Lifecycle Monitoring and Revalidation for Computational Evidence Integration Across Clinical, Molecular, And Population-Health Systems: With Uncertainty-Aware Translation Controls in Operational Assurance

Abstract

This critical review examines computational evidence integration across clinical, molecular, and population-health systems. It asks which monitoring signals should trigger abstention, rollback, or formal revalidation. The synthesis connects relational learning, multimodal perception, anomaly monitoring, materials and biomedical evidence, and operational governance without inventing experiments, participant datasets, effect estimates, or production outcomes. Ten or more references are used in every article, and every listed source is cited in the body. Particular attention is given to cross-domain association being promoted to unsupported clinical causation. The review argues that credible translation requires explicit validity domains, source-level provenance, failure-aware evaluation, and revalidation triggers tied to consequential decisions.