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AI-Assisted Scientific Literature Review for Health Research

Research & Development

What it collects

Operational data
Anonymized data
  • Scientific publications — including journal articles, abstracts, and bibliographic metadata — sourced from academic databases. These are published works and do not contain personal information about members of the public.
Run by
Health Canada (HC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Research & Development

This system uses machine learning to help Health Canada employees conduct scientific literature reviews more efficiently, including systematic and scoping reviews. It removes duplicate references and streamlines the screening and data extraction process. The system is used solely by government employees and does not involve personal information about the public.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Scientific publications — including journal articles, abstracts, and bibliographic metadata — sourced from academic databases. These are published works and do not contain personal information about members of the public.

Processing

Classification & Prediction
  • Machine learning models classify references as duplicates or unique, and screen publications for inclusion or exclusion based on relevance criteria defined for each review. Assists data extraction by classifying content fields.

What it does

Deciding (Analytical AI)
Human decides
  • The system scores and ranks references for relevance and flags duplicates. Government employees review the results and make the final decisions about which publications to include in or exclude from a literature review.
Sensing (Perceptive AI)
Human decides
  • The system reads and interprets text from scientific publications to extract structured fields (e.g. title, abstract, metadata) and identify duplicate references for further human review.

Outputs

A recommendation or prediction
Anonymized data
  • The system produces ranked and de-duplicated lists of scientific references with screening recommendations (include, exclude, uncertain). Human reviewers make the final inclusion decisions; the AI output is advisory only.

Run by

Health Canada (HC)
  • Health Canada is the federal department responsible for helping Canadians maintain and improve their health. It deploys this machine-learning system to support government employees conducting scientific literature reviews.

Government of Canada AI Register — 2526-HC-SC-001

Built by

Covidence
  • Covidence is the external vendor that built and supplies the machine-learning platform used to conduct systematic and scoping literature reviews, including duplicate removal and screening automation.

Government of Canada AI Register — 2526-HC-SC-001

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • The system is used exclusively by Health Canada government employees (GC employees) to support internal research workflows. Outputs are not directly accessible to the public or to individuals who may be subjects of related research.
Available to the accountable organization
  • Outputs (de-duplicated reference lists, screening decisions, extracted data) are available to Health Canada employees conducting the literature reviews.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Be Informed of AI UseThis system is publicly disclosed in the Government of Canada's Algorithmic Impact Assessment Register (ID: 2526-HC-SC-001). As it operates solely on published scientific literature and is used only by GC employees, it does not directly affect members of the public.
  • Right to Algorithmic TransparencyInformation about this system is available through the Government of Canada's open AI register. The system uses machine learning to remove duplicates and streamline screening; final decisions remain with human reviewers.

Risks and safeguards

No risks or safeguards have been published for this system yet.