弥合癌症护理的差距:利用大型语言模型提供可访问的饮食建议.
Julia A Logan1, Sriya Sadhu1, Cameo Hazlewood1
1Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA.
Nutrients
|April 12, 2025
概括
大型语言模型 (LLM) 可以提供个性化的癌症营养建议,适应患者的需求,如文化和预算. 这项技术可以改善那些无法获得专业护理的人的健康公平.
科学领域:
- 在瘤学瘤学.
- 营养科学 营养科学
- 人工智能的人工智能
背景情况:
- 体重管理显著影响癌症复发和存活率.
- 营养瘤咨询面临保险覆盖的差距,造成患者护理的差异.
- 对癌症患者来说,个性化的营养建议至关重要,需要创新的输送方法.
研究的目的:
- 评估大型语言模型 (LLM) 在为乳腺癌患者提供个性化的饮食建议方面的能力.
- 为了比较LLM生成的饮食计划与由瘤学饮食师创建的饮食计划.
主要方法:
- 31个提示模板评估了八个变量 (阶段,并发症,位置,文化,年龄,准则,预算,商店) 的ChatGPT和Gemini响应.
- 为了进行比较,四位经过董事会认证的瘤营养师回答了七个提示.
- 量化分析了营养含量,质量方面,卡路里和宏观营养素的坚持.
主要成果:
- 根据地理位置,文化和预算,LLMs适应了餐饮计划,但不适应年龄,疾病阶段或并发症.
- 双子座提供了更详细的答案,包括视觉和定价.
- 通过LLM生成的计划显示,比饮食计划更好地遵守可接受的宏观营养素分布范围的宏观营养素比例.
- 对美国农业部估计的卡路里含量遵守在营养学家生成的饮食中更高.
结论:
- 士可以提供个性化的饮食建议,增强癌症患者的健康公平性,而癌症患者获得护理的机会有限.
- 通过LLM生成的杂货清单和餐饮计划可以适应各种食物获取,社会经济地位和文化偏好.
- 虽然存在差异,但LLM和营养师的饮食计划没有显示出明显的整体变化,这表明LLM的适用性.
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