营养和饮食学的语言模型:一个范围审查
Cathal O'Hara1, Gráinne Kent1, Clarissa L Leydon1
1School of Population Health, Royal College of Surgeons in Ireland, Dublin, Ireland.
The American journal of clinical nutrition
|November 29, 2025
概括
生成型人工智能,或AI,显示了个性化的营养建议的希望,但需要更多的现实世界研究. 目前关于人工智能在饮食学的研究有限,突出需要更广泛的证据和公众意见.
科学领域:
- 营养科学中的人工智能 营养科学 人工智能
- 饮食学和计算健康学
背景情况:
- 生成型人工智能 (AI) 通过理解和生成文本提供了个性化营养的潜力.
- 然而,人工智能在营养方面存在公共卫生风险,包括不准确性,偏见和数据隐私问题.
- 对人工智能在营养和饮食学中的应用研究的范围尚不清楚.
研究的目的:
- 系统地审查语言模型在营养和饮食学的应用.
- 确定与这些人工智能应用相关的机遇和挑战.
主要方法:
- 在10个主要数据库 (Medline,EMBASE,PsycINFO,CINAHL,PubMed,全球健康,科学网,Scopus,DBLP和IEEE Xplore) 进行了全面的文献搜索.
- 审查遵循了预先定义的协议,包括对所有包含的文章进行前向和后向引用追逐.
- 在同行评审期刊上发表的原始研究被纳入了分析.
主要成果:
- 在2118篇确定文章中,包括了40篇,全部发表在2022年至2024年之间.
- 使用了23种不同的语言模型,其中9项研究通过检索增强生成或微调开发了特定领域的模型.
- 主要应用包括饮食建议 (21项研究),食品成分估计 (9项研究),食计划创建 (7项研究) 和数据提取/总结 (5项研究).
结论:
- 语言模型在营养和饮食学中的应用是一个新兴领域,现实世界的实施数据有限.
- 需要在实际环境中进行进一步的研究,以建立更强大的证据基础.
- 未来的研究应该纳入营养专业人员,患者和公众的观点.
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