*AI-Enabled Data Validation, Anomaly Detection, and Fraud Identification

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.