基于HOF的光响应平台,用于大语言模型辅助的代谢疾病多式诊断
1Shanghai Key Lab of Chemical Assessment and Sustainability, School of Chemical Science and Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China.
Analytical chemistry
|October 23, 2025
概括
这项研究介绍了一种使用新型传感器和GPT-4o的AI驱动的嗅觉诊断平台. 它可以通过体味分析快速,准确地检测代谢疾病,以便获得医疗解释.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI),特别是大型语言模型 (LLM),正在改变各种科学领域.
- 嗅觉诊断,分析身体气味,为非侵入性疾病检测提供了一个有希望的途径.
- 现有的诊断方法可能具有侵入性或缺乏可访问性.
研究的目的:
- 开发一个人工智能增强的嗅觉诊断平台,将光响应传感器与GPT-4o模型集成在一起.
- 为了能够选择性和快速检测关键的气味分子,表明代谢障碍.
- 提供基于人工智能对传感器数据分析的临床解释性诊断.
主要方法:
- 制造了一种用欧添加的以2,5-基碳酸为基础的结有机框架 (Eu-FDA@HOF) 传感器.
- 利用传感器的光发光特性,对酸 (PPA) 和肌素 (Cr) 进行选择性检测.
- 采用多式联网GPT-4o模型,以人为本的策略为指导,用于数据分析和诊断生成.
主要成果:
- 欧-FDA@HOF传感器展示了超长的室温光和对PPA和Cr的选择性检测.
- 达到了PPA (1.53μM) 和Cr (1.18μM) 的低检测极限.
- 人工智能平台准确预测了分析物度,并产生了临床相关的诊断解释.
结论:
- 一个集成的人工智能嗅觉诊断平台,结合先进材料和LLMs已经成功演示.
- 这个平台提供了一个基于体味分析的智能医疗诊断的多功能和可访问的方法.
- 光发光传感和AI解释之间的协同作用为新型诊断工具铺平了道路.
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