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The Bovine Lung in Biomedical Research: Visually Guided Bronchoscopy, Intrabronchial Inoculation and In Vivo Sampling Techniques
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Language Models in Veterinary Clinical Practice: Applications, Risks, and Practical Guidance.

Nathan Bollig1, Jonathan L Lustgarten2, Elizabeth Venit3

  • 1Association for Veterinary Informatics, Madison, WI, USA.

The Veterinary Clinics of North America. Small Animal Practice
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence language models offer significant potential for veterinary clinical practice, enhancing efficiency and accuracy in areas like client communication and medical records. Responsible implementation is key to maximizing benefits while mitigating inherent risks for veterinary professionals.

Keywords:
AI in veterinary medicineArtificial intelligenceClinical decision supportLarge language modelsNatural language processingResponsible AI

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Area of Science:

  • Veterinary Medicine
  • Artificial Intelligence
  • Computer Science

Background:

  • Veterinary clinical practice increasingly utilizes advanced computer systems.
  • Artificial intelligence (AI) language models offer novel capabilities for language interpretation and generation.
  • These technologies have the potential to transform various aspects of veterinary workflows.

Purpose of the Study:

  • To review computer systems leveraging AI language models in veterinary practice.
  • To provide guidance on integrating large language models (LLMs) into clinical workflows.
  • To address inherent risks and offer recommendations for responsible LLM use in veterinary medicine.

Main Methods:

  • Literature review of AI language model applications in veterinary settings.
  • Analysis of systems for client communication, medical records, clinical decision support, and practice assessment.
  • Synthesis of guidance for workflow integration and risk mitigation.

Main Results:

  • AI language models can support diverse veterinary applications, including client interaction and record management.
  • Integration of LLMs can enhance clinical efficiency, accuracy, and provider performance.
  • Key risks associated with LLM use have been identified.

Conclusions:

  • LLM-powered systems present valuable opportunities for advancing veterinary clinical practice.
  • Strategic implementation and adherence to responsible use guidelines are crucial for veterinary professionals.
  • Further research and development are needed to optimize AI integration in veterinary medicine.