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Implications of Artificial Intelligence in Clinical Nutrition and Non-Communicable Chronic Related Diseases of Obesity for Patients and Health Professionals

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

Deadline for manuscript submissions: 25 June 2026 | Viewed by 4529

Special Issue Editor

Dr. Daniel-Antonio de Luis Roman

E-Mail Website
Guest Editor
1. Servicio de Endocrinología y Nutrición, Hospital Clínico Universitario de Valladolid, Av. Ramón y Cajal, 3, 47003 Valladolid, Spain
2. Instituto de Endocrinología y Nutrición (IENVA), Universidad de Valladolid, Av. Ramón y Cajal, 3, 47003 Valladolid, Spain
Interests: obesity; nutrigenetics; enteral nutrition; malnutrition related to the disease
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) is reshaping numerous scientific disciplines, and clinical nutrition is experiencing a significant transformation as a result. This Special Issue of Nutrients is dedicated to exploring how AI-driven technologies are revolutionizing the field by addressing critical challenges, refining research methodologies, and enabling the development of highly personalized and precise nutritional interventions and new potential diagnostic tools. By leveraging AI, researchers can uncover complex relationships between diet, obesity, non-communicable chronic diseases related to obesity, and malnutrition related to disease. These new potentialities will allow for new diagnostic approaches with images and various biological signals in this type of disease, approaches to the analysis of large databases with the discovery of new diagnostic or prognostic algorithms, and even the design of new educational strategies for health professionals and new methodologies in clinical and basic research, as well as translational research.

This new Special Issue will feature pioneering studies and innovative applications of AI in clinical nutrition. Topics of interest include the use of general artificial intelligence in clinical nutrition, virtual assistants, or chat bots for training health professionals, metaverse tools, deep learning and machine learning techniques in nutritional diseases databases, medical image segmentation tools for nutritional diagnosis, and new explanatory artificial intelligences and their uses in clinical nutrition, as well as new educational and research strategies using cutting-edge tools in artificial intelligence.

As Guest Editor, I warmly encourage researchers, professors, and professionals in the field of clinical nutrition to submit original studies, meta-analyses, and comprehensive reviews that explore contemporary challenges, introduce ground-breaking AI methodologies, or demonstrate the real-world applications of AI in this broad field of clinical nutrition and diseases. Through this collective effort, we aim to deepen our knowledge of AI’s transformative potential in clinical nutrition and to inspire future innovations, with the aim to improve the health of our patients.

Dr. Daniel-Antonio de Luis Roman
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
  • chat-bots
  • clinical nutrition
  • deep and machine learning
  • education
  • explainable artificial intelligence
  • generative artificial intelligence
  • image analysis-based AI
  • malnutrition
  • non-communicable chronic diseases related to obesity

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Published Papers (3 papers)

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Research

38 pages, 7535 KB  
Article
Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios
by Marcela Rodríguez-Jiménez, Gustavo Daniel Martín-del-Campo-Becerra, Sandra Sumalla-Cano, Jorge Crespo-Álvarez and Iñaki Elio
Nutrients 2025, 17(22), 3613; https://doi.org/10.3390/nu17223613 - 19 Nov 2025
Viewed by 737
Abstract
Background/Objectives: Estimating energy and macronutrients from food images is clinically relevant yet challenging, and rigorous evaluation requires transparent accuracy metrics with uncertainty and clear acknowledgement of reference data limitations across heterogeneous sources. This study assessed ChatGPT-5, a general-purpose vision-language model, across four [...] Read more.
Background/Objectives: Estimating energy and macronutrients from food images is clinically relevant yet challenging, and rigorous evaluation requires transparent accuracy metrics with uncertainty and clear acknowledgement of reference data limitations across heterogeneous sources. This study assessed ChatGPT-5, a general-purpose vision-language model, across four scenarios differing in the amount and type of contextual information provided, using a composite dataset to quantify accuracy for calories and macronutrients. Methods: A total of 195 dishes were evaluated, sourced from Allrecipes.com, the SNAPMe dataset, and Home-prepared, weighed meals. Each dish was evaluated under Case 1 (image only), Case 2 (image plus standardized non-visual descriptors), Case 3 (image plus ingredient lists with amounts), and Case 4 (replicates Case 3 but excluding the image). The primary endpoint was kcal Mean Absolute Error (MAE); secondary endpoints included Median Absolute Error (MedAE) and Root Mean Square Error (RMSE) for kcal and macronutrients (protein, carbohydrates, and lipids), all reported with 95% Confidence Intervals (CIs) via dish-level bootstrap resampling and accompanied by absolute differences (Δ) between scenarios. Inference settings were standardized to support reproducibility and variance estimation. Source stratified analyses and quartile summaries were conducted to examine heterogeneity by curation level and nutrient ranges, with additional robustness checks for error complexity relationships. Results and Discussion: Accuracy improved from Case 1 to Case 2 and further in Case 3 for energy and all macronutrients when summarized by MAE, MedAE, and RMSE with 95% CIs, with absolute reductions (Δ) indicating material gains as contextual information increased. In contrast to Case 3, estimation accuracy declined in Case 4, underscoring the contribution of visual cues. Gains were largest in the Home-prepared dietitian-weighed subset and smaller yet consistent for Allrecipes.com and SNAPMe, reflecting differences in reference curation and measurement fidelity across sources. Scenario-level trends were concordant across sources, and stratified and quartile analyses showed coherent patterns of decreasing absolute errors with the provision of structured non-visual information and detailed ingredient data. Conclusions: ChatGPT-5 can deliver practically useful calorie and macronutrient estimates from food images, particularly when augmented with standardized nonvisual descriptors and detailed ingredients, as evidenced by reductions in MAE, MedAE, and RMSE with 95% CIs across scenarios. The decline in accuracy observed when the image was omitted, despite providing detailed ingredient information, indicates that visual cues contribute meaningfully to estimation performance and that improvements are not solely attributable to arithmetic from ingredient lists. Finally, to promote generalizability, it is recommended that future studies include repeated evaluations across diverse datasets, ensure public availability of prompts and outputs, and incorporate systematic comparisons with non-artificial-intelligence baselines. Full article
(This article belongs to the Special Issue Implications of Artificial Intelligence in Clinical Nutrition and Non-Communicable Chronic Related Diseases of Obesity for Patients and Health Professionals)
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16 pages, 508 KB  
Article
Prognostic Value of Computed Tomography-Derived Muscle Density for Postoperative Complications in Enhanced Recovery After Surgery (ERAS) and Non-ERAS Patients
by Fiorella X. Palmas, Marta Ricart, Amador Lluch, Fernanda Mucarzel, Raul Cartiel, Alba Zabalegui, Elena Barrera, Nuria Roson, Aitor Rodriguez, Eloy Espin-Basany and Rosa M. Burgos
Nutrients 2025, 17(14), 2264; https://doi.org/10.3390/nu17142264 - 9 Jul 2025
Cited by 1 | Viewed by 1351
Abstract
Background: Prehabilitation programs improve postoperative outcomes in vulnerable patients undergoing major surgery. However, current screening tools such as the Malnutrition Universal Screening Tool (MUST) may lack the sensitivity needed to identify those who would benefit most. Muscle quality assessed by Computed Tomography [...] Read more.
Background: Prehabilitation programs improve postoperative outcomes in vulnerable patients undergoing major surgery. However, current screening tools such as the Malnutrition Universal Screening Tool (MUST) may lack the sensitivity needed to identify those who would benefit most. Muscle quality assessed by Computed Tomography (CT), specifically muscle radiodensity in Hounsfield Units (HUs), has emerged as a promising alternative for risk stratification. Objective: To evaluate the prognostic performance of CT-derived muscle radiodensity in predicting adverse postoperative outcomes in colorectal cancer patients, and to compare it with the performance of the MUST score. Methods: This single-center cross-sectional study included 201 patients with non-metastatic colon cancer undergoing elective laparoscopic resection. Patients were stratified based on enrollment in a multimodal prehabilitation program, either within an Enhanced Recovery After Surgery (ERAS) protocol or a non-ERAS pathway. Nutritional status was assessed using MUST, SARC-F questionnaire (strength, assistance with walking, rise from a chair, climb stairs, and falls), and the Global Leadership Initiative on Malnutrition (GLIM) criteria. CT scans at the L3 level were analyzed using automated segmentation to extract muscle area and radiodensity. Postoperative complications and hospital stay were compared across nutritional screening tools and CT-derived metrics. Results: MUST shows limited sensitivity (<27%) for predicting complications and prolonged hospitalization. In contrast, CT-derived muscle radiodensity demonstrates higher discriminative power (AUC 0.62–0.69), especially using a 37 HU threshold. In the non-ERAS group, patients with HU ≤ 37 had significantly more complications (33% vs. 15%, p = 0.036), longer surgeries, and more severe events (Clavien–Dindo ≥ 3). Conclusions: Opportunistic CT-based assessment of muscle radiodensity outperforms traditional screening tools in identifying patients at risk of poor postoperative outcomes, and may enhance patient selection for prehabilitation strategies like the ERAS program. Full article
(This article belongs to the Special Issue Implications of Artificial Intelligence in Clinical Nutrition and Non-Communicable Chronic Related Diseases of Obesity for Patients and Health Professionals)
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13 pages, 854 KB  
Article
Predicting Weight Loss Success After Gastric Sleeve Surgery: A Machine Learning-Based Approach
by Mónica Casas Domínguez, Isabel Herrena Montano, Juan José López Gómez, Beatriz Ramos Bachiller, Daniel Antonio de Luis Román and Isabel de la Torre Díez
Nutrients 2025, 17(8), 1391; https://doi.org/10.3390/nu17081391 - 21 Apr 2025
Viewed by 1324
Abstract
Background/Objectives: Obesity is a global health issue, and in this context, bariatric surgery is considered the most effective treatment for severe cases. However, postoperative outcomes vary widely among individuals, driving the development of tools to predict body weight loss success. The main objective [...] Read more.
Background/Objectives: Obesity is a global health issue, and in this context, bariatric surgery is considered the most effective treatment for severe cases. However, postoperative outcomes vary widely among individuals, driving the development of tools to predict body weight loss success. The main objective of this paper is to evaluate predictive variables for successful weight loss one year after Sleeve bariatric surgery, defining success as a weight loss exceeding 30%. Methods: A dataset of 94 cases was included in this study. Data were collected between 2013 and 2018 from the Nutrition Section of the Endocrinology and Nutrition Department in the Eastern Area of Valladolid, Spain. Machine learning algorithms applied included Random Forest, Multilayer Perceptron, XGBoost, Decision Tree, Logistic Regression, and Support Vector Machines (SVMs). Results: The SVM model demonstrated the best performance, attaining an accuracy of 88% and an area under the curve (AUC) of 0.76 with a 95% CI between 0.5238 and 0.9658. The main predictive variables identified were potassium (K), folic acid, alkaline phosphatase (ALP), height, transferrin, weight, body mass index (BMI), triglyceride (Tg), Beck Depression Test score, and insulin levels. Conclusions: In conclusion, this study highlights the potential of machine learning models, particularly Support Vector Machines (SVMs), in predicting successful weight loss after Sleeve bariatric surgery. The key predictive variables identified include biochemical markers, anthropometric measures, and psychological factors, emphasizing the multifactorial nature of postoperative weight loss outcomes. Full article
(This article belongs to the Special Issue Implications of Artificial Intelligence in Clinical Nutrition and Non-Communicable Chronic Related Diseases of Obesity for Patients and Health Professionals)
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