From Static Outputs to Living Evidence: AI for Integrated Knowledge Translation in Canadian Health Research (Preprint)

UNSTRUCTURED Integrated knowledge translation (iKT) still relies on static reports, presentations, and manuscripts that cannot adapt to decision-makers’ evolving questions. Retrieval-augmented large language models (LLMs) can add a secure, auditable conversational layer over curated program outputs and selected research materials, enabling rapid, traceable synthesis between meetings and across portfolios. Treating these tools as governed infrastructure, with mandatory provenance, privacy protections, transparent documentation, and equity-by-design, could help reduce friction in evidence exchange and shorten the lag between knowledge creation and use.