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| The application value of artificial intelligence in nutritional management focusing on elderly and cancer patients |
| 1Liu Chengyu,2Lu Xinlian,1Yu Jianchun |
| 1Department of General Surgery Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union
Medical College Beijing 100730 China
2National Key Laboratory of Human Factors Engineering China Astronaut Research and
Training Center Beijing 100094 China |
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Abstract Artificial intelligence AI is being increasingly applied in the healthcare field gradually extending to nutritional
management and offering new solutions to the global challenge of malnutrition among elderly and cancer patients. Traditional nutritional
management methods are often inefficient subjective and difficult to personalize. Through machine learning natural language
processing and multimodal data analysis AI enables efficient and precise nutritional screening assessment intervention and
monitoring. In the area of nutritional screening and assessment AI-based automated tools such as a facial image recognition model
can quickly identify high-risk patients. Multidimensional data-driven predictive models contribute to more accurate determination and
grading of nutritional status. In the intervention phase AI technology is used to explore the relationships between individual datagenomic microbial metabolomic and behavioral-and nutritional influences thereby designing personalized dietary and nutritional
support plans. For monitoring and prognosis AI utilizes technologies such as image recognition and wearable devices to track
nutritional status in real time and dynamically adjust intervention strategies. Machine learning models can also predict complications
survival rates and changes in physical function based on nutritional indicators assisting in clinical prognosis evaluation. Although AI
shows great potential in nutritional management it still faces challenges such as insufficient data standardization and ethical privacy
concerns. Future efforts should focus on constructing high-quality multi-center datasets developing interpretable algorithms and
validating clinical applications to promote the standardized and scalable use of AI in nutritional management ultimately improving
patients' quality of life and health outcomes.
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