人工智能生成的信息对慢性病患者的可理解性和可操作性
Emi Furukawa1, Tsuyoshi Okuhara2, Hiroe Suzuki-Chiba3
1University Hospital Medical Information Network (UMIN) Center, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Patient education and counseling
|February 18, 2026
概括
人工智能 (AI) 对慢性病 (CKD) 的反应是可以理解的,但与专家信息相比,缺乏可行的建议. 医疗保健专业人员必须改进人工智能生成的CKD内容,以提高患者的清晰度和安全性.
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:
- 慢性病 (CKD) 患者教育对于管理至关重要.
- 人工智能 (AI) 工具为可访问的健康信息提供了潜力.
- 与专家来源对比AI产生的健康内容的评估是必不可少的.
研究的目的:
- 评估AI产生的慢性病 (CKD) 反应的可理解性和可操作性.
- 将人工智能生成的CKD信息质量与专家认可的患者教育材料进行比较.
- 确定AI在提供CKD健康信息方面的优缺点.
主要方法:
- 量化分析比较AI响应 (ChatGPT,Copilot,Gemini) 和专家CKD网页.
- 使用了日本版本的患者教育材料评估工具.
- 评估了180个AI响应和88个专家项目的可理解性和可操作性.
主要成果:
- 人工智能响应的可理解性与专家信息的可理解性相比 (67.2%对63.4%).
- 专家信息比人工智能响应 (37.1%对27.9%) 具有显著的可操作性.
- 双子座在可理解性和可操作性方面都超过了其他AI模型.
结论:
- 人工智能生成的内容是可以理解的,但需要提高可操作性和全面性.
- 人工智能擅长简化医疗术语,但可能缺乏逻辑流和特定指令.
- 在患者传播之前,医疗保健专业人员应该审查和增强AI生成的CKD信息.
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