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

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).
• 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.