Logan Julia A, Sadhu Sriya, Hazlewood Cameo, Denton Melissa, Burke Sara E, Simone-Soule Christina A, Black Caroline, Ciaverelli Corey, Stulb Jacqueline, Nourzadeh Hamidreza, Vinogradskiy Yevgeniy, Leader Amy, Dicker Adam P, Choi Wookjin, Simone Nicole L
Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA.
Sidney Kimmel Comprehensive Cancer Center, Thomas Jefferson University Hospitals, Philadelphia, PA 19107, USA.
Nutrients. 2025 Mar 28;17(7):1176. doi: 10.3390/nu17071176.
: Weight management is directly linked to cancer recurrence and survival, but unfortunately, nutritional oncology counseling is not typically covered by insurance, creating a disparity for patients without nutritional education and food access. Novel ways of imparting personalized nutrition advice are needed to address this issue. Large language models (LLMs) offer a promising path toward tailoring dietary advice to individual patients. This study aimed to assess the capacity of LLMs to offer personalized dietary advice to patients with breast cancer. : Thirty-one prompt templates were designed to evaluate dietary recommendations generated by ChatGPT and Gemini with variations within eight categorical variables: cancer stage, comorbidity, location, culture, age, dietary guideline, budget, and store. Seven prompts were selected for four board-certified oncology dietitians to also respond to. Responses were evaluated based on nutritional content and qualitative observations. A quantitative comparison of the calories and macronutrients of the LLM- and dietitian-generated meal plans via the Acceptable Macronutrient Distribution Ranges and United States Department of Agriculture's estimated calorie needs was performed. : The LLMs generated personalized grocery lists and meal plans adapting to location, culture, and budget but not age, disease stage, comorbidities, or dietary guidelines. Gemini provided more comprehensive responses, including visuals and specific prices. While the dietitian-generated diets offered more adherent total daily calorie contents to the United States Department of Agriculture's estimated calorie needs, ChatGPT and Gemini offered more adherent macronutrient ratios to the Acceptable Macronutrient Distribution Range. Overall, the meal plans were not significantly different between the LLMs and dietitians. LLMs can provide personalized dietary advice to cancer patients who may lack access to this care. Grocery lists and meal plans generated by LLMs are applicable to patients with variable food access, socioeconomic means, and cultural preferences and can be a tool to increase health equity.
体重管理与癌症复发和生存率直接相关，但不幸的是，营养肿瘤学咨询通常不在保险覆盖范围内，这给缺乏营养教育和食物获取途径的患者造成了差异。需要新的方式来提供个性化营养建议以解决这一问题。大语言模型为根据个体患者量身定制饮食建议提供了一条有前景的途径。本研究旨在评估大语言模型为乳腺癌患者提供个性化饮食建议的能力。设计了31个提示模板，以评估ChatGPT和Gemini生成的饮食建议，这些建议在八个分类变量中有所不同：癌症分期、合并症、地点、文化、年龄、饮食指南、预算和商店。选择了七个提示让四位获得董事会认证的肿瘤营养师进行回应。根据营养成分和定性观察对回复进行评估。通过可接受的宏量营养素分布范围和美国农业部估计的卡路里需求，对大语言模型和营养师生成的饮食计划的卡路里和宏量营养素进行了定量比较。大语言模型生成了适应地点、文化和预算但不适应年龄、疾病分期、合并症或饮食指南的个性化购物清单和饮食计划。Gemini提供了更全面的回复，包括视觉效果和具体价格。虽然营养师生成的饮食提供的每日总卡路里含量更符合美国农业部估计的卡路里需求，但ChatGPT和Gemini提供的宏量营养素比例更符合可接受的宏量营养素分布范围。总体而言，大语言模型和营养师生成的饮食计划没有显著差异。大语言模型可以为可能无法获得此类护理的癌症患者提供个性化饮食建议。大语言模型生成的购物清单和饮食计划适用于食物获取途径、社会经济状况和文化偏好各不相同的患者，并且可以成为促进健康公平的工具。
