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Machine learning tools match physician accuracy in multilingual text annotation.

Marta Zielonka1, Andrzej Czyżewski2, Dariusz Szplit3

  • 1Faculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, 80-233, Gdańsk, Poland. marta.zielonka@pg.edu.pl.

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Medical text annotation accuracy for non-English languages shows no significant difference between human experts and current AI tools. Further research is needed to improve machine performance in recognizing medical terms.

Keywords:
AI in HealthcareComparative study of text annotation methodsMedical Speech RecognitionMedical text annotation

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Area of Science:

  • Medical Informatics
  • Natural Language Processing (NLP)
  • Computational Linguistics

Background:

  • Text annotation in medicine categorizes clinical data, crucial for tools like speech recognition systems that reduce physician burnout.
  • Annotating medical texts in non-English languages presents unique challenges, requiring advanced AI models.
  • Physician burnout is a significant issue, with up to 60% of medical staff reporting it.

Purpose of the Study:

  • To evaluate the performance of various AI tools and models in recognizing medical terms in non-English languages.
  • To compare the accuracy of AI-driven medical text annotation with human expert annotations.
  • To investigate challenges in medical text annotation for languages other than English.

Main Methods:

  • Evaluated AI tools and models on recognizing medical terms across categories like 'Drugs', 'Diseases and Symptoms', 'Procedures', and 'Other Medical Terms'.
  • Compared AI performance on translated texts with annotations made by medical experts.
  • Utilized statistical analysis to compare outcomes between human experts and AI tools.

Main Results:

  • No statistically significant differences were found between the performance of human experts and the tested AI tools/models.
  • The evaluated multilingual chatbots and NLP tools demonstrated comparable effectiveness to human experts in recognizing medical terms.
  • The study identified challenges in achieving human-level accuracy in machine-based medical text annotation, particularly in non-English contexts.

Conclusions:

  • Current AI tools and models show similar average effectiveness to human experts for medical term recognition in the tested non-English context.
  • The findings underscore the need for continued development and refinement of AI technologies for medical text annotation.
  • Bridging the accuracy gap between human and machine annotation in diverse linguistic medical settings remains an ongoing research objective.