Artificial intelligence in cancer-related malnutrition and cachexia: a transformative tool in clinical nutrition

Malnutrition and cachexia are common complications in cancer patients, and they negatively influence prognosis, treatment efficacy, and tolerability as well as quality of life [[1], [2], [3]]. Accurately identifying and effectively managing malnutrition and cachexia in this population remains a clinical challenge. Conventional validated screening tools may lack the sensitivity and specificity required for early detection and personalized intervention in diverse cancer types and treatment settings [4,5]. Over the last decade, the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) strategies, has shown promising results in clinical nutrition, with the potential to revolutionize nutritional care by providing more precise and scalable tools [6]. In the recent issue of the Journal, Sguanci et al. [7] investigated the role of AI in identifying and managing malnutrition and cachexia in cancer patients. The authors conducted a systematic review involving over 52,000 individuals to investigate AI’s potential for nutritional assessment, body composition monitoring, dietary adherence, and clinical outcomes in patients with cancer