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Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey.

Anastassia Shaitarova1, Jamil Zaghir2,3, Alberto Lavelli4

  • 1Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.

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This survey highlights progress in biomedical and clinical Natural Language Processing (NLP) for languages other than English. While transformer models and datasets are increasing, more resources are needed for low-resource languages in medical NLP.

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics
  • Natural Language Processing

Background:

  • Biomedical and clinical Natural Language Processing (NLP) research is increasingly expanding beyond English.
  • Multilingual NLP presents unique challenges in data availability and model development.

Purpose of the Study:

  • To survey the current state of biomedical and clinical NLP in languages other than English (LoE).
  • To focus on data resources, language models, and common NLP tasks in LoE.
  • To identify research gaps and future opportunities in multilingual medical NLP.

Main Methods:

  • Literature review of clinical and biomedical NLP publications from 2020-2022.
  • Focus on multilinguality and LoE challenges.
  • Database queries and manual selection of relevant studies, supplemented by existing review papers.

Main Results:

  • Transformer-based language models are increasingly used for medical NLP tasks in LoE.
  • Annotated datasets for clinical NLP in LoE, especially European languages, have grown.
  • Common tasks include information extraction, named entity recognition, and negation detection.
  • Need for tailored datasets and models for low-resource languages remains.

Conclusions:

  • Significant progress in medical NLP for languages other than English is evident.
  • Opportunities exist for developing specialized resources for underrepresented languages.
  • Further research is needed to address the unique challenges of multilingual medical text processing.