Clinical Trial Predictor
Description
Applications that propose clinical trials that are submitted to NOFOs that do not allow clinical trials cannot be funded, no matter how well they do in review, because they were not reviewed using all appropriate clinical trial criteria. This application allows NIH to identify clinical trial applications submitted to NOFOs that do not allow clinical trials so they can be withdrawn before being reviewed and potentially transferred to a NOFO that does allow clinical trials.
Detailed example
Input: IMPAC II application data, including titles, abstracts, narratives, specific aims, and research strategies. Output: Prediction of possible clinical trial submitted to a non-CT NOFO.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
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 AI tool predicts whether grant applications may involve clinical trials based on the text of their titles, abstracts, narratives, specific aims, and research strategies. It is very difficult to deal with misclassified CTs that make to review on a CT not allowed FOA: no matter how good the score is, the IC cannot fund them. The CT prediction algorithm is used to help identify potential CTs on CT not allowed NOFOs, mainly the parent R01.
Controls / human review
ATO: No; PIA: Not published
Data needed
All data come from the internal NIH IMPAC II database.