Veterinary medicine is undergoing a quiet but profound transformation. While the world debates about ChatGPT and Claude, a different revolution is underway in the most advanced veterinary clinics: specialized language models that assist veterinarians in real time during clinical consultations. Not generalist models hosted in the cloud, but local LLMs trained on real veterinary clinical cases that work without an internet connection and protect each patient’s privacy. That is Howl Vision.
This article explores in depth how Howl Vision is redefining the concept of veterinary PMS (Practice Management System), why local LLMs are superior to cloud solutions for clinical diagnosis, and how specialization by veterinary discipline makes the difference between a useful tool and an indispensable one.
The Next-Generation Veterinary PMS: Beyond Administrative Management
Traditional veterinary management systems were built to solve administrative problems: appointment management, billing, patient records, pharmaceutical inventory control. They are, in essence, ERPs adapted to the veterinary sector. They fulfill their function, but they don’t help the veterinarian where it matters most: at the moment of diagnosis.
Howl Vision breaks with this paradigm. It is not a PMS with AI added as a marketing afterthought. It is a system designed from the ground up with diagnostic assistance as its central pillar, where administrative management is a natural consequence of the clinical workflow, not the other way around.
The Clinical Workflow as the Central Axis
When a veterinarian opens a patient file in Howl Vision, they don’t see pending invoices first. They see the complete clinical history, diagnostic images, laboratory results, and — most importantly — the specialized AI system’s suggestions based on all that aggregated information. The system analyzes patterns that the human eye might miss: correlations between symptoms separated by months, trends in analytical values, similarities with previously resolved cases.
Research on the impact of AI on veterinary medicine demonstrates that diagnostic assistance systems reduce diagnosis time by 35% and improve accuracy by 22% — figures that translate directly into better animal care and greater clinic efficiency.
Local LLMs vs Cloud: Why Local Execution Is Not Optional
The first question that arises with any clinical AI system is: why not simply use the GPT-4 or Claude API? The answer involves three critical factors that make local execution not a preference, but a technical and legal necessity.
Clinical Data Privacy
Veterinary clinical data includes sensitive information: owner details, detailed medical histories, diagnostic images, and in the European context, they are subject to GDPR. Sending this information to external servers — even to reputable providers like OpenAI or Anthropic — introduces regulatory compliance risks that many clinics cannot assume.
With local LLMs, data never leaves the clinic’s infrastructure. The model runs on the clinic’s own server or on dedicated hardware provided by Howl Vision. This enormously simplifies regulatory compliance and eliminates dependence on third parties for sensitive data processing.
Latency: Milliseconds vs Seconds
In a veterinary consultation, time is critical. A veterinarian cannot wait 5-10 seconds for a cloud API to process their query while the patient is on the examination table. Howl Vision’s local LLMs offer response times of 200-500 milliseconds, compared to the typical 2-8 seconds from cloud APIs — and that’s under ideal connectivity conditions.
Reliability: No Internet Dependency
Rural veterinary clinics, mobile care units, and veterinary hospitals during peak activity cannot depend on a stable internet connection. A local model works always, regardless of network status. This reliability is not a luxury: it is a fundamental requirement for any tool that integrates into the daily clinical workflow.
Research published in Nature Digital Medicine on AI in clinical diagnosis supports this trend toward local execution, emphasizing that clinical AI systems must prioritize availability and privacy over raw model power.
Specialization by Discipline: One Model for Each Clinical Area
This is where Howl Vision’s most significant innovation lies. Instead of using a single generalist model for all clinical queries, the system deploys models specialized by veterinary discipline. Each model has been trained on thousands of real clinical cases from its specialty, validated by expert veterinarians.
Veterinary Dermatology
The dermatology module combines image analysis with natural language processing. The veterinarian can photograph a skin lesion and describe additional symptoms (pruritus, temporal evolution, previous treatments), and the model generates a differential diagnosis ranked by probability. Trained on over 15,000 dermatological cases, the model recognizes patterns in atopic dermatitis, pyoderma, dermatophytosis, cutaneous neoplasms, and autoimmune diseases with accuracy exceeding 91% in the top-3 diagnosis.
Traumatology and Orthopedics
The traumatology module analyzes radiographs of limbs, spine, and pelvis, identifying fractures, luxations, dysplasias, and degenerative joint diseases. But it goes beyond detection: it suggests evidence-based treatment protocols, including surgical and conservative options, with prognosis estimates based on similar previously resolved cases. Computer vision applied to veterinary medicine is one of the fields where AI demonstrates the greatest immediate impact.
Veterinary Oncology
The oncology module assists in neoplasm classification, tumor staging, and therapeutic planning. It integrates data from cytologies, histopathologies, and diagnostic images to generate a comprehensive view of the oncological case. One of its greatest contributions is the early detection of suspicious patterns in routine blood work — subtle elevations in hepatic or renal parameters that, correlated with the patient’s age, breed, and medical history, could indicate the need for additional testing.
Veterinary Ophthalmology
The ophthalmology module analyzes fundus images, anterior segment photographs, and results from tests such as the Schirmer test or tonometry. Specialized in detecting cataracts, glaucoma, corneal ulcers, uveitis, and retinopathies, this module is especially valuable in breeds with genetic predisposition to ocular pathologies.
Internal Medicine
The internal medicine module is the broadest and most complex. It analyzes results from complete blood counts, biochemistry panels, urinalysis, abdominal and thoracic ultrasounds, and correlates them with reported symptomatology. It is capable of identifying patterns consistent with endocrine diseases (Cushing’s, Addison’s, hypothyroidism), chronic kidney disease, hepatopathies, and early-stage cardiac conditions.
How the Veterinarian Interacts with the System During Consultations
The most advanced technology is useless if it disrupts the clinical workflow. Howl Vision has been designed with user experience as the absolute priority, working with practicing veterinarians throughout the entire development process.
Contextual Interface
The system does not require the veterinarian to «consult the AI» as a separate step. Assistance is integrated contextually into every screen of the clinical workflow. When recording symptoms, the system suggests additional relevant questions. When viewing laboratory results, it highlights anomalous values and suggests correlations. When reviewing images, it offers automatic annotations.
Natural Language
The veterinarian can interact with the system using natural language, dictating clinical notes that the system processes in real time. «The patient presents lameness in the right hind limb, pain on palpation of the knee, positive drawer test» — and the system automatically suggests «Cranial cruciate ligament rupture» with a 94% confidence level, along with confirmatory diagnostic protocols and therapeutic options.
Transparency in Suggestions
Each system suggestion includes its clinical justification: what data supports it, what similar cases were found in the knowledge base, and what the confidence level is. The veterinarian always retains the final decision — the system assists, never replaces.
Specialized Models vs Generalist Models: The Critical Difference
Why not simply use GPT-4 or Claude for veterinary diagnostic assistance? The answer is nuanced but clear.
Depth vs Breadth
Generalist models like GPT-4 have broad but superficial knowledge of veterinary medicine. They can answer general questions correctly, but they fail on the clinical nuances that make the difference between a good diagnosis and a mediocre one. A model specialized in veterinary dermatology knows the subtle differences between canine atopic dermatitis and an adverse food reaction — differences that a generalist model might not capture.
Training Data
Generalist models are trained on internet data, where veterinary information is scarce, outdated, and frequently incorrect. Howl Vision’s models are trained on real clinical cases, validated by specialists, with confirmed outcomes and patient follow-up. The quality of training data determines the quality of the resulting model.
Hallucinations in Clinical Context
A generalist model’s hallucinations in a conversational context are annoying. In a clinical context, they can be dangerous. Howl Vision’s specialized models exhibit hallucination rates 87% lower than generalist models in the veterinary domain, according to internal evaluations with expert veterinarian panels.
The American Veterinary Medical Association (AVMA) has published guidelines on AI use in veterinary practice that emphasize the need for clinically validated models — exactly the approach Howl Vision adopts.
The Technical Ecosystem: How It Works Under the Hood
Howl Vision deploys its specialized models on a technical architecture designed to maximize performance and minimize hardware requirements.
Quantized Models
Models are distributed in quantized format (GGUF/GPTQ), enabling execution on accessible hardware. A consumer GPU with 12-16 GB of VRAM is sufficient to run the most complex model in the catalog. For clinics without a dedicated GPU, Howl Vision offers a dedicated edge device that connects to the clinic’s local network.
Incremental Updates
Models are periodically updated with new clinical cases through incremental fine-tuning. Updates are downloaded as deltas — only the modified weights — minimizing the required bandwidth. The clinic can contribute its own (anonymized) cases to the training pool, improving the model for the entire community.
Interoperability
Howl Vision integrates with diagnostic imaging equipment (digital X-rays, ultrasound machines), laboratory analyzers, and existing management systems through standard APIs and HL7/FHIR protocols adapted to the veterinary context.
The Future: Predictive Diagnosis in Veterinary Medicine
The next phase of Howl Vision, currently under development, is predictive diagnosis. Using each patient’s accumulated history, the specialized models will be able to identify future risks before they manifest clinically. A dog of a breed predisposed to hip dysplasia with certain growth patterns could receive preventive intervention months before the first symptoms appear.
Studies available on PubMed on veterinary AI confirm that predictive medicine is the next great leap in animal care, and specialized LLMs are the tool that makes it possible.
Howl Vision is currently in the pilot clinic deployment phase. For more information about the project and its capabilities, visit the product page or check out our research on AI in veterinary medicine.
