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Related Experiment Videos

Clinical Artificial Intelligence and Machine Learning Metrics 101: How to Evaluate Artificial Intelligence Tools for

Brian Hur1, Aparna Elangovan2, Laura Hardefeldt3

  • 1Veterinary Information Network, 777 W. Covell, Davis, CA, USA; Department of Veterinary Biosciences, Melbourne Veterinary School, University of Melbourne, Melbourne, Victoria, Australia.

The Veterinary Clinics of North America. Small Animal Practice
|May 7, 2026
PubMed
Summary

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Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this; it...

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Veterinary professionals need clear guidelines for evaluating artificial intelligence (AI) tools. Understanding key AI performance metrics beyond simple accuracy is crucial for informed adoption in veterinary medicine.

Area of Science:

  • Veterinary Medicine
  • Artificial Intelligence
  • Biomedical Informatics

Background:

  • Artificial intelligence (AI) tools are increasingly integrated into veterinary practice.
  • Clinicians currently lack standardized frameworks for assessing AI tool performance.
  • Evaluating AI requires understanding metrics beyond basic accuracy.

Purpose of the Study:

  • To provide a practical guide for veterinary professionals on evaluating AI performance.
  • To explain essential AI evaluation metrics and their significance.
  • To emphasize the need for evaluation literacy in AI adoption.

Main Methods:

  • Discussion of key AI evaluation metrics: sensitivity, specificity, precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC).
Keywords:
Artificial intelligenceClinical decision supportDiagnostic AIExternal validationMachine learningModel validationPerformance metricsVeterinary medicine

Related Experiment Videos

  • Explanation of the importance of interannotator agreement for setting performance benchmarks.
  • Highlighting the necessity of external validation and modality-specific considerations (AI scribes, digital imaging, pathology).

    • Main Results:

    • Accuracy alone is an insufficient metric for evaluating AI in veterinary medicine.
    • Interannotator agreement is vital for establishing realistic performance ceilings.
    • External validation and modality-specific metrics are critical for robust AI assessment.

    Conclusions:

    • Veterinary professionals must develop AI evaluation literacy due to the absence of regulatory oversight.
    • Informed decision-making regarding AI adoption depends on a thorough understanding of performance metrics.
    • Standardized evaluation frameworks are needed to ensure the effective and safe integration of AI in veterinary care.