Why medical AI needs multiple safety checks
At Delphyr we don't trust a single filter or a one-time check. Trust is earned by layering safety across the entire lifecycle of an AI interaction.
Even a well-trained medical AI model can produce outputs that are off: hallucinated values, instruction-override attempts, responses that drift outside the medical domain. These aren't edge cases. In clinical settings, a single incorrect output carries real stakes.
The article describes how Delphyr approaches this with layered guardrails across three dimensions: security (blocking prompt injection and malicious inputs), accuracy (requiring exact-quote citations for every claim), and focus (keeping the model inside its medical domain). Checks run before, during, and after generation, each one designed to catch what the others might miss.
Published on the Delphyr Engineering blog, March 2026.