CAIHL read · Jun 8, 2026
Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal
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
mixed
Both patients and clinicians interact directly with 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
neutral
Neither clearly expanding nor constraining patient agency.
Editor's CAIHL read
One-sentence synthesis
Meta-analysis of governance frameworks; institutional hosting and interest; the patient is the object of governance rather than the agent.
In the scan
How this item appeared in the daily scan
Editor's note: The systematic review counts the frameworks; nobody has yet built the systematic review that counts the binding instruments. The first sentence of every framework review has to start with the disclaimer that frameworks are not enforcement.
Summary: JMIR: Systematic review of AI governance frameworks deployed inside health systems, with an integrative model that maps where existing frameworks converge and where they leave gaps.
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.