CAIHL read · Jun 8, 2026
Do GLP-1 drugs reduce the risk of cancer?
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
patient
Patients, families, and care partners are the primary users of this AI.
Hosting
public
Hosted for public use (ChatGPT, Claude, consumer apps). Anyone with a device can use it.
Interests
patient-aligned
Interest structure prioritizes patients. Operates on a philanthropic, public-service, or advocacy footing.
Agency
expanding
Expands patient capabilities, supports their questions, increases their ability to act on their own values across and beyond health systems.
Editor's CAIHL read
One-sentence synthesis
Clinician-voice public deconstruction of a populator-press claim; expanding patient-side critical-evaluation capacity.
In the scan
How this item appeared in the daily scan
Editor's note: The patient evaluator of a GLP-1 question is now evaluating a claim three steps removed from a trial — a press release of a meta-analysis of an observational study. The CAIHL competence required to do this in 2026 is materially harder than in 2024.
Summary: Vinay Prasad: Substack post interrogating the claim that GLP-1 receptor agonists reduce cancer risk — walks the reader through observational-versus-causal evidence at the level patients are now hearing about it.
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.