How Veterinary AI Models Work: A Practical Guide to Evaluating AI in Vet Med

AI is rapidly expanding across veterinary medicine, powering everything from diagnostics and imaging to herd health and client communication.

But with so many tools emerging, how do you know which ones are clinically ready and which are still experimental?

Whether you're reviewing research papers, evaluating commercial software, or planning your own AI project, understanding how these models are developed gives you the insight to ask sharper questions and make smarter decisions.

This guide breaks down the six key stages of AI model development, helping you recognize where a model is in its lifecycle, so you can distinguish between promising concepts and proven, real-world solutions – and more importantly, which models should raise a red flag.

1. Problem Selection: The Foundation of Practical AI

What happens at this stage

Researchers identify and clearly define the specific veterinary problem the AI model will solve. They determine what kind of data is needed, how success will be measured, and whether AI is even the right tool for the job.

Why it's crucial

A good AI model starts with a well-defined problem. If the goal is vague or irrelevant, everything that follows (training, validation, deployment) risks being flawed or meaningless. Misaligned objectives are often the hidden reason a model doesn’t translate into real-world use.

Think of this like a misdiagnosis. If you’re solving the wrong problem, it doesn’t matter how smart your solution is.

What to look for in publications

Papers at this stage will often focus on literature reviews, gap analyses, and early frameworks for applying AI to a specific veterinary use case. These papers may be conceptual or theoretical; they usually don’t include a working model yet.

Want to get technical? 

Look for the following phrases and measurements:

• Literature reviews identifying gaps in current diagnostic or predictive methods

• Problem framing using domain-specific knowledge (e.g., “predicting calving difficulty based on tail movement”)

• Selection of supervised vs. unsupervised learning

• Discussion of input/output structure (“given X, predict Y”)

• Initial metrics or business outcomes proposed (e.g., sensitivity target of 90%)

Questions to ask when reading

• Is the problem clearly defined and relevant to vet med?

• Are the outcomes measurable and actionable?

• Does the paper explain why AI is the right tool here?

• Is there evidence of collaboration between clinicians and data scientists?

Research example 

A paper proposing the use of computer vision to detect subtle changes in gait for early lameness detection in dairy cattle. No model is built, but the authors review existing tools, identify a clinical need, and define performance goals for future model development.

How to Understand Red Flags: Problem Selection

Conclusion 

If you can't clearly state the problem the AI is solving in one sentence, the researchers probably couldn’t either.

2. Data Preparation: The Critical Building Blocks

There was a phrase that somehow I didn’t learn until business school: Garbage in, garbage out – and that fully applies to AI model data. If you don’t quality-control the data you put into your model, your entire model is going to be garbage.

What happens at this stage

This is where researchers gather the raw data, clean it up, label it, and shape it into a format that a machine learning model can actually learn from. Think of this as prepping the ingredients before you ever start cooking.

Why it's crucial 

AI is only as good as the data it’s trained on. If your input data is biased, messy, or incomplete, your model will be too. And most AI projects fail - not during modeling - but because of poor data prep that occurred during modeling.

There was a phrase that somehow I didn’t learn until business school: Garbage in, garbage out – and that fully applies to AI model data. If you don’t quality-control the data you put into your model, your entire model is going to be garbage.

What to look for in publications

Papers focused on this stage often describe data sources, cleaning processes, and how cases were selected or excluded. They might not build the final model yet, but they’re laying the groundwork by organizing the data pipeline.

Want to get technical? 

Look for the following phrases and measurements:

• Source of the data: Was it clinic records? Wearable sensors? Imaging archives? How many sources were used, and how varied were those populations?

• Data labeling protocols: Was a board-certified specialist involved? Were multiple reviewers used?

• Cleaning techniques: Were outliers removed? Were missing values substituted or excluded?

• Data splitting: Was there a training set, validation set, and test set?

• Augmentation methods: Did they simulate more data using rotations, crops, or other methods? Did they find more sources to widen the variety of their data?

• Annotation tools: Especially important for imaging (e.g., bounding boxes or pixel-level segmentation)

Questions to ask when reading:

• Is the dataset large enough and diverse enough to support the goal?

• Are inclusion and exclusion criteria clearly stated?

• How were errors, duplicates, or inconsistencies handled?

• Does the data reflect the range of cases you'd see in real-world vet med?

• How are edge cases represented (e.g., rare breeds, unusual presentations)?

Research example

A study describing the creation of a 15,000-image dataset of canine thoracic radiographs from multiple clinics. The paper outlines labeling by two board-certified radiologists, describes how they handled poor image quality, and details their process for splitting data into training and test sets.

How to Understand Red Flags: Data Preparation

Conclusion

If you wouldn’t use that dataset to make a medical decision, don’t trust an AI model trained on it.

3. Model Training: Teaching the Machine What to “See”

What happens at this stage

This is where the AI learns. Researchers feed the cleaned, labeled data into a selected algorithm (like a neural network) and tweak its settings until it starts making predictions. It’s not yet tested in the real world: this is “school,” not “practice.”

Why it's crucial

This step defines the model’s brain. Done well, the AI learns true patterns reflective of

real-life data. Done poorly, it memorizes unique aspects of the training data, but falls apart when faced with anything new. An important concept here is overfitting, which is when a model is trained so well on its training data that it can’t extend that knowledge to anything beyond that set, called generalizability.

What to look for in publications

Training-stage papers often focus on how the model was built, why they chose that architecture, and how they adjusted it during training. These papers tend to feature performance metrics. But be careful: these results often reflect only what the model saw in training, not in the real world.

Want to get technical? 

Look for the following phrases and measurements:

• Hyperparameters: learning rate, batch size, number of epochs

• Training loss & training accuracy (not to be confused with validation accuracy)

• Overfitting/underfitting indicators: performance drops or flatlines

• Augmentation: techniques to prevent overfitting by modifying inputs

Want to get crazy technical?

• Model architecture: A lot of technical info here if you really want to know

• Optimization methods: Review article here

Questions to ask when reading:

• Why was this particular model architecture chosen?

• Were training metrics too good to be true (e.g., 99% accuracy)? There might be overfitting happening.

• Was any effort made to prevent overfitting or underfitting?

• Did they compare different training setups or just one?

• How reproducible is the training process? This is a really important concept in AI, but also the scientific method in general. If it’s not reproducible, that’s a problem.

Research example

A study training three deep learning models to identify ringworm in pet photos. The researchers describe which model worked best, how long training took, and how they tuned parameters to avoid overfitting, while acknowledging that training accuracy reached 95%, but generalizability wasn’t yet proven.

How to Understand Red Flags: Training

Conclusion

Training is where models get smart (or overconfident!).

4. Model Validation: The First Real Test

This step separates memorization from true understanding. A model might work amazingly on its training data, but validation is where you find out if it can actually handle new situations.

What happens at this stage

Now the model is graded on data it hasn’t seen before, and many models fail to

extrapolate their learning outside of their training data. Here, researchers feed it a new set of examples called validation data to see how well it performs when it’s not just running through the motions of what it was trained on.

Why it's crucial

Validation checks for overfitting and underfitting, ensuring the model performs reliably on unseen data, which is critical for trust and usability in practical scenarios.

This step separates memorization from true understanding. A model might work amazingly on its training data, but validation is where you find out if it can actually handle new situations.

What to look for in publications

Validation-stage papers present performance metrics on unseen data—this is where results start to matter more. You’ll often see confusion matrices, ROC curves, and comparison charts. Be sure to clarify that data has come from new data, not the same training data as you read these.

Want to get technical? 

Look for the following phrases and measurements:

• Validation accuracy (on a separate dataset)

• Precision, Recall, F1-Score

• ROC-AUC, confusion matrix, and other statistical metrics

• Cross-validation methods

• Loss curves that compare training vs validation performance

• Generalization performance is discussed clearly

Questions to ask when reading:

• Did they use a separate validation dataset?

• Are metrics reported only on training data? (This should be a red flag.)

• How does the model handle edge cases or outliers?

• Were results replicated across different sites or datasets?

• Did they mention bias in results (e.g., works better on one species, breed, or setting)?

Research example

A study validating a deep learning tool for detecting feline heart disease. It uses 2,000 new ultrasound images from clinics not involved in training. It compared the model’s predictions to those of board-certified cardiologists, reporting a ROC-AUC of 0.92, and discusses areas where it still struggled, such as uncommon structural abnormalities.

How to Understand Red Flags: Validation

Conclusion

Validation isn’t optional: it’s the difference between a theory and a reliable tool.

5. Model Deployment: Real-World Implementation

What happens at this stage

Now it gets really real. The validated model leaves the lab and enters the field and is now used by real veterinary professionals, on real data, in real clinical environments. End-users integrate the AI into workflows, mobile apps, equipment, or software platforms.

Why it's crucial

A model might perform beautifully in a controlled setting, but that doesn’t guarantee it can handle the messiness of real-life cases. I mean, have you seen vet med? Deployment exposes all the unexpected variables, such as poor-quality inputs, time constraints, and user error, that AI must navigate to be effective in a real-world environment.

What to look for in publications

Look for evidence that the model has been used outside of research environments, such as in a clinical pilot program, as a commercial tool, or as part of routine decision-making. These papers should describe the performance after deployment, challenges encountered during implementation, and practical results.

Want to get technical? 

Look for the following phrases and measurements:

• Real-world performance metrics (accuracy, precision, recall) on new, live data

• Field-tested integration: mobile apps, clinic software, wearable tech

• Time-to-decision or workflow impact measurements

• End-user feedback or adoption rates

• Evidence of real-world error patterns and adaptations

• Mentions of unexpected implementation challenges or retraining needs

Questions to ask when reading:

• Has the model been tested in actual veterinary environments?

• What adjustments were needed during real-world use?

• How did the results compare to validation metrics?

• Was the model used by multiple people and locations to test scalability?

• Did users understand and trust the outputs?

Research example

A study describing the deployment of an AI diagnostic tool in five companion animal practices. It tracked user engagement, workflow changes, and diagnostic accuracy over a three-month period. The researchers noted that accuracy dropped slightly compared to validation, prompting a model adjustment.

How to Understand Red Flags: Deployment

Conclusion

If it can’t prove its reliability in the real world, it doesn’t belong in your workflow.

6. Post-Deployment: Ongoing Evaluation and Real-World Reality Checks

This stage reveals the cracks. That "confident" AI diagnosis on a poorly positioned X-ray? That’s not just a one-off: it might signal a deeper training flaw. The most dangerous models are the ones that are wrong, but sound sure of themselves.

What happens at this stage

The model is now live, running in real-world veterinary settings and producing real outputs. Researchers (and users) must monitor its performance over time, identify issues that didn’t appear during testing, and make updates or retrain the model with new data as needed.

Why it's crucial

This is where things get real. Just because a model performed well in validation

doesn’t guarantee it will hold up in the messiness of everyday clinical use. Data patterns change, new “edge” cases appear, and the model can "drift" away from its original accuracy. Without ongoing monitoring, performance quietly deteriorates, and most users won’t know until it fails them.

This stage reveals the cracks. That "confident" AI diagnosis on a poorly positioned X-ray? That’s not just a one-off: it might signal a deeper training flaw. The most dangerous models are the ones that are wrong, but sound sure of themselves.

What to look for in publications

Look for long-term results, not just a snapshot in time. Strong studies will share multi-month or multi-year performance data, flag model drift, and explain how they responded to these changes in data. Bonus points if they disclose when the model didn't work as expected.

Want to get technical?

Look for the following phrases and measurements:

• Performance decay curves

• Drift detection metrics

• Precision/recall over time

• Retraining protocols

• Feedback loop structures (if any)

Questions to ask when reading:

• How has the model performed in real-world use over time?

• Were any unexpected issues uncovered after deployment?

• What retraining or adjustments were made, and why?

• Does the system account for evolving data inputs (e.g., new diseases, equipment, protocols)?

• What safety checks are in place for incorrect outputs?

Research example

A follow-up study examining two years of post-deployment data from an AI tool used to support antimicrobial stewardship in veterinary hospitals. The study tracked evolving accuracy rates, retraining cycles, and practical limitations identified during clinical use.

How to Understand Red Flags: Post-Deployment

Conclusion

A model that isn’t monitored will fail you - and you won’t know until it does.

Beyond the Hype: Turning Insight Into Action

Understanding how AI models are built — and where they break — gives you more than just tech literacy. It gives you power. The power to ask better questions, push back on weak claims, and spot real innovation before it becomes mainstream.

Here’s what to remember:

• Early-stage research can sound impressive but often lacks practical utility. Be cautious.

• “High accuracy” means nothing without context. Ask how, where, and on what it was tested.

• Validation isn’t the finish line. Deployment is. And post-deployment is where the truth comes out.

• Transparency about limitations is a sign of scientific integrity, not weakness. You should weigh ethically implemented models higher than those without transparency.

Veterinary AI is evolving fast. However, with an understanding of how these models work, you can make informed, strategic, and ethical choices for yourself, your business, your employees, and your patients.

Want to go deeper into veterinary AI?

Explore practical use cases, real-world applications, and my latest insights on The Vet AI Hub, or check out custom AI tools and services (coming soon!)