患者从大语言模型的角度来看,对莫亚莫亚的问题做出反应
Marcella R Ruppert-Gomez1, Joon Hyeok Choi1, Steven J Staffa2
1Department of Neurosurgery, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
Acta neurochirurgica
|February 26, 2026
概括
公共可用的大型语言模型 (LLM) 提供关于莫亚莫亚病的信息. 虽然被认为是医生质量的,但LLM有安全性和准确性的限制,影响了患者的信任.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 脑血管疾病 脑血管疾病
背景情况:
- 大型语言模型 (LLM) 越来越多地用于健康信息.
- 对于非专家社区来说,Moyamoya疾病信息的可访问性至关重要.
研究的目的:
- 评估公开可用的LLMs的有用性,安全性,有用性和准确性,以获得莫亚莫亚病信息.
- 为了比较来自ChatGPT-4o和Gemini 1.5 Flash的响应.
主要方法:
- 直接提示聊天GPT-4o和双子座1.5闪光灯与10个常见的moyamoya问题.
- 通过Moyamoya基金会网站的调查收集社区反.
- 临床医生评估了LLM反应的安全性和准确性.
主要成果:
- 社区受访者发现LLM的答案与医生提供的信息相比或更好.
- 有关程序,药物和何时寻求医疗护理的关键安全信息经常被LLM遗漏.
- 医生评价者指出,LLM反应缺乏最近的研究,并未解决紧急症状.
结论:
- 法律学的响应被认为是高质量的,但在安全性和数据完整性方面存在重大限制.
- 在LLM生成的健康信息中遗漏可能会对患者与医生的关系和患者安全产生负面影响.
- 为了可靠的健康信息传播,需要进一步完善LLM.
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