Lalani Benjamin, Herur Rohan, Zade Daniel, Collins Grace, Dishong Devin M, Mehta Setu, Shim Jalene, Valdez Yllka, Mathioudakis Nestoras
Division of Endocrinology, Diabetes & Metabolism, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
J Diabetes Sci Technol. 2025 Jul 8:19322968251351995. doi: 10.1177/19322968251351995.
Prediabetes is a prevalent condition in which early detection and lifestyle interventions can prevent or delay progression to diabetes. Artificial intelligence (AI) and machine learning (ML) offer enhanced tools for diagnosis, risk stratification, and scalable delivery of lifestyle interventions. This review synthesizes current applications of AI/ML in patients with prediabetes.
We conducted a scoping review using PubMed, EMBASE, and Web of Science (through May 2025) to identify original studies applying AI/ML to prediabetes prediction or management. Population-level forecasting and models combining prediabetes with other conditions were excluded. Data were extracted via structured REDCap instruments and validated through secondary review. Descriptive statistics summarized findings.
Of 2072 records screened, 149 studies met criteria: 118 prediction model studies, 20 intervention studies, and 11 miscellaneous. Machine learning models primarily targeted prediction of prediabetes, progression to diabetes, diabetic complications, and glucose metrics. Overall model performance was favorable (mean C-statistic 0.81), with random forests, neural networks, and support vector machines showing better performance. Only 20 studies reported external validation, few compared ML to standard risk tools, and data/code availability was limited. Six AI-based diabetes prevention programs showed positive clinical outcomes, though randomized controlled trial (RCT) evidence was limited. Three personalized nutrition interventions showed mixed efficacy.
Most AI/ML research in prediabetes focused on predictive modeling, which shows promise but limited translation to real-world settings. Artificial intelligence-based interventions may scale behavioral change support but need further evaluation versus standard care. Future efforts should prioritize external validation, assess added value over standard tools, and address barriers to integration into care.
糖尿病前期是一种普遍存在的状况，早期检测和生活方式干预可以预防或延缓其发展为糖尿病。人工智能（AI）和机器学习（ML）为诊断、风险分层以及可扩展的生活方式干预提供了更强大的工具。本综述综合了AI/ML在糖尿病前期患者中的当前应用情况。
我们使用PubMed、EMBASE和Web of Science（截至2025年5月）进行了一项范围综述，以识别将AI/ML应用于糖尿病前期预测或管理的原始研究。排除了人群水平的预测以及将糖尿病前期与其他疾病相结合的模型。通过结构化的REDCap工具提取数据，并通过二次审查进行验证。描述性统计总结了研究结果。
在筛选的2072条记录中，149项研究符合标准：118项预测模型研究、20项干预研究和11项其他研究。机器学习模型主要针对糖尿病前期的预测、发展为糖尿病的预测、糖尿病并发症的预测以及血糖指标的预测。总体模型性能良好（平均C统计量为0.81），随机森林、神经网络和支持向量机表现出更好的性能。只有20项研究报告了外部验证情况，很少有研究将ML与标准风险工具进行比较，并且数据/代码的可用性有限。六个基于AI的糖尿病预防项目显示出了积极的临床结果，尽管随机对照试验（RCT）的证据有限。三项个性化营养干预显示出混合疗效。
大多数关于糖尿病前期的AI/ML研究都集中在预测建模上，这显示出了前景，但在实际应用中的转化有限。基于人工智能的干预措施可能会扩大行为改变支持，但与标准护理相比还需要进一步评估。未来的努力应优先进行外部验证，评估相对于标准工具的附加价值，并解决整合到护理中的障碍。
