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Currently submitted to: JMIR AI

Date Submitted: Apr 24, 2026
Open Peer Review Period: Apr 27, 2026 - Jun 22, 2026
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

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

  • Zack van Allen; 
  • Jayne Beselt; 
  • Jerry M Maniate; 
  • Samuel Hickcox; 
  • Joshua A. Rash; 
  • Kumanan Wilson; 
  • Douglas Archibald; 
  • Arun Radhakrishnan

ABSTRACT

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.


 Citation

Please cite as:

van Allen Z, Beselt J, Maniate JM, Hickcox S, Rash JA, Wilson K, Archibald D, Radhakrishnan A

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

JMIR Preprints. 24/04/2026:99386

DOI: 10.2196/preprints.99386

URL: https://preprints.jmir.org/preprint/99386

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