摘要
人工智能(AI)在医疗健康领域的应用日益深入,逐渐拓展至营养管理,为老年及肿瘤患者营养不良这一全球性挑战提供新的解决方案。传统营养管理方法效率低、主观性强且难以个性化,AI通过机器学习、自然语言处理及多模态数据分析,实现了高效、精准的营养筛查、评估、干预与监测。在营养筛查与评估方面,基于AI的自动化工具(如面部图像识别模型)可快速识别高风险人群;多维度数据驱动的预测模型有助于实现更精准的营养状况判定及分级。在干预环节,借助AI技术探索个体数据(基因组、微生物、代谢组和行为)和影响营养之间的相互关系,从而设计个性化的膳食与营养支持方案。在监测与预后方面,AI借助图像识别、可穿戴设备等技术实时追踪营养状况,动态调整干预策略;机器学习模型还能够基于营养指标预测并发症、生存率及身体功能变化,辅助临床预后判断。尽管AI应用于营养管理展现出巨大潜力,仍面临数据标准化不足和伦理隐私等挑战。未来应聚焦高质量多中心数据集构建、可解释算法开发及临床转化验证,推动AI技术在营养管理中的规范化、规模化应用,最终改善患者生活质量和健康结局。
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 data-genomic,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.
作者
刘承宇
陆薪莲
于健春
Liu Chengyu;Lu Xinlian;Yu Jianchun(Department of General Surgery,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100730,China;National Key Laboratory of Human Factors Engineering,China Astronaut Research and Training Center,Beijing 100094,China)
出处
《肿瘤代谢与营养电子杂志》
2025年第5期548-553,共6页
Electronic Journal of Metabolism and Nutrition of Cancer
基金
国家重点研发计划(2022YFF1100400)。
关键词
人工智能
营养管理
老年
肿瘤代谢
精准营养
机器学习
Artificial intelligence
Nutritional management
Elderly
Cancer metabolism
Precision nutrition
Machine learning
