Security and clinical ML share a hard problem

Cybersecurity and clinical machine learning look unrelated, but they fail in the same ways. Both domains change after deployment — new attack patterns, new patient populations, new devices. Both contain rare, high-cost cases that standard training underweights. And both punish confident mistakes: a missed intrusion and a missed diagnosis are not rounding errors.

That makes robustness, out-of-distribution detection, interpretable evidence, and careful validation shared design requirements across our cyber and health projects. Methods we build for one domain — retrieval-based adaptation, uncertainty-aware prediction, prospective validation — transfer directly to the other.