生成型人工智能为患者教育提供关于胃癌预防的材料
Tommy Rizkala1, Natasha Muench2, Cesare Hassan1,3
1Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy.
Endoscopy
|February 13, 2026
概括
像ChatGPT-4o这样的大型语言模型 (LLM) 生成了与现有资源相比的胃癌前期患者摘要. 然而,无论是LLM还是现有的摘要,都需要改进可读性,以改善患者教育.
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:
- 评估大型语言模型 (LLM) 在为胃癌前期管理创建患者友好的教育材料方面的有效性.
- 评估人工智能生成的内容与已建立的患者信息资源对比.
研究的目的:
- 为了比较ChatGPT-4o生成的胃癌前期管理的简要概要质量与来自欧洲消化癌症 (DiCE) 的简要概要质量.
- 从专家医生和患者的角度评估准确性,完整性,可理解性和满意度.
- 评估人工智能生成的患者教育材料的可读性.
主要方法:
- 一项试点研究采用了两期,交叉,盲目的设计,比较了ChatGPT-4o和DiCE摘要.
- 专家医生和患者咨询委员会对关键指标的摘要进行了评级.
- 使用弗莱什-金凯德等级水平 (FKGL) 和SMOG指数分析可读性.
主要成果:
- 专家和患者对ChatGPT-4o和DiCE摘要的评价在准确性,完整性,可理解性和满意度方面统计上相似.
- 这两种类型的摘要都未能满足基于FKGL和SMOG分数的推可读性准则.
- 患者的反与专家医生的评估密切结合.
结论:
- 聊天GPT-4o可以制作患者教育材料,其质量与DiCE等既定组织的材料相提并论.
- 人工智能生成的和现有的摘要都需要优化可读性.
- 建议进一步研究涉及人类监督和各种人工智能模型.
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