HHS · no agency ID · record R0908

Clinical Trial Predictor

Department of Health and Human Services · HHS/NIH

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2023-05 (month precision)Source: May-23
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Administrative Functions
Operational functions: Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

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allows NIH to identify clinical trial applications submitted to NOFOs that do not allow clinical trials so they can be withdrawn before being reviewed and potentially transferred to a NOFO that does allow clinical trials.

Expected benefits

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.

System outputs

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.

Technology

AI classification: Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
System name(s): Not reported
Custom-developed code: Yes
Public source code: It is not publicly available.

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: All data come from the internal NIH IMPAC II database.
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported