CAIHL read · Jun 8, 2026
From raw audio to structure: an agent-based pipeline that boosts medical LLM performance
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
mixed
Multiple stakeholder interests in tension; the alignment is not stable.
Agency
constraining
Channels patients toward predetermined pathways or substitutes for patient capabilities.
Editor's CAIHL read
One-sentence synthesis
Ambient-audio-to-structure pipeline; constraining patient agency because the consent envelope arrives after the technical envelope.
In the scan
How this item appeared in the daily scan
Editor's note: The substrate the next clinical LLM will be trained on is the patient's voice, in real time, in the consultation. The patient should know what the consent envelope looks like before that pipeline is built into the room.
Summary: npj Digital Medicine: An agent-based pipeline converts raw clinical audio (consultations, dictation) into structured clinical input that improves downstream LLM medical-task performance.
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.