VETNET：为 AI 驱动的兽医创新构建基准数据集
VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation
VETNET 是一个由实时基准数据集与评测框架组成的生态，旨在通过标准化高质量数据集和跨机构协作加速动物科学与兽医领域的 AI 创新。该框架试图弥合人类医学 AI 进展与兽医 AI 开发碎片化之间的差距。
Authors:Katherine J. Koebel, Renata Ivanek, Parminder Basran, Jennifer Sun, Casey Cazer, Gayatri Anil, Tommaso Banzato, Liz Barton, Dottie Cimino Brown, Leszek Bukowski, Marta Castelhano, Simon Coghlan, Christopher De Sa, Nikki Dutta, Hala Ghali, Julio Giordano, Joshua Glass, David C. Hogg, Nicholas Hollingshead, Adam Kiehl, Ziang Liu, Monica Meduri, Param Mohapatra, Kim Nayyer, Amelia Nelson, Peter-John Mantyla Noble, Christopher Pinard, Beryl Rabindran, Scott Ross, Ron Seccia, Moira Sheehan, Fei Wang, Tracy L. Webb, Kilian Q. Weinberger, Heather M. Whitney, Chen Xin
Abstract:VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through standardized, high-quality datasets and multi-institutional collaboration across academic and non-academic sectors. This framework addresses the gap between AI advances in human medicine and the fragmented state of veterinary AI development.
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.14149 [cs.AI] |
| (or arXiv:2610.14149v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14149 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Katherine Koebel [view email]
[v1]
Fri, 9 Oct 2026 15:41:38 UTC (1,342 KB)
来源：arXiv：cs.AI · arxiv.org