在处理医学上不准确的信息时,对自然语言处理进行范围审查:错误,错误信息和幻觉
Zhaoyi Sun1, Wen-Wai Yim2, Özlem Uzuner3
1Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA.
Journal of biomedical informatics
|July 24, 2025
概括
自然语言处理 (NLP) 在识别和纠正错误信息和幻觉等医学不准确性方面表现有前途. 解决数据隐私和背景方面的挑战对于可靠的医疗保健NLP应用至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 医疗保健中的人工智能
背景情况:
- 医学上不准确的信息对患者安全和公共卫生沟通构成风险.
- 自然语言处理 (NLP) 为检测和纠正这些不准确性提供了潜在的解决方案.
- 现有的研究强调了NLP在这个领域的能力和局限性.
研究的目的:
- 审查使用NLP用于检测,纠正和减轻医疗错误,错误信息和幻觉的潜力和挑战.
- 统一这些概念的理解,强调共享的方法和不同的医疗保健影响.
- 为医疗保健提供更可靠,更透明的NLP工具的开发提供指导.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 从五个数据库中分析了2020年至2024年间发表的研究.
- 选择的研究重点是NLP应用来解决医学上不准确的信息.
主要成果:
- 在错误检测和纠正,错误信息检测和纠正以及幻觉检测和缓解方面,NLP显示出潜力.
- 确定的主要挑战包括数据隐私问题,上下文依赖性以及对标准化评估指标的需求.
- 在与医疗准确性相关的各种NLP任务中注意到了进展.
结论:
- 在它对抗医学上不准确的信息的能力方面,NLP正在取得进展.
- 持续存在的挑战需要集中精力对现实世界的数据集,上下文理解和幻觉管理.
- 未来的工作应该优先发展强大和透明的NLP解决方案,以改善医疗保健结果.
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