MLM-AI Concepts

Learn more about how MLM-AI works and key concepts that deliver more efficient workflows.

Literature Monitoring with MLM-AI

To introduce how MLM-AI makes Medical Literature Monitoring more efficient, below is an outline of the traditional workflow for this process:

A typical literature monitoring workflow comprises these stages:

• One or more databases (PubMed, Embase, Google Scholar, etc.) are searched, each with their specific query language

• Results (title and abstract) are extracted and collated together

• Articles are de-duplicated, removing repeated and articles previously seen on past runs

• Abstracts are then screened for relevant safety events

• If screen is positive, the article may then proceed for a full text review

• Results are quality controlled (QC) and exported for further processing downstream

All the above steps were taking into account when building MLM-AI's integrated workflow:

• MLM-AI automates search and de-duplication of results from multiple sources, presenting a single, clean view of all results

  • See the FAQ on data sources and the latest release notes for more information

• Users can also upload their own search results from sources that export as csv or a reference management format

• Using AI Tags (see below) users can rank and filter articles for faster screening. This further removes time from processing large volumes of articles

• Finally, the remaining steps of the workflow are performed from within a single tool, with all actions recorded into a permanent audit log

Monitors and Reviews

In MLM-AI, the screening process has the following steps:

• Users configure a monitor defining the search criteria for a product, literature sources to search, and workflow details. This is a one-time setup

• Once configured, Monitors produce results periodically, or on request.

• Results are de-duplicated and presented to users as Reviews. They are also tagged by AI models for faster screening (see AI Tags below)

• Users can then screen abstracts in reviews according to their workflow, and export results upon screening completion

AI Tags

Every article presented to users receives a number of tags based on AI predictions. These tags can be used to filter and rank results, facilitating more efficient workflows.

Example tags associated to an abstract

Tag	Description
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).
Animal/In-Vitro	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)

Why "suspected" adverse events?

A "suspected" event indicates an event may have been described in the full text and indirectly mentioned in the abstract and was considered by MLM-AI as worth further inspection.

Suspected events are drug-agnostic: they can refer to any drug or treatment mentioned in the abstract.

Learn more:

• See our page on AI-Enabled Screening Workflows for approaches that integrate AI tags into your screening workflow.

• See the FAQ on AI models for more details about how MLM-AI makes predictions.

Next Steps

Follow the links below to:

• Configure your first Monitor

• Submit an review to retrieve your first results

• Learn how to use AI Tags for faster screening