概括
本研究引入了一个框架,以检测AI在瘤学中的伪造,确保AI生成的临床笔记保持瘤学推理和程序逻辑,以获得可靠的AI集成.
科学领域:
- 人工智能在医学中的应用
- 瘤学 信息学 信息学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在瘤学中越来越多地用于临床文档.
- LLM的流利程度可以掩盖冒充瘤学家的风险,而没有真正的瘤学推理.
- 现有的方法缺乏对人工智能生成的瘤学内容进行特定领域的检查.
研究的目的:
- 提出一个框架,用于检测域级角色冒充在AI产生的瘤传播.
- 在人工智能生成的临床笔记中量化语义忠实性和程序连贯性.
- 保障生成性AI在瘤实践中的可靠整合.
主要方法:
- 开发了使用语义忠实性和程序连贯性 (PC) 的角色冒充指数 (RII).
- 将生成的内容与来自本体学 (SNOMED CT,UMLS,NCIt,OncoTree) 的专家知识保持一致.
- 评估了4800份临床瘤学笔记 (人类与人工智能的作者).
主要成果:
- 该框架检测了瘤术语,阶段,生物标志物逻辑和治疗序列的不一致性.
- 在区分人类和人工智能作者方面获得了94%的F1得分和0.96的AUROC.
- 在不依赖于肤浅的语言线索的情况下,成功识别了假冒.
结论:
- 拟议的框架提供了一个原则上的保障,防止AI在瘤学中的角色模仿.
- 将知识图对齐与程序逻辑评分相结合,是检测假冒的新方法.
- 这种方法支持在癌症护理文档中可靠使用生成AI.
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