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Artificial Intelligence in Veterinary Clinical Pathology-An Introduction and Review.

Samuel V Neal1, Daniel G Rudmann2, Kara N Corps1

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This summary is machine-generated.

Artificial intelligence (AI) can improve veterinary clinical pathology workflows. Veterinarians must guide AI development and ensure its responsible use in practice.

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Area of Science:

  • Veterinary Medicine
  • Artificial Intelligence
  • Clinical Pathology

Background:

  • Artificial intelligence (AI), encompassing machine learning and deep learning, offers significant potential to advance veterinary clinical pathology.
  • Current veterinary clinical pathology workflows can benefit from AI-driven enhancements.

Purpose of the Study:

  • To introduce fundamental AI concepts in a non-technical manner for veterinary professionals.
  • To explore the qualification and integration strategies for AI tools within veterinary clinical pathology.
  • To emphasize the crucial role of veterinary clinical pathologists in AI implementation.

Main Methods:

  • Review of AI concepts relevant to veterinary diagnostics.
  • Exploration of AI qualification frameworks.
  • Discussion on practical integration of AI into clinical pathology.

Main Results:

  • AI presents viable opportunities for enhancing veterinary clinical pathology.
  • A structured approach to AI qualification and integration is necessary.
  • Veterinary pathologists are essential for guiding AI's role.

Conclusions:

  • AI, including machine learning and deep learning, can optimize veterinary clinical pathology.
  • Veterinary clinical pathologists must actively participate in defining AI's application, design, and qualification.
  • Responsible monitoring and implementation plans are critical for successful AI adoption in veterinary practice.