Part 3: Smart Help Where It Counts: AI Opportunities in Practice
A 5-Part Blog Series on Integrating AI into Veterinary Practice
From reading radiographs to booking appointments, AI can streamline many aspects of veterinary practice. This post looks at the clinical and operational areas where AI can help most, and how to make sure it really adds value.
Opportunities to Improve Clinical and Administrative Tasks with AI
Where can AI actually help in a veterinary practice? The short answer: pretty much anywhere you deal with lots of data, routine tasks, or pattern recognition. Let’s explore some prime opportunities in both clinical and administrative realms where AI has the potential to lighten the load or enhance our capabilities:
Diagnostic Imaging (Radiology & Beyond): This is one of the most mature and exciting areas for AI. In human medicine, AI tools are already reading radiographs, CT’s, and MRI’s alongside radiologists, and in vet med we’re not far behind. AI in radiology can act like a tireless junior radiologist: it can pre-screen images, highlight areas of concern, and even draft initial reports for the veterinarian to review. For example, AI software can automatically orient an X-ray correctly and mark possible abnormalities, speeding up the interpretation process. It might flag a tiny lung nodule or subtle fracture line that could be overlooked on a busy day. The vet still makes the final call, but now they have a “second set of eyes.” Some AI systems can also populate a radiology report with preliminary findings, which the clinician can then edit rather than starting from scratch. This can save a lot of time on documentation. There’s growing evidence that these tools are effective, a recent study showed that a commercial AI for reading canine and feline radiographs performed about as well as expert veterinary radiologists in overall accuracy. Interestingly, the AI was particularly strong at confirming normal cases (high specificity), it excelled at saying “this radiograph looks normal” correctly, though it was a bit less sensitive in catching some abnormalities. In practice, that kind of AI could be hugely valuable for screening: imagine it quickly sorting out the routine clear X-rays from those that likely have issues, so your radiology consultations can focus on the tricky ones. The bottom line is AI can improve imaging workflow efficiency, and potentially diagnostic accuracy, by handling the initial heavy lift. This frees up vets (or specialist radiologists) to concentrate on the findings that truly need their expertise. It’s like having an apprentice who never gets tired or distracted. Besides radiographs, similar AI tech is being explored for ultrasound images and even advanced imaging like CT scans, though those are still in early stages for veterinary use.
Pathology & Laboratory Analysis: Think about the quantities of lab results and slides we deal with. AI is making inroads here too. For instance, in cytology or histopathology, AI image analysis can count cells or identify patterns much faster than a person. AI is being used in pathology for things like counting mitotic figures on histology slides, a task that can be tedious and time-consuming for pathologists. An AI program can scan a slide image and pinpoint dividing cells in minutes, if not seconds. Similarly, AI-driven analysers might flag abnormal blood smear cells or find parasites in a fecal exam image automatically. In human medicine, there are AI systems to scan Pap smears or blood smears for abnormalities, which increases throughput. In vet labs, we’re seeing early versions of this. The benefit is speed and consistency. AI won’t miss that one atypical cell because it had 50 other cases to run; it looks at everything methodically. For in-house use, a general practice might someday have a smart microscope that, when you load a slide, an AI gives you a preliminary read (e.g., “likely lymphoma” on a lymph node aspirate, or “no parasites seen” on a skin scraping). Even if that’s still on the horizon, larger diagnostic labs are already leveraging AI, which means faster turnaround and potentially lower costs for the clinics that use those labs. It’s an area to watch and, if you do a lot of in-clinic cytology, maybe an area to pilot AI in the future.
Clinical Decision Support & Diagnostics: Beyond imaging and pathology, AI can assist in synthesising clinical data to support diagnoses or treatment decisions. This includes those expert system-like tools and newer AI that can take patient signs, history, maybe some lab results, and suggest possible diagnoses or alert to certain risks. In human healthcare, AI-driven decision support might, for example, help identify early sepsis or recommend cancer treatment options based on big data. In veterinary practice, we’re starting to see AI in things like triage and case prioritisation. Picture an AI that, when a client calls or fills an online form about their pet’s symptoms, can triage the case: if certain red flags are present (e.g., difficulty breathing, bleeding), it alerts the team for an urgent appointment, whereas routine issues might be scheduled for later or handled via telemedicine. AI chatbots for veterinary triage are emerging, they can ask owners a series of questions (via a website or app) and determine if the pet needs to be seen immediately, can wait a few days, or perhaps doesn’t need a vet at all. This helps ensure emergencies are seen promptly and the schedule isn’t clogged with non-urgent visits. Some platforms are using AI chatbots to assist in patient screening and triage, letting vets focus on cases that truly need their attention. Even in the clinic, an AI could prioritise the appointment book if multiple walk-ins occur at once, by analysing presenting complaints and helping the reception team to prioritize true emergencies. Another decision-support use is treatment or diagnostics suggestions: e.g., an AI might remind you “Coughing in older large-breed dog: consider chest radiographs to check for cardiomegaly” based on learning from many similar cases. While these are in early days, they represent how AI can act as a safety net or an idea generator, ensuring we don’t miss possibilities. Generative AI like large language models (think ChatGPT) are also being explored by veterinary teams here, because they can “converse” with a clinician and answer questions, one could imagine asking an AI, “What are some causes of chronic weight loss in a senior cat that I might be overlooking?” and it could list out differential diagnoses or even relevant recent research. In fact, human clinicians are already testing such AI for decision support and documentation, and vets are not far behind. The risk is that AI can get it wrong, and make things up, it can make mistakes, and it is vitally important to remember this: a vet always needs to be the final decision maker. The key is that AI can help with the cognitive load, processing lots of information and providing insights, while the vet applies the final layer of judgment taking into account the individual patient (and client).
Client Communication & Service: On the client-facing side, AI can improve how we engage and inform pet owners. One practical example is AI-driven chatbots for client questions or appointment scheduling. Many veterinary websites now have chat features; imagine if after hours or during busy times, an AI could answer common questions (“My dog just ate chocolate, what do I do?” or “Do you have any appointments tomorrow?”). These chatbots can provide immediate advice (usually following protocols set by vets for emergency triage, e.g., “Please go to the ER, chocolate can be toxic” or “Here’s an open slot at 3 PM”) and even book appointments. This not only offers clients quick help but also reduces phone/tag-up burden on your front desk staff. AI can also personalise client education: e.g., after a diagnosis, the AI could automatically email the client a summary of the condition and care instructions (reviewed by you). Some systems use AI to draft these communications in layman’s terms. Additionally, AI can monitor client satisfaction by analysing feedback or reviews, perhaps alerting you to a negative trend (like multiple mentions of long wait times) so you can address it proactively. And let’s not forget social media and marketing: there are AI tools that help draft social media posts or reply to reviews in a consistent tone, which can help maintain an online presence without consuming a staff member’s entire day. While these uses aren’t “clinical,” they are part of running a modern practice and keeping clients happy. Aside from imaging, administrative tasks are a top area where vets are using AI, and indeed reducing the workload for administrative staff was one of the most cited benefits of AI integration. So, leveraging AI for client service and office management is a big opportunity. It means your front-of-house team can spend more time on personal calls or in-clinic client interactions while the AI handles the routine stuff in the background.
Medical Records and Documentation: Every vet knows the mantra “if it’s not in the record, it didn’t happen,” but writing up those records (and reading past records) can eat up hours. AI to the rescue here comes in a couple forms. Voice-to-text transcription has improved dramatically with AI. Many vets are now using AI-powered dictation: you talk through your exam findings and plan, and the AI transcribes it directly into the SOAP format in your electronic record system. Voice-to-text is a common AI application in vet settings, and it’s easy to see why: it can speed up record-keeping and is generally more accurate than the old clunky dictation software from a decade ago. Beyond transcription, summarisation AI can help condense long histories. For example, if you get a new client and they bring records from three different clinics spanning 10 years, an AI could potentially summarise the pet’s medical history into a digestible narrative or timeline. Similarly, AI can ensure consistency and completeness in records by prompting for missing info (“BP reading not entered, would you like to add it?”). And looking forward, large language model AIs are being explored to generate discharge instructions or referral letters, you input the key facts and it drafts a coherent, client-friendly letter. Human medicine is actively experimenting with AI scribes that sit in on the exam (through a microphone) and produce full visit notes, orders, and a patient summary. It’s not hard to imagine vet clinics doing the same soon. Think about how nice it would be to finish an appointment and have the record already written and the client’s home-care instructions printed, all thanks to an AI assistant. That’s time saved and a more consistent output.
Inventory and Business Analytics: While clinical care is paramount, running a practice has a business side, and AI can help there too. For instance, AI can analyse your inventory usage patterns and help forecast orders so you don’t run out of important meds or overstock slow movers. It can consider seasonal trends (flea products, allergy meds) and even local disease outbreaks. On the analytics front, an AI could crunch years of practice data to find, say, which services are growing or which demographics of clients might be underserved, helping you make strategic decisions. Some practice management systems are starting to integrate AI for reporting, identifying anomalies (e.g., “revenue was unusually low last Tuesday, possibly due to X”) or even suggesting efficiency improvements (like detecting if appointments often run overtime on Mondays and flagging that schedule adjustments might be needed). These are more about practice management than direct care, but they ultimately contribute to a smoother operation and better client/patient service.
In all these areas, it’s important to note that AI’s role is to assist and augment the veterinary team, not to take over. Each opportunity comes with the need for oversight, e.g., a vet should verify the AI’s radiology report or double-check a critical chatbot triage decision initially. But if implemented thoughtfully, AI can handle the repetitive or data-heavy tasks in seconds, tasks that might take us minutes or hours, thus freeing up the human team to focus on what truly requires clinical judgment, empathy, and decision-making. The evidence and case studies in vet med are starting to pile up: we have AIs reading images nearly as well as specialists, AIs routinely handling admin duties in some clinics, and more on the horizon. And we’ve seen analogous successes in human medicine we can borrow from, such as AI systems that reliably assist in diagnosing certain conditions or streamline hospital workflows. So, there’s a lot of low-hanging fruit where AI can make an immediate impact in a vet practice, from getting that lab result interpreted faster to making sure no appointment request falls through the cracks.
Next up: Having identified where AI can help, the next big question is evaluating which AI solutions to trust and use. Before you adopt an AI tool, you’ll want to kick the tires, so to speak. With so many AI tools available, how do you choose one that’s right for your practice? In Part 4, I’ll look at the key questions to ask to ensure any AI system is useful, safe, and a good fit.