10 techniques to make LLMs safer in healthcare
LLMs are inherently probabilistic. Here are ten practical methods we use or are actively developing to reduce risk and improve reliability when applying them in clinical systems.
LLMs don't always produce the same answer twice. That's fine for most applications. In healthcare, where small differences can carry real consequences, it requires a different kind of system design: not making models deterministic, but designing around their variability.
The article covers ten practical techniques used at Delphyr: output guards, structured outputs for critical data fields, citation verification, hallucination detection via input perturbation, deterministic routing for simple queries, and scenario-based regression testing. The through-line is the same idea from clinical practice: manage uncertainty through layered systems, not by pretending it doesn't exist.
Published on the Delphyr Engineering blog, April 2026.