教育水平对自然语言处理在心血管健康沟通中的影响
Stanley Joseph1, Ashna Bhardwaj1, Justin Skariah1
1Medical College of Georgia, Augusta University, Augusta, GA, United States.
Frontiers in public health
|December 1, 2025
概括
像ChatGPT和MediSearch这样的人工智能 (AI) 模型为不同的教育水平调整心血管疾病 (CVD) 信息. 然而,他们的反应往往仍然过于复杂的平均素养,表明需要人工智能改进患者教育.
科学领域:
- 医学沟通 医学沟通
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,强调需要清晰的健康信息.
- 人工智能 (AI) 工具为改善健康沟通可访问性提供了潜力.
- 评估AI反应的准确性和可读性对于有效的患者教育至关重要.
研究的目的:
- 评估自然语言处理 (NLP) 模型在回答心血管疾病 (CVD) 问题时如何适应不同的教育背景.
- 为了比较ChatGPT (免费和高级) 和MediSearch的可读性和响应相似性.
主要方法:
- 35个与心血管疾病有关的问题适用于初中,高中和大学毕业生教育水平.
- 提示被输入到ChatGPT免费 (GPT-4o mini),ChatGPT优惠 (GPT-4o) 和MediSearch (v1.1.4) 中.
- 使用Flesch-Kincaid分数测量可读性,使用基于BERT的共弦相似性评估响应相似性,使用包括ANOVA,Kruskal-Wallis和Pearson相关性在内的统计分析.
主要成果:
- 在所有测试的AI模型中,随着教育水平的提高,可读性显著下降 (p < 0.001).
- 聊天GPT免费提供了比MediSearch (p < 0.001) 更多可读的响应,聊天GPT模型显示了更高的响应相似性.
- 聊天GPT高级显示了最强的适应用户教育水平,解释了最大的可读性差异 (r = 0.350,p < 0.001).
结论:
- NLP模型可以根据用户的教育水平调整响应可读性.
- 尽管进行了调整,但人工智能产生的健康信息的复杂性经常超过平均识字水平.
- 人工智能工具的进一步完善是必要的,以优化它们对患者教育的有效性,并弥合健康知识的差距.
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