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

Abstract

Malnutrition is a critical complication among cancer patients, affecting ≤80% of individuals depending on cancer type, stage, and treatment. Artificial intelligence (AI) has emerged as a promising tool in healthcare, with potential applications in nutritional management to improve early detection, risk stratification, and personalized interventions. This systematic review evaluated the role of AI in identifying and managing malnutrition in cancer patients, focusing on its effectiveness in nutritional status assessment, prediction, clinical outcomes, and body composition monitoring. A systematic search was conducted across PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica Database from June to July 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quantitative primary studies investigating AI-based interventions for malnutrition detection, body composition analysis, and nutritional optimization in oncology were included. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools, and evidence certainty was evaluated with the Oxford Centre for Evidence-Based Medicine framework. Eleven studies (n = 52,228 patients) met the inclusion criteria and were categorized into 3 overarching domains: nutritional status assessment and prediction, clinical and functional outcomes, and body composition and cachexia monitoring. AI-based models demonstrated high predictive accuracy in malnutrition detection (area under the curve >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).

Keywords: artificial intelligence; body composition; cachexia; cancer patients; clinical outcomes; deep learning; machine learning; malnutrition; nutritional assessment; oncology.

FIGURE 1

PRISMA flowchart.

FIGURE 2

Performance of artificial intelligence (AI) models for nutritional status assessment. Forest plot showing area under the curve (AUC) values reported by the included studies evaluating AI models for nutritional status assessment in cancer patients. All models demonstrated good to excellent discriminative ability, with AUCs ranging from 0.813 to 0.964.

FIGURE 3

AI in the management of malnutrition in cancer patients. AI, artificial intelligence; AUC, area under the curve; CART, classification and regression tree.