CAIHL read · Jun 9, 2026
Patient and Urologist Perspectives on Unmet Needs, Communication Gaps, and Trust in Bladder Cancer
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
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
Patient-urologist trust-gap research; expanding agency by naming the gap explicitly.
In the scan
How this item appeared in the daily scan
Editor's note: The trust gap the paper names is the gap the AI tool will land in. Whether the AI closes it or widens it depends on whether the AI is the patient's tool or the clinician's tool.
Summary: UroToday: Mixed-perspective qualitative work on unmet needs, communication gaps, and trust in bladder cancer between patients and urologists — the relational substrate the AI-mediated consultation will rearrange.
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.