The AI Debate in Veterinary Medicine: Friend, Foe, or Something in Between?

AI is already reshaping human healthcare. Now, it’s creeping into the veterinary world – from automated booking systems to diagnostic support tools. But the question remains: should we trust it? Is AI the future of veterinary care, or are we stepping into a technological minefield?

Let’s unpack what’s real, what’s hype, and what every veterinary professional needs to know – right now.

AI as a Friend: Real-World Applications in Veterinary Practice

We’re already using AI more than we realise. Here’s where it’s showing the most promise:

1. Imaging & Diagnostics

• Tools like Vetology AI and SignalPET use machine learning to detect abnormalities in radiographs.
• These systems don’t replace radiologists – but they flag issues faster, support clinical decision-making, and reduce human error, especially in time-pressured environments.
• A study published in Frontiers suggests AI performs on par with top radiologists in radiograph interpretation, especially in low-ambiguity cases, suggesting it could support efficiency in veterinary diagnostics – but human oversight remains essential, particularly for complex cases.

Caution: These tools are only as good as the data they’re trained on. If the datasets are limited or biased (e.g. mostly images of large-breed dogs), accuracy can drop sharply.

2. Practice Management & Workflow Automation

• AI-driven triage bots guide owners on whether a visit is necessary.
• AI can automatically schedule appointments, predict no-shows, or suggest follow-ups.
• Some tools analyse staff workload to identify inefficiencies, helping managers fine-tune rotas or flag burnout risk.

Watch for over-automation: If human oversight is removed from these processes, mistakes like misbooked appointments or missed clinical flags can slip through.

3. Client Engagement

• Natural Language Processing (NLP) models can generate custom post-visit summaries, follow-up reminders, or even educational content based on patient history.
• Some PMS platforms are beginning to integrate AI to identify clients at risk of lapsing and send personalised communications to bring them back.

AI as a Foe: The Emerging Risks

Let’s not sugar-coat it – there are significant risks with AI in vet med, especially when adoption outpaces understanding or regulation.

1. The Black Box Problem

Many AI systems operate as “black boxes” – they provide an output (e.g., “this radiograph is normal”) but offer little or no insight into how that conclusion was reached.

In a clinical setting, this lack of transparency is a concern. Veterinary professionals remain fully responsible – both medically and legally – for any decisions made, even when informed by AI tools. If the technology gets it wrong, the liability still rests with the human practitioner.

While the Royal College of Veterinary Surgeons (RCVS) has not yet released AI-specific guidance, its existing Code of Professional Conduct applies. This means vets must ensure any tools used are appropriate, evidence-based, and do not replace their own clinical judgement.

Some newer veterinary-specific tools, such as LAIKA and Vet Pulse, are attempting to address this by positioning themselves not as decision-makers, but as collaborative, explainable support systems. LAIKA claims to assist across the full clinical process – from anamnesis and differential diagnoses to treatment recommendations – while emphasising that it does not replace clinical judgment, but enhances it. Tools like this may help shift AI from “black box” to “glass box” thinking, but transparency and professional oversight remain essential.

2. Data Bias and Representation

AI systems are only as good as the data they’re trained on. If the training datasets lack diversity in species, breeds, environments, or disease presentations, the resulting outputs may be biased or incomplete.

For instance, a tool primarily trained on cases from urban referral hospitals involving purebred dogs may struggle to accurately interpret presentations from mixed-breed animals, exotic species, or cases more typical of general or rural practice.

Without representative data, these systems risk reinforcing existing diagnostic blind spots, limiting their reliability and potentially compromising patient care in underrepresented contexts.

3. Cybersecurity and Data Ownership

• AI requires vast amounts of client and patient data to function well.
• Practices need to be crystal clear on:
  • Where this data is stored
  • Whether it’s anonymised
  • Who owns it
  • Whether it’s being sold or used to train third-party models

The Information Commissioner’s Office (ICO) treats animal data linked to humans (i.e., owners) as personal data under UK GDPR. Data breaches could have legal consequences and damage client trust.

The Middle Ground: AI as a Tool, Not a Teammate

We don’t need to worship or fear AI – but we do need to think critically about it.

When AI works best:

• As a co-pilot – not a driver.
• When it supports, not replaces, the professional judgement of a qualified vet or nurse.
• When it is transparent, explainable, and accountable.

Questions Vets Should Ask Before Using AI Tools

Transparency isn’t a nice-to-have, it’s a necessity. Before implementing AI, ask providers:

1. What dataset was the model trained on?
2. Is the model explainable? Can it show why it made a suggestion?
3. What are the error rates, and in what scenarios?
4. What happens if the AI makes a mistake and who’s liable?
5. Where is the data stored, and who has access to it?
6. Can I opt out of my data being used for training future versions?

What’s Next: The Need for Regulation and Ethical Standards

While the RCVS has issued some guidance on digital veterinary services, there’s no formal AI regulation yet. Compare this to the EU, where the AI Act is already being drafted, classifying medical AI as “high risk.”

Without formal standards in the UK vet space, practices need to set their own internal governance and prioritise informed consent when client or patient data is involved.

Final Thoughts: Where Do You Stand?

AI isn’t going away. In fact, it’s only going to become more embedded in how we practice – clinically and operationally. The challenge is not whether we should adopt AI, but how to do it responsibly, and in a way that enhances, rather than undermines, veterinary care.

So, what’s your verdict? Is AI your trusted colleague, a problematic partner – or something in between?