Toward collaborative artificial intelligence development for animal well-being

Abstract

This review focuses on opportunities and challenges of future AI developments in veterinary medicine, from the perspective of computer science researchers in developing AI systems for animal behavior analysis. We examine the paradigms of supervised learning, self-supervised learning, and foundation models, highlighting their applications and limitations in automating animal behavior analysis. These emerging technologies present future challenges in data, modeling, and evaluation in veterinary medicine. To address this, we advocate for a collaborative approach that integrates the expertise of AI researchers, veterinary professionals, and other stakeholders to navigate the evolving landscape of AI in veterinary medicine. Through cross-domain dialogue and an emphasis on human and animal well-being, we can shape AI development to advance veterinary practice for the benefit of all.