Keywords
health
systems
evidence synthesis
reliability
provenance
revalidation
Abstract
This critical review examines knowledge-graph-guided drug-target affinity modelling and evidence translation. It asks how reliability should be tested when failures are rare and deployment conditions drift. 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 semantic enrichment being mistaken for causal or pharmacological validation. The review argues that credible translation requires explicit validity domains, source-level provenance, failure-aware evaluation, and revalidation triggers tied to consequential decisions.
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