{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:27:15Z","timestamp":1785414435721,"version":"3.56.0"},"reference-count":73,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010607","name":"University of Perugia","doi-asserted-by":"publisher","award":["00448820548"],"award-info":[{"award-number":["00448820548"]}],"id":[{"id":"10.13039\/501100010607","id-type":"DOI","asserted-by":"publisher"}]},{"award":["00448820548"],"award-info":[{"award-number":["00448820548"]}],"id":[{"id":"https:\/\/ror.org\/00x27da85","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background: Critical illness is characterized by profound and rapidly evolving metabolic derangements driven by systemic inflammation, hypercatabolism, fluid shifts, and endocrine dysregulation. These dynamic changes markedly limit the accuracy of predictive equations, increasing the risk of both underfeeding and overfeeding. Indirect Calorimetry Energy represents the gold standard for measuring energy expenditure, while bioelectrical impedance vector analysis (BIVA) provides complementary insights into hydration status, cellular integrity, and body cell mass. In palliative care, AI-supported integration of indirect calorimetry and BIVA enables goal-concordant artificial nutrition by aligning energy delivery with real-time metabolic status while minimizing symptom burden. Artificial intelligence (AI) has emerged as a promising tool to integrate these heterogeneous data streams and support adaptive nutritional strategies. Methods: We conducted a structured narrative review of the literature published between 2000 and 2025 using PubMed, Scopus, Embase, and Web of Science. Artificial intelligence was not used to perform the literature search or study selection. Instead, AI was analyzed as a clinical and technological component within the included studies and explored as a future enabling strategy. Eligible publications involved adult critically ill patients and addressed indirect calorimetry, BIVA-derived parameters, or AI-based metabolic modeling applied to nutritional support. Given the heterogeneity of study designs and outcomes, findings were synthesized qualitatively. Results: Predictive equations showed substantial inaccuracy in unstable metabolic states, with errors frequently exceeding \u00b120\u201340%. Indirect calorimetry enabled individualized assessment of energy expenditure but remained limited by intermittent availability. Serial BIVA assessments consistently identified clinically relevant alterations in hydration status, body cell mass, and phase angle, the latter being strongly associated with adverse outcomes. Studies incorporating AI demonstrated improved integration of calorimetry, BIVA, and clinical variables, allowing identification of metabolic phenotypes, anticipation of metabolic shifts, and generation of adaptive nutritional recommendations. Conclusions: This narrative review highlights the complementary roles of Indirect Calorimetry and BIVA in characterizing metabolic needs in critical illness. Artificial intelligence does not replace these tools but enhances their clinical utility by integrating multidimensional data into dynamic, patient-specific nutritional strategies. The combined AI\u2013IC\u2013BIVA approach represents a promising framework for metabolic precision nutrition in the ICU, warranting prospective validation.<\/jats:p>","DOI":"10.3390\/nu18091387","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:09:33Z","timestamp":1777388973000},"page":"1387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Guided Artificial Nutrition in Critical Illness: Integrating Indirect Calorimetry and BIVA for Metabolic Precision"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2470-7057","authenticated-orcid":false,"given":"Marialaura","family":"Scarcella","sequence":"first","affiliation":[{"name":"Anesthesia and Intensive Care Department, Perugia University, 06121 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0154-0418","authenticated-orcid":false,"given":"Antonella","family":"Cotoia","sequence":"additional","affiliation":[{"name":"Anesthesia and Intensive Care Unit, Department of Medical Surgical Science, University Hospital of Foggia, 71121 Foggia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"Vetrugno","sequence":"additional","affiliation":[{"name":"Anesthesia and Intensive Care Unit, Department of Emergency, Health Integrated Agency of Friuli Centrale, Tolmezzo Hospital, 33028 Tolmezzo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2101-1118","authenticated-orcid":false,"given":"Emidio","family":"Scarpellini","sequence":"additional","affiliation":[{"name":"Internal Medicine and Nutritional Unit, \u2018Madonna del Soccorso\u2019 General Hospital, 63074 San Benedetto del Tronto, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5119-0850","authenticated-orcid":false,"given":"Gian Marco","family":"Petroni","sequence":"additional","affiliation":[{"name":"Anesthesia and Intensive Care Unit, Azienda Ospedaliera Santa Maria of Terni, 05100 Terni, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1626-3177","authenticated-orcid":false,"given":"Cristian","family":"Deana","sequence":"additional","affiliation":[{"name":"Anesthesia and Neurointensive Care Unit, Department of Anesthesia and Intensive Care, Health Integrated Agency of Friuli Centrale, 33100 Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7361-4052","authenticated-orcid":false,"given":"Rachele","family":"Simonte","sequence":"additional","affiliation":[{"name":"Anesthesia and Intensive Care Department, Perugia University, 06121 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Monti","sequence":"additional","affiliation":[{"name":"Anesthesia and Operating Room Department, Azienda Ospedaliera Santa Maria di Terni, 05100 Terni, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita","family":"Commissari","sequence":"additional","affiliation":[{"name":"Anesthesia and Intensive Care Unit, Azienda Ospedaliera Santa Maria of Terni, 05100 Terni, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9182-6964","authenticated-orcid":false,"given":"Edoardo","family":"De Robertis","sequence":"additional","affiliation":[{"name":"Anesthesia and Intensive Care Department, Perugia University, 06121 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9985-4126","authenticated-orcid":false,"given":"Elena","family":"Bignami","sequence":"additional","affiliation":[{"name":"Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"ref_1","first-page":"61","article-title":"Hypermetabolism and energy expenditure variability in severe trauma and burns","volume":"51","author":"Mogensen","year":"2019","journal-title":"Shock"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1093\/bja\/aeu187","article-title":"Metabolic response to the stress of critical illness","volume":"113","author":"Preiser","year":"2014","journal-title":"Br. J. Anaesth."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1007\/s00134-017-4880-3","article-title":"Early goal-directed nutrition versus standard of care in adult intensive care patients: The single-centre, randomised, outcome assessor-blinded EAT-ICU trial","volume":"43","author":"Allingstrup","year":"2017","journal-title":"Intensive Care Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"569","DOI":"10.3945\/ajcn.110.005074","article-title":"Permissive underfeeding and intensive insulin therapy in critically ill patients: A randomized controlled trial","volume":"93","author":"Arabi","year":"2011","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1056\/NEJMoa1502826","article-title":"Permissive Underfeeding or Standard Enteral Feeding in Critically Ill Adults","volume":"372","author":"Arabi","year":"2015","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1016\/j.clnu.2022.10.010","article-title":"Prognostic value of phase angle and bioelectrical impedance vector in critically ill patients: A systematic review and meta-analysis of observational studies","volume":"41","author":"Lima","year":"2022","journal-title":"Clin. Nutr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1016\/j.clnu.2020.01.017","article-title":"The clinical evaluation of the new indirect calorimeter developed by the ICALIC project","volume":"39","author":"Oshima","year":"2020","journal-title":"Clin. Nutr."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Falcioni, L., Guidetti, L., Baldari, C., Posada, A.S., Wing, C., Dover, L., and Meucci, M. (2025). Accuracy and repeatability of the COSMED\u00ae Q-NRG max mobile metabolic system. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0319394"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.clnu.2012.05.008","article-title":"Bioelectrical phase angle and impedance vector analysis\u2014Clinical relevance and applicability of impedance parameters","volume":"31","author":"Norman","year":"2012","journal-title":"Clin. Nutr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s11154-023-09803-7","article-title":"Future lines of research on phase angle: Strengths and limitations","volume":"24","author":"Bellido","year":"2023","journal-title":"Rev. Endocr. Metab. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1186\/s12967-025-07311-7","article-title":"Predicting mortality in critically ill patients: A machine learning approach to electrolyte imbalances and clinical risk factors","volume":"23","author":"Keryakos","year":"2025","journal-title":"J. Transl. Med."},{"key":"ref_12","first-page":"193","article-title":"Artificial intelligence-guided nutritional therapy in the ICU","volume":"29","author":"Peng","year":"2026","journal-title":"Curr. Opin. Clin. Nutr. Metab. Care"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Briassoulis, G., and Briassouli, E. (2025). AI-Enabled Precision Nutrition in the ICU: A Narrative Review and Implementation Roadmap. Nutrients, 18.","DOI":"10.3390\/nu18010110"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Buddrus, U., Kutza, J.O., Thye, J., Esdar, M., H\u00fcbner, U.H., and Liebe, J.D. (2026). PICO-based assessment and categorization of evidence for digital health interventions: An inductive framework development. Front. Digit. Health, 8.","DOI":"10.3389\/fdgth.2026.1755598"},{"key":"ref_15","first-page":"344","article-title":"Bioelectric impedance analysis for body composition measurement and other potential clinical applications in critical illness","volume":"27","author":"Moonen","year":"2021","journal-title":"Curr. Opin. Crit. Care"},{"key":"ref_16","first-page":"693","article-title":"Dynamic metabolic changes measured by indirect calorimetry during the early phase of septic shock: A prospective observational pilot study","volume":"76","author":"Emilie","year":"2021","journal-title":"Eur. J. Clin. Nutr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1016\/j.clnu.2024.07.048","article-title":"The usefulness of a new indirect calorimeter in critically ill adult patients","volume":"43","author":"Lakenman","year":"2024","journal-title":"Clin. Nutr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1186\/s13054-021-03830-z","article-title":"Changes in carbon dioxide production and oxygen uptake evaluated using indirect calorimetry in mechanically ventilated patients with sepsis","volume":"25","author":"Hirayama","year":"2021","journal-title":"Crit. Care"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1038\/ki.1994.305","article-title":"A new method for monitoring body fluid variation by bioimpedance analysis:the RXc graph","volume":"46","author":"Piccoli","year":"1994","journal-title":"Kidney Int."},{"key":"ref_20","first-page":"76","article-title":"Body fluid overload and bioelectrical impedance analysis in renal patients","volume":"22","author":"Piccoli","year":"1996","journal-title":"Miner. Electrolyte Metab."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1146\/annurev-nutr-062122-031443","article-title":"Energy Expenditure in Humans: Principles, Methods, and Changes Throughout the Life Course","volume":"44","author":"Ravussin","year":"2024","journal-title":"Annu. Rev. Nutr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.clnu.2020.06.024","article-title":"Indirect calorimetry: The 6 main issues","volume":"40","author":"Achamrah","year":"2021","journal-title":"Clin. Nutr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.clnu.2016.06.010","article-title":"Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group","volume":"36","author":"Oshima","year":"2017","journal-title":"Clin. Nutr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.clnesp.2024.07.1055","article-title":"How to interpret and apply the results of indirect calorimetry studies: A case-based tutorial","volume":"63","author":"Berger","year":"2024","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1177\/014860710302700121","article-title":"Clinical use of the respiratory quotient obtained from indirect calorimetry","volume":"27","author":"McClave","year":"2003","journal-title":"J. Parenter. Enter. Nutr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.clnesp.2024.11.009","article-title":"Course of measured energy expenditure over the first 10 days of critical illness: A nested prospective study in an adult surgical ICU","volume":"65","author":"Veldsman","year":"2025","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1002\/jpen.1450","article-title":"Optimization of Nutrition Therapy with the Use of Calorimetry to Determine and Control Energy Needs in Mechanically Ventilated Critically Ill Patients: The ONCA Study, a Randomized, Prospective Pilot Study","volume":"43","author":"Schollenberger","year":"2019","journal-title":"J. Parenter. Enter. Nutr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3875","DOI":"10.1016\/j.clnu.2021.05.012","article-title":"ANZICS Clinical Trials Group. Measured energy expenditure in mildly hypothermic critically ill patients with traumatic brain injury: A sub-study of a randomized controlled trial","volume":"40","author":"Ridley","year":"2021","journal-title":"Clin. Nutr."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Spolidoro, G.C.I., D\u2019Oria, V., De Cosmi, V., Milani, G.P., Mazzocchi, A., Akhondi-Asl, A., Mehta, N.M., Agostoni, C., Calderini, E., and Grossi, E. (2021). Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children. Nutrients, 13.","DOI":"10.3390\/nu13113797"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Scarcella, M., Scarpellini, E., Abenavoli, L., Ceccarelli, A., Commissari, R., Monti, R., Tack, J., Cotoia, A., and De Robertis, E. (2025). Energy Requirements in the Post-ICU Period: An Exploratory Multicenter Observational Study. Nutrients, 17.","DOI":"10.3390\/nu17122046"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"n160","DOI":"10.1136\/bmj.n160","article-title":"PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1159\/000356366","article-title":"Fluid management in the intensive care unit: Bioelectrical impedance vector analysis as a tool to assess hydration status and optimal fluid balance in critically ill patients","volume":"36","author":"Basso","year":"2013","journal-title":"Blood Purif."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"381","DOI":"10.5603\/AIT.2014.0061","article-title":"The use of bio-electrical impedance analysis (BIA) to guide fluid management, resuscitation and deresuscitation in critically ill patients: A bench-to-bedside review","volume":"46","author":"Malbrain","year":"2014","journal-title":"Anaesthesiol. Intensive Ther."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Papanastasiou, P., Chaloulakou, S., Karayiannis, D., Almperti, A., Poupouzas, G., Vrettou, C.S., Issaris, V., Jahaj, E., Vassiliou, A.G., and Dimopoulou, I. (2025). Phase Angle Trajectory Among Critical Care Patients: Longitudinal Decline Predicts Mortality Independent of Clinical Severity Scores. Healthcare, 13.","DOI":"10.3390\/healthcare13121463"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"\u0160ostakait\u0117, G., \u0160al\u010di\u016bt\u0117-\u0160im\u0117n\u0117, E., Svetikien\u0117, M., Danilenko, S., Klima\u0161auskas, A., and \u0160ipylait\u0117, J. (2026). Assessment of muscle wasting in intensive care unit patients with and without COVID-19 using ultrasound imaging and bioimpedance analysis. BMC Anesthesiol., 26.","DOI":"10.1186\/s12871-026-03659-5"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e70200","DOI":"10.1111\/jhn.70200","article-title":"Artificial Intelligence in Nutrigenomics: A Critical Review on Functional Food Insights and Personalized Nutrition Pathways","volume":"39","author":"Balamurugan","year":"2026","journal-title":"J. Hum. Nutr. Diet."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Salehi, A., Ebrahimzadeh, F., Behzadfar, A., Hashemi, M., and Yarahmadi, S. (2026). Effect of probiotic colonization on oral hygiene and ventilator-associated pneumonia in ICU patients: A triple-blind randomized controlled trial. BMC Infect. Dis., 26.","DOI":"10.1186\/s12879-026-12711-1"},{"key":"ref_38","first-page":"15598276251359185","article-title":"Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review","volume":"2025","author":"Seyedi","year":"2025","journal-title":"Am. J. Lifestyle Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.clnesp.2025.05.031","article-title":"Machine-learning modeL based on computed tomography body composition analysis for the estimation of resting energy expenditure: A pilot study","volume":"68","author":"Palmas","year":"2025","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.clnesp.2021.04.002","article-title":"Effect of an intensified individual nutrition therapy on serum metabolites in critically ill patients\u2014A targeted metabolomics analysis of the ONCA study","volume":"43","author":"Seethaler","year":"2021","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"100289","DOI":"10.1016\/j.jnha.2024.100289","article-title":"Precision nutrition impact on metabolic health and quality of life in aging population after a 3-month intervention: A randomized intervention","volume":"28","author":"Galarregui","year":"2024","journal-title":"J. Nutr. Health Aging"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Quan, W., Zhou, J., Wang, J., Huang, J., and Du, L. (2025). Machine Learning-Driven Precision Nutrition: A Paradigm Evolution in Dietary Assessment and Intervention. Nutrients, 18.","DOI":"10.3390\/nu18010045"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Stoian, M., Andone, A., B\u00e2ndil\u0103, S.R., Oni\u0219or, D., Bab\u0103, D.F., Niculescu, R., Stoian, A., and Azamfirei, L. (2025). Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition. Nutrients, 17.","DOI":"10.3390\/nu17101659"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vilne, B., \u0136ibilds, J., Siksna, I., Lazda, I., Valci\u0146a, O., and Kr\u016bmi\u0146a, A. (2022). Could Artificial Intelligence\/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease. Front. Microbiol., 13.","DOI":"10.3389\/fmicb.2022.627892"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1093\/advances\/nmac103","article-title":"Machine Learning in Nutrition Research","volume":"13","author":"Kirk","year":"2022","journal-title":"Adv. Nutr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41387-022-00226-y","article-title":"Machine learning modeling practices to support the principles of AI and ethics in nutrition research","volume":"12","author":"Thomas","year":"2022","journal-title":"Nutr. Diabetes"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1002\/ncp.70100","article-title":"Lean mass loss in the intensive care unit and its restoration: A narrative review","volume":"41","author":"Weimann","year":"2026","journal-title":"Nutr. Clin. Pract."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Theodorakis, N., Kreouzi, M., Pappas, A., and Nikolaou, M. (2024). Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management-A State-of-the-Art Narrative Review. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms252413438"},{"key":"ref_49","first-page":"73","article-title":"Disorders of nutritional status in sepsis\u2014Facts and myths","volume":"12","author":"Wachowska","year":"2017","journal-title":"Prz. Gastroenterol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1186\/s13054-016-1269-6","article-title":"Impact of hyperhydration on the mortality risk in critically ill patients admitted in intensive care units: Comparison between bioelectrical impedance vector analysis and cumulative fluid balance recording","volume":"20","author":"Samoni","year":"2016","journal-title":"Crit. Care"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s13668-025-00635-2","article-title":"Assessing the Links Between Artificial Intelligence and Precision Nutrition","volume":"14","author":"Ferreira","year":"2025","journal-title":"Curr. Nutr. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"118152","DOI":"10.1016\/j.bios.2025.118152","article-title":"Machine learning-driven prediction model of adult body weight based on integrated bioelectrical impedance and infrared imaging","volume":"293","author":"Zheng","year":"2026","journal-title":"Biosens. Bioelectron."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"106556","DOI":"10.1016\/j.clnu.2025.106556","article-title":"Nocturnal enteral feeding and mechanical ventilation risk in intensive care unit patients: A deep Learning-Based causal inference study","volume":"57","author":"Kang","year":"2026","journal-title":"Clin. Nutr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"110976","DOI":"10.12998\/wjcc.v13.i33.110976","article-title":"Muscle matters: Transforming the care of intensive care unit acquired sarcopenia and myosteatosis","volume":"13","author":"Kataria","year":"2025","journal-title":"World J. Clin. Cases"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1007\/s00134-025-08186-4","article-title":"The intensive care medicine research agenda for the management of ICU acquired weakness: A multinational, interprofessional perspective","volume":"51","author":"Eggmann","year":"2025","journal-title":"Intensive Care Med."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ge, Y., Hu, D., Wang, Z., and Zhang, C. (2025). Association between the nutritional inflammation index and mortality among patients with sepsis: Insights from traditional methods and machine learning-based mortality prediction. BMC Infect. Dis., 25.","DOI":"10.1186\/s12879-025-11429-w"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1002\/ncp.11194","article-title":"Role of artificial intelligence in critical care nutrition support and research","volume":"39","author":"Kittrell","year":"2024","journal-title":"Nutr. Clin. Pract."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ejim.2023.11.009","article-title":"Early prediction of ventilator-associated pneumonia with machine learning models: A systematic review and meta-analysis of prediction model performance","volume":"121","author":"Frondelius","year":"2024","journal-title":"Eur. J. Intern. Med."},{"key":"ref_59","first-page":"e83112","article-title":"Review of Adult Critical Care Calls to the Out-of-Hours Resident Pharmacist Service: A Retrospective Single-Center Observation Study","volume":"17","author":"Wang","year":"2025","journal-title":"Cureus"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"108653","DOI":"10.1016\/j.cmpb.2025.108653","article-title":"Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care","volume":"263","author":"Susnjak","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e70836","DOI":"10.2196\/70836","article-title":"Public Perspectives on Palliative and Hospice Care: Social Media Content Analysis Using Topic Modeling and Multiclass Sentiment Analysis","volume":"27","author":"Kim","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"00774-2023","DOI":"10.1183\/23120541.00774-2023","article-title":"Comprehensive geriatric assessment for people with both COPD and frailty starting pulmonary rehabilitation: A mixed-methods feasibility trial","volume":"10","author":"Brighton","year":"2024","journal-title":"ERJ Open Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1017\/S000711452510384X","article-title":"Explainable deep learning model WAL-net for individualised assessment of potentially reversible malnutrition in patients with cancer: A multicentre cohort study","volume":"134","author":"Yin","year":"2025","journal-title":"Br. J. Nutr."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.ajcnut.2025.01.006","article-title":"Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: A multicenter cohort study","volume":"121","author":"Yin","year":"2025","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_65","first-page":"e0659","article-title":"The Future of Critical Care: Optimizing Technologies and a Learning Healthcare System to Potentiate a More Humanistic Approach to Critical Care","volume":"4","author":"Meissen","year":"2022","journal-title":"Crit. Care Explor."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"\u0141ukasik, S., Tobis, S., Wieczorowska-Tobis, K., and Suwalska, A. (2018). Could Robots Help Older People with Age-Related Nutritional Problems? Opinions of Potential Users. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15112535"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/bs.afnr.2025.11.005","article-title":"Utilizing artificial intelligence to enhance sustainable high-quality food production: Current state, challenges, and future directions","volume":"118","author":"Dalakleidi","year":"2026","journal-title":"Adv. Food Nutr. Res."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Monlezun, D.J., Marshall, G., Omutoko, L., Oduor, P., Kokonya, D., Rayel, J., Sotomayor, C., Sinyavskiy, O., Aksamit, T., and MacKay, K. (2025). The Responsible Health AI Readiness and Maturity Index (RHAMI): Applications for a Global Narrative Review of Leading AI Use Cases in Public Health Nutrition. Nutrients, 18.","DOI":"10.3390\/nu18010038"},{"key":"ref_69","first-page":"254","article-title":"Artificial intelligence: Making decisions about artificial nutrition and hydration","volume":"8","author":"Gordon","year":"2004","journal-title":"J. Nutr. Health Aging"},{"key":"ref_70","first-page":"595","article-title":"A Present Where AI is Enhancing Laboratory Medicine, A Future Where It Redefines Healthcare","volume":"36","author":"Gruson","year":"2025","journal-title":"EJIFCC"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"100377","DOI":"10.1016\/j.advnut.2025.100377","article-title":"Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact","volume":"16","author":"Linseisen","year":"2025","journal-title":"Adv. Nutr."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e455","DOI":"10.1016\/S2214-109X(25)00473-5","article-title":"Research priorities for data science and artificial intelligence in global health: An international consensus exercise","volume":"14","author":"Song","year":"2026","journal-title":"Lancet Glob. Health"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1515\/cclm-2025-1314","article-title":"From automation to agentic artificial intelligence in laboratory medicine: An opinion of the IFCC Division on Emerging Technologies","volume":"64","author":"Gruson","year":"2025","journal-title":"Clin. Chem. Lab. Med."}],"container-title":["Nutrients"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/9\/1387\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:57:21Z","timestamp":1778497041000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/9\/1387"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":73,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["nu18091387"],"URL":"https:\/\/doi.org\/10.3390\/nu18091387","relation":{},"ISSN":["2072-6643"],"issn-type":[{"value":"2072-6643","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]}}}