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Related Experiment Video

Updated: May 22, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Disease and Health Surveillance in Companion Animals Using Artificial Intelligence and Machine Learning.

Peter-John Mäntylä Noble1, Sean Oliver Farrell2

  • 1Department of Small Animal Clinical Science, University of Liverpool, Leahurst Campus, Chester High Road, Neston, Wirral, UK.

The Veterinary Clinics of North America. Small Animal Practice
|May 20, 2026
PubMed
Summary

Artificial intelligence and electronic health records enhance companion animal disease surveillance. Computational methods, including transformer models, analyze veterinary clinical text for improved disease coding and syndromic surveillance, while addressing challenges like privacy and sustainability.

Keywords:
Disease surveillanceElectronic health recordsLanguage models

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Last Updated: May 22, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Area of Science:

  • Veterinary Medicine
  • Computational Biology
  • Artificial Intelligence

Background:

  • Companion animal disease surveillance is increasingly reliant on electronic health records (EHRs) and artificial intelligence (AI).
  • Analyzing unstructured veterinary clinical text presents unique challenges and opportunities for public health.
  • Advancements in AI offer novel approaches to process and interpret this complex data.

Purpose of the Study:

  • To review computational approaches for analyzing unstructured veterinary clinical text.
  • To highlight the capabilities of modern AI models, such as transformer networks, in veterinary surveillance.
  • To identify key challenges and future directions for AI in companion animal health.

Main Methods:

  • Review of computational techniques, including rule-based systems, traditional neural networks, and transformer models.
  • Discussion of domain-adapted encoders (e.g., PetBERT) for disease coding and syndromic surveillance.
  • Exploration of topic modeling for unsupervised pattern discovery in clinical narratives.

Main Results:

  • Transformer models and domain-adapted encoders show promise for efficient disease coding and syndromic surveillance.
  • Generative models and topic modeling offer new avenues for data analysis and pattern discovery.
  • Key challenges identified include model generalization, data privacy, standardized evaluation, and environmental sustainability.

Conclusions:

  • Strategic implementation of AI, particularly appropriately sized models, can significantly advance One Health surveillance.
  • Addressing challenges in generalization, privacy, and evaluation is crucial for reliable veterinary AI applications.
  • Balancing technological advancement with environmental responsibility is essential for sustainable AI deployment in animal health.