Introduction and Background
Veterinary medicine is on the cusp of a technological revolution. Artificial Intelligence (AI) is rapidly being adopted to enhance diagnostic imaging and radiology in animal healthcare, promising faster and more accurate diagnoses for our animal patients. As the Founder of the Voice of Stray Dogs (VOSD) and an entrepreneur with a deep technology background, I have witnessed firsthand how AI can transform industries. I previously led India’s first AI conference for CXOs, TWB_’s Automationshift 2017 at the World Trade Center Bangalore, underscoring early on how automation and AI would reshape business. Now, as VOSD’s founder – running India’s largest dog rescue, the country’s largest referral hospital for stray dogs, a national helpdesk, and India’s first AI-based chatbot for dog rescue and care – I am focused on bringing the power of AI to veterinary science.
VOSD (The Voice of Stray Dogs) today stands as India’s largest dog healthcare system, which includes a 7-acre “VOSD City of Dogs” sanctuary housing over 1,700 rescued dogs & running the largest super-specialty referral hospital for dogs in India that has at any time 100+ dogs in life critical IPD care and over 300 critical treatments daily. This unique vantage point – at the intersection of cutting-edge technology and hands-on veterinary care – shapes my perspective on how AI can be harnessed to improve animal health. In 2024, VOSD launched VOSD AI, the world’s first AI virtual health assistant for dogs, to provide pet owners with instant veterinary advice and access to thousands of India-specific treatments via a chatbot. As I noted during its launch, our vision is for VOSD AI to train on diagnostic images and radiological data, so that veterinarians across India can enhance diagnostic accuracy and streamline radiological analysis. This paper builds on that vision, examining the state of AI in veterinary radiology and diagnostic imaging – globally and in India – and exploring the road ahead.
The Rise of AI in Veterinary Imaging
Across the world, veterinarians are beginning to embrace AI tools to augment their diagnostic capabilities. Diagnostic imaging – especially the interpretation of X-rays (radiographs) – has emerged as one of the earliest and most impactful applications of AI in veterinary practice. Recent industry analyses note that roughly two dozen companies now market AI-powered software and services for veterinary practices, with the majority focusing on radiograph image analysis and related tasks. These AI tools can rapidly analyze medical images to detect abnormalities, assist in measurements, and even improve image quality.
Clinical adoption is following. In a 2024 survey by the American Animal Hospital Association, about 30% of veterinarians reported they already use some form of AI in their practice on a daily or weekly basis. This is a striking number, indicating that veterinarians, traditionally slower to adopt new tech, are increasingly “early adopters” of AI. The drivers are clear: AI promises to save time, improve accuracy, and reduce workloads in busy clinics. Routine tasks like scanning an X-ray for signs of disease can be automated to an extent, allowing vets and radiologists to focus on complex cases and spend more time with patients. AI algorithms can also serve as a “second set of eyes” – identifying subtleties in an image that a human might miss, or flagging potential issues for closer review. For example, an AI system might highlight a faint nodule on a dog’s lung radiograph that could be an early tumor, ensuring it isn’t overlooked. In this way, AI acts as a diagnostic assistant, enhancing the accuracy and confidence of veterinarians.
Table 1 – Key AI Use Cases in Veterinary Diagnostic Imaging
As shown in Table 1, AI’s role spans the imaging spectrum – from interpreting standard radiographs to analyzing cytology slides and streamlining workflow. Notably, AI is not about replacing veterinarians or radiologists, but augmenting their capabilities. Modern veterinary AI systems typically serve as clinical decision support: they provide a quick preliminary read or flag on an image, but final diagnosis and treatment still rest with the veterinarian’s judgment. In human medical radiology, it’s said that AI won’t replace radiologists, but radiologists who use AI may replace those who don’t. The same is likely true in veterinary medicine – practitioners leveraging AI will have an edge in efficiency and insight, while those ignoring it could fall behind.
Key Players and Technologies Driving AI Adoption
The veterinary industry’s AI landscape is a mix of innovative startups and established animal health companies. Below, we highlight some of the prominent players, technologies, and their applications:
SignalPET – A pioneering startup from the U.S., SignalPET offers an AI-powered radiology platform used in over 2,300 veterinary clinics worldwide. It can instantly analyze companion-animal X-rays and generate a report on over 50 radiographic findings (covering conditions from heart enlargement to fractures). In early 2024, SignalPET launched SignalSTAT, a rapid radiology service blending AI with human radiologist oversight to guarantee interpretation within 45 minutes, 24/7. This hybrid approach addresses the shortage of veterinary radiologists by triaging urgent cases with AI and delivering a full expert-verified report in under an hour. SignalPET’s platform is currently interpreting 50,000+ X-ray films each week for veterinarians – a testament to how AI can scale imaging diagnostics cost-effectively.
Vetology – One of the early veterinary AI radiology providers, Vetology (USA) integrates AI screening with cloud-based teleradiology. Vetology’s “virtual radiologist” AI tool produces an initial radiograph report within 5–10 minutes of image upload. It highlights key findings – e.g. signs of thoracic issues like heart failure or lung patterns, and abdominal issues like organ enlargement, kidney stones, or even detects pregnancy by spotting fetal skeletons. Hundreds of clinics use Vetology’s AI for quick second opinions, especially valuable for after-hours cases or solo practitioners. The AI is continually validated and updated by board-certified veterinary radiologists to ensure it remains a reliable screening tool. Vetology’s approach shows how AI can be seamlessly integrated into daily workflow – images from existing X-ray machines are auto-sent to the cloud, and a report returns to the vet’s email or practice management system shortly after, with no PACS infrastructure needed.
IDEXX Laboratories – As a global leader in veterinary diagnostics and imaging hardware, IDEXX has been incorporating AI across its product lines. Notably, IDEXX’s SediVue Dx analyzer was one of the first in-clinic veterinary AI tools, using machine learning to automatically identify cells and crystals in urine samples (a tedious manual microscopic task). In diagnostic imaging, IDEXX offers digital radiography systems and a cloud PACS; they have patented AI methods to improve imaging workflows – for example, an algorithm to recognize the body part and species in an X-ray, then auto-rotate and sort images in a standardized order for review. This AI can prompt if an expected view is missing (e.g., a lateral view not taken) and check exposure quality, ensuring a more complete radiographic study. While IDEXX has not announced an autonomous radiographic diagnosis product yet, it has AI-enabled features in its software, such as automatic lesion measurement tools and an “image quality scoring” that gives clinics feedback to improve their radiograph technique. Moreover, IDEXX’s recent developments indicate future AI decision-support systems: the company’s cloud platform VetMedStat began offering AI-based image analysis notes in radiology reports in 2024, and its upcoming DecisionIQ service will use AI on combined patient data to flag subtle health pattern changes. For a company serving tens of thousands of vet clinics, integrating AI into its ecosystem can rapidly mainstream the technology.
Zoetis – The world’s largest animal health company (known for vaccines and pharmaceuticals) has also moved into AI diagnostics via its acquisition of startup Scopio Labs and launch of the Vetscan Imagyst platform. Imagyst is an in-clinic digital scanner and AI analysis system – essentially a microscope with AI brains. It started with AI Fecal (automated parasite egg detection in stool samples) and AI Blood Smear (identifying blood cell anomalies). As of 2025, Zoetis’ Imagyst offers seven AI applications in one device – including AI Dermatology (to find yeast or bacteria on skin cytology slides), AI Urine Sediment, and notably the newly released AI Masses module. AI Masses can screen needle aspirate slides from lymph nodes or skin tumors for potentially cancerous cells, giving an in-clinic result in minutes for cases that used to require a pathologist’s review. This development is significant – it effectively brings a mini pathology lab with AI into the veterinary clinic. According to Zoetis, the addition of AI Masses makes Vetscan Imagyst “the most capable veterinary AI analyzer in the world,” able to handle cytology, hematology, and parasitology tests on-site. All Imagyst AI tests are validated against expert evaluations and continue to involve expert telepathologist review for confirmation when needed, offering a blended AI+human diagnostic workflow. By leveraging deep learning on thousands of scanned
AI Enhanced veterinary diagnosis
images, Zoetis’s platform exemplifies how AI can shorten diagnostic turnaround (e.g., identifying a malignant lymphoma cell while the pet is still in the exam room) and reduce dependence on scarce specialists. This is especially impactful in regions where access to veterinary pathologists or radiologists is limited.
Mars Petcare (Antech Diagnostics) – Mars, which owns Antech Laboratories and major vet hospital chains, has been investing in data analytics and AI through its Science & Diagnostics division. One outcome is RenalTech, an AI-based predictive tool that mines 20+ years of Banfield Pet Hospital records to predict chronic kidney disease in cats up to 2 years before traditional diagnosis. While RenalTech is not an imaging tool (it uses lab test data), it demonstrates the power of large veterinary datasets for AI-driven insights. On the imaging front, Antech’s imaging arm (Sound™) provides digital radiography equipment and teleradiology services; they have begun integrating AI to assist radiologists. In 2024, Antech hosted webinars to educate vets on “Artificial Intelligence in Veterinary Radiology”, highlighting that AI can improve efficiency but also stressing the need for quality control and human oversight when using AI in practice. Antech’s RapidRead service (which provides radiology reports for vets) is likely to incorporate AI for preliminary reads, ensuring faster turnaround. Mars Petcare’s involvement suggests that corporate veterinary networks see AI as essential to scaling diagnostics, especially as their hospitals generate massive numbers of images that can be used to train better algorithms.
Other noteworthy entrants include SK Telecom’s foray with an AI radiology service for pets in South Korea, and boutique startups like Pixotope/PicoxIA in Europe offering automated pet X-ray analysis via cloud. Even open-source and academic projects are emerging, such as algorithms to automatically detect common findings in feline chest radiographs. In summary, the ecosystem of AI in veterinary imaging is vibrant and growing: startups are pushing the envelope on AI accuracy and speed, while established veterinary companies bring distribution, large datasets, and integration into workflow – a combination accelerating adoption globally.
Bridging the Gap: Veterinary vs. Human Medical Imaging AI
While progress is rapid, it’s important to recognize that AI in veterinary imaging is still in its early days compared to human medical radiology. The differences between the fields create both challenges and opportunities:
Data and Training: Modern AI (deep learning) thrives on large quantities of labeled data. Human radiology benefits from enormous image datasets – thousands of hospitals contributing millions of patient scans – enabling highly refined algorithms for, say, detecting lung cancer on CT or fractures on X-rays. Veterinary medicine, in contrast, faces a fragmented and smaller data pool. There are many species (dogs, cats, horses, etc.) and breeds with varying anatomies, and far fewer total imaging studies captured than in human medicine. It is telling that some of the vet AI tools focus on dogs and cats initially – these are the largest groups with more data available – and even then, algorithms may need to be species-specific (an AI trained on canine hip X-rays won’t automatically generalize to felines). This lack of vast data can impact accuracy and the range of conditions an AI can detect. However, as more clinics go digital and contribute images (often via cloud systems), datasets are growing. Collaborative initiatives, perhaps spearheaded by universities or large corporations, could create shared veterinary image repositories to fuel the next generation of AI models.
Regulatory Oversight and Validation: In human healthcare, AI diagnostic tools usually require regulatory approval (e.g., FDA clearance in the US or CE marking in Europe) and rigorous validation through clinical trials. This ensures a baseline of safety and efficacy before algorithms are used on patients. In veterinary medicine, there is currently no formal regulation or standardized validation for AI algorithms. Veterinary AI tools can enter the market directly, placing the onus on vets to judge their reliability. On one hand, this lack of regulatory hurdle allows faster innovation and deployment in vet practices. On the other hand, it raises concerns about quality – an unreliable AI could miss a serious condition or cause false alarms. The veterinary radiology community is aware of this gap and has begun publishing independent evaluations. For example, academic studies have compared AI radiograph interpretations to those of board-certified radiologists, often finding the AI to be surprisingly on par for certain findings, but weaker in others (e.g., AI might be very good at noticing lung patterns but might overlook subtleties that an experienced radiologist catches). The consensus is that AI should complement, not replace, expert judgment, and that vendors should work closely with veterinary radiologists to improve and validate algorithms. We may eventually see industry guidelines or even certifications for veterinary AI software to ensure minimum performance standards.
Species and Condition Diversity: Human radiology AI development tends to prioritize a relatively small number of common modalities and pathologies (e.g., chest X-ray for pneumonia, mammograms for breast cancer, etc.). In veterinary practice, the range of species and conditions is broader, from scanning a cow for early mastitis using thermal imaging to detecting a tumor in a pet snake via CT. This diversity means opportunities for AI startups to target niche problems that might be too small for human healthcare giants. For instance, an AI model could be trained specifically to detect canine hip dysplasia on pelvic X-rays and help breeders/vets screen for it early – a valuable tool in veterinary orthopedics. Or AI could assist in equine medicine by analyzing ultrasound videos of horse tendons to spot lesions. These specific applications have a high impact in their domain but require domain-specific data to build. OEMs that manufacture veterinary imaging equipment (X-ray, ultrasound machines, etc.) might integrate AI modules for certain high-value use cases (much as some human ultrasound machines now have AI presets for cardiac measurements). We are already seeing signs of this: for example, some veterinary dental X-ray software now uses AI to automatically chart teeth and detect dental lesions in pets, analogous to human dental AI software. Over time, the gap between human and veterinary imaging AI capabilities will narrow, especially as transfer learning (using knowledge from human models to apply to animal images) becomes more feasible.
Economics and Market Forces: The market for human radiology AI is huge (billions of dollars) which has attracted significant investment, whereas veterinary clinics operate on tighter budgets and the ROI of AI must be clear. However, the value proposition of AI in a vet clinic is compelling when it directly improves revenue or care: for example, a general practice vet can in-house more radiograph interpretations with AI decision support, rather than referring every X-ray to a specialist, thus offering faster answers to pet owners and saving referral costs. Early adopters (specialty hospitals, large vet chains) are demonstrating these advantages, which will drive broader adoption as costs come down. Cloud-based services with subscription models (as offered by SignalPET, Vetology, etc.) make AI affordable even for small clinics – typically these services charge per study or a flat monthly fee that is far less than hiring additional staff. Moreover, as pet owners become aware of AI (through marketing or veterinarians educating them), it can even be a differentiator: “AI-enhanced diagnostics” can assure owners that their pet is getting state-of-the-art care, much like hospitals tout advanced technology.
Collaborative Diagnostics: A key difference in veterinary medicine is that general practice veterinarians handle a much wider array of cases (versus human doctors who quickly refer to specialists). AI can act as a “multiplier” for the generalist vet, providing specialist-level input on demand. We see a future where a veterinarian, armed with AI tools, can confidently interpret an emergency radiograph or a tricky pathology slide at 2 AM, but also knows when to call in a specialist. In human medicine, AI triage systems are used in radiology departments to prioritize critical cases (e.g., flagging a probable pneumothorax on an X-ray so it’s read first). Similarly, in veterinary settings, AI could triage images – a finding of suspected GDV (gastric dilatation volvulus) on an abdominal radiograph could immediately alert the vet team to prep for surgery even before a radiologist confirms it. This kind of human-AI collaboration will become routine, improving outcomes for animals.
In short, veterinary imaging AI is catching up with human healthcare, but must contend with unique challenges of scale and scope. Addressing these gaps – through data sharing initiatives, establishing validation standards, and focusing on species-specific solutions – represents a huge opportunity. It is an opportunity not only for companies and startups but for the entire veterinary profession to elevate its diagnostic acumen.
Opportunities for Veterinary OEMs and Startups
The confluence of need and nascent technology in veterinary imaging presents fertile ground for innovation. Veterinary equipment manufacturers (OEMs) and agile startups alike can seize this moment:
Enhancing Imaging Hardware with AI: OEMs that build X-ray, ultrasound, CT, or MRI machines for vet use can embed AI for real-time assistance. For example, an ultrasound machine with AI could guide a user to the optimal view or automatically measure organ dimensions in an exam (features already appearing in high-end human ultrasound units). X-ray systems could have built-in AI that immediately analyzes each shot – providing a pop-up suggestion if a retake is needed (poor positioning) or highlighting a possible pathology for the vet to investigate further. By incorporating such AI features, OEMs add value to their devices and differentiate in a competitive market. It also encourages clinics to upgrade to new technology for better care. Companies like IDEXX and Sound (Antech) are already moving in this direction with their software suites, as evidenced by patent activity on automated image sorting and quality checks. We can expect future vet imaging machines to come “AI-ready” out of the box.
New AI Services and SaaS Models: Startups can offer Software-as-a-Service platforms that analyze images from any source. This is essentially what cloud AI teleradiology is – a vet uploads images and gets results. But beyond radiographs, consider services like an AI that a farm veterinarian can upload cattle thermography images to, for early mastitis detection across the herd, or an AI for aquaculture that analyzes fish images for disease signs. These targeted solutions could be delivered via web or smartphone apps. In India, for instance, a Chennai-based startup introduced a smartphone app for pet owners that uses AI to assess pets’ health indicators from photos – a sort of general wellness scan. Such approaches, if medically validated, could reduce the load on vets by handling initial screening and directing only the true issues to clinics (much like human telehealth apps do for people). Telemedicine for animals combined with AI is another frontier – e.g., an AI that listens to a dog’s cough via an app and analyzes the sound for patterns (cough AI exists in human pediatrics for pneumonia detection). Startups can fill these niche needs quickly, and if proven, scale them globally.
Addressing the Specialist Gap: There is a well-known shortage of veterinary radiologists and pathologists, particularly in countries like India, where the concept of veterinary specialists is still emerging. AI can democratize access to specialist-level interpretation. This opens opportunities for partnerships: A startup might develop AI for, say, ultrasound interpretation and partner with a network of general vets or chains to deploy it. Or an OEM like Mindray or GE, which sells a lot of vet ultrasound machines, might sponsor a startup to build an AI module for their devices, adding value to their customers. Startups focusing on single high-impact problems – e.g., early detection of certain zoonotic diseases via imaging in livestock – could find support from government or industry grants as well, since these have public health implications. The bottom line is that AI can extend the reach of scarce expertise to every vet clinic, and companies that achieve this will not only profit but also solve a critical bottleneck in animal healthcare delivery.
Integrating AI with Veterinary Practice Management: Beyond pure imaging, there is an opportunity to integrate AI into the workflow and decision-making process of clinics. For example, AI could analyze a clinic’s database to identify trends (perhaps flag that “8 out of 10 diabetic cat patients show radiographic signs of liver lipidosis; consider screening for it”). Practice management software could have AI-driven reminders or diagnostic checklists once an image and lab results are in – essentially an expert system guiding the vet through possibilities. These types of clinical decision support systems (CDSS) are standardizing in human hospitals; veterinary software firms could collaborate with AI developers to build analogous systems for veterinary use.
Education and Training: Another area is using AI as a training tool. Veterinary colleges could use AI applications to teach radiology to students – an interactive system where students interpret an X-ray and the AI provides feedback or points out missed findings, accelerating learning. Startups or academic spin-offs might create “AI tutors” for radiology, which could be very attractive in markets with new vet schools or fewer senior radiologists to mentor students. This not only creates future customers for the AI (as new vets will be familiar with the tool), but also improves the overall skill level in interpreting images.
It’s worth noting that as these opportunities are pursued, multidisciplinary collaboration will be key. Successful AI solutions will require veterinary domain experts working hand-in-hand with data scientists and software engineers. In my experience organizing AI conferences and leading tech businesses, I’ve seen the best outcomes when we break silos between technologists and practitioners. The veterinary industry must do the same – vets should be involved in AI development from day one to ensure the tools genuinely solve clinical pain points and are user-friendly in practice.
Conclusion: Towards an AI-Driven Future in Animal Healthcare
Artificial intelligence is set to become an indispensable part of veterinary radiology and diagnostic imaging, driving a paradigm shift in how we care for animals. What we are witnessing now – AI algorithms reading pet X-rays, analyzing lab images, predicting diseases – is likely just the beginning. As the technology matures, we can envision a future where AI-powered diagnostics are as routine as blood tests in veterinary clinics. This future is one of augmented veterinarians: professionals armed with AI tools that amplify their expertise, enabling them to deliver faster, more precise, and more personalized care to every animal, whether a beloved pet or a valuable livestock animal.
However, realizing this future requires continued innovation, validation, and education. There are open questions and valid concerns that the community must address: ensuring AI tools are accurate and unbiased across breeds and species, keeping data secure and private, training practitioners to interpret AI results correctly, and maintaining the humane touch in medicine. These discussions and developments will take center stage at forums dedicated to veterinary AI.
In that spirit, VOSD is proud to host the upcoming AI4Dogs 2025: 1st International AI + Veterinary Science Summit in September 2025. This event – the first of its kind in India – will convene veterinarians, AI researchers, industry leaders, and policymakers from around the world. Our objectives with AI4Dogs 2025 are to share knowledge, showcase emerging technologies, and collaboratively shape the vision for AI in animal healthcare. Key themes will include advancements in veterinary radiology AI (many highlighted in this paper), AI applications in surgery and critical care, the role of AI in preventive health and epidemiology, and how to bridge the gap between human and veterinary medicine through cross-learning. We’ll also tackle the challenges head-on: panels on ethics and regulation for veterinary AI, workshops on validating AI tools in clinical settings, and discussions on how to make AI accessible and affordable to shelters and rescue organizations, not just high-end hospitals.
My vision – forged from decades in technology and a passion for improving dogs’ lives – is that AI becomes a great equalizer in veterinary medicine. Just as VOSD has used technology to create a nationwide rescue helpline and the VOSD AI chatbot for instant pet care advice, we believe AI can ensure that every vet, whether in a metropolitan hospital or a rural clinic, can offer world-class diagnostic services. The end beneficiaries of this are the animals themselves. When a street dog with an unknown injury can get a quick AI-assisted X-ray screening that flags a hairline fracture, or when a cow on a farm has an emerging illness caught early by an AI monitor, we prevent suffering and save lives.
The year 2025 will likely be remembered as a breakout time for AI in veterinary science. By the time of the AI4Dogs Summit, we expect to celebrate not just the technological achievements, but also real stories of impact – cases where AI made the difference in a diagnosis or outcome. And we will chart a roadmap for the years ahead, identifying gaps (like those between human and vet AI capabilities) and forging partnerships to address them. In conclusion, the marriage of AI and veterinary radiology is a game-changer that aligns perfectly with our mission at VOSD: to bring the best of science and compassion together for the welfare of animals. The journey is just beginning, and through collaboration and innovation, the future of animal healthcare looks brighter than ever.