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The Importance of Modern Digital for Diet Assessment, Self-Testing and Weight Management

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition Methodology & Assessment".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 843

Special Issue Editor


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Guest Editor
Hamlyn Centre, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
Interests: food AI; dietary assessment; deep learning for healthcare applications; food recommendation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionizing the field of food recognition and nutrition estimation, driving innovations in diet assessment, self-testing, and weight management. With its ability to analyze complex datasets, AI provides solutions that promote healthier eating habits, prevent diet-related diseases, and deliver personalized nutritional care. These advancements are reshaping both individual and public health strategies.

Breakthroughs in AI technologies, including the development of Large Language Models (LLMs), are enhancing the accuracy of nutrition estimation and enabling adaptive, personalized dietary interventions. Furthermore, the integration of AI with IoT devices, wearables, and advanced imaging systems offers holistic solutions for nutrition monitoring, assessment, and intervention. These systems address the limitations of current methods by providing scalable, efficient, and highly personalized approaches within the field of nutrition.

This Special Issue of Nutrients, entitled “The Importance of Modern Digital for Diet Assessment, Self-Testing and Weight Management”, welcomes the submission of high-quality original studies and review articles that explore recent advancements in the application of AI and digital technologies to nutrition science and health. We also welcome clinical studies that leverage modern digital tools for the monitoring and analysis of nutrition, further advancing the integration of AI into real-world healthcare applications.

Best regards,

Dr. Frank Po Wen Lo
Guest Editor

Manuscript Submission Information

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Keywords

  • Artificial Intelligence (AI)
  • digital tools
  • large language models
  • dietary assessment
  • food recommendation
  • IoT devices
  • nutrition estimation
  • weight management

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Published Papers (1 paper)

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Research

11 pages, 1096 KiB  
Article
Bridging Gaps in Cancer Care: Utilizing Large Language Models for Accessible Dietary Recommendations
by Julia A. Logan, Sriya Sadhu, Cameo Hazlewood, Melissa Denton, Sara E. Burke, Christina A. Simone-Soule, Caroline Black, Corey Ciaverelli, Jacqueline Stulb, Hamidreza Nourzadeh, Yevgeniy Vinogradskiy, Amy Leader, Adam P. Dicker, Wookjin Choi and Nicole L. Simone
Nutrients 2025, 17(7), 1176; https://doi.org/10.3390/nu17071176 - 28 Mar 2025
Viewed by 511
Abstract
Background/Objectives: 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 [...] Read more.
Background/Objectives: 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. Methods: 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. Conclusions: 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. Full article
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