Differential ranking
Clinical cases are transformed into ranked hypotheses rather than a single opaque answer.
- Species-specific context
- Symptom-driven graph priors
- Confidence scores for every result
Runtime publication controls
CIRE signals show differential concentration, perturbation pressure, and publication state alongside inference output.
- phi_hat concentration signal
- Runtime perturbation score
- Safety state in the response
Closed-loop validation
Outcome events link confirmed diagnoses back to the original inference so diagnostic quality can be measured over time.
- 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 platform accepts species-typed inputs and has public content for common veterinary workflows, with graph work focused first on canine and feline disease-symptom relationships.
Can diagnostic AI be used without outcome feedback?
It is decision support only. Confirmed outcomes are required to measure calibration and clinical performance.