"对大型语言模型与NHS 111在线分组对应急眼科的比较分析"
Shaheryar Ahmed Khan1, Chrishan Gunasekera2
1Ophthalmology Department, Moorfields Eye Hospital, London, UK. javakhan@hotmail.com.
Eye (London, England)
|January 21, 2025
概括
大型语言模型 (LLM) 在应对眼科紧急情况方面表现出高准确性,可与英国国家卫生服务111在线系统相比较. 这表明LLM可以成为患者指导和医疗保健可访问性的有价值工具.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 对医疗应用的大型语言模型 (LLM) 的评估至关重要.
- 眼科紧急情况需要准确和及时的信息.
- 为了验证人工智能的性能,需要与已建立的医疗保健系统进行比较.
研究的目的:
- 综合评估LLM在眼科紧急情况的响应过程中的表现.
- 将LLM生成的响应与英国国家卫生服务 (NHS) 111在线系统的准确性进行比较.
主要方法:
- 从NHS 111分类算法中利用了21个眼科紧急情况.
- 从NHS 111在线与各种LLM聊天机器人 (ChatGPT-3.5,Google Bard,Bing聊天,ChatGPT-4.0) 进行了比较.
- 评估准确性使用两个提示策略和2 (非常差) 到3 (优秀) 的评分表.
主要成果:
- 总体而言,LLM的准确性很好,其中93%的答案得分为"好" (≥1).
- 一个"好"的分数表明正确的信息没有不准确.
- 在用于评估的两个提示之间没有观察到任何显著差异.
结论:
- LLM反应的高准确性和安全性表明它们作为患者指导的有效工具的潜力.
- 在数字时代,LLM在改善患者护理和医疗保健可访问性方面表现有前途.
- 在医疗保健中进一步整合LLM可以提高及时的信息传递.
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