Application prospects of artificial intelligence in nutritional support assessment for critically ill patients
10.3969/j.issn.1008-9691.2025.02.023
- VernacularTitle:人工智能在危重症营养支持治疗效果评估中的应用前景
- Author:
Junhua HUANG
1
;
Jie FANG
1
;
Jingkai LIN
1
Author Information
1. 舟山医院急诊重症监护室,浙江 舟山 316000
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Critical illness;
Nutritional support;
Treatment evaluation;
Clinical decision making
- From:
Chinese Journal of Integrated Traditional and Western Medicine in Intensive and Critical Care
2025;32(2):238-241
- CountryChina
- Language:Chinese
-
Abstract:
Nutritional support is essential for improving survival and prognosis in critical care medicine.However,traditional nutritional assessment methods have limitations such as strong subjectivity,dependence on clinical experience,lack of real-time data support,especially in complex environments such as intensive care unit(ICU).In recent years,the development of artificial intelligence(AI)technology has provided new opportunities for precise nutrition management.AI can analyze a large amount of clinical data through machine learning algorithms,monitor the physiological state of patients in real time,and dynamically adjust nutritional regimens to optimize the nutritional support effect of critically ill patients.At present,the application of AI in critical nutrition assessment mainly focuses on nutritional risk screening and tolerance assessment,but it is still in the preliminary exploration stage.The application of machine learning,deep learning and data mining technologies in the medical field provides more objective and efficient tools for nutritional assessment,such as personalized intervention by analyzing multi-dimensional data of patients(eating habits,physiological indicators,disease history,etc.).This paper analyzes the research prospect of AI in nutritional support treatment evaluation of critically ill patients,the application status,potential advantages and challenges of AI technology in nutritional assessment,focuses on the application of AI in data analysis,personalized nutrition plan formulation and clinical decision support,and discusses how to improve the effectiveness and safety of nutritional treatment by integrating multiple data sources,thus providing direction and ideas for future research.And through in-depth understanding of the application potential of AI,clinical medical staff can provide more accurate and scientific evaluation basis for nutritional support treatment of critically ill patients,and finally improve the clinical treatment effect of patients.However,widespread adoption of AI still faces challenges such as data privacy,ethical norms,algorithmic bias,and interdisciplinary collaboration.In the future,it is necessary to further optimize algorithm models,strengthen multidisciplinary cooperation(such as the collaboration of clinicians,nutritionists,and data engineers),and solve issues such as data standardization,cost-effectiveness,and patient privacy protection to achieve more accurate and personalized nutrition management strategies,ultimately improving the clinical outcomes of critically ill patients.
