Uptrends in AI technology in veterinary medicine today

Within the past few years, the use of artificial intelligence (AI) in veterinary medicine has grown all expectations. Initially, AI was incorporated for basic data analysis, including managing large databases of patient records, tracking health parameters, and improving management efficiency.

However, the role of AI has expanded dramatically due to continuous improvements in technology. Today, AI tools are used not only for diagnostic purposes but also in a variety of other fields, changing the way veterinarians approach their work.

Main trends

Veterinarian on your phone. It's only a matter of time before people all over the world use their mobile devices to check their pet's health. Currently, many trending applications are being released in Asian countries. They use picture analysis techniques to detect early signs of pet health problems.

Simply take a photo of your pet's skin, eyes and other affected areas, and the app can provide primary analysis and suggest whether a veterinarian visit is required. Additionally, this tool, which offers online consultations with professionals, can help reduce costs for both pet owners and veterinarians by minimizing unnecessary visits. On the other hand, how can you ensure that such applications do not harm your pet by misinterpreting the results or lacking any potentially serious ones?

AI diagnostic scan. This valuable AI tool is used for X-ray, ultrasound and MRI analysis. Increase patient safety by allowing experts to find the smallest deviations in the early stages without waiting for interpretation from highly skilled professionals. This type of equipment can predict the onset of chronic kidney disease (CKD) in cat patients up until two years before it occurred.

A database containing thousands of images can be used to detect problems faster, easier, and cheaper than traditional methods. But in such a complicated situation, can we really rely on AI? It sounds very optimistic and promising, but the use of such software is still in its early stages and requires constant direct human supervision. As a clinician, I stated: “There is a possibility for diagnosis such as radiographs, but they are by no means fully developed or perfect.”

AI-equipped animal health monitor. PET accessories such as collars and trackers use advanced AI algorithms and sensors to monitor a variety of important parameters and biometric data, including activity level, heart rate, breathing rate, temperature, and more, to detect the onset of health problems.

Health Records Management AI. AI helps with data management, allowing veterinarians to spend more time with patients rather than focusing on documents. But can you make mistakes when taking medical history, especially given industry-specific abbreviations that are unique to each practice?

Oncology AI. “AI is a pioneer in oncology,” these are future headlines for many news articles. It is already possible to predict cancer treatment outcomes using software equipped with AI. By analyzing cancer cells and using machine learning algorithms, AI can propose the most effective chemotherapy treatments for lymphoma dogs. This personalized approach helps you choose the best cancer treatments, reduce the trial and error process, and improve your success rate. However, as explained before, important work is being done to develop this technology, and great caution is required before entrusting AI with many decisions or relying entirely on it.

AI in drug development and clinical testing interpretation. The use of AI in drug discovery and development is rapidly advancing. AI algorithms can analyze vast datasets of compounds, biological interactions, and clinical outcomes much faster than traditional methods.

This has created a more efficient drug development process. Veterinary medicine has applied AI technology to develop new treatments for a variety of conditions, ranging from parasitic infections to chronic diseases, and to intensify clinical testing.

Senior lecturer Iain Richards at the University of Central Lancashire said: “One important thing to keep in mind is that AI can be essentially accurate when analyzing pixels in a scan. Biological testing, especially biochemistry, has its own unique sensitivity and specificity cutoffs.

“AIs using these tests take the risk of the same error. For example, many lab profiles define a range of “normal” as within one or two standard deviations from the average, but results outside that range are flagged as anomaly.

“A good example is ALT (alanine aminotransferase), which usually needs to be 2-3 times the upper limit to be considered clinically significant.”

AI Pros in Veterinary Medicine

Improved diagnosis. AI can analyze complex datasets to identify the smallest deviations and often make predictions beyond human capabilities. This can detect early symptoms of a potential disease and generally improve the accuracy of the diagnosis.

Data entry efficiency. The automated system can handle routine tasks and allow veterinarians to focus more on patients and client communication.

Individual medical care. AI allows for more personalized treatment plans by analyzing the medical history of thousands of patients and allowing them to predict the most efficient drugs set for a particular case.

Minimizing cost. Using AI as a triage tool (telemedicine) provides easy access to veterinary consultation services, reducing costs such as transportation, sedation of highly uneasy patients. AI online triage is beneficial for stress management for both patients, owners and consultants.

Cons of AI in veterinary medicine

High initial cost. Implementing AI technology can be expensive. This can be a cost-related issue for small practices.

Data privacy concerns: Digitalizing patient records and confidentiality raises concerns about data security and privacy.

Dependence on technology. An excessive reliance on AI can reduce practical diagnostic skills among new veterinarians, and even lead to potential human exchanges in certain workplaces.

Ethical issues. The use of AI in veterinary care raises ethical issues, such as how errors made by AI systems are handled and the implications of reducing human surveillance.

Lack of regulations. Currently, there are few regulations governing the use of AI in veterinary medicine. It raises concerns about safety standards, fair competition and widespread misinformation.

Senior staff's opinion on the use of AI in Veterinary Medicine (UCLAN)

“We are a veterinarian coach and mentor (education and learning) Laura Katherine Jenkinson said: “AI technology is rapidly advancing and is quickly commonly used in everyday life. AI has been around for several years in science and technology. We are beginning to leverage the capabilities of everyday tasks.

“When used properly, it's a great way to streamline research and quickly refine resources. The key factor is to use it properly and accurately. It's extremely beneficial to use it with free CPD, which educates users how to safely source AI technology.”

Adrian Nelson-Pratt of VSGD shared some important questions to consider before relying entirely on AI technology.

“When AI, automation and integration are routine components, what does it mean to run a veterinary business? Where is the value proposition, what kind of staff will be needed, and what will the nature of the job be?

“Do consumers who adapt more quickly than the veterinary industry expect us to be involved in these opportunities? How will new expectations arise? What are the regulatory burdens associated with AI in veterinary medicine? It may be beneficial to compare it to human medicine.

“How does AI affect the health and well-being of a veterinarian occupation, taking into account changes in job patterns, stress levels and expectations?

“Finally, what is the evolutionary role of animals in society?”

Using AI in Veterinary Research: A Personal View

As a veterinary student in my second year, I witnessed how AI is integrated into my research. My personal experience using AI shows an increase in academic achievement and allows me to diversify revisions through the generation of useful quizzes, revision materials and infographics.

With AI-powered virtual simulations, students can practice surgical and diagnostic procedures in a safe environment, increasing their skills and confidence before working with living animals. The AI-based platform is also used to analyse student performance and provides personalized feedback and resources to improve learning outcomes. This not only prepares us better for real-world scenarios, but also raises future generations of veterinarians who are more adaptive and informed.

Conclusion

The integration of AI in veterinary medicine is a game changer, offering new possibilities for diagnosis, treatment, education, and overall animal care. Therefore, multiple challenges are inevitable. However, the benefits of using AI technology cannot be denied. From predicting diseases to optimizing treatment plans to strengthening veterinary education, AI is set to play an increasingly important role in this field.

However, AI is good at summarizing information, but human surveillance is important even in these applications. Our veterinarian colleagues are exposed to errors and potential lawsuits if they cannot read and edit everything AI Summaryzer has produced carefully.

Finally, AI still has a long path to development, hopefully combining the strengths of both human and artificial intelligence to work together to achieve a new scientific breakthrough.

• Appears in Veterinary time (2025), Vol. 55, Issue 27, pages 17-18