Keywords
health
Abstract
This review examines lifecycle governance for molecular models, content systems, and UAV planning through an evidence-centered design lens. The analysis asks how change control should trigger revalidation. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to adaptive systems drifting beyond their evidence boundary. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports high-consequence adaptive systems while distinguishing component promise from system readiness.
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