[大型语言模型在儿童白内障患者健康教育中的应用]
概括
国内大型语言模型 (LLM) 在儿科白内障健康教育方面表现有前途,为家长提供准确和完整的信息. 虽然这些人工智能工具很有价值,但由于持续存在的挑战,它们不能取代医生.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 儿科白内障需要及时干预,以防止视力丧失.
- 父母的健康素养和自我管理对于治疗坚持至关重要.
- 人工智能为可访问的健康信息资源提供了潜力.
研究的目的:
- 评估国内开源大型语言模型 (LLM) 的准确性,完整性和可重复性.
- 评估LLM作为儿童白内障患者在线健康信息工具的潜力.
- 将Kimi聊天,Doubao,ERNIE Bot 3.5和DeepSeek的性能进行比较.
主要方法:
- 收集和分类了40个高度关注的儿科白内障问题.
- 专家提供了手册参考答案.
- 四个国内的LLM被测试了重复查询,包括角色提示.
- 白内障专家盲目评分了LLM答案的准确性,完整性和可重复性.
主要成果:
- 基米聊天在准确性,完整性和可重复性方面超过了其他LLM.
- LLM的答案通常是准确的 (83.8%) 和完整的 (77.9%).
- 评价者更喜欢Kimi聊天答案,尽管注意到一些问题,如冗余.
结论:
- 国内的LLM,特别是Kimi chat,显示了儿童白内障健康教育的潜力.
- 关键的挑战包括信息安全,幻觉和偏见.
- 法律学士是医生有价值的补充,而不是取代个性化医疗保健.
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