01
Why we need principles
The ICU generates more data than any other clinical environment on earth. Machine learning is inevitable there. The question is not whether AI reaches the bedside, but under what conditions. These are our conditions.
AI Principles
Artificial intelligence in intensive care must be explainable, supervised, and clinically safe. These six principles govern every model we build or endorse.
Version
v1.0 · Draft
Last Updated
12 July 2026
Effective
12 July 2026
Languages
EN · TR
AI-01
Explainable AI
Every model surfaced at the bedside must be interpretable by the clinician using it. If a physician cannot ask why, we do not deploy it.
AI-02
Human Oversight
AI augments clinical reasoning; it does not replace it. A qualified human remains accountable for every decision that reaches the patient.
AI-03
Transparency
We publish model cards: training data, intended use, performance envelope, known failure modes, and the version deployed.
AI-04
Bias Awareness
We test for demographic, institutional, and physiological bias before deployment and continuously in production, and we act on what we find.
AI-05
Clinical Safety
Models are validated against physiological ground truth, not only statistical benchmarks. Silent failure is treated as an incident.
AI-06
Accountability
Every model has a named owner. Every incident has a root-cause review. Every review is shared with the community that depends on us.
In practice
From principle to deployment.
Every model that carries the YODA name passes a governance checklist — intended use, dataset provenance, bias audit, validation cohort, interpretability review, human-in-the-loop plan, and a post-deployment monitoring schedule. Checklists are published alongside the model.
Questions about this document? contact@yoda.academy
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