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机器学习工具与医生在多语言文本注释中的准确性相匹配.

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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概括

非英语语言的医学文本注释准确性显示,人类专家和当前的人工智能工具之间没有显著差异. 需要进一步的研究来提高机器在识别医疗术语方面的性能.

关键词:
医疗保健中的AI对文本注释方法的比较研究.医学语音识别 医学语音识别医学文本注释 医学文本注释

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科学领域:

  • 医疗信息学 医疗信息学
  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 计算语言学 计算语言学

背景情况:

  • 医学中的文本注释对临床数据进行分类,这对于诸如语音识别系统等工具至关重要,可以减少医生倦怠.
  • 在非英语语言注释医疗文本提出了独特的挑战,需要先进的AI模型.
  • 医生倦怠是一个重大问题,高达60%的医务人员报告了它.

研究的目的:

  • 评估各种AI工具和模型在识别非英语语言中的医疗术语方面的性能.
  • 将人工智能驱动的医学文本注释与人类专家注释的准确性进行比较.
  • 调查医学文本注释在英语以外的语言中的挑战.

主要方法:

  • 评估了AI工具和模型,用于识别"药物"",疾病和症状"",程序"和"其他医疗术语"等类别的医疗术语.
  • 将翻译文本的AI性能与医学专家的注释进行比较.
  • 利用统计分析来比较人类专家和人工智能工具之间的结果.

主要成果:

  • 在人类专家和测试的人工智能工具/模型的表现之间没有发现统计学上显著的差异.
  • 评估的多语言聊天机器人和NLP工具在识别医疗术语方面表现出与人类专家相似的有效性.
  • 该研究确定了在基于机器的医学文本注释中实现人类水平准确性的挑战,特别是在非英语环境中.

结论:

  • 当前的人工智能工具和模型在测试的非英语环境中对医学术语识别的平均有效性与人类专家相似.
  • 这些发现强调需要继续开发和改进用于医学文本注释的AI技术.
  • 在不同的语言医学环境中,弥合人类和机器注释之间的准确性差距仍然是一个正在进行的研究目标.