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Natural language processing in veterinary pathology: A review.

Lev Stimmer1, Raoul V Kuiper2, Laura Polledo3

  • 1Paris Brain Institute, CNRS UMR 7225, INSERM U1127, Sorbonne Université, Paris, France.

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This summary is machine-generated.

Natural language processing (NLP) can advance veterinary pathology by improving diagnostics and research. While offering speed and accuracy, careful validation is crucial due to potential biases and data limitations.

Keywords:
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Area of Science:

  • Veterinary Pathology
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Natural language processing (NLP) is a field of artificial intelligence focused on computer-human language interaction.
  • NLP offers potential to enhance knowledge sourcing, text generation, and data searchability in veterinary pathology.
  • The integration of NLP can drive innovation and efficiency in healthcare settings.

Purpose of the Study:

  • To explore the applications of NLP in veterinary pathology.
  • To emphasize NLP's potential role in diagnostics, pathologist training, and research.
  • To highlight the advantages and challenges of implementing NLP in this field.

Main Methods:

  • This review synthesizes current knowledge on NLP applications in veterinary pathology.
  • It examines the potential benefits, such as accuracy, speed, and cost reduction for tasks like report generation.
  • It also addresses limitations, including data bias, interpretation challenges, and privacy concerns.

Main Results:

  • NLP can significantly improve routine tasks like text summarization and report generation.
  • Potential benefits include enhanced diagnostic accuracy, accelerated research, and cost efficiencies.
  • Challenges include the need for expert-validated algorithms and the risk of introducing biases or errors from training data.

Conclusions:

  • NLP holds transformative potential for veterinary pathology, offering enhanced diagnostic accuracy and research capabilities.
  • Critical evaluation and integration with human expertise are essential for valid and credible outcomes.
  • The technology promises to add value and drive innovation, but careful implementation is required.