*AI-Enabled Data Validation, Anomaly Detection, and Fraud Identification
Past New Requirement Updated
- Agency
-
Food and Drug Administration
Department of Health and Human Services
- Solicitation expected
- Estimated value
- $1,500,000 to $3,000,000
- Set-aside
- No set-aside
Forecast facts
- Anticipated award
- NAICS code
- Not on record
- Points of contact
-
- CDRH Acquisition Liaison cdhracquisitions@fda.hhs.gov
Forecast details come from HHS's procurement forecast. Agencies' dates and values are estimates.
About this forecast
The Food and Drug Administration is procuring discovery and early development services, including prototype development, to integrate AI-enabled data validation, anomaly detection, and fraud identification capabilities into the FDA Unified Registration and Listing System (FURLS) and its Device Registration and Listing Module (DRLM), with an estimated solicitation release date of July 1, 2026 and an estimated award date of January 1, 2027. The modernization effort will embed machine learning models into FURLS/DRLM submission intake and data management workflows to proactively flag anomalous, invalid, or potentially fraudulent medical device establishment registrations and device listings, surfacing prioritized AI-generated alerts for human adjudication by Center for Devices and Radiological Health (CDRH) staff. The initiative will also incorporate cross-domain validation logic that leverages unified data from the companion GUDID Migration to FURLS Platform proposal, enabling consistency checks across establishment registration, device listing, and unique device identification (UDI) records to strengthen regulatory oversight of the U.S. medical device ecosystem.
The estimated award value range is $1.5 million to $3 million. Work will modernize FURLS/DRLM, which serves as the authoritative federal repository for medical device establishment registrations and listings and is foundational to CDRH's Total Product Life Cycle regulatory activities, including import screening, inspection planning, and enforcement prioritization. Inquiries should be directed to the Food and Drug Administration at cdhracquisitions@fda.hhs.gov.
Forecast text
This proposal recommends discovery and early development (e.g., prototype) to modernize the FDA Unified Registration and Listing System (FURLS) and its Device Registration and Listing Module (DRLM) through the integration of AI-enabled data validation, anomaly detection, and fraud identification capabilities. FURLS/DRLM serves as the authoritative federal repository for medical device establishment registrations and device listings, and the accuracy of this data is foundational to CDRH's Total Product Life Cycle (TPLC) regulatory activities, including import screening, inspection planning, and enforcement prioritization. However, the system currently relies on static, rule-based validation and manual review processes that are insufficient to detect fraudulent submissions, identify cross-record inconsistencies, or scale to meet growing submission volumes.This initiative will seek to embed machine learning models directly into FURLS/DRLM submission intake and ongoing data management workflows to proactively flag anomalous, invalid, or potentially fraudulent records - surfacing prioritized, AI-generated alerts for human adjudication by qualified CDRH staff while preserving meaningful reviewer oversight at every stage. As a direct companion to the GUDID Migration to FURLS Platform proposal, this effort will also seek to incorporate cross-domain validation logic that leverages the unified data environment created by that migration, enabling consistency checks across establishment registration, device listing, and unique device identification (UDI) records to deliver a more cohesive and reliable regulatory view of the U.S. medical device ecosystem.
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