现实世界营养评估的大型语言模型:结构化提示,多模型验证和专家监督
Aia Ase1, Jacek Borowicz2, Kamil Rakocy3
1Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, 02-097 Warsaw, Poland.
Nutrients
|January 10, 2026
概括
先进的人工智能 (AI) 模型在分类波兰食品方面显示出高准确度,有助于营养评估. 虽然人工智能提供了效率,但专家审查对于可靠的饮食数据至关重要.
科学领域:
- 在营养方面的人工智能
- 计算语言学 计算语言学
- 饮食评估方法的方法论
背景情况:
- 传统的饮食评估方法存在报告偏差和可扩展性问题.
- 大型语言模型 (LLM) 显示出对自动食品分类的承诺.
- 对于波兰语等复杂的非英语语言的法学士学位存在有限的验证.
研究的目的:
- 以波兰语验证先进的LLMs用于自动化食品分类.
- 为了比较不同LLM和提示策略的性能.
- 在饮食分类中对人类专家的共识进行LLM准确性评估.
主要方法:
- 对1992年波兰长期护理机构 (LTCF) 队列中的食品进行分析.
- 使用了三个LLM (克劳德·奥普斯4.5,双子星3pro,GPT-5.1-聊天-最新).
- 采用了两个提示策略:结构化双步 (NOVA,WHO标准) 和简化单步.
- 将LLM分类与两个人类专家的共识判断进行了比较.
主要成果:
- 所有LLM都表现出与人类专家 (90.3-94.2%) 的高度一致.
- 在对对LLM比较中观察到的统计学意义上的差异 (p < 0.001).
- 结构化的提示为不健康的项目提供了高回忆率,但具有较低的特异性;简化的提示提供了更好的整体准确性和平衡的配置文件.
结论:
- 先进的LLM实现了波兰饮食分类的近专家准确性,提高了工作流程的效率.
- 专家监督对于验证人工智能驱动的营养评估至关重要.
- 多模型共识和特定语言的验证提高了AI在营养方面的可靠性.
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