By Cory Penn, DVM, Head of Vetscan Imagyst, Immunoassay, and Virtual Laboratory– Zoetis Global Diagnostics & Kristin Owens, DVM, DACVP, Clinical Pathologist, Vetscan Imagyst Development, Medical Affairs – Zoetis Global Diagnostics • Zoetis Diagnostics US Cytology is an extremely useful and minimally invasive diagnostic tool, that provides rapid, critical information to veterinary practitioners. Widely used in both the diagnosis and treatment decisions of various pathologies – inclusive of inflammation, injury, infection, hyperplasia and neoplasia1 – the field has faced remarkable evolution in recent decades.2 Alongside this, there has been a significant increase in pet ownership and pet life expectancy, with a corresponding rise in demand for veterinary services.3 As of 2024, 86.9 million households in the US own a pet: an increase of 56% in less than four decades.4 Diagnostic tools, including cytology testing, provide veterinarians the ability of establishing a working diagnosis, aiding in prognosis and supporting individualized therapeutic interventions. Pet owners have a growing awareness of the importance of complete diagnostic workups; challenging cases are increasing in frequency.2 Advances in artificial intelligence (AI) are having a notable impact on the veterinary industry, rapidly advancing the field of diagnostics and addressing challenges faced within cytology. Identifying masses The importance of cytology lies in the fact that it identifies the etiology of the pathology (i.e., neoplasia, inflammation, etc.), the abnormal growth of tissue.5 In instances of inflammation, cytology can often identify the underlying cause of etiology.6 In the case of neoplastic samples, the same is true in terms of determining the type of tissue involved. What’s more is that it can tell veterinary teams whether the neoplasm is malignant (cancerous)or benign (non-cancerous). With the cause of the problem identified, diagnosis can be provided and individualized steps towards the best treatment options can be taken. Another element to consider is that cytology in its conventional state does not provide readily shareable results. While some veterinarians may develop familiarity with common cytologic patterns over time, accurate interpretation requires the expertise of a boarded pathologist. Studies have indicated that up to 48% of veterinarians have historically chosen to send their samples to a reference laboratory.⁷ However, for the diagnostic evaluation to be of high quality, so too must be the sample -there is a direct correlation. More recently, point-of-care analyzers with a digital cytology capability have been introduced, allowing access to same-day results. Veterinarians scan samples which are sent to pathologists to verify the critical information needed to diagnose. Currently, slightly more than half of cytology cases are submitted to a clinical pathologist for review. There are, however, many instances where cytology testing is often not performed; reasons include time constraints, fear of non-diagnostic samples and cost. As we will come to see, AI is uniquely positioned to support with these challenges, and further the field of cytology. Challenges facing cytology – and the wider industry Let’s examine some of the pressing issues facing traditional cytology, and how they intertwine with the challenges facing the wider veterinary industry. Time and labor constraints The increase in pet ownership witnessed worldwide requires a veterinary workforce that can keep pace with this. While there is ongoing debate about whether the workplace shortage will be addressed,8 it is clear to all that the demand for veterinary care is higher than ever. In a 91-country survey, almost half of all clinics reported an increase in caseloads, and although in the US we’ve seen a recent downturn in visits, time per visit is up.3,9 For a profession that continues to be impacted by demanding work schedules and a perceived lack of work-life balance, this can result in intense strain on practices and individuals. With more animals to care for and yet less time to attend to patients, the quality of service offered can be affected – in turn, impacting relationships with clients. Research has found this takes a significant toll on veterinary mental health; burnout and stress are on the rise across the profession.3 Costs The rising cost of living remains a concern across the US; prices continue upwards in multiple aspects of everyday life, including pet ownership. Over the past decade, the expense of veterinary care has increased more than 60%, outpacing inflation.10 The cost of vet bills is a burden for some pet owners, affecting how often their animals are taken to the vet. While veterinary associations recommend animals should seek care on an annual basis at a minimum, only 40% visit their veterinarian that frequently.3 Additionally, revenue growth for practices has slowed from nearly 7% pre-COVID, to under 4% in 20249. Concerningly, more than a quarter (27%) of dog owners said they would not pay for surgery for their dogs (elective or emergency) if budgets were tight, while 17% said they would reduce veterinary checkups.4 Veterinary care has become more sophisticated over the years, which explains the rising costs; similar increases have been seen across other health care sectors. While not entirely attributable to the new technologies and medical equipment introduced to the field, ensuring veterinarians are able to deliver the most effective, up-to-date care for their patients does play a part.11 These diagnostic and other tools allow veterinarians to take earlier individualized preventative action, ensuring better patient outcomes. Then there is the fact that as our pets age, new unique health needs emerge – the risk of certain health problems (i.e., cancer, liver disease, diabetes, joint disease) grow later in an animal’s life.3 As such, additional veterinary care is required, and the life-time cost of an animal’s care increases. Historically, cytology has been considered a cost-effective tool in veterinary care. However, in some instances, the outsourcing of cytology analyses to pathologists can be costly (especially when samples are non-diagnostic) which reduces accessibility for some pet owners who are already concerned about expenses, delaying diagnosis and therefore treatment. Sample quality Non-diagnostic and low-quality samples are a frustration in the field and can lead to increased costs. For accurate diagnosis, the sample must contain enough cells and be free from contamination or degradation. While “skin masses and lymph nodes” can be easier to diagnose due to ease of access, in cases where cells are challenging to reach (deep-seated organs, small lesions), obtaining high-quality samples can be difficult. Even with easy to access lesions such as lymph nodes and skin masses, non-diagnostic samples can be upwards of 20-30%.12,13 Fine-needle aspirations regularly face sample limitations, with lack of cells a common problem. In many instances, the cell characteristics may not be sufficient to yield a definitive diagnosis or to indicate the probable behavior of the lesion. Additionally, a good quality sample is required for full cytologic interpretation. Reference laboratories on the receiving end speak of samples not being stained appropriately,14 or samples being inappropriately spread – making it difficult for pathologists to recognize what cells they’re evaluating, or the cells’ morphology. Skilled pathologists can only provide highly reliable interpretations with samples of high enough quality; however, manual cytology review can be time-intensive and is also subject to variability in interpretation. It is clear that the wider veterinary industry, as well as the practice of cytology in particular, require tools to help streamline workflow, improve cost efficient access to care, and maintain consistent accuracy over time. While not a singular solution for the entire industry’s woes, AI-driven devices can relieve pressure on veterinary teams and enhance patient care. The introduction of AI Newer to veterinary medicine than human medicine, AI is predicted to have a “profound” impact on the profession in years to come.15 Its potential and realized applications range from precision medicine and predictive models through to appointment schedule and workflow automation.16 Rapidly advancing the diagnostics field, AI is improving both the speed and accuracy of diagnoses.16 Able to repeatedly and methodically analyze diagnostic images or data sources, it has the capacity to detect patterns, correlations and abnormalities that may not immediately be apparent, in near real time with high accuracy.16 This allows for earlier detection of diseases or other health concerns, assisting veterinarians in making informed clinical decisions and confirming treatment plans, sooner – giving pet owners greater peace of mind. For example, Zoetis’ Vetscan Imagyst® is a 7-in-1 diagnostic analyzer that includes AI-Blood Smear, AI Fecal, AI Dermatology, AI Equine Fecal Egg Count, AI Urine Sediment, and AI Masses, along with a Digital Cytology capability. For the AI applications, a deep learning AI is utilized. A convolutional deep neural network sees each sample slide undergo layers of precisely trained neuro inputs for finely-tuned feature identification and recognition. The object detection algorithm was trained using thousands of samples and elements identified by board-certified specialist clinical pathologists and parasitology experts. Looking at cytology specifically, AI-powered tools have the potential to quickly provide a screening rule in test for lymph node and subcutaneous masses, which can be crucial in complex cases where an immediate treatment decision is required17 – such as instances of cancer. Vetscan Imagyst’s Digital Cytology application accelerates access to results; samples consisting of cells from blood, internal organs or bodily fluids can be identified by board-certified clinical pathologists in as little as two hours.18 AI Masses, the latest addition to the Vetscan Imagyst platform, also utilizes deep learning and image recognition. In doing so, it is able to quickly analyze common lymph node and subcutaneous masses that are suggestive of cancer at point-of-care. Together, these AI-driven solutions provide support to veterinary teams, pet patients and their owners. Efficiency Cytology is streamlined with Vetscan Imagyst. Tried and true sample preparation and rapid AI-powered analysis of common lesions can see results provided in a time frame as quick as 15 minutes, freeing up valuable time for veterinary teams to spend on patient care. In instances where the screening test may indicate that a less common pathology is present, expert pathologist insights are available within a matter of hours. Ensuring timely clinical decisions and shorter wait times for pet owners can decrease client anxiety and speed up treatment decisions, supporting better patient outcomes. Cost effectiveness Sending a sample to a lab for review, only to be told it is a non-diagnostic sample, can be an expensive waste of time. With tools at point-of-care that can make this determination while the animal is still in clinic, this expense can be all but eliminated. AI has the ability to accurately identify common lesions, which are then examined and confirmed by the veterinarian – meaning there are two ports of call for diagnosis prior to a reference lab; the need to send samples externally can be reduced. Although still available as a safety blanket for clinicians, reducing the instances of send-outs makes high-quality diagnostics more accessible and affordable for clinics, and for pet owners. Consistency AI possesses the ability to scan vast datasets – including hundreds of diagnostic parameters – with great sensitivity and speed, and without fatigue. Its ability to automate data-intensive tasks is beneficial to veterinary practices; surveys of professionals in the field perceive it as holding significant promise in terms of improving productivity and saving time (60.6%), reducing administrative workload (56.1%) and increasing the efficiency of diagnosis and treatment (46.1%).19 Complementary to the expertise of veterinary professionals, AI helps to guide clinical judgement and enhance patient outcomes.20 Looking to the future AI’s potential to reshape how veterinary medicine is practiced is irrefutable; we are already witnessing its impact. However, while 43.1% of veterinary professionals are optimistic about the adoption of AI in veterinary medicine, 36.9% have expressed skepticism19. The success of AI will be dependent on the guidelines established that govern its use16; the most prevalent concern is the reliability and accuracy of AI systems (70.3%), followed by data security and privacy (53.9%).19 Combining AI-driven diagnostics and the ability for add on expert review – the aforementioned safety net of expert human oversight, as offered by the Virtual Laboratory by Zoetis – helps alleviate these instances of skepticism and concern, building trust in a product’s diagnostic capability.21 Making the customer aware of the algorithm behind these devices is critical to establishing confidence, too. The integration of AI into veterinary cytology is revolutionizing the speed in which diseases and other issues are detected and diagnosed. Offering improved efficiency alongside this, the expediting of vital medical results and treatment initiation has a positive effect on patient outcomes and client experience. Poised to continue this evolution, AI diagnostic technology will only continue to grow – with cytology set to remain an invaluable in-clinic tool for the veterinary profession. References O’Brien, P.J. & Balan, M. 2024. “Practical diagnostic cytology for practitioners”. Royal Canin Academy. https://academy.royalcanin.com/en/veterinary/practical-diagnostic-cytology-for-practitioners Marrinhas, C., Malhão, F., Lopes, C., Sampaio, F., Moreira, R., Caniatti, M., Santos, M. & Marcos, R. 2022. “Doing more with less: multiple uses of a single slide in veterinary cytology. A practical approach.” Veterinary Research Communications 46. https://doi.org/10.1007/s11259-022-09953-0 HealthforAnimals. 2022. “Global State of Pet Care.” HealthforAnimals. https://healthforanimals.org/wp-content/uploads/2022/07/Global-State-of-Pet-Care.pdf Megna, M. 2025. “Pet Ownership Statistics 2025”. Forbes. https://www.forbes.com/advisor/pet-insurance/pet-ownership-statistics/ Llera, R., Stoewen, D., Ruotsalo, K. & Tant, M. R. 2023. “Cytology – General”. VCA Animal Hospitals. https://vcahospitals.com/know-your-pet/cytology Lopez, J. 2022. “A closer look at the benefits of cytology.” Veterinary Practice News. https://www.veterinarypracticenews.com/a-closer-look-at-the-benefits-of-cytology/ Christopher MM, Hotz CS, Shelly SM, Pion PD. Use of cytology as a diagnostic method in veterinary practice and assessment of communication between veterinary practitioners and veterinary clinical pathologists. J Am Vet Med Assoc. 2008 Mar 1;232(5):747-54 Veterinary Practice News. 2024. “Vet colleges set to meet pet care demand through 2035, study finds.” Veterinary Practice News. https://www.veterinarypracticenews.com/veterinary-colleges-meet-demand-2035/ Vetsource. 2025. “Behavior shift: Implications of today’s pet owner trends on veterinary medicine in 2025. White paper. Vetsource Veterinary Analytics. https://veterinaryanalytics.com/veterinary-trends-white-paper/ Thomas, K. 2024. “The Costs of Caring for Pets”. The New York Times. https://www.nytimes.com/2024/06/23/briefing/the-costs-of-caring-for-pets.html Carbonaro, G. 2024. “Americans Can No Longer Afford Their Pets. Newsweek Magazine. https://www.newsweek.com/americans-no-longer-afford-pets-1884626 Skeldon N, Dewhurst E. The perceived and actual diagnostic utility of veterinary cytologic samples. J Small Anim Pract.2009 Apr;50(4):180-5 Amores-Fuster, I., Cripps, P., Graham, P., Marrington, A. M., & Blackwood, L. (2011). The diagnostic utility of lymph node cytology samples in dogs and cats. Journal of Small Animal Practice, 52(6), 299-306. American Veterinary Editorial Staff. 2016. “Challenges with Performing Cytology in the Veterinary Practice. dvm360. https://www.dvm360.com/view/challenges-with-performing-cytology-in-the-veterinary-practice Appleby, R. B. & Basran, P. 2022. “Artificial intelligence in veterinary medicine.” Journal of the American Veterinary Medical Association 260(8):1-6. https://doi.org/10.2460/javma.22.03.0093 Sobkowich, K. E. 2025. “Demystifying article intelligence for veterinary professionals: practical applications and future potential.” American Journal of Veterinary Research 86(S1). https://doi.org/10.2460/ajvr.24.09.0275 Zoetis. N.d. “Transforming Veterinary Care: How AI is revolutionizing diagnostics in busy practices.” Zoetis Knowledge Hub. MM-35750. https://www2.zoetis.ie/about-us/knowledge-hub/article-1 Zoetis. N.d. “Why Digital Cytology?” Zoetis, Vetscan IMAGYST™. VTS-00181R3. https://www.vetscanimagyst.com/why-digital-cytology Gabor, S, Danylenko, G. & Voegeli, B. 2025. “Familiarity with artificial intelligence drives optimism and adoption among veterinary professionals: 2024 survey.” American Journal of Veterinary Research 86(S1). https://doi.org/10.2460/ajvr.24.10.0293 Akinsulie, O.C, Idris, I, Aliyu, V. A., Shahzad, S., Banwo, O. G., Ogunleye, S. C., Olorunshola, M., Okedoyin, D. O., Ugwu, C., Oladapo, I. P., Gbadegoye, J. O., Akanda, Q. A., Babawale, P., Rostami, S. & Soetan, K. O. 2024. “The potential application of artificial intelligence in veterinary clinical practice and biomedical research.” Frontiers in Veterinary Science 11. https://doi.org/10.3389/fvets.2024.1347550 Zoetis Diagnostics. N.d. “Digital Diagnostic Artificial Intelligence in Veterinary Medicine: The Future is Now.” Zoetis. VTS-00956. https://www.zoetisdiagnostics.com/assets/Resources/PDF/Digital-Diagnostic-Artificial-Intelligence-in-Veterinary-Medicine-The-Future-Is-Now.pdf
