Caduceus: Clinical AI Platform
Caduceus is the Go backend for a stroke-rehab telehealth platform. Patients train in a mixed-reality headset, a multimodal model scores each rep from the video, and the result lands in their clinical record while the session is still running. Behind it, a Kafka, Redis, and Cassandra pipeline with idempotent writes, because a duplicate rep is a corrupted chart, not a minor bug.
Decisions
Exactly-once by composite key
Results were keyed on exercise and rep number, so a retry from a flaky headset could overwrite a different patient's row. Widening the conflict key to exercise, patient, rep, and step makes a retry idempotent instead of destructive.
Let the compiler judge the model
New exercises are generated as Scenic programs that must compile against a fixed library of allowed objects and actions. Prose can lie fluently. A program either compiles or it does not.
A boolean you can chart, reasoning a clinician can read
Each rep evaluation returns a bool for the dashboard and a paragraph for the edge case review. One is measurable across model versions, the other is accountable to a human.
Boring beats clever under load
Circuit breakers and retries keep one slow dependency from cascading, and the target is p99, not the mean. Trust lives in the slowest one percent.
— Amisha
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