用大型语言模型重新定义数字健康接口
Fergus Imrie1, Paulius Rauba2, Mihaela van der Schaar2
1Department of Statistics, University of Oxford, Oxford, United Kingdom.
Frontiers in artificial intelligence
|October 13, 2025
概括
大型语言模型 (LLM) 可以通过作为外部工具的代理来增强数字健康工具,提高临床可用性和信任. 这种方法解决了LLM的局限性,例如医疗保健应用中的幻觉.
科学领域:
- 数字健康数字健康
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 数字健康工具提供了潜在的医疗保健改进,但由于可用性和信任问题而面临采用障碍.
- 大型语言模型 (LLM) 展示了先进的文本处理能力,表明了广泛的医疗保健应用.
- 由于潜在的不一致性和无意义的输出 (幻觉),LLM的直接临床使用具有挑战性.
研究的目的:
- 展示基于LLM的系统,利用外部工具作为数字健康的新型接口.
- 增强数字医疗工具和人工智能模型的实用性和实际影响.
- 为了解决临床环境中LLM目前的局限性,例如幻觉.
主要方法:
- 基于LLM的系统被设计为与外部工具交互的代理.
- 开发一种新的接口,通过LLMs将临床医生与数字技术联系起来.
- 基于LLM的接口的定量评估,使用心血管疾病和中风风险预测示例.
主要成果:
- 基于LLM的系统有效地利用了外部工具,创建了一个功能界面.
- 在预测心血管疾病和中风风险方面,对表现进行了定量评估.
- 与传统的数字工具接口相比,新型接口展示了优势.
结论:
- 基于LLM的代理系统为将先进的人工智能集成到临床工作流程中提供了可行的解决方案.
- 这些系统提高了数字健康工具的可用性和可信度.
- 该方法减轻了LLM特定的挑战,如幻觉,为更广泛的AI在医疗保健中的采用铺平了道路.
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