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Artificial Intelligence

Learn how AI can transform your veterinary business. Explore educational content, digital tools, and expert insights to integrate cutting-edge technology into your operations and stay ahead of the curve.

A note about graphics, and an introduction:

Many of the graphics on this site are AI-generated, which you may have guessed by the magnetic stethoscope on the back of the vet's neck above. But I loved this video otherwise, and I couldn't get Sora to give up the stethoscope, so here it is.

 

While stethoscopes are a struggle now, remember that a year ago fingers were iffy (at best). Look again at this video and see how far they have come!

I'm glad you've joined me on the journey of learning about AI. It's developing fast, and I wholeheartedly believe that we need to face this new chapter of vet medicine head-on, or we'll be left behind.

 

I started my AI journey in complete amazement with little regard to what I saw as the negative consequences being echoed by many in our field - that it is going to steal our jobs.

My viewpoint has completely changed now. I still think AI is amazing, but I understand far better the issues at hand - they are not what I expected, and they are certainly not what are being commonly discussed in our field.

Is AI going to steal your job? Maybe...if you completely ignore it. But you're here, so that's probably not you.

But there are far more serious consequences of not understanding AI, but using AI-based technology anyway on our patients. I intend to dive into those issues on this site as I add more content.

AI is an amazing, game-changing tool, but it needs to be used responsibly, especially when lives are at risk. Understanding its use takes education, just like any other medical tool in our arsenal.

Continue on to start learning about Artificial Intelligence!

Artificial Intelligence in Veterinary Medicine

Neurotechnology

How do A.I. models work?

Traditional Library

A.I. Publications in Vet Med

Doctor analyzing brain scans

One Health A.I. Publications

Earth with Data Connections

A.I. Resources
 

How Do Artificial Intelligence Models Work?

1

Problem Selection

What it entails: This stage involves identifying and clearly defining the problem that the AI model will solve, including specifying the type of AI needed and the desired outcomes.


Why it's crucial: Proper problem selection ensures the AI development efforts are aligned with business needs and objectives, setting a clear goal that guides the entire project.

2

Data Preparation

What it entails: Data preparation includes collecting, cleaning, and preprocessing data to remove errors, handle missing values, and potentially augmenting data to improve model training.


Why it's crucial: The quality and appropriateness of data directly impact the model’s ability to learn and make accurate predictions, making this step foundational for model performance.

3

Model Training

What it entails: This involves selecting a suitable model architecture and using the prepared data to train the model, adjusting its parameters to minimize error and maximize prediction accuracy.


Why it's crucial: Effective model training is essential for developing a robust model that can accurately interpret data and make predictions, directly influencing the model's effectiveness in real-world applications.

4

Model Validation

What it entails: After training, the model is tested against a separate validation dataset to evaluate its performance and ensure it generalizes well to new data.


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.

5

Model Deployment

What it entails: Deploying the model involves integrating it into the existing production environment where it will receive and process real-world data to perform its functions.


Why it's crucial: Successful deployment makes the model accessible to users and applications, turning it from a concept into a functional tool that adds real value.

6

Post-Deployment

What it entails: This stage includes monitoring the model’s performance in the production environment, updating it as necessary to handle changes in data or requirements, and potentially retraining it with new data.


Why it's crucial: Continuous monitoring and maintenance ensure the model remains effective and relevant over time, addressing drift in data patterns and evolving requirements which are common in dynamic environments.

Traditional Library

Peer-Reviewed A.I. in Vet Med

> Not all A.I. is clinic-worthy. <

I've searched far and wide to find all of the peer-reviewed artificial intelligence papers in veterinary medicine.

Traditional Library

One Health

Artificial Intelligence Publications from Human Medicine

Exploring AI advancements from human health studies with potential applications in veterinary medicine, including zoonotic diseases and shared health challenges under the One Health framework. These articles highlight innovations that bridge human and animal health for better cross-species outcomes.

Love for animals

More Resources on Artificial Intelligence

This field is quickly evolving, so staying educated is essential! Click on the resources below to continue your journey.

artificial intelligence

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Once posts are published, you’ll see them here.
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