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

IF 1.3 3区 农林科学 Q2 VETERINARY SCIENCES
Hesha J Duggirala, Jennifer L Johnson, Daniel A Tadesse, Chih-Hao Hsu, Alexis L Norris, Joseph Faust, Linda Walter-Grimm, Tristan Colonius
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Abstract

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.

兽医学中的人工智能和机器学习:当前举措和未来前景的监管视角。
美国食品和药物管理局兽医中心(CVM)通过将人工智能和机器学习(ML)整合到其监管框架和科学计划中,正在推进其在兽医科学方面的领导地位。本文探讨了CVM利用这些技术通过支持创新产品和方法来增强人类和动物健康的战略方针。重点领域包括调控适应、基因组研究和信息技术现代化。动物和兽医创新议程概述了该中心在应对新出现的挑战的同时促进兽医创新的承诺。这包括开发人工智能/机器学习驱动的工具,用于抗微生物药物耐药性研究、基因组编辑安全性和上市后安全监测。本文讨论了CVM参与FDA在制定人工智能监管决策指导文件方面的作用。在基因组研究方面,CVM正在利用AI/ML研究抗菌素耐药性并改进基因组编辑技术。这些技术增强了对抗性机制的理解,并促进了基因改变的精确识别。人工智能也是信息技术现代化工作的关键,旨在简化数据管理和提高运营效率。本文重点介绍了在安全监控中集成AI/ML的努力,包括信号检测和案件处理。它强调了以人为主导的治理、数据质量和模型验证在确保人工智能技术的道德部署方面的重要性。CVM的倡议代表着向更有效和创新的监管方法的转型转变。论文最后呼吁研究人员、行业和监管机构继续合作,推进人工智能整合,实现动物卫生领域的共同目标。
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来源期刊
CiteScore
1.70
自引率
10.00%
发文量
186
审稿时长
3 months
期刊介绍: The American Journal of Veterinary Research supports the collaborative exchange of information between researchers and clinicians by publishing novel research findings that bridge the gulf between basic research and clinical practice or that help to translate laboratory research and preclinical studies to the development of clinical trials and clinical practice. The journal welcomes submission of high-quality original studies and review articles in a wide range of scientific fields, including anatomy, anesthesiology, animal welfare, behavior, epidemiology, genetics, heredity, infectious disease, molecular biology, oncology, pharmacology, pathogenic mechanisms, physiology, surgery, theriogenology, toxicology, and vaccinology. Species of interest include production animals, companion animals, equids, exotic animals, birds, reptiles, and wild and marine animals. Reports of laboratory animal studies and studies involving the use of animals as experimental models of human diseases are considered only when the study results are of demonstrable benefit to the species used in the research or to another species of veterinary interest. Other fields of interest or animals species are not necessarily excluded from consideration, but such reports must focus on novel research findings. Submitted papers must make an original and substantial contribution to the veterinary medicine knowledge base; preliminary studies are not appropriate.
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