对医生和患者的生成人工智能的应用瘤学 Letters-AI-OncLetters
Adel Shahnam1, Udit Nindra2, Nadia Hitchen1
1Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
JCO clinical cancer informatics
|May 2, 2025
概括
像ChatGPT这样的大型语言模型可以在瘤学中生成准确的医生和患者信件. 虽然通常是安全和可理解的,但为了最佳的临床使用,需要在细节和流量方面进行微小的改进.
科学领域:
- 医学瘤学 医学瘤学
- 医疗保健中的人工智能
- 临床交流 临床沟通
背景情况:
- 大型语言模型 (LLM) 在临床环境中表现有前途,但它们在医学瘤学中的有效性需要正式评估.
- 评估LLMs的质量,准确性和有效性对于生成临床文档至关重要.
研究的目的:
- 评估ChatGPT在从瘤学临床病例笔记中生成医生和患者信件的能力.
- 评估人工智能生成的医疗通信的准确性,相关性,可读性和安全性.
主要方法:
- 瘤学家创建了合成瘤病例笔记,以便快速开发和生成信件.
- 通过ChatGPT生成的医生和患者信件被瘤学家和消费者使用利克特尺度评估.
- 患者的信件进一步使用患者教育材料评估工具 (PEMAT-P) 和可读性指数进行了评估.
主要成果:
- 95%的瘤学家认为医生信非常准确,全面和相关,没有安全问题.
- 患者的信件达到七年级的阅读水平和高可理解度得分 (PEMAT-P>80%).
- 虽然通常是明确的,但一些患者的信件有轻微的遗漏和风格不一致;72%的消费者会接受人工智能生成的信件.
结论:
- 通过结构化的提示,ChatGPT产生高质量的临床和患者通信信件,符合既定标准.
- 没有发现临床安全问题,但建议对细节和自然语言流量进行改进.
- 进一步研究将人工智能生成的和人类写的字母进行比较是有必要的,以优化实践中的实用性.
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