About / methodology
Public-interest reviews of AI that affects patient agency.
HugoScore is the applied review layer for Critical AI Health Literacy: a public way to ask who health AI serves, who controls it, and whether it expands a patient's practical ability to understand, question, decide, challenge, navigate, and act.
Methodology
How Profiles Are Reviewed
Reviews are led by Hugo Campos using the CAIHL-derived HugoScore patient agency framework, with AI-assisted public-source research and human editorial review before publication. Evidence includes official product documentation, privacy and security materials, patient-facing notices, health-system explainers, peer-reviewed studies, credible independent reporting, and submitted corrections. The 0-100 number is an axis position, not an overall quality grade: 0 means institution-directed control and 100 means patient-directed choice and control, with placement shaped by service alignment, visibility, consent, contestability, action support, privacy, equity, safety boundaries, evidence quality, and disclosed unknowns. Profiles update when substantial new evidence appears, when readers submit supported corrections, or during scheduled evidence refreshes; each profile shows its last-reviewed date, review method, status, and confidence.
Reviewer
Hugo-led CAIHL review, supported by structured research drafts and human judgment before publication.
Evidence
Official sources, policies, patient materials, implementation notes, studies, independent reporting, and reader-supplied evidence.
Score
0 to 100 locates the tool on the institutional-to-patient-directed axis; it is not a safety, quality, or procurement score.
Updates
Profiles refresh when evidence changes, corrections are accepted, or a scheduled review pass is completed.