Foundation Models and the Future of Veterinary AI

In March 2023, a language model was asked to pass the veterinary licensing exam. Not a specialized veterinary AI — just a general-purpose system trained on internet text. It passed, not spectacularly, but convincingly. This wasn't the point of building the model. It was just something it could do.

This example illustrates the strange new reality of foundation models — massive AI systems trained on broad data that acquire unexpected capabilities. They're reshaping assumptions about what AI can do, how quickly it can be deployed, and what the future holds. Understanding them is essential for anyone thinking about AI's trajectory in veterinary medicine.

## What Are Foundation Models?

Traditional AI development follows a simple paradigm: collect data relevant to your task, train a model for that task, deploy it for that purpose. Want an AI to detect heart disease on radiographs? Collect labeled radiographs, train a classifier, deploy to clinics. Each application requires its own data collection and training effort.

Foundation models invert this paradigm. Instead of training narrow models for specific tasks, researchers train massive models on enormous amounts of general data. These models learn broad representations of language, vision, or other domains. Then, with minimal additional training, they can be adapted to countless specific tasks.

The most prominent foundation models are large language models (LLMs) like GPT-4 and Claude. They're trained on hundreds of billions of words from the internet, books, articles, code, and other text sources. Through this training, they acquire remarkable capabilities: generating coherent text, answering questions, summarizing documents, writing code, and much more.

Vision foundation models like CLIP learn to understand images and their relationships to language. Multimodal models combine vision and language capabilities. New architectures continue to emerge.

## Why This Matters

Foundation models matter for veterinary AI for several reasons.

Reduced data requirements. Traditional AI requires substantial task-specific data. If you want to build a classifier for a rare condition, you need many examples of that condition. Foundation models can often be adapted to new tasks with very few examples — sometimes just a description of what you want. This dramatically expands what's feasible with limited veterinary data.

Unexpected capabilities. Foundation models develop abilities that weren't explicitly trained. A model trained to predict the next word in text somehow learns to answer medical questions, translate languages, and write poetry. These emergent capabilities are difficult to predict and sometimes surprising even to researchers. What else might be lurking in these systems, waiting to be discovered?

Rapid deployment. Because foundation models can be adapted through prompting rather than retraining, new applications can be developed quickly. Instead of months of data collection and model training, a skilled practitioner can sometimes create useful tools in hours. This accelerates innovation cycles.

Accessibility. You don't need to be a machine learning expert to use foundation models. APIs let anyone access their capabilities. This democratizes AI development, enabling veterinary professionals to experiment directly rather than relying entirely on specialized vendors.

## Veterinary Applications

Foundation models enable veterinary applications that would have been impractical with traditional approaches.

Flexible documentation assistance. Rather than training a specialized scribe for each documentation format, foundation models can adapt to your preferred style, structure, and terminology through prompting. They can handle the variety and nuance of real clinical documentation without task-specific training.

Instant knowledge access. Foundation models trained on medical literature can answer clinical questions, explain concepts, and surface relevant information. They're not perfect — hallucination remains a problem — but they provide faster access to knowledge than traditional search.

Client communication generation. Drafting client communications that are accurate, empathetic, and appropriately pitched is time-consuming. Foundation models can generate first drafts that capture the key information and appropriate tone, which you then refine.

Code and automation. Foundation models can write code. This enables custom automation — scripts that process data, format reports, integrate systems — without requiring programming expertise. Describe what you want; the model writes the implementation.

Multimodal analysis. Models that understand both language and vision can analyze images while considering textual context. Describe what you're looking for; show the image; get relevant analysis. This flexibility is impossible with traditional single-modality systems.

## Limitations and Concerns

Foundation models are powerful but imperfect, and their limitations have serious implications for clinical use.

Hallucination persists. Despite rapid improvement, foundation models still generate plausible-sounding falsehoods. They may cite papers that don't exist, describe drug interactions incorrectly, or provide confident answers to questions they shouldn't answer confidently. Every output requires verification.

Knowledge cutoffs. Models are trained on data up to a certain date. They don't know about recent research, new drug approvals, or current events. For fast-moving clinical domains, this matters.

Reasoning limitations. Foundation models excel at pattern matching but struggle with genuine reasoning. They can retrieve information and generate plausible responses, but complex clinical reasoning — weighing multiple factors, considering rare possibilities, integrating disparate information — remains challenging.

Black box concerns. Foundation models are even more opaque than traditional AI. With hundreds of billions of parameters, understanding why they produce specific outputs is nearly impossible. This limits accountability and debugging.

Environmental and cost concerns. Training and running foundation models requires enormous computing resources with significant environmental impact. Commercial access can be expensive at scale. These are practical constraints on adoption.

## The Trajectory

Where are foundation models heading?

Continued capability growth. Each new generation of models has been more capable than the last. There's debate about how long this will continue, but near-term models will likely be substantially more capable than current ones.

Multimodal integration. Models that seamlessly combine text, images, audio, and structured data are emerging. For medicine, this means systems that can analyze images, read clinical notes, and consider lab values simultaneously.

Domain specialization. General foundation models are being fine-tuned for specific domains, including medicine. These specialized models retain general capabilities while improving performance on domain-specific tasks.

Smaller, faster models. Techniques for compressing foundation model capabilities into smaller, faster systems are advancing. This could enable on-device deployment without cloud dependencies.

Agentic systems. Research is exploring AI that can take actions — browse the web, execute code, interact with software — not just generate text. This could enable more autonomous AI applications, with corresponding opportunities and risks.

## Practical Guidance

For veterinary professionals, the practical implications are:

Experiment. Foundation models are accessible through simple interfaces. Try them for documentation, research, communication drafting. Develop intuition for their capabilities and limits through direct experience.

Verify everything. Never trust foundation model output for clinical purposes without verification. Use them as first drafts and idea generators, not authoritative sources.

Stay current. The field moves faster than any other in technology. Capabilities that are impossible today may be routine in a year. Follow developments to recognize opportunities as they emerge.

Engage with vendors. Ask AI vendors how they're incorporating foundation model capabilities. The best veterinary AI tools will leverage these powerful systems while mitigating their limitations.

The foundation model revolution is just beginning. How it unfolds will shape the future of AI in veterinary medicine — and in most other domains of human endeavor.