Harnessing AI for Personalized Nutrition in Enhancing Immunotherapy Outcomes: A Systematic Review

Abstract

Immunotherapy has emerged as a transformative approach in cancer treatment, yet its efficacy is often limited by interpatient variability and immune resistance mechanisms. Recent advances suggest that nutritional status and dietary interventions can significantly influence the immune microenvironment, thereby impacting immunotherapy outcomes. This systematic review explores the potential of AI-driven approaches to design personalized nutritional interventions aimed at enhancing the effectiveness of immunotherapy. A comprehensive search of biomedical databases was conducted, focusing on studies published between 2010 and 2024 that examined the intersection of artificial intelligence, nutrition, and immunotherapy. The review highlights how machine learning models are being utilized to analyze large-scale dietary, genomic, and metabolomic datasets, identifying key nutritional biomarkers and tailoring interventions to individual patients. Furthermore, AI-driven simulations provide insights into the synergistic effects of specific nutrients, gut microbiota modulation, and immune activation pathways. Despite promising results, challenges remain in integrating AI-derived recommendations into clinical practice, including data standardization, ethical concerns, and the need for longitudinal studies. This review underscores the transformative potential of AI-driven nutritional strategies to optimize immunotherapy and calls for collaborative efforts to bridge gaps in research and implementation.