Study Section Clustering Tool (SSCT)
Description
Efficiently organizing grant applications into appropriate study sections based on scientific similarity.
Detailed example
The AI system’s outputs are lists of study sections grouped together based on the scientific similarity of grant application texts. Specifically, it generates clusters of applications that should be reviewed collectively because they share related scientific topics. These groupings serve as recommendations to subject matter experts, who use them to finalize the organization of study sections for peer review panels.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
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
The Study Section Clustering Tool (SSCT) enhances the agency’s mission by automating and streamlining the organization of grant applications into scientifically relevant study sections, improving efficiency and reducing manual effort. It ensures applications are reviewed by experts with appropriate expertise, adapting over time to changes in scientific fields through periodic model updates. This data-driven support leads to higher-quality peer review processes, promoting more effective research funding decisions
Controls / human review
ATO: Yes; PIA: It does not have a standalone publicly available Privacy Impact Assessment (PIA). However, it operates within the Center for Scientific Review General Support System (CSR GSS), which is a FISMA-reportable system that has an associated PIA covering the overall system environment where the AI tool functions.
Data needed
text of grant applications submitted to the Center for Scientific Review (CSR).