Reliability is a design constraint, not an afterthought. Predictive models in biomedicine can fail silently — returning confident, wrong answers on molecules or variants unlike anything in their training data. In safety-critical settings, a confidently wrong prediction is worse than no prediction at all.
We engineer architectures that recognise the edge of their own competence. Hybrid models pair the pattern-finding of deep learning with the explicit, inspectable memory of classical methods, and fall back to the more conservative expert when the data drifts out of distribution. Uncertainty is surfaced, not hidden.
“A model that can say ‘I don’t know’ is safer than one that is confidently wrong.”