Differential ranking
Clinical cases are transformed into ranked hypotheses rather than a single opaque answer.
- Species-specific context
- Supporting and contradicting evidence per condition
- A reported probability for every ranked condition
Runtime publication controls
CIRE checks the declared probability semantics and structure of each differential and returns a publication state with the inference output. Structural signals are not clinical reliability.
- Differential concentration (structural)
- Input integrity and mechanistic consistency
- Publication state: NOMINAL, REVIEW, CAUTION, or SUPPRESSED
Outcome linkage
Outcome events link confirmed diagnoses back to the original inference so diagnostic quality can be measured over time. Reliability is claimed only for clinical slices that meet published thresholds.
- Confirmed outcome capture
- No duplicate outcome events
- Append-only audit trail
Why this matters
VetIOS is built as infrastructure rather than a standalone chatbot. The platform connects structured veterinary inputs, graph priors, model execution, runtime integrity signals, outcomes, simulations, and public-health research surfaces into one auditable loop.
Frequently asked questions
How does VetIOS rank veterinary differentials?
VetIOS combines structured clinical inputs with graph priors and deterministic inference, then returns ranked differential labels with confidence and runtime publication metadata.
What species does VetIOS support?
The engine accepts species-typed input, but condition coverage is uneven: canine has by far the most modeled conditions, and some species have few or none. Per-species counts are published in the documentation.
Can diagnostic AI be used without outcome feedback?
It is decision support only. Confirmed outcomes are required to measure calibration and clinical performance.