Project Summary Antibiotic resistance stands as a formidable challenge in both human and veterinary medicine, demanding comprehensive strategies to monitor and regulate antimicrobial usage. This FDA proposal brings together the esteemed Food Animal Residue Avoidance Databank (FARAD) and the pioneering data analytics prowess of the 1DATA consortium to confront this urgent issue head-on. With a dual focus, the project aims to (AIM 1) extract antimicrobial use data for major livestock and poultry species and (AIM 2) extend data collection efforts to encompass minor species and companion animals. FARAD, a stalwart institution with over four decades of experience, serves as the bedrock of evidence-based withdrawal recommendations in veterinary practice. Through a collaborative network spanning prominent veterinary colleges nationwide, FARAD has cultivated databases and tools to meticulously curate and analyze antimicrobial usage data across diverse animal demographics. Harnessing FARAD's reservoir of expertise, this project endeavors to birth the Long-term Antimicrobial Use with AI web-crawler (LAMU-AI), a revolutionary platform poised to bridge existing data lacunae. LAMU-AI emerges as a beacon of innovation, amalgamating data streams from FARAD's secure case repository, regulatory bodies, veterinary medical teaching hospitals, and online repositories to furnish real-time insights into antimicrobial utilization trends. Armed with cutting-edge data analytics, machine learning, artificial intelligence methodologies, and a large language processing model, LAMU-AI promises scalable and multifaceted visualization of antimicrobial deployment patterns. This groundbreaking approach empowers stakeholders—be it veterinarians, producers, regulatory agencies, or researchers—with the ammunition to make judicious decisions regarding antimicrobial stewardship and public health. Central to this initiative is the integration of disparate data sources, including FARAD's databases and regulatory testing datasets, to furnish a complete view of antimicrobial utilization practices. The advent of a sophisticated Big Data Dashboard and Visualization system promises to democratize the analysis and interpretation of intricate datasets, fostering collaboration and knowledge propagation across sectors. Furthermore, robust data security protocols will safeguard the sanctity and confidentiality of sensitive information, assuring stakeholders of the integrity of the data ecosystem. In summation, this project represents a paradigm shift in veterinary medicine—a concerted effort to confront critical data lacunae through the fusion of advanced data analytics and artificial intelligence. By marrying FARAD's unparalleled expertise with the avant-garde technology of the 1DATA consortium, we aspire not only to redefine antimicrobial surveillance but also to catalyze global endeavors aimed at combating antibiotic resistance at its core.

Project Narrative: Leveraging the extensive database of FARAD and advanced analytics of the 1DATA consortium, this project aims to develop the Long-term Antimicrobial Use with AI web-crawler (LAMU-AI) to revolutionize antimicrobial stewardship in veterinary medicine. By integrating multiple data sources and deploying sophisticated visualization tools, LAMU-AI will provide real-time insights into antimicrobial usage trends across diverse animal populations, enabling informed decision- making for stakeholders. Through collaboration with FARAD scientists and veterinary experts, we seek to address critical data gaps and enhance our understanding of antimicrobial deployment patterns to combat antibiotic resistance effectively.