大语言模型质量对辐射瘤学的反应 病人护理问题
Amulya Yalamanchili1, Bishwambhar Sengupta1, Joshua Song1
1Robert H. Lurie Comprehensive Cancer Center, Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
JAMA network open
|April 2, 2024
概括
大型语言模型 (LLM) 为放射瘤患者的问题提供准确,完整和简洁的答案,其性能与人类专家相似,风险最小. 虽然他们的语言处于更高的阅读水平,但LLM显示出作为患者资源的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 放射瘤学 通信 辐射瘤学
背景情况:
- 人工智能 (AI) 大型语言模型 (LLM) 在类似人类的对话模拟方面表现有前途.
- 在放射性瘤学中,LLM的应用对于准确的患者-临床医生沟通仍然未被探索.
研究的目的:
- 评估LLM对辐射瘤学患者护理问题的答案的质量.
- 使用特定领域的专业知识和域异的指标评估响应.
主要方法:
- 截面研究从NCI和RSNA网站获取问题 (2023年2月至3月).
- 使用ChatGPT版本3.5来生成对这些查询的响应 (2023年2月至4月).
- 辐射瘤学家和物理学家使用利克特尺度和计算域不可知度量对LLM响应与专家答案进行了评估.
主要成果:
- 与专家答案相比,LLM的答案在正确性 (94%),完整性 (77%) 和简洁性 (91%) 方面获得了很高的评分.
- 在115个答案中,只有2个涉及潜在的危害.
- 法学士答案的可读性得分 (大学水平) 比专家答案 (10年级) 高.
结论:
- 在辐射瘤学中,LLM可以产生准确,全面和简洁的反应,损害最小.
- 法学士生成的语言与专家相比,但在更高的阅读水平.
- 进一步的再培训可能会增强LLM作为在瘤学和其他医疗领域的宝贵患者资源.
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