Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review

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dc.contributor.authorSguanci, Marco
dc.contributor.authorMorales Palomares, Sara
dc.contributor.authorCangelosi, Giovanni
dc.contributor.authorPetrelli, Fabio
dc.contributor.authorSandri, Elena
dc.contributor.authorFerrara, Gaetano
dc.contributor.authorMancin, Stefano
dc.date.accessioned2026-10-27T08:41:02Z
dc.date.available2026-10-27T08:41:02Z
dc.date.issued2025-07
dc.date.updated2026-10-26T08:56:58Z
dc.descriptionAuthor contributions The authors’ responsibilities were as follows – MS, SMP: performed conceptualization, methodology, and wrote the original draft, collaborating in review and editing process; FP, GF, ES: performed methodology, investigation, visualization and proofreading; SM: defined the scope and structure and supervised the review process; MS, SMP: provided an equal contribution as first author in design, writing, and final content of the manuscript; GF, SM: contributed equally as last author; and all authors: contributed substantial intellectual input to the manuscript in line with the International Committee of Medical Journal Editors (ICMJE) criteria for authorship, and read and approved the final manuscript.en
dc.description.abstractMalnutrition is a critical complication among cancer patients, affecting 0.80). Machine learning algorithms, including decision trees, random forests, and support vector machines, outperformed conventional screening tools. Deep learning models applied to medical imaging achieved high segmentation accuracy (Dice similarity coefficient: 0.92-0.94), enabling early cachexia detection. AI-driven virtual dietitian systems improved dietary adherence (84%) and reduced unplanned hospitalizations. AI-enhanced workflows streamlined dietitian referrals, reducing referral times by 2.4 d. AI demonstrates significant potential in optimizing malnutrition screening, body composition monitoring, and personalized nutritional interventions for cancer patients. Its integration into oncology nutrition care could enhance patient outcomes and optimize healthcare resource allocation. Further research is necessary to standardize AI models and ensure clinical applicability. This systematic review followed a protocol registered prospectively on Open Science Framework (https://doi.org/10.17605/OSF.IO/A259M).en
dc.description.disciplineNutrición humana y dietética
dc.identifier.doihttps://doi.org/10.1016/j.advnut.2025.100438
dc.identifier.issn2161-8313
dc.identifier.urihttps://hdl.handle.net/20.500.12466/7787
dc.issue.number7
dc.journal.titleAdvances in Nutrition. a
dc.language.isoeng
dc.page.final12
dc.page.initial1
dc.relationAppendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.advnut.2025.100438en
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourcehttps://advances.nutrition.org/
dc.subject.keywordArtificial intelligenceen
dc.subject.keywordMalnutritionen
dc.subject.keywordOncologyen
dc.subject.keywordCachexiaen
dc.subject.keywordMachine learningen
dc.subject.keywordDeep learningen
dc.subject.keywordNutritional assessmenten
dc.subject.keywordBody composition,en
dc.subject.keywordClinical outcomesen
dc.subject.keywordCancer patientsen
dc.subject.unesco3201.01 Oncología
dc.subject.unesco3206 Ciencias de la Nutrición
dc.titleArtificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Reviewen
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number16

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