基于人工智能的大型语言模型可以促进患者教育
Xochitl Bryson1, Marleni Albarran1, Nicole Pham1
1Stanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Journal of the Pediatric Orthopaedic Society of North America
|August 12, 2025
概括
人工智能 (AI) 大型语言模型 (LLM) 为儿科脊椎病提供准确的答案,与专业社会内容相匹配. 人工智能和专家撰写的回复都显示了类似的同意率,突出了AI在医疗信息传播方面的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 脊柱手术研究 脊柱手术研究
背景情况:
- 越来越多的患者依赖人工智能获得医疗信息,需要评估人工智能工具,如大语言模型 (LLM).
- 了解LLM在提供准确的医疗信息方面的能力和局限性,对于患者教育至关重要.
- 这项研究解决了需要将人工智能生成的内容与儿科脊柱病的既定医疗资源进行比较的需求.
研究的目的:
- 为了比较大语言模型 (LLM) 产生的答案的准确性和适当性,与专业协会为儿科脊椎病常见问题撰写的答案进行比较.
- 评估专家医生在人工智能生成的医疗信息和人类撰写的医疗信息之间的协议和偏好.
主要方法:
- 三个LLM生成了来自主要骨科协会的15个儿科脊椎病常见问题答案.
- 一个由儿科脊椎外科医生组成的专家小组评估了人工智能生成和医生撰写的答案的准确性,适当性和偏好.
- 使用开源计算器评估可读性,并根据利克特级别对答案进行分级.
主要成果:
- 专家小组的共识几乎相当于人工智能生成的答案和医生生成的答案.
- 专家小组赞成人工智能和专业社会的回答 (40%分别),13%的人均排名相同,7%的人均排名一致.
- 人工智能和专业社会的答案都存在分歧,这表明这两个来源都有争议的领域.
结论:
- 人工智能为儿科脊椎病常见问题所提供的答案与专业社会网站上的答案一样准确和适当.
- 人工智能和专业社会产生的内容都表现出类似的专家小组分歧率.
- 虽然人工智能对医疗信息传播充满希望,但需要进一步的研究来完善其准确性和解决局限性.
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