Large Language Models in Animal Healthcare: Survey, Challenges, and Future Directions

Abstract

Large Language Models (LLMs) are beginning to support veterinary care in diagnosis, symptom triage, documentation, and client communication. This paper surveys language-first systems used in daily workflows and outlines enabling methods such as retrieval-augmented generation (RAG), quantization, and hybrid deployment. Extending prior descriptive reviews, we integrate quantitative comparisons of veterinary LLMs (e.g., PetBERT, VetLLM) and highlight differences between peer-reviewed and industry-reported results. We summarize emerging evidence for diagnostic reasoning, triage accuracy, and record summarization, and discuss practical risks, data quality issues, hallucinations, and governance. A deployment-focused lens links quantization levels to veterinary use cases. We conclude with future opportunities in multimodal integration, predictive analytics, and One Health–aligned governance for safe and effective adoption in animal healthcare.

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