What’s in the box? A toolbox for safe deployment of artificial intelligence in veterinary medicine

Author:

Basran Parminder S.1,Appleby Ryan B.2

Affiliation:

1. Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY

2. Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada

Abstract

Abstract This report describes a comprehensive framework for applying artificial intelligence (AI) in veterinary medicine. Our framework draws on existing research on AI implementation in human medicine and addresses the challenges of limited technology expertise and the need for scalability. The critical components of this framework include assembling a diverse team of experts in AI, promoting a foundational understanding of AI among veterinary professionals, identifying relevant use cases and objectives, ensuring data quality and availability, creating an effective implementation plan, providing team training, fostering collaboration, considering ethical and legal obligations, integrating AI into existing workflows, monitoring and evaluating performance, managing change effectively, and staying up-to-date with technological advancements. Incorporating AI into veterinary medicine requires addressing unique ethical and legal considerations, including data privacy, owner consent, and the impact of AI outputs on decision-making. Effective change management principles aid in avoiding disruptions and building trust in AI technology. Furthermore, continuous evaluation of AI’s relevance in veterinary practice ensures that the benefits of AI translate into meaningful improvements in patient care.

Publisher

American Veterinary Medical Association (AVMA)

Reference47 articles.

1. ImageNet classification with deep convolutional neural networks;Krizhevsky A,2012

2. Explainable AI in medical imaging: an overview for clinical practitioners - beyond saliency-based XAI approaches;Borys K,2023

3. Explanation in artificial intelligence: insights from the social sciences;Miller T,2019

4. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI);Adadi A,2018

5. Transparency of AI in healthcare as a multilayered system of accountabilities: between legal requirements and technical limitations;Kiseleva A,2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3