.
Editor's CAIHL read
One-sentence synthesis
Field-level reporting on patient-and-clinician AI use at state-system scale; agency direction depends on which specific tools and protocols are deployed.
In the scan
How this item appeared in the daily scan
Editor's note: State-public-radio coverage of clinician-and-patient AI use captures a register the national press misses. The Wisconsin clinic encounter is what the consent envelope looks like in the average US-Midwest health system.
Summary: Wisconsin Public Radio: Mid-market state-level reporting on the practical AI patterns inside Wisconsin clinics and patient households — interviews with both clinicians and patients on the same encounter.
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.