Natural language processing in veterinary pathology: A review

Abstract

Natural language processing (NLP), a branch of artificial intelligence that focuses on the interaction between computers and human language, has potential in advancing veterinary pathology through its ability to source knowledge efficiently from vast data sets, generate high-quality text rapidly, and enhance data searchability. This review explores the applications of NLP in veterinary pathology, emphasizing its potential role in diagnostics, training pathologists, and research. NLP might offer many advantages, such as accuracy, speed, and cost reduction, especially for routine tasks including text summarization and report generation. These benefits make NLP a promising technology for achieving precision, adding value, and driving innovation in health care. However, caution is warranted, as NLP models may introduce biases and errors due to the quality of the data they are trained on, have limitations in interpreting nuanced or context-specific information, and lead to private data leakage. Furthermore, the multifaceted nature of veterinary pathology data may require specifically trained and expert-validated algorithms for accurate interpretation. To ensure the credibility and validity of research findings, pathologists must critically evaluate and complement obtained outputs with human expertise and judgment. This article highlights the transformative potential of NLP in veterinary pathology, underscores the importance of integrating this technology into the field for enhanced diagnostic accuracy and research advancements, and gives real-life examples from pathologists for pathologists, which illustrate how NLP can be applied in veterinary pathology.

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