模拟同情互动与合成LLM生成的癌症患者人格
Rezaur Rashid1, Saba Kheirinejad1, Brianna M White1
1Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center (UTHSC), Memphis, Tennessee, USA.
Studies in health technology and informatics
|October 3, 2025
概括
本研究引入了使用大型语言模型 (LLM) 模拟患者互动的同情AI框架,旨在改善放射治疗 (RT) 期间的心理社会支持,并提高治疗坚持.
科学领域:
- 在瘤学中使用人工智能
- 临床心理学 临床心理学
- 医疗信息学 医疗信息学
背景情况:
- 无计划中断放射治疗 (RT) 带来临床风险.
- 目前,针对瘤患者的个性化心理社会支持是有限的.
- 健康的社会决定因素 (SDoH) 显著影响患者的护理和遵守.
研究的目的:
- 提出一个概念验证框架,用于模拟和评估同情的人工智能患者互动.
- 探索使用大型语言模型 (LLM) 来生成合成患者人格和同情反应.
- 建立一个开发人工智能驱动干预措施的基础,以改善RT遵守.
主要方法:
- 开发了一个使用双重LLM的框架:一个用于人格生成,另一个用于同情反应.
- 使用非识别的人口,临床和SDoH数据创建了现实的合成瘤患者人物.
- 评估模拟对话使用统计相似性,定量指标 (BERTScore,SDoH相关性,同理心) 和定性人类评估.
主要成果:
- 证明了可扩展,安全和上下文意识的对话生成的可行性,用于早期AI开发.
- 验证了框架能够创建独特和相关的患者人格的能力.
- 证实了人工智能提供细微和适合背景的同情反应的潜力.
结论:
- 开发的框架提供了一种可行的方法,用于对同情临床支持工具的伦理测试.
- 这种方法可以促进人工智能驱动干预措施的发展,以增强患者的支持和RT坚持.
- 未来的工作可以在这个基础上扩展到支持性瘤护理中更复杂的AI应用.
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