CAIHL read · Jun 8, 2026
Have a Thorny Medical Question? Your Doctor May Be Using A.I. for That.
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
clinician
Clinicians or care teams are the primary users. Patients are affected downstream.
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
Clinician-side AI use in the consultation without patient disclosure; constraining patient agency because the disclosure layer is missing.
In the scan
How this item appeared in the daily scan
Editor's note: The AI is in the consultation; it is just not on the consent form. The patient is the only party to the encounter who cannot tell which sentence came from the clinician and which came from the model.
Summary: NYT: Doctors increasingly reach for AI tools — ChatGPT, ambient scribes, condition-specific copilots — for the harder questions inside the consultation, often without disclosing the AI's involvement to the patient.
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.