Beyond Human Medical and Generalist AI: Why Veterinary-Specific AI is Crucial for Better Patient Care
Apr 2, 2025
We're hearing a lot about Artificial Intelligence (AI) in healthcare, and rightly so! From faster diagnoses to personalized treatment plans, AI holds incredible promise. But what happens when we try to apply tools developed primarily for human medicine or even the general population directly to veterinary care? Our recent study sheds light on this important question – and highlights why creating veterinary-specific AI is absolutely essential.
The Promise of AI in Veterinary Medicine
Imagine a world where analyzing patient records is faster, more accurate, and frees up your time to focus on what matters most: the animals in your care. That's the potential of AI! Specifically, technologies like Natural Language Processing (NLP) – which allows computers to understand and process human language – can automate tasks like extracting key information from medical records, identifying trends, and even suggesting clinical trial matches for patients.
The Challenge: Human vs. Animal Medicine Isn't the Same
Our recent research looked at using a pre-existing AI tool (called BioEN) designed to understand medical records – but originally trained on human medical data. While it showed some promise, we found significant limitations when applied to veterinary records. Why? Because veterinary medicine isn’t just a scaled-down version of human medicine.
Here's what we discovered:
Missed Details: The AI struggled with terminology specific to veterinary oncology – things like surgical margin descriptions, microscopic findings (like "superficial"), and even critical bloodwork values and units of measurement.
Human Expertise Still Matters: Experienced clinicians often pick up on subtle clues in records that the AI missed, demonstrating the importance of clinical judgment. The AI sometimes overlooked information deemed clinically relevant by human observers.
Different Record Keeping: Veterinary record-keeping practices can differ from those used in human medicine, further complicating the process.
Why Veterinary-Specific Models are a Must
The key takeaway is this: AI tools trained on human data simply can't fully understand the nuances of veterinary medicine. To truly unlock the benefits of AI for our animal patients, we need to develop models specifically tailored to veterinary records.
What Does This Mean for You?
More Accurate Summaries: Veterinary-specific NLP tools will be able to extract and categorize information more accurately, leading to better summaries of patient histories and treatment plans.
Improved Diagnostics & Treatment: By understanding the unique language and data patterns in veterinary records, AI can help us diagnose diseases earlier and develop more effective treatments.
Reduced Clinician Burden: Automating tedious tasks frees up valuable time for clinicians to focus on direct patient care and complex decision-making.
The Future is Collaborative
Developing these specialized AI tools will require a collaborative effort between veterinary practitioners, data scientists, and AI experts. By working together, we can ensure that AI in veterinary medicine is both clinically relevant and technologically sound – ultimately leading to better outcomes for the animals we serve.
Reference:
Pinard CJ, Poon AC, Lagree A, Wu KC, Li J, Tran WT. Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary Oncology. Vet Comp Oncol. 2025 Mar;23(1):102-108. doi: 10.1111/vco.13035. Epub 2024 Dec 23. PMID: 39711253; PMCID: PMC11830456.