人工智能在普通语言写作的实用性:一个范围审查
Mohamed Ugas1, Jimmy Huynh2, Ashley Lenarcik-Packham2
1Cancer Health Literacy Research Centre, Cancer Education, Princess Margaret Cancer Centre, Toronto, Canada.
Patient education and counseling
|January 21, 2026
概括
生成型人工智能显示出创造简单语言患者教育材料的潜力,提高健康素养. 然而,人工智能模型的性能各不相同,需要对行业标准和各种语言环境进行更多的研究.
科学领域:
- 卫生沟通健康沟通
- 医疗保健中的人工智能
- 卫生识字 卫生识字
背景情况:
- 医学术语阻碍了患者对医疗保健信息的理解.
- 简单的语言 (PL) 写作对于有效的健康沟通至关重要,但面临着资源限制.
- 生成型人工智能 (AI) 正在成为解决PL写作挑战的潜在解决方案.
研究的目的:
- 进行范围审查,探索目前对生成AI平台的知识,以简单的语言写作.
- 确定如何利用生成性AI以简单的语言创建患者教育材料 (PEM).
- 了解生成性AI在生产可访问的健康信息方面的有效性和局限性.
主要方法:
- 在2024年夏天,使用9个学术数据库的搜索进行了全面的范围审查.
- 包括的研究评估了生成人工智能平台以普通语言编写患者教育材料 (PEMs).
- 评估的关键指标包括可读性和遵守健康沟通的最佳实践.
主要成果:
- 该综述包括47篇文章,其中大多数研究来自美国.
- 提示工程策略涉及指定阅读等级水平,受众,健康状况和资源类型.
- 人工智能生成的PEM显示可读性有所改善:28.3%的研究显示阅读等级水平有所改善,46.2%的研究显示阅读易度有所改善.
结论:
- 生成性人工智能在撰写患者教育材料和提高健康素养方面具有重大潜力.
- 不同的人工智能模型在生成和编辑普通语言内容的性能存在差异.
- 进一步的研究是必要的,以验证AI的能力与行业标准和非英语语境.
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