How Competent is the Co-Pilot? Understanding the Strengths, Limitations, and Expectations of Diagnostic AI in Veterinary Medicine

Artificial intelligence (AI) is reshaping veterinary medicine, particularly in diagnostic imaging, but its adoption requires careful evaluation. While AI has demonstrated impressive capabilities, ensuring transparency, clinical validation, and regulatory oversight is crucial for responsible implementation.
Current State of AI in Veterinary Medicine

AI in veterinary diagnostics primarily relies on machine learning (ML) and deep learning (DL) models. These systems analyze medical images, predict disease risk, and support diagnostic decisions. Some commercially available AI tools claim high accuracy, yet critical questions remain about how they are trained, tested, and evaluated.

Although studies suggest AI can outperform veterinarians in specific tasks, real-world validation is limited. Many AI models lack publicly available peer-reviewed evidence or transparency in their development. For instance, proprietary systems often withhold key details such as dataset sources, ground truth definitions, and model retraining protocols.
Challenges and Ethical Considerations

A key concern is the lack of regulation in veterinary AI. Unlike human healthcare AI, which undergoes rigorous FDA oversight, veterinary AI operates in an unregulated environment. Transparency is necessary to ensure accountability, patient safety, and clinician trust. Additionally, ethical concerns arise regarding data ownership, bias in training datasets, and medico-legal responsibility for AI-driven errors.

The quality and reliability of AI models depend on multiple factors, including:

• Dataset Quality: How the AI was trained, labeled, and validated.


• Performance Metrics: Accuracy, sensitivity, specificity, and real-world applicability.


• Post-Implementation Monitoring: How AI performance is tracked and adjusted over time.


• Human Oversight: AI should augment—not replace—clinical and specialist level expertise.