一个以患者为中心的大型语言模型的比较分析,用于外周止痛教育
Akshar P Patel1, Stanley Joseph1, Ashna Bhardwaj1
1From the Medical College of Georgia, Augusta University, Augusta, Georgia.
A&A practice
|August 5, 2025
概括
这项研究比较了两个人工智能聊天机器人,用于患者在外周止痛治疗方面的教育. MediSearch提供了更可靠的信息,而ChatGPT提供了更容易理解的解释,揭示了准确性和可读性之间的权衡.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 产科 产科 产科 产科 产科
背景情况:
- 患者对外周止痛治疗的教育对于知情同意和管理期望至关重要.
- 当前的教育方法可能无法充分解决患者对风险和益处的担忧.
- 大型语言模型 (LLM) 为改善患者信息传递提供了潜在的途径.
研究的目的:
- 评估两个LLM的有效性,ChatGPT和MediSearch,在教育患者关于外周止痛.
- 为了比较这些AI工具产生的响应的可靠性和可读性.
主要方法:
- 使用罗斯威尔系统制定了100个关于外围止痛的以患者为中心的问题.
- 来自ChatGPT和MediSearch的响应使用DISCERN可靠性评分进行了评估.
- 使用弗莱什-金凯德等级水平和科尔曼-利乌指数来测量可读性.
主要成果:
- 与ChatGPT相比,MediSearch的可靠性得分明显更高 (P < .0001).与ChatGPT相比,MediSearch的可靠性得分明显更高.
- 聊天GPT实现了卓越的可读性,由较低的Flesch-Kincaid等级水平和Coleman-Liau指数得分 (P =.0013) 表示.
- 两种LLM的可靠性和可读性之间存在不平衡.
结论:
- 虽然这两种LLM都显示出患者教育的潜力,但这两种LLM都没有最佳的平衡外围止痛信息的可靠性和可读性.
- 医疗保健提供者在考虑人工智能工具用于患者教育时应该意识到这些不同的优势.
- 需要进一步开发以提高人工智能生成的医疗信息的准确性和可访问性.
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