Medical interpretation AI
No Barrier
No Barrier provides institution-purchased medical interpretation. Its terms allow deletion or anonymization and continuing derivative-data uses, while its security page makes broader deletion and no-repurposing promises. Patient alternatives and correction remain deployment-dependent.
Published September 8, 2026 as an AI-assisted draft. The public report separates documented facts, agency judgments and unresolved questions.
Summary judgment · 43 out of 100 toward patient-directed
Institution-purchased medical interpretation used directly with patients. The July 20 baseline is 43, Mixed, institution-led language-access aid.
Mixed, institution-led language-access aid
No Barrier provides institution-purchased medical interpretation. Its terms allow deletion or anonymization and continuing derivative-data uses, while its security page makes broader deletion and no-repurposing promises. Patient alternatives and correction remain deployment-dependent.
Patient agency
How this tool changes agency
Visual explanation and bilingual notes may improve comprehension. Patient-accessible comparison with source speech, qualified review of important summaries and a durable correction route remain unestablished by this pass.
Patient notice, refusal experience, interpreter escalation, deployment-specific BAA and derivative-data handling were not observed.
Patient agency assessment
Who sets and changes the goal?
Language access can let patients express goals and challenge care decisions. The organization selects and configures the platform. A contractual alternative is meaningful, but it does not establish that a patient can trigger it promptly or correct a disputed translation afterward.
What can the patient understand, question, or do?
Visual explanation and bilingual notes may improve comprehension. Patient-accessible comparison with source speech, qualified review of important summaries and a durable correction route remain unestablished by this pass.
Can the patient evaluate the conditions of use?
The concrete distinction between deletion and anonymization matters to informed participation. Broad no-repurposing messaging does not explain surviving derivative-data permissions or which agreement controls a particular encounter.
Text findings
Conditions of use
Published controls and their limits
The concrete distinction between deletion and anonymization matters to informed participation. Broad no-repurposing messaging does not explain surviving derivative-data permissions or which agreement controls a particular encounter.
What remains unknown?
Not tested or not established
Patient notice, refusal experience, interpreter escalation, deployment-specific BAA and derivative-data handling were not observed.
Who evaluated this?
AI-assisted public-source draft
Vendor statements describe published conditions, not independently verified behavior. No clinical, security, accessibility, or legal validation is claimed. Earlier evidence remains dated in the report and history.
Sources checked
- https://www.nobarrier.ai/legal/terms-of-service
- https://www.nobarrier.ai/security
- https://www.nobarrier.ai/ai-interpreting
Source-specific findings and retrieval limitations are recorded in the full report.
Review provenance
Criteria
CAIHL-derived HugoScore framework and September 7 qualitative review priorities. Draft v1.2 numerical anchors remain unadopted.
Reviewer
AI-assisted public-source reassessment prepared in OpenAI Codex.
AI / model
OpenAI Codex / GPT-6
Human review
Hugo Campos authorized publication of these AI-assisted draft reassessments on September 8, 2026. This does not claim comprehensive human verification of every finding.
Review date
2026-09-08
Limitations
Patient notice, refusal experience, interpreter escalation, deployment-specific BAA and derivative-data handling were not observed. No live product use, patient-data upload, account creation, code audit, clinical evaluation, or independent implementation validation.
Review method
Focused public-source reassessment using CAIHL: patient authority, critical capacity, and informed control. Existing evidence plus one focused primary-source pass and at most one targeted follow-up. No live product testing. Numerical scores remain provisional editorial placements, not a new calculation.
AI-assisted draft · Medium for published conditions, low for encounter-level implementation (AI-assisted draft)