Research
We treat medical AI as a constrained system. How signals enter, states are represented, actions occur, and errors are detected must remain auditable layer by layer.
Derive verifiable state from radar, vision, thermal imaging, bedside mechanical signals, and environmental sensing. The goal is not simply more data, but a clear intended use, reference standard, missingness model, and deployment burden.
Represent phases, events, anatomical relations, and changing risk over time. We prioritize integrity, severe-error control, and uncertainty before stronger prediction or interaction.
Constrain outputs to authorized, interruptible, and reversible action spaces. High-risk settings retain human supervision, and systems must express when they do not know or cannot act.
Track provenance, versions, timelines, and the basis of conclusions so automation does not alter meaning during evidence organization, summarization, or migration.
Method
Write unacceptable severe errors, denominators, endpoints, and stop rules before modeling.
Challenge the riskiest assumption with public or synthetic data before expanding scope.
Research, preview, available, and archived states are explicit in the software catalog.
No ethics, rights, reference standard, and site validation means no clinical-effectiveness claim.