González-Rivas Juan P, Seyedi Seyed Arsalan, Mechanick Jeffrey I
Departments of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA (JPGR).
Research Department, Foundation for Clinic, Public Health, and Epidemiological Research of Venezuela (FISPEVEN), Caracas, Venezuela (JPGR).
Am J Lifestyle Med. 2025 Jul 17:15598276251359185. doi: 10.1177/15598276251359185.
To examine the applications of artificial intelligence (AI) in lifestyle medicine focused on diabetes care as a narrative review. Relevant keywords were identified and searched using PubMed to find relevant studies on AI in diabetes lifestyle management. AI applications in diabetes care were divided into four primary categories: 1- predictive models for diabetes risk and complications, which can utilize random forest and deep learning, demonstrating high accuracy rates (>80%); 2- personalized lifestyle recommendations, which can utilize clustering techniques and causal forest analysis to adapt interventions, leading to enhanced glycemic control and weight reduction; 3- remote monitoring and self-management tools, which can utilize digital twin technology and machine learning for behavior modeling, showing improved patient adherence and clinical results; and 4- clinical decision support systems, which assess various data sources for enhanced diagnosis and treatment suggestions. AI technologies show significant potential in improving diabetes care through multiple modalities that offer scalable cost-effective solutions, improved patient outcomes, and more efficient resource distribution.
作为一篇叙述性综述，探讨人工智能（AI）在以糖尿病护理为重点的生活方式医学中的应用。通过使用PubMed识别并搜索相关关键词，以查找关于AI在糖尿病生活方式管理方面的相关研究。AI在糖尿病护理中的应用主要分为四类：1-糖尿病风险和并发症的预测模型，可利用随机森林和深度学习，显示出较高的准确率（>80%）；2-个性化生活方式建议，可利用聚类技术和因果森林分析来调整干预措施，从而改善血糖控制和减轻体重；3-远程监测和自我管理工具，可利用数字孪生技术和机器学习进行行为建模，显示出患者依从性和临床效果的改善；4-临床决策支持系统，评估各种数据源以提供增强的诊断和治疗建议。AI技术通过多种方式在改善糖尿病护理方面显示出巨大潜力，这些方式提供了可扩展的成本效益解决方案、改善的患者预后以及更有效的资源分配。
