# MLM-AI Concepts
## Literature Monitoring with MLM-AI
[Biologit MLM-AI](https://www.biologit.com/factsheet) addresses all productivity challenges on literature monitoring workflows for safety surveillance. The diagram below outlines key stages in a typical workflow, and their productivity challenges:
* One or more literature databases (PubMed, Embase, 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 must be **exported in a suitable format** for further processing downstream
The Biologit platform addresses the above challenges with an integrated system that is easy to adopt and to scale to your needs. In Biologit the following components were built from the ground up with the needs of literature monitoring of safety events, working seamlessly together:
* **Biologit Database**: comprehensive database of scientific and medical literature covering global and regional sources
* Robust and validated **AI models** tailored for pharmacovigilance and safety surveillance
* Easy to use web application delivering integrated workflow, collaboration, reporting and integration capabilities
## The Biologit Database
The [Biologit Database](https://www.biologit.com/database) is a comprehensive and continuously updated repository of scientific literature, ready for compliant regulatory searches, integrating databases of global and regional reach into an easy to use interface. It provides:
* High quality results for literature monitoring of adverse events and possible new risks for human and veterinary products, medical devices, nutraceuticals and cosmetics.
* Full integration into [biologit MLM-AI ](https://www.biologit.com/factsheet)for fast and audit-ready literature screening.
## Application Concepts
### Monitors and Reviews
The literature screening process can be setup in the platform with these simple steps:
* Users [configure a monitor](https://docs.biologit.com/configuration/monitor-configuration) 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.
* Search results [can optionally be uploaded](https://docs.biologit.com/application/upload-data) from an external source.
* Results are de-duplicated and presented to users as [Reviews](https://docs.biologit.com/application/reviews). They are also tagged by AI models for faster screening. (see AI Tags below)
* Users can then [complete assessments ](https://docs.biologit.com/application/article-screening)according to their workflow. during article assessment:
* Full text articles can be uploaded to the workflow as [attachments](https://docs.biologit.com/application/article-screening/attachments-and-full-text-screening), they are permanently stored in the platform for future reference.
* [Article translations](https://docs.biologit.com/application/article-screening/attachments-and-full-text-screening) can be directly requested from the system.
* On completion assessment results can be [exported ](https://docs.biologit.com/application/article-screening#e2b)in various formats for further processing (addition to the safety database) or reporting.
### 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.](https://docs.biologit.com/topics/ai-enabled-screening-workflows)
| 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.
{% hint style="success" %}
**Learn more:**
* See our page on [**AI-Enabled Screening Workflows**](https://docs.biologit.com/topics/ai-enabled-screening-workflows) for approaches that integrate AI tags into your screening workflow.
* See the [**FAQ on AI models**](https://docs.biologit.com/more-help/faq#ai-models) for more details about how MLM-AI makes predictions.
{% endhint %}
## Next Steps
Follow the links below to:
* [Configure your first Monitor](https://docs.biologit.com/configuration/monitor-configuration#creating-monitors)
* [Submit an review to retrieve your first results](https://docs.biologit.com/application/reviews#submitting-reviews-on-demand)
* [Learn how to use AI Tags for faster screening](https://docs.biologit.com/topics/ai-enabled-screening-workflows)
