HHS · no agency ID · record R0871

AI-Assisted Systematic Review and Validation of Analytical Worksheets

Department of Health and Human Services · HHS/FDA/OCS

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Law Enforcement
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Manual review and validation of analytical worksheets can create bottlenecks in regulatory processes, introduces potential human error, and limits scalability of quality assurance procedures across FDA operations. Analytical worksheets serve as critical evidence in legal proceedings, enforcement actions, and regulatory decisions affecting public health and safety. These worksheets are the legal documents that will be used and referenced in a court of law and legal proceedings if FDA determines regulatory action should be taken in accordance with the Federal Food, Drug, and Cosmetic Act and subsequent amending supplements codified in Title 21of the United States Code.

Expected benefits

Increased efficiency in worksheet validation processes, standardized review procedures ensuring consistency in legal documents, faster turnaround times for analytical work supporting enforcement actions, enhanced quality assurance for evidence used in court, reduced human error in legally significant documents, and improved consistency in regulatory processes supporting FDA's mission to protect public health.

System outputs

Assessment summaries identifying errors and inconsistencies in court-admissible documents

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
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: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
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