Frequently asked questions about biologit MLM-AI
Newly generated reviews may present pre-screened abstracts that were tagged at the duplicate detection stage. Duplicate abstracts relate to a corresponding abstract in the same review, or in a previous review for the same product.
The "Duplicates" tab indicates pre-screened articles.
MLM-AI performs duplicate detection by ID, DOI and content. This is further detailed in the page:
Articles are flagged as duplicates only when:
- They appear in the same review results. One of the articles will be automatically reviewed as "duplicate".
- An article is a duplicate of another article seen in a past review for the same Monitor.
Reviews are categorized into tabs by status:
Reviews stay "In Progress" while there are still abstracts to be screened, and move to "Completed" once all abstracts have been screened ie. all abstracts were saved with a decision by a user or by MLM-AI via automated action.
Search results obtained from PubMed, EMBASE or EBSCO can be directly uploaded to the tool for screening.
The supported formats and help with the export steps are detailed in:
For uploaded reviews, duplicate detection only works by ID. Currently, uploaded results are not de-duplicated against other results retrieved from the MLM-AI database.
MLM-AI ingests data from established and open access databases to achieve a broad literature reach. Currently, we automatically scan and upload the following sources:
- PubMed - Comprehensive repository of biomedical and life sciences literature comprising over 33 million citations.
- Abstracts from all sources is ingested daily into our database.
- Abstracts are searched for every active monitor, according to the specified product keywords and synonyms, and the specified sources to be searched.
Note that searches are performed in the medication keywords only, as specified by your monitor configuration. No additional keyword-based filtering occurs.
In the example below, the key terms associated with "Betamethasone" are OR'ed together, and searched across all sources specified in the monitor.
Example key terms from Medication Monitor
These additional sources broaden the reach of scientific literature available to you, and can contribute in significant ways in identifying relevant satefy information.
We have conducted a comparative study showing that additional safety information can be found from these sources. Learn more here:
MLM-AI models are drug-agnostic, having been trained with a cross-section of scientific literature covering a broad range of drug classes.
There are various strategies that can be employed for the safe implementation of AI-based screening. In AI-Enabled Screening Workflows we discuss techniques to leverage AI into your workflows.
All MLM-AI models are configured for high recall on the target category of interest (Suspected AE, Suspected Case etc). This means AI tags produced by MLM-AI are "conservative" in that it tolerates some amount of false positives so that missing a relevant abstract is highly unlikely.
There are many ways in which users can take advantage of AI Tags produced by MLM-AI. Please see the section on AI-Enabled Screening Workflows to learn more and use one that best suits your needs.
For performance results please refer to the latest fact sheet:
The best resource on how to safely integrate AI tags is our Guide to AI-Enabled Screening Workflows.
Title-only citations may sometimes appear, depending on how journals decide to publish their data.
- Without an abstract there is insufficient information to reliably make model predictions
- Hence, all "No Abstract" articles are tagged as "Suspected AE", indicating they should be screened
- "No Abstract" citations are also presented in a separate "No Abstarct" tab in Review results, to facilitate a separate screening workflow
MLM-AI models were designed so that relevant safety information is not missed. With this in mind, we have labeled and curated datasets that capture how likely safety information is present either in the abstract or in the full text of an article.
Consider the samples below extracted from abstracts. The most ambiguous cases may still be relevant, and will likely need review of the full text of the article:
By looking for "suspected" safety information, MLM-AI can still tag abstracts with incomplete information without missing important safety data.
See our article discussing the the use of AI for literature screening:
See also the model technical specification and intended uses in the FactSheet:
Finally, our technical paper goes in more details on our AI development methodology and experimental results:
Medication synonyms presented in the Monitor Configuration pages are periodically (at least quarterly) collected from the following public sources: MeSH, ChemID, OpenFDA and the EMA list of marketed products.
Any modification to monitors apply only to Reviews created after the change.
Existing reviews always use the monitor configuration present at the time of review creation. This is to ensure consistent behavior with the then current settings. This also applies to "Scheduled" reviews that are already in progress.
- Maximum number of synonyms used in a monitor:
- Maximum number of days in a review:
- Oldest start date of a review (number of days from today):
- Date where data starts in the MLM-AI article database:
By default every user is subject to the following password rules:
- Minimum password length:
- At least
2numbers in password
- At least
- At least
- At least
1special character (