从字节到咬:应用大型语言模型来增强营养建议
Karin Bergling1, Lin-Chun Wang2, Oshini Shivakumar3
1Artificial Intelligence Translational Innovation Hub, Renal Research Institute, New York, NY, USA.
Clinical kidney journal
|April 14, 2025
概括
大型语言模型 (LLM) 显示了慢性病 (CKD) 个性化营养的前景. 目前的LLM工具需要改进以进行准确的营养分析,但未来的AI进步预计将提高CKD患者的护理.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 在营养方面的人工智能
- 慢性病管理 慢性病管理
背景情况:
- 大型语言模型 (LLM) 正在融入日常生活,在医疗保健领域有潜在的应用.
- 个性化营养指导对于慢性病 (CKD) 患者至关重要.
- 医疗保健专业人员,患者,护理人员和LLM专家之间的合作对于有效的LLM整合至关重要.
研究的目的:
- 审查目前关于在CKD中使用LLM进行营养建议的文献.
- 确定在CKD中基于LLM的营养支持的局限性和潜在改进.
- 探索生成AI在CKD营养中的未来作用.
主要方法:
- 综合文献综述,重点关注CKD营养的LLM.
- 包括糖尿病和肥胖管理等相邻领域的发现.
- 专家对经过审查的文献进行策划.
主要成果:
- 目前对CKD营养支持的LLM应用程序有限,需要增强.
- LLM可以生成食谱,但往往低估了电解质和卡路里等关键营养成分.
- 人工智能技术的进步有望改善营养分析和脏健康的食物识别.
结论:
- 对CKD患者的基于LLM的营养支持处于早期阶段,但正在迅速发展.
- 未来的AI进步可能会实现准确的营养分析,视觉辅助工具和餐厅菜单建议.
- 为了在CKD营养护理中对AI采取平衡和乐观的方法,社区参与至关重要.
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