CAIHL read · Jun 8, 2026
The Evolution of Artificial Intelligence in Oncology: Impact on Trials, Workflows, and Outcomes
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
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
neutral
Neither clearly expanding nor constraining patient agency.
Editor's CAIHL read
One-sentence synthesis
Sector review of AI across an oncology workflow; neutral because the patient-facing surface is mediated by the oncologist.
In the scan
How this item appeared in the daily scan
Editor's note: Oncology is the field where the AI-versus-clinician comparator is least controversial (imaging, pathology) and most consequential (decision support, prognostication). The patient framing is whose oncologist will read what the AI wrote.
Summary: CancerNetwork: Field-state review of AI integration across oncology — trial design, workflow embedding, patient-outcome reporting — written for the practicing oncology audience.
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.