AI-Enabled Screening Workflows

Learn how to make the most of AI-derived tags in MLM-AI
In MLM-AI results are divided into smaller subsets using AI tags, where relevant safety information are highly likely to be found, including candidates for ICSRs, special situations, new signals and safety information for aggregate reports.
Users can follow screening processes that focus efforts on the results more likely to contain relevant information:
The following AI Tags can be used for searching and filtering of abstracts in MLM-AI:
Suspected AE
The abstract or full text may contain the description of an adverse event. Thus, MLM-AI flagged the abstract as a candidate for further screening.
Suspected Case
The abstract or full text may contain the description of a case (identifiable patient).
The abstract describes animal or in-vitro studies.
Special Situations
Any detected special situation (elderly, pediatric, pregnancy) is also flagged as a tag.
Identifiable Patient
A confirmed patient mention (ex: "a 72-year old woman" etc)
With AI tags, it is possible to implement workflows that focus on the most relevant abstracts. For example, users can:
  • Prioritize abstracts classified as Suspected AE, by ranking or filtering results.
  • Use less resource intensive screening methods where safety events are unlikely. For example:
    • Batch Screening of Non Suspected AE abstracts.
    • Automatically filter articles by Tag, skipping screening and perform only QC.
    • Employing a quality-checked method for reviewing Non Suspected AE abstracts less frequently.
Important: Ensure any screening approach using AI Tags is implemented with adequate quality controls in place, and results are validated before roll out.

Options for Screening workflows with AI Tags

In this section we discuss possible scenarios that employ AI tags for faster screening workflows:

Prioritized screening of Suspected AE abstracts

Abstracts in a review can be ranked according to their AI Tags. In the Reviews page simply sort the abstracts by "Suspected AE".
The ranking is preserved once users start reviewing articles in the detail page, so that users will always screen "Suspected AE" articles first:

Screening for Not Suspected AE abstracts

Abstracts not tagged as Suspected AE can in principle receive less scrutiny during reviews. Examples of how this can be done:
  • Update your screening process so that the "Not Suspected AE" abstracts receive fewer QC steps.
  • Screen quicker with Batch Review.

Example with Batch Review

Batch review enables "quick screening" of many abstracts at once, and may be used in articles likely to be irrelevant for your screening needs.
For example, Batch Review can be used to refute all animal/in vitro studies at once (typically not a valid ICSR):
See also the Batch Screening page for more on how to use this feature.

Automatic Filtering by Tag

Users can further reduce effort by configuring monitors to automatically pre-screen articles based on the predictions made by MLM-AI:
All pre-screened articles remain visible in the results. They are presented in the "Reviewed" tab and can still be reviewed as part of the quality control process.
See also Monitor Configuration for more details on this feature.