A sick person is a poor historian — they forget, they minimise, they rehearse. The cabin reads face, voice and symptom before a single question is asked, so the patient arrives already understood rather than interrogated.
Every signal is traceable to the model that produced it. The clinician decides; the machine only ever hands them evidence.
Most telemedicine is a funnel — book, call, leave. CabinOS closes: what the cabin learns today becomes context for the next visit.
The cabin greets the patient and takes consent before anything is recorded.
A single frame reads visible signs of distress. No video is kept.
Symptoms in the patient's own words, plus tone, stress and speech rate.
A weighted score places them in the queue by clinical need, not arrival time.
The doctor joins already holding the report — and a copilot that listens.
A drafted SOAP note the clinician edits and signs. Never auto-finalised.
Then it remembers. The completed visit is embedded and stored, so the next time this patient walks in, the doctor sees what changed since last time — without asking them to remember it.
Four signals, each from a real model, each shown to the clinician with its provenance attached.
Seven-class expression read from one captured frame, returned with a confidence score and a clinical note.
Vision transformer
Valence, arousal, vocal tremor, speech rate and pain likelihood — the things a patient rarely reports accurately.
Speech emotion model + DSP
Symptoms, medications and conditions lifted from the transcript as structured terms a doctor can scan.
Medical NER
Past visits retrieved by meaning, so “heart attack” finds “myocardial infarction”. Empty on a first visit — never invented.
Medical embeddings + vector search
The same system, seen from where you stand in it.
Medical software earns trust by what it refuses to do. These are constraints in the system, not promises in a policy.