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AI and Precision Nutrition: Digital Innovations for Dietary Assessment and Self-Testing

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

Deadline for manuscript submissions: 15 March 2026 | Viewed by 5520

Special Issue Editor

Dr. Frank Po Wen Lo

E-Mail Website
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 “AI and Precision Nutrition: Digital Innovations for Dietary Assessment and Self-Testing”, 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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (3 papers)

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Research

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16 pages, 978 KB  
Article
Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight
by Aia Ase, Jacek Borowicz, Kamil Rakocy and Barbara Piekarska
Nutrients 2026, 18(1), 23; https://doi.org/10.3390/nu18010023 - 20 Dec 2025
Viewed by 154
Abstract
Background: Traditional dietary assessment methods face limitations including reporting bias and scalability challenges. Large language models (LLMs) offer potential for automated food classification, yet their validation in morphologically complex, non-English languages like Polish remains limited. Methods: We analyzed 1992 food items from a [...] Read more.
Background: Traditional dietary assessment methods face limitations including reporting bias and scalability challenges. Large language models (LLMs) offer potential for automated food classification, yet their validation in morphologically complex, non-English languages like Polish remains limited. Methods: We analyzed 1992 food items from a Polish long-term care facility (LTCF) cohort using three advanced LLMs (Claude Opus 4.5, Gemini 3 pro, and GPT-5.1-chat-latest) with two prompting strategies: a structured double-step prompt integrating NOVA and World Health Organization (WHO) criteria, and a simplified single-step prompt. Classifications were compared against consensus judgments from two human experts. Results: All LLMs showed high agreement with human experts (90.3–94.2%), but there were statistically significant differences in all pairwise comparisons (χ2 = 1174.5–1897.1; p < 0.001). The structured prompt produced very high Recall for UNHEALTHY items at the cost of lower Specificity, whereas the simplified prompt achieved higher overall Accuracy and a more balanced Recall–Specificity profile, indicating a trade-off between strict guideline adherence and alignment with general human judgment. Conclusions: Advanced LLMs demonstrate near-expert accuracy in Polish-language dietary classification, enhancing workflow efficiency by shifting effort toward validation. Expert oversight remains essential, and multi-model consensus alongside language-specific validation can improve AI reliability in nutrition assessment. Full article
(This article belongs to the Special Issue AI and Precision Nutrition: Digital Innovations for Dietary Assessment and Self-Testing)
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11 pages, 1096 KB  
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
Cited by 2 | Viewed by 1896
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
(This article belongs to the Special Issue AI and Precision Nutrition: Digital Innovations for Dietary Assessment and Self-Testing)
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Review

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45 pages, 3194 KB  
Review
The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature
by Kaitlyn Ngo, Simone Mekhail, Virginia Chan, Xinyi Li, Annabelle Yin, Ha Young Choi, Margaret Allman-Farinelli and Juliana Chen
Nutrients 2025, 17(22), 3515; https://doi.org/10.3390/nu17223515 - 10 Nov 2025
Viewed by 2751
Abstract
Background/objectives: The nutrition care process (NCP) is an evidence-based practice framework used in Medical Nutrition Therapy for the prevention, treatment, and management of non-communicable chronic health conditions. This review aimed to explore available artificial intelligence (AI)-integrated technologies across the NCP in dietetic [...] Read more.
Background/objectives: The nutrition care process (NCP) is an evidence-based practice framework used in Medical Nutrition Therapy for the prevention, treatment, and management of non-communicable chronic health conditions. This review aimed to explore available artificial intelligence (AI)-integrated technologies across the NCP in dietetic primary care, their uses, and their impacts on the NCP and patient outcomes. Method: Six databases were searched: MEDLINE, Embase, PsycINFO, Scopus, IEEE, and ACM digital library. Eligible studies were published between January 2007 and August 2024 and included human adult studies, AI-integrated technologies in the dietetic primary care setting, and patient-related outcomes. Extracted details focused on participant characteristics, dietitian involvement, and the type of AI system and its application in the NCP. Results: Ninety-seven studies were included. Three different AI systems (image or audio recognition, chatbots, and recommendation systems) were found. These were implemented in web-based or smartphone applications, wearable sensor systems, smart utensils, and software. Most AI-integrated technologies could be incorporated into one or more NCP stages. Seventy-nine studies reported user- or patient-related outcomes, with mixed findings, but all highlighted efficiencies of using AI. Higher patient engagement was observed with Chatbots. Seventeen studies raised concerns encompassing ethics and patient safety. Conclusions: AI systems show promise as a clinical support tool across most stages of the NCP. Whilst they have varying degrees of accuracy, AI demonstrates potential in improving efficiency, supporting personalised nutrition, and enhancing chronic disease management outcomes. Integrating AI education into dietetic training and professional development will be essential to ensure safe and effective use in practice. Full article
(This article belongs to the Special Issue AI and Precision Nutrition: Digital Innovations for Dietary Assessment and Self-Testing)
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