用生成性人工智能重新想象癌症护理:大型语言模型的承诺
Ji-Eun Irene Yum1, Syed Arsalan Ahmed Naqvi1, Ben Zhou2
1Mayo Clinic, Phoenix, AZ.
JCO clinical cancer informatics
|December 1, 2025
概括
大型语言模型 (LLM) 通过分析广泛的患者数据,为癌症护理提供了新的机会. 未来的研究应该关注患者的观点和安全整合的现实世界测试.
科学领域:
- 人工智能在医学中的应用
- 瘤学 信息学 信息学
背景情况:
- 大型语言模型 (LLM) 展示了先进的自然语言处理能力,开辟了医疗保健领域的新途径.
- 癌症患者的旅程产生了丰富的纵向数据,非常适合LLM分析.
研究的目的:
- 审查LLM的基本概念,包括架构,培训和优化.
- 为患者,临床医生,研究人员和学习者探索LLM在瘤学的应用.
- 解决与LLM在癌症护理中的实施相关的局限性和风险.
主要方法:
- 对LLM基础的审查:定义,架构,培训和优化技术 (提示工程,检索增强生成).
- 探索LLMs的多机构框架.
- 综合目前关于LLM在瘤学的好处和风险的研究.
- 确定未来的研究方向.
主要成果:
- 由于广泛的患者数据,LLM可以在各种NLP任务中概括,在瘤学中具有显著的潜力.
- 潜在的好处涵盖患者,瘤学家,研究人员和学习者,增强癌症护理的各个方面.
- 确定的风险包括模型幻觉,数据偏差,患者隐私问题和潜在的临床医生失业.
结论:
- 在转变瘤学方面,LLM显著有前途,但仔细考虑风险至关重要.
- 未来的研究必须优先考虑患者的观点,临床基准,现实世界的验证,以及对LLM推理的更深入理解.
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