The Rise of Decision Support Tools: What They Do (and Don’t) Replace in Veterinary Practice

Artificial intelligence (AI) is becoming increasingly present in veterinary practice – and one of the most prominent areas of adoption is radiology. From highlighting abnormalities to aiding diagnosis, AI-powered decision support tools are designed to complement clinical judgement. But as these tools evolve, so do the conversations around their role. Are they a silent assistant – or a subtle substitute? And how do we ensure we’re using them wisely?

Let’s take a closer look at what decision support tools can do – and just as importantly, what they can’t.

AI as a Support, Not a Substitute

There’s growing evidence that AI tools can match – and in some cases outperform – humans in interpreting certain radiographic findings, particularly in straightforward, low-complexity cases. That opens up exciting possibilities: repetitive tasks can be streamlined, decision fatigue reduced, and vets freed to focus on the clinical reasoning, conversations, and care that machines simply can’t replicate.

But these tools are not replacements. They’re reinforcements – additional layers of insight to inform, not overrule, clinical judgement.

In more nuanced or ambiguous cases, AI’s performance varies just as human interpretation does. The more complex the case, the more essential the vet becomes. Patient history, context, behavioural cues, and the conversation with the client – these remain squarely within the vet’s expertise.

Where AI Shines: Efficiency and Workflow

In practice, AI’s greatest strength lies in improving efficiency. Some tools can pre-screen radiographs, flagging images likely to be normal and allowing vets to triage their time. Others offer a ‘second set of eyes’ – a safety net for junior clinicians or when a second opinion isn’t immediately available.

These tools can accelerate turnaround on image-heavy cases, reduce bottlenecks, and help practices work smarter. That means less time chasing backlogs, and more time with patients – which can translate to quicker diagnoses, faster treatments, and better care.

But these benefits only materialise when the tools are well integrated into daily workflows. Like any system, AI is only as effective as its implementation.

Why Clinical Context Still Reigns

No matter how sophisticated, AI doesn’t create differential diagnoses. It doesn’t weigh up clinical history, behavioural cues, or owner feedback. It doesn’t ask clarifying questions or make judgment calls when data is incomplete or conflicting.

It’s important to remember that even the best tools are still susceptible to false positives and false negatives. That’s why final decision-making must stay with the vet. AI can support decisions – but it can’t make them.

Collaboration, Not Competition

For decision support tools to bring genuine value, they need to be designed with veterinary workflows in mind. That’s why collaboration between developers and clinicians is so crucial. Tools must be transparent, easy to interpret, and clearly positioned as support – not decision-makers.

Equally important is education. Veterinary teams need to know where AI fits, how to use it responsibly, and how to respond when a tool’s suggestions don’t align with their clinical instincts.

Used correctly, AI can become a trusted support system. But it’s not a silver bullet. It’s a tool – not a replacement for professional judgement, empathy, or experience.

Final Thought

As decision support tools continue to develop, the focus should remain on enhancement, not replacement. When used well, AI can streamline diagnostics, ease workloads, and improve patient outcomes. But that only happens when it’s working with the vet – not instead of them.

The aim isn’t to automate care. It’s to empower those who deliver it.