7 real applications of AI in veterinary medicine (and how clinics use them today)

Artificial intelligence (AI) in veterinary medicine is already reshaping how clinics diagnose, document, and deliver care. From auto-generating consult notes to flagging abnormalities in imaging scans, AI tools are helping veterinary teams improve accuracy, reduce burnout, and make more informed decisions.

This article explores seven real-world applications of AI in veterinary medicine, with examples of how clinics are already using these tools to optimize workflows and enhance patient outcomes. Whether you run a busy small-animal practice or support specialty diagnostics, understanding these use cases can help you stay ahead of emerging trends—and start implementing AI in a way that works for your team.

We’ll also touch on common considerations, like ensuring clinical oversight and data quality, to give you a balanced view of AI’s capabilities and limitations. If you're curious about what’s possible right now (not five years from now), this guide is for you.

AI-assisted documentation and automated medical records

One of the most practical uses of AI in veterinary medicine today is automated documentation. Tools like CoVet’s AI scribe use natural language processing (NLP) to generate real-time medical notes during consultations, automatically structuring them into clear, customizable SOAP notes formats. Instead of typing or handwriting notes after a busy consult, vets can focus on their patients while the AI records and organizes key details in the background. This also helps clinics improve veterinary record keeping with consistent and accurate entries.

Benefits

• Saves time: Reduces hours spent on documentation after hours or between appointments

• Reduces burnout: Eases cognitive and administrative load for veterinarians and support staff

• Improves consistency: Ensures medical records follow best-practice structures and are easy to review or transfer

For example, a multi-location small-animal clinic using CoVet sees a significant reduction in documentation time after integrating AI scribes into their daily workflow. The AI captures patient history, physical exam findings, and treatment plans—all while syncing with their existing veterinary practice management system (PMS).

Use CoVet to highlight and extract detailed insights from any consultation or document.

💡Key consideration: Vet oversight is still crucial. AI scribes aren’t fully autonomous—they generate drafts, not final records. Veterinarians should always review and approve the note before it’s added to the electronic health record (EHR) to ensure clinical accuracy and legal compliance.

AI-driven diagnostics in everyday clinical decision-making

Veterinarians face an overwhelming amount of patient data, from clinical signs and lab results to medical history and breed-specific risk factors. AI-driven diagnostics help make sense of this complexity by offering real-time support during the diagnostic process.

Tools powered by machine learning analyze structured data from EHRs, symptom inputs, and prior cases to generate a shortlist of potential diagnoses. These AI systems don’t replace a vet’s expertise, but they can flag overlooked possibilities, identify patterns across cases, and offer differential diagnoses that prompt deeper investigation.

Benefits

• Supports complex decision-making: Especially helpful for newer vets or when handling unfamiliar presentations

• Reduces diagnostic delays: Speeds up time-to-treatment in urgent cases

• Uncovers hidden patterns: Helps surface less obvious differential diagnoses

For example, some platforms integrate with systems like CoVet, using structured multi-pet vet records and veterinary dictation software to feed more consistent and reliable data into diagnostic algorithms. This structured input improves the AI’s accuracy and helps it return more context-aware suggestions.

A clinic might enter a combination of symptoms like lethargy, vomiting, and abdominal tenderness into the system, and the AI could highlight pancreatitis, foreign body ingestion, or toxin exposure as top differentials, prompting immediate next steps like imaging or bloodwork.

💡Key consideration: AI diagnostic tools rely heavily on the quality of training data. If rare conditions or atypical presentations aren’t well represented, the system might miss them. It’s essential that clinics treat these tools as decision support—not decision makers.

Veterinary imaging powered by machine learning

Imaging plays a central role in veterinary diagnosis, but interpreting radiographs, ultrasounds, and CT scans can be time-consuming and subjective, especially in busy clinics. That’s where machine learning comes in.

AI-powered imaging tools use computer vision to analyze medical images for patterns and anomalies that may be hard to detect with the naked eye. These tools can flag subtle indicators of disease, suggest measurements (like heart size or bone alignment), and even prioritize urgent cases for review.

Benefits

• Faster interpretation: Useful during after-hours or when radiologists are unavailable

• Increased consistency: Reduces human error in image reading

• Early detection: Helps flag subtle issues before they escalate

For instance, let’s say a patient presents with intermittent lameness. The vet orders an X-ray, and the AI flags a possible early-stage hip dysplasia based on joint spacing and bone structure, prompting further orthopedic consultation. In another case, the AI might detect signs of foreign body ingestion or pulmonary nodules that weren’t obvious on first review.

These systems are already in use at some specialty centers and progressive clinics, often integrated with their veterinary imaging software.

💡Key consideration: While AI is powerful, it doesn’t replace clinical judgment. Factors like image quality, positioning, and uncommon conditions still require trained eyes. Clinics should view AI as a second opinion, not the final word.

Remote monitoring and predictive analytics for chronic care

Chronic conditions like diabetes, arthritis, or post-surgical recovery often require ongoing observation—something that’s not always feasible with occasional in-clinic visits. That’s where remote monitoring powered by AI can transform veterinary care.

Wearable sensors and smart collars now collect real-time data on a pet’s vital signs, activity levels, temperature, and even respiratory rate. AI then processes that data using predictive analytics, identifying patterns and flagging abnormalities before they become emergencies.

Benefits

• Improved outcomes: Early intervention means fewer complications

• Better client communication: Data-backed updates reassure pet owners

• Less in-clinic stress: Reduces the need for frequent transport and re-checks

For example, imagine a dog recovering from TPLO surgery. A smart sensor picks up changes in gait and decreased activity overnight. The AI system triggers an alert to the clinic, prompting a technician to call the owner and schedule a check-up, potentially catching a complication like swelling or infection early.

This technology isn’t just useful for veterinarians. Registered Veterinary Technicians (RVTs) and Veterinary Assistants (VAs) can also benefit by using AI alerts to manage follow-up care more proactively.

💡Key consideration: AI depends on accurate, consistent input. If a pet chews off the monitor or wears it loosely, the data may be misleading. Clinics should educate clients on proper device use and interpret alerts within context.

AI in telemedicine and triage support

In a world where pet parents expect instant answers, AI-powered triage tools and telemedicine assistants are redefining how veterinary clinics manage demand.

These systems use chatbots, symptom checkers, and NLP-powered questionnaires to screen cases before they reach the clinic. By asking structured questions about symptoms, behavior, and medical history, AI can help determine the urgency of a visit and recommend next steps.

Benefits

• Reduces phone time for receptionists with automated intake

• Improves client experience with faster, more accurate answers

• Helps prioritize emergencies and reduce wait times

Take this example: A pet owner reports their cat has been vomiting for a day, but is still playful and eating. An AI triage tool assesses the input and suggests a non-emergency consult within 48 hours instead of an urgent drop-off. This helps the front desk prioritize true emergencies while offering reassurance to the client.

This kind of pre-visit triage improves workflow and avoids unnecessary stress on clinical staff. It also helps receptionists and techs better manage appointment blocks based on the expected severity of cases.

💡Key consideration: While these tools can guide scheduling, they must respect veterinary scope of practice. AI shouldn’t diagnose or prescribe, and pet owners need clear messaging that AI output is not a replacement for professional care.

Genomic and pathology insights through AI

As veterinary medicine advances, AI is playing an increasingly powerful role in pathology and genomics—areas once limited to specialist labs. These tools can now detect abnormalities in tissue samples or genetic data much faster and, in some cases, with greater consistency than manual methods alone.

Benefits

• Accelerates diagnostic turnaround in clinics and diagnostic labsReduces manual errors by highlighting high-risk samples

• Enhances personalized medicine based on breed or genetic risk

For example, AI algorithms trained on thousands of pathology slides can highlight cellular anomalies that indicate malignancies—such as mast cell tumors or lymphomas—enabling earlier intervention. Similarly, in genomics, AI tools can sift through large sets of sequencing data to spot disease markers or inherited conditions, helping vets make informed care decisions for breeds predisposed to certain illnesses. 

Knowing that, a referral oncology clinic might use an AI platform to pre-screen histology samples. The software can flag suspicious regions for pathologists to review, cutting analysis time in half and improving reporting accuracy.

💡Key consideration: While the potential is massive, most general veterinary clinics aren't yet using genomic AI daily. The tech still requires high-quality data, specialist interpretation, and integration with lab workflows. But for specialty clinics and research-focused hospitals, it's quickly becoming indispensable.

AI tools for veterinary education and team training

AI is reshaping clinical care, including how veterinary teams learn and improve their skills. From simulation-based learning to real-time feedback on documentation, AI-powered tools help veterinarians, vet techs, and assistants sharpen their expertise efficiently. 

Let's dive in: 

Simulation-based learning and case walkthroughs

AI-driven simulators create realistic scenarios that let veterinary professionals practice diagnosing and treating cases in a safe, controlled environment. These virtual cases often include branching outcomes based on decisions made, helping learners understand complex clinical reasoning.

For instance, a veterinary technician training module might simulate a complex feline patient presenting with respiratory distress. As the learner progresses, the AI adapts based on their choices—whether they order diagnostic tests, administer medications, or perform procedures—providing immediate feedback and guidance to improve clinical decision-making skills.

Improving documentation and clinical skills

Tools like CoVet use AI to provide instant feedback on SOAP notes, ensuring veterinary teams capture comprehensive and consistent patient information. This not only improves record accuracy but also aids ongoing professional development and offers tips for SOAP writing that enhance documentation skills.

 CoVet seamlessly creates detailed SOAP notes across general practice to exotics cases

For example, licensed Veterinary Technicians (LVTs) in training programs benefit from AI-powered platforms that review their case notes and clinical decisions, highlighting areas for improvement and reinforcing best practices. 

Discover more helpful vet tech tips for your practice.

The growing impact of AI in veterinary medicine: What clinics need to know today

From streamlining documentation and diagnostics to enhancing imaging and remote monitoring, AI is empowering clinics to deliver faster, more accurate, and more personalized care.

While challenges remain—such as ensuring data quality, maintaining human oversight, and addressing ethical concerns—the benefits of AI in veterinary medicine are already clear. Clinics that adopt these technologies not only improve workflow efficiency and clinical outcomes but also enhance team training and client engagement.

Staying informed about the latest AI applications helps veterinary professionals prepare for a future where intelligent systems will be integral to delivering exceptional animal care. Tools like CoVet’s AI-powered scribe and copilot exemplify how advanced technology can seamlessly integrate into daily workflows, reducing administrative burden and improving accuracy.

Frequently asked questions about AI in veterinary medicine

What is the role of AI in veterinary medicine? 

AI in veterinary medicine plays a crucial role in enhancing clinical decision-making, improving diagnostic accuracy, automating administrative tasks like documentation, and supporting remote patient monitoring.

AI-powered scribes and copilots like CoVet help veterinarians save time by automatically generating medical records, allowing them to focus more on patient care. When considering these technologies, clinics should also evaluate scribe pricing and alternatives to select solutions that best fit their needs and budgets.

What's the importance of AI in veterinary medicine? 

AI improves the efficiency and quality of veterinary care by saving time on documentation, enabling earlier disease detection, and providing personalized treatment insights. It helps clinics manage increasing patient loads, reduces burnout, and supports better outcomes for animals and clients.

What are examples of veterinary AI software? 

Examples include CoVet, which uses natural language processing to generate real-time consult notes and structured medical records; AI platforms for diagnostic support that analyze symptoms and patient data; machine learning-powered imaging tools that detect anomalies in radiographs; and telemedicine chatbots that assist with appointment triage.