How to deploy artificial intelligence in veterinary practice

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

Parminder S. Basran and Ryan B. Appleby

Background

This paper discusses the increasing integration of Artificial Intelligence (AI) into veterinary medicine and the necessity for a comprehensive framework to safely implement these technologies. It highlights the rapid growth of AI applications within this field and the challenges posed by a lack of regulatory frameworks and the need for transparency, particularly regarding how AI algorithms make decisions.

Methods

The authors propose a structured framework for AI implementation in veterinary practices that draws upon methodologies from human medical research and AI applications. The framework emphasizes the importance of assembling a diverse expert team, enhancing foundational AI knowledge among veterinary professionals, and identifying relevant use cases. It also includes steps for ensuring data quality, creating effective implementation plans, and continuous performance evaluation.

Results

Key components of the proposed AI implementation framework include:

• Developing a deep understanding of AI among veterinary staff.

• Identifying and clearly defining relevant use cases for AI applications.

• Ensuring high standards of data quality and availability.

• Establishing ethical guidelines and considering legal obligations.

• Integrating AI into existing workflows without disruption.

• Ongoing monitoring and evaluation of AI applications in practice.

The framework aims to address the unique challenges in veterinary medicine, such as data privacy, owner consent, and the practical integration of AI tools.

Limitations

The article discusses inherent challenges with AI technologies, such as the "black-box" nature of AI algorithms and the difficulty in understanding their decision-making processes. There is also concern about the steep learning curve for veterinary professionals and the need for greater transparency in AI applications.

Conclusions

The authors conclude that implementing AI in veterinary medicine requires careful consideration of various ethical, legal, and practical factors. They suggest that effective change management and continuous evaluation are crucial to ensuring that AI technologies benefit patient care without compromising safety or ethical standards. The proposed framework serves as a guideline to navigate the complexities of AI implementation in veterinary settings, promoting a responsible and informed approach.

Disclaimer: The summary generated in this email was created by an AI large language model. Therefore errors may occur. Reading the article is the best way to understand the scholarly work. The figure presented here remains the property of the publisher or author and subject to the applicable copyright agreement. It is reproduced here as an educational work. If you have any questions or concerns about the work presented here, reply to this email.