Evidence Boundaries and Benchmark Design for Computational Evidence Integration Across Clinical, Molecular, And Population-Health Systems: For Reproducible Human-Ai Collaboration in Conceptual Foundations
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Keywords

artificial intelligence
health
systems
evidence synthesis
reliability
provenance
revalidation

Abstract

This critical review examines computational evidence integration across clinical, molecular, and population-health systems. It asks which evidence boundaries and comparators are needed before a cross-domain model can support action. 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.

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