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对人工智能聊天机器人进行评估,以提供IR患者教育材料:与社会网站内容的比较
Colin J McCarthy1, Seth Berkowitz1, Vijay Ramalingam1
1Division of Vascular and Interventional Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
Journal of vascular and interventional radiology : JVIR
|June 17, 2023
概括
像ChatGPT这样的机器学习模型可能会产生不准确的患者教育材料,比网站内容更难阅读. 医疗保健提供者应了解这些限制,以确保安全的患者沟通.
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:
- 患者教育材料对于明智的医疗保健决策至关重要.
- 像ChatGPT这样的AI工具的出现为内容生成提供了潜力.
- 评估人工智能产生的患者教育质量至关重要.
研究的目的:
- 评估人工智能生成的患者教育材料的准确性,完整性和可读性.
- 将人工智能生成的内容与来自知名社会网站的材料进行比较.
主要方法:
- 来自干预性放射病患者中心协会的内容被用作基准.
- 聊天GPT被提示了问题,并分析了它的输出.
- 使用验证的工具 (PEMAT-P,Flesch-Kincaid) 评估了可读性,事实正确性和适用性.
主要成果:
- 在多个尺度上,ChatGPT输出比网站内容更长,更难读.
- 11.5%的聊天GPT生成的答案是事实上不正确的.
- 在可打印材料的患者教育材料评估工具 (PEMAT-P) 上,人工智能生成的内容得分较低.
结论:
- 聊天GPT可能会产生不完整或不准确的患者教育内容.
- 医疗保健提供者必须意识到人工智能在患者教育中的当前局限性.
- 对大型语言模型的进一步改进可能会提高它们对患者教育的适用性.
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