Detection/Identification of Reviewer Expertise and Grant Application Content
Description
The AI is designed to detect and identify grant application content and reviewer expertise such as pediatric or other domains to support accurate matching and more efficient, informed decision-making
Detailed example
The AI system’s outputs are classifications or labels indicating whether a grant application involves specific content areas (e.g., pediatric or other domains) and assessments of reviewer expertise based on biosketches, publications, and related data. These outputs are used to support accurate matching between applications and qualified reviewers.
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 tool is expected to enhance the agency’s mission by improving the accuracy, fairness, and efficiency of processes such as reviewer assignment and grant application analysis. By automating the detection of relevant content and expertise, it reduces manual workload, ensures better alignment between reviewers and applications, and supports more informed decision-making. This leads to more equitable and effective funding outcomes, ultimately benefiting the general public through improved support for research and programs that address critical needs.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
This AI use case does not involve training or fine-tuning models. Instead, it uses predefined rules and prompts to analyze existing grant application texts and reviewer information to identify relevant content and expertise. Evaluation is based on validating the accuracy of these prompt-based classifications against known examples.