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

Malnutrition is a critical complication among cancer patients, affecting up to 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 evaluates 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 (JBI) 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 three 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 (AUC >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 days.

AI has demonstrated 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. 10.17605/OSF.IO/A259M STATEMENT OF SIGNIFICANCE: This systematic review highlights the transformative potential of Artificial Intelligence in oncology nutrition by demonstrating its superior accuracy in malnutrition detection, body composition monitoring, and personalized dietary interventions. Unlike previous studies, which focused on isolated AI applications, this work comprehensively evaluates AI-driven models across multiple clinical domains, emphasizing their integration into routine cancer care to enhance early detection, treatment personalization, and overall patient outcomes.