HHS · no agency ID · record R1018
Leveraging NLP and LLMs to identify and characterize NIH prevention research via 160-topic taxonomy
Department of Health and Human Services · HHS/NIH
Overview
- Development stage
- PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- 2025-04 (month precision)Source: Apr-25
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Health and Medical
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
Semi-automate manual curation
Expected benefits
More timely assessments of NIH spending in specific areas within prevention research.
System outputs
Classification of grants by health conditions, risk factors, study designs, and prevention research type.
Technology
- AI classification
- Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
- System name(s)
- Microsoft Azure
- Custom-developed code
- Yes
- Public source code
- Not reported
Sourcing
- How it was built
- Contract and in-houseSource: c) Developed with both contracting and in-house resources
- Vendor (as reported)
- Microsoft Azure, Westat
- Vendors (standardized)
- Microsoft
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
- Yes
- Demographic features
- k) None of the above
- Training and evaluation data
- Publicly available grant information (ApplID, Grant Number, Title, Abstract, Public Health Relevance).
- 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