Living Literature Review: Semi-automated Literature Screening
Description
The Agency conducts reviews of scientific literature in many contexts, including periodic revision of NAAQS as mandated by the Clean Air Act, other standards, but also one-time assessments of published evidence. Given the scope of some of those reviews and the very large of peer-reviewed articles to consider, a system was required to rank and prioritize them for faster screening, based on past reviews and ongoing expert criteria.
Detailed example
The AI system queues up references for expert review with the references most likely to be relevant first, and continuously improves the queue as the experts go through it.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Faster, more responsive evidence reviews.
Audit / financial statement impact
The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
Controls / human review
ATO: No; PIA: Not published
Data needed
AI is simple machine learning that trains anew for each literature screening project, using user decisions to select relevant articles and deselect irrelevant ones. Users can also provide a warmup set of past relevant articles on a per-project basis.