AI assistant for safer surgeries in vet clinics
A reputable U.S.-based veterinary clinic network offering comprehensive animal healthcare services across general surgery, preventive care, dental procedures, and emergency treatment.
USA
Veterinary
Since 2022Key achievements
| 90% | and more accuracy in anesthesia dosage calculations |
| 30% | reduction in surgical prep time |
| 24% | increase in vet team productivity |
Solution
As part of our veterinary software solutions development services, we created an assistant for safer surgeries as a central component of a comprehensive SaaS platform.
Integrated into a mobile app as well, our veterinary software solution uses real-time data and adaptive decision-making to help veterinarians make safer and faster decisions during surgeries and patient management – right at the point of care.
Anesthesia dosage optimization
The assistant, using machine learning (ML) technology, calculates anesthesia dosage using historical medical data, patient vitals, and medication profiles. It factors in unique patient traits (breed, age, previous surgeries, comorbidities) and adjusts dosages in real time as vitals change. This reduces dosage errors and adapts to patient responses mid-procedure.
Pre-op risk assessment & triage
Before surgery, the tool scans the patient’s history and current vitals to flag potential anesthesia risks like cardiac history or abnormal lab results and suggests additional tests if needed. It classifies surgical risk levels and provides confidence scores for proceeding.
Real-time vitals interpretation
During surgery, the ML-powered tool continuously monitors vital sign charts and identifies patterns that may indicate distress, such as potential hypoxia, arrhythmia, or anesthesia overdose. It alerts the vet team and recommends intervention steps.
Personalized protocol generator
Based on species, weight, prior medical history, and typical outcomes, the assistant creates personalized anesthetic protocols for each patient. These templates evolve over time as the system learns from historical success rates and updates best practices.
Dental and wellness insights
For dental procedures, the assistant interprets historical dental charts, predicts future risks (e.g., likelihood of periodontal disease), and offers preventive treatment recommendations based on breed and dietary history.
Conversational AI interface
An in-app chatbot allows the veterinary team to ask natural-language questions during procedures or case reviews, such as: ‘Show last 3 surgeries with similar vitals’ or ‘Show optimal anesthesia for an American Akita with low HGB.’ The assistant responds with clear, concise guidance backed by data.
Offline predictive support
Even without an internet connection, the platform provides surgical risk scores and dosage guidance using a condensed on-device model, ensuring uninterrupted use in field clinics or mobile operations.
Project scope
To build the smart vet assistant, available on both desktop and mobile, we took a phased approach that we always adhere to when building AI solutions for veterinary.
Discovery phase
- Analyzed practitioner workflows, patient data flows, and operational bottlenecks through audits and interviews.
- Mapped accuracy risks in anesthesia and inefficiencies in cross-team coordination into priority use cases.
Data science
- Defined AI applications across case management, anesthesia dosing, vitals monitoring, triage, and workflow optimization.
- Built lightweight models for mobile-side calculations and larger predictive/monitoring models trained on AWS SageMaker.
- Training data included vitals, species, medication protocols, and historical case outcomes.
Frontend & UX
- Embedded AI features into a mobile app for vets/nurses and two admin panels for managers and platform admins.
- Designed smart templates, suggestion widgets, and monitoring alerts to blend seamlessly into clinical routines.
Backend & cloud infrastructure
- Deployed as a cloud-native service on AWS using ECS Fargate for elastic scaling, RDS for database reliability, and CloudFront for fast global access.
- Blue-green deployments ensured safe, continuous updates.
- Staged rollout to high-usage clinics with feedback loops for iterative improvements in accuracy and operational efficiency.
Project screenshots
Team
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Project Manager x1
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AI/ML Engineer x2
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Front-end developer x1
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Back-end developer x1
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QA Engineer x1
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UX/UI Designer x1
Tools & Technologies
Insights
Clinical adoption depends on trust in recommendations
At first, some veterinarians treated the assistant as a second opinion rather than a primary decision tool. Over time, as the system consistently produced accurate dosage calculations and flagged potential risks, teams started relying on it more during surgical preparation.
Workflow improvements mattered as much as AI accuracy
The biggest efficiency gains did not come only from machine learning models. Bringing patient data, vitals, and lab results into a single interface reduced the time spent switching between systems and allowed clinicians to prepare for procedures faster.
Early versions generated too many alerts
During testing, the system sometimes flagged too many potential risks, which could distract surgeons during procedures. The models were adjusted to prioritize clinically relevant alerts and reduce unnecessary notifications.
Key takeaways
| Embed AI directly into existing workflows. |
 | Prioritize clear and actionable recommendations. |
 | Design for reliability in different environments. |
 | Balance automation with human oversight. |
From prep to post-op – make every step smarter with AI.
Contact us to learn how we can improve your veterinary services with our smart veterinary software solutions.
FAQ
AI systems are designed to support veterinary teams by analyzing patient data, monitoring vitals, and suggesting possible actions. However, the final medical decisions always remain with the veterinarian.
AI systems typically use patient medical records, vital signs, treatment protocols, medication data, and historical case outcomes. The quality and structure of this data strongly affect the accuracy of predictions.
Yes. AI assistants can be integrated into existing veterinary management systems, mobile apps, or clinical software through APIs and shared databases, allowing clinics to improve workflows without replacing their current platforms.