标题： 自然语言处理在解决医学信息不准确方面的范围审查：错误、误导信息和幻觉 显示英文标题

标题： A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination

摘要： 目标：本综述旨在探讨使用自然语言处理（NLP）检测、纠正和缓解医疗信息不准确性的潜力和挑战，包括错误、误导信息和幻觉。通过统一这些概念，综述强调了它们在方法论上的共同基础及其对医疗保健的独特影响。我们的目标是提高患者安全，改善公共卫生沟通，并支持开发更可靠和透明的医疗保健领域的NLP应用。 方法：根据PRISMA指南进行了范围综述，分析了从2020年到2024年的五个数据库中的研究。根据研究是否使用NLP来解决医疗信息不准确性进行选择，并按主题、任务、文档类型、数据集、模型和评估指标进行分类。 结果：NLP在以下任务中显示出解决医疗信息不准确性的潜力：（1）错误检测（2）错误纠正（3）误导信息检测（4）误导信息纠正（5）幻觉检测（6）幻觉缓解。然而，数据隐私、上下文依赖性和评估标准方面仍存在挑战。 结论：本综述突出了在应用NLP解决医疗信息不准确性方面的进展，同时强调需要解决持续存在的挑战。未来的工作应集中在开发现实世界的数据集、完善上下文方法和改进幻觉管理，以确保可靠的、透明的医疗保健应用。 显示英文摘要

摘要： Objective: This review aims to explore the potential and challenges of using Natural Language Processing (NLP) to detect, correct, and mitigate medically inaccurate information, including errors, misinformation, and hallucination. By unifying these concepts, the review emphasizes their shared methodological foundations and their distinct implications for healthcare. Our goal is to advance patient safety, improve public health communication, and support the development of more reliable and transparent NLP applications in healthcare. Methods: A scoping review was conducted following PRISMA guidelines, analyzing studies from 2020 to 2024 across five databases. Studies were selected based on their use of NLP to address medically inaccurate information and were categorized by topic, tasks, document types, datasets, models, and evaluation metrics. Results: NLP has shown potential in addressing medically inaccurate information on the following tasks: (1) error detection (2) error correction (3) misinformation detection (4) misinformation correction (5) hallucination detection (6) hallucination mitigation. However, challenges remain with data privacy, context dependency, and evaluation standards. Conclusion: This review highlights the advancements in applying NLP to tackle medically inaccurate information while underscoring the need to address persistent challenges. Future efforts should focus on developing real-world datasets, refining contextual methods, and improving hallucination management to ensure reliable and transparent healthcare applications.