CAIHL read · Jun 10, 2026
AI in Health Care: The Governance Gap
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
institutional
Hosted inside a health system, insurer, or large employer. Access controlled by the institution.
Interests
institutional
Prioritizes institutional efficiency, compliance, risk management, or revenue.
Agency
constraining
Channels patients toward predetermined pathways or substitutes for patient capabilities.
Editor's CAIHL read
One-sentence synthesis
Legal-frame analysis confirming patient absence from the contractual governance layer; constraining agency by structural exclusion.
In the scan
How this item appeared in the daily scan
Editor's note: When statutes lag, contract law fills the floor. The patient is never a party to the contract between the health system and the AI vendor. The protective layer the patient gets is the layer the two corporate parties leave intact.
Summary: Spencer Fane: Legal-press analysis arguing that the AI-in-health-care governance gap is now wide enough that vendor-side liability allocation will become the operational regulatory instrument by default, in the absence of a federal floor.
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.