CAIHL read · Jun 12, 2026
AI bias in health care reads the writer, not the symptom
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
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 on AI bias as corpus inheritance rather than presentation reading; expanding agency by naming the structural source.
In the scan
How this item appeared in the daily scan
Editor's note: The 'reads the writer not the symptom' framing is the right framing. The bias the patient encounters in the AI tool's output is not the bias of the patient's clinical presentation; it is the bias of the population that produced the documentation the model trained on.
Summary: KevinMD: Clinician-essay framing AI bias in health care as a function of who wrote the training corpus rather than what the patient presents with — the corpus is the demographic, and the demographic is the bias the model inherits.
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.