对于正确和不正确的医学诊断,用自然语言进行解释性论证
Benjamin Molinet1, Santiago Marro2, Elena Cabrio2
1Université Côte d'Azur, CNRS, Inria, I3S, Rte des Lucioles, Sophia Antipolis, 06900, Alpes-Maritimes, France. benjamin.molinet@univ-cotedazur.fr.
Journal of biomedical semantics
|May 30, 2024
概括
这项研究引入了一个人工智能管道,为医学诊断生成自然语言解释,提高透明度并帮助临床教育. 该系统提取症状和发现,以解释诊断推理,改进现有方法.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 目前的人工智能诊断工具缺乏透明度,阻碍了它们在医学教育中的使用.
- 可解释性对于医疗保健中的AI至关重要,以建立信任并促进学习.
研究的目的:
- 开发一种自动化管道,用于生成医学诊断的自然语言解释.
- 提高AI驱动的医疗诊断支持系统的可解释性.
- 为培训临床住院人员创建人工智能辅助的教育框架.
主要方法:
- 开发了一个完整的管道,从临床病例描述中生成自然语言解释.
- 综合信息提取症状和发现与医学本体学和生物界限.
- 创建了新的语言资源:UMLS注释的临床病例数据集和共同发现边界的数据库.
主要成果:
- 该系统自动生成自然语言解释,澄清正确和不正确的诊断.
- 该管道将临床病例数据与经过验证的医学知识相结合.
- 与现有方法相比,拟议的方法显示出更高的性能.
结论:
- 开发的框架为临床居民提供了人工智能辅助的教育支持.
- 它旨在提高住院人员为患者制定明确和全面的诊断解释的能力.
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