Artificial intelligence and machine learning in nutritional management of esophageal cancer: A narrative review

The nutritional management of patients with esophageal cancer (EC) presents significant complexities, with traditional approaches facing inherent limitations in data collection, real-time decision-making, and personalized care. This narrative review explores the transformative potential of artificial intelligence (AI) and machine learning (ML), particularly deep learning (DL) and reinforcement learning (RL), in revolutionizing nutritional support for this vulnerable patient population. DL has demonstrated remarkable capabilities in enhancing the accuracy and objectivity of nutritional assessment through precise, automated body composition analysis from medical imaging, offering valuable prognostic insights. Concurrently, RL enables the dynamic optimization of nutritional interventions, adapting them in real time to individual patient responses, paving the way for truly personalized care paradigms. Although AI/ML offers potential advantages in efficiency, precision, and personalization by integrating multidimensional data for superior clinical decision support, its widespread adoption is accompanied by critical challenges. These include safeguarding data privacy and security, mitigating algorithmic bias, ensuring transparency and accountability, and establishing rigorous clinical validation. Early evidence suggests the feasibility of applying AI/ML in nutritional risk stratification and workflow optimization, but high-quality prospective studies are needed to demonstrate the direct impact on clinical outcomes, including complications, readmissions, and survival. Overcoming these hurdles necessitates robust ethical governance, interdisciplinary collaboration, and continuous education. Ultimately, the strategic integration of AI/ML holds immense promise to profoundly improve patient outcomes, enhance quality of life, and optimize health care resource utilization in the nutritional management of esophageal cancer.