CAIHL read · Jun 5, 2026
Por que você não deve trocar seu médico pelo ChatGPT
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
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
Major-press normalization-then-warning frame in Portuguese; constraining the substitution practice that's already the patient default.
In the scan
How this item appeared in the daily scan
Editor's note: The Brazilian mainstream press has to write the 'don't replace your doctor' piece because the alternative is the actual practice. The Anglophone mainstream press is still writing it as a hypothetical.
Summary: O Globo (BR): Portuguese-language major-press piece arguing against substituting ChatGPT for a physician — directed at a Brazilian readership where patient AI use is already widespread and the physician-shortage problem the substitution implicitly solves is real.
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.