营养科学中的GenAI (GAINS):未来研究的系统审查和报告框架
Bettina Hieronimus1, Maria-Laura Lopez-Aguirre2, Marc Birringer3
1Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.
Nutrition research (New York, N.Y.)
|October 25, 2025
概括
大型语言模型 (LLM) 驱动的聊天机器人显示出营养建议的潜力,但由于复杂案例的限制和偶尔的不准确性,它们尚未准备好不受监督的使用. 需要进一步的研究来提高它们的可靠性.
科学领域:
- 营养科学 营养科学
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 越来越多地融入医疗保健.
- 通过LLM驱动的聊天机器人正在探索其提供营养指导的潜力.
- 关于聊天机器人提供的营养建议质量的现有证据需要综合.
研究的目的:
- 系统地审查和综合LLM驱动的聊天机器人对营养建议质量的证据.
- 评估聊天机器人的适用性,以提供有关代谢疾病,食物过敏,营养摄入量和怀孕/哺乳期营养的建议.
- 识别当前研究中的局限性和方法上的差距.
主要方法:
- 进行了预先注册的系统审查 (CRD42025619448).
- 一项广泛的搜索确定了2469个记录,其中13项研究符合纳入标准.
- 进行了标准化的数据提取和质量评估.
主要成果:
- 用LLM驱动的聊天机器人显示出潜力,但在复杂的案例中扎,并且可能提供不正确的信息.
- 在评估标准和研究设计中发现了实质性的方法异质性.
- 限制包括主观测量和缺乏可复制性测试.
结论:
- 基于LLM的聊天机器人显示出支持营养建议的潜力,但尚未适合无监督使用.
- 显著的方法差距需要在未来的研究中提高严格性,透明度和可比性.
- 一项拟议的报告准则,即营养科学中的生成人工智能 (GAINS),旨在解决这些差距.
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