Lifecycle Monitoring and Revalidation for Traceable Graph, Multimodal, And Systems Methods For Interdisciplinary Decision Support: With Uncertainty-Aware Translation Controls in Model Development

Abstract

This critical review examines traceable graph, multimodal, and systems methods for interdisciplinary decision support. 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 method transfer proceeding without an explicit validity bridge. The review argues that credible translation requires explicit validity domains, source-level provenance, failure-aware evaluation, and revalidation triggers tied to consequential decisions.