Artificial Intelligence (AI) is revolutionizing veterinary medicine, offering tools that improve efficiency, enhance diagnostics, and streamline communication. From AI scribes that reduce documentation time to diagnostic platforms that flag abnormalities in lab work, these technologies have the potential to elevate patient care. But before we embrace AI fully, we need to understand how it works, its limitations, and how to implement it responsibly.

My concern for the profession isn’t whether AI is good or bad—it’s how we are adopting it. Too often, we see new technologies introduced with little understanding of how they function, how they fail, and what ethical considerations we should be discussing. AI can provide incredible support, but only if we ensure transparency in training models, continuously update its knowledge base, and establish policies that guide its use in veterinary practices.

Understanding How AI Works (and How It Fails)

Before adopting AI in veterinary medicine, we need to ask platform providers:

• How is this AI model trained? Transparency is critical. If we don’t know how an AI tool is learning and evolving, how can we trust its recommendations?
• How often is it updated? AI, like a veterinarian, needs continuing education. If it’s pulling from outdated data, we risk subpar patient outcomes and declining standards of care.
• How does it handle uncertainty? AI is designed to provide answers, but we must understand how it arrives at conclusions—and, more importantly, where it might go wrong.
• What are its limitations? No AI is perfect. Understanding where a system struggles helps us mitigate risks and use it as a tool, not a decision-maker.

AI can fail in multiple ways: incorrect assumptions due to biased data, misinterpretation of clinical signs, or limitations in recognizing rare conditions. By knowing where it struggles, we can work around its weaknesses and strengthen its impact on patient care.

AI Scribes: Less Time on Notes, More Time with Clients

One of the most exciting applications (and widely adopted) of AI in veterinary medicine is AI scribes, which significantly reduce the time veterinarians spend documenting cases. While some frame this as simply a time-saving tool, I believe it’s more than that.

AI scribes don’t just free up time—they enable vets to be more present in the exam room. By reducing the burden of documentation, AI allows for more empathetic, less chaotic conversations with pet owners. This, in turn, enhances the client’s perception of veterinary services, fostering trust, engagement, and better compliance with treatment recommendations.

AI in Diagnostics: A Co-Pilot for Veterinary Teams

AI-powered diagnostic tools are now built into many lab platforms, helping veterinarians identify trends in bloodwork that might otherwise be missed. These tools function like a board-certified specialist that is literally boarded in every speciality reviewing every case in the background, flagging abnormalities and potential disease patterns.

I always tell veterinarians to view AI-powered diagnostics as a co-pilot—an extra set of eyes that ensures nothing slips through the cracks. It doesn’t replace clinical judgment, but it can enhance decision-making and improve patient outcomes.

As a non-veterinarian, I typically refrain from discussing more in-depth medical applications, such as AI-driven radiology interpretation, I do believe we should be having open conversations about how these tools work and how they impact clinical decision-making.

Setting an AI Policy in Veterinary Practices

Every veterinary practice should have a formal policy on AI adoption and use. Without one, team members could go rogue, signing up for AI services that haven’t been vetted by practice leadership. A structured approach ensures:

• AI tools align with practice standards and ethics.
• Team members understand when and how AI should be used.
• There’s accountability for monitoring AI performance.

Additionally, veterinarians should have a reporting system for documenting AI failures. If an AI tool consistently misses certain conditions or provides misleading recommendations, those patterns need to be logged, analyzed, and addressed. This ensures we are learning from AI’s shortcomings and refining our approach to improve outcomes.

AI is Here to Stay—Let’s Use it Wisely

Artificial intelligence is transforming veterinary medicine, but adoption without understanding is risky. We must be intentional, asking the right questions, recognizing its limitations, and ensuring ongoing learning and transparency in AI development.

I recommend companies that are open about how their AI models are trained and continuously updated. Without regular updates, AI risks providing dated, inaccurate information that could lower the standard of care.

At its best, AI is a powerful tool that supports veterinary teams, enhances patient care, and improves client communication. But like any tool, it’s only as effective as how well we understand and implement it.

By choosing AI solutions wisely, setting clear policies, and maintaining a critical eye on its performance, we can ensure that technology remains an asset, not a liability, in veterinary medicine.