使用ChatGPT3.5和GPT4进行快速工程,以改善患者对视网膜疾病的教育
Hoyoung Jung1, Jean Oh1, Kirk A J Stephenson2
1Faculty of Medicine, University of British Columbia, Vancouver BC, Canada.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
|September 8, 2024
概括
快速工程提高了大语言模型 (LLM) 对视网膜疾病问题的响应可读性,但降低了准确性和全面性. 需要进一步研究LLM在患者教育中的实用性和伦理.
科学领域:
- 人工智能在医学中的应用
- 眼科医生 眼科 眼科
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 显示了医疗保健中患者教育的潜力.
- 优化LLM响应的清晰度和准确性对于医疗应用至关重要.
- 提示工程是一种指导LLM输出的方法.
研究的目的:
- 评估快速工程对LLM对患者关于视网膜疾病的查询产生的反应的影响.
- 评估LLM响应的准确性,全面性,可读性和同理心的变化.
主要方法:
- 涉及视网膜专家的前性定性研究.
- 使用ChatGPT 3.5和GPT-4来回答20个患者关于视网膜状况的问题.
- 没有提示,优化提示 (A) 或优化提示与长度/阅读水平约束 (B) 的响应被生成.
- 专家对准确性和全面性进行了评分;可读性使用在线工具进行了评估.
主要成果:
- 在ChatGPT 3.5和GPT-4之间没有发现显著的性能差异.
- 与没有提示或提示A (p < 0.001) 相比,提示B显著降低了响应的准确性和全面性.
- 提示工程,特别是提示B,显著降低了答案的平均阅读等级水平 (p < 0.001).
结论:
- 快速工程提高了关于视网膜疾病的LLM反应的可读性.
- 这种可读性改善是以减少准确性和全面性为代价的.
- 需要进一步调查,以确定使用LLM用于患者教育的临床实用性和伦理考虑.
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