As a consultant and speaker who works closely with veterinary practices across the world, I see daily how artificial intelligence (AI) is reshaping our profession. From client communication platforms to diagnostic tools, AI in veterinary medicine promises efficiency, speed, and new ways to support both teams and pet owners. Yet the rise of artificial intelligence also comes with ethical concerns that we cannot ignore.

The question is not whether AI belongs in veterinary medicine. The question is how we, as veterinary professionals, ensure it is used responsibly, transparently, and in ways that improve—not compromise—patient care and client trust.

Why AI Ethics Matters in Veterinary Practice

AI can help reduce phone call backlogs, automate reminders, and even assist in interpreting medical records. But these efficiencies should never come at the expense of our professional oath or the human–animal bond that defines our profession. In my work with practices, the same concerns surface again and again:

• Patient safety: Are AI-driven recommendations validated by veterinarians and medically reliable?
• Client trust: Do pet owners feel confident they are being cared for by people, not just systems?
• Team confidence: Does AI support staff workflows, or does it create more hidden complexity?

These ethical questions must guide every decision when a veterinary practice considers adopting artificial intelligence.

Biases in How AI Models Are Trained

A critical but often overlooked issue in AI in veterinary medicine is bias in training data. Every model is only as good as the information used to build it. If the training data is incomplete, unbalanced, or not representative of the diverse cases veterinary professionals see every day, the output can be misleading.

Examples of how bias shows up:

• Species and breed gaps: If a model is trained mostly on canine data, its recommendations may be less accurate for cats or exotic pets.
• Geographic differences: Disease prevalence, parasites, and treatment protocols vary by region. A model trained only on North American data may not apply well to practices in other countries.
• Socioeconomic context: AI that overlooks financial realities of clients may recommend treatment plans that are unrealistic, leading to frustration and distrust.

This is why transparency about how models are trained is not optional—it is essential. Veterinary professionals must know where the data comes from, how it is validated, and what steps companies are taking to correct for bias. Without this knowledge, we risk applying artificial intelligence in ways that unintentionally disadvantage patients or clients.

Why Companies Must Employ Full-Time Veterinarians

One of my strongest recommendations to every veterinary practice is to partner only with companies that employ full-time veterinarians. Having practicing veterinarians embedded in development teams helps ensure that tools are trained, validated, and monitored with real-world veterinary experience in mind.

Veterinarians on staff help guide:

• Model training with accurate veterinary data sets
• Clinical validation that matches real exam-room conditions
• Ethical safeguards to prevent AI from prioritizing efficiency over patient health

If companies only consult veterinarians occasionally, they miss critical nuances that affect daily practice. Full-time veterinary leadership is the only way to build AI responsibly.

Questions Every Veterinary Practice Should Ask Vendors

When exploring AI in veterinary medicine, practices should ask vendors:

• Do you employ full-time veterinarians?
• How do you train your models, and what data sources are used?
• How do you identify and reduce bias in your AI tools?
• How do you ensure security and privacy of client and patient data?
• How often are your tools validated and updated?

If a vendor cannot answer these questions clearly, that should raise serious concerns.

Transparency and Standards Are Non-Negotiable

To protect patients and strengthen trust, the veterinary profession should advocate for:

1. Transparent training methods so practices know how tools are built.
2. Veterinarian-led oversight with full-time professionals shaping AI design.
3. Independent validation through peer review, not just marketing claims.
4. Bias monitoring and corrections to ensure equitable, reliable outcomes.
5. Continuous updates so AI keeps pace with evolving medicine.

Moving Forward Together

Artificial intelligence will continue to play a growing role in veterinary practice. Used responsibly, it can ease workloads, improve client communication, and support better patient outcomes. But AI is only as trustworthy as the people and processes behind it.

My recommendation as a consultant and speaker is clear: work only with companies that employ veterinarians full time, disclose how they train their models, and address bias openly. By demanding transparency and ethical standards now, we ensure that AI strengthens veterinary medicine rather than undermining it.

✅ Bottom line: AI in veterinary medicine is powerful, but it must be ethical, transparent, and veterinarian-led to truly support practices, patients, and clients.