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从人工智能聊天机器人对化疗心脏毒性患者教育材料的质量和可读性进行评估:一项观察性横截面研究
Christoph A Stephenson-Moe1, Benjamin J Behers2, Rebecca M Gibons1
1Florida State University College of Medicine, Tallahassee, FL.
Medicine
|April 14, 2025
概括
人工智能 (AI) 聊天机器人生成高质量的患者教育材料 (PEM) 关于化疗引起的心脏毒性,但可读性很差. 提醒患者注意人工智能生成的PEM可读性,因为聊天机器人不能替代医疗保健提供者.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 大型语言模型 (LLM) 聊天机器人越来越多地用于医疗保健中的患者查询.
- 研究表明,人工智能生成的患者教育材料 (PEM) 通常具有可读性差但可接受的质量.
- 化疗引起的心脏毒性是关于人工智能产生的PEM的研究不足的领域.
研究的目的:
- 评估人工智能聊天机器人生成的关于化疗诱导心脏毒性的PEM的质量和可读性.
- 为了比较四个领先的人工智能聊天机器人 (ChatGPT,Copilot,Gemini,Meta AI) 在生成这种特定类型的患者信息方面的性能.
主要方法:
- 在2024年8月进行了一项观察性横截面研究.
- 对四个人工智能聊天机器人提出了关于化疗引起的心脏毒性的十个问题.
- 使用七种标准工具 (例如,Flesch阅读易度,Flesch-Kincaid等级水平) 评估可读性.
- 使用改版的患者教育材料评估工具 (PEMAT) 和DISCERN.使用评估质量.
主要成果:
- 所有聊天机器人的平均阅读等级水平为13.7,表明可读性差.
- 平均DISCERN分数高 (4.2/5),表明质量令人满意.
- 对于可理解性 (91.7%) 和可操作性 (75%) 的 PEMAT 平均得分一般都很好,但聊天机器人之间存在差异.
结论:
- 人工智能聊天机器人可以制作高质量的患者教育材料,以了解化疗引起的心脏毒性.
- 人工智能生成的PEM的可读性仍然是一个重大问题,需要患者谨慎.
- 人工智能聊天机器人应该补充,而不是取代,医疗保健提供者为患者教育.
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