Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects

The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into its regulatory framework and scientific initiatives. This paper explores the CVM's strategic approach to harnessing these technologies to enhance human and animal health by supporting innovative products and methods. Key areas of focus include regulatory adaptation, genomic research, and information technology modernization. The Animal and Veterinary Innovation Agenda outlines the Center's commitment to fostering innovation in veterinary medicine while addressing emerging challenges. This includes developing AI/ML-driven tools for antimicrobial resistance research, genome editing safety, and postmarketing safety surveillance. The paper discusses the CVM's participation in the FDA's role in shaping guidance documents for AI in regulatory decision making. In genomic research, the CVM is utilizing AI/ML to study antimicrobial resistance and improve genomic editing techniques. These technologies enhance the understanding of resistance mechanisms and facilitate the precise identification of genetic alterations. Artificial intelligence is also pivotal in information technology modernization efforts, aimed at streamlining data management and enhancing operational efficiency. The paper highlights the efforts to integrate AI/ML in safety surveillance, including signal detection and case processing. It emphasizes the importance of human-led governance, data quality, and model validation in ensuring the ethical deployment of AI technologies. The CVM's initiatives represent a transformative shift toward more efficient and innovative regulatory approaches. The paper concludes with a call for continued collaboration among researchers, industry, and regulatory bodies to advance AI integration and achieve mutual goals in animal health.