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AI co-pilot for veterinary medical records

The Aristek team developed an AI-powered solution to streamline veterinary medical record processing, enhancing efficiency and decision-making for veterinarians. The smart AI co-pilot functions as an add-on and can be easily integrated into various platforms, including any modern ai veterinary software platform.

Data science & AIVeterinary
Icon 1USA
Icon 2Veterinary
Icon 3Since 2024

Summary

This case study explains how an AI co-pilot was developed to help veterinarians review and analyze medical records faster. The AI veterinary software solution processes large volumes of structured and handwritten records, highlights important health indicators, and suggests relevant past cases. In this material, we describe the implementation process, the system architecture, and the results achieved on a veterinary telemedicine platform.

Key achievements

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40%

less time spent on medical records review

25%

increase in early detection of health issues

30%

reduction in diagnostic errors

Challenge

The client, the owner of a telemedicine platform, highlighted the growing challenge of managing the increasing vet staff workload. Many specialists were conducting virtual consultations after hours, evenings, or weekends, while others were squeezing them between in-person clinic appointments.

The platform allowed veterinarians to conduct follow-up, post-operative exams, and minor medical consultations remotely, reducing unnecessary clinic visits.

However, each telemedicine consultation required a thorough review of the pet’s medical history, which slowed down the process considerably.

As demand grew, this manual approach limited the platform’s ability to scale effectively, making it difficult to accommodate more pet owners and maximize potential revenue.

There were also challenges in processing veterinary medical records because of:

  • Large volumes of historical medical records in a variety of formats, including handwritten notes from different clinic departments.

  • Unstructured and inconsistent data, making it difficult to extract meaningful information and highlighting the need for AI for improving pet records accuracy.

  • The need to provide relevant information to animal owners without causing undue anxiety.

  • The need to accurately analyze health indicators and benchmarks that vary with conditions such as age and pre-existing diseases.

Solution

To support this process, the Aristek team proposed augmenting the telemedicine platform with an AI veterinary software solution. This allows veterinarians to make informed decisions faster, combining their professional expertise with AI-driven data analysis.

However, the true value lies in their ability to interpret the data effectively, which depends on their knowledge and experience. This expertise is further enhanced by other specialists’ knowledge-sharing, research in veterinary medicine, and the pet’s medical history.

To support this process, the Aristek team proposed augmenting the telemedicine platform with an AI-based solution. This allows veterinarians to make informed decisions faster, leveraging both their expertise and the power of AI.

  • Accurate diagnostic support

    AI provides deeper data-driven interpretations of diagnostic images, medical history, and test results, speeding up decision-making and improving accuracy.

  • Dynamic medical analysis

    The solution analyzes medical records in real time, adjusting for factors like age, pre-existing conditions, and current health status.

  • Identification of rare conditions

    AI helps spot rare conditions or correlations that may be challenging for even experienced specialists to detect immediately.

  • Fast case retrieval

    Veterinarians can quickly access relevant past cases and treatment plans, enabling them to identify similar symptoms and make faster, more accurate decisions.

  • Dynamic health monitoring

    AI helps to analyze patient trends continuously, flagging potential health risks based on historical data for proactive care.

Project scope

The main goal of the solution was to analyze all available medical cases to identify potential issues and determine proactive actions that could be taken.

Here’s how the team approached the development and integration process step by step:

  • Comprehensive data collection

    The team gathered diverse veterinary medical records, including handwritten notes, ensuring compliance with privacy standards. Data was stored securely in AWS S3, allowing easy access for processing and training.

  • Data cleaning & preprocessing

    Handwritten text was digitized using Optical Character Recognition (OCR) powered by AWS Textract. The data was then extensively cleaned and standardized through custom-built processing pipelines.

  • Custom AI model development

    A hybrid AI model using NLP and Hugging Face Transformers was developed for text analysis, with GPT-4o powering a complex multi-agent system coordinated by a supervisory agent for task delegation and communication.

  • Seamless platform integration

    The AI-powered tool was integrated into the telemedicine platform using AWS Lambda for serverless execution and AWS API Gateway for secure API communication. This ensured real-time processing of patient data, generating summaries on demand for faster decision-making.

  • Backend

    The backend was powered by Python-based microservices deployed on AWS Lambda for serverless execution. AI processing pipelines used AWS Glue for ETL tasks and OpenSearch for efficient case retrieval. Secure and scalable communication between the platform and the AI engine was established using AWS API Gateway and IAM. This ensured real-time data processing and robust system performance under growing consultation volumes.

  • Frontend

    The AI assistant and dynamic summaries were integrated into the existing React-based interface. Our team ensured a seamless user experience where veterinarians could interact with AI-generated case insights, view flagged risks, and retrieve similar historical cases – all from within the same consultation screen. The design emphasized intuitive UX with a minimal learning curve.

How it works

Let’s say a registered physician on the telemedicine platform received a request from a pet owner about their cat, which had elevated statins in its urine. The veterinarian decided to consult the AI co-pilot for insights into the pet’s medical history.

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    The vet input the request into the telemedicine platform, specifying the cat’s condition. The AI system was immediately called upon to assess and offer assistance.

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    The AI searched through the cat’s medical history. Upon reviewing the pre-parsed and indexed content, the AI identified that this wasn’t the first instance of elevated statins and highlighted recurring patterns in previous cases.

  • 3

    Based on the historical data, the AI detected that elevated statins could indicate an emerging trend, pointing to diabetes. It prompted the veterinarian to consider steps for further diagnostics.

  • 4

    The AI pulled up similar cases of cats with elevated statins, presenting how other veterinarians diagnosed and treated those conditions. It analyzed data to assess consistency and offered insights into the condition’s progression.

  • 5

    By examining long-term trends in the cat’s medical history, the AI predicted potential future risks or complications and suggested tests for blood glucose levels and an oral glucose tolerance test.

  • 6

    The veterinarian reviewed the AI-generated insights and decided to schedule a follow-up test. The system also offered recommendations for the most appropriate specialists and appointment slots.

Screenshots

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Disclaimer

The final decision regarding the pet’s diagnosis and treatment remains in the hands of the veterinarian, with the AI acting as an additional tool to enhance accuracy and decision-making efficiency.

The AI summarizer does not replace the veterinarian but is designed to assist by:

  • Aggregating similar cases and identifying patterns.

  • Analyzing previous diagnoses and treatments by other doctors.

  • Analyzing long-term health trends in the pet’s medical records.

  • Offering contextual recommendations based on data, not subjective judgment.

Team

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    AI/ML Engineer x1

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    Front-end developer x1

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    Back-end developer x1

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    Subject matter expert x1

Tools & technologies

GPT-4
OpenAI
PostgreSQL
TensorFlow
spaCy
AWS Lambda
FastAPI

Project results

The Aristek team leveraged AI to enhance veterinary telemedicine by developing a smart AI assistant that processes pet medical histories, diagnostic images, and treatment plans. This AI veterinary software solution analyzes large datasets to provide veterinarians with quick and reliable insights, supporting faster and more accurate decisions.

  • Time savings for staff

    The AI-powered tool saved up to 40% of the time veterinarians spent reviewing medical records by instantly extracting key information, identifying relevant patterns, and summarizing past cases.

  • Early diagnosis improvement

    The AI solution enabled a 25% increase in the earlier detection of health issues in pets by flagging trends and symptoms in their medical history. This allowed veterinarians to intervene sooner and improve treatment outcomes.

  • 30% fewer diagnostic errors

    The AI co-pilot reduced diagnostic errors by 30% by cross-referencing similar cases and leveraging expert insights. Veterinarians arrived at the correct diagnosis more quickly and accurately, ensuring better patient outcomes.

  • 95% accuracy in parsing past records

    The AI achieved over 95% accuracy in digitizing and parsing handwritten medical records, ensuring that even handwritten notes from various clinics could be seamlessly integrated and processed. This demonstrates how AI for improving pet records accuracy can significantly improve the usability of historical veterinary data.

  • Flexible integration across industries

    The AI assistant integrates seamlessly into existing systems without requiring full redesigns. Its adaptable architecture supports use cases in healthcare, finance, education, retail, and beyond.

Optimize your veterinary practice with AI-powered diagnostics

Insights

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    Most of the real value came from structuring messy historical data

    At the start, the team expected the main challenge to be building the AI model. In practice, the biggest effort went into cleaning and standardizing years of inconsistent medical notes from different clinics. Once the data became searchable and structured, the AI started producing much more useful insights.

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    Veterinarians trusted the system more when it showed evidence

    Early versions generated summaries and recommendations, but adoption improved when the system began showing links to similar past cases and specific parts of medical records. Seeing the reasoning behind suggestions helped veterinarians feel more comfortable using AI during consultations.

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    Workflow placement mattered as much as model quality

    The AI tool worked best when embedded directly in the consultation screen rather than as a separate module. When vets could see summaries and flagged risks without leaving their workflow, usage increased and time savings became noticeable.

Key takeaways

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Prepare medical data before training AI models
Cleaning and standardizing historical records greatly improves the quality of AI analysis.

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Provide traceable insights, not only recommendations
Showing similar cases or record excerpts builds trust in AI-assisted decisions.

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Embed AI directly into existing workflows
Tools that require switching between systems are less likely to be adopted.

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Design AI as a support tool, not a replacement for specialists
Keeping the final decision with the veterinarian encourages practical adoption.

FAQ

Yes. In this project, handwritten notes were digitized using OCR technology and then processed by NLP models, allowing historical records from different clinics to be analyzed together.

No. The AI assistant provides summaries, pattern detection, and suggestions based on historical data. The veterinarian still reviews the information and makes the final medical decision.

Yes. The system in this case study was built as an add-on that can connect to existing platforms through APIs, allowing organizations to add AI functionality without rebuilding their software.

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