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大型语言模型聊天机器人在回答患者问题方面是否比已有的患者信息资源表现更好? 一项关于黑色素瘤的比较研究
Nadia C W Kamminga1, June E C Kievits1,2, Peter W Plaisier2
1Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
The British journal of dermatology
|October 4, 2024
概括
大型语言模型 (LLM) 对黑色素瘤的患者信息有希望,比传统资源更完整和个性化. 然而,在广泛采用之前,准确性和可重复性需要改进.
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:
- 大型语言模型 (LLM) 为增强患者信息传播提供了潜在的途径.
- 通过LLM生成的健康信息的质量需要与已有的资源进行严格评估.
研究的目的:
- 将领先的大型语言模型 (LLM) 与荷兰患者信息资源 (PIR) 的响应质量进行比较,用于黑色素瘤特定查询.
- 评估LLM和PIR响应的准确性,完整性,个性化和可读性.
主要方法:
- 评估了来自ChatGPT (3.5和4.0版本) 和Gemini以及3个荷兰黑色素瘤PIR对50个患者问题的反应.
- 评估结果包括医学准确性,完整性,个性化和可读性,在LLMs中也评估了可重复性.
- 使用弗里德曼的Anova进行集团内部比较和Wilcoxon签名等级测试来比较最好的LLM与黄金标准PIR.
主要成果:
- 在LLM中,ChatGPT-3.5在准确度方面领先;双子座在完整性和个性化方面表现出色.
- 患者信息资源 (PIR) 显示出一致的准确性和完整性,全科医生网站在个性化和可读性方面排名第一.
- 最好的LLM在完整性和个性化方面超过了黄金标准PIR,但准确性和可读性较差.
- 随着时间的推移,LLM反应的可复制性下降,表明了变化.
结论:
- 大型语言模型 (LLM) 显示出提供个性化和全面的黑色素瘤患者信息的巨大潜力.
- 提高准确性,可复制性和可访问性对于LLM来说至关重要,以有效地补充或取代传统的患者信息资源.
- 需要进一步的研究,以确保安全有效地将LLMs纳入患者教育策略.
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